A method and system for substation equipment image detection based on rapid fault identification

Abstract

The present invention discloses a method and system for detecting substation equipment images based on rapid fault identification, relating to the field of image recognition technology. The method comprises: collecting substation equipment images and preprocessing the images, obtaining a substation equipment image set, extracting substation equipment image fault feature information from the substation equipment image set and classifying and grading the images, obtaining a substation equipment image classification and grading fault feature set, constructing a rapid fault identification model based on the substation equipment image classification and grading fault feature set, obtaining substation equipment fault image detection results, verifying the substation equipment fault image detection results based on manual detection results, and optimizing the rapid fault identification model based on the verification results. The present invention can rapidly identify fault-related factors such as the faulty substation equipment, location, and impact of the fault on substation operation through substation equipment images, allowing maintenance personnel to quickly obtain fault-related factors and handle the fault.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to a method and system for detecting substation equipment images based on rapid fault recognition. Background Art

[0002] At the current stage of my country's national economy's continuous development, people's demand for electric energy is increasing, which, to a certain extent, increases the importance of substation equipment fault detection. However, judging from the current status of my country's substation equipment fault detection work, due to the influence of various factors, there are still many inevitable deficiencies. Substation equipment fault identification has become a practical problem to be solved in related research fields. It not only plays a role in ensuring the safe and reliable operation of the power system, but also has very important practical significance for the sustainable development of society. Summary of the Invention

[0003] The present invention provides a substation equipment image detection method based on rapid fault identification, comprising:

[0004] Step S1: The cloud platform collects substation equipment images through image acquisition equipment and pre-processes the images to obtain a substation equipment image set;

[0005] Step S2: The cloud platform extracts fault feature information of the substation equipment images from the substation equipment image set and classifies and grades the images to obtain a classified and graded fault feature set of the substation equipment images;

[0006] Step S3: The cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading, and obtains the substation equipment fault image detection results;

[0007] Step S4: The platform verifies the substation equipment fault image detection results based on the manual detection results, and optimizes the rapid fault identification model based on the verification results.

[0008] In the above-mentioned substation equipment image detection method based on rapid fault identification, the cloud platform collects substation equipment images through image acquisition equipment and pre-processes the images. The sub-steps for obtaining the substation equipment image set are as follows:

[0009] Step S11: The cloud platform collects images of substation equipment through an image acquisition device to obtain collected images of the substation equipment;

[0010] Step S12: The cloud platform pre-processes the substation equipment collected images to obtain a substation equipment image set.

[0011] In the above-mentioned method for detecting substation equipment images based on rapid fault identification, the cloud platform extracts fault feature information of substation equipment images from a substation equipment image set and classifies and grades the image. The sub-steps for obtaining the classified and graded fault feature set of substation equipment images are as follows:

[0012] Step S21: The cloud platform extracts fault features from the substation equipment image set to obtain a substation equipment image fault feature set;

[0013] Step S22: The cloud platform classifies and grades the substation equipment image fault feature set to obtain the classified and graded substation equipment image fault feature set.

[0014] In the above-mentioned method for detecting substation equipment images based on rapid fault identification, the cloud platform constructs a rapid fault identification model based on a set of fault features for classifying and grading substation equipment images. The sub-steps for obtaining the substation equipment fault image detection results are as follows:

[0015] Step S31: The cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading;

[0016] Step S32: The platform verifies the substation equipment fault image detection results based on the manual detection results, and optimizes the rapid fault identification model based on the verification results.

[0017] In the above-mentioned method for detecting substation equipment images based on rapid fault identification, the cloud platform verifies the substation equipment fault image detection results based on manual detection results, and the sub-steps of optimizing the rapid fault identification model based on the verification results are as follows:

[0018] Step S41: The cloud platform verifies the accuracy of the substation equipment fault image detection result based on the manual detection result, and obtains the accurate value of the substation equipment fault image detection result;

[0019] Step S42: The cloud platform determines the detection accuracy of the fast fault identification model based on the accuracy value of the substation equipment fault image monitoring result. If the detection accuracy is low, the fast fault identification model is optimized based on the manual detection result.

