Method and device for detecting living body in machine table of production equipment of battery assembly

Abstract

The invention provides a living body detection method and device in a production equipment machine table of a battery assembly, relates to the technical field of photovoltaic battery pieces, and solves the technical problem of low accuracy of living body detection in the production equipment machine table. The method comprises the following steps: monitoring temperature change data in an equipment cabinet through an infrared sensor, and acquiring a real-time image in the equipment cabinet through an image acquisition device; based on the temperature change data, performing data analysis through a threshold method to obtain a data analysis result; performing image analysis through a YOLOv10 target detection algorithm based on the real-time image to obtain an image analysis result; performing data fusion on the data analysis result and the image analysis result to obtain a comprehensive analysis result; and judging whether a living body object exists in the equipment machine according to the comprehensive analysis result to obtain a living body detection result in the equipment machine.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic cells, and in particular to a method and device for detecting living bodies in a production equipment platform of a battery assembly. Background Art

[0002] At present, with the widespread application of automated equipment in the production workshops of the photovoltaic cell manufacturing industry, production efficiency has been greatly improved, but it has also brought new safety hazards, especially the possibility of human bodies or other living objects entering the equipment, which may lead to serious safety accidents. Therefore, it is very important to conduct real-time detection of living things in equipment. In order to deal with this problem, ultrasonic detection is currently mainly used. However, the detection range of ultrasonic detection technology is limited, the resolution is low, and it is difficult to accurately identify small living objects, which affects its detection effect and leads to low accuracy of live detection in production equipment. Summary of the invention

[0003] The object of the present invention is to provide a method and device for detecting living bodies in a production equipment platform of a battery assembly, so as to solve the technical problem of low accuracy of detecting living bodies in the production equipment platform.

[0004] In a first aspect, the present application provides a method for detecting liveness in a battery assembly production equipment platform, the method comprising: Monitor the temperature change data in the equipment platform through infrared sensors, and obtain real-time images in the equipment platform through image acquisition devices; Performing data analysis based on the temperature change data by using a threshold method to obtain a data analysis result; Performing image analysis based on the real-time image using a YOLOv10 (You Only Look Once version 10) target detection algorithm to obtain an image analysis result; Fusing the data analysis result and the image analysis result to obtain a comprehensive analysis result; According to the comprehensive analysis result, it is determined whether there is a living object in the equipment, and a living object detection result in the equipment is obtained.

[0005] In a possible implementation, after monitoring the temperature change data in the equipment platform by using an infrared sensor and acquiring a real-time image in the equipment platform by using an image acquisition device, the method further includes: Performing infrared data filtering processing on the temperature change data to obtain a data preprocessing result; Perform image preprocessing on the real-time image to obtain an image preprocessing result.

[0006] In a possible implementation, performing image analysis based on the real-time image by using a YOLOv10 target detection algorithm to obtain an image analysis result includes: The target object in the image preprocessing result is identified and located by the YOLOv10 target detection algorithm to obtain a recognition result and a positioning result for the target object, and the recognition result and the positioning result are used as the image analysis result of the real-time image.

[0007] In a possible implementation, after determining whether there is a living object in the device according to the comprehensive analysis result and obtaining a living object detection result in the device, the method further includes: If it is determined that there is a living object in the equipment, triggering an audible and visual alarm and sending an alarm signal, and controlling the target device corresponding to the equipment to stop running; An alarm log is recorded according to the presence of a living object in the equipment.

[0008] In a possible implementation, after recording an alarm log according to the presence of a living object in the equipment, the method further includes: Controlling the removal of the living object from within the equipment and confirming the removal result of the living object; If the clearing result is that the living object has been cleared, resetting the alarm state of the sound and light alarm, and detecting the system state corresponding to the target device; If the system state belongs to the specified normal state, the target device is controlled to resume operation, and the steps of monitoring the temperature change data in the device through the infrared sensor and acquiring the real-time image in the device through the image acquisition device are continued.

[0009] In a possible implementation, after the controlling clears the living object from the equipment and confirms the clearing result of the living object, the method further includes: If the clearing result is that the living object has not been cleared, the alarm process continues, and the step of controlling the clearing process of the living object from the equipment is executed again.

[0010] In a possible implementation, after determining whether there is a living object in the device according to the comprehensive analysis result and obtaining a living object detection result in the device, the method further includes: If it is determined that there is no living object in the equipment platform, the steps of monitoring the temperature change data in the equipment platform by using an infrared sensor and acquiring a real-time image in the equipment platform by using an image acquisition device are performed.

