Modular train braking method, system, apparatus, and storage medium

Abstract

The application relates to the technical field of train braking, and provides a modular train braking method, system, device and storage medium, which solves the problems of lack of space pertinence in brake pressure adjustment, local heat accumulation aggravation and low heat management efficiency in the prior art. The method comprises the following steps: identifying a local hotspot area with a temperature exceeding a preset safety range from a temperature distribution image, and extracting target geometric feature parameters of the local hotspot area; combining current running state information of the train, and generating brake pressure adjustment parameters of the train by using a heat conduction model; optimizing the brake pressure adjustment parameters to obtain pressure adjustment parameters, and sending the pressure adjustment parameters to a pneumatic braking device in a modular train braking system, so that the pneumatic braking device applies a dynamically changed braking force based on the optimized brake pressure adjustment parameters. The application can realize local overheating feature identification, dynamically adjust the brake pressure to accurately control the temperature, and ensure braking safety and reliability.

Description

Technical Field

[0001] This application relates to the field of train braking technology, and in particular to a modular train braking method, system, device and storage medium. Background Technology

[0002] During high-speed braking of a train, the area between the brake disc and the pneumatic device is prone to instantaneous high temperatures due to intense energy conversion, which can reach over 800°C in some areas. This can lead to material thermal degradation, thermal cracking, and a decline in braking performance. To ensure safety and stability under emergency braking conditions, it is necessary to acquire the temperature field distribution characteristics of the friction interface in real time, accurately identify abnormally overheated areas, and achieve rapid adaptive adjustment of braking force distribution based on multi-dimensional dynamic parameters to avoid the risk of braking failure caused by local thermal runaway.

[0003] The existing solution uses an infrared thermal imager to monitor the temperature distribution of the brake disc, extracts the overheated area through threshold segmentation, and calculates the overall brake pressure compensation value by combining a fixed weight algorithm. Finally, the braking force is uniformly adjusted through the brake control system.

[0004] However, existing solutions rely solely on simple area statistics of high-temperature regions, resulting in a lack of spatial targeting in brake pressure adjustments, exacerbating local heat accumulation, and reducing the effectiveness of thermal management. Summary of the Invention

[0005] This application provides a modular train braking method, system, device, and storage medium to solve the problems of insufficient spatial targeting of braking pressure adjustment, aggravated local heat accumulation, and low thermal management efficiency in the prior art.

[0006] In a first aspect, this application provides a modular train braking method, comprising:

[0007] Acquire temperature distribution data for a target area on the train, where the target area is the region on the train where the brake disc surface contacts the pneumatic braking device.

[0008] Identify local hotspot areas whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extract the target geometric feature parameters of the local hotspot areas from the temperature distribution image;

[0009] Based on the target geometric feature parameters of the local hotspot area and combined with the current operating status information of the train, the braking pressure adjustment parameters of the train are generated using a heat conduction model.

[0010] The brake pressure adjustment parameters are optimized to obtain the optimized brake pressure adjustment parameters;

[0011] The optimized braking pressure adjustment parameters are sent to the pneumatic braking device in the modular train braking system, so that the pneumatic braking device applies dynamically changing braking force based on the optimized braking pressure adjustment parameters.

[0012] Optionally, the step of identifying local hotspot regions whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extracting target geometric feature parameters of the local hotspot regions from the temperature distribution image, includes:

[0013] In the temperature distribution image, local hotspot areas where the temperature value exceeds the preset safe temperature range are identified by using a preset safe temperature range threshold.

[0014] Boundary detection is performed on the local hotspot region to obtain the boundary contour of the local hotspot region, and the initial geometric feature parameters of the local hotspot region are calculated based on the boundary contour.

[0015] Based on the initial geometric feature parameters, morphological analysis is performed on the local hotspot region to obtain the structural distribution characteristics of the local hotspot region;

[0016] The initial geometric feature parameters are fused with the structural distribution features to generate intermediate geometric feature parameters for local hotspot regions;

[0017] The intermediate geometric feature parameters are optimized to remove redundant parameters and retain feature parameters whose contribution to the description of local hotspot regions is greater than a preset threshold, thereby generating target geometric feature parameters for local hotspot regions.

[0018] Secondly, this application provides a modular train braking system, comprising:

[0019] The acquisition module is used to acquire temperature distribution data of a target area on the train, wherein the target area is the area on the train where the brake disc surface is in contact with the pneumatic braking device.

[0020] The extraction module is used to identify local hotspot areas whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and to extract the target geometric feature parameters of the local hotspot areas from the temperature distribution image;

[0021] The generation module is used to generate the train's braking pressure adjustment parameters based on the target geometric feature parameters of the local hot spot area and the train's current operating status information, using a heat conduction model.

[0022] An optimization module is used to optimize the brake pressure adjustment parameters to obtain optimized brake pressure adjustment parameters;

[0023] The sending module sends the optimized braking pressure adjustment parameters to the pneumatic braking device in the modular train braking system, so that the pneumatic braking device applies dynamically changing braking force based on the optimized braking pressure adjustment parameters.

[0024] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a modular train braking method as described in any of the first aspects.

[0025] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a modular train braking method as described in any of the first aspects.

[0026] The beneficial effects of this application are:

[0027] The modular train braking method provided in this application has the following advantages: By acquiring the temperature distribution data of the contact area between the brake disc surface and the pneumatic braking device, it achieves an upgrade from traditional single-point temperature measurement to two-dimensional temperature field monitoring, which can comprehensively capture the temperature changes of the area, avoid missing local high-temperature points, and provide a high-resolution data foundation for subsequent analysis; then, based on the temperature distribution image, it identifies local hot spots that exceed the safe range and extracts their geometric feature parameters, transforming thermal anomalies into quantifiable physical indicators. This step uses image processing algorithms to achieve intelligent diagnosis, reduce errors from manual intervention, and provide accurate input parameters for the heat conduction model.

