Automatic lifting horizontal protection method and device

Abstract

The invention discloses an automatic lifting horizontal protection method and device, and relates to the technical field of industrial safety protection. According to the method, industrial operation conditions and personnel distribution data are collected, a dynamic danger area model containing sliding plate geometric features and personnel motion states is constructed, falling dynamics analysis is introduced, the sliding speed and impact kinetic energy of personnel on the surface of a sliding plate are calculated, the sliding speed and impact kinetic energy are converted into kinetic energy equivalent height and superposed to geometric safety height, and the safety of the sliding plate is improved. The self-adaptive calculation of the protection height is realized; meanwhile, a hierarchical interlocking control strategy is generated in combination with personnel behavior intention recognition and space-time risk scoring. According to the device part, a multi-section rigid frame and a gear and rack synchronous driving mechanism are adopted, and stable lifting is executed based on a multi-target optimized trajectory planning instruction. The problems that the height of a traditional protection facility is fixed, dynamic sliding impact cannot be handled, and control logic is rigid are solved, and the active safety and the intelligent level of industrial operation are improved.

Description

Technical Field

[0001] This invention belongs to the field of industrial safety protection technology, and in particular to automatic lifting and leveling protection methods and devices. Background Technology

[0002] The geotextile laying equipment is a core component in engineering operations, equipment construction, and pipeline laying. The sliding plate area at its working end is a critical working surface for splicing, positioning, and conveying geotextiles and flexible geotextiles to the work interface. Because the sliding plate extends to the work interface at a significant angle, workers must perform high-intensity operations on an exposed, humid, and moving geotextile surface, facing extremely high risks of falls and slippage. Therefore, the level of safety protection in this area directly impacts project progress and personnel safety.

[0003] Currently, the protection of work platform edges mainly relies on physical barriers. Common forms include fixed guardrails, which are steel pipe structures directly welded to the edge of the work platform, with a constant height and position; and flexible safety nets, which form a mesh surface by tensioning steel wire ropes or nylon ropes between posts. These facilities primarily provide passive boundary definition through their physical form, and are usually maintained in a single state during operation, relying on the structural rigidity or flexible deformation to provide basic fall protection.

[0004] However, in complex and dynamic working environments and slope-variable operations, the aforementioned passive protection methods suffer from a technical challenge of mismatch between static protection capabilities and dynamic risk evolution. Specifically, this manifests in the following deep-seated defects: existing protection height settings are based solely on static geometric dimensions, failing to consider the dynamics of personnel slipping and falling after losing stability on inclined, slippery surfaces. This neglects the fact that the kinetic energy generated by personnel slipping requires higher interception potential energy to offset it, resulting in insufficient protection effectiveness in high-risk conditions. Existing interlocking control logic is too rigid, mostly based on binary logic triggered by infrared single points, unable to analyze personnel behavioral characteristics and movement intentions, such as normal operations and dangerous crossings, leading to frequent false alarms or missed alarms, making it difficult to balance safety with operational efficiency. The lifting and lowering of protective devices lacks multi-target trajectory optimization, and the start-up and shutdown processes are accompanied by severe mechanical impacts and energy waste, making it difficult to adapt to the frequently adjusted working conditions required in industrial operations. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic lifting and lowering horizontal protection method and device, in order to solve the above-mentioned problems of the prior art.

[0006] Technical solution: An automatic lifting horizontal protection method, applied to a liftable protection unit configured on the edge of a work platform, comprising:

[0007] Collect real-time operational parameters and personnel distribution data in the skateboard area;

[0008] A dynamic hazard zone model is constructed based on the working condition parameters, and personnel distribution data is mapped to the dynamic hazard zone model;

[0009] The real-time target protection height that satisfies the current security boundary constraints is calculated based on the mapping results.

[0010] Based on the real-time target protection height, a lifting control command is generated to drive the protection unit to adjust its vertical height to match the real-time target protection height.

[0011] Beneficial effects: This invention solves the problems of traditional protective facilities being highly fixed, unable to cope with dynamic sliding impacts, and having rigid control logic, thereby improving the active safety and intelligence level of industrial operations. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating the steps of an automatic lifting and lowering horizontal protection method in an embodiment of this application.

[0013] Figure 2 This is a flowchart illustrating the steps of generating lifting control commands based on the real-time target protection height in an embodiment of this application.

[0014] Figure 3 This is a flowchart illustrating the steps of risk trend analysis and dynamic correction based on spatiotemporal segments in an embodiment of this application.

[0015] Figure 4 This is a flowchart illustrating the steps of integrated fault diagnosis and fault-tolerant control in the embodiments of this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0017] Example 1, such as Figure 1 As shown, the physical structure, electrical components, and basic adaptive control process based on the geometric operating condition model of the industrial protective device are described.

[0018] Step 1101, an automatic lifting horizontal protection method is applied to a liftable protection unit configured on the edge of the work slide, including: collecting real-time work condition parameters and personnel distribution data in the slide area.

[0019] In this embodiment, the sensing and monitoring system serves as the hardware front-end for data acquisition, responsible for acquiring various environmental and operational data. Specifically, a liftable protective unit is installed on both sides of the work platform to create a physical barrier to prevent personnel from falling. The hardware structure includes a multi-segment rigid frame, preferably welded from 6061-T6 aluminum alloy profiles, balancing strength and lightweight requirements. Several rigid frame segments are connected by hinges to form a chain structure that can change its folding angle with height, similar to the diamond-shaped telescopic principle of a pantograph. A metal protective net is fixed to the inner side of the rigid frame segments, with a mesh size preferably 5cm*5cm, to prevent tools or personnel from falling. A guide slider is installed at the bottom of the rigid frame segments, which is embedded in a C-shaped steel guide rail laid along the length of the edge of the work platform. The C-shaped steel guide rail is preferably made of Q345B steel, with a layer of UHMW-PE wear-resistant lining on its inner side to reduce frictional resistance and noise. The guide slider is embedded in the C-shaped steel guide rail and slides with it to constrain the lifting trajectory of the liftable protective unit.

[0020] The sensing and monitoring system includes a tilt sensor, preferably an SCA100T model, mounted on the skateboard structure to collect the real-time tilt angle θ of the skateboard relative to the horizontal plane. The system also includes multi-point ranging sensors, such as infrared proximity switches, preferably an E3F-DS30C4 model, or lidar sensors, positioned on top of or around the protective unit to collect personnel distribution data within the skateboard area. Furthermore, the system also needs to collect the reference height H of the work interface. w This data can be obtained through existing level gauges or measured in real time using ultrasonic sensors installed at the underside of the slide. Personnel distribution data specifically refers to digital signals that reflect the coordinates of personnel positions, their distance from the edge, and the presence of personnel.

