A forklift electronic coach practical training intelligent safety protection control system and method

Abstract

The application provides a forklift electronic coach practical training intelligent safety protection control system and method, the system comprises: a main control processing unit, a personnel safety identification module, a vehicle body state acquisition module, a fork operation monitoring module, an environment anti-collision detection module and a hierarchical safety execution module. Each module is used for collecting the state of the practical training personnel, the running state of the vehicle body, the fork operation working condition and the surrounding environment information, and sending to the main control processing unit. The main control processing unit judges the risk level based on the received information, and outputs differentiated safety control instructions to the hierarchical safety execution module according to the risk level, so as to execute early warning, operation permission flow limiting or emergency braking and other hierarchical intervention actions. The application also provides a corresponding method. Through multi-dimensional data acquisition and intelligent analysis, the application realizes active risk perception and closed-loop intervention of the forklift practical training process, and improves the practical training safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent training technology for special equipment, and in particular to an intelligent safety protection control system and method for electronic coach training of forklifts. Background Technology

[0002] Forklifts, as core special equipment in warehousing, logistics, and industrial production, require highly specialized and risky operation. Therefore, it is mandatory for operators to undergo systematic practical training and obtain a special equipment operation certificate before being allowed to work. Currently, forklift electronic training systems are widely used in various training and assessment institutions. Through automated programs, standardized teaching and unmanned scoring effectively overcome the industry drawbacks of traditional manual teaching, such as inconsistent standards, high labor costs, and significant subjective scoring errors.

[0003] However, existing electronic driving instructor systems generally suffer from functional deficiencies. Their core functions are limited to teaching, scoring, and recording the process, lacking the ability to proactively detect safety risks, predict risks, and implement hazard interventions. They are essentially "teaching-only, not prevention-only" teaching devices. Trainees are mostly novices with weak safety awareness, and are prone to dangerous behaviors such as speeding, overloading, driving with forks high, operating on slopes improperly, tilting the vehicle significantly, extending limbs out of the vehicle, and driving close to obstacles.

[0004] Under current technological conditions, these dangerous behaviors cannot be automatically identified and stopped by the system, and can only rely on on-site safety personnel for manual supervision. Manual supervision is not only inefficient, but also suffers from blind spots and limitations in attention, making it difficult to achieve effective monitoring around the clock. This greatly increases the risk of safety accidents such as forklift rollovers, falling goods, vehicle collisions, and personnel injuries. Furthermore, traditional practical training safety management lacks standardized risk grading mechanisms, automatic intervention methods, and a complete safety data traceability system. Its crude safety control and passive accident prevention are no longer sufficient to meet the current requirements for intelligent, standardized, and safe practical training and assessment of special equipment.

[0005] Therefore, existing technologies in the field of forklift training safety suffer from technical problems such as a lack of proactive protection systems, insufficient risk intervention methods, low level of intelligence, and high dependence on manual labor. Summary of the Invention

[0006] This invention provides an intelligent safety protection control system and method for forklift electronic coach training, which can perform full-dimensional intelligent monitoring and hierarchical proactive intervention in the forklift training process. It aims to overcome the technical defects of existing forklift electronic coach training systems, which lack proactive safety protection capabilities, cannot automatically identify and block dangerous operations, and result in training safety relying heavily on manual supervision and having blind spots.

[0007] To achieve the above objectives, the present invention provides an intelligent safety protection control system for forklift electronic training, comprising: Main control processing unit; The personnel safety identification module is connected to the main control processing unit and is used to collect the status information of the trainees. The vehicle status acquisition module is connected to the main control processing unit and is used to acquire the vehicle operating status data of the forklift. The fork operation monitoring module is connected to the main control processing unit and is used to collect fork operation status data of the forklift. An environmental collision avoidance detection module, connected to the main control processing unit, is used to detect environmental information around the forklift; A graded safety execution module is connected to the main control processing unit and interfaces with the forklift's electronic control system; The main control processing unit is used to receive the status information, the vehicle body operating status data, the fork operating condition data and the environmental information, and to judge the risk level based on the information and data, and then output differentiated safety control instructions to the graded safety execution module according to the risk level.

[0008] Optionally, the graded safety execution module includes: a voice and visual warning unit; an operation permission limiting unit; and an emergency power-off braking unit. The setup of these three units provides a physical basis for executing safety intervention actions of varying intensities, enabling the system to take a range of measures, from gentle prompts to mandatory interventions, depending on the severity of the risk.

[0009] Optionally, the main control processing unit is configured to: when a first risk level is determined, instruct the voice and light warning unit to perform a warning action; when a second risk level is determined, instruct the operation permission limiting unit to perform an action to restrict operation permissions; and when a third risk level is determined, instruct the emergency power-off braking unit to perform an emergency braking action. This configuration clarifies the correspondence between risk levels and intervention actions, constructing a clear and progressive differentiated intervention strategy of "first-level warning, second-level limiting, and third-level braking," ensuring the timeliness and effectiveness of safety intervention, correcting minor violations and forcibly preventing major dangers.

[0010] Optionally, the main control processing unit is further configured to lock the entire vehicle after the emergency power-off braking unit performs the emergency braking action, and to unlock and restart only after receiving authorization from the administrator. This additional process addresses the problem that trainees might restart the equipment and continue dangerous operations without authorization after a serious incident. By introducing the administrator authorization step, a closed loop of safety management is added, ensuring that serious risk events are effectively reviewed and handled.

[0011] Optionally, the action of restricting operating permissions is as follows: when the main control processing unit determines that the current vehicle speed exceeds a preset safe speed threshold, it reduces the forklift's power output according to a preset proportional relationship based on the extent to which the current vehicle speed exceeds the preset safe speed threshold. This proportional speed limiting method is a more refined control strategy compared to fixed speed limiting. By dynamically adjusting the power output, it can effectively control the vehicle speed while providing the driver with a smoother operating experience, avoiding operational errors or secondary risks caused by abrupt intervention.