[0011] In a second aspect, the present application provides a liveness detection device in a battery assembly production equipment platform, comprising: An acquisition module is used to monitor the temperature change data in the equipment platform through an infrared sensor and acquire a real-time image in the equipment platform through an image acquisition device; A first analysis module, used to perform data analysis based on the temperature change data by a threshold method to obtain a data analysis result; A second analysis module is used to perform image analysis based on the real-time image through a YOLOv10 target detection algorithm to obtain an image analysis result; A fusion module, used for fusing the data analysis result and the image analysis result to obtain a comprehensive analysis result; The judgment module is used to judge whether there is a living object in the equipment according to the comprehensive analysis result, and obtain the living object detection result in the equipment.

[0012] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0013] In a fourth aspect, the present application further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method described in the first aspect.

[0014] This application brings the following beneficial effects: The present application provides a method and device for detecting living things in a production equipment platform of a battery assembly. The method and device can monitor the temperature change data in the equipment platform through an infrared sensor, obtain a real-time image in the equipment platform through an image acquisition device, perform data analysis based on the temperature change data through a threshold method to obtain a data analysis result, perform image analysis based on the real-time image through a YOLOv10 target detection algorithm to obtain an image analysis result, fuse the data analysis result and the image analysis result to obtain a comprehensive analysis result, judge whether there is a living object in the equipment platform based on the comprehensive analysis result, and obtain a liveness detection result in the equipment platform. In this scheme, a dynamic liveness detection method in equipment and machine platforms is developed by combining infrared detection and YOLOv10 deep learning algorithm. Infrared sensors are used to detect temperature changes, and YOLOv10 algorithm is used for real-time image recognition. The infrared sensor can effectively detect temperature changes and timely discover living objects in the machine platform. The YOLOv10 deep learning algorithm can quickly identify and locate these living objects with its high precision and real-time advantages. This method significantly improves the sensitivity and accuracy of detection, effectively reduces the false alarm rate, improves the accuracy of liveness detection in production equipment and machines, and solves the technical problem of low accuracy of liveness detection in production equipment and machines.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a method for detecting a live body in a battery assembly production equipment according to an embodiment of the present application; Figure 2 Another schematic diagram of the process of the method for detecting a living body in a battery assembly production equipment provided by an embodiment of the present application; Figure 3 A schematic diagram of the structure of a liveness detection device in a battery assembly production equipment platform provided in an embodiment of the present application; Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0019] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0020] At present, in order to conduct real-time detection of living things in equipment and machines, the existing technologies mainly use technologies such as ultrasonic detection. These technologies achieve liveness detection through different principles, but in the complex and changeable production environment, they each face different challenges and limitations. The detection range of ultrasonic detection technology is limited, the resolution is low, and it is difficult to accurately identify small living objects, which affects its detection effect. Although multi-sensor fusion technology can improve the accuracy of detection, the system complexity is high, the data fusion and analysis are difficult, and the development and maintenance costs are high. These shortcomings and deficiencies make it difficult for existing technologies to fully meet the requirements of efficiency, safety, and reliability in practical applications, resulting in low accuracy of liveness detection in production equipment and machines.

[0021] Based on this, an embodiment of the present application provides a method and device for detecting living things in a production equipment platform of a battery assembly, by which the technical problem of low accuracy of living things detection in a production equipment platform can be solved.

[0022] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0023] Figure 1 A schematic diagram of a method for detecting a live body in a battery assembly production equipment provided in an embodiment of the present application. Figure 1 As shown, the method includes: Step S110, monitoring the temperature change data in the equipment platform through an infrared sensor, and acquiring a real-time image in the equipment platform through an image acquisition device.

[0024] For example, Figure 2As shown, the infrared sensor continuously detects temperature changes, and the camera continuously captures images inside the equipment. After this step, the method may also include the following steps: performing infrared data filtering on the temperature change data to obtain data preprocessing results; performing image preprocessing on the real-time image to obtain image preprocessing results. The preprocessing process can improve the efficiency of subsequent data analysis of the preprocessing results.

[0025] Step S120, performing data analysis based on the temperature change data by using a threshold method to obtain a data analysis result.

[0026] As an optional implementation, Figure 2 As shown, in the data fusion and analysis process, based on the data preprocessing result obtained by performing infrared data filtering processing on the temperature change data, data analysis is performed through the threshold method to obtain the data analysis result corresponding to the temperature change data.

[0027] Step S130, performing image analysis based on the real-time image using the YOLOv10 target detection algorithm to obtain an image analysis result.