[0021] Step 1102: Construct a dynamic hazard zone model based on the work condition parameters and map the personnel distribution data into the dynamic hazard zone model; extract the vertical height of the skateboard relative to the work interface and the effective height and thickness of the soft material on the skateboard surface from the work condition parameters; obtain the maximum body height data of the workers in upright or bent-over postures.

[0022] In this embodiment, the intelligent control system is electrically connected to both the sensing and monitoring system and the lifting drive system. It is equipped with a memory and a processor; the memory stores a computer program, and the processor executes the program to implement an automatic lifting and horizontal protection method. For example, a PLC controller uses the collected parameters to construct a dynamic hazard zone model. In the basic implementation, the dynamic hazard zone model is primarily constructed based on geometric relationships. The system establishes a coordinate system with the fixed end of the sliding plate as the origin O.

[0023] The vertical height z of any point x on the skateboard at time t s (x,t) is calculated by the following formula: z s (x,t)=H s (t)+(xL)sinθ(t). Where, H s (t) is the vertical height of the lower end of the skateboard in the equipment coordinate system, L is the total length of the skateboard, sinθ(t) is the tilt angle of the skateboard, and x is the position coordinate along the direction of the skateboard, with a value range of [0,L].

[0024] The vertical height H of any point on the skateboard relative to the working interface clear (x,t) is calculated by the following formula:

[0025] H clear (x,t)=z s (x,t)-H w (t) where H w (t) represents the reference height of the current working interface.

[0026] A coordinate x is defined along the length of the skateboard, where x=0 represents the fixed end, x=L represents the lower end of the skateboard, and L is the total length of the skateboard. The system is based on the skateboard tilt angle θ and the vertical height H of the lower end of the skateboard in the equipment coordinate system. s Calculate the vertical height z of any point x on the skateboard. s (x,t). Vertical height H clear (x,t) is defined as a point on the skateboard surface relative to the current H. w The vertical distance, i.e., H clear (x,t)=z s (x,t)-H w .

[0027] Furthermore, the operating parameters also include the geometric parameters of the flexible board, especially the effective height thickness t of the flexible board on the slide surface. eff This parameter can be manually preset or acquired in real time via a contour scanner mounted above the skateboard. When the soft panel is present, it effectively raises the worker's standing reference plane. Maximum body height data H max The preset value covers the height of most workers, such as 1.8 meters or 1.9 meters, or can be set according to ergonomic statistics to characterize the upper limit of the height that a person's center of gravity or head may reach in an upright or bent-over posture.

[0028] Step 1103: Calculate the real-time target protection height that satisfies the current safety boundary constraints based on the mapping results; generate lifting control commands based on the real-time target protection height to drive the protection unit to adjust its vertical height to match the real-time target protection height; and superimpose the data of vertical free height, effective pad thickness, and maximum body height to obtain the geometric safety height that prevents personnel from climbing over.

[0029] In this embodiment, the control system calculates the minimum physical height required to prevent a person from falling. Geometric safety height h geo This refers to the height of the upper edge of the protective barrier that, under static or quasi-static conditions, can prevent people from climbing over or falling. The specific calculation formula is: h safe (x,t)=t eff (x,t)+H max -ΔH allow , where ΔH allow The minimum allowable height margin is a preset safety redundancy value, such as 0.2 meters. The physical meaning of this formula is that the upper edge of the protective device must be higher than the highest point of a person standing on the soft platform, with a certain safety margin.

[0030] To achieve a uniform control objective across the entire length of the skateboard, at each position x, the local safety height requirement h after the soft padding is raised is considered. safe (x,t) is calculated by the following formula: h safe (x,t)=t eff (x,t)+H max -△H allow ; where t eff (x,t) represents the effective height and thickness of the soft padding at position x, H max △H represents the maximum height of the worker. allow This represents the permissible geometric safety margin.

[0031] Taking the maximum value along the entire length of the slide, we obtain the minimum required protection height under the current working conditions:

[0032] h req (t)=max x∈[0,L] h safe (x,t);

[0033] The initial target height after adding a safety margin is:

[0034] h 0 (t)=h req (t)+△h margin ;

[0035] The final real-time target protection height is subject to structural limiting:

[0036] h *(t)=clip(h 0 (t),h min ,h max );

[0037] The clipping function is defined as follows:

[0038] clip(x,a,b)={a,x<a;x,a≤x≤b;b,x>b};

[0039] That is, h req (t)=max(h safe (x,t)), where x takes values ​​between 0 and L. For further security, the system can also add an additional safety height margin Δh. margin The final real-time target protection height h*(t) needs to be limited by the physical structure of the device, and is limited by the Clip function: h*(t) = clip(h req (t)+Δh margin ,h min ,h max ), where h min and h max These represent the minimum (e.g., 0.3 meters) and maximum (e.g., 1.5 meters) allowable height of the protective unit's mechanical structure, respectively. The Clip function restricts the calculation results to [h...]. min ,h max Within the closed interval of ].

[0040] In a preferred compatibility mode or basic logic mode, to address sensor failures or debugging needs, the control system can preset simplified threshold logic. For example, when the tilt sensor detects a skateboard angle θ greater than 15 degrees, the system directly sets the real-time target protection height h*(t) to 1.5 meters; when the skateboard angle θ is less than 5 degrees, the system sets it to 0.3 meters. This mode, as a special case of the algorithm, is compatible with the aforementioned continuous calculation model under specific parameter configurations, providing system robustness.

[0041] Step 1104: Generate lifting control commands based on the real-time target protection height to drive the protection unit to adjust its vertical height to match the real-time target protection height.

[0042] In this embodiment, the intelligent control system converts the calculated real-time target protection height h*(t) into an electrical signal to control the motor. The lifting drive system, mechanically connected to the liftable protection unit, provides the power to drive its vertical movement. It includes a DC electric actuator, preferably a DTZ300, mounted at the bottom hinge of the rigid frame section, with a rated thrust of 3000 Newtons and a running speed of 50 mm / s. This actuator drives the rigid frame section to unfold or fold through its extension / retraction stroke. The DC electric actuator integrates an electromagnetic brake to automatically lock its stroke position in the event of a power outage. The control system compares the current actual height of the protection unit with the target height h*(t) fed back by the displacement sensor WYC-100, generating commands for forward rotation, reverse rotation, or stop.

[0043] To ensure the consistency of action of multiple protective units, a rack and pinion synchronization mechanism is installed between adjacent rigid frame sections. This mechanism forces adjacent sections to maintain synchronized lifting and lowering movements. Even if the loads on the push rods are uneven, mechanical meshing ensures the consistency of the overall height, with synchronization errors controlled within 2 millimeters. When the target height is reached or the system is powered off, the electromagnetic brake automatically locks the stroke position of the DC electric push rod, preventing the protective unit from accidentally sliding down due to gravity and achieving reliable physical position maintenance. Through the above process, the device achieves closed-loop control from operational condition sensing to physical protective action.

