A forklift truck safety on-line industrial monitoring management system

Abstract

The present application relates to the technical field of industrial vehicle safety control, in particular to a forklift safety online industrial monitoring and management system, comprising: a data fusion unit for collecting kinematic data, position and attitude information and load distribution data of the vehicle, and performing space-time alignment processing on the collected data to generate a real-time state vector; a margin calculation unit for calculating a plurality of independent safety margin components and performing fusion processing based on the safety margin components to generate a comprehensive safety margin; a risk prediction unit for predicting a future safety margin value based on the time series of the comprehensive safety margin, and comparing and analyzing the future safety margin value with a preset safety threshold and a preset warning threshold to generate a risk level; an intervention control unit for generating and executing a corresponding hierarchical intervention strategy in response to the risk level; the present application constructs a complete technical link from multi-source data perception to closed-loop control, realizing the transition from passive safety to active safety.

Description

Technical Field

[0001] This invention relates to the field of industrial vehicle safety control technology, specifically to an online industrial monitoring and management system for forklift safety. Background Technology

[0002] In the current field of industrial vehicle applications, forklifts, as core material handling equipment, are directly related to the safety of personnel, goods, and equipment. To ensure operational safety, forklifts are usually equipped with basic alarm systems. However, the design of these systems often relies on the driver's experience and judgment, or only triggers alarms after a dangerous situation occurs, which is a passive response safety strategy. Traditional safety assurance methods lack continuous and real-time comprehensive monitoring and analysis of multi-dimensional state parameters during forklift operation, such as kinematic data, position and posture, and load distribution, making it difficult to identify the evolution trend of potential risks before a danger occurs.

[0003] In existing technologies, safety monitoring methods are usually relatively simple and cannot effectively integrate heterogeneous data from different sensors to form an accurate and quantitative assessment of the vehicle's hazardous status. Existing systems generally lack predictive capabilities, meaning they cannot infer the risk level within a short future time window based on the changing trends of current status data, thus missing the best intervention opportunity. In addition, when a risk is identified, traditional intervention measures are often simple, non-tiered alarm prompts, failing to take corresponding, automatically executed closed-loop control interventions based on the urgency of the risk, making it difficult to achieve a dynamic balance between ensuring safety and maintaining operational efficiency.

[0004] Therefore, how to provide a forklift safety online industrial monitoring and management system that can integrate multi-source data and realize hierarchical active intervention based on safety margin calculation and prediction is a technical problem that urgently needs to be solved by those skilled in the art.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention discloses an online industrial monitoring and management system for forklift safety. Specifically, the technical solution includes:

[0007] A forklift safety online industrial monitoring and management system, comprising:

[0008] The data fusion unit is used to collect vehicle kinematic data, position and attitude information and load distribution data, and to perform spatiotemporal alignment processing on the collected data to generate real-time state vectors.

[0009] The margin calculation unit is used to calculate multiple independent safety margin components based on the real-time state vector, and to perform fusion processing based on the safety margin components to generate a comprehensive safety margin.

[0010] The risk prediction unit is used to predict future safety margin values ​​based on the time series of comprehensive safety margins, and compare and analyze the future safety margin values ​​with preset safety thresholds and preset warning thresholds to generate risk levels.

[0011] The intervention control unit is used to generate and execute corresponding graded intervention strategies in response to risk levels.

[0012] Preferably, the real-time state vector generation process is as follows: kinematic data is acquired through the vehicle data bus, position and attitude information is acquired using the positioning system, and load distribution data is acquired through the linear force sensor array; the acquired data is time-aligned using a timestamp correction algorithm, and the aligned data is unified under the vehicle's centroid coordinate system to generate a real-time state vector;

[0013] The real-time state vector includes the vehicle's lateral acceleration, yaw rate, shortest collision time, load resultant force vector, and load torque vector.

[0014] Preferably, the process for generating the comprehensive safety margin is as follows:

[0015] The rollover stability margin was calculated.

[0016] The load stability margin is calculated.

