Fire identification method, system and equipment used in charging pile scene, and medium

Abstract

The invention relates to the technical field of fire identification, and discloses a fire identification method, system, equipment and medium used in a charging pile scene, and the method comprises the steps: obtaining a time sequence image and a time sequence environment parameter of a to-be-identified charging pile region, the time sequence image comprising a bitmap image and an infrared image, and the time sequence environment parameter comprising time sequence smoke data; inputting the bitmap image into a target recognition model to obtain a target image, and selecting an infrared target image from the infrared images according to the target image; extracting temperature data from the infrared target image of each infrared image to obtain time sequence temperature data; and inputting a target image, the smoke data and the temperature data into a fire identification model to obtain a fire identification result of the charging pile area. According to the method, the flame and the charging pile can be accurately and efficiently recognized through the target recognition model, multiple features are fused through the fire recognition model, the model recognition accuracy is improved, and the safety of the charging environment is further ensured.

Description

Technical Field

[0001] The present invention relates to the field of fire identification technology, and in particular to a fire identification method, system, device and medium for use in a charging pile scenario. Background Art

[0002] With the development of electric vehicles, electric vehicles and electric bicycles occupy a large proportion in people's daily lives. Due to the characteristics of electric vehicles, electric vehicles need to be charged frequently, so charging piles have emerged. At present, the charging pile equipment on the market is generally installed in basements, parking sheds, and parking lots. Because there are many lines in the existing charging piles, and the charging current is very large when charging electric vehicles, the equipment is relatively seriously aged and damaged. At the same time, most charging piles are generally set in an open-air environment, and it is difficult to avoid high temperature, low temperature, and humid working environment. In this way, charging piles are extremely prone to fire; and most charging pile equipment is deployed in relatively remote places or is unattended. Once a fire occurs, it is difficult to provide timely warning and timely disposal. The fire develops and spreads quickly, and the consequences will be more serious, causing greater economic losses.

[0003] Existing charging piles generally integrate temperature sensors to monitor the temperature of the vehicle during the charging process. However, temperature sensors alone cannot monitor the smoke, sparks and other characteristics that may be generated during the charging process, and cannot detect fire hazards in the first place. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a fire identification method, system, device and medium for use in a charging pile scenario, so as to solve the problem of untimely discovery of fire hazards and achieve the effect of accurately and efficiently identifying fire hazards of charging piles.

[0005] In a first aspect, the present invention provides a fire identification method for a charging pile scenario, the method comprising:

[0006] Acquire time-series images and time-series environmental parameters of the charging pile area to be identified, wherein the time-series images include a bitmap image and a corresponding infrared image, and the time-series environmental parameters include time-series smoke data;

[0007] Inputting the bitmap image into a target recognition model to obtain a target image, and selecting a corresponding infrared target image from the infrared image according to the target image;

[0008] Extracting temperature data from the infrared target image of each of the infrared images, and obtaining time series temperature data based on the extracted temperature data;

[0009] The target image, the time-series smoke data and the time-series temperature data are input into a fire recognition model to obtain a fire recognition result of a charging pile area.

[0010] Furthermore, the step of inputting the bitmap image into a target recognition model to obtain a target image comprises:

[0011] Preprocessing the bitmap data, and inputting the preprocessed bitmap data into a pre-trained target recognition model to recognize the flame and the charging pile to obtain a target image, wherein the target image includes a flame image and a charging pile image;

[0012] Among them, the target recognition model is constructed using the YOLO model based on GhostNet, and an attention module and a multi-layer receptive field module are set in the backbone network of the YOLO model. The attention module is a hybrid attention module based on the convolutional block attention mechanism and the coordinate attention mechanism.

[0013] Furthermore, the step of extracting temperature data from the infrared target image of each of the infrared images and obtaining time series temperature data according to the extracted temperature data includes:

[0014] Acquire calibration data of an infrared camera corresponding to the infrared image, and extract pixel values ​​corresponding to an infrared target image of the infrared image;

[0015] Converting the pixel value into a radiation intensity value according to the calibration data, and converting the radiation intensity value into temperature data according to Planck's law;

[0016] The time series temperature data is obtained according to the temperature data corresponding to each of the infrared images.

