CN116226687B - Reservoir daily initial water level estimation method and device based on data mining technology - Google Patents
Reservoir daily initial water level estimation method and device based on data mining technology Download PDFInfo
- Publication number
- CN116226687B CN116226687B CN202310519152.1A CN202310519152A CN116226687B CN 116226687 B CN116226687 B CN 116226687B CN 202310519152 A CN202310519152 A CN 202310519152A CN 116226687 B CN116226687 B CN 116226687B
- Authority
- CN
- China
- Prior art keywords
- data mining
- water level
- reservoir
- daily
- feature vector
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 183
- 238000007418 data mining Methods 0.000 title claims abstract description 115
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000005516 engineering process Methods 0.000 title claims abstract description 20
- 239000013598 vector Substances 0.000 claims abstract description 115
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 24
- 230000002068 genetic effect Effects 0.000 claims abstract description 24
- 230000001419 dependent effect Effects 0.000 claims abstract description 15
- 238000013524 data verification Methods 0.000 claims abstract description 13
- 230000006870 function Effects 0.000 claims description 44
- 230000015654 memory Effects 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000012795 verification Methods 0.000 claims description 18
- 238000010606 normalization Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 8
- 238000010248 power generation Methods 0.000 claims description 7
- 108090000623 proteins and genes Proteins 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 4
- 230000006978 adaptation Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 description 6
- 230000005611 electricity Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005065 mining Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 235000019800 disodium phosphate Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Biophysics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Physiology (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Genetics & Genomics (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Feedback Control In General (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method and a device for estimating the daily initial water level of a reservoir based on a data mining technology, wherein the method comprises the following steps: performing data verification on historical operation data of the cascade hydropower station to generate a data mining library; taking the water consumption rate in the data mining library as a dependent variable, taking other data in the data mining library as independent variables, and generating a historical feature vector by using a correlation coefficient method; constructing an objective function based on the data mining library, and solving the objective function by utilizing a genetic algorithm to generate a weight coefficient; acquiring a feature vector of a target day, traversing a data mining library, and calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient; determining a daily initial water level estimated value based on the similarity between the characteristic vector of the target day and the historical characteristic vector; the daily initial water level estimated value is used for carrying out optimal scheduling on the cascade reservoir. The method effectively improves the accuracy of estimating the daily initial water level of the reservoir, and ensures the safe and economic operation of the power station.
Description
Technical Field
The invention relates to the technical field of reservoir scheduling, in particular to a method and a device for estimating the daily initial water level of a reservoir based on a data mining technology.
Background
The daily initial water level estimation is a necessary condition for making a power generation plan of the reservoir before the day, and has important significance for improving the water resource utilization efficiency and reducing the reservoir running risk. Currently, a dispatcher mainly adopts 'electricity to fix water' to estimate the daily initial water level of a reservoir, namely, the actual water level of the reservoir at the current moment and a reservoir power generation plan of a subsequent period are known, and the daily initial water level of the reservoir at the next day is calculated by utilizing 'electricity to fix water' in a period-by-period manner. However, the problems of water abandon, water level out-of-limit and the like of reservoirs are easy to occur due to the influences of factors such as deviation, complex cascade hydraulic connection, difficult prediction of warehouse-in flow and the like in the power generation plan execution, the daily water level is difficult to accurately estimate, the influences on reservoirs with poor regulation performances such as daily regulation and radial flow are particularly remarkable, and the safe and economic operation of a power station is influenced.
Disclosure of Invention
Therefore, the technical scheme of the invention mainly solves the defects that the existing reservoir daily initial water level estimation method is low in accuracy and affects the safe and economic operation of a power station, and provides the reservoir daily initial water level estimation method and device based on the data mining technology.
In a first aspect, an embodiment of the present invention provides a method for estimating a daily initial water level of a reservoir based on a data mining technique, including:
acquiring historical operation data of the cascade hydropower station in a target flow field, and performing data verification on the historical operation data of the cascade hydropower station to generate a data mining library;
taking the water consumption rate in the data mining library as a dependent variable, taking other data in the data mining library as independent variables, and generating a historical feature vector by using a correlation coefficient method;
constructing an objective function based on the data mining library, and solving the objective function by utilizing a genetic algorithm to generate a weight coefficient;
acquiring a feature vector of a target day, traversing the data mining library, and calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient;
determining a daily initial water level estimated value based on the similarity between the characteristic vector of the target day and the historical characteristic vector; the daily initial water level estimated value is used for carrying out optimal scheduling on the cascade reservoir.
According to the reservoir daily initial water level estimation method based on the data mining technology, data verification is carried out on historical operation data of the cascade hydropower station, a data mining library is generated, correlation analysis is carried out on the data mining library, correlation between water consumption rate and other variables is mined from massive historical operation data of the cascade hydropower station, factors with strong correlation with the water consumption rate of the power station and weights thereof are mined through technologies such as coupling correlation analysis, similarity calculation and genetic algorithm, daily initial water level estimation is carried out, daily initial water level estimation accuracy can be effectively improved, water consumption rate is taken as a mining analysis object, water level estimation deviation caused by electricity water determination in a time interval in the prior art is avoided, optimal scheduling of the cascade reservoir is guided for science, water energy utilization efficiency is improved, and decision support is provided for power station operation risk reduction.
