Resource detection method and device of power equipment, electronic equipment and medium

Abstract

The invention provides a resource detection method and device for power equipment, electronic equipment and a medium, and can utilize a material fluctuation prediction model to perform fluctuation analysis on material resource data corresponding to the power equipment to obtain a fluctuation analysis result; matching the adaptive parameter information corresponding to each to-be-detected target with the condition information of the corresponding position to obtain a matching result; obtaining abnormal execution data of each to-be-detected target for the power equipment; an abnormal degree index is obtained through abnormal degree analysis by using a support vector machine model; each to-be-detected target corresponds to a target resource value of the power equipment; therefore, the fluctuation analysis result, the matching result, the abnormal execution data, the abnormal degree index and the target resource value of the to-be-detected target can be combined together to determine the resource detection result, the resource corresponding to the highest resource detection result screened based on the resource detection result is used for determining the target, the accuracy is higher, and the requirements of electronic equipment are better met.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a resource detection method, device, electronic device and medium for power equipment. Background Art

[0002] Resource detection of power equipment is generally performed directly according to the corresponding resource values, and the detection target with the largest resource value is selected as the final result.

[0003] However, detection based only on resource values ​​is not accurate enough, so how to combine various detection schemes to screen the most suitable detection targets for electronic devices has become a problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the purpose of the present application is to propose a resource detection method, device, electronic device and medium for power equipment to solve or partially solve the above-mentioned technical problems.

[0005] Based on the above purpose, the present application provides a resource detection method for power equipment, comprising:

[0006] Determine a plurality of targets to be detected corresponding to the electric power equipment;

[0007] Obtaining material resource data corresponding to the power equipment, inputting the material resource data into a pre-built material fluctuation prediction model to perform fluctuation analysis on the material resource quantity, and obtaining a fluctuation analysis result, wherein the material fluctuation prediction model is a model pre-built based on a neural network model, and the material fluctuation prediction model can analyze the fluctuation of the material resource quantity corresponding to the material resource data;

[0008] For each target to be detected: determine the position information corresponding to the target to be detected, retrieve the condition information of the position information, obtain the adaptation parameter information of the target to be detected, match the adaptation parameter information with the condition information, and obtain the matching result of the target to be detected;

[0009] Acquire abnormal execution data of each target to be detected on the power equipment;

[0010] For each target to be detected: collecting the index parameter information of the target to be detected, inputting the index parameter information into a pre-built support vector machine model to perform abnormality analysis, and obtaining an abnormality index;

[0011] Obtain target resource values ​​for each target to be detected for the power equipment;

[0012] For each target to be detected, the following steps are performed: combining the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected to obtain the resource detection result;

[0013] The various targets to be detected are sorted according to the resource detection results, and the target to be detected with the highest resource detection result is selected from the sorting as the resource determination target, and the resource determination target is output and displayed.

[0014] Based on the same inventive concept, the present application also provides a resource detection device for electric power equipment, comprising:

[0015] A to-be-detected target determination module is configured to determine a plurality of to-be-detected targets corresponding to the electric power equipment;

[0016] A fluctuation analysis module is configured to obtain material resource data corresponding to the power equipment, input the material resource data into a pre-built material fluctuation prediction model to perform fluctuation analysis on the material resource quantity, and obtain a fluctuation analysis result, wherein the material fluctuation prediction model is a model pre-built based on a neural network model, and the material fluctuation prediction model can analyze the fluctuation of the material resource quantity corresponding to the material resource data;

[0017] The matching module is configured to, for each target to be detected, determine the position information corresponding to the target to be detected, retrieve the condition information of the position information, obtain the adaptation parameter information of the target to be detected, match the adaptation parameter information with the condition information, and obtain the matching result of the target to be detected;

[0018] An abnormal execution acquisition module is configured to acquire abnormal execution data of each target to be detected for the electric power equipment;

[0019] The abnormality degree analysis module is configured to collect the index parameter information of each target to be detected, input the index parameter information into a pre-built support vector machine model to perform abnormality degree analysis, and obtain an abnormality degree index;

[0020] A resource data acquisition module is configured to acquire a target resource value of each target to be detected for the electric power equipment;

[0021] The resource detection module is configured to perform, for each target to be detected, combining the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected to obtain a resource detection result;

[0022] The resource determination module is configured to sort the various targets to be detected according to the resource detection results, select the target to be detected with the highest resource detection result from the sorting as the resource determination target, and output and display the resource determination target.

[0023] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0024] Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the method as described above.