[0028] Among them, YOLOv10, as an advanced target detection algorithm, has high precision and strong real-time performance. In the embodiment of the present application, the characteristics of YOLOv10 are used to process image data in a short time and quickly identify living objects in the machine, ensuring the safety of the equipment during operation and improving detection accuracy and real-time performance. Moreover, the YOLOv10 algorithm has strong real-time performance and environmental adaptability, and can run stably in complex production environments.

[0029] In an optional implementation, this step may specifically include the following steps: using the YOLOv10 target detection algorithm to identify and locate the target object in the image preprocessing result, obtaining the recognition result and the positioning result for the target object, and using the recognition result and the positioning result as the image analysis result of the real-time image. Using the YOLOv10 deep learning algorithm to identify and locate the image, with its high precision and real-time advantages, these living objects can be quickly identified and located.

[0030] Step S140, fusing the data analysis result and the image analysis result to obtain a comprehensive analysis result.

[0031] Existing infrared detection technology has poor sensitivity and is easily disturbed by changes in ambient temperature, resulting in a high false alarm rate. It also has poor detection effects on static living objects. Although image processing technology has high recognition accuracy, it has high computational complexity, high hardware requirements, and poor adaptability in complex environments such as lighting changes, dust, and obstructions, which affects its stability and reliability in actual production environments.

[0032] In the present application embodiment, Figure 2 As shown in the figure, by integrating infrared and image processing technology, the shortcomings of a single technology in complex production environments are overcome. Infrared detection is not affected by light, and the YOLOv10 algorithm is highly robust to light changes, dust, and obstructions, which enables the method to run stably in various complex environments and enhance environmental adaptability.

[0033] Moreover, the embodiment of the present application combines infrared detection technology with the YOLOv10 deep learning algorithm, uses infrared sensors to detect temperature changes within the equipment, and performs real-time image analysis through the YOLOv10 algorithm, thereby achieving accurate detection of dynamic living things. This combination not only improves the sensitivity and accuracy of detection, but also reduces the false alarm rate.

[0034] Furthermore, the embodiment of the present application adopts the combination of infrared detection and YOLOv10 algorithm, which simplifies the system architecture and reduces the complexity of the overall system. Compared with multi-sensor fusion technology, the present invention reduces the types of sensors and data processing procedures, reduces development and maintenance costs, improves the operability and stability of the system, achieves a higher system integration and simplifies the architecture.

[0035] By simplifying the system architecture and reducing the reliance on high-performance hardware, the embodiment of the present application reduces the cost of the overall system. At the same time, it reduces the complexity of multi-sensor data fusion, making system maintenance and upgrades more convenient and economical, thereby reducing system costs and maintenance difficulties.

[0036] Step S150, judging whether there is a living object in the equipment according to the comprehensive analysis result, and obtaining the living object detection result in the equipment.

[0037] like Figure 2 As shown in the figure, starting from data collection, through sensor monitoring, data collection and preprocessing, data fusion and analysis, and judgment result processing, the entire process is intuitively displayed. Each step has its specific function, which is organically connected together to form a complete and efficient system.

[0038] In an embodiment of the present application, a method for dynamic liveness detection in equipment and machine platforms based on infrared detection and YOLOv10 is provided, which improves detection sensitivity and accuracy and reduces false alarms by combining the temperature detection of infrared sensors and the image recognition advantages of the YOLOv10 deep learning algorithm. In addition, the method reduces the complexity and maintenance cost of the overall system by simplifying the system architecture, and realizes efficient, safe and reliable liveness detection in equipment and machine platforms. The method provided in an embodiment of the present application solves the problems of low sensitivity, high false alarm rate, poor environmental adaptability and high system complexity in the prior art of liveness detection in equipment and machine platforms in manufacturing production workshops.

[0039] By combining infrared detection and YOLOv10 deep learning algorithm, the dynamic liveness detection method in the equipment platform is realized, the infrared sensor is used to detect temperature changes, and the YOLOv10 algorithm is used for real-time image recognition. The infrared sensor can effectively detect temperature changes and timely find live objects in the platform, and the YOLOv10 deep learning algorithm can quickly identify and locate these live objects with its high precision and real-time advantages. This method significantly improves the sensitivity and accuracy of detection, effectively reduces the false alarm rate, and improves the accuracy of liveness detection in production equipment platforms. At the same time, the environmental robustness of the YOLOv10 algorithm is utilized to enhance the adaptability of the system in complex production environments. In addition, the embodiment of the present application simplifies the system architecture by combining infrared detection with the YOLOv10 algorithm, reduces the complexity and maintenance cost of the overall system, thereby realizing efficient, safe and reliable liveness detection. This method not only improves production safety, but also improves the economy and practicality of the system.