With reference to the first aspect, in a possible implementation manner, the performing data verification on the historical operation data of the cascade hydropower station to generate a data mining database includes:
checking the historical operation data of the cascade hydropower station to generate checked historical operation data of the cascade hydropower station;
and integrating the verified historical operation data of the cascade hydropower station into a time sequence data set by taking the date as an index, and storing the time sequence data set to generate the data mining library.
With reference to the first aspect, in another possible implementation manner, the verifying the historical operating data of the cascade hydropower station, generating the verified historical operating data of the cascade hydropower station includes:
and respectively carrying out repeatability verification, non-null verification, value range verification and association relation verification on the step hydropower station historical operation data to generate the step hydropower station historical operation data after verification.
With reference to the first aspect, in another possible implementation manner, the generating a historical feature vector by using a correlation coefficient method with the water consumption rate in the data mining library as a dependent variable and other data in the data mining library as independent variables includes:
calculating a pearson correlation coefficient based on the dependent variable and the independent variable;
comparing the pearson correlation coefficient with a preset condition, and selecting a variable related to the water consumption rate based on a comparison result;
and converting the variable related to the water consumption rate into the historical feature vector.
With reference to the first aspect, in another possible implementation manner, the constructing an objective function based on the data mining library and solving the objective function by using a genetic algorithm, generating a weight coefficient includes:
constructing the objective function based on the days in the data mining library, the actual value of the daily initial water level, the estimated value of the daily initial water level, the highest water level of the reservoir and the lowest water level of the reservoir; wherein, the objective function takes the minimum error of daily initial water level estimation as the objective;
acquiring weight variables and auxiliary variables, and constructing constraint conditions of the objective function based on the weight variables, the auxiliary variables and the data mining library;
and solving the objective function by using the genetic algorithm to generate the weight coefficient.
With reference to the first aspect, in another possible implementation manner, the traversing the data mining library, calculating a similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient includes:
normalizing the characteristic vector of the target day and the historical characteristic vector;
and calculating the similarity between the feature vector of the target day after normalization processing and the historical feature vector based on the weight coefficient.
With reference to the first aspect, in another possible implementation manner, the determining the daily initial water level estimation value based on the similarity between the feature vector of the target day and the historical feature vector includes:
sorting the similarity between the feature vector of the target day and the historical feature vector, and selecting the date with the maximum similarity with the feature vector of the target day based on the sorting result;
acquiring a daily average water consumption rate corresponding to the date with the maximum feature vector similarity of the target day from the data mining library, and taking the daily average water consumption rate as the average water consumption rate of the target day;
and calculating the daily initial water level estimated value through a water balance equation based on the average water consumption rate of the target day.
In a second aspect, an embodiment of the present invention further provides a device for estimating a daily initial water level of a reservoir based on a data mining technology, including:
the verification module is used for acquiring historical operation data of the cascade hydropower station in the target flow area, carrying out data verification on the historical operation data of the cascade hydropower station and generating a data mining library;
the analysis module is used for taking the water consumption rate in the data mining library as a dependent variable, taking other data in the data mining library as independent variables and generating a historical feature vector by using a correlation coefficient method;
the construction module is used for constructing an objective function based on the data mining library, solving the objective function by utilizing a genetic algorithm and generating a weight coefficient;
the calculation module is used for acquiring the feature vector of the target day, traversing the data mining library and calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient;
and the determining module is used for determining a daily initial water level estimated value based on the similarity between the characteristic vector of the target day and the historical characteristic vector.
In a third aspect, an embodiment of the present invention further discloses an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of a method for estimating reservoir daily initial water level based on data mining techniques as described in the first aspect or any of the alternative embodiments of the first aspect.
In a fourth aspect, the present invention further discloses a computer readable storage medium, on which a computer program is stored, the computer program implementing the steps of a reservoir daily initial water level estimation method based on the data mining technology according to the first aspect or any optional implementation manner of the first aspect when being executed by a processor.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a reservoir daily initial water level estimation method based on a data mining technology provided by an embodiment of the invention;
fig. 2 is a flowchart of S101 provided in an embodiment of the present invention;
FIG. 3 is a flowchart of S102 provided in an embodiment of the present invention;
fig. 4 is a flowchart of S103 provided in an embodiment of the present invention;
FIG. 5 is a flowchart of S104 provided in an embodiment of the present invention;
fig. 6 is a flowchart of S105 provided in an embodiment of the present invention;
FIG. 7 is a block diagram of a reservoir daily initial water level estimation device based on a data mining technology according to an embodiment of the present invention;
fig. 8 is a diagram illustrating an embodiment of an electronic device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. 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.
In the description of the present invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, unless explicitly stated or limited otherwise, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, mechanically connected, or electrically connected; or can be directly connected, or can be indirectly connected through an intermediate medium, or can be communication between the two elements, or can be wireless connection or wired connection. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The embodiment of the invention provides a reservoir daily initial water level estimation method based on a data mining technology, which is shown in fig. 1 and comprises the following steps:
s101, acquiring historical operation data of the cascade hydropower station in a target flow area, and performing data verification on the historical operation data of the cascade hydropower station to generate a data mining library.
