Control method and system of power distribution network equipment based on power distribution network CIM model mapping

Abstract

The application discloses a kind of based on distribution network CIM model mapping distribution network equipment control method and system, method includes obtaining distribution network CIM model of distribution internet of things terminal and distribution network access equipment, the name similarity and structure similarity of two different versions distribution network CIM model node are calculated respectively, and the comprehensive matching degree is calculated by using principal component analysis method adjustment weight, the node tree of the comprehensive matching degree higher than matching threshold is mapped, and the current mapping result is analyzed, the current threshold compliance is calculated, according to preset step and preset adjustment condition, constantly improve the current preset matching threshold, constantly obtain the current threshold compliance, determine the maximum value corresponding to mapping result of threshold compliance, realize the optimal mapping of different versions distribution network CIM model, and according to optimal mapping result, distribution network access equipment is accessed control.This embodiment realizes the optimal mapping of different versions distribution network CIM model, improves equipment compatibility.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control, and in particular to a control method and system for power distribution network equipment based on the CIM model mapping of the power distribution network. Background Technology

[0002] The distribution Internet of Things (IoT) is a key node in the extension of the new power system from the medium-voltage side to the low-voltage side. With the continuous advancement of the new power system construction, the large-scale access and rapid iteration of smart terminals and monitoring equipment have generated serious compatibility issues. The Common Information Model (CIM) is a universal standard in the power sector, directly affecting equipment access and status control in the distribution network. Achieving correct mapping between different versions of the distribution network CIM model is a crucial link and data support for realizing the observability, measurability, and controllability of the new power system.

[0003] However, due to version differences, existing distribution network CIM models suffer from incompatible mappings between different versions of CIM models when a large number of distributed terminal devices are connected. This leads to mapping errors between different versions of distribution network CIM models, which seriously hinders the access and control of new devices on the distribution network side. Summary of the Invention

[0004] This invention provides a control method and system for distribution network equipment based on the mapping of the distribution network CIM model, which realizes the optimal mapping of different versions of the distribution network CIM model and improves the compatibility of distribution IoT terminals and distribution network access equipment.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a control method for distribution network equipment based on distribution network CIM model mapping, comprising: Obtain distribution network CIM models from distribution IoT terminals and distribution network access equipment to obtain different versions of distribution network CIM models; The node names of different versions of the distribution network CIM model are similar to calculate the name similarity. Based on the name similarity, the different versions of the distribution network CIM model are compared and analyzed in a tree structure to calculate the structural similarity. Principal component weight analysis was performed on name similarity and structural similarity to obtain the overall matching degree; The node tree with a comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold matching degree. Based on the preset step size and preset adjustment conditions, the current preset matching threshold is continuously adjusted until the maximum value of the threshold compliance is obtained. The mapping result corresponding to the maximum value of the threshold compliance is found, and the optimal mapping result of different versions of the distribution network CIM model is obtained. Among them, whenever the current preset matching threshold is adjusted, the node tree with the comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold compliance. Based on the optimal mapping result, the access control of the distribution network access equipment is implemented.

[0006] This invention involves acquiring distribution network CIM models for distribution IoT terminals and distribution network access devices. The name similarity and structural similarity of nodes in two different versions of the distribution network CIM models are calculated. Principal component analysis is used to adjust weights to calculate the comprehensive matching degree. Node trees with a comprehensive matching degree higher than a matching threshold are mapped, and the current mapping results are analyzed to calculate the current threshold compliance. Based on preset step sizes and preset adjustment conditions, the current preset matching threshold is continuously improved and adjusted to continuously obtain the current threshold compliance. The mapping result corresponding to the maximum threshold compliance is determined, achieving optimal mapping of different versions of the distribution network CIM models. Based on the optimal mapping result, the distribution network access devices are controlled for access. This solves the compatibility problem of mapping between different versions of CIM models under massive distributed terminal device access, efficiently manages the access and control of new devices on the distribution network side, and improves the compatibility of distribution IoT terminals and distribution network access devices.

[0007] As a preferred approach, principal component weight analysis is performed on name similarity and structural similarity to obtain the comprehensive matching degree, specifically: The name similarity and structural similarity scores are matrixed to obtain a standard value matrix; Principal component analysis was performed on the standard value matrix to calculate the similarity weights. Based on the similarity weights, the name similarity and structural similarity are weighted and summed to obtain the overall matching degree.

