CN118644268B - Electric power marketing management system and method based on Internet of things - Google Patents
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Abstract
The invention discloses an electric power marketing management system and method based on the Internet of things, which relate to the technical field of electric power marketing and comprise the following steps: the system comprises an electricity consumption environment monitoring and analyzing module, an electricity consumption behavior monitoring and analyzing module, an electricity consumption quality monitoring and analyzing module, an electricity consumption abnormality degree evaluating module and an electricity marketing database. The invention realizes the fine management of the electricity consumption behavior and the electricity consumption quality of the user, and is beneficial to improving the electricity consumption efficiency. The environment data and the electricity consumption behavior are monitored in real time, a user is guided to optimize the electricity consumption behavior, personalized service is provided, the electricity consumption quality is improved by electricity consumption quality monitoring, resource allocation is optimized, the supply and demand relationship is balanced, excessive power generation or power grid overload is reduced, and stable and reliable power supply is ensured. Through the power consumption abnormality degree evaluation module, the power failure or abnormal situation can be rapidly identified, and a response mechanism is started immediately, so that the fault recovery time is shortened, and the influence caused by the power abnormality is reduced.
Description
Technical Field
The invention relates to the technical field of electric power marketing, in particular to an electric power marketing management system and method based on the Internet of things.
Background
Along with the rapid development of the power industry and the gradual opening of the power market, the construction and optimization of the power marketing management system become important means for improving the operation efficiency, enhancing the customer satisfaction and realizing the fine management of power enterprises. The power marketing management system aims at integrating all links of the power marketing business through an informatization means so as to realize efficient configuration of power resources and accurate provision of services. With the wide application of technologies such as big data, cloud computing, artificial intelligence, etc., the electric power marketing management system gradually develops to the direction of intellectualization, automation and integration.
For example, the invention patent with publication number CN116503023B is a method for checking abnormal power information based on a power marketing management system, which comprises the following steps: acquiring a power network structure to be checked; segmenting connecting wires formed by all nodes and power transmission lines in the power network structure according to a preset segmentation strategy to obtain a plurality of power supply segments; counting power supply sections which are not directly connected with the grid-connected power supply node as first power supply sections and obtaining a first section set, and determining the power supply sections which are directly connected with the grid-connected power supply node as second power supply sections; obtaining first line loss information of all the first power supply sections according to the mains supply information and the first power information, and obtaining second line loss information of each second power supply section according to the mains supply information, the grid-connected power supply information and the second power consumption information; positioning the abnormal power supply main section or power supply branch section and adding a corresponding abnormal inspection label; and positioning the abnormal power supply main section or the abnormal power supply branch section and adding a corresponding abnormal inspection label.
For example, the invention patent with publication number CN110717784B is an electric power marketing system and method based on an intelligent management platform, comprising: a server with a built-in data processor, wherein the server is connected to an Internet network; a database storing data, the database being connected to an internet network; the power marketing data management platform is connected with the server through an Internet network, classifies information related to power data stored in the database through the server, and performs information interaction with the database through the Internet; the data output end of the data acquisition and processing terminal is connected with the electric power marketing data management platform through an Internet network; the data acquisition terminal of the data acquisition processing terminal is connected with the intelligent ammeter through an Internet network, acquires electric quantity data information of the intelligent ammeter, and transmits the electric quantity data information to the electric power marketing data management platform after processing; the power marketing data management platform comprises a login module, a user input module and a system function module.
However, in the process of implementing the technical scheme of the embodiment of the application, the application discovers that the above technology has at least the following technical problems: at present, in the aspect of power marketing management, although a system can collect a large amount of electricity consumption data, the processing and analysis of the data are often limited to basic statistics and report generation, so that the system is difficult to timely and accurately capture abnormal changes of the electricity consumption behaviors of users, and comprehensive understanding and accurate management of the electricity consumption behaviors of the users are affected.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a power marketing management system and method based on the Internet of things, which can effectively solve the problems related to the background technology.
In order to achieve the above purpose, the invention is realized by the following technical scheme: the first aspect of the invention provides an electric power marketing management system based on the Internet of things, which comprises: and the electricity utilization environment monitoring and analyzing module is used for collecting the environment data of the target area and analyzing to obtain the first factor influence index.
And the electricity consumption behavior monitoring and analyzing module is used for monitoring the electricity consumption behavior data and comprehensively analyzing the electricity consumption behavior data by combining the first factor influence indexes to obtain the electricity consumption behavior deviation degree value of the user.
And the electricity quality monitoring and analyzing module is used for monitoring the electricity quality data and obtaining the electricity quality deviation degree value of the user through processing.
The power consumption abnormal degree evaluation module is used for acquiring the characterization data of the data acquisition equipment, analyzing to obtain a second factor influence index, comprehensively analyzing to obtain a user power consumption abnormal degree evaluation index according to the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value, evaluating the user power consumption abnormal degree according to the second factor influence index and the user power consumption abnormal degree evaluation index, obtaining an evaluation result and feeding back.
As a further method, the analysis obtains a first factor influence index, and the specific analysis process is as follows: the target area environment data comprises the number of public transportation stations, the target area, the building density and the number of floors of each building.
