Variable constraint control method for stage equipment based on risk perception and dynamic security domain

Abstract

The invention belongs to the technical field of stage equipment boundary safety protection control, and particularly relates to a variable constraint control method for stage equipment based on risk perception and a dynamic safety domain. The characteristic that stage equipment is usually in a fixed application scene in the performance process is utilized, the inherent safety level and active protection capacity of a stage equipment system are greatly improved through tight combination of real-time collection and variable constraint, and therefore safer, more accurate and more smooth control is achieved. According to the method, a construction algorithm is embedded in software, a dynamic region division and segmentation threshold adjustment strategy is utilized, and a multi-stage braking redundancy cooperation mechanism is triggered, so that the anti-interference capability and fault tolerance performance of the control system are effectively enhanced, the implementation cost is low, a large amount of manpower and material debugging can be saved, and the system is suitable for large-scale popularization and application. And a high-precision and high-robustness safety control scheme is provided for large stage machinery such as a seat vehicle platform and a rotating stage.

Description

Technical Field

[0001] This invention relates to the field of boundary safety protection and control technology for stage equipment, and in particular to a variable constraint control method for stage equipment based on risk perception and dynamic safety domain. Background Technology

[0002] With the continuous innovation of stage art and the deep integration of performing arts technology, modern stage equipment systems are rapidly developing towards high integration, diversified motion trajectories, and multi-machine collaborative linkage. Whether it's the multi-layered lifting stage of a large concert, the moving scenery in a theater, or the intelligent lighting rigging of a music festival, while stage machinery presents complex and dynamic stage effects, the safety and reliability of its operation have become crucial, directly affecting the integrity of expensive equipment, the safety of performers, and the success or failure of the entire performance. Currently, when controlling stage equipment to avoid fixed obstacles, the mainstream approach relies on pre-programmed procedures or the operator's experience and judgment; its essence is preset-driven. This traditional method has significant limitations in perception: firstly, it is difficult to continuously and accurately monitor key operating states of the equipment, such as real-time wear and tear of mechanical structures, speed and acceleration overshoot during movement, and instantaneous changes in load; secondly, it lacks the dynamic perception capability of surrounding environmental conditions, such as the relative distance between moving equipment, the activity area of ​​performers on stage, and the impact of ambient temperature and humidity on the equipment. Especially when dealing with equipment movement affected by obstacles, this invisibility and lag can easily lead to the control system being in a blind control state, unable to identify and handle sudden anomalies in a timely manner, thus creating potential safety hazards. Generally speaking, existing stage equipment obstacle avoidance motion control technology usually relies on laser rangefinders to detect the real-time distance between the moving equipment and obstacles, thereby enabling real-time, safe, and precise control of the mechanical equipment to achieve stage art effects. However, this method has proven ineffective in practice. The reason is that the equipment's running path is relatively long, with scenery props, lighting equipment, or temporary obstacles along the way that can easily block the laser beam, causing the rangefinder signal to be interrupted. More importantly, the vibrations generated when the equipment moves at high speeds directly affect the stability of the laser emitter, preventing it from forming stable markings on the wall. This leads to jumps in measurement data, false alarms, and triggers unexplained sudden stops or malfunctions of the equipment, disrupting the performance rhythm and potentially causing additional wear and tear on the mechanical structure due to frequent starts and stops. Summary of the Invention

[0003] The purpose of this invention is to provide a variable constraint control method for stage equipment based on risk perception and dynamic safety domain. By dynamically dividing the area, integrating multi-dimensional risks, and adjusting variable constraint parameters, the method improves the safety, anti-interference ability, and scene adaptability of stage equipment, while reducing mechanical wear and debugging costs, thereby solving the aforementioned problems in the prior art.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A variable constraint control method for stage equipment based on risk perception and dynamic safety domain includes:

[0006] Based on the stage space structure and the physical movement properties of the stage equipment, a multi-dimensional stage space is constructed, and stage areas are dynamically divided within the multi-dimensional stage space;

[0007] Collect real-time motion attributes of stage equipment;

[0008] Calculate multidimensional risk factors;

[0009] The multi-dimensional risk factors are integrated into a comprehensive risk coefficient using a weighted summation method.

[0010] Establish a risk baseline for the dynamic safety domain, and determine the variable constraint parameter strategy set for the current device by combining the current stage area where the device is located and the value of the comprehensive risk coefficient.