[0008] As a preferred approach, the name similarity and structural similarity are matrixed to obtain a standard value matrix, specifically: A similarity matrix is ​​constructed based on name similarity and structural similarity. Based on the similarity matrix, the average similarity is calculated using the following formula:

[0009] in, This represents the average similarity. n represents the number of child nodes to be compared in the nodes of the two different versions of the distribution network CIM model. The value can be 1 or 2. For the first The similarity of the names of the nodes For the first Structural similarity of nodes; The similarity variance is calculated based on the similarity matrix and the mean value, using the following formula:

[0010] in, The value can be 1 or 2. Let V be the variance of node name similarity. The variance of the node structural similarity; The average similarity and variance of similarity are standardized to obtain a standard value matrix, as shown in the formula:

[0011] in, For the standard value matrix, , This is the standard value.

[0012] As a preferred approach, principal component analysis is performed on the standard value matrix to calculate the similarity weights, specifically as follows: Based on the standard value matrix, the correlation coefficient matrix is ​​calculated using the following formula:

[0013] in, The correlation coefficient matrix, These are the elements of the correlation coefficient matrix. ; Calculate the eigenvalues ​​of the correlation coefficient matrix based on the correlation coefficient matrix; And based on the eigenvalues ​​of the correlation coefficient matrix, the similarity weight is calculated using the following formula:

[0014] in, For similarity weights, These are the eigenvalues ​​of the correlation coefficient matrix.

[0015] As a preferred approach, the name similarity and structural similarity are weighted and summed to obtain the overall matching degree, as shown in the formula:

[0016] in, For overall matching accuracy, For similarity weights, For name similarity, For structural similarity.

[0017] As a preferred approach, the node names of different versions of the distribution network CIM model are similarity measured to obtain the name similarity score, specifically: Calculate the minimum number of operations required to convert node names from one version of the distribution network CIM model to node names from another version of the distribution network CIM model; where the minimum number of operations includes the insertion, deletion, and replacement of single characters; The name similarity is calculated based on the minimum number of operations, using the following formula:

[0018] in, For name similarity, It is the minimum number of operands.

[0019] As a preferred approach, based on name similarity, different versions of the distribution network CIM model are compared and analyzed in a tree structure to calculate structural similarity, specifically: Based on name similarity, a tree-structured comparison analysis is performed on the two nodes. The CIM model version information is used as the root node, the classes contained in the CIM model are used as child nodes, and the attributes contained in each class are used as leaf nodes. The similarity between nodes of different versions of the CIM model is calculated to obtain the structural similarity, using the following formula:

[0020] in, For structural similarity, and These are the child nodes to be compared in the nodes of two different versions of the CIM model. The number of leaf nodes in each child node. This represents the total number of child nodes to be compared in the nodes of the two different versions of the CIM model.

[0021] As a preferred approach, the node tree with a comprehensive matching degree higher than the current preset matching threshold is subjected to mapping statistical analysis to obtain the current threshold compliance degree, specifically: Map the node tree with a comprehensive matching degree higher than the current preset matching threshold to obtain the current mapping result; Calculate the current mapping results using statistical nodes to obtain the mapping accuracy and mapping omission rate; The current threshold compliance is calculated based on the mapping accuracy and mapping omission rate, using the following formula:

[0022] in, For the current threshold compliance, For mapping accuracy, This represents the mapping omission rate.

[0023] As a preferred approach, the current mapping results are statistically analyzed to obtain the mapping accuracy and mapping omission rate, specifically: Based on the preset extraction ratio, the current mapping results are statistically analyzed to obtain node data; the node data includes the total number of nodes, the number of missed mapping nodes, the number of correctly mapped nodes, and the number of incorrectly mapped nodes. The mapping accuracy is calculated based on the number of correctly mapped nodes and the number of incorrectly mapped nodes, using the following formula:

[0024] in, For mapping accuracy, To map the correct number of nodes, This represents the number of nodes that were mapped incorrectly. The mapping omission rate is calculated based on the number of correctly mapped nodes and the number of missed mapped nodes, using the following formula:

[0025] in, For mapping the omission rate, This represents the number of missing mapping nodes.

