Crawler-type articulated vehicle braking system applied to low-temperature environment

Abstract

The technical scheme of the invention relates to the technical field of control and adjustment, in particular to a crawler-type articulated vehicle braking system applied to a low-temperature environment. Through multi-module collaborative optimization and a closed-loop control mechanism, the problems of unbalanced dynamic distribution of braking force, insufficient redundancy fault tolerance and signal distortion under the low-temperature working condition are solved. The data acquisition module is used for acquiring a hinging angle, an inertial measurement unit attitude, a braking temperature and a vehicle state signal in real time; the optimization control module performs multi-objective optimization on the dynamic weight coefficient, the redundancy switching threshold value and the fuzzy rule parameter based on a genetic algorithm to generate a weight distribution scheme of a hinge angle compensation mode and an inertial measurement unit dominant mode; the adjusting module generates a brake pressure reference value through temperature self-adaptive analysis and synchronizes hydraulic control units of a front vehicle and a rear vehicle, and all the modules remarkably improve the dynamic stability and the low-temperature working condition fault-tolerant capability of a brake system through full-link cooperation of environment perception, parameter iteration and redundancy execution.

Description

Technical Field

[0001] The present invention relates to the technical field of control and regulation, and in particular to a braking system for a tracked articulated vehicle applied to a low-temperature environment. Background Art

[0002] When a tracked articulated vehicle operates in a low-temperature environment, the multi-degree-of-freedom motion coupling characteristics and the multiple constraints of the low-temperature environment on mechanical and hydraulic systems form a complex dynamic control problem. Due to the low temperature causing changes in material stiffness, deterioration of lubrication performance, and abnormal transmission clearances of actuators, the existing open-loop or single-feedback control modes are difficult to dynamically compensate for the non-linear disturbances caused by articulated steering and load changes; at the same time, the sensor signal drift and actuator response lag under low-temperature environments will weaken the system state identification accuracy, making the control strategy based on fixed thresholds unable to effectively coordinate the motion trajectories and torque distributions of the front and rear vehicle bodies, and thus resulting in vehicle steering instability or traction loss. To address the above problems, it is necessary to adopt a multi-mode control architecture with environmental parameter adaptability, integrate multi-source state perception and dynamic parameter identification algorithms, and construct an actuator collaborative control mechanism with redundant fault tolerance capabilities, so as to achieve real-time decision-making and adaptive reconstruction of vehicle kinematic and dynamic constraints under working conditions.

[0003] In a low-temperature environment, the braking system of a tracked articulated vehicle suffers from performance degradation of hydraulic and mechanical components due to the low-temperature effect. The core technical pain point is that the existing control architecture cannot achieve dynamic coordination and redundant fault tolerance of multiple braking units. The existing braking solutions rely on independent control logics for the front and rear vehicles. Under low-temperature working conditions, the control parameters of each unit are easily deviated due to environmental disturbances, resulting in unbalanced braking force distribution and unable to compensate for the actuator response delay caused by low temperature through real-time interaction; at the same time, the separate control mechanisms for parking braking and service braking weaken the system state monitoring and fault reconstruction capabilities. When a single control link fails due to low temperature, there is no adaptive redundant switching strategy, resulting in the system being unable to maintain dynamic stability and emergency braking functions in low-temperature and sudden fault scenarios. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a braking system for a tracked articulated vehicle applied to a low-temperature environment, which is used to solve the problems of unbalanced dynamic distribution of braking force, insufficient low-temperature failure tolerance, and unstable emergency braking function caused by the lack of coordination of the control architecture and the weakness of redundancy of multiple braking units of a tracked articulated vehicle in a low-temperature environment.

[0005] To solve the above technical problems, the specific technical solutions of the present invention are as follows: A braking system for a tracked articulated vehicle applied to a low-temperature environment according to the present invention includes: A data acquisition module, which is used to obtain sensor data. The sensor data includes articulated angle sensor signals, brake pedal displacement signals, pitch angle and yaw angle data output by an inertial measurement unit, vehicle slope signals, gear state signals, and brake component temperature data; An optimization control module, which is used to receive sensor data and perform multi-objective optimization on dynamic weight coefficients, redundancy switching thresholds, fuzzy rule parameters, thermal management parameters, and Kalman filter noise covariance matrices through a genetic algorithm. Among them, the multi-objectives include brake pressure deviation, yaw angle stability index, and temperature maintenance energy consumption; An adjustment module, which is used to analyze the driver's intention according to the optimized dynamic weight coefficient, generate a brake pressure reference value, and transmit the reference value to the pre-installed front vehicle hydraulic control unit and rear vehicle hydraulic control unit through a communication bus; An execution module, which is used to switch to a standby control core when the main control link communication is abnormal based on the optimized redundancy switching threshold and the reference value of the adjustment module, and call the autonomous braking curve parameters generated by the optimization control module to control the rear vehicle hydraulic control unit; A decision-making module, which is used to cooperate with a preset electronic parking brake controller and a vehicle controller to generate a parking brake force release timing instruction according to the optimized fuzzy rule parameters. The fuzzy rule parameters include a temperature compensation coefficient and a priority mapping relationship related to the slope; A thermal management module, which is 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; A Kalman filter module, which 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 multi-source sensor data, and output the dynamic load distribution to the adjustment module.

[0006] Furthermore, the present invention is applied to a braking system of a tracked articulated vehicle in a low-temperature environment. The optimization control module includes: A dynamic weight optimization unit, which is connected to the data acquisition module, and is used to perform genetic algorithm iterative optimization on the weight coefficients of the articulated angle, pedal displacement, and inertial measurement unit data with the deviation between the brake pressure reference value and the actual demand as the fitness function, and output the optimized weight coefficients to the redundancy strategy optimization unit and the adjustment module; A redundancy strategy optimization unit, which is connected to the dynamic weight optimization unit and the execution module, and is used to use the weight coefficients output by the dynamic weight optimization unit as the initial constraint conditions, and optimize the master-slave switching threshold and the autonomous braking curve trigger condition with 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 through a communication bus; A fuzzy rule evolution unit is connected to the decision module and the electronic parking brake controller, and 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; Among them, the weight coefficient output by the dynamic weight optimization unit is used as the initial input constraint of the redundant strategy optimization unit; the autonomous braking curve trigger condition generated by the redundant strategy optimization unit is bound to the backup control core parameter of the execution module; the rule table output by the fuzzy rule evolution unit is dynamically updated through the firmware interface of the electronic parking brake controller.

[0007] Furthermore, the present invention is applied to a brake system of a tracked articulated vehicle in a low temperature environment, and the adjustment module comprises: Ambient temperature sensor, integrated in the data acquisition module, used to collect temperature data of the brake components in real time; a multi-modal analysis submodule, connected to the ambient temperature sensor and the dynamic weight optimization unit, for matching the weight mode according to the ambient temperature sensor data, enabling the articulation angle compensation mode when the temperature is lower than a preset low temperature threshold, enabling the inertial measurement unit data dominant mode in a preset temperature range, and transmitting the mode selection instruction to the reference value correction submodule through an internal bus; The reference value correction submodule is connected to the front vehicle hydraulic control unit, the rear 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 front vehicle hydraulic control unit, synchronously update the reference value parameter library of the rear 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; 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 front vehicle hydraulic control unit and the rear vehicle hydraulic control unit shares the communication bus resources with the standby core activation unit of the execution module.

[0008] Furthermore, the present invention is applied to a crawler-type articulated vehicle braking system in a low-temperature environment, and the execution module includes: Wheel speed sensor, integrated in the data acquisition module, 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, and is used to count the packet loss rate and delay time of the communication bus. When the cumulative signal loss exceeds the set threshold, the autonomous decision instruction is triggered, and the bus status data is transmitted to the standby core activation unit through the internal interrupt signal; Standby nuclear activation unit, connected to the communication monitoring unit, reference value correction sub-module and rear vehicle hydraulic control unit, is used to call the reference value correction algorithm generated by the reference value correction sub-module of the adjustment module, complete the missing control instructions by interpolating the wheel speed sensor data, and transmit the interpolation instructions to the redundant control interface of the rear vehicle hydraulic control unit through the CAN bus; Among them, the bus status data of the communication monitoring unit triggers the start of the interpolation algorithm of the standby nuclear activation unit; the interpolation instructions generated by the standby nuclear activation unit and the reference value correction parameters of the adjustment module are synchronized and bound through timestamps.

[0009] Furthermore, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The decision-making module includes: Vehicle controller, preset in the vehicle control system, is used to provide vehicle real-time slope and gear state signals; Historical hysteresis database, stored in the data acquisition module, is used to record the action delay data of the braking mechanism; Low-temperature hysteresis compensation unit, connected to the thermal management module and the fuzzy rule evolution unit, is used to superimpose a compensation coefficient on the fuzzy rule output according to 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; Timing priority mapping unit, connected to the vehicle controller and the electronic parking brake controller, is used to dynamically adjust the release timing of the parking braking force based on the compensation coefficient according to the vehicle slope and gear state 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; 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 and the real-time data of the vehicle controller are synchronized through timestamps; the release timing instruction triggers the firmware execution program of the electronic parking brake controller.

[0010] Furthermore, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The thermal management module includes: Rear vehicle thermal insulation bin thermostat, set in the braking component bin of the rear vehicle body, is used to receive temperature control instructions and adjust the working states of the heating tape and the semiconductor refrigeration sheet; Thermal management optimization unit, integrated in the optimization control module, is used to receive deformation compensation parameters and update the thermal management strategy; Hierarchical heating unit, connected to the data acquisition module and the rear vehicle thermal insulation bin thermostat, is used to start the full-power operation of the heating tape 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 equalization mode of the semiconductor refrigeration sheet in the preset temperature range, and transmit the temperature control instruction to the rear vehicle thermal insulation bin thermostat through the CAN bus; A deformation compensation unit, connected to the optimization control module and the historical deformation database, 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 feedback the output parameters to the thermal management optimization unit through the communication bus; Among them, the control signal of the thermoelectric cooler of the hierarchical heating unit is bound to the temperature equalization logic of the temperature controller of the rear vehicle insulation bin; the output parameters of the deformation compensation unit trigger the genetic algorithm parameter iteration of the thermal management optimization unit.