[0017] The environmental collision avoidance margin is calculated;

[0018] The calculated rollover stability margin, load stability margin, and environmental collision avoidance margin are then multiplied to generate a comprehensive safety margin.

[0019] Preferably, the rollover stability margin is used to quantify the ratio between the vehicle's restoring moment and overturning moment, and is calculated based on the vehicle's lateral acceleration obtained from the real-time state vector, combined with the pre-calibrated vehicle equivalent center of gravity height and vehicle wheelbase.

[0020] Preferably, the load stability margin is used to assess whether the torque generated by the load is close to the design bearing limit. It is calculated based on the load torque obtained from the real-time state vector and combined with the maximum allowable load torque preset as a safety threshold parameter.

[0021] Preferably, the environmental collision avoidance margin is used to characterize the nonlinear growth characteristics of collision risk. It is calculated using an exponential decay model based on the shortest collision time obtained from the real-time state vector and combined with the calibrated time sensitivity coefficient.

[0022] Preferably, the risk level is generated as follows: the time series of the comprehensive safety margin is subjected to first-order differencing to estimate the margin change rate; based on the margin change rate, the future safety margin value is predicted using linear extrapolation.

[0023] When the future safety margin value is greater than the preset safety threshold, the risk level is determined to be a safe zone;

[0024] When the future safety margin value is less than or equal to the preset safety threshold and greater than the preset warning threshold, the risk level is determined to be the warning zone;

[0025] When the future safety margin value is less than or equal to the preset warning threshold, the risk level is determined to be a danger zone.

[0026] Preferably, the graded intervention strategy includes a primary intervention strategy; the primary intervention strategy is executed in response to a risk level of warning zone, and the primary intervention strategy includes performing power output limiting operation, performing steering angular velocity limiting operation, and performing haptic feedback operation.

[0027] Preferably, the graded intervention strategy includes a secondary intervention strategy; the secondary intervention strategy is executed in response to a risk level of danger zone, and the secondary intervention strategy includes performing active smooth braking operation, performing maximum steering angle limiting operation, and performing mast or fork motion locking operation.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The online industrial monitoring and management system for forklift safety disclosed in this invention, compared with existing safety strategies that rely on driver experience or passive alarms, constructs a complete technical link from multi-source data perception to closed-loop control, realizing the transformation from passive safety to active safety;

[0030] 2. This system forms a complete closed-loop control loop through a data fusion unit, a margin calculation unit, a risk prediction unit, and an intervention control unit. First, the data fusion unit can acquire the vehicle's kinematic data, position and attitude information, and load distribution data, and use a timestamp correction algorithm to perform spatiotemporal alignment processing, ultimately unifying the data into the vehicle's center of mass coordinate system to generate a high-fidelity and delay-free real-time state vector. This process solves the technical challenge of multi-source heterogeneous data fusion, laying a solid data foundation for subsequent accurate risk assessment.

[0031] 3. The margin calculation unit of this invention receives the real-time state vector and transforms it into an intuitive and quantifiable safety indicator. The unit calculates three independent safety margin components with clear physical meanings in parallel: rollover stability margin, load stability margin, and environmental collision avoidance margin. These components are nonlinearly fused using product operations to generate a comprehensive safety margin. This multiplicative fusion design conforms to the principle of the weakest link effect in safety systems and can more accurately and sensitively capture the critical dangerous state of the system as a whole, solving the drawback of the traditional weighted summation method, which cannot effectively reflect the extremely dangerous situation in a certain dimension.

[0032] 4. The risk prediction unit of the present invention continuously analyzes the time series of the comprehensive safety margin, estimates the margin change rate through first-order difference processing, and predicts the future safety margin value using linear extrapolation. This predictive decision-making mechanism enables the system to anticipate the decay trend of the safety margin before the actual occurrence of the danger, thereby reserving valuable reaction time for the subsequent intervention and control module.

