Power grid fault response detection method and device, computer device, readable storage medium and program product

Abstract

The application relates to a power grid fault response detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring first device information of a plurality of associated power grid devices corresponding to a target power grid device from a preset knowledge graph; detecting the device correlation between the target power grid device and each associated power grid device according to each first device information and second device information corresponding to the target power grid device to obtain a correlation detection result; detecting a coupling grouping device set corresponding to the target power grid device from each associated power grid device according to the correlation detection result; and performing device fault response detection based on N-1 calculation on the target power grid device according to the coupling grouping device set. The method can improve the accuracy of power grid fault response detection.

Description

Technical Field

[0001] This application relates to the field of power grid fault response detection technology, and in particular to a power grid fault response detection method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the development of power grid fault response detection technology, power grid fault response detection technology has emerged, which can be used to detect the impact of power grid equipment failures on power grid operation.

[0003] In traditional technologies, data from power grid equipment is typically collected first, and then power grid fault response detection is performed based on that data.

[0004] However, power grid equipment data is usually massive, and it is often difficult to filter out effective data from the massive amount of data for power grid fault response detection, which leads to low accuracy of power grid fault response detection. Summary of the Invention

[0005] Therefore, it is necessary to provide a power grid fault response detection method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of power grid fault response detection in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a power grid fault response detection method, including:

[0007] Obtain the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a pre-defined knowledge graph;

[0008] Based on the first device information and the second device information corresponding to the target power grid device, the device correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result.

[0009] Based on the correlation detection results, detect the set of coupling and grouping devices corresponding to the target power grid device from each of the associated power grid devices;

[0010] Based on the set of coupled grouped equipment, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0011] In one embodiment, the coupled grouping equipment set includes multiple coupled grouping power grid devices; the step of performing equipment fault response detection on the target power grid device based on N-1 calculation according to the coupled grouping equipment set includes:

[0012] Obtain the marshalling equipment information for each of the aforementioned coupled marshalling network devices from a pre-defined knowledge graph;

[0013] Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features;

[0014] Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0015] Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0016] In one embodiment, detecting the N-1 calculation model type for the target power grid equipment in the N-1 calculation based on the characteristics of each of the grouped equipment includes:

[0017] Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph;

[0018] Based on the device association information, a corresponding feature weight is matched for each of the grouping device features;

[0019] Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features;

[0020] Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

[0021] In one embodiment, detecting the device correlation between the target power grid device and each of the associated power grid devices based on the first device information and the second device information corresponding to the target power grid device includes:

[0022] The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment;

[0023] The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment;

[0024] Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

[0025] In one embodiment, the correlation detection result includes a device correlation value; the step of detecting the set of coupled marshalling equipment corresponding to the target power grid device from each of the associated power grid devices based on the correlation detection result includes:

[0026] If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device;

[0027] If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device;

[0028] All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0029] In one embodiment, the method further includes:

[0030] Obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices;

[0031] Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained.

[0032] Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

[0033] Secondly, this application also provides a power grid fault response detection device, comprising:

[0034] The acquisition module is used to obtain the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a preset knowledge graph;

[0035] The first detection module is used to detect the device correlation between the target power grid equipment and each of the associated power grid equipment based on the first device information and the second device information corresponding to the target power grid equipment, and to obtain the correlation detection result.

[0036] The second detection module is used to detect the set of coupling and grouping devices corresponding to the target power grid device from each of the associated power grid devices based on the correlation detection results.

[0037] The equipment fault response detection module is used to perform equipment fault response detection on the target power grid equipment based on N-1 calculation according to the coupled grouped equipment set.

[0038] In one embodiment, the coupled marshalling equipment set includes multiple coupled marshalling power grid devices; the equipment fault response detection module is further configured to:

[0039] Obtain the marshalling equipment information for each of the aforementioned coupled marshalling network devices from a pre-defined knowledge graph;

[0040] Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features;

[0041] Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0042] Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0043] In one embodiment, the device fault response detection module is further configured to:

[0044] Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph;

[0045] Based on the device association information, a corresponding feature weight is matched for each of the grouping device features;

[0046] Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features;

[0047] Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

[0048] In one embodiment, the first detection module is further configured to:

[0049] The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment;

[0050] The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment;

[0051] Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

[0052] In one embodiment, the correlation detection result includes a device correlation value; the second detection module is further configured to:

[0053] If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device;

[0054] If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device;

[0055] All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0056] In one embodiment, the device further includes:

[0057] A knowledge graph construction module is used to obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices;

[0058] Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained.