[0026] To address the same technical problem, this invention also provides a control system for distribution network equipment based on distribution network CIM model mapping, including: a version acquisition module, a similarity calculation module, a matching degree analysis module, an initial mapping module, an optimal mapping module, and an access control module; Among them, the version acquisition module is used to acquire the distribution network CIM model of distribution IoT terminals and distribution network access equipment, and obtain different versions of distribution network CIM model; The similarity calculation module is used to measure and calculate the similarity of node names in different versions of the distribution network CIM model, obtain name similarity, and compare and analyze the different versions of the distribution network CIM model according to the tree structure based on the name similarity to calculate the structural similarity. The matching degree analysis module is used to perform principal component weight analysis on name similarity and structural similarity to obtain the comprehensive matching degree; The initial mapping module is used to perform mapping statistical analysis on the node tree with a comprehensive matching degree higher than the current preset matching threshold to obtain the current threshold matching degree; The optimization mapping module is used to continuously adjust the current preset matching threshold according to the preset step size and preset adjustment conditions until the maximum value of the threshold compliance is obtained, find the mapping result corresponding to the maximum value of the threshold compliance, and obtain the optimal mapping result of different versions of the distribution network CIM model; wherein, each time the current preset matching threshold is adjusted, the node tree with the comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold compliance. The access control module is used to control the access of distribution network access equipment based on the optimal mapping result. Attached Figure Description

[0027] Figure 1 : A flowchart illustrating an embodiment of the control method for distribution network equipment based on distribution network CIM model mapping provided by the present invention; Figure 2 : A flowchart of the distribution network CIM model mapping process, which is an embodiment of the control method for distribution network equipment based on distribution network CIM model mapping provided by the present invention; Figure 3 This is a schematic diagram of an embodiment of the control system for distribution network equipment based on the CIM model mapping of the distribution network provided by the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 Please refer to Figure 1 This is a flowchart illustrating the control method for distribution network equipment based on distribution network CIM model mapping provided in an embodiment of the present invention. The distribution network CIM model mapping process is as follows: Figure 2 As shown. This embodiment improves the compatibility of distribution IoT terminals and distribution network access equipment by mapping different versions of the distribution network CIM model. The control method for the distribution network equipment includes steps 101 to 106, each step as follows: Step 101: Obtain the distribution network CIM model of the distribution IoT terminal and distribution network access equipment to obtain different versions of the distribution network CIM model.

[0030] Step 102: Calculate the similarity of node names in different versions of the distribution network CIM model to obtain name similarity. Based on the name similarity, compare and analyze the different versions of the distribution network CIM model according to the tree structure to calculate the structural similarity.

[0031] In this embodiment, the name similarity is obtained by weighted averaging of the syntactic and semantic similarities of the node names in two different XML documents corresponding to two different versions of the distribution network CIM model. Then, the structural similarity and comprehensive matching degree of the two distribution network CIM models are calculated by tree structure comparison analysis of the two XML documents to resolve the naming conflict problem. Finally, the heterogeneous XML document data are merged and modeled to obtain the optimal mapping.

[0032] Optionally, the node names of different versions of the distribution network CIM model can be similarized to obtain the name similarity score. Specifically: Calculate the minimum number of operations required to convert node names from one version of the distribution network CIM model to node names from another version of the distribution network CIM model; where the minimum number of operations includes the insertion, deletion, and replacement of single characters; The name similarity is calculated based on the minimum number of operations, using the following formula:

[0033] in, For name similarity, It is the minimum number of operands.

[0034] In this embodiment, the Wagner-Fischer similarity metric is used to calculate the similarity of node names in two different XML documents, and the minimum number of operations required to convert the node names of one version of the distribution network CIM model to the node names of another version of the distribution network CIM model is calculated. Specifically, this includes the insertion, deletion, and replacement of individual characters. At this point, and These are the names of nodes in two different versions of the CIM model.