[0011] Furthermore, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The Kalman filter module includes: a multi-modal analysis sub-module, arranged in the adjustment module, for receiving dynamic load distribution data and analyzing the weight patterns of multi-source sensors; A temperature compensation interface unit, connected to the thermal management module and the hierarchical heating unit, 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; A dynamic load feedback unit, connected to the temperature compensation interface unit and the multi-modal analysis sub-module, is used to fuse and calibrate the optimized load distribution data based on the noise covariance matrix parameters, and transmit it to the multi-modal analysis sub-module of the adjustment module through the SPI bus; Among them, the temperature gradient data of the temperature compensation interface unit comes from the hierarchical heating unit of the thermal management module; the fusion calibration logic of the dynamic load feedback unit and the weight pattern of the multi-modal analysis sub-module are synchronized by timestamps; the calibrated load distribution data triggers the reference value correction operation of the adjustment module.

[0012] Furthermore, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The redundancy strategy optimization unit further includes: A simulation environment server, deployed on the cloud platform of the vehicle control system, is used to simulate the communication bus intermittent connection fault scenario; An OTA upgrade module, integrated in the rear vehicle hydraulic control unit of the execution module, is used to receive and write the autonomous braking curve parameters; A hardware-in-the-loop test unit, connected to the simulation environment server and the standby core activation unit of the execution module, is used to inject communication bus intermittent connection faults in the simulation environment, generate a Pareto front solution set, and transmit the solution set to the parameter linkage unit through the Ethernet; A parameter linkage unit, connected to the hardware-in-the-loop test unit, the OTA upgrade module and the standby core activation 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 through OTA, and share the master-slave switching threshold data with the standby core activation unit of the execution module through the FlexRay bus; Among them, the Pareto solution set 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 writing operation of the OTA upgrade module is compatible with the redundant control interface protocol of the rear vehicle hydraulic control unit.

[0013] Furthermore, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The low-temperature hysteresis compensation unit further includes: A screw mechanism sensor, integrated in the data acquisition module, for real-time acquisition of the displacement and torque data of the screw mechanism; A screw mechanism lubrication monitoring sub-module, connected to the screw mechanism sensor, for calculating the action delay amount according to the displacement and torque data of the screw mechanism, generating a historical hysteresis database, and inputting the delay data into the compensation coefficient update sub-module through the SPI bus; A compensation coefficient update sub-module, connected to the fuzzy rule evolution unit and the screw mechanism lubrication monitoring sub-module, for dynamically adjusting the compensation coefficient according to the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module, in combination with the historical hysteresis data, and feeding back the updated compensation coefficient to the rule base learning interface of the fuzzy rule evolution unit through the CAN bus; Among them, the delay calculation logic of the screw mechanism lubrication monitoring sub-module is bound to the storage rule of the historical hysteresis database; the parameter adjustment of the compensation coefficient update sub-module triggers the iterative optimization of the membership function of the fuzzy rule evolution unit.

[0014] Furthermore, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The hierarchical heating unit further includes: A thermoelectric cooler array, integrated in the rear vehicle insulation bin thermostat, for performing temperature equalization control; A parameter self-learning unit, arranged in the thermal management module, for updating the thermal management parameters according to the warning signal; A local overheating suppression sub-module, connected to the thermoelectric cooler array and the Kalman filter module, for equalizing the temperature distribution in the insulation bin through the thermoelectric cooler and transmitting the real-time temperature data to the seal aging warning sub-module through the I2C bus; A seal aging warning sub-module, connected to the deformation compensation unit and the parameter self-learning unit, for predicting the seal life according to the deformation threshold parameters output by the deformation compensation unit and transmitting the warning signal to the parameter self-learning unit through the FlexRay bus; Among them, the temperature equalization logic of the local overheating suppression sub-module is bound to the control signal of the thermoelectric cooler array; the life prediction model of the seal aging warning sub-module is generated based on the historical learning curve of the deformation threshold parameters; the update operation of the parameter self-learning unit triggers the iterative optimization of the thermal management parameters of the optimization control module.

[0015] Advantages of the present invention The advantages of the present invention lie in significantly improving the dynamic stability and redundant fault tolerance ability of the braking system of tracked articulated vehicles in low-temperature environments through multi-module collaborative optimization and closed-loop control mechanisms: The data acquisition module obtains multi-source sensor signals in real time. The optimization control module dynamically adjusts the weight coefficients and redundant strategy parameters based on the genetic algorithm to solve the problem of unbalanced priority allocation of sensor signals caused by low temperature; The adjustment module realizes the dynamic adaptation of the braking force distribution between the front and rear vehicles through temperature-adaptive multi-modal analysis and reference value correction; The execution module triggers the standby core activation mechanism when the main control link is abnormal, and generates redundant control instructions by interpolating wheel speed data to ensure the continuity of the braking function; The thermal management module suppresses the deterioration of the performance of sealing materials through hierarchical heating and deformation compensation. The Kalman filter module adjusts the noise model according to the temperature gradient to improve the reliability of signal fusion; The low-temperature hysteresis compensation unit corrects the output of fuzzy rules by combining historical action delay data, and collaboratively optimizes the parking brake release logic through the mapping of timing priorities. The above technical solutions effectively solve the problems of unbalanced braking force distribution, insufficient low-temperature failure tolerance, and unstable emergency braking under low-temperature working conditions through the full-link collaboration of environmental perception, parameter iterative optimization, and redundant execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the drawings.

[0017] Figure 1 It is a system architecture diagram of a braking system for a tracked articulated vehicle applied to a low-temperature environment provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, 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 described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below in conjunction with the drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0019] Please refer to Figure 1 , a braking system for a tracked articulated vehicle applied to a low-temperature environment according to the present invention, includes: A data acquisition module, configured to acquire articulated angle sensor signals, brake pedal displacement signals, pitch angle and yaw angle data output by an inertial measurement unit, vehicle slope signals, gear state signals, and brake component temperature data; An optimization control module, connected to the data acquisition module, configured to receive sensor data and perform multi-objective optimization on dynamic weight coefficients, redundancy switching thresholds, fuzzy rule parameters, thermal management parameters, and Kalman filter noise covariance matrices through a genetic algorithm. The multi-objectives include brake pressure deviation, yaw angle stability index, and temperature maintenance energy consumption; An adjustment module, connected to the optimization control module, configured to analyze the driver's intention according to the optimized dynamic weight coefficients, generate a brake pressure reference value, and transmit the reference value to the front vehicle hydraulic control unit and the rear vehicle hydraulic control unit through a communication bus; An execution module, connected to the optimization control module and the adjustment module, configured to switch to a standby control core when the main control link communication is abnormal based on the optimized redundancy switching threshold and the reference value of the adjustment module, and call the autonomous braking curve parameters generated by the optimization control module to control the rear vehicle hydraulic control unit; A decision-making module, connected to the optimization control module and the execution module, configured to generate a parking brake force release timing instruction in cooperation with a preset electronic parking brake controller and a vehicle controller according to the optimized fuzzy rule parameters. The fuzzy rule parameters include a temperature compensation coefficient and a slope-related priority mapping relationship; A thermal management module, connected to the data acquisition module and the Kalman filter module, configured 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; A Kalman filter module, connected to the data acquisition module and the adjustment module, configured to adjust the noise covariance matrix according to the temperature compensation parameters of the thermal management module, fuse multi-source sensor data, and output a dynamic load distribution to the adjustment module; Among them, the articulated 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 coefficients 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 vehicle state data with the decision-making 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.

[0020] The present invention provides a braking system for a tracked articulated vehicle applied to a low-temperature environment. Its technical solution realizes dynamic control and redundancy fault tolerance in a low-temperature environment through multi-module collaborative optimization.

[0021] The data acquisition module obtains vehicle operation status data in real time through multi-source sensors, including articulation angle sensor signals, brake pedal displacement signals, pitch angle and yaw angle data output by the inertial measurement unit, vehicle slope signals, gear status signals and brake component temperature data. Among them, the inertial measurement unit data is used to capture vehicle posture changes, and the brake component temperature data is collected through distributed temperature control probes to provide ambient temperature gradient input for subsequent thermal management modules. All sensor signals are transmitted to the optimization control module through a high-speed communication bus, forming a multi-modal data input basis.

[0022] The optimization control module receives the sensor data from the data acquisition module, and uses the genetic algorithm to perform multi-objective optimization on the dynamic weight coefficient, redundant switching threshold, fuzzy rule parameters, thermal management parameters and Kalman filter noise covariance matrix. In the specific implementation, the genetic algorithm uses the brake pressure deviation, yaw angle 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 uses the mean square error between the brake pressure reference value and the actual demand as the evaluation index to generate the weight parameter set; the redundant strategy optimization unit uses the communication delay and braking distance as constraints to optimize the master-slave switching threshold; the fuzzy rule evolution unit optimizes the membership function parameters based on the parking brake force release timing error. The optimized parameters are synchronized to the adjustment module and the execution module through the communication bus.

[0023] The adjustment module analyzes the driver's intention according to the optimized dynamic weight coefficient and generates a brake pressure reference value. The multimodal analysis submodule matches the weight mode according to the ambient temperature sensor data: when the temperature is lower than the preset low temperature threshold, the weight mode focusing on articulation angle compensation is enabled; within the preset temperature range, it switches to the inertial measurement unit data-dominated mode. The reference value correction submodule loads the optimized weight coefficient to the hydraulic control unit of the leading vehicle, and synchronously updates the reference value parameter library of the hydraulic control unit of the trailing vehicle through the communication bus. The corrected brake pressure reference value is fed back to the execution module in real time as the reference input for redundant control.

