Safety control method, system and equipment for tail board of freight vehicle and medium

Abstract

The invention discloses a safety control method, a safety control system, safety control equipment and a safety control medium for a tail board of a freight vehicle in the field of automobile auxiliary equipment safety. Performing real-time monitoring on personnel, obstacles and loading states in a tail board working area; the central control unit receives signals of all the detection modules, carries out data fusion and logic decision making, and triggers protection mechanisms such as tail board action stop and sound-light alarm when a dangerous working condition is recognized. The problems that a traditional tail board protection means is single, and potential safety hazards are prominent are solved, all-directional active protection is achieved, the safety and the intelligent level of tail board operation are improved, meanwhile, data recording and tracing functions are achieved, and the safety production requirement of the modern logistics industry is met.

Description

Technical Field

[0001] This invention relates to the field of automotive auxiliary equipment safety, specifically to a safety control method, system, device, and medium for the tailgate of a freight vehicle. Background Technology

[0002] As a commonly used cargo loading and unloading auxiliary equipment in logistics, warehousing, and distribution industries, tailgates can significantly improve loading and unloading efficiency and reduce manual labor intensity, making them an important feature of freight vehicles. However, in actual operation, due to factors such as the complex operating environment, frequent personnel movement, and the inherent characteristics of the equipment, safety accidents frequently occur.

[0003] Traditional safety protection methods for automotive tailgates mostly rely on mechanical protection or single photoelectric sensors, which have significant limitations: mechanical protection only works upon physical contact and cannot achieve proactive prevention; single photoelectric sensors are easily interfered with by ambient light, dust, rain, etc., leading to false alarms or missed alarms, and are difficult to cover all risk points in the tailgate's working area. For example, if an operator accidentally enters the tailgate's lifting path, relying solely on infrared beam detection may fail to trigger protection because the operator is in a blind spot of the beam; if overweight cargo is not detected in time, long-term use can lead to structural damage to the tailgate and even cause a fall accident.

[0004] With the increasing sophistication of safety regulations and the development of intelligent technologies, existing tailgate protection solutions can no longer meet the safety requirements of complex operating environments. Therefore, those skilled in the art have provided safety control methods, systems, equipment, and media for freight vehicle tailgates to address the problems mentioned in the background section. Summary of the Invention

[0005] The purpose of this invention is to provide a safety control method, system, device, and medium for the tailgate of freight vehicles to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The safety control method for the tailgate of a freight vehicle includes the following steps: S1. Parameter Configuration: Obtain the model, rated load and working range parameters of the target freight vehicle's tailgate, and set the detection threshold, response time and safety control logic threshold of each detection module; S2. Multi-source data acquisition: The infrared diffuse reflection sensor collects the presence signal of objects under the tailgate and in a specific area; the lidar sensor scans the tailgate lifting path and the distance signal of obstacles within a preset range; the video AI human detection camera collects image data of the tailgate working area and identifies key human body parts; the infrared beam obstruction signal is collected through the infrared beam curtain; and the weight of the tailgate is collected through the weighing sensor. S3. Data Processing and Fusion: The raw signals collected by each detection module are filtered and denoised to remove interference data; the multi-source detection data are fused and analyzed based on the multi-source data fusion algorithm to determine whether there is personnel intrusion, obstacle obstruction or overload in the current working environment of the tailgate; S4. Safety Decision and Execution: If the fusion analysis results show that there is a safety risk, the central control unit immediately sends an action stop command to the tailgate actuator, and at the same time triggers the audible and visual alarm module to issue a warning signal, and records the current risk type, occurrence time and data of each sensor; if the fusion analysis results show no safety risk, the tailgate is allowed to perform lifting or tilting actions according to the preset operation instructions.

[0007] As a further aspect of the present invention: In step S2, the video AI human detection camera realizes the identification of key human body parts through the edge computing AI module. The key human body parts include the head, shoulders, hands and feet. When at least one key body part is identified and the body part is within the tailboard working area, it is determined to be a human intrusion signal.

[0008] As a further aspect of the present invention: In step S3, the multi-source data fusion algorithm includes the following logic: When any one of the following conditions is met, the infrared diffuse reflection sensor detects an object signal, the lidar sensor detects an obstacle distance less than a preset safe distance, the video AI human detection identifies a person intrusion signal, or the infrared beam blocking light curtain detects a beam obstruction signal, a preliminary judgment is made that there is a safety risk; combined with the weighing sensor data, if the weight exceeds the rated load, it is directly judged as high risk and the highest level of protection mechanism is triggered.

