State indicator lamp positioning method and device for complex electrical instrument equipment

Abstract

The invention discloses a state indicating lamp positioning method and device for complex electrical instrument equipment, and relates to the technical field of state indicating lamp positioning, and the method comprises the steps: obtaining data which comprises the image data, collected in real time, of the state indicating lamp of the electrical instrument equipment; preprocessing the data; the preprocessed data is input into a state indicator light detection model based on a deep neural network, positioning information of each indicator light is output, the state indicator light detection model based on the deep neural network is used for carrying out feature extraction on the preprocessed data to obtain image features of different scales, feature fusion is carried out on the image features of different scales, and positioning information of each indicator light is obtained; processing the feature map based on a region suggestion network and a binary classifier according to the feature map to obtain positioning information of the indicator lamp; and according to the positioning information of each indicating lamp, based on spatial proximity analysis, outputting accurate positioning information of each indicating lamp. According to the invention, the detection capability of the multi-scale indicating lamp is enhanced, and false detection and missing detection are effectively reduced.

Description

Technical Field

[0001] The invention relates to the technical field of status indicator light positioning, and in particular to a status indicator light positioning method and device for complex electrical instrument equipment. Background Art

[0002] Traditional deep learning technology has been widely used in the field of target detection, but in complex industrial scenarios, there are still many bottlenecks in the detection and positioning of status indicator lights. First, the color and brightness characteristics of status indicator lights are easily significantly affected by lighting conditions. In strong light, low light or even shadow environments, the detection performance of existing methods is generally poor. Secondly, the multi-indicator light scene in a complex background further exacerbates the difficulty of detection. Due to the diverse equipment design and significant background interference, false detection and missed detection are frequent in traditional methods. In addition, the detection of unlit indicator lights is more difficult. Existing algorithms have a high reliance on brightness features, and the recognition accuracy is greatly reduced when dealing with such situations. Finally, in multi-indicator light scenes, the spatial distribution information of each indicator light is not fully utilized in existing methods, which leads to insufficient reliability of positioning results and inaccurate positioning.

[0003] Therefore, the applicant has developed a status indicator light positioning method for complex electrical instrument equipment to solve the above problems. Summary of the invention

[0004] The present invention provides a status indicator light positioning method for complex electrical instrument equipment to solve the problem of inaccurate positioning of the existing status indicator lights.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] On one hand, the present invention provides a method for locating a status indicator light of a complex electrical instrument device, comprising:

[0007] Acquiring data, the data including real-time collected image data of status indicator lights of electrical instrument equipment;

[0008] Preprocessing the data to obtain preprocessed data;

[0009] Input the preprocessed data into a status indicator light detection model based on a deep neural network, and output the location information of each indicator light. The status indicator light detection model based on a deep neural network is used to extract features from the preprocessed data to obtain image features of different scales, perform feature fusion on the image features of different scales to obtain a feature map, and process the feature map based on a region proposal network and a binary classifier to obtain the location information of the indicator light.

[0010] Based on the spatial proximity analysis according to the positioning information of each indicator light, the precise positioning information of each indicator light is output.

[0011] Furthermore, data is obtained, including:

[0012] Collecting image data of the status indicator light of the first electrical instrument device in real time, wherein the image data of the status indicator light of the first electrical instrument device is a visible light image;

[0013] Collecting image data of the status indicator light of the second electrical instrument device in real time, wherein the image data of the status indicator light of the second electrical instrument device is an infrared image;

[0014] The data is obtained by fusing the first electrical instrument equipment status indicator light image data and the second electrical instrument equipment status indicator light image data.

[0015] Further, the data is preprocessed to obtain preprocessed data, including:

[0016] Performing data cleaning on the data to obtain cleaned data;

[0017] Perform image enhancement processing on the cleaned data to obtain preprocessed data.

[0018] Furthermore, the preprocessed data is input into a status indicator light detection model based on a deep neural network, and the location information of each indicator light is output, including:

[0019] Performing feature extraction on the preprocessed data to obtain a multi-layer feature map;

[0020] Capture image features of different scales based on multi-layer feature maps;

[0021] Based on a feature pyramid network, the image features of different scales are integrated to obtain a feature map;

[0022] Generate an anchor point on each of the feature maps based on a region proposal network through a preset sliding window;

[0023] Scoring each of the anchor points through a binary classifier to obtain anchor box parameters, and predicting the anchor box offset and scaling factor;

[0024] Generate a candidate region frame on the feature map according to the anchor frame parameters, the offset and the scaling factor;

[0025] Whether the candidate area frame contains an indicator light is determined according to the binary classifier, and the category probability distribution of each candidate area frame is calculated. According to the category probability distribution, the indicator light category corresponding to the category probability distribution with the maximum value in the category set is taken as the candidate area frame category, and the candidate area frame containing the indicator light is optimized based on the regression head to obtain the positioning information of each indicator light.