[0020] The present invention also provides a substation equipment image detection system based on rapid fault identification, comprising:

[0021] An image acquisition and processing module collects substation equipment images through an image acquisition device and pre-processes the images to obtain a substation equipment image set;

[0022] A classification and grading fault feature set acquisition module extracts fault feature information of substation equipment images from the substation equipment image set and performs classification and grading to obtain a classification and grading fault feature set of substation equipment images;

[0023] Image detection module, which builds a fast fault recognition model based on the fault feature set of substation equipment image classification and obtains substation equipment fault image detection results;

[0024] The fast fault identification model optimization module verifies the substation equipment fault image detection results based on manual detection results, and optimizes the fast fault identification model based on the verification results.

[0025] In the above-mentioned substation equipment image detection system based on rapid fault identification, the image acquisition and processing module specifically includes:

[0026] The image acquisition submodule acquires images of substation equipment through an image acquisition device to obtain images of substation equipment;

[0027] The image processing submodule pre-processes the collected images of the substation equipment to obtain a substation equipment image set.

[0028] In the above-mentioned substation equipment image detection system based on rapid fault identification, the classification and grading fault feature set acquisition module specifically includes:

[0029] The fault feature extraction submodule extracts fault features from the substation equipment image set to obtain the substation equipment image fault feature set;

[0030] In the classification and grading submodule, the platform classifies and grades the fault feature set of substation equipment images to obtain the classified and graded fault feature set of substation equipment images.

[0031] In the above-mentioned substation equipment image detection system based on rapid fault identification, the image detection module specifically includes:

[0032] The fast fault identification model construction submodule builds a fast fault identification model based on the fault feature set of the substation equipment image classification and grading;

[0033] The image detection result acquisition submodule transmits the real-time collected images of the substation equipment to be detected to the fast fault identification model to obtain the substation equipment fault image detection results.

[0034] In the above-mentioned substation equipment image detection system based on rapid fault identification, the rapid fault identification model optimization module specifically includes:

[0035] In the result verification submodule, the cloud platform verifies the accuracy of the substation equipment fault image detection results based on the manual detection results, and obtains the accurate value of the substation equipment fault image detection results;

[0036] The model optimization submodule determines the detection accuracy of the rapid fault identification model based on the accuracy of the substation equipment fault image monitoring results. If the detection accuracy is low, the rapid fault identification model is optimized based on the manual detection results.

[0037] The beneficial effects achieved by the present invention are as follows: the present invention can quickly identify fault-related factors such as the equipment and location of the substation equipment failure and the degree of impact of the fault on the substation operation through the substation equipment image, so that maintenance personnel can quickly obtain fault-related factors and handle the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 This is a flow chart of a method for image detection of substation equipment based on rapid fault identification provided in Example 1 of the present application;

[0040] Figure 2 This is a schematic diagram of a substation equipment image detection system based on rapid fault identification provided in Example 2 of the present application. DETAILED DESCRIPTION

[0041] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1 As shown, the first embodiment of the present application provides a substation equipment image detection method based on rapid fault identification, the method comprising the following steps:

[0044] Step S1: The cloud platform collects substation equipment images through image acquisition equipment and pre-processes the images to obtain a substation equipment image set;

[0045] Furthermore, the cloud platform collects substation equipment images through image acquisition equipment and pre-processes the images. The sub-steps for obtaining the substation equipment image set are as follows:

[0046] Step S11: The cloud platform collects images of substation equipment through an image acquisition device to obtain collected images of the substation equipment;

[0047] Specifically, substation equipment acquisition images include primary equipment acquisition images and secondary equipment acquisition images; primary equipment refers to equipment that directly produces, transmits, distributes and uses electric energy, including transformers, high-voltage circuit breakers, disconnectors, busbars, lightning arresters, capacitors, reactors, etc.; secondary equipment refers to equipment that measures, monitors, controls and protects the operating conditions of primary equipment and systems, including relay protection devices, automatic devices, measurement and control devices, metering devices, automation systems and DC equipment that provides power to secondary equipment.

[0048] Step S12: The cloud platform pre-processes the substation equipment collected images to obtain a substation equipment image set;

[0049] Specifically, the images collected by substation equipment are subjected to preprocessing operations such as image denoising, size unification, and color normalization to ensure the consistency and comparability of the data.