[0025] From the above, it can be seen that the resource detection method, device, electronic device and medium of the power equipment provided by the present application can use the material fluctuation prediction model to perform fluctuation analysis on the material resource data corresponding to the power equipment and obtain the fluctuation analysis results; after matching the corresponding adaptation parameter information of each target to be detected with the condition information of its corresponding position, the matching results of each target to be detected are obtained; and the abnormal execution data of each target to be detected for the power equipment are obtained; and the abnormal degree index of each target to be detected is obtained after the abnormal degree analysis using the support vector machine model; and the target resource value of each target to be detected for the power equipment; in this way, the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected can be combined to determine the resource detection result, so that the resource detection result is more in line with various detection-related data, and the resource corresponding to the highest resource detection result screened based on the resource detection result is determined with higher accuracy and more in line with the needs of electronic equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A flow chart of a method for detecting resources of an electric power device according to an embodiment of the present application;

[0028] Figure 2 This is a structural block diagram of a resource detection device for electric power equipment according to an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0031] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0032] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0033] The present application embodiment proposes a method for detecting resources of an electric power device, such as Figure 1 As shown, including:

[0034] Step 101: determine a plurality of targets to be detected corresponding to the electric power equipment.

[0035] During specific implementation, the target to be detected includes at least one of the following: a user, an enterprise, or a team.

[0036] Step 102, obtaining material resource data corresponding to the power equipment, inputting the material resource data into a pre-constructed material fluctuation prediction model to perform a fluctuation analysis of the material resource quantity, and obtaining a fluctuation analysis result, wherein the material fluctuation prediction model is a model pre-constructed based on a neural network model, and the material fluctuation prediction model can analyze the fluctuation of the material resource quantity corresponding to the material resource data.

[0037] In a specific implementation, the material resource data is at least one of the name, type, price, quantity, and source information of the raw materials required for processing tasks related to the power equipment.

[0038] Step 103, for each target to be detected: determine the position information corresponding to the target to be detected, retrieve the condition information of the position information, obtain the adaptation parameter information of the target to be detected, match the adaptation parameter information with the condition information, and obtain the matching result of the target to be detected.

[0039] In specific implementation, since different locations have different requirements for the conditions of the target to be detected, the adaptation parameter information of the target to be detected will be matched with the condition information of its location, and then the matching results of each target to be detected will be obtained. The matching results represent the degree to which the target to be detected meets the condition information of its location.

[0040] Step 104: Acquire abnormal execution data of each target to be detected for the electric power equipment.

[0041] In specific implementation, if there are records of processing power equipment in the history of each target to be detected, the records will be retrieved to see whether there are abnormal situations that cannot be executed or situations where the processing can be executed smoothly when the processing is performed on the power equipment. These are all abnormal execution data.

[0042] Step 105 , for each target to be detected: collect index parameter information of the target to be detected, input the index parameter information into a pre-built support vector machine model to perform abnormality analysis, and obtain an abnormality index.

[0043] In specific implementation, the support vector machine (SVM) model is a pre-built abnormality index that can predict whether interruptions, errors, abnormal stops, and other abnormal situations will occur when executing the current power equipment's tasks based on some indicator parameter information of each target to be detected.

[0044] The index parameter information includes: the resource volume of the target to be detected, the size of the area, the type of tools, the number of each type of tools, the type of materials and the number of each type of materials, etc.

[0045] Step 106, obtaining the target resource value of each target to be detected for the electric power equipment.

[0046] In a specific implementation, the target resource value is the resource demand provided by the target to be detected to execute the task of the power equipment.

[0047] Step 107, for each target to be detected, is performed: the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected are combined to obtain a resource detection result.

[0048] Step 108, sorting the various targets to be detected according to the resource detection results, selecting the target to be detected with the highest resource detection result from the sorting as the resource determination target, and outputting the resource determination target for display.

[0049] Through the above scheme, the material fluctuation prediction model can be used to perform fluctuation analysis on the material resource data corresponding to the power equipment and obtain the fluctuation analysis results; after matching the corresponding adaptation parameter information of each target to be detected with the condition information of its corresponding position, the matching results of each target to be detected are obtained; and the abnormal execution data of each target to be detected for the power equipment are obtained; and after the abnormality degree analysis is performed using the support vector machine model, the abnormality degree index of each target to be detected is obtained; and the target resource value of each target to be detected for the power equipment is obtained; in this way, the fluctuation analysis result, matching result, abnormal execution data, abnormality degree index and target resource value of the target to be detected can be combined to determine the resource detection result, so that the resource detection result is more in line with various detection-related data, and the resource corresponding to the highest resource detection result screened based on the resource detection result is determined with higher accuracy and more in line with the needs of electronic equipment.

[0050] In some embodiments, the process of constructing the material fluctuation prediction model includes:

[0051] Step A1, obtaining a plurality of historical material resource data corresponding to the power equipment arranged in chronological order in a first time period in history, and graphing the plurality of historical material resource data to obtain a first curve graph.

