Electric energy terminal and ammeter data fusion and storage method

Abstract

The invention discloses an electric energy terminal and electric meter data fusion and storage method, and relates to the technical field of electric data processing, and the method comprises the steps: determining an association relationship between electric meter nodes, constructing a self-changing mode of each electric meter node through the historical electric data of each electric meter node, and storing the self-changing mode of each electric meter node; a synchronization mode of the electric meter nodes is constructed in combination with an association relationship between the electric meter nodes; according to the electricity data and the self-changing mode and the synchronization mode of the electricity meter nodes, whether deviation data exist in each electricity meter node is detected; performing transverse and longitudinal correction on the deviation data by combining a self-changing mode and a synchronization mode of the electric meter node; and analyzing attributes of each type of electric data features, and performing partition storage on the electric data features according to the attributes. Through mutual verification of data of a plurality of electricity meters, possible errors or inconsistency can be identified. The integration and collaborative analysis effects between the terminal and the electric meter node are improved, and the adaptability of integration between the terminal and the electric meter node is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electric data processing, and in particular to a method for integrating and storing electric energy terminal and electric meter data. Background Art

[0002] With the development of smart grids, electric energy terminals are responsible for real-time collection and processing of meter data to ensure efficient management of energy supply. Through advanced data collection, transmission and storage technologies, accurate integration and efficient storage of meter data are achieved, providing real-time energy consumption analysis, prediction and optimization suggestions for power suppliers, energy management companies and users, and promoting the rational allocation and utilization of energy.

[0003] In the prior art, single-point analysis is performed only based on the numerical value of the meter data, resulting in poor fusion and integration effect of the meter data and low adaptability of data integration.

[0004] Therefore, how to improve the effect and adaptability of meter data fusion and integration is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of poor effect and low adaptability of electric meter data fusion and integration in the prior art, and to propose a method for fusion and storage of electric energy terminal and electric meter data, the method comprising:

[0006] Obtaining a meter logic diagram of an electric energy terminal and an electric meter, determining the association relationship between the electric meter nodes according to the meter logic diagram of the electric energy terminal and the electric meter, constructing an independent variable mode of each electric meter node through historical electric data of each electric meter node, and constructing a synchronization mode of the electric meter nodes in combination with the association relationship between the electric meter nodes;

[0007] Obtain the current electricity data of all meter nodes for a period of time, and summarize them on the electric energy terminal. Detect whether there is deviation data for each meter node based on the electricity data and the self-changing mode and synchronization mode of the meter node;

[0008] If there is deviation data, the deviation data is corrected horizontally and vertically by combining the independent change mode and the synchronization mode of the meter node, so as to correct the deviation data;

[0009] The electric data of all electric meter nodes are standardized and integrated to form a unified standard electric data set. The electric data features are extracted from the electric data set, the attributes of each type of electric data features are analyzed, and the electric data features are partitioned and stored according to the attributes.

[0010] In some embodiments of the present application, determining the association relationship between the nodes of the electric meter according to the electric energy terminal and the electric meter logic diagram of the electric meter includes:

[0011] Each point in the electric energy terminal and the electric meter logic diagram represents an electric meter node, and different colors are set on each electric meter node to represent the functional type of a specific electric device in the power system, and each electric meter node is marked with location coordinate information. Multiple electric meter nodes are connected by connecting lines, and the connecting lines represent physical connections or data communication connections;

[0012] The association relationship includes a connection relationship, a position relationship, a position function relationship and a multiple association relationship. The connection relationship is determined by connecting lines of multiple meter nodes in the meter logic diagram. Multiple meters within a first preset distance are regarded as a position relationship through the position coordinate information on the meter nodes. Multiple meters of the same functional type within a second preset distance are regarded as a position function relationship through the color and position coordinate information on the meter nodes.

[0013] The corresponding electric meter node lists are constructed according to the connection relationship, position relationship and position function relationship respectively, and multiple electric meter node lists are compared. Multiple repeated electric meter nodes are regarded as multiple association relationships, and electric meter node lists corresponding to the multiple association relationships are constructed.

