A power distribution network diagram data intelligent management system

Abstract

This invention discloses an intelligent management system for distribution network diagram and model data, belonging to the field of distribution network diagram and model data management technology. It includes an application analysis module, an optimization analysis module, and an optimization management module. The application analysis module performs application analysis on the diagram and model data to determine optimization items, which are data items requiring optimization of diagram and model data transmission. The optimization analysis module performs optimization analysis on the optimization items, obtains diagram and model flow association data for each optimization item, generates a diagram and model flow information diagram for each optimization item based on the association data, identifies the transmission items present in each optimization item based on the flow information diagram, determines the baseline optimization method for each transmission item, determines the evaluation order of each transmission item based on the baseline optimization method, performs optimization analysis based on the evaluation order, and forms an optimization combination curve. The optimization management module manages the optimization items for each diagram and model.

Description

Technical Field

[0001] This invention belongs to the field of distribution network diagram and model data management technology, specifically a distribution network diagram and model data intelligent management system. Background Technology

[0002] With the large-scale construction of distribution automation and smart grids, the intelligent management system for distribution network diagram data is playing an increasingly important role in the power industry. This system integrates multiple data sources, such as Geographic Information Systems (GIS), dispatch automation systems, and power metering systems, to achieve unified exchange, verification, updating, and maintenance of distribution network diagram data. However, despite its significant achievements in improving distribution network operation and management and ensuring the safe and stable operation of the power grid, some problems still need to be addressed. Among these, the timeliness issue is particularly prominent. In the actual operation of the distribution network, the network structure frequently changes due to equipment additions and subtractions, line modifications, and other reasons. However, the current intelligent management system for distribution network diagram data suffers from data lag in updates, failing to reflect the latest status of the distribution network in real time. Especially when manual adjustments are made to the residential side, the frequent and complex adjustments easily lead to untimely data updates. This not only affects the accuracy and consistency of the distribution network diagram data but may also pose a potential threat to the safe and stable operation of the power grid.

[0003] In order to solve the above problems, this invention provides an intelligent management system for distribution network diagram data. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides an intelligent management system for distribution network diagram data.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A power distribution network diagram model data intelligent management system includes an application analysis module, an optimization analysis module, and an optimization management module;

[0007] The application analysis module is used to perform application analysis on the image data and determine the image optimization items. The image optimization items are the data items that need to be optimized for image data transmission.

[0008] Furthermore, the methods for determining the optimization terms of the graphical model include:

[0009] Real-time acquisition of application records, setting optimization item definitions for graph model optimization items, wherein the optimization item is defined as a data item that causes application abnormalities due to untimely updates of graph model data;

[0010] An identification and judgment model is established, and the application records and optimization item definitions are analyzed through the identification and judgment model to determine the candidate options; the application records are classified according to the candidate options to obtain the candidate analysis data corresponding to the candidate options, and the candidate analysis data consists of application records belonging to the corresponding candidate options;

[0011] Based on the candidate analysis data, determine the candidate probability and candidate value of each candidate option; calculate the optimized value of each candidate option using the optimization formula, which is:

[0012] YB=DZ×(e μ -1);

[0013] In the formula: YB is the optimized value; DZ is the candidate value; μ is the candidate probability; e is the natural constant;

[0014] Candidates whose optimization value is greater than the threshold X1 are marked as graphical optimization items.

[0015] Furthermore, the expression for the identification and judgment model is:

[0016]

[0017] In the formula: (s, Y) represents the input data, s represents the application record, Y represents the optimization term definition, s→Y indicates that the corresponding application record conforms to the optimization term definition, and the output data is the recognition judgment value YA(s, Y), which is 1 or 0.

[0018] When the recognition value is 1, the data item recorded by the corresponding application is marked as a candidate.

[0019] When the recognition value is 0, no corresponding operation is performed.

[0020] The optimization analysis module is used to perform optimization analysis on the graph model optimization items, obtain the graph model flow association data of the graph model optimization items, generate the graph model flow information graph of the graph model optimization items based on the graph model flow association data, and identify the transmission items of the graph model optimization items based on the graph model flow information graph.