[0028] Then, based on the current operating status of the train, the development trend of local hot spots is calculated using a heat conduction model, and targeted braking pressure adjustment parameters are generated. This process takes into account the thermo-mechanical coupling effect to ensure that the dynamic adjustment of braking pressure can effectively suppress the deterioration of hot spots without excessively affecting the overall braking performance. Subsequently, the braking pressure adjustment parameters are optimized, and a balance is achieved between cooling requirements and braking efficiency through a multi-objective optimization algorithm, while eliminating interference factors such as sensor noise and improving control stability.

[0029] Based on this, the optimized braking pressure adjustment parameters are sent to the actuator of the modular train braking system, applying dynamically varying braking force to the area corresponding to local hot spots to achieve precise control. This method can effectively suppress the risk of failures such as thermal cracking and brake disc deformation, extend the life of the braking system, and adapt to the braking thermal management requirements under different operating conditions.

[0030] Furthermore, this method achieves high-precision quantification of the geometric characteristics of hotspot regions through multi-level feature fusion and optimization; combining boundary contour and morphological analysis can comprehensively describe the spatial distribution of hotspots, overcoming the limitations of traditional single parameters such as maximum temperature; the normalization and nonlinear superposition process adaptively balances the contributions of geometric and structural features, improving the predictive ability of features for hotspot evolution trends, and after eliminating redundant parameters, it reduces the computational burden while ensuring the correlation of input parameters of the heat conduction model, thereby enhancing the accuracy and real-time performance of braking pressure control.

[0031] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A flowchart of a modular train braking method provided in this application embodiment;

[0034] Figure 2 This is a schematic diagram of a modular train braking system provided in an embodiment of this application;

[0035] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0036] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0037] In some of the processes described in the specification and accompanying drawings of this application, multiple operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 11, 12, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] Figure 1 A flowchart of a modular train braking method provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0040] S11. Obtain temperature distribution data of the target area on the train. The target area is the area on the train where the brake disc surface is in contact with the pneumatic braking device.

[0041] Among them, pneumatic braking device can refer to pneumatic relay, so the target area can specifically refer to the interface between the train brake disc and the pneumatic relay.

[0042] The temperature distribution data includes the temperature value and spatial distribution characteristics of each coordinate point in the region, and the temperature distribution data can be collected by multiple temperature sensors arranged on the surface of the brake disc.

[0043] Furthermore, this application is equipped with a controller, which establishes a stable data connection with temperature sensors distributed on the surface of the brake disc and at the contact position of the pneumatic brake device, and receives temperature data collected by each temperature sensor in real time.

[0044] Then, the received raw temperature data is preprocessed, including noise removal, data calibration, and data format conversion, to ensure the accuracy and consistency of the data. Next, based on the preprocessed temperature data, a three-dimensional data model reflecting the temperature distribution of the target area is constructed, providing an accurate data foundation for the subsequent generation of temperature distribution images.

[0045] S12. Identify local hotspot areas whose temperatures exceed the preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extract the target geometric feature parameters of the local hotspot areas from the temperature distribution image.

[0046] Among them, the local hot spot region refers to the continuous high temperature region where the temperature exceeds the allowable threshold of the material, and the target geometric feature parameters include hot spot area, perimeter, and shape factor.

[0047] This step may include the following process: Based on a predetermined image generation algorithm, the temperature distribution data is transformed into an intuitive temperature distribution image, in which the grayscale value or color value of each pixel corresponds to the actual temperature value at the corresponding location within the target area; on the generated temperature distribution image, by setting an appropriate temperature threshold, an image recognition algorithm is used to automatically identify local hotspot areas where the temperature exceeds a preset safety range, and these identified hotspot areas are marked; for the marked local hotspot areas, image analysis techniques, such as edge detection and contour extraction algorithms, are used to accurately calculate the geometric feature parameters of the local hotspot areas, and these geometric feature parameters will serve as an important basis for subsequent analysis.

[0048] S13. Based on the target geometric feature parameters of the local hot spot area and combined with the current operating status information of the train, the braking pressure adjustment parameters of the train are generated using the heat conduction model.

[0049] Among them, the braking pressure adjustment parameter is a dynamic pressure correction coefficient for each hot spot area, such as 0.8~1.2, used for local braking force adjustment; the operating status information includes the train's speed, load status, and braking history records.

[0050] The construction and application of the heat conduction model are explained as follows: A heat conduction model considering the material properties, heat conduction characteristics, and fluid dynamics characteristics of the train braking system is established; the geometric characteristic parameters of the local hot spot area, the current operating status information of the train, and relevant parameters such as the heat conduction coefficient of the brake disc and pneumatic braking device are input into the heat conduction model; based on the input parameters, the heat conduction model simulates and calculates the temperature change trend and heat transfer process of the local hot spot area under different braking pressures.

[0051] It should be noted that this embodiment does not impose specific limitations on the specific structure and representation of the heat conduction model, and can be set accordingly based on the actual situation.

[0052] S14. Optimize the brake pressure adjustment parameters to obtain the optimized brake pressure adjustment parameters.

[0053] The optimization process can be solved through multi-objective constraints, which can balance temperature suppression and braking stability.

[0054] S15. Send the optimized braking pressure adjustment parameters to the pneumatic braking device in the modular train braking system so that the pneumatic braking device can adjust the braking pressure based on the optimized braking pressure parameters.

[0055] "Applying" can refer to independent pressure control for each brake cylinder based on a pressure parameter table.

[0056] The modular train braking system is a braking system based on a distributed control architecture. This system can consist of multiple independent functional modules, such as control units, pressure regulation modules, and sensor networks, interconnected through a high-speed communication bus, supporting on-demand configuration, dynamic reconfiguration, and fault isolation.

[0057] The local hot spot area is the part where the brake disc and the pneumatic device actually come into contact. Its spatial distribution and pressure load change dynamically with the braking conditions. It is the core area for pneumatic braking energy conversion and heat generation.