Specifically, historical operation data of the cascade hydropower station takes days as a scale, and comprises a storage capacity (such as a day initial storage capacity, a day final storage capacity and the like), a water level (such as a day initial water level, a day final water level and the like), a flow (such as a warehouse-in flow, a section flow, a warehouse-out flow, a power generation flow and the like), an output (such as average output, maximum output, minimum output, daily electric quantity and the like), a water consumption rate and the like.
S102, using the water consumption rate in the data mining library as a dependent variable, using other data in the data mining library as independent variables, and generating a historical feature vector by using a correlation coefficient method.
Specifically, the other data are the remaining data in the data mining library except for the water consumption rate.
S103, constructing an objective function based on the data mining library, and solving the objective function by utilizing a genetic algorithm to generate a weight coefficient.
S104, obtaining the feature vector of the target day, traversing the data mining database, and calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient.
S105, determining a daily initial water level estimated value based on the similarity between the characteristic vector of the target day and the historical characteristic vector; the daily initial water level estimated value is used for carrying out optimal scheduling on the cascade reservoir.
According to the reservoir daily initial water level estimation method based on the data mining technology, data verification is carried out on historical operation data of the cascade hydropower station, a data mining base is generated, correlation analysis is carried out on the data mining base, correlation between water consumption rate and other variables is mined from massive historical operation data of the cascade hydropower station, factors and weights with strong correlation with the water consumption rate of the power station are mined through technologies such as coupling correlation analysis, similarity calculation and genetic algorithm, daily initial water level estimation is carried out, daily initial water level estimation accuracy can be effectively improved, water consumption rate is taken as a mining analysis object, water level estimation deviation caused by electricity water determination in a time interval in the prior art is avoided, optimal scheduling of the cascade reservoir is guided scientifically, water energy utilization efficiency is improved, and decision support is provided for power station operation risk reduction.
As an optional embodiment of the present invention, as shown in fig. 2, the step S101, that is, performing data verification on the historical operation data of the cascade hydropower station, generates a data mining library, and includes:
s1011, checking the historical operation data of the cascade hydropower station, and generating the checked historical operation data of the cascade hydropower station.
And the step hydropower station historical operation data after verification is generated by checking the correctness and the integrity of the daily operation data of the power station and deleting unqualified data.
Further, repeatability checking: taking the date as an index, checking whether repeated dates exist in historical operation data of the cascade hydropower station; if so, deleting the repeated daily operation data.
Further, non-empty checking: traversing all the historical operation data of the cascade hydropower stations, and checking whether the historical operation data of the cascade hydropower stations are empty; and if the null value exists, deleting the daily operation data.
Further, checking the value range: defining the value ranges of different data variables and defining the upper limit and the lower limit of the value range; traversing all the step hydropower station historical operation data, and checking whether the step hydropower station historical operation data is in a value domain; if the data is not in the value range, the data is wrongly displayed, and the data is deleted.
Further, the association relation is verified: taking the date as an index, checking whether the daily operation data meet a water balance equation, a hydroelectric generation equation and a cascade hydraulic connection equation; if the water quantity balance equation is not satisfied, the description data is wrong, and the daily operation data is deleted, wherein the water quantity balance equation is as follows:
（1）
in the above-mentioned method, the step of,representing reservoir->First->Day-to-day stock capacity, ->Representing reservoir->First->The storage capacity at the end of the day,representing reservoir->First->Daily warehouse-in flow rate,/->Representing reservoir->First->Daily delivery flow, < > on the way>Representing the time conversion constant.
Further, the hydroelectric generation equation is as follows:
（2）
in the above-mentioned method, the step of,representing reservoir->First->Day of force->Representing reservoir->First->Daily power flow rate, < > on>Representing reservoir->First->Daily water consumption rate.
Further, the cascade hydraulic connection equation is as follows:
（3）
in the above-mentioned method, the step of,representing reservoir->First->Daily interval flow,/->Representing reservoir->-1->Daily delivery flow, wherein, reservoir ∈>-1 is reservoir->Upstream reservoir of (a).
S1012, integrating the verified historical operation data of the cascade hydropower station into a time series data set by taking the date as an index, and storing the time series data set to generate the data mining library.
Specifically, the historical operation data of the step hydropower station after verification is integrated into a time sequence data set by taking the date as an index and stored as an XML (Extensible Markup Language extensible markup language) text file to form a data mining library.
As an alternative embodiment of the present invention, as shown in fig. 3, the step S102 of generating a historical feature vector using a correlation coefficient method with the water consumption rate in the data mining library as a dependent variable and other data in the data mining library as independent variables includes:
s1021, calculating the Pearson correlation coefficient based on the dependent variable and the independent variable.
Specifically, the correlation between the water consumption rate and other variables is evaluated by using the pearson correlation coefficient as a statistical index by using a correlation coefficient method, as shown in the following formula:
（4）
in the above-mentioned method, the step of,indicating water consumption rate, < >>Represents any variable except water consumption rate, < ->Representing the variable->And variable->Pearson correlation coefficient between +.>Representing the variable->And variable->Covariance of->、/>Respectively represent the variables->Variable->Standard deviation of (2).
S1022, comparing the pearson correlation coefficient with a preset condition, and selecting a variable related to the water consumption rate based on a comparison result.
Specifically, the pearson correlation coefficient is filtered out>0.6 and significance results according to the significance test methodP<A variable of 0.05 reaching a level of statistical significance was considered to have a strong correlation with water consumption rate.
S1023, converting the variable related to the water consumption rate into the history feature vector.
