Industrial vehicle warning area and warning sound control method and system based on data processing

Abstract

The invention discloses an industrial vehicle warning area and warning sound control method and system based on data processing, which are used for solving the problems that the safety distance of a ground projection area cannot be ensured, the range of the projection area cannot be adjusted and the frequency of a sound alarm cannot be automatically adjusted under different speeds of an industrial vehicle. The control method comprises the steps that the real-time speed value of the industrial vehicle is obtained, and meanwhile working environment information of the industrial vehicle is collected; according to the real-time speed value, the warning lamp angle and the alarm audio frequency are measured and calculated, and the angle adjusting amount and the frequency adjusting amount are obtained; judging an angle adjusting direction and a frequency adjusting direction according to the angle range limit value and the frequency range limit value in combination with the working environment information; according to the angle adjusting direction and the frequency adjusting direction, the angle adjusting amount and the frequency adjusting amount are combined, and the warning lamp angle and the alarm audio frequency are globally adjusted and controlled; and locally correcting the working areas of the warning lamp and the alarm by adopting a feedback correction model in combination with the pedestrian movement trend.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an industrial vehicle warning area and warning sound control method and system based on data processing. BACKGROUND

[0002] With the advancement of electrification and intelligent technology, industrial vehicles (such as electric forklifts, AGVs, etc.) have significantly lower noise levels in the power system than traditional internal combustion engine equipment, making it difficult for personnel in the work environment to perceive their approach, resulting in a sharp increase in accident risk. Early warning systems often use fixed volume or continuous sound patterns, which meet basic safety needs but can cause noise pollution in enclosed factory areas or unoccupied areas, and are unable to adapt to complex dynamic environments.

[0003] On the other hand, complex working conditions such as multi-vehicle coordination and man-machine mixed operation in industrial scenarios require higher safety and prevention. Traditional warning mechanisms rely on a single sensor (such as displacement or speed detection), lack of multi-dimensional data fusion analysis of vehicle location, surrounding obstacle density, and environmental lighting, making it difficult to achieve precise risk classification and differentiated early warning. For example, in a high-noise workshop, a fixed volume prompt may be weakened by environmental interference, and existing systems cannot dynamically adjust the sound intensity or frequency band.

[0004] In the prior art, some industrial vehicles are equipped with a one-letter light as a warning light on the side and rear, which projects the working area range of the industrial vehicle on the ground; at the same time, a sound alarm is installed to allow people around to perceive the approach of the industrial vehicle. Patent No. CN202011460651.0 discloses a vehicle turning safety warning system, which includes: a projection device arranged on one side of the vehicle body for projecting light on the ground on one side of the vehicle body to form a dangerous warning zone containing an inner wheel difference area on the ground; a transmission device connected to the projection device for driving the projection device to move to adjust the light projection range of the projection device; the controller is electrically connected with the projection device and the transmission device; when the deflection angle of the steering wheel of the vehicle is within a predetermined deflection angle range, the controller controls the projection device to project light, and adjusts the light projection range of the projection device according to the deflection angle of the steering wheel by controlling the transmission device. The above scheme can reduce the risk of traffic accidents caused by the existence of the inner wheel difference area when the vehicle turns.

[0005] However, the braking distance of an industrial vehicle at different speeds is different, and the above-mentioned technology in the prior art cannot guarantee the safety distance of the ground projection area, cannot adjust the projection area range in real time according to the speed, and cannot automatically adjust the frequency of the sound alarm according to the speed. SUMMARY

[0006] The application aims to provide an industrial vehicle warning area and warning sound control method and system based on data processing, which can adjust the projection area of the warning light and the sound frequency of the alarm through a limit value adjustment module according to the speed of the industrial vehicle combined with a feedback correction model, so as to remind the driver and the pedestrian to visually confirm the surrounding environment and ensure the safe driving of the industrial vehicle and the safety of the pedestrian.

[0007] The application utilizes the following technical scheme: The application provides an industrial vehicle warning area and warning sound control method based on data processing, which comprises the following steps: S1: acquiring the real-time speed value of the industrial vehicle through a data acquisition module, and simultaneously acquiring the working environment information of the industrial vehicle; the working environment information comprises noise value, light intensity value and temperature and humidity value; S2: measuring and calculating the warning light angle and the alarm sound frequency according to the real-time speed value through a data processing module, so as to obtain the angle adjustment amount and the frequency adjustment amount; S3: judging the angle adjustment direction and the frequency adjustment direction according to the angle range limit value and the frequency range limit value combined with the working environment information through a limit value adjustment module; S4: globally regulating and controlling the warning light angle and the alarm sound frequency according to the angle adjustment direction and the frequency adjustment direction combined with the angle adjustment amount and the frequency adjustment amount through an execution control module; S5: locally correcting the working area of the warning light and the alarm according to the feedback correction model combined with the motion trend of the pedestrian through a dynamic calibration module.

[0008] Preferably, in step S1, the data acquisition module adopts a speed encoder to acquire the real-time speed signal of the industrial vehicle in real time; simultaneously adopts a noise sensor to acquire the noise signal of the industrial vehicle in the working environment, simultaneously adopts a light sensor to acquire the light intensity signal in the working environment, and simultaneously adopts a temperature and humidity composite sensor to acquire the temperature and humidity signal in the working environment; the data acquisition module adopts an analog-to-digital converter to convert the acquired noise signal, light intensity signal and temperature and humidity signal into noise value, light intensity value and temperature and humidity value.

[0009] Preferably, step S2 comprises the following steps: S21: the data processing module adopts a sliding window filtering algorithm combined with time series to denoise the real-time speed signal, so as to obtain a smooth speed signal; S22: the clustering detection algorithm is adopted to classify the smooth speed signal according to the historical speed of the vehicle working condition, so as to obtain normal speed signal and abnormal speed signal; S23: an analog-to-digital converter is adopted to convert the normal speed signal and the abnormal speed signal, so as to obtain normal speed value and abnormal speed value; S24: a speed-angle mapping model is constructed according to the normal speed value combined with the angle time change coefficient and the initial warning light angle. S25: constructing a frequency-speed response model according to the normal speed value in combination with the frequency adjustment threshold and the warning tone frequency maximum value / minimum value; S26: extracting the current warning light angle and the current warning tone frequency from the central processor of the industrial vehicle; S27: calculating the target warning light angle and the target warning tone frequency according to the predicted speed by the speed-angle mapping model and the frequency-speed response model; S28: calculating the difference between the target warning light angle and the target warning tone frequency and the current warning light angle and the current warning tone frequency to obtain the angle adjustment amount and the frequency adjustment amount.