[0058] Here is a specific example: Taking a high-speed train emergency braking scenario as an example, when the train brakes suddenly at 350 km / h, the fiber optic sensor detects a 720℃ hot spot with a diameter of 8 mm at the 3 o'clock position on brake disc No. 3, and generates a temperature distribution matrix; then the hot spot area is segmented, and its area is calculated to be 50 mm². 2 The shape factor is 0.42, thus identifying it as a regular circular high-temperature zone. Then, combined with the current vehicle speed and axle load of 23t, the pressure correction coefficient for this area needs to be reduced from 1.0 to 0.85 through the heat conduction model. After optimization by the multi-objective optimization algorithm (Nondominated Sorting Genetic Algorithm II, NSGA-II), the coefficient is adjusted to 0.82. Hardware-in-the-loop testing shows that the temperature can be reduced to 655℃ and the braking force fluctuation is 2.7%. The brake controller sends a pressure command of 0.82 times to brake cylinder No. 3 in the next 10ms cycle. After synchronous adjustment, the hot spot temperature drops back to 630℃ within 2 seconds.

[0059] By executing S11~S15, the embodiments of this application achieve active suppression of thermal runaway at the braking interface through high-resolution real-time temperature field sensing, accurate extraction of hot spot geometric features, multi-physics field coupled modeling, and distributed dynamic pressure control. Under emergency braking conditions at 350km / h, the peak temperature rise of local hot spots can be reduced by 18%~22%, the life of the brake disc can be extended to more than 400,000 kilometers, and at the same time, the pressure synchronization error of the entire train braking unit is less than 1.5%, and the braking distance fluctuation rate is controlled within the standard requirement of 2.8%.

[0060] In one possible embodiment, S12 involves identifying local hotspot regions whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extracting target geometric feature parameters of the local hotspot regions from the temperature distribution image, including:

[0061] Step 121: In the temperature distribution image, identify local hotspot areas where the temperature value exceeds the preset safe temperature range by using a preset safe temperature range threshold.

[0062] The preset safe temperature range threshold is a temperature threshold for a dynamic temperature range set according to the thermal decay characteristics of the brake disc material. In this embodiment, neither the upper nor lower limit of the threshold is specifically limited. The local hot spot region refers to the connected region formed by consecutive over-temperature pixels in the temperature distribution image.

[0063] In step 121, the processing flow of the temperature distribution image begins with adaptive threshold segmentation technology. This technology intelligently identifies local hotspot areas that exceed the safe range through a dynamic multi-threshold segmentation algorithm. Specifically, after the infrared thermal imager captures the surface temperature data of the brake disc, it first applies nonlocal mean filtering to eliminate motion blur caused by the rotation of the brake disc. Then, it automatically generates a dual-threshold segmentation scheme based on the maximum inter-class variance method, dividing the temperature field into a safe zone, a transition zone, and an over-temperature zone. Next, when performing 8-neighborhood connected component labeling on the over-temperature zone, it combines morphological closing operations to filter out noise interference and accurately label continuous high-temperature areas.

[0064] This process fully embodies intelligent features: the threshold parameters are adjusted in real time based on the thermal degradation characteristics of the brake disc material, connected component analysis ensures that only areas with persistent thermal risk are marked, and the filtering algorithm is specifically optimized to cope with high-speed rotation conditions. The entire identification mechanism is closely linked to the system's main control process.

[0065] For example, taking the brake disc temperature monitoring under emergency braking conditions of a train as an example, the real-time temperature distribution image captured by the infrared thermal imager has a resolution of 1024×768 pixels and a temperature range of 0~800℃. Based on this, a safe temperature threshold of 650℃ is set, and an improved multi-threshold segmentation algorithm combined with morphological gradient edge detection is used to identify hotspots: non-local mean denoising is performed on the original thermal image, such as filtering parameter h=0.8, to eliminate motion blur caused by brake disc rotation.

[0066] The Otsu's method is used to automatically determine dual thresholds in the temperature histogram, with an upper temperature threshold T1 = 630℃ and a lower temperature threshold T2 = 650℃. The image is then divided into a safe zone of T < 630℃, a transition zone of 630℃ ≤ T < 650℃, and an over-temperature zone of T ≥ 650℃. The over-temperature zone is labeled with 8-neighbor connected components, and two local hotspots are identified: hotspot A and hotspot B.

[0067] Step 122: Perform boundary detection on the local hotspot region to obtain the boundary contour of the local hotspot region, and calculate the initial geometric feature parameters of the local hotspot region based on the boundary contour.

[0068] Among them, the boundary contour refers to the continuous pixel chain on the periphery of the hot spot region, and the initial geometric feature parameters include area, perimeter, and aspect ratio of the minimum bounding rectangle.

[0069] Boundary detection is used to identify the boundary lines between local hot spots and surrounding areas in temperature distribution images. Its core is to locate transition regions in the temperature field that undergo significant changes by calculating pixel gradients or contrast abrupt changes.

[0070] Boundary contours are closed geometric figures generated by polygon fitting from the discrete set of edge points output by boundary detection. They are used to characterize the shape, size, and topology of local hotspot regions.

[0071] In step 122, after hotspot identification is completed, the boundary detection stage begins. Specifically, an improved edge detection algorithm is used, and sub-pixel level contour extraction is achieved through Gaussian kernel filtering and non-maximum suppression techniques. After obtaining the hotspot boundary chain code, the algorithm applies Green's theorem to convert the region area integral into the boundary curve integral, and at the same time establishes a transformation matrix between image coordinates and the physical position of the brake disc.

[0072] The boundary detection results in this stage directly affect the quality of subsequent features. Its accuracy advantage enables the system to accurately quantify the morphological characteristics of hot spots, providing reliable input for heat conduction modeling.

[0073] For example, in a high-speed train brake disc temperature monitoring scenario, a 1024×768 resolution temperature image acquired by an infrared thermal imager shows a local overheated area. An improved edge detection algorithm is used for boundary detection, with a Gaussian kernel parameter σ of 1.5 and dual thresholds set to 630℃ and 650℃ respectively. After non-maximum suppression of the overheated area, a closed boundary contour is obtained through 8-neighbor connected component analysis, where the hotspot A contour contains 85 pixels and is elliptical in shape.

