Power system operation optimization method based on marketing and distribution connection data

Abstract

The invention discloses a power system operation optimization method based on marketing and distribution connection data, and belongs to the technical field of power grid data processing. The marketing and distribution connection data is divided into two categories of equipment data and user data, a corresponding association relationship is constructed according to a user side and an equipment side, and then the reliability of the marketing and distribution connection data is evaluated; according to the method, the abnormal marketing and distribution connection data which does not meet the evaluation standard is corrected, and finally, the operation of the power system is optimized according to the corrected marketing and distribution connection data, so that the relationship among various types of data in the marketing and distribution connection data is more concise, and the association degree among the various types of data in the marketing and distribution connection data is improved; the effectiveness of the marketing and distribution connection data is improved through real-time evaluation of the marketing and distribution connection data, so that the reliability of the operation planning scheme of the power system is improved, and the operation benefit of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data processing, and in particular to a method for optimizing power system operation based on marketing and distribution interoperability data. Background Art

[0002] In order to accelerate the promotion of marketing and distribution data governance, improve the basic archives of marketing and distribution, tap potential and increase efficiency with lean management, empower and empower intelligence with digital technology, and help enterprises improve quality and efficiency, it is a development trend of the current power system to connect marketing data and distribution operation data. However, the amount of marketing data and distribution operation data is too large. Therefore, a lot of effort is required to connect the parts related to marketing data and distribution operation data. While ensuring the interconnection of marketing data and distribution operation data, the accuracy and effectiveness of the data must also be guaranteed. If the marketing data cannot be associated with the distribution operation data in real time, it will affect the subsequent operation strategy adopted by the power system based on the marketing and distribution connection data, resulting in the operation effect of the power system failing to achieve the best expected effect.

[0003] Chinese patent, publication number: CN118114761A, publication date: May 31, 2024, discloses a knowledge base-based marketing and distribution data processing method, by collecting historical operation data of the distribution system in the area and the archive data of each electricity user, a marketing and distribution knowledge base is constructed based on the historical operation data and the archive data, and the marketing and distribution knowledge base is stored in a distributed database; a topological map of all distribution equipment and power-using equipment in the area is generated based on the marketing and distribution knowledge base, and the nodes of the topological map are distribution equipment or power-using equipment; real-time operation data of each distribution equipment in the distribution system and real-time power consumption data of the power-using equipment are collected in real time and mapped to the nodes corresponding to the topological map, based on the real-time operation data and real-time power consumption data of each node in the topological map as well as the historical operation data and the archive data, erroneous line transformers are identified and the topological map is calibrated, although the distribution operation data is classified and stored and the data is calibrated, the connection between marketing data and distribution operation data is not considered. Summary of the invention

[0004] In view of the problem that the prior art is difficult to ensure the reliability of the marketing and distribution through-communication data when the operation of the power system is adjusted in real time based on the marketing and distribution through-communication data, resulting in poor adjustment effect, the present invention proposes a power system operation optimization method based on the marketing and distribution through-communication data, by dividing the marketing and distribution through-communication data into two categories: equipment data and user data, and building corresponding association relationships according to the user side and the equipment side, and then evaluating the reliability of the marketing and distribution through-communication data, correcting the abnormal marketing and distribution through-communication data that does not meet the evaluation criteria, and finally optimizing the operation of the power system according to the corrected marketing and distribution through-communication data, so that the relationship between various types of data in the marketing and distribution through-communication data is more concise, and the degree of association between various types of data in the marketing and distribution through-communication data is improved. The effectiveness of the marketing and distribution through-communication data is improved through real-time evaluation of the marketing and distribution through-communication data, thereby improving the reliability of the power system operation planning scheme, thereby improving the operation efficiency of the power system.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is: a method for optimizing power system operation based on operation and distribution interconnection data, comprising the following steps: S1. Obtain target marketing and distribution data, and establish a first association relationship based on device information and geographic information in the target marketing and distribution data; and establish a second association relationship based on supply and demand information and user information in the target marketing and distribution data; S2. Construct a marketing and distribution integration data indicator evaluation system, evaluate the target marketing and distribution integration data based on the marketing and distribution integration data indicator evaluation system, and obtain abnormal marketing and distribution integration data based on the evaluation results; S3, based on the abnormal marketing and distribution data and in combination with the first association relationship and the second association relationship, obtaining abnormal device information, abnormal device location and abnormal user information; S4. Obtain historical marketing and distribution data, and correct abnormal device information, abnormal device location, and abnormal user information based on the change trend of the historical marketing and distribution data to obtain marketing and distribution correction data; S5. Analyze the operation and distribution correction data, and optimize the operation of the power system based on the analysis results.

