Cloud operation and maintenance analysis method and system applied to intelligent transformer area

Abstract

The invention provides a cloud operation and maintenance analysis method and system applied to an intelligent transformer area, and belongs to the technical field of electric power operation and maintenance. Firstly, equipment operation and line transmission state information in the intelligent transformer area is collected and associated to form an operation and maintenance basic association set; constructing an operation association link based on the operation association relationship in the operation maintenance basic association set; secondly, capturing abnormal state characteristics according to the operation association link, and predicting an abnormal influence range; calling a preset linkage rule according to an abnormal influence prediction result and the like, and generating an operation and maintenance linkage disposal scheme; and finally, synchronizing the operation and maintenance linkage processing scheme to the cloud platform and the transformer area terminal equipment for execution, and collecting state feedback information to optimize link conduction characteristics and linkage rules. According to the invention, intelligentization, systematization and dynamic optimization of operation and maintenance of the intelligent transformer area are realized, and the operation and maintenance efficiency and reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of power operation and maintenance technology, and more specifically, to a cloud-based operation and maintenance analysis method and system applied to smart transformer substations. Background Technology

[0002] In power systems, smart distribution substations, as a crucial component, undertake key tasks in power distribution and management. With the increasing number of power devices and the growing complexity of power grid structures, the operation and maintenance management of smart distribution substations faces numerous challenges.

[0003] Traditional smart transformer substation operation and maintenance methods mainly rely on regular manual inspections and simple equipment monitoring techniques. Manual inspections not only consume significant manpower, resources, and time, but also struggle to achieve real-time, comprehensive monitoring, failing to promptly identify potential problems during equipment operation. Furthermore, traditional methods often lack effective correlation analysis for line transmission status, making it difficult to accurately determine the operational relationship between equipment and lines. This results in difficulties in quickly locating the source of a fault and accurately assessing its impact when one occurs.

[0004] Furthermore, existing operation and maintenance management methods lack a systematic and coordinated response mechanism when dealing with abnormal situations. They often handle individual devices or local lines independently without fully considering the mutual influence and transmission relationship between devices and lines, resulting in low operation and maintenance efficiency and difficulty in ensuring the stable operation of smart distribution areas. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a cloud-based operation and maintenance analysis method applied to smart transformer substations, the method comprising: The system collects operational status information generated by devices during operation and transmission status information generated during line transmission within the smart distribution area. It then associates the operational status information with the transmission status information to form a basic set of operational and maintenance associations. This basic set of operational and maintenance associations includes the operational association relationships and status parameter correspondences between devices and lines. Based on the operational association relationships in the basic operational and maintenance association set, the connection node information and transmission path data of equipment and lines are extracted to construct operational association links. The operational association links take equipment and lines as nodes and connection relationships as links, including the status parameter identifiers, status parameter correspondences, and link transmission characteristics of the associated nodes. Based on the operational associated links, the abnormal state characteristics of the nodes in the operational associated links are captured. The initial abnormal nodes and associated links are tracked and determined based on the abnormal state characteristics. Combined with the link propagation characteristics in the operational associated links, the scope of the abnormal impact is predicted, and the abnormal impact prediction results are obtained. The abnormal impact prediction results include the sequence of nodes that may be affected by the abnormality, the order of impact, and the degree of impact. Based on the anomaly impact prediction results, the initial anomaly nodes, and the status characteristics of the affected nodes, the preset operation and maintenance linkage rules are retrieved, the corresponding linkage handling strategies are matched, and an operation and maintenance linkage handling plan is generated. The operation and maintenance linkage handling plan includes the handling operations for each node, the order of operations, and the operation connection requirements. The synchronized operation and maintenance linkage response plan is transmitted to the cloud operation and maintenance management platform and the terminal equipment in the distribution area. The operation and maintenance linkage response operation is executed, and the status feedback information during the operation is collected. Based on the status feedback information, the link transmission characteristics and preset operation and maintenance linkage rules in the operation-related links are optimized.

[0006] Furthermore, embodiments of the present invention also provide a cloud-based operation and maintenance analysis system applied to smart transformer substations, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned cloud-based operation and maintenance analysis method for smart distribution areas by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the aforementioned cloud-based operation and maintenance analysis method applied to smart distribution areas.

[0008] Based on the above, by comprehensively collecting equipment operation status information and line transmission status information within the smart distribution area and associating them to form a basic operation and maintenance association set, the system can present the operation association relationships and status parameter correspondences between equipment and lines. Then, based on the operation association links constructed from this set, with equipment and lines as nodes and connections as links, and associating node status parameter identifiers, correspondences, and link transmission characteristics, the system can quickly capture abnormal state characteristics, accurately track initial abnormal nodes and associated links, and accurately predict the scope of abnormal impact by combining link transmission characteristics. This generates anomaly impact prediction results that include descriptions of potentially affected node sequences, impact order, and impact severity, significantly improving the accuracy of fault location and impact assessment. Simultaneously, based on the anomaly impact prediction results, the system retrieves preset operation and maintenance linkage rules to generate operation and maintenance linkage handling plans that include handling operations for each node, the order of operations, and operation connection requirements. This achieves systematic and collaborative operation and maintenance handling, effectively improving operation and maintenance efficiency. Finally, by synchronizing the operation and maintenance linkage response plan to the cloud-based operation and maintenance management platform and the terminal equipment in the distribution area, and by collecting status feedback information during the operation process to optimize the link transmission characteristics and preset operation and maintenance linkage rules in the operation-related links, it is possible to continuously adapt to the actual operation changes of the smart distribution area, continuously improve the intelligence level and reliability of operation and maintenance management, and ensure the stable and efficient operation of the smart distribution area. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the cloud-based operation and maintenance analysis method for smart transformer substations provided in this embodiment of the invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of a cloud-based operation and maintenance analysis system for smart distribution areas provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a cloud-based operation and maintenance analysis method for smart transformer substations, provided by an embodiment of the present invention. The following is a detailed description of this cloud-based operation and maintenance analysis method for smart transformer substations.

[0012] Step S110: Collect the operating status information generated by the equipment during operation and the transmission status information generated during line transmission within the smart distribution area, associate the operating status information and the transmission status information to form a basic association set for operation and maintenance; the basic association set for operation and maintenance includes the operating association relationship and the corresponding relationship of status parameters between the equipment and the line.

[0013] In this embodiment, the smart distribution area includes various devices such as transformers, smart meters, switching equipment, reactive power compensation devices, and power lines connecting these devices, including high-voltage lines, low-voltage lines, and control lines. During the data acquisition phase, for transformers, their built-in sensors and data acquisition modules collect real-time operating status information, specifically including parameters such as three-phase voltage, three-phase current, active power, reactive power, temperature, oil level, and tap changer position. Smart meters collect operating status information for each user, such as voltage, current, electricity consumption, and power factor. Switching equipment collects switch opening and closing status, number of operations, and mechanical characteristic parameters. Reactive power compensation devices collect capacitor switching status, compensation capacity, and power factor. For line transmission status information, high-voltage and low-voltage lines collect parameters such as line current, voltage, power, temperature, insulation status, icing condition, and light wind vibration through monitoring devices installed on the lines. Control lines collect parameters such as signal transmission strength, bit error rate, and communication delay.

[0014] During the data collection process, all operational and transmission status information is timestamped and includes a unique device / line identifier to ensure data accuracy and traceability. Furthermore, for privacy-sensitive electricity data, such as individual user electricity consumption curves, data anonymization techniques are employed. Specifically, user identification information is anonymized and replaced, separating the user's real identity information from the electricity data for separate storage, retaining only the association between the device identifier and the electricity data. Additionally, encryption algorithms are used to encrypt the data during transmission to prevent theft or leakage.

[0015] After collecting the above data, the operational status information and transmission status information are correlated. Specifically, based on the topology information of the smart distribution area, the physical connection relationships between each device and line are determined. For example, a transformer is connected to the upstream power grid through a specific high-voltage line and to a smart meter through a specific low-voltage line. Then, according to the connection relationship between the device and the line, the operational status information of the device is correlated with the transmission status information of the line. For example, the output voltage and current information of the transformer are correlated with the input voltage and current information of the low-voltage line connected to the transformer, and the voltage information of the smart meter is correlated with the voltage information of the low-voltage line connected to the smart meter. Through the above correlation, a basic correlation set for operation and maintenance is formed. This set not only includes the operational correlation relationships between devices and lines, such as device A being connected to device C through line B, but also the correspondence relationships of status parameters, such as the correspondence between the output current of device A and the transmission current of line B, and the correspondence between the input voltage of device C and the output voltage of line B, etc.

[0016] Step S120: Based on the operation association relationship in the operation and maintenance basic association set, extract the connection node information and transmission path data of the equipment and line, and construct the operation association link; the operation association link takes the equipment and line as nodes and the connection relationship as the link, and includes the status parameter identifier, status parameter correspondence and link transmission characteristics of the associated nodes.

[0017] In this embodiment, after obtaining the basic association set for operation and maintenance, the connection node information and transmission path data are extracted based on the operation association relationships therein to construct the operation association link.

[0018] Step S121: Extract the operation association relationships from the operation and maintenance basic association set, and split them to obtain the line list corresponding to each device and the device list corresponding to each line.

[0019] From the basic operation and maintenance association set, based on the connection relationships between devices and lines, all lines connected to each device are extracted to form a line list corresponding to that device. For example, for the transformer in the aforementioned smart distribution area, its line list includes high-voltage lines connected to the upstream power grid and multiple low-voltage lines connected to various smart meters; the line list for smart meters consists of low-voltage lines connected to the transformer. Similarly, the device list corresponding to each line is also extracted. For example, the device list corresponding to a certain low-voltage line includes the transformer and multiple smart meters connected to that line; the device list corresponding to a certain control line includes the control devices and controlled devices connected to that line.

[0020] Step S122: Retrieve the physical deployment data of the smart distribution area, match the connection location information of each device and line, and determine the connection node of the device and line; the connection node includes device interface information, line port information, and the matching relationship between interface and port.

[0021] The physical deployment data of the smart distribution area is retrieved, which details the installation location, interface type, port number, and other information of each device and line. Based on the list of lines corresponding to the devices and the list of devices corresponding to the lines, the connection location information between each device and line is matched. For example, the high-voltage side interface of a transformer is a specific type of bushing interface, and the port of the high-voltage line connected to the transformer is a terminal head that matches the bushing interface. By matching the model, specifications, and location information of both, the connection node between the transformer and the high-voltage line is determined. The device interface information of this connection node includes the model, rated voltage, rated current, and other parameters of the transformer's high-voltage side bushing interface, while the line port information includes the model, specifications, and other parameters of the high-voltage line terminal head. The matching relationship between the interface and the port is that both are compatible in model and have matching electrical parameters. Similarly, for the connection node between a smart meter and a low-voltage line, the device interface information includes the type, quantity, and rated current of the smart meter's terminals, while the line port information includes the type and specifications of the low-voltage line's terminals. The matching relationship between the interface and the port is that the physical dimensions and electrical parameters of the terminals and ports match.

[0022] Step S123: Treat the equipment and lines as independent nodes and assign a unique identifier to each node; the identifier includes node type information and location information.

[0023] All equipment and lines within the smart distribution area are treated as independent nodes, each assigned a unique identifier. Node type information distinguishes between equipment nodes and line nodes. Equipment node type information includes transformers, smart meters, switching equipment, reactive power compensation devices, etc.; line node type information includes high-voltage lines, low-voltage lines, control lines, etc. Location information is based on the physical deployment data of the smart distribution area, using a combination of zone number and equipment / line number. For example, a transformer located at location 1 in zone A will have a location information of A1 in its node identifier; a low-voltage line located at path 3 in zone B will have a location information of B3 in its node identifier. Through this method, the unique identifier of each node clearly reflects its type and location within the smart distribution area.

[0024] Step S124: Using the connection node as the basis for connection between nodes, associate the device node and the line node according to the actual connection relationship to form the initial link framework.