[0017] Furthermore, the fire recognition model is constructed using a neural network model, including a first recurrent neural network model, a second recurrent neural network model, a convolutional neural network model, a feature fusion layer and a fully connected layer;

[0018] Wherein, the input data of the first recurrent neural network model is time series smoke data, and the output data of the first recurrent neural network model is smoke feature data;

[0019] The input data of the second recurrent neural network model is time series temperature data, and the output data of the second recurrent neural network model is temperature characteristic data;

[0020] The input data of the convolutional neural network model is the target image, and the output data of the convolutional neural network model is image feature data;

[0021] The input data of the feature fusion layer is the smoke feature data, temperature feature data and image feature data, and the output data of the feature fusion layer is fused feature data;

[0022] The input data of the fully connected layer is the fused feature data, and the output data of the fully connected layer is the fire identification result.

[0023] In a second aspect, the present invention provides a fire identification system for a charging pile scenario, the system comprising:

[0024] A data acquisition module, used to acquire time-series images and time-series environmental parameters of the charging pile area to be identified, wherein the time-series images include a bitmap image and a corresponding infrared image, and the time-series environmental parameters include time-series smoke data;

[0025] A target recognition module, used for inputting the bitmap image into a target recognition model to obtain a target image, and selecting a corresponding infrared target image from the infrared image according to the target image;

[0026] A temperature acquisition module, used to extract temperature data from the infrared target image of each of the infrared images, and obtain time series temperature data based on the extracted temperature data;

[0027] The fire identification module is used to input the target image, the time-series smoke data and the time-series temperature data into a fire identification model to obtain a fire identification result of the charging pile area.

[0028] Furthermore, the target recognition module is further used to pre-process the bitmap data, and input the pre-processed bitmap data into a pre-trained target recognition model to recognize the flame and the charging pile to obtain a target image, wherein the target image includes a flame image and a charging pile image;

[0029] Among them, the target recognition model is constructed using the YOLO model based on GhostNet, and an attention module and a multi-layer receptive field module are set in the backbone network of the YOLO model. The attention module is a hybrid attention module based on the convolutional block attention mechanism and the coordinate attention mechanism.

[0030] Furthermore, the temperature acquisition module is also used to acquire calibration data of an infrared camera corresponding to the infrared image, and extract pixel values ​​corresponding to the infrared target image of the infrared image;

[0031] Converting the pixel value into a radiation intensity value according to the calibration data, and converting the radiation intensity value into temperature data according to Planck's law;

[0032] The time series temperature data is obtained according to the temperature data corresponding to each of the infrared images.

[0033] Furthermore, the fire identification module also includes a fire identification model, which is constructed using a neural network model, including a first recurrent neural network model, a second recurrent neural network model, a convolutional neural network model, a feature fusion layer and a fully connected layer;

[0034] Wherein, the input data of the first recurrent neural network model is time series smoke data, and the output data of the first recurrent neural network model is smoke feature data;

[0035] The input data of the second recurrent neural network model is time series temperature data, and the output data of the second recurrent neural network model is temperature characteristic data;

[0036] The input data of the convolutional neural network model is the target image, and the output data of the convolutional neural network model is image feature data;

[0037] The input data of the feature fusion layer is the smoke feature data, temperature feature data and image feature data, and the output data of the feature fusion layer is fused feature data;

[0038] The input data of the fully connected layer is the fused feature data, and the output data of the fully connected layer is the fire identification result.

[0039] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0040] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0041] The present invention provides a fire identification method, system, device and medium for use in a charging pile scenario. The present invention can accurately and efficiently identify flames and charging piles through a target recognition model, and obtain corresponding temperature data based on the recognition results, thereby providing accurate data support for subsequent data analysis. In addition, the present invention integrates multiple features through a multi-input fire recognition model, effectively improving the accuracy of model recognition. The recognition method provided by the present invention can accurately and efficiently identify fire hazards in charging piles, further ensuring the safety of the charging environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of a fire identification method according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of the structure of a target recognition model in an embodiment of the present invention;

[0044] Figure 3 yes Figure 2 Schematic diagram of the module structure of the target recognition model;

[0045] Figure 4 yes Figure 2 Schematic diagram of the structure of the attention module of the target recognition model;

[0046] Figure 5 is a schematic diagram of the structure of a fire identification system in an embodiment of the present invention;

[0047] Figure 6 It is a diagram of the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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. 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.