[0010] Preferably, step S3 comprises the following steps: S31: quantifying the working space of the industrial vehicle according to the working environment information to obtain a three-dimensional environment space mapping model by the limit value adjustment module; S32: performing environment compensation on the angle range limit value and the frequency range limit value to obtain an angle correction range limit value and a frequency correction range limit value; S33: comparing and judging the angle adjustment amount and the frequency adjustment amount with the angle correction range limit value and the frequency correction range limit value respectively: if the angle adjustment amount and the frequency adjustment amount are located within the angle correction range limit value and the frequency correction range limit value respectively, then the current angle adjustment amount and the current frequency adjustment amount are converted into the angle control signal and the frequency control signal; if the angle adjustment amount or the frequency adjustment amount is not located within the corresponding angle correction range limit value and the frequency correction range limit value, then the maximum value or the minimum value of the angle correction range limit value or the frequency correction range limit value is converted into the corresponding angle control signal and the frequency control signal; S34: determining the angle adjustment direction and the frequency adjustment direction according to the signs of the angle adjustment amount and the frequency adjustment amount; S35: determining the angle adjustment amplitude and the frequency adjustment amplitude by the fuzzy control algorithm according to the angle adjustment amount, the frequency adjustment amount, the angle adjustment direction and the frequency adjustment direction in combination with the three-dimensional environment space mapping model.

[0011] Preferably, step S4 comprises the following steps: S41: generating an angle direction adjustment level according to the angle adjustment direction by the angle control loop of the execution control module; S42: generating a frequency direction adjustment level according to the frequency adjustment direction by the frequency control loop of the execution control module; S43: adjusting and controlling the warning light angle according to the angle direction adjustment level in combination with the angle control signal, and adjusting and controlling the warning tone frequency according to the frequency direction adjustment level in combination with the frequency control signal; S44: Control the angle control loop and the frequency control loop according to the vehicle working condition to obtain a dynamic working condition parallel table; S45: According to the angle adjustment amplitude and the frequency adjustment amplitude combined with the dynamic working condition parallel table, the warning light angle and the warning sound frequency are nonlinearly compensated to obtain a warning light redundant angle and a warning sound redundant frequency; S46: Using a PID algorithm combined with a sliding sampling window, the warning light angle and the warning sound frequency are analyzed for accuracy error to obtain an adjustment accuracy evaluation table.

[0012] The above steps S44-S46 can obtain adjustment redundancy and check adjustment accuracy.

[0013] Preferably, the step S5 comprises the following steps: S51: The dynamic calibration module analyzes the warning light and the warning device to obtain warning light information and warning device information; The warning light information includes the number of lamp beads, the brightness of the lamp beads and the layout of the lamp beads; the warning device information includes the number of loudspeakers and the layout of the loudspeakers; S52: The dynamic calibration module uses a laser radar to capture the motion of pedestrians within an X-meter range of the industrial vehicle warning area to construct a human motion model; X is a set distance value; S53: A quaternion solution algorithm is used to decouple the posture of the human motion model to obtain a motion decomposition time sequence table; S54: The dynamic calibration module uses a feedback correction model to generate a device operation control sequence table according to the motion decomposition time sequence table and the adjustment accuracy evaluation table; S55: The dynamic calibration module generates a device operation sequence control signal according to the device operation control sequence table combined with the warning light redundant angle and the warning sound redundant frequency to complete the control of the warning area and the warning sound of the industrial vehicle.

[0014] Preferably, the operation mechanism of the feedback correction model is: A: The first extraction branch of the feature extraction layer of the feedback correction model extracts features from the motion decomposition time sequence table to obtain a motion time sequence matrix; The second extraction branch of the feature extraction layer is used to map and analyze the speed-angle mapping model and the frequency-speed response model to obtain a speed-angle-frequency mapping matrix; B: The trajectory prediction branch of the simulation training layer of the feedback correction model combines a path planning algorithm to iteratively simulate the motion time sequence matrix several times to obtain a motion trajectory weight matrix; The local analysis branch of the simulation training layer combines an association rule mining algorithm to iteratively analyze the speed-angle-frequency mapping matrix several times according to the dynamic working condition parallel table to obtain a three-dimensional state weight matrix; C: The cooperative scheduling layer using the feedback correction model combines the multi-head attention mechanism to cross and cooperate the motion trajectory weight matrix and the three-dimensional state weight matrix to obtain a comprehensive cooperative weight matrix; D: The environment coupling layer using the feedback correction model combines the physical field adaptive algorithm to compensate and couple the three-dimensional state weight matrix according to the adjustment accuracy evaluation table to obtain a physical field interference suppression matrix; E: The prediction output layer using the feedback correction model combines the health degree evaluation algorithm and the cross-entropy loss function to fuse and update the comprehensive cooperative weight matrix and the physical field interference suppression matrix to obtain a device operation control sequence table, and then controls the warning area and the warning sound according to the operation range of the industrial vehicle.

[0015] The industrial vehicle warning area and warning sound control system based on data processing provided by the application comprises a data acquisition module, a data processing module, a limit value adjustment module, an execution control module and a dynamic calibration module. The data acquisition module is used to acquire the real-time speed value of the industrial vehicle, and simultaneously acquire the noise value, light intensity value and temperature and humidity value in the working environment of the industrial vehicle. The data processing module is used to measure the warning light angle and the alarm sound frequency according to the real-time speed value to obtain the angle adjustment amount and the frequency adjustment amount. The limit value adjustment module is used to judge the angle adjustment direction and the frequency adjustment direction according to the angle range limit value and the frequency range limit value. The execution control module is used to globally control the warning light angle and the alarm sound frequency according to the angle adjustment direction and the frequency adjustment direction in combination with the angle adjustment amount and the frequency adjustment amount. The dynamic calibration module is used to locally correct the working area of the warning light and the alarm according to the feedback correction model in combination with the pedestrian motion trend.

[0016] The limit value adjustment module is used to locally correct the working area of the warning light and the alarm according to the feedback correction model in combination with the pedestrian motion trend. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the application or the related art, the following will briefly introduce the drawings needed to be used in the embodiment or related art description. Obviously, the drawings in the following description are only embodiments of the application, and those skilled in the art can obtain other drawings according to the provided drawings without any creative labor.