[0009] As a further aspect of the present invention: in step S4, the safety risks include primary risks and secondary risks; the primary risks include personnel intrusion, overloading, and obstruction by nearby obstacles, which trigger the tailgate to immediately stop its operation, continuously activate the audible and visual alarm, and push a remote alarm; the secondary risks include temporary obstruction by non-living objects, which triggers the tailgate to decelerate and intermittently activate the audible and visual alarm. If the obstruction signal does not disappear within 5 seconds, it is upgraded to a primary risk.

[0010] A safety control system for the tailgate of freight vehicles, comprising a detection layer, a control layer, and an execution and alarm layer; The detection layer includes an infrared diffuse reflection sensor, a lidar sensor, a video AI human detection camera, an infrared beam curtain, a weighing sensor, and a remote data monitoring module. The infrared diffuse reflection sensor is used to detect objects below the tailplate and in specific areas. The lidar sensor is used to scan the distance to obstacles around the tailplate. The video AI human detection camera, combined with an edge computing AI module, is used to identify key human body parts within the tailplate's working area. The infrared beam curtain is used to form a protective beam and detect obstruction signals. The weighing sensor is used to collect the weight carried by the tailplate. The remote data monitoring module is used to store operation data and alarm information. The control layer includes a central control unit, which is an MCU or PLC used to receive signals from the detection layer, perform data fusion analysis, and send control commands. The execution and alarm layer includes a tailgate actuator, an audible and visual alarm module, and a status indicator module. The tailgate actuator includes a motor and a solenoid valve, used to perform tailgate lifting and tilting actions. The audible and visual alarm module is used to issue a danger warning. The status indicator module includes a power indicator, a running indicator, and a fault indicator, used to display the system's working status.

[0011] As a further aspect of the present invention: the infrared diffuse reflection sensor has a waterproof and dustproof rating of not less than IP65, a detection distance range of 0.1-2m, and a response time of not more than 10ms; the lidar sensor has a scanning angle of not less than 120°, a ranging accuracy of ±0.01m, and is suitable for vehicle environment temperatures ranging from -30℃ to 70℃.

[0012] As a further aspect of the present invention: the video AI human detection camera has a resolution of not less than 1080P, a minimum illumination of not more than 0.01Lux, a wide-angle shooting function, and a horizontal field of view of not less than 110°; the human recognition accuracy of the edge computing AI module is not less than 95%, and the false alarm rate is not higher than 1%.

[0013] 8. An electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements steps of a safety control method for a cargo vehicle tailgate.

[0014] A computer-readable storage medium storing a computer program, which, when executed by a processor, comprises steps of a safety control method for a cargo vehicle tailgate.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. Comprehensive safety protection: Integrating multiple detection technologies such as infrared detection, laser detection, AI vision detection, and weighing detection, it comprehensively covers core risk points such as personnel intrusion, obstacle obstruction, and overload during tailgate operation, effectively making up for the limitations of single protection methods, eliminating detection blind spots, and significantly reducing the probability of safety accidents such as pinching, squeezing, and collision.

[0016] 2. Proactive and Intelligent Prevention and Control: Relying on multi-source data fusion algorithms, potential hazards can be identified and early warning mechanisms can be triggered in advance before risks are fully formed (such as when personnel are about to enter a dangerous area), enabling rapid response to potential dangers. Through video AI human body recognition technology, it can accurately distinguish between personnel and non-living objects in the work area, reducing unnecessary downtime caused by misjudgment and ensuring the continuity and stability of the tailgate operation process. Compared with the traditional passive protection mode, it further improves the initiative and intelligence level of safety prevention and control.

[0017] 3. Data-driven management and traceability: Equipped with remote data recording capabilities, it can store and easily retrieve daily operation data and fault alarm information for the tailgate for extended periods. Enterprises can conduct safety management analysis based on the stored data, such as optimizing work processes by analyzing alarm patterns under different operating scenarios; in the event of a safety incident, the cause of the incident can be quickly clarified and the responsibility assigned by tracing historical data, providing data support for subsequent safety improvements.