[0052] In a specific implementation, each historical material resource data is classified according to raw materials, for example, classified into: raw material (1), raw material (2), raw material (3), raw material (4).

[0053] Then, for each category of raw materials corresponding to the historical material resource data, a graph is drawn, and each category of raw materials corresponds to a first curve graph. The first curve graph is a curve graph formed by taking time as the horizontal axis and the value of the corresponding historical material resource data as the vertical axis, and the trend of the historical material resources of the raw materials of this category over time can be learned from the first curve graph.

[0054] Step A2, determining the fluctuation index corresponding to each historical material resource data according to the first curve chart, and using the fluctuation index to mark each historical material resource data, combining multiple marked historical material resource data together to form a first training set, wherein the first training set includes multiple first training samples, and each first training sample is a marked historical material resource data.

[0055] In a specific implementation, the curvature corresponding to each time point is calculated according to the first curve graph, and the curvature is used as a fluctuation index. A positive value of the fluctuation index indicates an increasing fluctuation, and a negative value of the fluctuation index indicates a decreasing fluctuation.

[0056] In this way, each historical material resource data can be marked with a corresponding fluctuation index in chronological order, and these marked historical material resource data can be combined as first training samples into a first training set.

[0057] Step A3, constructing a neural network model including an input layer, multiple hidden layers and an output layer.

[0058] In a specific implementation, the input layer includes multiple input ports, each of which corresponds to a type of raw material and is used to receive historical material resource data of the raw material of the type. One historical material resource data may correspond to multiple raw materials.

[0059] The hidden layers include a plurality of hidden layers, each of which corresponds to a category of raw materials and is connected to a corresponding input port, and is used for performing fluctuation analysis on the historical material resource data of the raw materials of the category received by the input port.

[0060] The output layer is connected to each hidden layer to receive the fluctuation analysis results obtained by each hidden layer and integrate these fluctuation analysis results.

[0061] Step A4, input the first training sample in the first training set from the input layer to the neural network model in chronological order. After the input layer preprocesses the first training sample, the preprocessed first training sample is input to each hidden layer for fluctuation analysis. Each hidden layer sends the analysis result to the output layer, and the output layer is used to organize and output the fluctuation analysis training results.

[0062] In the specific implementation, the first training sample to be input is determined in chronological order. After the input layer determines the input port corresponding to the first training sample, it reaches the corresponding hidden layer, and is sent to the output layer after performing fluctuation analysis using the corresponding hidden layer. The output layer is used to integrate the fluctuation analysis results of each hidden layer to obtain the final fluctuation analysis training result output.

[0063] Step A5, determine the corresponding loss value according to the difference between the fluctuation analysis training result and the marked fluctuation index, determine the adjustment amount of each layer parameter according to the loss value, adjust the parameters of each layer of the neural network model according to the adjustment amount of each layer parameter, and complete the training process for the first training sample.

[0064] In specific implementation, each time a first training sample is trained, the difference between its fluctuation analysis training result and the marked fluctuation index is determined. For example, the absolute value of the difference between the two is calculated as the loss value. If the loss value is larger, the corresponding parameter adjustment amount is also larger, and there is a positive correlation.

[0065] In this way, the neural network model is adjusted once each time a first training sample is trained, and when the next first training sample is trained, the previously adjusted neural network model is used to continue training and adjusting.

[0066] Step A6, after determining that all first training samples in the first training set have been trained, the finally obtained neural network model is used as the material fluctuation prediction model.

[0067] In specific implementation, after all the first training samples in the first training set are trained, the neural network model obtained after the last first training sample is trained and adjusted will be used as the material fluctuation prediction model.

[0068] Through the above scheme, a material fluctuation prediction model is obtained which can accurately analyze the fluctuation of material resource data.

[0069] In some embodiments, step A6 includes:

[0070] Step A61, after determining that all first training samples in the first training set have been trained, obtain a plurality of historical material resource data corresponding to the power equipment arranged in chronological order in the second time period in history, and graph the plurality of historical material resource data to obtain a second curve graph.

[0071] The process of drawing the second curve graph is the same as that of drawing the first curve graph, and will not be described again here.

[0072] Step A62, determine the volatility index corresponding to each historical material resource data based on the second curve chart, and use the volatility index to mark each historical material resource data in the second time period, and combine multiple marked historical material resource data together to form a first test set, wherein the first test set includes multiple first test samples, and each first test sample is the historical material resource data in the second time period after marking.

[0073] Step A63, input each first test sample in the first test set into the finally obtained neural network model in chronological order to perform fluctuation analysis test processing, and obtain fluctuation analysis test results.

[0074] Step A64, comparing the fluctuation analysis test results corresponding to each first test sample with the marked fluctuation index, and calculating the test accuracy rate corresponding to the test process.