Specifically, the variable set having a strong correlation with the water consumption rate is expressed in the form of a feature vector, as shown in the following formula:
（5）
in the above-mentioned method, the step of,for the historical feature vector, ++>For the number of variables>Is the variable number->Indicate->A variable related to water consumption rate.
Further, all power station operation time sequence data in the data mining library are converted into historical feature vectors, and the daily feature vectors are as follows:
（6）
in the above-mentioned method, the step of,representing reservoir->First->Characteristic vector of day, ">Representing reservoir->First->Variable +.>Is a value of (a).
As an alternative embodiment of the present invention, as shown in fig. 4, S103, that is, the constructing an objective function based on the data mining library, and solving the objective function by using a genetic algorithm, generates a weight coefficient, including:
s1031, constructing an objective function based on the days in the data mining library, the actual value of the daily initial water level, the estimated value of the daily initial water level, the highest water level of the reservoir and the lowest water level of the reservoir; wherein, the objective function aims at the minimum error of the daily initial water level estimation.
Specifically, the expression of the objective function is:
（7）
in the above-mentioned method, the step of,for the number of days contained in the power station operational time sequence data set, < >>、/>Is respectively a reservoir->First->Actual value, estimated value of daily water level of day, +.>、/>Is respectively a reservoir->First->Day maximum water level, minimum water level.
S1032, obtaining weight variables and auxiliary variables, and constructing constraint conditions of the objective function based on the weight variables, the auxiliary variables and the data mining library.
Specifically, the constraint conditions are as follows:
（8）
（9）
（10）
（11）
（12）
（13）
in the above-mentioned method, the step of,for the variables->Corresponding weight coefficient, ++>For the auxiliary variables introduced ∈ ->For the time conversion factor, +.>Representing reservoir in data mining library->First->Daily warehouse-in flow rate,/->、/>Respectively is a reservoir in a data mining library>First->Daily planned power generation, estimated value of change of storage capacity, < >>Representing AND +.>Date number corresponding to feature vector with maximum similarity, +.>Reservoir in data mining library->First->Daily rate of consumption, ->Is a reservoir->First->Actual value of day-to-day stock volume, +.>Reservoir in data mining library->First->Day-to-day reservoir capacity estimate, +.>Is a storage capacity-water level relation curve.
S1033, solving the objective function by utilizing a genetic algorithm to generate the weight coefficient.
In particular, it is advantageous toThe process of solving the objective function by using a genetic algorithm is as follows: (1) setting genetic algorithm parameters and initializing population: setting crossover probabilityProbability of mutation->Population size->I.e. all weight variables) and a maximum number of iterations +.>Coding by adopting a real number coding mode and randomly generating population scale +.>Initial population->The method comprises the steps of carrying out a first treatment on the surface of the (2) Evaluating initial population fitness: evaluation of initial population->Is adapted to the degree of adaptation of (a); let iteration number +.>=1; (3) generating a population of offspring: for->Performing roulette selection operations from parent population +.>Screening out population size->Is a progeny population of (a); execution cross probability->Pairing offspring individuals pairwise, and exchanging part of genes; execution ofMutation probability->Randomly altering the genes of the offspring individuals to obtain the population size +.>New offspring population->The method comprises the steps of carrying out a first treatment on the surface of the (4) Calculating population fitness: calculating new offspring population->The fitness of each individual; (5) judging a calculation termination condition: if the current iteration number is less than the maximum iteration number +.>Let->Repeating step (3); otherwise, the individual with the optimal fitness is selected as the optimal solution, and the calculation is stopped and the weight coefficient corresponding to each selected variable (namely, the variable with strong correlation with the water consumption rate) is output.
As an alternative embodiment of the present invention, as shown in fig. 5, the step S104 of traversing the data mining library to calculate the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient includes:
s1041, normalizing the characteristic vector of the target day and the historical characteristic vector.
Specifically, to eliminate the dimensional influence among different variables, the daily feature vector and the feature vector of the target day are normalized, and the values of the variables are mapped to [0,1 ]]In sections of reservoirsFirst->Japanese foodSyndrome vector->For example, the normalization process is as follows:
（14）
in the above-mentioned method, the step of,for normalized +.>，/>、/>Respectively indicate water reservoir->Variable +.>Maximum and minimum values of (a) and (b).
S1042, calculating the similarity between the feature vector of the target day after normalization processing and the historical feature vector based on the weight coefficient.
Specifically, traversing the data mining library, and calculating the similarity between the feature vector of the target day and the historical feature vector by adopting cosine similarity, wherein the similarity is shown in the following formula:
（15）
in the above-mentioned method, the step of,representing reservoir->First->Day (i.e. target day) and +.>Similarity of feature vectors between days, +.>Representing the variable->Corresponding weight coefficient, ++>Represents reservoir after normalization treatment->First->Variable +.>Value of->Represents reservoir after normalization treatment->First->Variable +.>Is a value of (2).
As an alternative embodiment of the present invention, as shown in fig. 6, the step S105 of determining the estimated daily water level based on the similarity between the characteristic vector of the target day and the historical characteristic vector includes:
s1051, sorting the similarity between the feature vector of the target day and the historical feature vector, and selecting the date with the maximum similarity with the feature vector of the target day based on the sorting result.