[0050] Step S2: The cloud platform extracts fault feature information of the substation equipment images from the substation equipment image set and classifies and grades the images to obtain a classified and graded fault feature set of the substation equipment images;

[0051] Furthermore, the cloud platform extracts fault feature information of the substation equipment images from the substation equipment image set and classifies and grades them. The sub-steps for obtaining the substation equipment image classification and grading fault feature set are as follows:

[0052] Step S21: The cloud platform extracts fault features from the substation equipment image set to obtain a substation equipment image fault feature set;

[0053] Specifically, the substation equipment image is processed by image processing technology to improve the accuracy and efficiency of image feature extraction, wherein the image processing technology includes grayscale transformation, filtering, edge detection, image segmentation, etc.; the fault feature information of the processed substation equipment image is extracted by image extraction technology, wherein the image feature extraction technology includes edge feature extraction technology, color feature extraction technology, texture feature extraction technology, shape feature extraction technology, etc.; the substation equipment image fault feature set includes but is not limited to transformer fault features, disconnector fault features, capacitor fault features, and relay protection device fault features.

[0054] Step S22: The cloud platform classifies and grades the substation equipment image fault feature set to obtain a classified and graded substation equipment image fault feature set;

[0055] Specifically, the substation equipment image fault feature set is classified according to the different characteristics of the substation equipment to obtain the substation equipment image classification fault feature set. The substation equipment image classification fault feature set includes but is not limited to a transformer equipment fault feature subset, a switch equipment fault feature subset, a protection equipment fault feature subset, a reactive compensation equipment fault feature subset, a measurement equipment fault feature subset, a control equipment fault feature subset, and a protection and regulation equipment fault feature subset.

[0056] According to the fault impact caused by the substation equipment failure, the power station equipment image classification fault feature set is classified, and the substation equipment importance vector set SB = {sb1, sb2…sb m}, the importance vector set of substation equipment failure factors YS = {ys1, ys2…ys n}, fault location importance vector set WZ={wz1,wz2…wz n}, fault repair time importance vector set XF = {xf1, xf2…xf n The weight of the fault factor, location, and repair time is determined according to their impact on substation operation. Fault factors include but are not limited to equipment aging, lack of maintenance, overload, environmental factors, and human error.

[0057] Furthermore, the fault impact degree expression is as follows:

[0058]

[0059] Among them, GZD is the fault impact, sb j is the importance of the j-th substation equipment, m is the total number of equipment in the substation, λ1 is the weight value of the fault factor importance, ys i is the importance of the i-th fault factor in the j-th substation equipment, λ2 is the weight value of the importance of the fault location, wz i is the importance of the location of the i-th fault in the j-th substation equipment, λ3 is the weight value of the importance of the repair time used for the fault, xf i is the importance of the repair time of the i-th fault in the j-th substation equipment, n is the total number of substation equipment faults in the j-th substation equipment, where λ1+λ2+λ3=1.

[0060] Specifically, according to the fault impact, the substation equipment fault level is divided into high priority fault level, medium priority fault level, low priority fault level, and no fault level. Among them, the high priority fault level refers to the fault that has a serious impact on the operation of the substation; the medium priority fault level refers to the fault that has a certain impact on the operating efficiency of the substation; the low priority fault level refers to the fault that has a relatively small impact on the operation of the substation; the no fault level means that the normal operation of the equipment will not affect the operation of the substation.

[0061] Step S3: The cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading, and obtains the substation equipment fault image detection results;

[0062] Furthermore, the cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading. The sub-steps for obtaining the substation equipment fault image detection results are as follows:

[0063] Step S31: The cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading;

[0064] Specifically, a fast fault recognition model training set and a fast fault recognition model test set are generated based on the substation equipment image classification and grading fault feature set, wherein the fast fault recognition model training set is constructed based on the substation equipment image set to construct the model training input vector set

[0065] Among them, [(jd1,jd2…jd t …jd w )1,(jd1,jd2…jd t …jd w )2…(jd1,jd2…jd t …jd w ) j …(jd1,jd2…jd t …jd w ) m ] i is the i-th substation equipment image, (jd1, jd2…jd t …jd w ) j is the jth fault image of the i-th device, jd t For the t-th angle image of the j-th fault of the i-th device, the fast fault recognition model is trained with the input vector set and the fault impact degree GZD to train the sub-recognition model ψ y (x), according to the formula The set of weights {γ1,γ2…γ Y}, through each sub-recognition model {ψ1(x),ψ2(x)…ψ Y (x)} and its corresponding weight values ​​{γ1,γ2…γ Y}Through the formula Determine the fault level of substation equipment; construct a model training output vector SC={gz1,gz2…gz n}, where gz1, gz2…gz n For information related to substation equipment faults, including but not limited to substation equipment type, fault factors, fault location, and fault level, the input vector set and output vector set of the rapid fault recognition model training are input into the machine learning model to train a rapid fault recognition model. The performance of the rapid fault recognition model is evaluated through the rapid fault recognition model test set, so that the model can recognize the substation equipment type, fault factors, fault location, fault level and other fault-related information of the image to be identified based on the substation equipment image, so that the staff can quickly locate the faulty equipment, the degree of impact on the substation operation and the fault repair priority based on the substation image fault detection results.