[0075] Step A65, in response to determining that the test accuracy is greater than or equal to the accuracy threshold, the finally obtained neural network model is used as the material fluctuation prediction model.

[0076] In specific implementation, if the test accuracy is less than the accuracy threshold, the first test set will be used as the first training set to continue training the final neural network model according to the process of steps A1 to A6, and the first test set will be re-determined to be tested according to the process of the above steps A61 to A64, and it will be repeated until it is determined that the test accuracy is greater than or equal to the accuracy threshold, and the final neural network model will be used as the material fluctuation prediction model.

[0077] Through the above scheme, the accuracy of the final neural network model is tested using the first test set, which can make the final material fluctuation prediction model predict fluctuations more accurately.

[0078] In some embodiments, the support vector machine model construction process includes:

[0079] Step B1, obtaining multiple historical indicator parameter information of various targets to be detected, and marking the corresponding abnormality degree for each historical indicator parameter information.

[0080] In specific implementation, the historical indicator parameter information includes: the resource volume, area size, tool type, quantity of each type of tool, material type and quantity of each type of material of each target to be detected in the historical time period.

[0081] And the degree of abnormality of each historical indicator parameter information will be known in advance (for example, abnormality is 0.5 and normality is 1).

[0082] Step B2, normalizing the multiple marked historical indicator parameter information of each target to be detected to obtain a normalized result.

[0083] During specific implementation, in order to ensure the consistency of each historical indicator parameter information, it is normalized.

[0084] Step B3: taking the normalization result as the second training set, wherein the second training set includes a plurality of second training samples, and each second training sample is historical indicator parameter information of the abnormality degree after normalization.

[0085] Step B4, constructing an initial support vector machine model.

[0086] In a specific implementation, the initial support vector machine is: Support Vector Machine, SVM, a generalized linear classifier that performs binary classification on data in a supervised learning manner.

[0087] Step B5: input each second training sample in the second training set into the initial support vector machine model for abnormality analysis to obtain the abnormality degree of the sample.

[0088] Step B6, determine the corresponding loss value according to the difference between the abnormality degree of the sample and the abnormality degree of the mark, determine the adjustment amount of each layer parameter of the initial support vector machine model according to the loss value, adjust the initial support vector machine model according to the adjustment amount of each layer parameter, and complete the training process for the second training sample.

[0089] In specific implementation, the loss value is calculated by performing hinge loss function calculation based on the abnormality degree of the sample and the abnormality degree of the label to obtain the loss value.

[0090] The hinge loss function has a relatively small amount of calculation and is relatively fast in the training process, which can better meet the needs of real-time prediction. In addition, the hinge loss function can also be applied to different types of data, such as discrete, continuous, and binary. In short, the hinge loss function is a very effective classification loss function that can better simulate data, improve the generalization ability of the model, and effectively suppress overfitting.

[0091] Step B7: After all the second training samples in the second training set are determined to have been trained, the final initial support vector machine model is used as the support vector machine model.

[0092] In specific implementation, according to the process of steps B1 to B3 above, a second test set is re-collected, and the test set includes multiple second test samples, each of which is the historical indicator parameter information of the abnormal degree after normalization. The second test set can be the same as or different from the second training set.

[0093] Each second test sample in the second test set is input into the final initial support vector machine model for processing, the processing result is compared with the abnormal degree of the mark, and the test accuracy of the support vector machine model corresponding to the second test set is calculated. If the test accuracy is less than the accuracy threshold, the second test set is used as the second training set to continue training the final initial support vector machine model according to the process of steps B1 to A7, and the second test set is re-determined for testing, and the process is repeated until the test accuracy is determined to be greater than or equal to the accuracy threshold, and the final initial support vector machine is used as the support vector machine model.

[0094] Through the above scheme, a support vector machine model of anomaly program capable of accurately analyzing the index parameter information of the corresponding target to be detected is obtained.

[0095] In some embodiments, step B5 includes:

[0096] Step B51, adding a linear kernel function processing layer, a polynomial kernel function processing layer, a Gaussian kernel function processing layer and an activation processing layer to the initial support vector machine model.

[0097] Step B52, input each second training sample in the second training set into the initial support vector machine model and process it in sequence using a linear kernel function processing layer, a polynomial kernel function processing layer, a Gaussian kernel function processing layer and an activation processing layer.