[0012] Optionally, the personnel safety identification module is an in-vehicle high-definition AI vision camera, equipped with algorithms for recognizing human posture and the wearing status of items. Employing an AI vision solution, it can identify complex unsafe behaviors that traditional mechanical switches cannot detect, such as limbs protruding from the vehicle body, thereby more comprehensively collecting personnel status information and improving the breadth and accuracy of identifying unsafe behaviors.

[0013] Optionally, the environmental collision avoidance detection module employs lidar and is arranged around the forklift body so that the lidar's detection range covers the perimeter of the forklift. Using lidar with perimeter coverage allows for high-precision detection of the forklift's surroundings. Compared to ultrasonic or single-point radar, it more reliably identifies close-range collision risks, providing reliable data support for high-level interventions such as emergency braking.

[0014] This invention also provides an intelligent safety protection control method for forklift electronic coach training, comprising the following steps: synchronously collecting the status information of trainees, the vehicle body operation status data of the forklift, the fork operation condition data, and the environmental information around the forklift; analyzing the status information and data to determine the risk level; and executing differentiated graded safety intervention actions according to the risk level.

[0015] Optionally, the step of executing differentiated graded safety intervention actions includes: if the risk level is the first risk level, then executing an audible and visual warning action; if the risk level is the second risk level, then executing an action to restrict operation permissions; if the risk level is the third risk level, then executing an emergency braking action. This step concretizes the abstract graded intervention, clarifies the specific operations corresponding to different risk levels, and makes the method more feasible.

[0016] Optionally, the method further includes: performing power-on self-test and access control before executing the aforementioned steps, including collecting the status information of the trainees; if the status information indicates that the trainees' safety attire is not compliant, locking the forklift's start-up permission; and performing real-time data archiving to record the status information, the vehicle's operating status data, the forklift's operating condition data, the environmental information, and the risk level and the recorded graded safety intervention actions. By adding the "pre-access" and "post-archiving" steps, the core safety management process is expanded into a closed-loop management method covering the entire process from "pre-event to during-event" to "post-event," further enhancing the system's overall safety assurance capabilities from the two dimensions of pre-event prevention and post-event traceability.

[0017] The beneficial effects of this invention are as follows: It achieves proactive closed-loop protection: Through the closed-loop logic of "multi-source perception - intelligent judgment - hierarchical intervention", this invention can proactively identify and intervene in dangerous operations, which helps to prevent safety accidents and solves the defect of existing technologies that can only "record after the fact".

[0018] The invention enhances the comprehensiveness of risk identification by setting up a personnel safety identification module, a vehicle status acquisition module, a fork operation monitoring module, and an environmental collision avoidance detection module. It can comprehensively collect safety data from four dimensions: people, vehicles, goods, and site. In particular, it incorporates core risk sources unique to forklifts, such as overloading and high-position fork movement, into the monitoring, thereby improving the comprehensiveness and accuracy of risk identification.

[0019] Enhanced effectiveness of safety intervention: Through a graded safety execution module, this invention can take mandatory physical intervention measures such as operation permission limiting and emergency power-off braking to ensure that dangerous states can be forcibly terminated under severe risks. Compared with the single alarm prompt of existing technologies, the intervention means are more effective and the safety guarantee is more reliable.

[0020] A traceable safety management system has been established: by archiving data and intervention events in real time throughout the entire process, objective and complete data support is provided for post-event analysis, student evaluation and teaching improvement, thereby enhancing the scientific nature and refinement of safety management. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the intelligent safety protection control system for forklift electronic coach training according to an embodiment of the present invention; Figure 2 This is a flowchart of a forklift electronic coach training intelligent safety protection control method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the sensor deployment positions on a forklift according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a hierarchical security intervention logic according to an embodiment of the present invention; Explanation of reference numerals in the attached figures: 10. Main control processing unit; 20. Personnel safety identification module; 30. Vehicle body status acquisition module; 40. Forklift operation monitoring module; 50. Environmental collision avoidance detection module; 60. Graded safety execution module; 21. Vehicle-mounted high-definition AI camera; 31. Tilt sensor; 41. Height sensor; 42. Pressure sensor; 51. LiDAR; 302. Cabin; 303. Vehicle body; 304. Forklift mast; 305. Forks; 61. Voice and light warning unit; 62. Operation permission limiting unit; 63. Emergency power-off braking unit. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used in the embodiments of this application have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the embodiments of this application is for the purpose of describing the embodiments of this application only and is not intended to limit this application.

[0025] Before providing a further detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0026] (1) Main control processing unit: refers to a computing device, such as an embedded controller, programmable logic controller (PLC) or industrial computer. Its core function is to act as the central processing unit of the entire system, responsible for receiving and processing data streams from various sensor modules, running built-in safety judgment logic based on preset rules or algorithms to analyze and assess potential risks, and generating and sending specific control instructions to the execution module based on the analysis results.

[0027] (2) Risk level: refers to a quantitative or classification assessment of the degree of potential danger. In this technical solution, it specifically refers to the classification of the danger level of the current forklift operation status by the main control processing unit after comparing and comprehensively calculating the multi-dimensional data collected in real time with the preset safety threshold. For example, it can be divided into the first risk level (corresponding to slight risk), the second risk level (corresponding to moderate risk), and the third risk level (corresponding to severe risk), with each level corresponding to different degree of danger and intervention strategies.

[0028] (3) Differentiated safety control commands: These are control commands issued by the main control processing unit based on different risk levels, with varying content, intensity, and objectives. This differentiation reflects the precision and adaptability of the control strategy. For example, a non-mandatory audible and visual warning command is issued for the first risk level, while a mandatory emergency braking command is issued for the third risk level, thereby achieving precise response to different dangerous situations.