[0044] Example 2 describes how to introduce a fall dynamics model to convert the sliding speed and kinetic energy of a person on a skateboard into an equivalent protective height requirement, thus solving the problem that a simple geometric model cannot cope with the dynamic sliding risk.

[0045] Step 1201: Construct a sliding motion model of the person on the skateboard surface based on the working condition parameters; extract the skateboard tilt angle and the skateboard friction coefficient from the working condition parameters; calculate the difference between the tangential component of gravity along the skateboard and the friction resistance to obtain the tangential acceleration of the person sliding down the skateboard.

[0046] In this embodiment, the system introduces a dynamic perspective to assess risk. In addition to geometric parameters, the system needs to preset or calibrate the dynamic friction coefficient μ of the skateboard surface. This coefficient depends on the skateboard material (usually steel), the material of the person's shoe sole, and the surface slipperiness (affected by weather and working environment). The system establishes a force model of the person on the inclined skateboard. The person is subjected to gravity mg, a support force N, and an upward frictional force f along the skateboard.

[0047] Tangential acceleration a t This refers to the acceleration of a person sliding downwards on a skateboard after losing balance. According to Newton's second law, the net force F along the tangential direction of the skateboard is... t =mg*sin(θ)-μ*mg*cos(θ). Therefore, the tangential acceleration at It can be done through formula a t The equation = g*sin(θ) - μ*cos(θ) is calculated. Here, g is the acceleration due to gravity, taken as 9.8 m / s²; θ is the skateboard angle; and sin and cos are sine and cosine functions, respectively. This model quantifies the physical fact that the steeper the skateboard angle and the slipperier the surface, the greater the risk of slipping for the user.

[0048] Step 1202: Calculate the relative sliding velocity of the person reaching the edge of the skateboard using the sliding motion model; calculate the tangential terminal velocity of the person reaching the edge of the skateboard by combining the sliding distance determined by the personnel distribution data, and project the tangential terminal velocity onto the normal direction of the protective unit to obtain the impact component velocity.

[0049] In this embodiment, the system uses collected personnel distribution data to determine the distance *s* along the bottom edge of the skateboard from the current position of the person. Assuming the initial velocity *v0* of the person is 0, the most common accidental slip scenario or a preset initial velocity is considered. According to kinematic equations, the final tangential velocity *v* when the person reaches the edge of the skateboard is... e It can be done through formula v e =sqrt(v0 2 +2*a t The result is calculated using *s), where sqrt represents the square root operation.

[0050] Since protective devices are usually installed perpendicular to or at an angle to the skateboard, the speed at which a person impacts the protective device is not full speed (v). e Rather, it is the component of the impact velocity v in the direction normal to the protective device. The system calculates the impact velocity v based on the angle φ between the tangential direction of the sliding plate and the normal direction of the protective surface. n =v e *cos(φ). The geometric definition of the included angle φ is as follows: Taking the tangential direction of the slide plate as a reference, the protective unit is usually installed perpendicular to the surface of the slide plate, and the angle between its normal direction and the tangential direction of the slide plate is φ. In a typical installation method, the protective device is installed perpendicular to the slide plate, φ=0, at which point cosφ=1, and the impact velocity component is equal to the tangential terminal velocity; if the protective device is installed at a certain angle to the slide plate, then φ takes the corresponding angle value. The complete formula for calculating the impact velocity component is: v n =v e *cosφ; where v e Let φ be the final tangential velocity of the person reaching the edge of the skateboard, and φ be the angle between the tangential direction of the skateboard and the normal direction of the protective device. For example, if the protective device is installed perpendicular to the skateboard, then φ = 0, v n =v e This step converts the sliding energy of a person on the skateboard into impact potential energy against the protective device.

[0051] For example, in a specific numerical calculation case, assume the skateboard tilt angle θ is 25 degrees, the coefficient of kinetic friction μ is 0.3, and the sliding distance s of the person from the edge is 2 meters. Calculate the tangential acceleration a. t =9.8*sin(25°)-0.3*9.8*cos(25°)≈4.14-2.66=1.48 m / s², calculate the terminal tangential velocity v at the edge. e =sqrt(0+2*1.48*2)≈2.43 meters per second. Assuming a perpendicular impact, then v n ≈2.43 meters per second.

[0052] Step 1203: Based on the principle of energy conservation, the relative sliding velocity is converted into the kinetic energy equivalent height; the ratio of the square of the impact velocity component to twice the gravitational acceleration is calculated to obtain the kinetic energy equivalent height.

[0053] In this embodiment, the system uses the impact velocity component v calculated above. n The equivalent height value is called the kinetic energy equivalent height h. eq The physical principle is: assuming the person moves at a speed of v... n The kinetic energy generated by the impact on the protective device and the attempt to climb over it is 0.5 mv. n 2 It can be converted into potential energy m*g*h eq This helps personnel overcome a certain height. In order to intercept personnel with this kinetic energy, the protective device must add this height to the geometric height.

[0054] The calculation formula is h eq =v n 2 / (2*g). Continuing with the numerical example above, if v n ≈2.43 meters per second, then h eq =2.43 2 / (2*9.8)≈0.30 meters. Under the above conditions, in addition to considering the geometric obstruction of height, the protective device needs to be raised by an additional 0.3 meters to effectively counteract the kinetic energy generated by the person sliding and prevent them from rushing out of the guardrail due to inertia.

[0055] Step 1204: Obtain the geometric safety height determined based on human body size and working posture; superimpose the geometric safety height with the kinetic energy equivalent height, and add the preset dynamic safety margin to obtain the real-time target protection height.

[0056] In this embodiment, the system calculates the geometric safety height h. geo Equivalent height h of the calculated kinetic energy eq The new real-time target protection height h*(t) is calculated as follows: h*(t) = hgeo +h eq +Δh dyn Among them, Δh dyn This serves as a dynamic safety margin to compensate for errors introduced by model simplification, such as neglecting air resistance and non-point-mass effects of the human body.

[0057] Through overlay logic, the system achieves dual protection through both static and dynamic mechanisms. When the water is calm and the skateboard is gently gliding, h eq When the value is close to 0, the system primarily provides geometric protection; however, in conditions of high winds and waves, steep skids, or slippery surfaces, h... eq Increasing the protection level will automatically raise the system's protection height, demonstrating its adaptability to high-risk operating conditions. The final h*(t) will also be clipped by the Clip function to ensure the instruction remains within the hardware's limits.