[0021] Determine the baseline optimization method for the transmission item; determine the evaluation order of the transmission item based on the baseline optimization method;

[0022] Optimization analysis is performed based on the evaluation order to form an optimal combination curve.

[0023] Furthermore, methods for determining the baseline optimization method for the transmission term include:

[0024] The platform provider establishes a transmission time detail table, which is used to calculate the baseline time corresponding to different transmission items.

[0025] Match the benchmark time consumption of the transmission item according to the transmission time consumption breakdown明细表; identify the historical time consumption of the transmission item;

[0026] Calculate the time consumption ratio of the transmission item according to the ratio formula, and the ratio formula is:

[0027] BZ = LZ ÷ HZ;

[0028] In the formula: BZ is the time consumption ratio; HZ is the benchmark time consumption; LZ is the historical time consumption;

[0029] When the time consumption ratio is not greater than 1, the benchmark optimization method is none;

[0030] When the time consumption ratio is greater than 1, determine the benchmark optimization method according to the time consumption ratio.

[0031] Further, the method for determining the benchmark optimization method of the transmission item includes:

[0032] Determine the candidate optimization methods of the transmission item; identify the optimization duration of the candidate optimization methods;

[0033] Estimate the optimization cost and optimization difficulty value corresponding to the candidate optimization methods;

[0034] Calculate the method value of the candidate optimization method according to the first formula, and the first formula is:

[0035]

[0036] In the formula: WA is the method value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 < 1, 0 < b2 < 1; NB is the optimization difficulty value; CB is the optimization cost;

[0037] Form a candidate coordinate according to the method value and optimization duration of the candidate optimization method; input the candidate coordinate into the coordinate system, and form a candidate analysis curve according to the candidate coordinate;

[0038] Calculate the slope of the candidate analysis curve at the candidate coordinate, denoted as the candidate analysis value; determine the benchmark optimization method according to the candidate analysis value.

[0039] Further, the method for determining the evaluation order of the corresponding transmission item according to the benchmark optimization method includes:

[0040] Calculate the method value of the benchmark optimization method and identify the optimization duration of the benchmark optimization method;

[0041] Calculate the priority value of the transmission item according to the priority formula, and the priority formula is:

[0042] WY = T ÷ (WA + 1);

[0043] In the formula: WY is the priority value; T is the optimization duration; WA is the mode value;

[0044] The evaluation order of the transmission items is determined according to their priority values.

[0045] Furthermore, methods for optimization analysis based on the evaluation order include:

[0046] Step SA1: Generate an evaluation sequence according to the evaluation order; combine the first transport item in the evaluation sequence into a merged combination to form a new evaluation sequence; determine the combined optimization value and combined condition value of the merged combination;

[0047] Set up an optimized coordinate system, with the horizontal axis representing the combination condition values ​​and the vertical axis representing the combination optimization values; then, input the combination optimization values ​​and combination condition values ​​corresponding to the initial combination into the optimized coordinate system to form a combination coordinate.

[0048] Step SA2: Combine the first-ranked transmission item in the evaluation sequence with the merged combination to form a new merged combination and a new evaluation sequence; determine the combined optimization value and combined condition value of the merged combination; and input the combined optimization value and combined condition value of the merged combination into the optimization coordinate system to form a new combined coordinate system.

[0049] Step SA3: Repeat step SA2 until there are no more transport terms in the evaluation sequence, and form an optimized combination curve based on the combined coordinates in the optimized coordinate system.

[0050] Furthermore, the formula for the combined optimal value is:

[0051]

[0052] In the formula: UH is the combined optimization value; Ti is the optimization time of the corresponding transmission item in the combined combination, i = 1, 2, ..., n, and n is the number of transmission items in the combined combination.

