Vehicle control unit of industrial vehicle and control method of vehicle control unit

Abstract

The invention relates to the technical field of vehicle program control, in particular to a vehicle control unit of an industrial vehicle and a control method of the vehicle control unit, and the vehicle control unit comprises a pressure intensity gathering monitoring module, a response grade regulation and control module, a direction concentration judgment module, a speed zone window adjustment module and a running state feedback module. According to the method, the data of the pressure sensors at the wheel edge connecting points of the frame supporting part and the end part of the axle are collected, and sequence integration and trend identification are carried out on the load change in time and space dimensions, so that the real-time gathering situation of local load distribution of the vehicle body can be constructed, and pre-judgment in the early stage of load offset is realized; and the response identification capability of the stress state of the structure is improved. In the aspect of speed control, by extracting and sorting dynamic fluctuation information between a speed change instruction and a node response speed and combining a local load concentration state to carry out adaptability analysis, elimination of a mismatched instruction and dynamic adjustment of a speed grade interval can be realized, so that speed regulation control is more stable and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle program control, and in particular to a whole vehicle controller of an industrial vehicle and a control method thereof. Background Art

[0002] The field of vehicle program control technology encompasses centralized management and logical control of the vehicle's various functional controllers. The core concept is to pre-set program logic within the electronic control unit, which processes input information and outputs control signals to adjust the vehicle's operating state. This technology encompasses engine control, vehicle drive controller control, steering and braking control, power management, and safety mechanisms.

[0003] The vehicle controller for industrial vehicles is a program-controlled controller that coordinates and manages the overall operating status of the vehicle's drive unit, brake actuator, steering control vehicle controller, body posture adjustment structure, and operating functional components during operation. This includes the drive output command generation mechanism, energy distribution logic, speed and load coordination parameter setting method, steering control signal conversion and processing flow, vehicle state perception and feedback triggering logic, and its impact on control commands, completing the orchestration and management of the vehicle control process.

[0004] Traditional vehicle controllers only coordinate control of modules such as drive, braking, steering, and operational functions, lacking in-depth understanding of the correlation between changes in vehicle structural forces and dynamic operating conditions. Regarding load variations, existing technologies typically collect single-point data from nodes, lacking temporal integration and spatial trend analysis of pressure changes between nodes. This makes it difficult to identify load accumulation paths, resulting in the inability to provide timely warnings before load concentration forms. Regarding speed control, the discrepancy between speed commands and response speeds is often not dynamically recorded and analyzed. Control commands fail to promptly eliminate levels showing signs of mismatch, which can easily lead to localized vehicle vibration and speed regulation failure. In directional control, traditional solutions fail to capture directional consistency data from angle changes, resulting in weak identification of vehicle posture disturbances and difficulty assessing structural stability risks. Regarding operational status feedback mechanisms, control strategies fail to effectively integrate comprehensive assessments of load and slope changes, lacking a coordinated regulatory mechanism between structural posture anomalies and speed control. For example, when a vehicle is operating at high speed on a slope and the load is concentrated on one side, traditional systems fail to identify the operational risks caused by posture instability, potentially causing structural deflection or delayed control response. The above limitations result in the vehicle control system lacking flexible adaptability and predictive capabilities, and its stability and safety are easily affected under complex working conditions. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a vehicle controller for an industrial vehicle and a control method thereof.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a vehicle controller for an industrial vehicle, the vehicle controller comprising: The pressure concentration monitoring module obtains feedback data from pressure sensors at the frame support parts, including wheel side connection points and axle ends, monitors the load change trend of the nodes, determines whether a critical concentration situation has formed, and generates local pressure concentration analysis results; The response level control module compares the clustering coefficient change trajectory with the node group classified and marked as critical in the local pressure clustering analysis result, determines whether there is a mismatch trend in the speed command, and obtains the speed limit level range; The direction concentration determination module continuously samples the angle between the pressure vector and the vehicle's running direction according to the vehicle's driving state after the speed limit level interval is adjusted, calculates the directional concentration factor, and generates a directional concentration offset analysis result based on the direction periodic change characteristics; The speed zone window adjustment module queries the corresponding balance interval in the speed mapping table based on the structural state reflected by the directional concentrated offset analysis result, resets the speed scheduling boundary, and obtains the speed control window; The running state feedback module screens the adaptable speed levels based on the speed limit level interval and the speed control window, and generates a vehicle speed control instruction.

[0007] The improvements of the present invention are that the local pressure concentration analysis results include load-intensive area position marks, critical state classification of concentration coefficients, and load distribution concentration trend levels; the speed limit level interval includes low response level boundaries, jump frequency control constraints, and speed level elimination lists; the direction concentration offset analysis results include directional concentration factor values, offset rate change indicators, and structural disturbance risk types; the speed control window is specifically the speed balance interval boundary value, the conflicting speed level exclusion table, and the speed scheduling section adjustment parameters; the vehicle speed control instructions include executable speed level groups, load structure matching status labels, and vehicle speed regulation execution control parameters.

[0008] The present invention is improved in that the pressure accumulation monitoring module includes: The load change acquisition submodule acquires data from pressure sensors at the wheel-side connection points and axle ends in the frame support area, collects load value change information at the detection nodes within a set period, integrates the data differences between nodes in a time series, and, based on the distance structure between nodes, extracts the synchronous performance of load changes in space and determines whether there is a concentrated direction of load changes, thereby generating load change distribution characteristics. The central tendency identification submodule, based on the load change distribution characteristics, calls the structural arrangement characteristics of the load change amplitude and distribution path between nodes, identifies whether the load change direction tends to a single centralized path in space, makes a cumulative judgment on the centralized performance, and calculates the clustering coefficient reflecting the load concentration state; The critical state marking submodule extracts the upper pressure limit data recorded at the same node site based on the clustering coefficient, and determines whether the current clustering state is close to the original bearing limit of the node. If the clustering coefficient is close to the structural boundary, the state of the area where the node is located is marked and the central tendency level is divided to obtain the local pressure concentration analysis results.