[0024] The execution module switches to the standby control core when the communication of the main control link is abnormal based on the optimized redundant switching threshold and the reference value of the adjustment module. The communication monitoring unit counts the packet loss rate and delay time of the communication bus. When the cumulative signal loss exceeds the set threshold, the standby core activation unit is triggered. The standby core activation unit calls the reference value correction algorithm of the adjustment module, combines the wheel speed sensor data to interpolate and complete the missing control instructions, and transmits them to the rear vehicle hydraulic control unit through the private communication bus. The interpolation algorithm uses a linear regression model and uses the wheel speed change rate as input to dynamically correct the braking instruction.

[0025] The decision-making module generates a parking brake force release timing instruction according to the optimized fuzzy rule parameters. The low-temperature hysteresis compensation unit receives the temperature data from the thermal management module, superimposes a compensation coefficient based on the historical hysteresis database on the output of the fuzzy rule, and suppresses the action delay caused by the lubrication deterioration of the screw mechanism. The timing priority mapping unit takes the vehicle slope and gear state provided by the vehicle controller as inputs and dynamically adjusts the parking brake force release priority. The optimized instruction is transmitted to the electronic parking brake controller via the CAN bus to achieve the coordinated control of parking and driving braking.

[0026] The thermal management module adjusts the heating power of the heating tape based on the optimized thermal management parameters. The hierarchical heating unit triggers the heating strategy according to the temperature data of the data acquisition module: when the temperature is lower than the preset low-temperature threshold, the heating tape is started to operate at full power; in the preset temperature range, it switches to the temperature equalization mode of the thermoelectric cooler to prevent local overheating. The deformation compensation unit dynamically adjusts the compensation threshold of the deformation mechanism according to the stress-strain curve of the sealing material and the historical deformation database, and outputs the parameters to the thermal management optimization unit of the optimized control module to form a parameter self-learning closed loop.

[0027] The Kalman filter module adjusts the noise covariance matrix according to the temperature compensation parameters of the thermal management module. The temperature compensation interface unit receives the temperature gradient data, dynamically increases the weight of the process noise covariance, and suppresses the signal drift of the inertial measurement unit in a low-temperature environment. After fusing the multi-source sensor data, the dynamic load feedback unit transmits the optimized load distribution data to the multi-modal analysis sub-module of the adjustment module via the bus, as the input basis for the weight mode switching, and forms a positive feedback on the sensor data quality.

[0028] The articulated angle, inertial measurement unit data, and temperature data of the data acquisition module are input to the optimized control module to drive the genetic algorithm parameter optimization; The dynamic weight coefficient of the optimized control module is transmitted to the adjustment module to generate a brake pressure reference value and feedback it to the execution module; The vehicle state data of the execution module is shared with the decision-making module to jointly generate a parking control instruction; The temperature compensation parameters of the thermal management module are input to the Kalman filter module. After adjusting the noise model, the dynamic load data is output to the adjustment module; The dynamic load distribution data of the Kalman filter module is reversely input to the adjustment module to form a closed-loop iterative optimization link.

[0029] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The optimized control module includes: A dynamic weight optimization unit for iteratively optimizing the weight coefficients of the articulated angle, pedal displacement, and inertial measurement unit data using the deviation between the brake pressure reference value and the actual demand as the fitness function by means of a genetic algorithm; The redundancy strategy optimization unit, connected to the dynamic weight optimization unit, is used to optimize the master-slave switching threshold and the triggering condition of the autonomous braking curve with the weight coefficients output by the dynamic weight optimization unit as the initial constraint conditions, and with communication delay, yaw angle stability, and braking distance as the multi-objective fitness functions; The fuzzy rule evolution unit is used to optimize the parameters of the fuzzy membership function and the structure of the rule base with the parking brake force release timing error and the probability of mechanical interference as the constraints; The switching threshold parameter input execution module of the redundancy strategy optimization unit; the rule table of the fuzzy rule evolution unit is updated to the electronic parking brake controller.

[0030] The optimization control module of the present invention realizes parameter optimization and strategy generation through multi-unit collaboration. The specific technical solution and logical relationship are as follows: The dynamic weight optimization unit receives the articulated angle, brake pedal displacement, and inertial measurement unit data of the data acquisition module, uses the deviation between the brake pressure reference value and the actual demand as the fitness function, and iteratively optimizes the weight coefficients of each sensor data through the genetic algorithm. The initialization of the genetic algorithm includes the candidate solution set of the weight coefficients, injects low-temperature disturbance variables into the simulation environment, and screens the optimal weight combination with the smallest mean square error through crossover and mutation operations. The optimized weight coefficients are synchronized to the front vehicle hydraulic control unit through the communication bus and used as the initial constraint conditions for the redundancy strategy optimization unit. The temperature gradient data is input through the thermal management module to drive the dynamic weight optimization unit to switch the weight mode in different temperature ranges.

[0031] The redundancy strategy optimization unit constructs a multi-objective fitness function including communication delay, yaw angle stability, and braking distance with the weight coefficients output by the dynamic weight optimization unit as the constraints, and optimizes the master-slave switching threshold and the triggering condition of the autonomous braking curve. Simulate the CAN bus intermittent connection fault in the hardware-in-the-loop test platform, generate the Pareto front solution set, and screen the optimal parameters that take into account both response speed and stability. The optimized master-slave switching threshold is input to the communication monitoring unit of the execution module through the parameter linkage unit, and the autonomous braking curve parameters are written into the parameter library of the rear vehicle hydraulic control unit through OTA to form a dynamic update mechanism for the redundancy control strategy.

[0032] The fuzzy rule evolution unit optimizes the parameters of the fuzzy membership function and the structure of the rule base with the parking brake force release timing error and the probability of mechanical interference as the constraint conditions. The historical hysteresis database records the action delay data of the screw mechanism at low temperature, and iteratively optimizes the center point of the membership function and the weights of the rule antecedent / consequent through the genetic algorithm. The optimized fuzzy rule table is updated to the electronic parking brake controller through the flashing tool, and at the same time receives the temperature data of the thermal management module, and superimposes the temperature compensation coefficient on the rule output. The compensation coefficient is dynamically adjusted according to the real-time temperature gradient to suppress the control conflict caused by lubrication deterioration.

[0033] The data flow between each unit forms a closed loop: the output parameters of the dynamic weight optimization unit drive the redundancy strategy optimization unit to generate a switching threshold; the independent braking curve parameters of the redundancy strategy optimization unit interact with the execution module; 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. The above collaborative mechanism enables the multi-objective optimization parameters to penetrate the control link, improving the dynamic collaborative accuracy and fault tolerance of the braking system in low-temperature environments.

[0034] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The adjustment module includes: A multi-modal analysis sub-module for matching the weight mode according to the ambient temperature sensor data. When the temperature is lower than the preset low-temperature threshold, the articulated angle compensation mode is enabled, and the inertial measurement unit data dominant mode is enabled in the preset temperature range. A reference value correction sub-module for loading the weight coefficient output by the dynamic weight optimization unit of the optimization control module into the front vehicle hydraulic control unit and synchronously updating the reference value parameter library of the rear vehicle hydraulic control unit through the communication bus. The multi-modal analysis sub-module receives the temperature sensor data and transmits the mode selection instruction to the reference value correction sub-module; the reference value correction sub-module inputs the corrected braking pressure reference value into the execution module.

[0035] The adjustment module of the present invention realizes the dynamic adaptation of the braking pressure reference value through the collaborative mechanism of multi-modal analysis and reference value correction. The specific technical solution and logical relationship are as follows: The multi-modal analysis sub-module receives the real-time temperature data collected by the ambient temperature sensor and matches the weight mode according to the preset low-temperature threshold and the temperature range division rule. When the temperature is lower than the preset low-temperature threshold, the system enables the articulated angle compensation mode, increasing the weight coefficient of the articulated angle sensor data to compensate for the abnormal mechanical transmission gap caused by low temperature; within the preset temperature range, it switches to the inertial measurement unit data dominant mode, enhancing the contribution of the pitch angle and yaw angle data output by the inertial measurement unit in the braking pressure calculation. The mode selection instruction is transmitted to the reference value correction sub-module through the internal data bus, triggering the dynamic loading logic of the weight coefficient.

[0036] The reference value correction sub-module receives the set of weight coefficients 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 front vehicle hydraulic control unit. The weight coefficient is synchronized to the reference value parameter library of the rear vehicle hydraulic control unit through the communication bus to achieve the collaborative update of the braking pressure reference values of the front and rear vehicles. During the synchronization process, a differential verification mechanism is adopted to compare the parameter version consistency of the front and rear vehicles. If a version difference is detected, the weight coefficient retransmission process is triggered. The corrected braking pressure reference value is input into the standby core activation unit of the execution module through the high-speed bus as the reference input for generating redundant control instructions.

[0037] The multimodal analysis sub-module and the reference value correction sub-module form a closed-loop interaction link: the temperature data drives the mode switch, the mode instruction triggers the update of the weight coefficient, and the corrected reference value is fed back to the execution module to control the braking pressure output. This link uses a weight distribution mechanism that adapts to the ambient temperature to suppress the sensor signal drift and actuator response lag caused by low temperature, enabling the braking pressure reference value to dynamically match the real-time working conditions. The data flow between the modules runs through the temperature perception, parameter loading, and instruction execution links.

[0038] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The execution module includes: A communication monitoring unit, which is used to count the packet loss rate and delay time of the communication bus, and triggers an autonomous decision-making instruction when the cumulative signal loss exceeds a set threshold; A spare core activation unit, which is used to call the reference value correction algorithm generated by the reference value correction sub-module of the adjustment module, and interpolate and complete the missing control instructions in combination with the wheel speed sensor data; The communication monitoring unit inputs the bus status data into the spare core activation unit; the interpolation instruction output by the spare core activation unit is transmitted to the rear vehicle hydraulic control unit through the communication bus.