[0006] In this scheme, in order to reduce the difficulty of finding the required data from the massive amount of marketing and distribution data, the marketing and distribution data are divided into two categories: user side and device side according to the role type represented in the power grid. First, the information of each device is associated with the geographic information to build a first association relationship, and the information such as the location and connection relationship of each device is integrated. When the above information needs to be queried, all relevant information can be mapped out according to the first association relationship, which reduces the difficulty of data query; and the supply and demand information is basically related to user demand. The supply and demand information is associated with the user information to build a second association relationship, which is convenient for mapping the corresponding supply and demand information according to the user ID and user location information, and also reduces the difficulty of users querying their own data; then the target marketing and distribution data is evaluated for data quality. If it does not meet the evaluation requirements, it is corrected according to the historical marketing and distribution data, and then the corrected marketing and distribution data is used as the data support for the operation of the power system to build the best operation plan for the future, which solves the problem of data query difficulties caused by the excessive amount of marketing and distribution data, and also ensures the reliability of the obtained marketing and distribution data, thereby improving the operation efficiency of the power system.

[0007] Preferably, in S1, obtaining target distribution data includes the following steps: Obtain marketing and distribution data, pre-process the data, remove invalid and erroneous data, and obtain target marketing and distribution data.

[0008] In this solution, before analyzing the marketing and distribution data, it is necessary to preprocess it first, because the data collected by various acquisition devices are in different formats and types and there is a large amount of invalid data. These invalid data need to be cleaned, and then the data type and data format need to be unified to facilitate subsequent analysis and reduce the impact of invalid data on data analysis, thereby improving analysis efficiency.

[0009] Preferably, in S1, building a first association relationship based on the equipment information and geographic information in the target marketing and distribution data includes the following steps: Acquire equipment information in the target distribution and interconnection data, wherein the equipment information at least includes power grid equipment operation data and power grid topology data; The positions of the power grid equipment and each power grid topology are associated with the geographic information to obtain a first association relationship.

[0010] In this solution, in order to make a one-to-one correspondence between power grid equipment and various topological structures in the power grid and geographic information, the power grid equipment operation data including equipment model, commissioning time, maintenance records, etc. and the power grid topological structure data including line connection relationship, substation distribution, etc. are associated with the geographic information to construct a first association relationship, and the location of each device in the power grid and the topological structure and distribution between each device are obtained. When it is necessary to query data for a certain device, according to the data associated in the first association relationship, the relevant equipment location, topological structure and other information can be directly mapped according to the device number, which significantly improves the efficiency of data query and also strengthens the correlation between various types of data in the marketing and distribution data.

[0011] Preferably, in S1, building a second association relationship based on the supply and demand information and user information in the target marketing and distribution data includes the following steps: Acquire supply and demand information in the target marketing and distribution data, wherein the supply and demand information includes at least load data and power data; The load data and the power data are associated with the user information to obtain a second association relationship, wherein the user information at least includes the user's power meter information and the user's name.

[0012] In this solution, in order to make a one-to-one correspondence between the supply and demand information and the user information in the power grid, the supply and demand information including electricity data, electricity fee data, load data and power grid capacity data and the user information including name, address, contact information, etc. are associated with the supply and demand information to construct a second association relationship, and the load data and electricity fee data corresponding to each user are obtained. Based on these data, the power supply capacity required by the power grid can be obtained. When it is necessary to query data for a certain user, according to the data associated in the second association relationship, the corresponding load data and electricity fee data can be directly mapped according to the user's name, address, contact information, etc., which significantly improves the efficiency of data query and also strengthens the association between various types of data in the marketing and distribution data.

[0013] Preferably, in S2, a marketing and distribution through data indicator evaluation system is constructed, target marketing and distribution through data is evaluated based on the marketing and distribution through data indicator evaluation system, and abnormal marketing and distribution through data is obtained based on the evaluation result, including the following steps: constructing a marketing and distribution through data indicator evaluation system based on at least the data integrity, timeliness and accuracy of the marketing and distribution through data; evaluating the target marketing and distribution through data based on the marketing and distribution through data indicator evaluation system to obtain a data evaluation value; The target marketing and distribution data that is lower than the data evaluation threshold is regarded as abnormal marketing and distribution data.