[0018] 4. High adaptability and compatibility: The system supports customized configurations based on the specific model, load capacity, and actual operating environment of the tailgate, meeting the needs of different types of freight vehicles. The sensors and central control unit can seamlessly integrate with existing tailgate systems without requiring the replacement of the entire system, reducing upgrade and modification costs for businesses. Furthermore, the system possesses strong environmental adaptability, operating stably in harsh environments with varying temperatures, humidity, and dust levels, making it suitable for different regions and operating scenarios. Attached Figure Description

[0019] Figure 1 This is a diagram illustrating the architecture of the safety control system for the tailgate of a freight vehicle according to the present invention. Figure 2 This diagram illustrates the installation positions of each sensor in the detection layer of the present invention. Figure 1 ; Figure 3 This diagram illustrates the installation positions of each sensor in the detection layer of the present invention. Figure 2 ; Figure 4 This diagram illustrates the installation positions of each sensor in the detection layer of the present invention. Figure 3 ; Figure 5 This diagram illustrates the installation positions of each sensor in the detection layer of the present invention. Figure 4 ; Figure 6This diagram illustrates the installation positions of each sensor in the detection layer of the present invention. Figure 5 ; Figure 7 This is a flowchart of the safety control method for the tailgate of a freight vehicle according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figures 1-7 In this embodiment of the invention, a safety control method, system, device, and medium for the tailgate of a freight vehicle are described. The safety control method for the tailgate of a freight vehicle collects real-time data on the tailgate's operating environment using multi-source sensors, and then performs safety decisions after data fusion analysis. The specific steps are as follows: Step S1. Parameter Configuration: First, obtain the basic parameters of the target tailgate, including model, rated load (e.g., 1.5T, 3T, etc.) and working range (e.g., lifting height, tilting angle). Based on this, set the technical thresholds for each detection module. For example, the safe distance threshold for the lidar is set to 0.5m, the overload threshold for the weighing sensor is set to 1.1 times the rated load, and the recognition sensitivity for the video AI human detection is set to prioritize the recognition of the head and shoulders.

[0022] Step S2. Multi-source data acquisition: All detection modules work synchronously: Infrared diffuse reflection sensors are installed at the four corners below the tailgate to detect whether there are people's feet or fallen goods; LiDAR sensors are installed on both sides of the tailgate to scan the lifting path and obstacles within a 1.5m radius; Video AI human detection cameras are installed on the frame above the tailgate to capture real-time images of the working area and identify key human body parts; Infrared beam curtains are installed on the upper and lower edges of the tailgate to form a vertical protective beam; Weighing sensors are embedded in the tailgate support structure to collect the load weight in real time.

[0023] Step S3. Data Processing and Fusion: The central control unit filters the raw signals from each sensor, for example, by using a moving average algorithm to remove transient interference signals from the infrared diffuse reflection sensor; then, data fusion is performed based on a fuzzy logic algorithm. If the video AI identifies a head signal (determining intrusion), and the lidar detects an obstacle at a distance of 0.3m (less than the safety threshold), it is determined to be high risk. If only the infrared beam curtain is briefly blocked by a non-living object (such as a cargo packaging bag), and other sensors show no abnormalities, it is determined to be low risk.

[0024] Step S4. Safety Decision and Execution: Implement corresponding measures according to the risk level: In the case of Level 1 risk (personnel intrusion, overload, etc.), the central control unit immediately cuts off the power to the tailgate actuator, stops the lifting / tilting action, and the audible and visual alarm module emits a red light and a buzzer warning (frequency 2Hz). At the same time, the alarm information is pushed to the management terminal through the remote module; In the case of Level 2 risk (temporary obstruction), the tailgate decelerates to 50% of its original speed, and the audible and visual alarm module emits a yellow light and an intermittent buzzer (frequency 1Hz). If the obstruction is not removed within 5 seconds, it is upgraded to Level 1 risk; In the case of no risk, the tailgate executes the operating instructions normally.

[0025] Safety control system for tailgate of freight vehicles The system consists of a detection layer, a control layer, and an execution and alarm layer, with each layer working together to achieve safety control functions. The detection layer is the system's "sensory organ," containing six core components: Infrared diffuse reflection sensor: Select the model with IP65 waterproof and dustproof rating, detection distance 0.1-2m, response time ≤10ms, to ensure stable operation in rainy and dusty environments and avoid missed detection due to environmental interference.

[0026] LiDAR sensor: It adopts a miniaturized vehicle-mounted model with a scanning angle of 120°, a ranging accuracy of ±0.01m, and an operating temperature of -30℃ to 70℃. It is adaptable to different regional climate conditions and can accurately identify nearby obstacles (such as walls and other vehicles).

[0027] AI-powered human detection video camera: 1080P resolution, minimum illumination of 0.01Lux, supports shooting in nighttime or low-light environments; equipped with an edge computing AI module, using the YOLOv5 lightweight algorithm, human recognition accuracy is ≥95%, false alarm rate is ≤1%, can quickly distinguish between people and goods, and reduce unnecessary downtime.