[0058] Example 3: Introducing artificial intelligence algorithms to perform in-depth analysis of human behavior, achieving a leap from simply identifying people to understanding their intentions. By constructing behavioral feature vectors and risk scoring models, intelligent hierarchical interlocking control is realized.

[0059] Step 1301: Analyze the temporal distribution data of personnel, extract the minimum distance between the personnel and the edge of the skateboard, the approach rate towards the danger zone, the dwell time in the danger zone, and the body posture code; combine the minimum distance, approach rate, dwell time, and body posture code to construct the behavioral feature vector of each detected personnel.

[0060] In this embodiment, the control system no longer focuses solely on the location of people at a single moment, but rather processes the personnel distribution data sequence over a time window Δt. For each detected object j, the system extracts multi-dimensional features: minimum distance d. j This refers to the nearest Euclidean distance between a person and the edge of the skateboard; the approach rate v d , j It refers to the distance d j The rate of change over time, with positive values ​​indicating moving away and negative values ​​indicating moving closer; the dwell time τ j This refers to the length of time a person can continuously exist within a pre-defined high-risk area, such as within 1 meter of the edge.

[0061] In addition, the system also uses image processing algorithms or posture sensor data to generate body posture codes β. j The encoding can be a discrete variable or a continuous vector; for example, 0 represents standing, 1 represents bending over, 2 represents squatting, and 3 represents climbing or stepping. Ultimately, the system combines these components into a behavioral feature vector φ. j [k]=[d j [k],v d ,j[k],τ j [k],βj [k] T , where k represents the current time step and T represents the vector transpose. This vector fully digitizes the behavioral state of the person at the current moment.

[0062] Step 1302: Input the behavior feature vector into the preset behavior classification model to identify and output the behavior intention category of the current detection personnel.

[0063] In this embodiment, the system utilizes a pre-trained classification model to classify the feature vector φ j [k] is used for classification. Behavioral classification models can employ linear classifiers, support vector machines (SVMs), or simple logistic regression models. For each predefined intent category c, such as c1 = safe passage, c2 = normal operation, c3 = suspicious crossing, the model calculates an unnormalized score s. j c[k]=w c T *φ j [k]+b c , where w c It is the weight vector of category c, b c It is a bias term.

[0064] To convert ratings into probabilities, the system preferably uses the Softmax function for processing. The Softmax function converts the ratings for each category into a normalized probability distribution, where P is the probability that the j-th person belongs to intention category c at time k. j (c|k) is calculated by the following formula: P j (c|k)=exp(s j ,c[k])c'∈Cexp(s j ,c'[k]); where exp(·) represents the natural exponential function; s j Let c[k] be the unnormalized score of the j-th person in category c; C be the set of all predefined behavioral categories; and the denominator be the exponential sum of the scores for all categories, ensuring that the sum of the probabilities of each category is 1. This probability distribution not only provides the most likely intention category but also quantifies the confidence level of the classification, providing a more refined weighting basis for subsequent risk scoring. The system selects the category with the highest probability as the output behavioral intention category. For example, if a person is very close to the edge, has a high approach rate, and their posture indicates climbing, the model will output a suspicious crossing category with a high probability.

[0065] Step 1303: Calculate the behavioral risk score by assigning corresponding risk weights according to the behavioral intent category, and calculate the spatial risk score based on the distance information in the personnel distribution data; perform a weighted summation of the behavioral risk score and the spatial risk score to obtain a comprehensive risk score that represents the current safety status of personnel.

[0066] In this embodiment, the system converts qualitative intention categories into quantitative risk values. Each intention category c is assigned a risk weight γ c , for example, γ cross >γ work >γ pass . The personal behavior risk score R jbeh [k] is calculated as the weighted sum of all category probabilities: R jbeh [k]=∑ c γ c *P j (c|k). Meanwhile, the system calculates the spatial risk score R sp [k] purely based on geometric location. Generally, the closer the distance, the higher R sp .

[0067] The comprehensive risk score R tot [k] is the result of the fusion of the two: R tot [k]=λ sp *R sp [k]+λ beh *R beh [k], where λ sp and λ beh are the weight coefficients of spatial risk and behavior risk respectively. This fusion mechanism ensures that the system neither ignores physical hazards at close range (dominated by R sp ) nor fails to capture dangerous behaviors at a distance but with intent (dominated by R beh ), such as a person rushing towards the edge quickly at a relatively far distance.

[0068] Step 1304: Compare the comprehensive risk score with the preset multi-level risk thresholds to determine a hierarchical interlock strategy including warning, restricting actions, or forced elevation; use the hierarchical interlock strategy to correct the initial instruction generated based on the real-time target protection height to obtain the final lifting control instruction.

[0069] In this embodiment, the system presets hierarchical thresholds R1 < R2 < R3. The control logic is as follows: When R tot [k] < R1, the system operates normally and only executes the calculated basic protection instruction. When R1 ≤ R tot [k] < R2, the warning mode is triggered. The system issues an audible and visual alarm and adds a small correction amount Δh1 to the basic height. When R2 ≤ R tot [k] < R3, the restriction mode is triggered. The system restricts the lowering speed of the soft dam and adds a large height correction amount Δh2.

[0070] When R totWhen [k]≥R3, the forced intervention mode is triggered. At this time, regardless of the basic calculation results, the system forcibly sets the real-time target protection height to the maximum value h. max The system immediately cuts off the power to the software until the risk score drops. This tiered strategy avoids the efficiency losses caused by traditional one-size-fits-all shutdowns, achieving an intelligent balance between safety and efficiency. Finally, the corrected or overridden altitude instructions are sent to the drive system for execution.

[0071] Example 4, such as Figure 3 As shown, this paper describes how to construct discrete risk zones using spatial discretization and introduce time-dimensional rate of change analysis. When the risk has not yet reached the critical value but shows a rapid upward trend, the protection height can be dynamically adjusted in advance to achieve a proactive safety strategy that prevents problems before they occur.

[0072] Step 801: Define several continuous discrete risk segments along the length of the skateboard, and project the personnel distribution data into the corresponding discrete risk segments.

[0073] In this embodiment, the control system transforms the continuous physical space into discrete logical units for computation. Specifically, the system divides the edge of the work skateboard into N continuous discrete risk segments along the length L of the work skateboard, i.e., the direction extending from the work end to the work interface. For example, if the skateboard is 30 meters long, it can be divided into 15 segments, with each segment being 2 meters long, and the index k ranging from 1 to 15. Each segment corresponds to a specific physical space range.

[0074] The system utilizes personnel distribution data collected by multi-point sensors, particularly the longitudinal x-coordinate of each person in the skateboard coordinate system, and projects this data onto the corresponding k-th segment. For example, if a person is located 5.5 meters from a fixed end, the system determines that the person belongs to the 3rd segment. This type of spatial discretization simplifies complex continuous field problems into finite-dimensional vector computation problems, facilitating efficient implementation in industrial controllers such as PLCs.