[0040] The above steps are described in detail below.

[0041] In some embodiments, after step S150, the method may further include the following steps: if it is determined that there is a living object in the equipment, triggering an audible and visual alarm and sending an alarm signal, and controlling the target device corresponding to the equipment to stop running; and recording an alarm log according to the presence of a living object in the equipment.

[0042] As an optional implementation, Figure 2 As shown, after the data fusion and analysis phase is completed, the judgment result processing phase is executed. For example, if it is determined through comprehensive analysis that there is a living object in the equipment, the sound and light alarm is triggered, an alarm signal is sent, the equipment is stopped, and an alarm log is recorded. In this way, the impact of the living object in the equipment on the equipment production process can be timely and effectively avoided.

[0043] In some embodiments, after recording an alarm log according to the presence of a living object in the equipment, the method may further include the following steps: Control the removal of living objects from the equipment and confirm the removal results of living objects; If the clearing result is that the living object has been cleared, the alarm state of the sound and light alarm is reset, and the system state corresponding to the target device is detected; If the system status belongs to the specified normal status, the target device is controlled to resume operation, and the steps of monitoring the temperature change data in the device through the infrared sensor and acquiring the real-time image in the device through the image acquisition device are continued.

[0044] like Figure 2 As shown, as an optional implementation, during the alarm processing and recovery process, it is confirmed whether the living organism has been cleared. If it is confirmed that the living organism has been cleared, the alarm state is reset, and then the system state is checked. In this way, the timely removal of the living organism in the equipment and the normal operation of the equipment can be ensured, and the living organism in the equipment can be prevented from affecting the production process of the equipment.

[0045] In some embodiments, after the above control performs a clearing process on the living object from the equipment and confirms the clearing result of the living object, the method may further include the following steps: If the result of the removal is that the living object has not been removed, the alarm processing continues, and the step of controlling the removal of the living object from the equipment is executed again.

[0046] like Figure 2 As shown, as another optional implementation, after continuing the alarm processing in the previous step, the living object is then controlled to be removed from the equipment. In this way, timely alarm processing of the living object in the equipment can be ensured to avoid affecting the production process of the equipment due to incomplete removal.

[0047] In some embodiments, after step S150, the method may further include the following steps: If it is determined that there is no living object in the equipment platform, the steps of monitoring the temperature change data in the equipment platform by using an infrared sensor and acquiring a real-time image in the equipment platform by using an image acquisition device are performed.

[0048] As an optional implementation, Figure 2 As shown, after the data fusion and analysis phase is completed, the judgment result processing phase is executed. For example, if it is determined through comprehensive analysis that there is no living object in the equipment, the above step S110 is executed repeatedly. In this way, it is possible to realize real-time monitoring of whether there is a living object in the equipment.

[0049] Figure 3 A schematic diagram of the structure of a liveness detection device in a battery assembly production equipment is provided. Figure 3 As shown, the living body detection device 300 in the production equipment of the battery assembly includes: An acquisition module 301 is used to monitor the temperature change data in the equipment platform through an infrared sensor and acquire a real-time image in the equipment platform through an image acquisition device; A first analysis module 302, configured to perform data analysis based on the temperature change data by using a threshold method to obtain a data analysis result; A second analysis module 303 is used to perform image analysis based on the real-time image using a YOLOv10 target detection algorithm to obtain an image analysis result; A fusion module 304 is used to fuse the data analysis result and the image analysis result to obtain a comprehensive analysis result; The judgment module 305 is used to judge whether there is a living object in the equipment according to the comprehensive analysis result, and obtain the living object detection result in the equipment.

[0050] The living body detection device in a battery assembly production equipment machine provided in the embodiment of the present application has the same technical features as the living body detection method in a battery assembly production equipment machine provided in the above-mentioned embodiment, and therefore can also solve the same technical problems and achieve the same technical effects.

[0051] An electronic device provided in an embodiment of the present application is Figure 4 As shown, the electronic device 400 includes a processor 402 and a memory 401, wherein the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method provided in the above embodiment when executing the computer program.

[0052] See also Figure 4 The electronic device further includes: a bus 403 and a communication interface 404, a processor 402, a communication interface 404 and a memory 401 are connected via the bus 403; the processor 402 is used to execute an executable module stored in the memory 401, such as a computer program.