[0035] Optionally, based on name similarity, different versions of the distribution network CIM model are compared and analyzed in a tree structure to calculate structural similarity, specifically: Based on name similarity, a tree-structured comparison analysis is performed on the two nodes. The CIM model version information is used as the root node, the classes contained in the CIM model are used as child nodes, and the attributes contained in each class are used as leaf nodes. The similarity between nodes of different versions of the CIM model is calculated to obtain the structural similarity, using the following formula:

[0036] in, For structural similarity, and These are the child nodes to be compared in the nodes of two different versions of the CIM model. The number of leaf nodes in each child node. This represents the total number of child nodes to be compared in the nodes of the two different versions of the CIM model.

[0037] In this embodiment, two different versions of the distribution network CIM model are expanded according to a tree structure of root node-child node-leaf node. The structural similarity of the nodes of the two distribution network CIM models is calculated. That is, based on the calculated name similarity, a tree structure comparison analysis is performed on the two nodes. The CIM model version information is used as the root node, the classes contained in the CIM model are used as child nodes, and the attributes contained in each class are used as leaf nodes. The structural similarity of the nodes of the different versions of the CIM model is calculated.

[0038] Step 103: Perform principal component weight analysis on name similarity and structural similarity to obtain the overall matching degree.

[0039] In this embodiment, the weights of name similarity and structural similarity are calculated using principal component analysis, and the weighted sum of name similarity and structural similarity is used to calculate the overall matching degree.

[0040] Optionally, step 103 specifically includes steps 1031 to 1033, each step being as follows: Step 1031: Perform matrix processing on the name similarity and structural similarity to obtain the standard value matrix.

[0041] Optionally, step 1031 specifically includes: Based on name similarity and structural similarity Establish a similarity matrix; similarity matrix , .

[0042] Based on the similarity matrix, the average similarity is calculated using the following formula:

[0043] in, This represents the average similarity. n represents the number of child nodes to be compared in the nodes of the two different versions of the distribution network CIM model. The value can be 1 or 2. For the first The similarity of the names of the nodes For the first Structural similarity of nodes; The similarity variance is calculated based on the similarity matrix and the mean value, using the following formula:

[0044] in, The value can be 1 or 2. Let V be the variance of node name similarity. The variance of the node structural similarity; The average similarity and variance of similarity are standardized to obtain a standard value matrix, as shown in the formula:

[0045] in, For the standard value matrix, , This is the standard value.

[0046] Step 1032: Perform principal component analysis on the standard value matrix and calculate the similarity weights.

[0047] Optionally, step 1032 specifically includes: Based on the standard value matrix, the correlation coefficient matrix is ​​calculated using the following formula:

[0048] in, The correlation coefficient matrix, These are the elements of the correlation coefficient matrix. ; Calculate the eigenvalues ​​of the correlation coefficient matrix based on the correlation coefficient matrix; And based on the eigenvalues ​​of the correlation coefficient matrix, the similarity weight is calculated using the following formula:

[0049] in, For similarity weights, These are the eigenvalues ​​of the correlation coefficient matrix.

[0050] Step 1033: Based on the similarity weights, calculate the weighted sum of name similarity and structural similarity to obtain the overall matching degree.

[0051] Optionally, based on the similarity weights, the name similarity and structural similarity are weighted and summed to obtain the comprehensive matching degree, as shown in the formula:

[0052] in, For overall matching accuracy, For similarity weights, and , For name similarity, For structural similarity.

[0053] Step 104: Perform mapping statistical analysis on the node tree with a comprehensive matching degree higher than the current preset matching threshold to obtain the current threshold compliance degree.

[0054] Optionally, step 104 specifically includes steps 1041 to 1043, each of which is as follows: Step 1041: Map the node tree with a comprehensive matching degree higher than the current preset matching threshold to obtain the current mapping result.

[0055] In this embodiment, for naming conflicts, the problem can be solved by using standard names from a given dictionary. Replacement is performed during the initial setup. The overall matching degree Higher than Map the node tree. This is the current preset matching threshold.

[0056] Step 1042: Perform statistical node calculations on the current mapping results to obtain the mapping accuracy and mapping omission rate.