[0049] See also Figure 1 The first embodiment of the present invention provides a fire identification method for a charging pile scenario, which includes steps S10 to S40:

[0050] Step S10, obtaining a time-series image and a time-series environmental parameter of a charging pile area to be identified, wherein the time-series image includes a bitmap image and a corresponding infrared image, and the time-series environmental parameter includes time-series smoke data;

[0051] Step S20, inputting the bitmap image into a target recognition model to obtain a target image, and selecting a corresponding infrared target image from the infrared image according to the target image;

[0052] Step S30, extracting temperature data from the infrared target image of each of the infrared images, and obtaining time series temperature data based on the extracted temperature data;

[0053] Step S40: input the target image, the time-series smoke data and the time-series temperature data into a fire recognition model to obtain a fire recognition result of the charging pile area.

[0054] The present invention adopts a method based on the combination of image analysis and data analysis to identify fire hazards of charging piles. The images used include bitmap images extracted from surveillance videos and infrared images taken by infrared cameras. The data uses environmental parameters of the charging pile area. Specifically, smoke data collected by smoke sensors can be used. In order to reduce the amount of calculation and improve calculation efficiency, the bitmap images and infrared images used in the present invention are in a corresponding relationship, and each bitmap image corresponds to an infrared image.

[0055] In the actual application scenario of charging piles, the fire hazard occurrence areas can be divided into two types: one is the fire caused by the combustion of the charging pile itself, and the other is the fire caused by the presence of combustibles in the area around the charging pile. In order to accurately identify multiple fire hazards, in a preferred embodiment, firstly, the image within the charging pile area is identified and screened, and the specific steps include:

[0056] Preprocessing the bitmap data, and inputting the preprocessed bitmap data into a pre-trained target recognition model to recognize the flame and the charging pile to obtain a target image, wherein the target image includes a flame image and a charging pile image;

[0057] The target recognition model is constructed using the YOLO model based on GhostNet. An attention module and a multi-layer receptive field module are set in the backbone network of the YOLO model. The attention module is a hybrid attention module based on the convolutional block attention mechanism and the coordinate attention mechanism.

[0058] In this embodiment, the images of the charging pile area used are a group of images sorted in time series, also known as time series images. The following takes a single image in the time series image as an example to illustrate the target recognition steps in this embodiment.

[0059] For a single bitmap image, image preprocessing is first performed, including operations such as cropping, padding, and normalization. Then the preprocessed bitmap image is input into a pre-established target recognition model for target recognition, which includes fire target recognition and charging pile target recognition, thereby obtaining target images, namely flame images and charging pile images.

[0060] The target recognition model in this embodiment is constructed using the YOLOv5 model based on GhostNet. Figure 2The target recognition model is mainly composed of the backbone BackBone, neck and head. The BackBone part is composed of GhostCBS module and GSP1 module, which is mainly used for sufficient feature extraction. The GhostCBS module replaces the traditional convolution Conv module to achieve a significant lightweight backbone network. At the same time, the CBAM-CA attention mechanism and ASPP multi-layer receptive field are added to the tail of the backbone network to increase the feature extraction ability of the backbone network; the Neck part is composed of feature pyramid network and path aggregation network, which reduces the number of model parameters while improving the feature fusion ability of the model. The Head part is composed of 3 detectors, whose function is to complete the detection of target position and category.

[0061] The module structure of the target recognition model is as follows: Figure 3 As shown, specifically, the GhostCBS module is composed of Ghost Conv (Ghost convolution), BN (normalization) and SILU (activation function). The Ghost convolution unit extracts the preliminary feature map through the standard convolution layer, and then further generates the Ghost feature map through several deep convolution layers. These deep convolution layers perform channel-by-channel convolution operations on the preliminary feature map, and the generated Ghost feature map can maintain the feature expression capability while reducing the computational workload and the number of parameters. Compared with the conventional YOLOv5 model, this embodiment uses the Ghost Conv unit to replace the conventional Conv unit, so the structure and function of other modules in the model, such as ResUnit and GSP, will not be repeated.