[0009] The present invention is improved in that the response level control module includes: The node fluctuation extraction submodule extracts the change information between the speed control command and the current response speed in the corresponding control record based on the node group marked as critical in the local pressure concentration analysis results, calculates the fluctuation frequency and amplitude of the speed response in the area where the node is located, and summarizes and identifies the speed change trend to obtain the speed control response fluctuation characteristics; The speed mismatch identification submodule uses the speed regulation response fluctuation characteristics and combines the clustering coefficient of the node area to determine whether there is a command mismatch between the speed response trend and the local load concentration state. Based on whether the fluctuation anomaly and the load concentration condition occur at the same time, the speed level control effectiveness in the area is determined to obtain the speed response adaptation state analysis results. The level interval adjustment submodule filters out non-adaptive jump speed instructions based on the speed response adaptation state analysis results, retains the level intervals in which the instruction fluctuation amplitude is lower than the fluctuation threshold from the current speed level structure as the level range with execution stability, and obtains the speed limit level interval.

[0010] The present invention is improved in that the direction concentration discrimination module includes: The angle acquisition submodule collects the angle data between the pressure vector direction of the load contact node and the vehicle's travel direction based on the vehicle's driving state after the speed limit level interval is adjusted, continuously records the change trajectory of the angle within a time segment, extracts the offset angle and change rate of each detection point within a specified period as basic data, and obtains the angle offset change index; The factor calculation submodule extracts the offset angle value and change rate data of each detection point based on the angle offset change index, performs normalization judgment on the degree of aggregation of the directional offset trend in space, and calculates and generates a directional concentration factor for representing the load directional concentration state; The disturbance judgment submodule calls the directional concentration factor, combines the changing rhythm of the directional concentration factor within multiple detection cycles, compares it with the set vehicle posture disturbance cycle reference value, determines the interference potential of the fluctuation trend on the vehicle body stability, and obtains the directional concentration offset analysis result.

[0011] The present invention is improved in that the speed zone window adjustment module includes: The interval query submodule extracts the directional concentration factor value based on the structural state reflected by the directional concentration offset analysis result, searches the window mapping table indexed by the directional concentration factor in the speed control architecture, calls the speed level item corresponding to the current factor value in the table, determines the matched upper and lower limits of the speed balance boundary, and obtains the speed balance interval; The boundary reconstruction submodule extracts the upper and lower boundary settings corresponding to the current control speed level based on the speed balance interval, and determines whether there is a speed instruction beyond the balance interval range in the original level boundary. If there is a deviation item, the speed level structure is reconstructed based on the balance interval to obtain the speed level boundary adjustment result; The instruction screening submodule calls the speed level boundary adjustment result, identifies all speed instructions in the original speed control level that are not within the adjusted boundary range, and removes them from the speed scheduling path, retaining only the instructions within the reconstructed boundary to obtain the speed control window.

[0012] The present invention is improved in that the operating status feedback module includes: The posture recognition submodule collects slope sensor data and wheel-side load sensor data at the vehicle's current position based on the speed control range defined by the speed limit level interval and the speed control window, identifies the current structural posture change area, and determines whether there is posture instability caused by uneven load, thereby obtaining a posture instability area recognition result; The level matching submodule calls the posture unstable area identification result, judges the adaptability between the speed command and the current slope value and load distribution in the target area, selects a speed level set that can maintain structural stability under the joint constraints of direction deviation and pressure aggregation, and obtains an adaptive speed level group; The instruction generation submodule synchronizes the executable level to the instruction interface of the vehicle control according to the adaptive speed level group, covering all level instructions in the original speed control path that are not in the set, and uses the adaptive level as the current upper and lower speed limit boundaries of the vehicle to obtain the vehicle speed control instruction.

[0013] A whole vehicle control method for an industrial vehicle is provided. The whole vehicle control method for an industrial vehicle is performed based on the whole vehicle controller of the industrial vehicle, and comprises the following steps: S1: Obtain feedback data from pressure sensors at the frame support locations, including wheel-side connection points and axle ends, monitor the load change trend at the nodes, determine whether a critical concentration situation has formed, and generate local pressure concentration analysis results; S2: Based on the node group classified and marked as critical in the local pressure concentration analysis result, compare the clustering coefficient change trajectory to determine whether there is a mismatch trend in the speed command, and obtain the speed limit level range; S3: Based on the vehicle driving state after the speed limit level interval is adjusted, continuously sampling the angle between the pressure vector and the vehicle running direction, calculating the directional concentration factor, and generating a directional concentration offset analysis result based on the directional periodic change characteristics; S4: Based on the structural state reflected by the directional concentrated offset analysis result, query the corresponding balance interval in the speed mapping table, reset the speed scheduling boundary, and obtain the speed control window; S5: Based on the speed limit level interval and the speed control window, the adaptable speed levels are screened and a vehicle speed control instruction is generated.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by collecting pressure sensor data from the wheel-side connection points and axle ends of the frame support area, and performing sequential integration and trend identification of load changes in time and space dimensions, it is possible to construct a real-time aggregation situation of the local load distribution of the vehicle body, achieve early prediction of load offset, and improve the response identification capability of the structural stress state. In terms of speed control, by extracting and sorting the dynamic fluctuation information between the speed change command and the node response speed, and combining it with the adaptability analysis of the local load concentration state, it is possible to eliminate mismatched commands and dynamically adjust the speed level range, making the speed control more stable and reliable. In the direction determination process, the node directional concentration factor is calculated by continuous sampling of the angle offset and the pressure vector direction, and the risk of structural posture disturbance is identified by comparing the periodic change trend, effectively improving the ability to assess the consistency of the vehicle body's running direction. By combining the directional offset results with the speed mapping table, the speed balance interval boundaries that meet the structural stability requirements are set, and a controllable speed control window is established, which can avoid structural conflicts caused by excessive speed regulation. During the feedback phase, unstable areas of the structural posture are identified based on slope and load sensor data, and the appropriate speed level is further selected to ensure that speed control commands accurately cover the vehicle's operating status. This multi-dimensional data coupling mechanism and the coordinated adjustment of the speed control path form a dynamically adaptive vehicle control strategy, significantly improving the vehicle's structural stability, speed regulation accuracy, and fault warning capabilities under complex operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1This is a module diagram of the vehicle controller of the present invention; Figure 2 This is a framework diagram of the vehicle controller of the present invention; Figure 3 Schematic diagram of a pressure accumulation monitoring module of the present invention; Figure 4 Schematic diagram of the response level control module of the present invention; Figure 5 Schematic diagram of the direction-focused discrimination module of the present invention; Figure 6 Schematic diagram of the speed zone window adjustment module of the present invention; Figure 7 Schematic diagram of the operating status feedback module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: a vehicle controller for an industrial vehicle, the vehicle controller comprising: The pressure concentration monitoring module obtains feedback data from pressure sensors at the frame support parts, including wheel side connection points and axle ends, monitors the load change trend of the nodes, determines whether a critical concentration situation has formed, and generates local pressure concentration analysis results; The response level control module compares the clustering coefficient change trajectory of the node groups classified as critical in the local pressure clustering analysis results, determines whether there is a mismatch trend in the speed command, and obtains the speed limit level range; The directional concentration discrimination module continuously samples the angle between the pressure vector and the vehicle's running direction based on the vehicle's driving state after the speed limit level interval is adjusted, calculates the directional concentration factor, and generates the directional concentration offset analysis results based on the directional periodic change characteristics; The speed zone window adjustment module queries the corresponding balance interval in the speed mapping table based on the structural state reflected by the directional concentrated offset analysis results, resets the speed scheduling boundary, and obtains the speed control window; The running status feedback module screens the adaptable speed levels based on the speed limit level range and speed control window and generates the vehicle speed control instructions; The results of local pressure concentration analysis include the location mark of load-intensive area, critical state classification of concentration coefficient, and load distribution concentration trend level. The speed limit level interval includes low response level boundary, jump frequency control constraint, and speed level elimination list. The results of directional concentration offset analysis include directional concentration factor value, offset rate change index, and structural disturbance risk type. The speed control window is specifically the speed balance interval boundary value, conflict speed level exclusion table, and speed scheduling section adjustment parameters. The vehicle speed control instructions include executable speed level group, load structure matching status label, and vehicle speed regulation execution control parameters.