[0066] Step S32: The cloud platform transmits the substation equipment images collected in real time to the rapid fault identification model to obtain the substation equipment fault image detection results;

[0067] Specifically, during the image transmission process, multiple transmission channels are established according to the different characteristics of the image, so that the image can be efficiently transmitted to the fast fault recognition model, so that the substation image detection results can be quickly generated to detect the faults of the substation equipment and handle the faults.

[0068] Step S4: The cloud platform verifies the substation equipment fault image detection results based on the manual detection results, and optimizes the rapid fault identification model based on the verification results;

[0069] Furthermore, the cloud platform verifies the substation equipment fault image detection results based on the manual inspection results. The sub-steps of optimizing the fast fault identification model based on the verification results are as follows:

[0070] Step S41: The cloud platform verifies the accuracy of the substation equipment fault image detection result based on the manual detection result, and obtains the accurate value of the substation equipment fault image detection result;

[0071] Specifically, the accuracy of the substation equipment fault image detection results is calculated based on the manual detection results, and the detection vector set is constructed based on the manual detection results, the substation equipment fault image detection results and the importance of the detection information items. Among them, {rg1,rg2…rg n} are different manual detection results, {mx1,mx2…mx n} are the fault image detection results of different substation equipment, is the weight of different detection items, n is the total number of detection information items; according to the detection vector set, the formula Calculate the accuracy of substation equipment fault image detection results.

[0072] Step S42: The cloud platform determines the detection accuracy of the fast fault identification model based on the accuracy value of the substation equipment fault image monitoring result. If the detection accuracy is low, the fast fault identification model is optimized based on the manual detection result.

[0073] Specifically, if the precision value JZ is larger, the detection accuracy of the rapid fault identification model is lower. According to the accuracy, detection information items with low detection accuracy are obtained. According to the detection information items, a large number of substation equipment images are obtained as a training set for the rapid fault identification model and the model is optimized and trained, so that the accuracy of the detection results is improved after the rapid fault identification model is optimized.

[0074] Example 2

[0075] like Figure 2 As shown, the second embodiment of the present application provides a substation equipment image detection system based on rapid fault identification, including:

[0076] An image acquisition and processing module 21 acquires substation equipment images through an image acquisition device and pre-processes the images to obtain a substation equipment image set;

[0077] Furthermore, the image acquisition and processing module 21 includes the following submodules:

[0078] The image acquisition submodule acquires images of substation equipment through an image acquisition device to obtain images of substation equipment;

[0079] Specifically, substation equipment acquisition images include primary equipment acquisition images and secondary equipment acquisition images; primary equipment refers to equipment that directly produces, transmits, distributes and uses electric energy, including transformers, high-voltage circuit breakers, disconnectors, busbars, lightning arresters, capacitors, reactors, etc.; secondary equipment refers to equipment that measures, monitors, controls and protects the operating conditions of primary equipment and systems, including relay protection devices, automatic devices, measurement and control devices, metering devices, automation systems and DC equipment that provides power to secondary equipment.

[0080] The image processing submodule pre-processes the collected images of substation equipment to obtain a set of substation equipment images;

[0081] Specifically, the images collected by substation equipment are subjected to image denoising, size unification, color normalization and other operations to ensure the consistency and comparability of the data.

[0082] The classification and grading fault feature set acquisition module 22 extracts fault feature information of the substation equipment image from the substation equipment image set and performs classification and grading to obtain the classification and grading fault feature set of the substation equipment image;

[0083] Furthermore, the classification and grading fault feature set acquisition module 22 includes the following submodules:

[0084] The feature extraction submodule extracts fault features from the substation equipment image set to obtain the substation equipment image fault feature set;

[0085] Specifically, the substation equipment image is processed by image processing technology to improve the accuracy and efficiency of image feature extraction, wherein the image processing technology includes grayscale transformation, filtering, edge detection, image segmentation, etc.; the fault feature information of the processed substation equipment image is extracted by image extraction technology, wherein the image feature extraction technology includes edge feature extraction technology, color feature extraction technology, texture feature extraction technology, shape feature extraction technology, etc.; the substation equipment image fault feature set includes but is not limited to transformer fault features, disconnector fault features, capacitor fault features, and relay protection device fault features.

