CN103065271A - Power supply reliability and customer satisfaction quantitative relation model establishment method - Google Patents
Power supply reliability and customer satisfaction quantitative relation model establishment method Download PDFInfo
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Abstract
The invention discloses a power supply reliability and customer satisfaction quantitative relation model establishment method. The method includes the steps of firstly establishing four power supply reliability indexes and customer satisfaction quantitative relation models which respectively are a prearranged power failure time model, a prearranged power failure frequency model, a fault power failure time model and a fault power failure frequency model according to customer satisfaction research data, then calculating weight of the four power supply reliability indexes, and finally establishing a power supply reliability and customer overall satisfaction quantitative relation model. The method is simple and accurate, not only can be used for establishing the four power supply reliability indexes and customer satisfaction quantitative relation models which respectively are the prearranged power failure time model, the prearranged power failure frequency model, the fault power failure time model and the fault power failure frequency model, but also can be used for determining a weight coefficient of the importance of the four indexes according to customers. In addition, the method can reflect the quantitative relation between the four power supply reliability indexes and the customer satisfaction, and can reflect the conditions of customer satisfaction change when the power supply reliability indexes are changed.
Description
Technical Field
The invention belongs to the technical field of a customer satisfaction quantitative model establishing method, and particularly relates to a power supply reliability and customer satisfaction quantitative model establishing method.
Background
The customer satisfaction degree of the power is the pleasant and disappointed feeling state formed by comparing the perception effect of the power user on the power utilization or the power utilization service with the expected value of the power utilization service, and comprehensively evaluates the information of the power supply company on the aspects of service quality, power supply reliability, power supply stability and the like from the perspective of the user. With the advance of the market reformation of electric power in China, the environment and status of power supply enterprises are deeply changed and face the environment transition from monopoly to competition. The operation environment of the power supply enterprise is changed from distribution of electric power and limitation of power utilization into a service management type guiding customer power utilization. Therefore, the quality of the power supply quality and the quality of the power supply service directly influence the survival of power supply enterprises. The power supply customer satisfaction evaluation work is developed, various factors influencing the customer satisfaction are researched, the specific influence degree of the various factors on the customer satisfaction is analyzed, the power supply enterprises can be helped to find the problems existing in the power supply quality and the power supply service and various factors influencing the customer satisfaction and further influencing the power supply service quality, and accordingly targeted improvement measures can be made in time and the power supply service quality is further improved.
The satisfaction degree of the power customer is influenced by a plurality of factors, and the influence degrees of different influencing factors are different. At present, the research on the satisfaction degree of power customers at home and abroad mainly focuses on the qualitative analysis of influence factors, including a customer satisfaction degree index model construction method and customer satisfaction degree influence factor analysis.
The customer satisfaction index model is a unique system structure consisting of a plurality of satisfaction basic indexes, and comprises the relationship between the satisfaction basic indexes and the customer satisfaction. And (4) designing and researching a questionnaire according to the customer satisfaction index model to evaluate the satisfaction degree of the customer on the basic indexes, and carrying out weighted average on the satisfaction degree to obtain the overall satisfaction degree of the customer. As in the article "power industry user satisfaction index model construction and demonstration research" in "university of wuhan's theory of engineering (information and management edition) at volume 3, 28, 2006, the disclosure is to use the idea of multi-level analytic hierarchy process to construct a multi-level and multi-index power user satisfaction evaluation system, and provide 19 basic indexes from 5 aspects of power supply quality, standard service, consultation service, electric charge payment and service management, thereby designing an questionnaire to evaluate the customer satisfaction. For analyzing the satisfaction degree of a customer on the power supply reliability, the model is too rough, the related satisfaction degree influence factors are too wide, qualitative analysis is included, the satisfaction degree of the customer on the current power supply reliability level can be given, the specific requirement of the customer on the power supply reliability cannot be represented, and the rule that the customer satisfaction degree changes along with the power supply reliability level cannot be given.
Disclosure of Invention
Aiming at the defects of power supply reliability in the existing satisfaction evaluation calculation, the invention aims to provide a power supply reliability and customer satisfaction relation quantitative model establishing method, which is simpler and more accurate, not only establishes the relation between the four power supply reliability indexes of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times and the customer satisfaction, but also determines a weight coefficient according to the attention degree of the customer to the four indexes; the quantitative relation between the four power supply reliability indexes and the customer satisfaction degree can be reflected; the change situation of the customer satisfaction degree when the power supply reliability index changes can be reflected.
The technical solution of the invention for realizing the above purpose is as follows:
a power supply reliability and customer satisfaction quantitative relation model building method is characterized in that according to customer satisfaction investigation data, a power supply reliability index and customer satisfaction quantitative relation model of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times is built respectively, then weights of the four power supply reliability indexes are calculated, and finally a quantitative relation model of power supply reliability and customer overall satisfaction is built.