[0074] The area integral of the region is converted into the boundary curve integral using Green's theorem, and the area of ​​hotspot A and its proportion of the contact surface are calculated. Then, the ratio of the major axis to the minor axis is calculated using vertex chain code. If the major axis is 15 pixels and the minor axis is 7 pixels, the ratio of the major axis to the minor axis is approximately 2.14. For example, the offset Δx relative to the center of the brake disc is +12.3 mm. The sampling interval is 0.1 mm, and the maximum gradient calculated along the boundary normal is 78°C / mm.

[0075] Compared with the 3D contour reconstructed by laser scanning point cloud, this method effectively reduces the boundary positioning error and the relative error of area calculation. Furthermore, by integrating the integral transformation of Discrete Green's theorem with the boundary encoding of vertex chain codes, it achieves sub-pixel level geometric feature extraction, which improves computational efficiency compared with the traditional chain code method.

[0076] Step 123: Based on the initial geometric feature parameters, perform morphological analysis on the local hotspot region to obtain the structural distribution characteristics of the local hotspot region.

[0077] The core of morphological analysis is to analyze the geometric complexity and internal defects of the high-temperature region. This embodiment does not specifically limit the type of algorithm used for morphological analysis. It can be set according to the actual situation. For example, a circular kernel or a rectangular kernel can be used to perform topological operations on the shape of the target region to extract its internal structure, hole distribution and edge features.

[0078] Among them, the structural distribution characteristics of local hot spots are quantitative parameters that describe the internal topological morphology and edge irregularities of hot spots, including: pore density, skeleton branching degree, convex hull defect rate, Euler number, etc.

[0079] In step 123, the internal structure of the hot spot is analyzed topologically based on mathematical morphology principles. Then, erosion-dilation operations are performed using an optimized circular core structure to effectively separate the hot spot core region from the transition region. During this process, the algorithm calculates the fractal dimension to quantify the irregular characteristics of the boundary and analyzes the impact of the heat dissipation structure on the heat conduction path using Thiessen polygon diagrams.

[0080] In practice, the morphological analysis may include a multi-layered processing flow that is executed sequentially or selectively, in order to progressively deepen the understanding of the hot spot structure and ensure the reliability of the analysis.

[0081] The first step, in the morphological analysis stage of local hotspot areas, dynamically adjusts the processing strategy based on the detected initial geometric feature parameters. For example, it automatically calculates and generates structural elements of optimal size based on the hotspot area to ensure that the diameter of the circular core is precisely matched with the hotspot size. At the same time, it intelligently selects rectangular or circular core types based on shape factors to effectively adapt to the processing needs of hotspots of different shapes. For hotspots with spatial offset, it rotates the orientation of the structural elements in real time according to the centroid offset to make them accurately match the spatial orientation characteristics of the target area. Through this step, an adaptive structural element configuration system can be established.

[0082] The second step involves performing deep topological analysis through a combination of multi-level morphological operations, based on the configuration of structural elements. For example, firstly, directional erosion is applied to remove the temperature transition region around the hot spot, and then the high-temperature core region is accurately extracted based on the connected domain recombination algorithm, while simultaneously calculating the area ratio parameter between the core region and the overall region. In the internal structure analysis layer, differential topological analysis can be used to identify pores and defects inside the hot spot, establish a thermal map model of pore distribution, and quantify its density and spatial distribution characteristics. For complex boundary characteristics, multi-scale boundary unfolding operations are performed to accurately calculate the fractal dimension index.

[0083] In another embodiment, based on the morphological features obtained from the above morphological analysis, a heat conduction path prediction model is further constructed; anisotropic analysis reveals the difference in heat conduction efficiency of hot spots in different directions, a mapping relationship between morphological features and the spatial distribution of heat dissipation fins of the brake disc is established, and the formation trend of high gradient risk areas is predicted based on morphological features; the above conduction model intelligently associates geometric features with actual thermodynamic behavior, providing a physical mechanism-level decision basis for brake pressure regulation.

[0084] In other embodiments, to ensure the accuracy of morphological features, this embodiment constructs a closed-loop verification and optimization mechanism. Specifically, X-ray tomography is used to establish a mapping relationship between morphological features and material micro-damage, and to verify the spatial correlation between pore distribution and material phase transition regions. Then, three-dimensional reconstruction is used to confirm that the morphological features and microstructure have a high degree of consistency. Based on the verification results, structural elements are dynamically calibrated, and a structural confidence assessment system is established.

[0085] For example, in the temperature monitoring of a high-speed train brake disc, based on initial geometric characteristic parameters, such as the area of ​​hotspot A being 38 mm², 2 The structural distribution characteristics were extracted using morphological spatial pattern analysis, taking into account factors such as shape factor 2.14 and centroid offset of 12.3 mm.

[0086] First, an erosion-dilation operation was performed using a 3×3 circular core structure to separate the continuous high-temperature region, determining the core area of ​​hotspot A to be 32 mm². 2 The area, accounting for 84.2% of the overall region, has an equivalent diameter of 6.2 mm and a fractal dimension of 1.28 at its boundary. The fractal dimension of 1.28 indicates local anisotropy in the heat conduction path. Subsequently, temperature data was sampled along the boundary normal, and a gradient distribution curve was fitted, showing a steep gradient increase on the northwest side with a peak of 92℃ / mm. This area corresponds to the region where the heat dissipation fins of the brake disc are missing. Further calculation of the Euler number and shape index, combined with the Thiessen polygon diagram, revealed that the hot spot spacing d follows a power-law distribution with d=2.3 mm. Then, X-ray tomography results showed that the structural distribution characteristics obtained from the above morphological spatial pattern analysis had a spatial matching degree of up to 89% with the microcrack distribution of the brake disc, confirming that the high-temperature zone has a "core-shell" structure, with the core being the martensitic phase transformation region and the periphery being the heat-affected zone.

[0087] This stage employs automated hole identification algorithms to accurately quantify internal defects, parameterized descriptions of gradient distribution curves to reveal temperature transfer patterns, and X-ray verification mechanisms to ensure spatial matching between features and physical damage. This deep analysis enables the system to capture the essential laws governing hotspot evolution, improving the accuracy of thermal stress prediction.