[0028] Infrared beam light curtain: Select the length of the light curtain (e.g., 2m, 3m) according to the width of the tail plate, and the beam spacing is ≤50mm to ensure no detection blind spots. When a person or object blocks any beam of light, an obstruction signal is output immediately.

[0029] Weighing sensor: It adopts strain gauge type sensor, with a range covering 1.5 times the rated load of the tailgate and an accuracy of ±0.5%. It is installed at the support beam connecting the tailgate and the frame to monitor the weight of the cargo in real time and avoid overloading that could cause structural damage.

[0030] Remote data monitoring module: integrates a 4G / 5G communication module, supports real-time data upload to the cloud server, and also has local storage function (storage capacity ≥16GB) to prevent data loss when the network is interrupted.

[0031] Control layer: This is the "brain" of the system, with the central control unit at its core. High-performance MCUs or PLCs are used, depending on the system complexity: MCUs can be used for small tailgates (load ≤ 2T) due to their low cost and fast response; PLCs are used for large tailgates (load > 2T) due to their strong expandability and support for more sensor connections.

[0032] The central control unit has at least 16 digital input interfaces (for receiving sensor signals), 8 digital output interfaces (for controlling actuators and alarm modules), and 2 serial ports (for communicating with remote modules) to ensure stable communication between components.

[0033] Execution and Alarm Layer: This is the system's "executive organ," containing three types of components: Tailgate actuator: It consists of a lifting motor, a tilting solenoid valve and a relay. It receives instructions from the central control unit to realize the raising, lowering and tilting actions of the tailgate. The relay adopts a double contact design to ensure that the motor power can be reliably cut off when the power is off, and to prevent the action from going out of control in case of failure.

[0034] Audible and visual alarm module: Includes red and yellow LED lights and a buzzer, installed in a conspicuous position on the side of the tailgate, with an alarm sound level of ≥85dB, ensuring that operators can promptly detect the warning in noisy logistics parks.

[0035] Status indicator module: Includes power indicator (green, lights up when the system is powered on), operation indicator (blue, flashes when the tailboard is working), and fault indicator (red, lights up when the system is abnormal), which makes it easy for operators to quickly judge the system status.

[0036] Electronic devices and computer-readable storage media Electronic equipment includes processors (such as CPUs and GPUs), memory (RAM and ROM), communication interfaces, and input / output interfaces. The memory stores computer programs, and the processor executes the programs to implement the steps of the above-mentioned safety control methods. It can be integrated into the tailgate control box or used as an independent control unit to interface with the existing tailgate system.

[0037] Computer-readable storage media include USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), optical discs, etc. When the stored computer programs are executed by the processor, security control logic can be implemented on different devices, facilitating system upgrades and maintenance.

[0038] Example 1: Safety control of tailgate of small freight vehicle Equipment selection: Tailgate parameters: Folding type, rated load 1.5T, lifting height 1.8m, tilting angle 90°.

[0039] Detection layer: Infrared diffuse reflection sensor (IP65, detection distance 0.1-2m); LiDAR sensor (range accuracy ±0.01m, operating temperature -20℃-60℃); Video AI human detection camera (1080P, 0.01Lux); Infrared beam-through light curtain (length 2m, beam spacing 50mm); Weighing sensor (range 2T, accuracy ±0.5%); Remote data monitoring module (supports 4G communication).

[0040] Control layer: The central control unit uses an STM32H743 MCU, which has 16 DI and 8 DO interfaces.

[0041] Execution and alarm layer: lifting motor (power 0.75KW); audible and visual alarm module (85dB buzzer + red and yellow light); status indicator lights (green LED, blue LED, red LED).

[0042] System Deployment: Sensor installation: Infrared diffuse reflection sensors are fixed at the four corners below the tailgate, facing the ground; LiDAR sensors are installed on both sides of the tailgate edge, with the scanning direction perpendicular to the tailgate lifting path; video AI camera is installed above the rear of the frame, with a downward angle of 30°, covering the tailgate and a surrounding area of ​​1.5m; infrared beam curtains are installed on the upper and lower edges of the tailgate to form a vertical protective surface; weighing sensors are embedded in the tailgate support beam.

[0043] Programming: Write the control program in the MCU, set the LiDAR safe distance to 0.5m, the weighing overload threshold to 1.65T, and prioritize head detection for video AI human body recognition; the data fusion logic is: trigger a preliminary judgment when any sensor detects a risk signal, and confirm the risk level by combining the weighing data.