[0019] See also Figure 2 and Figure 3 , the pressure accumulation monitoring module includes: The load change acquisition submodule acquires data from pressure sensors at the wheel-side connection points and axle ends in the frame support area, collects load value change information at the detection nodes within a set period, integrates the data differences between nodes in a time series, and, based on the distance structure between nodes, extracts the synchronous performance of load changes in space and determines whether there is a concentrated direction of load changes, thereby generating load change distribution characteristics. The pressure sensor data of the wheel side connection point and the axle end in the frame support part is obtained. The system is arranged to install high-precision pressure sensors at key node positions on both sides of the front and rear axles of the vehicle. Each node, such as the left side of the front wheel is P1, the right side of the front wheel is P2, the left side of the rear axle is P3, and the right side is P4. All nodes record the pressure value changes within the unit cycle with a sampling period of 0.2 seconds. Each node records the pressure of the current cycle and compares it with the previous cycle to form a pressure difference sequence. For example, the pressure of P1 in the current cycle is 2.40 MPa, and the pressure in the previous cycle is 2.25 MPa, with a difference of 0.15 MPa, all differences are entered into the database and paired with time series by timestamp. The system synchronously obtains the spatial structure coordinate information between nodes and calculates the node spacing, such as the spacing from P1 to P2 is 1.6 meters, and from P1 to P3 is 2.4 meters. The pressure differences paired with these distances and times are used to identify whether there are node pairs that change simultaneously and in the same direction. If the change directions between multiple nodes are consistent and the pressure change difference does not exceed 10%, it is considered that there is a synchronous change performance, and these performances are written into the synchronous change area record table. If the synchronous relationship persists for more than 3 cycles and involves no less than 3 nodes, the group is marked as a stable synchronous change area. Finally, the pressure changes, spatial distribution and trend combinations between nodes in the area are summarized to generate a load change distribution feature dataset.

[0020] The central tendency identification submodule is based on the load change distribution characteristics and calls the structural arrangement characteristics of the load change amplitude and distribution path between nodes to identify whether the load change direction tends to a single centralized path in space. It makes a cumulative judgment on the centralized performance and calculates the clustering coefficient reflecting the load concentration state. The identified synchronous change area is used as the input source, and the pressure change amplitude between its internal nodes and its path structure arrangement information are combined and judged. The system first analyzes the pressure change value of each pair of adjacent nodes to identify whether there are node pairs with consistent directions and similar amplitudes in each path. For example, the pressure of path P1-P2-P3 in the 1st to 2nd cycle and the 4th cycle shows an increasing trend of 0.15, 0.16, and 0.14 MPa respectively. It is considered that the path is a load concentration trend path in the corresponding cycle. Then the system establishes a record of the duration of the concentrated performance of each path in each cycle and calculates whether it appears continuously. If it appears continuously, the continuity factor is assigned to 1.0. If it appears again after an interruption, the continuity factor of this performance is set to 0.5 to attenuate its contribution. The system also counts the number of nodes in each path and converts the number of nodes into the number of node pairs in the path to measure the importance of the path length structure. Then the performance time of all concentrated paths is counted to calculate the clustering coefficient , which is used to reflect whether the load in the entire structural area is concentrated on some paths. The calculation formula is as follows: ; in, is the clustering coefficient (dimensionless), which measures the overall strength of the central tendency of all paths during the observation time; The total monitoring time is in seconds (s), which indicates the total time the system runs and collects data in one identification cycle. For example, if 5 cycles are collected, each cycle is 0.2 seconds, then Second; It indicates the number of all paths with central tendency identified by the system during the identification cycle, for example, path P1-P2-P3 is one; It is The total number of nodes contained in a path, an integer value, representing the complexity of the path topology. For example, the path P1-P2-P3 has 3 nodes, i.e. ; This is for the The number of node pairs in a path, a valid representation of the path length, e.g. 3 nodes contain 2 node pairs; It is The number of times a path is identified as having a central tendency state during the monitoring period. For example, if it is identified as having a central tendency twice in the 1st to 2nd period and the 4th period respectively, then ; It is The path is in its The duration of the secondary concentrated state is in seconds (s). If two consecutive cycles are in the concentrated state, and the cycle length is 0.2 seconds, the duration of the secondary concentrated state is Second; It is The path is in its The time continuity factor in the secondary concentrated state is dimensionless. If the secondary state is continuous with the previous state, then take , if it is interrupted from the previous state, then the attenuation factor is Indicates discontinuity; Indicates the The sum of the concentrated time corrected for continuity in all periods of the path identified as concentrated state; Used to normalize the performance of all paths to the total monitoring time to ensure the final clustering coefficient It is a relative value per unit time, and its value range is usually between.