[0053] Furthermore, the formula for the combined condition value is:

[0054] ZT = ZT′ + ZO;

[0055] In the formula: ZT is the combination condition value of the current merged combination; ZT′ is the condition combination value of the previous merged combination; and ZO is the value obtained by converting the newly added combination value of the current merged combination according to the preset conversion method.

[0056] The optimization management module is used to optimize and manage the graphic model optimization items. It displays the optimized combination curves of the graphic model optimization items to the relevant management personnel, who then determine which graphic model optimization items to optimize and optimize them accordingly.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] By leveraging the collaborative efforts of the application analysis module, optimization analysis module, and optimization management module, continuous optimization of distribution network diagram data management is achieved, ensuring timely updates to distribution network diagram data and avoiding potential safety hazards caused by data lag. This helps reduce grid faults caused by inaccurate or inconsistent data, thereby enhancing the safety and stability of the power grid. For frequent and complex adjustments at the residential side, this invention simplifies the data update process through automation and intelligent means, improving the efficiency and accuracy of data updates. Furthermore, the implementation of this invention contributes to promoting the construction and development of smart grids. By providing real-time and accurate distribution network diagram data, it provides strong support for the scheduling, control, and optimization of smart grids, contributing to the intelligent, automated, and efficient operation of the power grid. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0061] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, a power distribution network diagram model data intelligent management system includes an application analysis module, an optimization analysis module, and an optimization management module;

[0063] The application analysis module is used to analyze the application records of the application model data and determine each model optimization item. The model optimization item is the data item that needs to be optimized for data transmission of the model data, such as the data item corresponding to the process from the completion of the adjustment to the entry into the model management system for the electricity meter adjustment of newly added residents in the jurisdiction.

[0064] In one embodiment, the image model optimization items can be determined based on existing technologies, such as by feedback settings from staff at the corresponding application end; the application end is a system or platform that has the corresponding image model data; or, based on existing intelligent algorithms, a corresponding intelligent model can be established to analyze the application records and determine the image model optimization items that meet the requirements.

[0065] In one embodiment, the method for determining the graph model optimization term includes:

[0066] It connects to various application clients and acquires application records in real time. Application records include application model data, application result data, usage data, and other related data. Application results include whether the application is normal, whether it is abnormal, and the reasons for the abnormality and the losses caused by the abnormality. That is, the application failed to achieve or meet the corresponding application purpose due to the corresponding model data. This could be due to the model data itself or other reasons. Therefore, the corresponding reasons for the abnormality are supplemented, such as the abnormality caused by the untimely update of the model data or the abnormality caused by the incorrect model data. Since the corresponding results are already available, the corresponding comprehensive application result data can be obtained directly.

[0067] The criteria for defining the optimization items for the map model are set, that is, abnormal situations caused by untimely updates of map model data are marked as optimization item definitions;

[0068] An identification and judgment model is established. The identification and judgment model is used to analyze each application record according to the definition of the optimization item, and to determine whether the application record meets the definition of the optimization item. When it is determined that the optimization item definition is met, the data item corresponding to the application record is determined and marked as a candidate item.

[0069] By analyzing each application record and optimization item definition through an identification and judgment model, each candidate option is determined. Based on each candidate option, the corresponding application records are classified to obtain the candidate analysis data corresponding to each candidate option. Because each application record is analyzed, there may be multiple application records corresponding to the same candidate option.

[0070] Based on the candidate analysis data, determine the candidate probability and candidate value of the corresponding candidate options. The candidate probability refers to the probability that the data item becomes a candidate option, which is determined by comparing the number of candidate analysis data with the total number corresponding to the data item. The candidate value is set based on the loss of the candidate option, such as using the mean, mode, etc. to set the candidate value, or the staff can directly preset the candidate value of each data item for subsequent matching.

[0071] The optimized value of the corresponding candidate item is calculated based on the optimization formula, which is:

[0072] YB=DZ×(e μ -1);

[0073] In the formula: YB is the optimized value; DZ is the candidate value; μ is the candidate probability; e is the natural constant;

[0074] The platform sets a threshold X1 based on user needs. Only items exceeding the threshold X1 are considered as image optimization items. This threshold X1 is set by those skilled in the art based on actual conditions or obtained through large-scale data simulation. If no setting is made, the default threshold X1 can be 0.