[0029] (4) Operational access control unit: This refers to a specific functional unit within the hierarchical safety execution module. Its function is to receive instructions from the main control processing unit and, through interaction with the forklift's electronic control system (e.g., via CAN bus), restrict, rather than completely prohibit, certain functions or performance of the vehicle. This "current control" can be limited by restricting the vehicle's maximum permissible speed, limiting the maximum lifting height of the forks, or reducing the percentage of output power of the power system, etc., aiming to reduce risk while allowing operators to continue operating or correct errors under restricted conditions.

[0030] (5) Status information: In this application, it mainly refers to safety status information directly related to the trainees, including but not limited to information obtained through image information analysis such as the helmet wearing status, seat belt fastening status, body posture (such as whether limbs are extended out of the cockpit), and whether they are in a permitted seat.

[0031] Please see Figure 1 and Figure 2 This application aims to address the common technical shortcomings of existing forklift electronic coach training technologies, namely, the lack of proactive safety perception and intervention capabilities, which often result in "teaching without prevention." To achieve this goal, this application provides an intelligent safety protection control system and corresponding method for forklift electronic coach training. Its core idea is to construct a full-process proactive safety protection system encompassing "multi-source information collection, intelligent risk assessment, and closed-loop hierarchical intervention."

[0032] Reference Figure 1This application provides an intelligent safety protection control system for forklift electronic training. The system, through the inclusion of a personnel safety identification module 20, a vehicle status acquisition module 30, a forklift operation monitoring module 40, and an environmental collision avoidance detection module 50, achieves comprehensive and real-time collection of safety status data across four core dimensions: "person, vehicle, goods, and environment," in forklift training scenarios. This multi-source data acquisition method overcomes the monitoring blind spots and information limitations caused by relying on single video feeds or manual observation in previous technologies.

[0033] Specifically, the personnel safety identification module 20 is used to acquire the status information of trainees. Its design aims to include non-compliant personnel behavior within the system's monitoring scope, addressing the pain point of traditional technologies that cannot automatically identify whether operators are wearing safety equipment correctly or engaging in dangerous actions (such as extending limbs outside the vehicle). By introducing monitoring of "people," this solution strengthens safety access control and process supervision from the source.

[0034] The vehicle status acquisition module 30 is used to collect data on the forklift's operating status, such as current speed, vehicle tilt posture (lateral tilt angle, longitudinal pitch angle), and start / stop status. This information collection aims to monitor the forklift's operational stability in real time, enabling timely detection of potential rollover risks caused by speeding, sharp turns, or driving on uneven surfaces, thus compensating for the shortcomings of existing technologies in perceiving risks related to the vehicle's posture.

[0035] The forklift operation monitoring module 40 is used to collect forklift forklift operation data. This is specifically designed to address the unique operational risks associated with forklifts as specialized equipment. Background electronic training systems typically do not consider forklift load and height, which are precisely the main causes of serious accidents such as overloading and tipping, and instability due to high-level cargo movement. By collecting forklift operation data, this solution can accurately identify the unique operational risks of forklifts.

[0036] The environmental collision avoidance detection module 50 is used to detect environmental information around the forklift. Its purpose is to address collisions caused by driver blind spots or misjudgments when the forklift is operating in complex or narrow spaces. By actively detecting surrounding obstacles or people, the system can anticipate collision risks and proactively prevent them.

[0037] Optionally, it also includes a data storage module and a display terminal. The data storage module is used to store the training operation data, violation records, and risk intervention logs throughout the process, forming a traceable training safety file. The display terminal is used to visualize and display the forklift's operating parameters, safety status, violation prompts, and safety assessment results in real time.

[0038] The information and data collected by each of the above modules are sent to the main control processing unit 10. The main control processing unit 10 is the decision-making core of the entire system. It performs comprehensive analysis and judgment based on the received multi-dimensional information to determine whether a risk exists and its severity, i.e., to assess the risk level. This intelligent judgment based on multi-source data fusion has higher accuracy, real-time performance, and reliability compared to manual judgment.

[0039] After assessing the risk level, the main control processing unit 10 outputs differentiated safety control commands to the graded safety execution module 60. The graded safety execution module 60 is then responsible for translating these commands into actual intervention actions on the forklift's electronic control system. By establishing a closed-loop control link from risk assessment to physical intervention, this solution overcomes the shortcomings of the prior art in lacking effective intervention methods when danger occurs, and effectively prevents dangerous operations.

[0040] The entire system adopts an independent architecture design, which is compatible with a variety of mainstream forklift electronic coach teaching systems on the market. It does not require modification of the original teaching program, scoring algorithm and hardware structure, and realizes the parallel and independent operation of the two systems with complementary functions.

[0041] Furthermore, in a preferred embodiment, the internal structure of the hierarchical safety execution module 60 is specified to achieve hierarchical intervention. See also... Figure 4 This module may include a voice and light warning unit 61, an operation permission limiting unit 62, and an emergency power-off braking unit 63. These three units represent three different intervention intensities, from weak to strong, providing a physical basis for implementing differentiated control strategies. Through this structural design, the system can take appropriate intervention measures according to the severity and urgency of the risks, serving both as a warning and education for minor violations and as a decisive measure in emergency situations, thus ensuring a balance between safety and user experience.

[0042] Based on the above structure, the core control logic of this application is realized. Specifically, the main control processing unit 10 is configured to execute specific logic bindings: when a first risk level is determined (such as slight speeding, slight fork height deviation, slight non-standard operation), only the voice and light warning unit 61 is instructed to perform a warning action, prompting the operator to make immediate corrections; when a second risk level is determined (such as severe speeding, slight overloading, forks traveling at a high position, slight vehicle tilt), the operation permission limiting unit 62 is instructed to perform an action to restrict operation permissions, forcibly reducing the risk; and when a third risk level is determined (such as vehicle tilt reaching the overturning threshold, severe overloading, close-range collision risk, personnel entering the training area), the emergency power-off braking unit 63 is instructed to perform an emergency braking action to prevent an accident from occurring. By mapping abstract risk levels one-to-one with specific intervention actions, this solution constructs a clear, explicit, and progressively advancing differentiated intervention model of "first-level warning, second-level limiting, and third-level braking," which is the key to achieving the core technical effect of this invention.