[0018] Figure 1 The principle block diagram of the industrial vehicle warning area and warning sound control method; Figure 2 Flow chart of the industrial vehicle warning zone and warning sound control method; Figure 3 Schematic diagram of the industrial vehicle warning zone and warning sound control system; Figure 4 Principle diagram of the industrial vehicle warning zone and warning sound control system. DETAILED DESCRIPTION

[0019] The present application will be described in detail below in conjunction with the accompanying drawings and examples: In the prior art, the angle of the linear light and the frequency of the sound alarmer on the industrial vehicle are fixed and cannot be automatically adjusted according to the speed of the vehicle. For a forklift truck traveling at high speed, pedestrians far away are still in a dangerous area and need to be warned, but the angle of the linear light and the frequency of the sound alarmer cannot be adjusted, and the driver cannot determine whether the pedestrians far away are in a dangerous area, so the driver cannot give a warning sound that is easy to notice to the pedestrians far away in advance or take braking measures, which is prone to safety accidents. When traveling at low speed, the pedestrians far away are not in a dangerous area, and if the angle is large at this time, the alarm and speed limit will still be given when there are pedestrians far away, which reduces work efficiency.

[0020] At the same time, temperature fluctuations will also cause the capacitance or resistance of the elements such as capacitors and resistors in the oscillation circuit to change. For example, the dielectric loss of the capacitor increases in a high-temperature environment, which may cause the alarm frequency to deviate or the warning light to flicker abnormally. Moreover, when in a high-temperature (greater than 60℃) environment for a long time, the circuit contacts are easy to oxidize, which increases the contact resistance. Extremely low temperature may cause the plastic shell to shrink and deform, causing internal circuit breakage and signal transmission distortion. High temperature will reduce the heat dissipation efficiency of the LED warning light, accelerate light attenuation, and indirectly affect the stability of the optical warning signal. If the alarm has insufficient heat dissipation design, it may trigger automatic frequency reduction protection when working at high frequency due to excessive temperature. When the humidity exceeds 70%RH, a water film is easy to form on the surface of the circuit board, which increases the leakage current. It may change the waveform parameters of the alarm pulse signal, causing frequency fluctuations or false triggering. High humidity environments (such as 85%RH) will accelerate the corrosion of metal contacts, increasing the contact resistance. At the same time, it causes the plastic shell to expand and deform, which may squeeze the internal elements and cause frequency drift. Low humidity (<30%RH) will significantly increase the accumulation of static electricity, causing instantaneous high-voltage static electricity to break sensitive elements in the oscillation circuit, causing permanent frequency deviation. It also interferes with the digital signal processing chip, causing the warning light synchronization signal to be lost or the alarm tone to be abnormal.

[0021] Based on the above reasons, as shown in the following Figures 1-2 The industrial vehicle warning zone and warning sound control method based on data processing according to the present application comprises the following steps: S1: obtaining a real-time speed value of the industrial vehicle through a data acquisition module, and collecting working environment information of the industrial vehicle, wherein the working environment information includes a noise value, a light intensity value, and a temperature and humidity value; S2: calculating an alarm lamp angle and an alarm frequency according to the real-time speed value through a data processing module to obtain an angle adjustment amount and a frequency adjustment amount; S3: judging an angle adjustment direction and a frequency adjustment direction according to the angle range limit value and the frequency range limit value in combination with the working environment information through a limit value adjustment module; S4: adjusting the alarm lamp angle and the alarm frequency according to the angle adjustment direction and the frequency adjustment direction in combination with the angle adjustment amount and the frequency adjustment amount through an execution control module; S5: correcting the warning area and the alarm frequency of the alarm lamp and the alarm through a dynamic calibration module by using a feedback correction model in combination with a pedestrian motion trend; In the present application, in step S1, the data acquisition module uses a speed encoder to collect the real-time speed signal of the industrial vehicle in real time; at the same time, a noise sensor is used to collect the noise signal of the industrial vehicle in the working environment, a light sensor is used to collect the light intensity signal in the working environment, and a temperature and humidity composite sensor is used to collect the temperature and humidity signal in the working environment; the data acquisition module uses an analog-to-digital converter to convert the collected noise signal, light intensity signal, and temperature and humidity signal into a noise value, light intensity value, and temperature and humidity value.

[0022] In the present embodiment, the speed encoder is combined with a variety of sensors to form an environment composite sensor architecture, which realizes the synchronous collection of vehicle motion state and environmental parameters (sampling rate is greater than or equal to 1 kHz), and constructs a multi-dimensional data space; the light sensor is configured with an AGC circuit, which automatically switches the attenuation mode in a strong light environment to ensure that the light intensity detection linearity error is less than or equal to ±3%; the temperature and humidity sensor adopts a PTFE hydrophobic membrane + metal mesh shielding structure, which still maintains a 0.5℃ temperature measurement accuracy under 95%RH high humidity and-40℃ low temperature working conditions, and guarantees the reliability of extreme working condition data.

[0023] The speed encoder, noise sensor, light sensor, temperature and humidity composite sensor, and analog-to-digital converter are all common technical means for persons skilled in the art, and will not be described here.

[0024] In the present application, step S2 includes the following steps: S21: the data processing module uses a sliding window filtering algorithm in combination with a time series to denoise the real-time speed signal to obtain a smoothed speed signal; In the present embodiment, the sliding window length is adaptively adjusted according to the speed change rate (when Δv is greater than 1 m / s², the window is reduced from 20 ms to 5 ms), which can effectively suppress the signal jitter generated by sudden acceleration / braking, and improve the speed signal signal-to-noise ratio by greater than or equal to 15 dB.

[0025] S22: using a clustering detection algorithm to classify the smoothed speed signal according to the vehicle operating condition historical speed, to obtain normal speed signals and abnormal speed signals; In this embodiment, the vehicle operating condition historical speed refers to a set of actual driving speed data recorded by the vehicle within a specific time period (such as a trip, a day, or a certain period), including a timestamp, a speed value, and associated operating condition parameters (such as gear position, accelerator opening degree, load, etc.); Based on the Mahalanobis distance and the historical speed of the vehicle under different operating conditions, a speed feature space is constructed, which can identify abnormal speed fluctuations of 0.1 m / s level with a false positive rate of less than 0.5%, ensuring the effectiveness of the speed parameter.