[0059] Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

[0060] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0061] First equipment information of multiple associated power grid devices corresponding to the target power grid device is obtained from a preset knowledge graph; based on each of the first equipment information and the second equipment information corresponding to the target power grid device, the equipment correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result; based on the correlation detection result, the set of coupled grouped devices corresponding to the target power grid device is detected from each of the associated power grid devices; based on the set of coupled grouped devices, the target power grid device is subjected to equipment fault response detection based on N-1 calculation.

[0062] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0063] First equipment information of multiple associated power grid devices corresponding to the target power grid device is obtained from a preset knowledge graph; based on each of the first equipment information and the second equipment information corresponding to the target power grid device, the equipment correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result; based on the correlation detection result, the set of coupled grouped devices corresponding to the target power grid device is detected from each of the associated power grid devices; based on the set of coupled grouped devices, the target power grid device is subjected to equipment fault response detection based on N-1 calculation.

[0064] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0065] First equipment information of multiple associated power grid devices corresponding to the target power grid device is obtained from a preset knowledge graph; based on each of the first equipment information and the second equipment information corresponding to the target power grid device, the equipment correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result; based on the correlation detection result, the set of coupled grouped devices corresponding to the target power grid device is detected from each of the associated power grid devices; based on the set of coupled grouped devices, the target power grid device is subjected to equipment fault response detection based on N-1 calculation.

[0066] The aforementioned power grid fault response detection method, apparatus, computer equipment, computer-readable storage medium, and computer program product first obtain first equipment information of multiple associated power grid devices corresponding to the target power grid device from a preset knowledge graph; based on each first equipment information and the second equipment information corresponding to the target power grid device, the device correlation between the target power grid device and each associated power grid device is detected to obtain the correlation detection result. Using the correlation detection result, the set of coupled grouped devices corresponding to the target power grid device can be detected from each associated power grid device. This achieves the goal of finding the set of coupled grouped devices related to power grid fault response detection for the target power grid device from a massive amount of power grid equipment. Therefore, by utilizing the effective equipment information of the coupled grouped device set, the target power grid device can be subjected to equipment fault response detection based on N-1 calculation. This achieves the goal of obtaining effective data from massive amounts of power grid equipment data for equipment fault response detection, thus improving the accuracy of equipment fault response detection. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart illustrating a power grid fault response detection method in one embodiment of this application;

[0069] Figure 2 This is a schematic diagram of the process for detecting equipment fault response of a target power grid device based on N-1 calculation in one embodiment of this application;

[0070] Figure 3 This is a structural block diagram of a power grid fault response detection device in one embodiment of this application;

[0071] Figure 4 This is an internal structural diagram of a computer device in one embodiment of this application. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0073] In one exemplary embodiment, such as Figure 1 As shown, a power grid fault response detection method is provided, including steps 202 to 208. Wherein:

[0074] Step 202: Obtain the first device information of multiple associated power grid devices corresponding to the target power grid device from the preset knowledge graph.

[0075] In this embodiment, a preset knowledge graph is set for the target power grid. The preset knowledge graph includes multiple entities, each entity corresponding to a power grid device in the target power grid. The entity information contained in the entity can be the power grid device parameter information of the power grid device.

[0076] It should be noted that as the power grid equipment parameter information is continuously collected in real time, the entity information in the preset knowledge graph will also be dynamically updated in real time.

[0077] As an example, power grid equipment parameter information can include transformer information, terminal equipment information, switch information, and power grid statistics. Among them, transformer information can include transformer model, rated power, connection method, and transformer type; terminal equipment information can include terminal equipment type and terminal equipment installation location; switch information can include switch equipment model, installation location, and switch status; and power grid statistics can include the total load and peak load of the power grid area.

[0078] As an example, step 202 includes: locating the target entity corresponding to the target power grid device from a preset knowledge graph based on the device number of the target power grid device; detecting multiple associated entities that have a connection relationship with the target entity in the preset knowledge graph, and taking the power grid device corresponding to the associated entity as the associated power grid device; and extracting the power grid device parameter information corresponding to each associated power grid device from the preset graph as the first device information.