[0044] Test and verification: Personnel intrusion test: Personnel were deliberately arranged to enter the tailgate lifting path. The video AI camera identified the head signal within 0.3 seconds, the central control unit immediately stopped the tailgate lifting action, and the sound and light alarm module activated the red light and buzzer. The test success rate was 100%.

[0045] Overload test: When a 1.7T load is applied, the weighing sensor detects that the weight exceeds the threshold, the tailgate is prohibited from starting, the fault indicator light illuminates, and the test success rate is 100%.

[0046] Obstacle test: Place an obstacle 0.3m high in the tailgate lifting path. When the lidar detects the obstacle at a distance of 0.3m, the tailgate stops moving and the audible and visual alarm is activated. The test success rate is 100%.

[0047] Example 2: Safety Control of Tailgate of Large Freight Vehicles Equipment selection: Tailgate parameters: Vertical type, rated load 3T, lifting height 2.2m, tilting angle 180°.

[0048] Detection layer: Infrared diffuse reflection sensor (IP67, detection distance 0.1-3m); LiDAR sensor (scanning angle 120°, ranging accuracy ±0.01m); video AI human detection camera; infrared beam-through light curtain (beam spacing 30mm); weighing sensor (range 5T, accuracy ±0.3%); remote data monitoring module (supports 5G communication).

[0049] Control layer: The central control unit is a Siemens S7-1200 PLC, which has 24 DI and 16 DO interfaces.

[0050] Execution and alarm layer: lifting motor (power 2.2KW); audible and visual alarm module (100dB buzzer + red and yellow lights); status indicator light (three-color LED screen, displaying detailed status).

[0051] System Deployment: Sensor installation: Infrared diffuse reflection sensors are installed below and on both sides of the tailgate to cover a larger area; LiDAR sensors are installed on the top of the tailgate, with a scanning range covering a 2m perimeter; video AI cameras are installed on both sides of the vehicle frame, with dual cameras stitched together to cover the entire panorama; infrared beam-beaming curtains are installed around the tailgate to form a closed protective zone; and weighing sensors are installed at the four support points of the tailgate.

[0052] Test and verification: Complex environment test: When operating in rainy or dusty conditions, the infrared diffuse reflection sensor and lidar sensor had no missed or false alarms; when operating at night, the video AI camera could still accurately identify people with an accuracy rate of 98%.

[0053] Multiple risk superposition test: Simultaneously simulate personnel intrusion and cargo overload. The system prioritizes the risk of personnel intrusion, stopping the tailgate movement within 0.2 seconds, and simultaneously alarming to indicate dual risks. The test success rate is 100%.

[0054] Analysis shows that this invention integrates multiple detection technologies, including infrared, laser, AI vision, and weighing, covering core risk points in tailgate operations such as personnel intrusion, obstacle obstruction, and overloading. It eliminates blind spots in single-protection methods, increasing the accident prevention rate by over 95%. Through multi-source data fusion algorithms, it achieves early risk identification (e.g., triggering an early warning before personnel have fully entered the danger zone), with a response time of ≤0.5 seconds. Compared to traditional passive protection, it curbs accidents at their nascent stage. Video AI human recognition accurately distinguishes between people and objects, reducing the false alarm rate to below 1%, minimizing unnecessary downtime and improving operational efficiency. 5%-20%; The remote data recording function supports long-term storage and retrieval of work data and alarm information, facilitating safety management analysis for enterprises (such as statistical analysis of alarm frequency of tailgates in a certain area to optimize work processes). It also allows for rapid tracing of causes and identification of responsibilities after an accident. The system can be customized according to the tailgate model, load capacity, and operating environment. The sensors and central control unit support interface with existing tailgate systems without requiring replacement of the entire equipment, reducing enterprise upgrade costs. It also adapts to a temperature range of -30℃ to 70℃ and has an IP65 or higher protection rating, meeting the usage needs of different regions and scenarios. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A safety control method for a cargo vehicle tailgate, characterized by, Comprising the following steps: S1. Parameter configuration: Obtain the model, rated load and working range parameters of the target freight vehicle tailgate, set the detection threshold, response time and safety control logic threshold of each detection module; S2. Multi-source data acquisition: Collect object existence signals under the tailgate and in specific areas through infrared diffuse reflection sensors, scan obstacle distance signals in the tailgate lifting path and the preset range around through laser radar sensors, collect image data of the tailgate working area and identify human key part signals through video AI human body detection cameras, collect light beam blocking signals through infrared light curtain, and collect tailgate load weight signals through weighing sensors; S3. Data processing and fusion: Filter and denoise the original signals collected by each detection module to eliminate interference data; based on the multi-source data fusion algorithm, the multi-source detection data is fused and analyzed to determine whether there is a personnel intrusion, obstacle blocking or overload in the current tailgate working environment; S4. Safety decision and execution: If the fusion analysis result shows that there is a safety risk, the central control unit immediately sends a stop action command to the tailgate actuator, triggers the sound and light alarm module to issue a warning signal, and records the current risk type, occurrence time and sensor data; If there is no safety risk in the fusion analysis result, the tailgate is allowed to perform lifting or turning actions according to the preset operation instructions.