[0014] In some embodiments of the present application, the self-variation mode of each electric meter node is constructed by using the historical electric data of each electric meter node, including:

[0015] A historical electricity data time series is obtained based on the historical electricity data of the meter node, and the historical electricity data time series is split into multiple small electricity data time series with the same time length according to a fixed period. Each small electricity data time series is mapped to a numerical curve that changes with time. Multiple sample points are randomly selected on the numerical curve, and the slope change value between every two adjacent sample points is calculated to obtain the average slope change value of the entire numerical curve. The average slope change value of each numerical curve is integrated to determine the smoothing coefficient of the meter node;

[0016] The electric data time series prediction model of each electric meter node is constructed by using the smoothing coefficient and the historical electric data time series, and the independent variation mode of the electric meter node is realized by using the electric data time series prediction model.

[0017] In some embodiments of the present application, the synchronization mode of the electric meter nodes is constructed in combination with the association relationship between the electric meter nodes, including:

[0018] According to the multi-association relationship electric meter node list, duplicate electric meter nodes in the electric meter node list of connection relationship, position relationship and position function relationship are eliminated, and the electric meter node list of connection relationship, position relationship and position function relationship is obtained again, and the connection relationship, position relationship and position function relationship are recorded as a single association relationship;

[0019] According to the meter node list of single association relationship and multiple association relationship, the historical electricity data of the corresponding meter node is collected, the common item set of all meters in the meter node list is defined on the historical electricity data of multiple meters in the meter node list, the frequency of the common item set in the historical electricity data is calculated, the frequent common item set is screened out from the common item set, the frequent common item set is analyzed to generate association rules, and the confidence and support of the association rules are calculated;

[0020] Different weights are assigned to each association rule through single association relationships and multiple association relationships. The confidence and support of each association rule are weighted to obtain the association strength of the association rule. The association rules of single association relationships and multiple association relationships are screened based on the association strength. A synchronization model is trained through the association rules to realize the synchronization mode between the meter nodes.

[0021] In some embodiments of the present application, detecting whether each meter node has deviation data according to the electric data and the self-changing mode and the synchronization mode of the meter node includes:

[0022] Predicting the first calibration electric data of the electric meter node within a current period of time through the self-variable mode, and outputting the second calibration electric data of the electric meter node within a current period of time through the synchronous mode;

[0023] The electrical data, the first proofreading electrical data and the second proofreading electrical data are time aligned, the deviations of the electrical data from the first proofreading electrical data and the second proofreading electrical data are calculated, the deviations of the two are combined to obtain a comprehensive deviation, and whether there is deviation data is determined by the comprehensive deviation.

[0024] In some embodiments of the present application, judging whether there is deviation data by comprehensive deviation degree includes:

[0025] Construct a box plot of each meter node, determine the discreteness of the meter node through the box plot, and set the comprehensive deviation threshold of each meter node according to the discreteness;

[0026] If the comprehensive deviation is less than the comprehensive deviation threshold of the meter node, then there is no deviation data for the meter node;

[0027] If the comprehensive deviation is not less than the comprehensive deviation threshold of the meter node, then the meter node has deviation data.

[0028] In some embodiments of the present application, the deviation data is corrected horizontally and vertically in combination with the self-changing mode and the synchronous mode of the electric meter node, thereby correcting the deviation data, including:

[0029] Compare the values ​​of the first calibration electrical data and the second calibration electrical data at each time point, and if the values ​​are the same, replace the deviation value at the same time point with the value;

[0030] If the values ​​are different, a target value is determined based on the first calibration electrical data and the second calibration electrical data, and the deviation value at the same time point is replaced by the target value;

[0031]

[0032] Among them, D i is the target value at the i-th time point, D1 i is the first calibration value at the i-th time point, D2 i is the second calibration value at the i-th time point, exp is an exponential function, and k is a preset constant.

[0033] In some embodiments of the present application, extracting electrical data features from an electrical data set includes:

[0034] A feature set is determined according to the electrical data analysis requirements. The feature set includes all the required electrical data features, and the electrical data features in the feature set are extracted from the electrical data set.

[0035] In some embodiments of the present application, the attributes of each type of electrical data feature are analyzed, and the electrical data features are partitioned and stored according to the attributes, including:

[0036] The attributes of electrical data features include access frequency and update frequency. The flow intensity of each type of electrical data feature is determined in combination with the access frequency and update frequency. Different types of electrical data features are stored in different areas of the database based on the flow intensity.