[0014] In this scheme, in order to ensure the timeliness and effectiveness of the marketing and distribution data, it is necessary to evaluate the marketing and distribution data. By constructing an evaluation system for marketing and distribution data indicators, the evaluation indicators include data integrity, data consistency, data timeliness, data accuracy and other indicators, which respectively ensure that there is no data missing in the marketing and distribution data, the data is consistent with the equipment or user that actually generates the data, the data is the current data without delay, and the data is the same as the data collected by the acquisition device without deviation. The reliability of the marketing and distribution data is guaranteed by evaluating the marketing and distribution data according to the above indicators. If it is found during the evaluation process that one or more indicators of the marketing and distribution data do not meet the requirements, it will be regarded as abnormal marketing and distribution data. The abnormal data needs to be corrected before it can be used as data support for the operation of the power system. By checking the marketing and distribution data, the data reliability is significantly improved, and the feasibility of the power system operation strategy is improved.

[0015] Preferably, in S3, based on the abnormal marketing and distribution data and in combination with the first association relationship and the second association relationship, obtaining abnormal device information, abnormal device location and abnormal user information includes the following steps: Obtaining a data evaluation value of the abnormal marketing and distribution through-communication data, and obtaining an abnormal type of the abnormal marketing and distribution through-communication data based on the proportion of each evaluation index in the marketing and distribution through-communication data index evaluation system occupied by the data evaluation value; Based on the abnormal type and in combination with the first association relationship, abnormal device information and abnormal device location are acquired; based on the abnormal type and in combination with the second association relationship, abnormal user information is acquired.

[0016] In this scheme, in order to obtain the abnormal device information or user information based on the abnormal marketing and distribution data, the abnormal type of the abnormal marketing and distribution data is obtained based on the proportion of the data evaluation value in each evaluation index in the marketing and distribution data index evaluation system. For example, if the data integrity index accounts for the largest proportion, the abnormal type may be equipment operation failure. If the data consistency index accounts for the largest proportion, the abnormal type may be user address error, user meter number error, etc. After determining the abnormal type, according to whether the abnormal type belongs to the user side or the device side, all the required information is mapped according to the corresponding association relationship to locate the area where the abnormality occurs, and then the equipment or users in the abnormal area are processed according to the abnormal type, which greatly improves the positioning accuracy of the abnormal area and the efficiency of identifying abnormal data.

[0017] Preferably, in S4, historical marketing and distribution data are obtained, and based on the change trend of the historical marketing and distribution data, the abnormal device information, the abnormal device location and the abnormal user information are corrected to obtain the marketing and distribution correction data, including the following steps: obtaining historical marketing and distribution data, and obtaining the predicted marketing and distribution data in the same time domain as the abnormal marketing and distribution data based on the deep learning neural network model and the historical marketing and distribution data; Based on the data deviation between the abnormal marketing and distribution data and the predicted marketing and distribution data, part of the abnormal marketing and distribution data that exceeds the deviation threshold is replaced with the predicted marketing and distribution data, and the abnormal equipment information, abnormal equipment location and abnormal user information are corrected based on the replaced data to obtain the marketing and distribution correction data.

[0018] In this scheme, in order to improve the reliability of the marketing and distribution through-connection data, by collecting historical marketing and distribution through-connection data, a deep learning neural network model is used to analyze the changing trend of the historical marketing and distribution through-connection data, and the marketing and distribution through-connection data is predicted based on the analysis results. The predicted data is compared with the actual data, and the data of some areas with large deviations are removed. The predicted data of the area is used as filling to correct the abnormal marketing and distribution through-connection data, thereby obtaining the marketing and distribution through-connection corrected data, thereby improving the reliability of the marketing and distribution through-connection data, so that the power system will not infer the subsequent operation mode based on the abnormal marketing and distribution through-connection data, thereby improving the operation efficiency of the power system.

[0019] Preferably, in S5, analyzing the distribution and penetration correction data, and optimizing the operation of the power system based on the analysis result, comprises the following steps: Adjust the corresponding equipment operation mode based on the data area corrected by the marketing and distribution correction data; Provide personalized power service plans to corresponding users based on the data area corrected by the marketing and distribution correction data; The operation of the power system is optimized based on the adjusted equipment operation mode and the user's personalized power service plan.

[0020] In this scheme, since the abnormal part of the abnormal operation and distribution data is replaced by the predicted operation and distribution data, the replaced data can be used to obtain the optimal operation mode required by the corresponding equipment according to the first association relationship mapping, and the corresponding user's optimal curve of electricity consumption and time relationship can be obtained according to the second association relationship mapping. A series of user optimal electricity consumption plans, when the optimal operation mode on the equipment side and the optimal electricity consumption plan on the user side in the power system are determined, the optimal operation plan of the power system can be obtained, thereby realizing the optimization and adjustment of the power system according to the operation and distribution data.