2. The safety control method for a cargo vehicle tailgate according to claim 1, characterized by, In step S2, the video AI human body detection camera realizes human key part recognition through an edge computing AI module, and the human key parts include head, shoulder, hand and foot. When at least one key part is recognized and the part is in the tailgate working area, it is determined as a personnel intrusion signal.

3. The safety control method for a cargo vehicle tailgate as claimed in claim 1, characterized by, In step S3, the multi-source data fusion algorithm includes the following logic: when any one of the infrared diffuse reflection sensor detects an object signal, the laser radar sensor detects an obstacle distance less than a preset safety distance, the video AI human body detection identifies a personnel intrusion signal, or the infrared light curtain detects a light beam blocking signal, it is preliminarily determined that there is a safety risk; combined with the weighing sensor data, if the weight exceeds the rated load, it is directly determined as a high risk and the highest level protection mechanism is triggered.

4. The safety control method for a cargo vehicle tailgate according to claim 1, wherein, In step S4, the safety risk includes a first level risk and a second level risk; the first level risk includes personnel intrusion, overload and close-range obstacle blocking, triggering the tailgate to stop immediately, the sound and light alarm to start continuously, and remote alarm to push; the second level risk includes non-living object temporary blocking, triggering the tailgate to slow down, the sound and light alarm to start intermittently, and if the blocking signal does not disappear within 5 seconds, it is upgraded to a first level risk.

5. A safety control system for a cargo vehicle tailgate, characterized by, Comprising a detection layer, a control layer, and an execution and alarm layer; The detection layer includes an infrared diffuse reflection sensor, a laser radar sensor, a video AI human body detection camera, an infrared opposite light curtain, a weighing sensor and a remote data monitoring module; the infrared diffuse reflection sensor is used to detect objects below the tail plate and in a specific area; the laser radar sensor is used to scan the distance of obstacles around the tail plate; the video AI human body detection camera is matched with an edge computing AI module and is used to identify key parts of human bodies in the working area of the tail plate; the infrared opposite light curtain is used to form a protective light beam and detect a shielding signal; the weighing sensor is used to collect the load of the tail plate; and the remote data monitoring module is used to store operation data and alarm information. The control layer includes a central control unit, which adopts an MCU or a PLC and is used to receive signals of the detection layer, perform data fusion analysis and send control instructions; and the execution and alarm layer includes a tail plate execution mechanism, an audible and light alarm module and a state indication module.

6. A safety control system for a cargo vehicle tailgate according to claim 5, characterised in that, The tail plate execution mechanism includes a motor and an electromagnetic valve and is used to execute lifting and overturning actions of the tail plate; the audible and light alarm module is used to issue a danger warning; and the state indication module includes a power indicator, a running indicator and a fault indicator and is used to display the working state of the system.

7. The safety control system for a cargo vehicle tailgate of claim 5, wherein, The waterproof and dustproof grade of the infrared diffuse reflection sensor is not less than IP65, the detection distance range is 0.1-2 m, and the response time is not greater than 10 ms; the scanning angle of the laser radar sensor is not less than 120°, the ranging accuracy is ±0.01 m, and the vehicle-mounted environment temperature range is-30℃-70℃.

8. The safety control system for a cargo vehicle tailgate of claim 5, wherein, The resolution of the video AI human body detection camera is not less than 1080P, the minimum illumination is not greater than 0.01Lux, it has a wide-angle shooting function, and the horizontal field of view is not less than 110°; the human body recognition accuracy of the edge computing AI module is not less than 95%, and the false alarm rate is not higher than 1%.

9. An electronic device, characterized by A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the safety control method for a tail plate of a cargo vehicle.

10. A computer readable storage medium, characterized in that, A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the safety control method for a tail plate of a cargo vehicle.

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