[0079] Step 204: Based on the information of each first device and the information of the second device corresponding to the target power grid device, detect the device correlation between the target power grid device and each associated power grid device, and obtain the correlation detection result.

[0080] As an example, step 204 includes: extracting power grid equipment parameter information of the target power grid equipment from a preset knowledge graph as second equipment information; and detecting the equipment correlation between the target power grid equipment and each associated power grid equipment based on the information correlation between each first equipment information and the second equipment information, thereby obtaining the correlation detection result.

[0081] As an example, based on the first device information and the second device information corresponding to the target power grid device, the device correlation between the target power grid device and each associated power grid device is detected, including:

[0082] Equipment features are extracted from the first equipment information to obtain the first equipment features corresponding to the associated power grid equipment; equipment features are extracted from the second equipment information of the target power grid equipment to obtain the second equipment features corresponding to the target power grid equipment; based on the feature similarity between the first equipment features and the second equipment features, the equipment correlation between the target power grid equipment and the associated power grid equipment is detected.

[0083] Specifically, the first device information is one-hot encoded to obtain a first one-hot encoded feature, and the first one-hot encoded feature is dimensionality reduced to obtain the first device feature corresponding to the associated power grid device; the second device information of the target power grid device is one-hot encoded to obtain a second one-hot encoded feature of the target power grid device, and the second one-hot encoded feature is dimensionality reduced to obtain the second device feature of the target power grid device; the feature distance between the first device feature and the second device feature is calculated; this feature distance is mapped to the device correlation value between the target power grid device and the associated power grid device, wherein the feature distance is used to characterize the feature similarity between the first device feature and the second device feature. The larger the feature distance, the lower the feature similarity and the smaller the device correlation value. The lower the device correlation, the smaller the feature distance, and the higher the feature similarity. The larger the device correlation value, the higher the device correlation.

[0084] Step 206: Based on the correlation detection results, detect the set of coupled marshalling equipment corresponding to the target power grid equipment from each associated power grid equipment.

[0085] The correlation detection results include the equipment correlation value, which is used to characterize the correlation between the target power grid equipment and the associated power grid equipment. The higher the equipment correlation value, the higher the correlation between the target power grid equipment and the associated power grid equipment; the lower the equipment correlation value, the lower the correlation between the target power grid equipment and the associated power grid equipment.

[0086] As an example, the correlation detection results include equipment correlation values; based on the correlation detection results, the set of coupled marshalling equipment corresponding to the target power grid equipment is detected from each associated power grid equipment, including:

[0087] If the device correlation value is greater than the preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device; if the device correlation value is not greater than the preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device; all first type of associated power grid devices are set together as the set of coupled grouping devices corresponding to the target power grid device.

[0088] Specifically, if the equipment correlation value is greater than the preset correlation threshold, it indicates that after the target power grid equipment fails, the associated power grid equipment corresponding to the equipment correlation value is likely to be affected by the correlation. Therefore, the associated power grid equipment corresponding to the equipment correlation value is marked as the first type of associated power grid equipment. If the equipment correlation value is not greater than the preset correlation threshold, it indicates that after the target power grid equipment fails, the associated power grid equipment corresponding to the equipment correlation value is likely to be affected by the correlation. Therefore, the associated power grid equipment corresponding to the equipment correlation value is marked as the second type of associated power grid equipment. All the first type of associated power grid equipment are set together as the set of coupled grouping equipment corresponding to the target power grid equipment.

[0089] Step 208: Based on the set of coupled grouped equipment, perform equipment fault response detection on the target power grid equipment using N-1 calculation.

[0090] In this embodiment, the detection of the fault response in the power grid after the target power grid equipment sends a fault is achieved by performing N-1 calculation on the target power grid equipment; the above-mentioned set of coupled and grouped equipment includes multiple coupled and grouped power grid equipment.

[0091] It should be noted that the purpose of the N-1 calculation is to ensure that, under normal operating conditions, when any component (such as a line, generator, transformer, etc.) is fault-free or disconnected due to a fault, the power grid can maintain stable operation and normal power supply, other components are not overloaded, and voltage and frequency are within the allowable range, that is, the power grid response is normal and it can operate normally.

