Fire monitoring method of charging station, electronic equipment and storage medium

Abstract

The invention discloses a fire monitoring method for a charging station, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current charging station image of the charging station when determining that the charging station has a fire risk; the current charging station image is input to a fire monitoring model, a fire monitoring result is obtained, and the fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images marked with fire information. According to the method, when it is detected that the charging station has the fire risk, fire monitoring is carried out on the charging station based on the fire monitoring model, the situation that flames of the charging station are missed due to manual inspection of the charging station can be avoided, and the monitoring accuracy of fire monitoring on the charging station is improved.

Description

Technical Field

[0001] This application belongs to the technical field of image processing, and particularly relates to a method for fire monitoring of a charging station, an electronic device, and a storage medium. Background Art

[0002] With the development of electric vehicles, electric cars and electric bicycles account for a large proportion in people's daily lives. Due to the characteristics of electric vehicles, they need to be charged frequently, so charging stations have emerged.

[0003] Most charging stations are installed in open-air sites, and it is difficult to avoid high-temperature working environments. In addition to the harsh working environment, the battery components are in a high-current working state for a long time, resulting in relatively serious aging and damage. Especially in the hot summer season, the surface temperature is as high as more than 40 degrees. These factors cause the battery components to heat up rapidly and face a great risk of electrical fire. Therefore, it is urgent to monitor the fire of the charging station.

[0004] Currently, the fire monitoring of the charging station mainly relies on manual inspections to determine whether a fire has occurred. However, during the manual inspection process, due to the visual fatigue of the inspectors, it is easy to miss detecting the flame, resulting in low monitoring accuracy for the fire monitoring of the charging station. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a method for fire monitoring of a charging station, an electronic device, and a storage medium to overcome the above problems of the prior art.

[0006] In a first aspect, the embodiments of this application provide a method for fire monitoring of a charging station, including: When it is determined that there is a fire risk at the charging station, obtain the current charging station image of the charging station; Input the current charging station image into a fire monitoring model to obtain a fire monitoring result, where the fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images marked with fire information.

[0007] Wherein, in some optional embodiments, before the step of "when it is determined that there is a fire risk at the charging station, obtain the current charging station image of the charging station", the method for fire monitoring of the charging station further includes: Obtain the environmental point cloud data of the charging station; Determine whether there is the fire risk at the charging station according to the environmental point cloud data.

[0008] Wherein, in some optional embodiments, the step of "determine whether there is the fire risk at the charging station according to the environmental point cloud data" includes: When there is target environmental data greater than or equal to the first environmental data threshold in the environmental point cloud data, it is determined that the charging station has the fire risk, and the first environmental data threshold is used to represent the minimum environmental data for the charging station to have the fire risk; When all the environmental data in the environmental point cloud data are less than the first environmental data threshold, it is determined that the charging station does not have the fire risk.

[0009] Among them, in some alternative embodiments, before inputting the current charging station image into the fire monitoring model to obtain a fire monitoring result, the fire monitoring method further includes: Determine the target area image corresponding to the target environmental data in the current charging station image; Crop the target area image from the current charging station image to obtain a risk area image; The inputting the current charging station image into the fire monitoring model to obtain a fire monitoring result includes: Input the risk area image into the fire monitoring model to obtain the fire monitoring result.

[0010] Among them, in some alternative embodiments, after determining that the charging station has the fire risk, the fire monitoring method of the charging station further includes: When the target environmental data is greater than or equal to the second environmental data threshold, control the fire sprinkler to spray the fire extinguishing medium on the target charging station area corresponding to the target environmental data, and the second environmental data threshold is greater than the first environmental data threshold.

[0011] Among them, in some alternative embodiments, the fire monitoring method of the charging station further includes: When the fire monitoring result includes a first monitoring result, control the fire sprinkler to spray the fire extinguishing medium on the charging station, and the first monitoring result is used to represent that there is a fire source in the charging station.

[0012] Among them, in some alternative embodiments, before controlling the fire sprinkler to spray the fire extinguishing medium on the charging station, the fire monitoring method of the charging station further includes: Determine the fire source location of the fire source; The controlling the fire sprinkler to spray the fire extinguishing medium on the charging station includes: When the fire source location is within the preset spraying area of the fire sprinkler, control the fire sprinkler to spray the fire extinguishing medium.

[0013] Among them, in some alternative embodiments, the fire monitoring method of the charging station further includes: When the position of the fire source is outside the preset spraying area, determine the rotation angle of the fire sprinkler according to the position of the fire source and the preset spraying area; Control the fire sprinkler to rotate by the rotation angle so that the position of the fire source is within the current spraying area of the fire sprinkler; Control the fire sprinkler to spray the fire extinguishing medium.

[0014] In a second aspect, an embodiment of the present application provides a fire monitoring device for a charging station. The fire monitoring device for the charging station includes a first acquisition module and an input module.

[0015] The first acquisition module is configured to acquire the current charging station image when it is determined that there is a fire risk at the charging station; The input module is configured to input the current charging station image into a fire monitoring model to obtain a fire monitoring result. The fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images annotated with fire information.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, including a memory; one or more processors coupled to the memory; and one or more application programs. Among them, the one or more application programs are stored in the memory and are configured to be executed by the one or more processors. The one or more application programs are configured to execute the fire monitoring method for the charging station provided in the first aspect as described above.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which program code is stored. The program code can be called by a processor to execute the fire monitoring method for the charging station provided in the first aspect as described above.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a computer device, it causes the computer device to execute the fire monitoring method for the charging station provided in the first aspect as described above.