[0043] In another preferred embodiment, the intervention measures for the third risk level are further enhanced. After the emergency power-off braking unit 63 performs the emergency braking action, the main control processing unit 10 is also configured to immediately lock the entire vehicle, preventing it from being restarted by the trainee without authorization. Unlocking and restarting the vehicle requires an authorization signal from an administrator with higher authority (such as an on-site instructor or safety officer). This design combines technical intervention with management processes, constructing a closed-loop management system of "high-risk event handling - mandatory locking - authorization review - unlocking and restarting". Its technical effect is that it not only effectively avoids the risk of trainees immediately restarting the vehicle due to wishful thinking or improper operation after a serious accident, leading to a secondary accident, but also mandates that management personnel intervene, ensuring effective review and traceability of high-risk events, and enhancing the overall safety of the system and the rigor of management.

[0044] In one optional implementation, the "restriction of operating authority" action in the second risk level is finely designed. When the main control processing unit 10 determines that the current vehicle speed exceeds a preset safe speed threshold, it does not simply limit the speed to a fixed value. Instead, it dynamically reduces the forklift's power output according to a preset proportional relationship based on the extent to which the current speed exceeds the threshold. For example, the greater the speeding, the greater the power reduction. This "proportional speed limiting" method, compared to abrupt speed reduction or fuel cut-off, provides a smoother intervention process, allowing the driver to clearly feel the decrease in vehicle power response, thus guiding them to actively and smoothly reduce the vehicle speed. Through this intervention method, vehicle power can be smoothly controlled, avoiding operational problems that may be caused by abrupt intervention and improving the operating experience.

[0045] Furthermore, in a preferred embodiment, the specific implementation of the personnel safety identification module 20 is defined. This module can specifically be one or more vehicle-mounted high-definition AI vision cameras 21, equipped with dedicated algorithms for recognizing human posture and the wearing status of items. By employing an AI vision solution, the system can not only identify simple binary states such as "whether a helmet is being worn," but also more complex and concealed dynamic unsafe behaviors such as "the driver leaning out of the cockpit," "leaving the vehicle illegally," and "looking down at a mobile phone while driving." The technical advantage of this solution is that it expands the monitoring scope and identification dimensions of unsafe personnel behaviors, achieving intelligent supervision and preventing more types of potential accidents.

[0046] In another preferred embodiment, the specific implementation of the environmental collision avoidance detection module 50 is defined. This module can employ LiDAR 51, which is arranged around the forklift body, for example, at the front, rear, left, and right sides, so that the detection range of each LiDAR 51 complements each other and covers the perimeter of the forklift body. Compared to ultrasonic or infrared sensors, LiDAR 51 has advantages such as longer detection range, higher accuracy, stronger resistance to environmental interference, and the ability to generate high-density point cloud data. This layout effectively eliminates blind spots around the vehicle, achieving comprehensive detection of the forklift body's perimeter. The technical effect is to improve the reliability and accuracy of collision avoidance warnings, especially in complex scenarios such as low-speed turning, reversing into a parking space, or mixed traffic of people and vehicles, enabling earlier and more accurate detection of potential collision risks.

[0047] Please see Figure 2 This application also provides an intelligent safety protection control method for forklift electronic coach training, which is a procedural manifestation of the above-mentioned system functions. The method first includes synchronously collecting the status information of the trainees, the forklift's vehicle operating status data, the fork operating condition data, and the environmental information surrounding the forklift. This step is the basis for all subsequent decisions, emphasizing the synchronicity and comprehensiveness of data collection, ensuring that the information used for risk assessment is real-time and complete.

[0048] Next, the method includes analyzing the status information and data to determine the risk level. This step corresponds to the core function of the main control processing unit 10, which fuses and analyzes heterogeneous data from different sensors and compares it with a built-in safety rule base (e.g., safety thresholds specified by national standards) to make a comprehensive judgment on the degree of danger of the current situation.

[0049] Finally, the method includes implementing differentiated, tiered security interventions based on the risk level. This step is the final execution stage of the method, translating the assessed risk level into specific, intensity-matched physical interventions, forming a closed loop from perception to action, thereby achieving the goal of proactive security protection.

[0050] Furthermore, to make the logic of tiered intervention clearer, the steps for executing differentiated tiered safety intervention actions can specifically include: if the risk level is the first risk level, then execute an audible and visual warning action; if the risk level is the second risk level, then execute an action to restrict operation permissions; if the risk level is the third risk level, then execute an emergency braking action. This corresponds completely to the three-level intervention logic in the aforementioned system embodiment, ensuring consistency between the method and the system in core functions.

[0051] In a more complete implementation, this method can be extended to build a safety management system covering the entire process from pre-event to in-event to post-event. Specifically, before performing the aforementioned synchronous data collection step, a power-on self-check and access control step is added. This step utilizes the personnel safety identification module 20 to check whether the trainees' safety attire is compliant (e.g., whether they are wearing safety helmets) before the forklift is started. If it is not compliant, the forklift's starting permission is directly locked to prevent the vehicle from being started in an non-compliant state. Simultaneously, the method also includes a real-time data archiving step, recording all status information, vehicle data, fork data, environmental information, as well as the risk level determined by the system and the intervention actions performed during the training process, in real time and tamper-proofly. By adding these two steps, this method forms a complete closed loop: access control beforehand, monitoring and intervention during the event, and data traceability afterward. This improves the scientific rigor and scientific nature of training safety management, providing data support for accident analysis, trainee evaluation, and teaching improvement.