[0037] By applying the above technical solutions, the synchronization mode and self-variation mode of the meter nodes can be constructed by combining the association between historical electricity data and meter nodes, so as to establish the change of a single meter node and the synchronous change of multiple meter nodes, so as to accurately detect whether there is deviation data in the meter, realize mutual verification of data from multiple meters, and identify possible errors or inconsistencies; and correct the deviation data horizontally and vertically, and use the data of other normal meters to supplement or correct it, so as to improve the integration and collaborative analysis effect between the terminal and the meter nodes, and ensure the adaptability of the integration of the terminal and the meter nodes. The reliability of data storage is improved by partitioning and storing the characteristics of the electricity data according to the attributes. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention provides a flow chart of a method for integrating and storing data between an electric energy terminal and an electric meter. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0040] Reference Figure 1 , a method for integrating and storing electric energy terminal and electric meter data, comprising the following steps:

[0041] Step S101, obtain the meter logic diagram of the electric energy terminal and the electric meter, determine the association relationship between the meter nodes according to the meter logic diagram of the electric energy terminal and the electric meter, construct the independent variable mode of each meter node through the historical electric data of each meter node, and construct the synchronization mode of the meter nodes in combination with the association relationship between the meter nodes.

[0042] In this embodiment, the electric energy terminal acts as a data integrator, and its main task is to aggregate the data from each electric meter. These data may come from different nodes, and each node has its own unique electric meter responsible for collecting data. Therefore, when the terminal integrates these data, it is necessary to ensure the accuracy and consistency of the data. As a data collector, the electric meter is responsible for collecting the electric energy data of each node in real time and accurately. These data include key indicators such as voltage, current, and power, and are the basis for electric energy measurement and management. When collecting data, the electric meter needs to follow unified standards and protocols to ensure the accuracy and comparability of the data.

[0043] In this embodiment, the electric meter logic diagram is a graphical representation method that represents the connection and relationship between the electric meter nodes and the terminals, which can help us intuitively understand the relationship between the various electric meter nodes. It is necessary to clarify the relationship between the electric meter nodes, including physical connection, functional association, location association, etc. These associations determine the similarities or trends that may exist between the electric meter node data. Multiple electric meter data with these associations often show similarities or the same trends. For example, if the electric meter nodes are directly connected through physical media such as wires and cables, their electricity consumption data may show synchronization or trends to a large extent. For example, the electricity consumption data of lighting equipment on the same floor or area may also have a certain correlation.

[0044] It can be understood that the independent change mode of the meter node is a change prediction mode constructed only based on the historical data changes of the meter itself, and the synchronization mode of the meter node is a common change mode constructed based on the data of all meter nodes under the association relationship.

[0045] In some embodiments of the present application, determining the association relationship between the nodes of the electric meter according to the electric energy terminal and the electric meter logic diagram of the electric meter includes:

[0046] Each point in the electric energy terminal and the electric meter logic diagram represents an electric meter node, and different colors are set on each electric meter node to represent the functional type of a specific electric device in the power system, and each electric meter node is marked with location coordinate information. Multiple electric meter nodes are connected by connecting lines, and the connecting lines represent physical connections or data communication connections;

[0047] The association relationship includes a connection relationship, a position relationship, a position function relationship and a multiple association relationship. The connection relationship is determined by connecting lines of multiple meter nodes in the meter logic diagram. Multiple meters within a first preset distance are regarded as a position relationship through the position coordinate information on the meter nodes. Multiple meters of the same functional type within a second preset distance are regarded as a position function relationship through the color and position coordinate information on the meter nodes.

[0048] The corresponding electric meter node lists are constructed according to the connection relationship, position relationship and position function relationship respectively, and multiple electric meter node lists are compared. Multiple repeated electric meter nodes are regarded as multiple association relationships, and electric meter node lists corresponding to the multiple association relationships are constructed.