[0062] Another difference from the conventional YOLOv5 model is that in this embodiment, the CBAM-CA attention mechanism and the ASPP multi-layer receptive field are added to the tail of the backbone network, where the CBAM-CA attention mechanism module consists of the convolutional block attention mechanism CBAM and the coordinate attention mechanism CA, as shown in Figure 4 As shown in the figure, the convolutional block attention mechanism CBAM includes a channel attention mechanism and a spatial attention mechanism. First, the feature map is globally averaged pooled through the channel attention mechanism, and the feature map of each channel is compressed into a scalar value to obtain the global spatial features of each channel. The fully connected layer is used to perform a nonlinear transformation on the features after global average pooling to extract the correlation between channels. The transformation process usually includes a ReLU activation function to increase nonlinearity, and a sigmoid activation function to compress the output value between 0 and 1, generate a channel attention weight, and multiply the channel attention weight by the original feature map element by element to obtain a channel weighted feature map. This step emphasizes the channels that contribute most to the task and suppresses irrelevant or redundant channels.

[0063] The channel-weighted feature map is then processed by the coordinate attention mechanism, that is, the channel-weighted feature map is globally averaged pooled in the width and height directions respectively to capture the global features in the two directions. This step generates two feature maps, representing the global features in the width and height directions respectively. The global feature maps in the two directions are merged to generate a new feature layer. The merged feature layer is further processed using a convolutional layer to extract the features required for coordinate attention. During the processing, normalization and activation functions can be applied to enhance the expressiveness of the model. Then, a 1x1 convolution is used to adjust the number of channels of the merged feature layer to match the number of channels of the input feature map, and a sigmoid activation function is applied to generate attention scores in the width and height dimensions, that is, coordinate attention weights. Finally, the coordinate attention weights are element-wise multiplied with the channel-weighted feature map to obtain the coordinate-weighted feature map. This step emphasizes the areas in the feature map that contribute most to the task and suppresses irrelevant or redundant areas.

[0064] Finally, the spatial attention mechanism processes the spatial dimension of the coordinate-weighted feature map, including channel average pooling and channel maximum pooling, thereby compressing the channel dimension while retaining the spatial information, splicing the results of the global maximum pooling and the global average pooling, generating a feature descriptor containing spatial information, and further processing the spliced ​​feature descriptor using a convolutional layer to generate spatial attention weights, and multiplying the spatial attention weights by the coordinate-weighted feature map element by element to obtain the final hybrid attention weighted feature map. Through the hybrid attention mechanism in this embodiment, the features of the input data can be captured more comprehensively, thereby improving the accuracy of target recognition.

[0065] Furthermore, the ASPP module in this embodiment includes multiple parallel convolutional layers and maximum pooling layers for acquiring features of different receptive fields, and fusing the features of different receptive fields through merging layers and splicing layers, thereby capturing a wider range of context and feature information in the image.

[0066] After obtaining the target recognition model of the above structure, the bitmap image can be input into the target recognition model to identify the flame and the charging pile, thereby obtaining the flame image and the charging pile image. The reason why the charging pile needs to be identified is that when no fire hazard is found, the charging pile image is combined with the subsequent temperature processing, so that the situation where the charging pile temperature is too high and spontaneous combustion occurs is included in the scope of fire monitoring. For the target images identified by the target recognition model, these target areas are used as the corresponding areas of interest in the infrared image, and the temperature data is extracted from these areas of interest. The specific steps include:

[0067] Acquire calibration data of an infrared camera corresponding to the infrared image, and extract pixel values ​​corresponding to an infrared target image of the infrared image;

[0068] Converting the pixel value into a radiation intensity value according to the calibration data, and converting the radiation intensity value into temperature data according to Planck's law;

[0069] The time series temperature data is obtained according to the temperature data corresponding to each of the infrared images.