[0011] The variable constraint parameter strategy set is input into the controller to generate optimal control commands, which drive the stage equipment to adjust its motion state.

[0012] Preferably, the following specific steps are included:

[0013] S1, based on the stage space structure and the physical movement properties of the stage equipment, construct a multi-dimensional stage space Ω, and dynamically divide the stage area Ω(t) = (Ω) within the multi-dimensional stage space Ω. CR (t)∪Ω TR (t)∪Ω BR (t)), where Ω CR (t) represents the core region, Ω TR (t) represents the transition region, Ω BR (t) represents the boundary region, in which the equipment operates at full performance in the core region, in which the constraint parameters smoothly transition in the transition region, and in which the equipment operates under strict restrictions in the boundary region.

[0014] S2, collects the real-time motion attributes (DEV) of the stage equipment. motion =[Pos(t),Vel(t),Acc(t)] T , where Pos(t) is the real-time position of the device, Vel(t) is the real-time velocity of the device, and Acc(t) is the real-time acceleration of the device;

[0015] S3, calculate the multi-dimensional risk factors, which include the geometric risk factor R. geo Dynamic risk factor R dyn Time risk factor R time External risk factor R envand equipment health risk factor R hea ;

[0016] S4. The multi-dimensional risk factors are integrated into a comprehensive risk coefficient R using a weighted summation method. sum The weights satisfy ω geo +ω dyn +ω time +ω env +ω hea =1, ω geo ω dyn ω time ω env ω hea These are the weights of each risk factor;

[0017] S5: Establish a risk baseline for dynamic security domains. low R med R high Based on the current stage area where the equipment is located and R sum The value determines the current device's variable constraint parameter strategy set.

[0018] S6: Configure the variable constraint parameter strategy set The input controller generates the optimal control command U(t), which drives the stage equipment to adjust its motion state.

[0019] Preferably, the geometric risk factor R in step S3 geo = 1 - K(t), where K(t) is the safety factor function:

[0020] When D(t)≥D CR (t) and DEV motion ∈Ω CR When (t), K(t) = 1;

[0021] When D(t) ≤ D BR (t) and DEV motion ∈Ω BR When (t), K(t) = 0;

[0022] When D BR (t) <D(t)<D CR (t) and DEV motion ∈Ω TR When K(t) ∈ (0,1), K(t) ∈ (0,1).

[0023] D(t) is the distance from the device's current position Pos(t) to the nearest physical boundary. CR (t) represents the boundary distance threshold between the core area and the transition area, D BR (t) represents the boundary distance threshold between the transition zone and the boundary zone.

[0024] Preferably, the dynamic risk factor R mentioned in step S3 dyn =max(0, (Vel(t)*n / V) max _perf)), where n is the unit normal vector pointing outside the nearest safe boundary, Vel(t)*n is the projection of the velocity in the danger direction, V max _perf represents the maximum speed allowed for devices in the kernel area.

[0025] Preferably, the time risk factor R mentioned in step S3 time =min(1, (t_unsafe / t_max)), where t unsafe For the equipment in Ω TR (t)∪Ω BR The duration of dwell within (t) is dynamically adjusted by t_max based on the device type.

[0026] Preferably, the external risk factor R mentioned in step S3 env =R peo ∪R obj , where R peo To mitigate the risk of unauthorized entry, R obj To mitigate the risk of trajectory conflicts between multiple devices.

[0027] Preferably, the equipment health risk factor R mentioned in step S3 hea =∑(current) Pro -pro normal ) / (pro max -pro normal ), where the device attributes Pro = [Temp(t), Amp(t), Noi(t), Loa(t), Ele(t)] represent the device's temperature, amplitude, noise, load, and current characteristics, respectively.

[0028] Preferably, the set of variable constraint parameter strategies in step S5 includes,

[0029]

[0030]

[0031] in For use in the core area, the device movement uses the optimal performance mode; For use in the transition zone, the constraints are tighter, the safety margin is increased, and the equipment is in conservative mode. Used in boundary areas with extremely strict constraints, aiming for a slow stop, the equipment is in braking mode. Used for hazardous areas within the boundary region, triggering hardware-level safety functions and enabling emergency stop mode; Vmax A max J max , representing the maximum speed, maximum acceleration, and maximum jerk (Jerk) allowed in the region, respectively.