[0033] 5. The intervention control unit of this invention translates the predicted risk level into specific, graded, and executable vehicle control actions. When the system is in the warning zone, a first-level intervention strategy is executed, which aims to guide the driver to avoid risks and minimize the impact on normal operating efficiency by limiting power output, steering angle, and providing tactile feedback. When the system enters the danger zone, a second-level intervention strategy is forcibly executed, including active and gentle braking, maximum steering angle limitation, and mast or fork locking, to decisively and effectively prevent accidents from occurring. This graded, closed-loop control logic achieves a dynamic balance between safety and operating efficiency, ensuring the inherent safety of the vehicle in complex industrial scenarios. Attached Figure Description

[0034] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0035] Figure 1 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0037] Example 1:

[0038] Please see Figure 1 A forklift safety online industrial monitoring and management system, comprising:

[0039] The data fusion unit is used to collect vehicle kinematic data, position and attitude information and load distribution data, and to perform spatiotemporal alignment processing on the collected data to generate real-time state vectors.

[0040] The margin calculation unit is used to calculate multiple independent safety margin components based on the real-time state vector, and to perform fusion processing based on the safety margin components to generate a comprehensive safety margin.

[0041] The risk prediction unit is used to predict future safety margin values ​​based on the time series of comprehensive safety margins, and compare and analyze the future safety margin values ​​with preset safety thresholds and preset warning thresholds to generate risk levels.

[0042] The intervention control unit is used to generate and execute corresponding graded intervention strategies in response to risk levels.

[0043] This embodiment discloses an online industrial monitoring and management system for forklift safety. The core purpose of this system is to achieve a shift from passive alarm to active intervention by continuously and multi-dimensionally monitoring and predicting the operating status of forklifts, thereby nipping potential safety risks in the bud without affecting normal operating efficiency. The system disclosed in this embodiment logically constitutes a complete closed-loop control loop, characterized by including a data fusion unit, a margin calculation unit, a risk prediction unit, and an intervention control unit.

[0044] The data fusion unit is used to provide a unified, synchronous and high-precision state input for the entire system. This unit is responsible for collecting vehicle kinematic data, position and attitude information and load distribution data from multiple heterogeneous sensors. Since the sampling frequency and transmission delay of these data sources are different, the data fusion unit performs spatiotemporal alignment processing to finally generate a real-time state vector that can comprehensively describe the instantaneous dangerous state of the forklift.

[0045] The margin calculation unit is used to transform complex, multi-dimensional state information into an intuitive, quantifiable single safety metric. This unit receives a real-time state vector generated by the data fusion unit and calculates multiple independent safety margin components for assessing specific risk dimensions based on the specific components in the vector. Subsequently, this unit performs nonlinear fusion processing on these components to generate a comprehensive safety margin. This comprehensive metric can sensitively reflect the risk of any single dimension.

[0046] The risk prediction unit is used to achieve the leading role of safety management, that is, to provide early warning before the actual occurrence of a dangerous situation. The unit continuously receives and analyzes the time series of comprehensive safety margins, and obtains a future safety margin value by predicting its changing trend within a short time window in the future. Then, the unit compares and analyzes this predicted value with the preset safety threshold and warning threshold in real time, thereby dynamically classifying the current risk level of the vehicle.

[0047] The intervention control unit is used to translate the results of risk assessment into specific, executable vehicle control actions to form a closed loop. In response to the risk level output by the risk prediction unit, the unit automatically generates and executes a set of corresponding graded intervention strategies. For example, it executes a soft prompt at a lower risk level and a mandatory vehicle dynamic parameter limit at a higher risk level, thereby actively keeping the vehicle within the safe operating envelope.

[0048] Through the collaborative work of the four units—data fusion, margin calculation, risk prediction, and intervention control—this invention constructs a complete technical chain from data perception to closed-loop control. Compared to the passive safety strategies in existing technologies that rely on driver experience or only issue alarms after a hazard occurs, this system can predict the decay trend of safety margin in advance and take proactive intervention measures that match the risk level. This not only greatly improves the inherent safety of forklift operations but also minimizes unnecessary impacts on normal operating efficiency through graded intervention, achieving a dynamic balance between safety and efficiency.