[0019] The solution provided by the present application, when it is determined that there is a fire risk at the charging station, acquires the current charging station image of the charging station, and inputs the current charging station image into the fire monitoring model to obtain a fire monitoring result. The fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images annotated with fire information, realizing fire monitoring of the charging station based on the fire monitoring model when it is detected that there is a fire risk at the charging station, which can avoid missed detection of the flames of the charging station caused by manual inspection of the charging station, and improve the monitoring accuracy of fire monitoring for the charging station.

[0020] Moreover, by combining the fire risk detection results and the fire monitoring model to monitor the charging station for fire, it is possible to suppress the problem of misjudging that a fire has occurred at the charging station when only based on the misdetection of the sensor that there is a fire risk at the charging station, which is beneficial to further improve the monitoring accuracy of fire monitoring for the charging station. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 Fig. shows a schematic scenario diagram of a charging station fire monitoring system provided by an embodiment of the present application.

[0023] Figure 2 Fig. shows a schematic flowchart of a method for monitoring the fire of a charging station provided by an embodiment of the present application.

[0024] Figure 3 Fig. shows another schematic flowchart of a method for monitoring the fire of a charging station provided by an embodiment of the present application.

[0025] Figure 4 Fig. shows still another schematic flowchart of a method for monitoring the fire of a charging station provided by an embodiment of the present application.

[0026] Figure 5 Fig. shows still another schematic flowchart of a method for monitoring the fire of a charging station provided by an embodiment of the present application.

[0027] Figure 6 Fig. shows a schematic block diagram of a charging station fire monitoring device provided by an embodiment of the present application.

[0028] Figure 7 Fig. shows a functional block diagram of an electronic device provided by an embodiment of the present application.

[0029] Figure 8 Fig. shows a computer-readable storage medium for storing or carrying program codes for implementing the method for monitoring the fire of a charging station provided by an embodiment of the present application.

[0030] Figure 9 Fig. shows a computer program product for storing or carrying program codes for implementing the method for monitoring the fire of a charging station provided by an embodiment of the present application. Detailed Embodiments

[0031] To make the objects, features, and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0032] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0033] It should also be understood that the terms used in this specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0034] It should be further understood that the term "and / or" used in this specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0035] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used for differential description and cannot be understood as indicating or implying relative importance.

[0036] With the development of electric vehicles, electric cars and electric bicycles account for a large proportion in people's daily lives. Due to the characteristics of electric vehicles, electric vehicles need to be charged frequently, so charging stations have emerged.

[0037] Most charging stations are installed in open-air sites, making it difficult to avoid high-temperature working environments. The harsh working environment, combined with the long-term high-current working state of the battery components, makes their aging and damage relatively serious. Especially in the hot summer season, the surface temperature can reach over 40 degrees Celsius. These factors cause the battery components to heat up rapidly, facing a great risk of electrical fires. Therefore, it is urgent to monitor the fires in charging stations.

[0038] Currently, the fire monitoring of charging stations mainly relies on manual inspections to check whether a fire has occurred. However, during the manual inspection process, due to the visual fatigue of the inspectors, it is easy for the inspectors to miss detecting the flames, resulting in relatively low monitoring accuracy for the fire monitoring of charging stations.

[0039] In view of the above problems, the fire monitoring method, electronic device, and storage medium of the charging station provided in the embodiments of the present application, when it is determined that there is a fire risk in the charging station, obtain the current charging station image, and input the current charging station image into the fire monitoring model to obtain the fire monitoring result. The fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images marked with fire information. When it is detected that there is a fire risk in the charging station, the fire monitoring of the charging station is realized based on the fire monitoring model, which can avoid missing the detection of the flame of the charging station caused by manual inspection of the charging station, and improve the monitoring accuracy of the fire monitoring of the charging station.

[0040] Moreover, by combining the fire risk detection result and the fire monitoring model to conduct fire monitoring on the charging station, it is possible to suppress the problem of misjudging that a fire has occurred in the charging station when it is only misdetected by the sensor that there is a fire risk in the charging station, which is beneficial to further improving the monitoring accuracy of the fire monitoring of the charging station.

[0041] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application.

[0042] Please refer to Figure 1 , which shows a schematic diagram of an application scenario of a charging station fire monitoring system provided in the embodiments of the present application. The charging station fire monitoring system may include a charging station 100, a camera 200, and a processing device 300. The camera 200 can be connected to the processing device 300 through a network and perform data interaction with the processing device 300 through the network.

[0043] Among them, the charging station 100 may include any one of an alternating current (AC) charging station or a direct current (DC) charging station, etc., and no limitation is made here.

[0044] The camera 200 can be used to collect images of the charging station 100, obtain the charging station image, and send the charging station image to the processing device 300 through the network.

[0045] The camera 200 may include any one of a wide-angle camera, a macro camera, an ultra-wide-angle camera, a panoramic camera, a depth camera, a monocular camera, and a binocular camera, etc. The type of the camera 200 is not limited here and can be specifically set according to actual needs.