[0070] In this embodiment, the calibration data of the infrared camera is first obtained. Usually, the calibration data of the infrared camera will provide a lookup table or a mathematical equation to describe the relationship between the pixel value and the radiation intensity value. For example, if the camera provides a lookup table, the radiation intensity value corresponding to the pixel value can be directly obtained through the lookup table. If the camera provides a mathematical equation, the pixel value can be substituted into the equation for calculation to obtain the radiation intensity value. After obtaining the radiation intensity value, the radiation intensity value can be converted into a temperature value using Planck's law, which describes the relationship between the spectral radiance of blackbody radiation and the wavelength and temperature. However, for infrared imaging, what is usually concerned is the radiation intensity within a specific wavelength range, so a simplified form of Planck's law or a formula at a specific wavelength can be used. For example, for the medium-wave or long-wave infrared bands commonly used by infrared cameras, a simplified formula in the following form can be used:

[0071]

[0072] Where T is the temperature (in degrees Kelvin or Celsius), C1 and C2 are the calibration constants of the camera, R is the radiation intensity value, and ε is the target emissivity.

[0073] For each image at a time point, repeat the above temperature data extraction steps to obtain the temperature data at that time point. Arrange these data in chronological order to construct time series temperature data.

[0074] Through the above steps, the target image identified by the target recognition model, the time-series temperature data corresponding to the target image, and the time-series smoke data collected from the smoke sensor can be obtained. For these images and data, the present invention adopts a multi-input neural network model to construct a fire recognition model, and processes these data through the fire recognition model to identify fire hazards. Specifically, the fire recognition model is composed of a convolutional neural network and a recurrent neural network, wherein the convolutional neural network CNN is used to extract features of the target image, and the recurrent neural network RNN ​​includes a first recurrent neural network and a second recurrent neural network, and these two recurrent neural networks are used to extract features of the time-series smoke data and the time-series temperature data, respectively, that is, the three neural networks are parallel structures. It should be noted here that the convolutional neural network model is used to extract features of the flame image. When the target recognition model does not recognize the flame image, the convolutional neural network model will directly output the default value feature value, because the features of the charging pile image are not of great significance for the identification of fire hazards. For the neural network model structure used in this embodiment, refer to the conventional model structure, which will not be described one by one here.

[0075] The image features, temperature features, and smoke features extracted by the model will be input into the feature fusion layer, and the features will be fused by merging and splicing. The obtained fused features will be input into the fully connected layer. There are multiple fully connected layers here. ReLU, ELU and other activation functions can be used after each fully connected layer. The last fully connected layer uses the softmax activation function for classification, so as to obtain the fire recognition result.

[0076] The present embodiment provides a fire identification method for a charging pile scenario. The present invention can accurately and efficiently identify flames and charging piles through a target recognition model, and obtain corresponding temperature data based on the recognition result, thereby providing accurate data support for subsequent data analysis. In addition, the present invention integrates multiple features through a multi-input fire recognition model, thereby effectively improving the accuracy of model recognition. The recognition method provided by the present invention can accurately and efficiently identify fire hazards in charging piles, further ensuring the safety of the charging environment.

[0077] See also Figure 5 Based on the same inventive concept, a fire identification system for a charging pile scenario proposed in the second embodiment of the present invention includes:

[0078] A data acquisition module 10 is used to acquire a time-series image and a time-series environmental parameter of a charging pile area to be identified, wherein the time-series image includes a bitmap image and a corresponding infrared image, and the time-series environmental parameter includes time-series smoke data;

[0079] A target recognition module 20 is used to input the bitmap image into a target recognition model to obtain a target image, and select a corresponding infrared target image from the infrared image according to the target image;

[0080] The temperature acquisition module 30 is used to extract temperature data from the infrared target image of each of the infrared images, and obtain time series temperature data according to the extracted temperature data;

[0081] The fire identification module 40 is used to input the target image, the time-series smoke data and the time-series temperature data into a fire identification model to obtain a fire identification result of the charging pile area.

[0082] Furthermore, the target recognition module 20 is further used to pre-process the bitmap data, and input the pre-processed bitmap data into a pre-trained target recognition model to recognize the flame and the charging pile to obtain a target image, wherein the target image includes a flame image and a charging pile image;

[0083] Among them, the target recognition model is constructed using the YOLO model based on GhostNet, and an attention module and a multi-layer receptive field module are set in the backbone network of the YOLO model. The attention module is a hybrid attention module based on the convolutional block attention mechanism and the coordinate attention mechanism.