[0075] Candidates whose optimization value is greater than the threshold X1 are marked as graphical optimization items.

[0076] In one embodiment, the identification and judgment model can be established based on existing identification and judgment technologies, and it is sufficient to achieve the corresponding identification and judgment. The main purpose is to compare the application result data in the application record with the definition of optimization items. Normal applications do not need to be analyzed, while abnormal applications and their corresponding abnormal causes are analyzed.

[0077] In one embodiment, the expression for the identification and judgment model is:

[0078]

[0079] In the formula: (s, Y) are the input data, s is the application record; Y is the definition of the optimization item; s→Y means that the corresponding application record meets the definition of the optimization item, and the judgment is made according to the corresponding abnormal application and the cause of the abnormality; the output data is the identification judgment value YA(s, Y), and the identification judgment value is 1 or 0.

[0080] When the recognition value is 1, the data item recorded by the corresponding application is marked as a candidate.

[0081] When the recognition value is 0, no corresponding operation is performed.

[0082] The optimization analysis module is used to perform optimization analysis on the diagram model, identify the diagram model optimization items, obtain data related to the diagram model data transmission process corresponding to the diagram model optimization items, and mark them as diagram model process-related data, such as the transmission process and related information such as the transmission method and equipment of each transmission node; for example, if an electrician uses the corresponding mobile phone program to enter the updated diagram model data, the corresponding department will conduct step-by-step approval through the corresponding method, and after the approval is qualified, it will be transmitted to the distribution network diagram model data management system.

[0083] Based on the associated data of the graph model process, a graph model process information diagram for the graph model optimization item is generated. The graph model process information diagram is used to represent relevant information such as transmission process, transmission nodes, transmission methods, and equipment used. Based on the graph model process information diagram, the various transmission items that can optimize and shorten transmission time for the graph model optimization item are identified, such as process optimization, transmission method optimization, and the use of more efficient equipment. Existing technologies are used to identify and determine each transmission item.

[0084] Analyze each transmission item to determine the baseline optimization method for each transmission item; determine the evaluation order of each transmission item based on the baseline optimization method.

[0085] Optimization analysis is performed based on the evaluation order to form an optimal combination curve.

[0086] In one embodiment, the baseline optimization method for the transmission item can be determined based on existing methods, such as building an optimization analysis model based on neural networks such as CNN or DNN, manually building a corresponding training set for training, and using the successfully trained optimization analysis model to analyze the corresponding transmission item to obtain the most likely optimization method for the current transmission item if optimization adjustments are made, and marking this optimization method as the baseline optimization method; alternatively, the baseline optimization method can be specified directly by manual means.

[0087] In one embodiment, a method for determining the baseline optimization method for a transmission item includes:

[0088] The platform provider pre-sets the baseline time for each possible transmission item, i.e. the normal usage time of the transmission item, and integrates and establishes a detailed table of transmission time.

[0089] Match the baseline time of each transmission item to the transmission time details table; determine the actual time of each transmission item based on historical data and mark it as historical time.

[0090] The time ratio of the corresponding transmission item is calculated according to the ratio formula, which is:

[0091] BZ = LZ ÷ HZ;

[0092] In the formula: BZ is the time consumption ratio; HZ is the baseline time consumption; LZ is the historical time consumption;

[0093] When the time consumption ratio is not greater than 1, no optimization is needed, that is, the baseline optimization method is none.

[0094] When the time consumption ratio is greater than 1, the optional optimization methods are determined. The baseline optimization method is determined based on the reduction ratio of the predicted time consumption of the optional optimization methods and the time consumption ratio. That is, the reduction ratio of the time consumption should be no less than the time consumption ratio of more than 1. The closest one is taken as the baseline optimization method; or the optimization method with the closest time consumption is directly taken as the baseline optimization method.