[0039] The execution module of the present invention realizes the fault-tolerant control of the braking system through the collaborative mechanism of communication status monitoring and redundant control instruction generation. The specific technical solution and logical relationship are as follows: The communication monitoring unit continuously collects the packet transmission status of the communication bus, and counts the packet loss rate and delay time through the sliding window algorithm. The packet loss rate is calculated based on the ratio of the number of lost packets to the total number of sent packets within a unit time, and the delay time is measured by the timestamp difference 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 according to the reliability test data of the communication link in a low-temperature environment. For example, it is dynamically adjusted based on the median of the historical failure rate at -40°C. The bus status data is transmitted to the spare core activation unit through the internal register, triggering the redundant control mode switch.

[0040] After receiving the bus status data from the communication monitoring unit, the spare core activation unit calls the reference value correction algorithm generated by the reference value correction sub-module of the adjustment module. The reference value correction algorithm is based on the weight coefficient loaded by the front vehicle hydraulic control unit, and combines the real-time wheel speed change rate collected by the wheel speed sensor to complete the missing control instructions using the linear interpolation algorithm. During the interpolation process, the wheel speed sensor data is used as the input to predict the braking pressure demand curve during the missing time period, generating a smooth redundant control instruction. The interpolated instruction is transmitted to the rear vehicle hydraulic control unit through the private communication bus. The bus protocol uses a time-triggered mechanism to complete the instruction distribution within a fixed time slot, avoiding bus conflicts.

[0041] The communication monitoring unit and the standby nuclear activation unit form a closed-loop control link: the abnormal bus state triggers the autonomous decision-making process, the redundant instructions are dynamically generated based on the reference value correction algorithm and real-time sensor data, and the interpolation results are sent for execution through a dedicated communication channel. This link suppresses the intermittent problem of control instructions caused by low temperature through a communication state perception and data-driven interpolation compensation mechanism, ensuring the dynamic coordination of the braking pressures of the front and rear vehicles. The data interaction between modules covers the state monitoring, algorithm call, and instruction transmission links.

[0042] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The decision-making module includes: A low-temperature hysteresis compensation unit, which is used to superimpose a compensation coefficient based on historical hysteresis data on the output of the fuzzy rule according to the temperature data output by the thermal management module; A timing priority mapping unit, which is used to adjust the release timing of the parking braking force according to the vehicle slope and gear state provided by the vehicle controller; 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.

[0043] The decision-making module of the present invention realizes the precise control of the parking braking force through the collaborative mechanism of temperature compensation and priority mapping. Its specific technical solution and logical relationship are as follows: The low-temperature hysteresis compensation unit receives the real-time temperature data output by the thermal management module, combines the action delay data of the screw mechanism recorded in the historical hysteresis database, and generates a temperature-hysteresis characteristic mapping table. The compensation coefficient is dynamically calculated according to the current temperature gradient and the fitting result of the historical hysteresis curve, and is superimposed on the output of the fuzzy rule. 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 linkage adjustment of the compensation coefficient and the membership function. When the temperature is lower than the preset threshold, the compensation coefficient is corrected based on the exponentially weighted average value of the historical hysteresis data, suppressing the mechanism response delay caused by the increase in the lubricant viscosity.

[0044] The timing priority mapping unit receives the vehicle slope angle and gear state signals provided by the vehicle controller, and analyzes the release priority of the parking braking force through the state machine model. After being preprocessed by the Kalman filter module, the slope angle signal is divided into multiple discrete intervals, and each interval corresponds to a different braking force release time window. The gear state signal triggers the adjustment of the initial loading threshold of the parking braking force. For example, the release delay is shortened when in reverse gear. The priority mapping table is dynamically updated by querying the fuzzy rule parameters generated by the optimization control module, and the compensation coefficient is input to further adjust the weight distribution of the timing logic.

[0045] The low-temperature hysteresis compensation unit and the timing priority mapping unit form a data interaction closed-loop: after the compensation coefficient corrects the output of the fuzzy rule, it is input to the timing priority mapping unit to adjust the time window parameters; the execution result of the timing logic is fed back to the fuzzy rule evolution unit to drive the optimization of the next round of the rule base. The optimized parking brake release instruction is transmitted to the electronic parking brake controller through the CAN bus, and the instruction includes timestamp synchronization information for timing alignment with the service brake instruction. The data flow between modules covers temperature compensation, rule iteration, and timing coordination links to achieve conflict-free control of parking brake and service brake at low temperatures.

[0046] Specifically, the present invention is applied to a braking system of a tracked articulated vehicle in a low-temperature environment. The thermal management module includes: A hierarchical heating unit for starting the full-power operation of the heating tape according to the temperature data of the data acquisition module when the temperature is lower than the preset low-temperature threshold and switching to the temperature equalization mode of the thermoelectric cooler in the preset temperature range; A deformation compensation unit for dynamically adjusting the compensation threshold of the deformation mechanism according to the stress-strain curve of the sealing material and the historical deformation database; The temperature control instruction of the hierarchical heating unit is input to the rear vehicle insulation bin thermostat; the output parameters of the deformation compensation unit are fed back to the thermal management optimization unit of the optimization control module.

[0047] The thermal management module of the present invention realizes the low-temperature adaptability adjustment of the braking components through the coordinated control of hierarchical heating and deformation compensation. The specific technical solution and logical relationship are as follows: The hierarchical heating unit receives the real-time temperature data of the data acquisition module and divides the heating strategy execution interval according to the preset low-temperature threshold. When the temperature is lower than the preset low-temperature threshold, the full-power operation mode of the heating tape is started. The heating tape is evenly distributed along the brake pipeline, and the heating power is maintained stable through the PID control algorithm; when the temperature enters the preset temperature range, it switches to the temperature equalization mode of the thermoelectric cooler, and the thermoelectric cooler adjusts the cooling power according to the temperature gradient distribution to suppress local overheating. The temperature control instruction is transmitted to the rear vehicle insulation bin thermostat through a pulse width modulation signal. The thermostat is internally provided with a multi-channel relay to control the power on and off of the heating tape and the thermoelectric cooler, and at the same time monitors the abnormal loop current.

[0048] The deformation compensation unit generates a dynamic compensation threshold based on the stress-strain curve of the sealing material and the historical deformation database. The stress-strain curve is calibrated through a material mechanics experiment, and the historical deformation database records the deformation data of the seal at different temperatures. The sliding average algorithm is used to predict the deformation trend corresponding to the current temperature. The compensation threshold is dynamically adjusted according to the difference between the predicted deformation amount and the real-time deformation sensor data, and the output parameters are fed back to the thermal management optimization unit of the optimization control module through the communication bus to drive the iterative optimization of the thermal management parameters by the genetic algorithm.

[0049] The hierarchical heating unit and the deformation compensation unit form a data interaction closed-loop: after the temperature control instruction of the hierarchical heating unit is executed, the temperature controller of the rear vehicle insulation bin feeds back the actual temperature data to the deformation compensation unit to correct the deformation prediction model; the output parameters of the deformation compensation unit are input into the optimization control module to adjust the preset temperature range of the heating strategy and the power distribution ratio of the semiconductor refrigeration chips. The coordinated control between modules is realized through the temperature-deformation coupling model to suppress the embrittlement of the sealing material and the sudden change of the hydraulic oil viscosity caused by low temperature. The thermal management optimization unit synchronizes the updated parameters to the Kalman filter module to enhance the compensation accuracy of the temperature drift in the dynamic load estimation.

[0050] The above technical solution reduces the influence of the low-temperature environment on the reliability of the braking system through the temperature adaptive heating strategy and the material deformation dynamic compensation mechanism.

[0051] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The Kalman filter module includes: a temperature compensation interface unit for adjusting the process noise covariance weight according to the temperature gradient data output by the thermal management module; a dynamic load feedback unit for transmitting the optimized load distribution data to the multi-modal analysis sub-module of the adjustment module through a bus; The temperature compensation interface unit receives the temperature gradient data of the thermal management module and adjusts the Kalman filter parameters; The output data of the dynamic load feedback unit is input into the multi-modal analysis sub-module of the adjustment module.

[0052] The Kalman filter module of the present invention improves the multi-source data fusion accuracy through the coordinated mechanism of temperature compensation and dynamic load feedback. The specific technical solution and logical relationship are as follows: The temperature compensation interface unit receives the temperature gradient data output by the thermal management module and analyzes the temperature distribution differences of different braking components. Dynamically adjust the weights of the diagonal elements of the process noise covariance matrix of the Kalman filter according to the temperature gradient data. The sensor data in the low-temperature area corresponds to an increase in the noise covariance weight to suppress the signal drift interference caused by material shrinkage. The adjusted noise parameters are input into the state prediction model of the Kalman filter to optimize the load estimation accuracy in the vehicle kinematic equation. The temperature gradient data is normalized through the historical temperature change record library of the thermal management module to match the temperature-noise correlation characteristic curve in the low-temperature environment.

[0053] The dynamic load feedback unit transmits the optimized load distribution data to the multi-modal analysis sub-module of the adjustment module through a high-speed communication bus. The load distribution data includes the dynamic pressure distribution ratio of the front and rear vehicle bodies, which serves as the input basis for the weight mode switching of the multi-modal analysis sub-module. When the load distribution data detects that the pressure ratio of the rear vehicle exceeds the preset threshold, it triggers the multi-modal analysis sub-module to switch to the inertial measurement unit data-dominated mode, increasing the weight coefficient of the yaw angle data. The feedback data is verified for integrity through a checksum mechanism, and abnormal data packets trigger retransmission requests to ensure the real-time generation of control commands.

[0054] The temperature compensation interface unit and the dynamic load feedback unit form a closed-loop data link: the temperature compensation parameters optimize the state estimation results of the Kalman filter, and the dynamic load data is reversely input into the adjustment module to correct the weight distribution; the braking pressure reference value generated by the adjustment module further affects the temperature control strategy of the thermal management module, driving the iterative update of the temperature gradient data. The collaborative control between modules is through the linkage mechanism of noise suppression and load feedback, reducing the impact of sensor signal distortion in low-temperature environments on braking decisions.