1) The quantitative relation model of the average power failure time prearranged by the user and the customer satisfaction degree is established according to the following steps:
1.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 11% and expected pre-scheduled blackout time averageT 11The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 12% and expected pre-scheduled blackout time averageT 12The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 13% and expected pre-scheduled blackout time averageT 13The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 14% and expected pre-scheduled blackout time averageT 14And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 15% and expected pre-scheduled blackout time averageT 15；
1.2) input feeder Preset annual average blackout time and sequencing
1.1) inputting all the information into the power supply stationnThe average power failure time is prearranged by users of the feeder lines and is arranged according to the ascending order;
1.3) calculating the proportion of customers deemed to have met the satisfaction level
1.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 1% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 1%= a 11%+ a 12%+ a 13%
1.4) calculating the average power failure time of the user prearranged corresponding to the qualified satisfaction degree score
1.3) after the step is finished, setting the satisfaction degree and the qualification scorecThe first rank in the ascending sequence of the prearranged average power failure time of the feeder line is takenn×b 1% bit user pre-scheduled average outage time as the pre-scheduled user average outage time to achieve satisfaction and qualification scored；
1.5) setting the average power failure time of the prearranged users corresponding to the score of the full mark satisfaction degree
1.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the corresponding average power failure time of the user to be 0;
1.6) calculating the satisfaction score corresponding to very satisfactory in satisfaction
1.5) after the step is finished, selecting the rank before the rank in the ascending sequence of the prearranged average power failure timen×a 11% of all the values, the average value of which is calculatedA 11Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 11；
1.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
1.6) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 11% ton×（a 11+a 12) % of all the values, the average value of which is calculatedA 12Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 12；
1.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
1.7) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 12% ton×（a 12+a 13) % of all the values, the average value of which is calculatedA 13Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 13；
1.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
1.8), selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 13% ton×（a 13+a 14) % of all the values, the average value of which is calculatedA 14Then, it is calculated to be very full as followsSatisfaction score of intention correspondenceS 14；
1.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
1.9) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 14% ton×（a 14+a 15) % of all the values, the average value of which is calculatedA 15Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 15；
1.11) establishing a quantitative relation model between the user prearranged average power failure time and the customer satisfaction
After the step 1.10) is finished, calculating a satisfaction degree score series corresponding to five satisfaction degrees according to the step 1.6), the step 1.7), the step 1.8), the step 1.9) and the step 1.10) to obtainS 1={S 11,S 12,S 13,S 14,S 15And expected prearranged blackout time sequenceT 1={T 11,T 12,T 13,T 14,T 15Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 1, S 1) Fitting a user prearranged average power failure time and satisfaction degree score relation curve, and calculating a curve analysis expression;
2) the quantitative relation model of the average power failure times prearranged by the user and the customer satisfaction degree is established according to the following steps:
2.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 21% and expected pre-scheduled blackout number averageT 21The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 22% and expected pre-scheduled blackout number averageT 22The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 23% and expected pre-scheduled blackout number averageT 23The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 24% and expected pre-scheduled blackout number averageT 24And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 25% and expected pre-scheduled blackout number averageT 25；
2.2) input feeder Preset annual average blackout times and sequencing
2.1) inputting all the information into the power supply stationnThe average power failure times are prearranged by users of the feeder lines and are arranged according to the ascending order;
2.3) calculating the proportion of customers deemed to have met the satisfaction level
2.2) calculating the proportion of the customers with the research selections of 'very satisfied', 'comparatively satisfied' and 'general' to the total number of the research customersb 2% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 2%= a 21%+ a 22%+ a 23%
2.4) calculating the average power failure times of the users corresponding to the qualified satisfaction degree scores
2.3) after the step is finished, setting the satisfaction degree and the qualification scorecTaking feeder line to prearrange average power failure timesRank first in ascending sequencen×b 2% bit user pre-scheduled average outage times as pre-scheduled user average outage times to achieve satisfaction and qualification scored；
2.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
2.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
2.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
2.5) after finishing, selecting the rank before ranking in the ascending sequence of the pre-arranged average power failure timesn×a 21% of all the values, the average value of which is calculatedA 21Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 21；
2.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
2.6) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 21% ton×（a 21+a 22) % of all the values, the average value of which is calculatedA 22Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 22；
2.8) calculating the satisfaction score corresponding to the general satisfaction
Step 2.7) completionThen, the ranking in the ascending sequence of the pre-arranged average power failure times is selectedn×a 22% ton×（a 22+a 23) % of all the values, the average value of which is calculatedA 23Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 23；
2.9) calculating satisfaction scores corresponding to less than satisfactory ones of the degrees of satisfaction
2.8) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 23% ton×（a 23+a 24) % of all the values, the average value of which is calculatedA 24Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 24；
2.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
2.9) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 24% ton×（a 24+a 25) % of all the values, the average value of which is calculatedA 25Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 25；
2.11) establishing a quantitative relation model between the pre-arranged average power failure times of the users and the customer satisfaction
After the step 2.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 2.6), the step 2.7), the step 2.8), the step 2.9) and the step 2.10) to obtain a satisfaction degree score seriesS 2={S 21,S 22,S 23,S 24,S 25And expected prearranged blackout times sequenceT 2={T 21,T 22,T 23,T 24,T 25Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 2, S 2) Fitting a relation curve of the average power failure times and the satisfaction degree score prearranged by the user, and calculating a curve analysis expression;
3) the quantitative relation model of the average power failure time of the user fault and the customer satisfaction degree is established according to the following steps:
3.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 31% and expected mean time to failureT 31The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 32% and expected mean time to failureT 32The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 33% and expected mean time to failureT 33The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 34% and expected mean time to failureT 34And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 35% and expected mean time to failureT 35；
3.2) inputting the annual average power failure time of feeder line fault and sequencing
3.1) inputting all the information into the power supply stationnAverage power failure time of user faults of the feeder lines is arranged according to ascending order;
3.3) calculating the proportion of customers deemed to have met the satisfaction level
3.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 3% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 3%= a 31%+ a 32%+ a 33%
3.4) calculating the average power failure time of the user corresponding to the qualified satisfaction degree score
3.3) after the completion of the step, setting the satisfaction degree and the qualification scorecTaking the ranking in the ascending sequence of the mean power failure time of the feeder line faultn×b 3% bit user fault mean time to outage as a pre-scheduled average number of user outages to achieve satisfaction and qualification scored；
3.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
3.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
3.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
3.5) after finishing, selecting the rank before the sequence in the ascending sequence of the mean time to power off of the faultn×a 31% of all the values, the average value of which is calculatedA 31Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 31；
3.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
3.6) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 31% ton×（a 31+a 32) % of all the values, the average value of which is calculatedA 32Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 32；
3.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
3.7) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 32% ton×（a 32+a 33) % of all the values, the average value of which is calculatedA 33Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 33；
3.9) calculating satisfaction scores corresponding to the comparative dissatisfaction in the satisfaction
3.8) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 33% ton×（a 33+a 34) % of all the values, the average value of which is calculatedA 34Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 34；
3.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
3.9) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 34% ton×（a 34+a 35) % of all the values, the average value of which is calculatedA 35Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 35；
3.11) establishing a quantitative relation model between the average power failure time of the user fault and the customer satisfaction
After the step 3.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 3.6), the step 3.7), the step 3.8), the step 3.9) and the step 3.10) to obtain a satisfaction degree score seriesS 3={S 31,S 32,S 33,S 34,S 35And expected failure blackout time sequenceT 3={T 31,T 32,T 33,T 34,T 35Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 3, S 3) Fitting a user fault average power failure time and satisfaction degree score relation curve, and calculating a curve analysis expression;
4) the satisfaction degree score corresponding to each satisfaction degree of the average failure times is calculated according to the following steps:
4.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: select "very satisfactory"the ratio of the number of customers to the total number of usersa 41% and average expected number of failed outagesT 41The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 42% and average expected number of failed outagesT 42The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 43% and average expected number of failed outagesT 43The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 44% and average expected number of failed outagesT 44And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 45% and average expected number of failed outagesT 45；