[0026] Furthermore, the regression formula for generating the candidate region box on the feature map is as follows:

[0027]

[0028] Furthermore, the calculation formula for determining the category of the candidate region box is as follows:

[0029] The anchor box parameters are , They represent the horizontal coordinate, vertical coordinate, width and height of the center point of the anchor box respectively.

[0030] The anchor box offset is , respectively represent the horizontal and vertical offsets of the center point of the candidate box relative to the center point of the anchor box, and the scaling factors of the width and height of the candidate box relative to the width and height of the anchor box.

[0031] The candidate region box is represented as , Respectively represent the horizontal coordinate of the center point of the candidate box, the vertical coordinate of the center point, the width and height.

[0032] The category probability distribution is P(c), which represents the probability that the candidate region box belongs to category c.

[0033] The category set is C, and the candidate area box category is c*.

[0034] Further, according to the location information of each indicator light, based on the spatial proximity analysis, the precise location information of each indicator light is output, including:

[0035] Obtaining the actual two-dimensional coordinates of each indicator light according to the positioning information of each indicator light;

[0036] Obtaining the two-dimensional predicted coordinates of each indicator light according to the predicted information of each indicator light;

[0037] Constructing a spatial proximity matrix according to the actual coordinates of each of the indicator lights, and calculating an expansion vector of the spatial proximity matrix;

[0038] Constructing a prediction space proximity matrix according to the prediction coordinates of each of the indicator lights, and calculating an expansion vector of the prediction space proximity matrix;

[0039] Calculating cosine similarity based on the expansion vector of the spatial proximity matrix and the expansion vector of the predicted spatial proximity matrix;

[0040] According to the cosine similarity-based matrix dimension reduction and similarity optimization method, redundant or erroneous detection items in the spatial proximity matrix and the predicted spatial proximity matrix are eliminated to obtain accurate positioning results of each indicator light.

[0041] Furthermore, the calculation formula for calculating the cosine similarity according to the expansion vector of the spatial proximity matrix and the expansion vector of the predicted spatial proximity matrix is:

[0042]

[0043] Wherein, A represents the expansion vector of the spatial proximity matrix, B represents the expansion vector of the predicted spatial proximity matrix, m is the total length of the vector, i.e., the total number of indicator light pairs, k is the vector index, and A is k represents the actual distance between the kth pair of indicator lights, B k represents the predicted distance between the kth pair of indicator lights, represents the cosine similarity.

[0044] Furthermore, removing redundant or erroneous detection items in the spatial proximity matrix and the predicted spatial proximity matrix includes:

[0045] First, gradually remove indicator light information from a matrix with a higher dimension in the spatial proximity matrix and the predicted spatial proximity matrix until the matrix dimensions of the predicted spatial proximity matrix match those of the spatial proximity matrix;

[0046] The number of indicator light information removed during the dimensionality reduction process is fixed, but different indicator light information can be selected for removal, and the cosine similarity is calculated for all possible dimensionality reduction combinations;

[0047] The group with the highest cosine similarity is selected for matching. At this time, each dimension in the predicted spatial proximity matrix and the spatial proximity matrix is ​​matched, and a mapping between the predicted coordinates of the indicator light and the actual coordinates of the indicator light can be established to obtain the precise positioning results of each indicator light.

[0048] Furthermore, it also includes:

[0049] A preset state mapping table includes an indicator light number, the actual two-dimensional coordinates of the indicator light, an indicator light state flag, and a device state description corresponding to the indicator light state, wherein the indicator light number uniquely identifies each indicator light, and the indicator light state flag describes the indicator light state;

[0050] Acquire status information and positioning information of all indicator lights, wherein the status information is image data of status indicator lights of electrical instrument equipment and the status flags, and the positioning information is the precise positioning result of each indicator light;

[0051] The state information and the positioning information are converted into a readable device state according to the preset state mapping table.