[0055] Specifically, for the braking system of a tracked articulated vehicle applied in a low-temperature environment, the redundancy strategy optimization unit further includes: A hardware-in-the-loop test unit, which is used to inject intermittent connection faults into the communication bus in a simulation environment to generate a Pareto front solution set; A parameter linkage unit, which 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 OTA, and share the master-slave switching threshold data with the standby core activation unit of the execution module; The parameter linkage unit and the standby core activation unit exchange switching threshold data through the communication bus.

[0056] The redundancy strategy optimization unit of the present invention realizes the 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: The hardware-in-the-loop test unit simulates intermittent connection faults of the communication bus at low temperatures in a simulation environment, uses a fault injector to periodically insert signal loss events, and triggers the calculation of the fitness function of the multi-objective optimization model. The Pareto front solution set is generated through genetic algorithm iteration. The algorithm takes communication delay, braking distance deviation, and yaw angle stability as optimization objectives, and screens out a set of autonomous braking curve parameters that balance response speed and dynamic stability. After the solution set is generated, it is compared with the actual vehicle test data through an offline verification platform, and parameter combinations with deviations from the actual working conditions exceeding the preset threshold are excluded, and the optimal solution is retained as the parameter to be updated for the rear vehicle hydraulic control unit.

[0057] The parameter linkage unit receives the optimal autonomous braking curve parameters generated by the hardware-in-the-loop test unit and transmits them to the parameter library of the rear vehicle hydraulic control unit in segments through the wireless communication protocol (OTA) encryption. During the transmission process, the cyclic redundancy check code (CRC) is used to verify the data integrity, and the breakpoint resumption mechanism is triggered when the verification fails. The master-slave switching threshold data is shared in real-time with the standby core activation unit of the execution module through the private communication bus. The shared data includes the switching delay time, the bus health score, and the redundant instruction priority label. The standby core activation unit dynamically adjusts the trigger conditions for autonomous decision-making according to the shared threshold data. For example, when the bus health is lower than the critical value, the redundant control core is activated in advance.

[0058] The hardware-in-the-loop test unit and the parameter linkage unit form a closed-loop optimization link: the parameter set generated by the simulation test is updated to the real vehicle control system through OTA, and the real vehicle operation data is fed back to the hardware-in-the-loop test unit in reverse to correct the fault model; the shared threshold data drives the collaborative response of the standby core activation unit and the parameter linkage unit to achieve the dynamic matching of offline optimization and online control. The data interaction between modules covers the links of fault simulation, parameter distribution, and threshold coordination, and improves the redundant fault tolerance ability of the braking system in low-temperature environments through multi-objective optimization and real-time data fusion.

[0059] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The low-temperature hysteresis compensation unit further includes: A screw mechanism lubrication monitoring sub-module for collecting the action delay data of the screw mechanism and generating a historical hysteresis database; A compensation coefficient update sub-module for dynamically adjusting the compensation coefficient according to the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module; The data of the screw mechanism lubrication monitoring sub-module is input into the compensation coefficient update sub-module; the output parameters of the compensation coefficient update sub-module are fed back to the fuzzy rule evolution unit.

[0060] 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 update. Its specific technical solution and logical relationship are as follows: The screw mechanism lubrication monitoring sub-module collects the action delay data of the screw mechanism through a high-precision displacement sensor and calculates the time difference between the actual displacement and the command displacement using the timestamp difference method. The delay data is classified and stored in the historical hysteresis database according to the temperature range. The database is dynamically updated using the sliding window algorithm, and the typical hysteresis characteristics under recent low-temperature working conditions are retained. The temperature sensor collects the surface temperature of the screw mechanism in real-time and generates a temperature-hysteresis characteristic curve in association with the delay data. The curve describes the mapping relationship between the hysteresis amount and the temperature through the polynomial fitting algorithm.

[0061] The compensation coefficient update sub-module receives the fuzzy rule parameters output by the fuzzy rule evolution unit of the optimization control module, and analyzes the center points of the membership functions and the weights of the rule antecedents. The compensation coefficient is dynamically adjusted according to the temperature compensation weight factor in the fuzzy rule parameters, and the exponential weighted average algorithm is used to fuse the current delay data and the predicted values of the historical hysteresis characteristic curves. The adjusted compensation coefficient is superimposed on the fuzzy rule output quantity to correct the hysteresis compensation amount in the parking brake force release timing instruction. The compensation coefficient update period is synchronized with the action frequency of the screw mechanism to avoid instruction conflicts.

[0062] The screw mechanism lubrication monitoring sub-module and the compensation coefficient update sub-module form a two-way data flow: the delay data drives the update of the historical hysteresis database, and the characteristic curve output by the database is input for the calculation of the compensation coefficient; the compensation coefficient is fed back to the fuzzy rule evolution unit to trigger the iterative optimization of the membership function parameters. The fuzzy rule evolution unit adjusts the priority weight of the temperature compensation term in the rule base according to the feedback compensation effect data, forming a co-evolution mechanism between the rule parameters and the compensation coefficient.

[0063] The above technical solution suppresses the influence of the deterioration of the lubricant performance caused by low temperature on the braking timing through real-time monitoring and closed-loop control of rule iteration.

[0064] Specifically, the present invention is applied to the braking system of a tracked articulated vehicle in a low-temperature environment. The hierarchical heating unit further includes: A local overheating suppression sub-module for equalizing the temperature distribution in the thermal insulation bin through a thermoelectric cooler; A seal aging warning sub-module for predicting the life of the seal according to the deformation threshold parameters output by the deformation compensation unit; The temperature data of the local overheating suppression sub-module is input into the seal aging warning sub-module; The warning signal of the seal aging warning sub-module is transmitted to the parameter self-learning unit of the thermal management module through the bus for updating the thermal management parameters.

[0065] The hierarchical heating unit of the present invention optimizes the thermal management strategy through the coordinated control of temperature equalization and life prediction. The specific technical solution and logical relationship are as follows: The local overheating suppression sub-module collects the real-time temperature data of multiple regions in the thermal insulation bin through a distributed temperature sensor array to identify the local overheating position. The thermoelectric cooler dynamically adjusts the cooling power according to the temperature distribution difference, and uses the PID control algorithm to equalize the temperature gradient in the bin. The working state of the cooler is controlled by a pulse width modulation signal, and the cooling power distribution ratio is linearly related to the temperature deviation value, suppressing the concentration of material thermal stress caused by uneven heating of the heat tracing tape. The equalized temperature data is transmitted to the seal aging warning sub-module through the internal bus as the input parameter for seal life prediction.

[0066] The seal aging warning sub-module receives the deformation threshold parameters output by the deformation compensation unit, combines the temperature data of the local overheat suppression sub-module, and predicts the remaining life of the seal based on the Arrhenius accelerated aging model. The prediction model calibrates the activation energy parameter according to the 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. The warning signal is transmitted to the parameter self-learning unit of the thermal management module through the CAN bus, driving the genetic algorithm to iteratively optimize the heating power distribution coefficient and the start threshold of the thermoelectric cooler.

[0067] The data flow between modules forms a closed-loop feedback link: the temperature balance result of the local overheat suppression sub-module is input into the seal aging warning sub-module to correct the temperature influence factor 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 through the hierarchical heating unit, forming a dynamic adjustment cycle of temperature control-life prediction-parameter update. This technical solution realizes the adaptive control of the thermal management of the braking component in a low-temperature environment through multi-physical field coupling analysis.

[0068] Explanation of the technical feature terms of the present invention is as follows: Data acquisition module: A combination of hardware and software for real-time acquisition of vehicle operating state data, including a hinge angle sensor (measuring the mechanical angle change at the connection between the front and rear vehicle bodies), an inertial measurement unit (outputting pitch angle and yaw angle data to reflect the vehicle attitude), a temperature sensor (monitoring the temperature of the braking component), a vehicle slope sensor (detecting the terrain tilt angle), and a gear state sensor. The multi-source data is transmitted to the optimization control module through a high-speed communication bus, providing an input basis for subsequent parameter optimization.

[0069] Optimization control module: The core of control parameter optimization based on the genetic algorithm, including: Dynamic weight optimization unit: Using the deviation between the braking pressure reference value and the actual demand as the fitness function, adjusting the weight coefficients of the hinge angle, pedal displacement, and inertial measurement unit data to solve the problem of dynamic allocation of sensor signal priorities at low temperatures.

[0070] Redundancy strategy optimization unit: Simulating 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, improving the reliability of redundant control.

[0071] Fuzzy rule evolution unit: Optimizing the membership function and rule base based on the parking brake timing error, and dynamically adjusting the control logic in combination with the temperature compensation coefficient to suppress the influence of mechanical hysteresis.

[0072] Adjustment module: Responsible for parsing the driver's intention and generating the braking pressure reference value, including: Multimodal Analysis Sub-module: Switch the weight mode according to the temperature data. For example, when the temperature is below -40°C, enable the articulated angle compensation mode to offset the mechanical clearance error; in the range of -20°C to -40°C, adopt the inertial measurement unit dominant mode to improve the attitude control accuracy.

[0073] Reference Value Correction Sub-module: Load the optimized weight coefficients into the front and rear vehicle hydraulic control units, synchronize the parameter library through the differential calibration mechanism, and achieve dynamic consistency in the braking force distribution between the front and rear vehicles.

[0074] Execution Module: Implement redundant control and instruction execution, including: Communication Monitoring Unit: Statistically analyze the bus packet loss rate and latency time. When the abnormal accumulation exceeds the threshold (such as 5 seconds), trigger the activation of the standby core.

[0075] Standby Core Activation Unit: Invoke the reference value correction algorithm, interpolate and complete the missing instructions in combination with the wheel speed sensor data, and transmit them to the rear vehicle hydraulic control unit through the private CAN bus to ensure braking continuity when the main control link fails.

[0076] Thermal Management Module: Solve the problem of material property degradation in low-temperature environments, including: Hierarchical Heating Unit: When the temperature is below -40°C, start the full-power operation of the heating tape to prevent the hydraulic oil from solidifying; in the range of -20°C to -40°C, switch to the thermoelectric cooler to balance the temperature in the bin and suppress local overheating.