[0046] As an example, the resolution of the camera 200 may be 1920x1440 pixels, and no limitation is made here.

[0047] The processing device 300 can be used to receive the charging station image sent by the camera 200 and conduct fire monitoring on the charging station according to the charging station image.

[0048] The processing device 300 can be any one of a server or a terminal device, etc., which is not limited here and can be specifically set according to actual requirements.

[0049] The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), big data, and artificial intelligence platforms, etc.

[0050] The terminal device can be a mobile terminal device (for example, in-vehicle terminal, Personal Digital Assistant (PDA), Tablet Personal Computer (TabletPC), laptop computer, etc.), or a fixed terminal device (desktop computer, smart panel, etc.), etc.

[0051] The network can be any one of a ZigBee network, Bluetooth (BT) network, Wireless Fidelity (Wi-Fi) network, Thread network, Long Range Radio (LoRa) network, Low-Power Wide-Area Network (LPWAN), infrared network, Narrow Band Internet of Things (NB-IoT), Controller Area Network (CAN), Digital Living Network Alliance (DLNA) network, Wide Area Network (WAN), Local Area Network (LAN), Metropolitan Area Network (MAN), or Wireless Personal Area Network (WPAN), etc., which is not limited here.

[0052] In some embodiments, the charging station fire monitoring system may further include an environmental sensor, and the environmental sensor can be connected to the processing device 300 through the network and perform data interaction with the processing device 300 through the network.

[0053] An environmental sensor can be installed relative to the charging station 100, and the environmental sensor can be used to collect environmental data of the environment where the charging station 100 is located to obtain environmental point cloud data.

[0054] The environmental sensor can include at least any one of an array temperature sensor and an array smoke sensor, etc. The environmental point cloud data can include at least any one of temperature point cloud data and smoke concentration point cloud data, etc., which is not limited here.

[0055] The array temperature sensor can be formed by arranging a plurality of temperature sensor units in an array. The array temperature sensor can be used to collect the environmental temperature at different points in the environmental space where the charging station 100 is located to obtain temperature point cloud data.

[0056] The temperature sensor unit can be any one of a resistive temperature sensor, a capacitive temperature sensor, a voltage temperature sensor, or a grating temperature sensor, etc., which is not limited here.

[0057] As an example, the array temperature sensor can be a 90640 temperature sensor, and the 90640 temperature sensor can output data of a 32x24 matrix.

[0058] The array smoke sensor can be formed by arranging a plurality of smoke sensor units in an array. The array smoke sensor can be used to collect the smoke concentration at different points in the environmental space where the charging station 100 is located to obtain smoke concentration point cloud data.

[0059] The smoke sensor unit can be any one of a photoelectric smoke sensor, an ionization smoke sensor, a gas-sensitive smoke sensor, or an infrared smoke sensor, etc., which is not limited here.

[0060] Please refer to Figure 2 , which shows a flowchart of a fire monitoring method for a charging station provided by an embodiment of the present application. In a specific embodiment, the fire monitoring method for the charging station can be applied to a processing device 300 in a charging station fire monitoring system as shown in Figure 1 . Taking the processing device 300 as an example, the Figure 2 shown process will be elaborated in detail. The fire monitoring method for the charging station can include the following steps 110 to step 120.

[0061] Step 110: When it is determined that there is a fire risk at the charging station, obtain the current charging station image.

[0062] In an embodiment of the present application, when the processing device determines that there is a fire risk at the charging station, it can send a first acquisition instruction to the camera through the network. The camera receives and responds to the first acquisition instruction, acquires an image of the charging station to obtain the current charging station image, and sends the current charging station image to the processing device through the network. The processing device receives the current charging station image returned by the camera.

[0063] Step 120: Input the current charging station image into the fire monitoring model to obtain a fire monitoring result.

[0064] In an embodiment of the present application, when the processing device determines that there is a fire risk at the charging station, after acquiring the current charging station image of the charging station, the current charging station image can be input into the fire monitoring model. The fire monitoring model receives and responds to the current charging station image, and outputs a fire monitoring result to the processing device. The processing device receives the fire monitoring result output by the fire monitoring model, realizing fire monitoring of the charging station based on the fire monitoring model when it is detected that there is a fire risk at the charging station, which can avoid missing the detection of the flame of the charging station caused by manual inspection of the charging station, and improve the monitoring accuracy of fire monitoring of the charging station.

[0065] Moreover, combining the fire risk detection result and the fire monitoring model for fire monitoring of the charging station can suppress the problem of misjudging that a fire has occurred at the charging station when the sensor only misdetects that there is a fire risk at the charging station, which is beneficial to further improving the monitoring accuracy of fire monitoring of the charging station.

[0066] Among them, the fire monitoring model can be obtained by training a deep learning neural network model based on historical charging station images marked with fire information.

[0067] The fire information can include at least any one of flame color, flame shape, and flame dynamics, etc., which is not limited here.

[0068] The fire monitoring result can include a first monitoring result for indicating that there is a fire source at the charging station and a second monitoring result for indicating that there is no fire source at the charging station.