4.2) inputting annual average power failure times of feeder line faults and sequencing
4.1) inputting all the information into the power supply stationnAverage power failure times of user faults of the feeder lines are arranged in ascending order;
4.3) calculating the proportion of customers deemed to have met the satisfaction level
4.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 4% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 4%= a 41%+ a 42%+ a 43%
4.4) calculating the average power failure times of the user corresponding to the qualified satisfaction degree scores
4.3) after the step is finished, setting the satisfaction degree and the qualification scorecTaking the ranking of the feeder line fault average power failure times in the ascending sequencen×b 4% bit average number of subscriber failures as pre-scheduled average number of subscriber outages to achieve satisfaction and qualification scored；
4.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
4.4) after the completion of the step, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
4.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
4.5) after finishing the step, firstly selecting the rank before the rank in the ascending sequence of the average power failure times of the faultsn×a 41% of all the values, the average value of which is calculatedA 41Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 41；
4.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
4.6) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 41% ton×（a 41+a 42) % of all the values, the average value of which is calculatedA 42Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 42；
4.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
4.7) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 42% ton×（a 42+a 43) % of all the values, the average value of which is calculatedA 43Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 43；
4.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
4.8) after the step is finished, firstly selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 43% ton×（a 43+a 44) % of all the values, the average value of which is calculatedA 44Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 44；
4.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
4.9) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 44% ton×（a 44+a 45) % of all the values, the average value of which is calculatedA 45Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 45；
4.11) establishing a quantitative relation model between the average power failure times of the user faults and the customer satisfaction degree
After the step 4.10) is finished, the steps 4.6), 4.7), 4.8) and 4Step 9) and step 4.10) to obtain a satisfaction degree score sequence corresponding to the five satisfaction degreesS 4={S 41,S 42,S 43,S 44,S 45And expected failure outage times sequenceT 3={T 41,T 42,T 43,T 44,T 45Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 4, S 4) Fitting a relation curve between the average power failure times of the user faults and the satisfaction degree score, and calculating a curve analysis expression;
5) the weight calculation method of the four power supply reliability indexes comprises the following steps:
5.1) input satisfaction investigation data
Inputting importance sorting data of customers counted in satisfaction investigation on four indexes of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times, wherein the importance sorting data comprises the proportion of times of selecting each power supply reliability index as 1, 2,3 and 4 in the total number of investigation customers;
5.2) calculating importance weight of each ranking
5.1) after the step is finished, calculating the weight of each ranking in the importance ranking by adopting an analytic hierarchy process, firstly setting the scale of each ranking to obtain an importance judgment matrix, and then obtaining the importance weight through normalization processing;
5.3) calculating the weight of each power supply reliability index
5.2) after the step is finished, counting the times of selecting each power supply reliability index as 1, 2,3 and 4 respectively and calculating the proportion of the power supply reliability index to the total number of investigation customers, wherein the weight of each power supply reliability index is calculated according to the following formula
Wherein,w i is shown asiThe overall weight of each power supply reliability index,r ij when representing the ordering will beiThe individual power supply reliability index is selected asjThe named customers account for the proportion of the total survey customers,
rank first in the representation importance rankingjThe corresponding weight.
5.4) establishing a power supply reliability and customer comprehensive satisfaction quantitative relation model according to the following method:
according to the quantitative relational expression of the power supply reliability indexes and the customer satisfaction degree established in the steps 1), 2), 3) and 4), combining the weights of the indexes obtained by calculation in the step 5.3), and establishing a quantitative relational model of the power supply reliability indexes and the customer comprehensive satisfaction degree according to the following formula;
whereinSIIn order to integrate the satisfaction scores for the customers,f i (x) Is shown asiAnd quantifying a relational expression between the power supply reliability index and the customer satisfaction degree.
After the technical scheme is adopted, the invention mainly has the following effects:
the method overcomes the defects of the existing power customer satisfaction evaluation model, not only can a user calculate the satisfaction score of the customer on the current reliability level, but also can indicate the change rule of the customer satisfaction along with the power supply reliability, and is convenient for a power supply company to provide differentiated services for the customer;
the influence of four indexes of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times in power supply reliability management can be considered, the method is more approximate to the actual operation condition of the reliability management of a power supply bureau, and the model accuracy is high;
the attention degrees of different customers to the four indexes of the prearranged power failure time, the prearranged power failure times, the fault power failure time and the fault power failure times can be considered, the index weight is determined according to the importance sequence of the users to the four power supply reliability indexes, and the actual conditions of the customers are better met.
The method has the advantages of simple algorithm, convenience for popularization and application, high accuracy of model establishment, capability of reflecting specific orders of actual conditions of customers and the like. The method is widely applied to the establishment of the quantitative model of the relation between the power supply reliability and the customer satisfaction degree, and is particularly suitable for the power supply reliability and the customer satisfaction degree management of a power supply bureau.
Detailed Description
According to the method for establishing the quantitative relation model between the power supply reliability and the customer satisfaction degree, according to customer satisfaction degree investigation data, a quantitative relation model between four power supply reliability indexes of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times and the customer satisfaction degree is established respectively, then the weights of the four power supply reliability indexes are calculated, and finally the quantitative relation model between the power supply reliability and the customer overall satisfaction degree is established.
Wherein: 1) the quantitative relation model of the average power failure time prearranged by the user and the customer satisfaction degree is established according to the following steps:
1.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 11% and expected pre-scheduled blackout time averageT 11The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 12% and expected pre-scheduled blackout time averageT 12The number of customers who choose 'general' accounts for the total researchPercentage of householdsa 13% and expected pre-scheduled blackout time averageT 13The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 14% and expected pre-scheduled blackout time averageT 14And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 15% and expected pre-scheduled blackout time averageT 15；
1.2) input feeder Preset annual average blackout time and sequencing
1.1) inputting all the information into the power supply stationnThe average power failure time is prearranged by users of the feeder lines and is arranged according to the ascending order;
1.3) calculating the proportion of customers deemed to have met the satisfaction level
1.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 1% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 1%= a 11%+ a 12%+ a 13%
1.4) calculating the average power failure time of the user prearranged corresponding to the qualified satisfaction degree score
1.3) after the step is finished, setting the satisfaction degree and the qualification scorecThe first rank in the ascending sequence of the prearranged average power failure time of the feeder line is takenn×b 1% bit user pre-scheduled average outage time as the pre-scheduled user average outage time to achieve satisfaction and qualification scored；
1.5) setting the average power failure time of the prearranged users corresponding to the score of the full mark satisfaction degree
1.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the corresponding average power failure time of the user to be 0;
1.6) calculating the satisfaction score corresponding to very satisfactory in satisfaction
1.5) after the step is finished, selecting the rank before the rank in the ascending sequence of the prearranged average power failure timen×a 11% of all the values, the average value of which is calculatedA 11Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 11；
1.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
1.6) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 11% ton×（a 11+a 12) % of all the values, the average value of which is calculatedA 12Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 12；
1.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
1.7) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 12% ton×（a 12+a 13) % of all the values, the average value of which is calculatedA 13Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 13；
1.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
1.8), selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 13% ton×（a 13+a 14) % of all the values, the average value of which is calculatedA 14Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 14；
1.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
1.9) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 14% ton×（a 14+a 15) % of all the values, the average value of which is calculatedA 15Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 15；
1.11) establishing a quantitative relation model between the user prearranged average power failure time and the customer satisfaction
After the step 1.10) is finished, calculating a satisfaction degree score series corresponding to five satisfaction degrees according to the step 1.6), the step 1.7), the step 1.8), the step 1.9) and the step 1.10) to obtainS 1={S 11,S 12,S 13,S 14,S 15And expected prearranged blackout time sequenceT 1={T 11,T 12,T 13,T 14,T 15Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 1, S 1) Fitting a user prearranged average power failure time and satisfaction degree score relation curve, and calculating a curve analysis expression;
2) the quantitative relation model of the average power failure times prearranged by the user and the customer satisfaction degree is established according to the following steps:
2.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 21% and expected pre-scheduled blackout number averageT 21The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 22% and expected pre-scheduled blackout number averageT 22The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 23% and expected pre-scheduled blackout number averageT 23The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 24% and expected pre-scheduled blackout number averageT 24And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 25% and expected pre-scheduled blackout number averageT 25；
