A crawler-type articulated vehicle braking system for use in low-temperature environments

Abstract

The technical solution of the present invention relates to the field of control and regulation technology, specifically a tracked articulated vehicle braking system for use in low-temperature environments. The present invention solves the problems of imbalanced dynamic distribution of braking force, insufficient redundant fault tolerance and signal distortion under low-temperature conditions through multi-module collaborative optimization and closed-loop control mechanism. The data acquisition module acquires the articulation angle, inertial measurement unit posture, brake temperature and vehicle status signal in real time; the optimization control module performs multi-objective optimization on the dynamic weight coefficient, redundant switching threshold and fuzzy rule parameters based on the genetic algorithm to generate a weight distribution scheme for the articulation angle compensation mode and the inertial measurement unit dominant mode; the adjustment module generates a brake pressure reference value through temperature adaptive analysis and synchronizes the front and rear vehicle hydraulic control units. Each module significantly improves the dynamic stability and low-temperature fault tolerance of the braking system through full-link collaboration of environmental perception, parameter iteration and redundant execution.

Description

Technical Field

[0001] The present invention relates to the field of control and regulation technology, and in particular to a crawler-type articulated vehicle braking system used in a low-temperature environment. Background Art

[0002] When tracked articulated vehicles operate in low-temperature environments, their multi-degree-of-freedom motion coupling characteristics and the multiple constraints imposed by the low-temperature environment on mechanical and hydraulic systems create complex dynamic control problems. Because low temperatures cause changes in material stiffness, deterioration in lubrication performance, and abnormal actuator transmission clearances, existing open-loop or single feedback control modes struggle to dynamically compensate for nonlinear disturbances caused by articulated steering and load changes. At the same time, sensor signal drift and actuator response lag in low-temperature environments weaken the accuracy of system state identification, making it impossible for fixed-threshold-based control strategies to effectively coordinate the motion trajectories and torque distribution of the front and rear vehicles, leading to vehicle steering instability or traction loss. To address these issues, a multi-mode control architecture with adaptive environmental parameters is required, integrating multi-source state perception and dynamic parameter identification algorithms to construct an actuator collaborative control mechanism with redundant fault tolerance, thereby enabling real-time decision-making and adaptive reconstruction of vehicle kinematic and dynamic constraints under working conditions.

[0003] In low-temperature environments, the braking system of tracked articulated vehicles experiences performance degradation of hydraulic and mechanical components due to the low-temperature effect. The core technical pain point lies in the inability of existing control architectures to achieve dynamic coordination and redundant fault tolerance across multiple brake units. Existing braking solutions rely on independent control logic for the front and rear vehicles. Under low-temperature conditions, the control parameters of each unit are susceptible to deviations due to environmental disturbances, leading to unbalanced braking force distribution and an inability to compensate for actuator response delays caused by low temperatures through real-time interaction. Furthermore, the separate control mechanisms for parking and service brakes weaken the system's state monitoring and fault reconstruction capabilities. When a single control link fails due to low temperatures, there is a lack of an adaptive redundant switching strategy, rendering the system unable to maintain dynamic stability and emergency braking capabilities in low-temperature and sudden failure scenarios. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a tracked articulated vehicle braking system for use in low-temperature environments, which is used to solve the problems of imbalance in dynamic distribution of braking force, insufficient fault tolerance for low-temperature failures, and instability of emergency braking functions caused by the lack of coordination and weak redundancy of the control architecture of multiple braking units of tracked articulated vehicles in low-temperature environments.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] The present invention provides a crawler-type articulated vehicle braking system for use in a low-temperature environment, comprising:

[0007] A data acquisition module is used to acquire sensor data, including articulation angle sensor signals, brake pedal displacement signals, pitch and yaw angle data output by the inertial measurement unit, vehicle slope signals, gear status signals, and brake component temperature data;

[0008] An optimization control module receives sensor data and uses a genetic algorithm to perform multi-objective optimization of dynamic weight coefficients, redundant switching thresholds, fuzzy rule parameters, thermal management parameters, and the Kalman filter noise covariance matrix. The multi-objectives include brake pressure deviation, yaw stability index, and temperature maintenance energy consumption.

[0009] An adjustment module is used to analyze the driver's intention based on the optimized dynamic weight coefficient, generate a brake pressure reference value, and transmit the reference value to the pre-installed leading vehicle hydraulic control unit and the trailing vehicle hydraulic control unit via a communication bus;

[0010] An execution module is used to switch to the backup control core based on the optimized redundancy switching threshold and the reference value of the adjustment module when the main control link communication is abnormal, and to call the autonomous braking curve parameters generated by the optimization control module to control the hydraulic control unit of the following vehicle;

[0011] A decision module, configured to generate a parking brake force release timing instruction in coordination with a preset electronic parking brake controller and a vehicle controller based on optimized fuzzy rule parameters, including a temperature compensation coefficient and a priority mapping relationship associated with slope;

[0012] A thermal management module, used to adjust the heating power of the heating tape based on the optimized thermal management parameters, and input the temperature compensation parameters into the temperature compensation interface of the Kalman filter module;

[0013] The Kalman filter module is connected to the data acquisition module and the adjustment module. It is used to adjust the noise covariance matrix according to the temperature compensation parameters of the thermal management module, fuse the multi-source sensor data and output the dynamic load distribution to the adjustment module.

[0014] Furthermore, the present invention is applied to a braking system of a tracked articulated vehicle in a low-temperature environment, and the optimization control module includes:

[0015] The dynamic weight optimization unit is connected to the data acquisition module and is used to iteratively optimize the weight coefficients of the articulation angle, pedal displacement, and inertial measurement unit data using a genetic algorithm, using the deviation between the reference value of brake pressure and the actual demand as the fitness function. The optimized weight coefficients are then output to the redundancy strategy optimization unit and the adjustment module.

[0016] a redundant strategy optimization unit connected to the dynamic weight optimization unit and the execution module, configured to optimize the master-slave switching threshold and the autonomous braking curve triggering condition using the weight coefficient output by the dynamic weight optimization unit as the initial constraint condition and communication delay, yaw angle stability, and braking distance as the multi-objective fitness function, and transmit the switching threshold parameters to the master-slave switching interface of the execution module via the communication bus;

[0017] a fuzzy rule evolution unit, connected to the decision module and the electronic parking brake controller, for optimizing the fuzzy membership function parameters and rule base structure based on the parking brake force release timing error and the mechanical interference probability as constraints, and writing the updated rule table to the electronic parking brake controller via the CAN bus of the vehicle control system;

[0018] Among them, the weight coefficient output by the dynamic weight optimization unit serves as the initial input constraint of the redundant strategy optimization unit; the trigger condition of the autonomous braking curve generated by the redundant strategy optimization unit is bound to the backup control core parameters of the execution module; and the rule table output by the fuzzy rule evolution unit is dynamically updated through the firmware interface of the electronic parking brake controller.

[0019] Furthermore, the present invention is applied to a brake system of a tracked articulated vehicle in a low-temperature environment, and the adjustment module includes:

[0020] Ambient temperature sensor, integrated into the data acquisition module, is used to collect temperature data of the brake components in real time;

[0021] a multimodal analysis submodule, connected to the ambient temperature sensor and the dynamic weight optimization unit, configured to match a weight mode based on ambient temperature sensor data, enable an articulation angle compensation mode when the temperature is below a preset low-temperature threshold, enable an inertial measurement unit data-dominated mode within a preset temperature range, and transmit a mode selection instruction to the reference value correction submodule via an internal bus;

[0022] The reference value correction submodule is connected to the leading vehicle hydraulic control unit, the trailing vehicle hydraulic control unit, and the dynamic weight optimization unit, and is used to load the weight coefficient output by the dynamic weight optimization unit of the optimization control module into the leading vehicle hydraulic control unit, synchronously update the reference value parameter library of the trailing vehicle hydraulic control unit via the communication bus, and input the corrected brake pressure reference value into the communication monitoring unit of the execution module;

[0023] Among them, the mode selection instruction of the multimodal analysis submodule triggers the weight coefficient loading operation of the reference value correction submodule; the reference value parameter library update of the leading vehicle hydraulic control unit and the trailing vehicle hydraulic control unit shares the communication bus resources with the spare core activation unit of the execution module.

[0024] Furthermore, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the execution module includes:

[0025] Wheel speed sensor, integrated into the data acquisition module, is used to collect the wheel speed signal of the rear vehicle in real time;

[0026] The communication monitoring unit is connected to the communication bus and the reference value correction submodule of the adjustment module. It is used to count the packet loss rate and delay time of the communication bus. When the cumulative signal loss exceeds the set threshold, it triggers the autonomous decision instruction and transmits the bus status data to the standby core activation unit through the internal interrupt signal;

[0027] The standby core activation unit is connected to the communication monitoring unit, the reference value correction submodule, and the rear vehicle hydraulic control unit. It is used to call the reference value correction algorithm generated by the reference value correction submodule of the adjustment module, interpolate the missing control instructions based on the wheel speed sensor data, and transmit the interpolation instructions to the redundant control interface of the rear vehicle hydraulic control unit via the CAN bus.

[0028] The bus status data of the communication monitoring unit triggers the start of the interpolation algorithm of the standby core activation unit; the interpolation instruction generated by the standby core activation unit is synchronously bound to the reference value correction parameter of the adjustment module through a timestamp.

[0029] Furthermore, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the decision module includes:

[0030] The vehicle controller is preset in the vehicle control system and is used to provide the vehicle's real-time slope and gear status signals;

[0031] The historical hysteresis database is stored in the data acquisition module and is used to record the brake mechanism action delay data;

[0032] A low-temperature hysteresis compensation unit is connected to the thermal management module and the fuzzy rule evolution unit, and is used to superimpose a compensation coefficient on the fuzzy rule output based on the temperature data output by the thermal management module and the delay data in the historical hysteresis database, and transmit the compensation coefficient to the timing priority mapping unit through the internal data bus;

[0033] a timing priority mapping unit connected to the vehicle controller and the electronic parking brake controller, for dynamically adjusting the parking brake force release timing based on a compensation coefficient according to the vehicle slope and gear status provided by the vehicle controller, and transmitting the release timing instruction to the electronic parking brake controller via the CAN bus of the vehicle control system;

[0034] Among them, the fuzzy rule parameters received by the low-temperature hysteresis compensation unit come from the fuzzy rule evolution unit of the optimization control module; the adjustment logic of the timing priority mapping unit is synchronized with the real-time data of the vehicle controller through timestamps; and the release timing instruction triggers the firmware execution program of the electronic parking brake controller.