[0025] Based on the connection nodes of the equipment and lines determined in step S122, the equipment nodes and line nodes are associated according to their actual physical connection relationships. For example, the transformer equipment node is associated with the high-voltage line node through its connection node with the high-voltage line; the high-voltage line node is associated with the upper-level power grid connection equipment node through its connection node with the upper-level power grid connection equipment; the transformer equipment node is also associated with each low-voltage line node through its connection node with the low-voltage line; and each low-voltage line node is then associated with the smart meter equipment node through its connection node with the smart meter. Through the above association methods, an initial link framework is formed based on equipment nodes and line nodes, with connection nodes as the connection basis. This framework initially reflects the connection structure of equipment and lines within the smart distribution area.

[0026] Step S125: Trace the complete path of the signal emitted by each device node through the line node to other device nodes, mark the node order and connection nodes in the path to form transmission path information, integrate the transmission path information into the initial link framework, mark the transmission direction and path priority between nodes, and form a preliminary operational association link.

[0027] Step S1251: Select a device node in the running associated link as the starting device node, mark it as the current starting node, extract the line list corresponding to the current starting node, determine all line nodes connected to the current starting node, and mark them as the current line nodes.

[0028] Based on the initial link framework, the transmission path information is traced. First, a device node is selected as the starting device node, for example, the transformer device node in the smart distribution area mentioned above is selected as the current starting node. The list of lines corresponding to the current starting node is extracted, that is, the high-voltage lines and multiple low-voltage lines connected to the transformer. The line nodes corresponding to these lines are identified and marked as the current line nodes, such as high-voltage line node L1, low-voltage line nodes L2, L3, etc.

[0029] Step S1252: Retrieve the connection node information corresponding to the current starting node and each current line node, and record it as path connection nodes.

[0030] Retrieve the connection node information corresponding to the transformer equipment node and the high-voltage line node L1, that is, the matching information between the high-voltage side bushing interface of the transformer and the high-voltage line terminal head, and record it as path connection node J1; retrieve the connection node information corresponding to the transformer equipment node and the low-voltage line node L2, and record it as path connection node J2; retrieve the connection node information corresponding to the low-voltage line node L3, and record it as path connection node J3, etc.

[0031] Step S1253: Extract the device list corresponding to each current line node, remove the current starting node, obtain the other device nodes connected to each current line node, mark them as the next device node, and arrange them in the order of current starting node, path connection node, current line node, and next device node to form an initial path segment.

[0032] Extract the equipment list corresponding to the current line node L1 (high-voltage line node). This list includes the transformer equipment node (current starting node) and the upstream grid connection equipment node. After removing the current starting node, the upstream grid connection equipment node is obtained and marked as the next equipment node D1. Arrange the current starting node (transformer), path connection node J1, current line node L1, and next equipment node D1 in that order to form the initial path segment: Transformer -> J1 -> L1 -> D1.

[0033] For the current line node L2 (low-voltage line node), its corresponding equipment list includes a transformer equipment node (the current starting node) and multiple smart meter equipment nodes. After removing the current starting node, we get smart meter equipment nodes D2, D3, etc., which are marked as the next equipment nodes. Arrange them in the order of the current starting node (transformer), path connection node J2, current line node L2, and next equipment node D2 to form the initial path segment: Transformer -> J2 -> L2 -> D2; similarly, we form the initial path segment: Transformer -> J2 -> L2 -> D3, etc.

[0034] Step S1254: Take each next device node as the new current starting node, repeat the above steps of extracting line nodes, connection nodes and next device nodes, extend the initial path segment until the end node of the path segment is a device node without subsequent connection lines or a device node that has been traversed.

[0035] Take the next device node D1 (upper-level grid connection device node) in the above initial path segment as the new current starting node, extract its corresponding line list, and assume that the device node is only connected to the transformer through the high-voltage line node L1 and has no other line connection. Then the path segment extension terminates and the end node is D1.

[0036] Taking the next device node D2 (smart meter device node) as the new current starting node, extract its corresponding line list. This list only contains the low-voltage line node L2 (current line node), with no other line connections. Therefore, the extension of this path segment terminates, with the end node being D2. Similarly, for the next device node D3, etc., perform similar processing to extend the corresponding initial path segments.

[0037] Step S1255: When a path segment extends to a device node that has already been traversed, terminate the extension of the path segment; when a path segment extends to a device node with no subsequent connecting lines, mark it as the end path segment.

[0038] During the extension of a path segment, if a device node that has already been traversed is encountered, such as when the new starting node of a path segment is a device node that has already been processed, the extension of that path segment is terminated. When a path segment extends to a device node with no subsequent connecting lines, such as the upstream power grid connection device node D1, smart meter device nodes D2 and D3 mentioned above, these path segments are marked as the endpoint path segments.

[0039] Step S1256: Record the node order in each path segment and determine the alternating arrangement order of device nodes and line nodes.

[0040] For each endpoint path segment, record the node order. For example, the node order for the path segment Transformer -> J1 -> L1 -> D1 is Transformer (equipment node), J1 (connection node), L1 (line node), D1 (equipment node). It can be seen that equipment nodes and line nodes are arranged alternately. Similarly, the node order for the path segment Transformer -> J2 -> L2 -> D2 is Transformer (equipment node), J2 (connection node), L2 (line node), D2 (equipment node), also showing an alternating arrangement of equipment nodes and line nodes.

[0041] Step S1257: Mark the connection node information between adjacent nodes in each path segment and associate it with the corresponding node pair.

[0042] In each path segment, the connection node information between adjacent nodes is labeled and associated with the corresponding node pair. For example, in the path segment Transformer->J1->L1->D1, the connection node information between the adjacent node pair Transformer and L1 is J1, and the connection node information between the adjacent node pair L1 and D1 is the connection node corresponding to L1 and D1 (let's assume it's J4). J1 is associated with the node pair (Transformer, L1), and J4 is associated with the node pair (L1, D1).

[0043] Step S1258: Select the device nodes in the running associated link that are not the starting device nodes, repeat the above path segment generation steps, cover the signal transmission paths of all device nodes, integrate all generated path segments, remove completely duplicate path segments, and mark the path segments containing the same node sequence but in opposite directions as bidirectional transmission paths.

[0044] Select other device nodes in the smart distribution area that are not the starting device node, such as smart meter device node D2, switch device node, reactive power compensation device node, etc., and repeat steps S1251 to S1257 to generate their respective path segments. For example, using smart meter device node D2 as the starting device node, generate the path segment D2->J2->L2->Transformer->J1->L1->D1, etc. Integrate all generated path segments. For path segments that are exactly the same, such as those generated from different starting device nodes with the same node order or connection node information, discard them. For path segments that contain the same node sequence but in opposite directions, such as Transformer->J2->L2->D2 and D2->J2->L2->Transformer, mark them as bidirectional transmission paths, indicating that the signal can be transmitted bidirectionally on this path.

[0045] Step S1259: Assign a unique path identifier to each path segment, the unique path identifier including a start node identifier and an end node identifier.

[0046] Each path segment is assigned a unique path identifier, which is a combination of the start node identifier and the end node identifier of the path segment. For example, in the path segment transformer->J1->L1->D1, the start node identifier is the unique identifier of the transformer, and the end node identifier is the unique identifier of D1. Therefore, the unique path identifier of this path segment is the start node identifier + the end node identifier.

[0047] Step S12510: Record the transmission direction of each path segment to determine the direction of signal transmission from the starting node to the ending node.

[0048] Record the transmission direction of each path segment. For unidirectional transmission path segments, specify the direction of signal transmission from the starting node to the ending node; for bidirectional transmission paths, record the transmission directions in both directions. For example, the transmission direction of the path segment Transformer->J2->L2->D2 is from the transformer to D2, and the transmission direction of its reverse path segment D2->J2->L2->Transformer is from D2 to the transformer.

[0049] Step S12511: Integrate path identifiers, node order, connection node information, path direction, and transmission direction to form transmission path information. Associate the transmission path information with the nodes and connection nodes in the running associated link and mark the path identifier to which each node and connection node belongs.

[0050] The above path identifiers, node order, connection node information, path direction, and transmission direction are integrated to form transmission path information. Then, this transmission path information is associated with the nodes and connection nodes in the running linked links, and the path identifier to which it belongs is marked on each node and connection node. For example, the path identifiers of all path segments to which it belongs are marked on the transformer node, and the path identifiers of the path segments containing the connection node are marked on the connection node J2, etc.

[0051] After integrating the aforementioned transmission path information into the initial link framework, the conduction direction between nodes is marked according to the path direction. For example, in the path segment Transformer->J2->L2->D2, the conduction direction is marked from the transformer to L2, and then from L2 to D2. Simultaneously, path priorities are marked based on factors such as path importance and transmission capacity. For instance, high-voltage lines connecting transformers to the upper-level power grid have a higher priority than low-voltage lines connecting transformers to smart meters, and low-voltage lines serving important users have a higher priority than those serving ordinary users. Through these processes, a preliminary operational interconnected link is formed.

[0052] Step S126: Extract the status parameter identifier of each device node and the status parameter identifier of each line node from the basic association set of operation and maintenance, and associate them with the corresponding nodes in the initial operation association link.

[0053] From the basic operation and maintenance association set, extract the status parameter identifiers for each device node. For example, the status parameter identifiers for transformers include "three-phase voltage (phase A)," "three-phase voltage (phase B)," "three-phase voltage (phase C)," "three-phase current (phase A)," "three-phase current (phase B)," "three-phase current (phase C)," "temperature," and "oil level," etc.; the status parameter identifiers for smart meters include "voltage," "current," "power consumption," and "power factor," etc. Similarly, extract the status parameter identifiers for each line node. For example, the status parameter identifiers for high-voltage lines include "line current (phase A)," "line current (phase B)," "line current (phase C)," "line voltage (phase A)," "line voltage (phase B)," "line voltage (phase C)," "temperature," and "insulation status," etc.; the status parameter identifiers for control lines include "signal transmission strength," "bit error rate," and "communication delay," etc. Associate these status parameter identifiers with the corresponding device nodes and line nodes in the initial operation association link, respectively. For example, associate the status parameter identifiers of transformers with the transformer nodes in the initial operation association link, and associate the status parameter identifiers of high-voltage lines with the high-voltage line nodes.

[0054] Step S127: Extract the correspondence of status parameters in the basic association set of operation and maintenance, associate them with the corresponding node pairs and connection links in the preliminary operation association link, and mark the association rules between equipment status parameters and line status parameters.

[0055] Extract the correspondences of state parameters from the basic operation and maintenance association set. For example, there is a correspondence between the output current of a transformer and the transmission current of the low-voltage line connected to the transformer, that is, the output current of a certain phase of the transformer is equal to the transmission current of the low-voltage line of that phase (under ideal conditions where line losses are ignored); there is a correspondence between the output voltage of the transformer and the input voltage of the low-voltage line, that is, the input voltage of the low-voltage line is equal to the output voltage of the transformer (under ideal conditions where line voltage drop is ignored). Associate these state parameter correspondences with the corresponding node pairs and connection links in the preliminary operation association link. For example, associate the correspondence between the output current of the transformer and the transmission current of the low-voltage line with the node pair consisting of the transformer node and the low-voltage line node and the connection links between them, and mark the association rules between the equipment state parameters and the line state parameters, such as "the output current of phase A of the transformer is basically equal to the transmission current of phase L2A of the low-voltage line, with an error range within a specific percentage".

[0056] Step S128: Traverse all nodes and links in the initial operation association link, check whether there are any nodes without associated status parameter identifiers and links without associated status parameter corresponding relationships, and supplement the missing association information.

[0057] Iterate through all device nodes, line nodes, and connecting links in the initial operation linkage, checking whether each node has been associated with a status parameter identifier and whether each connecting link has been associated with a status parameter correspondence. If a node without an associated status parameter identifier is found, such as a newly added monitoring device node, its status parameter identifier is promptly extracted from the operation and maintenance basic linkage set and associated. If a link without an associated status parameter correspondence is found, such as a newly laid line connecting to equipment, the corresponding status parameter correspondence is searched for and extracted from the operation and maintenance basic linkage set and associated, ensuring that all nodes and links in the initial operation linkage are associated with complete status parameter identifiers and status parameter correspondences.

[0058] Step S129: Based on the actual operation process of the smart transformer area, adjust the arrangement order of nodes in the initial operation association link and the transmission direction identifier of the link so that the link structure conforms to the actual operation logic.

[0059] In the actual operation of a smart distribution area, there are specific directions for energy and information flow. For example, energy is transmitted from the upstream power grid to the transformer via high-voltage lines, and then to each smart meter and user equipment via low-voltage lines; control signals are transmitted from the control center to each device via control lines, and the device's operating status information is fed back to the control center via control lines. Based on these actual operational processes, the arrangement of nodes in the initial operational linkage is adjusted to ensure that the node arrangement conforms to the actual paths of energy and information flow. Simultaneously, the transmission direction markings of the links are adjusted to ensure that the transmission direction is consistent with the actual direction of energy and information flow, making the link structure fit the actual operational logic of the smart distribution area.

[0060] Step S1210: Collect historical operating data of nodes and links, extract data related to transmission delay and signal attenuation between nodes, label them to the corresponding links, improve the transmission characteristic description of the links, and form an operational association link with devices and lines as nodes, connection relationships as links, and associated node status parameter identifiers, status parameter correspondences and link transmission characteristics.

[0061] Historical operational data for each node and link is collected, including changes in node status parameters and link transmission signals over a period of time. Propagation delay data between nodes is extracted from this historical data, such as the time it takes for a signal to travel from device node A to device node B. Signal attenuation data is also extracted, such as the degree of signal strength or amplitude reduction during link transmission. This propagation delay and signal attenuation data are then labeled to the corresponding links in the initial operational association link, refining the link's propagation characteristic description. The link's propagation characteristics include signal propagation rate, state attenuation patterns, and inter-node propagation delay. After these steps, the final operational association link is formed. This link uses devices and lines as nodes and connections as links, associating the corresponding status parameter identifiers of the nodes, the corresponding status parameter relationships, and the link's propagation characteristics.

[0062] Step S130: Based on the running associated links, capture the abnormal state characteristics of the nodes in the running associated links, track and determine the initial abnormal nodes and associated links based on the abnormal state characteristics, and combine the link transmission characteristics in the running associated links to predict the scope of abnormal impact and obtain the abnormal impact prediction results; the abnormal impact prediction results include the sequence of nodes that may be affected by the abnormality, the order of impact, and the degree of impact.

[0063] In this embodiment, after the operational linkage is constructed, the abnormal state features are captured, the initial abnormal nodes and linkages are determined, and the scope of the abnormal impact is predicted based on the linkage.

[0064] Step S131: Extract the real-time status parameters of all nodes in the running associated link, associate the historical normal status parameter range of each node, compare the real-time status parameters with the historical normal status parameter range, filter out abnormal status parameters that exceed the historical normal status parameter range, and form abnormal status features; the abnormal status features include abnormal parameter type, parameter value and occurrence time.

[0065] Step S1311: Call the status acquisition interface of each node in the running associated link, and extract the real-time status parameters of the node according to the preset acquisition interval; the real-time status parameters include the device's working mode parameters, component operating parameters, output parameters, and the line's signal transmission parameters, port status parameters, and load parameters.

[0066] The system calls the status acquisition interfaces of each node in the associated link, with preset acquisition intervals set according to the characteristics and monitoring needs of different nodes. For example, the acquisition interval for transformer status parameters is a specific time interval, the acquisition interval for smart meters is another specific time interval, and the acquisition interval for lines is also set according to their importance and the frequency of parameter changes. Through the status acquisition interface, real-time status parameters of the equipment are extracted, including: equipment operating mode parameters such as the transformer's operating position, the smart meter's metering mode, and the operating status of switchgear (open / closed); component operating parameters such as the operating status of the transformer's cooling fan and oil pump, and the operating mechanism status of switchgear; output parameters such as the transformer's output voltage, output current, and output power, and the compensation capacity of the reactive power compensation device. Real-time status parameters of the lines include: signal transmission parameters such as the signal transmission strength and bit error rate of control lines, and the voltage, current, and power of power lines; port status parameters such as the connection status, temperature, and insulation resistance of the ports at both ends of the line; and load parameters such as the size and type of the load carried by the line.

[0067] Step S1312: Retrieve historical operating data of each node from the historical database in the cloud, filter the dataset in which the node is in normal operating state, mark it as normal dataset, and determine the fluctuation range of each parameter based on the normal dataset as the historical normal state parameter range for each node.

[0068] Historical operational data from a cloud-based historical database is retrieved for each node over a relatively long period. This database stores the status parameter records of each node under different operating conditions. Through data analysis, datasets showing nodes in normal operating condition are selected. The criteria for determining whether a node is in normal operating condition are that all status parameters of the node are within the rated range of the equipment or line, and that no faults, alarms, or other abnormalities have occurred in the equipment or line. These selected datasets are marked as normal datasets. For each parameter in the normal dataset, statistical analysis methods are used to calculate its mean, standard deviation, and other statistics. Based on these statistics, the fluctuation range of each parameter is determined. For example, the standard deviation plus or minus a specific multiple of the mean is used as the historical normal state parameter range for that parameter. This range reflects the normal fluctuation interval of the parameter under normal operating conditions.

[0069] Step S1313: Establish a parameter comparison table. Fill the comparison table with the real-time status parameters of each node and the corresponding historical normal status parameter range one by one. Compare the relationship between the real-time status parameters and the historical normal status parameter range in the parameter comparison table one by one, and mark the parameter entries whose real-time status parameters exceed the historical normal status parameter range.

[0070] Create a parameter comparison table where rows represent different nodes and columns represent the status parameter types of the nodes. Each cell in the table contains the real-time status parameter value for each node and its corresponding historical normal status parameter range. Then, compare each real-time status parameter in the comparison table with its corresponding historical normal status parameter range to determine if the real-time status parameter falls within the historical normal range. If a real-time status parameter exceeds the historical normal range (e.g., the real-time value is greater than the upper limit or less than the lower limit), mark that parameter entry as abnormal.

[0071] Step S1314: Extract the parameter names from the marked parameter entries to determine the abnormal parameter types; extract the corresponding real-time parameter values ​​and record them as parameter values.

[0072] For parameter entries marked as abnormal, extract the parameter name from the entry, such as "transformer A-phase current" or "low-voltage line L2 temperature," and determine the abnormal parameter type based on the parameter name, i.e., which category of state parameter the parameter belongs to. Simultaneously, extract the corresponding real-time parameter value from the abnormal parameter entry and record it as the parameter value. This value reflects the specific magnitude of the parameter under the current abnormal state.

[0073] Step S1315: Retrieve the time record of the status acquisition interface, obtain the acquisition time of the real-time status parameter corresponding to the abnormal parameter type, and use it as the occurrence time. Arrange the abnormal parameter types and corresponding parameter values ​​in the order of the occurrence time to form the initial draft of the abnormal status features.

[0074] The system retrieves the time information recorded by the status acquisition interface when collecting real-time status parameters. This time information is accurate to the second or millisecond level. Based on the real-time status parameter corresponding to the abnormal parameter type, the system finds its acquisition time in the status acquisition interface and uses this time as the occurrence time of the abnormal status feature. Then, the abnormal parameter types and their corresponding parameter values ​​are arranged in chronological order to form an initial draft of the abnormal status features. This initial draft records the order of occurrence of each abnormal parameter and its corresponding value.

[0075] Step S1316: Compare the abnormal parameter types and parameter values ​​at different acquisition times of the same node, and remove parameter items that are temporarily out of range due to acquisition errors; the acquisition error is determined by the parameter values ​​of adjacent acquisition intervals returning to the historical normal state parameter range and the fluctuation range meeting the preset requirements.

[0076] For the same node, compare the types and values ​​of abnormal parameters that appear at different collection times. If a certain type of abnormal parameter exceeds the historical normal state parameter range at a certain collection time point, but the parameter value returns to the historical normal state parameter range at the next adjacent collection time point, and the fluctuation range of the parameter value between the two collection time points is small and meets the preset collection error fluctuation range requirement, then it is determined that the abnormal parameter entry is a temporary out-of-range caused by collection error, and it is removed from the draft of abnormal state features to avoid misjudgment caused by collection error.

[0077] Step S1317: Supplement the node identifiers corresponding to the abnormal parameter types, determine the node to which each abnormal parameter type belongs, and thus integrate the abnormal parameter type, parameter value, occurrence time and node identifier to form abnormal state characteristics.

[0078] Based on the initial draft of the abnormal state characteristics, a node identifier corresponding to each abnormal parameter type is added. This node identifier is a unique identifier for the node in the operational linkage, and the specific node to which each abnormal parameter type belongs can be determined through the node identifier. By integrating the abnormal parameter type, parameter value, occurrence time, and node identifier, a complete abnormal state characteristic is formed. This characteristic can clearly reflect which node, at what time, has which type of abnormal parameter and its specific value.

[0079] Step S1318: Perform unit unification processing on the parameter values ​​in the abnormal state features, mark the parameter change trend of each abnormal parameter type in the abnormal state features, and output the abnormal state features including abnormal parameter type, parameter value, occurrence time, node identifier and parameter change trend; the parameter change trend includes numerical increase, numerical decrease and numerical fluctuation.

[0080] The units of parameters in the abnormal state characteristics are standardized to ensure that parameters of different nodes and types have a consistent unit of measurement, facilitating subsequent analysis and comparison. For example, the unit for all voltage parameters is standardized to volts, and the unit for current parameters is standardized to amperes. Simultaneously, the changes in parameter values ​​for abnormal parameter types at multiple acquisition time points are analyzed, and their trends are marked. If the parameter value gradually increases over time, it is marked as an upward trend; if it gradually decreases, it is marked as a downward trend; if it fluctuates within a certain range, it is marked as a fluctuation trend. The final output of the abnormal state characteristics includes the abnormal parameter type, parameter value, occurrence time, node identifier, and parameter change trend.

[0081] Step S132: Based on the abnormal state characteristics, retrieve the association relationship and link transmission record of the nodes in the running associated link, trace the node association path corresponding to the abnormal state characteristics, and mark the node that first appears the abnormal state characteristics in the node association path as the initial abnormal node.

[0082] Based on the abnormal state characteristics formed in step S131, which include information such as abnormal parameter type, occurrence time, and node identifier, the corresponding node is located in the running associated link according to the node identifier. Then, the association relationship of that node in the running associated link is retrieved, that is, the connection relationship between that node and other nodes, the transmission path, and the link conduction record, which contains the historical transmission of signals or state parameters in the link. Based on the occurrence time in the abnormal state characteristics, the node association path corresponding to the abnormal state characteristics is traced, that is, the path through which the abnormal state may be transmitted from one node to other nodes. In the traced node association paths, the time when the abnormal state characteristics of each node appear is compared, and the node with the earliest occurrence of the abnormal state characteristics is marked. This node is the initial abnormal node and the source of the abnormality.

[0083] Step S133: Extract all connection links corresponding to the initial abnormal node as associated links; collect the transmission medium information, connection method information and historical transmission data of the associated links to determine the transmission characteristics of the associated links; the transmission characteristics include signal transmission rate, state attenuation law and inter-node transmission delay.

[0084] Extract all connection links corresponding to the initial anomalous node in the associated links. These links are the channels connecting the initial anomalous node to other nodes and are considered as associated links. For each associated link, collect its transmission medium information, such as the material of the line (copper, aluminum, optical fiber, etc.), cross-sectional area, insulation type, etc.; connection method information, such as the connection method between the line and the equipment (bolted connection, welding, plug-in, etc.), mechanical strength, electrical performance, etc. of the connection parts; and historical transmission data, such as the signal transmission rate of the link over a period of time, the attenuation of state parameters during transmission, and the transmission delay between nodes. Based on the collected transmission medium information, connection method information, and historical transmission data, analyze and determine the transmission characteristics of the associated links. Signal transmission rate refers to the speed at which the signal is transmitted in the link; state attenuation law refers to the attenuation pattern of state parameters or signals with distance or time during link transmission; and inter-node transmission delay refers to the time required for the signal to travel from one node to another in the link.

[0085] Step S134: Based on the abnormal state characteristics of the initial abnormal node and the transmission characteristics of the associated links, simulate the transmission process of the abnormal state on the associated links and record the change data of the abnormal state parameters during the transmission process.

[0086] Step S1341: Extract the abnormal parameter type, parameter value, and parameter change trend from the abnormal state features of the initial abnormal node as the initial simulation data.

[0087] From the abnormal state characteristics of the initial abnormal node, extract the abnormal parameter type, such as "transformer temperature" and "line current"; parameter value, that is, the specific value of the abnormal parameter at the initial abnormal node; parameter change trend, such as the value rising, falling or fluctuating trend. Use this information as the initial simulation data for simulating the abnormal state transmission process.

[0088] Step S1342: Extract the conduction rate, attenuation pattern and conduction delay from the conduction characteristics of the associated link as simulation parameters.

[0089] From the transmission characteristics of the associated links, we extract the transmission rate, i.e. the speed at which a signal or state is transmitted in the link; the attenuation law, i.e. the attenuation pattern of state parameters with distance or time during transmission; and the transmission delay, i.e. the time required for a signal to be transmitted from one end of the link to the other. We use this information as simulation parameters.

[0090] Step S1343: Set up the conduction simulation environment by inputting the initial simulation data and simulation parameters into the simulation environment.

[0091] In this embodiment, a conduction simulation environment is built to simulate the transmission process of abnormal states. This environment can be a simulation platform built based on computer software. The initial simulation data extracted in step S1341 and the simulation parameters extracted in step S1342 are input into this conduction simulation environment to provide initial conditions and parameter settings for the simulation process.

[0092] Step S1344: Set the simulation time step, determine the transmission position of the abnormal state on the associated link at each time step according to the transmission rate, determine the associated link position corresponding to each time step, and, based on the attenuation law of the associated link, correct the abnormal parameter value after each time step, and record the associated link position, the corrected abnormal parameter value and the simulation time corresponding to each time step to form a transmission process data entry.

[0093] In the conduction simulation environment, a simulation time step is set. This time step is the time interval during the simulation process. An appropriate time step is set based on the conduction rate and the length of the associated link to ensure simulation accuracy. According to the conduction rate, the distance the abnormal state travels on the associated link within each time step is calculated, thus determining the transmission location of the abnormal state at each time step, i.e., the corresponding associated link location. Simultaneously, based on the attenuation law of the associated link, the values ​​of the abnormal parameters after each time step are corrected. For example, if the attenuation law is linear with distance, the attenuation amount is calculated based on the transmission distance of the abnormal state, and this attenuation amount is subtracted from the initial parameter values ​​to obtain the corrected abnormal parameter values. The associated link location, corrected abnormal parameter values, and simulation time corresponding to each time step are recorded to form conduction process data entries. These entries record the conduction of the abnormal state on the associated link in chronological order.

[0094] Step S1345: When the simulated abnormal state is transmitted to the end node of the associated link, record the reception time of the end node and the abnormal parameter value at the time of reception, and repeat the above simulation process, adjust the transmission delay in the simulation parameters, simulate the transmission process under different delay conditions, and form multiple sets of transmission process data entries.

[0095] When the simulated abnormal state is transmitted to the end node of the associated link, the reception time of the abnormal state and the value of the abnormal parameters at the time of reception are recorded at the end node. Then, the simulation process in step S1344 is repeated, and the conduction delay in the simulation parameters is adjusted, for example, by setting different conduction delay values, to simulate the conduction process of the abnormal state on the associated link under different conduction delay conditions, forming multiple sets of conduction process data entries to analyze the impact of conduction delay on the conduction of abnormal state.

[0096] Step S1346: Compare the changes in abnormal parameter values ​​and transmission time differences in multiple sets of transmission process data entries to determine the influence of transmission delay on abnormal state transmission.

[0097] By comparing the data entries of the transmission process under multiple sets of different transmission delays, the changes in the values ​​of abnormal parameters during the transmission process and the differences in transmission time are analyzed. For example, when the transmission delay is large, the time for the abnormal state to be transmitted from the initial abnormal node to the terminal node is longer, and the values ​​of abnormal parameters may change differently due to attenuation during transmission; when the transmission delay is small, the transmission time is shorter, and the changes in parameter values ​​may also be different. Through comparative analysis, the influence of transmission delay on the transmission of abnormal states is determined, such as the relationship between transmission delay and transmission time, and the impact of transmission delay on the degree of attenuation of abnormal parameter values.

[0098] Step S1347: Extract all nodes through which the abnormal state passes during the transmission process, and record the simulation time of each node receiving the abnormal state and the corresponding abnormal parameter values.

[0099] From the data entries of the transmission process, extract all nodes that the abnormal state passes through during the transmission process. These nodes are intermediate or terminal nodes on the associated link. Record the simulated time when each node receives the abnormal state, that is, the time when the abnormal state is transmitted to the node, and the corresponding abnormal parameter value at the time of reception. This value is the value after correction for transmission attenuation on the associated link.

[0100] Step S1348: Mark the parameter change trend when each node receives an abnormal state, and analyze the cause of the change trend in combination with the attenuation law.

[0101] Based on the changes in the abnormal state during propagation, the parameter change trend of each node when receiving the abnormal state is marked, such as whether the abnormal parameter value at that node continues to rise, begins to fall, or remains fluctuating. Combined with the attenuation law of the associated link, the causes of the parameter change trend of each node are analyzed. For example, if the attenuation law causes the parameter value to decrease with increasing transmission distance, while the initial parameter change trend of the abnormal node is upward, then the parameter change trend of a certain node when receiving the abnormal state may be upward, but the rate of increase slows down. This is the result of the combined effect of the initial upward trend and propagation attenuation.

[0102] Step S1349: Integrate the reception time, abnormal parameter values, and parameter change trends of each node during the conduction process to form a conduction process parameter change table. Correlate the conduction rate, attenuation law, and conduction delay in the simulation parameters with the conduction process parameter change table to determine the influence of different simulation parameters on the abnormal parameter value changes. Output the conduction process abnormal state parameter change data, which includes the conduction process parameter change table and the simulation parameter influence analysis.

[0103] The reception time, abnormal parameter values, and parameter change trends of each node during the transmission process are integrated and compiled into a transmission process parameter change table. This table records relevant information about abnormal reception states at each node in node order. Then, the transmission rate, attenuation law, and transmission delay in the simulated parameters are correlated with the transmission process parameter change table to analyze the impact of different simulated parameter values ​​on the changes in abnormal parameter values. For example, how the magnitude of the transmission rate affects the arrival time and value of abnormal parameters at the node, and how different forms of attenuation laws lead to differences in the degree of parameter attenuation. The final output includes the transmission process parameter change table and the analysis of the impact of simulated parameters, providing data on the changes in abnormal state parameters during the transmission process.

[0104] Step S135: Based on the change data of abnormal state parameters during the transmission process, track all nodes involved in the transmission process of abnormal state, arrange the involved nodes in the order of transmission to form a node sequence; mark the time when each node receives the abnormal state, and determine the order of influence between nodes.

[0105] Based on the abnormal state parameter change data output in step S134, the transmission process parameter change table records all nodes passed through during the abnormal state transmission process and the reception time of each node. These nodes are tracked and arranged according to the order of abnormal state transmission, i.e., nodes that receive the abnormal state first are listed first, and those that receive it later are listed later, forming a node sequence. Simultaneously, the time when each node receives the abnormal state is marked. Based on the order of these times, the order of influence between nodes is determined, i.e., which node is affected by the abnormality first and which node is affected later.

[0106] Step S136: Compare the parameter changes of each node after receiving the abnormal state with the abnormal parameters of the initial abnormal node, and combine the conduction attenuation law of the associated link to obtain a description of the degree of influence of each node.

[0107] For each node in the node sequence, the parameter changes after receiving an abnormal state are compared with the abnormal parameters of the initial abnormal node, comparing the magnitude of the parameter values ​​and the similarity of the changing trends. Combining the propagation attenuation law of the associated links, the degree of attenuation during the propagation of the abnormal state from the initial abnormal node to this node is analyzed. The greater the attenuation, the less likely the node is affected; the smaller the attenuation, the greater the potential impact. Simultaneously, considering the node's own characteristics, such as its sensitivity to abnormal parameters and its importance in the system, a comprehensive description of the impact degree of each node is obtained. This description can be qualitative (e.g., severe impact, moderate impact, slight impact) or quantitative (e.g., percentage of impact).

[0108] Step S137: Integrate the node sequence, the order of influence, and the description of the degree of influence to form the abnormal influence prediction result, and associate the abnormal state characteristics of the initial abnormal node with the abnormal influence prediction result, and mark the difference in the influence range corresponding to different abnormal parameter types.

[0109] The node sequence formed in step S135, the determined order of influence, and the description of the degree of influence obtained in step S136 are integrated to form the anomaly influence prediction result. Then, the anomalous state characteristics of the initial anomalous node are correlated with this anomaly influence prediction result, indicating that the prediction result is caused by the specific anomalous state of the initial anomalous node. Simultaneously, if the initial anomalous node has multiple anomalous parameter types, the anomaly influence prediction result corresponding to each anomalous parameter type is analyzed separately, and the differences in the influence range of different anomalous parameter types are marked. For example, one anomalous parameter type may affect a larger number of nodes, while another anomalous parameter type may affect a smaller number of nodes, or the affected node types may be different.

[0110] Step S138: Retrieve historical anomaly handling records, compare the current anomaly impact prediction results with the impact range records of similar historical anomalies, and adjust the impact degree description.

[0111] Historical anomaly handling records are retrieved from the cloud-based historical database. These records contain information such as the impact range, handling process, and results of past anomalies similar to the current anomaly type. The impact range (node ​​sequence) in the current anomaly impact prediction result is compared with the impact range records of similar historical anomalies to analyze the similarities and differences. If there is a discrepancy between the current predicted impact range and the impact range in the historical records, the impact degree description of the corresponding node in the current anomaly impact prediction result is adjusted based on the actual impact degree of each node in the historical anomaly events to make the prediction result more accurate and reliable.

[0112] Step S139: Supplement the transmission correction coefficients of abnormal states under different environmental conditions, and adjust the node sequence and impact order in the abnormal impact prediction results based on the current environmental information of the smart transformer area, so as to output the abnormal impact prediction results containing node sequence, impact order, impact degree description and environmental correction description.

[0113] The propagation process of abnormal states can be affected by environmental conditions. Factors such as temperature, humidity, wind speed, and icing can influence the transmission characteristics of lines, thus affecting the propagation of abnormal states. Beforehand, correction coefficients for abnormal state propagation under different environmental conditions are determined through experiments or data analysis. For example, increased line resistance in high-temperature environments may lead to a decrease in signal propagation speed and an increase in attenuation, requiring a specific correction coefficient. Based on the current environmental information of the smart distribution area, such as current temperature, humidity, and weather conditions, appropriate propagation correction coefficients are selected to adjust the node sequence and impact order in the abnormal impact prediction results. For instance, in high-temperature environments, the propagation speed of an abnormal state on a certain line decreases, causing a delay in the arrival of an abnormal state that was originally predicted to arrive at a certain node at a specific time, potentially altering the node sequence or impact order. Finally, the output includes the abnormal impact prediction results, which include the node sequence, impact order, description of the impact degree, and environmental correction explanations. The environmental correction explanations clarify how current environmental information affects the propagation of abnormal states and the adjustments made to the prediction results.

[0114] Step S140: Based on the anomaly impact prediction results, the initial anomaly nodes and the status characteristics of the affected nodes, retrieve the preset operation and maintenance linkage rules, match the corresponding linkage handling strategies, and generate an operation and maintenance linkage handling plan; the operation and maintenance linkage handling plan includes the handling operations of each node, the order of operations, and the operation connection requirements.

[0115] In this embodiment, after obtaining the abnormal impact prediction results, an operation and maintenance linkage response plan is generated by combining the status characteristics of the initial abnormal node and the affected node.

[0116] Step S141: Retrieve preset operation and maintenance linkage rules; the preset operation and maintenance linkage rules include handling strategy entries corresponding to different anomaly types and different impact ranges; each handling strategy entry includes the applicable anomaly parameter type, handling operation content, operation resource requirements and associated operation logic.

[0117] The system retrieves preset operation and maintenance linkage rules from the rule database of the cloud-based operation and maintenance management platform. These rules are formulated based on the operation and maintenance experience of the smart distribution area, equipment characteristics, and industry standards. The preset operation and maintenance linkage rules include various anomaly types, such as overvoltage, overcurrent, overtemperature, and equipment failure, as well as different impact ranges, such as affecting a single device, a local line, or the entire distribution area, with corresponding handling strategy entries. Each handling strategy entry targets a specific anomaly. The applicable anomaly parameter type clarifies which anomaly parameter type(s) the strategy entry applies to; the handling operation content details the specific operational steps to be taken in response to the anomaly, such as adjusting equipment parameters, disconnecting lines, or replacing components; the operation resource requirements list the manpower, tools, spare parts, and other resources required to perform the handling operation; and the associated operation logic explains the coordination relationship between this handling operation and other related operations, such as what preparatory operations need to be completed before performing a certain operation and what follow-up operations are required after the operation.

[0118] Step S142: Extract the abnormal parameter type, node sequence and impact degree description from the abnormal impact prediction results, compare with the disposal strategy items in the preset operation and maintenance linkage rules, filter the applicable disposal strategy items, and form a candidate strategy set.

[0119] The abnormal parameter types (i.e., the specific parameter types causing the abnormality) and node sequences (i.e., the list of nodes affected by the abnormality) are extracted from the abnormality impact prediction results. The impact severity description (i.e., the degree of impact on each node) is then compared with the applicable abnormal parameter types of the handling strategy entries in the preset operation and maintenance linkage rules. Handling strategy entries that match the current abnormal parameter type are selected. Simultaneously, considering the impact range and impact severity description reflected by the node sequences, further handling strategy entries adapted to the current abnormality's impact range and severity are selected, forming a candidate strategy set.

[0120] Step S143: Extract the state features of the initial abnormal nodes and the state features of the affected nodes, compare the applicable conditions of each disposal strategy entry in the candidate strategy set, remove entries that do not match the state features, and obtain the matching strategy set.

[0121] Extract the state characteristics of the initial abnormal node, including its device type, operating status, and specific abnormal parameters; and the state characteristics of the affected nodes, including their type, number, current operating parameters, and role in the system. Each disposal strategy entry in the candidate strategy set has its applicable conditions, such as a specific device type or a specific operating status range. Compare the state characteristics of the initial abnormal node and affected nodes with the applicable conditions of each disposal strategy entry in the candidate strategy set. If the applicable conditions of a disposal strategy entry do not match the current node's state characteristics—for example, if the strategy entry applies to a specific device model that the current node's device model does not match—the entry is removed from the candidate strategy set. After removal, a set of matching strategies that match the current abnormal situation and node state characteristics is obtained.

[0122] Step S144: For each node in the abnormal impact prediction results, select the corresponding handling operation content from the matching strategy set, and determine the operation steps, operation tool type and operation parameter requirements for each node.

[0123] For each node in the node sequence of the anomaly impact prediction results, based on information such as the node's type, the degree of impact, and the type of anomaly parameters, the most suitable handling operation is selected from the matching strategy set. For example, for a line node affected by overcurrent, the handling operation for line overcurrent in the matching strategy set is selected. Based on the selected handling operation, the specific operation steps for each node are determined, such as which checks to perform in the first step, which adjustments to execute in the second step, and which tests to complete in the third step; the type of operating tools, such as the need to use multimeters, oscilloscopes, operating levers, wrenches, etc.; and the required operating parameters, such as the target parameter range when adjusting equipment parameters and the voltage and current ranges to be maintained during operation.

[0124] Step S145: Extract the order of impact from the anomaly impact prediction results, and determine the order of operations between nodes by combining the prerequisite operation requirements for each disposal operation; the prerequisite operation requirements include the preceding node operations to be completed and the operation completion flag.

[0125] The order of impacts from the anomaly impact prediction results is extracted, reflecting the sequence in which nodes are affected by the anomaly. Simultaneously, each handling operation has its prerequisite operations, i.e., other operations that must be completed before executing the current operation. These prerequisite operations may involve handling operations on other nodes. For example, before de-energizing a node on a line, the load nodes connected to that line must first be transferred or de-energized. These load node de-energization operations are the prerequisite operations for de-energizing the line node. The preceding node operation is the de-energization of the load nodes, and the completion indicator is that the power supply to the load nodes has been disconnected and no voltage has been confirmed. Combining the impact order and prerequisite operation requirements, the order of operations between nodes is determined, ensuring that subsequent operations are performed only after the prerequisite operations are completed, thus guaranteeing the safety and effectiveness of the operations.

[0126] Step S146: Analyze the processing operation content of adjacent nodes and determine the operation connection requirements; the operation connection requirements include the completion status standard of the preceding operation, the connection time interval, and the range of status parameters during connection.

[0127] For example, step S1461: Extract the handling operation content of adjacent nodes in the operation and maintenance linkage handling plan, mark it as the preceding operation content and the following operation content, analyze the operation objectives and operation steps of the preceding operation content, and determine the state standard that should be achieved when the preceding operation is completed, as the completion state standard of the preceding operation; the completion state standard includes the number of operation steps completed, the execution results of key operations, and the state parameter requirements of the node.

[0128] Extract the handling operations of adjacent nodes in the operation and maintenance linkage response plan, marking the operations performed earlier as preceding operations and the operations performed later as subsequent operations. Analyze the operational objectives of the preceding operations, i.e., the desired purpose of the operation and the specific operational steps. Based on the operational objectives and steps, determine the state standards that should be achieved upon completion of the preceding operations. For example, all operational steps should be completed, the execution results of key operations should meet expectations, and the relevant state parameters of the node should reach specific ranges. The number of operational steps completed in the completion state standards refers to all operational steps included in the preceding operations being executed; the execution results of key operations refer to the results of operational steps that play a decisive role in the operation effect, such as after a switch disconnection operation, the switch should be in the open position and the auxiliary contact signal should be correct; the state parameter requirements of the node refer to the relevant state parameters of the node being within the normal range after the preceding operations are completed, such as the equipment temperature dropping to a normal level and the line voltage returning to the rated value.

[0129] Step S1462: Retrieve the operation duration data corresponding to the preceding and subsequent operation content, and combine it with the connection time data of similar operations in the historical processing records to determine the time interval from the completion of the preceding operation to the start of the subsequent operation, as the connection time interval.

[0130] Retrieve the operation duration data of the preceding and subsequent operations from the historical processing records, i.e., the time spent performing similar operations in the past. Simultaneously, search the historical processing records for the connection time data of similar adjacent operations, i.e., the time interval between the completion of the preceding operation and the start of the subsequent operation. Combining the operation duration data and historical connection time data, and considering factors such as the complexity of the operation, equipment response time, and safety waiting time, determine a reasonable time interval between the completion of the current preceding operation and the start of the subsequent operation, as the connection time interval. For example, if the preceding operation is equipment parameter adjustment and the subsequent operation is parameter testing, and the equipment needs a certain amount of time to stabilize after adjustment, then the connection time interval should include the time required for equipment stabilization.

[0131] Step S1463: Extract the key parameters in the preceding operation that affect the subsequent operation, and determine the value range of the key parameter when the preceding operation is completed, as the state parameter range when connecting.

[0132] Analyze the preceding operations to identify key parameters that directly affect the execution of subsequent operations. For example, if the preceding operation is adjusting the line voltage and the subsequent operation is measuring the line current, the voltage parameter is the key parameter affecting the current measurement. Determine the value range that this key parameter should be within after the preceding operation is completed. This range should ensure the smooth execution of the subsequent operation and the accuracy of the measurement results. Use this value range as the state parameter range for the transition.

[0133] Step S1464: If there is a parameter transfer relationship between the preceding and following operations, determine the parameter transfer method, transfer time, and parameter format requirements during transfer, and supplement them to the operation connection requirements; if the preceding operation involves equipment start-up, shutdown, or mode switching, determine the time requirement for stable equipment operation after the preceding operation is completed, and adjust the connection time interval.

[0134] If there is a parameter passing relationship between the preceding and following operations, such as data generated by the preceding operation needing to be used as input parameters for the following operation, then the parameter passing method must be determined, such as transmission via data bus, storage in a shared database, or manual recording and passing; the passing time, i.e., how long after the preceding operation is completed will the parameters be passed to the following operation; and the parameter format requirements during passing, such as data type, precision, unit, encoding method, etc., should be added to the operation connection requirements. If the preceding operation involves starting, stopping, or switching operating modes of equipment, such as starting a transformer, stopping power supply to a line, or switching the operating mode of equipment, the equipment needs a certain amount of time to stabilize after starting, stopping, or switching modes. Therefore, it is necessary to determine the time requirement for stable equipment operation and adjust the connection time interval accordingly to ensure that the following operation begins after the equipment is running stably.

[0135] Step S1465: Analyze the operation conditions of the subsequent operation and confirm whether the completion status criteria of the preceding operation meet the start conditions of the subsequent operation. If not, adjust the description of the completion status criteria of the preceding operation or the start conditions of the subsequent operation.

[0136] Analyze the operational conditions for subsequent operations, i.e., the various conditions that must be met to initiate the subsequent operation, such as specific equipment status, parameter ranges, and environmental conditions. Compare the completion status criteria of the preceding operation with the initiation conditions of the subsequent operation to confirm whether the initiation conditions of the subsequent operation can be met after the completion of the preceding operation. If not, for example, if a parameter range in the completion status criteria of the preceding operation is inconsistent with the parameter range required by the initiation conditions of the subsequent operation, then the completion status criteria of the preceding operation need to be adjusted to meet the initiation conditions of the subsequent operation, or the description of the initiation conditions of the subsequent operation can be appropriately adjusted without affecting the operational effect.

[0137] Step S1466: Retrieve historical records of similar adjacent operations, compare the differences between the currently determined completion status standard, connection time interval, and status parameter range and the historical records, and adjust the parameters based on the historical connection effect data.

[0138] Retrieve historical records of similar adjacent operations. These records contain completion status criteria, connection time intervals, status parameter ranges, and operation connection effect data for past executions of similar preceding and subsequent operations, such as whether the subsequent operation started smoothly and whether the operation results met expectations. Compare the currently determined completion status criteria, connection time intervals, and status parameter ranges with the historical records and analyze the differences. Based on historical connection effect data, if a certain parameter setting historically resulted in good connection effects, the current parameter settings should be as close to those effects as possible; if a certain parameter setting historically caused connection problems, similar settings should be avoided currently, thereby adjusting the current completion status criteria, connection time intervals, and status parameter ranges.

[0139] Step S1467: If the disposal operations of adjacent nodes involve the same operation and maintenance resources, determine the connection logic between resource release and occupation, mark the status flag of resource release and the start time of resource occupation, and supplement it to the operation connection requirements.

[0140] If adjacent node operations require the use of the same operation and maintenance resources, such as the same testing instrument, the same group of operators, or the same spare parts, then the connection logic between resource release and occupancy needs to be determined. This includes clarifying how resources are released after a preceding operation has finished using them, and what the status flags for resource release are (e.g., turning off the power and returning the instrument to its storage location after use, or issuing a resource release signal after personnel have completed their operation); and when subsequent operations can occupy the resource, i.e., the resource occupancy initiation timing (e.g., subsequent operations can only begin occupying the resource after the preceding operation has released the resource and issued a release status flag). These connection logics, status flags, and initiation timings for resource release and occupancy should be added to the operation connection requirements.

[0141] Step S1468: For adjacent operations involving line nodes, determine the stability requirements for line signal transmission and supplement the signal fluctuation range standard during connection to the operation connection requirements.

[0142] For adjacent operations involving line nodes, such as signal testing after adjusting line parameters, the stability of line signal transmission has a significant impact on the results of subsequent operations. Therefore, it is necessary to determine the stability requirements for line signal transmission, such as the amplitude and duration of signal fluctuations during transmission, and to supplement the signal fluctuation range standard to the operation connection requirements based on these requirements. That is, after the preceding operation is completed, the fluctuation range of line signal transmission should be within this standard before the subsequent operation can begin.

[0143] Step S147: For the initial abnormal node, increase the priority setting of the handling operation, and stipulate that the start time of the handling operation of the initial abnormal node is earlier than the start time of the handling operation of the affected node.

[0144] Since the initial abnormal node is the source of the anomaly, timely handling of it can prevent the anomaly from escalating further and reduce its impact on other nodes. Therefore, when generating the operation and maintenance linkage response plan, the handling operations for the initial abnormal node are given a higher priority. Specifically, the start time of the handling operations for the initial abnormal node is stipulated to be earlier than the start time of the handling operations for affected nodes, ensuring that the initial abnormal node is handled first, controlling the anomaly at its source.

[0145] Step S148: Based on the handling operation content and operation resource requirements of each node, match the corresponding operation and maintenance resource information, associate it with each handling operation, determine the resource allocation time and resource usage order, and integrate the handling operation content, operation sequence, operation connection requirements and resource allocation information of each node to form a preliminary operation and maintenance linkage handling plan.

[0146] Based on the handling operation content and resource requirements of each node, corresponding operation and maintenance resource information is matched from the smart grid area's operation and maintenance resource database. This database records information such as the type, quantity, storage location, and status of available resources including manpower, tools, equipment, and spare parts. The matched resource information is then associated with each handling operation to clarify which resources are required for each operation. Next, based on the operation sequence and coordination requirements, the resource allocation time (the time it takes for resources to be moved from storage to the operation location) and the resource usage order (the allocation order when multiple operations require the same resource) are determined. Finally, by integrating the handling operation content, operation sequence, coordination requirements, and resource allocation information for each node, a preliminary operation and maintenance coordinated handling plan is formed.

[0147] Step S149: Retrieve historical handling plans and execution effect records for similar anomalies, compare the differences between the preliminary operation and maintenance linkage handling plan and the historical plans, adjust the handling operation content and the order of operations, and supplement the anomaly response plan for each handling operation. This forms an operation and maintenance linkage handling plan that includes the handling operation content, the order of operations, the operation connection requirements, resource allocation information, and anomaly response plan for each node. The anomaly response plan includes adjustment steps and alternative operation content when parameter anomalies occur during the operation.

[0148] Historical records of handling similar anomalies were retrieved and their execution results were analyzed. The analysis covered the operational content, sequence, resource allocation, and contingency plans of these historical plans, as well as their post-implementation effects, such as whether the anomaly was successfully eliminated, the degree of impact on the system, and the handling time. The preliminary operation and maintenance coordinated handling plan was compared with historical plans to identify differences in operational content, sequence, and resource allocation. Based on historical execution results, if certain operational steps in the historical plan were effective, they were adopted into the current plan; if certain operations in the current plan might be risky or inefficient, adjustments were made based on historical experience. Simultaneously, an anomaly contingency plan was added to each handling operation. This plan addressed potential parameter anomalies during operation, such as parameters exceeding expected ranges or equipment unresponsiveness, specifying corresponding adjustment steps, such as how to modify operational parameters, what additional measures to take, and alternative operations—methods that could be used when the original operation could not be performed. Through these adjustments and additions, a final operation and maintenance coordinated handling plan was formed, including the handling operation content, sequence, operational coordination requirements, resource allocation information, and anomaly contingency plans for each node.

[0149] Step S150: Synchronize the operation and maintenance linkage response plan to the cloud operation and maintenance management platform and the terminal equipment in the distribution area, execute the operation and maintenance linkage response operation, collect status feedback information during the operation process, and optimize the link transmission characteristics and preset operation and maintenance linkage rules in the operation-related links based on the status feedback information.

[0150] In this embodiment, after generating the operation and maintenance linkage response plan, the plan is synchronized, executed, and optimized.

[0151] Step S151: Analyze the operation and maintenance linkage response plan, and break it down into response operation data, sequence data, connection data and resource data according to data type. After unifying the data format, standardized plan data is formed.

[0152] The operation and maintenance coordination response plan is analyzed, and the information in the plan is broken down according to data type. Operational data includes the operation steps, tool types, and parameter requirements for each node; sequence data includes the order of operations and the start time of each node's operation; coordination data includes completion status standards, coordination time intervals, and status parameter ranges in the coordination requirements; resource data includes resource allocation information such as resource type, quantity, allocation time, and usage order. The various data types are then standardized by adopting specific encoding formats, data structures, and units to ensure compatibility and consistency between different data types, forming standardized plan data that facilitates data transmission, storage, and parsing.

[0153] Step S152: Establish an encrypted communication link between the cloud-based operation and maintenance management platform and the terminal equipment in the distribution area to transmit standardized solution data. This allows the cloud-based operation and maintenance management platform to receive the standardized solution data, store it in the operation and maintenance solution database, synchronize it with the scheduling module in the cloud-based operation and maintenance management platform, and allow the terminal equipment in the distribution area to receive the standardized solution data, parse it to obtain its corresponding handling operation content and execution requirements.

[0154] An encrypted communication link is established between the cloud-based operation and maintenance management platform and the terminal equipment in the distribution area through security authentication and key negotiation. Encryption algorithms are used to encrypt transmitted data, ensuring the confidentiality and integrity of standardized solution data during transmission. After receiving the standardized solution data, the cloud-based operation and maintenance management platform stores it in the operation and maintenance solution database for subsequent querying and traceability, and synchronizes it to the scheduling module within the cloud-based operation and maintenance management platform. The scheduling module is responsible for coordinating and controlling the execution of each stage based on the solution data. After receiving the standardized solution data through the encrypted communication link, the terminal equipment in the distribution area parses it and extracts the relevant handling operations and execution requirements based on its own node identifier, such as operation steps, operation parameters, and time requirements.

[0155] Step S153: The scheduling module of the cloud-based operation and maintenance management platform sends operation start instructions to the corresponding terminal devices in the order of operation. The start instructions include the operation start time, operation parameters, and connection requirements.

[0156] The scheduling module of the cloud-based operation and maintenance management platform sends operation start commands to the corresponding terminal devices in the corresponding areas according to the sequential data in the standardized scheme data and the order of operations. The start command includes the operation start time, specifying when the terminal device should start performing the disposal operation; operation parameters, i.e., the parameter requirements that should be followed when performing the operation, such as the target parameter value to be adjusted, the allowable error range, etc.; and connection requirement prompts, reminding the terminal device to pay attention to the connection requirements with the preceding or subsequent operations, such as the completion status standard of the preceding operation, the connection time interval, etc.

[0157] Step S154: After receiving the start command, the terminal equipment in the distribution area executes the corresponding processing operation, collects real-time status parameters during the operation process according to the preset time interval, and forms status feedback information; the status feedback information includes operation steps, real-time parameters, operation time and parameter change trend.

[0158] After receiving the operation start command, the terminal equipment in the distribution area executes the corresponding handling operations according to the operation steps and parameters in the command. During the operation, the terminal equipment collects real-time status parameters, such as equipment operating parameters and line transmission parameters, at preset time intervals through its own sensors or data acquisition modules. The collected real-time status parameters are combined with the operation steps, operation time (the time of parameter collection), and parameter change trends (how the parameters change over time) to form status feedback information.

[0159] Step S155: The terminal equipment in the distribution area transmits the status feedback information to the cloud operation and maintenance management platform in real time. The cloud operation and maintenance management platform stores the status feedback information and associates it with the corresponding handling operation.

[0160] The terminal equipment in the distribution area transmits status feedback information to the cloud-based operation and maintenance management platform in real time via an encrypted communication link. After receiving the status feedback information, the cloud-based operation and maintenance management platform stores it in the status feedback database and associates the status feedback information with the corresponding handling operation according to the operation identifier, so as to track the execution process and status changes of each handling operation.

[0161] Step S156: Compare the real-time parameters in the status feedback information with the preset parameters in the operation and maintenance linkage response plan, and analyze the parameter differences; if the parameter differences are within the allowable range, continue to perform subsequent operations; if the parameter differences exceed the allowable range, trigger the abnormal response plan and adjust the response operation parameters.

[0162] The cloud-based operation and maintenance management platform compares the real-time parameters in the status feedback information with the preset parameters corresponding to the operation in the operation and maintenance linkage response plan, and analyzes the parameter differences between the two. The preset parameters are the target parameters expected to be achieved by the response operation or the parameter range that should be maintained during the operation. If the parameter difference is within the preset allowable range, it indicates that the operation is executing normally, and subsequent operations continue. If the parameter difference exceeds the allowable range, it indicates that an anomaly has occurred during the operation. The cloud-based operation and maintenance management platform triggers the anomaly response plan corresponding to the response operation, and adjusts the response operation parameters according to the adjustment steps in the plan, such as modifying the target parameter value or adjusting the operation rhythm, to eliminate the parameter difference and ensure that the operation can be completed smoothly.

[0163] Step S157: After all the handling operations are completed, collect the final status parameters of each node, compare the final status parameters with the historical normal status parameter range, and evaluate the handling effect.

[0164] Once all handling operations are completed, the cloud-based operation and maintenance management platform collects the final status parameters of each node through the terminal devices in the control area. These parameters represent the stable operating parameters of each node after the handling operations are completed. The final status parameters are compared with the historical normal status parameter ranges for each node. If all final status parameters are within the historical normal status parameter range, it indicates that the anomaly has been eliminated and the handling effect is good. If some parameters still exceed the historical normal status parameter range, it indicates that the handling was not completely successful, and further analysis of the causes and supplementary measures are required.

[0165] Step S158: Extract parameter transmission data between the initial abnormal node and the affected node from the status feedback information, determine the actual transmission rate, attenuation degree and delay time, and replace the transmission characteristic description of the corresponding link in the running associated link.

[0166] For example, step S1581: Filter the abnormal parameter types and corresponding parameter value change records of the initial abnormal node from the status feedback information and mark them as source parameter data; the source parameter data includes parameter values ​​and corresponding timestamps.

[0167] Information related to the initial abnormal node is filtered from the status feedback information. The abnormal parameter types and corresponding parameter value changes are extracted, specifically the changes in the abnormal parameter values ​​over time before and after the handling operation. This information is marked as source parameter data. The source parameter data includes parameter values—the specific values ​​of the parameters at different time points—and corresponding timestamps, recording the time the parameter values ​​were collected.

[0168] Step S1582: Filter the parameter value change records after the affected node receives the abnormal parameter type from the status feedback information and mark them as target parameter data; the target parameter data includes the parameter value and the corresponding timestamp.

[0169] Similarly, information about affected nodes is filtered from the status feedback information. Records of parameter value changes at affected nodes after receiving the abnormal parameter type propagated by the initial abnormal node are extracted; that is, the changes in the parameter value at different time points for the affected nodes are marked as target parameter data. The target parameter data also includes the parameter value and the corresponding timestamp.

[0170] Step S1583: Match the parameter change trends of the same abnormal parameter type in the source parameter data and the target parameter data, determine the timestamp when the parameter in the source parameter data begins to change abnormally, and use it as the source start time; determine the timestamp when the parameter in the target parameter data begins to change, and use it as the target start time; determine the difference between the target start time and the source start time, and use it as the actual propagation delay time.

[0171] By comparing the parameter change trends of the same abnormal parameter type in the source and target parameter data, the time point when the parameter in the source parameter data begins to show abnormal changes is identified, and the timestamp corresponding to this time point is determined as the source start time. Similarly, the time point when the parameter in the target parameter data begins to change (affected by the anomaly) is identified, and the corresponding timestamp is determined as the target start time. The difference between the target start time and the source start time is calculated; this difference represents the actual propagation delay time of the abnormal parameter from the initial abnormal node to the affected node.

[0172] Step S1584: Extract the link length information between the initial abnormal node and the affected node, obtain the identifier and corresponding physical length data of the link from the running associated links; and, based on the physical length data of the link and the actual transmission delay time, obtain the actual transmission rate.

[0173] Obtain the identifier of the link between the initial abnormal node and the affected node from the running associated links, and find the corresponding physical length data based on the identifier, i.e., the actual physical length of the link. Based on the physical length data of the link and the actual conduction delay time obtained in step S1583, calculate the ratio of the physical length to the actual conduction delay time to obtain the actual conduction rate of the abnormal parameter in the link.

[0174] Step S1585: Extract the peak value of the parameter after abnormal change in the source parameter data as the source peak value; extract the peak value of the parameter after change in the target parameter data as the target peak value, and obtain the actual attenuation degree based on the relationship between the source peak value and the target peak value.

[0175] The peak values ​​after abnormal parameter changes are extracted from the source parameter data, i.e., the maximum value reached by the abnormal parameter at the initial abnormal node, and are taken as the source peak value. The peak values ​​after parameter changes are extracted from the target parameter data, i.e., the maximum value reached by the abnormal parameter at the affected node, and are taken as the target peak value. By comparing the source peak value and the target peak value, the difference or ratio between the two is calculated to obtain the actual attenuation degree of the abnormal parameter during the link propagation process. For example, the attenuation amount is the source peak value minus the target peak value, and the attenuation rate is (source peak value - target peak value) / source peak value.

[0176] Step S1586: Repeat the above steps to determine the actual transmission rate, actual attenuation, and actual transmission delay time of the link between the initial abnormal node and all affected nodes.

[0177] For each link between the initial abnormal node and all affected nodes, repeat steps S1581 to S1585 to determine the actual transmission rate, actual attenuation, and actual transmission delay time of each link.

[0178] Step S1587: Extract the original conduction feature description of the corresponding link from the running associated link. The conduction feature description includes the original conduction rate, the original attenuation law, and the original conduction delay. Replace the original conduction rate of the corresponding link with the determined actual conduction rate, replace the attenuation value in the original attenuation law with the actual attenuation degree, and replace the original conduction delay with the actual conduction delay time. If there are multiple sets of actual conduction rate, actual attenuation degree, and actual conduction delay time data for the same link, obtain replacement values ​​based on the multiple sets of data, and replace the original conduction feature description with the replacement values.

[0179] Extract the original conduction characteristic description of each corresponding link from the running associated links, including the original conduction rate, original attenuation law (such as attenuation value, attenuation formula, etc.), and original conduction delay. Replace the original conduction rate with the actual conduction rate determined in step S1586; replace the attenuation value in the original attenuation law with the actual attenuation degree. If the attenuation value of a certain link in the original attenuation law is a specific value, replace that value with the actual calculated attenuation degree; replace the original conduction delay with the actual conduction delay time. If multiple sets of actual conduction rate, actual attenuation degree, and actual conduction delay time data are obtained for the same link due to multiple affected nodes or multiple conduction processes, perform statistical analysis on the multiple sets of data, such as calculating the average, median, etc., to obtain replacement values, and use these replacement values ​​to replace the original conduction characteristic description.

[0180] Step S1588: Mark the update time and basis of the transmission feature description, associate the updated transmission feature description with the corresponding link identifier, and form a link transmission feature update table. The update basis is the status feedback information of this operation and maintenance linkage.

[0181] The updated conduction characteristic description is marked with the update time, i.e., the time of this optimization operation; the update basis is clearly stated as being based on the status feedback information of this operation and maintenance linkage. The updated conduction characteristic description is associated with the corresponding link identifier to form a link conduction characteristic update table, which records the updated conduction rate, attenuation degree, conduction delay, and other information corresponding to each link identifier.

[0182] Step S1589: Synchronize the link transmission characteristic update table to the storage module of the running associated link, generate a transmission characteristic update report, which includes the updated link identifier, original parameters, updated parameters and update time, and store it in the cloud operation and maintenance log database.

[0183] The link conduction characteristic update table is synchronized to the storage module of the associated running links, updating the conduction characteristic descriptions of the corresponding links in the storage module. Simultaneously, a conduction characteristic update report is generated, containing the updated link identifier, the original parameters of each link before the update (original conduction rate, original attenuation value, original conduction delay), the updated parameters (actual conduction rate, actual attenuation level, actual conduction delay time), and the update time. This conduction characteristic update report is stored in the cloud-based operation and maintenance log database as a basis for subsequent auditing, analysis, and traceability.

[0184] Step S159: Associate the abnormal state characteristics, abnormal impact prediction results, operation and maintenance linkage response plan and response effect of this anomaly to form a response case record. Analyze the response operation adjustment and effect data in the response case record, optimize the corresponding response strategy entries in the preset operation and maintenance linkage rules, and update the applicable conditions and operation content.

[0185] The abnormal state characteristics, predicted impact, joint operation and maintenance response plan, and evaluation results of the response effectiveness are correlated to form a complete response case record. The adjustments made to the response operations in this case record are analyzed, such as whether the emergency response plan was triggered, how the operation parameters were adjusted, and the post-adjustment effect data, such as whether the abnormality was successfully eliminated, system recovery time, and resource utilization efficiency. Based on the analysis results, the corresponding response strategy entries in the preset joint operation and maintenance rules are optimized. If certain adjustments in this response operation are effective, they are integrated into the operation content of the corresponding response strategy entries; if the applicable conditions of the original response strategy entries are found to be limited, the descriptions of the applicable conditions are updated to better reflect the actual situation.

[0186] Step S1510: Store the treatment case record in the historical case database and associate it with the corresponding node and link in the running association link.

[0187] The resulting case handling records are stored in a historical case database, which is used to accumulate and manage experience in handling various anomalies. Simultaneously, these case handling records are associated with corresponding nodes and links in the operational chain. For example, case records are linked to the initial anomaly node, affected nodes, and related links, facilitating quick retrieval and reference of historical cases when similar anomalies occur in subsequent nodes or links.

[0188] Step S1511: Based on the optimized operation association links and preset operation and maintenance linkage rules, update the operation and maintenance analysis model in the cloud operation and maintenance management platform.

[0189] The operation and maintenance analysis model in the cloud-based operation and maintenance management platform is built upon operation-related links and preset operation and maintenance linkage rules. It is used for anomaly analysis, impact prediction, and response plan generation. After optimizing the transmission characteristics of the operation-related links and the preset operation and maintenance linkage rules, the operation and maintenance analysis model in the cloud-based operation and maintenance management platform is updated based on the updated operation-related links and preset operation and maintenance linkage rules. This involves adjusting the model's parameter settings and logical judgment conditions to improve the accuracy of model analysis and prediction, ensuring it can better adapt to the actual operation of the smart distribution area.

[0190] Based on the same inventive concept, please refer to Figure 2 This paper shows a schematic block diagram of a cloud-based operation and maintenance analysis system 100 for executing the cloud-based operation and maintenance analysis method for smart substations, provided in an embodiment of this application. The cloud-based operation and maintenance analysis system 100 for smart substations may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0191] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located in the cloud-based operation and maintenance analysis system 100 applied to smart distribution areas, and are separately configured and interchangeable. The machine-readable storage medium 120 can also be integrated into the processor 130, and can communicate and interact with external systems through the communication unit 110. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the cloud-based operation and maintenance analysis method for smart distribution areas provided in the aforementioned method embodiment.

[0192] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A cloud-based operation and maintenance analysis method applied to smart transformer substations, characterized in that, The method includes: The system collects operational status information generated by devices during operation and transmission status information generated during line transmission within the smart distribution area. It then associates the operational status information with the transmission status information to form a basic set of operational and maintenance associations. This basic set of operational and maintenance associations includes the operational association relationships and status parameter correspondences between devices and lines. Based on the operational association relationships in the basic operational and maintenance association set, the connection node information and transmission path data of equipment and lines are extracted to construct operational association links. The operational association links take equipment and lines as nodes and connection relationships as links, including the status parameter identifiers, status parameter correspondences, and link transmission characteristics of the associated nodes. Based on the operational associated links, the abnormal state characteristics of the nodes in the operational associated links are captured. The initial abnormal nodes and associated links are tracked and determined based on the abnormal state characteristics. Combined with the link propagation characteristics in the operational associated links, the scope of the abnormal impact is predicted, and the abnormal impact prediction results are obtained. The abnormal impact prediction results include the sequence of nodes that may be affected by the abnormality, the order of impact, and the degree of impact. Based on the anomaly impact prediction results, the initial anomaly nodes, and the status characteristics of the affected nodes, the preset operation and maintenance linkage rules are retrieved, the corresponding linkage handling strategies are matched, and an operation and maintenance linkage handling plan is generated. The operation and maintenance linkage handling plan includes the handling operations for each node, the order of operations, and the operation connection requirements. The synchronized operation and maintenance linkage response plan is transmitted to the cloud operation and maintenance management platform and the terminal equipment in the distribution area. The operation and maintenance linkage response operation is executed, and the status feedback information during the operation is collected. Based on the status feedback information, the link transmission characteristics and preset operation and maintenance linkage rules in the operation-related links are optimized.

2. The cloud-based operation and maintenance analysis method for smart distribution areas according to claim 1, characterized in that, Based on the operational linkages, the abnormal state characteristics of nodes in the operational linkages are captured. Initial abnormal nodes and linkages are determined based on these abnormal state characteristics. Combined with the link propagation characteristics in the operational linkages, the scope of the abnormal impact is predicted, resulting in an abnormal impact prediction. This includes: Extract real-time status parameters of all nodes in the running associated link, associate the historical normal status parameter range of each node, compare the real-time status parameters with the historical normal status parameter range, filter out abnormal status parameters that exceed the historical normal status parameter range, and form abnormal status features; the abnormal status features include abnormal parameter type, parameter value and occurrence time. Based on the abnormal state characteristics, retrieve the association relationship and link transmission record of the nodes in the running associated link, trace the node association path corresponding to the abnormal state characteristics, and mark the node with the earliest abnormal state characteristics in the node association path as the initial abnormal node. Extract all connection links corresponding to the initial abnormal node as associated links; collect transmission medium information, connection method information and historical conduction data of associated links to determine the conduction characteristics of associated links; conduction characteristics include signal conduction rate, state attenuation law and conduction delay between nodes; Based on the abnormal state characteristics of the initial abnormal node and the transmission characteristics of the associated links, the transmission process of the abnormal state on the associated links is simulated, and the change data of the abnormal state parameters during the transmission process are recorded. Based on the change data of abnormal state parameters during the transmission process, all nodes involved in the transmission process of abnormal state are tracked, and the nodes involved are arranged in the order of transmission to form a node sequence; the time when each node receives the abnormal state is marked to determine the order of influence between nodes. By comparing the parameter changes of each node after receiving an abnormal state with the abnormal parameters of the initial abnormal node, and combining the conduction attenuation law of the associated link, a description of the degree of influence of each node is obtained. The node sequence, impact order, and impact degree description are integrated to form the anomaly impact prediction result. The anomaly state characteristics of the initial anomaly node are associated with the anomaly impact prediction result, and the differences in the impact range corresponding to different anomaly parameter types are marked. Retrieve historical anomaly handling records, compare the current anomaly impact prediction results with historical records of the impact range of similar anomalies, and adjust the impact degree description accordingly; The transmission correction coefficients of abnormal states under different environmental conditions are supplemented. Based on the current environmental information of the smart distribution area, the node sequence and impact order in the abnormal impact prediction results are adjusted to output abnormal impact prediction results that include node sequence, impact order, impact degree description and environmental correction description.

3. The cloud-based operation and maintenance analysis method for smart transformer substations according to claim 1, characterized in that, The operation association is constructed by extracting connection node information and transmission path data of equipment and lines based on the operation and maintenance basic association set. The operation association link uses equipment and lines as nodes and connection relationships as links. The status parameter identifiers, status parameter correspondences, and link transmission characteristics of the associated nodes include: Extract the operational relationships from the basic operational and maintenance relationship set, and split them to obtain the line list corresponding to each device and the device list corresponding to each line; Retrieve physical deployment data of the smart distribution area, match the connection location information of each device and line, and determine the connection nodes of the devices and lines; the connection nodes include device interface information, line port information, and the matching relationship between interfaces and ports; Each device and line is treated as an independent node, and a unique identifier is assigned to each node; the identifier includes node type information and location information. Using connection nodes as the basis for connections between nodes, device nodes and line nodes are associated according to their actual connection relationships to form an initial link framework; Trace the complete path of the signal emitted by each device node through the line node to other device nodes, mark the node order and connection nodes in the path to form transmission path information, integrate the transmission path information into the initial link framework, mark the transmission direction and path priority between nodes, and form a preliminary operational association link. Extract the status parameter identifier of each device node and the status parameter identifier of each line node from the basic association set of operation and maintenance, and associate them with the corresponding nodes in the initial operation association link; Extract the correspondence of status parameters from the basic association set of operation and maintenance, associate them with the corresponding node pairs and connection links in the preliminary operation association link, and mark the association rules between equipment status parameters and line status parameters; Traverse all nodes and links in the initial operation association link, check for any nodes without associated status parameter identifiers and links without associated status parameter corresponding relationships, and supplement the missing association information; Based on the actual operation process of the smart transformer area, the arrangement order of nodes in the initial operation link and the transmission direction identification of the link are adjusted to make the link structure conform to the actual operation logic. Collect historical operational data of nodes and links, extract data related to transmission delay and signal attenuation between nodes, label them to the corresponding links, improve the transmission characteristic description of the links, and form an operational association link with devices and lines as nodes, connection relationships as links, and associated node status parameter identifiers, status parameter correspondences, and link transmission characteristics.

4. The cloud-based operation and maintenance analysis method for smart transformer substations according to claim 1, characterized in that, Based on the anomaly impact prediction results, the initial anomaly nodes, and the state characteristics of the affected nodes, the system retrieves preset operation and maintenance linkage rules, matches corresponding linkage response strategies, and generates an operation and maintenance linkage response plan, including: Retrieve preset operation and maintenance linkage rules; preset operation and maintenance linkage rules include handling strategy entries corresponding to different anomaly types and different impact ranges; each handling strategy entry includes the applicable anomaly parameter type, handling operation content, operation resource requirements and associated operation logic; Extract the abnormal parameter types, node sequences, and impact degree descriptions from the abnormal impact prediction results, compare them with the disposal strategy items in the preset operation and maintenance linkage rules, filter the applicable disposal strategy items, and form a candidate strategy set; Extract the state features of the initial abnormal nodes and the state features of the affected nodes, compare the applicable conditions of each disposal strategy entry in the candidate strategy set, and remove entries that do not match the state features to obtain the matching strategy set. For each node in the abnormal impact prediction results, select the corresponding handling operation content from the matching strategy set, and determine the operation steps, operation tool type and operation parameter requirements for each node; Extract the order of impact from the anomaly impact prediction results, and determine the order of operations between nodes by combining the prerequisite operation requirements for each disposal operation; the prerequisite operation requirements include the preceding node operations to be completed and the operation completion flags; Analyze the processing operations of adjacent nodes to determine the operation connection requirements; the operation connection requirements include the completion status criteria of the preceding operation, the connection time interval, and the range of status parameters during the connection. For initial abnormal nodes, the priority setting of the handling operation is increased, and the handling operation start time of the initial abnormal node is specified to be earlier than the handling operation start time of the affected node; Based on the handling operation content and operation resource requirements of each node, the corresponding operation and maintenance resource information is matched and associated with each handling operation. The resource allocation time and resource usage order are determined, and the handling operation content, operation sequence, operation connection requirements and resource allocation information of each node are integrated to form a preliminary operation and maintenance linkage handling plan. Retrieve historical handling plans and execution effect records for similar anomalies, compare the differences between the preliminary operation and maintenance linkage handling plan and historical plans, adjust the handling operation content and sequence, and supplement the anomaly response plan for each handling operation. This forms an operation and maintenance linkage handling plan that includes the handling operation content, sequence, operation connection requirements, resource allocation information, and anomaly response plan for each node. The anomaly response plan includes adjustment steps and alternative operation content when parameter anomalies occur during operation.

5. The cloud-based operation and maintenance analysis method for smart transformer substations according to claim 1, characterized in that, The synchronized operation and maintenance linkage response plan is transmitted to the cloud-based operation and maintenance management platform and the terminal equipment in the distribution area. Operation and maintenance linkage response operations are executed, and status feedback information is collected during the operation process. Based on the status feedback information, the link transmission characteristics in the operation-related links and the preset operation and maintenance linkage rules are optimized to achieve dynamic optimization of smart distribution area cloud-based operation and maintenance, including: The operation and maintenance linkage response plan is analyzed and broken down according to data type to obtain response operation data, sequence data, connection data and resource data. After unifying the data format, standardized plan data is formed. An encrypted communication link is established between the cloud-based operation and maintenance management platform and the terminal equipment in the distribution area to transmit standardized solution data. This enables the cloud-based operation and maintenance management platform to receive the standardized solution data, store it in the operation and maintenance solution database, synchronize it to the scheduling module in the cloud-based operation and maintenance management platform, and enable the terminal equipment in the distribution area to receive the standardized solution data, parse it to obtain its own corresponding handling operation content and execution requirements. The scheduling module of the cloud-based operation and maintenance management platform sends operation start commands to the corresponding terminal devices in the order of operation. The start command includes the operation start time, operation parameters, and connection requirements. After receiving the start command, the terminal equipment in the distribution area executes the corresponding processing operation and collects real-time status parameters during the operation at preset time intervals to form status feedback information. The status feedback information includes operation steps, real-time parameters, operation time, and parameter change trends. The terminal equipment in the distribution area transmits status feedback information to the cloud-based operation and maintenance management platform in real time. The cloud-based operation and maintenance management platform stores the status feedback information and associates it with the corresponding handling operations. Compare the real-time parameters in the status feedback information with the preset parameters in the operation and maintenance linkage response plan, and analyze the parameter differences; if the parameter differences are within the allowable range, continue to execute subsequent operations; if the parameter differences exceed the allowable range, trigger the abnormal response plan and adjust the response operation parameters. After all the handling operations are completed, the final status parameters of each node are collected, and the final status parameters are compared with the historical normal status parameter range to evaluate the handling effect. Extract parameter transmission data between the initial abnormal node and the affected node from the status feedback information, determine the actual transmission rate, attenuation degree and delay time, and replace the transmission characteristic description of the corresponding link in the running associated link; By associating the abnormal state characteristics, abnormal impact prediction results, operation and maintenance linkage response plan and response effect of this anomaly, a response case record is formed. The response operation adjustment and effect data in the response case record are analyzed to optimize the corresponding response strategy entries in the preset operation and maintenance linkage rules and update the applicable conditions and operation content. The recorded cases are stored in the historical case database and associated with the corresponding nodes and links in the operational linkage. Based on the optimized operation association links and preset operation and maintenance linkage rules, the operation and maintenance analysis model in the cloud operation and maintenance management platform is updated.

6. The cloud-based operation and maintenance analysis method for smart transformer substations according to claim 2, characterized in that, The process involves extracting real-time status parameters from all nodes in the operational linkage, associating these parameters with the historical normal status parameter ranges corresponding to each node, comparing the real-time status parameters with the historical normal status parameter ranges, filtering out abnormal status parameters that exceed the historical normal status parameter ranges, and forming abnormal status features, including: The status acquisition interface of each node in the associated link is invoked to extract the real-time status parameters of the nodes according to the preset acquisition interval; the real-time status parameters include the device's working mode parameters, component operating parameters, output parameters, and the line's signal transmission parameters, port status parameters, and load parameters. Historical operation data of each node is retrieved from the historical database in the cloud. Data sets in which the nodes are in normal operation are filtered out and marked as normal datasets. The fluctuation range of each parameter is determined based on the normal datasets and used as the historical normal state parameter range for each node. Establish a parameter comparison table, fill in the real-time status parameters of each node with the corresponding historical normal status parameter range into the comparison table one by one, compare the relationship between the real-time status parameters and the historical normal status parameter range in the parameter comparison table one by one, and mark the parameter entries whose real-time status parameters exceed the historical normal status parameter range. Extract the parameter names from the marked parameter entries to determine the abnormal parameter types; extract the corresponding real-time parameter values ​​and record them as parameter values. Retrieve the time record of the status acquisition interface, obtain the acquisition time of the real-time status parameter corresponding to the abnormal parameter type, and use it as the occurrence time. Arrange the abnormal parameter types and corresponding parameter values ​​in the order of the occurrence time to form the initial draft of the abnormal status features. Compare the abnormal parameter types and parameter values ​​at different collection times of the same node, and remove parameter items that are temporarily out of range due to collection errors; the basis for judging collection errors is that the parameter values ​​of adjacent collection intervals return to the historical normal state parameter range and the fluctuation range meets the preset requirements. Supplement the node identifiers corresponding to the abnormal parameter types, determine the node to which each abnormal parameter type belongs, and thus integrate the abnormal parameter type, parameter value, occurrence time and node identifier to form abnormal state characteristics; The unit of the parameter values ​​in the abnormal state features is standardized, the parameter change trend of each abnormal parameter type in the abnormal state features is marked, and the abnormal state features containing abnormal parameter type, parameter value, occurrence time, node identifier and parameter change trend are output; the parameter change trend includes numerical increase, numerical decrease and numerical fluctuation.

7. The cloud-based operation and maintenance analysis method for smart transformer substations according to claim 2, characterized in that, Based on the abnormal state characteristics of the initial abnormal node and the propagation characteristics of the associated links, the process of simulating the propagation of the abnormal state on the associated links is described, and the changes in abnormal state parameters during the propagation process are recorded, including: Extract the abnormal parameter types, parameter values, and parameter change trends from the abnormal state features of the initial abnormal nodes, and use them as the initial simulation data; The conduction rate, attenuation pattern, and conduction delay of the associated links are extracted as simulation parameters; Set up a conduction simulation environment and input the initial simulation data and simulation parameters into the simulation environment; Set the simulation time step, determine the transmission position of the abnormal state on the associated link at each time step according to the transmission rate, determine the associated link position corresponding to each time step, and, based on the attenuation law of the associated link, correct the abnormal parameter value after each time step, and record the associated link position, the corrected abnormal parameter value and the simulation time corresponding to each time step to form a transmission process data entry. When the simulated abnormal state is transmitted to the end node of the associated link, the receiving time of the end node and the abnormal parameter value at the time of reception are recorded, and the above simulation process is repeated. The transmission delay in the simulation parameters is adjusted to simulate the transmission process under different delay conditions, forming multiple sets of transmission process data entries. By comparing the changes in abnormal parameter values ​​and the differences in transmission time in multiple sets of transmission process data entries, the influence of transmission delay on the transmission of abnormal states can be determined. Extract all nodes through which the abnormal state passes during the transmission process, and record the simulation time and corresponding abnormal parameter values ​​for each node when it receives the abnormal state. Mark the parameter change trend when each node receives abnormal states, and analyze the cause of the change trend in combination with the attenuation law; By integrating the reception time, abnormal parameter values, and parameter change trends of each node during the conduction process, a conduction process parameter change table is formed. The conduction rate, attenuation law, and conduction delay in the simulation parameters are correlated with the conduction process parameter change table to determine the influence of different simulation parameters on the changes in abnormal parameter values. The output includes the conduction process parameter change table and the simulation parameter influence analysis, showing the abnormal state parameter change data of the conduction process.

8. The cloud-based operation and maintenance analysis method for smart transformer substations according to claim 3, characterized in that, The process of tracing the complete path of signals emitted by each device node through line nodes to other device nodes, marking the order of nodes and connecting nodes in the path, forms transmission path information, including: Select a device node in the running associated link as the starting device node and mark it as the current starting node. Extract the list of lines corresponding to the current starting node, determine all line nodes connected to the current starting node, and mark them as the current line nodes. Retrieve the connection node information corresponding to the current starting node and each current line node, and record it as path connection nodes; Extract the device list corresponding to each current line node, remove the current starting node, obtain the other device nodes connected to each current line node, mark them as the next device node, and arrange them in the order of current starting node, path connection node, current line node, and next device node to form the initial path segment; Take each next device node as the new current starting node, repeat the above steps of extracting line nodes, connection nodes and next device nodes, extend the initial path segment until the end node of the path segment is a device node without subsequent connection lines or a device node that has been traversed. When a path segment extends to a device node that has already been traversed, the extension of the path segment is terminated; when a path segment extends to a device node with no subsequent connecting lines, it is marked as the end path segment. Record the node order in each path segment to determine the alternating order of device nodes and line nodes; Label the connection node information between adjacent nodes in each path segment and associate it with the corresponding node pairs; Select a device node in the running associated link that is not the starting device node, repeat the above path segment generation steps to cover the signal transmission paths of all device nodes, integrate all generated path segments, remove completely duplicate path segments, and mark path segments containing the same node sequence but in opposite directions as bidirectional transmission paths. Each path segment is assigned a unique path identifier, which includes a start node identifier and an end node identifier. Record the transmission direction of each path segment to determine the direction of signal transmission from the starting node to the ending node; The path identifier, node order, connection node information, path direction, and transmission direction are integrated to form transmission path information. The transmission path information is then associated with the nodes and connection nodes in the running linked links, and the path identifier to which each node and connection node belongs is marked.

9. A cloud-based operation and maintenance analysis system applied to smart transformer substations, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the cloud-based operation and maintenance analysis method for smart distribution areas as described in any one of claims 1 to 8 by executing the machine-executable instructions.

10. A computer program product, characterized in that, The computer program product includes machine-executable instructions stored in a computer-readable storage medium. The processor of the computer device reads the machine-executable instructions from the computer-readable storage medium and executes the machine-executable instructions, causing the computer device to perform the cloud-based operation and maintenance analysis method for smart distribution areas as described in any one of claims 1 to 8.

Images