[0052] like Figure 1 As shown, this embodiment of the invention provides an intelligent safety protection control system for forklift electronic coach training, which addresses the technical problem that existing forklift electronic coaching equipment focuses solely on teaching and scoring while lacking proactive safety protection capabilities. This system aims to proactively predict and intervene in potential risks during training through multi-dimensional perception and intelligent decision-making.

[0053] The system includes a main control processing unit 10, and multiple functional modules that interact with the main control processing unit 10 for data and control. These modules include: a personnel safety identification module 20, a vehicle status acquisition module 30, a forklift operation monitoring module 40, an environmental collision avoidance detection module 50, and a graded safety execution module 60.

[0054] In a basic embodiment, the main control processing unit 10 can be a high-performance embedded industrial computer pre-installed with software algorithms for data processing and risk assessment. This main control processing unit 10 connects to all other modules via standardized communication interfaces (such as CAN bus, Ethernet, or RS485), forming the control center of the system.

[0055] The personnel safety identification module 20 is functionally used to collect the status information of the trainees. In a specific implementation, it can be a general-purpose sensor device installed in the cockpit that can monitor the driver's upper body. For example, it can determine whether the driver is seated and whether the seat belt is fastened by detecting seat pressure and seat belt buckle switch signals.

[0056] The vehicle status acquisition module 30 is used to collect data on the operating status of the forklift. This can consist of a set of basic sensors, such as a pulse sensor connected to the forklift wheels or drive shaft to measure and calculate the current vehicle speed; and a general attitude sensor mounted on the chassis to measure the tilt angle of the vehicle.

[0057] The forklift operation monitoring module 40 is used to collect forklift operation data. This can also be achieved using a set of general-purpose sensors, such as a sensor to measure the lifting height of the forks and a sensor to sense the weight of the load on the forks. These sensors parameterize the forklift-specific operational risks, providing a basis for the decision-making of the main control processing unit 10.

[0058] The environmental collision avoidance detection module 50 is used to detect environmental information around the forklift. In the basic configuration, it can be a general-purpose reversing radar installed at the rear of the forklift to provide obstacle distance information when reversing to prevent collisions.

[0059] The graded safety execution module 60 is the final execution end for realizing the control closed loop. It is physically connected to the forklift's electronic control system, for example, by controlling one or more relays to connect to the forklift's horn, warning lights, throttle control circuit, or main power supply circuit.

[0060] These modules work together. During the training, the personnel safety identification module 20, the vehicle status acquisition module 30, the forklift operation monitoring module 40, and the environmental collision avoidance detection module 50 continuously send the raw data or preliminarily processed information they have collected to the main control processing unit 10.

[0061] After receiving this multi-dimensional data from different dimensions, the main control processing unit 10 will activate its internal risk assessment algorithm. The core of this algorithm is a multi-condition logical judgment engine. For example, the algorithm will compare the current vehicle speed reported by the vehicle status acquisition module 30 with a preset safe speed threshold (for example, in an indoor scenario, this threshold can be set to 5 km / h); at the same time, it will compare the load data reported by the forklift operation monitoring module 40 with the forklift's rated load.

[0062] Based on these comparisons and judgments, the main control processing unit 10 will rate the current overall safety status, that is, assess the risk level. For example, if the vehicle speed only slightly exceeds the threshold, it is judged as the first risk level; if the load exceeds the rated value, it is judged as the second risk level; if the vehicle body tilt angle is close to the overturning threshold, it is judged as the third risk level.

[0063] Subsequently, the main control processing unit 10 issues differentiated safety control commands to the graded safety execution module 60 based on the assessed risk level. For example, for the first risk level, the graded safety execution module 60 is instructed to turn on the warning lights and buzzer; for the second risk level, it is instructed to restrict the throttle signal, thereby reducing the power output of the forklift; and for the third risk level, it is instructed to cut off the main power supply of the entire vehicle to achieve an emergency stop.

[0064] Based on the above structure and working principle, this basic embodiment constructs a complete "perception-decision-execution" proactive safety protection closed loop. It can automatically and in real-time monitor multiple risk sources during forklift training and take corresponding intervention measures according to the severity of the risks, thereby effectively filling the gaps in safety protection of existing electronic training systems and improving the safety of training.

[0065] In a preferred embodiment, based on the above embodiments, the internal structure of the hierarchical security execution module 60 is further specified. Please refer to [link to specific embodiment]. Figure 4 The hierarchical safety execution module 60 specifically includes a voice and visual warning unit 61, an operation permission current limiting unit 62, and an emergency power-off braking unit 63. The voice and visual warning unit 61 typically consists of a high-decibel buzzer and a high-brightness LED warning light. The operation permission current limiting unit 62 can be a controller capable of intervening in and modifying the forklift's CAN bus signals, or a module capable of adjusting the electronic throttle signal voltage. The emergency power-off braking unit 63 is typically a high-current relay connected in series in the forklift's main power circuit. This specific structure allows the hierarchical intervention strategy to be clearly mapped to the physical execution units, enhancing the feasibility of the solution.

[0066] Furthermore, such as Figure 4As shown, the control logic of the main control processing unit 10 is precisely bound to these three units. When the risk level is determined to be the first level, the main control processing unit 10 only sends a trigger signal to the voice and light warning unit 61 to achieve a flexible warning. When the risk level is determined to be the second level, the main control processing unit 10 will simultaneously trigger the voice and light warning unit 61 and send an instruction to the operation permission limiting unit 62, for example, instructing it to limit the maximum speed of the forklift to a preset value (such as 3 km / h) or prohibit the forks from continuing to lift. When the risk level is determined to be the third level, the main control processing unit 10 directly sends a high-level signal to the emergency power-off braking unit 63 to immediately disconnect the main power supply, achieving forced intervention. This clear logical binding ensures that the intensity of the intervention measures is strictly matched with the risk level, achieving precise protection.