[0035] Furthermore, the present invention is applied to a braking system of a tracked articulated vehicle in a low-temperature environment, and the thermal management module includes:

[0036] The rear vehicle insulation compartment thermostat is installed in the brake assembly compartment of the rear vehicle body and is used to receive temperature control instructions and adjust the working status of the heating belt and semiconductor refrigeration plate;

[0037] Thermal management optimization unit, integrated into the optimization control module, is used to receive deformation compensation parameters and update thermal management strategies;

[0038] The hierarchical heating unit is connected to the data acquisition module and the thermostat of the rear vehicle insulation compartment. It is used to start the heating tape at full power when the temperature is lower than the preset low temperature threshold according to the temperature data of the data acquisition module, switch to the temperature balancing mode of the semiconductor refrigeration chip in the preset temperature range, and transmit the temperature control command to the thermostat of the rear vehicle insulation compartment through the CAN bus;

[0039] The deformation compensation unit is connected to the optimization control module and the historical deformation database, and is used to dynamically adjust the compensation threshold of the deformation mechanism according to the stress-strain curve of the sealing material and the historical deformation database, and feed back the output parameters to the thermal management optimization unit through the communication bus;

[0040] Among them, the semiconductor refrigeration plate control signal of the hierarchical heating unit is bound to the temperature balancing logic of the thermostat of the rear vehicle insulation compartment; the output parameters of the deformation compensation unit trigger the genetic algorithm parameter iteration of the thermal management optimization unit.

[0041] Furthermore, the present invention is applied to a tracked articulated vehicle braking system in a low-temperature environment, wherein the Kalman filter module includes: a multimodal analysis submodule, disposed within the adjustment module, for receiving dynamic load distribution data and analyzing a multi-source sensor weight pattern;

[0042] The temperature compensation interface unit is connected to the thermal management module and the hierarchical heating unit, and is used to adjust the process noise covariance weight according to the temperature gradient data output by the thermal management module, and input the adjusted noise covariance matrix parameters into the dynamic load feedback unit;

[0043] The dynamic load feedback unit is connected to the temperature compensation interface unit and the multimodal analysis submodule, and is used to perform fusion calibration on the optimized load distribution data based on the noise covariance matrix parameters, and transmit the data to the multimodal analysis submodule of the adjustment module via the SPI bus;

[0044] Among them, the temperature gradient data of the temperature compensation interface unit comes from the graded heating unit of the thermal management module; the fusion calibration logic of the dynamic load feedback unit and the weight mode of the multimodal analysis submodule are synchronized through timestamps; the calibrated load distribution data triggers the baseline value correction operation of the adjustment module.

[0045] Furthermore, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the redundancy strategy optimization unit further includes:

[0046] The simulation environment server is deployed on the cloud platform of the vehicle control system to simulate intermittent connection failure scenarios of the communication bus;

[0047] The OTA upgrade module is integrated into the rear vehicle hydraulic control unit of the execution module and is used to receive and write autonomous braking curve parameters;

[0048] The hardware-in-the-loop test unit is connected to the simulation environment server and the standby core activation unit of the execution module, and is used to inject intermittent connection failures of the communication bus into the simulation environment, generate a Pareto front solution set, and transmit the solution set to the parameter linkage unit via Ethernet;

[0049] The parameter linkage unit is connected to the hardware-in-the-loop test unit, the OTA upgrade module, and the standby core activation unit. It is used to write the autonomous braking curve parameters generated by the hardware-in-the-loop test unit to the follower vehicle hydraulic control unit via OTA, and share the master-slave switching threshold data with the standby core activation unit of the execution module via the FlexRay bus;

[0050] Among them, the Pareto solution generated by the hardware-in-the-loop test unit triggers the dynamic parameter update of the parameter linkage unit; the parameter linkage unit and the spare core activation unit ensure the consistency of the switching threshold data through the timestamp synchronization mechanism; the write operation of the OTA upgrade module is compatible with the redundant control interface protocol of the rear vehicle hydraulic control unit.

[0051] Furthermore, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the low-temperature hysteresis compensation unit further comprises:

[0052] The screw mechanism sensor is integrated into the data acquisition module and is used to collect the displacement and torque data of the screw mechanism in real time;

[0053] The screw mechanism lubrication monitoring submodule is connected to the screw mechanism sensor and is used to calculate the action delay based on the displacement and torque data of the screw mechanism, generate a historical hysteresis database, and input the delay data into the compensation coefficient update submodule through the SPI bus;

[0054] The compensation coefficient updating submodule is connected to the fuzzy rule evolution unit and the screw mechanism lubrication monitoring submodule, and is used to dynamically adjust the compensation coefficient based on the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module and the historical hysteresis data, and feed back the updated compensation coefficient to the rule base learning interface of the fuzzy rule evolution unit through the CAN bus;

[0055] Among them, the delay calculation logic of the screw mechanism lubrication monitoring submodule is bound to the historical hysteresis database storage rules; the parameter adjustment of the compensation coefficient update submodule triggers the iterative optimization of the membership function of the fuzzy rule evolution unit.

[0056] Furthermore, the present invention is applied to a brake system of a tracked articulated vehicle in a low-temperature environment, and the staged heating unit further comprises:

[0057] The semiconductor refrigeration chip array is integrated into the thermostat of the rear vehicle insulation compartment to perform temperature balance control;

[0058] A parameter self-learning unit is provided in the thermal management module and is used to update thermal management parameters according to the early warning signal;

[0059] The local overheating suppression submodule is connected to the semiconductor refrigeration chip array and the Kalman filter module. It is used to balance the temperature distribution in the insulation chamber through the semiconductor refrigeration chips and transmit the real-time temperature data to the seal aging warning submodule through the I2C bus.

[0060] The seal aging warning submodule is connected to the deformation compensation unit and the parameter self-learning unit, and is used to predict the seal life according to the deformation threshold parameter output by the deformation compensation unit and transmit the warning signal to the parameter self-learning unit via the FlexRay bus;

[0061] Among them, the temperature balancing logic of the local overheating suppression submodule is bound to the control signal of the semiconductor refrigeration array; the life prediction model of the seal aging warning submodule is generated based on the historical learning curve of the deformation threshold parameter; the update operation of the parameter self-learning unit triggers the iterative optimization of the thermal management parameters of the optimization control module.

[0062] Beneficial effects of the present invention:

[0063] The present invention significantly improves the dynamic stability and redundancy fault tolerance of the braking system of a tracked articulated vehicle in low-temperature environments through multi-module collaborative optimization and closed-loop control mechanisms. The data acquisition module acquires multi-source sensor signals in real time, while the optimization control module dynamically adjusts weight coefficients and redundancy strategy parameters based on a genetic algorithm to address the imbalance in sensor signal priority distribution caused by low temperatures. The adjustment module dynamically adapts the braking force distribution between the front and rear vehicles through temperature-adaptive multimodal analysis and baseline value correction. The execution module triggers the backup core activation mechanism when the main control link is abnormal, and generates redundant control instructions based on wheel speed data interpolation to ensure the continuity of the braking function. The thermal management module suppresses the degradation of the sealing material performance through graded heating and deformation compensation, and the Kalman filter module adjusts the noise model based on the temperature gradient to improve the reliability of signal fusion. The low-temperature hysteresis compensation unit corrects the fuzzy rule output based on historical action delay data, and coordinates the timing priority mapping to optimize the parking brake release logic. Through the full-link collaboration of environmental perception, parameter iterative optimization, and redundant execution, the above technical solution effectively addresses the problems of unbalanced braking force distribution, insufficient low-temperature failure tolerance, and emergency braking instability in low-temperature conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0065] Figure 1 This is a system architecture diagram of a tracked articulated vehicle braking system for use in low-temperature environments, provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0067] See also Figure 1 The present invention provides a crawler-type articulated vehicle braking system for use in a low-temperature environment, comprising:

[0068] Data acquisition module, used to obtain articulation angle sensor signals, brake pedal displacement signals, pitch and yaw angle data output by the inertial measurement unit, vehicle slope signals, gear status signals, and brake component temperature data;

[0069] An optimization control module, connected to the data acquisition module, receives sensor data and uses a genetic algorithm to perform multi-objective optimization on dynamic weight coefficients, redundant switching thresholds, fuzzy rule parameters, thermal management parameters, and the Kalman filter noise covariance matrix. The multi-objectives include brake pressure deviation, yaw angle stability index, and temperature maintenance energy consumption.

[0070] an adjustment module connected to the optimization control module, configured to analyze the driver's intention based on the optimized dynamic weight coefficient, generate a brake pressure reference value, and transmit the reference value to the leading vehicle hydraulic control unit and the trailing vehicle hydraulic control unit via a communication bus;

[0071] An execution module, connected to the optimization control module and the adjustment module, is used to switch to the backup control core based on the optimized redundancy switching threshold and the reference value of the adjustment module when the main control link communication is abnormal, and to call the autonomous braking curve parameters generated by the optimization control module to control the hydraulic control unit of the following vehicle;

[0072] a decision module, connected to the optimization control module and the execution module, for generating a parking brake force release timing instruction in coordination with a preset electronic parking brake controller and a vehicle controller based on optimized fuzzy rule parameters, wherein the fuzzy rule parameters include a temperature compensation coefficient and a priority mapping relationship associated with a slope;

[0073] A thermal management module connected to the data acquisition module and the Kalman filter module, used to adjust the heating power of the heating tape based on the optimized thermal management parameters, and input the temperature compensation parameters into the temperature compensation interface of the Kalman filter module;

[0074] The Kalman filter module is connected to the data acquisition module and the adjustment module, and is used to adjust the noise covariance matrix according to the temperature compensation parameters of the thermal management module, fuse the multi-source sensor data and output the dynamic load distribution to the adjustment module;

[0075] Among them, the articulation angle, inertial measurement unit data and temperature data of the data acquisition module are input into the optimization control module; the optimization control module transmits the optimized dynamic weight coefficient to the adjustment module and the execution module; the brake pressure reference value output by the adjustment module is fed back to the execution module; the execution module shares the vehicle status data with the decision module; the temperature compensation parameters of the thermal management module are input into the Kalman filter module; the dynamic load distribution data of the Kalman filter module is reversely input into the adjustment module to form a closed-loop optimization link.

[0076] The present invention provides a crawler-type articulated vehicle braking system for use in low-temperature environments. The technical solution thereof realizes dynamic control and redundant fault tolerance in low-temperature environments through multi-module collaborative optimization.

[0077] The data acquisition module acquires real-time vehicle operating status data from multiple sensors, including signals from the articulation angle sensor, brake pedal displacement, pitch and yaw angle data from the inertial measurement unit (IMU), vehicle slope information, gear position information, and brake component temperature data. The IMU data is used to capture changes in vehicle posture, while brake component temperature data is collected via distributed temperature control probes, providing ambient temperature gradient input for the subsequent thermal management module. All sensor signals are transmitted to the optimization control module via a high-speed communication bus, forming the basis for multimodal data input.