[0098] In a specific implementation, the linear kernel function processing layer is used to analyze the linear performance of the second training sample and determine the linear value corresponding to the abnormality degree of the second training sample;

[0099] The polynomial kernel function processing layer is used to analyze the performance of the polynomial function of the second training sample and determine the multinomial values ​​corresponding to the second training sample and the abnormality degree;

[0100] The Gaussian kernel function processing layer is used to analyze the performance of the Gaussian distribution of the second training sample and determine the Gaussian value corresponding to the abnormality degree of the second training sample;

[0101] The activation processing layer is used to analyze the activation performance of the second training sample and determine the activation value corresponding to the abnormality degree of the second training sample.

[0102] Step B53, integrating the processing results of each processing layer, determining the abnormal separation plane according to the integration result, calculating the distance of the second training sample from the abnormal separation plane, and determining the degree of sample abnormality according to the distance.

[0103] In specific implementation, the functional relationship between the linear value and the degree of abnormality, the functional relationship between the multinomial value and the degree of abnormality, the functional relationship between the Gaussian value and the degree of abnormality, and the functional relationship between the activation value and the degree of abnormality are integrated to determine a common plane functional relationship, and the plane functional relationship is used as the abnormal separation plane. In this way, the distance of each second training sample from the abnormal separation plane can be calculated, and then the degree of sample abnormality can be obtained according to the size of the distance.

[0104] Through the above scheme, the process of the degree of abnormality of the sample can be accurately determined.

[0105] In some embodiments, the process performed for each target to be detected in step 107 includes:

[0106] Step 1071, determining the fluctuation degree value corresponding to the fluctuation analysis result of the target to be detected.

[0107] Step 1072, determining the matching degree value corresponding to the matching result of the target to be detected.

[0108] Step 1073: Determine the abnormal execution value corresponding to the abnormal execution data of the target to be detected.

[0109] Step 1074, determining the abnormality level value corresponding to the abnormality level index of the target to be detected.

[0110] Step 1075, multiplying the fluctuation degree value, the matching degree value, the abnormal execution value, the abnormal degree value and the target resource data, and taking the multiplication result as the resource detection result.

[0111] Through the above scheme, the resource detection result can be obtained by combining the fluctuation degree value, matching degree value, abnormal execution value, abnormal degree value and target resource data, so that the most suitable resource determination target can be more accurately screened out from multiple targets to be detected based on the resource detection result.

[0112] In some embodiments, the process performed for each target to be detected in step 107 includes:

[0113] Step 1071 ′, determining the fluctuation degree value corresponding to the fluctuation analysis result of the target to be detected.

[0114] Step 1072', determining the matching degree value corresponding to the matching result of the target to be detected.

[0115] Step 1073', determining the abnormal execution value corresponding to the abnormal execution data of the target to be detected.

[0116] Step 1074', determining the abnormality level value corresponding to the abnormality level index of the target to be detected.

[0117] Step 1075', weighted summing the fluctuation degree value, matching degree value, abnormal execution value, abnormal degree value and target resource data is performed, and the weighted summing result is used as the resource detection result.

[0118] Through the above scheme, the resource detection result can be obtained by combining the fluctuation degree value, matching degree value, abnormal execution value, abnormal degree value and target resource data, so that the most suitable resource determination target can be more accurately screened out from multiple targets to be detected based on the resource detection result.

[0119] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0120] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a resource detection device for electric power equipment.

[0122] refer to Figure 2 , the device comprises:

[0123] A to-be-detected target determination module is configured to determine a plurality of to-be-detected targets corresponding to the electric power equipment;

[0124] A fluctuation analysis module is configured to obtain material resource data corresponding to the power equipment, input the material resource data into a pre-built material fluctuation prediction model to perform fluctuation analysis on the material resource quantity, and obtain a fluctuation analysis result, wherein the material fluctuation prediction model is a model pre-built based on a neural network model, and the material fluctuation prediction model can analyze the fluctuation of the material resource quantity corresponding to the material resource data;

[0125] The matching module is configured to, for each target to be detected, determine the position information corresponding to the target to be detected, retrieve the condition information of the position information, obtain the adaptation parameter information of the target to be detected, match the adaptation parameter information with the condition information, and obtain the matching result of the target to be detected;

[0126] An abnormal execution acquisition module is configured to acquire abnormal execution data of each target to be detected for the electric power equipment;

[0127] The abnormality degree analysis module is configured to: for each target to be detected: collect the index parameter information of the target to be detected, input the index parameter information into a pre-built support vector machine model to perform abnormality degree analysis, and obtain an abnormality degree index;

[0128] A resource data acquisition module is configured to acquire a target resource value of each target to be detected for the electric power equipment;

[0129] The resource detection module is configured to perform, for each target to be detected, combining the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected to obtain a resource detection result;

[0130] The resource determination module is configured to sort the various targets to be detected according to the resource detection results, select the target to be detected with the highest resource detection result from the sorting as the resource determination target, and output and display the resource determination target.