[0021] Preferably, the abnormality types include at least a user address error, a user meter number error, asynchrony of equipment operation data, an equipment location error and an equipment operation failure.

[0022] In this scheme, since the evaluation indicators of abnormal operation and distribution data include data integrity, data consistency, data timeliness, data accuracy and other indicators, each indicator corresponds to a different type of abnormality. For example, the abnormality type corresponding to the data integrity indicator may be equipment operation failure, and the abnormality type corresponding to the data consistency indicator may be user address error, user meter number error, etc., the evaluation indicators of operation and distribution data are linked to the corresponding abnormality types to locate the area where the abnormality occurs according to the evaluation results, so that the abnormal area can be handled in time, thereby improving the operation stability of the power system.

[0023] Preferably, the geographic information includes at least the geographic location of the power grid equipment and the user distribution area.

[0024] In this solution, geographic information includes the geographical location of the device on the device side and the distribution area of ​​the user. By using geographic information as the association node between the user side and the device side, the correlation of various types of data in the marketing and distribution data is improved.

[0025] The beneficial effects of the present invention are as follows: (1) The present invention improves the processing efficiency of massive amounts of marketing and distribution data by dividing the marketing and distribution data into two categories: device data and user data, and constructing corresponding association relationships based on the user side and the device side. At the same time, all information related to the query content can be easily queried based on the association relationships, thereby improving the efficiency of data query and the integrity of query data; (2) The present invention performs data quality assessment on the marketing and distribution through communication data, determines whether the marketing and distribution through communication data needs to be revised based on the assessment results, and uses the processed marketing and distribution through communication data as data support for planning future operation plans of the power system to obtain an optimized operation plan of the power system, thereby improving the reliability of the marketing and distribution through communication data as data support, thereby improving the operating efficiency of the power system.

[0026] The above invention content is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0028] Figure 1 The present invention is a flow chart of a method for optimizing power system operation based on operation and distribution data. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0031] Example: Figure 1 As shown, in order to solve the problem that the reliability of the operation and distribution through data is difficult to ensure when the operation of the power system is adjusted in real time according to the operation and distribution through data in the prior art, resulting in poor adjustment effect, this embodiment proposes a power system operation optimization method based on the operation and distribution through data, including the following steps: S1: Acquire target marketing and distribution data, and establish a first association relationship based on the equipment information and geographic information in the target marketing and distribution data; establish a second association relationship based on the supply and demand information and user information in the target marketing and distribution data.

[0032] In this embodiment, obtaining target marketing and distribution data includes the following steps: Obtain marketing and distribution data, pre-process the data, remove invalid and erroneous data, and obtain target marketing and distribution data.

[0033] Before analyzing the marketing and distribution data, this embodiment needs to preprocess it first. Because the data collected by various acquisition devices are of different formats and types and contain a large amount of invalid data, these invalid data need to be cleaned and then the data type and data format need to be unified to facilitate subsequent analysis and reduce the impact of invalid data on the analysis data, thereby improving analysis efficiency.

[0034] When acquiring the data of the distribution and distribution, this embodiment can select the appropriate type and specification of intelligent sensors according to the monitoring requirements and equipment characteristics, such as voltage sensors and current sensors with high-precision measurement capabilities, and integrated sensors that can monitor multiple parameters at the same time. The sensors are installed strictly in accordance with engineering specifications and safety standards to ensure that they are firm, stable, and in the best monitoring position. For sensors installed outdoors, waterproof, dustproof, and anti-electromagnetic interference packaging designs can be used to ensure long-term reliable operation in harsh environments. At the same time, the sensors are equipped with remote monitoring and diagnostic functions to understand the working status of the sensors in real time and to promptly discover and solve possible faults.

[0035] When the collected data is transmitted in this embodiment, a hybrid transmission architecture can be established with the 5G network as the backbone and dedicated optical fiber as the auxiliary. In urban centers and densely populated areas, the high speed, low latency and large capacity of the 5G network are fully utilized to achieve rapid data upload. For areas in remote areas with poor communication conditions or extremely high requirements for data security, such as important substations and transmission line crossing points, dedicated optical fiber lines are laid to ensure the stability and reliability of data transmission. Deploy data caching and breakpoint resumption mechanisms. When the network signal is unstable or interrupted, the sensor can temporarily store the data and automatically resume transmission after the network is restored to ensure data integrity. Network slicing technology can also be used to divide dedicated network channels for power data transmission to ensure data transmission priority and service quality.