[0069] The deep learning neural network model can be any one of a convolutional neural network (CNN) model, a deep belief network (DBN) model, a stacked auto encoder network (SAE) model, a recurrent neural network (RNN) model, a deep neural network (DNN) model, a long short-term memory (LSTM) network model, or a gated recurring unit (GRU) model, etc. The type of the deep learning neural network model is not limited here and can be specifically set according to actual needs.

[0070] In the solution provided by this application, when it is determined that there is a fire risk at the charging station, the current charging station image of the charging station is obtained, and the current charging station image is input into the fire monitoring model to obtain a fire monitoring result. The fire monitoring model is trained based on the historical charging station images marked with fire information for the deep learning neural network model. When it is detected that there is a fire risk at the charging station, the charging station is monitored for fire based on the fire monitoring model, which can avoid missed detection of the flames at the charging station caused by manual inspection of the charging station, and improve the monitoring accuracy of fire monitoring for the charging station.

[0071] Moreover, by combining the fire risk detection result and the fire monitoring model to monitor the charging station for fire, the problem of misjudging that a fire has occurred at the charging station when the sensor erroneously detects that there is a fire risk at the charging station can be suppressed, which is beneficial to further improving the monitoring accuracy of fire monitoring for the charging station.

[0072] Please refer to Figure 3 , which shows a flowchart of a method for monitoring a fire at a charging station provided in another embodiment of this application. In a specific embodiment, the method for monitoring a fire at a charging station can be applied to a processing device 300 in a charging station fire monitoring system as shown in Figure 1 . Taking the processing device 300 as an example, the process shown in Figure 3 will be elaborated in detail below. The method for monitoring a fire at a charging station may include the following steps 210 to step 240.

[0073] Step 210: Obtain the environmental point cloud data of the charging station.

[0074] In this embodiment, the charging station fire monitoring system may further include an environmental sensor. The processing device may send a second acquisition instruction to the environmental sensor through a network. The environmental sensor receives and responds to the second acquisition instruction, acquires environmental data of the environment where the charging station is located to obtain environmental point cloud data, and sends the environmental point cloud data to the processing device through the network. The processing device receives the environmental point cloud data returned by the environmental sensor.

[0075] In some embodiments, the environmental sensor may be an array temperature sensor, and the environmental point cloud data may be temperature point cloud data.

[0076] The processing device may send a second acquisition instruction to the array temperature sensor through a network. The array temperature sensor receives and responds to the second acquisition instruction, acquires the environmental temperature of the environment where the charging station is located to obtain temperature point cloud data, and sends the temperature point cloud data to the processing device through the network. The processing device receives the environmental point cloud data returned by the array temperature sensor.

[0077] In some embodiments, the environmental sensor may be an array smoke sensor, and the environmental point cloud data may be smoke concentration point cloud data.

[0078] The processing device may send a second acquisition instruction to the array smoke sensor through a network. The array smoke sensor receives and responds to the second acquisition instruction, acquires the smoke concentration of the environment where the charging station is located to obtain smoke concentration point cloud data, and sends the smoke concentration point cloud data to the processing device through the network. The processing device receives the smoke concentration point cloud data returned by the array smoke sensor.

[0079] Step 220: Determine whether there is a fire risk at the charging station according to the environmental point cloud data.

[0080] In this embodiment, after the processing device acquires the environmental point cloud data of the charging station, it may determine whether there is a fire risk at the charging station according to the environmental point cloud data, realizing fire risk detection for the charging station based on the acquired environmental point cloud data of the charging station, and improving the detection accuracy of fire risk detection for the charging station.

[0081] When there is target environmental data greater than or equal to the first environmental data threshold in the environmental point cloud data, it is determined that there is a fire risk at the charging station; when all the environmental data in the environmental point cloud data are less than the first environmental data threshold, it is determined that there is no fire risk at the charging station.

[0082] Among them, the first environmental data threshold can be used to represent the minimum environmental data when there is a fire risk at the charging station. Judging whether there is a fire risk at the charging station based on the environmental point cloud data and the first environmental data threshold is beneficial to improving the judgment accuracy of fire risk judgment for the charging station.

[0083] In some embodiments, the environmental point cloud data may be temperature point cloud data, and the first environmental data threshold may be the first temperature threshold.

[0084] When there is a target temperature greater than or equal to the first temperature threshold in the temperature point cloud data, it is determined that there is a fire risk at the charging station; when all the temperatures in the temperature point cloud data are less than the first temperature threshold, it is determined that there is no fire risk at the charging station.

[0085] Among them, the first temperature threshold can be used to represent the minimum temperature at which there is a fire risk at the charging station. Judging whether there is a fire risk at the charging station based on the temperature point cloud data and the first temperature threshold is beneficial to improving the accuracy of the judgment on the fire risk of the charging station.

[0086] In some embodiments, the environmental point cloud data may be smoke concentration point cloud data, and the first environmental data threshold may be the first smoke concentration threshold.

[0087] When there is a target smoke concentration greater than or equal to the first smoke concentration threshold in the smoke concentration point cloud data, it is determined that there is a fire risk at the charging station; when all the smoke concentrations in the smoke concentration point cloud data are less than the first smoke concentration threshold, it is determined that there is no fire risk at the charging station.