And carrying out ratio operation on the number of public transportation stations and the area of the target area to obtain the density of the public transportation stations, obtaining the density of the critical public transportation stations, the density of the critical buildings and the number of floors of the critical buildings from the electric power marketing database, and comprehensively analyzing to obtain the first factor influence index.
As a further method, the comprehensive analysis obtains the deviation degree value of the electricity consumption behavior of the user, and the specific analysis process is as follows: the electricity consumption behavior data comprise the electricity consumption and average load of each monitoring period.
And acquiring critical electricity consumption, critical average load, preset electricity consumption, average load and electricity consumption behavior deviation influence weight corresponding to the first factor influence index from the electricity marketing database, and comprehensively analyzing to obtain a user electricity consumption behavior deviation degree value.
As a further method, the processing obtains a user electricity quality deviation degree value, and the specific analysis process is as follows: the electricity quality data comprise actual electricity consumption voltage and frequency deviation of the user at each monitoring time point.
And acquiring reference standard voltage, allowable deviation voltage and critical frequency deviation from the electric power marketing database, and comprehensively analyzing to obtain the user electricity quality deviation degree value.
As a further method, the analysis obtains a second factor influence index, and the specific analysis process is as follows: the characterization data of the data acquisition equipment comprises the accumulated working time length and the environment floating dust concentration of each data acquisition equipment.
And acquiring the critical working time length and the critical floating dust concentration of each data acquisition device from the electric power marketing database, and comprehensively analyzing to obtain a second factor influence index.
As a further method, the comprehensive analysis is used for obtaining an evaluation index of the abnormal degree of the electricity consumption of the user, and the specific analysis process is as follows: and comprehensively analyzing the degree of abnormality of the electricity consumption of the user according to the degree of deviation value of the electricity consumption behavior of the user and the degree of deviation value of the electricity consumption quality of the user, wherein the degree of abnormality of the electricity consumption of the user is estimated by the degree of abnormality estimation index of the electricity consumption of the user, and the degree of abnormality of the electricity consumption data is comprehensively analyzed from two angles of the electricity consumption behavior and the electricity consumption quality of the user, so that data support is provided for electric power marketing management.
As a further method, the evaluation of the abnormal electricity consumption degree of the user comprises the following specific processes: and according to the second factor influence index, matching the power marketing database to obtain a user power consumption abnormality degree evaluation index threshold.
And comparing the user electricity consumption abnormality degree evaluation index with a user electricity consumption abnormality degree evaluation index threshold, evaluating the user electricity consumption behavior as abnormal if the user electricity consumption abnormality degree evaluation index is larger than or equal to the user electricity consumption abnormality degree evaluation index threshold, and evaluating the user electricity consumption behavior as normal if the user electricity consumption abnormality degree evaluation index is smaller than the user electricity consumption abnormality degree evaluation index threshold.
The second aspect of the invention provides a power marketing management method based on the Internet of things, which comprises the following steps: and collecting the environmental data of the target area, and analyzing to obtain a first factor influence index.
And monitoring the electricity consumption behavior data, and comprehensively analyzing by combining the first factor influence index to obtain the electricity consumption behavior deviation degree value of the user.
And monitoring the electricity quality data, and processing to obtain the electricity quality deviation degree value of the user.
And obtaining characterization data of the data acquisition equipment, analyzing to obtain a second factor influence index, comprehensively analyzing to obtain a user electricity consumption abnormality degree evaluation index according to the user electricity consumption behavior deviation degree value and the user electricity consumption quality deviation degree value, evaluating the user electricity consumption abnormality degree according to the second factor influence index and the user electricity consumption abnormality degree evaluation index, obtaining an evaluation result and feeding back.
Compared with the prior art, the embodiment of the invention has at least the following beneficial effects:
(1) According to the electric power marketing management system and method based on the Internet of things, the user electricity consumption behavior and electricity consumption quality are finely managed, and the improvement of electricity consumption efficiency is facilitated. The user is guided to optimize the electricity consumption behavior by monitoring the environment data and the electricity consumption behavior in real time, personalized service is provided, the electricity consumption quality is improved by monitoring the electricity consumption quality, the resource allocation is optimized, and stable and reliable power supply is ensured.
(2) The invention is helpful for predicting the electricity behavior mode of the user by comprehensively analyzing the density of public transportation stations, the building density and the number of building floors, so that the power grid company can more accurately predict and schedule the power supply, and the reliability and the stability of power supply are improved. Meanwhile, through optimizing public transportation network layout and reasonably controlling building density and height, the waste phenomenon in the electricity consumption behavior of users can be further reduced, and the energy utilization efficiency is improved.
(3) According to the invention, through comprehensively analyzing the accumulated working time of the data acquisition equipment and the concentration of the environmental floating dust, the maintenance period of the equipment can be predicted, necessary maintenance and calibration work can be arranged in advance, and inaccurate or interrupted data caused by equipment faults can be prevented. By identifying the influence of the concentration of floating dust and the working time on the data quality, corresponding measures can be taken in the data processing stage, and the accuracy of final data analysis is improved.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
Fig. 1 is a schematic diagram of a system module connection according to the present invention.
FIG. 2 is a schematic flow chart of the method of the present invention.