[0078] The optimization control module receives sensor data from the data acquisition module and uses a genetic algorithm to perform multi-objective optimization of the dynamic weight coefficients, redundancy switching thresholds, fuzzy rule parameters, thermal management parameters, and the Kalman filter noise covariance matrix. Specifically, the genetic algorithm uses brake pressure deviation, yaw stability index, and temperature maintenance energy consumption as fitness functions, and iteratively selects the optimal parameter combination through crossover and mutation operations. The dynamic weight coefficient optimization unit generates a set of weight parameters using the mean squared error between the brake pressure baseline and the actual demand as an evaluation metric. The redundancy strategy optimization unit optimizes the master-slave switching threshold using communication delay and braking distance as constraints. The fuzzy rule evolution unit optimizes membership function parameters based on the parking brake release timing error. The optimized parameters are synchronized to the adjustment and execution modules via the communication bus.

[0079] The adjustment module analyzes the driver's intention based on the optimized dynamic weight coefficients and generates a brake pressure baseline value. The multimodal analysis submodule matches the weighting mode based on ambient temperature sensor data. When the temperature falls below a preset low-temperature threshold, the weighting mode prioritizes articulation angle compensation; within a preset temperature range, the mode switches to inertial measurement unit data-based mode. The baseline correction submodule loads the optimized weight coefficients into the leading vehicle's hydraulic control unit and simultaneously updates the baseline parameter library of the trailing vehicle's hydraulic control unit via the communication bus. The corrected brake pressure baseline value is fed back to the execution module in real time as the reference input for redundant control.

[0080] Based on the optimized redundancy switching threshold and the baseline value of the adjustment module, the execution module switches to the backup control core when communication anomalies occur on the primary control link. The communication monitoring unit measures the packet loss rate and latency of the communication bus. When the cumulative signal loss exceeds a set threshold, the backup core activation unit is triggered. The backup core activation unit invokes the baseline value correction algorithm of the adjustment module, interpolating missing control commands based on wheel speed sensor data and transmitting them to the following vehicle hydraulic control unit via a private communication bus. The interpolation algorithm uses a linear regression model, dynamically correcting braking commands based on the wheel speed change rate as input.

[0081] The decision module generates parking brake release timing instructions based on optimized fuzzy rule parameters. The low-temperature hysteresis compensation unit receives temperature data from the thermal management module and superimposes a compensation coefficient based on a historical hysteresis database on the fuzzy rule output to mitigate delays caused by lubrication deterioration in the screw mechanism. The timing priority mapping unit dynamically adjusts the parking brake release priority based on vehicle grade and gear status provided by the vehicle controller. The optimized instructions are transmitted to the electronic parking brake controller via the CAN bus, enabling coordinated control of parking and service braking.

[0082] The thermal management module adjusts the heating power of the heating cable based on optimized thermal management parameters. The staged heating unit triggers the heating strategy based on temperature data from the data acquisition module: when the temperature falls below the preset low-temperature threshold, the heating cable starts operating at full power; within the preset temperature range, it switches to the semiconductor cooling plate temperature balancing mode to prevent local overheating. The deformation compensation unit dynamically adjusts the compensation threshold of the deformation mechanism based on the seal material stress-strain curve and a historical deformation database. The output parameters are fed back to the thermal management optimization unit of the optimization control module, forming a closed-loop parameter self-learning loop.

[0083] The Kalman filter module adjusts the noise covariance matrix based on the temperature compensation parameters of the thermal management module. The temperature compensation interface unit receives temperature gradient data and dynamically increases the process noise covariance weight to suppress inertial measurement unit signal drift in low-temperature environments. After fusing multi-source sensor data, the dynamic load feedback unit transmits the optimized load distribution data via a bus to the multimodal analysis submodule of the adjustment module. This data serves as input for weight mode switching, providing positive feedback on sensor data quality.

[0084] The articulation angle, inertial measurement unit data, and temperature data from the data acquisition module are input into the optimization control module to drive the genetic algorithm parameter optimization;

[0085] The dynamic weight coefficient of the optimization control module is transmitted to the adjustment module to generate a brake pressure reference value and feed it back to the execution module;

[0086] The vehicle status data of the execution module is shared with the decision module to collaboratively generate parking control instructions;

[0087] The temperature compensation parameters of the thermal management module are input into the Kalman filter module, which adjusts the noise model and outputs the dynamic load data to the adjustment module;

[0088] The dynamic load distribution data of the Kalman filter module is input back into the adjustment module to form a closed-loop iterative optimization link.

[0089] Specifically, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the optimized control module includes:

[0090] A dynamic weight optimization unit is used to iteratively optimize the weight coefficients of the articulation angle, pedal displacement, and inertial measurement unit data using a genetic algorithm, using the deviation between the reference brake pressure value and the actual demand as the fitness function;

[0091] The redundant strategy optimization unit is connected to the dynamic weight optimization unit and is used to optimize the master-slave switching threshold and the autonomous braking curve triggering condition using the weight coefficient output by the dynamic weight optimization unit as the initial constraint condition and the communication delay, yaw angle stability and braking distance as the multi-objective fitness function;

[0092] A fuzzy rule evolution unit is used to optimize the fuzzy membership function parameters and rule base structure based on the parking brake force release timing error and mechanical interference probability as constraints;

[0093] The switching threshold parameter of the redundant strategy optimization unit is input into the execution module; the rule table of the fuzzy rule evolution unit is updated to the electronic parking brake controller.

[0094] The optimization control module of the present invention realizes parameter optimization and strategy generation through multi-unit collaboration. Its specific technical solutions and logical relationships are as follows:

[0095] The dynamic weight optimization unit receives data from the data acquisition module regarding articulation angle, brake pedal displacement, and inertial measurement unit (IMU). Using the deviation between the baseline brake pressure and the actual demand as the fitness function, it iteratively optimizes the weight coefficients for each sensor data set using a genetic algorithm. The genetic algorithm initializes a candidate solution set containing the weight coefficients, injects low-temperature disturbance variables into the simulation environment, and uses a crossover mutation operation to select the optimal weight combination that minimizes the mean squared error. The optimized weight coefficients are synchronized to the preceding vehicle's hydraulic control unit via a communication bus and serve as initial constraints for the redundancy strategy optimization unit. Temperature gradient data is input via the thermal management module, driving the dynamic weight optimization unit to switch weight modes between different temperature ranges.

[0096] The redundant strategy optimization unit uses the weight coefficients output by the dynamic weight optimization unit as constraints to construct a multi-objective fitness function encompassing communication latency, yaw stability, and braking distance. This function optimizes the master-slave switching threshold and the triggering conditions for the autonomous braking curve. Intermittent CAN bus connection failures are simulated in a hardware-in-the-loop test platform to generate a Pareto front solution set, identifying the optimal parameters that balance response speed and stability. The optimized master-slave switching threshold is input to the execution module's communication monitoring unit via the parameter linkage unit. The autonomous braking curve parameters are then written to the parameter library of the follower vehicle's hydraulic control unit via over-the-air (OTA) transmission, establishing a dynamic update mechanism for the redundant control strategy.

[0097] The fuzzy rule evolution unit optimizes the fuzzy membership function parameters and rule base structure using the parking brake force release timing error and mechanical interference probability as constraints. A historical hysteresis database records the screw mechanism's action delay data at low temperatures. A genetic algorithm is used to iteratively optimize the membership function center point and the rule antecedent / consequent weights. The optimized fuzzy rule table is updated to the electronic parking brake controller via a flashing tool. Simultaneously, the controller receives temperature data from the thermal management module and incorporates a temperature compensation coefficient into the rule output. This compensation coefficient is dynamically adjusted based on the real-time temperature gradient to mitigate control conflicts caused by lubrication degradation.

[0098] The data flow between these units forms a closed loop: the output parameters of the dynamic weight optimization unit drive the redundant strategy optimization unit to generate switching thresholds; the autonomous braking curve parameters of the redundant strategy optimization unit interact with the execution module; and the rule table of the fuzzy rule evolution unit is updated to the electronic parking brake controller, and its compensation coefficient is fed back to the thermal management module. This collaborative mechanism allows multi-objective optimization parameters to be integrated throughout the control chain, improving the dynamic coordination accuracy and fault tolerance of the braking system in low-temperature environments.

[0099] Specifically, the present invention is applied to a brake system of a tracked articulated vehicle in a low-temperature environment, and the adjustment module includes:

[0100] A multimodal analysis submodule is used to match weighted modes based on ambient temperature sensor data, enabling articulation angle compensation mode when the temperature falls below a preset low-temperature threshold, and enabling inertial measurement unit data-dominant mode within a preset temperature range;

[0101] The reference value correction submodule is used to load the weight coefficient output by the dynamic weight optimization unit of the optimization control module into the hydraulic control unit of the leading vehicle, and synchronously update the reference value parameter library of the hydraulic control unit of the trailing vehicle through the communication bus;

[0102] The multimodal analysis submodule receives the temperature sensor data and transmits the mode selection instruction to the reference value correction submodule; the reference value correction submodule inputs the corrected brake pressure reference value into the execution module.

[0103] The adjustment module of the present invention realizes dynamic adaptation of the brake pressure reference value through a collaborative mechanism of multimodal analysis and reference value correction. The specific technical solution and logical relationship are as follows:

[0104] The multimodal analysis submodule receives real-time temperature data from the ambient temperature sensor and matches a weighting pattern based on a preset low-temperature threshold and temperature range division rules. When the temperature falls below the preset low-temperature threshold, the system activates articulation compensation mode, increasing the weighting coefficient of the articulation sensor data to compensate for mechanical transmission clearance abnormalities caused by low temperatures. Within the preset temperature range, the system switches to inertial measurement unit data-dominant mode, increasing the contribution of the pitch and yaw angle data output by the inertial measurement unit in brake pressure calculations. The mode selection command is transmitted to the reference value correction submodule via the internal data bus, triggering the dynamic loading logic of the weighting coefficient.

[0105] The reference value correction submodule receives the weight coefficient set output by the dynamic weight optimization unit of the optimization control module and loads the weight coefficient corresponding to the current temperature range into the control algorithm of the leading vehicle hydraulic control unit. The weight coefficient is synchronized to the reference value parameter library of the trailing vehicle hydraulic control unit via a communication bus, enabling coordinated updates of the brake pressure reference values ​​of the leading and trailing vehicles. A differential verification mechanism is used during synchronization to compare the parameter versions of the leading and trailing vehicles for consistency. Any detected version discrepancies trigger a weight coefficient retransmission process. The corrected brake pressure reference value is input to the standby core activation unit of the execution module via a high-speed bus and serves as the reference input for generating redundant control instructions.