[0086] The classification and grading submodule classifies and grades the fault feature set of the substation equipment image to obtain the classification and grading fault feature set of the substation equipment image;

[0087] Specifically, the substation equipment image fault feature set is classified according to the different characteristics of the substation equipment to obtain the substation equipment image classification fault feature set. The substation equipment image classification fault feature set includes but is not limited to a transformer equipment fault feature subset, a switch equipment fault feature subset, a protection equipment fault feature subset, a reactive compensation equipment fault feature subset, a measurement equipment fault feature subset, a control equipment fault feature subset, and a protection and regulation equipment fault feature subset.

[0088] According to the fault impact caused by the substation equipment failure, the power station equipment image classification fault feature set is classified, and the substation equipment importance vector set SB = {sb1, sb2…sb m}, the importance vector set of substation equipment fault factors YS={ys1,ys2…ysn}, the importance vector set of fault location WZ={wz1,wz2…wz n}, fault repair time importance vector set XF = {xf1, xf2…xf nThe weight of the fault factor, location, and repair time is determined according to their impact on substation operation. Fault factors include but are not limited to equipment aging, lack of maintenance, overload, environmental factors, and human error.

[0089] Furthermore, the fault impact degree expression is as follows:

[0090]

[0091] Among them, GZD is the fault impact, sb j is the importance of the j-th substation equipment, m is the total number of equipment in the substation, λ1 is the weight value of the fault factor importance, ys i is the importance of the i-th fault factor in the j-th substation equipment, λ2 is the weight value of the importance of the fault location, wz i is the importance of the location of the i-th fault in the j-th substation equipment, λ3 is the weight value of the importance of the repair time used for the fault, xf i is the importance of the repair time of the i-th fault in the j-th substation equipment, n is the total number of substation equipment faults in the j-th substation equipment, where λ1+λ2+λ3=1.

[0092] Specifically, according to the fault impact, the substation equipment fault level is divided into high priority fault level, medium priority fault level, low priority fault level, and no fault level. Among them, the high priority fault level refers to the fault that has a serious impact on the operation of the substation; the medium priority fault level refers to the fault that has a certain impact on the operating efficiency of the substation; the low priority fault level refers to the fault that has a relatively small impact on the operation of the substation; the no fault level means that the normal operation of the equipment will not affect the operation of the substation.

[0093] Image detection module 23, which builds a fast fault recognition model based on the fault feature set of the substation equipment image classification and obtains the substation equipment fault image detection results;

[0094] Furthermore, the image detection module 23 includes the following submodules:

[0095] The fast fault identification model construction submodule: The cloud platform constructs a fast fault identification model based on the fault feature set of the substation equipment image classification and grading;

[0096] Specifically, a fast fault recognition model training set and a fast fault recognition model test set are generated based on the substation equipment image classification and grading fault feature set, wherein the fast fault recognition model training set is constructed based on the substation equipment image set to construct the model training input vector set Among them, [(jd1,jd2…jd t …jdw )1,(jd1,jd2…jd t …jd w )2…(jd1,jd2…jd t …jd w ) j …(jd1,jd2…jd t …jd w ) m ] i is the i-th substation equipment image, (jd1, jd2…jd t …jd w ) j is the jth fault image of the i-th device, jd t For the t-th angle image of the j-th fault of the i-th device, the fast fault recognition model is trained with the input vector set and the fault impact degree GZD to train the sub-recognition model ψ y (x), according to the formula The set of weights {γ1,γ2…γ Y}, through each sub-recognition model {ψ1(x),ψ2(x)…ψ Y (x)} and its corresponding weight values ​​{γ1,γ2…γ Y}Through the formula Determine the fault level of substation equipment; construct a model training output vector SC={gz1,gz2…gz n}, where gz1, gz2…gz n For information related to substation equipment faults, including but not limited to substation equipment type, fault factors, fault location, and fault level, the input vector set and output vector set of the rapid fault recognition model training are input into the machine learning model to train a rapid fault recognition model. The performance of the rapid fault recognition model is evaluated through the rapid fault recognition model test set, so that the model can recognize the substation equipment type, fault factors, fault location, fault level and other fault-related information of the image to be identified based on the substation equipment image, so that the staff can quickly locate the faulty equipment, the degree of impact on the substation operation and the fault repair priority based on the substation image fault detection results.