FIG. 3 is a schematic diagram of the frequency deviation as a function of the user power quality deviation level value according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, a first aspect of the present invention provides an electric power marketing management system based on the internet of things, including: and the electricity utilization environment monitoring and analyzing module is used for collecting the environment data of the target area and analyzing to obtain the first factor influence index.
And the electricity consumption behavior monitoring and analyzing module is used for monitoring the electricity consumption behavior data and comprehensively analyzing the electricity consumption behavior data by combining the first factor influence indexes to obtain the electricity consumption behavior deviation degree value of the user.
And the electricity quality monitoring and analyzing module is used for monitoring the electricity quality data and obtaining the electricity quality deviation degree value of the user through processing.
The power consumption abnormal degree evaluation module is used for acquiring the characterization data of the data acquisition equipment, analyzing to obtain a second factor influence index, comprehensively analyzing to obtain a user power consumption abnormal degree evaluation index according to the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value, evaluating the user power consumption abnormal degree according to the second factor influence index and the user power consumption abnormal degree evaluation index, obtaining an evaluation result and feeding back.
Specifically, the first factor influence index is obtained through analysis, and the specific analysis process is as follows: the target area environment data comprises the number of public transportation stations, the target area, the building density and the number of floors of each building.
It should be understood that the number of public transportation stations in this embodiment refers to the total number of stations in the target area for providing transportation services to the public, including bus stations, subway stations, and light rail stations. These stations are key nodes in public transportation networks that connect different traffic lines so that passengers can easily transfer and reach destinations.
It should be understood that the building density in this embodiment refers to an indicator of the ratio of building footprint to total footprint in the target area. The building density typically has a value between 0% and 100%. In practice, building densities typically do not exceed 40% to 50% in order to make enough space available for road, greening, plaza, parking lot functions, etc.
And carrying out ratio operation on the number of public transportation stations and the area of the target area to obtain the density of the public transportation stations, obtaining the density of the critical public transportation stations, the density of the critical buildings and the number of floors of the critical buildings from the electric power marketing database, and comprehensively analyzing to obtain the first factor influence index.
In a specific embodiment, the preset electricity utilization behavior deviation influence factors corresponding to the density of public transportation stations, the density of buildings and the number of floors of buildings are extracted from the electricity marketing database, and the first factor influence index is obtained through comprehensive analysis, wherein the specific numerical expression is as follows:
；
In the method, in the process of the invention, Indicating that the first factor affects the indicator,Representing the density of the public transportation sites,Representing a critical mass transit stop density,Representing the density of the building in question,Representing the critical building density of the building,Indicating the number of floors of the i-th building,Indicating the number of floors of the critical building,Indicating the electricity behavior deviation influencing factor corresponding to the preset public transportation site density,Represents the electricity utilization behavior deviation influence factor corresponding to the preset building density,The electricity consumption behavior deviation influence factor corresponding to the preset number of building floors is represented by i, i=1, 2,3, & gt, n, and the total number of building floors is represented by i.
It should be understood that, in this embodiment, the value range of the electricity consumption behavior deviation influencing factor corresponding to the public transportation site density, the building density and the number of building floors is between 0 and 1, the mapping set of the public transportation site density, the building density and the number of building floors and the corresponding electricity consumption behavior deviation influencing factor is constructed by the relationship between the public transportation site density, the building density and the number of building floors and the first factor influencing index in the history data, the electricity consumption behavior deviation influencing factor corresponding to the public transportation site density, the electricity consumption behavior deviation influencing factor corresponding to the building density and the electricity consumption behavior deviation influencing factor corresponding to the number of building floors are input, and the electricity consumption behavior deviation influencing factor corresponding to the public transportation site density, the building floor number is obtained from the mapping set.
Table 1 first factor impact index data example
As shown in table 1, in one particular embodiment,For the average number of floors of a building,=5 Stations/km,=15%，The number of layers is =10,=0.2，=0.3，=0.5. The first factor influence index in this embodiment is a quantization index obtained by comprehensively analyzing the density of public transportation sites, the density of buildings and the number of floors of buildings in the target area, and is used for quantitatively evaluating the influence degree of the environmental data in the target area on the electricity consumption behavior of the user. The smaller the density of public transportation sites is, the larger the building density and the number of building floors are, and the larger the corresponding first factor influence indexes are, which shows that under the interference of the environmental factors of the target area, the larger the electricity demand of the user is.
It should be appreciated that the density of public transportation sites, the density of buildings, and the number of floors of the building in this embodiment may have some impact on the user's electricity usage. The high density of public transportation sites helps reduce the dependence of residents on private vehicles, thereby reducing the power demand generated by the traveling of private vehicles. Also, in urban areas of high density, buildings are typically taller and more dense, which can lead to poor natural ventilation and solar conditions between buildings, requiring more mechanical ventilation and artificial lighting, increasing power requirements. On the other hand, high-rise buildings are often equipped with elevators, central air conditioning systems, etc., thereby increasing power consumption.