S1052, obtaining a daily average water consumption rate corresponding to the date with the maximum feature vector similarity of the target day from the data mining library, and taking the daily average water consumption rate as an average water consumption rate of the target day.
S1053, calculating the estimated value of the daily initial water level through a water balance equation based on the average water consumption rate of the target day.
Specifically, the above formulas (11) - (13) are combined, and the average water consumption rate on the target day is taken asAnd (3) inputting the water level estimated value of the end-of-day of the target day into a simultaneous formula, and calculating and generating the water level estimated value of the beginning-of-day of the next day.
The embodiment of the invention also discloses a reservoir daily initial water level estimation device based on the data mining technology, which is shown in fig. 7 and comprises:
the verification module 71 is configured to obtain historical operation data of the cascade hydropower station in the target flow area, perform data verification on the historical operation data of the cascade hydropower station, and generate a data mining library; see the description of S101 in the method embodiment above for details.
An analysis module 72, configured to generate a historical feature vector using a correlation coefficient method, with the water consumption rate in the data mining library as a dependent variable and other data in the data mining library as independent variables; for details, see the description of S102 in the above method embodiment.
A construction module 73, configured to construct an objective function based on the data mining library, and solve the objective function by using a genetic algorithm to generate a weight coefficient; see the relevant description of S103 in the above method embodiment for details.
A calculation module 74, configured to obtain a feature vector of a target day, traverse the data mining library, and calculate a similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient; see the description of S104 in the method embodiment.
A determining module 75, configured to determine a daily initial water level estimation value based on a similarity between the feature vector of the target day and the historical feature vector; see the relevant description of S105 in the above method embodiment for details.
According to the reservoir daily initial water level estimation device based on the data mining technology, data verification is carried out on historical operation data of a cascade hydropower station, a data mining base is generated, correlation analysis is carried out on the data mining base, correlation between water consumption rate and other variables is mined from massive historical operation data of the cascade hydropower station, factors and weights with strong correlation with the water consumption rate of the power station are mined through technologies such as coupling correlation analysis, similarity calculation and genetic algorithm, daily initial water level estimation is carried out, daily initial water level estimation accuracy can be effectively improved, water consumption rate is taken as a mining analysis object, water level estimation deviation caused by electricity water determination in a time interval in the prior art is avoided, optimal scheduling of the cascade reservoir is guided scientifically, water energy utilization efficiency is improved, and decision support is provided for reducing power station operation risks.
As an alternative embodiment of the present invention, the verification module 71 includes: the verification sub-module is used for verifying the historical operation data of the cascade hydropower station and generating the verified historical operation data of the cascade hydropower station; and the storage sub-module is used for integrating the verified historical operation data of the cascade hydropower station into a time sequence data set by taking the date as an index, storing the time sequence data set and generating the data mining library.
As an alternative embodiment of the present invention, the analysis module 72 includes: a first calculation sub-module for calculating a pearson correlation coefficient based on the dependent variable and the independent variable; wherein the other data is the remaining data except the water consumption rate in the data mining library; the comparison sub-module is used for comparing the pearson correlation coefficient with a preset condition and selecting a variable related to the water consumption rate based on a comparison result; and the conversion sub-module is used for converting the variable related to the water consumption rate into the historical characteristic vector.
As an alternative embodiment of the present invention, the above-mentioned construction module 73 includes: the first construction submodule is used for constructing an objective function based on the days in the data mining library, the actual value of the daily initial water level, the estimated value of the daily initial water level, the highest water level of the reservoir and the lowest water level of the reservoir; wherein, the objective function takes the minimum error of daily initial water level estimation as the objective; the second construction submodule is used for acquiring weight variables and auxiliary variables and constructing constraint conditions of the objective function based on the weight variables, the auxiliary variables and the data mining library; and the solving sub-module is used for solving the objective function by utilizing a genetic algorithm to generate the weight coefficient.
As an alternative embodiment of the present invention, the calculating module 74 includes: the normalization processing sub-module is used for carrying out normalization processing on the characteristic vector of the target day and the historical characteristic vector; and the second computing sub-module is used for computing the similarity between the feature vector of the target day after normalization processing and the historical feature vector based on the weight coefficient.
As an alternative embodiment of the present invention, the determining module 75 includes: the sorting sub-module is used for sorting the similarity between the feature vector of the target day and the historical feature vector, and selecting the date with the maximum similarity with the feature vector of the target day based on the sorting result; the acquisition submodule is used for acquiring the daily average water consumption rate corresponding to the date with the maximum feature vector similarity of the target day from the data mining library, and taking the daily average water consumption rate as the average water consumption rate of the target day; and the third calculation sub-module is used for calculating the daily initial water level estimated value through a water balance equation based on the average water consumption rate of the target day.
In addition, an electronic device is provided in an embodiment of the present invention, as shown in fig. 8, where the electronic device may include a processor 110 and a memory 120, where the processor 110 and the memory 120 may be connected by a bus or other manner, and in fig. 8, the connection is exemplified by a bus. In addition, the electronic device further includes at least one interface 130, where the at least one interface 130 may be a communication interface or other interfaces, and the embodiment is not limited thereto.
The processor 110 may be a central processing unit (Central Processing Unit, CPU). The processor 110 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), field programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above.
The memory 120 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the video compositing method according to the embodiments of the present invention. The processor 110 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 120, i.e. implements a method for estimating the daily initial water level of a reservoir based on a data mining technique in the above-described method embodiment.