[0067] In another preferred embodiment, the personnel safety identification module 20 is technically upgraded. For example... Figure 3 As shown, this module is specifically a vehicle-mounted high-definition AI camera 21 installed inside the cockpit 302, facing the driver. This vehicle-mounted high-definition AI camera 21 has a built-in edge computing chip, enabling it to run deep learning algorithms locally. These algorithms are specially trained to analyze video streams in real time, accurately identifying whether the driver is wearing a helmet, fastening their seatbelt, and even using human key point detection technology to determine if the driver is in a dangerous posture such as extending their arms or head out of the vehicle body 303. Compared to the mechanical sensors in the basic embodiment, the AI ​​vision solution can identify richer and more concealed unsafe behaviors, expanding the dimension of safety monitoring from the state of objects to human behavior, thus improving the comprehensiveness and intelligence of risk identification.

[0068] In another preferred embodiment, the environmental collision avoidance detection module 50 is also technically upgraded. For example... Figure 3 As shown, this module specifically employs multiple LiDARs 51. For example, one LiDAR 51 is installed at each of the four corners of the forklift body 303: front, rear, left, and right. The main control processing unit 10, by fusing and analyzing the data transmitted from these four LiDARs 51, can construct an environmental model around the forklift, accurately identifying the position, shape, and distance of obstacles. Compared to the ultrasonic radar in the basic embodiment, the LiDARs 51 have advantages such as a wider detection range, higher accuracy, faster refresh rate, and stronger resistance to adverse weather conditions. They can provide the system with more reliable and richer environmental information, thereby improving the accuracy and response speed of collision avoidance warnings and emergency braking.

[0069] Furthermore, the sensor configurations of the vehicle status acquisition module 30 and the fork operation monitoring module 40 are further refined. For example... Figure 3As shown, the vehicle status acquisition module 30 can integrate a high-precision three-axis tilt sensor 31, which is fixed at the center of the forklift chassis. This sensor can monitor the lateral roll angle and longitudinal pitch angle of the vehicle body 303 in real time during driving and turning, providing core data for the main control processing unit 10 to determine the risk of tipping over. The fork operation monitoring module 40 can integrate a height sensor 41 (such as a wire encoder or laser rangefinder) mounted on the fork mast 304 to accurately measure the lifting height of the forks 305; and a pressure sensor 42 mounted on the forks 305 or its load-bearing cylinder to monitor the weight of the goods in real time. Through these dedicated sensors, the system can accurately detect various high-risk operating conditions such as speeding, overloading, forks traveling at high positions, and significant vehicle tilting.

[0070] In complex real-world training scenarios, multiple risk factors may occur simultaneously or sequentially. To achieve accurate and comprehensive assessment of overall risk and avoid misjudgment or omission due to a single factor, the main control processing unit 10 of this system incorporates a comprehensive risk assessment model based on a fusion of weighted scoring and rule triggering. This model can achieve multi-condition comprehensive assessment through the following steps: Step 1: Construct a multi-dimensional risk indicator layer The main control processing unit 10 analyzes the received multi-source data in real time into the following five independent risk indicators, each of which is quantified into a continuous risk value of 0-100: Personnel Behavior Risk Value (R1): Based on the recognition results of AI vision camera 21. If the clothing is compliant and the posture is normal, R1=0; if the seat belt is not fastened, R1=60; if the limbs are extended outside the vehicle, R1=80; if the person is away from their post or using a mobile phone, R1=100. Intermediate states can be linearly interpolated.

[0071] Vehicle attitude risk value (R2): Based on tilt sensor 31 data. When the lateral tilt angle is <3° and the longitudinal tilt angle is <5°, R2=0; when the lateral tilt angle is 3-8° or the longitudinal tilt angle is 5-10°, R2=40-80 increases linearly; when the lateral tilt angle is >8° or the longitudinal tilt angle is >10°, R2=100.

[0072] Driving Operation Risk Value (R3): Based on vehicle speed and steering data. When the vehicle speed is less than the safety threshold (e.g., 5 km / h), R3 = 0; when the speeding range is 0-50%, R3 = 30-90; when the speeding range is greater than 50% or the acceleration during a sharp turn is greater than 0.5g, R3 = 100.

[0073] Forklift Operation Risk Value (R4): Based on pressure sensor 42 and height sensor 41. When the overload rate is <10% and the lifting height is <1m, R4=20-50; when the overload rate is >20% or when driving at a high position (lifting height >1.5m and speed >3km / h), R4=100.

[0074] Environmental collision risk value R5: Based on LiDAR 51 data. When the obstacle distance is >3m, R5=0; when the distance is 1-3m, R5=40-80; when the distance is <1m, R5=100.

[0075] Step 2: Dynamically weighted and fused calculation of comprehensive risk score The main control processing unit 10 dynamically adjusts the weight coefficients W1-W5 of each indicator according to the current training scenario (such as "site slalom", "cargo stacking", "hill start"), and calculates the comprehensive risk score S=(W1×R1+W2×R2+W3×R3+W4×R4+W5×R5) / ΣW.

[0076] For example, in the "cargo stacking" scenario, the weights are preset as follows: W1=0.2, W2=0.2, W3=0.1, W4=0.4, W5=0.1, emphasizing forklift operation safety. However, in the "site slalom" scenario, the weights are adjusted to: W1=0.3, W2=0.3, W3=0.3, W4=0.05, W5=0.05, emphasizing personnel, posture, and speed risks.