2.2) input feeder Preset annual average blackout times and sequencing
2.1) inputting all the information into the power supply stationnThe average power failure times are prearranged by users of the feeder lines and are arranged according to the ascending order;
2.3) calculating the proportion of customers deemed to have met the satisfaction level
2.2) calculating the proportion of the customers with the research selections of 'very satisfied', 'comparatively satisfied' and 'general' to the total number of the research customersb 2% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 2%= a 21%+ a 22%+ a 23%
2.4) calculating the average power failure times of the users corresponding to the qualified satisfaction degree scores
2.3) after the step is finished, setting the satisfaction degree and the qualification scorecThe feeder line is taken to be ranked the first in the ascending sequence of the average power failure timesn×b 2% bit user pre-scheduled average outage times as pre-scheduled user average outage times to achieve satisfaction and qualification scored；
2.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
2.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
2.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
2.5) after finishing, selecting the rank before ranking in the ascending sequence of the pre-arranged average power failure timesn×a 21% of all the values, the average value of which is calculatedA 21Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 21；
2.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
2.6) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 21% ton×（a 21+a 22) % of all the values, the average value of which is calculatedA 22Then calculating the satisfaction of the very satisfactory correspondence according to the following formulaDegree scoreS 22；
2.8) calculating the satisfaction score corresponding to the general satisfaction
2.7) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 22% ton×（a 22+a 23) % of all the values, the average value of which is calculatedA 23Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 23；
2.9) calculating satisfaction scores corresponding to less than satisfactory ones of the degrees of satisfaction
2.8) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 23% ton×（a 23+a 24) % of all the values, the average value of which is calculatedA 24Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 24；
2.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
2.9) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 24% ton×（a 24+a 25) % of all the values, the average value of which is calculatedA 25Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 25；
2.11) establishing a quantitative relation model between the pre-arranged average power failure times of the users and the customer satisfaction
After the step 2.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 2.6), the step 2.7), the step 2.8), the step 2.9) and the step 2.10) to obtain a satisfaction degree score seriesS 2={S 21,S 22,S 23,S 24,S 25And expected prearranged blackout times sequenceT 2={T 21,T 22,T 23,T 24,T 25Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 2, S 2) Fitting a relation curve of the average power failure times and the satisfaction degree score prearranged by the user, and calculating a curve analysis expression;
3) the quantitative relation model of the average power failure time of the user fault and the customer satisfaction degree is established according to the following steps:
3.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 31% and expected mean time to failureT 31The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 32% and expected mean time to failureT 32The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 33% and expected mean time to failureT 33The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 34% and expected mean time to failureT 34And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 35% and expected mean time to failureT 35；
3.2) inputting the annual average power failure time of feeder line fault and sequencing
3.1) inputting all the information into the power supply stationnAverage power failure time of user faults of the feeder lines is arranged according to ascending order;
3.3) calculating the proportion of customers deemed to have met the satisfaction level
3.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 3% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 3%= a 31%+ a 32%+ a 33%
3.4) calculating the average power failure time of the user corresponding to the qualified satisfaction degree score
3.3) after the completion of the step, setting the satisfaction degree and the qualification scorecTaking the ranking in the ascending sequence of the mean power failure time of the feeder line faultn×b 3% bit user fault mean time to outage as a pre-scheduled average number of user outages to achieve satisfaction and qualification scored；
3.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
3.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
3.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
3.5) after the step is finished, firstlySelecting rank front in ascending sequence of mean time of power failuren×a 31% of all the values, the average value of which is calculatedA 31Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 31；
3.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
3.6) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 31% ton×（a 31+a 32) % of all the values, the average value of which is calculatedA 32Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 32；
3.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
3.7) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 32% ton×（a 32+a 33) % of all the values, the average value of which is calculatedA 33Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 33；
3.9) calculating satisfaction scores corresponding to the comparative dissatisfaction in the satisfaction
3.8) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 33% ton×（a 33+a 34) % of all the values, the average value of which is calculatedA 34Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 34；
3.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
3.9) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 34% ton×（a 34+a 35) % of all the values, the average value of which is calculatedA 35Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 35；
3.11) establishing a quantitative relation model between the average power failure time of the user fault and the customer satisfaction
After the step 3.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 3.6), the step 3.7), the step 3.8), the step 3.9) and the step 3.10) to obtain a satisfaction degree score seriesS 3={S 31,S 32,S 33,S 34,S 35And expected failure blackout time sequenceT 3={T 31,T 32,T 33,T 34,T 35Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 3, S 3) Fitting user eventsCalculating a curve analysis expression according to a relation curve between the average power failure time of the obstacle and the satisfaction degree score;
4) the satisfaction degree score corresponding to each satisfaction degree of the average failure times is calculated according to the following steps:
4.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 41% and average expected number of failed outagesT 41The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 42% and average expected number of failed outagesT 42The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 43% and average expected number of failed outagesT 43The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 44% and average expected number of failed outagesT 44And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 45% and average expected number of failed outagesT 45；
4.2) inputting annual average power failure times of feeder line faults and sequencing
4.1) inputting all the information into the power supply stationnAverage power failure times of user faults of the feeder lines are arranged in ascending order;
4.3) calculating the proportion of customers deemed to have met the satisfaction level
4.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 4% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 4%= a 41%+ a 42%+ a 43%
4.4) calculating the average power failure times of the user corresponding to the qualified satisfaction degree scores
4.3) after the step is finished, setting the satisfaction degree and the qualification scorecTaking the ranking of the feeder line fault average power failure times in the ascending sequencen×b 4% bit average number of subscriber failures as pre-scheduled average number of subscriber outages to achieve satisfaction and qualification scored；
4.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
4.4) after the completion of the step, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
4.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
4.5) after finishing the step, firstly selecting the rank before the rank in the ascending sequence of the average power failure times of the faultsn×a 41% of all the values, the average value of which is calculatedA 41Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 41；
4.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
4.6) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 41% ton×（a 41+a 42) % of all the values, the average value of which is calculatedA 42Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 42；
4.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
4.7) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 42% ton×（a 42+a 43) % of all the values, the average value of which is calculatedA 43Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 43；
4.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
4.8) after the step is finished, firstly selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 43% ton×（a 43+a 44) % of all the values, the average value of which is calculatedA 44Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 44；
4.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
4.9) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 44% ton×（a 44+a 45) % of all the values, the average value of which is calculatedA 45Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 45；
4.11) establishing a quantitative relation model between the average power failure times of the user faults and the customer satisfaction degree
After the step 4.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 4.6), the step 4.7), the step 4.8), the step 4.9) and the step 4.10) to obtain a satisfaction degree score seriesS 4={S 41,S 42,S 43,S 44,S 45And expected failure outage times sequenceT 3={T 41,T 42,T 43,T 44,T 45Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 4, S 4) Fitting a relation curve between the average power failure times of the user faults and the satisfaction degree score, and calculating a curve analysis expression;
5) the weight calculation method of the four power supply reliability indexes comprises the following steps:
5.1) input satisfaction investigation data
Inputting importance sorting data of customers counted in satisfaction investigation on four indexes of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times, wherein the importance sorting data comprises the proportion of times of selecting each power supply reliability index as 1, 2,3 and 4 in the total number of investigation customers;
5.2) calculating importance weight of each ranking
5.1) after the step is finished, calculating the weight of each ranking in the importance ranking by adopting an analytic hierarchy process, firstly setting the scale of each ranking to obtain an importance judgment matrix, and then obtaining the importance weight through normalization processing;
5.3) calculating the weight of each power supply reliability index
5.2) after the step is finished, counting the times of selecting each power supply reliability index as 1, 2,3 and 4 respectively and calculating the proportion of the power supply reliability index to the total number of investigation customers, wherein the weight of each power supply reliability index is calculated according to the following formula
Wherein,w i is shown asiThe overall weight of each power supply reliability index,r ij when representing the ordering will beiThe individual power supply reliability index is selected asjThe named customers account for the proportion of the total survey customers,rank first in the representation importance rankingjThe corresponding weight.