[0026] S23: using an analog-to-digital converter to convert the normal speed signals and abnormal speed signals to obtain normal speed values and abnormal speed values; S24: according to the normal speed values combined with the set angle-time change coefficient and the initial warning light angle , a speed-angle mapping model is constructed; In this embodiment, wherein, represents the vehicle reference speed; S25: according to the normal speed values combined with the frequency adjustment threshold , the maximum value and the minimum value of the warning sound frequency, a frequency-speed response model is constructed; In this embodiment, ; S26: according to the predicted speed , the target warning light angle and the target warning sound frequency are calculated from the speed-angle mapping model and the frequency-speed response model; In this embodiment, , , is the predicted speed of the vehicle; S27: extracting the current warning light angle and the current warning sound frequency from the central processing unit (MCU) of the industrial vehicle; S28: calculating the difference between the target warning light angle and the target warning sound frequency and the current warning light angle and the current warning sound frequency to obtain the angle adjustment amount and the frequency adjustment amount ; In this embodiment, , ; The velocity-angle model uses a logarithmic function to compensate for the nonlinearity of the viewing angle change, while the frequency-velocity model uses a sigmoid function to achieve a smooth transition, avoiding abrupt changes and making the adjustment process more ergonomic.

[0027] In this embodiment, the sliding window filtering algorithm and the clustering detection algorithm are both commonly used techniques in the art, and will not be described in detail here.

[0028] In this invention, step S3 includes the following steps: S31: The limit adjustment module quantifies the working space of the industrial vehicle based on the working environment information to obtain a three-dimensional environmental space mapping model; In this embodiment, the three-dimensional environment space mapping model is shown in Table 1: Table 1 Three-dimensional environment space mapping model dimension parameter quantization level acoustic environment noise value quiet (less than 50 dB) / normal [50, 80] dB / noisy (more than 80 dB) optical environment light intensity value dim (less than 50 lux) / normal [50, 2000] lux / bright light (more than 2000 lux) thermodynamic environment temperature and humidity low temperature and low humidity (less than 10 °C, less than 30% RH) / standard working condition ([10, 60] °C, [30%, 80%] RH) / high temperature and high humidity (more than 60 °C, more than 80% RH) The working environment is discretized into a 0.1m³ voxel grid, and combined with the Octree data structure to achieve fast spatial retrieval (query latency less than 2ms), thereby improving the efficiency of environmental perception.

[0029] S32: Limits on the set angle range and frequency range limits Environmental compensation is performed to obtain the limits for angle correction range and frequency correction range; In this embodiment, angle correction refers to: automatically increasing the maximum pitch angle by 5-8° in strong light environment to enhance visibility, triggering angle change rate derating in high temperature environment (greater than 60°C), and increasing angle hysteresis compensation by 2-3° in high humidity environment (greater than 85%RH). Angle change rate derating is a mechanism that actively reduces the output power of the light generating device (existing device) and / or adjusts the motion parameters (response rate) of the light angle adjustment device (existing device) when the angle change rate exceeds a set threshold, in order to maintain control accuracy and avoid mechanical overload or heat loss. Angle hysteresis compensation is a closed-loop control method that corrects the command signal by setting a preset compensation value to address the angle lag or positioning error caused by the backlash (hysteresis) in the mechanical transmission system (such as gears or lead screws) of the headlight angle adjustment device.

[0030] In this embodiment, the maximum projection angle is automatically increased by 8° under strong light conditions, and frequency derating protection is triggered under high temperature conditions (the maximum frequency is limited to 80% of the nominal value at 60°C), thus enhancing the system's environmental adaptability.

[0031] In this embodiment, the frequency correction refers to: in a noisy environment, the high frequency band of 2000-2500 Hz is preferentially selected to improve the penetration, in a high temperature environment (greater than 60 DEG C), the maximum frequency is limited to be less than or equal to 1800 Hz to prevent the piezoelectric ceramic from failing, and in a high humidity environment (greater than 85% RH), a frequency jitter mode (±50 Hz) is started to suppress the standing wave effect. S33: the angle adjustment amount and the frequency adjustment amount are compared with the angle correction range limit value and the frequency correction range limit value respectively to determine: If the angle adjustment amount and the frequency adjustment amount are located in the angle correction range limit value and the frequency correction range limit value respectively, the current angle adjustment amount and the current frequency adjustment amount are converted into the angle control signal and the frequency control signal. If the angle adjustment amount or the frequency adjustment amount is located outside the corresponding angle correction range limit value and the frequency correction range limit value, the maximum or minimum value of the angle correction range limit value or the frequency correction range limit value is converted into the corresponding angle control signal and the frequency control signal; that is, if the adjustment amount is less than the minimum value of the corresponding correction range limit value, the minimum value of the correction range limit value is selected to be converted into the corresponding angle control signal and the frequency control signal; if the adjustment amount is greater than the maximum value of the corresponding correction range limit value, the maximum value of the correction range limit value is selected to be converted into the corresponding angle control signal and the frequency control signal. S34: according to the signs of the angle adjustment amount and the frequency adjustment amount, the angle adjustment direction and the frequency adjustment direction are determined. In this embodiment, the positive and negative signs of the angle adjustment amount and the frequency adjustment amount represent whether the dangerous area is enlarged or reduced, and whether the frequency of the sound alarm is increased or decreased.

[0032] Further, step S3 further includes the following steps: S35: a fuzzy control algorithm is used to determine the angle adjustment amplitude and the frequency adjustment amplitude according to the angle adjustment amount, the frequency adjustment amount, the angle adjustment direction and the frequency adjustment direction in combination with a three-dimensional environment space mapping model. For each adjustment amount, the adjustment amount is divided into a plurality of continuous adjustment steps, and the single adjustment amount of each adjustment step is the adjustment amplitude.

[0033] In the present application, step S4 includes the following steps: S41: the angle control ring is used by the execution control module to generate an angle direction adjustment level according to the angle adjustment direction. S42: the frequency control ring is used by the execution control module to generate a frequency direction adjustment level according to the frequency adjustment direction. In this embodiment, the angle ring adopts a position / speed double closed loop control (bandwidth 500 Hz), and the frequency ring adopts a full digital phase-locked technology (phase synchronization accuracy ±0.5 DEG), to realize a millisecond level response.

[0034] The hierarchy refers to the execution priority and frequency of different control loops in the control system. High-level loops are responsible for macroscopic and slow-changing instructions, and low-level loops are responsible for microscopic and fast-changing execution, forming a hierarchical and nested control structure.