[0053] The memory 401 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 404 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0054] The bus 403 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0055] Among them, the memory 401 is used to store programs, and the processor 402 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the present application can be applied to the processor 402 or implemented by the processor 402.

[0056] The processor 402 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 402. The above processor 402 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module may be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401 and completes the steps of the above method in combination with its hardware.

[0057] Corresponding to the above-mentioned method for detecting living bodies in a production equipment platform of a battery assembly, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned method for detecting living bodies in a production equipment platform of a battery assembly.

[0058] The liveness detection device in the production equipment of the battery assembly provided in the embodiment of the present application can be specific hardware on the equipment or software or firmware installed on the equipment. The implementation principle and technical effects of the device provided in the embodiment of the present application are the same as those of the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0059] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0060] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0061] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0062] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0063] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method for detecting live bodies in the production equipment of the battery assembly described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0064] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0065] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solution of the present application, rather than to limit it. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solution recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiment of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for detecting liveness in a battery assembly production equipment, characterized in that: The method comprises: Monitor the temperature change data in the equipment platform through infrared sensors, and obtain real-time images in the equipment platform through image acquisition devices; Performing data analysis based on the temperature change data by using a threshold method to obtain a data analysis result; Perform image analysis based on the real-time image using the YOLOv10 target detection algorithm to obtain an image analysis result; Fusing the data analysis result and the image analysis result to obtain a comprehensive analysis result; According to the comprehensive analysis result, it is determined whether there is a living object in the equipment, and a living object detection result in the equipment is obtained.

2. The method according to claim 1, characterized in that After monitoring the temperature change data in the equipment platform by the infrared sensor and acquiring the real-time image in the equipment platform by the image acquisition device, the method further includes: Performing infrared data filtering processing on the temperature change data to obtain a data preprocessing result; Perform image preprocessing on the real-time image to obtain an image preprocessing result.

3. The method according to claim 2, characterized in that The image analysis is performed based on the real-time image by using the YOLOv10 target detection algorithm to obtain the image analysis result, including: The target object in the image preprocessing result is identified and located by the YOLOv10 target detection algorithm to obtain a recognition result and a positioning result for the target object, and the recognition result and the positioning result are used as the image analysis result of the real-time image.

4. The method according to claim 1, characterized in that: After determining whether there is a living object in the equipment according to the comprehensive analysis result and obtaining the living object detection result in the equipment, the method further includes: If it is determined that there is a living object in the equipment, triggering an audible and visual alarm and sending an alarm signal, and controlling the target device corresponding to the equipment to stop running; An alarm log is recorded according to the presence of a living object in the equipment.

5. The method according to claim 4, characterized in that After recording an alarm log according to the presence of a living object in the equipment, the method further includes: Controlling the removal of the living object from within the equipment and confirming the removal result of the living object; If the clearing result is that the living object has been cleared, resetting the alarm state of the sound and light alarm, and detecting the system state corresponding to the target device; If the system state belongs to the specified normal state, the target device is controlled to resume operation, and the steps of monitoring the temperature change data in the device through the infrared sensor and acquiring the real-time image in the device through the image acquisition device are continued.

6. The method according to claim 5, characterized in that After the control performs a clearing process on the living object from the equipment and confirms the clearing result of the living object, the method further includes: If the clearing result is that the living object has not been cleared, the alarm process continues, and the step of controlling the clearing process of the living object from the equipment is executed again.

7. The method according to claim 1, characterized in that After determining whether there is a living object in the equipment according to the comprehensive analysis result and obtaining the living object detection result in the equipment, the method further includes: If it is determined that there is no living object in the equipment platform, the steps of monitoring the temperature change data in the equipment platform by using an infrared sensor and acquiring a real-time image in the equipment platform by using an image acquisition device are performed.

8. A liveness detection device in a battery assembly production equipment, characterized in that: include: An acquisition module is used to monitor the temperature change data in the equipment platform through an infrared sensor and acquire a real-time image in the equipment platform through an image acquisition device; A first analysis module, used to perform data analysis based on the temperature change data by a threshold method to obtain a data analysis result; A second analysis module is used to perform image analysis based on the real-time image through a YOLOv10 target detection algorithm to obtain an image analysis result; A fusion module, used for fusing the data analysis result and the image analysis result to obtain a comprehensive analysis result; The judgment module is used to judge whether there is a living object in the equipment according to the comprehensive analysis result, and obtain the living object detection result in the equipment.

9. An electronic structure comprising a memory and a processor, wherein the memory stores a computer program that can be run 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 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

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