[0075] Step 802: Calculate the segment risk value of each discrete risk segment at the current time based on the distance weight between the person and the edge of the skateboard; use exponential weighted moving average to perform time-series smoothing on the segment risk value to obtain the smoothed segment risk value.

[0076] In this embodiment, the system quantifies the instantaneous risk level within each segment. For the k-th segment, the system assigns different weights based on the lateral distance d between the detected person and the edge of the skateboard within that segment. Specifically, a set of weight parameters can be preset, such as a proximity weight w. near Mid-distance weight w mid and long-distance weight w far And satisfy w nearGreater than w mid Greater than w far A relationship greater than 0.

[0077] Section risk value R k The calculation of (t) uses a weighted summation method. The specific formula is: R k (t)=∑(I(d=d class )*w class Where ∑ represents the summation of all detected targets within the segment, I represents the indicator function, which takes the value 1 when the distance to a person belongs to a certain category and 0 otherwise, w class This corresponds to the distance weight. For example, if within a certain segment, one person is in the close-range area and another person is in the medium-range area, and w... near Set to 1.0, w mid If we set it to 0.5, then the risk value for this segment is 1.5. In this way, the system generates a risk distribution vector R(t) = [R1(t),...,R...] that varies over time. n (t)].

[0078] In some alternative implementations, the weighting can also take into account the density of people. For example, when more than three people gather in the same area, the system introduces an additional crowding coefficient to multiply and enhance the calculated risk value, reflecting the uncontrollable risks brought about by crowd gathering.

[0079] To suppress the interference of transient noise on risk assessment, the system performs an exponentially weighted moving average over time on the segment risk value. The smoothed segment risk value R' k (t) is calculated by the following formula: R' k (t)=α·R k (t)+(1-α)·R' k (t-ΔT); where α is the smoothing coefficient, ranging from (0,1), with a preferred value of 0.3 to 0.5; ΔT is the time step; R k (t) represents the original segment risk value at the current time; R' k (t-ΔT) represents the smoothed risk value at the previous time step. This smoothing mechanism makes risk assessment more sensitive to persistent threats and more robust to incidental disturbances.

[0080] Step 803: Calculate the rate of change of the smoothed segment risk value within a preset time window to obtain a risk change trend index characterizing the speed of risk increase. Based on the smoothed segment risk value and the risk change trend index, the system calculates the global risk score R. global (t), the formula is as follows:

[0081] R global (t)=max k R'k (t)+λ·max k ΔR k (t) + ;where max k R' k (t) represents the maximum smoothed risk value of all segments, reflecting the absolute risk level of the most dangerous segment at present; max k ΔR k (t) + λ represents the maximum positive value of the risk change rate across all segments, reflecting the maximum rate of risk deterioration; λ is the trend weighting coefficient, used to adjust the sensitivity to risk change trends; [·] + The operator for taking the positive part is defined as: [x] + =max(x,0); This global risk score integrates two dimensions: static risk level and dynamic deterioration trend, providing a more comprehensive basis for subsequent interlocking control decisions.

[0082] In this embodiment, the system introduces a time dimension to capture dynamic trends. The system sets a time window ΔT, for example, 1 second or 2 seconds. For each segment k, the system calculates its risk change ΔR. k (t)=R k (t)-R k (t-ΔT). This change is the indicator of risk change trend. When ΔR k When (t) is positive and large, it indicates that the risk in the segment is accumulating rapidly, such as people rushing to the edge or the number of people surging in a short period of time.

[0083] To obtain the global trend, the system can extract the maximum value of the rate of change across all segments as the system's global trend indicator, i.e., Trend(t) = max(ΔR). k (t)). This indicator can keenly capture local emergencies. Even if the global average risk has not yet reached the alarm threshold, as long as there is a severe deterioration trend in a local area, the system can identify it through this indicator.

[0084] Step 804: Generate a trend correction coefficient using the global risk score, and use the trend correction coefficient to dynamically adjust the real-time target protection height.

[0085] In this embodiment, the system transforms trend indicators into specific control increments. The system uses a preset mapping function or lookup table logic to map risk change trend indicators to a trend correction coefficient α. trend For example, when Trend(t) exceeds a preset trend threshold, α trend Set it to 1.2, otherwise keep it at 1.0. Alternatively, use a linear mapping: α trend =1+k trend*Trend(t), where k trend This is the gain coefficient.

[0086] The system uses this coefficient to correct the foundation protection height h*(t), and the correction formula is h final (t)=h*(t)*α trend Alternatively, the system can directly calculate the additional height Δh. trend This dynamic gain correction mechanism ensures that the protective device responds not only to the current danger but also to the speed at which the danger develops, thus gaining valuable protective time before the danger occurs.

[0087] Example 5, as follows Figure 2 As shown, this paper describes how to use optimal control theory to transform a single target height command into a smooth, energy-efficient, and safe motion trajectory, thus solving the problems of equipment impact and high energy consumption caused by traditional step control.

[0088] Step 601: Set the future prediction time interval and define the protection height trajectory function that changes continuously with time within the prediction time interval.

[0089] In this embodiment, the intelligent control system no longer focuses solely on the current moment but looks ahead to a future time. The system sets a prediction time interval [t0, t0+T], where t0 is the current moment and T is the prediction time domain length, for example, 5 seconds. Within this interval, the system defines the protection altitude trajectory function h(t) to be solved.

[0090] The protective height trajectory function h(t) can be represented in polynomial form, such as a cubic or quintic polynomial, ensuring the continuity of its first derivative (velocity) and second derivative (acceleration). In other implementations, h(t) can also be represented as a B-spline curve or a discrete point sequence of a piecewise linear function to facilitate numerical optimization. This function describes the complete spatiotemporal path of the protective device from its current position to the target position.

[0091] Step 602: Establish a comprehensive objective function to measure the operating cost of the system, and set the real-time target protection height as the safety lower limit constraint of the protection height trajectory function.

[0092] In this embodiment, the system constructs a scalar function J to quantify the quality of the trajectory. Simultaneously, the system transforms security into mathematical constraints. Specifically, the real-time target protection height h... safe (t) is set as a hard constraint lower bound, which requires that at any time t within the prediction interval, the planned trajectory h(t) must be greater than or equal to h. safe (t).

[0093] In addition, the system also needs to incorporate physical constraints, including the mechanical travel limit h of the protection unit. min and h max and the maximum speed v of the drive motor max and maximum acceleration a max These constraints collectively define the space of feasible solutions, ensuring that the planned trajectory is physically feasible and safe.

[0094] Step 701: Calculate the difference between the real-time target protection height and the protection height trajectory function, and record the portion that does not reach the real-time target protection height as the safety cost density.