[0095] In one embodiment, a method for determining the baseline optimization method for a transmission item includes:

[0096] Identify the possible optimization methods for a transmission item and mark them as candidate optimization methods. For example, use big data and other methods to identify the various applicable transmission methods for the transmission item, identify their corresponding time consumption, and then compare and determine the candidate optimization methods.

[0097] Estimate the optimization cost and optimization difficulty value corresponding to each candidate optimization method. The optimization cost can be estimated based on the prior art; the optimization difficulty value is set according to the implementation difficulty of adjusting from the existing method to the candidate optimization method, and then the corresponding difficulty quantification is performed to obtain the optimization difficulty value. For example, the platform can preset different optimization difficulty standards and corresponding optimization difficulty values, and then perform an optimization difficulty similarity match to determine the corresponding optimization difficulty value. It is also possible to preset the value range of the optimization difficulty value, preset different standards, and calculate the optimization difficulty values of different candidate optimization methods by combining interpolation methods, etc. It is also possible to evaluate the optimization difficulty value based on other existing methods, such as using a neural network and other neural networks to establish a difficulty evaluation model, and training it through an artificial method to establish a corresponding training set. The training set includes input data and output data. The input data is the candidate optimization method and the existing corresponding transmission method; the output data is the optimization difficulty value; analyze through the difficulty evaluation model after successful training.

[0098] Identify the optimization duration of each candidate optimization method, that is, estimate the shortened duration.

[0099] Remove the dimension and take its numerical value for calculation, and calculate the method value of the corresponding candidate optimization method according to the first formula. The first formula is:

[0100]

[0101] In the formula: WA is the method value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 < 1, 0 < b2 < 1; NB is the optimization difficulty value; CB is the optimization cost.

[0102] Set the corresponding candidate coordinates for the candidate optimization method according to the method value and the optimization duration, that is, the horizontal axis is the method value and the vertical axis is the optimization duration; input each candidate coordinate into the coordinate system, and form a candidate analysis curve according to each candidate coordinate.

[0103] Calculate the slope of the candidate analysis curve at each candidate coordinate, and mark it as the candidate analysis value; select the candidate optimization method corresponding to the candidate coordinate with the largest candidate analysis value as the reference optimization method.

[0104] In one embodiment, the method for determining the evaluation order of each transmission item according to each reference optimization method includes:

[0105] Calculate the method value of each reference optimization method according to the method value calculation method in the above embodiment; identify the optimization duration of the reference optimization method.

[0106] Calculate the priority value of the corresponding transmission item according to the priority formula. The priority formula is:

[0107] WY = T ÷ (WA + 1);

[0108] In the formula: WY is the priority value; T is the optimization duration; WA is the mode value;

[0109] The evaluation order of the corresponding transmission items is determined according to their priority values.

[0110] In one embodiment, the method for performing optimization analysis according to the evaluation order includes:

[0111] Step SA1: Generate an evaluation sequence according to the evaluation order; form a merged combination from the first transport item in the evaluation sequence, remove the merged transport item from the evaluation sequence to form a new evaluation sequence; that is, initially form a merged combination from a transport item; determine the combined optimization value and combined condition value of the merged combination;

[0112] The combined optimal value is the sum of the optimization times of each transmission term within the combination; that is, the formula for the combined optimal value is:

[0113]

[0114] Where: UH is the combined optimization value; Ti is the optimization duration of the corresponding transmission item in the corresponding merged combination, i = 1, 2, ..., n, and n is the number of transmission items in the merged combination;

[0115] The combination condition value is set based on the newly added method value, compared to the previous combination. The platform presets the horizontal axis increase corresponding to different newly added method values. As the method value increases, the horizontal axis amplitude corresponding to the same unit method value increases. For example, previously, 1 method value corresponded to 1 horizontal axis unit length, but as the method value increases, 1 method value corresponds to 5 horizontal axis unit lengths. The platform presets the combination adjustment value corresponding to different method values, and can also set corresponding amplification coefficients according to different method value ranges, thereby determining the corresponding combination condition value. The combination condition value is the previous combination condition value plus the number of unit lengths corresponding to the newly added method value; expressed by the formula:

[0116] ZT = ZT′ + ZO;

[0117] In the formula: ZT is the combination condition value of the current combination, ZT′ is the condition combination value of the previous combination, and ZO is the added condition combination value determined according to the newly added method value;

[0118] This means that the new method value needs to be determined first, and then the method value is converted according to the conversion method preset by the platform to obtain the new combination value. The condition combination value of the previous combination is added to it to obtain the condition combination value of the current merged combination.