[0092] As an example, step 208 includes: obtaining power grid equipment parameter information corresponding to each coupled grouped power grid equipment from a preset knowledge graph as coupled equipment information; and performing equipment fault response detection on the target power grid equipment based on N-1 calculation according to the coupled equipment information.

[0093] In the aforementioned power grid fault response detection method, firstly, the first equipment information of multiple associated power grid devices corresponding to the target power grid device is obtained from a preset knowledge graph. Based on the first equipment information and the second equipment information corresponding to the target power grid device, the equipment correlation between the target power grid device and each associated power grid device is detected to obtain the correlation detection result. Then, using the correlation detection result, the set of coupled grouped devices corresponding to the target power grid device can be detected from each associated power grid device. This achieves the goal of finding the set of coupled grouped devices related to the target power grid device for power grid fault response detection from a massive amount of power grid devices. Thus, by using the effective equipment information of the set of coupled grouped devices, the target power grid device can be subjected to equipment fault response detection based on N-1 calculation. This achieves the goal of obtaining effective data from massive power grid device data for equipment fault response detection, thereby improving the accuracy of equipment fault response detection.

[0094] It should be noted that power grid equipment data is usually massive. On the one hand, it is often difficult to filter out effective data from the massive data for power grid fault response detection, which leads to low accuracy of power grid fault response detection. On the other hand, it is also often difficult to provide a matching calculation model for the massive data for power grid fault response detection, which further reduces the accuracy of power grid fault response detection.

[0095] In one exemplary embodiment, such as Figure 2 As shown, the coupled grouping equipment set includes multiple coupled grouping power grid devices; based on the coupled grouping equipment set, the target power grid device is subjected to equipment fault response detection based on N-1 calculation, including:

[0096] Step 302: Obtain the marshalling equipment information for each coupled marshalling network device from the preset knowledge graph;

[0097] As an example, the power grid equipment parameter information corresponding to each coupled group of power grid equipment can be obtained from a pre-defined knowledge graph as the coupled equipment information.

[0098] Step 304: Extract features from the information of each group of equipment to obtain multiple group of equipment features;

[0099] As an example, step 304 includes: for each group of equipment information, performing one-hot encoding on the grouping equipment information to obtain one-hot encoded features; inputting the one-hot encoded features into a feature extraction model for feature dimensionality reduction, and outputting grouping equipment features. The grouping equipment features can be embedded feature vectors, i.e. vector.

[0100] Step 306: Based on the characteristics of each group of equipment, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0101] As an example, step 306 includes: splicing the features of each group of equipment to obtain the spliced ​​features of the group of equipment; mapping the spliced ​​features of the group of equipment to the corresponding feature classification identifier according to a preset classifier; and using the calculation model type corresponding to the feature classification identifier as the target power grid equipment for N-1 calculation.

[0102] As an example, based on the characteristics of each group of equipment, the type of N-1 calculation model used for N-1 calculation of the target power grid equipment is detected, including:

[0103] The system detects the device association information between each coupled group of power grid equipment and the target power grid equipment from the preset knowledge graph; based on the device association information, it matches the corresponding feature weights for each group of equipment features; based on the corresponding feature weights for each group of equipment features, it performs weighted fusion of the features of each group of equipment to obtain the fused device features; based on the fused device features and the second device features corresponding to the second device information, it detects the N-1 calculation model type for the target power grid equipment to perform N-1 calculations.

[0104] Among them, the connection edges between entities in the preset knowledge graph can describe the relationship information between different entities, that is, the relationship information between different power grid devices.

[0105] Specifically, the process involves: obtaining equipment association information between each coupled and grouped power grid device and the target power grid device from a pre-defined knowledge graph; extracting features from each equipment association information to obtain equipment association features corresponding to each coupled and grouped power grid device; normalizing each equipment association feature to obtain feature weights for matching the features of the coupled and grouped power grid device; weighting each feature of the coupled and grouped power grid device according to its feature weights to obtain weighted features; fusing the weighted features to obtain fused equipment features, where the feature fusion method can be concatenation or summation; extracting features from the second equipment information of the target power grid device to obtain second equipment features; concatenating the fused equipment features and the second equipment features to obtain target concatenated features; mapping the target concatenated features to corresponding feature classification identifiers according to a pre-defined classifier; and using the calculation model type corresponding to the feature classification identifier as the target power grid device for N-1 calculation. In this embodiment, the association information between the target power grid equipment and each coupled grouped power grid equipment in the preset knowledge graph is used to evaluate the type of N-1 calculation model for the target power grid equipment. The evaluation basis is richer and can improve the evaluation accuracy of the type of N-1 calculation model for the target power grid equipment.