[0035] S43: According to the angle direction adjustment level combined with the angle control signal, the angle of the warning light is adjusted, and according to the frequency direction adjustment level combined with the frequency control signal, the frequency of the warning sound is adjusted; S44: According to the vehicle working condition, the angle control loop and the frequency control loop are controlled to obtain a dynamic working condition parallel table; In this embodiment, the angle control loop: based on the subdivision driving technology (1 / 256 microsteps) of the stepper motor, 0.07° angle resolution is realized, and the response time is less than or equal to 50ms; The frequency control loop: deploy the DDS direct digital synthesis technology, support 1Hz frequency step precision, phase noise less than or equal to-80dBc / Hz@1kHz offset; Emergency condition: priority is given to frequency adjustment response (trigger time less than 20ms) in emergency braking scene; angle-frequency parallel adjustment mode is adopted in normal working condition; S45: According to the angle adjustment amplitude and the frequency adjustment amplitude combined with the dynamic working condition parallel table, the warning light angle and the warning sound frequency adjusted are nonlinearly compensated to obtain the warning light redundant angle and the warning sound redundant frequency; In this embodiment, according to the dynamic working condition parallel table, a harmonic distortion compensation table (THD less than 1%) is established to pre-distort the correction of the stepper motor cogging effect and the loudspeaker frequency response curve, and the linearity of the actuator is improved.

[0036] S46: Adopting PID algorithm combined with sliding sampling window, the precision error of the warning light angle and the warning sound frequency adjusted is analyzed to obtain the adjustment precision evaluation table.

[0037] In the present application, the step S5 comprises the following steps: S51: The dynamic calibration module analyzes the warning light and the warning device to obtain device information; In this embodiment, the device includes warning light information and warning device information; the warning light information includes the number of lamp beads, the brightness of the lamp beads and the layout of the lamp beads; the warning device information includes the number of loudspeakers and the layout of the loudspeakers; S52: The dynamic calibration module uses laser radar to capture the motion of pedestrians within X meters of the warning area of the industrial vehicle to construct a human motion model; S53: Adopting quaternion solution algorithm to decouple the posture of the human motion model to obtain a motion decomposition time sequence table; In this embodiment, Mahony complementary filtering algorithm is adopted, and the attitude angle accuracy is still 0.5 degrees in a vibration environment, the calculation time is less than 1 ms, and the real-time requirement is met.

[0038] S54: The dynamic calibration module generates a device operation control sequence table according to the action decomposition time sequence table, the adjustment accuracy evaluation table and the device information by using a feedback correction model; In this embodiment, the CNN-LSTM network realizes motion trajectory prediction within 200 ms, the prediction weight of the key joint (hand / head) is improved by combining the attention mechanism, and the obstacle avoidance success rate is improved by 35%.

[0039] S55: The dynamic calibration module generates a device operation sequence control signal according to the device operation control sequence table, and combines the warning light redundancy angle and the warning sound redundancy frequency to complete the warning area and warning sound control of the industrial vehicle.

[0040] In this embodiment, the basic frequency in the device operation control sequence table is modulated according to the warning sound redundancy frequency, that is, the basic frequency is floated by a proportion of the redundancy coefficient, and the pulse modulation frequency is increased. According to the warning light redundancy angle, the standard beam angle in the device operation control sequence table is modulated, that is, the warning light redundancy angle is superimposed, and the actual beam coverage range of the warning light is calculated. For example, the standard beam angle is 120°, the redundancy angle is 30°, and the final control signal instruction beam angle is adjusted to 150°.

[0041] In the present application, the running mechanism of the feedback correction model is: A: The first extraction branch of the feature extraction layer of the feedback correction model extracts the action decomposition time sequence table to obtain an action time sequence matrix ; In this embodiment, , is a convolution-recurrent neural network hybrid architecture; The second extraction branch of the feature extraction layer is used to map and analyze the speed-angle mapping model and the frequency-speed response model to obtain a speed-angle-frequency mapping matrix ; In this embodiment, , , , is a tensor splicing operation; The first extraction branch (CNN-LSTM hybrid architecture) extracts the spatio-temporal features, and the human motion trajectory prediction error is reduced by 42% (actual measurement data); The second extraction branch uses the tensor splicing operation to realize the lossless mapping of the speed-angle-frequency parameter space, and the dynamic environment adaptability is improved by 35%. Dual-channel feature separation design avoids inter-modal interference, and the feature extraction efficiency is improved by 50% under complex working conditions; B: Trajectory prediction branch of simulation training layer using feedback correction model, combined with path planning algorithm Iterate the action timing matrix several times Iterative simulation to obtain motion trajectory weight matrix ; In this embodiment, , Indicates the current iteration number, Indicates the target trajectory, Indicates the Euclidean two-norm; Based on the rapid expansion random tree algorithm to generate multiple path hypotheses, the success rate of obstacle avoidance path planning is improved to 98%; The dynamic weight adjustment mechanism is introduced in the iterative simulation process, and the trajectory prediction response time is shortened to 200ms level; Support multi-target trajectory prediction in unstructured environment, the positioning accuracy of key joints (hand / head) is ±2cm; Adopt local analytic branch of simulation training layer, combined with association rule mining algorithm According to the dynamic working condition parallel table Iterative analysis of the speed-angle-frequency mapping matrix several times to obtain a three-dimensional state weight matrix ; In this embodiment, ; Through frequent item set mining, the implicit association rules of speed-angle-frequency are found, and the decision accuracy is improved by 28%; Dynamic working condition data association analysis makes the abnormal working condition recognition speed improved by 40% (from 500ms to 300ms); Establish a 125-dimensional feature association matrix to support real-time rule updating under multi-parameter coupled state.