[0052] Another aspect of the present invention provides a status indicator light positioning device for complex electrical instrument equipment, comprising:

[0053] An acquisition module, the acquisition module is used to acquire data, the data including image data of status indicator lights of electrical instrument equipment collected in real time;

[0054] A preprocessing module, wherein the preprocessing module is used to preprocess the data to obtain preprocessed data;

[0055] A detection module, wherein the detection module is used to input the preprocessed data into a status indicator light detection model based on a deep neural network, and output the location information of each indicator light. The status indicator light detection model based on the deep neural network is used to extract features from the preprocessed data to obtain image features of different scales, perform feature fusion on the image features of different scales to obtain a feature map, and process the feature map based on the feature map based on a region proposal network and a binary classifier;

[0056] An analysis module is used to output accurate positioning information of each indicator light based on spatial proximity analysis according to the positioning information of each indicator light.

[0057] The beneficial effects of the present invention are:

[0058] The present invention proposes a method and device for locating status indicator lights for complex electrical instrument equipment, based on a comprehensive algorithm framework of deep learning and spatial proximity matrix. The framework is based on a status indicator light detection model of a deep neural network, introduces a feature pyramid network, and enhances the model's detection capability for multi-scale indicator lights. Aiming at the positioning problem in multi-indicator light scenarios, an optimization strategy based on a spatial proximity matrix is ​​designed in the algorithm. This optimization strategy utilizes the spatial distribution characteristics of the indicator lights, achieves precise positioning through matrix matching, and effectively reduces the occurrence of false detection and missed detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A framework flow chart of a method for locating a status indicator light of a complex electrical instrument device in an embodiment of the present application;

[0060] Figure 2This is a data processing flow chart of a status indicator light detection model based on a deep neural network in an embodiment of the present application;

[0061] Figure 3 Flow chart of the method for spatial proximity analysis in an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0063] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0065] Furthermore, the terms “first”, “second”, etc. are merely used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0066] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0067] like Figure 1 As shown, this embodiment provides a method for locating a status indicator light of a complex electrical instrument device, including:

[0068] Data collection and preprocessing steps: Get data The data includes real-time acquisition of status indicator light image data of electrical instrument equipment, and preprocessing the data to obtain preprocessed data. In this embodiment, the monitoring device is moved to the specified target position through the slide rail system to acquire the status image of the device in real time. These acquired images undergo strict preprocessing steps to eliminate noise and adjust image quality, thereby improving the recognition accuracy and robustness of subsequent models in complex environments;

[0069] The data obtained include:

[0070] Collecting image data of the status indicator light of the first electrical instrument device in real time, wherein the image data of the status indicator light of the first electrical instrument device is a visible light image;

[0071] Collecting image data of the status indicator light of the second electrical instrument device in real time, wherein the image data of the status indicator light of the second electrical instrument device is an infrared image;

[0072] The data is obtained by fusing the image data of the first electrical instrument equipment status indicator light and the image data of the second electrical instrument equipment status indicator light. The invention adopts multimodal data fusion technology, combines the complementarity of visible light and infrared images, and significantly improves the robustness of status indicator light detection, so that the algorithm can still maintain a high detection accuracy under different lighting and complex backgrounds.

[0073] The data is preprocessed to obtain preprocessed data, including:

[0074] Performing data cleaning on the data to obtain cleaned data. The data cleaning stage mainly filters the image quality and removes abnormal data caused by occlusion, blur or excessive noise;

[0075] The cleaned data is subjected to image enhancement processing to obtain preprocessed data, and brightness adjustment, contrast enhancement, and data amplification are applied to optimize image quality.

[0076] After data cleaning and image enhancement, the processed data I pre .

[0077] Indicator light detection and positioning step: input the preprocessed data into a status indicator light detection model based on a deep neural network, output the positioning information of each indicator light, the status indicator light detection model based on a deep neural network is used to extract features from the preprocessed data, obtain image features of different scales, perform feature fusion on image features of different scales, obtain a feature map, process the feature map based on a region proposal network and a binary classifier according to the feature map, obtain the positioning information of the indicator light, and output the precise positioning information of each indicator light based on the spatial proximity analysis according to the positioning information of each indicator light. Using a detection algorithm based on deep learning, the area of ​​the indicator light is automatically extracted from the device image. The detected indicator light is then accurately positioned using a positioning algorithm based on a spatial proximity matrix to ensure that the detected indicator light can be accurately mapped to a specific location on the device to avoid false detection and missed detection.