[0021] The path P1-P2-P3 is the central tendency path identified by the system ( ), the path contains 3 nodes ( , the number of node pairs is 2), there are 2 concentrated performances in the identification cycle, the first one appears continuously in the 1st to 2nd cycle, seconds, the continuity factor is 1.0; the second time appears in the fourth cycle, but is interrupted from the previous one. , seconds, the total monitoring time is seconds, then: ; Final gathering coefficient If the system threshold is set to 0.20, the path enters the high-concentration risk zone and enters the subsequent status marking module for processing. This formula can be extended to multi-path, multi-period, and multi-level centralized trend identification scenarios. All parameters are time dimensions or dimensionless Boolean values with clear and consistent units, ensuring parameter consistency and traceability during project implementation.

[0022] The critical state marking submodule extracts the upper pressure limit data recorded at the same node site based on the clustering coefficient, and determines whether the current clustering state is close to the original bearing limit of the node. If the clustering coefficient is close to the structural boundary, the state of the node area is marked and the central tendency level is divided to obtain the local pressure concentration analysis results. First, the upper pressure limit of each node is extracted from the structural design data. For example, node P1 is a high-strength steel structure with a maximum design pressure of 2.5 MPa. The measured pressure in the current cycle is 2.38 MPa, which has reached 95% of the pressure limit. The system sets the regional grade judgment criteria as green (<80%), yellow (80%-90%), and red (≥90%). Based on the current value, P1 is judged to have entered the red critical zone. The clustering coefficient of the path it is in and its pressure ratio are then combined to determine the value. For example, if the clustering coefficient of P1's path is 0.25, which exceeds the clustering threshold, the system marks the node and the entire path as a "critical concentration zone." If multiple nodes in the same area meet the high pressure ratio and high clustering coefficient conditions simultaneously, the entire area is marked as a severe concentration area. In the output results, the system uses red, orange, and yellow color zoning to identify different grade areas. The pressure value, proportion, and clustering trend value of each node are also listed, forming a final local pressure concentration analysis table for reference during maintenance or structural verification.

[0023] See also Figure 2 and Figure 4 , the response level control module includes: The node fluctuation extraction submodule extracts the change information between the speed control command and the current response speed in the corresponding control record based on the node group marked as critical in the local pressure concentration analysis results. It then calculates the fluctuation frequency and amplitude of the speed response in the node area, and identifies the speed change trend to obtain the speed response fluctuation characteristics. First, read the node number and regional mapping that have entered the high-risk area. For example, the path with a clustering coefficient of 0.25 exceeding the threshold of 0.20 contains node numbers P1, P2, and P3. The system extracts the speed control instruction data and speed response records in the control records of these three nodes in the same time period. The speed control instruction is sent in periodic units, such as once every 0.2 seconds, and the record value is the target speed instruction. For example, in the current cycle, the speed control instruction of the P1 node is 8.0 meters per second, and the response speed record is 7.2 meters per second. The change value is 0.8 meters per second. The response speed change amplitude of each node in the time window is extracted by difference, and the fluctuation frequency within five consecutive cycles is analyzed in sliding window mode. For example, the speed change of the P2 node exceeds ±0.5 meters 3 times in 5 cycles. per second, the fluctuation frequency is 60%. The system sets 0.5 meters per second as the basic fluctuation threshold. The basis is that the equipment speed regulation mechanism responds basically without lag when the change is within 0.3 meters per second, and there are occasional lag fluctuations in the range of 0.4 to 0.6 meters per second. When it is higher than 0.6 meters per second, there will be obvious response deviation. Therefore, 0.5 is set as the minimum fluctuation stability boundary. Then the fluctuation frequency and maximum amplitude of each node are combined and recorded, and the directionality of the speed response sequence is judged. If the speed continues to decrease or increase in three consecutive cycles, it is classified as a unidirectional trend change. If the speed fluctuates alternately up and down, it is determined to be an unstable trend. The fluctuation trend type and time characteristics of each node are further counted to generate a speed regulation response fluctuation feature data set corresponding to each node as the basis for subsequent processing.

[0024] The speed mismatch identification submodule uses the speed regulation response fluctuation characteristics and combines them with the clustering coefficient of the node area to determine whether there is a command mismatch between the speed response trend and the local load concentration state. Based on whether the fluctuation anomaly and load concentration conditions occur simultaneously, the speed level control effectiveness in the area is determined, and the speed response adaptation state analysis results are obtained; First, the response fluctuation value and trend type of each node under the control of the speed control command are obtained, and the corresponding clustering coefficient is matched. For example, the speed control command of node P2 is 8.0 meters per second, the actual response is 7.1 meters per second, the fluctuation value is 0.9 meters per second, the trend is rising fluctuation, and the node clustering coefficient is 0.25, which exceeds the high concentration risk area threshold of 0.20 determined by the system. According to the judgment conditions, the system sets that when the fluctuation value exceeds 0.6 meters per second and the clustering coefficient is greater than 0.20, the node is regarded as having a command mismatch state, of which 0.6 meters per second is the speed response mismatch critical value, which is obtained based on historical data statistics. In more than 90% of the normal operating cycles, the response error is less than 0.6. The situation exceeding this value is often accompanied by mechanical structure impact or electronic control instability. Therefore, it is used as a hard benchmark for identifying abnormal fluctuations, and the clustering coefficient threshold of 0.20 is based on the structural assessment model. The load concentration trend boundary is set. When it is lower than 0.20, the structure does not show significant load concentration. When it exceeds 0.20, the structure has partially entered the high-risk area. The number and distribution range of all nodes that meet this condition are counted. If more than two nodes in a certain area meet the fluctuation anomaly and clustering coefficient anomaly at the same time, the system determines that there is a speed level instruction mismatch problem in the area, and then checks all the corresponding records of instructions and responses. In each cycle, the behavior with an instruction change amplitude greater than 0.5 meters per second but a response lag greater than 0.3 seconds is marked as a serious mismatch behavior, and a graded judgment is made based on the number of consecutive mismatch cycles. For example, if the P1 node shows an increase in speed control instructions but no response or reverse change in speed in three consecutive cycles, its mismatch level is a high-level failure. The system finally summarizes the adaptation status of each node and outputs the speed response adaptation status analysis results of each area for subsequent use.