In a particular embodiment, areas of high mass transit site density tend to mean higher accessibility and convenience, which may attract more residents and business collections, thereby driving an increase in the building density of the area. However, too high a building density may also result in traffic congestion and insufficient public space, which in turn affects the operating efficiency of the public transportation system. Areas with high density of public transportation sites may attract building projects of higher floors due to high traffic convenience. High-rise buildings can better utilize land resources, reduce ground traffic pressure, and provide more living or business space. On the other hand, since the land area occupied by high-rise buildings is relatively small, the higher the average number of building floors, the lower the building density is generally.
In the embodiment, the comprehensive analysis of the density of public transportation sites, the building density and the number of building floors is beneficial to predicting the electricity utilization behavior mode of users, so that the power grid company can more accurately predict and schedule the power supply, and the reliability and stability of power supply are improved. The relationship among the density of public transportation stations, the density of buildings and the number of building floors is comprehensively analyzed, so that the urban planning and construction in the future can be guided. Through optimizing public transportation network layout and reasonably controlling building density and height, the waste phenomenon in the electricity consumption behavior of users can be further reduced, and the energy utilization efficiency is improved.
Specifically, the comprehensive analysis obtains the deviation degree value of the electricity consumption behavior of the user, and the specific analysis process is as follows: the electricity consumption behavior data comprise the electricity consumption and average load of each monitoring period.
And acquiring critical electricity consumption, critical average load, preset electricity consumption, average load and electricity consumption behavior deviation influence weight corresponding to the first factor influence index from the electricity marketing database, and comprehensively analyzing to obtain a user electricity consumption behavior deviation degree value.
In a specific embodiment, the numerical expression of the deviation degree value of the user electricity behavior is:
；
In the method, in the process of the invention, Indicating the deviation degree value of the electricity consumption behavior of the user,Representing a functional expression for the independent variables as electricity consumption and average load,，Indicating the power consumption of the j-th monitoring period,Indicating the critical power consumption amount,Representing the average load of the jth monitoring period,Represents the critical average load of the load cell,Indicating that the first factor affects the indicator,Indicating the power utilization behavior deviation influence weight corresponding to the preset first factor influence index,Indicating the deviation influence weight of the electricity consumption behavior corresponding to the preset electricity consumption,And (3) representing the power consumption behavior deviation influence weight corresponding to the preset average load, j representing the number of each monitoring period, j=1, 2, 3.
The algorithm of the embodiment combines the first factor influence index, the electricity consumption and the average load, and comprehensively analyzes to obtain the electricity consumption behavior deviation degree value of the user. The first factor affects the index, the electricity consumption, and the average load, and the first factor affects the index directly to the electricity consumption demand of the user, for example, the lighting demand and the automobile charging demand directly increase the electricity consumption. There is a direct link between electricity usage and average load, but average load also takes into account electricity usage patterns in the time dimension, providing a time-varying relationship for electricity usage. The first factor influence index, the electricity consumption and the average load are comprehensively analyzed, so that more accurate electricity consumption behavior prediction can be provided, the power grid is helped to be finely managed, and personalized service can be provided based on specific electricity consumption behaviors of users. The comprehensive analysis result can identify the abnormal situation of electricity consumption behavior, provide energy-saving advice for users, and help them to better manage own electricity consumption requirements. The utility company may take demand side management measures, such as encouraging the user to use electricity during off-peak hours, to relieve grid stress, based on the user's pattern of electricity usage behavior.
It should be understood that, in this embodiment, the value range of the power consumption, average load and power consumption behavior deviation influence weight corresponding to the first factor influence index is between 0 and 1, and a mapping set of the power consumption, average load and first factor influence index and the corresponding power consumption behavior deviation influence weight is constructed by the relation between the power consumption, average load and first factor influence index and the power consumption behavior deviation degree value of the user in the historical data, the power consumption, average load and first factor influence index are input, and the power consumption behavior deviation influence weight corresponding to the first factor influence index, the power consumption behavior deviation influence weight corresponding to the power consumption and the power consumption behavior deviation influence weight corresponding to the average load are obtained from the mapping set.
It should be understood that, in this embodiment, the deviation degree value of the user power consumption behavior is used to quantify the abnormality degree of the user power consumption behavior, and the smaller the first factor influence index is, the larger the power consumption and the average load are, the larger the corresponding deviation degree value of the user power consumption behavior is, which indicates that the user power consumption behavior is abnormal. In this embodiment, the user electricity consumption behavior deviation degree value is mainly aimed at the situation that electricity consumption is abnormally increased, and the first factor influence index is used for explaining the situation that electricity consumption is abnormally increased. When the deviation degree value of the electricity consumption behavior of the user is equal to zero, the situation that the electricity consumption is abnormally increased is indicated.
Specifically, the user electricity quality deviation degree value is obtained through processing, and the specific analysis process is as follows: the electricity quality data comprise actual electricity consumption voltage and frequency deviation of the user at each monitoring time point.
It should be understood that in this embodiment, the frequency deviation refers to the deviation between the grid frequency and the nominal frequency, and may be monitored by a digital frequency table, where the nominal frequency refers to a standard frequency value set by the grid system in design and operation, typically 50Hz. The rotation speed of the motor is directly proportional to the frequency of the power grid, the frequency deviation can cause the rotation speed of the motor to change, the precision and the efficiency of the production process are affected, and meanwhile, the frequency deviation can change the relative frequency of harmonic waves in a power system, so that the harmonic waves are amplified or interacted, and the quality of electric energy is affected.