[0078] The pre-processed data is input into a status indicator light detection model based on a deep neural network, and the location information of each indicator light is output, including:

[0079] Performing feature extraction on the preprocessed data to obtain a multi-layer feature map;

[0080] Capture image features of different scales based on multi-layer feature maps;

[0081] Based on a feature pyramid network, the image features of different scales are integrated to obtain a feature map;

[0082] Generate an anchor point on each of the feature maps based on a region proposal network through a preset sliding window;

[0083] Scoring each of the anchor points through a binary classifier to obtain anchor box parameters, and predicting the anchor box offset and scaling factor;

[0084] Generate a candidate region frame on the feature map according to the anchor frame parameters, the offset and the scaling factor;

[0085] Determine whether the candidate area frame contains an indicator light according to the binary classifier, and calculate the category probability distribution of each candidate area frame;

[0086] According to the category probability distribution, the indicator light category corresponding to the category probability distribution with the maximum value in the category set is taken as the candidate area frame category, and the candidate area frame containing the indicator light is optimized based on the regression head to obtain the positioning information of each indicator light.

[0087] Specifically:

[0088] The input image is , H, W, and C represent the height, width, and number of channels of the image, respectively. After feature extraction, a multi-layer feature map is obtained. The resolution of each layer of feature maps decreases layer by layer with the depth of the network, thereby capturing image features of different scales.

[0089] Through the top-down feature fusion strategy of the feature pyramid network, low-level detail information is introduced by upsampling on the high-level semantic features to obtain the fused feature map , the fusion formula is:

[0090]

[0091] The region proposal network (RPN) generates anchors on each feature map through a sliding window, and uses a binary classifier to score each anchor to obtain the anchor box parameters. , generate candidate region boxes on the feature map The regression formula for calculating the candidate box is:

[0092]

[0093] The binary classifier determines whether the candidate area contains the indicator light and calculates the category probability distribution of each area. The regression head further optimizes the area frame to make it closer to the actual target and finally outputs a set of accurately located detection frames B.

[0094] For the n indicator lights in the image, their two-dimensional coordinates are expressed as ,in The distance d between indicator lights i and j ij It can be expressed as:

[0095]

[0096] Then construct the spatial proximity matrix D:

[0097]

[0098] In the same way, the predicted spatial proximity matrix is ​​constructed based on the detected indicator light coordinates. , calculate D and The expanded vectors A and B are used to calculate the cosine similarity ρ. The closer ρ is to 1, the more consistent the detected indicator light layout is with the known indicator lights on the device. The formula for calculating cosine similarity is:

[0099]

[0100] Using matrix dimensionality reduction and similarity optimization methods, redundant or false detection items in the matrix are removed to make the two matrices of the same dimension. First, some indicator light information is gradually removed from the matrix with higher dimension until the prediction matrix The dimension of is matched with the original matrix D, the cosine similarity is calculated for all possible dimensionality reduction combinations, and the group with the highest similarity is selected for matching. The accurate positioning result of the indicator light is obtained

[0101] Status reporting: preset status mapping table, including indicator light number, the actual two-dimensional coordinates of the indicator light, indicator light status mark and device status description of the corresponding indicator light state; obtain the status information and positioning information of all indicator lights, the status information is the status image of the device and the status mark, and the positioning information is the precise positioning result of each indicator light; according to the preset status mapping table, the status information and positioning information are converted into readable device status. The system obtains the status information of each indicator light through identification and positioning, and integrates this information with the overall status of the device to form a complete status report.

[0102] According to the preset state mapping table Integrate the status information S and location information B of all indicator lights and convert them into readable device status R. The status mapping formula is:

[0103]

[0104] like Figure 2 As shown in the figure, the data processing flow of the status indicator light detection model based on the deep neural network includes:

[0105] The preprocessed data first enters the basic feature extraction module, and then passes through four residual blocks in sequence. The specifications of the images output by the four residual blocks are: height / 4, width / 4; height / 8, width / 8; height / 16, width / 16; height / 32, width / 32; the output of each residual block is input into each first convolution layer, and the convolution kernel of the first convolution layer is 1X1 convolution kernel. Then, the output of each first convolution layer is input into the corresponding second convolution layer after 2 times upsampling, and the convolution kernel of each second convolution layer is 3X3 convolution kernel. Thus, the feature map with image features of different scales output from each second convolution layer is obtained, and the image features of different scales are fused based on the feature pyramid network to obtain the feature map. The convolutional neural network is used for feature extraction, and the high-level features of the image are captured layer by layer through multiple residual blocks to ensure that the significant features of the indicator light can be separated from the complex background. The feature pyramid network further improves the detection capability of multi-scale indicator lights, and uses feature maps of different scales to ensure that the indicator light can be accurately detected at both high resolution and low resolution.