[0106] It should be noted that there are usually multiple types of N-1 computation models. Different types of N-1 computation models are suitable for different N-1 computation scenarios. For example, the N-1 computation model mentioned above can be an N-1 computation module without complementary relationships or an N-1 computation model with complementary relationships greater than 2, etc.

[0107] Step 308: Based on the target N-1 calculation model corresponding to the N-1 calculation model type, perform equipment fault response detection on the target power grid equipment based on N-1 calculation.

[0108] As an example, step 308 includes: obtaining a target N-1 calculation model under the N-1 calculation model type; performing N-1 calculation on the target power grid equipment by inputting the second equipment information of the target power grid equipment and the grouping equipment information of each coupled grouped power grid equipment into the target N-1 calculation model; and performing equipment fault response detection on the target power grid equipment based on N-1 calculation.

[0109] In this embodiment, the grouping equipment information of each coupled and grouped power grid device is first obtained from a preset knowledge graph. Then, feature extraction is performed on each grouping equipment information to obtain multiple grouping equipment features. Based on the features of each grouping equipment, the type of N-1 calculation model for the target power grid device is detected. Thus, based on the type of N-1 calculation model for the target power grid device, the corresponding target N-1 calculation model is accurately matched for the target power grid device. Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the second equipment information of the target power grid device, and the grouping equipment information of each coupled and grouped power grid device, the device fault response detection of the target power grid device is performed based on N-1 calculation. On the one hand, it realizes the screening of effective data (second equipment information and grouping equipment information) from massive data for power grid fault response detection. On the other hand, it also realizes the provision of a matching target N-1 calculation model for effective data for power grid fault response detection, thus improving the accuracy of power grid fault response detection.

[0110] As an example, power grid fault response detection methods also include:

[0111] Obtain power grid data text, which includes equipment parameter text of multiple basic power grid devices; detect the pairwise relationships between the multiple basic power grid devices based on their equipment parameter text, and obtain the relationship detection results; construct a pre-defined knowledge graph based on the relationship detection results and the equipment parameter text of the multiple basic power grid devices.

[0112] In this embodiment, the power grid equipment data collected is usually in the form of power grid data text. Therefore, a preset knowledge graph can be constructed by performing some text processing on the power grid data text.

[0113] Specifically, the process involves acquiring power grid data text, which includes equipment parameter text from multiple power grid devices; for each equipment parameter text, text processing is performed to obtain processed text, where text processing methods can include text segmentation, synonym replacement, and text classification; text features are extracted from each processed text to obtain multiple text features; based on these multiple text features, the relationships between power grid devices are detected to obtain relationship detection results; based on the multiple processed texts, multiple entities in a pre-defined knowledge graph are constructed, where each entity corresponds to a power grid device, and the entity information contained in each entity is the processed text; based on the relationship detection results, connection edges are constructed for each entity to obtain the pre-defined knowledge graph, where the connection edges between entities represent the relationships between entities.

[0114] In this embodiment, a preset knowledge graph corresponding to each power grid device can be constructed, laying the foundation for power grid fault response detection of the target power grid device.

[0115] In a complete embodiment, firstly, the target entity corresponding to the target power grid device is located from a preset knowledge graph based on the device number of the target power grid device; multiple associated entities that have a connection relationship with the target entity are detected in the preset knowledge graph, and the power grid devices corresponding to the associated entities are regarded as associated power grid devices; the power grid device parameter information corresponding to each associated power grid device is extracted from the preset graph as the first device information.

[0116] Furthermore, the first device information is one-hot encoded to obtain the first one-hot encoded feature, and the first one-hot encoded feature is dimensionality reduced to obtain the first device feature corresponding to the associated power grid device; the second device information of the target power grid device is one-hot encoded to obtain the second one-hot encoded feature of the target power grid device, and the second one-hot encoded feature is dimensionality reduced to obtain the second device feature of the target power grid device; the feature distance between the first device feature and the second device feature is calculated; this feature distance is mapped to the device correlation value between the target power grid device and the associated power grid device, wherein the feature distance is used to characterize the feature similarity between the first device feature and the second device feature. The larger the feature distance, the lower the feature similarity and the smaller the device correlation value. The lower the device correlation, the smaller the feature distance, and the higher the feature similarity. The larger the device correlation value, the higher the device correlation.