[0049] Example 2:

[0050] The real-time state vector generation process is as follows: kinematic data is acquired through the vehicle data bus, position and attitude information is acquired through the positioning system, and load distribution data is acquired through the linear force sensor array; the acquired data is time-aligned using a timestamp correction algorithm, and the aligned data is unified under the vehicle's centroid coordinate system to generate the real-time state vector.

[0051] The real-time state vector includes the vehicle's lateral acceleration, yaw rate, shortest collision time, load resultant force vector, and load torque vector.

[0052] In the system of Example 1, the process of generating real-time state vectors was specifically implemented; the purpose of this was to ensure that the process of generating state vectors was reliable, accurate and engineerable, providing a high-quality data foundation for all subsequent calculations.

[0053] The specific workflow of the data fusion unit is as follows:

[0054] Firstly, the vehicle's kinematic data is acquired through the forklift's built-in onboard data bus; simultaneously, a high-precision positioning system is used to acquire the forklift's high-frequency position and attitude information in the global coordinate system; in addition, load distribution data is monitored and acquired in real time through a linear force sensor array installed on the forks or mast.

[0055] Before further processing, the data fusion unit first performs validity checks and filtering on the raw data from each sensor to eliminate outliers that may be caused by sensor noise or communication interference. Simultaneously, it uses onboard LiDAR or vision sensors to perceive dynamic and static obstacles around the vehicle. The data fusion unit processes this perceived information, identifies potential collision targets, and predicts their trajectories over a future period based on their current state and the vehicle's kinematic data, thereby calculating the shortest collision time. ;

[0056] Secondly, to address the issue of inconsistent time references among multi-source data, this embodiment employs a Kalman filter-based timestamp correction algorithm to perform time alignment processing on the acquired data. This algorithm can accurately estimate and compensate for the transmission delay of each data point, ensuring that all data logically correspond to the same moment. Finally, to eliminate the impact of vehicle pose changes on data interpretation, all time-aligned data is unified under the vehicle's centroid coordinate system to generate a real-time state vector. ;

[0057] In this embodiment, the real-time state vector It is explicitly defined as a vector containing five core components: ;in, It is the vehicle's lateral acceleration; It is the yaw rate; It is the shortest collision time; It is the resultant force vector of the load; It is the load torque vector; where the yaw rate is... With the resultant force vector of the load Reserved for more advanced dynamic stability models or driving behavior analysis, subsequent calculations in this embodiment will focus on the core risk components.

[0058] By clearly defining the data acquisition source, specifying the processing algorithm, and unifying the coordinate reference, this embodiment ensures the real-time state vector. The generation is high-fidelity and latency-free; this method not only solves the technical challenge of multi-source heterogeneous data fusion, but also defines... The specific composition provides a direct and effective input for the subsequent accurate calculation of safety margin, greatly improving the accuracy and reliability of the entire system risk assessment.

[0059] Example 3:

[0060] The process for generating the overall safety margin is as follows:

[0061] The rollover stability margin was calculated.

[0062] The load stability margin is calculated.

[0063] The environmental collision avoidance margin is calculated;

[0064] The calculated rollover stability margin, load stability margin, and environmental collision avoidance margin are then multiplied to generate a comprehensive safety margin.

[0065] The rollover stability margin is used to quantify the ratio between the vehicle's restoring moment and overturning moment. It is calculated based on the vehicle's lateral acceleration obtained from the real-time state vector, combined with the pre-calibrated vehicle equivalent center of gravity height and vehicle track width.

[0066] The load stability margin is used to assess whether the torque generated by the load is close to the design bearing limit. It is calculated based on the load torque obtained from the real-time state vector and combined with the maximum allowable load torque preset as a safety threshold parameter.

[0067] The environmental collision avoidance margin is used to characterize the nonlinear growth characteristics of collision risk. It is calculated using an exponential decay model based on the shortest collision time obtained from the real-time state vector and combined with the calibrated time sensitivity coefficient.

[0068] In the system of Example 1, the process of generating the comprehensive safety margin was specifically implemented. The underlying logic of this decomposition and fusion strategy is to break down a general safety concept into multiple risk dimensions with clear physical meaning and independent modeling, and to fuse them in a nonlinear way that can reflect the veto safety principle, so as to more accurately assess the overall danger status of the system.