[0106] The multimodal analysis submodule and the reference value correction submodule form a closed-loop interactive link: temperature data drives mode switching, mode commands trigger weight coefficient updates, and the corrected reference value is fed back to the execution module to control brake pressure output. This link, through a temperature-adaptive weight distribution mechanism, suppresses sensor signal drift and actuator response lag caused by low temperatures, ensuring that the brake pressure reference value dynamically matches real-time operating conditions. Data flow between modules spans temperature sensing, parameter loading, and command execution.

[0107] Specifically, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the execution module includes:

[0108] The communication monitoring unit is used to count the packet loss rate and delay time of the communication bus, and trigger the autonomous decision-making instruction when the cumulative signal loss exceeds the set threshold;

[0109] A spare core activation unit is used to call the reference value correction algorithm generated by the reference value correction submodule of the adjustment module and interpolate the wheel speed sensor data to complete the missing control instructions;

[0110] The communication monitoring unit inputs the bus status data into the standby core activation unit; the interpolation instruction output by the standby core activation unit is transmitted to the rear vehicle hydraulic control unit via the communication bus.

[0111] The execution module of the present invention realizes fault-tolerant control of the braking system through a collaborative mechanism of communication status monitoring and redundant control instruction generation. The specific technical solution and logical relationship are as follows:

[0112] The communication monitoring unit collects data packet transmission status from the communication bus in real time and uses a sliding window algorithm to calculate packet loss rate and delay time. The packet loss rate is calculated based on the ratio of the number of lost packets to the total number of packets sent per unit time, and delay time is measured using the timestamp differential method. When the cumulative number of signal losses exceeds a preset threshold, the autonomous decision-making instruction generation process is triggered. The preset threshold is set based on reliability test data of the communication link in low-temperature environments. For example, at -40°C, it is dynamically adjusted based on the median historical failure rate. Bus status data is transmitted to the backup core activation unit via internal registers, triggering a switch to the redundant control mode.

[0113] After receiving bus status data from the communication monitoring unit, the standby core activation unit invokes the baseline value correction algorithm generated by the baseline value correction submodule of the adjustment module. This algorithm uses linear interpolation to complete missing control commands based on the weight coefficients loaded by the leading vehicle's hydraulic control unit and the real-time wheel speed change rate collected by the wheel speed sensors. The interpolation process uses wheel speed sensor data as input to predict the brake pressure demand curve during the missing time period and generate redundant control commands with a smooth transition. The interpolated commands are then transmitted to the trailing vehicle's hydraulic control unit via a private communication bus. The bus protocol utilizes a time-triggered mechanism to distribute commands within fixed time slots to avoid bus conflicts.

[0114] The communication monitoring unit and the standby core activation unit form a closed-loop control link. Bus status anomalies trigger an autonomous decision-making process. Redundant commands are dynamically generated based on a baseline correction algorithm and real-time sensor data. Interpolated results are then distributed and executed via a dedicated communication channel. This link utilizes communication status awareness and a data-driven interpolation compensation mechanism to mitigate intermittent control commands caused by low temperatures and ensure dynamic coordination of braking pressure between the front and rear vehicles. Data exchange between modules encompasses status monitoring, algorithm invocation, and command transmission.

[0115] Specifically, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the decision module includes:

[0116] A low-temperature hysteresis compensation unit is used to superimpose a compensation coefficient based on historical hysteresis data on the fuzzy rule output according to the temperature data output by the thermal management module;

[0117] A timing priority mapping unit is used to adjust the parking brake force release timing according to the vehicle slope and gear status provided by the vehicle controller;

[0118] The low-temperature hysteresis compensation unit receives the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module, and inputs the compensation coefficient into the timing priority mapping unit; the output instruction of the timing priority mapping unit is transmitted to the electronic parking brake controller.

[0119] The decision module of the present invention realizes precise control of parking brake force through the coordinated mechanism of temperature compensation and priority mapping. The specific technical solution and logical relationship are as follows:

[0120] The low-temperature hysteresis compensation unit receives real-time temperature data output by the thermal management module and, combined with the screw mechanism action delay data recorded in the historical hysteresis database, generates a temperature-hysteresis characteristic mapping table. The compensation coefficient is dynamically calculated based on the current temperature gradient and the fitting results of the historical hysteresis curve and is added to the fuzzy rule output. The fuzzy rule parameters output by the fuzzy rule evolution unit are input into the low-temperature hysteresis compensation unit through the parameter configuration interface, driving the coordinated adjustment of the compensation coefficient and the membership function. When the temperature is below the preset threshold, the compensation coefficient is corrected based on the exponentially weighted average of the historical hysteresis data to suppress the mechanism response delay caused by the increased lubricant viscosity.

[0121] The sequential priority mapping unit receives the vehicle's bank angle and gear status signals from the vehicle controller and uses a state machine model to analyze the parking brake release priority. The bank angle signal is preprocessed by the Kalman filter module and divided into multiple discrete intervals, each corresponding to a different brake release time window. The gear status signal triggers adjustments to the initial parking brake force threshold, for example, shortening the release delay when in reverse gear. The priority mapping table is dynamically updated by querying the fuzzy rule parameters generated by the optimization control module. Compensation coefficients are then input to further adjust the weight distribution of the sequential logic.

[0122] The low-temperature hysteresis compensation unit and the timing priority mapping unit form a closed data loop. The compensation coefficient corrects the fuzzy rule output and is then fed into the timing priority mapping unit to adjust the time window parameters. The results of the timing logic execution are fed back to the fuzzy rule evolution unit to drive the next round of rule base optimization. The optimized parking brake force release command, including timestamp synchronization information, is transmitted to the electronic parking brake controller via the CAN bus to align the timing with the service brake command. The inter-module data flow encompasses temperature compensation, rule iteration, and timing coordination, enabling conflict-free control of the parking and service brakes at low temperatures.

[0123] Specifically, the present invention is applied to a braking system of a tracked articulated vehicle in a low-temperature environment, and the thermal management module includes:

[0124] The hierarchical heating unit is used to start the heating tape at full power when the temperature is lower than the preset low temperature threshold according to the temperature data of the data acquisition module, and switch to the temperature balancing mode of the semiconductor refrigeration chip in the preset temperature range;

[0125] A deformation compensation unit is used to dynamically adjust the compensation threshold of the deformation mechanism according to the stress-strain curve of the sealing material and the historical deformation database;

[0126] The temperature control instructions of the graded heating unit are input into the thermostat of the rear vehicle insulation compartment; the output parameters of the deformation compensation unit are fed back to the thermal management optimization unit of the optimization control module.

[0127] The thermal management module of the present invention achieves low-temperature adaptability adjustment of the brake assembly through the coordinated control of graded heating and deformation compensation. The specific technical solution and logical relationship are as follows:

[0128] The hierarchical heating unit receives real-time temperature data from the data acquisition module and divides the heating strategy execution range according to the preset low-temperature threshold. When the temperature falls below the preset low-temperature threshold, the heating cable is activated in full-power mode. The heating cable is evenly distributed along the brake line, and the PID control algorithm maintains stable heating power. When the temperature enters the preset temperature range, the unit switches to the semiconductor cooler temperature balancing mode. The semiconductor cooler adjusts the cooling power according to the temperature gradient distribution to prevent local overheating. The temperature control command is transmitted to the rear vehicle's insulation compartment thermostat via a pulse-width modulation signal. The thermostat has built-in multiple relays to control the power supply to the heating cable and semiconductor cooler, while also monitoring for abnormal circuit current.

[0129] The deformation compensation unit generates dynamic compensation thresholds based on the seal material's stress-strain curve and a historical deformation database. The stress-strain curve is calibrated through material mechanics experiments, and the historical deformation database records seal deformation data at different temperatures. A sliding average algorithm is used to predict the deformation trend corresponding to the current temperature. The compensation threshold is dynamically adjusted based on the difference between the predicted deformation and the real-time deformation sensor data. The output parameters are fed back to the thermal management optimization unit of the optimization control module via a communication bus, driving the genetic algorithm to iteratively optimize the thermal management parameters.

[0130] The staged heating unit and the deformation compensation unit form a closed data loop. After the staged heating unit's temperature control command is executed, the rear vehicle's insulation compartment thermostat feeds actual temperature data back to the deformation compensation unit to refine the deformation prediction model. The deformation compensation unit's output parameters are then fed into the optimization control module to adjust the heating strategy's preset temperature ranges and the power allocation ratio for the semiconductor cooler. Coordinated control between these modules is achieved through a temperature-deformation coupling model, suppressing low-temperature-induced embrittlement of sealing materials and sudden changes in hydraulic oil viscosity. The thermal management optimization unit synchronizes these updated parameters with the Kalman filter module to enhance the accuracy of temperature drift compensation in dynamic load estimation.

[0131] The above technical solution reduces the impact of low temperature environment on the reliability of the braking system through temperature adaptive heating strategy and material deformation dynamic compensation mechanism.

[0132] Specifically, the present invention is applied to a tracked articulated vehicle braking system in a low-temperature environment, wherein the Kalman filter module includes: a temperature compensation interface unit for adjusting the process noise covariance weight according to temperature gradient data output by the thermal management module;

[0133] A dynamic load feedback unit, used to transmit the optimized load distribution data to the multi-modal analysis submodule of the adjustment module via a bus;

[0134] The temperature compensation interface unit receives the temperature gradient data from the thermal management module and adjusts the Kalman filter parameters;

[0135] The output data of the dynamic load feedback unit is input into the multi-modal analysis submodule of the adjustment module.

[0136] The Kalman filter module of the present invention improves the accuracy of multi-source data fusion through the collaborative mechanism of temperature compensation and dynamic load feedback. Its specific technical solution and logical relationship are as follows:

[0137] The temperature compensation interface unit receives temperature gradient data from the thermal management module and analyzes temperature distribution differences among various brake components. The diagonal elements of the Kalman filter's process noise covariance matrix are dynamically weighted based on the temperature gradient data. The noise covariance weights corresponding to sensor data in low-temperature regions are increased to suppress signal drift interference caused by material shrinkage. These adjusted noise parameters are input into the Kalman filter's state prediction model to optimize the load estimation accuracy in the vehicle's kinematic equations. The temperature gradient data is normalized using the thermal management module's historical temperature change database to match the temperature-noise correlation characteristic curve in low-temperature environments.

[0138] The dynamic load feedback unit transmits optimized load distribution data via a high-speed communication bus to the multimodal analysis submodule of the adjustment module. This load distribution data, including the dynamic pressure distribution ratio between the front and rear vehicle bodies, serves as an input for the multimodal analysis submodule to switch weighting modes. When the load distribution data detects that the rear vehicle pressure ratio exceeds a preset threshold, the multimodal analysis submodule switches to inertial measurement unit data-dominant mode, increasing the weighting factor for the yaw angle data. The feedback data is verified for integrity using a checksum mechanism, and abnormal data packets trigger retransmission requests to ensure real-time control command generation.