[0057] Optionally, step 1042 specifically involves: according to a preset extraction ratio, statistically analyzing the current mapping results to obtain node data; wherein, the node data includes the total number of nodes, the number of missed mapping nodes, the number of correctly mapped nodes, and the number of incorrectly mapped nodes; In this embodiment, the total number of nodes is obtained by analyzing the mapping results of the distribution network CIM model with a preset extraction ratio (e.g., 10%). Omission of mapping node number Map the correct number of nodes Number of mapping error nodes Calculate the mapping accuracy respectively and mapping omission rate .

[0058] The mapping accuracy is calculated based on the number of correctly mapped nodes and the number of incorrectly mapped nodes, using the following formula:

[0059] in, For mapping accuracy, To map the correct number of nodes, This represents the number of nodes that were mapped incorrectly. The mapping omission rate is calculated based on the number of correctly mapped nodes and the number of missed mapped nodes, using the following formula:

[0060] in, For mapping the omission rate, This represents the number of missing mapping nodes.

[0061] Step 1043: Calculate the current threshold compliance based on the mapping accuracy and mapping omission rate, using the following formula:

[0062] in, For the current threshold compliance, For mapping accuracy, This represents the mapping omission rate.

[0063] Step 105: Based on the preset step size and preset adjustment conditions, continuously adjust the current preset matching threshold until the maximum value of the threshold compliance is obtained. Find the mapping result corresponding to the maximum value of the threshold compliance and obtain the optimal mapping result of different versions of the distribution network CIM model. Whenever the current preset matching threshold is adjusted, the node tree with the comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold compliance.

[0064] In this embodiment, the preset adjustment condition is: when the mapping accuracy... When the threshold is low, increase the threshold; when the mapping omission rate is low... When the threshold is low, decrease it. Preset step size. Based on this, the current preset matching threshold is adjusted, and step 104 is repeated after the threshold adjustment to obtain the current threshold compliance. According to the preset step size and preset adjustment conditions, the current preset matching threshold is continuously adjusted, and step 104 is continued until the threshold compliance reaches its maximum value. Using this threshold as the benchmark, all mapping results of the distribution network CIM model are output, that is, the optimal mapping results of different versions of the distribution network CIM model are obtained.

[0065] Step 106: Based on the optimal mapping result, control the access of the distribution network access equipment.

[0066] This invention involves acquiring distribution network CIM models for distribution IoT terminals and distribution network access devices. The name similarity and structural similarity of nodes in two different versions of the distribution network CIM models are calculated. Principal component analysis is used to adjust weights to calculate the comprehensive matching degree. Node trees with a comprehensive matching degree higher than a matching threshold are mapped, and the current mapping results are analyzed to calculate the current threshold compliance. Based on preset step sizes and preset adjustment conditions, the current preset matching threshold is continuously improved and adjusted to continuously obtain the current threshold compliance. The mapping result corresponding to the maximum threshold compliance is determined, achieving optimal mapping of different versions of the distribution network CIM models. Based on the optimal mapping result, the distribution network access devices are controlled for access. This solves the compatibility problem of mapping between different versions of CIM models under massive distributed terminal device access, efficiently manages the access and control of new devices on the distribution network side, and improves the compatibility of distribution IoT terminals and distribution network access devices.

[0067] Example 2 Accordingly, see Figure 3 , Figure 3 This is a schematic diagram of a second embodiment of the control system for distribution network equipment based on the CIM model mapping of the distribution network provided by the present invention. Figure 3 As shown, the control system for distribution network equipment based on the distribution network CIM model mapping includes a version acquisition module 301, a similarity calculation module 302, a matching degree analysis module 303, an initial mapping module 304, an optimal mapping module 305, and an access control module 306. Among them, the version acquisition module 301 is used to acquire the distribution network CIM model of the distribution IoT terminal and the distribution network access equipment, and obtain different versions of the distribution network CIM model; The similarity calculation module 302 is used to perform similarity measurement calculation on the node names of different versions of the distribution network CIM model, obtain name similarity, and compare and analyze the different versions of the distribution network CIM model according to the tree structure based on the name similarity to calculate the structural similarity. The matching degree analysis module 303 is used to perform principal component weight analysis on name similarity and structural similarity to obtain the comprehensive matching degree; The initial mapping module 304 is used to perform mapping statistical analysis on the node tree with a comprehensive matching degree higher than the current preset matching threshold to obtain the current threshold matching degree; The optimization mapping module 305 is used to continuously adjust the current preset matching threshold according to the preset step size and preset adjustment conditions until the maximum value of the threshold compliance is obtained, find the mapping result corresponding to the maximum value of the threshold compliance, and obtain the optimal mapping result of different versions of the distribution network CIM model; wherein, whenever the current preset matching threshold is adjusted, the node tree with the comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold compliance. The access control module 306 is used to control the access of distribution network access equipment according to the optimal mapping result.