[0036] When the transmitted data is saved in this embodiment, since the amount of data collected is relatively large, a data processing center based on cloud computing can be built, and the powerful computing and storage resources of the cloud service provider can be used to achieve elastic expansion and high availability. A distributed server cluster architecture, such as a Hadoop cluster, can also be used to improve the data processing speed through parallel processing. High-speed solid-state drives (SSDs) and large-capacity disk arrays (RAID) are configured for data storage to meet the storage needs of massive data. Data compression and archiving technology can also be used to reasonably compress and classify historical data for storage, reduce storage costs, and ensure data retrievability and availability. At the same time, a data backup and recovery mechanism is established, and full and incremental backups of data are regularly performed, and stored in a remote data center to cope with possible disaster events.

[0037] In this embodiment, building a first association relationship based on the device information and geographic information in the target marketing and distribution data includes the following steps: Acquire equipment information in the target distribution and interconnection data, wherein the equipment information at least includes power grid equipment operation data and power grid topology data; The positions of the power grid equipment and each power grid topology are associated with the geographic information to obtain a first association relationship.

[0038] In order to make a one-to-one correspondence between power grid equipment and various topological structures in the power grid and geographic information, this embodiment associates power grid equipment operation data including equipment model, commissioning time, maintenance records, etc. and power grid topological structure data including line connection relationship, substation distribution, etc. with geographic information to construct a first association relationship, thereby obtaining the location of each device in the power grid and the topological structure and distribution between each device. When data query is required for a certain device, the relevant equipment location, topological structure and other information can be directly mapped according to the device number based on the data associated in the first association relationship, which significantly improves the efficiency of data query and also strengthens the association between various types of data in the marketing and distribution data.

[0039] In this embodiment, building a second association relationship based on the supply and demand information and user information in the target marketing and distribution data includes the following steps: Acquire supply and demand information in the target marketing and distribution data, wherein the supply and demand information includes at least load data and power data; The load data and the power data are associated with the user information to obtain a second association relationship, wherein the user information at least includes the user's power meter information and the user's name.

[0040] In order to make a one-to-one correspondence between the supply and demand information and the user information in the power grid, this embodiment associates the supply and demand information including power consumption data, electricity fee data, load data and power grid capacity data with the user information including name, address, contact information, etc., and constructs a second association relationship by associating the supply and demand information with the user information, thereby obtaining the load data and electricity fee data corresponding to each user. Based on these data, the power supply capacity required by the power grid can be obtained. When it is necessary to query data for a certain user, the corresponding load data and electricity fee data can be directly mapped according to the user's name, address, contact information, etc. based on the data associated in the second association relationship, thereby significantly improving the efficiency of data query and also strengthening the association between various types of data in the marketing and distribution data.

[0041] S2: Construct an evaluation system for marketing and distribution integration data indicators, evaluate the target marketing and distribution integration data based on the evaluation system, and obtain abnormal marketing and distribution integration data based on the evaluation results.

[0042] In this embodiment, a marketing and distribution integration data indicator evaluation system is constructed, target marketing and distribution integration data is evaluated based on the marketing and distribution integration data indicator evaluation system, and abnormal marketing and distribution integration data is obtained based on the evaluation results, including the following steps: At least based on the data integrity, timeliness and accuracy of the marketing and distribution integration data, a marketing and distribution integration data indicator evaluation system is established; based on the marketing and distribution integration data indicator evaluation system, the target marketing and distribution integration data is evaluated to obtain a data evaluation value; The target marketing and distribution data that is lower than the data evaluation threshold is regarded as abnormal marketing and distribution data.

[0043] In order to ensure the timeliness and effectiveness of the marketing and distribution data, this embodiment needs to evaluate the marketing and distribution data. By constructing an evaluation system for marketing and distribution data indicators, the evaluation indicators include data integrity, data consistency, data timeliness, data accuracy and other indicators. This is an important basis for realizing information management and decision support for power grid enterprises. The data integrity indicator measures the coverage of the marketing system (such as customer information and electricity consumption data) and the distribution system (such as equipment information and line topology) data to ensure that no key information between the two is missed.

[0044] The data consistency indicator is to check whether the customer access point information in the marketing system corresponds to the grid wiring diagram in the distribution system, including the matching degree of transformers, lines, user access points, etc.

[0045] The data timeliness indicator evaluates the synchronization speed of data updates and ensures that changes on the marketing side (such as new user access, user change information) can be quickly reflected in the distribution system.