5.4) establishing a power supply reliability and customer comprehensive satisfaction quantitative relation model according to the following method:
according to the quantitative relational expression of the power supply reliability indexes and the customer satisfaction degree established in the steps 1), 2), 3) and 4), combining the weights of the indexes obtained by calculation in the step 5.3), and establishing a quantitative relational model of the power supply reliability indexes and the customer comprehensive satisfaction degree according to the following formula;
whereinSIIn order to integrate the satisfaction scores for the customers,f i (x) Is shown asiAnd quantifying a relational expression between the power supply reliability index and the customer satisfaction degree.
Examples
The method for establishing the quantitative model of the relationship between the power supply reliability and the customer satisfaction degree in a certain area comprises the following specific steps:
1) quantitative relation model establishment of average power failure time prearranged by user and customer satisfaction
1.1) inputting customer satisfaction investigation data with power supply reliability as a target, wherein the customer satisfaction investigation data comprises 23 percent of the number of customers with 'very satisfactory' in the total number of investigation users, 49 percent of the number of customers with 'comparatively satisfactory' in the total number of investigation users, 26 percent of the number of customers with 'general' in the total number of investigation users, 1 percent of the number of customers with 'comparatively unsatisfactory' in the total number of investigation users and 1 percent of the number of customers with 'very unsatisfactory' in the total number of investigation users, and the average value of the expected prearranged average power failure time of the corresponding customers of each satisfaction is as follows:
| degree of satisfaction | Is very satisfactory | Is relatively satisfied | In general | Is less satisfactory | Is very unsatisfactory |
| Prearranged blackout time expectation | 2.2 | 3.4 | 4.6 | 6.3 | 7.8 |
1.2) inputting annual average power off time of feeder and sequencing
1.1) after the step is finished, inputting all users of 3532 feeder lines of a power supply office to pre-arrange average power failure time and arranging the users in ascending order;
1.3) calculating the proportion of customers deemed to have met the satisfaction level
1.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 1% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 1%=23%+49%+26%= 98%
1.4) calculating the average power failure time of the user prearranged corresponding to the qualified satisfaction degree score
1.3) after the step is finished, setting the satisfaction degree and the qualification scorecTaking the pre-scheduled average power failure time of the 3461-th user in the ascending sequence of the pre-scheduled average power failure time of the feeder line as the pre-scheduled average power failure time of the user reaching the satisfaction degree and the qualification gradedAt 21.4 hours;
1.5) setting the average power failure time of the prearranged users corresponding to the score of the full mark satisfaction degree
1.4) after the step is finished, setting the satisfaction degree full mark fractioneThe average power failure time of the corresponding user is set to be 0 when the average power failure time is 100 minutes;
1.6) calculating the satisfaction score corresponding to very satisfactory in satisfaction
1.5), selecting all values of 812 before ranking in the ascending sequence of the prearranged average power failure time, and calculating the average valueT 11=3.0 hours, then the fraction of satisfaction corresponding to very satisfactory was calculated as followsS 11；
1.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
1.6), selecting all the values from 812 th to 2543 th in the ascending sequence of the prearranged average power failure time, and calculating the average valueT 12=7.0 hours, then the fraction of satisfaction corresponding to very satisfactory was calculated as followsS 12；
1.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
1.7), selecting all the values from 2543 to 3461 in the ascending sequence of the prearranged average power failure time, and calculating the average valueT 13=14.1 hours, and then the fraction of satisfaction corresponding to very satisfactory was calculated as followsS 13；
1.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
1.8), selecting all the values ranked from 3461 to 3497 in the ascending sequence of the prearranged average power failure time, and calculating the average valueT 14=26.4 hours, and then the fraction of satisfaction corresponding to very satisfactory was calculated as followsS 14；
1.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
1.9), firstly selecting all the values ranked from 3497 to 3532 in the ascending sequence of the prearranged average power failure time, and calculating the average valueT 15=30.4 hours, and then the fraction of satisfaction corresponding to very satisfactory was calculated as followsS 15；
1.11) establishing a quantitative relation model between the user prearranged average power failure time and the customer satisfaction
After the step 1.10) is finished, calculating a satisfaction degree score series corresponding to five satisfaction degrees according to the step 1.6), the step 1.7), the step 1.8), the step 1.9) and the step 1.10) to obtainS 1={93.3,84.3,68.3,40.8,31.9} and expected Pre-scheduled blackout time sequenceT 1= {2.2,3.4,4.6,6.3,7.8}, call piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (kop) (2.2, 3.4,4.6,6.3, 7.8) }T 1, S 1) And fitting a relation curve of the average power failure time prearranged by the user and the satisfaction degree score, and calculating a curve analysis expression.
2) User pre-arrangement average power failure times and customer satisfaction quantitative relation model establishment
Inputting a user expected prearranged power failure number average value sequence corresponding to five satisfaction degrees according to the calculation method in the step 1)T 2= 1.2,2.3,3.4,4.8,6.2 and the pre-scheduled average outage times of all feeder users, the pre-scheduled average outage times for users are calculated as indexes "very satisfactory", "comparatively satisfactory", "general", "comparatively unsatisfactory" and"very unsatisfied" five satisfaction degree corresponding to satisfaction degree score sequenceS 2={S 21,S 22,S 23,S 24,S 25Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 2, S 2) And fitting a quantitative relation curve of the average power failure times and the customer satisfaction of the user prearranged, and calculating a curve analysis expression.