[0088] Among them, the first smoke concentration threshold can be used to represent the minimum smoke concentration at which there is a fire risk at the charging station. Judging whether there is a fire risk at the charging station based on the smoke concentration point cloud data and the first smoke concentration threshold is beneficial to improving the accuracy of the judgment on the fire risk of the charging station.

[0089] In some embodiments, after the processing device obtains the environmental point cloud data of the charging station, it can perform interpolation processing on the environmental point cloud data to obtain interpolated point cloud data, and determine whether there is a fire risk at the charging station according to the interpolated point cloud data.

[0090] When there is a target environmental data greater than or equal to the first environmental data threshold in the interpolated point cloud data, it is determined that there is a fire risk at the charging station; when all the environmental data in the interpolated point cloud data are less than the first environmental data threshold, it is determined that there is no fire risk at the charging station.

[0091] Among them, the interpolation processing can be any one of single linear interpolation processing, bilinear interpolation processing, cubic interpolation processing, etc., which is not limited here.

[0092] Step 230: When it is determined that there is a fire risk at the charging station, obtain the current charging station image.

[0093] Step 240: Input the current charging station image into the fire monitoring model to obtain the fire monitoring result.

[0094] In this embodiment, steps 230 and 240 may refer to the corresponding steps in the foregoing embodiments, and will not be elaborated here.

[0095] In some embodiments, when the processing device determines that there is a fire risk at the charging station, after obtaining the current charging station image of the charging station, it may determine the target area image corresponding to the target environmental data in the current charging station image, crop the target area image from the current charging station image to obtain a risk area image, and input the risk area image into the fire monitoring model. The fire monitoring model receives and responds to the risk area image, outputs the fire monitoring result to the processing device. The processing device receives the fire monitoring result output by the fire monitoring model, crops the risk area image from the charging station image based on the fire risk detection result, and performs fire monitoring on the risk area image based on the fire monitoring model, which can reduce the computational load of the model and is beneficial to improving the monitoring efficiency of fire monitoring at the charging station.

[0096] Among them, the processing device may determine the target area image corresponding to the current charging station image according to the target spatial position of the target environmental data.

[0097] In some embodiments, the charging station fire monitoring system may further include a fire sprinkler. The fire sprinkler is connected to the processing device through a network and performs data interaction with the processing device through the network. The fire sprinkler can be used to spray a fire extinguishing medium.

[0098] After the processing device determines that there is a fire risk at the charging station and when the target environmental data is greater than or equal to the second environmental data threshold, it may send a first control instruction to the fire sprinkler through the network. The fire sprinkler receives and responds to the first control instruction, and sprays the fire extinguishing medium on the target charging station area corresponding to the target environmental data, realizing automatic control of the fire sprinkler to perform fire extinguishing treatment on the corresponding area of the charging station when a relatively high fire risk is detected at the charging station, which can avoid serious damage to the charging station caused by the spread of the fire and is beneficial to reducing the damage to the charging station.

[0099] Among them, the second environmental data threshold is greater than the first environmental data threshold. For example, the first environmental data threshold may be 70 °C, and the second environmental data threshold may be 120 °C, which is not limited here.

[0100] The fire extinguishing medium may be any one of water, sand, soil, asbestos powder, asbestos felt, foam, dry powder, carbon dioxide, or heptafluoropropane, etc., which is not limited here.

[0101] The solution provided in this embodiment obtains the environmental point cloud data of the charging station, determines whether there is a fire risk at the charging station based on the environmental point cloud data, and when it is determined that there is a fire risk at the charging station, obtains the current charging station image of the charging station and inputs the current charging station image into the fire monitoring model to obtain the fire monitoring result. By detecting the fire risk of the charging station based on the collected environmental point cloud data of the charging station, the detection accuracy of the fire risk detection of the charging station is improved.

[0102] Please refer to Figure 4 , which shows a flowchart of a fire monitoring method for a charging station provided in another embodiment of the present application. In a specific embodiment, the fire monitoring method for a charging station can be applied to a processing device 300 in a charging station fire monitoring system as shown in Figure 1 . Taking the processing device 300 as an example, the following will elaborate in detail on the Figure 4 shown process. The fire monitoring method for a charging station may include the following steps 310 to step 330.

[0103] Step 310: When it is determined that there is a fire risk at the charging station, obtain the current charging station image of the charging station.

[0104] Step 320: Input the current charging station image into the fire monitoring model to obtain the fire monitoring result.

[0105] In this embodiment, steps 310 and 320 may refer to the content of the corresponding steps in the foregoing embodiment, and will not be elaborated here.

[0106] Step 330: When the fire monitoring result includes a first monitoring result, control the fire sprinkler to spray a fire extinguishing medium on the charging station.

[0107] In this embodiment, the charging station fire monitoring system may further include a fire sprinkler. When the fire monitoring result includes a first monitoring result, a second control instruction may be sent to the fire sprinkler through the network. The fire sprinkler receives and responds to the second control instruction to spray a fire extinguishing medium on the charging station, realizing automatic control of the fire sprinkler to extinguish the fire at the charging station when a fire occurs at the charging station, which can avoid serious damage to the charging station caused by the spread of the fire and is beneficial to reducing the damage to the charging station.