It should be understood that in this embodiment, the actual voltage of the electricity consumption of the user is monitored by the voltage sensor, the fluctuation of the actual voltage accelerates the aging process of the electrical equipment, shortens the service life of the equipment, and meanwhile, influences the measurement accuracy of the electric energy meter, and reduces the accuracy of electric quantity statistics.
In this embodiment, by monitoring the actual voltage and frequency deviation, it is helpful to find out that the voltage level deviates from the normal range in time, so as to avoid the degradation of the device performance. Meanwhile, the accuracy of electric energy metering is guaranteed, and the overall stability of the power system is improved.
And acquiring reference standard voltage, allowable deviation voltage and critical frequency deviation from the electric power marketing database, and comprehensively analyzing to obtain the user electricity quality deviation degree value.
In a specific embodiment, a power quality deviation influencing factor corresponding to preset actual voltage and frequency deviation is extracted from a power marketing database, and a user power quality deviation degree value is obtained through comprehensive analysis, wherein a specific numerical expression is as follows:
；
In the method, in the process of the invention, Indicating the deviation degree value of the electricity quality of the user,The actual voltage of the user electricity consumption at the r-th monitoring time point is represented,Which means that with reference to a standard voltage,Indicating the voltage of the allowed deviation (v/v),Indicating the frequency deviation of the r-th monitoring time point,Indicating the deviation of the critical frequency,Indicating the power quality deviation influence factor corresponding to the preset actual voltage,Represents the power quality deviation influence factor corresponding to the preset frequency deviation, r represents the number of each monitoring time point, r=1, 2,3,..h, h represents the total number of monitoring time points.
The algorithm of the embodiment combines the actual voltage and frequency deviation of the user electricity consumption, and comprehensively analyzes the actual voltage and frequency deviation to obtain the quality deviation degree value of the user electricity consumption. Deviations between voltage and frequency are often not independent, and have an interactive relation, for example, when the voltage of the system is reduced, the output power of the generator is reduced, and thus the frequency of the system is reduced, meanwhile, the voltage is also influenced by the change of the frequency, and when the frequency is reduced, the reactive power requirement of the system is increased, and the voltage is possibly reduced. The deviation of voltage and frequency jointly determines the quality of electric energy, and the deviation of the actual voltage and frequency of the user electricity consumption can be comprehensively analyzed to accurately evaluate the quality of electric energy and improve the fault diagnosis capability. By continuously monitoring the voltage and frequency deviations, the utility company can take appropriate regulation measures to maintain the power quality within an acceptable range, thereby improving the overall operating efficiency of the grid. The power quality deviation degree value of the user is obtained through comprehensive analysis, abnormal conditions in the power utilization process of the user can be accurately detected, an early warning mechanism is built, and possible power quality problems can be predicted in advance.
It should be understood that, in this embodiment, the value range of the power quality deviation influencing factor corresponding to the actual voltage and frequency deviation is between 0 and 1, and a mapping set of the actual voltage and frequency deviation and the corresponding power quality deviation influencing factor is constructed through the relation between the actual voltage and frequency deviation and the user power quality deviation degree value in the historical data, the actual voltage and frequency deviation is input, and the power quality deviation influencing factor corresponding to the actual voltage and the power quality deviation influencing factor corresponding to the frequency deviation are obtained from the mapping set.
As shown in fig. 3, in one particular embodiment, h=1,=220V，=5V，=0.5Hz，==1. When the actual voltage of the user electricity consumption is 210V, the functional relation between the frequency deviation and the degree of deviation of the quality of the user electricity consumption is shown as a curve a; when the actual voltage of the user electricity consumption is 218V, the functional relation between the frequency deviation and the degree of deviation of the quality of the user electricity consumption is shown as a curve b; when the actual voltage of the user electricity consumption is 225V, the frequency deviation and the degree of deviation of the quality of the user electricity consumption are functionally related as shown in a curve c. The user electricity quality deviation degree value is used for quantitatively evaluating the abnormality degree of the user electricity quality, and the more the actual voltage of the user electricity deviates from the reference standard voltage, the larger the frequency deviation is, and the corresponding user electricity quality deviation degree value indicates that the user electricity quality is abnormal.
Specifically, the second factor influence index is obtained through analysis, and the specific analysis process is as follows: the characterization data of the data acquisition equipment comprises the accumulated working time length and the environment floating dust concentration of each data acquisition equipment.
It should be appreciated that in this embodiment, the suspended particulate matter concentration sensor may be used to monitor, and the concentration of the floating dust may have a direct or indirect effect on the accuracy of the power marketing data collection device, and the floating dust particles may be deposited on the surface or inside the device, resulting in reduced performance of the device. After the airborne dust particles enter the interior of the device, wear of the mechanical components may be accelerated, resulting in a slow response or inaccurate measurement of the device.
And acquiring the critical working time length and the critical floating dust concentration of each data acquisition device from the electric power marketing database, and comprehensively analyzing to obtain a second factor influence index.