3) User fault average power failure time and customer satisfaction quantitative relation model establishment
Inputting a user expected failure power failure time average value sequence corresponding to five satisfaction degrees according to the calculation method in the step 1)T 3Calculating satisfaction score sequences corresponding to five satisfaction degrees of 'very satisfactory', 'comparatively satisfactory', 'common', 'comparatively unsatisfactory' and 'very unsatisfactory' respectively for user fault average power failure time indexes of {0.6,2.4,4.5,6.6,8.7} and all feeder user fault average power failure timeS 3={S 31,S 32,S 33,S 34,S 35Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 3, S 3) And fitting a quantitative relation curve of the average power failure time of the user and the customer satisfaction degree, and calculating a curve analysis expression.
4) Calculating the satisfaction degree score corresponding to each satisfaction degree of the average failure times
Inputting a user fault power failure number average value sequence corresponding to five satisfaction degrees according to the calculation method in the step 1)T 4Calculating satisfaction degree score sequences corresponding to five satisfaction degrees of 'very satisfactory', 'comparatively satisfactory', 'common', 'comparatively unsatisfactory' and 'very unsatisfactory' respectively for user fault average power failure indexes of {0.4,1.6,2.6,4,5.2} and all feeder line user fault average power failure timesS 4={S 41,S 42,S 43,S 44,S 45Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 4, S 4) And fitting a quantitative relation curve of the average power failure time of the user and the customer satisfaction degree, and calculating a curve analysis expression.
5) Calculating power supply reliability index weight
5.1) input satisfaction investigation data
The importance ranking data of the customers counted in the satisfaction investigation on the four indexes of the prearranged power failure time, the prearranged power failure times, the failure power failure time and the failure power failure times is input, and the importance ranking data comprises the proportion of the times of selecting each power supply reliability index as 1, 2,3 and 4 in the total number of investigation customers, and is shown in the following table.
| Rank of name | Prearranged blackout times | Prearranged power failure times | Time of power failure | Number of power failures |
| 1 | 0.073 | 0.161 | 0.210 | 0.589 |
| 2 | 0.097 | 0.169 | 0.452 | 0.282 |
| 3 | 0.726 | 0.073 | 0.129 | 0.056 |
| 4 | 0.105 | 0.597 | 0.210 | 0.073 |
5.2) calculating importance weight of each ranking
And 5.1) after the step is finished, calculating the weight of each ranking in the importance ranking by adopting an analytic hierarchy process.
Firstly, setting the scale of each ranking
| Rank of name | 1 | 2 | 3 | 4 |
| Scale | 8 | 6 | 4 | 2 |
Obtaining an importance judgment matrix
| Rank of name | 1 | 2 | 3 | 4 |
| 1 | 1 | 2 | 4 | 6 |
| 2 | 0.5 | 1 | 2 | 4 |
| 3 | 0.25 | 0.5 | 1 | 2 |
| 5 | 0.166667 | 0.25 | 0.5 | 1 |
Then, the importance weight is obtained through normalization processing
| Product per row | Root of 5 times | Summing | Normalization process (weight) |
| 48 | 2.63 | 5.133 | 0.513 |
| 4 | 1.41 | 0.275 | |
| 0.25 | 0.71 | 0.138 | |
| 0.020833 | 0.38 | 0.074 |
The index weights ranked 1 to 4 are 0.513, 0.275, 0.138, and 0.074, respectively.
5.3) calculating the weight of each power supply reliability index
After the step 5.2), the weight of the reliability index is calculated according to the following formula
Wherein,w i is shown asiThe overall weight of each power supply reliability index,r ij when representing the ordering will beiThe individual power supply reliability index is selected asjThe named customers account for the proportion of the total survey customers,
rank first in the representation importance rankingjA corresponding weight;
obtain the following table of the weight of each index
| Prearranged blackout times | Prearranged power failure times | Time of power failure | Number of power failures | |
| Weight of | 0.169 | 0.179 | 0.262 | 0.390 |
5.4) establishing a power supply reliability and customer comprehensive satisfaction quantitative relation model
And establishing a power supply reliability index and customer comprehensive satisfaction quantitative relation model according to the following formula by combining each index weight obtained by calculation in the step 5.3) according to the quantitative relation expression of each power supply reliability index and customer satisfaction established in the steps 1), 2), 3) and 4).
WhereinSIIn order to integrate the satisfaction scores for the customers,y i (t) Is shown asiAnd quantifying a relational expression between the power supply reliability index and the customer satisfaction degree.
Results of the experiment
The method is applied to the establishment of the quantitative relation model of the relationship between the power supply reliability of the Dongguan power supply bureau and the customer satisfaction degree of Guangdong power grid company. Before the model is established, questionnaire investigation on power supply reliability and customer satisfaction is widely carried out, and data related to customer satisfaction obtained by investigation is counted.
The calculated satisfaction degree scores corresponding to the five satisfaction degrees and four power supply reliability index values of the prearranged power failure time, the prearranged power failure times, the fault power failure time and the fault power failure times are as follows
The quantitative model expression of the relationship between the prearranged power failure time and the customer satisfaction degree is as follows:
the quantitative model expression of the relationship between the prearranged power failure times and the customer satisfaction degree is as follows:
the quantitative model expression of the relationship between the fault power failure time and the customer satisfaction degree is as follows:
the quantitative model expression of the relationship between the failure power failure times and the customer satisfaction degree is as follows:
the relation model of the power supply reliability index and the comprehensive satisfaction degree of the customer is as follows:
according to the results, the method for establishing the quantitative relation model between the power supply reliability and the customer satisfaction is that four power supply reliability management indexes, namely the prearranged power failure time, the prearranged power failure times, the fault power failure time and the fault power failure times, and the quantitative relation between the customer satisfaction can be obtained in the establishing process, and the method is closer to the engineering practice; the index weight calculation is based on the actual condition of the client, so that the method is more persuasive; the method has simple interface, is convenient for engineering personnel to learn and is practical, has better universality and can effectively know the change condition of the customer satisfaction degree.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (4)
1. A power supply reliability and customer satisfaction relation quantitative model building method is characterized in that: according to the customer satisfaction investigation data, firstly, a quantitative relation model of the customer satisfaction and four power supply reliability indexes of prearranged power failure time, prearranged power failure times, fault power failure time and fault power failure times is established respectively, then the weights of the four power supply reliability indexes are calculated, and finally, a quantitative relation model of the power supply reliability and the customer overall satisfaction is established.