[0139] The temperature compensation interface unit and the dynamic load feedback unit form a closed-loop data link. Temperature compensation parameters optimize the Kalman filter's state estimation results, and dynamic load data is fed back into the adjustment module to correct weight distribution. The brake pressure reference value generated by the adjustment module further influences the thermal management module's temperature control strategy, driving the iterative update of temperature gradient data. The coordinated control between these modules, through the linkage of noise suppression and load feedback, reduces the impact of sensor signal distortion on braking decisions in low-temperature environments.

[0140] Specifically, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the redundancy strategy optimization unit further includes:

[0141] Hardware-in-the-loop test unit, used to inject intermittent connection failures of the communication bus into the simulation environment and generate the Pareto front solution set;

[0142] The parameter linkage unit is used to write the autonomous braking curve parameters generated by the hardware-in-the-loop test unit into the rear vehicle hydraulic control unit via OTA, and share the master-slave switching threshold data with the standby core activation unit of the execution module;

[0143] The parameter linkage unit and the standby core activation unit interactively switch threshold data through a communication bus.

[0144] The redundant strategy optimization unit of the present invention realizes dynamic optimization of the control strategy through the collaborative mechanism of hardware-in-the-loop testing and parameter linkage. The specific technical solution and logical relationship are as follows:

[0145] The hardware-in-the-loop test unit simulates intermittent communication bus connection failures at low temperatures in a simulation environment. A fault injector periodically inserts signal loss events to trigger the fitness function calculation of the multi-objective optimization model. A Pareto front solution set is iteratively generated using a genetic algorithm. The algorithm optimizes communication latency, braking distance deviation, and yaw angle stability, selecting a set of autonomous braking curve parameters that balances response speed and dynamic stability. Once generated, the solution set is compared with real-vehicle test data using an offline verification platform. Parameter combinations that deviate from actual operating conditions by more than a preset threshold are eliminated, and the optimal solution is retained as the updated parameters for the hydraulic control unit of the follower vehicle.

[0146] The parameter linkage unit receives the optimal autonomous braking curve parameters generated by the hardware-in-the-loop test unit and transmits them in encrypted segments to the parameter library of the downstream vehicle's hydraulic control unit via an over-the-air (OTA) wireless communication protocol. A cyclic redundancy check (CRC) is used to verify data integrity during transmission, triggering a breakpoint resume mechanism if the check fails. Master-slave switching threshold data is shared in real time with the execution module's backup core activation unit via a private communication bus. This shared data includes switching delay time, bus health score, and redundant instruction priority tags. The backup core activation unit dynamically adjusts the triggering conditions for autonomous decision-making based on this shared threshold data, for example, preemptively activating the redundant control core when bus health falls below a critical value.

[0147] The hardware-in-the-loop (HIL) test unit and the parameter linkage unit form a closed-loop optimization chain: the parameter set generated by simulation testing is updated to the actual vehicle control system via OTA, and the actual vehicle operation data is fed back into the HIL test unit to correct the fault model. Shared threshold data drives the coordinated response of the backup core activation unit and the parameter linkage unit, achieving dynamic matching of offline optimization and online control. Data interaction between modules covers fault simulation, parameter distribution, and threshold coordination. Through multi-objective optimization and real-time data fusion, the redundant fault tolerance of the braking system in low-temperature environments is improved.

[0148] Specifically, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the low-temperature hysteresis compensation unit further includes:

[0149] The screw mechanism lubrication monitoring submodule is used to collect the action delay data of the screw mechanism and generate a historical hysteresis database;

[0150] The compensation coefficient updating submodule is used to dynamically adjust the compensation coefficient according to the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module;

[0151] The data of the screw mechanism lubrication monitoring submodule is input into the compensation coefficient updating submodule; the output parameters of the compensation coefficient updating submodule are fed back to the fuzzy rule evolution unit.

[0152] The low-temperature hysteresis compensation unit of the present invention suppresses the low-temperature action delay of the screw mechanism through a closed-loop mechanism of lubrication monitoring and coefficient updating. The specific technical solution and logical relationship are as follows:

[0153] The screw mechanism lubrication monitoring submodule uses a high-precision displacement sensor to collect screw mechanism motion delay data and calculates the time difference between actual and commanded displacement using a timestamp differential method. This delay data is categorized by temperature range and stored in a historical hysteresis database. This database is dynamically updated using a sliding window algorithm to preserve typical hysteresis characteristics under recent low-temperature operating conditions. A temperature sensor collects the screw mechanism surface temperature in real time and correlates it with the delay data to generate a temperature-hysteresis characteristic curve. This curve uses a polynomial fitting algorithm to describe the mapping between hysteresis and temperature.

[0154] The compensation coefficient update submodule receives the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module and analyzes the membership function center point and rule antecedent weights. The compensation coefficient is dynamically adjusted based on the temperature compensation weight factor in the fuzzy rule parameters. An exponentially weighted average algorithm is used to fuse current delay data with the predicted values ​​of the historical hysteresis characteristic curve. The adjusted compensation coefficient is added to the fuzzy rule output to correct the hysteresis compensation in the parking brake force release timing command. The compensation coefficient update cycle is synchronized with the screw mechanism's operating frequency to avoid command conflicts.

[0155] The screw mechanism lubrication monitoring submodule and the compensation coefficient update submodule form a bidirectional data flow: delay data drives the update of the historical hysteresis database, and the database output characteristic curve is input into the compensation coefficient calculation. The compensation coefficient is fed back to the fuzzy rule evolution unit, triggering the iterative optimization of the membership function parameters. Based on the feedback of compensation effect data, the fuzzy rule evolution unit adjusts the priority weights of the temperature compensation items in the rule base, forming a co-evolution mechanism for the rule parameters and compensation coefficients.

[0156] The above technical solution suppresses the impact of lubricant performance degradation caused by low temperature on braking timing through real-time monitoring and closed-loop control of rule iteration.

[0157] Specifically, the present invention is applied to a brake system of a tracked articulated vehicle in a low-temperature environment, and the staged heating unit further comprises:

[0158] The local overheating suppression submodule is used to balance the temperature distribution in the insulation chamber through semiconductor refrigeration chips;

[0159] The seal aging warning submodule is used to predict the seal life according to the deformation threshold parameter output by the deformation compensation unit;

[0160] The temperature data of the local overheating suppression submodule is input into the seal aging warning submodule;

[0161] The warning signal of the seal aging warning submodule is transmitted to the parameter self-learning unit of the thermal management module through the bus for updating the thermal management parameters.

[0162] The hierarchical heating unit of the present invention optimizes the thermal management strategy through the coordinated control of temperature balance and life prediction. The specific technical solution and logical relationship are as follows:

[0163] The local overheating suppression submodule uses a distributed array of temperature sensors to collect real-time temperature data from multiple areas within the insulation chamber, identifying localized overheating locations. Semiconductor refrigeration units dynamically adjust cooling power based on temperature distribution differences, employing a PID control algorithm to balance temperature gradients within the chamber. The refrigeration units' operating state is controlled by pulse-width modulation signals, with cooling power allocation proportionally proportional to temperature deviation, thus suppressing thermal stress concentration in the material caused by uneven heating of the heating tape. This balanced temperature data is transmitted via an internal bus to the seal aging warning submodule, serving as input for seal life prediction.

[0164] The seal aging warning submodule receives the deformation threshold parameters output by the deformation compensation unit and, combined with temperature data from the local overheating suppression submodule, predicts the remaining life of the seal based on the Arrhenius accelerated aging model. The prediction model calibrates the activation energy parameter based on seal failure cases in the historical deformation database. When the real-time deformation threshold exceeds the preset safety range, a life warning signal is triggered. This warning signal is transmitted via the CAN bus to the parameter self-learning unit of the thermal management module, which drives the genetic algorithm to iteratively optimize the heating power allocation coefficient and the cooling plate activation threshold.

[0165] The data flow between modules forms a closed-loop feedback loop: the temperature equalization results of the local overheating suppression submodule are fed into the seal aging warning submodule to correct the temperature influencing factors of the aging prediction model. After the warning signal triggers the parameter self-learning unit to update the thermal management strategy, the optimized heating parameters are reloaded and executed by the hierarchical heating unit, forming a dynamic regulation cycle of temperature control, life prediction, and parameter update. This technical solution achieves adaptive control of brake component thermal management in low-temperature environments through multi-physics field coupling analysis.

[0166] The technical features of the present invention are explained as follows:

[0167] The data acquisition module is a hardware and software combination used to collect real-time vehicle operating status data. It includes an articulation angle sensor (which measures the mechanical angle change at the front and rear vehicle body connections), an inertial measurement unit (IMU), which outputs pitch and yaw angle data to reflect vehicle posture, a temperature sensor (which monitors brake component temperature), a vehicle grade sensor (which detects terrain inclination), and a gear position sensor. This multi-source data is transmitted to the optimization control module via a high-speed communication bus, providing input for subsequent parameter optimization.

[0168] Optimization control module: The control parameter optimization core based on genetic algorithm, including:

[0169] Dynamic weight optimization unit: Using the deviation between the brake pressure baseline value and the actual demand as the fitness function, it adjusts the weight coefficients of the articulation angle, pedal displacement, and inertial measurement unit data to solve the problem of dynamic allocation of sensor signal priorities at low temperatures.

[0170] Redundancy strategy optimization unit: Simulates communication link failures through hardware-in-the-loop testing to generate master-slave switching thresholds and autonomous braking curve parameters that balance response speed and stability, thereby improving redundant control reliability.

[0171] Fuzzy rule evolution unit: Optimizes the membership function and rule base based on the parking brake timing error, dynamically adjusts the control logic in combination with the temperature compensation coefficient, and suppresses the influence of mechanical hysteresis.

[0172] Adjustment module: Responsible for analyzing the driver's intention and generating a brake pressure reference value, including:

[0173] Multimodal analysis submodule: Switches weight modes based on temperature data. For example, when the temperature is below -40°C, the articulation angle compensation mode is enabled to offset mechanical clearance errors. In the -20°C to -40°C range, the inertial measurement unit-dominated mode is used to improve attitude control accuracy.

[0174] Baseline value correction submodule: loads the optimized weight coefficients to the front and rear vehicle hydraulic control units, synchronizes the parameter library through the differential verification mechanism, and achieves dynamic consistency in the braking force distribution of the front and rear vehicles.

[0175] Execution module: implements redundant control and instruction execution, including:

[0176] Communication monitoring unit: Counts bus packet loss rate and delay time, and triggers the activation of the standby core when the accumulated abnormality exceeds a threshold (such as 5 seconds).