[0108] The solution provided in this embodiment, when it is determined that there is a fire risk at the charging station, obtains the current charging station image of the charging station, inputs the current charging station image into the fire monitoring model to obtain the fire monitoring result, and when the fire monitoring result includes a first monitoring result, controls the fire sprinkler to spray a fire extinguishing medium on the charging station, realizing automatic control of the fire sprinkler to extinguish the fire at the charging station when a fire occurs at the charging station, which can avoid serious damage to the charging station caused by the spread of the fire and is beneficial to reducing the damage to the charging station.

[0109] Please refer to Figure 5 , which shows a flowchart of a fire monitoring method for a charging station provided by another embodiment of the present application. In a specific embodiment, the fire monitoring method for the charging station can be applied to a processing device 300 in a charging station fire monitoring system as shown in Figure 1 . Taking the processing device 300 as an example, the process shown in Figure 5 will be elaborated in detail below. The fire monitoring method for the charging station may include the following steps 410 to step 440.

[0110] Step 410: When it is determined that there is a fire risk at the charging station, obtain the current charging station image.

[0111] Step 420: Input the current charging station image into the fire monitoring model to obtain a fire monitoring result.

[0112] In this embodiment, steps 410 and 420 may refer to the content of the corresponding steps in the foregoing embodiment, which will not be elaborated herein.

[0113] Step 430: When the fire monitoring result includes a first monitoring result, determine the fire source location of the fire source.

[0114] In this embodiment, when the fire monitoring result includes a first monitoring result, the fire source location of the fire source at the charging station can be determined according to the pixel position of the fire source in the current charging station image.

[0115] Step 440: When the fire source location is within the preset spraying area of the fire sprinkler, control the fire sprinkler to spray a fire extinguishing medium.

[0116] In this embodiment, when the fire source location is within the preset spraying area of the fire sprinkler, the processing device can send a third control instruction to the fire sprinkler through the network. The fire sprinkler receives and responds to the third control instruction to spray the fire extinguishing medium, realizing automatic control of the fire sprinkler to spray the fire extinguishing medium when it is detected that a fire has occurred at the charging station and the fire source location is within the preset spraying area of the fire sprinkler, so that the spraying range of the fire extinguishing medium can cover the fire source, which is beneficial to improving the control accuracy of controlling the fire situation.

[0117] Among them, the preset spraying area is the spraying range pre-adjusted by the user.

[0118] In some embodiments, when the position of the fire source is outside the preset spraying area, the processing device can calculate the rotation angle of the fire sprinkler according to the position of the fire source and the preset spraying area, and send the rotation angle to the fire sprinkler through the network. The fire sprinkler receives and responds to the rotation angle, rotates by the rotation angle, so that the position of the fire source is within the current spraying area of the fire sprinkler, and sends a fourth control instruction to the fire sprinkler through the network. The fire sprinkler receives and responds to the fourth control instruction and sprays the fire extinguishing medium. When a fire occurs at the charging station is detected and the position of the fire source is outside the preset spraying area of the fire sprinkler, the fire sprinkler is controlled to rotate to a position where the spraying range can cover the fire source, and the fire extinguishing medium is sprayed, so that the spraying range of the fire extinguishing medium can cover the fire source, which is beneficial to improving the control accuracy of the fire situation.

[0119] For the solution provided in this embodiment, when it is determined that there is a fire risk at the charging station, the current charging station image of the charging station is obtained and input into the fire monitoring model to obtain the fire monitoring result. When the fire monitoring result includes the first monitoring result, the position of the fire source of the fire is determined. And when the position of the fire source is within the preset spraying area of the fire sprinkler, the fire sprinkler is controlled to spray the fire extinguishing medium. When a fire occurs at the charging station is detected and the position of the fire source is within the preset spraying area of the fire sprinkler, the fire sprinkler is automatically controlled to spray the fire extinguishing medium, so that the spraying range of the fire extinguishing medium can cover the fire source, which is beneficial to improving the control accuracy of the fire situation.

[0120] Please refer to Figure 6 , which shows the fire monitoring device 500 of the charging station provided in another embodiment of the present application. In a specific embodiment, the fire monitoring device 500 of the charging station can be applied to the processing device 300 in the charging station fire monitoring system as shown in Figure 1 . Taking the processing device 300 as an example below, the fire monitoring device 500 of the charging station shown in Figure 6 will be elaborated in detail. The fire monitoring device 500 of the charging station may include a first acquisition module 510 and an input module 520.

[0121] The first acquisition module 510 can be used to obtain the current charging station image when it is determined that there is a fire risk at the charging station; the input module 520 can be used to input the current charging station image into the fire monitoring model to obtain the fire monitoring result. The fire monitoring model can be obtained by training a deep learning neural network model based on historical charging station images marked with fire information.

[0122] In some embodiments, the fire monitoring device 500 of the charging station may further include a second acquisition module and a first determination module.

[0123] The second acquisition module can be used to acquire the environmental point cloud data of the charging station before the first acquisition module 510 acquires the current charging station image when it is determined that there is a fire risk at the charging station; the first determination module can be used to determine whether there is a fire risk at the charging station according to the environmental point cloud data.

[0124] In some embodiments, the first determination module may include a first determination unit and a second determination unit.