[0106] The feature map is input into the region proposal network, and a 3X3 convolution kernel is applied to each position of the feature map. The convolution kernel is further decomposed into two 1X1 convolution kernels for parallel processing of different tasks. The output of the first 1X1 convolution kernel is processed by tensor deformation, Softmax activation function, and tensor deformation again to finally complete the category prediction. The second 1X1 convolution kernel focuses on bounding box regression and calculates the bounding box coordinates of each candidate region to obtain the precise location of the candidate region. The outputs of these two convolution kernels are aggregated to obtain the final target region proposal.

[0107] The feature map and the target region proposal are jointly input into the region of interest pooling module, and then the output of the region of interest pooling module is input into the fully connected layer. After the fully connected layer, the binary classifier classifies each candidate box, and the regression head predicts the precise boundary of the candidate box to obtain the predicted category and the predicted bounding box.

[0108] like Figure 3 As shown in Figure 2, the spatial proximity analysis process includes:

[0109] Dimension matching: Reduce the dimensions of the original matrix and the predicted matrix to match the dimension information;

[0110] Similarity optimization: Calculate the similarity of the matrix after dimension matching, and continuously select the better results;

[0111] Best match: Get the result with the highest similarity from all the results, and then get the best match between the predicted matrix and the original matrix.

[0112] Another aspect of the present invention provides a status indicator light positioning device for complex electrical instrument equipment, comprising:

[0113] An acquisition module, the acquisition module is used to acquire data, the data including image data of status indicator lights of electrical instrument equipment collected in real time;

[0114] A preprocessing module, wherein the preprocessing module is used to preprocess the data to obtain preprocessed data;

[0115] A detection module, wherein the detection module is used to input the preprocessed data into a status indicator light detection model based on a deep neural network, and output the location information of each indicator light. The status indicator light detection model based on the deep neural network is used to extract features from the preprocessed data to obtain image features of different scales, perform feature fusion on the image features of different scales to obtain a feature map, and process the feature map based on the feature map based on a region proposal network and a binary classifier;

[0116] An analysis module is used to output accurate positioning information of each indicator light based on spatial proximity analysis according to the positioning information of each indicator light.

[0117] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for locating status indicator lights of complex electrical instrument equipment, characterized in that: include: Acquiring data, the data including real-time collected image data of status indicator lights of electrical instrument equipment; Preprocessing the data to obtain preprocessed data; Input the preprocessed data into a status indicator light detection model based on a deep neural network, and output the location information of each indicator light. The status indicator light detection model based on a deep neural network is used to extract features from the preprocessed data to obtain image features of different scales, perform feature fusion on the image features of different scales to obtain a feature map, and process the feature map based on a region proposal network and a binary classifier to obtain the location information of the indicator light. Based on the spatial proximity analysis according to the positioning information of each indicator light, the precise positioning information of each indicator light is output.

2. A method for locating status indicator lights of complex electrical instrument equipment according to claim 1, characterized in that: Get data, including: Collecting image data of the status indicator light of the first electrical instrument device in real time, wherein the image data of the status indicator light of the first electrical instrument device is a visible light image; Collecting image data of the status indicator light of the second electrical instrument device in real time, wherein the image data of the status indicator light of the second electrical instrument device is an infrared image; The data is obtained by fusing the first electrical instrument equipment status indicator light image data and the second electrical instrument equipment status indicator light image data.

3. A method for locating status indicator lights of complex electrical instrument equipment according to claim 1 or 2, characterized in that: Preprocessing the data to obtain preprocessed data includes: Performing data cleaning on the data to obtain cleaned data; Perform image enhancement processing on the cleaned data to obtain preprocessed data.

4. A method for locating status indicator lights of complex electrical instrument equipment according to claim 1, characterized in that: The pre-processed data is input into a status indicator light detection model based on a deep neural network, and the location information of each indicator light is output, including: Performing feature extraction on the preprocessed data to obtain a multi-layer feature map; Capture image features of different scales based on multi-layer feature maps; Based on a feature pyramid network, the image features of different scales are integrated to obtain a feature map; Generate an anchor point on each of the feature maps based on a region proposal network through a preset sliding window; Scoring each of the anchor points through a binary classifier to obtain anchor point box parameters, offsets, and scaling factors; Generating a candidate region frame on the feature map according to the anchor frame parameters, the offset and the scaling factor; Determine whether the candidate area frame contains an indicator light according to the binary classifier, and calculate the category probability distribution of each candidate area frame; According to the category probability distribution, the indicator light category corresponding to the maximum category probability distribution in the category set is taken as the candidate area frame category, and the candidate area frame containing the indicator light is optimized based on the regression head to obtain the positioning information of each indicator light.