[0088] Step 124: Fuse the initial geometric feature parameters with the structural distribution features to generate intermediate geometric feature parameters for local hotspot regions.

[0089] In step 124, the feature fusion stage can use a dynamic weighting mechanism to optimize parameters. For example, firstly, the geometric features and structural distribution features are normalized to eliminate dimensional differences. Then, the contribution weight of each feature is dynamically calculated using the entropy weighting method. In the weight allocation stage, a spatial decay function can be introduced to automatically increase the weight coefficient of the edge region. Finally, the hyperbolic tangent function is applied to achieve nonlinear superposition of features.

[0090] It should be noted that the specific implementation process of step 124 can be referred to steps a1~a3, and will not be repeated here.

[0091] The core of this process lies in establishing a dynamic correlation between weight allocation and thermal coupling strength: when a high-gradient region is detected, the weight of structural distribution features is automatically increased; while for homogeneous temperature regions, geometric features are strengthened. This adaptive balancing mechanism ensures that high-risk regions receive higher weights in decision-making, enabling the generated feature vectors to accurately reflect the evolution trend of hot spots.

[0092] Step 125: Optimize the intermediate geometric feature parameters to remove redundant parameters and retain feature parameters whose contribution to the description of local hotspot regions is greater than a preset threshold, thereby generating target geometric feature parameters for local hotspot regions.

[0093] In step 125, feature dimensionality reduction and selection can be achieved through parameter optimization. In this stage, the intermediate feature vector generated in step 124 is used as input, and machine learning technology is used to select the core parameters with the greatest representational ability.

[0094] Specifically, firstly, high-dimensional features are mapped to the principal component space using principal component analysis, automatically identifying and retaining the feature dimensions that contribute the most to the hotspot state variance. Based on this, a filtering algorithm based on a preset threshold is applied to directly remove parameters with low correlation to the principal components or those with redundant information. Next, to verify the physical meaning and predictive value of the retained features, an importance assessment is performed using a random forest model. For example, multiple decision trees are constructed, and the average decrease in impurity caused by each feature during tree node splitting is calculated, thereby quantifying its predictive importance for the hotspot evolution trend. The entire optimization process, through the three-level processing mechanism of principal component screening, threshold filtering, and model evaluation, ensures that only core feature parameters with a contribution exceeding the preset threshold are retained.

[0095] This application embodiment achieves efficient transformation from raw temperature data to high-value decision parameters through an end-to-end intelligent processing flow. While meeting the real-time requirements of the braking control system, it provides a precise and reliable basis for thermal state assessment and decision-making for modular braking systems.

[0096] Here is a specific example: In a hotspot analysis scenario for a certain type of train brake disc, when the train brakes at 300 km / h, an infrared thermal imager detects an abnormal area on the brake disc surface with a temperature reaching 680℃. The system first performs threshold correction on this area, dynamically increasing the threshold to 665℃, and marks three valid hotspots accordingly. Subsequently, edge detection is performed on the largest hotspot, fitting a 28-sided contour, and then calculating its initial geometric features, such as an area of ​​82 mm². 2 The perimeter is 36.7 mm and the length-to-width ratio is 1.8.

[0097] Based on this, a morphological analysis was further performed on the hotspot. The analysis results showed that the hotspot contained two holes, accounting for 7% of the area; its skeleton branching degree was 3, and the maximum convex hull depression depth was 0.32 mm. Then, the 15-dimensional features obtained above were weighted and fused to generate an intermediate feature vector. In this vector, the area and the number of holes became the main components.

[0098] Finally, through joint screening using principal component analysis and minimum absolute shrinkage and selection operator algorithms, the four core parameters of area, perimeter, number of holes, and convex hull depth were retained, with their total contribution reaching 91%.

[0099] This application's embodiments improve the accuracy of hot spot geometric feature extraction to the sub-millimeter level through dynamic threshold correction, multi-scale morphological analysis, and feature contribution optimization. Furthermore, in high-speed braking scenarios at 300km / h, it can accurately identify hot spots with a diameter >3mm, reduce feature parameter redundancy by 60%, provide highly reliable input for subsequent thermo-mechanical coupling control, and ultimately control the local temperature rise prediction error of the brake disc within ±8℃, thereby reducing the risk of thermal cracking.

[0100] In one possible embodiment, step 124, fusing the initial geometric feature parameters with the structural distribution features to generate intermediate geometric feature parameters for the local hotspot region, includes:

[0101] Step a1: Normalize the initial geometric feature parameters and structural distribution features respectively to construct the first and second tensors that are spatially aligned with the local hotspot regions.

[0102] The initial geometric feature parameters include basic geometric quantities such as the area, perimeter, and shape factor of the hotspot region.

[0103] It should be noted that this embodiment does not impose specific limitations on the calculation formula for the shape factor, and can be set accordingly based on the actual situation.

[0104] Step a2: Calculate the dynamic weight coefficients corresponding to the first tensor and the second tensor respectively.

[0105] Among them, the dynamic weighting coefficient reflects the contribution of geometric feature parameters and structural distribution characteristics to the final result, and varies with spatial location.

[0106] In this embodiment, the information entropy can be dynamically calculated using the entropy weight method, and then the corresponding weight can be determined based on the entropy value. Furthermore, this embodiment can also introduce a spatial decay function in the calculation process of the weight coefficient, thereby achieving spatial adaptation of the weight coefficient.

[0107] It should be noted that the expressions for the entropy weight method and the spatial decay function can be found in relevant techniques, and will not be elaborated here.

[0108] Step a3: Based on the dynamic weight coefficients, nonlinearly superimpose the first tensor and the second tensor to obtain the intermediate geometric feature parameters of the local hotspot region.

[0109] Nonlinear superposition enhances feature interaction through weighted fusion and nonlinear transformation.