[0046] The data accuracy indicator verifies the numerical consistency by comparing the relevant data of the marketing and distribution systems, such as the accuracy of electricity metering and the degree of consistency between user load and distribution network load.

[0047] The above indicators respectively ensure that there is no missing data in the marketing and distribution through-connection data, the data is consistent with the equipment or user that actually generates the data, the data is the current data without delay, and the data is the same as the data collected by the acquisition device without deviation. The reliability of the marketing and distribution through-connection data is ensured by evaluating the marketing and distribution through-connection data according to the above indicators. If it is found during the evaluation process that one or more indicators of the marketing and distribution through-connection data do not meet the requirements, it will be regarded as abnormal marketing and distribution through-connection data, and the abnormal data needs to be corrected before it can be used as data support for the operation of the power system. By checking the marketing and distribution through-connection data, the data reliability is significantly improved, and the feasibility of the power system operation strategy is improved.

[0048] S3: Based on the abnormal marketing and distribution data and in combination with the first association relationship and the second association relationship, abnormal device information, abnormal device location and abnormal user information are obtained.

[0049] In this embodiment, based on the abnormal marketing and distribution data and in combination with the first association relationship and the second association relationship, abnormal device information, abnormal device location and abnormal user information are obtained, including the following steps: Obtaining a data evaluation value of the abnormal marketing and distribution through-communication data, and obtaining an abnormal type of the abnormal marketing and distribution through-communication data based on the proportion of each evaluation index in the marketing and distribution through-communication data index evaluation system occupied by the data evaluation value; Based on the abnormal type and in combination with the first association relationship, abnormal device information and abnormal device location are acquired; based on the abnormal type and in combination with the second association relationship, abnormal user information is acquired.

[0050] In order to obtain abnormal device information or user information based on abnormal marketing and distribution data, this embodiment obtains the abnormal type of abnormal marketing and distribution data based on the proportion of each evaluation index in the marketing and distribution data index evaluation system accounted for by the data evaluation value. For example, if the data integrity index accounts for the largest proportion, the abnormal type may be equipment operation failure. If the data consistency index accounts for the largest proportion, the abnormal type may be user address error, user meter number error, etc. After determining the abnormal type, according to whether the abnormal type belongs to the user side or the device side, all the required information is mapped according to the corresponding association relationship to locate the area where the abnormality occurs, and then the equipment or users in the abnormal area are processed according to the abnormal type, which greatly improves the positioning accuracy of the abnormal area and the efficiency of identifying abnormal data.

[0051] This embodiment can introduce cutting-edge machine learning and artificial intelligence algorithms when acquiring abnormal areas, such as using convolutional neural networks (CNN) in deep learning for image recognition to identify faulty images of equipment, such as using recurrent neural networks (RNN) for time series prediction to predict power loads, and acquiring areas where abnormal loads occur based on the prediction results. Customized analysis models can also be developed for different business scenarios, such as fault tree models for power grid fault diagnosis, optimal power flow models for optimizing power grid operations, etc. A data visualization platform is established to present the analysis results to decision makers in the form of intuitive and clear charts and reports, supporting real-time interaction and in-depth analysis.

[0052] S4: Obtain historical marketing and distribution data, and correct abnormal device information, abnormal device location, and abnormal user information based on the change trend of the historical marketing and distribution data to obtain marketing and distribution correction data.

[0053] In this embodiment, historical marketing and distribution data are obtained, and based on the change trend of the historical marketing and distribution data, the abnormal device information, the abnormal device location and the abnormal user information are corrected to obtain the marketing and distribution correction data, including the following steps: obtaining historical marketing and distribution data, and obtaining the predicted marketing and distribution data in the same time domain as the abnormal marketing and distribution data based on the deep learning neural network model and the historical marketing and distribution data; Based on the data deviation between the abnormal marketing and distribution data and the predicted marketing and distribution data, part of the abnormal marketing and distribution data that exceeds the deviation threshold is replaced with the predicted marketing and distribution data, and the abnormal equipment information, abnormal equipment location and abnormal user information are corrected based on the replaced data to obtain the marketing and distribution correction data.

[0054] In order to improve the reliability of the marketing and distribution connection data, this embodiment collects historical marketing and distribution connection data, adopts a deep learning neural network model to analyze the changing trend of the historical marketing and distribution connection data, predicts the marketing and distribution connection data according to the analysis results, compares the predicted data with the actual data, removes the data of some areas with large deviations, and uses the predicted data of the area as filling to correct the abnormal marketing and distribution connection data, thereby obtaining the marketing and distribution connection corrected data, thereby improving the reliability of the marketing and distribution connection data, and preventing the power system from inferring the subsequent operation mode based on the abnormal marketing and distribution connection data, thereby improving the operation efficiency of the power system.