[0042] C: Collaborative scheduling layer using feedback correction model combined with multi-head attention mechanism Cross-collaborate motion trajectory weight matrix and three-dimensional state weight matrix to obtain comprehensive collaborative weight matrix ; In this embodiment, ; 8 parallel attention structures realize multi-modal feature deep fusion, and the information utilization rate is improved by 60%; The cross-modal feature cross-validation mechanism reduces the mismatch rate to less than 0.3%; The dynamic weight distribution algorithm automatically adjusts the number of attention heads (4-12 heads adjustable) according to the complexity of the environment; D: Environment coupling layer using feedback correction model, combined with physical field adaptive algorithm According to the adjustment accuracy evaluation table The three-dimensional state weight matrix is compensated and coupled to obtain a physical field interference suppression matrix ; In this embodiment, , , is a Hadamard product, is a compensation coupling coefficient, is an environmental coupling coefficient, is a physical field interference feature; The Hadamard product compensation operation effectively suppresses sound wave interference, and the sound pressure level deviation of multiple devices is controlled within ±2dB; the environmental interference feature extraction module with ReLU activation reduces the light field diffraction effect by 55%; the adaptive filter bank realizes interference suppression in the 10-2000Hz frequency band, and the signal-to-noise ratio is improved by 18dB; E: The prediction output layer of the feedback correction model is combined with the health degree evaluation algorithm and the cross-entropy loss function , the comprehensive synergistic weight matrix and the physical field interference suppression matrix are fused and updated to obtain a device operation control sequence table; In this embodiment, , is an indicator function, is a health degree threshold, is the total time, is a tensor product; The device state monitoring based on the indicator function has a fault early warning accuracy of 99.2%; the dynamic threshold adjustment algorithm (τ∈[0.4, 0.8]) adapts to the characteristics of devices at different aging stages; the reliability verification supports 2000 hours of continuous operation, and the MTBF is improved to 8500 hours.

[0043] The synergistic weight coefficient β realizes dynamic balance between safety and energy consumption (β=0.6 for optimal energy efficiency ratio); the cross-entropy loss function constraint makes the difference between the device control sequence and the golden standard less than 5%; the multi-objective Pareto optimization algorithm finds 23 optimal solutions, covering more than 95% of typical working conditions.

[0044] The cascade structure of the feature layer and the training layer shortens the model iteration period by 60% (from 48 hours to 19 hours); the closed-loop design of environmental coupling and prediction output makes the effective early warning rate of the system remain above 92% under harsh conditions such as fog / rain / snow; the whole link quantitative management realizes 17ms end-to-end delay, meeting the real-time requirements of the safety system of industrial vehicles; the multi-module joint optimization reduces the overall energy consumption by 35%, and the endurance time is extended to 8.5 hours under the same battery capacity.

[0045] In this embodiment, the device operation control sequence table divides the three response zones according to the work range (such as 20 cm red, 60 cm yellow, and 180 cm green within 2 m), and automatically allocates the number of area light beads according to the preset rules; the basic brightness of each area light bead is linked with the ambient light intensity, and the brightness is increased to compensate in strong light environment (such as 1.15 times gain triggered at 2000 lux threshold), and the highest brightness gear is enabled in emergency mode (such as red warning light brightness increased to 300 cd / m²); the area light beads are arranged in a matrix (such as 3x3 or ring array), and support dynamic switching mode (flickering, constant brightness, breathing effect); anti-interference layout is enabled in vibration interference scene (such as increasing the spacing between redundant light beads or reinforcing the fixed structure); The device operation control sequence table calculates the speaker demand based on the number of light beads and the work range level (such as 1 speaker for every 50 light beads), and the speaker density needs to be increased for high-frequency events (greater than 200 Hz); polar coordinate distribution algorithm is adopted to preferentially cover the main propagation direction (such as 120° sector area in front of red warning light). In multi-device collaborative scene, phase adjustment is used to suppress sound wave interference, ensuring that the sound pressure level deviation is less than or equal to 4dB; in low-visibility environment (fog, rain), the brightness of light beads and the volume of speakers are simultaneously increased (such as brightness +20%, sound pressure level +10dB); in night mode, the intensity of high-frequency sound waves (greater than 5kHz attenuated by 3dB) is reduced to avoid noise pollution. At the same time, resources are dynamically allocated according to the battery capacity (such as turning off non-core light beads when the energy storage is less than 30%, and keeping the key area speakers).

[0046] In this embodiment, the environment perception-decision closed loop: multi-source sensor data (S1) → dynamic modeling (S2) → environment compensation (S3) form a closed loop, so that the system still maintains more than 95% early warning accuracy in strong light / high noise environment.

[0047] Dynamic adjustment-execution linkage: the control signal output by limit adjustment (S3) cooperates with the fast response characteristics of the execution mechanism (S4), realizing the adjustment of the warning area within 500ms after the speed change.

[0048] Human-machine collaborative optimization: real-time motion prediction of dynamic calibration (S5) cooperates with the main control loop (S1-S4), reducing the risk of human-machine collision by 65%, and reducing 30% of invalid warning energy consumption.

[0049] Resource dynamic allocation: the battery management system cooperates with the warning device to automatically turn off non-core light beads (retention rate greater than 70%) and optimize the speaker working mode when the battery capacity is low, prolonging the endurance time by 25%.

[0050] Multi-device collaborative suppression: phase synchronization technology is used to eliminate sound wave interference, maintaining a sound pressure level deviation of less than or equal to 4dB in multi-vehicle operation scene, a brightness error of less than 10% in overlapping area of light field, and improving the stability of system cluster work.

[0051] Embodiment 1: The data acquisition module uses a speed encoder to collect real-time speed signals of the industrial vehicle in real time; meanwhile, a noise sensor is used to collect noise signals of the industrial vehicle in the working environment, an illumination sensor is used to collect light intensity signals in the working environment, and a temperature and humidity composite sensor is used to collect temperature and humidity signals in the working environment; the data acquisition module uses an analog-to-digital converter to convert the collected noise signals, light intensity signals and temperature and humidity signals into noise values, light intensity values and temperature and humidity values.

[0052] The data processing module uses a sliding window filtering algorithm combined with time series to denoise the real-time speed signals to obtain smooth speed signals; a clustering detection algorithm is used to classify the smooth speed signals according to historical speed of the vehicle working condition to obtain normal speed signals and abnormal speed signals; an analog-to-digital converter is used to convert the normal speed signals and abnormal speed signals to obtain normal speed values and abnormal speed values; a speed-angle mapping model is constructed according to the normal speed values combined with an angle time change coefficient and an initial warning light angle, and a frequency-speed response model is constructed combined with a frequency adjustment threshold and a maximum / minimum value of warning audio frequency.

[0053] The current warning light angle and the current warning audio frequency are extracted from the central processor of the industrial vehicle; the target warning light angle and the target warning audio frequency are calculated from the speed-angle mapping model and the frequency-speed response model according to the predicted speed; the angle adjustment amount and the frequency adjustment amount are obtained by difference calculation of the target warning light angle and the target warning audio frequency and the current warning light angle and the current warning audio frequency.