[0110] This application's embodiments can eliminate the dimensional differences between geometric feature parameters and structural distribution features through normalized alignment, constructing a spatially aligned standardized vector; then, based on dynamic weight allocation, the contribution of geometric feature parameters and structural distribution features is adaptively adjusted; finally, nonlinear superposition is used to enhance feature interaction and generate high-precision intermediate geometric feature parameters. Therefore, in scenarios such as hot spot detection of high-speed rail brake discs, this method reduces positioning errors and improves the robustness of analysis under complex working conditions.

[0111] In one possible embodiment, S13, based on the target geometric feature parameters of the local hotspot region and combined with the train's current operating status information, the train's braking pressure adjustment parameters are generated using a heat conduction model, including:

[0112] Step 131: Spatial discretization of the target geometric feature parameters in the local hot spot area to generate a geometric parameter matrix aligned with the grid cells of the brake disc.

[0113] Among them, the local hot spot area can refer to the part of the brake disc surface where the temperature is significantly higher than the surrounding area due to the heat generated by the conversion of pneumatic braking energy; the target geometric feature parameters include spatial attributes such as the radius of curvature, area, and depth of the hot spot area;

[0114] The geometric parameter matrix is ​​a numerical matrix that is discretized into target geometric feature parameters by using finite element mesh generation technology, and corresponds one-to-one with the mesh element of the brake disc.

[0115] Step 132: Extract the real-time speed, braking duration, and ambient temperature parameters from the train's current operating status information to construct an operating status vector.

[0116] The operating state vector consists of three dimensions: real-time speed, braking duration, and ambient temperature parameters, and is used to characterize the dynamic thermal boundary conditions during the train braking process.

[0117] Step 133: Couple the geometric parameter matrix with the running state vector in multiple dimensions to generate a joint input feature set.

[0118] Among them, multidimensional coupling refers to associating geometric parameter matrices with dynamic vectors in spatial and temporal dimensions through tensor operations.

[0119] Step 134: Based on the joint input feature set, calculate the dynamic evolution process of the surface temperature field and internal thermal stress field of the brake disc through the heat conduction equation in the heat conduction model, and generate spatiotemporal distribution data to characterize the temperature gradient and stress concentration region.

[0120] The heat conduction equation can be represented by the transient Fourier equation, and the specific expression of the heat conduction equation is not specifically limited in the embodiments of this application. It can be set according to the actual situation.

[0121] The spatiotemporal distribution data is a set of spatiotemporal coordinates of temperature gradient and stress concentration region.

[0122] Step 135: Based on the spatiotemporal distribution data, with the goal of homogenizing the thermal load on the brake disc, the brake pressure adjustment parameters are solved iteratively in reverse, and the effectiveness of the parameters is verified through preset boundary constraints. The brake pressure adjustment parameters of the train are then output.

[0123] Among them, the brake pressure adjustment parameter refers to the brake cylinder pressure value that needs to be optimized; the boundary constraints include the maximum allowable temperature and stress threshold, such as 600℃ and 80% of the material yield strength.

[0124] This application embodiment achieves accurate prediction of the brake disc thermal-mechanical field through geometric feature discretization and dynamic state coupling. Then, through reverse optimization, the uniformity of thermal load distribution is improved by 35%, avoiding the risk of thermal cracks caused by hot spots, while the braking distance is shortened by 8%.

[0125] In one possible embodiment, S14, the brake pressure adjustment parameters are optimized to obtain optimized brake pressure adjustment parameters, including:

[0126] Step 141: Discretize the brake pressure adjustment parameters to generate a parameter distribution matrix that maps one-to-one with the grid cells of the brake disc.

[0127] Among them, the braking pressure adjustment parameters include control variables such as target pressure and pressure gradient;

[0128] The parameter distribution matrix is ​​a two-dimensional matrix that establishes a mapping relationship between the coordinates of the finite element mesh of the brake disc and the pressure parameters.

[0129] Step 142: Based on the preset mechanical structure constraints of the brake disc, perform boundary stability testing on the parameters corresponding to each grid cell in the parameter distribution matrix, and select the adjustable parameter range.

[0130] Among them, the mechanical structure constraints include the maximum allowable stress, such as 271 MPa, and the thermal deformation threshold, such as 0.15 mm; the adjustable parameter range may include the pressure range, which is an interval consisting of the lower pressure limit and the upper pressure limit.

[0131] Step 143: Within the adjustable parameter range, based on the dynamic coupling relationship between braking pressure and temperature field, iteratively weight the parameters of adjacent units to generate an initial parameter sequence.

[0132] The adjustable parameter range can avoid exceeding the allowable stress of the material; the embodiments of this application do not specifically limit the expression of the above dynamic coupling relationship and its application process in the weight allocation process, and can be set accordingly according to the actual situation;

[0133] Furthermore, this embodiment does not specifically limit the type of parameters; the initial parameter sequence refers to the set of parameters generated through dynamic coupling and iterative weight allocation during the braking pressure optimization process.

[0134] Step 144: Perform multiphysics joint verification on the initial parameter sequence to obtain the verified parameter sequence.

[0135] If a parameter causes a sudden change in stress or an abnormal temperature gradient in a local mesh element, the system reverts to the pre-adjustment state and reduces the adjustment step size. The validated parameter sequence refers to the set of optimized parameters that, after joint verification within a multiphysics environment, satisfy all physical constraints and performance indicators. These parameters may include material properties, boundary conditions, and load parameters, and their validation ensures their effectiveness and reliability in a multiphysics environment.

[0136] It should be noted that this embodiment does not specifically limit the process, specific method, or type of algorithm used for multiphysics joint verification.

[0137] Step 145: Generate incremental update rules based on the verified parameter sequence. Based on the incremental update rules and combined with the real-time pressure feedback data of the braking system, generate optimized braking pressure adjustment parameters.

[0138] Among them, the incremental update rule is a dynamic adjustment strategy that generates a recursive formula for parameter adjustment based on the verified parameter sequence, such as the verified braking pressure and temperature field distribution, through mathematical modeling or machine learning methods.

[0139] It should be noted that the embodiments of this application do not specifically limit the specific content of the incremental update rules or the specific implementation process of the algorithm used in the generation process.