[0055] S5: Analyze the operation and distribution correction data, and optimize the operation of the power system based on the analysis results.

[0056] In this embodiment, the operation of the power system is optimized based on the analysis results of the analysis, including the following steps: Adjust the corresponding equipment operation mode based on the data area corrected by the marketing and distribution correction data; Provide personalized power service plans to corresponding users based on the data area corrected by the marketing and distribution correction data; The operation of the power system is optimized based on the adjusted equipment operation mode and the user's personalized power service plan.

[0057] In this embodiment, the abnormal part of the abnormal operation and distribution data is replaced by the predicted operation and distribution data, and the replaced data can be used to obtain the optimal operation mode required by the corresponding equipment according to the first association relationship mapping, and a series of user optimal power consumption plans such as the optimal curve of the relationship between power consumption and time of the corresponding user can be obtained according to the second association relationship mapping. When the optimal operation mode on the equipment side and the optimal power consumption plan on the user side in the power system are determined, the optimal operation plan of the power system can be obtained, thereby realizing the optimization and adjustment of the power system according to the operation and distribution data.

[0058] In this embodiment, the abnormality types include at least a user address error, a user meter number error, a device operation data asynchrony, a device location error, and a device operation failure.

[0059] In this embodiment, since the evaluation indicators of abnormal operation and distribution data include indicators such as data integrity, data consistency, data timeliness, and data accuracy, each indicator corresponds to a different type of abnormality. For example, the abnormality type corresponding to the data integrity indicator may be equipment operation failure, and the abnormality type corresponding to the data consistency indicator may be user address error, user meter number error, etc., the evaluation indicators of operation and distribution data are linked to the corresponding abnormality types to locate the area where the abnormality occurs according to the evaluation results, so that the abnormal area can be handled in time, thereby improving the operating stability of the power system.

[0060] In this embodiment, the geographic information includes at least the geographic location of the power grid equipment and the user distribution area.

[0061] The geographic information of this embodiment includes the geographical location of the device on the device side and the distribution area of ​​the user. By using the geographic information as the association node between the user side and the device side, the correlation of various types of data in the marketing and distribution data is improved.

[0062] In order to reduce the difficulty of finding required data from massive marketing and distribution data, this embodiment divides the marketing and distribution data into two categories: user side and device side according to the role type represented in the power grid. First, the information of each device is associated with the geographic information to build a first association relationship, and the information such as the location and connection relationship of each device is integrated. When the above information needs to be queried, all relevant information can be mapped out according to the first association relationship, which reduces the difficulty of data query; and the supply and demand information is basically related to user demand. The supply and demand information is associated with the user information to build a second association relationship, which is convenient for mapping the corresponding supply and demand information according to the user ID and user location information, and also reduces the difficulty of users querying their own data; then the target marketing and distribution data is evaluated for data quality. If it does not meet the evaluation requirements, it is corrected according to the historical marketing and distribution data, and then the corrected marketing and distribution data is used as data support for the operation of the power system to build the best operation plan for the future, which solves the problem of data query difficulty caused by the excessive amount of marketing and distribution data, and ensures the reliability of the obtained marketing and distribution data, thereby improving the operation efficiency of the power system.

[0063] It can be seen from the above embodiments that at least the following substantial effects are achieved: (1) The present invention improves the processing efficiency of massive amounts of marketing and distribution data by dividing the data into two categories: device data and user data, and building corresponding association relationships based on the user side and the device side. At the same time, all information related to the query content can be easily queried based on the association relationships, thereby improving the efficiency of data query and the integrity of query data; (2) The present invention performs data quality assessment on the marketing and distribution through communication data, determines whether the marketing and distribution through communication data needs to be revised based on the assessment results, and uses the processed marketing and distribution through communication data as data support for planning future operation plans of the power system to obtain an optimized operation plan of the power system, thereby improving the reliability of the marketing and distribution through communication data as data support, thereby improving the operating efficiency of the power system.