[0084] Furthermore, the temperature acquisition module 30 is also used to acquire calibration data of the infrared camera corresponding to the infrared image, and extract pixel values ​​corresponding to the infrared target image of the infrared image;

[0085] Converting the pixel value into a radiation intensity value according to the calibration data, and converting the radiation intensity value into temperature data according to Planck's law;

[0086] The time series temperature data is obtained according to the temperature data corresponding to each of the infrared images.

[0087] Furthermore, the fire identification module 40 also includes a fire identification model, which is constructed using a neural network model, including a first recurrent neural network model, a second recurrent neural network model, a convolutional neural network model, a feature fusion layer and a fully connected layer;

[0088] Wherein, the input data of the first recurrent neural network model is time series smoke data, and the output data of the first recurrent neural network model is smoke feature data;

[0089] The input data of the second recurrent neural network model is time series temperature data, and the output data of the second recurrent neural network model is temperature characteristic data;

[0090] The input data of the convolutional neural network model is the target image, and the output data of the convolutional neural network model is image feature data;

[0091] The input data of the feature fusion layer is the smoke feature data, temperature feature data and image feature data, and the output data of the feature fusion layer is fused feature data;

[0092] The input data of the fully connected layer is the fused feature data, and the output data of the fully connected layer is the fire identification result.

[0093] The technical features and technical effects of the fire identification system for the charging pile scenario proposed in the embodiment of the present invention are the same as the method proposed in the embodiment of the present invention, and will not be repeated here. Each module in the above-mentioned fire identification system for the charging pile scenario can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0094] In addition, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0095] See also Figure 6 , an internal structure diagram of a computer device in an embodiment, the computer device may specifically be a terminal or a server. The computer device includes a processor, a memory, a network interface, a display, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a fire identification method for a charging pile scenario is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0096] It can be understood by those skilled in the art that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have the same component arrangement.

[0097] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0098] In summary, the embodiment of the present invention proposes a fire identification method, system, device and medium for a charging pile scene. The method obtains a time-series image and a time-series environmental parameter of the charging pile area to be identified, wherein the time-series image includes a bitmap image and a corresponding infrared image, and the time-series environmental parameter includes time-series smoke data; the bitmap image is input into a target recognition model to obtain a target image, and a corresponding infrared target image is selected from the infrared image according to the target image; temperature data is extracted from the infrared target image of each infrared image, and time-series temperature data is obtained according to the extracted temperature data; the target image, the time-series smoke data and the time-series temperature data are input into a fire recognition model to obtain a fire recognition result of the charging pile area. The present invention can accurately and efficiently identify flames and charging piles through a target recognition model, and obtain corresponding temperature data based on the recognition result, thereby providing accurate data support for subsequent data analysis, and the present invention integrates multiple features through a multi-input fire recognition model, effectively improving the accuracy of model recognition, and the recognition method provided by the present invention can accurately and efficiently identify fire hazards of charging piles, further ensuring the safety of the charging environment.

[0099] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-mentioned embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims

1. A fire identification method for a charging pile scenario, characterized in that: include: Acquire time-series images and time-series environmental parameters of the charging pile area to be identified, wherein the time-series images include a bitmap image and a corresponding infrared image, and the time-series environmental parameters include time-series smoke data; Inputting the bitmap image into a target recognition model to obtain a target image, and selecting a corresponding infrared target image from the infrared image according to the target image; Extracting temperature data from the infrared target image of each of the infrared images, and obtaining time series temperature data based on the extracted temperature data; The target image, the time-series smoke data and the time-series temperature data are input into a fire recognition model to obtain a fire recognition result of a charging pile area.

2. The fire identification method for charging pile scenarios according to claim 1 is characterized in that: The step of inputting the bitmap image into the target recognition model to obtain the target image comprises: Preprocessing the bitmap data, and inputting the preprocessed bitmap data into a pre-trained target recognition model to recognize the flame and the charging pile to obtain a target image, wherein the target image includes a flame image and a charging pile image; Among them, the target recognition model is constructed using the YOLO model based on GhostNet, and an attention module and a multi-layer receptive field module are set in the backbone network of the YOLO model. The attention module is a hybrid attention module based on the convolutional block attention mechanism and the coordinate attention mechanism.