Memory 120 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created by the processor 110, etc. In addition, memory 120 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 120 may optionally include memory located remotely from processor 110, which may be connected to processor 110 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
In addition, at least one interface 130 is used for communication of the electronic device with external devices, such as with a server or the like. Optionally, at least one interface 130 may also be used to connect peripheral input, output devices, such as a keyboard, display screen, etc.
The one or more modules are stored in the memory 120 and when executed by the processor 110, perform a method of reservoir daily initial water level estimation based on data mining techniques as in the embodiment shown in fig. 1.
The specific details of the electronic device may be understood correspondingly with respect to the corresponding related descriptions and effects in the embodiment shown in fig. 1, which are not repeated herein.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. The storage medium may be a magnetic Disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (6)
1. The method for estimating the daily initial water level of the reservoir based on the data mining technology is characterized by comprising the following steps of:
acquiring historical operation data of the cascade hydropower station in a target flow field, and performing data verification on the historical operation data of the cascade hydropower station to generate a data mining library;
taking the water consumption rate in the data mining library as a dependent variable, taking other data in the data mining library as independent variables, and generating a historical feature vector by using a correlation coefficient method;
constructing an objective function based on the data mining library, and solving the objective function by utilizing a genetic algorithm to generate a weight coefficient;
acquiring a feature vector of a target day, traversing the data mining library, and calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient;
determining a daily initial water level estimated value based on the similarity between the characteristic vector of the target day and the historical characteristic vector; the daily initial water level estimated value is used for carrying out optimal scheduling on the cascade reservoir;
the generating a historical feature vector by using the water consumption rate in the data mining library as a dependent variable and other data in the data mining library as independent variables and using a correlation coefficient method comprises the following steps:
calculating a pearson correlation coefficient based on the dependent variable and the independent variable;
comparing the pearson correlation coefficient with a preset condition, and selecting a variable related to the water consumption rate based on a comparison result;
converting the variable related to the water consumption rate into the historical feature vector;
the construction of the objective function based on the data mining library, and the solving of the objective function by utilizing a genetic algorithm, the generation of the weight coefficient comprises the following steps:
constructing the objective function based on the days in the data mining library, the actual value of the daily initial water level, the estimated value of the daily initial water level, the highest water level of the reservoir and the lowest water level of the reservoir; wherein, the objective function takes the minimum error of daily initial water level estimation as the objective; the expression of the objective function is:
in the above-mentioned method, the step of,for the number of days contained in the power station operational time sequence data set, < >>、/>Is respectively a reservoir->First->Actual value, estimated value of daily water level of day, +.>、/>Is respectively a reservoir->First->Daily maximum water level and daily minimum water level;
acquiring weight variables and auxiliary variables, and constructing constraint conditions of the objective function based on the weight variables, the auxiliary variables and the data mining library; wherein the constraint conditions are as follows:
in the above-mentioned method, the step of,for the variables->Corresponding weight coefficient, ++>For the auxiliary variables introduced ∈ ->For the time conversion factor, +.>Representing reservoir in data mining library->First->Daily warehouse-in flow rate,/->、/>Respectively is a reservoir in a data mining library>First->Daily planned power generation, estimated value of change of storage capacity, < >>Representing AND +.>Date number corresponding to feature vector with maximum similarity, +.>Reservoir in data mining library->First->The daily water consumption rate of the water-saving type water-saving device,is a reservoir->First->Actual value of day-to-day stock volume, +.>Reservoir in data mining library->First->Day-to-day reservoir capacity estimate, +.>Is a storage capacity-water level relation curve;
solving the objective function by utilizing the genetic algorithm to generate the weight coefficient; the process of solving the objective function by using the genetic algorithm is as follows: step 1, setting genetic algorithm parameters and initializing population: setting crossover probabilityProbability of mutation->Population size->And maximum number of iterations->Coding by adopting a real number coding mode and randomly generating population scale +.>Initial population->The method comprises the steps of carrying out a first treatment on the surface of the Step 2, evaluating initial population fitness: evaluation of initial population->Is adapted to the degree of adaptation of (a); let iteration number +.>=1; step 3, generating a offspring population: for->Performing roulette selection operations from parent population +.>Screening out population size->Is a progeny population of (a); execution cross probability->Pairing offspring individuals pairwise, and exchanging part of genes; execution variant probability->Randomly altering the genes of the offspring individuals to obtain the population size +.>New offspring population->The method comprises the steps of carrying out a first treatment on the surface of the Step 4, calculating population fitness: calculating new offspring population->The fitness of each individual; step 5, judging the calculation termination condition: if the current iteration number is less than the maximum iteration number +.>Order in principleRepeating step 3; otherwise, selecting an individual with the optimal fitness as an optimal solution, terminating calculation and outputting weight coefficients corresponding to the selected variables;
the traversing the data mining library, calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient, comprising:
normalizing the characteristic vector of the target day and the historical characteristic vector;
calculating the similarity between the feature vector of the target day after normalization processing and the historical feature vector based on the weight coefficient;
the determining a daily initial water level estimation value based on the similarity between the characteristic vector of the target day and the historical characteristic vector comprises:
sorting the similarity between the feature vector of the target day and the historical feature vector, and selecting the date with the maximum similarity with the feature vector of the target day based on the sorting result;
acquiring a daily average water consumption rate corresponding to the date with the maximum feature vector similarity of the target day from the data mining library, and taking the daily average water consumption rate as the average water consumption rate of the target day;
and calculating the daily initial water level estimated value through a water balance equation based on the average water consumption rate of the target day.