[0032] Preferably, based on the actual value of the comprehensive risk factor and the marked movement area of ​​the equipment, the control parameters to be referenced by the current equipment are determined:

[0033] when So, what is the set of variable constraint parameter strategies that the current device should adopt?

[0034] when The current variable constraint parameters of the equipment are in and Smooth transitions between values ​​prevent equipment jitter caused by sudden changes in equipment control parameters. Where V max _cur=V max _perf+(V max _slow-V max Similarly, we can obtain A by _perf)*R. max _cur、J max _cur;

[0035] when The set of variable constraint parameter strategies that the current device should adopt.

[0036] When R sum If none of the above three situations apply, then the current device should adopt the following set of constraint parameter strategies.

[0037] Preferred,

[0038] It also includes a multi-level braking redundancy mechanism: when R sum ≥R high When this happens, software pre-braking is triggered first; if R... sum If it does not descend, the hardware emergency braking is triggered;

[0039] and / or

[0040] It also includes an environmental compensation coefficient γ, where γ = 1 - 0.01·(Temp env -25)+0.005·(Hum env -50), where: Temp env For ambient temperature, Hum env The system measures ambient humidity and compensates for the impact of temperature and humidity on equipment performance by adjusting constraint parameters.

[0041] The beneficial effects of this invention are:

[0042] This invention presents a variable constraint control method for stage equipment based on risk perception and dynamic safety domains. Leveraging the characteristic that stage equipment typically operates in a fixed scenario during performances, the method first dynamically divides the area of ​​equipment movement. Then, using the equipment's real-time operating status, such as position, speed, and acceleration, different parameter standards are constrained in different areas. The control method dynamically adjusts constraints such as speed and acceleration limits. This aims to significantly improve the inherent safety level and active protection capabilities of stage equipment systems through the close integration of real-time data acquisition and variable constraints, thereby achieving safer, more precise, and smoother control. The method embeds a construction algorithm in the software, utilizing dynamic region division and segmented threshold adjustment strategies to trigger a multi-level braking redundancy collaborative mechanism. This effectively enhances the anti-interference capability and fault tolerance performance of the control system, achieving low cost and saving significant manpower and resources for debugging. It provides a high-precision, highly robust safety control solution for large stage machinery such as stage carriages and rotating stages. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the dynamic area division of the equipment during stage movement in an embodiment of the present invention;

[0044] Figure 2 This is a flowchart illustrating the variable constraint control method for stage equipment based on risk perception and dynamic safety domain of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] As attached Figure 1-2 As shown, the stage equipment variable constraint control method based on risk perception and dynamic safety domain of the present invention includes the following steps:

[0047] Step 1: Dynamic Stage Area Division

[0048] Based on the three-dimensional dimensions of the stage space (length × width × height) and the physical motion attributes of the equipment (such as maximum range of motion and turning radius), a multi-dimensional stage space Ω is constructed; using a "dynamic update of distance threshold" algorithm, Ω is divided into three functional areas:

[0049] Core area Ω CR (t): Distance from the physical boundary (such as the edge of the stage or a fixed set) D(t) ≥ D CR (t)(e.g., rising stage D) CR(t) = 5m, seat platform D CR (t)=4m), the equipment activates its optimal performance mode in this area. Allowed to run at maximum speed and acceleration;

[0050] Transition region Ω TR (t): Distance D from the physical boundary BR (t) <D(t)<D CR (t), for example, D BR (t) = 2m, the equipment uses conservative mode in this area. The constraint parameters transition smoothly with distance;

[0051] Boundary region Ω BR (t): Distance from the physical boundary D(t) ≤ D BR (t), the equipment activates braking mode in this area. The system strictly limits speed and acceleration with the goal of "gradual stopping"; if there is a danger (such as intrusion), the emergency stop mode is triggered.

[0052] Step 2: Real-time motion and state awareness

[0053] Data is collected from devices through multi-sensor fusion.

[0054] Motion attributes: Pos(t) is obtained using an encoder (accuracy ±0.1mm), Vel(t) is obtained using a tachometer (accuracy ±0.01m / s), and Acc(t) is obtained using an accelerometer (accuracy ±0.05m / s²).