[0117] Furthermore, if the device correlation value is greater than the preset correlation threshold, it indicates that after the target power grid device fails, the associated power grid device corresponding to the device correlation value is likely to be affected by the correlation. Therefore, the associated power grid device corresponding to the device correlation value is marked as the first type of associated power grid device. If the device correlation value is not greater than the preset correlation threshold, it indicates that after the target power grid device fails, the associated power grid device corresponding to the device correlation value is likely to be affected by the correlation. Therefore, the associated power grid device corresponding to the device correlation value is marked as the second type of associated power grid device. All first type of associated power grid devices are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0118] Furthermore, the marshalling equipment information for each coupled and grouped power grid device is obtained from a pre-defined knowledge graph. For each marshalling equipment information, one-hot encoding is performed to obtain one-hot encoded features. These one-hot encoded features are then input into a feature extraction model for feature dimensionality reduction, outputting the marshalling equipment features. The marshalling equipment features can be embedded feature vectors, i.e. Vector; Obtain equipment association information between each coupled grouped power grid device and the target power grid device from a preset knowledge graph; Extract features from each equipment association information to obtain equipment association features corresponding to each coupled grouped power grid device; Normalize each equipment association feature to obtain feature weights for matching grouped equipment features corresponding to each coupled grouped power grid device; Weight each grouped equipment feature according to its feature weights to obtain weighted grouped equipment features; Fuse the weighted grouped equipment features to obtain fused equipment features, where the feature fusion method can be concatenation or summation, etc.; Extract features from the second equipment information of the target power grid device to obtain second equipment features; Concatenate the fused equipment features and the second equipment features to obtain target concatenated features; Map the target concatenated features to corresponding feature classification identifiers according to a preset classifier; Use the calculation model type corresponding to the feature classification identifier as the target power grid device for N-1 calculation.

[0119] Furthermore, the target N-1 calculation model under the N-1 calculation model type is obtained; by inputting the second equipment information of the target power grid equipment and the grouping equipment information of each coupled grouped power grid equipment into the target N-1 calculation model for N-1 calculation, the equipment fault response detection of the target power grid equipment based on N-1 calculation is performed.

[0120] In this embodiment, on the one hand, it enables the selection of effective data (second equipment information and grouping equipment information) from massive amounts of data for power grid fault response detection; on the other hand, it also enables the provision of a matching target N-1 calculation model for effective data for power grid fault response detection, thus improving the accuracy of power grid fault response detection.

[0121] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0122] Based on the same inventive concept, this application also provides a power grid fault response detection device for implementing the power grid fault response detection method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more power grid fault response detection device embodiments provided below can be found in the limitations of the power grid fault response detection method described above, and will not be repeated here.

[0123] In one exemplary embodiment, such as Figure 3 As shown, a power grid fault response detection device is provided, comprising: an acquisition module 402, a first detection module 404, a second detection module 406, and an equipment fault response detection module 408, wherein:

[0124] The acquisition module 402 is used to acquire the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a preset knowledge graph.

[0125] The first detection module 404 is used to detect the device correlation between the target power grid device and each of the associated power grid devices based on the first device information and the second device information corresponding to the target power grid device, and obtain the correlation detection result.

[0126] The second detection module 406 is used to detect the set of coupling and grouping devices corresponding to the target power grid device from each of the associated power grid devices based on the correlation detection results.

[0127] The equipment fault response detection module 408 is used to perform equipment fault response detection on the target power grid equipment based on N-1 calculation according to the coupled grouped equipment set.