[0069] The margin calculation unit receives the real-time state vector. As input, three independent safety margin components are computed in parallel, each of which is normalized to... The interval is defined as follows: 0 represents extreme danger, and 1 represents absolute safety.

[0070] The first step is to calculate the rollover stability margin. ;

[0071] Rollover stability margin The lateral rollover moment is used to quantify the proportional relationship between the vehicle's restoring moment and rollover moment, and is a key indicator for evaluating the vehicle's ability to resist lateral rollover. To ensure real-time calculation, this embodiment uses a quasi-static model that can effectively reflect rollover risk under most operating conditions for approximate calculation; its calculation is based on the real-time state vector. Vehicle lateral acceleration obtained from And combined with the pre-calibrated inherent physical parameters of the vehicle, namely the vehicle's equivalent center of gravity height and vehicle wheelbase The specific calculation formula is as follows: ;

[0072] in: To obtain from the real-time state vector The absolute value of the lateral acceleration obtained in the middle; and These are the inherent physical parameters of the vehicle that have been pre-calibrated through 3D modeling or experimental methods. This refers to gravitational acceleration; in the formula... The function is used to apply lower bound constraints to ensure that the margin is 0 rather than negative when the vehicle is in a supercritical state.

[0073] The second step is to calculate the load stability margin. ;

[0074] Load stability margin It is used to assess whether the torque generated by the current load is close to the design load limit of the forks or mast; its calculation is based on the real-time state vector. The load torque obtained from And combined with the maximum permissible load torque preset as a safety threshold parameter. The specific calculation formula is as follows:

[0075] ;in: To obtain from the real-time state vector The modulus of the real-time load torque obtained from the data; The safety threshold parameters are determined based on the manufacturer's design manual or through finite element analysis; the design employs a squared term, the technical consideration of which is to improve the sensitivity of the margin when approaching the limit state;

[0076] The third step is to calculate the environmental collision avoidance margin. ;

[0077] Environmental collision avoidance margin It is used to characterize the nonlinear growth of collision risk over time; its calculation is based on the real-time state vector. The shortest collision time obtained And combined with the calibrated time sensitivity coefficient The results were obtained using the exponential decay model:

[0078] ;in: To obtain from the real-time state vector Obtain from; This is a time sensitivity coefficient, derived from statistical analysis of a large amount of historical driving behavior data, and is a value that best reflects the driver's sense of urgency; this formula ensures a non-linear characteristic mapping of collision risk.

[0079] The calculated rollover stability margin Load stability margin Collision avoidance margin with the environment Perform product operations to generate a comprehensive safety margin. : ;

[0080] By decomposing the overall safety issue into three orthogonal dimensions—rollover, load, and collision avoidance—and modeling them separately, this embodiment achieves a precise characterization of risk sources. More importantly, it generates data using a multiplicative fusion method rather than an additive fusion method. This causes the margin component in any dimension to approach zero, which will lead to a decrease in the overall safety margin. Approaching zero; this design conforms to the principle of the weakest link effect in safety systems, and compared with the traditional weighted summation method, it can more accurately and sensitively capture the critical danger state of the entire system.

[0081] Example 4:

[0082] The risk level is generated as follows: the time series of the comprehensive safety margin is subjected to first-order differencing to estimate the margin change rate; based on the margin change rate, the future safety margin value is predicted using linear extrapolation.

[0083] When the future safety margin value is greater than the preset safety threshold, the risk level is determined to be a safe zone;

[0084] When the future safety margin value is less than or equal to the preset safety threshold and greater than the preset warning threshold, the risk level is determined to be the warning zone;

[0085] When the future safety margin value is less than or equal to the preset warning threshold, the risk level is determined to be a danger zone.

[0086] In the system of Example 1, the process of generating risk levels was implemented in a specific way; the purpose of this is to establish an objective and dynamic risk decision-making mechanism that not only focuses on the current margin status, but more importantly, can predict its future evolution trend, thereby achieving the leading role of control intervention.