[0064] The specific implementation described above is a preferred implementation of the power system operation optimization method based on distribution and operation data of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for optimizing power system operation based on marketing and distribution data, characterized in that: The following steps are involved: S1. Obtain target marketing and distribution data, and establish a first association relationship based on device information and geographic information in the target marketing and distribution data; and establish a second association relationship based on supply and demand information and user information in the target marketing and distribution data; S2. Construct a marketing and distribution integration data indicator evaluation system, evaluate the target marketing and distribution integration data based on the marketing and distribution integration data indicator evaluation system, and obtain abnormal marketing and distribution integration data based on the evaluation results; S3, based on the abnormal marketing and distribution data and in combination with the first association relationship and the second association relationship, obtaining abnormal device information, abnormal device location and abnormal user information; S4. Obtain historical marketing and distribution data, and correct abnormal device information, abnormal device location, and abnormal user information based on the change trend of the historical marketing and distribution data to obtain marketing and distribution correction data; S5. Analyze the operation and distribution correction data, and optimize the operation of the power system based on the analysis results.

2. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S1, obtaining target marketing and distribution data includes the following steps: Obtain marketing and distribution data, pre-process the data, remove invalid and erroneous data, and obtain target marketing and distribution data.

3. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S1, a first association relationship is established based on the equipment information and geographic information in the target operation and distribution interconnection data, including the following steps: obtaining the equipment information in the target operation and distribution interconnection data, wherein the equipment information at least includes power grid equipment operation data and power grid topology structure data; The positions of the power grid equipment and each power grid topology are associated with the geographic information to obtain a first association relationship.

4. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S1, a second association relationship is established based on the supply and demand information and user information in the target marketing and distribution data, including the following steps: Acquire supply and demand information in the target marketing and distribution data, wherein the supply and demand information includes at least load data and power data; The load data and the power data are associated with the user information to obtain a second association relationship, wherein the user information at least includes the user's power meter information and the user's name.

5. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S2, a marketing and distribution integration data indicator evaluation system is constructed, target marketing and distribution integration data is evaluated based on the marketing and distribution integration data indicator evaluation system, and abnormal marketing and distribution integration data is obtained based on the evaluation results, including the following steps: At least build a marketing and distribution integration data indicator evaluation system based on the data integrity, timeliness and accuracy of the marketing and distribution integration data; Based on the marketing and distribution integration data indicator evaluation system, the target marketing and distribution integration data is evaluated to obtain the data evaluation value; The target marketing and distribution data that is lower than the data evaluation threshold is regarded as abnormal marketing and distribution data.

6. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S3, based on the abnormal marketing and distribution data and in combination with the first association relationship and the second association relationship, abnormal device information, abnormal device location and abnormal user information are obtained, including the following steps: Obtaining a data evaluation value of the abnormal marketing and distribution through-communication data, and obtaining an abnormal type of the abnormal marketing and distribution through-communication data based on the proportion of each evaluation index in the marketing and distribution through-communication data index evaluation system occupied by the data evaluation value; Based on the abnormal type and in combination with the first association relationship, abnormal device information and abnormal device location are acquired; based on the abnormal type and in combination with the second association relationship, abnormal user information is acquired.

7. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S4, historical marketing and distribution data are obtained, and based on the change trend of the historical marketing and distribution data, the abnormal device information, the abnormal device location and the abnormal user information are corrected to obtain marketing and distribution correction data, including the following steps: Obtain historical distribution and interconnection data, and obtain predicted distribution and interconnection data in the same time domain as the abnormal distribution and interconnection data based on the deep learning neural network model and historical distribution and interconnection data; Based on the data deviation between the abnormal marketing and distribution data and the predicted marketing and distribution data, part of the abnormal marketing and distribution data that exceeds the deviation threshold is replaced with the predicted marketing and distribution data, and the abnormal equipment information, abnormal equipment location and abnormal user information are corrected based on the replaced data to obtain the marketing and distribution correction data.

8. The method for optimizing power system operation based on operation and distribution data according to claim 1, characterized in that: In S5, the operation and distribution through correction data are analyzed, and the operation of the power system is optimized based on the analysis results, including the following steps: Adjust the corresponding equipment operation mode based on the data area corrected by the marketing and distribution correction data; Provide personalized power service plans to corresponding users based on the data area corrected by the marketing and distribution correction data; The operation of the power system is optimized based on the adjusted equipment operation mode and the user's personalized power service plan.

9. The method for optimizing power system operation based on operation and distribution data according to claim 6, characterized in that: The abnormality types include at least a user address error, a user meter number error, asynchrony of equipment operation data, an equipment location error, and an equipment operation failure.

10. A method for optimizing power system operation based on operation and distribution data according to claim 1 or 3, characterized in that: The geographical information at least includes the geographical location of the power grid equipment and the user distribution area.

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