[0025] The level interval adjustment submodule filters out non-adaptive jump speed instructions based on the speed response adaptation status analysis results, retains the level intervals where the instruction fluctuation amplitude is lower than the fluctuation threshold from the current speed level structure, and uses them as the level range with execution stability to obtain the speed limit level interval; First, read the speed control instruction change records of each node within the specified cycle range, and define the instructions with speed changes greater than 0.5 meters per second per cycle as jump instructions. The threshold is set according to the stable response bandwidth of the drive system to the speed control instruction. In actual operation, when the speed instruction changes within two consecutive cycles are within 0.3 meters per second, the internal servo mechanism of the system can stably maintain a response error of ±0.05 meters per second. When the change amplitude exceeds 0.5 meters per second, the feedback delay begins to increase and may cause step oscillation. Therefore, 0.5 meters per second is used as the jump judgment benchmark. If the speed instruction of node P2 in five consecutive cycles is 6.0, 6.7, 6.1, 6.9, and 7.0 meters per second, the change amplitude is 0.7, 0.6, 0.8, and 0.1 meters per second respectively, and three jump behaviors are marked. If the jump ratio is set to be greater than 50%, it is considered that the node has non-adaptive control behavior. These unsuitable instructions are screened out from all current speed level structures, and the instruction interval with an instruction change amplitude of less than 0.3 meters per second and a response delay of less than 0.1 seconds within the corresponding cycle is marked as a stably executable level interval. The 0.1-second response delay threshold is derived from the controller feedback refresh frequency and the motor minimum response time data. If a certain instruction response delay continues to exceed this value, the system feedback signal will have an integral error expansion, resulting in a decrease in overall control accuracy. For example, the instruction change control of node P3 within the cycle is between 6.0 and 6.2 meters per second, and the response change lag is controlled within 0.08 seconds. In this case, the speed interval is a stable instruction level. The system outputs these level structures as speed limit level intervals for subsequent selection and use by the speed regulation module.

[0026] See also Figure 2 and Figure 5 , the direction concentration discrimination module includes: The angle acquisition submodule collects the angle data between the pressure vector direction of the load contact node and the vehicle's travel direction based on the vehicle's driving state after the speed limit level interval is adjusted. It continuously records the change trajectory of the angle within the time segment, extracts the offset angle and change rate of each detection point within the specified period as basic data, and obtains the angle offset change index; First, collect the load contact node information of the current position of the vehicle body, including the pressure direction vector of each node at the current moment. The direction value is recorded by the three-axis pressure sensor and converted into angle data relative to the forward direction of the vehicle body. For example, if the vehicle body is traveling in the north (0°), and the pressure direction of the P1 node is 30° northeast, then the angle is 30°. Continuously record the angle data of each node to form an angle sequence over time. For example, if the recording period is 0.2 seconds, 5 angle samples can be obtained per second. In 10 seconds, a 50-point angle time series can be formed. Calculate the angle of any detection point in 5 weeks. The difference between the maximum and minimum angles within a period (1 second) is the offset angle. For example, if the angle of node P2 changes from 15° to 37°, the offset angle is 22°. The difference in angles on the time axis is calculated and divided by the number of cycles to get the rate of change. 22° divided by 1 second is a rate of 22° per second. The maximum angle offset and the rate of change of each node are paired to generate an angle fluctuation sample table, and the four indicators of node number, timestamp, offset value, and rate value are used to construct an analysis basic table. Finally, the angle offset change indicators of all detection points in their respective period segments are obtained for subsequent modules to calculate the directional trend aggregation status.

[0027] The factor calculation submodule extracts the offset angle value and change rate data of each detection point based on the angle offset change index, normalizes the degree of aggregation of the directional offset trend in space, and calculates and generates a directional concentration factor to represent the load directional concentration state; First, the offset angle sequence and change rate sequence of each node within a 10-second window are extracted, and their extreme values, means, and variances are counted. Multiple nodes in the same area are combined as directional aggregation units. For example, the angle offsets of nodes P1, P2, and P3 within 5 seconds are 18°, 22°, and 19°, respectively, and the change rates are 20°, 22°, and 21° per second, respectively. The system recognizes that the change trends of these three points are consistent and the offset degrees are similar, and believes that there is directional concentration. According to the definition, the directional concentration factor is used to measure the concentration of the offset directions of multiple nodes. The calculation formula is as follows: ; in, Indicates the directional concentration factor, with a value range of 0 to 1. To detect the number of nodes, For the The angle offset value of each node in the current cycle (unit: degree), is the mean of all node offset angles, and are the maximum and minimum angle offset values in the current node group respectively. This formula evaluates the directional aggregation by normalizing the difference between each node and the overall mean. When the offset angles of all nodes are almost the same, The direction concentration factor is very small and approaches 1, indicating that the direction is highly concentrated. If the offset value is scattered and the difference is close to the extreme, the direction concentration factor approaches 0. In actual application, if the number of nodes is 5 and the angle offset is 18°, 19°, 21°, 22°, and 20°, then , the maximum value is 22°, the minimum value is 18°, the range is 4°, and the contributions of the direction concentration factors of each node are: 1-2 / 4=0.5, 1-1 / 4=0.75, 1-1 / 4=0.75, 1-2 / 4=0.5, 1-0 / 4=1.0, and the average is taken. , and finally output the directional concentration factor D=0.7 and store it in the directional concentration analysis data table of the current period.

[0028] The disturbance judgment submodule uses the directional concentration factor, combines its changing rhythm over multiple detection cycles, and compares it with the set vehicle posture disturbance cycle reference value to determine the potential interference of the fluctuation trend on the vehicle's stable state and obtain the directional concentration offset analysis results. The directional concentration factor is called and combined with the concentration factor sequence of the detection nodes in each path in multiple cycles to form a time trend map. Based on 5 cycles per second, the system analyzes the fluctuation curve of the directional concentration factor with a 10-second window, extracts the number of peaks, rising slope and fluctuation period, and compares the fluctuation rhythm of the directional concentration factor with the vehicle posture disturbance period reference value set by the system. The reference value is set based on the fact that when the vehicle is traveling at a constant speed of 60 kilometers per hour under typical road conditions, the normal posture change frequency of the vehicle body is about 0.6 to 1.2 Hz, corresponding to the disturbance period. The period is 0.8 to 1.7 seconds, and the system uses a disturbance period threshold of 1.2 seconds as the middle reference line. If the concentration factor fluctuation period is less than this threshold and the amplitude change exceeds ±0.3, it is considered that there is abnormal disturbance behavior. For example, within 5 seconds, the directional concentration factor rises from 0.3 to 0.9 and then drops to 0.4, forming a high-frequency pulsation curve with a period of 1 second and an amplitude of 0.6. The system determines it as a directional offset disturbance event. If the event repeats in two consecutive 10-second windows, it forms an interference potential accumulation. The system records the area as a posture disturbance sensitive area, and finally forms the directional concentration offset analysis result.