2. The method for estimating the daily primary water level of a reservoir based on a data mining technology according to claim 1, wherein the step hydropower station historical operation data is subjected to data verification to generate a data mining library, and the method comprises the following steps:
checking the historical operation data of the cascade hydropower station to generate checked historical operation data of the cascade hydropower station;
and integrating the verified historical operation data of the cascade hydropower station into a time sequence data set by taking the date as an index, and storing the time sequence data set to generate the data mining library.
3. The method for estimating the daily initial water level of a reservoir based on a data mining technology according to claim 2, wherein the step hydropower station historical operation data is verified, and the step hydropower station historical operation data after verification is generated comprises:
and respectively carrying out repeatability verification, non-null verification, value range verification and association relation verification on the step hydropower station historical operation data to generate the step hydropower station historical operation data after verification.
4. The utility model provides a reservoir day water level estimation device based on data mining technique which characterized in that includes:
the verification module is used for acquiring historical operation data of the cascade hydropower station in the target flow area, carrying out data verification on the historical operation data of the cascade hydropower station and generating a data mining library;
the analysis module is used for taking the water consumption rate in the data mining library as a dependent variable, taking other data in the data mining library as independent variables and generating a historical feature vector by using a correlation coefficient method;
the construction module is used for constructing an objective function based on the data mining library, solving the objective function by utilizing a genetic algorithm and generating a weight coefficient;
the calculation module is used for acquiring the feature vector of the target day, traversing the data mining library and calculating the similarity between the feature vector of the target day and the historical feature vector based on the weight coefficient;
the determining module is used for determining a daily initial water level estimated value based on the similarity between the characteristic vector of the target day and the historical characteristic vector;
the analysis module comprises: a first calculation sub-module for calculating a pearson correlation coefficient based on the dependent variable and the independent variable; wherein the other data is the remaining data except the water consumption rate in the data mining library; the comparison sub-module is used for comparing the pearson correlation coefficient with a preset condition and selecting a variable related to the water consumption rate based on a comparison result; the conversion sub-module is used for converting the variable related to the water consumption rate into the history feature vector;
the construction module comprises:
the first construction submodule is used for constructing an objective function based on the days in the data mining library, the actual value of the daily initial water level, the estimated value of the daily initial water level, the highest water level of the reservoir and the lowest water level of the reservoir; wherein, the objective function takes the minimum error of daily initial water level estimation as the objective; the expression of the objective function is:
in the above-mentioned method, the step of,for the number of days contained in the power station operational time sequence data set, < >>、/>Is respectively a reservoir->First->Actual value, estimated value of daily water level of day, +.>、/>Is respectively a reservoir->First->Daily maximum water level and daily minimum water level;
the second construction submodule is used for acquiring weight variables and auxiliary variables and constructing constraint conditions of the objective function based on the weight variables, the auxiliary variables and the data mining library; wherein the constraint conditions are as follows:
in the above-mentioned method, the step of,for the variables->Corresponding weight coefficient, ++>For the auxiliary variables introduced ∈ ->For the time conversion factor, +.>Representing reservoir in data mining library->First->Daily warehouse-in flow rate,/->、/>Respectively is a reservoir in a data mining library>First->Daily planned power generation, estimated value of change of storage capacity, < >>Representing AND +.>Date number corresponding to feature vector with maximum similarity, +.>Reservoir in data mining library->First->The daily water consumption rate of the water-saving type water-saving device,is a reservoir->First->Actual value of day-to-day stock volume, +.>Reservoir in data mining library->First->Day-to-day reservoir capacity estimate, +.>Is a storage capacity-water level relation curve;
the solving sub-module is used for solving the objective function by utilizing a genetic algorithm to generate the weight coefficient; the process of solving the objective function by using the genetic algorithm is as follows: step 1, setting genetic algorithm parameters and initializing population: setting crossover probabilityProbability of mutation->Population size->And maximum number of iterations->Coding by adopting a real number coding mode and randomly generating population scale +.>Initial population->The method comprises the steps of carrying out a first treatment on the surface of the Step 2, evaluating initial population fitness: evaluation of initial population->Is adapted to the degree of adaptation of (a); let iteration number +.>=1; step 3, generating a offspring population: for->Performing roulette selection operations from parent population +.>Screening out population size->Is a progeny population of (a); execution cross probability->Pairing offspring individuals pairwise, and exchanging part of genes; execution variant probability->Randomly altering the genes of the offspring individuals to obtain the population size +.>New offspring population->The method comprises the steps of carrying out a first treatment on the surface of the Step 4, calculating population fitness: calculation of New offspring populationsThe fitness of each individual; step 5, judging the calculation termination condition: if the current iteration number is smaller than the maximum iteration numberLet->Repeating step 3; otherwise, selecting an individual with the optimal fitness as an optimal solution, terminating calculation and outputting weight coefficients corresponding to the selected variables;
the computing module comprises: the normalization processing sub-module is used for carrying out normalization processing on the characteristic vector of the target day and the historical characteristic vector; the second calculation sub-module is used for calculating the similarity between the feature vector of the target day after normalization processing and the historical feature vector based on the weight coefficient;
the determining module includes: the sorting sub-module is used for sorting the similarity between the feature vector of the target day and the historical feature vector, and selecting the date with the maximum similarity with the feature vector of the target day based on the sorting result; the acquisition submodule is used for acquiring the daily average water consumption rate corresponding to the date with the maximum feature vector similarity of the target day from the data mining library, and taking the daily average water consumption rate as the average water consumption rate of the target day; and the third calculation sub-module is used for calculating the daily initial water level estimated value through a water balance equation based on the average water consumption rate of the target day.