3. The fire identification method for charging pile scenarios according to claim 1 is characterized in that: The step of extracting temperature data from the infrared target image of each of the infrared images and obtaining time series temperature data according to the extracted temperature data comprises: Acquire calibration data of an infrared camera corresponding to the infrared image, and extract pixel values ​​corresponding to an infrared target image of the infrared image; Converting the pixel value into a radiation intensity value according to the calibration data, and converting the radiation intensity value into temperature data according to Planck's law; The time series temperature data is obtained according to the temperature data corresponding to each of the infrared images.

4. The fire identification method for charging pile scenarios according to claim 1, characterized in that: The fire recognition model is constructed by using a neural network model, including a first recurrent neural network model, a second recurrent neural network model, a convolutional neural network model, a feature fusion layer and a fully connected layer; Wherein, the input data of the first recurrent neural network model is time series smoke data, and the output data of the first recurrent neural network model is smoke feature data; The input data of the second recurrent neural network model is time series temperature data, and the output data of the second recurrent neural network model is temperature characteristic data; The input data of the convolutional neural network model is the target image, and the output data of the convolutional neural network model is image feature data; The input data of the feature fusion layer is the smoke feature data, temperature feature data and image feature data, and the output data of the feature fusion layer is fused feature data; The input data of the fully connected layer is the fused feature data, and the output data of the fully connected layer is the fire identification result.

5. A fire identification system for charging pile scenarios, characterized in that: include: A data acquisition module, used to acquire time-series images and time-series environmental parameters of the charging pile area to be identified, wherein the time-series images include a bitmap image and a corresponding infrared image, and the time-series environmental parameters include time-series smoke data; A target recognition module, used for inputting the bitmap image into a target recognition model to obtain a target image, and selecting a corresponding infrared target image from the infrared image according to the target image; A temperature acquisition module, used to extract temperature data from the infrared target image of each of the infrared images, and obtain time series temperature data based on the extracted temperature data; The fire identification module is used to input the target image, the time-series smoke data and the time-series temperature data into a fire identification model to obtain a fire identification result of the charging pile area.

6. The fire identification system for charging pile scenarios according to claim 5 is characterized in that: The target recognition module is further used to pre-process the bitmap data, and input the pre-processed bitmap data into a pre-trained target recognition model to recognize the flame and the charging pile to obtain a target image, wherein the target image includes a flame image and a charging pile image; Among them, the target recognition model is constructed using the YOLO model based on GhostNet, and an attention module and a multi-layer receptive field module are set in the backbone network of the YOLO model. The attention module is a hybrid attention module based on the convolutional block attention mechanism and the coordinate attention mechanism.

7. The fire identification system for charging pile scenarios according to claim 5 is characterized in that: The temperature acquisition module is further used to acquire calibration data of the infrared camera corresponding to the infrared image, and extract pixel values ​​corresponding to the infrared target image of the infrared image; Converting the pixel value into a radiation intensity value according to the calibration data, and converting the radiation intensity value into temperature data according to Planck's law; The time series temperature data is obtained according to the temperature data corresponding to each of the infrared images.

8. The fire identification system for charging pile scenarios according to claim 5 is characterized in that: The fire identification module also includes a fire identification model, which is constructed using a neural network model and includes a first recurrent neural network model, a second recurrent neural network model, a convolutional neural network model, a feature fusion layer, and a fully connected layer; Wherein, the input data of the first recurrent neural network model is time series smoke data, and the output data of the first recurrent neural network model is smoke feature data; The input data of the second recurrent neural network model is time series temperature data, and the output data of the second recurrent neural network model is temperature characteristic data; The input data of the convolutional neural network model is the target image, and the output data of the convolutional neural network model is image feature data; The input data of the feature fusion layer is the smoke feature data, temperature feature data and image feature data, and the output data of the feature fusion layer is fused feature data; The input data of the fully connected layer is the fused feature data, and the output data of the fully connected layer is the fire identification result.

9. A computer 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 computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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