[0077] Deformation Compensation Unit: Dynamically adjust the compensation threshold based on the stress-strain curve of the sealing material and the historical deformation database, and feedback it to the optimization control module to iterate the thermal management strategy.

[0078] Kalman Filter Module: Improve the accuracy of sensor data fusion, including: Temperature Compensation Interface Unit: Adjust the process noise covariance weight according to the temperature gradient data to suppress the signal drift of the inertial measurement unit.

[0079] Dynamic Load Feedback Unit: Input the fused load distribution data into the adjustment module to drive the weight mode switch. For example, when the pressure of the rear vehicle exceeds the threshold, trigger the inertial measurement unit dominant mode.

[0080] Redundant Strategy Optimization Unit: Hardware-in-the-loop Test Unit: Inject intermittent communication bus faults into the simulation environment, generate the Pareto optimal solution set, and screen out the parameter combinations that take into account both braking distance and yaw stability.

[0081] Parameter Linkage Unit: Encrypt and transmit the autonomous braking curve parameters to the rear vehicle hydraulic control unit through OTA, and share the switching threshold data (such as the bus health score) with the standby core to achieve dynamic threshold adjustment.

[0082] Low-temperature Hysteresis Compensation Mechanism: Screw mechanism lubrication monitoring sub-module: Records the screw action delay data at low temperatures and generates a temperature-hysteresis characteristic curve.

[0083] Compensation coefficient update sub-module: Dynamically adjusts the compensation coefficient based on fuzzy rule parameters. For example, at low temperatures, the exponential weighted average is used to correct the delay amount, and the feedback is sent to the fuzzy rule evolution unit to optimize the rule base.

[0084] Environmental perception and parameter optimization closed-loop: The data acquisition module provides multi-source inputs. The optimization control module generates dynamic weights and redundancy strategies through the genetic algorithm. The adjustment module loads the parameters into the execution unit to form a "perception-optimization-execution" closed-loop.

[0085] Redundant fault-tolerant link: The communication monitoring of the execution module and the standby core activation unit cooperate to seamlessly switch to redundant instructions when the main control is abnormal, and the autonomous curve parameters updated by OTA ensure the continuity of the braking function.

[0086] Temperature adaptive control: The hierarchical heating and deformation compensation of the thermal management module suppress the deterioration of material performance. The temperature compensation interface of the Kalman filter module improves the signal reliability, forming a "temperature-material-signal" collaborative optimization link.

[0087] Genetic algorithm optimization model: The genetic algorithm model is used for multi-objective optimization of dynamic weight coefficients, redundant switching thresholds, and fuzzy rule parameters. This model uses the braking pressure deviation, yaw angle stability index, and temperature maintenance energy consumption as fitness functions, and iteratively screens the optimal parameter combinations through operations such as selection, crossover, and mutation in biological evolution. For example, the dynamic weight optimization unit adjusts the weight distribution of the hinge angle, pedal displacement, and inertial measurement unit data through the genetic algorithm to solve the problem of 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 screens the master-slave switching threshold that takes into account both response speed and stability.

[0088] Fuzzy rule decision model: The fuzzy rule model is used to handle the uncertainty and nonlinearity of the parking brake force release timing. The model constructs membership functions and rule bases based on the historical hysteresis database and real-time temperature data, and corrects the output instructions through the temperature compensation coefficient. For example, the low-temperature hysteresis compensation unit correlates the screw mechanism action delay data with the temperature gradient, generates a compensation coefficient, and superimposes it on the fuzzy rule output quantity to suppress the timing error caused by lubrication deterioration. The fuzzy rule evolution unit optimizes the rule base structure through the genetic algorithm to improve the self-adaptability of the decision-making logic.

[0089] 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 weight through the temperature compensation interface unit to suppress the signal drift of the inertial measurement unit caused by low temperature; the dynamic load feedback unit fuses the articulated angle, wheel speed, and attitude data, and outputs the dynamic load distribution to the adjustment module. For example, when the proportion of the rear vehicle pressure exceeds the preset threshold, the model triggers the weight mode to switch to the inertial measurement unit dominant mode to improve the yaw angle control accuracy.

[0090] Pareto Front Solution Set Model: This model is used to screen the optimal parameter combinations in the redundant strategy optimization. The hardware-in-the-loop test unit simulates the intermittent faults of the communication bus, generates a multi-objective solution set including communication delay, braking distance, and yaw stability, and eliminates the solutions deviating from the actual working conditions through offline verification. The optimized autonomous braking curve parameters are encrypted and transmitted to the rear vehicle hydraulic control unit through OTA to realize the dynamic update of the redundant control strategy.

[0091] Temperature-Hysteresis Characteristic Compensation Model: The model constructs a temperature-hysteresis characteristic curve based on the lubrication monitoring data and temperature gradient of the screw mechanism to predict the mechanical delay at low temperatures. The compensation coefficient update sub-module uses the exponential weighted average algorithm to fuse the real-time delay data and the historical curve, and dynamically corrects the output of the fuzzy rule. For example, when the temperature is lower than -30°C, the model increases the weight of the compensation coefficient according to the historical hysteresis data to shorten the parking brake release delay.

[0092] Thermal Management Deformation Prediction Model: The model combines the stress-strain curve of the sealing material and the historical deformation database to predict the deformation threshold at different temperatures. The deformation compensation unit dynamically adjusts the compensation amount according to the difference between the predicted value and the real-time sensor data, and outputs the parameters to the optimization control module to iterate the thermal management strategy. For example, when the predicted deformation amount exceeds the safety threshold, the hierarchical heating unit is triggered to switch to the semiconductor refrigeration mode to inhibit the aging of the seal.

[0093] Data-Driven Optimization: The genetic algorithm model and the fuzzy rule model cooperate to achieve the dynamic adaptation of control parameters and decision-making logic through multi-objective optimization and rule iteration.

[0094] Redundancy and Fault Tolerance Linkage: The Pareto solution set model and the Kalman filter model are combined to ensure the continuity of the braking command during communication anomalies and suppress signal distortion at the same time.

[0095] 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.

[0096] The above models construct an intelligent control system with environment adaptability, redundancy and fault tolerance, and multi-objective optimization through algorithm fusion and data interaction.

[0097] In view of the problems of unbalanced dynamic distribution of braking force, insufficient redundancy and fault tolerance ability, and low-temperature signal distortion in the braking system of tracked articulated vehicles in low-temperature environments, the following specific implementation manners are proposed: The data acquisition module collects multi-source signals in real time through an articulated angle sensor, an inertial measurement unit, a temperature sensor, and a vehicle state sensor, including pitch angle, yaw angle, braking temperature, and slope data. After receiving the above data, the optimization control module performs multi-objective optimization on the dynamic weight coefficient, redundancy switching threshold, and fuzzy rule parameters by using a genetic algorithm: the dynamic weight optimization unit uses the braking pressure deviation as the fitness function and iteratively generates a weight distribution scheme for the articulated angle and inertial measurement unit data; the redundancy strategy optimization unit generates a Pareto optimal solution set by simulating a bus fault in a hardware-in-the-loop test based on the communication delay and yaw angle stability index, and optimizes the master-slave switching threshold; the fuzzy rule evolution unit updates the membership function parameters and rule base by combining the parking brake timing error and the probability of mechanical interference. The optimized parameters are synchronized to the adjustment module and the execution module through a communication bus.

[0098] The multi-modal analysis sub-module of the adjustment module switches the weight mode according to the real-time temperature data: when the temperature is lower than -40°C, the articulated angle compensation mode is enabled to increase the weight of the articulated angle to offset the mechanical transmission clearance error; in the range of -20°C to -40°C, it switches to the inertial measurement unit data dominant mode to improve the attitude control accuracy. The reference value correction sub-module loads the optimized weight coefficient into the front vehicle hydraulic control unit and synchronously updates the parameter library of the rear vehicle hydraulic control unit through a differential calibration mechanism to ensure the dynamic consistency of the braking reference values of the front and rear vehicles. The communication monitoring unit of the execution module continuously counts the bus packet loss rate and delay time. When the packet loss accumulates for more than 5 seconds, it triggers the standby core activation unit to call the reference value correction algorithm, generates a redundant control instruction by interpolating the wheel speed sensor, and transmits it to the rear vehicle hydraulic control unit through a private CAN bus to achieve seamless switching when the main control link is abnormal.

[0099] The hierarchical heating unit of the thermal management module dynamically adjusts the operating modes of the heating tape and the thermoelectric cooler according to the braking temperature: when the temperature is below -40°C, the heating tape is started for full-power heating to inhibit the sudden change in the viscosity of the hydraulic oil; when the temperature rises to -20°C, the thermoelectric cooler is switched to balance the temperature distribution in the chamber. The deformation compensation unit predicts the deformation threshold based on the stress-strain curve of the seal and the historical deformation database, and outputs parameters to feedback to the optimization control module to iterate the thermal management strategy. The temperature compensation interface unit of the Kalman filter module adjusts the process noise covariance weight according to the temperature gradient data to inhibit the signal drift of the inertial measurement unit; the dynamic load feedback unit inputs the fused load distribution data into the adjustment module to drive the adaptive switching of the weight mode. The low-temperature hysteresis compensation unit of the decision module generates a temperature compensation coefficient based on the historical action delay data of the screw mechanism, corrects the output of the fuzzy rule, and coordinates with the timing priority mapping unit to adjust the parking brake release timing to avoid control conflicts caused by lubrication deterioration.

[0100] Through multi-module closed-loop collaboration, dynamic parameter optimization, and redundant execution mechanisms, the above embodiments solve the problems of unbalanced braking force distribution and insufficient fault tolerance in low-temperature environments.