[0029] See also Figure 2 and Figure 6 , the speed zone window adjustment module includes: The interval query submodule extracts the directional concentration factor value based on the structural state reflected by the directional concentration offset analysis results, searches the window mapping table indexed by the directional concentration factor in the speed control architecture, calls the speed level item corresponding to the current factor value in the table, determines the matched upper and lower limits of the speed balance boundary, and obtains the speed balance interval; Read the directional concentration factor value stored in the system in the current cycle. This value comes from the analysis results of the previous module. For example, if the directional concentration factor of a certain cycle is 0.7, the system uses this factor as an index to retrieve the preset window mapping table in the speed control architecture. The table is established by empirical experiments and simulation results. The structure is a bidirectional mapping relationship between the factor value interval and the corresponding recommended speed level range. For example, if the directional concentration factor is between 0.6 and 0.8, the corresponding speed level is 6.5 to 7.5 meters per second. The system searches for a matching item in the table and finds that the current factor 0.7 is within this interval, confirming that the matching item is the speed lower limit 6 0.5 meters per second and an upper limit of 7.5 meters per second. This window is the speed balance interval of this cycle. The factor interval division in the window table is based on the stability distribution probability of the vehicle speed response under each factor in the experimental statistics. When the concentration factor is greater than 0.85, most paths show extremely concentrated directions, and the speed needs to be compressed to below 5.5 meters per second to slow down the propagation of disturbances. If the concentration factor is lower than 0.3, it is determined that the direction changes are irregular, and the corresponding speed control value can be increased to above 9.0 meters per second. The upper and lower limits of the speed balance boundary of this cycle are obtained through system table retrieval, and the matching result is the speed balance interval.

[0030] The boundary reconstruction submodule extracts the upper and lower boundary settings corresponding to the current control speed level based on the speed balance interval, and determines whether there is a speed instruction beyond the balance interval range in the original level boundary. If there is a deviation item, the speed level structure is reconstructed based on the balance interval to obtain the speed level boundary adjustment result; First, the vehicle speed level structure in the control strategy for the current cycle is extracted. For example, the original speed level structure defines an executable range of 6.0 to 8.5 meters per second. The speed balance interval obtained by the table lookup in the current cycle is 6.5 to 7.5 meters per second. The system then compares the upper and lower boundaries of the original speed level structure with the current cycle balance boundary to determine whether any speed command exceeds this range. If 6.0 meters per second is below the lower limit of 6.5 meters per second, it is judged as a lower boundary violation. If 8.5 meters per second is above the upper limit of 7.5 meters per second, it is judged as an upper boundary violation. Based on the over-limit judgment logic, the system determines that the current speed level structure does not meet the latest balance state requirements. The system then uses the balance interval as the new boundary basis and adjusts the upper and lower limits of the speed level structure to 6.5 meters per second and 7.5 meters per second. The reconstructed speed level boundary structure is recorded as the new speed strategy template for this cycle. The boundary reconstruction judgment is based on the fact that when any command in the original control speed level exceeds the balance interval range, it is considered an unstable boundary that needs to be updated, rather than a uniform compression or translation operation. The reconstruction result establishes sub-intervals according to the new boundaries.

[0031] The instruction screening submodule calls the speed level boundary adjustment result, identifies all speed instructions in the original speed control level that are not within the adjusted boundary range, and removes them from the speed scheduling path, retaining only the instructions within the reconstructed boundary to obtain the speed control window; The speed level boundary adjustment result is called to identify and screen all instructions in the original speed control level structure one by one. For example, the original instruction set is 6.0, 6.4, 6.7, 7.2, 7.5, 7.9, and 8.2 meters per second. The adjusted boundary range is 6.5 to 7.5 meters per second. The system compares each instruction in sequence to see if it is within the boundary. The four instructions 6.0, 6.4, 7.9, and 8.2 are not within the range and are identified as items to be eliminated. The system establishes a list of instructions to be excluded and removes them from the scheduling path table. The remaining three instructions 6.7, 7.2, and 7.5 meet the boundary constraints and are retained as the controllable instruction set for this cycle. The system excludes all instructions that are not cycle-adaptive based on the boundary screening principle to avoid the risk of speed regulation interference and directional offset resonance, and finally outputs the control instruction sequence that remains within the speed level boundary as the currently valid speed control window.

[0032] See also Figure 2 and Figure 7 , the operation status feedback module includes: The posture recognition submodule collects data from the slope sensor at the vehicle's current position and the wheel-side load sensor based on the speed control range defined by the speed limit level interval and the speed control window. It identifies the current structural posture change area and determines whether there is posture instability caused by uneven load, thereby obtaining the posture instability area identification result. First, read the upper and lower limit values of the vehicle's speed level in the current cycle. For example, the control window in the current cycle is 6.5 meters per second to 7.5 meters per second. Combined with this speed range, the current slope sensor and wheel load sensor data are synchronously extracted from the vehicle control system. The slope sensor records the longitudinal or lateral tilt angle of the vehicle. For example, the current slope value is 4.5 degrees. The wheel load sensor records the corresponding vertical load of each wheel. For example, the front left wheel is 3800 Newtons, the front right wheel is 2950 Newtons, the rear left wheel is 3400 Newtons, and the rear right wheel is 3400 Newtons. By calculating the left and right load difference, it is determined whether the load is symmetrical. The current left and right front wheel load difference is 850 Newtons, and the rear wheel difference is 0 Newton. The system is set The front wheel load symmetry tolerance threshold is set to 600 Newtons. The current difference exceeds this threshold, and it is determined that there is currently an uneven load. Combined with the fact that the vehicle slope value exceeds the 3.5-degree stable posture benchmark set by the system, the system considers that the current structural posture change area is an unstable state area. In the posture stability judgment, the slope judgment adopts an interval judgment strategy. Less than 3.5 degrees is a stable posture, 3.5 to 5.5 degrees is a critical posture, and greater than 5.5 degrees is a significantly unstable state. For uneven load, 500 Newtons per wheel is the normal fluctuation range, and more than 800 Newtons is a strong interference area. Finally, the front axle area including the front left and front right wheels is identified as the posture unstable area, and the structural posture unstable area recognition result is output.