[0125] The first determination unit can be used to determine that there is a fire risk at the charging station when there is target environmental data greater than or equal to the first environmental data threshold in the environmental point cloud data, and the first environmental data threshold can be used to represent the minimum environmental data for the charging station to have a fire risk; the second determination unit can be used to determine that there is no fire risk at the charging station when all the environmental data in the environmental point cloud data is less than the first environmental data threshold.

[0126] In some embodiments, the fire monitoring device 500 of the charging station may further include a second determination module and a cropping module.

[0127] The second determination module can be used to determine the target area image corresponding to the target environmental data in the current charging station image before the input module 520 inputs the current charging station image into the fire monitoring model to obtain the fire monitoring result; the cropping module can be used to crop the target area image from the current charging station image to obtain the risk area image.

[0128] In some embodiments, the input module 520 may include an input unit.

[0129] The input unit can be used to input the risk area image into the fire monitoring model to obtain the fire monitoring result.

[0130] In some embodiments, the fire monitoring device 500 of the charging station may further include a first control module.

[0131] The first control module can be used to control the fire sprinkler to spray the fire extinguishing medium on the target charging station area corresponding to the target environmental data when the first determination unit determines that there is a fire risk at the charging station and the target environmental data is greater than or equal to the second environmental data threshold, and the second environmental data threshold is greater than the first environmental data threshold.

[0132] In some embodiments, the fire monitoring device 500 of the charging station may further include a second control module.

[0133] The second control module can be used to control the fire sprinkler to spray the fire extinguishing medium on the charging station when the fire monitoring result includes the first monitoring result, and the first monitoring result can be used to represent that there is a fire source at the charging station.

[0134] In some embodiments, the fire monitoring device 500 of the charging station may further include a third determination module.

[0135] The third determination module may be configured to determine the fire source location of the fire source before the second control module controls the fire sprinkler to spray the fire extinguishing medium on the charging station.

[0136] In some embodiments, the second control module may include a control unit.

[0137] The control unit may be configured to control the fire sprinkler to spray the fire extinguishing medium when the fire source location is within the preset spraying area of the fire sprinkler.

[0138] In some embodiments, the fire monitoring device 500 of the charging station may further include a fourth determination module, a third control module, and a fourth control module.

[0139] The fourth determination module may be configured to determine the rotation angle of the fire sprinkler according to the fire source location and the preset spraying area when the fire source location is outside the preset spraying area; the third control module may be configured to control the fire sprinkler to rotate by the rotation angle so that the fire source location is within the current spraying area of the fire sprinkler; the fourth control module may be configured to control the fire sprinkler to spray the fire extinguishing medium.

[0140] The solution provided in this embodiment, when it is determined that there is a fire risk at the charging station, obtains the current charging station image of the charging station, and inputs the current charging station image into the fire monitoring model to obtain a fire monitoring result. The fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images marked with fire information. When it is detected that there is a fire risk at the charging station, the charging station is monitored for fire based on the fire monitoring model, which can avoid missed detection of the flames at the charging station caused by manual inspection of the charging station, and improve the monitoring accuracy of fire monitoring for the charging station.

[0141] Moreover, combining the fire risk detection result and the fire monitoring model to monitor the charging station for fire can suppress the problem of misjudging that a fire has occurred at the charging station when it is only misdetected by the sensor that there is a fire risk at the charging station, which is beneficial to further improving the monitoring accuracy of fire monitoring for the charging station.

[0142] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For device embodiments, since they are basically similar to method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments. For any processing method described in the method embodiments, it can be implemented by the corresponding processing module in the device embodiments, and will not be elaborated one by one in the device embodiments.

[0143] In addition, in each embodiment of the present application, each functional module can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module.

[0144] Please refer to Figure 7 , which shows a functional block diagram of an electronic device 600 provided by an embodiment of the present application. The electronic device 600 may include one or more of the following components: a memory 610, a processor 620, and one or more application programs, where one or more application programs may be stored in the memory 610 and configured to be executed by one or more processors 620, and one or more application programs are configured to execute the methods described in the foregoing method embodiments.

[0145] The memory 610 may include a random access memory (RAM) and may also include a read-only memory. The memory 610 may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 610 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as determining the existence of a fire risk, obtaining an image of the current charging station, inputting the image of the current charging station, the fire monitoring result, annotating fire information, training a deep learning neural network model, obtaining a fire monitoring model, obtaining environmental point cloud data, determining whether there is a fire risk, determining that there is no fire risk, determining the target area image, cropping the target area image, obtaining the risk area image, inputting the risk area image, controlling the fire sprinkler to spray a fire extinguishing medium, determining the fire source location, determining the rotation angle, and rotating the fire sprinkler, etc.), and instructions for implementing the following method embodiments. The data storage area may also store data created during the use of the electronic device 600 (such as a charging station, a fire risk, an image of the current charging station, a fire monitoring model, a fire monitoring result, fire information, a historical image of the charging station, a deep learning neural network model, environmental point cloud data, a first environmental data threshold, target environmental data, minimum environmental data, a target area image, a risk area image, a second environmental data threshold, a fire sprinkler, a target charging station area, a fire extinguishing medium, a first monitoring result, a fire source, a fire source location, a preset spraying area, and a rotation angle).