[0177] The standby core activation unit calls the reference value correction algorithm, combines the wheel speed sensor data to interpolate and complete the missing instructions, and transmits them to the rear vehicle hydraulic control unit through the private CAN bus to ensure braking continuity when the main control link fails.

[0178] Thermal Management Module: This module addresses the issue of material performance degradation in low-temperature environments, including:

[0179] Staged heating unit: When the temperature is below -40℃, the heating tape is started to run at full power to prevent the hydraulic oil from solidifying; in the range of -20℃ to -40℃, the semiconductor refrigeration chip is switched to balance the temperature in the chamber to suppress local overheating.

[0180] Deformation compensation unit: Dynamically adjusts the compensation threshold based on the sealing material stress-strain curve and historical deformation database, and feeds back to the optimization control module to iterate the thermal management strategy.

[0181] Kalman filter module: Improves sensor data fusion accuracy, including:

[0182] Temperature compensation interface unit: adjusts the process noise covariance weight according to the temperature gradient data to suppress the inertial measurement unit signal drift.

[0183] Dynamic load feedback unit: The fused load distribution data is input into the adjustment module to drive the weight mode switching. For example, when the pressure of the following vehicle exceeds the threshold, the inertial measurement unit dominant mode is triggered.

[0184] Redundancy strategy optimization unit: Hardware-in-the-loop test unit: Injects intermittent communication bus failures in a simulation environment to generate a Pareto optimal solution set and select a parameter combination that takes into account both braking distance and yaw stability.

[0185] Parameter linkage unit: The autonomous braking curve parameters are encrypted and transmitted to the hydraulic control unit of the following vehicle via OTA, and the switching threshold data (such as bus health score) is shared with the spare core to achieve dynamic threshold adjustment.

[0186] Low temperature hysteresis compensation mechanism:

[0187] Screw mechanism lubrication monitoring submodule: records the screw action delay data at low temperatures and generates a temperature-hysteresis characteristic curve.

[0188] Compensation coefficient update submodule: Dynamically adjust the compensation coefficient based on the fuzzy rule parameters, for example, use exponential weighted average to correct the delay at low temperatures, and feed it back to the fuzzy rule evolution unit to optimize the rule base.

[0189] Environmental perception and parameter optimization closed loop: The data acquisition module provides multi-source input, the optimization control module generates dynamic weights and redundancy strategies through genetic algorithms, and the adjustment module loads parameters to the execution unit, forming a "perception-optimization-execution" closed loop.

[0190] Redundant fault-tolerant link: The communication monitoring of the execution module cooperates with the spare core activation unit to seamlessly switch to redundant instructions when the main control is abnormal. Combined with the autonomous curve parameters updated by OTA, the continuity of the braking function is guaranteed.

[0191] Temperature adaptive control: The thermal management module's graded heating and deformation compensation suppress material performance degradation, while the Kalman filter module's temperature compensation interface improves signal reliability, forming a "temperature-material-signal" collaborative optimization link.

[0192] Genetic Algorithm Optimization Model: This model is used for multi-objective optimization of dynamic weight coefficients, redundant switching thresholds, and fuzzy rule parameters. This model uses brake pressure deviation, yaw angle stability index, and temperature maintenance energy consumption as fitness functions. By simulating the selection, crossover, and mutation operations used in biological evolution, iteratively selects the optimal parameter combination. For example, the dynamic weight optimization unit uses a genetic algorithm to adjust the weight distribution of articulation angle, pedal displacement, and inertial measurement unit data to address the dynamic adaptation of sensor signal priorities at low temperatures. The redundant strategy optimization unit combines communication delay and braking distance constraints to generate a Pareto-optimal solution set and select a master-slave switching threshold that balances response speed and stability.

[0193] Fuzzy rule decision model: The fuzzy rule model is used to address the uncertainty and nonlinearity of the parking brake release timing. The model constructs a membership function and rule base based on a historical hysteresis database and real-time temperature data, and modifies output commands using temperature compensation coefficients. For example, the low-temperature hysteresis compensation unit correlates screw mechanism delay data with temperature gradients to generate a compensation coefficient that is added to the fuzzy rule output to mitigate timing errors caused by lubrication degradation. The fuzzy rule evolution unit uses a genetic algorithm to optimize the rule base structure and enhance the adaptability of the decision logic.

[0194] Kalman filter fusion model: The Kalman filter model is used for multi-source sensor data fusion and noise suppression. The model adjusts the process noise covariance weights through the temperature compensation interface unit to suppress low-temperature-induced inertial measurement unit signal drift. The dynamic load feedback unit integrates articulation angle, wheel speed, and attitude data, outputting the dynamic load distribution to the adjustment module. For example, when the pressure contribution from the following vehicle exceeds a preset threshold, the model triggers a switch in weighting mode to inertial measurement unit-dominated mode, improving yaw angle control accuracy.

[0195] Pareto Front Solution Set Model: This model is used to select the optimal parameter combination in redundant strategy optimization. The hardware-in-the-loop test unit simulates intermittent communication bus failures to generate a multi-objective solution set, including communication delay, braking distance, and yaw stability. Solutions that deviate from actual operating conditions are eliminated through offline verification. The optimized autonomous braking curve parameters are transmitted to the follower vehicle's hydraulic control unit via encrypted over-the-air transmission (OTA), enabling dynamic updates of the redundant control strategy.

[0196] Temperature-Hysteresis Compensation Model: This model constructs a temperature-hysteresis characteristic curve based on screw mechanism lubrication monitoring data and temperature gradients to predict mechanical delay at low temperatures. The compensation coefficient update submodule uses an exponentially weighted average algorithm to integrate real-time delay data with historical curves and dynamically adjust the fuzzy rule output. For example, when the temperature drops below -30°C, the model increases the compensation coefficient weight based on historical hysteresis data to shorten the parking brake release delay.

[0197] Thermal Management Deformation Prediction Model: This model combines the seal material's stress-strain curve with a historical deformation database to predict deformation thresholds at different temperatures. The deformation compensation unit dynamically adjusts the compensation based on the difference between the predicted value and real-time sensor data. The output parameters are fed back to the optimization control module to iterate the thermal management strategy. For example, when the predicted deformation exceeds a safety threshold, the staged heating unit is triggered to switch to semiconductor cooling mode to prevent seal aging.

[0198] Data-driven optimization: The genetic algorithm model works in conjunction with the fuzzy rule model to achieve dynamic adaptation of control parameters and decision logic through multi-objective optimization and rule iteration.

[0199] Redundancy and fault tolerance linkage: The Pareto solution model is combined with the Kalman filter model to ensure the continuity of braking commands in the event of communication anomalies while suppressing signal distortion.

[0200] Temperature-material-control closed loop: The temperature-hysteresis model and the thermal management deformation model form a closed loop. Through temperature compensation and material deformation prediction, the heating strategy and mechanical compensation amount are dynamically adjusted to solve the problem of material performance degradation caused by low temperature.

[0201] The above model constructs an intelligent control system with environmental adaptation, redundant fault tolerance and multi-objective optimization through algorithm fusion and data interaction.

[0202] The present invention addresses the problems of unbalanced dynamic distribution of braking force, insufficient redundancy and fault tolerance, and low-temperature signal distortion in the braking system of a tracked articulated vehicle under low-temperature conditions, and proposes the following specific implementation methods:

[0203] The data acquisition module collects real-time signals from multiple sources, including pitch angle, yaw angle, brake temperature, and slope data, from the articulation angle sensor, inertial measurement unit (IMU), temperature sensor, and vehicle status sensor. Upon receiving this data, the optimization control module uses a genetic algorithm to perform multi-objective optimization of dynamic weight coefficients, redundancy switching thresholds, and fuzzy rule parameters. The dynamic weight optimization unit uses brake pressure deviation as a fitness function to iteratively generate a weight distribution scheme for articulation angle and IMU data. The redundancy strategy optimization unit simulates bus failures during hardware-in-the-loop testing based on communication latency and yaw angle stability indicators to generate a Pareto optimal solution set and optimize the master-slave switching threshold. The fuzzy rule evolution unit updates the membership function parameters and rule base based on parking brake timing errors and mechanical interference probability. The optimized parameters are synchronized to the adjustment and execution modules via the communication bus.

[0204] The adjustment module's multimodal analysis submodule switches weighting modes based on real-time temperature data: When the temperature drops below -40°C, the articulation angle compensation mode is enabled, increasing the articulation angle weight to offset mechanical transmission backlash errors; in the -20°C to -40°C range, the inertial measurement unit (IMU) data-driven mode is switched to improve attitude control accuracy. The reference value correction submodule loads the optimized weight coefficients into the leading vehicle's hydraulic control unit and synchronously updates the parameter library of the trailing vehicle's hydraulic control unit through a differential verification mechanism, ensuring dynamic consistency between the front and rear vehicle's braking reference values. The execution module's communication monitoring unit continuously measures bus packet loss rate and latency. When cumulative packet loss exceeds 5 seconds, the backup core activation unit is triggered to invoke the reference value correction algorithm. This generates redundant control commands based on wheel speed sensor interpolation and transmits them to the trailing vehicle's hydraulic control unit via the private CAN bus, enabling seamless switching in the event of a master control link anomaly.

[0205] The thermal management module's hierarchical heating unit dynamically adjusts the operating modes of the heating cables and semiconductor coolers based on brake temperature. When the temperature drops below -40°C, the heating cables activate full-power heating to suppress sudden changes in hydraulic oil viscosity. When the temperature rises to -20°C, the semiconductor coolers switch to balance the temperature distribution within the chamber. The deformation compensation unit predicts deformation thresholds based on the seal's stress-strain curve and a historical deformation database, outputting these parameters as feedback to the optimization control module for iterative thermal management strategies. The Kalman filter module's temperature compensation interface unit adjusts the process noise covariance weights based on temperature gradient data to suppress inertial measurement unit signal drift. The dynamic load feedback unit inputs the fused load distribution data into the adjustment module to drive adaptive weighting mode switching. The decision module's low-temperature hysteresis compensation unit combines historical screw mechanism action delay data to generate temperature compensation coefficients, modify the fuzzy rule output, and coordinates with the timing priority mapping unit to adjust the parking brake release sequence to avoid control conflicts caused by lubrication deterioration.

[0206] The above implementation method solves the problems of unbalanced braking force distribution and insufficient fault tolerance in low-temperature environments through multi-module closed-loop collaboration, dynamic parameter optimization and redundant execution mechanism.