[0055] Health attributes: Thermocouples are used to detect Temp(t) (range -20~150℃), vibration sensors are used to detect Amp(t) (range 0~5mm) and vibration frequency spectrum (sampling rate 1kHz), current sensors are used to detect Ele(t) (range 0~50A), and pressure sensors are used to detect Loa(t) (range 0~100kN);

[0056] Environmental attributes: R is detected using an infrared thermal imaging sensor. peo (Detection distance 0-10m), LiDAR modeling to obtain n (unit normal vector, update frequency 10Hz), temperature and humidity sensor to obtain Temp. env (range 0~40℃), Hum env (Range 20%–90% RH).

[0057] Step 3: Calculation of multi-dimensional risk factors

[0058] Geometric risk factor R geo : Reflects the risk of distance between equipment and the boundary, R geo= 1 - K(t), where K(t) is the safety factor function, R geo ∈[0,1] — The larger the value, the closer it is to the boundary, and the higher the risk;

[0059] Dynamic risk factor R dyn : Reflects the speed risk of equipment rushing towards the boundary, R dyn =max(0, (Vel(t)*n / V) max _perf)), for example, the device rushes towards the boundary V at 0.8 m / s. max If _perf = 1 m / s, then R dyn =0.8;

[0060] Time risk factor R time : Reflects the risk of equipment remaining in high-risk areas, R time =min(1, (t_unsafe / t_max)), for example, if the seat platform stays in the transition zone for 15s (t_max = 20s), then R time =0.75;

[0061] External risk factor R env : Reflects the risk of conflict between personnel and equipment, R env =R peo ∪R obj This includes whether anyone has broken into specific areas of the stage. The values ​​are: no one has a value of 0, some have a value of 1; and there is a risk of conflict with the predicted trajectories of other devices. The value for no conflict is 0, and the value for conflict is 1.

[0062] Health risk factor R hea : Reflects the risk of equipment performance degradation, R hea =∑(current) Pro -pro normal ) / (pro max -pro normal ), where the device attributes Pro = [Temp(t), Amp(t), Noi(t), Loa(t), Ele(t)], represent the device's temperature, amplitude, noise, load, and current characteristics, respectively. The value of this attribute is normalized and the range is [0,1]. This health risk factor indicates that any abnormality will cause this value to increase.

[0063] Step 4: Integration of comprehensive risk coefficients

[0064] A weighted summation method is used to merge multiple risk factors into a single comprehensive risk coefficient. R sum =ω geo *R geo +ω dyn *Rdyn +ω time *R time +ω env *R env +ω hea *R hea ,ω geo ω dyn … represents the weight of each risk factor, satisfying ∑ω geo +ω dyn +…ω hea =1. The weighting is determined based on expert experience, such as ω geo and ω dyn It is the most direct risk, therefore it has a higher weight. sum The value range is also [0,1]. In the embodiment, the initial weights are:

[0065] ω geo =0.3, ω dyn =0.3, ω time =0.1, ω env =0.2, ω hea =0.1 (satisfying the sum of weights being 1); the weights are optimized through an AI learning module (based on gradient descent algorithm). For example, if historical data shows that "failures caused by dynamic risks account for 40%", then ω will be... dyn Adjust to 0.4 to ensure R sum The degree of matching with actual safety hazards is ≥95%.

[0066] Step 5: Determining the dynamic safety region and constraint parameters

[0067] Risk baseline establishment: Collecting R data from 1000+ different performance scenarios (concerts, plays, music festivals) sum The data is used to establish a baseline based on the principle of "minimum - median - maximum": R low =0.3 (low risk), R med =0.6 (medium risk), R high =0.9 (high risk), and the database is updated in real time with new data;

[0068] Constraint parameter selection:

[0069] Ruodang Therefore, the set of variable parameter constraints that the current device should adopt when The current variable constraint parameters of the equipment are in and Smooth transitions between values ​​prevent equipment jitter caused by sudden changes in equipment control parameters. Where V max _cur=V max _perf+(V max_slow-V max Similarly, we can obtain A by _perf)*R. max _cur、J max _cur etc.; when The set of variable constraint parameters that the current device should use. When R sum If none of the above three situations apply, then the current device should use the following set of constraint parameters.