[0068] By implementing the embodiments of the present invention, the name similarity and structural similarity of nodes in two different versions of distribution network CIM models are calculated, and the weights are adjusted using principal component analysis to calculate the comprehensive matching degree. Node trees with a comprehensive matching degree higher than the matching threshold are mapped, and the mapping results are analyzed. The threshold compliance is calculated based on the mapping accuracy and mapping omission rate. The threshold is continuously improved to achieve the optimal mapping of different versions of distribution network CIM models, effectively solving the mapping compatibility problem between different versions of CIM models under the access of massive distributed terminal devices.

[0069] The control system for distribution network equipment based on the distribution network CIM model mapping described above can implement the control method for distribution network equipment based on the distribution network CIM model mapping described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0070] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A control method for distribution network equipment based on distribution network CIM model mapping, characterized in that, include: Obtain distribution network CIM models from distribution IoT terminals and distribution network access equipment to obtain different versions of distribution network CIM models; The node names of the different versions of the distribution network CIM model are similar to calculate the name similarity. Based on the name similarity, the different versions of the distribution network CIM model are compared and analyzed in a tree structure to calculate the structural similarity. Principal component weight analysis is performed on the name similarity and the structural similarity to obtain the comprehensive matching degree; The node tree with a comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold matching degree. Based on the preset step size and preset adjustment conditions, the current preset matching threshold is continuously adjusted until the maximum value of the threshold compliance is obtained. The mapping result corresponding to the maximum value of the threshold compliance is found, and the optimal mapping result of the different versions of the distribution network CIM model is obtained. Whenever the current preset matching threshold is adjusted, the node tree with the comprehensive matching degree higher than the current preset matching threshold is mapped and statistically analyzed to obtain the current threshold compliance. Based on the optimal mapping result, the power distribution network access equipment is controlled for access.

2. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 1, characterized in that, The step of performing principal component weight analysis on the name similarity and the structural similarity to obtain the comprehensive matching degree is as follows: The name similarity and structural similarity are matrixed to obtain a standard value matrix; Principal component analysis was performed on the standard value matrix to calculate the similarity weights. Based on the aforementioned similarity weights, the name similarity and the structural similarity are weighted and summed to obtain the overall matching degree.

3. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 2, characterized in that, The step of matrixing the name similarity and structural similarity to obtain a standard value matrix is ​​as follows: A similarity matrix is ​​established based on the name similarity and the structural similarity. Based on the similarity matrix, the average similarity is calculated using the following formula: in, The average similarity is... n represents the number of child nodes to be compared in the nodes of the two different versions of the distribution network CIM model. The value can be 1 or 2. For the first The name similarity of the nodes, For the first Structural similarity of nodes; Based on the similarity matrix and the average value, the similarity variance is calculated using the following formula: in, The value can be 1 or 2. Let V be the variance of node name similarity. The variance of the node structural similarity; The average similarity and the variance of similarity are standardized to obtain a standard value matrix, as shown in the formula: in, The standard value matrix, , This is the standard value.

4. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 2, characterized in that, The step of performing principal component analysis on the standard value matrix and calculating the similarity weights is as follows: Based on the aforementioned standard value matrix, the correlation coefficient matrix is ​​calculated using the following formula: in, The correlation coefficient matrix is... The elements of the correlation coefficient matrix are... ; Calculate the eigenvalues ​​of the correlation coefficient matrix based on the correlation coefficient matrix; And based on the eigenvalues ​​of the correlation coefficient matrix, the similarity weight is calculated using the following formula: in, The similarity weight, is the eigenvalue of the correlation coefficient matrix.

5. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 2, characterized in that, The comprehensive matching degree is calculated by weighting and summing the name similarity and structural similarity according to the similarity weight, and the formula is as follows: in, The overall matching degree, The similarity weight, The name similarity, The structural similarity is given.

6. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 1, characterized in that, The step of calculating the similarity of node names in the different versions of the distribution network CIM model to obtain name similarity is as follows: The minimum number of operations required to convert the node name of one version of the distribution network CIM model to the node name of another version of the distribution network CIM model is calculated; wherein the minimum number of operations includes the insertion, deletion and replacement of a single character; The name similarity is calculated based on the minimum number of operands, using the following formula: in, The name similarity, This is the minimum number of operands.

7. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 1, characterized in that, The step involves comparing and analyzing the different versions of the distribution network CIM model according to the name similarity, using a tree structure, and calculating the structural similarity, specifically as follows: Based on the name similarity, a tree-structure comparison analysis is performed on the two nodes. The CIM model version information is used as the root node, the classes contained in the CIM model are used as child nodes, and the attributes contained in each class are used as leaf nodes. The similarity of the nodes of the different versions of the CIM model is calculated to obtain the structural similarity, using the following formula: in, The structural similarity is... and These are the child nodes to be compared within the nodes of the two different versions of the CIM model. The number of leaf nodes in each child node. The total number of child nodes to be compared in the two different versions of the CIM model node.

8. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 1, characterized in that, The step of mapping and statistically analyzing the node tree with a comprehensive matching degree higher than the current preset matching threshold to obtain the current threshold matching degree is as follows: Map the node tree whose overall matching degree is higher than the current preset matching threshold to obtain the current mapping result; The current mapping result is statistically calculated to obtain the mapping accuracy and mapping omission rate. Based on the mapping accuracy and mapping omission rate, the current threshold compliance is calculated using the following formula: in, The current threshold compliance, The mapping accuracy is... The mapping omission rate is denoted as .

9. The control method for distribution network equipment based on distribution network CIM model mapping as described in claim 8, characterized in that, The step of performing statistical node calculations on the current mapping result to obtain the mapping accuracy and mapping omission rate is specifically as follows: Based on a preset extraction ratio, the extracted current mapping results are statistically analyzed to obtain node data; wherein, the node data includes the total number of nodes, the number of missed mapping nodes, the number of correctly mapped nodes, and the number of incorrectly mapped nodes; The mapping accuracy is calculated based on the number of correctly mapped nodes and the number of incorrectly mapped nodes, using the following formula: in, The mapping accuracy is... The number of correctly mapped nodes. The number of nodes with mapping errors; The mapping omission rate is calculated based on the number of correctly mapped nodes and the number of missed mapped nodes, using the following formula: in, The mapping omission rate, The number of the missing mapping nodes.

10. A control system for distribution network equipment based on distribution network CIM model mapping, characterized in that, include: The module includes a version acquisition module, a similarity calculation module, a matching degree analysis module, an initial mapping module, an optimal mapping module, and an access control module. The version acquisition module is used to acquire the distribution network CIM model of the distribution IoT terminal and the distribution network access equipment, and obtain different versions of the distribution network CIM model. The similarity calculation module is used to perform similarity measurement calculation on the node names of the different versions of the distribution network CIM model to obtain name similarity, and based on the name similarity, to compare and analyze the different versions of the distribution network CIM model according to the tree structure to calculate structural similarity. The matching degree analysis module is used to perform principal component weight analysis on the name similarity and the structural similarity to obtain the comprehensive matching degree; The initial mapping module is used to perform mapping statistical analysis on the node tree whose comprehensive matching degree is higher than the current preset matching threshold, and obtain the current threshold matching degree. The optimization mapping module is used to continuously adjust the current preset matching threshold according to the preset step size and preset adjustment conditions until the maximum value of the threshold compliance is obtained, find the mapping result corresponding to the maximum value of the threshold compliance, and obtain the optimal mapping result of the different versions of the distribution network CIM model; wherein, whenever the current preset matching threshold is adjusted once, the node tree with the comprehensive matching degree higher than the current preset matching threshold is subjected to mapping statistical analysis to obtain the current threshold compliance. The access control module is used to control the access of the power distribution network access equipment according to the optimal mapping result.

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