[0049] In this embodiment, physical connection or data communication connection, function type, location coordinate information are all associations that can characterize common changes. In order to improve the reliability of common changes of multiple meters within the association, the position function relationship is an association that combines the position and function type, that is, multiple meters are close to each other and have the same function type. The meter node list is all the corresponding meters under one of the above associations. Multiple associations refer to situations with two or three of the three: connection relationship, position relationship, and position function relationship. For example, meters that satisfy both the connection relationship and the position relationship are all from the same batch or group.

[0050] In some embodiments of the present application, the self-variation mode of each electric meter node is constructed by using the historical electric data of each electric meter node, including:

[0051] A historical electricity data time series is obtained based on the historical electricity data of the meter node, and the historical electricity data time series is split into multiple small electricity data time series with the same time length according to a fixed period. Each small electricity data time series is mapped to a numerical curve that changes with time. Multiple sample points are randomly selected on the numerical curve, and the slope change value between every two adjacent sample points is calculated to obtain the average slope change value of the entire numerical curve. The average slope change value of each numerical curve is integrated to determine the smoothing coefficient of the meter node;

[0052] The electric data time series prediction model of each electric meter node is constructed by using the smoothing coefficient and the historical electric data time series, and the independent variation mode of the electric meter node is realized by using the electric data time series prediction model.

[0053] In this embodiment, the average value of the slope change of each numerical curve is integrated to obtain the average value of the comprehensive slope change. Different average values ​​of the comprehensive slope change correspond to different smoothing coefficients. If the data is relatively stable, a smaller smoothing coefficient is selected to better capture the subtle changes in the data. If the data fluctuates greatly, a larger smoothing coefficient can be selected to better adapt to the changes in the data. Exponential smoothing is a time series prediction technology that generates predictions for future values ​​by weighted averaging of historical data. This method is used to implement the self-variable model.

[0054] In some embodiments of the present application, the synchronization mode of the electric meter nodes is constructed in combination with the association relationship between the electric meter nodes, including:

[0055] According to the multi-association relationship electric meter node list, duplicate electric meter nodes in the electric meter node list of connection relationship, position relationship and position function relationship are eliminated, and the electric meter node list of connection relationship, position relationship and position function relationship is obtained again, and the connection relationship, position relationship and position function relationship are recorded as a single association relationship;

[0056] According to the meter node list of single association relationship and multiple association relationship, the historical electricity data of the corresponding meter node is collected, the common item set of all meters in the meter node list is defined on the historical electricity data of multiple meters in the meter node list, the frequency of the common item set in the historical electricity data is calculated, the frequent common item set is screened out from the common item set, the frequent common item set is analyzed to generate association rules, and the confidence and support of the association rules are calculated;

[0057] Different weights are assigned to each association rule through single association relationships and multiple association relationships. The confidence and support of each association rule are weighted to obtain the association strength of the association rule. The association rules of single association relationships and multiple association relationships are screened based on the association strength. A synchronization model is trained through the association rules to realize the synchronization mode between the meter nodes.

[0058] In this embodiment, the common item set of all meters in the meter node list is the common change of all meters under a certain association relationship. For example, if there is a physical connection or functional association between two nodes, their power consumption may increase or decrease at the same time, showing a synchronous change trend. Some nodes may also have peak or trough power consumption at the same time in a specific time period. Some nodes may have large fluctuations in power consumption in different time periods, etc. The frequency of occurrence of high voltage of meter A and high voltage of meter B is high, so the common item set is a frequent common item set.

[0059] In this embodiment, association rules are generated from frequent item sets, and the form of the rules is usually "if... then...". The antecedent and consequent of the rule are the items in the frequent item set, and the antecedent and consequent cannot be the same. The confidence indicates the probability of the consequent appearing when the antecedent of the rule appears, and the support indicates the frequency of the antecedent and consequent appearing at the same time. A high confidence indicates that when the voltage of meter A is high, the possibility of meter B voltage being high is indeed very high; a high support indicates that this association relationship is very common in the data set.

[0060] In this embodiment, a synchronization model is trained by association rules and the involved electric data. The synchronization model can calculate the common changes of multiple electric meters with an associated relationship, so as to output the electric data of one of the electric meters under the synchronous changes of other electric meters.

[0061] Step S102, obtaining the electric data of all electric meter nodes in the current period, and summarizing them on the electric energy terminal, and detecting whether there is deviation data for each electric meter node according to the electric data and the independent change mode and synchronization mode of the electric meter node.