[0131] In some embodiments, the apparatus further comprises a first model building module configured to:

[0132] The process of constructing the material fluctuation prediction model includes:

[0133] Acquire a plurality of historical material resource data corresponding to the electric power equipment arranged in chronological order in a first time period in history, and graph the plurality of historical material resource data to obtain a first curve graph;

[0134] Determine the fluctuation index corresponding to each historical material resource data according to the first curve graph, and use the fluctuation index to mark each historical material resource data, and combine multiple marked historical material resource data to form a first training set, wherein the first training set includes multiple first training samples, each of which is a marked historical material resource data;

[0135] Construct a neural network model including an input layer, multiple hidden layers, and an output layer;

[0136] Input the first training sample in the first training set from the input layer to the neural network model in chronological order. After the input layer preprocesses the first training sample, the preprocessed first training sample is input to each hidden layer for fluctuation analysis. Each hidden layer sends the analysis result to the output layer, and the output layer is used to organize and output the fluctuation analysis training results.

[0137] Determine a corresponding loss value according to the difference between the fluctuation analysis training result and the marked fluctuation index, determine the adjustment amount of each layer parameter according to the loss value, adjust the parameters of each layer of the neural network model according to the adjustment amount of each layer parameter, and complete the training process for the first training sample;

[0138] After all the first training samples in the first training set are trained, the finally obtained neural network model is used as the material fluctuation prediction model.

[0139] In some embodiments, the first model building module is specifically configured to:

[0140] After determining that all first training samples in the first training set have been trained, obtaining a plurality of historical material resource data corresponding to the power equipment arranged in chronological order in the second time period in history, and graphing the plurality of historical material resource data to obtain a second curve graph;

[0141] Determine the fluctuation index corresponding to each historical material resource data according to the second curve graph, and use the fluctuation index to mark each historical material resource data in the second time period, and combine multiple marked historical material resource data together to form a first test set, wherein the first test set includes multiple first test samples, each first test sample is the marked historical material resource data in the second time period;

[0142] Inputting each first test sample in the first test set into the finally obtained neural network model in chronological order to perform fluctuation analysis test processing, and obtaining fluctuation analysis test results;

[0143] Compare the fluctuation analysis test results corresponding to each first test sample with the marked fluctuation index, and calculate the test accuracy rate corresponding to the test process;

[0144] In response to determining that the test accuracy is greater than or equal to the accuracy threshold, the finally obtained neural network model is used as the material fluctuation prediction model.

[0145] In some embodiments, the apparatus further comprises: a second model building module configured to:

[0146] The construction process of the support vector machine model includes:

[0147] Obtain multiple historical indicator parameter information of various targets to be detected, and mark the corresponding abnormality degree for each historical indicator parameter information;

[0148] Normalizing the multiple marked historical indicator parameter information of each target to be detected to obtain a normalized result;

[0149] The normalized result is used as a second training set, wherein the second training set includes a plurality of second training samples, and each second training sample is historical indicator parameter information of a normalized marked abnormality degree;

[0150] Build an initial support vector machine model;

[0151] Inputting each second training sample in the second training set into the initial support vector machine model for abnormality analysis processing to obtain the abnormality degree of the sample;

[0152] Determine a corresponding loss value according to the difference between the abnormality degree of the sample and the abnormality degree of the mark, determine the adjustment amount of each layer parameter of the initial support vector machine model according to the loss value, adjust the initial support vector machine model according to the adjustment amount of each layer parameter, and complete the training process for the second training sample;

[0153] After it is determined that all the second training samples in the second training set have been trained, the final initial support vector machine model is used as the support vector machine model.

[0154] In some embodiments, the second model building module is specifically configured to:

[0155] Adding a linear kernel function processing layer, a polynomial kernel function processing layer, a Gaussian kernel function processing layer, and an activation processing layer to the initial support vector machine model;

[0156] Input each second training sample in the second training set into the initial support vector machine model and process it in sequence using a linear kernel function processing layer, a polynomial kernel function processing layer, a Gaussian kernel function processing layer and an activation processing layer;

[0157] The processing results of each processing layer are integrated, and the abnormal separation plane is determined according to the integration result. The distance of the second training sample deviating from the abnormal separation plane is calculated, and the degree of sample abnormality is determined according to the distance.

[0158] In some embodiments, the resource detection module is specifically configured to:

[0159] For each target to be detected, execute:

[0160] Determine the fluctuation degree value corresponding to the fluctuation analysis result of the target to be detected;

[0161] Determine the matching degree value corresponding to the matching result of the target to be detected;

[0162] Determine the abnormal execution value corresponding to the abnormal execution data of the target to be detected;

[0163] Determine the abnormality level value corresponding to the abnormality level index of the target to be detected;

[0164] The fluctuation degree value, the matching degree value, the abnormal execution value, the abnormal degree value and the target resource data are multiplied, and the product result is used as the resource detection result.