[0101] The present invention solves the technical problems of the braking system in low-temperature environments through a multi-module collaborative control and dynamic parameter optimization mechanism. The specific technical solutions are as follows: The data acquisition module real-time obtains multi-modal sensor data such as the articulation angle, the attitude of the inertial measurement unit, and the braking temperature, and inputs it into the optimization control module for multi-objective optimization by genetic algorithm. The dynamic weight optimization unit generates weight coefficients for the articulation angle, pedal displacement, and inertial measurement unit data based on the braking pressure deviation and the yaw angle stability index; the multi-modal analysis sub-module dynamically switches the weight mode according to the temperature sensor data: the articulation angle compensation mode is enabled at low temperatures to inhibit mechanical clearance errors, and the inertial measurement unit data dominant mode is switched in the preset temperature range to improve the attitude control accuracy. The optimized weight coefficients are synchronously loaded into the front and rear vehicle hydraulic control units through the reference value correction sub-module to achieve dynamic adaptation of braking force distribution.

[0102] The redundant strategy optimization unit generates the master-slave switching threshold and the autonomous braking curve parameters with communication delay and braking distance as constraints, and the hardware-in-the-loop test unit simulates bus intermittent faults to generate a Pareto optimal solution set. The communication monitoring unit of the execution module real-time detects the bus packet loss rate. When the anomaly exceeds the threshold, the standby core activation unit calls the reference value correction algorithm to interpolate and complete the missing instructions, and generates redundant control signals in combination with the wheel speed sensor data. The parameter linkage unit updates the autonomous braking curve to the rear vehicle hydraulic control unit through OTA and shares the switching threshold with the standby core to form a seamless switching ability between the primary and standby links, ensuring the stability of the emergency braking function under working conditions.

[0103] The hierarchical heating unit of the thermal management module switches between full-power heating of the heating tape and the balanced mode of the thermoelectric cooler according to temperature data, suppressing local overheating and low-temperature embrittlement; the deformation compensation unit dynamically adjusts the compensation threshold of the seal according to the stress-strain curve and historical deformation data, and feeds the output parameters back to the optimization control module to iterate the thermal management strategy. The temperature compensation interface unit of the Kalman filter module adjusts the noise covariance weight according to the temperature gradient, suppressing the drift of the sensor signal; the dynamic load feedback unit inputs the fused load distribution data into the adjustment module to drive the weight mode switch. The low-temperature hysteresis compensation unit generates a temperature compensation coefficient based on the historical hysteresis database, corrects the output of the fuzzy rule, and coordinates with the timing priority mapping unit to adjust the parking brake release timing, reducing the control conflict caused by lubrication deterioration.

[0104] Through the closed-loop collaboration of environment perception, parameter optimization, redundant execution, and temperature compensation, the above technical solution realizes the dynamic stability and fault tolerance of the braking system at low temperatures.

[0105] The braking system of the tracked articulated vehicle applied to low-temperature environments provided by the present invention can effectively solve the problems of unbalanced braking force distribution, insufficient redundant fault tolerance, and signal distortion in the background technology in the following low-temperature operation scenarios. The specific embodiments are as follows: (The new embodiments are as follows) Example 1: In a low-temperature scientific research vehicle operating in an environment of -40°C, the data acquisition module obtains the angular change data of the connection part between the front and rear vehicle bodies in real time through a hinge angle sensor. The inertial measurement unit collects the pitch angle (range ±45°, accuracy 0.1°) and yaw angle (range ±90°, accuracy 0.05°) of the vehicle. The ambient temperature sensor monitors the temperature of the braking component (measurement range -60°C to 120°C, resolution 0.5°C). The wheel speed sensor collects the rear wheel speed signal. The above data is transmitted to the optimization control module through the CAN bus. The dynamic weight optimization unit of the optimization control module uses the mean square error between the braking pressure reference value and the actual demand as the fitness function, and iteratively optimizes the weight coefficients of the hinge angle, pedal displacement, and inertial measurement unit data through a genetic algorithm (population size 50, number of iterations 30) to generate a weight allocation scheme in the hinge angle compensation mode (hinge angle weight 0.7, inertial measurement unit weight 0.3). The multi-modal analysis sub-module of the adjustment module detects that the temperature of the braking component is -38°C (lower than the preset low-temperature threshold of -35°C), triggers the activation of the hinge angle compensation mode. The reference value correction sub-module loads the optimized weight coefficients into the front vehicle hydraulic control unit (pressure adjustment range 0 - 16 MPa, accuracy ±0.2 MPa), and synchronously updates the reference value parameter library of the rear vehicle hydraulic control unit through the CAN bus (the rear vehicle pressure reference value is 15% higher than that of the front vehicle). The hierarchical heating unit of the thermal management module detects that the temperature is lower than the preset low-temperature threshold, starts the full-power operation of the heating tape (heating power 800 W), and maintains the temperature of the braking pipeline at -25°C ± 2°C. The temperature controller of the rear vehicle insulation bin adjusts the power supply of the heating tape through the PID control algorithm (duty cycle 90%). The communication monitoring unit of the execution module real-time statistics the CAN bus packet loss rate (the threshold of the number of packet losses within 5 seconds per unit time is 3). When it detects that packet losses occur in two consecutive cycles (packet loss rate 6%), it triggers the standby core activation unit to call the reference value correction algorithm (linear interpolation model based on the wheel speed change rate), combines the wheel speed sensor data (rear wheel speed change rate 0.5 m / s²) to complete the missing control instructions, and transmits them to the redundant control interface of the rear vehicle hydraulic control unit through a private CAN bus (baud rate 1 Mbps). The rear vehicle hydraulic control unit adjusts the braking pressure according to the interpolation instruction (actual output pressure 14.5 MPa), and cooperates with the front vehicle hydraulic control unit (output pressure 12.6 MPa) to achieve balanced braking force distribution between the front and rear vehicles when the vehicle turns (the proportion of the rear vehicle braking force is 55%).

[0106] Example 2: In a mining articulated vehicle at an open-pit mine in the plateau at -30°C, the screw mechanism sensor of the data acquisition module continuously collects screw displacement (range 0 - 200 mm, accuracy 0.1 mm) and torque (range 0 - 500 N·m, accuracy 1 N·m). The historical hysteresis database records the action delay data at different temperatures (typical delay of 290 ms at -30°C). The fuzzy rule evolution unit of the optimization control module is constrained by the parking brake force release timing error (target error ≤ 50 ms) and the mechanical interference probability (target probability ≤ 2%), and optimizes the fuzzy membership function parameters (temperature compensation coefficient range 0.7 - 1.2) and the rule base structure (number of rules: 20) through a genetic algorithm. The low-temperature hysteresis compensation unit of the decision-making module receives the temperature data (-28°C) output by the thermal management module, combines the delay data in the historical hysteresis database, and superimposes a compensation coefficient of 0.85 on the fuzzy rule output (calculation formula: compensation coefficient = historical average delay / current delay × 0.9). The timing priority mapping unit receives the vehicle slope signal provided by the vehicle controller (corrected to 15.2° after Kalman filtering), dynamically adjusts the parking brake force release timing based on the compensation coefficient (original timing of 200 ms adjusted to 120 ms), and transmits the release timing instruction to the electronic parking brake controller via the CAN bus (response time ≤ 30 ms). When the hierarchical heating unit of the thermal management module detects that the temperature is within the preset range (-20°C to -40°C), it switches to the thermoelectric cooler temperature equalization mode (refrigeration power 200 W). The local overheating suppression sub-module equalizes the temperature distribution in the insulation bin (temperature difference controlled within 5°C) through an array of thermoelectric coolers (8 pieces, single-piece area 50 mm × 50 mm). The seal aging warning sub-module predicts the remaining life (remaining life ≥ 500 h) based on the deformation threshold parameter (seal deformation 2.5 mm) output by the deformation compensation unit, and transmits the warning signal to the parameter self-learning unit via the FlexRay bus to drive the iterative optimization of the thermal management parameters (the power of the heating tape is adjusted to 600 W).

[0107] Example 3: In an armored articulated vehicle operating in snow at -25°C, the inertial measurement unit 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 the temperature gradient data output by the thermal management module (IMU compartment temperature -22°C), dynamically adjusts the weight of the process noise covariance matrix (increased by 1.5 times), and outputs the dynamic load distribution (rear vehicle load ratio 60%) to the multi-modal analysis sub-module of the adjustment module after fusing multi-source sensor data (articulation angle, wheel speed, attitude). The redundancy strategy optimization unit of the optimization control module simulates the CAN bus intermittent connection fault through the hardware-in-the-loop test unit, generates a Pareto front solution set (including parameter combinations with communication delay ≤ 100 ms, braking distance ≤ 35 m, and yaw angle deviation ≤ 0.5°). The parameter linkage unit writes the autonomous braking curve parameters (slope 1.2 times) into the parameter library of the rear vehicle hydraulic control unit through the OTA upgrade module (transmission rate 1 Mbps). The communication monitoring unit of the execution module detects a bus delay of 80 ms (exceeding the threshold by 50 ms), triggers the standby core activation unit to call the reference value correction algorithm (cubic spline interpolation based on the wheel speed change rate), combines the wheel speed sensor interpolation to complete 3 missing wheel speed data points (completed rear wheel speed 11.1 m / s), transmits the interpolation instruction to the redundant control interface of the rear vehicle hydraulic control unit through the CAN bus, and the rear vehicle hydraulic control unit performs braking at 1.2 times the reference pressure (15.2 MPa). The final braking distance is 32 m, and the yaw angle deviation is 0.3°, meeting the emergency braking requirements.