[0070] Environmental compensation: through γ = 1 - 0.01·(Temp env -25)+0.005·(Hum env -50) Adjust constraint parameters, such as Temp. env =35℃, Hum env When the RH is 60%, γ = 0.85, then Vmax_cur = 0.68 × 0.85 ≈ 0.58 m / s, compensating for the influence of temperature and humidity on equipment performance.

[0071] Step 6: Control command output and execution

[0072] Will As optimization constraints for the underlying controller (such as PLC, servo controller), the optimal control command U(t) (including speed command, acceleration command, braking command) is solved by the model predictive control (MPC) algorithm to drive the equipment to adjust its motion state; the controller output frequency is 100Hz to ensure that the control delay is ≤10ms.

[0073] The following uses a "theater seating cart" as an example to illustrate the implementation process of this invention in detail:

[0074] 1. Equipment and Scene Parameters

[0075] Seating platform: 10m in length, 3m in width, maximum load 50kN, and range of motion is from left to right of the stage (0 to 20m, with the stage edge located at 20m).

[0076] Sensor configuration: Encoder (model E6B2-CWZ6C), Tachometer motor (model JS-120), Vibration sensor (model SDJ-1), Infrared thermal imaging sensor (model DS-2TD2617B), Temperature and humidity sensor (model SHT30);

[0077] Initial risk baseline: R low =0.3, R med =0.6, R high =0.9;

[0078] Initial constraint parameters:

[0079] Step 1: Dynamic area division

[0080] According to the movement range of the seat trolley (0 - 20m), set:

[0081] Core area Ω CR (t): D(t) ≥ 4m (i.e., the trolley position Pos(t) ≤ 16m);

[0082] Transition area Ω TR (t): 2m < D(t) < 4m (i.e., 16m < Pos(t) < 18m);

[0083] Boundary area Ω BR (t): D(t) ≤ 2m (i.e., Pos(t) ≥ 18m).

[0084] Step 2: Real - time data collection

[0085] The data collected at a certain moment is as follows:

[0086] Movement attributes: Pos(t) = 17m (D(t) = 3m, located in the transition area), Vel(t) = 0.8m / s (towards the stage edge, n = 1), Acc(t) = 0.3m / s 2 ;

[0087] Health attributes: Temp(t) = 40℃ (pro normal = 35℃, pro max = 50℃), Amp(t) = 0.5mm (normal), Ele(t) = 15A (normal);

[0088] Environmental attributes: Temp env = 30℃, Hum env = 60%RH, no personnel intrusion (R peo = 0), no equipment conflict (R obj = 0), t unsafe = 10s (residence time in the transition area).

[0089] Step 3: Risk factor calculation

[0090] K(t) = (3 - 2) / (4 - 2) = 0.5, R geo = 1 - 0.5 = 0.5;

[0091] R dyn = (0.8×1) / 1 = 0.8};

[0092] t max = 20s, R time = 10 / 20 = 0.5;

[0093] R env =0.6×0+0.4×0=0;

[0094] ΔTemp = (40-35) / (50-35) = 0.33, other health attributes Δ = 0, R hea =0.33 / 6≈0.055.

[0095] Step 4: Integration of comprehensive risk coefficients

[0096] Initial weight ω geo =0.3, ω dyn =0.3, ω time =0.1, ω env =0.2, ω hea =0.1, then:

[0097] R sum =0.3×0.5+0.3×0.8+0.1×0.5+0.2×0+0.1×0.055=0.15+0.24+0.05+0+0.0055=0.4455( <R med =0.6).

[0098] Step 5: Determine constraint parameters

[0099] The environmental compensation coefficient γ = 1 - 0.01 × (30 - 25) + 0.005 × (60 - 50) = 1 - 0.05 + 0.05 = 1;

[0100] Transition zone constraint parameters: V max_cur =1+(0.2-1)×0.4455≈0.644m / s, A max_cur =0.5+(0.1-0.5)×0.4455≈0.322m / s 2 ,Right now

[0101] Step 6: Execution of control commands

[0102] Low-level PLC receiver Then, control commands are generated using the MPC algorithm: reduce the vehicle speed from 0.8 m / s to 0.644 m / s, and decrease the acceleration from 0.3 m / s². 2 Adjusted to 0.322m / s 2 This ensures the vehicle operates smoothly in the transition zone, avoiding the risk of it running towards the boundary.