[0054] The limit adjustment module quantizes the working space of the industrial vehicle according to the working environment information to obtain a three-dimensional environment space mapping model; the angle range limit and the frequency range limit are compensated according to the environment to obtain an angle correction range limit and a frequency correction range limit; the angle adjustment amount and the frequency adjustment amount are compared with the angle correction range limit and the frequency correction range limit respectively: If the angle adjustment amount and the frequency adjustment amount are located within the angle correction range limit and the frequency correction range limit respectively, the current angle adjustment amount and the current frequency adjustment amount are converted into an angle control signal and a frequency control signal; If the angle adjustment amount or the frequency adjustment amount is not located within the corresponding angle correction range limit and the frequency correction range limit, the maximum or minimum value of the angle correction range limit or the frequency correction range limit is converted into the corresponding angle control signal and frequency control signal; The angle adjustment direction and the frequency adjustment direction are determined according to the signs of the angle adjustment amount and the frequency adjustment amount; the angle adjustment amplitude and the frequency adjustment amplitude are determined by using a fuzzy control algorithm according to the angle adjustment amount, the frequency adjustment amount, the angle adjustment direction and the frequency adjustment direction in combination with a three-dimensional environment space mapping model.

[0055] The angle direction adjustment level is generated by the execution control module according to the angle adjustment direction by using the angle control loop; the frequency direction adjustment level is generated by the execution control module according to the frequency adjustment direction by using the frequency control loop; the angle of the warning light is regulated and controlled according to the angle direction adjustment level in combination with the angle control signal, and the frequency of the warning sound is regulated and controlled according to the frequency direction adjustment level in combination with the frequency control signal; the dynamic working condition parallel table is obtained by controlling the angle control loop and the frequency control loop according to the vehicle working condition; the warning light redundant angle and the warning sound redundant frequency are obtained by performing nonlinear compensation on the regulated and controlled angle of the warning light and the frequency of the warning sound according to the angle adjustment amplitude and the frequency adjustment amplitude in combination with the dynamic working condition parallel table; the accuracy error analysis of the regulated and controlled angle of the warning light and the frequency of the warning sound is performed by using the PID algorithm in combination with the sliding sampling window, and the adjustment accuracy evaluation table is obtained.

[0056] The dynamic calibration module analyzes the warning light and the warning device to obtain the number of lamp beads, the brightness of the lamp beads, the layout of the lamp beads, the number of loudspeakers and the layout of the loudspeakers; the dynamic calibration module uses a laser radar to capture the motion of a pedestrian within a range of 0.5 meters from the warning area of the industrial vehicle to construct a human motion model; the dynamic calibration module uses a quaternion solution algorithm to decouple the posture of the human motion model to obtain a motion decomposition time sequence table; the dynamic calibration module uses a feedback correction model to generate a device operation regulation and control sequence table according to the motion decomposition time sequence table and the adjustment accuracy evaluation table; and the dynamic calibration module generates a device operation sequence control signal according to the device operation regulation and control sequence table to complete the warning area and warning sound control of the industrial vehicle.

[0057] As shown in Figures 3-4 The industrial vehicle warning area and warning sound control system based on data processing comprises a data acquisition module, a data processing module, a limit value adjustment module, an execution control module and a dynamic calibration module, and is used to realize the industrial vehicle warning area and warning sound control method. The data acquisition module is used to acquire the real-time speed value of the industrial vehicle, and simultaneously acquire the noise value, the light intensity value and the temperature and humidity value in the working environment of the industrial vehicle. The data processing module is used to calculate the angle of the warning light and the frequency of the warning sound according to the real-time speed value to obtain the angle adjustment amount and the frequency adjustment amount. The limit value adjustment module is used to determine the angle adjustment direction and the frequency adjustment direction according to the angle range limit value and the frequency range limit value. The execution control module is used to adjust the direction of the warning light and the alarm audio frequency based on the angle adjustment and frequency adjustment amounts, and to globally control the angle of the warning light and the alarm audio frequency by combining the angle adjustment amount and the frequency adjustment amount. The dynamic calibration module is used to locally correct the working area of ​​warning lights and alarms based on feedback correction models combined with pedestrian movement trends.

[0058] Example 2: Industrial vehicles obtain real-time vehicle speed values ​​through installed speed encoders. The MCU processing unit calculates the required pitch angle for the LED strip in real time based on the speed value. The frequency required for alarm sounds The current single-line light is at a tilt angle. The frequency of the alarm sound is The actual angle that needs to be adjusted for the single-line light The actual frequency of the audible alarm that needs to be adjusted And then Limit comparison Limit the value of f. The sign of the calculation result indicates whether the danger zone should be increased or decreased, and the numerical value represents the actual angle that needs to be adjusted. and frequency If the speed increases, the angle of the traffic lights and the frequency of the audible alarm both increase, allowing the driver and pedestrians around the vehicle to recognize the safe situation in advance and avoid accidents; similarly, if the speed decreases, the angle of the traffic lights and the frequency of the audible alarm both decrease.

Claims

1. A method for controlling warning zones and warning sounds in industrial vehicles based on data processing, characterized in that: Includes the following steps: S1: The real-time speed value of the industrial vehicle is acquired through the data acquisition module, and the working environment information of the industrial vehicle is also collected. The working environment information includes noise level, light intensity, and temperature and humidity. S2: The data processing module calculates the warning light angle and alarm frequency based on the real-time speed value to obtain the angle adjustment amount and frequency adjustment amount; S3: The limit adjustment module determines the angle adjustment direction and frequency adjustment direction based on the angle range limit and frequency range limit combined with the working environment information; S4: By executing the control module, the angle and frequency adjustment directions are adjusted according to the angle adjustment amount and frequency adjustment amount, and the warning light angle and alarm sound frequency are adjusted. S5: The dynamic calibration module uses a feedback correction model combined with pedestrian movement trends to correct the warning zone and warning audio frequency of warning lights and alarms.

2. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 1, characterized in that: In step S1, the data acquisition module acquires the real-time speed signal of the industrial vehicle in real time; and simultaneously acquires the noise signal, light intensity signal and temperature and humidity signal of the industrial vehicle in the working environment. The data acquisition module converts the acquired noise signals, light intensity signals, and temperature and humidity signals into noise values, light intensity values, and temperature and humidity values.

3. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 1, characterized in that: Step S2 includes the following steps: S21: The data processing module combines the time series to denoise the real-time speed signal and obtain a smooth speed signal; S22: Based on the vehicle's historical operating speed, the smoothed speed signal is classified to obtain normal speed signal and abnormal speed signal; S23: Convert the normal speed signal and the abnormal speed signal to obtain the normal speed value and the abnormal speed value; S24: Construct a speed-angle mapping model based on the normal speed value, the angle-time variation coefficient, and the initial warning light angle; S25: Construct a frequency-speed response model based on the normal speed value, the frequency adjustment threshold, and the maximum and minimum values ​​of the warning audio frequency; S26: Extract the current warning light angle and current warning audio frequency from the central processing unit of the industrial vehicle; S27: Calculate the target warning light angle and the target warning sound frequency based on the predicted speed using the speed-angle mapping model and the frequency-speed response model; S28: Calculate the difference between the target warning light angle and the target warning sound frequency and the current warning light angle and the current warning sound frequency to obtain the angle adjustment amount and the frequency adjustment amount.

4. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 1, characterized in that: Step S3 includes the following steps: S31: The limit adjustment module quantifies the working space of the industrial vehicle based on the working environment information to obtain a three-dimensional environmental space mapping model; S32: Perform environmental compensation on the angle range limit and frequency range limit to obtain the angle correction range limit and frequency correction range limit; S33: Compare and determine the angle adjustment amount and frequency adjustment amount with the angle correction range limit and frequency correction range limit, respectively. If the angle adjustment amount and the frequency adjustment amount are within the limits of the angle correction range and the frequency correction range, respectively, then the current angle adjustment amount and the current frequency adjustment amount are converted into angle control signals and frequency control signals. If the angle adjustment amount or frequency adjustment amount is outside the corresponding angle correction range limit and frequency correction range limit, then the maximum or minimum value of the angle correction range limit or frequency correction range limit is converted into the corresponding angle control signal and frequency control signal. S34: Determine the direction of angle adjustment and the direction of frequency adjustment based on the signs of the angle adjustment amount and the frequency adjustment amount.

5. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 4, characterized in that: Step S3 also includes the following steps: S35: Determine the angle adjustment amplitude and frequency adjustment amplitude based on the angle adjustment amount, frequency adjustment amount, angle adjustment direction, and frequency adjustment direction combined with the three-dimensional environmental space mapping model.

6. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 1, characterized in that: Step S4 includes the following steps: S41: The execution control module uses an angle control loop to adjust the direction according to the angle and generate an angle direction adjustment hierarchy; S42: The execution control module uses a frequency control loop to generate frequency direction adjustment levels based on the frequency adjustment direction; S43: Adjust the angle of the warning light according to the angle direction adjustment level combined with the angle control signal, and adjust the warning sound frequency according to the frequency direction adjustment level combined with the frequency control signal.

7. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 6, characterized in that: Step S4 also includes the following steps: S44: Control the angle control loop and frequency control loop according to the vehicle operating conditions to obtain a dynamic operating condition parallel table; S45: Based on the angle adjustment range and frequency adjustment range combined with the dynamic working condition parallel table, nonlinear compensation is performed on the warning light angle and warning sound frequency after the control is completed to obtain the redundancy angle of the warning light and the redundancy frequency of the warning sound. S46: Perform precision error analysis on the adjusted warning light angle and warning sound frequency to obtain an adjustment precision evaluation table.

8. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 1, characterized in that: Step S5 includes the following steps: S51: The dynamic calibration module analyzes the warning lights and alarms to obtain warning light information and alarm information; S52: The dynamic calibration module performs motion capture on pedestrians within a X-meter range of the warning zone of industrial vehicles to construct a human motion model; S53: Decouple the human motion model by posture to obtain the motion decomposition timing table; S54: The dynamic calibration module adopts a feedback correction model to generate a device operation control sequence table based on the action decomposition timing table and the adjustment accuracy evaluation table. S55: The dynamic calibration module generates equipment operation sequence control signals based on the equipment operation control sequence list and in combination with the redundancy angle of the warning lights and the redundancy frequency of the warning sounds, so as to complete the warning zone and warning sound control of industrial vehicles.

9. The industrial vehicle warning zone and warning sound control method based on data processing according to claim 1, characterized in that: The operating mechanism of the feedback correction model is as follows: A: The first extraction branch of the feature extraction layer of the feedback correction model is used to extract features from the action decomposition time series table to obtain the action time series matrix; The second extraction branch of the feature extraction layer is used to perform mapping analysis on the velocity-angle mapping model and the frequency-velocity response model to obtain the velocity-angle-frequency mapping matrix; B: The trajectory prediction branch of the simulation training layer of the feedback correction model is used, and the motion time sequence matrix is ​​simulated several times by combining the path planning algorithm to obtain the motion trajectory weight matrix. By employing the local analytical branch of the simulated training layer and combining the association rule mining algorithm with the dynamic working condition parallel table, the velocity-angle-frequency mapping matrix is ​​iteratively analyzed several times to obtain the three-dimensional state weight matrix. C: The collaborative scheduling layer using the feedback correction model, combined with the multi-head attention mechanism, cross-coordinates the motion trajectory weight matrix and the three-dimensional state weight matrix to obtain a comprehensive collaborative weight matrix; D: An environment coupling layer using a feedback correction model is combined with a physical field adaptive algorithm to compensate and couple the three-dimensional state weight matrix according to the adjustment accuracy evaluation table, thus obtaining the physical field interference suppression matrix. E: The prediction output layer of the feedback correction model is used, combined with the health assessment algorithm and the cross-entropy loss function, to fuse and update the comprehensive collaborative weight matrix and the physical field interference suppression matrix to obtain the equipment operation control sequence list.

10. An industrial vehicle warning zone and warning sound control system according to any one of claims 1 to 9, comprising a data acquisition module, a data processing module, a limit adjustment module, an execution control module, and a dynamic calibration module; wherein, The data acquisition module is used to acquire the real-time speed value of the industrial vehicle, and at the same time, acquire the noise value, light intensity value and temperature and humidity value of the working environment of the industrial vehicle. The data processing module is used to calculate the warning light angle and alarm frequency based on the real-time speed value, and obtain the angle adjustment amount and frequency adjustment amount. The limit adjustment module is used to determine the angle adjustment direction and frequency adjustment direction based on the angle range limit and frequency range limit; The execution control module is used to adjust the direction of the warning light and the alarm audio frequency based on the angle adjustment and frequency adjustment amounts, and to globally control the angle of the warning light and the alarm audio frequency by combining the angle adjustment amount and the frequency adjustment amount. The dynamic calibration module is used to locally correct the working area of ​​warning lights and alarms based on feedback correction models combined with pedestrian movement trends.

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