[0033] The level matching submodule uses the posture unstable area identification results to determine the adaptability between the speed command and the current slope value and load distribution in the target area. It then selects a set of speed levels that can maintain structural stability under the joint constraints of direction deviation and pressure aggregation to obtain an adaptive speed level group. The recognition result of the unstable posture area is called to judge the adaptability between the speed command and the current slope value and load distribution in the target area. First, the position number of each wheel and the corresponding slope and load data in the target unstable area are read. For example, the slope of the front left wheel is 4.5 degrees and the load is 3800 Newtons, and the slope of the front right wheel is 4.5 degrees and the load is 2950 Newtons. The system extracts all speed commands from the current cycle speed control candidate set, such as 6.5, 6.7, 7.0, 7.2, and 7.5 meters per second, calls the matching calculation model one by one, and compares the vehicle speed caused by the combination of slope and load at each speed. The body roll trend response, for example, at 7.5 meters per second, the roll model predicts that the front left wheel load reduction rate is 15%, and the front right wheel load increase rate is 23%, which exceeds the system-set allowable load fluctuation threshold of 20%. The system determines that the speed is not suitable. At 6.5 meters per second, the change rate on both sides is within 10%, which is marked as an adaptive speed. The system evaluates all candidate speed instructions in turn to form a set of speed levels that meet the direction offset and pressure aggregation constraints in the current cycle and do not trigger posture instability. Finally, three instructions such as 6.5, 6.7, and 7.0 are selected to form an adaptive speed level group and output.

[0034] The command generation submodule synchronizes the executable level to the vehicle control command interface based on the adaptive speed level group, overwriting all level commands in the original speed control path that are not in the set, and uses the adaptive level as the current upper and lower speed limits of the vehicle to obtain the vehicle speed control command; First, read the adaptive speed level group list obtained in the previous step, for example, 6.5, 6.7, and 7.0 meters per second, and cross-check and screen it with all the speed level instructions in the scheduling table in the current control path. The original path contains continuous level instructions from 6.0 to 8.0 meters per second, a total of 11 levels. The 8 instruction items that are not in the adaptability set are eliminated one by one, including 6.0, 6.2, 6.4, 7.2, 7.4, 7.6, 7.8, and 8.0 meters per second. The elimination operation is completed by setting the control path mask flag. The system sets each non-adaptive speed bit in the scheduling mapping matrix to a prohibited scheduling state, and at the same time constructs the remaining three adaptive speed levels into a new cycle schedulable set, and simultaneously sets 6.5 meters per second as the current speed lower limit and 7.0 meters per second as the current speed upper limit. The speed control instruction file structure of the vehicle control interface is generated and transmitted to the speed control main control module for actual instruction issuance.

[0035] A whole vehicle control method for an industrial vehicle is provided. The whole vehicle control method for an industrial vehicle is performed based on the whole vehicle controller of the industrial vehicle, and comprises the following steps: S1: Obtain feedback data from pressure sensors at the frame support locations, including wheel-side connection points and axle ends, monitor the load change trend at the nodes, determine whether a critical concentration situation has formed, and generate local pressure concentration analysis results; S2: Based on the node groups classified as critical in the local pressure concentration analysis results, compare the clustering coefficient change trajectory to determine whether there is a mismatch trend in the speed command and obtain the speed limit level range; S3: Based on the vehicle driving state after the speed limit level interval is adjusted, the angle between the pressure vector and the vehicle running direction is continuously sampled, the directional concentration factor is calculated, and the directional concentration offset analysis result is generated by combining the directional periodic change characteristics; S4: Based on the structural state reflected by the directional concentrated offset analysis results, the corresponding balance interval in the speed mapping table is queried, the speed scheduling boundary is reset, and the speed control window is obtained; S5: Based on the speed limit level range and speed control window, the adaptable speed levels are screened and the vehicle speed control command is generated.

[0036] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A vehicle controller for an industrial vehicle, characterized in that: The vehicle controller includes: The pressure concentration monitoring module obtains feedback data from pressure sensors at the frame support parts, including wheel side connection points and axle ends, monitors the load change trend of the nodes, determines whether a critical concentration situation has formed, and generates local pressure concentration analysis results; The response level control module compares the clustering coefficient change trajectory with the node group classified and marked as critical in the local pressure clustering analysis result, determines whether there is a mismatch trend in the speed command, and obtains the speed limit level range; The direction concentration determination module continuously samples the angle between the pressure vector and the vehicle's running direction according to the vehicle's driving state after the speed limit level interval is adjusted, calculates the directional concentration factor, and generates a directional concentration offset analysis result based on the direction periodic change characteristics; The speed zone window adjustment module queries the corresponding balance interval in the speed mapping table based on the structural state reflected by the directional concentrated offset analysis result, resets the speed scheduling boundary, and obtains the speed control window; The running state feedback module screens the adaptable speed levels based on the speed limit level interval and the speed control window, and generates a vehicle speed control instruction.

2. The vehicle controller for an industrial vehicle according to claim 1, characterized in that: The local pressure concentration analysis results include the position mark of the load-intensive area, the critical state classification of the concentration coefficient, and the load distribution concentration trend level. The speed limit level interval includes the low response level boundary, the jump frequency control constraint, and the speed level elimination list. The directional concentration offset analysis results include the directional concentration factor value, the offset rate change index, and the structural disturbance risk type. The speed control window is specifically the speed balance interval boundary value, the conflict speed level exclusion table, and the speed scheduling section adjustment parameter. The vehicle speed control instruction includes the executable speed level group, the load structure matching status label, and the vehicle speed regulation execution control parameter.

3. The vehicle controller for an industrial vehicle according to claim 1, characterized in that: The pressure accumulation monitoring module includes: The load change acquisition submodule acquires data from pressure sensors at the wheel-side connection points and axle ends in the frame support area, collects load value change information at the detection nodes within a set period, integrates the data differences between nodes in a time series, and, based on the distance structure between nodes, extracts the synchronous performance of load changes in space and determines whether there is a concentrated direction of load changes, thereby generating load change distribution characteristics. The central tendency identification submodule, based on the load change distribution characteristics, calls the structural arrangement characteristics of the load change amplitude and distribution path between nodes, identifies whether the load change direction tends to a single centralized path in space, makes a cumulative judgment on the centralized performance, and calculates the clustering coefficient reflecting the load concentration state; The critical state marking submodule extracts the upper pressure limit data recorded at the same node site based on the clustering coefficient, and determines whether the current clustering state is close to the original bearing limit of the node. If the clustering coefficient is close to the structural boundary, the state of the area where the node is located is marked and the central tendency level is divided to obtain the local pressure concentration analysis results.