[0119] Set up an optimized coordinate system, with the horizontal axis representing the combined condition values ​​and the vertical axis representing the combined optimized values;

[0120] The combined optimization value and combined condition value corresponding to the merged combination are used to form combined coordinates and then introduced into the optimization coordinate system;

[0121] Step SA2: Combine the first-ranked transfer item in the evaluation sequence with the merged combination to form a new merged combination and a new evaluation sequence. Determine the combined optimization value and combined condition value of the new merged combination. Then, use the combined optimization value and combined condition value corresponding to the new merged combination to form a new combined coordinate system.

[0122] Step SA3: Repeat step SA2 until there are no more transport terms in the evaluation sequence, and form an optimized combination curve based on the combined coordinates in the optimized coordinate system.

[0123] The optimization management module is used to optimize and manage the graphic model optimization items. It displays the optimized combination curves of each graphic model optimization item to the corresponding management personnel, who then determine which graphic model optimization items to optimize and optimize them accordingly.

[0124] In one embodiment, recommended points can be marked on the optimized combination curve. For example, the minimum time to be shortened can be determined based on application records, the minimum combined optimization value can be determined, the optimized combination curve can be marked based on the minimum combined optimization value, and the coordinate point with the largest slope can be selected as the recommended point from the curve segment that meets the requirements.

[0125] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0126] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power distribution network diagram data intelligent management system, characterized in that, It includes an application analysis module, an optimization analysis module, and an optimization management module; The application analysis module is used to perform application analysis on the image data and determine the image optimization items. The image optimization items are the data items that need to be optimized for image data transmission. The optimization analysis module is used to perform optimization analysis on the graph model optimization items, obtain the graph model flow association data of the graph model optimization items, generate the graph model flow information graph of the graph model optimization items based on the graph model flow association data, and identify the transmission items of the graph model optimization items based on the graph model flow information graph. Determine the baseline optimization method for the transmission item; The evaluation order of the transmission items is determined according to the aforementioned benchmark optimization method; Optimization analysis is performed based on the evaluation order to generate an optimal combination curve; The optimization management module is used to optimize and manage the graphic model optimization items, display the optimized combination curves of the graphic model optimization items to the relevant management personnel, and let the management personnel determine the graphic model optimization items to be optimized and optimize the corresponding graphic model optimization items. The methods for determining the optimization terms of the graphical model include: Real-time acquisition of application records, setting optimization item definitions for graph model optimization items, wherein the optimization item is defined as a data item that causes application abnormalities due to untimely updates of graph model data; An identification and judgment model is established, and the application records and optimization item definitions are analyzed through the identification and judgment model to determine the candidate options; the application records are classified according to the candidate options to obtain the candidate analysis data corresponding to the candidate options, and the candidate analysis data consists of application records belonging to the corresponding candidate options; Based on the candidate analysis data, determine the candidate probability and candidate value of each candidate option; calculate the optimized value of each candidate option using the optimization formula, which is: ； In the formula: YB is the optimized value; DZ is the candidate value; μ is the candidate probability; e is the natural constant; Candidates whose optimization value is greater than the threshold X1 are marked as graphical optimization items; Methods for optimization analysis based on evaluation order include: Step SA1: Generate an evaluation sequence according to the evaluation order; combine the first transport item in the evaluation sequence into a merged combination to form a new evaluation sequence; determine the combined optimization value and combined condition value of the merged combination; Set up an optimized coordinate system, with the horizontal axis representing the combination condition values ​​and the vertical axis representing the combination optimization values; then, input the combination optimization values ​​and combination condition values ​​corresponding to the initial combination into the optimized coordinate system to form a combination coordinate. Step SA2: Combine the first-ranked transmission item in the evaluation sequence with the merged combination to form a new merged combination and a new evaluation sequence; determine the combined optimization value and combined condition value of the merged combination; and input the combined optimization value and combined condition value of the merged combination into the optimization coordinate system to form a new combined coordinate system. Step SA3: Repeat step SA2 until there are no more transport terms in the evaluation sequence, and form an optimized combination curve based on the combined coordinates in the optimized coordinate system.