[0165] In some embodiments, the resource detection module is specifically configured to:

[0166] For each target to be detected, execute:

[0167] Determine the fluctuation degree value corresponding to the fluctuation analysis result of the target to be detected;

[0168] Determine the matching degree value corresponding to the matching result of the target to be detected;

[0169] Determine the abnormal execution value corresponding to the abnormal execution data of the target to be detected;

[0170] Determine the abnormality level value corresponding to the abnormality level index of the target to be detected;

[0171] The fluctuation degree value, matching degree value, abnormal execution value, abnormal degree value and target resource data are weighted and summed, and the weighted sum result is used as the resource detection result.

[0172] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0173] The device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0174] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any of the above embodiments when executing the program.

[0175] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0176] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0177] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0178] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0179] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0180] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0181] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0182] The electronic device of the above embodiment is used to implement the corresponding method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0183] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in any of the above embodiments.

[0184] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0185] The computer instructions stored in the storage medium of the above embodiments are used to enable the computer to execute the method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0186] Based on the same concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer executes the method described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0187] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0188] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0189] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0190] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A resource detection method for electric power equipment, characterized in that: include: Determine a plurality of targets to be detected corresponding to the electric power equipment; Obtaining material resource data corresponding to the power equipment, inputting the material resource data into a pre-built material fluctuation prediction model to perform fluctuation analysis on the material resource quantity, and obtaining a fluctuation analysis result, wherein the material fluctuation prediction model is a model pre-built based on a neural network model, and the material fluctuation prediction model can analyze the fluctuation of the material resource quantity corresponding to the material resource data; For each target to be detected: determine the position information corresponding to the target to be detected, retrieve the condition information of the position information, obtain the adaptation parameter information of the target to be detected, match the adaptation parameter information with the condition information, and obtain the matching result of the target to be detected; Acquire abnormal execution data of each target to be detected on the power equipment; For each target to be detected: collecting the index parameter information of the target to be detected, inputting the index parameter information into a pre-built support vector machine model to perform abnormality analysis, and obtaining an abnormality index; Obtain target resource values ​​for each target to be detected for the power equipment; For each target to be detected, the following steps are performed: combining the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected to obtain the resource detection result; The various targets to be detected are sorted according to the resource detection results, and the target to be detected with the highest resource detection result is selected from the sorting as the resource determination target, and the resource determination target is output and displayed.

2. The method according to claim 1, characterized in that: The process of constructing the material fluctuation prediction model includes: Acquire a plurality of historical material resource data corresponding to the electric power equipment arranged in chronological order in a first time period in history, and graph the plurality of historical material resource data to obtain a first curve graph; Determine the fluctuation index corresponding to each historical material resource data according to the first curve graph, and use the fluctuation index to mark each historical material resource data, and combine multiple marked historical material resource data to form a first training set, wherein the first training set includes multiple first training samples, each of which is a marked historical material resource data; Construct a neural network model including an input layer, multiple hidden layers, and an output layer; Input the first training sample in the first training set from the input layer to the neural network model in chronological order. After the input layer preprocesses the first training sample, the preprocessed first training sample is input to each hidden layer for fluctuation analysis. Each hidden layer sends the analysis result to the output layer, and the output layer is used to organize and output the fluctuation analysis training results. Determine a corresponding loss value according to the difference between the fluctuation analysis training result and the marked fluctuation index, determine the adjustment amount of each layer parameter according to the loss value, adjust the parameters of each layer of the neural network model according to the adjustment amount of each layer parameter, and complete the training process for the first training sample; After all the first training samples in the first training set are trained, the finally obtained neural network model is used as the material fluctuation prediction model.

3. The method according to claim 2, characterized in that After all the first training samples in the first training set are trained, the finally obtained neural network model is used as the material fluctuation prediction model, including: After determining that all first training samples in the first training set have been trained, obtaining a plurality of historical material resource data corresponding to the power equipment arranged in chronological order in the second time period in history, and graphing the plurality of historical material resource data to obtain a second curve graph; Determine the fluctuation index corresponding to each historical material resource data according to the second curve graph, and use the fluctuation index to mark each historical material resource data in the second time period, and combine multiple marked historical material resource data together to form a first test set, wherein the first test set includes multiple first test samples, each first test sample is the marked historical material resource data in the second time period; Inputting each first test sample in the first test set into the finally obtained neural network model in chronological order to perform fluctuation analysis test processing, and obtaining fluctuation analysis test results; Compare the fluctuation analysis test results corresponding to each first test sample with the marked fluctuation index, and calculate the test accuracy rate corresponding to the test process; In response to determining that the test accuracy is greater than or equal to the accuracy threshold, the finally obtained neural network model is used as the material fluctuation prediction model.