[0108] Example 4: In a forestry articulated vehicle in a forest area at -35°C, the environmental temperature sensor of the data acquisition module monitors the temperature of the braking component (-35°C). The multi-modal analysis sub-module of the adjustment module detects that the temperature is lower than the preset low-temperature threshold (-30°C), enables the articulated angle compensation mode, and the dynamic weight optimization unit outputs an articulated angle weight of 0.8 (inertial measurement unit weight of 0.2). The reference value correction sub-module loads the weight coefficient into the front vehicle hydraulic control unit (pressure 12 MPa) and synchronously updates the reference value parameter library of the rear vehicle hydraulic control unit through the CAN bus (rear vehicle pressure 13.8 MPa). The hierarchical heating unit of the thermal management module starts the full-power operation of the heating tape (heating power 800 W). The temperature controller of the rear vehicle insulation bin raises the temperature of the rear cabin to -28°C ± 2°C. The local overheat suppression sub-module equalizes the temperature between the front cabin (-30°C) and the rear cabin (-28°C) (temperature difference controlled within 3°C) through an array of thermoelectric coolers (10 pieces). The deformation compensation unit dynamically adjusts the compensation threshold (compensation amount 1.5 mm) according to the stress-strain curve of the sealing material (elastic modulus increases by 20% at low temperature) and the historical deformation database (deformation amount 2 mm), and outputs the parameters to the thermal management optimization unit of the optimization control module to drive the iteration of thermal management parameters (duty cycle of the heating tape adjusted to 85%). The communication monitoring unit of the execution module continuously monitors the bus status (packet loss rate threshold 5%). When instantaneous packet loss is detected (packet loss rate 6%), the standby core activation unit calls the reference value correction algorithm (linear interpolation based on wheel speed) to complete the instruction, ensuring that the braking force of the inner track of the rear vehicle is greater during a curve turn (inner side pressure 14.2 MPa, outer side 13.5 MPa), and the outer swing of the rear vehicle is controlled within 0.3 m.

[0109] In the above embodiments, the data acquisition module obtains multi-source sensor data in real time. The optimization control module dynamically optimizes the weight coefficient, redundancy switching threshold, and fuzzy rule parameters through a genetic algorithm. The adjustment module adaptively switches the weight mode based on temperature and corrects the braking pressure reference value. The execution module achieves redundant control through communication monitoring and standby core activation. The thermal management module suppresses the deterioration of material performance through hierarchical heating and deformation compensation. The Kalman filter module improves the signal fusion accuracy by combining temperature compensation. The decision module optimizes the parking brake logic through hysteresis compensation and timing mapping. Each module works in coordination to effectively solve the problems of unbalanced dynamic distribution of braking force, insufficient redundancy and fault tolerance, and signal distortion in a low-temperature environment, supporting the practical application of each technical feature in the claims.

Claims

1. A braking system for a tracked articulated vehicle applied to a low-temperature environment, characterized in that, Including: A data acquisition module for obtaining sensor data, which includes articulated angle sensor signals, brake pedal displacement signals, pitch angle and yaw angle data output by an inertial measurement unit, vehicle slope signals, gear state signals, and brake component temperature data; An optimization control module for receiving sensor data and performing multi-objective optimization on dynamic weight coefficients, redundancy switching thresholds, fuzzy rule parameters, thermal management parameters, and Kalman filter noise covariance matrices through a genetic algorithm, where the multi-objectives include brake pressure deviation, yaw angle stability index, and temperature maintenance energy consumption; An adjustment module for analyzing the driver's intention according to the optimized dynamic weight coefficient, generating a brake pressure reference value, and transmitting the reference value to a pre-installed front vehicle hydraulic control unit and a rear vehicle hydraulic control unit through a communication bus; An execution module for switching to a standby control core when the main control link communication is abnormal based on the optimized redundancy switching threshold and the reference value of the adjustment module, and calling the autonomous braking curve parameters generated by the optimization control module to control the rear vehicle hydraulic control unit; A decision module for generating a parking brake force release timing instruction in cooperation with a preset electronic parking brake controller and a vehicle controller according to the optimized fuzzy rule parameters, and the fuzzy rule parameters include a temperature compensation coefficient and a slope-related priority mapping relationship; A thermal management module for adjusting the heating power of the heating tape based on the optimized thermal management parameters and inputting the temperature compensation parameters into the temperature compensation interface of the Kalman filter module; A Kalman filter module, connected to the data acquisition module and the adjustment module, for adjusting the noise covariance matrix according to the temperature compensation parameters of the thermal management module, fusing multi-source sensor data, and outputting a dynamic load distribution to the adjustment module.

2. The braking system for a tracked articulated vehicle applied to a low-temperature environment according to claim 1, characterized in that, The optimization control module includes: A dynamic weight optimization unit, connected to the data acquisition module, for performing genetic algorithm iterative optimization on the weight coefficients of the articulated angle, pedal displacement, and inertial measurement unit data with the deviation between the brake pressure reference value and the actual demand as the fitness function, and outputting the optimized weight coefficients to the redundancy strategy optimization unit and the adjustment module; A redundancy strategy optimization unit, connected to the dynamic weight optimization unit and the execution module, for using the weight coefficients output by the dynamic weight optimization unit as the initial constraint conditions, and using communication delay, yaw angle stability, and braking distance as the multi-objective fitness function to optimize the master-slave switching threshold and the autonomous braking curve trigger condition, and transmitting the switching threshold parameters to the master-slave switching interface of the execution module through a communication bus; A fuzzy rule evolution unit, connected to the decision module and the electronic parking brake controller, for optimizing the fuzzy membership function parameters and the rule base structure with the parking brake force release timing error and the mechanical interference probability as the constraints, and writing the updated rule table into the electronic parking brake controller through the CAN bus of the vehicle control system.

3. The braking system for a tracked articulated vehicle applied to a low-temperature environment according to claim 2, wherein, The adjustment module includes: An ambient temperature sensor, integrated in the data acquisition module, for real-time collecting the temperature data of the brake component; a multi-modal analysis submodule, connected to the ambient temperature sensor and the dynamic weight optimization unit, for matching the weight mode according to the ambient temperature sensor data, enabling the articulation angle compensation mode when the temperature is lower than a preset low temperature threshold, enabling the inertial measurement unit data dominant mode in a preset temperature range, and transmitting the mode selection instruction to the reference value correction submodule through an internal bus; The reference value correction submodule is connected to the front vehicle hydraulic control unit, the rear 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 front vehicle hydraulic control unit, synchronously update the reference value parameter library of the rear 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 braking system of the crawler articulated vehicle applied to a low-temperature environment according to claim 3, characterized in that, The execution modules include: Wheel speed sensor, integrated in the data acquisition module, 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, and is used to count the packet loss rate and delay time of the communication bus. When the cumulative signal loss exceeds the set threshold, the autonomous decision instruction is triggered, and the bus status data is transmitted 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 braking system of the crawler articulated vehicle applied to a low-temperature environment according to claim 4, wherein The decision-making modules include: 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; A historical hysteresis database, stored in the data acquisition module, 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 compensation coefficient in the fuzzy rule output according to 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 an internal data bus; The timing priority mapping unit is connected to the vehicle controller and the electronic parking brake controller, and is used to dynamically adjust the parking brake force release timing based on the 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 braking system of the tracked articulated vehicle applied to a low-temperature environment according to claim 5, wherein, 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 state of the heating belt and semiconductor refrigeration plate; A thermal management optimization unit, integrated in the optimization control module, is used to receive deformation compensation parameters and update the thermal management strategy; The hierarchical heating unit is connected to the data acquisition module and the thermostat of the rear vehicle insulation compartment, and is used to start the heating belt to run 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 plate 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 feedback the output parameters to the thermal management optimization unit through the communication bus.

7. The braking system of the crawler articulated vehicle applied to a low-temperature environment according to claim 6, characterized in that, The Kalman filter module includes: a multi-modal analysis sub-module, which is arranged in the adjustment module and is used to receive the dynamic load distribution data and analyze the multi-source sensor weight mode; 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; The dynamic load feedback unit is connected to the temperature compensation interface unit and the multi-modal analysis sub-module, and is used to fuse and calibrate the optimized load distribution data based on the noise covariance matrix parameters, and transmit it to the multi-modal analysis sub-module of the adjustment module through the SPI bus.

8. The braking system of the tracked articulated vehicle applied to a low-temperature environment according to claim 7, wherein, The redundancy strategy optimization unit further includes: The simulation environment server is deployed on the cloud platform of the vehicle control system and is used to simulate the communication bus discontinuous connection fault scenario; The OTA upgrade module is integrated in the rear vehicle hydraulic control unit of the execution module and is used to receive and write the 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 the communication bus discontinuous connection fault in the simulation environment, generate the Pareto front solution set, and transmit the solution set to the parameter linkage unit through the Ethernet; The parameter linkage unit is connected to the hardware-in-the-loop test unit, the OTA upgrade module and the standby core activation unit, and 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 OTA, and share the master-slave switching threshold data with the standby core activation unit of the execution module through the FlexRay bus.

9. The braking system for a tracked articulated vehicle applied to a low-temperature environment according to claim 8, characterized in that, The low-temperature hysteresis compensation unit further includes: The screw mechanism sensor is integrated in 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 sub-module is connected to the screw mechanism sensor, and is used to calculate the action delay amount according to the displacement and torque data of the screw mechanism, generate the historical hysteresis database, and input the delay data into the compensation coefficient update sub-module through the SPI bus; The compensation coefficient update sub-module is connected to the fuzzy rule evolution unit and the screw mechanism lubrication monitoring sub-module, and 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, combined with the historical hysteresis data, and feedback the updated compensation coefficient to the rule library learning interface of the fuzzy rule evolution unit through the CAN bus.

10. The braking system of the tracked articulated vehicle applied to a low-temperature environment according to claim 9, characterized in that, The hierarchical heating unit further includes: The thermoelectric cooler array is integrated in the rear vehicle insulation bin thermostat and is used to perform temperature equalization control; The parameter self-learning unit is arranged in the thermal management module and is used to update the thermal management parameters according to the warning signal; The local overheat suppression sub-module is connected to the thermoelectric cooler array and the Kalman filter module, and is used to equalize the temperature distribution in the insulation bin through the thermoelectric cooler, and transmit the real-time temperature data to the seal aging warning sub-module through the I2C bus; The seal aging warning sub-module, which is connected to the deformation compensation unit and the parameter self-learning unit, is used to predict the life of the seal 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.

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