[0062] In this embodiment, it is first detected whether there is a deviated electric meter, and then the specific deviated part data is determined as the deviated data.

[0063] In some embodiments of the present application, detecting whether each meter node has deviation data according to the electric data and the self-changing mode and the synchronization mode of the meter node includes:

[0064] Predicting the first calibration electric data of the electric meter node within a current period of time through the self-variable mode, and outputting the second calibration electric data of the electric meter node within a current period of time through the synchronous mode;

[0065] The electrical data, the first proofreading electrical data and the second proofreading electrical data are time aligned, the deviations of the electrical data from the first proofreading electrical data and the second proofreading electrical data are calculated, the deviations of the two are combined to obtain a comprehensive deviation, and whether there is deviation data is determined by the comprehensive deviation.

[0066] In some embodiments of the present application, judging whether there is deviation data by comprehensive deviation degree includes:

[0067] Construct a box plot of each meter node, determine the discreteness of the meter node through the box plot, and set the comprehensive deviation threshold of each meter node according to the discreteness;

[0068] If the comprehensive deviation is less than the comprehensive deviation threshold of the meter node, then there is no deviation data for the meter node;

[0069] If the comprehensive deviation is not less than the comprehensive deviation threshold of the meter node, then the meter node has deviation data.

[0070] In this embodiment, a box plot of the electric meter nodes is constructed to determine the degree of dispersion, that is, the degree of stability. Different degrees of stability correspond to different comprehensive deviation thresholds.

[0071] Step S103: if there is deviation data, the deviation data is corrected horizontally and vertically in combination with the independent variable mode and the synchronous mode of the electric meter node, so as to correct the deviation data.

[0072] In this embodiment, the horizontal and vertical corrections refer to comprehensive corrections using the independent variable mode in the horizontal direction and the synchronous mode of other electric meters in the vertical direction, so as to supplement the deviation data.

[0073] In some embodiments of the present application, the deviation data is corrected horizontally and vertically in combination with the self-changing mode and the synchronous mode of the electric meter node, thereby correcting the deviation data, including:

[0074] Compare the values ​​of the first calibration electrical data and the second calibration electrical data at each time point, and if the values ​​are the same, replace the deviation value at the same time point with the value;

[0075] If the values ​​are different, a target value is determined based on the first calibration electrical data and the second calibration electrical data, and the deviation value at the same time point is replaced by the target value;

[0076]

[0077] Among them, D i is the target value at the i-th time point, D1 i is the first calibration value at the i-th time point, D2 i is the second calibration value at the i-th time point, exp is an exponential function, and k is a preset constant.

[0078] In this embodiment, the average value of the first calibration electric data and the second calibration electric data is corrected by the difference between the two. By integrating the data of the electric energy terminal and the electric meter, the mutual verification and supplementation of the data are achieved, and the accuracy and reliability of the data are improved. This step not only helps to reduce data errors, but also provides a solid foundation for global electric energy analysis and management.

[0079] Step S104, standardize the electric data of all electric meter nodes, integrate the electric data of all electric meter nodes to form a unified standard electric data set, extract electric data features from the electric data set, analyze the attributes of each type of electric data features, and partition and store the electric data features according to the attributes.

[0080] In this embodiment, the electrical data of all meter nodes are standardized to have a unified format and dimension. The selected electrical data features may include the average value, maximum value, minimum value, standard deviation, peak-to-peak value, etc. of the current, as well as the corresponding features of voltage and power.

[0081] In some embodiments of the present application, extracting electrical data features from an electrical data set includes:

[0082] A feature set is determined according to the electrical data analysis requirements. The feature set includes all the required electrical data features, and the electrical data features in the feature set are extracted from the electrical data set.

[0083] In some embodiments of the present application, the attributes of each type of electrical data feature are analyzed, and the electrical data features are partitioned and stored according to the attributes, including:

[0084] The attributes of electrical data features include access frequency and update frequency. The flow intensity of each type of electrical data feature is determined in combination with the access frequency and update frequency. Different types of electrical data features are stored in different areas of the database based on the flow intensity.