5. A method for locating status indicator lights of complex electrical instrument equipment according to claim 4, characterized in that: The calculation formula for generating a candidate region frame on the feature map is as follows: ； The anchor box parameters are , They represent the horizontal coordinate, vertical coordinate, width and height of the center point of the anchor point frame respectively. The offset of the anchor point frame is , They respectively represent the horizontal offset and vertical offset of the center point of the candidate box relative to the center point of the anchor box, and the scaling factor of the width and height of the candidate box relative to the width and height of the anchor box. The candidate region box is represented as , Respectively represent the horizontal coordinate of the center point of the candidate box, the vertical coordinate of the center point, the width and height.

6. A method for locating status indicator lights of complex electrical instrument equipment according to claim 1, characterized in that: Outputting accurate positioning information of each indicator light based on spatial proximity analysis according to the positioning information of each indicator light includes: Obtain the two-dimensional coordinates of each indicator light according to the positioning information of each indicator light; Constructing a spatial proximity matrix according to the two-dimensional coordinates of each of the indicator lights, and calculating an expansion vector of the spatial proximity matrix; Constructing a prediction space proximity matrix according to the two-dimensional coordinates of each of the indicator lights, and calculating an expansion vector of the prediction space proximity matrix; Calculating cosine similarity based on the expansion vector of the spatial proximity matrix and the expansion vector of the predicted spatial proximity matrix; According to the cosine similarity-based matrix dimension reduction and similarity optimization method, redundant or erroneous detection items in the spatial proximity matrix and the predicted spatial proximity matrix are eliminated to obtain accurate positioning results of each indicator light.

7. A method for locating status indicator lights of complex electrical instrument equipment according to claim 6, characterized in that: The calculation formula for calculating the cosine similarity according to the expansion vector of the spatial proximity matrix and the expansion vector of the predicted spatial proximity matrix is: ， Wherein, A represents the expansion vector of the spatial proximity matrix, B represents the expansion vector of the predicted spatial proximity matrix, m is the total length of the vector, i.e., the total number of indicator light pairs, k is the vector index, and A is k represents the actual distance between the kth pair of indicator lights, B k represents the predicted distance between the kth pair of indicator lights, represents the cosine similarity.

8. The method for locating status indicator lights of complex electrical instrument equipment according to claim 6, characterized in that: Eliminating redundant or erroneous detection items in the spatial proximity matrix and the predicted spatial proximity matrix, including: First, gradually remove indicator light information from a matrix with a higher dimension in the spatial proximity matrix and the predicted spatial proximity matrix until the matrix dimensions of the predicted spatial proximity matrix match those of the spatial proximity matrix; The number of indicator light information removed during the dimensionality reduction process is fixed, but different indicator light information can be selected for removal, and the cosine similarity is calculated for all possible dimensionality reduction combinations; The group with the highest cosine similarity is selected for matching, and a mapping between the predicted coordinates of the indicator light and the actual coordinates of the indicator light is constructed to obtain the precise positioning result of each indicator light.

9. The method for locating status indicator lights of complex electrical instrument equipment according to claim 1, characterized in that: Also includes: A preset state mapping table, wherein the state mapping table includes an indicator light number, a two-dimensional actual coordinate of the indicator light, an indicator light state flag, and a device state description corresponding to the indicator light state; Acquire status information and positioning information of all indicator lights, wherein the status information is image data of status indicator lights of electrical instrument equipment and the status flags, and the positioning information is the precise positioning result of each indicator light; The state information and the positioning information are converted into a readable device state according to the preset state mapping table.

10. A status indicator light positioning device for complex electrical instrument equipment, characterized in that: include: An acquisition module, the acquisition module is used to acquire data, the data including image data of status indicator lights of electrical instrument equipment collected in real time; A preprocessing module, wherein the preprocessing module is used to preprocess the data to obtain preprocessed data; A detection module, wherein the detection module is used to input the preprocessed data into a status indicator light detection model based on a deep neural network, and output the location information of each indicator light. The status indicator light detection model based on the deep neural network is used to extract features from the preprocessed data to obtain image features of different scales, perform feature fusion on the image features of different scales to obtain a feature map, and process the feature map based on the feature map based on a region proposal network and a binary classifier; An analysis module is used to output accurate positioning information of each indicator light based on spatial proximity analysis according to the positioning information of each indicator light.

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