2. The method for establishing the power supply reliability and customer satisfaction relation quantitative model according to claim 1, wherein:
1) the quantitative relation model of the average power failure time prearranged by the user and the customer satisfaction degree is established according to the following steps:
1.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 11% and expected pre-scheduled blackout time averageT 11The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 12% and expected pre-scheduled blackout time averageT 12The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 13% and expected pre-scheduled blackout time averageT 13The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 14% and expected pre-scheduled blackout time averageT 14And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 15% and expected pre-scheduled blackout time averageT 15；
1.2) input feeder Preset annual average blackout time and sequencing
1.1) inputting all the information into the power supply stationnThe average power failure time is prearranged by users of the feeder lines and is arranged according to the ascending order;
1.3) calculating the proportion of customers deemed to have met the satisfaction level
1.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 1% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 1%= a 11%+ a 12%+ a 13%
1.4) calculating the average power failure time of the user prearranged corresponding to the qualified satisfaction degree score
1.3) after the step is finished, setting the satisfaction degree and the qualification scorecGet the feeder lineRank one in the ascending sequence of the pre-scheduled average outage timen×b 1% bit user pre-scheduled average outage time as the pre-scheduled user average outage time to achieve satisfaction and qualification scored；
1.5) setting the average power failure time of the prearranged users corresponding to the score of the full mark satisfaction degree
1.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the corresponding average power failure time of the user to be 0;
1.6) calculating the satisfaction score corresponding to very satisfactory in satisfaction
1.5) after the step is finished, selecting the rank before the rank in the ascending sequence of the prearranged average power failure timen×a 11% of all the values, the average value of which is calculatedA 11Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 11；
1.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
1.6) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 11% ton×（a 11+a 12) % of all the values, the average value of which is calculatedA 12Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 12；
1.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
1.7) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 12% ton×（a 12+a 13) % of all the values, the average value of which is calculatedA 13Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 13；
1.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
1.8), selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 13% ton×（a 13+a 14) % of all the values, the average value of which is calculatedA 14Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 14；
1.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
1.9) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timen×a 14% ton×（a 14+a 15) % of all the values, the average value of which is calculatedA 15Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 15；
1.11) establishing a quantitative relation model between the user prearranged average power failure time and the customer satisfaction
After the step 1.10) is finished, calculating a satisfaction degree score series corresponding to five satisfaction degrees according to the step 1.6), the step 1.7), the step 1.8), the step 1.9) and the step 1.10) to obtainS 1={S 11,S 12,S 13,S 14,S 15And expected prearranged blackout time sequenceT 1={T 11,T 12,T 13,T 14,T 15Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 1, S 1) Fitting a user prearranged average power failure time and satisfaction degree score relation curve, and calculating a curve analysis expression;
2) the quantitative relation model of the average power failure times prearranged by the user and the customer satisfaction degree is established according to the following steps:
2.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 21% and expected pre-scheduled blackout number averageT 21The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 22% and expected pre-scheduled blackout number averageT 22The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 23% and expected pre-scheduled blackout number averageT 23The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 24% and expected pre-scheduled blackout number averageT 24And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 25% and expected pre-scheduled blackout number averageT 25；
2.2) input feeder Preset annual average blackout times and sequencing
2.1) inputting all the information into the power supply stationnThe average power failure times are prearranged by users of the feeder lines and are arranged according to the ascending order;
2.3) calculating the proportion of customers deemed to have met the satisfaction level
2.2) calculating the proportion of the customers with the research selections of 'very satisfied', 'comparatively satisfied' and 'general' to the total number of the research customersb 2% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 2%= a 21%+ a 22%+ a 23%
2.4) calculating the average power failure times of the users corresponding to the qualified satisfaction degree scores
2.3) after the step is finished, setting the satisfaction degree and the qualification scorecThe feeder line is taken to be ranked the first in the ascending sequence of the average power failure timesn×b 2% bit user pre-scheduled average outage times as pre-scheduled user average outage times to achieve satisfaction and qualification scored；
2.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
2.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
2.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
2.5) after finishing, selecting the rank before ranking in the ascending sequence of the pre-arranged average power failure timesn×a 21% of all the values, the average value of which is calculatedA 21Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 21；
2.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
2.6) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 21% ton×（a 21+a 22) % of all the values, the average value of which is calculatedA 22Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 22；
2.8) calculating the satisfaction score corresponding to the general satisfaction
2.7) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 22% ton×（a 22+a 23) % of all the values, the average value of which is calculatedA 23Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 23；
2.9) calculating satisfaction scores corresponding to less than satisfactory ones of the degrees of satisfaction
2.8) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 23% ton×（a 23+a 24) % of all the values, the average value of which is calculatedA 24Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 24；
2.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
2.9) after the step is finished, selecting the ranking in the ascending sequence of the pre-arranged average power failure timesn×a 24% ton×（a 24+a 25) % of all the values, the average value of which is calculatedA 25Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 25；
2.11) establishing a quantitative relation model between the pre-arranged average power failure times of the users and the customer satisfaction
After the step 2.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 2.6), the step 2.7), the step 2.8), the step 2.9) and the step 2.10) to obtain a satisfaction degree score seriesS 2={S 21,S 22,S 23,S 24,S 25And expected prearranged blackout times sequenceT 2={T 21,T 22,T 23,T 24,T 25Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 2, S 2) Fitting a relation curve of the average power failure times and the satisfaction degree score prearranged by the user, and calculating a curve analysis expression;
3) the quantitative relation model of the average power failure time of the user fault and the customer satisfaction degree is established according to the following steps:
3.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 31% and expected mean time to failureT 31The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 32% and expected mean time to failureT 32The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 33% and expected mean time to failureT 33The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 34% and expected mean time to failureT 34And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 35% and expected mean time to failureT 35；
3.2) inputting the annual average power failure time of feeder line fault and sequencing
3.1) inputting all the information into the power supply stationnAverage power failure time of user faults of the feeder lines is arranged according to ascending order;
3.3) calculating the proportion of customers deemed to have met the satisfaction level
3.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 3% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 3%= a 31%+ a 32%+ a 33%
3.4) calculating the average power failure time of the user corresponding to the qualified satisfaction degree score
3.3) after the completion of the step, setting the satisfaction degree and the qualification scorecTaking the ranking in the ascending sequence of the mean power failure time of the feeder line faultn×b 3% bit user fault mean time to outage as a pre-scheduled average number of user outages to achieve satisfaction and qualification scored；
3.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
3.4) after the step is finished, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
3.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