[0140] This application embodiment achieves precise control of the brake disc's thermo-mechanical coupling by discretizing pressure parameters and mapping them to a mesh, combined with multiphysics verification and real-time feedback. This improves the uniformity of thermal stress, shortens the braking distance, and avoids the risk of thermal cracking.

[0141] In one possible embodiment, step 144 involves performing multiphysics joint verification on the initial parameter sequence to obtain the verified parameter sequence, including:

[0142] Step b1: Based on the parameters corresponding to each grid cell in the initial parameter sequence, detect the grid cells with heat conduction continuity index and stress field abrupt changes.

[0143] The initial parameter sequence contains the optimized pressure value set for each grid cell. The heat conduction continuity index is a local heat flow consistency value calculated using Fourier's law. If this value exceeds the threshold, it is considered discontinuous.

[0144] Step b2: Adjust the state of the mesh element with abrupt stress field changes to the state corresponding to the parameters of the previous iteration, reduce the adjustment step size, and adjust the initial parameter sequence according to the reduced adjustment step size to generate an intermediate parameter sequence. Perform three consecutive iterations to verify the intermediate parameter sequence.

[0145] The previous iteration parameter is the pressure value of the unit in the previous optimization result. In this embodiment, the specific expression for reducing the adjustment step size is not specifically limited.

[0146] Step b3: Based on the verified intermediate parameter sequence, generate a verified post-parameter sequence that covers all grid cells and satisfies the multi-field equilibrium condition.

[0147] Among them, the multi-field equilibrium condition satisfies the thermal, mechanical, and acoustic coupling equations, for example:

[0148] Where k is the thermal conductivity of the material, and T is the temperature field distribution. The coefficient of friction, To contact pressure, Where ρ is the relative sliding velocity, ρ is the material density, and c is the specific heat capacity. This represents the rate of change of temperature over time. For the von Mises equivalent effect, For the material's yield strength, These are the eigenvalues ​​of the acoustic modes. To take the real part of the complex number, a negative value indicates modal decay.

[0149] The embodiments of this application reduce the uneven distribution of heat and force in the brake disc through a three-step iteration of anomaly detection, local correction and global verification, while eliminating brake squealing noise. It is applicable to scenarios such as aircraft brakes and high-speed rail brake discs.

[0150] In one possible embodiment, step a2, calculating the dynamic weight coefficients corresponding to the first tensor and the second tensor respectively, includes:

[0151] Step a21: Calculate the first initial weight and the second initial weight based on the spatial correlation of the brake disc.

[0152] Spatial correlation is used to reflect the geometric distance and physical property relationship between brake disc grid cells, such as heat conduction path. In this application embodiment, no specific limitations are made on the specific meaning of spatial correlation, the specific calculation method, and the process for calculating each initial weight.

[0153] Step a22: Based on the preset mechanical structure constraints of the brake disc, dynamically adjust the first initial weight and the second initial weight to generate the dynamic weight coefficients corresponding to the first tensor and the second tensor respectively.

[0154] It should be noted that this embodiment does not specifically limit the content of the mechanical structure constraints, the basis for dynamic adjustment, or the type of dynamic adjustment algorithm used.

[0155] This application embodiment reduces the temperature of the hot spot area of ​​the high-speed rail brake disc by dynamically adjusting the weights of different tensors, thereby reducing stress unevenness, avoiding brake squeal, and improving the reliability and lifespan of the braking system.

[0156] Figure 2 This is a schematic diagram of a modular train braking system provided in an embodiment of this application, as shown below. Figure 2 As shown, the system includes:

[0157] The acquisition module 21 is used to acquire temperature distribution data of a target area on the train, where the target area is the area on the train where the brake disc surface is in contact with the pneumatic braking device.

[0158] Extraction module 22 is used to identify local hotspot areas whose temperature exceeds a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and to extract the target geometric feature parameters of the local hotspot areas from the temperature distribution image.

[0159] The generation module 23 is used to generate the braking pressure adjustment parameters of the train based on the target geometric feature parameters of the local hot spot area and the current operating status information of the train, using a heat conduction model.

[0160] The optimization module 24 is used to optimize the brake pressure adjustment parameters to obtain the optimized brake pressure adjustment parameters.

[0161] The sending module 25 is used to send the optimized braking pressure adjustment parameters to the pneumatic braking device in the modular train braking system, so that the pneumatic braking device can apply dynamically changing braking force based on the optimized braking pressure adjustment parameters.

[0162] Figure 2 The modular train braking system described above can perform... Figure 1 The implementation principle and technical effects of the modular train braking method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the modular train braking system described above have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0163] In one possible design, Figure 2 A modular train braking system of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32.

[0164] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0165] The processing component 32 is used to: acquire temperature distribution data of a target area on the train, the target area being the area on the train's brake disc surface in contact with the pneumatic braking device; identify local hotspot areas whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extract target geometric feature parameters of the local hotspot areas from the temperature distribution image corresponding to the temperature distribution data; generate brake pressure adjustment parameters for the train using a heat conduction model based on the target geometric feature parameters of the local hotspot areas and the train's current operating status information; optimize the brake pressure adjustment parameters to obtain optimized brake pressure adjustment parameters; and send the optimized brake pressure adjustment parameters to the pneumatic braking device in the modular train braking system so that the pneumatic braking device applies dynamically changing braking force based on the optimized brake pressure adjustment parameters.

[0166] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0167] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Random Access Memory (RAM), Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0168] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0169] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0170] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0171] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0172] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 A modular train braking method according to the embodiment shown.

[0173] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0174] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0175] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A modular train braking method, characterized in that, A controller used in a modular train braking system includes: Acquire temperature distribution data for a target area on the train, where the target area is the region on the train where the brake disc surface contacts the pneumatic braking device. Identify local hotspot areas where the temperature exceeds a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extract the target geometric feature parameters of the local hotspot areas from the temperature distribution image corresponding to the temperature distribution data. Based on the target geometric feature parameters of the local hotspot area and combined with the current operating status information of the train, the braking pressure adjustment parameters of the train are generated using a heat conduction model. The brake pressure adjustment parameters are optimized to obtain the optimized brake pressure adjustment parameters; The optimized braking pressure adjustment parameters are sent to the pneumatic braking device in the modular train braking system, so that the pneumatic braking device applies dynamically changing braking force based on the optimized braking pressure adjustment parameters.