[0207] The present invention solves the technical problems of the braking system in low-temperature environments through multi-module coordinated control and dynamic parameter optimization mechanism. The specific technical solutions are as follows:

[0208] The data acquisition module acquires multimodal sensor data, including articulation angle, inertial measurement unit (IMU) attitude, and brake temperature, in real time. This data is fed into the optimization control module for a multi-objective optimization using a genetic algorithm. The dynamic weight optimization module generates weighting coefficients for articulation angle, pedal displacement, and IMU data based on brake pressure deviation and yaw stability indicators. The multimodal analysis submodule dynamically switches weighting modes based on temperature sensor data: articulation angle compensation mode is enabled at low temperatures to suppress mechanical backlash errors, while IMU data-driven mode is switched to improve attitude control accuracy within a preset temperature range. The optimized weighting coefficients are then synchronously loaded into the front and rear vehicle hydraulic control units via the baseline correction submodule, enabling dynamic adaptation of braking force distribution.

[0209] The redundancy strategy optimization unit generates master-slave switching thresholds and autonomous braking curve parameters based on communication delay and braking distance constraints. The hardware-in-the-loop testing unit simulates intermittent bus failures to generate a Pareto-optimal solution set. The execution module's communication monitoring unit monitors the bus packet loss rate in real time. When an anomaly exceeds a threshold, the backup core activation unit invokes a baseline value correction algorithm to interpolate missing instructions and generates redundant control signals based on wheel speed sensor data. The parameter linkage unit updates the autonomous braking curve to the rear vehicle hydraulic control unit via OTA and shares the switching threshold with the backup core, enabling seamless switching between the master and backup links and ensuring the stability of the emergency braking function under operating conditions.

[0210] The thermal management module's hierarchical heating unit switches between full-power heating of the heating tape and balanced semiconductor cooling mode based on temperature data, suppressing local overheating and low-temperature embrittlement. The deformation compensation unit dynamically adjusts the seal compensation threshold based on stress-strain curves and historical deformation data, and outputs parameters that are fed back to the optimization control module to iterate the thermal management strategy. The Kalman filter module's temperature compensation interface unit adjusts the noise covariance weights based on the temperature gradient to suppress sensor signal drift. The dynamic load feedback unit inputs the fused load distribution data into the adjustment module to drive weight mode switching. The low-temperature hysteresis compensation unit generates temperature compensation coefficients based on the historical hysteresis database, corrects the fuzzy rule output, and coordinates with the timing priority mapping unit to adjust the parking brake release timing to reduce control conflicts caused by lubrication degradation.

[0211] The above technical solution achieves dynamic stability and fault tolerance of the braking system at low temperatures through closed-loop coordination of environmental perception, parameter optimization, redundant execution and temperature compensation.

[0212] The crawler-type articulated vehicle braking system provided by the present invention for use in low-temperature environments can effectively solve the problems of unbalanced braking force distribution, insufficient redundancy and fault tolerance, and signal distortion in the background art in the following low-temperature operating scenarios. Specific embodiments are as follows: (New embodiments are as follows)

[0213] Example 1: In a low-temperature scientific research vehicle operating in a -40°C environment, the data acquisition module uses an articulation angle sensor to acquire real-time data on the angular changes at the front and rear vehicle body connections. The inertial measurement unit (IMU) collects the vehicle's pitch angle (range: ±45°, accuracy: 0.1°) and yaw angle (range: ±90°, accuracy: 0.05°). An ambient temperature sensor monitors brake assembly temperature (measurement range: -60°C to 120°C, resolution: 0.5°C). Wheel speed sensors collect rear wheel speed signals. All of this data is transmitted to the optimization control module via the CAN bus. The optimization control module's dynamic weight optimization unit uses the mean squared error between the reference brake pressure and the actual demand as its fitness function. Using a genetic algorithm (population size: 50, iterations: 30), it iteratively optimizes the weight coefficients for articulation angle, pedal displacement, and IMU data, generating a weight distribution scheme for articulation angle compensation mode (articulation angle weight: 0.7, IMU weight: 0.3). The adjustment module's multimodal analysis submodule detected a brake component temperature of -38°C (below the preset low-temperature threshold of -35°C), triggering the articulation angle compensation mode. The baseline correction submodule loaded the optimized weight coefficients into the leading vehicle's hydraulic control unit (pressure adjustment range: 0-16 MPa, accuracy: ±0.2 MPa) and simultaneously updated the baseline parameter library of the trailing vehicle's hydraulic control unit via the CAN bus (the trailing vehicle's pressure baseline is 15% higher than the leading vehicle's). The thermal management module's staged heating unit detected a temperature below the preset low-temperature threshold and activated the heating cable at full power (800W) to maintain the brake line temperature at -25°C ±2°C. The trailing vehicle's insulation compartment thermostat then adjusted the heating cable power supply using a PID control algorithm (90% duty cycle). The execution module's communication monitoring unit counts the CAN bus packet loss rate in real time (the packet loss threshold is 3 per 5-second period). Upon detecting two consecutive periods of packet loss (a 6% packet loss rate), the standby core activation unit is triggered to invoke a baseline correction algorithm (based on a linear interpolation model of wheel speed change rate). This algorithm, combined with wheel speed sensor data (rear wheel speed change rate of 0.5 m / s²), completes the missing control instructions. These instructions are then transmitted via a private CAN bus (baud rate of 1 Mbps) to the redundant control interface of the rear vehicle's hydraulic control unit. The rear vehicle's hydraulic control unit then adjusts the brake pressure (actual output pressure 14.5 MPa) based on the interpolated instructions, coordinating with the front vehicle's hydraulic control unit (output pressure 12.6 MPa) to achieve balanced braking force distribution between the front and rear vehicles during cornering (the rear vehicle's braking force accounts for 55%).

[0214] Example 2: In an articulated mining truck operating at a -30°C open-pit mine on a high plateau, the screw mechanism sensor in the data acquisition module collects real-time screw displacement (range 0-200mm, accuracy 0.1mm) and torque (range 0-500N·m, accuracy 1N·m). A historical hysteresis database records motion delay data at different temperatures (typical delay 290ms at -30°C). The fuzzy rule evolution unit in the optimization control module uses a genetic algorithm to optimize the fuzzy membership function parameters (temperature compensation coefficient range 0.7-1.2) and the rule base structure (number of rules: 20) using the parking brake release timing error (target error ≤ 50ms) and the mechanical interference probability (target probability ≤ 2%) as constraints. The low-temperature hysteresis compensation unit in the decision module receives temperature data (-28°C) output by the thermal management module and, combined with delay data from the historical hysteresis database, adds a compensation coefficient of 0.85 to the fuzzy rule output (calculated as: compensation coefficient = historical average delay / current delay × 0.9). The timing priority mapping unit receives the vehicle gradient signal (corrected to 15.2° after Kalman filtering) from the vehicle controller and dynamically adjusts the parking brake release timing based on a compensation factor (from 200ms to 120ms). This release timing command is then transmitted to the electronic parking brake controller via the CAN bus (response time ≤ 30ms). The thermal management module's staged heating unit detects that the temperature is within the preset range (-20°C to -40°C) and switches to a 200W thermal radiator (SEMI) temperature balancing mode. The local overheating suppression submodule uses an array of eight SEMI radiators (each 50mm x 50mm in size) to balance the temperature distribution within the insulation chamber (controlling the temperature difference within 5°C). The seal aging warning submodule predicts the remaining service life (≥ 500h) based on the deformation threshold parameter (seal deformation of 2.5mm) output by the deformation compensation unit. This warning signal is then transmitted to the parameter self-learning unit via the FlexRay bus, driving iterative optimization of thermal management parameters (heating cable power is adjusted to 600W).

[0215] Example 3: In an armored articulated vehicle operating in snow at -25°C, the inertial measurement unit (IMU) of the data acquisition module outputs yaw angle data (drift rate 0.5° / s). The temperature compensation interface unit of the Kalman filter module receives temperature gradient data (IMU cabin temperature -22°C) from the thermal management module, dynamically adjusts the process noise covariance matrix weights (increases them by 1.5 times), and fuses multi-source sensor data (articulation angle, wheel speed, and posture) to output a dynamic load distribution (with the rear vehicle load accounting for 60%) to the multimodal analysis submodule of the adjustment module. The redundancy strategy optimization unit of the optimization control module simulates intermittent CAN bus connection failures using a hardware-in-the-loop test unit to generate a Pareto frontier solution set (including parameter combinations with communication delay ≤ 100ms, braking distance ≤ 35m, and yaw angle deviation ≤ 0.5°). The parameter linkage unit writes the autonomous braking curve parameters (slope 1.2 times) to the parameter library of the rear vehicle hydraulic control unit via the OTA upgrade module (transmission rate 1Mbps). The communication monitoring unit of the execution module detected a bus delay of 80ms (exceeding the threshold of 50ms), triggering the standby core activation unit to call the reference value correction algorithm (based on cubic spline interpolation of the wheel speed change rate). Combined with the wheel speed sensor interpolation, the three missing wheel speed data points were supplemented (the wheel speed after supplementation was 11.1m / s). The interpolation command was transmitted to the redundant control interface of the rear vehicle hydraulic control unit via the CAN bus. The rear vehicle hydraulic control unit performed braking at 1.2 times the reference pressure (15.2MPa). The final braking distance was 32m, and the yaw angle deviation was 0.3°, meeting the emergency braking requirements.

[0216] Example 4: In an articulated forestry truck operating in a -35°C forest, the data acquisition module's ambient temperature sensor monitors the brake assembly temperature (-35°C). The adjustment module's multimodal analysis submodule detects that the temperature has fallen below the preset low-temperature threshold (-30°C), activates articulation angle compensation mode, and the dynamic weight optimization unit outputs an articulation angle weight of 0.8 (with an inertial measurement unit weight of 0.2). The baseline value correction submodule loads the weight coefficient into the leading vehicle's hydraulic control unit (pressure 12 MPa) and simultaneously updates the baseline value parameter library of the trailing vehicle's hydraulic control unit (pressure 13.8 MPa) via the CAN bus. The thermal management module's staged heating unit activates the heating tape at full power (800W). The rear vehicle's insulation compartment thermostat raises the rear cabin temperature to -28°C ± 2°C. The local overheating suppression submodule uses a semiconductor refrigeration array (10 elements) to balance the temperatures of the front cabin (-30°C) and the rear cabin (-28°C) (the temperature difference is controlled within 3°C). The deformation compensation unit dynamically adjusts the compensation threshold (compensation amount 1.5mm) based on the stress-strain curve of the sealing material (the elastic modulus increases by 20% at low temperatures) and the historical deformation database (deformation amount 2mm). The output parameters are fed back to the thermal management optimization unit of the optimization control module to drive the iteration of thermal management parameters (the duty cycle of the heating tape is adjusted to 85%). The execution module's communication monitoring unit monitors the bus status in real time (packet loss rate threshold: 5%). Upon detecting instantaneous packet loss (packet loss rate: 6%), the standby core activation unit invokes a baseline correction algorithm (linear interpolation based on wheel speed) to complete the instruction, ensuring greater inner track braking force (inner pressure 14.2 MPa, outer pressure 13.5 MPa) when turning around a curve, and limiting the rear vehicle's outward swing to within 0.3 m.