[0077] Step 3: Final determination of risk level based on rule triggers Simply using weighted scoring might mask an extreme risk in a single metric (such as severe overload but low levels in other metrics). Therefore, the system employs a final decision-making logic of "grading by score + triggering by hard rules": Default level determination: If S < 30 → No risk or Level 1 risk (low risk, issue an early warning). If 30≤S<70→ Second risk level (medium risk, execution operation permission rate restriction) If S≥70 → Level 3 Risk (High Risk, Execute Emergency Braking) Hard rule triggered (one-vote veto): If any of the following conditions are met, regardless of the S value, it will be directly judged as the third risk level: Rule R1: R4 (Forklift Operation Risk Value) = 100 (Severe overloading or excessively high-speed driving) Rule R2: R2 (vehicle attitude risk value) = 100 (overturning threshold) Rule R3: R5 (Environmental Collision Risk Value) = 100 (Obstacle < 1m) Rule R4: R1 (Personnel Behavior Risk Value) = 100 (serious violations such as leaving one's post or using a mobile phone) Based on the above settings, in a specific instance (multiple factors combined): During a stacking operation, the trainee is slightly overloaded by 10% (R4=40), with the fork lifting height at 1.2m and the vehicle speed at 4km / h (R4 contribution increases), and the vehicle body has a slight lateral tilt of 4° (R2=50), but the personnel are compliant (R1=0), and there is no collision risk (R5=0). Under the weight of "Stacking Scenario," S=42 is calculated, falling into the second risk level. The system implements operation permission flow restrictions (such as limiting the fork lifting speed and limiting the maximum vehicle speed).

[0078] Based on the above settings, in another specific embodiment (one-vote veto): when the trainee is navigating the cones, the vehicle speed only slightly exceeds the speed limit (R3=40), but the AI ​​detects that their arm is completely extended outside the vehicle (R1=80). The overall risk level is S=52, still belonging to the second risk level. However, according to strict rules, even if the limb extension does not reach 100 points, it is still considered a high-risk behavior. Therefore, the rule can be set to "if R1≥80, directly upgrade to the third risk level." The system executes emergency braking and locks the vehicle.

[0079] Through the model that combines weighted fusion with rigid rules, the system can accurately respond to complex and coupled training risks and achieve scientific, precise, and timely hierarchical safety intervention.

[0080] In a particularly preferred embodiment, the intervention strategy for the second risk level is optimized by employing a proportional speed limit method. When the main control processing unit 10 determines, via the vehicle speed sensor, that the current vehicle speed exceeds a preset safe speed threshold (e.g., in a straight section of the training area, this threshold is set to 10 km / h), it does not immediately force a speed reduction but instead performs a dynamic adjustment process. The main control processing unit 10 calculates the percentage by which the current vehicle speed exceeds the threshold and determines a power reduction coefficient based on a preset functional relationship (e.g., a linear or nonlinear lookup table). Then, it instructs the operation permission limiting unit 62 to reduce the control signal strength to the forklift power system (such as the engine throttle or motor controller) according to this coefficient. For example, if the current vehicle speed is 12 km / h, exceeding the threshold by 20%, the power output may be reduced by 30%; if the vehicle speed drops to 11 km / h, exceeding the threshold by 10%, the power output may only be reduced by 15%. This smooth, proportional-to-the-violation-level intervention provides drivers with clear negative feedback, guiding them to slow down proactively. This avoids the jolting and panic caused by sudden speed limits, and enhances the user-friendliness and safety of human-machine interaction.

[0081] In a particularly preferred embodiment, the intervention process for the third risk level is improved. When the system triggers the third risk level and the emergency power-off braking unit 63 performs an emergency braking stop, the main control processing unit 10 immediately enters the "high-risk event lockout" mode. In this mode, all conventional starting methods of the forklift, such as the ignition switch and start button, are blocked by software and cannot be restored even if the main power is cut off and then reconnected. At the same time, the system sends a high-risk alarm containing the time, location, and cause of the event to a designated administrator terminal (such as the instructor's mobile APP or the monitoring center's computer) via a wireless communication module (such as 4G or Wi-Fi). Unlocking the vehicle must be done by the administrator on-site through this terminal by sending an authenticated authorization unlocking command. This closed-loop process of "braking-locking-reporting-authorized unlocking" ensures that every serious dangerous event receives the attention and intervention of management personnel, prevents trainees from operating the vehicle without the necessary skills, provides a guarantee for accident investigation and targeted training, and achieves a deep integration of technical means and management systems.

[0082] In a specific workflow, the system operates as follows: After a trainee boards the vehicle, the onboard high-definition AI camera 21 performs facial recognition and verifies whether the trainee is wearing a helmet and seatbelt. Upon successful verification, the system announces "Identity verification successful, start permitted." Otherwise, the vehicle remains locked, and the system prompts "Please wear safety gear properly." During the slalom maneuver, if the trainee turns too sharply, the tilt sensor 31 detects a lateral tilt angle of 6 degrees (Level 1 risk threshold), and the system immediately issues a voice warning, "Caution: Vehicle speed is high, vehicle is tilting." Subsequently, when the trainee retrieves goods, the pressure sensor 42 detects a weight of 3.5 tons, exceeding the forklift's rated load of 3 tons. The main control processing unit 10 determines this as a Level 2 risk, immediately triggering an audible and visual alarm, "Overload operation, danger," and using the operation permission limiting unit 62 to prevent the forks 305 from continuing to lift, while simultaneously limiting the forklift's maximum speed to 2 km / h. During a reversing maneuver, the rear lidar 51 detects another trainee crossing 5 meters away. The system determines there is a collision risk and immediately triggers emergency braking, bringing the vehicle to a stop. After shutdown, the system automatically locks and displays "Level 3 Risk: Collision Warning, Vehicle Locked, Please Contact Administrator" on the driver's cab screen. Upon receiving the alert via the mobile app, the administrator goes to the site to confirm safety and remotely unlocks the vehicle using the app with a password. All data, violation records, and intervention logs throughout the process are fully recorded, forming the trainee's training safety report for that day.