3.5) after finishing, selecting the rank before the sequence in the ascending sequence of the mean time to power off of the faultn×a 31% of all the values, the average value of which is calculatedA 31Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 31；
3.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
3.6) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 31% ton×（a 31+a 32) % of all the values, the average value of which is calculatedA 32Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 32；
3.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
3.7) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 32% ton×（a 32+a 33) % of all the values, the average value of which is calculatedA 33Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 33；
3.9) calculating satisfaction scores corresponding to the comparative dissatisfaction in the satisfaction
3.8) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 33% ton×（a 33+a 34) % of all the values, the average value of which is calculatedA 34Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 34；
3.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
3.9) after the step is finished, selecting the ranking in the ascending sequence of the mean time to power failure of the faultn×a 34% ton×（a 34+a 35) % of all the values, the average value of which is calculatedA 35Then, the satisfaction degree of the very satisfactory correspondence is calculated as followsNumber ofS 35；
3.11) establishing a quantitative relation model between the average power failure time of the user fault and the customer satisfaction
After the step 3.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 3.6), the step 3.7), the step 3.8), the step 3.9) and the step 3.10) to obtain a satisfaction degree score seriesS 3={S 31,S 32,S 33,S 34,S 35And expected failure blackout time sequenceT 3={T 31,T 32,T 33,T 34,T 35Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 3, S 3) Fitting a user fault average power failure time and satisfaction degree score relation curve, and calculating a curve analysis expression;
4) the satisfaction degree score corresponding to each satisfaction degree of the average failure times is calculated according to the following steps:
4.1) inputting customer satisfaction investigation statistical data with power supply reliability as a target, comprising: selecting the proportion of the number of 'very satisfied' customers to the total number of users in investigationa 41% and average expected number of failed outagesT 41The proportion of the number of the selected 'more satisfied' customers to the total number of the users in the research is selecteda 42% and average expected number of failed outagesT 42The proportion of the number of the selected 'general' customers to the total number of the investigation usersa 43% and average expected number of failed outagesT 43The proportion of the number of the selected 'unsatisfactory' customers to the total number of the users in the research is selecteda 44% and average expected number of failed outagesT 44And selecting the proportion of the number of 'very unsatisfied' customers to the total number of users in the investigationa 45% and average expected number of failed outagesT 45；
4.2) inputting annual average power failure times of feeder line faults and sequencing
4.1) inputting all the information into the power supply stationnAverage power failure times of user faults of the feeder lines are arranged in ascending order;
4.3) calculating the proportion of customers deemed to have met the satisfaction level
4.2) calculating the proportion of the customers with the investigation selection of 'very satisfactory', 'comparatively satisfactory' and 'general' to the total number of investigation customersb 4% i.e. the proportion of customers deemed to have met the passing level, is calculated as:
b 4%= a 41%+ a 42%+ a 43%
4.4) calculating the average power failure times of the user corresponding to the qualified satisfaction degree scores
4.3) after the step is finished, setting the satisfaction degree and the qualification scorecTaking the ranking of the feeder line fault average power failure times in the ascending sequencen×b 4% bit average number of subscriber failures as pre-scheduled average number of subscriber outages to achieve satisfaction and qualification scored；
4.5) setting the average power failure times of the prearranged users corresponding to the score of the full mark satisfaction degree
4.4) after the completion of the step, setting the satisfaction degree full mark fractioneSetting the average power failure frequency of the corresponding user to be 0;
4.6) calculating a satisfaction score corresponding to a very satisfactory one of the degrees of satisfaction
4.5) after finishing the step, firstly selecting the rank before the rank in the ascending sequence of the average power failure times of the faultsn×a 41% of all the values, the average value of which is calculatedA 41Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 41；
4.7) calculating the satisfaction score corresponding to the comparative satisfaction in the satisfaction
4.6) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 41% ton×（a 41+a 42) % of all the values, the average value of which is calculatedA 42Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 42；
4.8) calculating the satisfaction score corresponding to the general satisfaction in the degree of satisfaction
4.7) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 42% ton×（a 42+a 43) % of all the values, the average value of which is calculatedA 43Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 43；
4.9) calculating the satisfaction score corresponding to the dissatisfaction among the degrees of satisfaction
4.8) after the step is finished, firstly selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 43% ton×（a 43+a 44) % of all the values, the average value of which is calculatedA 44Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 44；
4.10) calculating a satisfaction score corresponding to a very unsatisfactory one of the degrees of satisfaction
4.9) after the step is finished, selecting the ranking in the ascending sequence of the average power failure times of the faultsn×a 44% ton×（a 44+a 45) % of all the values, the average value of which is calculatedA 45Then, the satisfaction score corresponding to the very satisfactory is calculated as followsS 45；
4.11) establishing a quantitative relation model between the average power failure times of the user faults and the customer satisfaction degree
After the step 4.10) is finished, calculating a satisfaction degree score series corresponding to the five satisfaction degrees obtained according to the step 4.6), the step 4.7), the step 4.8), the step 4.9) and the step 4.10) to obtain a satisfaction degree score seriesS 4={S 41,S 42,S 43,S 44,S 45And expected failure outage times sequenceT 3={T 41,T 42,T 43,T 44,T 45Call the piecewise cubic Hermite interpolation fitting function PCHIP in Matlab program (T 4, S 4) And fitting a relation curve between the average power failure times of the user faults and the satisfaction degree score, and calculating a curve analysis expression.
3. The method for establishing the power supply reliability and customer satisfaction relation quantitative model according to claim 1 or 2, wherein: 5) the weight calculation method of the four power supply reliability indexes comprises the following steps:
5.1) input satisfaction investigation data
Inputting importance sorting data of customers counted in satisfaction investigation on four indexes of prearranged power failure time, prearranged power failure times, failure power failure time and failure power failure times, wherein the importance sorting data comprises the proportion of times of selecting each power supply reliability index as 1, 2,3 and 4 in the total number of investigation customers;
5.2) calculating importance weight of each ranking
5.1) after the step is finished, calculating the weight of each ranking in the importance ranking by adopting an analytic hierarchy process, firstly setting the scale of each ranking to obtain an importance judgment matrix, and then obtaining the importance weight through normalization processing;
5.3) calculating the weight of each power supply reliability index
5.2) after the step is finished, counting the times of selecting each power supply reliability index as 1, 2,3 and 4 respectively and calculating the proportion of the power supply reliability index to the total number of investigation customers, wherein the weight of each power supply reliability index is calculated according to the following formula
Wherein,w i is shown asiThe overall weight of each power supply reliability index,r ij when representing the ordering will beiThe individual power supply reliability index is selected asjThe named customers account for the proportion of the total survey customers,rank first in the representation importance rankingjThe corresponding weight.
4. The method for establishing the power supply reliability and customer satisfaction relation quantitative model according to claim 3, wherein: 5.4) establishing a power supply reliability and customer comprehensive satisfaction quantitative relation model according to the following method:
according to the quantitative relational expression of the power supply reliability indexes and the customer satisfaction degree established in the steps 1), 2), 3) and 4), combining the weights of the indexes obtained by calculation in the step 5.3), and establishing a quantitative relational model of the power supply reliability indexes and the customer comprehensive satisfaction degree according to the following formula;
whereinSIIn order to integrate the satisfaction scores for the customers,f i (x) Is shown asiAnd quantifying a relational expression between the power supply reliability index and the customer satisfaction degree.
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