[0103] The following is a specific embodiment of the present invention:

[0104] 1. Based on the structure of the stage space and the physical motion properties of the equipment, construct a multi-dimensional space Ω. Within space Ω, dynamically divide the stage area Ω(t) = (ΩCR (t)∪Ω BR (t)∪Ω TR (t)), where Ω CR (t) Located in the center of the stage space, the equipment can operate at full capacity within this area; Ω BR (t) In areas close to the safety boundary, equipment must operate under strict restrictions; Ω TR (t) The region between the core region and the boundary region, where the constraint parameters transition smoothly.

[0105] 2. Based on the dynamically divided regions, construct a set of control variable constraint comparison parameter strategies for each region.

[0106] in It can be used in the core area, and the optimal performance mode can be enabled when the device is in motion; For the transition zone, the constraints are tighter, the safety margin is increased, and the conservative mode is activated. Used in boundary areas with extremely strict constraints, aiming for a slow stop, the braking mode is activated. Used for hazardous areas within the boundary region, it can trigger hardware-level safety functions and enable emergency stop mode. V max A max J max , representing the maximum speed, maximum acceleration, and maximum jerk (Jerk) allowed in the region, respectively.

[0107] 3. Obtain the device's real-time motion attributes (DEV) motion =[Pos(t),Vel(t),Acc(t)] ∧T Let D(t) represent the distance between the current state of the equipment and the safety boundary, and define the safety factor function K(t). Then, the safety factor K(t) = (D(t) - dt) / (t+1) = 0. min ) / (d max -d min Where D(t) represents the distance from the device's current position Pos(t) to the nearest physical boundary; d min Indicates the minimum distance between the equipment and the safety boundary; d max This refers to the maximum distance between the equipment and the safety boundary.

[0108] 4. The safety factor function can be expressed as:

[0109] The equipment is marked, and when the safety factor K(t) is 1, it indicates that the current movement of the equipment is in the core area. When the result is 0, the device moves within the boundary region. When the result is between (0,1), the device movement is in the transition zone.

[0110] 5. Based on the safety factor K(t) obtained from the equipment's movement position in the previous step, the geometric risk factor R of the equipment can be obtained. geo =1-K(t), this value indicates that the closer to the boundary, the higher the geometric risk.

[0111] 6. Calculate the dynamic risk factor R of the equipment. dyn =max(0, (Vel(t)*n / V) max _perf)), R dyn This indicates the tendency of the device to move towards the danger zone at a high speed. Even if the current position is safe, rushing towards the boundary at high speed is a high-risk behavior. `n` represents the unit normal vector pointing outside the nearest safe boundary. `Vel(t)*n` represents the projection of the velocity in the danger direction. The larger this value, the faster the device moves towards the boundary, and the higher the dynamic risk. max _perf represents the maximum speed allowed by the device. Vel(t) refers to the current speed of the device.

[0112] 7. Assume the time risk factor R of the equipment. time = min(1, (t_unsafe / t_max)), where t_unsafe represents the duration of continuous stay in the unsafe zone within a certain standard time value; t_max represents the maximum allowed stay time. Beyond this time, R... time Reaching a maximum value of 1. Time risk factor R time This indicates the duration of time a device remains in a high-risk area. Longer stays may indicate jamming or loss of control, with risks accumulating over time.

[0113] 8. External risk factor R env =R peo ∪R obj This includes whether anyone has broken into specific areas of the stage. The values ​​are: no one has a value of 0, some have a value of 1; and there is a risk of conflict with the predicted trajectories of other devices. The value for no conflict is 0, and the value for conflict is 1.

[0114] 9. Equipment health risk factors are determined by R. hea =∑(current) Pro -pro normal ) / (pro max -pro normal), where the device attribute Pro = [Temp(t), Amp(t), Noi(t), Loa(t), Ele(t)...], represents the device's temperature, amplitude, noise, load, current and other characteristics, respectively. This attribute value is normalized and the value range is [0,1]. This health risk factor indicates that any abnormality will cause this value to increase.

[0115] 10. A weighted summation method is used to merge multiple risk factors into a single comprehensive risk coefficient. R sum =ω geo *R geo +ω dyn *R dyn +ω time *R time +ω env *R env +ω hea *R hea ,ω geo ω dyn … represents the weight of each risk factor, satisfying ∑ω geo +ω dyn +…ω hea =1. The weighting is determined based on expert experience, such as ω geo and ω dyn It is the most direct risk, therefore it has a higher weight. sum The range of values ​​is also [0,1].