[0217] In the above embodiment, the data acquisition module acquires multi-source sensor data in real time, the optimization control module dynamically optimizes the weight coefficient, redundant switching threshold and fuzzy rule parameters through genetic algorithms, the adjustment module adaptively switches the weight mode based on temperature and corrects the brake pressure reference value, the execution module realizes redundant control through communication monitoring and spare core activation, the thermal management module suppresses material performance degradation through graded heating and deformation compensation, the Kalman filter module improves signal fusion accuracy through temperature compensation, and the decision module optimizes the parking brake logic through hysteresis compensation and timing mapping. The modules work together to effectively solve the problems of imbalance in dynamic distribution of braking force, insufficient redundant fault tolerance and signal distortion in low temperature environments, and support the practical application of the various technical features in the claims.

Claims

1. A crawler-type articulated vehicle braking system for use in low-temperature environments, characterized in that: include: A data acquisition module is used to acquire sensor data, including articulation angle sensor signals, brake pedal displacement signals, pitch and yaw angle data output by the inertial measurement unit, vehicle slope signals, gear status signals, and brake component temperature data; An optimization control module receives sensor data and uses a genetic algorithm to perform multi-objective optimization of dynamic weight coefficients, redundant switching thresholds, fuzzy rule parameters, thermal management parameters, and the Kalman filter noise covariance matrix. The multi-objectives include brake pressure deviation, yaw stability index, and temperature maintenance energy consumption. An adjustment module is used to analyze the driver's intention based on the optimized dynamic weight coefficient, generate a brake pressure reference value, and transmit the reference value to the pre-installed leading vehicle hydraulic control unit and the trailing vehicle hydraulic control unit via a communication bus; An execution module is used to switch to the backup control core based on the optimized redundancy switching threshold and the reference value of the adjustment module when the main control link communication is abnormal, and to call the autonomous braking curve parameters generated by the optimization control module to control the hydraulic control unit of the following vehicle; A decision module, configured to generate a parking brake force release timing instruction in coordination with a preset electronic parking brake controller and a vehicle controller based on optimized fuzzy rule parameters, including a temperature compensation coefficient and a priority mapping relationship associated with slope; A thermal management module, used to adjust the heating power of the heating tape based on the optimized thermal management parameters, and input the temperature compensation parameters into the temperature compensation interface of the Kalman filter module; The Kalman filter module is connected to the data acquisition module and the adjustment module. It is used to adjust the noise covariance matrix according to the temperature compensation parameters of the thermal management module, fuse the multi-source sensor data and output the dynamic load distribution to the adjustment module.

2. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 1, characterized in that: The optimization control module includes: The dynamic weight optimization unit is connected to the data acquisition module and is used to iteratively optimize the weight coefficients of the articulation angle, pedal displacement, and inertial measurement unit data using a genetic algorithm, using the deviation between the reference value of brake pressure and the actual demand as the fitness function. The optimized weight coefficients are then output to the redundancy strategy optimization unit and the adjustment module. a redundant strategy optimization unit connected to the dynamic weight optimization unit and the execution module, configured to optimize the master-slave switching threshold and the autonomous braking curve triggering condition using the weight coefficient output by the dynamic weight optimization unit as the initial constraint condition and communication delay, yaw angle stability, and braking distance as the multi-objective fitness function, and transmit the switching threshold parameters to the master-slave switching interface of the execution module via the communication bus; The fuzzy rule evolution unit is connected to the decision module and the electronic parking brake controller. It is used to optimize the fuzzy membership function parameters and the rule base structure based on the parking brake force release timing error and the mechanical interference probability as constraints, and write the updated rule table into the electronic parking brake controller through the CAN bus of the vehicle control system.

3. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 2, characterized in that: The adjustment module includes: Ambient temperature sensor, integrated into the data acquisition module, is used to collect temperature data of the brake components in real time; a multimodal analysis submodule, connected to the ambient temperature sensor and the dynamic weight optimization unit, configured to match a weight mode based on ambient temperature sensor data, enable an articulation angle compensation mode when the temperature is below a preset low-temperature threshold, enable an inertial measurement unit data-dominated mode within a preset temperature range, and transmit a mode selection instruction to the reference value correction submodule via an internal bus; The reference value correction submodule is connected to the leading vehicle hydraulic control unit, the trailing vehicle hydraulic control unit and the dynamic weight optimization unit. It is used to load the weight coefficient output by the dynamic weight optimization unit of the optimization control module into the leading vehicle hydraulic control unit, synchronously update the reference value parameter library of the trailing vehicle hydraulic control unit through the communication bus, and input the corrected brake pressure reference value into the communication monitoring unit of the execution module.

4. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 3, characterized in that: The execution modules include: Wheel speed sensor, integrated into the data acquisition module, is used to collect the wheel speed signal of the rear vehicle in real time; The communication monitoring unit is connected to the communication bus and the reference value correction submodule of the adjustment module. It is used to count the packet loss rate and delay time of the communication bus. When the cumulative signal loss exceeds the set threshold, it triggers the autonomous decision instruction and transmits the bus status data to the standby core activation unit through the internal interrupt signal; The spare core activation unit is connected to the communication monitoring unit, the reference value correction submodule and the rear vehicle hydraulic control unit. It is used to call the reference value correction algorithm generated by the reference value correction submodule of the adjustment module, combine the wheel speed sensor data to interpolate and complete the missing control instructions, and transmit the interpolation instructions to the redundant control interface of the rear vehicle hydraulic control unit through the CAN bus.

5. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 4, characterized in that: The decision-making module includes: The vehicle controller is preset in the vehicle control system and is used to provide the vehicle's real-time slope and gear status signals; The historical hysteresis database is stored in the data acquisition module and is used to record the brake mechanism action delay data; A low-temperature hysteresis compensation unit is connected to the thermal management module and the fuzzy rule evolution unit, and is used to superimpose a temperature compensation coefficient on the fuzzy rule output based on the temperature data output by the thermal management module and the delay data in the historical hysteresis database, and transmit the temperature compensation coefficient to the timing priority mapping unit through the internal data bus; The timing priority mapping unit is connected to the vehicle controller and the electronic parking brake controller. It is used to dynamically adjust the parking brake force release timing based on the temperature compensation coefficient according to the vehicle slope and gear status provided by the vehicle controller, and transmit the release timing instruction to the electronic parking brake controller through the CAN bus of the vehicle control system.

6. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 5, characterized in that: The thermal management module includes: The rear vehicle insulation compartment thermostat is installed in the brake assembly compartment of the rear vehicle body and is used to receive temperature control instructions and adjust the working status of the heating belt and semiconductor refrigeration plate; Thermal management optimization unit, integrated into the optimization control module, is used to receive deformation compensation parameters and update thermal management strategies; The hierarchical heating unit is connected to the data acquisition module and the thermostat of the rear vehicle insulation compartment. It is used to start the heating tape at full power when the temperature is lower than the preset low temperature threshold according to the temperature data of the data acquisition module, switch to the temperature balancing mode of the semiconductor refrigeration chip in the preset temperature range, and transmit the temperature control command to the thermostat of the rear vehicle insulation compartment through the CAN bus; The deformation compensation unit is connected to the optimization control module and the historical deformation database, and is used to dynamically adjust the compensation threshold of the deformation mechanism according to the stress-strain curve of the sealing material and the historical deformation database, and feed back the output parameters to the thermal management optimization unit through the communication bus.

7. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 6, characterized in that: The Kalman filter module includes: a multimodal analysis submodule, which is arranged in the adjustment module and is used to receive dynamic load distribution data and analyze the multi-source sensor weight pattern; The temperature compensation interface unit is connected to the hierarchical heating unit of the thermal management module and is used to adjust the process noise covariance weight according to the temperature gradient data output by the thermal management module, and input the adjusted noise covariance matrix parameters into the dynamic load feedback unit; The dynamic load feedback unit is connected to the temperature compensation interface unit and the multimodal analysis submodule, and is used to fuse and calibrate the optimized load distribution data based on the noise covariance matrix parameters, and transmit it to the multimodal analysis submodule of the adjustment module through the SPI bus.

8. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 7, characterized in that: The redundancy strategy optimization unit also includes: The simulation environment server is deployed on the cloud platform of the vehicle control system to simulate intermittent connection failure scenarios of the communication bus; The OTA upgrade module is integrated into the rear vehicle hydraulic control unit of the execution module and is used to receive and write autonomous braking curve parameters; The hardware-in-the-loop test unit is connected to the simulation environment server and the standby core activation unit of the execution module, and is used to inject intermittent connection failures of the communication bus into the simulation environment, generate a Pareto front solution set, and transmit the solution set to the parameter linkage unit via Ethernet; The parameter linkage unit is connected to the hardware-in-the-loop test unit, the OTA upgrade module and the spare core activation unit. It is used to write the autonomous braking curve parameters generated by the hardware-in-the-loop test unit into the rear vehicle hydraulic control unit through the OTA upgrade module, and share the master-slave switching threshold data with the spare core activation unit of the execution module through the FlexRay bus.

9. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 8, characterized in that: The low temperature hysteresis compensation unit also includes: The screw mechanism sensor is integrated into the data acquisition module and is used to collect the displacement and torque data of the screw mechanism in real time; The screw mechanism lubrication monitoring submodule is connected to the screw mechanism sensor and is used to calculate the action delay based on the displacement and torque data of the screw mechanism, generate a historical hysteresis database, and input the delay data into the compensation coefficient update submodule through the SPI bus; The compensation coefficient updating submodule is connected to the fuzzy rule evolution unit and the screw mechanism lubrication monitoring submodule. It is used to dynamically adjust the temperature compensation coefficient based on the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module and the historical hysteresis data, and feed back the updated temperature compensation coefficient to the rule base learning interface of the fuzzy rule evolution unit through the CAN bus.

10. The crawler-type articulated vehicle braking system for use in low-temperature environments according to claim 9, characterized in that: The staged heating unit also includes: The semiconductor refrigeration chip array is integrated into the thermostat of the rear vehicle insulation compartment to perform temperature balance control; A parameter self-learning unit is provided in the thermal management module and is used to update thermal management parameters according to the early warning signal; The local overheating suppression submodule is connected to the semiconductor refrigeration chip array and the Kalman filter module. It is used to balance the temperature distribution in the insulation chamber through the semiconductor refrigeration chips and transmit the real-time temperature data to the seal aging warning submodule through the I2C bus. The seal aging warning submodule is connected to the deformation compensation unit and the parameter self-learning unit. It is used to predict the seal life according to the deformation threshold parameter output by the deformation compensation unit and transmit the warning signal to the parameter self-learning unit through the FlexRay bus.

Images