[0128] In one embodiment, the coupled marshalling equipment set includes multiple coupled marshalling power grid devices; the equipment fault response detection module is further configured to:

[0129] Obtain the marshalling equipment information for each of the aforementioned coupled marshalling network devices from a pre-defined knowledge graph;

[0130] Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features;

[0131] Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0132] Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0133] In one embodiment, the device fault response detection module is further configured to:

[0134] Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph;

[0135] Based on the device association information, a corresponding feature weight is matched for each of the grouping device features;

[0136] Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features;

[0137] Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

[0138] In one embodiment, the first detection module is further configured to:

[0139] The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment;

[0140] The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment;

[0141] Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

[0142] In one embodiment, the correlation detection result includes a device correlation value; the second detection module is further configured to:

[0143] If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device;

[0144] If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device;

[0145] All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0146] In one embodiment, the device further includes:

[0147] A knowledge graph construction module is used to obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices;

[0148] Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained.

[0149] Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

[0150] Each module in the aforementioned power grid fault response detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0151] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a power grid fault response detection method.

[0152] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0153] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0154] Obtain the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a pre-defined knowledge graph;

[0155] Based on the first device information and the second device information corresponding to the target power grid device, the device correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result.

[0156] Based on the correlation detection results, detect the set of coupling and grouping devices corresponding to the target power grid device from each of the associated power grid devices;

[0157] Based on the set of coupled grouped equipment, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0158] In one embodiment, the coupled marshalling equipment set includes multiple coupled marshalling power grid devices; the processor, when executing the computer program, further implements the following steps:

[0159] Obtain the marshalling equipment information for each of the aforementioned coupled marshalling network devices from a pre-defined knowledge graph;

[0160] Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features;

[0161] Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0162] Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0163] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0164] Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph;

[0165] Based on the device association information, a corresponding feature weight is matched for each of the grouping device features;

[0166] Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features;

[0167] Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

[0168] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0169] The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment;

[0170] The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment;

[0171] Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

[0172] In one embodiment, the correlation detection result includes a device correlation value; the processor, when executing the computer program, further implements the following steps:

[0173] If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device;

[0174] If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device;

[0175] All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0176] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0177] Obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices;

[0178] Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained.

[0179] Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0181] Obtain the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a pre-defined knowledge graph;

[0182] Based on the first device information and the second device information corresponding to the target power grid device, the device correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result.

[0183] Based on the correlation detection results, detect the set of coupling and grouping devices corresponding to the target power grid device from each of the associated power grid devices;

[0184] Based on the set of coupled grouped equipment, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0185] In one embodiment, the coupled marshalling equipment set includes multiple coupled marshalling power grid devices; when the computer program is executed by a processor, it further implements the following steps:

[0186] Obtain the marshalling equipment information for each of the aforementioned coupled marshalling network devices from a pre-defined knowledge graph;

[0187] Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features;

[0188] Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0189] Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0190] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0191] Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph;

[0192] Based on the device association information, a corresponding feature weight is matched for each of the grouping device features;

[0193] Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features;

[0194] Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

[0195] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0196] The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment;

[0197] The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment;

[0198] Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

[0199] In one embodiment, the correlation detection result includes a device correlation value; when the computer program is executed by a processor, it further performs the following steps:

[0200] If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device;

[0201] If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device;

[0202] All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0203] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0204] Obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices;

[0205] Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained.

[0206] Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

[0207] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0208] Obtain the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a pre-defined knowledge graph;

[0209] Based on the first device information and the second device information corresponding to the target power grid device, the device correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result.

[0210] Based on the correlation detection results, detect the set of coupling and grouping devices corresponding to the target power grid device from each of the associated power grid devices;

[0211] Based on the set of coupled grouped equipment, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0212] In one embodiment, the coupled marshalling equipment set includes multiple coupled marshalling power grid devices; when the computer program is executed by a processor, it further implements the following steps:

[0213] Obtain the marshalling equipment information for each of the aforementioned coupled marshalling network devices from a pre-defined knowledge graph;

[0214] Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features;

[0215] Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation;

[0216] Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

[0217] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0218] Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph;

[0219] Based on the device association information, a corresponding feature weight is matched for each of the grouping device features;

[0220] Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features;

[0221] Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

[0222] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0223] The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment;

[0224] The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment;

[0225] Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

[0226] In one embodiment, the correlation detection result includes a device correlation value; when the computer program is executed by a processor, it further performs the following steps:

[0227] If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device;

[0228] If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device;

[0229] All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

[0230] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0231] Obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices;

[0232] Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained.