[0097] The image detection result acquisition submodule transmits the real-time collected images of the substation equipment to be detected to the fast fault identification model to obtain the substation equipment fault image detection results;

[0098] Specifically, during the image transmission process, multiple transmission channels are established according to the different characteristics of the image, so that the image can be efficiently transmitted to the fast fault recognition model, so that the substation image detection results can be quickly generated to detect the faults of the substation equipment and handle the faults.

[0099] A fast fault identification model optimization module 24 verifies the substation equipment fault image detection results based on the manual detection results and optimizes the fast fault identification model based on the verification results;

[0100] Furthermore, the fast fault identification model optimization module 24 includes the following submodules:

[0101] The result verification submodule verifies the accuracy of the substation equipment fault image detection results based on the manual detection results and obtains the accurate value of the substation equipment fault image detection results;

[0102] Specifically, the accuracy of the substation equipment image detection results is calculated based on the manual detection results, and the detection vector set is constructed based on the importance of the manual detection results, substation image detection results and detection information items. Among them, {rg1,rg2…rg n} are different manual detection results, {mx1,mx2…mx n} are different substation image detection results, is the weight of different detection items, n is the total number of detection information items; according to the detection vector set, the formula Calculate the accurate value of substation equipment image detection results.

[0103] The model optimization submodule determines the detection accuracy of the fast fault identification model based on the accuracy of the substation equipment fault image monitoring results. If the detection accuracy is low, the fast fault identification model is optimized based on the manual detection results.

[0104] Specifically, if the precision value JZ is larger, the detection accuracy of the rapid fault identification model is lower. According to the accuracy, detection information items with low detection accuracy are obtained. According to the detection information items, a large number of substation equipment images are obtained as a training set for the rapid fault identification model and the model is optimized and trained, so that the accuracy of the detection results is improved after the rapid fault identification model is optimized.

[0105] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting substation equipment images based on rapid fault identification, characterized in that: include: Step S1: The cloud platform collects substation equipment images through image acquisition equipment and pre-processes the images to obtain a substation equipment image set; Step S2: The cloud platform extracts fault feature information of the substation equipment images from the substation equipment image set and classifies and grades the images to obtain a classified and graded fault feature set of the substation equipment images, including the following sub-steps: Step S21: The cloud platform extracts fault features from the substation equipment image set to obtain a substation equipment image fault feature set; Step S22: The cloud platform classifies and grades the substation equipment image fault feature set to obtain a classified and graded substation equipment image fault feature set; Specifically, the substation equipment image fault feature set is classified according to the different characteristics of the substation equipment to obtain the substation equipment image classification fault feature set, and the power station equipment image classification fault feature set is graded according to the fault impact caused by the substation equipment failure. Based on the substation equipment image classification fault feature set and combined with the substation operation factors, the substation equipment importance vector set is constructed. , importance vector set of substation equipment failure factors , fault location importance vector set , fault repair time importance vector set ,determine its weight based on the impact of fault factors, location, and repair time on substation operation; Furthermore, the fault impact expression is: ,in, is the fault impact, For the The importance of each substation equipment, is the total number of equipment in the substation, is the weight value of the importance of the fault factor, For the The first of the substation equipment The importance of each failure factor, is the weight value of the importance of the fault location, For the The first of the substation equipment The importance of the fault location, is the weight value of the importance of the repair time used for the fault, For the The first of the substation equipment The importance of the repair time for each fault, For the The total number of substation equipment failures in substation equipment, of which, ; According to the fault impact, the substation equipment fault level is divided into high priority fault level, medium priority fault level, low priority fault level, and no fault level; Step S3: The cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading, and obtains the substation equipment fault image detection results; Step S4: The platform verifies the substation equipment fault image detection results based on the manual detection results, and optimizes the rapid fault identification model based on the verification results.

2. A method for detecting substation equipment images based on rapid fault identification according to claim 1, characterized in that: The cloud platform collects substation equipment images through image acquisition equipment and preprocesses the images. The sub-steps for obtaining the substation equipment image set are as follows: Step S11: The cloud platform collects images of substation equipment through an image acquisition device to obtain collected images of the substation equipment; Step S12: The cloud platform pre-processes the substation equipment collected images to obtain a substation equipment image set.