[0095] In this embodiment, to reflect safety in the optimization objective, especially when dealing with soft constraints, the system defines a safety cost density J. s (t). Although hard constraints already exist, introducing soft costs can guide the trajectory as far away from the danger boundary as possible. The safety cost density is usually constructed as a barrier function or penalty term. For example, J s (t)=[h safe (t)-h(t)+ε]+ 2 , where []+ denotes the positive part operator, and ε is the desired safety margin. When the planned height h(t) is lower than the safety requirement, this value will increase sharply, forcing the optimization algorithm to raise the trajectory.

[0096] Step 702: Calculate the first derivative of the protection height trajectory function to obtain the ascent and descent velocity, and calculate the square term of the ascent and descent velocity as the energy consumption cost density; calculate the second derivative of the protection height trajectory function to obtain the ascent and descent acceleration, and calculate the square term of the ascent and descent acceleration as the ride comfort cost density; assign corresponding weight coefficients to the safety cost density, energy consumption cost density, and ride comfort cost density respectively, and then sum them to obtain the integrand used for integration.

[0097] In this embodiment, the system defines in detail the cost terms related to efficiency and comfort. The system obtains the velocity v(t) by taking the first derivative of h(t) and defines the energy consumption cost density J. e (t)=v(t) 2 This is based on the physical approximation that motor energy consumption is proportional to the square of speed; minimizing this term helps reduce battery consumption and heat generation. The system obtains the acceleration a(t) by taking the second derivative of h(t), and defines the smoothness cost density J. c (t)=a(t) 2 Minimizing the square of acceleration can suppress mechanical shocks and vibrations, and extend the service life of gears and racks.

[0098] The system assigns weight coefficients w to the above three costs. s w e and w cThe setting of weighting coefficients reflects the preferences of the control strategy; for example, if w s If w is significantly greater than the other two, the system prioritizes safety; if w e If the value is relatively large, it is considered energy-saving. The integrand L(t) = w s *J s (t)+w e *J e (t)+w c *J c (t).

[0099] Step 603: With minimizing the comprehensive objective function as the optimization objective, the optimal protection height trajectory function is solved; the optimal protection height trajectory function is then subjected to time discretization to generate a discrete height command sequence containing a series of time steps, and the discrete height command sequence is used as the lifting control command.

[0100] In this embodiment, the system calculates the comprehensive objective function J, which is the definite integral of the integrand L(t) over the prediction interval [t0, t0+T]. The optimization problem is formulated as: finding the optimal function h(t) that minimizes J and satisfies all the aforementioned constraints. Mathematically, this belongs to the variational method or optimal control problem. In practical engineering implementation, it is usually transformed into a quadratic programming (QP) problem or a nonlinear programming (NLP) problem, and solved numerically using the sequential quadratic programming (SQP) algorithm or the interior-point method.

[0101] After the solution is completed, the system obtains a continuous optimal curve h. opt (t). Since the PLC controller is a discrete system based on a scan cycle, the system needs to sample this curve. According to the control cycle Δt, for example 50 milliseconds, the system extracts a series of discrete height setpoints h. cmd [k]=h opt (t0+k*Δt). Before sending the discrete height command sequence to the drive system, the system performs amplitude limiting on each command point to ensure it is within the mechanically permissible range of the protective structure. The amplitude-limited command h * cmd [k] is calculated by the following formula: h * cmd [k]=clip(h cmd [k],h min ,h max ); where h cmd [k] represents the altitude command obtained from the optimized solution at the k-th time step, h min and h maxThese represent the minimum and maximum heights allowed by the mechanical structure of the protective unit, respectively. This limiting process ensures that even if the optimization algorithm produces a theoretical solution beyond the physical limits under extreme operating conditions, the final executed command remains safe and feasible. This series of command points is sequentially sent to the drive system, directing the protective unit to rise and fall smoothly and precisely along a pre-planned perfect curve.

[0102] Example 6, as Figure 4 As shown, this describes how residual analysis and health assessment mechanisms can be used to achieve degraded operation of the system when the performance of sensors or actuators degrades, thereby avoiding job interruptions caused by direct shutdown.

[0103] Step 1001: Establish state observation models for each key sensor and actuator, and calculate the residual sequence between the measured values ​​and model estimates of each component.

[0104] In this embodiment, the system establishes mathematical models or utilizes redundant relationships for key components. Taking a tilt sensor as an example, the system can use data fusion from a gyroscope and an accelerometer as an observation model to output an estimated tilt angle θ. est Simultaneously, the measured value θ from the tilt sensor is read. meas The system calculates the difference between the two, i.e., the residual r(t) = θ. meas (t)-θ est (t).

[0105] For electric linear actuators, the system can estimate the theoretical displacement x based on voltage, current, and load models. est And compared with the measured value x of the displacement sensor meas The results are compared to obtain the actuator residuals. The residual sequence r(t) contains important information about the component's health status. Under ideal fault-free conditions, the residuals should be close to zero or contain only Gaussian white noise; when the component drifts, jams, or becomes loose, the mean or variance of the residuals will increase.

[0106] Step 1002: Normalize the residual sequences of each component and calculate the component health index that characterizes the reliability of each component; take the minimum value of the component health index as the overall system health.

[0107] In this embodiment, to uniformly evaluate the health status of different types of components, the system normalizes the residuals. The system presets the maximum allowable normal error amplitude δ for each component. The dimensionless residual z(t) = |r(t)| / δ is calculated.

[0108] The system constructs a health index H(t), whose value ranges from 0 to 1. Preferably, it is defined using a Gaussian decay function: H(t) = exp(-z(t)). 2), where exp is the natural exponential function. When the residual z(t) is 0, the health status H(t) is 1, indicating a perfect state; as the residual increases, H(t) rapidly decreases and approaches 0. Nonlinear mapping can amplify the signal characteristics in the early stages of a fault, making the health status index more sensitive to anomalies.

[0109] Step 1003: When the overall health of the system is lower than the preset warning threshold but higher than the failure threshold, generate an additional degradation safety margin and limit the rise and fall speed; add the degradation safety margin to the real-time target protection height, and perform fault-tolerant degradation control according to the limited rise and fall speed.

[0110] In this embodiment, the system sets two key thresholds: the warning threshold H. warn For example, 0.8; failure threshold H safe For example, 0.4. The system monitors the overall health H of the system in real time. sys Overall system health H sys (t) The calculation is performed using the weakest link principle, that is, taking the minimum health value of all critical components: H sys (t)=min 1≤i≤M H i (t);

[0111] Where M is the total number of critical components in the system, and H... i (t) represents the health index of the i-th component at time t. This design ensures that any anomaly in a single component is reflected in the overall health of the system.