[0087] The risk prediction unit receives the comprehensive safety margin output by the margin calculation unit. The time series data is analyzed; to achieve prediction, the time series is first-differenced to estimate the margin change rate; subsequently, based on the estimated margin change rate, linear extrapolation is used to predict the future safety margin value. This prediction model is a simplified first-order approximation of the system's dynamic behavior, with low computational cost and strong real-time performance. This linear model is suitable for relatively stable operating conditions. For more dynamic, sudden events, other nonlinear prediction models can be used as supplements. The specific prediction formula is as follows:

[0088] ;

[0089] in: and From The margin values ​​obtained from the time series at the current and previous time points; The sampling time interval of the system; This is a preset prediction time window;

[0090] Preset safety threshold and preset warning thresholds It was determined through statistical analysis of a large amount of driving simulation data under hazardous conditions, combined with relevant safety standards. The preset prediction time window... It was optimized after comprehensively considering the typical response time of the vehicle and the driver's reaction time, aiming to reserve sufficient reaction time for the intervention control unit;

[0091] Based on the predicted future safety margin , and preset threshold and Compare and dynamically classify risk levels:

[0092] When the future safety margin value Greater than the preset safety threshold At that time, the risk level was determined to be a safe zone;

[0093] When the future safety margin value Less than or equal to the preset safety threshold And greater than the preset warning threshold At that time, the risk level was determined to be a warning zone;

[0094] When the future safety margin value Less than or equal to the preset warning threshold At that time, the risk level was determined to be a dangerous area;

[0095] By introducing a linear extrapolation prediction model based on the rate of change, this embodiment shifts the focus of risk assessment from the current state. Transferred to the future state This predictive decision-making mechanism enables the system to anticipate dangers, reserving valuable reaction time for subsequent intervention and control modules. This allows intervention measures to be executed smoothly and effectively before the safety margin is completely exhausted, thus achieving true proactive safety control.

[0096] Example 5:

[0097] The tiered intervention strategy includes a primary intervention strategy; the primary intervention strategy is implemented in response to a risk level of warning zone. The primary intervention strategy includes implementing power output limiting operations, implementing steering angular velocity limiting operations, and implementing tactile feedback operations.

[0098] The tiered intervention strategy includes a secondary intervention strategy. The secondary intervention strategy is implemented in response to a risk level of danger zone. The secondary intervention strategy includes performing active smooth braking, performing maximum steering angle limiting, and performing mast or fork motion locking.

[0099] In the system of Example 1, the graded intervention strategy was implemented in a specific way; the purpose of this was to transform the abstract risk level into precise and effective vehicle control commands, and to ensure that the intensity of the intervention matched the severity of the risk.

[0100] When the intervention control unit receives a signal indicating a risk level in the warning zone, it means that the system has predicted a potential risk and executes a Level 1 intervention strategy. The design goal of this strategy is to guide the driver to avoid the risk in a gentle and suggestive manner. The Level 1 intervention strategy specifically includes: executing power output limiting operations, such as limiting the maximum acceleration to 70% of the normal value; executing steering angle rate limiting operations, guiding the driver to make smoother steering by applying dynamic damping to the steering system; and executing haptic feedback operations, activating vibration motors in the seat or steering wheel to issue tactile warnings.

[0101] When the intervention control unit receives a signal indicating a danger zone, it means that the system predicts that danger is about to occur or has already occurred, and at this time, a secondary intervention strategy is executed. The design goal of this strategy is to forcibly and decisively intervene in vehicle control to ensure safety. The secondary intervention strategy specifically includes: executing active and gentle braking, in which the system automatically applies braking force, and the target deceleration applied is precisely limited to a range that will not cause secondary instability of the load; executing maximum steering angle limiting operation, which dynamically calculates and sets a maximum safe steering angle allowed under the current operating conditions based on the current vehicle speed and load status; and executing mast or fork action locking operation, which temporarily prohibits all mast or fork operations that may further deteriorate vehicle stability.