3. The method for detecting substation equipment images based on rapid fault identification according to claim 1, characterized in that: The cloud platform builds a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading. The sub-steps for obtaining the substation equipment fault image detection results are as follows: Step S31: The cloud platform constructs a fast fault recognition model based on the fault feature set of the substation equipment image classification and grading; Step S32: The platform verifies the substation equipment fault image detection results based on the manual detection results, and optimizes the rapid fault identification model based on the verification results.

4. A method for detecting substation equipment images based on rapid fault identification according to claim 1, characterized in that: The cloud platform verifies the substation equipment fault image detection results based on manual inspection results. The sub-steps for optimizing the fast fault identification model based on the verification results are as follows: Step S41: The cloud platform verifies the accuracy of the substation equipment fault image detection result based on the manual detection result, and obtains the accurate value of the substation equipment fault image detection result; Step S42: The cloud platform determines the detection accuracy of the fast fault identification model based on the accuracy value of the substation equipment fault image monitoring result. If the detection accuracy is low, the fast fault identification model is optimized based on the manual detection result.

5. A substation equipment image detection system based on rapid fault identification, characterized in that: include: An image acquisition and processing module collects substation equipment images through an image acquisition device and pre-processes the images to obtain a substation equipment image set; The classification and grading fault feature set acquisition module extracts fault feature information of substation equipment images from the substation equipment image set and classifies and grades them to obtain the classification and grading fault feature set of substation equipment images. It includes the following submodules: The feature extraction submodule extracts fault features from the substation equipment image set to obtain the substation equipment image fault feature set; The classification and grading submodule classifies and grades the fault feature set of the substation equipment image to obtain the classification and grading fault feature set of the substation equipment image; Specifically, the substation equipment image fault feature set is classified according to the different characteristics of the substation equipment to obtain the substation equipment image classification fault feature set, and the power station equipment image classification fault feature set is graded according to the fault impact caused by the substation equipment failure. Based on the substation equipment image classification fault feature set and combined with the substation operation factors, the substation equipment importance vector set is constructed. , importance vector set of substation equipment failure factors , fault location importance vector set , fault repair time importance vector set ,determine its weight based on the impact of fault factors, location, and repair time on substation operation; Furthermore, the fault impact expression is: ,in, is the fault impact, For the The importance of each substation equipment, is the total number of equipment in the substation, is the weight value of the importance of the fault factor, For the The first of the substation equipment The importance of each failure factor, is the weight value of the importance of the fault location, For the The first of the substation equipment The importance of the fault location, is the weight value of the importance of the repair time used for the fault, For the The first of the substation equipment The importance of the repair time for each fault, For the The total number of substation equipment failures in substation equipment, of which, ; According to the fault impact, the substation equipment fault level is divided into high priority fault level, medium priority fault level, low priority fault level, and no fault level; Image detection module, which builds a fast fault recognition model based on the fault feature set of substation equipment image classification and obtains substation equipment fault image detection results; The fast fault identification model optimization module verifies the substation equipment fault image detection results based on manual detection results, and optimizes the fast fault identification model based on the verification results.

6. A substation equipment image detection system based on rapid fault identification according to claim 5, characterized in that: Image acquisition and processing module, specifically including: The image acquisition submodule acquires images of substation equipment through an image acquisition device to obtain images of substation equipment; The image processing submodule pre-processes the collected images of the substation equipment to obtain a substation equipment image set.

7. The substation equipment image detection system based on rapid fault identification according to claim 5, characterized in that: Image detection module, specifically including: The fast fault identification model construction submodule builds a fast fault identification model based on the fault feature set of the substation equipment image classification and grading; The image detection result acquisition submodule transmits the real-time collected images of the substation equipment to be detected to the fast fault identification model to obtain the substation equipment fault image detection results.

8. The substation equipment image detection system based on rapid fault identification according to claim 5, characterized in that: Rapid fault identification model optimization module, specifically including: In the result verification submodule, the cloud platform verifies the accuracy of the substation equipment fault image detection results based on the manual detection results, and obtains the accurate value of the substation equipment fault image detection results; The model optimization submodule determines the detection accuracy of the rapid fault identification model based on the accuracy of the substation equipment fault image monitoring results. If the detection accuracy is low, the rapid fault identification model is optimized based on the manual detection results.

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