[0112] In the degraded security mode, in addition to increasing the degraded security margin △h deg In addition, the system also limits the maximum permissible speed of the drive motor. The maximum speed h in degraded mode... * ' max Calculated by the following formula: h * ' max =α v ·h * max ; where h * max α is the maximum permissible acceleration / deceleration speed in normal mode. v The velocity reduction coefficient, α, ranges from (0,1), with a preferred value of 0.4 to 0.6. The velocity reduction coefficient can be dynamically adjusted based on the overall system health; the lower the health, the higher the coefficient. v The smaller the value, the lower the risk of exercise. Typically, the minimum value of the health status of all key components is taken.

[0113] When H safe Less than or equal to H sys And H sys Less than Hwarn When the system determines it is in a sub-healthy or slightly faulty state, it activates a degraded safety mode. In this mode, the system prioritizes safety over efficiency. Specific measures include generating an additional degraded safety margin Δh. deg For example, 0.5 meters, which is forcibly superimposed on the real-time target protection height h*(t), i.e., h cmd =h*(t)+Δh deg Trading space for security.

[0114] At the same time, the system can also limit the maximum speed of the drive motor, turning v max Adjust to v max_deg For example, 50% of the original speed. This type of fault-tolerant control strategy allows the device to maintain basic protective functions even when sensor accuracy decreases or there is slight mechanical jamming, until the end of the shift before maintenance, thus improving equipment availability and project schedule assurance capabilities. When H sys Further reduce to H safe In the following situations, the system must perform a forced shutdown protection to prevent accidents from occurring.

[0115] Example 7 describes the multi-mode operation logic of the automatic lifting horizontal protection device in actual engineering applications, especially the implementation of basic operation processes such as automatic avoidance software lowering, manual intervention, and emergency stop.

[0116] Step 1105, an automatic lifting and lowering horizontal protection method, the method further includes: receiving a software lowering signal or a manual intervention command, and switching between different operating modes.

[0117] In this embodiment, the intelligent control system not only operates automatically based on a complex algorithm model, but also has a basic logic control loop to respond to external discrete signals. The system defines three main operating modes: fully automatic intelligent mode, semi-automatic operation mode, and manual maintenance mode. In fully automatic intelligent mode, the system executes advanced protection logic. However, when the main control system issues a software deployment signal, regardless of the currently calculated target height, the control system executes high-priority avoidance logic.

[0118] Specifically, when a signal to lower the soft shield is received, the PLC controller temporarily disables the calculations based on the danger zone model and forces the protective unit to descend to a preset avoidance height, such as 0.3 meters. This height maintains basic edge protection while allowing the soft shield to slide smoothly on the sliding surface, preventing mechanical interference or snagging between the protective device and the shield. When the signal to lower the soft shield disappears and there is a certain delay, such as 5 seconds, the system automatically returns to fully automatic intelligent mode, and the protective unit rises again to the target protection height calculated in real time.

[0119] Step 1106: The sensing and monitoring system also includes a human-machine interface for displaying system status, setting parameters, and receiving manual control commands.

[0120] In this embodiment, to facilitate operator monitoring and intervention, the system is equipped with a human-machine interface (HMI), preferably a touchscreen. This interface communicates with the PLC controller via Ethernet or a serial bus. On the interface, operators can view the current skateboard tilt angle, calculated risk score, health indicators of each sensor, and the real-time height of the protective unit in real time.

[0121] In addition, the interface provides parameter setting functions, allowing authorized personnel to modify parameters such as the maximum allowable height h. max Safety margin Δh margin And key parameters such as risk thresholds R1 and R2. In manual maintenance mode, operators can directly send jog up or jog down commands via soft buttons on the interface or physical buttons on the console. When executing manual commands, the system still uses displacement sensors and limit switches for hard limit protection to prevent mechanical impact.

[0122] Step 1107: The lifting drive system is also equipped with an emergency stop circuit, which is used to immediately cut off the power and lock the position in abnormal situations.

[0123] In this embodiment, to cope with unforeseen emergencies such as sensor failure, abnormal mechanical noise, or personnel entrapment, the system is designed with a highest priority emergency stop circuit. This circuit connects multiple physical emergency stop buttons in series on the control panel, the side of the slide, and the electrical control box. When any emergency stop button is pressed, the hardware circuit directly cuts off the power supply to the DC electric actuator.

[0124] Because the push rod integrates a power-off electromagnetic brake, once the power is cut off, the brake immediately locks the motor shaft, instantly locking the protective unit in its current position and preventing secondary damage from a fall due to gravity. The system can only be powered back on via a reset operation after the fault has been cleared and the emergency stop button has been reset. This multi-layered safety redundancy design ensures the passive safety of the device in extreme situations.

[0125] By calculating the tangential acceleration and final velocity of personnel sliding, the dynamic impact risk that is difficult to quantify is transformed into a specific kinetic energy equivalent height. This solves the problem that the proposed static protection cannot offset the high kinetic energy generated by personnel sliding, and realizes the adaptive gain of the protection height according to the severity of the working conditions, such as large tilt angle and small friction coefficient.

[0126] By constructing behavioral feature vectors, such as distance, speed, posture, and classification models, this solution achieves accurate identification of personnel intentions, such as distinguishing between passing by and crossing. Combined with spatial risk scoring, it solves the problems of rigid control logic and inability to distinguish between normal operation and dangerous behavior, thus avoiding unnecessary downtime while ensuring safety.

[0127] By using multi-objective trajectory planning and introducing safety, energy consumption, and smoothness cost functions, the problems of large mechanical impact and high energy consumption are solved, and the device's flexible start-up and precise control are realized.

Claims

1. An automatic lifting horizontal protection method, applied to a liftable protection unit configured on the edge of a work slide, characterized in that, include: Collect real-time operational parameters and personnel distribution data in the skateboard area; A dynamic hazard zone model is constructed based on the working condition parameters, and personnel distribution data is mapped to the dynamic hazard zone model; The real-time target protection height that satisfies the current security boundary constraints is calculated based on the mapping results. Based on the real-time target protection height, a lifting control command is generated to drive the protection unit to adjust its vertical height to match the real-time target protection height.

2. The method according to claim 1, characterized in that, The real-time target protection height that satisfies the current security boundary constraints, calculated based on the mapping results, includes: A sliding motion model of personnel on the skateboard surface is constructed based on the working condition parameters; The relative sliding velocity of a person reaching the edge of the skateboard is calculated using a sliding motion model. Based on the principle of energy conservation, relative sliding velocity is converted into kinetic energy equivalent height; Obtain the geometrical safety height determined based on human body dimensions and working posture; The geometric safety height is superimposed with the kinetic equivalent height, and a preset dynamic safety margin is added to obtain the real-time target protection height.