In a specific embodiment, a device measurement accuracy influence factor corresponding to a preset working time length and a floating dust concentration is extracted from an electric power marketing database, and a second factor influence index is obtained through comprehensive analysis, wherein a specific numerical expression is as follows:
；
In the method, in the process of the invention, Indicating that the second factor affects the indicator,Indicating the accumulated operating time of the p-th data acquisition device,Indicating the critical operating time of the p-th data acquisition device,Representing the ambient dust concentration of the p-th data acquisition device,Representing the critical fly ash concentration of the p-th data acquisition device,Indicating a device measurement accuracy influence factor corresponding to a preset working time length,Indicating a device measurement accuracy influence factor corresponding to the preset floating dust concentration, p denotes the number of each data acquisition device, p=1, 2,3,..k, k denotes the total number of data acquisition devices.
It should be understood that, in this embodiment, the range of values of the device measurement accuracy influencing factors corresponding to the working duration and the floating dust concentration is between 0 and 1, and a mapping set of the working duration and the floating dust concentration and the corresponding device measurement accuracy influencing factors is constructed by the relationship between the working duration and the floating dust concentration in the historical data and the second factor influencing index, the working duration and the floating dust concentration are input, and the device measurement accuracy influencing factors corresponding to the working duration and the floating dust concentration are obtained from the mapping set.
It should be appreciated that the second factor impact indicator in this embodiment is used to quantitatively evaluate the reliability of the power marketing data collection device. The greater the accumulated working time length of the equipment and the concentration of the environmental floating dust, the greater the corresponding second factor influence index, which indicates that the reliability of the electric power marketing data acquisition equipment is lower.
In a particular embodiment, the components of the data acquisition device may age over time, affecting their performance, resulting in reduced measurement accuracy. On the other hand, the floating dust concentration of the working environment of the data acquisition equipment also has an influence on the measurement precision of the data acquisition equipment, and meanwhile, the aging effect of the data acquisition equipment is aggravated due to the fact that the floating dust concentration is too high.
In the embodiment, the integrated analysis is performed on the accumulated working time length and the environmental floating dust concentration of the data acquisition equipment, so that the maintenance period of the equipment can be predicted, the maintenance and calibration work can be arranged in advance, and inaccurate or interrupted data caused by equipment faults can be prevented. By analyzing the relation between the concentration of floating dust and the working time of the equipment, the life expectancy of the equipment can be estimated more accurately, so that more reasonable investment decision can be made. Meanwhile, by identifying the influence of the concentration of floating dust and the working time on the data quality, corresponding measures can be taken in the data processing stage, such as using a data cleaning or correcting algorithm, so that the accuracy of final data analysis is improved.
Specifically, the comprehensive analysis is carried out to obtain an evaluation index of the abnormal degree of the electricity consumption of the user, and the specific analysis process is as follows: and comprehensively analyzing the degree of abnormality of the electricity consumption of the user according to the degree of deviation value of the electricity consumption behavior of the user and the degree of deviation value of the electricity consumption quality of the user, wherein the degree of abnormality of the electricity consumption of the user is estimated by the degree of abnormality estimation index of the electricity consumption of the user, and the degree of abnormality of the electricity consumption data is comprehensively analyzed from two angles of the electricity consumption behavior and the electricity consumption quality of the user, so that data support is provided for electric power marketing management. The larger the deviation degree value of the user electricity behavior and the deviation degree value of the user electricity quality, the larger the corresponding evaluation index of the abnormality degree of the user electricity is, and the more abnormal the user electricity data is indicated.
In a specific embodiment, a preset user electricity consumption behavior deviation degree value and an electricity consumption abnormality evaluation influence factor corresponding to the user electricity consumption quality deviation degree value are extracted from an electricity marketing database, and comprehensive analysis is performed to obtain an electricity consumption abnormality degree evaluation index of the user, wherein a specific numerical expression is as follows:
；
In the method, in the process of the invention, An evaluation index indicating the degree of abnormality of the power consumption of the user,Indicating the deviation degree value of the electricity consumption behavior of the user,Indicating the deviation degree value of the electricity quality of the user,The power utilization abnormality evaluation influence factors corresponding to the preset power utilization behavior deviation degree values of the users are represented,And the power utilization abnormality evaluation influence factor corresponding to the preset power utilization quality deviation degree value of the user is represented.
The algorithm of the embodiment combines the user electricity consumption behavior deviation degree value and the user electricity consumption quality deviation degree value, and comprehensively analyzes to obtain the user electricity consumption abnormality degree evaluation index. Deviations in electricity usage often interact with deviations in electricity quality, for example, if a user uses a lot of electricity during abnormal periods, it may result in voltage fluctuations in the local grid, while high harmonic content may originate from certain types of loads, and the usage patterns of these loads may be reflected in electricity usage. Comprehensively analyzing the deviation degree value of the electricity consumption behavior of the user and the deviation degree value of the electricity consumption quality of the user, more comprehensive information can be provided, the root cause of the problem can be identified, and long-term tracking of the indexes can help to predict the problem area possibly occurring in the future, so that preventive measures can be taken in advance. The two indexes are combined to obtain an evaluation index of the abnormal degree of the electricity consumption of the user, and the evaluation index can be used for accurately positioning the problem user and making a personalized solution. For the power company, the resource allocation can be planned better, and unnecessary loss is reduced while the stable operation of the power grid is ensured.