[0085] By applying the above technical scheme, the meter logic diagram of the electric energy terminal and the electric meter is obtained, the association relationship between the electric meter nodes is determined according to the meter logic diagram of the electric energy terminal and the electric meter, the self-changing mode of each electric meter node is constructed according to the historical electric data of each electric meter node, and the synchronization mode of the electric meter node is constructed in combination with the association relationship between the electric meter nodes; the electric data of all the electric meter nodes in the current period are obtained and summarized on the electric energy terminal, and whether there is deviation data for each electric meter node is detected according to the electric data and the self-changing mode and synchronization mode of the electric meter node; if there is deviation data, the deviation data is corrected horizontally and vertically in combination with the self-changing mode and synchronization mode of the electric meter node, so as to correct the deviation data; the electric data of all the electric meter nodes are standardized, and the electric data of all the electric meter nodes are integrated to form a unified standard electric data set, the electric data features are extracted from the electric data set, the attributes of each type of electric data features are analyzed, and the electric data features are partitioned and stored according to the attributes. This application combines the correlation between historical electricity data and meter nodes to construct the synchronization mode and self-changing mode of meter nodes, so as to establish the change of a single meter node and the synchronous change of multiple meter nodes, so as to accurately detect whether there is deviation data in the meter, realize mutual verification of data from multiple meters, and identify possible errors or inconsistencies. In addition, the deviation data is corrected horizontally and vertically, and the data of other normal meters is used for supplementation or correction, which improves the integration and collaborative analysis between the terminal and the meter node, and ensures the adaptability of the integration of the terminal and the meter node. The characteristics of the electrical data are partitioned and stored according to the attributes to improve the reliability of data storage.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0087] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for integrating and storing electric energy terminal and electric meter data, characterized in that: include: Obtaining a meter logic diagram of an electric energy terminal and an electric meter, determining the association relationship between the electric meter nodes according to the meter logic diagram of the electric energy terminal and the electric meter, constructing an independent variable mode of each electric meter node through historical electric data of each electric meter node, and constructing a synchronization mode of the electric meter nodes in combination with the association relationship between the electric meter nodes; Obtain the current electricity data of all meter nodes for a period of time, and summarize them on the electric energy terminal. Detect whether there is deviation data for each meter node based on the electricity data and the self-changing mode and synchronization mode of the meter node; If there is deviation data, the deviation data is corrected horizontally and vertically by combining the independent change mode and the synchronization mode of the meter node, so as to correct the deviation data; The electric data of all electric meter nodes are standardized and integrated to form a unified standard electric data set. The electric data features are extracted from the electric data set, the attributes of each type of electric data features are analyzed, and the electric data features are partitioned and stored according to the attributes.

2. The method for merging and storing electric energy terminal and electric meter data according to claim 1 is characterized in that: The association relationship between the electric meter nodes is determined according to the electric energy terminal and the electric meter logic diagram, including: Each point in the electric energy terminal and the electric meter logic diagram represents an electric meter node, and different colors are set on each electric meter node to represent the functional type of a specific electric device in the power system, and each electric meter node is marked with location coordinate information. Multiple electric meter nodes are connected by connecting lines, and the connecting lines represent physical connections or data communication connections; The association relationship includes a connection relationship, a position relationship, a position function relationship and a multiple association relationship. The connection relationship is determined by connecting lines of multiple meter nodes in the meter logic diagram. Multiple meters within a first preset distance are regarded as a position relationship through the position coordinate information on the meter nodes. Multiple meters of the same functional type within a second preset distance are regarded as a position function relationship through the color and position coordinate information on the meter nodes. The corresponding electric meter node lists are constructed according to the connection relationship, position relationship and position function relationship respectively, and multiple electric meter node lists are compared. Multiple repeated electric meter nodes are regarded as multiple association relationships, and electric meter node lists corresponding to the multiple association relationships are constructed.