2. The intelligent management system for distribution network diagram data according to claim 1, characterized in that, The expression for the recognition and judgment model is: ； In the formula: (s, Y) represents the input data, s represents the application record, Y represents the definition of the optimization term, s→Y indicates that the corresponding application record conforms to the definition of the optimization term, and the output data is the recognition judgment value YA(s, Y), which is 1 or 0. When the recognition judgment value is 1, mark the data item of the corresponding application record as an option; When the recognition judgment value is 0, no corresponding operation is performed.

3. The intelligent management system for distribution network diagram data according to claim 1, characterized in that, The method for determining the baseline optimization method of a transmission item includes: The platform party establishes a transmission time-consuming detail list, and the transmission time-consuming detail list is used to count the baseline time-consuming corresponding to different transmission items; Match the baseline time-consuming of the transmission item according to the transmission time-consuming detail list; identify the historical time-consuming of the transmission item; Calculate the time-consuming ratio of the transmission item according to the ratio formula, and the ratio formula is: BZ = LZ ÷ HZ; In the formula: BZ is the time-consuming ratio; HZ is the baseline time-consuming; LZ is the historical time-consuming; When the time-consuming ratio is not greater than 1, the baseline optimization method is none; When the time-consuming ratio is greater than 1, determine the baseline optimization method according to the time-consuming ratio.

4. The intelligent management system for distribution network diagram data according to claim 1, characterized in that, The method for determining the baseline optimization method of a transmission item includes: Determine the candidate optimization methods possessed by the transmission item; identify the optimization duration of the candidate optimization methods; Estimate the optimization cost and optimization difficulty value corresponding to the candidate optimization methods; Calculate the method value of the candidate optimization method according to the first formula, and the first formula is: ； In the formula: WA is the method value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 < 1, 0 < b2 < 1; NB is the optimization difficulty value; CB is the optimization cost; Form candidate coordinates according to the method value and optimization duration of the candidate optimization method; input the candidate coordinates into the coordinate system, and form a candidate analysis curve according to the candidate coordinates; Calculate the slope of the candidate analysis curve at the candidate coordinates, marked as the candidate analysis value; determine the baseline optimization method according to the candidate analysis value.

5. The intelligent management system for distribution network diagram data according to claim 1, characterized in that, The method for determining the evaluation order of the corresponding transmission item according to the baseline optimization method includes: Calculate the method value of the baseline optimization method, and identify the optimization duration of the baseline optimization method; Calculate the priority value of the transmission item according to the priority formula, and the priority formula is: WY = T ÷ (WA + 1); In the formula: WY is the priority value; T is the optimization duration; WA is the method value; Determine the evaluation order of the transmission item according to the order of the priority values.

6. The intelligent management system for distribution network diagram data according to claim 1, characterized in that, The combined optimization value formula is: ； In the formula: UH is the combined optimization value; Ti is the optimization duration of the corresponding transmission item within the merged combination, i = 1, 2,..., n, and n is the number of transmission items within the merged combination.

7. The intelligent management system for distribution network diagram data according to claim 1, characterized in that, The combined condition value formula is: ZT = ZT´ + ZO; In the formula: ZT is the combined condition value of the current merged combination; ZT´ is the condition combination value of the previous merged combination, and ZO is the new combined value, and the new combined value is obtained by transforming the new method value of the current merged combination according to the preset transformation method.

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