[0146] The processor 620 may include one or more processing cores. The processor 620 connects various parts within the entire electronic device 600 through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 610, and by invoking the data stored in the memory 610, it performs various functions of the electronic device 600 and processes data. Optionally, the processor 620 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 620 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the display content; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 620 and may be implemented separately through a communication chip.

[0147] Please refer to Figure 8 , which shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application. Program code 710 is stored in the computer-readable storage medium 700, and the program code 710 can be called by a processor to execute the method described in the above method embodiment.

[0148] The computer-readable storage medium 700 may be an electronic memory such as a flash memory, EEPROM (electrically erasable programmable read-only memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 700 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 700 has a storage space for the program code 710 that executes any method step in the above method. These program codes can be read out from or written into one or more computer program products. The program code 710 may be compressed in an appropriate form, for example.

[0149] Please refer to Figure 9, which shows a structural block diagram of a computer program product 800 provided by an embodiment of the present application. The computer program product 800 includes a computer program / instructions 810, and the computer program / instructions 810 are stored in a computer-readable storage medium of a computer device. When the computer program product 800 runs on the computer device, a processor of the computer device reads the computer program / instructions 810 from the computer-readable storage medium, and the processor executes the computer program / instructions 810, so that the computer device executes the method described in the above method embodiment.

[0150] In the solution provided by this embodiment, when it is determined that there is a fire risk at the charging station, the current charging station image of the charging station is acquired, and the current charging station image is input into the fire monitoring model to obtain a fire monitoring result. The fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images marked with fire information. When it is detected that there is a fire risk at the charging station, the charging station is monitored for fire based on the fire monitoring model, which can avoid missing the detection of the flame of the charging station caused by manual inspection of the charging station, and improve the monitoring accuracy of fire monitoring for the charging station.

[0151] Moreover, by combining the fire risk detection result and the fire monitoring model to monitor the charging station for fire, the problem of misjudging that a fire has occurred at the charging station when it is only detected by the sensor that there is a fire risk at the charging station can be suppressed, which is beneficial to further improving the monitoring accuracy of fire monitoring for the charging station.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A fire monitoring method for a charging station, characterized in that: include: When it is determined that there is a fire risk in the charging station, obtaining a current charging station image of the charging station; The current charging station image is input into a fire monitoring model to obtain a fire monitoring result. The fire monitoring model is obtained by training a deep learning neural network model based on historical charging station images marked with fire information.

2. The fire monitoring method according to claim 1, characterized in that: When it is determined that there is a fire risk in the charging station, before obtaining the current charging station image of the charging station, the fire monitoring method further includes: Acquire environmental point cloud data of the charging station; Determine whether the charging station has the fire risk based on the environmental point cloud data.

3. The fire monitoring method according to claim 2, characterized in that: The determining, according to the environmental point cloud data, whether the charging station has the fire risk includes: When there is target environmental data greater than or equal to a first environmental data threshold in the environmental point cloud data, it is determined that the charging station has the fire risk, and the first environmental data threshold is used to characterize the minimum environmental data of the charging station having the fire risk; When all environmental data in the environmental point cloud data are less than the first environmental data threshold, it is determined that the charging station does not have the fire risk.

4. The fire monitoring method according to claim 3, characterized in that: Before inputting the current charging station image into the fire monitoring model to obtain the fire monitoring result, the fire monitoring method further includes: Determining a target area image corresponding to the target environment data in the current charging station image; Cutting out the target area image from the current charging station image to obtain a risk area image; The inputting the current charging station image into the fire monitoring model to obtain the fire monitoring result includes: The risk area image is input into the fire monitoring model to obtain the fire monitoring result.

5. The fire monitoring method according to claim 3, characterized in that: After determining that the charging station has the fire risk, the fire monitoring method further includes: When the target environment data is greater than or equal to a second environment data threshold, the fire sprinkler is controlled to spray a fire extinguishing medium on a target charging station area corresponding to the target environment data, and the second environment data threshold is greater than the first environment data threshold.

6. The fire monitoring method according to any one of claims 1 to 5, characterized in that: Also includes: When the fire monitoring result includes a first monitoring result, the fire sprinkler is controlled to spray a fire extinguishing medium on the charging station, and the first monitoring result is used to indicate that a fire source exists in the charging station.

7. The fire monitoring method according to claim 6, characterized in that: Before controlling the fire sprinkler to spray the fire extinguishing medium on the charging station, the fire monitoring method further includes: determining a fire source location of the fire source; The controlling the fire sprinkler to spray the fire extinguishing medium to the charging station comprises: When the fire source is located within the preset spraying area of ​​the fire sprinkler, the fire sprinkler is controlled to spray the fire extinguishing medium.

8. The fire monitoring method according to claim 7, characterized in that: Also includes: When the fire source position is outside the preset spraying area, determining the rotation angle of the fire sprinkler head according to the fire source position and the preset spraying area; Controlling the fire sprinkler to rotate the rotation angle so that the fire source position is within the current spraying area of ​​the fire sprinkler; The fire sprinkler is controlled to spray the fire extinguishing medium.

9. An electronic device, characterized in that: include: Memory; One or more processors coupled to the memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the fire monitoring method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the fire monitoring method according to any one of claims 1 to 8.

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