[0083] This embodiment achieves synergistic efficiency by combining all preferred technical features. The in-vehicle high-definition AI camera 21 and LiDAR 51 provide comprehensive perception capabilities; dedicated sensors for tilt angle and pressure ensure accurate detection of core risks; refined control strategies such as proportional speed limiting and authorized unlocking balance safety and user experience; ultimately forming a closed-loop security protection solution. Compared to embodiments that only implement the basic solution, this embodiment improves the risk identification rate and provides digital management tools.

[0084] The intelligent safety protection control system and method for forklift electronic training provided by this invention has a wider range of applications. Firstly, it can be applied to various vocational and technical colleges, technical schools, and special equipment training institutions. In these settings, trainees are often novices with no prior experience, lack operational skills, and have weak safety awareness. This system can achieve all-weather, all-round monitoring, promptly correct non-standard behaviors, and decisively intervene in dangerous situations, thereby reducing the risk of accidents during training and ensuring the safety of teachers and students.

[0085] Secondly, this system is also applicable to standardized testing sites for special equipment in various regions. In forklift driver certification exams, this system can supplement existing electronic scoring systems. It not only records whether candidates violate operating procedures, but also forcibly terminates the exam in the event of seriously dangerous actions, thus improving the safety of the testing process. Simultaneously, its automatically generated detailed data reports, containing all violations and intervention events, can serve as important objective evidence for examiners to assess candidates' safety awareness and emergency response capabilities.

[0086] Furthermore, this system is also valuable for use in the internal training centers of large enterprises, especially those in logistics, warehousing, and manufacturing sectors with a large number of forklift operators. Enterprises can use this system to conduct pre-job safety training for new employees or to provide regular skills refresher training and safety awareness reinforcement for existing employees. By simulating various hazardous working conditions and allowing the system to intervene, it can effectively improve employees' safe operating skills and their ability to respond to emergencies, thereby reducing the accident rate in the company's daily production and operations.

[0087] Finally, for forklift rental companies, this system can be used as a standard or optional configuration for their rented equipment. For lessees, forklifts with active safety features mean a higher level of operational safety, helping them to better fulfill their primary responsibility for safe production.

[0088] In summary, the present invention, through its modular design and good compatibility, can be retrofitted and modified on various existing new and old forklifts. Whether used for teaching, examinations, or for enterprise use, it can improve the intelligence and safety level of forklift operation.

Claims

1. A smart safety protection control system for forklift electronic coach training, characterized in that, include: Main control processing unit; The personnel safety identification module is connected to the main control processing unit and is used to collect the status information of the trainees. The vehicle status acquisition module is connected to the main control processing unit and is used to acquire the vehicle status data of the forklift. The fork operation monitoring module is connected to the main control processing unit and is used to collect fork operation data of the forklift. An environmental collision avoidance detection module, connected to the main control processing unit, is used to detect environmental information around the forklift; A graded safety execution module is connected to the main control processing unit and interfaces with the forklift's electronic control system; The main control processing unit is used to receive the status information, the vehicle body operating status data, the fork operating condition data and the environmental information, and to judge the risk level based on the information and data, and then output differentiated safety control instructions to the graded safety execution module according to the risk level.

2. The intelligent safety protection control system for forklift electronic coach training according to claim 1, characterized in that, The hierarchical security execution module includes: Voice and light warning unit; Operation permission rate limiting unit; Emergency power-off braking unit.

3. The intelligent safety protection control system for forklift electronic coach training according to claim 2, characterized in that, The main control processing unit is configured as follows: Upon determining that the first risk level is identified, the voice and light warning unit is instructed to perform a warning action. Upon determining that the second risk level is identified, the operation permission limiting unit is instructed to perform an action that restricts operation permissions. Upon determining that the third risk level is reached, the emergency power-off braking unit is instructed to perform an emergency braking action.

4. The intelligent safety protection control system for forklift electronic coach training according to any one of claims 1 to 3, characterized in that, The personnel safety identification module is an in-vehicle high-definition AI vision camera, equipped with an algorithm for recognizing human posture and the wearing status of items.

5. The intelligent safety protection control system for forklift electronic coach training according to any one of claims 1 to 3, characterized in that, The environmental collision avoidance detection module uses a lidar and is arranged around the forklift body so that the detection range of the lidar covers the perimeter of the forklift body.

6. The intelligent safety protection control system for forklift electronic coach training according to claim 3, characterized in that, The main control processing unit is also configured to: After the emergency power-off braking unit performs the emergency braking action, the entire vehicle is locked and configured to be unlocked and restarted only after receiving authorization from the administrator.

7. The intelligent safety protection control system for forklift electronic coach training according to claim 3, characterized in that, The action of restricting operation permissions is as follows: when the main control processing unit determines that the current vehicle speed exceeds the preset safe speed threshold, it reduces the power output of the forklift according to a preset ratio based on the extent to which the current vehicle speed exceeds the preset safe speed threshold.

8. A method for intelligent safety protection and control in forklift electronic coach training, characterized in that, Includes the following steps: S1. Synchronously collect the status information of trainees, the vehicle body operation status data of forklifts, the working condition data of forks, and the environmental information around the forklifts; S2. Analyze the status information and data to determine the risk level; S3. Based on the risk level, implement differentiated and graded safety intervention actions.

9. The intelligent safety protection control method for forklift electronic coach training according to claim 8, characterized in that, The steps for implementing differentiated, tiered security intervention actions include: If the risk level is the highest risk level, then an audible and visual warning will be issued. If the risk level is the second risk level, then the action of restricting operation permissions will be executed; If the risk level is level three, then emergency braking will be performed.

10. The intelligent safety protection control method for forklift electronic coach training according to claim 8 or 9, characterized in that, The method also includes: Before executing step S1, a power-on self-test and access control are performed, including: collecting the status information of the trainees; if the status information indicates that the trainees' safety attire is not compliant, locking the forklift's start-up permission; and Real-time data archiving is performed to record the status information, vehicle operating status data, fork operation data, environmental information, risk level, and graded safety intervention actions.

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