3. The method for merging and storing electric energy terminal and electric meter data according to claim 1, characterized in that: The independent variation mode of each meter node is constructed through the historical electricity data of each meter node, including: A historical electricity data time series is obtained based on the historical electricity data of the meter node, and the historical electricity data time series is split into multiple small electricity data time series with the same time length according to a fixed period. Each small electricity data time series is mapped to a numerical curve that changes with time. Multiple sample points are randomly selected on the numerical curve, and the slope change value between every two adjacent sample points is calculated to obtain the average slope change value of the entire numerical curve. The average slope change value of each numerical curve is integrated to determine the smoothing coefficient of the meter node; The electric data time series prediction model of each electric meter node is constructed by using the smoothing coefficient and the historical electric data time series, and the independent variation mode of the electric meter node is realized through the electric data time series prediction model.

4. The method for merging and storing electric energy terminal and electric meter data according to claim 2, characterized in that: And the synchronization mode of the meter nodes is constructed by combining the association relationship between the meter nodes, including: According to the multi-association relationship electric meter node list, duplicate electric meter nodes in the electric meter node list of connection relationship, position relationship and position function relationship are eliminated, and the electric meter node list of connection relationship, position relationship and position function relationship is obtained again, and the connection relationship, position relationship and position function relationship are recorded as a single association relationship; According to the meter node list of single association relationship and multiple association relationship, the historical electricity data of the corresponding meter node is collected, the common item set of all meters in the meter node list is defined on the historical electricity data of multiple meters in the meter node list, the frequency of the common item set in the historical electricity data is calculated, the frequent common item set is screened out from the common item set, the frequent common item set is analyzed to generate association rules, and the confidence and support of the association rules are calculated; Different weights are assigned to each association rule through single association relationships and multiple association relationships. The confidence and support of each association rule are weighted to obtain the association strength of the association rule. The association rules of single association relationships and multiple association relationships are screened based on the association strength. A synchronization model is trained through the association rules to realize the synchronization mode between the meter nodes.

5. The method for merging and storing electric energy terminal and electric meter data according to claim 1, characterized in that: According to the electric data and the self-changing mode and synchronization mode of the electric meter node, each electric meter node is detected to have deviation data, including: Predicting the first calibration electric data of the electric meter node within a current period of time through the self-variable mode, and outputting the second calibration electric data of the electric meter node within a current period of time through the synchronous mode; The electrical data, the first proofreading electrical data and the second proofreading electrical data are time aligned, the deviations of the electrical data from the first proofreading electrical data and the second proofreading electrical data are calculated, the deviations of the two are combined to obtain a comprehensive deviation, and whether there is deviation data is determined by the comprehensive deviation.

6. The method for merging and storing electric energy terminal and electric meter data according to claim 5, characterized in that: Determine whether there is deviation data through comprehensive deviation, including: Construct a box plot of each meter node, determine the discreteness of the meter node through the box plot, and set the comprehensive deviation threshold of each meter node according to the discreteness; If the comprehensive deviation is less than the comprehensive deviation threshold of the meter node, then there is no deviation data for the meter node; If the comprehensive deviation is not less than the comprehensive deviation threshold of the meter node, then the meter node has deviation data.

7. The method for merging and storing electric energy terminal and electric meter data according to claim 5, characterized in that: The deviation data is corrected horizontally and vertically by combining the self-changing mode and the synchronous mode of the meter node, thereby correcting the deviation data, including: Compare the values ​​of the first calibration electrical data and the second calibration electrical data at each time point, and if the values ​​are the same, replace the deviation value at the same time point with the value; If the values ​​are different, a target value is determined based on the first calibration electrical data and the second calibration electrical data, and the deviation value at the same time point is replaced by the target value; Among them, D i is the target value at the i-th time point, D1 i is the first calibration value at the i-th time point, D2 i is the second calibration value at the i-th time point, exp is the exponential function, and k is a preset constant.

8. The method for merging and storing electric energy terminal and electric meter data according to claim 1, characterized in that: Extract electrical data features from electrical data sets, including: A feature set is determined according to the electrical data analysis requirements. The feature set includes all the required electrical data features, and the electrical data features in the feature set are extracted from the electrical data set.

9. The method for merging and storing electric energy terminal and electric meter data according to claim 1, characterized in that: Analyze the attributes of each type of electrical data feature and partition and store the electrical data features according to the attributes, including: The attributes of electrical data features include access frequency and update frequency. The flow intensity of each type of electrical data feature is determined in combination with the access frequency and update frequency. Different types of electrical data features are stored in different areas of the database based on the flow intensity.

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