[0116] 11. Through multiple training and testing sessions, collect and store the values ​​of the comprehensive risk factor under different application scenarios, and simultaneously establish the R-squared value of the comprehensive risk factor. sum The database, based on the principles of minimum, median, and maximum values, sets a risk baseline of R for the dynamic security domain. low R med R high When the overall risk factors change, the database is updated, and the reference values ​​for the risk levels are also updated accordingly.

[0117] 12. Based on the actual value of the comprehensive risk factor and the movement area marked on the equipment, determine the control parameters that the current equipment should refer to.

[0118] Ruodang Therefore, the set of variable parameter constraints that the current device should adopt

[0119] when The current variable constraint parameters of the equipment are in and Smooth transitions between values ​​prevent equipment jitter caused by sudden changes in equipment control parameters. Where Vmax _cur=V max _perf+(V max _slow-V max Similarly, we can obtain A by _perf)*R. max _cur、J max _cur etc.;

[0120] when The set of variable constraint parameters that the current device should use. When R sum If none of the above three situations apply, then the current device should use the following set of constraint parameters.

[0121] 13. Set the dynamic constraint parameter set As the constraint of the current moment's optimization problem, it is input to the underlying controller to obtain a set of optimal control instructions U(t), which drive the device's motion state to change.

[0122] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained:

[0123] This invention introduces advanced sensing technology and intelligent decision-making algorithms to construct a novel safety constraint control method based on high-precision, low-latency, and multi-dimensional state perception. Stage equipment typically operates in fixed application scenarios during performances. Leveraging this characteristic, the moving area of ​​the equipment is first dynamically divided. Then, using the equipment's real-time operating status, such as position, speed, and acceleration, different parameter standards are constrained in different areas. The control method's constraints, such as speed and acceleration limits, are dynamically adjusted. The aim is to significantly improve the inherent safety level and active protection capabilities of the stage equipment system through the close integration of real-time data acquisition and variable constraints, thereby achieving safer, more precise, and smoother control. This invention's method embeds a construction algorithm into the software, utilizing dynamic area division and segmented threshold adjustment strategies to trigger a multi-level braking redundancy collaborative mechanism. This effectively enhances the control system's anti-interference capability and fault tolerance performance, achieving low cost and saving significant manpower and resources for debugging. It provides a high-precision, highly robust safety control solution for large stage machinery such as stage carriages and rotating stages.

[0124] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A stage equipment variable constraint control method based on risk perception and dynamic security domain, characterized in that, Comprise: According to the stage space structure and the physical motion attribute of the stage equipment, a multi-dimensional stage space is constructed, and a stage area is dynamically divided in the multi-dimensional stage space; Collecting the real-time motion attribute of the stage equipment; Calculating multi-dimensional risk factors; The multi-dimensional risk factors are fused into a comprehensive risk coefficient by using weighted summation method; Establishing the risk baseline of the dynamic safety domain, combining the current stage area of the equipment and the numerical value of the comprehensive risk coefficient, to determine the variable constraint parameter strategy set of the current equipment; The variable constraint parameter strategy set is input into the controller to generate optimal control instructions to drive the stage equipment to adjust the motion state.

2. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 1, characterized in that, Comprise the following specific steps: S1, according to the stage space configuration and the physical motion attribute of the stage equipment, constructing a multi-dimensional stage space Ω, and dynamically dividing a stage area Ω(t) = (Ω CR (t)∪Ω TR (t)∪Ω BR (t)) in the multi-dimensional stage space Ω, wherein Ω CR (t) is a core area, Ω TR (t) is a transition area, Ω BR (t) is a boundary area, in which the equipment runs in full performance in the core area, the parameter is smoothly transitioned in the transition area, and the equipment is strictly limited in the boundary area. S2, collecting real-time motion attribute DEV of stage equipment motion = [Pos(t), Vel(t), Acc(t)] T wherein Pos(t) is real-time position of the equipment, Vel(t) is real-time speed of the equipment, and Acc(t) is real-time acceleration of the equipment. S3, calculating a multi-dimensional risk factor, including a geometric risk factor R geo , a dynamic risk factor R dyn , a temporal risk factor R time , an external risk factor R env , and a device health risk factor R hea ; S4, fusing the multi-dimension risk factors into a comprehensive risk coefficient R by weighted summation sum wherein the weights satisfy ω geo +ω dyn +ω time +ω env +ω hea =1, ω geo , ω dyn , ω time , ω env , ω hea are the weights of the respective risk factors; S5: Establishing the risk baseline R of the dynamic security domain low , R med , R high , in combination with the current stage area where the device is located and the value of R sum , determine the variable constraint parameter policy set of the current device S6: generate the variable constraint parameter policy set An input controller generates optimal control instructions U(t) to drive the stage device to adjust the motion state.

3. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, characterized in that, The geometric risk factor R in step S3 geo = 1 - K(t), where K(t) is a safety function: When D(t) ≥ D CR (t) and DEV motion ∈ Ω CR (t), K(t) = 1; when D(t) < D BR (t) and DEV motion ∈Ω BR (t), K(t) = 0; When D BR (t) < D CR (t) and DEV motion ∈ Ω TR (t), K(t) ∈ (0,1). D(t) is the distance of the current position of the device Pos(t) to the nearest physical border, D CR D(t) is the distance of the current position of the device Pos(t) to the nearest physical border, D BR D(t) is the distance of the current position of the device Pos(t) to the nearest physical border, D 4. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, characterized in that, The dynamic risk factor R in step S3 dyn max (0, (Vel(t) * n / V max _perf)), where n is a unit normal vector pointing out of the nearest safety margin, Vel(t) * n is the projection of the velocity in the dangerous direction, and V max _perf is the maximum device speed allowed in the core region.​ 5. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, wherein, The time risk factor R in step S3 time = min(1, (t_unsafe / t_max)), where t unsafe is the duration of the stay of the device within Ω TR (t)∪Ω BR (t), and t_max is dynamically adjusted according to the device type.

6. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, wherein, The external risk factor R in step S3 env = R peo ∪ R obj where R peo is the risk of a person breaking in, and R obj is the risk of a multi-device trajectory conflict.

7. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, wherein, The device health risk factor R in step S3 hea = ∑(current Pro -Pro normal ) / (pro max -pro normal ), wherein the device properties Pro = [Temp(t), Amp(t), Noi(t), Loa(t), Ele(t)] represent the temperature, amplitude, noise, load, and current characteristics of the device, respectively.

8. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, wherein, The variable constraint parameter strategy set in step S5 comprises, wherein For the core region, the device motion enables the optimal performance mode; For the transition region, the constraints are tighter, the safety margin is increased, and the device enables the conservative mode; For the boundary region, the constraints are extremely tight, the goal is to slow down to a stop, and the device enables the braking mode; For the dangerous region in the boundary region, the hardware-level safety function is triggered, and the emergency stop mode is enabled;V max , A max , J max , respectively, represent the maximum speed, maximum acceleration, and maximum jerk allowed in the region.

9. The risk perception and dynamic security domain based stage equipment variable constraint control method according to claim 2, wherein, According to the actual value of the comprehensive risk factor and the motion area marked by the equipment, the control parameter to be referred to by the current equipment is judged: When Then the set of variable constraint parameter policies that the current device should employ When The current device variable constraint parameter is smoothly transitioned between And Avoids device jitter caused by sudden changes in device control parameters, Where V max _cur=V max _perf+(V max _slow-V max _perf)*R, and similarly A max _cur, J max _cur; When the set of variable constraint parameter policies that the current device should employ When R sum When none of the above three cases apply, then the set of constraint parameter policies that the current device should employ is 10. The stage equipment variable constraint control method based on risk perception and dynamic safety domain according to any one of claims 2-9, characterized in that, Also included is a multi-level braking redundancy mechanism: when R sum ≥ R high , a software pre-brake is triggered first; if R sum does not decrease within 1 second, a hardware emergency brake is triggered. And / or Also included is an environmental compensation factor γ, which is given by γ = 1 - 0.01 · (Temp env - 25) + 0.005 · (Hum env - 50), where: Temp env is the ambient temperature, Hum env is the ambient humidity, and the effects of temperature and humidity on device performance are compensated for by adjusting the constraint parameters.

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