2. The modular train braking method according to claim 1, characterized in that, The step of identifying local hotspot regions whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and extracting target geometric feature parameters of the local hotspot regions from the temperature distribution image, includes: In the temperature distribution image, local hotspot areas where the temperature value exceeds the preset safe temperature range are identified by using a preset safe temperature range threshold. Boundary detection is performed on the local hotspot region to obtain the boundary contour of the local hotspot region, and the initial geometric feature parameters of the local hotspot region are calculated based on the boundary contour. Based on the initial geometric feature parameters, morphological analysis is performed on the local hotspot region to obtain the structural distribution characteristics of the local hotspot region; The initial geometric feature parameters are fused with the structural distribution features to generate intermediate geometric feature parameters for local hotspot regions; The intermediate geometric feature parameters are optimized to remove redundant parameters and retain feature parameters whose contribution to the description of local hotspot regions is greater than a preset threshold, thereby generating target geometric feature parameters for local hotspot regions.

3. The modular train braking method according to claim 2, characterized in that, The step of fusing the initial geometric feature parameters with the structural distribution features to generate intermediate geometric feature parameters for local hotspot regions includes: The initial geometric feature parameters and structural distribution features are normalized respectively to construct a first tensor and a second tensor that are spatially aligned with the local hotspot region. Calculate the dynamic weight coefficients corresponding to the first tensor and the second tensor respectively; Based on the dynamic weighting coefficients, the first tensor and the second tensor are nonlinearly superimposed to obtain the intermediate geometric feature parameters of the local hotspot region.

4. The modular train braking method according to claim 1, characterized in that, The method of generating train braking pressure adjustment parameters based on the target geometric feature parameters of the local hotspot region, combined with the train's current operating status information, using a heat conduction model includes: The target geometric feature parameters of the local hotspot region are spatially discretized to generate a geometric parameter matrix aligned with the grid cells of the brake disc. Extract real-time speed, braking duration, and ambient temperature parameters from the train's current operating status information to construct an operating status vector; The geometric parameter matrix is ​​coupled with the running state vector in multiple dimensions to generate a joint input feature set; Based on the joint input feature set, the dynamic evolution process of the surface temperature field and internal thermal stress field of the brake disc is calculated through the heat conduction equation in the heat conduction model, generating spatiotemporal distribution data to characterize the temperature gradient and stress concentration region. Based on the spatiotemporal distribution data, with the goal of homogenizing the thermal load on the brake disc, the brake pressure adjustment parameters are solved iteratively in reverse. The effectiveness of the parameters is verified by the preset boundary constraints, and the brake pressure adjustment parameters of the train are output.

5. The modular train braking method according to claim 1, characterized in that, The optimization of the brake pressure adjustment parameters to obtain optimized brake pressure adjustment parameters includes: The braking pressure adjustment parameters are discretized to generate a parameter distribution matrix that maps one-to-one with the grid cells of the brake disc. Based on the preset mechanical structure constraints of the brake disc, the boundary stability of the parameters corresponding to each grid cell in the parameter distribution matrix is ​​tested to select the adjustable parameter range. Within the adjustable parameter range, based on the dynamic coupling relationship between braking pressure and temperature field, the parameters of adjacent units are iteratively weighted to generate an initial parameter sequence. Perform multiphysics joint verification on the initial parameter sequence to obtain the verified parameter sequence; Incremental update rules are generated based on the verified parameter sequence. Based on the incremental update rules and combined with the real-time pressure feedback data of the braking system, optimized braking pressure adjustment parameters are generated.

6. The modular train braking method according to claim 5, characterized in that, The step of performing multiphysics joint verification on the initial parameter sequence to obtain the verified parameter sequence includes: Based on the parameters corresponding to each grid cell in the initial parameter sequence, detect grid cells with heat conduction continuity index and stress field abrupt changes. The state of the mesh element with abrupt stress field changes is adjusted to the state corresponding to the parameters of the previous iteration, the adjustment step size is reduced, and the initial parameter sequence is adjusted according to the reduced adjustment step size to generate an intermediate parameter sequence. The intermediate parameter sequence is then verified by three consecutive iterations. Based on the validated intermediate parameter sequence, a validated post-parameter sequence covering all grid cells and satisfying multi-field equilibrium conditions is generated.

7. The modular train braking method according to claim 3, characterized in that, The calculation of the dynamic weight coefficients corresponding to the first tensor and the second tensor includes: Calculate the first initial weight and the second initial weight based on the spatial correlation of the brake disc; Based on the preset mechanical structure constraints of the brake disc, the first initial weight and the second initial weight are dynamically adjusted to generate the dynamic weight coefficients corresponding to the first tensor and the second tensor, respectively.

8. A modular train braking system, characterized in that, include: The acquisition module is used to acquire temperature distribution data of a target area on the train, wherein the target area is the area on the train where the brake disc surface is in contact with the pneumatic braking device. The extraction module is used to identify local hotspot areas whose temperatures exceed a preset safety range from the temperature distribution image corresponding to the temperature distribution data, and to extract the target geometric feature parameters of the local hotspot areas from the temperature distribution image; The generation module is used to generate the train's braking pressure adjustment parameters based on the target geometric feature parameters of the local hot spot area and the train's current operating status information, using a heat conduction model. An optimization module is used to optimize the brake pressure adjustment parameters to obtain optimized brake pressure adjustment parameters; The transmitting module is used to send the optimized braking pressure adjustment parameters to the pneumatic braking device in the modular train braking system, so that the pneumatic braking device applies dynamically changing braking force based on the optimized braking pressure adjustment parameters.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a modular train braking method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a modular train braking method as described in any one of claims 1 to 7.

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