It should be understood that, in this embodiment, the value range of the power consumption abnormality evaluation influence factor corresponding to the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value is between 0 and 1, and a mapping set of the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value and the corresponding power consumption abnormality evaluation influence factor is constructed by the relation between the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value and the user power consumption abnormality evaluation index in the historical data, the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value are input, and the power consumption abnormality evaluation influence factor corresponding to the user power consumption behavior deviation degree value and the power consumption abnormality evaluation influence factor corresponding to the user power consumption quality deviation degree value are obtained from the mapping set.
Specifically, the abnormal electricity consumption degree of the user is evaluated, and the specific process is as follows: and according to the second factor influence index, matching the power marketing database to obtain a user power consumption abnormality degree evaluation index threshold.
It should be understood that, in this embodiment, a mapping set of a second factor influence index interval and a user electricity consumption abnormality degree evaluation index threshold is constructed according to historical data, a second factor influence index is input, and a user electricity consumption abnormality degree evaluation index threshold corresponding to the interval to which the second factor influence index belongs is obtained from the mapping set.
And comparing the user electricity consumption abnormality degree evaluation index with a user electricity consumption abnormality degree evaluation index threshold, evaluating the user electricity consumption behavior as abnormal if the user electricity consumption abnormality degree evaluation index is larger than or equal to the user electricity consumption abnormality degree evaluation index threshold, and evaluating the user electricity consumption behavior as normal if the user electricity consumption abnormality degree evaluation index is smaller than the user electricity consumption abnormality degree evaluation index threshold.
In a specific embodiment, when the electricity consumption behavior of the user is evaluated to be abnormal, a notification is sent to the user with the electricity consumption behavior evaluated to be abnormal, the user is informed of the reason of the abnormal electricity consumption behavior and the suggested improvement measures, or the user is reminded of the electricity consumption habit through a short message, an email or application program pushing mode and the like. And for users with higher power quality deviation degree values, fault checking service is provided, so that the power quality problem can be identified and solved.
And the power marketing database is used for storing power marketing management related data, and comprises reference indexes such as reference standard voltage, allowable deviation voltage, critical frequency deviation and the like. The data in the power marketing database can be obtained by extracting the intelligent ammeter data and the power grid running state monitoring data, and can also be directly obtained from a power supplier.
Referring to fig. 2, a second aspect of the present invention provides a power marketing management method based on the internet of things, including: and collecting the environmental data of the target area, and analyzing to obtain a first factor influence index.
And monitoring the electricity consumption behavior data, and comprehensively analyzing by combining the first factor influence index to obtain the electricity consumption behavior deviation degree value of the user.
And monitoring the electricity quality data, and processing to obtain the electricity quality deviation degree value of the user.
And obtaining characterization data of the data acquisition equipment, analyzing to obtain a second factor influence index, comprehensively analyzing to obtain a user electricity consumption abnormality degree evaluation index according to the user electricity consumption behavior deviation degree value and the user electricity consumption quality deviation degree value, evaluating the user electricity consumption abnormality degree according to the second factor influence index and the user electricity consumption abnormality degree evaluation index, obtaining an evaluation result and feeding back.
In a specific embodiment, the invention provides the electric power marketing management system and the electric power marketing management method based on the Internet of things, so that the fine management of the electricity consumption behavior and the electricity consumption quality of the user is realized, and the improvement of the electricity consumption efficiency is facilitated. The user is guided to optimize the electricity consumption behavior by monitoring the environment data and the electricity consumption behavior in real time, personalized service is provided, the electricity consumption quality is improved by monitoring the electricity consumption quality, the resource allocation is optimized, and stable and reliable power supply is ensured.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention and are intended to be within the scope of the invention without departing from the spirit and scope of the invention.