3. The method according to claim 2, characterized in that, The relative sliding velocity of a person reaching the edge of the skateboard is calculated using a sliding motion model. This relative sliding velocity is then converted into an equivalent kinetic energy height, including: Extract the skateboard tilt angle and skateboard friction coefficient from the working condition parameters; Calculate the difference between the tangential component of gravity along the skateboard and the frictional resistance to obtain the tangential acceleration of the person sliding down the skateboard; The tangential terminal velocity of the personnel reaching the edge of the skateboard is calculated by combining the sliding distance determined by the personnel distribution data, and the impact component velocity is obtained by projecting the tangential terminal velocity onto the normal direction of the protective unit. The kinetic energy equivalent height is obtained by calculating the ratio of the square of the impact velocity component to twice the gravitational acceleration.

4. The method according to claim 1, characterized in that, Mapping personnel distribution data to a dynamic hazard zone model also includes behavioral intent recognition: The temporal distribution data of personnel is analyzed to extract the minimum distance between personnel and the edge of the skateboard, the approach rate towards the danger zone, the dwell time in the danger zone, and the body posture code. The minimum distance, approach rate, dwell time, and body posture are combined to construct the behavioral feature vector of each inspector. Input the behavioral feature vector into the preset behavioral classification model to identify and output the behavioral intent category of the current detection personnel.

5. The method according to claim 4, characterized in that, Based on the real-time target protection height, the following lifting control commands are generated: Behavioral risk scores are calculated by assigning corresponding risk weights to behavioral intention categories and spatial risk scores are calculated based on distance information in personnel distribution data. The behavioral risk score and the spatial risk score are weighted and summed to obtain a comprehensive risk score that represents the current safety status of personnel. The comprehensive risk score is compared with the preset multi-level risk thresholds to determine the hierarchical interlocking strategy, which includes early warning, action restriction or mandatory escalation. The initial command generated based on the real-time target protection height is modified using a hierarchical interlocking strategy to obtain the final lifting control command.

6. The method according to claim 1, characterized in that, Based on the real-time target protection height, the following lifting control commands are generated: Define a future prediction time interval and define a protection height trajectory function that changes continuously with time within that prediction time interval; Establish a comprehensive objective function to measure the operating cost of the system, and set the real-time target protection height as the safety lower limit constraint of the protection height trajectory function; The optimal protection height trajectory function is obtained by minimizing the comprehensive objective function. The optimal protection height trajectory function is discretized in time to generate a discrete height command sequence containing a series of time steps, and the discrete height command sequence is used as the lifting control command.

7. The method according to claim 6, characterized in that, The comprehensive objective function consists of a weighted integral of multiple cost densities over the prediction time interval. The calculation of the cost density includes: Calculate the difference between the real-time target protection height and the protection height trajectory function, and record the portion that does not reach the real-time target protection height as the safety cost density; The first derivative of the protection height trajectory function is used to obtain the ascent and descent rates, and the square of the ascent and descent rates is calculated as the energy consumption cost density. The second derivative of the protection height trajectory function is used to obtain the elevation acceleration, and the square term of the elevation acceleration is calculated as the ride comfort cost density. After assigning corresponding weighting coefficients to the safety cost density, energy consumption cost density, and smoothness cost density, respectively, the summation yields the integrand used for integration.

8. The method according to claim 1, characterized in that, Mapping personnel distribution data to dynamic hazard zone models includes: Several continuous discrete risk zones are defined along the length of the skateboard, and the personnel distribution data is projected onto the corresponding discrete risk zones; Based on the distance weight between the person and the edge of the skateboard, calculate the segment risk value of each discrete risk segment at the current moment; An exponentially weighted moving average is used to smooth the segment risk value over time to obtain the smoothed segment risk value. Calculate the rate of change of the smoothed segment risk value within a preset time window to obtain a risk change trend index that characterizes the rate of risk increase. The global risk score is calculated based on the smoothed segment risk value and risk change trend index. A trend correction coefficient is generated using the global risk score, and then the real-time target protection height is dynamically adjusted using the trend correction coefficient.

9. The method according to claim 2, characterized in that, Obtaining the geometrical safe height based on human body dimensions and working posture includes: Extract the vertical height of the skateboard relative to the working interface and the effective height and thickness of the soft material on the surface of the skateboard from the working condition parameters. Acquire the maximum body height data of workers in upright or bent-over positions; By superimposing the data on vertical height above the ground, effective padding thickness, and maximum body height, the geometric safety height to prevent people from climbing over is obtained.

10. The method according to claim 1, characterized in that, The method further includes: Establish state observation models for each key sensor and actuator, and calculate the residual sequence between the measured values ​​and model estimates of each component; The residual sequences of each component are normalized, and the component health index, which characterizes the reliability of each component, is calculated. The minimum value of the health indicators of each component is taken as the overall health of the system; When the overall health of the system is lower than the preset warning threshold but higher than the failure threshold, an additional degradation safety margin is generated and the acceleration / deceleration rate is limited. The downgraded safety margin is superimposed on the real-time target protection height, and fault-tolerant downgrade control is performed according to the limited ascent and descent speed.

11. An automatic lifting horizontal protection device, characterized in that, include: The liftable protective unit is installed on both sides of the working platform to create a physical barrier to prevent personnel from falling. The lifting drive system is mechanically connected to the liftable protection unit and is used to provide power to drive the vertical movement of the liftable protection unit. The sensing and monitoring system is used to collect data on skateboard tilt angle and personnel distribution in real time; The intelligent control system is electrically connected to the sensing and monitoring system and the lifting drive system, respectively. The intelligent control system is equipped with a memory and a processor. The memory stores computer programs, and the processor executes the computer programs to realize the automatic lifting and lowering level protection method.

12. The apparatus according to claim 11, characterized in that, The liftable protection unit adopts a multi-section rigid frame structure, including: Several rigid frame segments are connected by hinges to form a chain structure that can change the folding angle with the height. Metal protective netting, fixed to the inside of the rigid frame section; Guide slider, located at the bottom of the rigid frame section; C-shaped steel guide rails are laid along the length of the edge of the working slide. The guide slider is embedded in the C-shaped steel guide rail and slides with it to constrain the lifting trajectory of the liftable protective unit.

13. The apparatus according to claim 12, characterized in that, The lifting drive system includes: A DC electric actuator, installed at the bottom hinge of the rigid frame section, is used to drive the rigid frame section to unfold or fold through its extension stroke. A rack and pinion synchronizing mechanism is installed between adjacent rigid frame sections to force adjacent sections to maintain synchronous lifting and lowering motion. An electromagnetic brake, integrated inside the DC electric actuator, is used to automatically lock the DC electric actuator's travel position when power is off.

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