[0102] By designing a two-tiered intervention strategy that precisely corresponds to warning and danger zones, this embodiment achieves on-demand allocation of intervention intensity. The flexible prompts of the first-level intervention ensure that the impact on operational efficiency is minimized when the risk is low. The mandatory intervention of the second-level intervention ensures that accidents can be decisively and effectively prevented in critical moments. This hierarchical, closed-loop control logic enables the entire system to work in coordination with the driver, ultimately achieving the core technical goal of keeping the vehicle within the safe operating envelope in complex industrial scenarios.

[0103] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A forklift safety online industrial monitoring and management system, characterized in that, include: The data fusion unit is used to collect vehicle kinematic data, position and attitude information and load distribution data, and to perform spatiotemporal alignment processing on the collected data to generate real-time state vectors. The real-time state vector generation process is as follows: kinematic data is acquired through the vehicle data bus, position and attitude information is acquired through the positioning system, and load distribution data is acquired through the linear force sensor array; the acquired data is time-aligned using a Kalman filter timestamp correction algorithm, and the aligned data is unified to the vehicle's centroid coordinate system to generate a real-time state vector. The real-time state vector includes the vehicle's lateral acceleration, yaw rate, shortest collision time, load resultant force vector, and load torque vector. The margin calculation unit is used to calculate multiple independent safety margin components based on the real-time state vector, and to perform fusion processing based on the safety margin components to generate a comprehensive safety margin. The process for generating the comprehensive safety margin is as follows: The rollover stability margin was calculated. The load stability margin is calculated. The environmental collision avoidance margin is calculated; The calculated rollover stability margin, load stability margin, and environmental collision avoidance margin are then multiplied to generate a comprehensive safety margin. The risk prediction unit is used to predict future safety margin values ​​based on the time series of comprehensive safety margins, and compare and analyze the future safety margin values ​​with preset safety thresholds and preset warning thresholds to generate risk levels. The intervention control unit is used to generate and execute corresponding graded intervention strategies in response to risk levels.

2. The forklift safety online industrial monitoring and management system according to claim 1, characterized in that, The rollover stability margin is used to quantify the ratio between the vehicle's restoring moment and overturning moment. It is calculated based on the vehicle's lateral acceleration obtained from the real-time state vector, combined with the pre-calibrated vehicle equivalent center of gravity height and vehicle wheelbase.

3. The forklift safety online industrial monitoring and management system according to claim 1, characterized in that, The load stability margin is used to assess whether the torque generated by the load is close to the design bearing limit. It is calculated based on the load torque obtained from the real-time state vector and combined with the maximum allowable load torque preset as a safety threshold parameter.

4. The forklift safety online industrial monitoring and management system according to claim 1, characterized in that, The environmental collision avoidance margin is used to characterize the nonlinear growth characteristics of collision risk. It is calculated using an exponential decay model based on the shortest collision time obtained from the real-time state vector and combined with the calibrated time sensitivity coefficient.

5. The forklift safety online industrial monitoring and management system according to claim 1, characterized in that, The risk level is generated as follows: the time series of the comprehensive safety margin is subjected to first-order differencing to estimate the margin change rate; based on the margin change rate, the future safety margin value is predicted using linear extrapolation. When the future safety margin value is greater than the preset safety threshold, the risk level is determined to be a safe zone; When the future safety margin value is less than or equal to the preset safety threshold and greater than the preset warning threshold, the risk level is determined to be the warning zone; When the future safety margin value is less than or equal to the preset warning threshold, the risk level is determined to be a danger zone.

6. The online industrial monitoring and management system for forklift safety according to claim 1, characterized in that, The tiered intervention strategy includes a primary intervention strategy; the primary intervention strategy is executed in response to a risk level of warning zone, and the primary intervention strategy includes performing power output limiting operation, performing steering angular velocity limiting operation, and performing haptic feedback operation.

7. The online industrial monitoring and management system for forklift safety according to claim 1, characterized in that, The graded intervention strategy includes a secondary intervention strategy; the secondary intervention strategy is executed in response to a risk level of danger zone, and the secondary intervention strategy includes performing active smooth braking operation, performing maximum steering angle limiting operation, and performing mast or fork motion locking operation.

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