4. The vehicle controller for an industrial vehicle according to claim 3, characterized in that: To calculate the clustering coefficient C, the formula is used: Among them, T obs is the total monitoring time, N is the number of paths with central tendency identified during the identification period, n i is the total number of nodes included in the i-th path, n i -1 is the number of node pairs in the i-th path, M i is the number of times the i-th path is identified as having a central tendency state during the monitoring period, is the length of time that the i-th path remains in its j-th concentrated state, λ ij is the time continuity factor of the i-th path in its j-th concentrated state, Used to normalize the performance of all paths to the total monitoring time.

5. The vehicle controller for an industrial vehicle according to claim 1, characterized in that: The response level control module includes: The node fluctuation extraction submodule extracts the change information between the speed control command and the current response speed in the corresponding control record based on the node group marked as critical in the local pressure concentration analysis results, calculates the fluctuation frequency and amplitude of the speed response in the area where the node is located, and summarizes and identifies the speed change trend to obtain the speed control response fluctuation characteristics; The speed mismatch identification submodule uses the speed regulation response fluctuation characteristics and combines the clustering coefficient of the node area to determine whether there is a command mismatch between the speed response trend and the local load concentration state. Based on whether the fluctuation anomaly and the load concentration condition occur at the same time, the speed level control effectiveness in the area is determined to obtain the speed response adaptation state analysis results. The level interval adjustment submodule filters out non-adaptive jump speed instructions based on the speed response adaptation state analysis results, retains the level intervals in which the instruction fluctuation amplitude is lower than the fluctuation threshold from the current speed level structure as the level range with execution stability, and obtains the speed limit level interval.

6. The vehicle controller for an industrial vehicle according to claim 1, characterized in that: The direction concentration discrimination module includes: The angle acquisition submodule collects the angle data between the pressure vector direction of the load contact node and the vehicle's travel direction based on the vehicle's driving state after the speed limit level interval is adjusted, continuously records the change trajectory of the angle within a time segment, extracts the offset angle and change rate of each detection point within a specified period as basic data, and obtains the angle offset change index; The factor calculation submodule extracts the offset angle value and change rate data of each detection point based on the angle offset change index, performs normalization judgment on the degree of aggregation of the directional offset trend in space, and calculates and generates a directional concentration factor for representing the load directional concentration state; The disturbance judgment submodule calls the directional concentration factor, combines the changing rhythm of the directional concentration factor within multiple detection cycles, compares it with the set vehicle posture disturbance cycle reference value, determines the interference potential of the fluctuation trend on the vehicle body stability, and obtains the directional concentration offset analysis result.

7. The vehicle controller for an industrial vehicle according to claim 6, characterized in that: For calculating the directivity concentration factor D, the formula is used: Among them, Q is the number of detection nodes, θ k is the angle offset value of the kth node in the current cycle, is the mean of all node offset angles, θ max and θ min They are the maximum and minimum angle offset values in the current node group respectively.

8. The vehicle controller for an industrial vehicle according to claim 1, characterized in that: The speed zone window adjustment module includes: The interval query submodule extracts the directional concentration factor value based on the structural state reflected by the directional concentration offset analysis result, searches the window mapping table indexed by the directional concentration factor in the speed control architecture, calls the speed level item corresponding to the current factor value in the table, determines the matched upper and lower limits of the speed balance boundary, and obtains the speed balance interval; The boundary reconstruction submodule extracts the upper and lower boundary settings corresponding to the current control speed level based on the speed balance interval, and determines whether there is a speed instruction beyond the balance interval range in the original level boundary. If there is a deviation item, the speed level structure is reconstructed based on the balance interval to obtain the speed level boundary adjustment result; The instruction screening submodule calls the speed level boundary adjustment result, identifies all speed instructions in the original speed control level that are not within the adjusted boundary range, and removes them from the speed scheduling path, retaining only the instructions within the reconstructed boundary to obtain the speed control window.

9. The vehicle controller for an industrial vehicle according to claim 1, characterized in that: The operating status feedback module includes: The posture recognition submodule collects slope sensor data and wheel-side load sensor data at the vehicle's current position based on the speed control range defined by the speed limit level interval and the speed control window, identifies the current structural posture change area, and determines whether there is posture instability caused by uneven load, thereby obtaining a posture instability area recognition result; The level matching submodule calls the posture unstable area identification result, judges the adaptability between the speed command and the current slope value and load distribution in the target area, selects a speed level set that can maintain structural stability under the joint constraints of direction deviation and pressure aggregation, and obtains an adaptive speed level group; The instruction generation submodule synchronizes the executable level to the instruction interface of the vehicle control according to the adaptive speed level group, covering all level instructions in the original speed control path that are not in the set, and uses the adaptive level as the current upper and lower speed limit boundaries of the vehicle to obtain the vehicle speed control instruction.

10. A whole vehicle control method for an industrial vehicle, characterized in that: The vehicle controller of an industrial vehicle according to any one of claims 1 to 9 is implemented, comprising the following steps: S1: Obtain feedback data from pressure sensors at the frame support locations, including wheel-side connection points and axle ends, monitor the load change trend at the nodes, determine whether a critical concentration situation has formed, and generate local pressure concentration analysis results; S2: Based on the node group classified and marked as critical in the local pressure concentration analysis result, compare the clustering coefficient change trajectory to determine whether there is a mismatch trend in the speed command, and obtain the speed limit level range; S3: Based on the vehicle driving state after the speed limit level interval is adjusted, continuously sampling the angle between the pressure vector and the vehicle running direction, calculating the directional concentration factor, and generating a directional concentration offset analysis result based on the directional periodic change characteristics; S4: Based on the structural state reflected by the directional concentrated offset analysis result, query the corresponding balance interval in the speed mapping table, reset the speed scheduling boundary, and obtain the speed control window; S5: Based on the speed limit level interval and the speed control window, the adaptable speed levels are screened and a vehicle speed control instruction is generated.

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