Claims (4)
1. Electric power marketing management system based on thing networking, its characterized in that: comprising the following steps:
the electricity utilization environment monitoring and analyzing module is used for collecting environment data of a target area and analyzing to obtain a first factor influence index;
the electricity consumption behavior monitoring and analyzing module is used for monitoring electricity consumption behavior data and comprehensively analyzing the electricity consumption behavior data by combining with the first factor influence index to obtain a deviation degree value of the electricity consumption behavior of the user;
The electricity quality monitoring and analyzing module is used for monitoring the electricity quality data and obtaining a user electricity quality deviation degree value through processing;
The power consumption abnormality degree evaluation module is used for acquiring the characterization data of the data acquisition equipment, analyzing to obtain a second factor influence index, comprehensively analyzing to obtain a user power consumption abnormality degree evaluation index according to the user power consumption behavior deviation degree value and the user power consumption quality deviation degree value, evaluating the user power consumption abnormality degree according to the second factor influence index and the user power consumption abnormality degree evaluation index, obtaining an evaluation result and feeding back;
the analysis obtains a first factor influence index, and the specific analysis process comprises the following steps: the target area environment data comprises the number of public transportation stations, the target area, the building density and the number of building floors;
dividing the number of public transportation sites by the area of the target area to obtain the density of public transportation sites, obtaining the density of critical public transportation sites, the density of critical buildings and the number of floors of critical buildings from an electric power marketing database, and comprehensively analyzing to obtain a first factor influence index;
the comprehensive analysis obtains the deviation degree value of the electricity consumption behavior of the user, and the specific analysis process comprises the following steps: the electricity consumption behavior data comprise electricity consumption and average load of each monitoring period;
Obtaining critical electricity consumption, critical average load, electricity consumption behavior deviation influence weight corresponding to preset electricity consumption, electricity consumption behavior deviation influence weight corresponding to preset average load and electricity consumption behavior deviation influence weight corresponding to preset first factor influence index from an electricity marketing database, and comprehensively analyzing to obtain a user electricity consumption behavior deviation degree value;
The processing obtains the user electricity quality deviation degree value, and the specific analysis process is as follows: the electricity quality data comprise actual electricity consumption voltage and frequency deviation of a user at each monitoring time point;
Acquiring reference standard voltage, allowable deviation voltage and critical frequency deviation from an electric power marketing database, and comprehensively analyzing to obtain a user electricity quality deviation degree value;
The analysis obtains a second factor influence index, and the specific analysis process comprises the following steps: the characterization data of the data acquisition equipment comprises the accumulated working time length of each data acquisition equipment and the environment floating dust concentration of each data acquisition equipment;
Acquiring critical working time length of each data acquisition device and critical floating dust concentration of each data acquisition device from an electric power marketing database, and comprehensively analyzing to obtain a second factor influence index;
The user electricity behavior deviation degree value comprises the following specific numerical expressions:
；
In the method, in the process of the invention, Indicating the deviation degree value of the electricity consumption behavior of the user,Representing a functional expression for the independent variables as electricity consumption and average load,，Indicating the power consumption of the j-th monitoring period,Indicating the critical power consumption amount,Representing the average load of the jth monitoring period,Represents the critical average load of the load cell,Indicating that the first factor affects the indicator,Indicating the power utilization behavior deviation influence weight corresponding to the preset first factor influence index,Indicating the deviation influence weight of the electricity consumption behavior corresponding to the preset electricity consumption,The method comprises the steps of representing the influence weight of the power consumption behavior deviation corresponding to a preset average load, j representing the number of each monitoring period, j=1, 2,3,..;
The user electricity quality deviation degree value has the following specific numerical expression:
；
In the method, in the process of the invention, Indicating the deviation degree value of the electricity quality of the user,The actual voltage of the user electricity consumption at the r-th monitoring time point is represented,Which means that with reference to a standard voltage,Indicating the voltage of the allowed deviation (v/v),Indicating the frequency deviation of the r-th monitoring time point,Indicating the deviation of the critical frequency,Indicating the power quality deviation influence factor corresponding to the preset actual voltage,Represents the power quality deviation influence factor corresponding to the preset frequency deviation, r represents the number of each monitoring time point, r=1, 2,3,..h, h represents the total number of monitoring time points.
2. The internet of things-based power marketing management system of claim 1, wherein: the comprehensive analysis is carried out to obtain an evaluation index of the abnormal degree of the electricity consumption of the user, and the specific analysis process is as follows:
And comprehensively analyzing the degree of abnormality of the electricity consumption of the user according to the degree of deviation value of the electricity consumption behavior of the user and the degree of deviation value of the electricity consumption quality of the user, wherein the degree of abnormality of the electricity consumption of the user is estimated by the degree of abnormality estimation index of the electricity consumption of the user, and the degree of abnormality of the electricity consumption data is comprehensively analyzed from two angles of the electricity consumption behavior and the electricity consumption quality of the user, so that data support is provided for electric power marketing management.
3. The internet of things-based power marketing management system of claim 2, wherein: the evaluation of the abnormal electricity consumption degree of the user comprises the following specific processes:
According to the second factor influence index, matching to obtain a user electricity consumption abnormality degree evaluation index threshold value from the electricity marketing database;
And comparing the user electricity consumption abnormality degree evaluation index with a user electricity consumption abnormality degree evaluation index threshold, evaluating the user electricity consumption behavior as abnormal if the user electricity consumption abnormality degree evaluation index is larger than or equal to the user electricity consumption abnormality degree evaluation index threshold, and evaluating the user electricity consumption behavior as normal if the user electricity consumption abnormality degree evaluation index is smaller than the user electricity consumption abnormality degree evaluation index threshold.
4. A method of using the internet of things-based power marketing management system of any of claims 1-3, characterized by: comprising the following steps:
collecting environmental data of a target area, and analyzing to obtain a first factor influence index;
monitoring electricity consumption behavior data, and comprehensively analyzing to obtain a deviation degree value of the electricity consumption behavior of a user by combining a first factor influence index;
monitoring the electricity quality data, and processing to obtain a user electricity quality deviation degree value;
And obtaining characterization data of the data acquisition equipment, analyzing to obtain a second factor influence index, comprehensively analyzing to obtain a user electricity consumption abnormality degree evaluation index according to the user electricity consumption behavior deviation degree value and the user electricity consumption quality deviation degree value, evaluating the user electricity consumption abnormality degree according to the second factor influence index and the user electricity consumption abnormality degree evaluation index, obtaining an evaluation result and feeding back.
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