[0233] Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

[0234] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0235] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0236] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power grid fault response detection method, characterized in that, The method includes: Obtain the first device information of multiple associated power grid devices corresponding to the target power grid device from a pre-defined knowledge graph; Based on the first device information and the second device information corresponding to the target power grid device, the device correlation between the target power grid device and each of the associated power grid devices is detected to obtain the correlation detection result. Based on the correlation detection results, a set of coupled marshalling equipment corresponding to the target power grid equipment is detected from each of the associated power grid equipment, wherein the set of coupled marshalling equipment includes multiple coupled marshalling power grid equipment; The grouping equipment information of each of the coupled grouping power grid devices is obtained from a preset knowledge graph, wherein the connection edges between entities in the preset knowledge graph are used to describe the association information between different power grid devices; Feature extraction is performed on each of the aforementioned grouping equipment information to obtain multiple grouping equipment features; Based on the characteristics of each of the grouping devices, detect the type of N-1 calculation model for the target power grid equipment in the N-1 calculation; Based on the target N-1 calculation model corresponding to the N-1 calculation model type, the target power grid equipment is subjected to equipment fault response detection based on N-1 calculation.

2. The method according to claim 1, characterized in that, The step of detecting the N-1 calculation model type for the target power grid equipment based on the characteristics of each of the grouping devices includes: Detect the device association information between each of the coupled grouped power grid devices and the target power grid device from the preset knowledge graph; Based on the device association information, a corresponding feature weight is matched for each of the grouping device features; Based on the feature weights matched to the features of each grouping device, the features of each grouping device are weighted and fused to obtain the fused device features; Based on the characteristics of the fused equipment and the second equipment characteristics corresponding to the second equipment information, the type of N-1 calculation model for the target power grid equipment is detected.

3. The method according to claim 1, characterized in that, The step of detecting the device correlation between the target power grid device and each of the associated power grid devices based on the first device information and the second device information corresponding to the target power grid device includes: The first equipment information is subjected to equipment feature extraction to obtain the first equipment feature corresponding to the associated power grid equipment; The second equipment information of the target power grid equipment is subjected to equipment feature extraction to obtain the second equipment feature corresponding to the target power grid equipment; Based on the feature similarity between the first device feature and the second device feature, the device correlation between the target power grid device and the associated power grid device is detected.

4. The method according to claim 1, characterized in that, The correlation detection result includes equipment correlation values; the step of detecting the set of coupled marshalling equipment corresponding to the target power grid equipment from each of the associated power grid equipment based on the correlation detection result includes: If the device correlation value is greater than a preset correlation threshold, the associated power grid device corresponding to the device correlation value is marked as a first type of associated power grid device; If the device correlation value is not greater than a preset correlation threshold, then the associated power grid device corresponding to the device correlation value is marked as a second type of associated power grid device; All associated power grid devices of the first type are collectively set as the set of coupled grouping devices corresponding to the target power grid device.

5. The method according to claim 1, characterized in that, The method further includes: Obtain power grid data text, wherein the power grid data text includes equipment parameter text of multiple basic power grid devices; Based on the equipment parameter text of the multiple basic power grid devices, the correlation between each pair of the multiple basic power grid devices is detected, and the correlation detection result is obtained. Based on the correlation detection results and the equipment parameter text of the multiple basic power grid devices, a preset knowledge graph is constructed.

6. A power grid fault response detection device, characterized in that, The device includes: The acquisition module is used to obtain the first equipment information of multiple associated power grid devices corresponding to the target power grid device from a preset knowledge graph; The first detection module is used to detect the device correlation between the target power grid equipment and each of the associated power grid equipment based on the first device information and the second device information corresponding to the target power grid equipment, and to obtain the correlation detection result. The second detection module is used to detect the set of coupled and grouped devices corresponding to the target power grid device from each of the associated power grid devices according to the correlation detection results, wherein the set of coupled and grouped devices includes multiple coupled and grouped power grid devices; The equipment fault response detection module is used to obtain the marshalling equipment information of each coupled marshalling power grid device from a preset knowledge graph, wherein the connection edges between entities in the preset knowledge graph are used to describe the association information between different power grid devices; to extract features from each of the marshalling equipment information to obtain multiple marshalling equipment features; to detect the N-1 calculation model type of the target power grid device for N-1 calculation based on each of the marshalling equipment features; and to perform equipment fault response detection based on N-1 calculation on the target power grid device according to the target N-1 calculation model corresponding to the N-1 calculation model type.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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