5. An electronic device comprising a processor and a memory, the memory coupled to the processor;
the memory has stored thereon computer readable program instructions which, when executed by the processor, implement the method of any of claims 1 to 3.
6. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310519152.1A CN116226687B (en) | 2023-05-10 | 2023-05-10 | Reservoir daily initial water level estimation method and device based on data mining technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310519152.1A CN116226687B (en) | 2023-05-10 | 2023-05-10 | Reservoir daily initial water level estimation method and device based on data mining technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116226687A CN116226687A (en) | 2023-06-06 |
| CN116226687B true CN116226687B (en) | 2023-07-21 |
Family
ID=86571683
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310519152.1A Active CN116226687B (en) | 2023-05-10 | 2023-05-10 | Reservoir daily initial water level estimation method and device based on data mining technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116226687B (en) |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102855524A (en) * | 2012-08-14 | 2013-01-02 | 贵州乌江水电开发有限责任公司 | Carry-over storage year-end fluctuating level prediction method and system |
| KR101870176B1 (en) * | 2016-11-08 | 2018-06-22 | 해양복류발전 합자회사 | Installation method of waterway to increase the water level of the reservoir water type small hydro power |
| CN108470249B (en) * | 2018-03-16 | 2019-04-09 | 大连理工大学 | A short-term power generation scheduling method for cascade hydropower station clusters coupled with cluster analysis and decision tree |
| CN109886461A (en) * | 2019-01-18 | 2019-06-14 | 昆仑(重庆)河湖生态研究院(有限合伙) | A kind of Runoff Forecast method and device |
| CN110969342B (en) * | 2019-11-14 | 2022-10-11 | 国能大渡河沙坪发电有限公司 | Method for balancing small-reservoir-capacity hydroelectric generation safety and flood control risk management and control |
| CN111080157B (en) * | 2019-12-26 | 2023-04-07 | 大连理工大学 | Method and system for scheduling phosphorus discharge amount of cascade hydropower station |
| CN111612292A (en) * | 2020-04-03 | 2020-09-01 | 贵州乌江水电开发有限责任公司 | Dispatching control system and method of cascade hydropower station based on key water level control |
| CN113344288B (en) * | 2021-06-28 | 2023-12-01 | 南京大学 | Water level prediction method, device and computer-readable storage medium for cascade hydropower station group |
-
2023
- 2023-05-10 CN CN202310519152.1A patent/CN116226687B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN116226687A (en) | 2023-06-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107506868B (en) | Method and device for predicting short-time power load | |
| CN112488395A (en) | Power distribution network line loss prediction method and system | |
| CN109633448A (en) | Identify the method, apparatus and terminal device of cell health state | |
| CN109325607A (en) | A short-term wind power prediction method and system | |
| CN117933316A (en) | Groundwater level probability forecasting method based on interpretable Bayesian convolution network | |
| CN115204698A (en) | Real-time analysis method for power supply stability of low-voltage transformer area | |
| CN113487080B (en) | Wind speed dynamic scene generation method, system and terminal based on wind speed classification | |
| CN118277093A (en) | A load prediction method for computing power networks | |
| CN115118015B (en) | A power supply stability monitoring system based on fusion terminal | |
| CN116561569A (en) | Industrial power load identification method based on EO feature selection and AdaBoost algorithm | |
| CN116091118A (en) | Electricity price prediction method, device, equipment, medium and product | |
| CN116226687B (en) | Reservoir daily initial water level estimation method and device based on data mining technology | |
| CN117633456B (en) | Marine wind power weather event identification method and device based on self-adaptive focus loss | |
| CN115622034B (en) | A method for identifying power loads in IoT meter based on improved genetic neural network | |
| CN107909219A (en) | A kind of day electricity sales amount Forecasting Methodology and system based on dual cluster | |
| CN116415741B (en) | A method and system for predicting coal consumption in coal-fired power plants based on deep learning | |
| CN117725496A (en) | Power theft monitoring method based on similarity measurement and decision tree support vector machine | |
| CN117493914A (en) | Multi-scale measurement and Louvain-based platform area phase and table box identification method and system | |
| CN115130924A (en) | Microgrid power equipment asset evaluation method and system under source grid storage background | |
| CN115905720A (en) | A method for recommending equipment combination schemes for combat missions | |
| CN115640896A (en) | Household user power load forecasting method and related equipment in multi-user scenario | |
| CN111429257A (en) | Transaction monitoring method and device | |
| CN117435870B (en) | A method, system, equipment and medium for real-time filling of load data | |
| CN119337214B (en) | A system for building equipment subtype standard point model | |
| CN119358184B (en) | Distribution network layout optimization method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |