Operation and maintenance alarm analysis method and system of power network

Abstract

The invention discloses an operation and maintenance alarm analysis method for a power network. The method comprises the steps of obtaining data information and historical operation data information of a target power network; constructing an operation and maintenance alarm database of the target power network; when the target power network is abnormal, obtaining corresponding abnormal data information; traversing the target power network by taking the abnormal point as an initial point to obtain corresponding abnormal point data information; and matching the obtained abnormal point data information with the constructed operation and maintenance alarm database by adopting a dynamic linear function so as to complete the operation and maintenance alarm analysis of the target power network. The invention also discloses a system for realizing the operation and maintenance alarm analysis method of the power network. According to the method, the database is constructed for the historical data information of the target power network, and the real-time abnormal data is matched in the constructed database, so that the operation and maintenance alarm analysis of the power network is realized, and the method is high in reliability, good in accuracy and relatively high in efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of electrical automation, and particularly relates to a method and system for analyzing operation and maintenance alarms of a power network. Background Art

[0002] With the development of economic technology and the improvement of people's living standards, electric energy has become an essential secondary energy source in people's production and life, bringing endless convenience to people's production and life. Therefore, ensuring the stable and reliable supply of electric energy has become one of the most important tasks of the power system.

[0003] Currently, the scale of the power network is getting larger and the nodes of the power network are also increasing. When an abnormal situation occurs in the power network, currently, it is often necessary for the operation and maintenance personnel of the power network to conduct manual inspections, and sometimes off-site inspections may also be required. This situation undoubtedly greatly increases the workload of the operation and maintenance personnel of the power network. Moreover, the efficiency of manual inspections is relatively low and their reliability is also relatively low. Summary of the Invention

[0004] One object of the present invention is to provide a method for analyzing operation and maintenance alarms of a power network with high reliability, good accuracy and high efficiency.

[0005] Another object of the present invention is to provide a system for implementing the method for analyzing operation and maintenance alarms of the power network.

[0006] The method for analyzing operation and maintenance alarms of the power network provided by the present invention includes the following steps:

[0007] S1. Obtain the data information and historical operation data information of the target power network;

[0008] S2. Construct an operation and maintenance alarm database of the target power network according to the data information obtained in step S1;

[0009] S3. When an abnormality occurs in the target power network, obtain the corresponding abnormal data information;

[0010] S4. According to the data information obtained in step S3, take the abnormal point as the initial point and traverse the target power network to obtain the corresponding abnormal point data information;

[0011] S5. Use a dynamic linear function to match the abnormal point data information obtained in step S4 with the operation and maintenance alarm database constructed in step S2 to complete the analysis of operation and maintenance alarms of the target power network.

[0012] The obtaining of the data information and historical operation data information of the target power network in step S1 specifically includes the following steps:

[0013] The obtained data information of the target power grid includes the parameter information, voltage direction, and current direction of each main line, each main device, and each slave device under each main device in the target power grid;

[0014] According to the obtained data information of the target power grid, using the voltage direction or current direction in the target power grid as the main trunk, and adopting a tree-like branch distribution method, hierarchically screen each device in the target power grid; after completing the hierarchical screening, establish a tree diagram, and use the set of all corresponding associated nodes on the path from any node on the tree diagram to the main trunk as the label code of this node;

[0015] The obtained historical operation data information of the target power grid includes the device parameter information, the label code of the device, and the operation information executed during the occurrence of the anomaly for each main line, each main device, and each slave device under each main device in the target power grid when the anomaly occurs.

[0016] According to the data information obtained in step S1 as described in step S2, construct an operation and maintenance warning database for the target power grid, which specifically includes the following steps:

[0017] In the constructed operation and maintenance warning database of the target power grid, the stored data information includes the label code of the device corresponding to each anomaly occurrence, the parameter information of the device and related devices corresponding to each anomaly occurrence, and the operation information executed during each anomaly occurrence.

[0018] According to the data information obtained in step S3 as described in step S4, taking the anomaly point as the initial point, traverse the target power grid to obtain the corresponding anomaly point data information, which specifically includes the following steps:

[0019] According to the data information obtained in step S3, taking the anomaly point as the initial point and in the reverse direction of the voltage direction or current direction in the target power grid, traverse the tree diagram constructed in step S1 until the device with the anomaly is located, and obtain the label code of the corresponding device as the anomaly point data information.

[0020] As described in step S5, adopt a dynamic linear function to match the anomaly point data information obtained in step S4 with the operation and maintenance warning database constructed in step S2 to complete the operation and maintenance warning analysis of the target power grid, which specifically includes the following steps:

[0021] Match the anomaly point data information obtained in step S4 in the operation and maintenance warning database constructed in step S2:

[0022] If a unique matching result can be obtained, the data information obtained by matching in the constructed operation and maintenance alarm database will be output to complete the operation and maintenance alarm analysis of the target power network; the output data information includes the data information of abnormal devices and the operation information executed when the corresponding abnormality occurs.

[0023] If a unique matching result cannot be obtained, the abnormal point data information obtained in step S4 will be fitted in the operation and maintenance alarm database constructed in step S2 to complete the operation and maintenance alarm analysis of the target power network.

[0024] The fitting of the abnormal point data information obtained in step S4 in the operation and maintenance alarm database constructed in step S2 to complete the operation and maintenance alarm analysis of the target power network specifically includes the following steps:

[0025] Obtain the weight value of the impact of the devices where each node corresponding to the abnormal point data information obtained in step S4 has on the abnormality.

[0026] Calculate the dynamic linear fitting value by the following steps

[0027]

[0028] where x i is the weight value of the impact of the device where node i is located on the abnormality; y i is the data detected by the device where node i is located during the abnormality; n is the number of device nodes; μ is the total number of nodes involved in the abnormal point data information.

[0029] In the operation and maintenance alarm database constructed in step S2, calculate the historical dynamic linear fitting value each time an abnormality occurs in the historical data; compare the dynamic linear fitting value with the historical dynamic linear fitting value in the operation and maintenance alarm database. On the premise that the stored marker code is the same as the abnormal point data information, select several historical dynamic linear fitting values closest to the dynamic linear fitting value The corresponding historical data is used as the final operation and maintenance alarm analysis result of the target power network; the historical data includes the operation information executed when the abnormality occurs.

[0030] The fitting of the abnormal point data information obtained in step S4 in the operation and maintenance alarm database constructed in step S2 to complete the operation and maintenance alarm analysis of the target power network specifically includes the following steps:

[0031] Obtain all the marker codes in the operation and maintenance alarm database constructed in step S2.

[0032] Compare the abnormal point data information obtained in step S4 with all the marker codes in the operation and maintenance warning database: delete the associated marker codes that exist under different paths, and establish an item set for the associated marker codes;

[0033] Use the Apriori algorithm to recursively process the obtained item set to obtain frequent K-item sets; K is the length of the marker code;

[0034] According to the obtained frequent K-item sets, extract the nodes in the frequent K-item sets of the marker codes obtained when completely covering the abnormal situation as associated nodes;

[0035] According to all the obtained associated nodes, set the weight values of the impact of the devices where each associated node is located on the occurrence of the abnormality, and calculate the corresponding dynamic linear fitting value For

[0036] In the operation and maintenance warning database constructed in step S2, calculate the historical dynamic linear fitting value each time an abnormality occurs in the historical data; the dynamic linear fitting value Compare with the historical dynamic linear fitting value in the operation and maintenance warning database. On the premise that the stored marker code is the same as the abnormal point data information, select several historical dynamic linear fitting values that are closest to the dynamic linear fitting value The corresponding historical data is used as the operation and maintenance warning analysis result of the final target power grid; the historical data includes the operation information executed when the abnormality occurs.

[0037] The present invention also provides a system for implementing the operation and maintenance warning analysis method of the power grid, including a data acquisition module, a database construction module, an abnormality acquisition module, an abnormality processing module, and an alarm analysis module; the data acquisition module, the database construction module, the abnormality acquisition module, the abnormality processing module, and the alarm analysis module are connected in series in sequence; the data acquisition module is used to acquire the data information and historical operation data information of the target power grid, and upload the data information to the database construction module; the database construction module is used to construct the operation and maintenance warning database of the target power grid according to the received data information, and upload the data information to the abnormality acquisition module; the abnormality acquisition module is used to acquire the corresponding abnormal data information when the target power grid has an abnormality according to the received data information, and upload the data information to the abnormality processing module; the abnormality processing module is used to traverse the target power grid with the abnormal point as the starting point according to the received data information to obtain the corresponding abnormal point data information, and upload the data information to the alarm analysis module; the alarm analysis module is used to match the obtained abnormal point data information with the constructed operation and maintenance warning database by using a dynamic linear function to complete the operation and maintenance warning analysis of the target power grid.

[0038] The operation and maintenance warning analysis method and system for the power grid provided by the present invention construct a database through the historical data information of the target power grid, and at the same time adopt the process of matching real-time abnormal data in the constructed database, which not only realizes the operation and maintenance warning analysis of the power grid, but also has high reliability, good accuracy and high efficiency. Brief Description of the Drawings

[0039] Figure 1 It is a schematic flow chart of the method of the present invention.

[0040] Figure 2 It is a schematic diagram of the functional modules of the system of the present invention. Detailed Embodiments

[0041] As Figure 1 shown in the schematic flow chart of the method of the present invention: The operation and maintenance warning analysis method for the power grid disclosed by the present invention includes the following steps:

[0042] S1. Obtain the data information and historical operation data information of the target power grid; specifically, it includes the following steps:

[0043] The obtained data information of the target power grid includes the parameter information, voltage direction and current direction of each main line, each main device and each slave device subordinate to each main device in the target power grid;

[0044] According to the obtained data information of the target power grid, using the voltage direction or current direction in the target power grid as the main trunk, and adopting the tree-like branch distribution method, each device in the target power grid is hierarchically screened; after the hierarchical screening is completed, a tree diagram is established, and the set of all corresponding associated nodes on the path from any node on the tree diagram to the main trunk is used as the label code of the node; for example, in a certain area, there are three main lines, each main line has three main devices, and each main device has three slave devices. In this case, the screening level is three, and there are three branches at each level. The intersection point of the main lines is the starting point of the tree diagram, and the formation of its trunk branches is traced according to the flow direction of current or voltage, so that it is easier to trace the associated power equipment in the subsequent distribution planning of the power grid; the main lines are marked as A1, A2, A3, the main devices are marked as a1, a2, a3, and the slave devices are marked as 1, 2, 3. If the main and slave devices are classified by numbers, the label code of the slave device 2 is A2-a2-2. Similarly, the label code of the main device a2 is A2-a2, and the label code of the main line A2 is A2;

[0045] The obtained historical operation data information of the target power grid includes the device parameter information, the identification codes of the devices, and the operation information executed during the occurrence of anomalies for each main line, each main device, and each slave device subordinate to each main device in the target power grid; the operations performed during the maintenance of the power line are to provide analysis samples, and the parameter information detected for each relevant device during the occurrence of anomalies is to facilitate the analysis and aggregation of data anomaly problems and enhance the accuracy of data analysis, while the identification codes of the relevant devices during the occurrence of anomalies are to classify and sort out the fault situations;

[0046] S2. Construct an operation and maintenance warning database for the target power grid based on the data information obtained in step S1; specifically, it includes the following steps:

[0047] The constructed operation and maintenance warning database for the target power grid stores data information including the identification codes of the devices corresponding to each anomaly occurrence, the parameter information of the devices and related devices corresponding to each anomaly occurrence, and the operation information executed during each anomaly occurrence;

[0048] S3. When an anomaly occurs in the target power grid, obtain the corresponding anomaly data information;

[0049] S4. Based on the data information obtained in step S3, traverse the target power grid with the anomaly point as the starting point to obtain the corresponding anomaly point data information; specifically, it includes the following steps:

[0050] Based on the data information obtained in step S3, traverse the tree diagram constructed in step S1 with the anomaly point as the starting point and in the reverse direction of the voltage direction or current direction in the target power grid until the device with the anomaly is located, and obtain the identification code of the corresponding device as the anomaly point data information;

[0051] S5. Use a dynamic linear function to match the anomaly point data information obtained in step S4 with the operation and maintenance warning database constructed in step S2 to complete the operation and maintenance warning analysis of the target power grid; specifically, it includes the following steps:

[0052] Match the anomaly point data information obtained in step S4 with the operation and maintenance warning database constructed in step S2:

[0053] An exception usually requires a carrier. Therefore, when an exception occurs, there should be corresponding parameter changes in the devices. When a device is abnormal, since the device has been marked, the corresponding devices with abnormalities can all trace back to the marking codes. These marking codes provide the first layer of screening. Since the marking codes are encoded rather than complex real - world situations, when using the marking codes as retrieval codes for data analysis, it takes less time and can quickly detect data. When an exception occurs, the parameters of each relevant device are usually unique, that is, if the parameters of all relevant devices are the same, then the exceptions that occur should also be the same. Therefore, when the parameter information is used as address information, the specific operations performed during the maintenance process it points to should also be unique. Therefore, if a unique matching result can be obtained, the data information matched in the constructed operation and maintenance warning database will be output to complete the operation and maintenance warning analysis of the target power network. The output data information includes the data information of the abnormal device and the operation information executed when the corresponding abnormality occurs.

[0054] In many cases, the node device where the exception occurs may be different from the node device that causes the exception. In this case, it is impossible to directly trace the processing solution based on the marking code of the device where the exception is located. Therefore, if a unique matching result cannot be obtained, the exception point data information obtained in step S4 will be fitted in the operation and maintenance warning database constructed in step S2 to complete the operation and maintenance warning analysis of the target power network. The specific steps are as follows:

[0055] Obtain the weight value of the influence of the devices where each node corresponding to the exception point data information obtained in step S4 has on the exception.

[0056] Calculate the dynamic linear fitting value using the following steps

[0057]

[0058] In the formula, x i is the weight value of the influence of the device where node i is located on the exception; y i is the data detected by the device where node i is located during the exception; n is the number of device nodes; μ is the total number of nodes involved in the exception point data information.

[0059] If the detected marking code is: A1 - a1 - 1, and the obtained node meaning is that the slave device 1 of the main device a1 under the main line A1 has an exception, then when calculating the dynamic linear fitting value it is specifically

[0060]

[0061] In the operation and maintenance alarm database constructed in step S2, calculate the historical dynamic linear fitting value each time an anomaly occurs in the historical data; the dynamic linear fitting value is compared with the historical dynamic linear fitting value in the operation and maintenance alarm database. On the premise that the stored marker code is the same as the anomaly point data information, select several historical dynamic linear fitting values closest to the dynamic linear fitting value The corresponding historical data is used as the operation and maintenance alarm analysis result of the final target power grid and is displayed or fed back; the historical data includes the operation information executed when the anomaly occurs; by displaying or feeding back multiple most approximate operation and maintenance alarm analysis results, it can provide better data reference for operation and maintenance personnel, thereby greatly improving the maintenance efficiency.

[0062] Alternatively, the anomaly point data information obtained in step S4 is fitted in the operation and maintenance alarm database constructed in step S2 to complete the operation and maintenance alarm analysis of the target power grid, which specifically includes the following steps:

[0063] Obtain all marker codes in the operation and maintenance alarm database constructed in step S2;

[0064] Compare the anomaly point data information obtained in step S4 with all marker codes in the operation and maintenance alarm database: delete the associated marker codes under different paths, and establish an association item set for the associated marker codes; for example, when an anomaly occurs, it may be that the main device a2 under the A2 line affects the slave device 1 of the main device a1 under the A1 line. In this case, when storing the marker code, it is usually In this case, an association item is obtained. Separate these association items from the database and organize them to establish an association item set;

[0065] Use the Apriori algorithm to recursively process the obtained association item set to obtain frequent K-item sets; K is the length of the marker code;

[0066] For example, first search for candidate 1-item sets and their corresponding support degrees, prune the 1-item sets with support degrees lower than the threshold to obtain frequent 1-item sets; connect the remaining frequent 1-item sets to obtain candidate frequent 2-item sets, and filter out the candidate frequent 2-item sets with support degrees lower than the threshold to obtain the true frequent 2-item sets; and so on, iterate until no frequent k + 1-item sets can be found. The set of corresponding frequent k-item sets is the output result of the algorithm; when the Apriori algorithm recursively processes the association item set, only need to ensure that the recursive length is the length of the associated nodes, and ensure that the frequent K-item sets completely cover the marker codes, then it can be ensured that the nodes included in the finally output association items cover the nodes in the marker codes. There may be other nodes in the association items, and all the existing other nodes are used to calculate the dynamic linear fitting value When the detected marker code is: A1-a1-1, during recursion, the recursion length is 3. If the associated item sets are as follows:

[0067] {A1-a1

[0068] The acquisition of frequent one-item sets is as follows. Among them, a2 and 2 only appear once, so they are deleted during the calculation of frequent one-item sets:

[0069] {A1}4

[0070] {a1}4

[0071] {1}2

[0072] {A2}2

[0073] {A3}2

[0074] {a3}2

[0075] In the above formula, the coordinates are the markers that have appeared, and the right side is the number of times the marker appears. The calculation of frequent two-item sets and frequent three-item sets is the same as above, both using the recursive method. When the result of the recursively calculated frequent three-item set is: A1-a1-1, then return all the associated items that contain this frequent three-item set. Taking the above example as a reference, the associated items corresponding to the returned frequent three-item set can be obtained all completely cover A1-a1-1; then perform subsequent steps;

[0076] According to the obtained frequent K-item sets, extract the nodes in the frequent K-item sets that completely cover the marker codes obtained in the abnormal situation as associated nodes;

[0077] According to all the obtained associated nodes, set the weight values of the influence of each device where the associated nodes are located on the abnormality, and calculate the corresponding dynamic linear fitting value as

[0078] In the operation and maintenance warning database constructed in step S2, calculate the historical dynamic linear fitting value each time an abnormality occurs in the historical data; the dynamic linear fitting value is compared with the historical dynamic linear fitting value in the operation and maintenance warning database. On the premise that the stored marker code is the same as the abnormal point data information, select the historical data corresponding to several historical dynamic linear fitting values that are closest to the dynamic linear fitting value as the operation and maintenance warning analysis result of the final target power network; the historical data includes the operation information executed when the abnormality occurs.

[0079] Such as Figure 2The following is a schematic diagram of the functional modules of the system of the present invention: The system for implementing the operation and maintenance alarm analysis method of the power network disclosed in the present invention includes a data acquisition module, a database construction module, an anomaly acquisition module, an anomaly processing module, and an alarm analysis module; the data acquisition module, the database construction module, the anomaly acquisition module, the anomaly processing module, and the alarm analysis module are connected in series in sequence; the data acquisition module is used to acquire the data information and historical operation data information of the target power network, and upload the data information to the database construction module; the database construction module is used to construct the operation and maintenance alarm database of the target power network according to the received data information, and upload the data information to the anomaly acquisition module; the anomaly acquisition module is used to acquire the corresponding anomaly data information when the target power network has an anomaly according to the received data information, and upload the data information to the anomaly processing module; the anomaly processing module is used to traverse the target power network with the anomaly point as the initial point according to the received data information to obtain the corresponding anomaly point data information, and upload the data information to the alarm analysis module; the alarm analysis module is used to match the obtained anomaly point data information with the constructed operation and maintenance alarm database by using a dynamic linear function according to the received data information to complete the operation and maintenance alarm analysis of the target power network.

Claims

1. A method for analyzing operation and maintenance alarms of a power network, comprising the following steps: S1. Obtain the data information and historical operation data information of the target power network; S2. Construct an operation and maintenance alarm database for the target power network according to the data information obtained in step S1; S3. When an abnormality occurs in the target power network, obtain the corresponding abnormal data information; S4. According to the data information obtained in step S3, taking the abnormal point as the starting point, traverse the target power network to obtain the corresponding abnormal point data information; S5. Use a dynamic linear function to match the abnormal point data information obtained in step S4 with the operation and maintenance alarm database constructed in step S2 to complete the analysis of operation and maintenance alarms of the target power network.

2. The operation and maintenance warning analysis method for the power grid according to claim 1, wherein The obtaining of the data information and historical operation data information of the target power network in step S1 specifically includes the following steps: The obtained data information of the target power network includes the parameter information, voltage direction, and current direction of each main line, each main device, and each slave device subordinate to each main device in the target power network; According to the obtained data information of the target power network, using the voltage direction or current direction in the target power network as the main trunk, and adopting a tree-like branch distribution method, hierarchically screen each device in the target power network; After completing the hierarchical screening, establish a tree diagram, and take the set of all corresponding associated nodes on the path from any node on the tree diagram to the main trunk as the label code of the node; The obtained historical operation data information of the target power network includes the device parameter information, the label code of the device, and the operation information executed during the occurrence of the abnormality of each main line, each main device, and each slave device subordinate to each main device in the target power network when the abnormality occurs.

3. The operation and maintenance alarm analysis method for the power grid according to claim 2, characterized in that The constructing of the operation and maintenance alarm database for the target power network according to the data information obtained in step S1 in step S2 specifically includes the following steps: The stored data information in the constructed operation and maintenance alarm database for the target power network includes the label code of the device corresponding to each occurrence of the abnormality, the parameter information of the device and related devices corresponding to each occurrence of the abnormality, and the operation information executed during each occurrence of the abnormality.

4. The operation and maintenance alarm analysis method for the power grid according to claim 3, characterized in that The traversing of the target power network with the abnormal point as the starting point to obtain the corresponding abnormal point data information according to the data information obtained in step S3 in step S4 specifically includes the following steps: According to the data information obtained in step S3, taking the abnormal point as the starting point and the voltage direction or current direction in the target power network as the reverse direction, traverse the tree diagram constructed in step S1 until the device with the abnormality is located, and obtain the label code of the corresponding device as the abnormal point data information.

5. The operation and maintenance alarm analysis method for the power network according to claim 4, characterized in that The using of a dynamic linear function to match the abnormal point data information obtained in step S4 with the operation and maintenance alarm database constructed in step S2 to complete the analysis of operation and maintenance alarms of the target power network in step S5 specifically includes the following steps: Match the abnormal point data information obtained in step S4 with the operation and maintenance alarm database constructed in step S2: If a unique matching result can be obtained, the data information obtained by matching in the constructed operation and maintenance warning database will be output to complete the operation and maintenance warning analysis of the target power network; the output data information includes the data information of abnormal devices and the operation information executed when the corresponding abnormality occurs. If a unique matching result cannot be obtained, the abnormal point data information obtained in step S4 will be fitted in the operation and maintenance warning database constructed in step S2 to complete the operation and maintenance warning analysis of the target power network.

6. The operation and maintenance warning analysis method for a power network according to claim 5, characterized in that The fitting of the abnormal point data information obtained in step S4 in the operation and maintenance warning database constructed in step S2 to complete the operation and maintenance warning analysis of the target power network specifically includes the following steps: Obtain the weight values of the devices where the respective nodes corresponding to the abnormal point data information obtained in step S4 have an impact on the abnormality. The dynamic linear fitting value is calculated by the following steps Where x i is the weight value of the impact of the device where node i is located on the abnormality; y i is the data detected by the device where node i is located when it is abnormal; n is the number of device nodes; μ is the total number of nodes involved in the abnormal point data information; In the operation and maintenance warning database constructed in step S2, calculate the historical dynamic linear fitting value each time an anomaly occurs in the historical data; the dynamic linear fitting value is compared with the historical dynamic linear fitting value in the operation and maintenance warning database. On the premise that the stored marker code is the same as the anomaly point data information, select the historical data corresponding to several historical dynamic linear fitting values that are closest to the dynamic linear fitting value as the operation and maintenance warning analysis result of the final target power network; the historical data includes the operation information executed when the anomaly occurs.

7. The operation and maintenance warning analysis method for the power network according to claim 5, characterized in that The fitting of the abnormal point data information obtained in step S4 in the operation and maintenance warning database constructed in step S2 to complete the operation and maintenance warning analysis of the target power network specifically includes the following steps: Obtain all the marker codes in the operation and maintenance warning database constructed in step S2. Compare the abnormal point data information obtained in step S4 with all the marker codes in the operation and maintenance warning database: delete the associated marker codes under different paths, and establish an association item set for the associated marker codes. Use the Apriori algorithm to recursively process the obtained association item set to obtain the frequent K-item set; K is the length of the marker code. According to the obtained frequent K-item set, extract the nodes in the frequent K-item set of marker codes that completely cover the abnormal situation as associated nodes. Based on all the obtained associated nodes, set the weight values of the devices where each associated node is located on the influence of the anomaly, and calculate the corresponding dynamic linear fitting values For In the operation and maintenance alarm database constructed in step S2, calculate the historical dynamic linear fitting value at the time of each anomaly occurrence in the historical data; the dynamic linear fitting value is compared with the historical dynamic linear fitting value in the operation and maintenance alarm database. On the premise that the stored marker code is the same as the anomaly point data information, select the historical data corresponding to several historical dynamic linear fitting values that are closest to the dynamic linear fitting value as the operation and maintenance alarm analysis result of the final target power grid; the historical data includes the operation information executed at the time of anomaly occurrence.

8. A system for implementing the operation and maintenance alarm analysis method of the power network according to any one of claims 1 to 7, characterized in that It includes a data acquisition module, a database construction module, an abnormality acquisition module, an abnormality processing module, and a warning analysis module; the data acquisition module, the database construction module, the abnormality acquisition module, the abnormality processing module, and the warning analysis module are connected in series in sequence; the data acquisition module is used to acquire the data information and historical operation data information of the target power network and upload the data information to the database construction module. The database construction module is used to construct the operation and maintenance warning database of the target power network according to the received data information and upload the data information to the abnormality acquisition module; the abnormality acquisition module is used to acquire the corresponding abnormal data information when the target power network has an abnormality according to the received data information and upload the data information to the abnormality processing module. The abnormality processing module is used to traverse the target power network with the abnormal point as the starting point according to the received data information to obtain the corresponding abnormal point data information and upload the data information to the warning analysis module. The warning analysis module is used to match the obtained abnormal point data information with the constructed operation and maintenance warning database by using a dynamic linear function to complete the operation and maintenance warning analysis of the target power network.

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