4. The method according to claim 1, characterized in that: The construction process of the support vector machine model includes: Obtain multiple historical indicator parameter information of various targets to be detected, and mark the corresponding abnormality degree for each historical indicator parameter information; Normalizing the multiple marked historical indicator parameter information of each target to be detected to obtain a normalized result; The normalized result is used as a second training set, wherein the second training set includes a plurality of second training samples, and each second training sample is historical indicator parameter information of a normalized marked abnormality degree; Build an initial support vector machine model; Inputting each second training sample in the second training set into the initial support vector machine model for abnormality analysis processing to obtain the abnormality degree of the sample; Determine a corresponding loss value according to the difference between the abnormality degree of the sample and the abnormality degree of the mark, determine the adjustment amount of each layer parameter of the initial support vector machine model according to the loss value, adjust the initial support vector machine model according to the adjustment amount of each layer parameter, and complete the training process for the second training sample; After it is determined that all the second training samples in the second training set have been trained, the final initial support vector machine model is used as the support vector machine model.

5. The method according to claim 4, characterized in that The step of inputting each second training sample in the second training set into the initial support vector machine model for abnormality analysis to obtain the abnormality degree of the sample includes: Adding a linear kernel function processing layer, a polynomial kernel function processing layer, a Gaussian kernel function processing layer, and an activation processing layer to the initial support vector machine model; Input each second training sample in the second training set into the initial support vector machine model and process it in sequence using a linear kernel function processing layer, a polynomial kernel function processing layer, a Gaussian kernel function processing layer and an activation processing layer; The processing results of each processing layer are integrated, and the abnormal separation plane is determined according to the integration result. The distance of the second training sample deviating from the abnormal separation plane is calculated, and the degree of sample abnormality is determined according to the distance.

6. The method according to claim 1, characterized in that The method of performing, for each target to be detected, combining the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected to obtain a resource detection result includes: For each target to be detected, execute: Determine the fluctuation degree value corresponding to the fluctuation analysis result of the target to be detected; Determine the matching degree value corresponding to the matching result of the target to be detected; Determine the abnormal execution value corresponding to the abnormal execution data of the target to be detected; Determine the abnormality level value corresponding to the abnormality level index of the target to be detected; The fluctuation degree value, the matching degree value, the abnormal execution value, the abnormal degree value and the target resource data are multiplied, and the product result is used as the resource detection result.

7. The method according to claim 1, characterized in that For each target to be detected, the fluctuation analysis result, matching result, abnormal execution data and abnormality index of the target to be detected are combined to obtain the resource detection result, including: For each target to be detected, execute: Determine the fluctuation degree value corresponding to the fluctuation analysis result of the target to be detected; Determine the matching degree value corresponding to the matching result of the target to be detected; Determine the abnormal execution value corresponding to the abnormal execution data of the target to be detected; Determine the abnormality level value corresponding to the abnormality level index of the target to be detected; The fluctuation degree value, matching degree value, abnormal execution value, abnormal degree value and target resource data are weighted and summed, and the weighted sum result is used as the resource detection result.

8. A resource detection device for electric power equipment, characterized in that: include: A to-be-detected target determination module is configured to determine a plurality of to-be-detected targets corresponding to the electric power equipment; A fluctuation analysis module is configured to obtain material resource data corresponding to the power equipment, input the material resource data into a pre-built material fluctuation prediction model to perform fluctuation analysis on the material resource quantity, and obtain a fluctuation analysis result, wherein the material fluctuation prediction model is a model pre-built based on a neural network model, and the material fluctuation prediction model can analyze the fluctuation of the material resource quantity corresponding to the material resource data; The matching module is configured to, for each target to be detected, determine the position information corresponding to the target to be detected, retrieve the condition information of the position information, obtain the adaptation parameter information of the target to be detected, match the adaptation parameter information with the condition information, and obtain the matching result of the target to be detected; An abnormal execution acquisition module is configured to acquire abnormal execution data of each target to be detected for the electric power equipment; The abnormality degree analysis module is configured to collect the index parameter information of each target to be detected, input the index parameter information into a pre-built support vector machine model to perform abnormality degree analysis, and obtain an abnormality degree index; A resource data acquisition module is configured to acquire a target resource value of each target to be detected for the electric power equipment; The resource detection module is configured to perform, for each target to be detected, combining the fluctuation analysis result, matching result, abnormal execution data, abnormal degree index and target resource value of the target to be detected to obtain a resource detection result; The resource determination module is configured to sort the various targets to be detected according to the resource detection results, select the target to be detected with the highest resource detection result from the sorting as the resource determination target, and output and display the resource determination target.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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