Underground rack rail vehicle brake pressure self-adaptive adjusting method and system

Abstract

The invention relates to the technical field of rack rail vehicle braking, in particular to an underground rack rail vehicle braking pressure self-adaptive adjusting method and system, and the braking pressure can be adjusted in a self-adaptive mode according to different underground driving working condition attributes. The method comprises the following steps: constructing a braking condition related influence matrix; in the braking working condition correlation influence matrix, different underground driving working condition attributes correspond to different braking performance theoretical attenuation amplitudes respectively; acquiring an underground driving condition attribute corresponding to a to-be-braked action of the rack rail vehicle in real time; traversing in a preset braking working condition related influence matrix by taking the underground driving working condition attribute obtained in real time as a target to obtain a braking performance theoretical attenuation amplitude corresponding to the action to be braked; a braking performance score after the last braking action is obtained, and a braking performance prediction score after the to-be-braked action is calculated by combining the braking performance theoretical attenuation amplitude; and on the basis of the braking performance prediction score after the to-be-braked action, a braking pressure adjusting strategy corresponding to the to-be-braked action is generated.

Description

Technical Field

[0001] The invention relates to the technical field of rack rail vehicle braking, and in particular to an adaptive adjustment method and system for braking pressure of an underground rack rail vehicle. Background Art

[0002] In modern mining operations, rack rail vehicles, as core transportation equipment, frequently shuttle through complex tunnels, and undertake the heavy task of material transfer and personnel transportation. The performance of their braking system is directly related to the safety and efficiency of the entire mine operation. The working conditions underground are complex, such as variable driving slopes, different driving speeds, frequent curves, and dynamic changes in vehicle loads, which will have a significant impact on the braking performance of rack rail vehicles. With the continuous expansion of mining depth and scale, the requirements for the braking system of underground rack rail vehicles are also getting higher and higher.

[0003] The existing underground rack rail vehicle brake pressure adjustment method is relatively simple, and most of them adopt a fixed brake pressure mode. Regardless of whether the vehicle is unloaded or fully loaded, and whether it is driving on a flat road or a steep slope, the brake pressure remains unchanged. This results in the fixed brake pressure being difficult to provide sufficient braking force when the vehicle is heavily loaded downhill, which can easily cause the vehicle to lose control; and when the vehicle is unloaded or driving on a flat road, excessive brake pressure will cause excessive wear of the brake components and unnecessary consumption of energy.

[0004] Therefore, there is an urgent need for an adaptive adjustment method of the brake pressure of an underground rack rail vehicle to solve the above problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for adaptively adjusting the brake pressure of an underground rack rail vehicle, which adaptively adjusts the brake pressure according to different underground driving conditions.

[0006] In a first aspect, the present invention provides a method for adaptively adjusting the braking pressure of an underground rack rail vehicle, the method comprising: Construct a braking condition-related influence matrix; in the braking condition-related influence matrix, different underground driving condition attributes correspond to different theoretical attenuation amplitudes of braking performance; Real-time acquisition of underground driving condition attributes corresponding to the braking action of the rack vehicle; Taking the underground driving condition attributes acquired in real time as the target, traverse the influence matrix related to the braking condition to obtain the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed; Obtain the braking performance score after the last braking action, and calculate the predicted braking performance score after the next braking action in combination with the theoretical attenuation amplitude of the braking performance; Based on the brake performance prediction score after the braking action to be performed, a brake pressure adjustment strategy corresponding to the braking action to be performed is generated.

[0007] In combination with the first aspect, in a possible design, a braking condition-related impact matrix is ​​constructed, including: The braking characteristic parameter set and the corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action are collected.

[0008] The preset braking performance evaluation model is used to quantitatively evaluate the braking characteristic parameter set to obtain the braking performance score of the underground rack rail vehicle after each braking; For each braking performance score, a performance decay analysis is performed with the previous and most recent braking performance score to obtain the braking performance decay amplitude corresponding to the braking action of the underground rack rail vehicle; The correlation between the braking performance attenuation amplitude and the corresponding underground driving condition attributes is analyzed, and the braking condition related influence matrix is ​​constructed based on the analysis results.

[0009] In combination with the first aspect, in a possible design, the braking characteristic parameter set includes brake cylinder pressure, brake pad wear, brake oil temperature, brake line pressure, and brake response time.

[0010] In combination with the first aspect, in a possible design, the underground driving condition attributes include vehicle load, driving slope, driving speed and curve curvature.

[0011] In combination with the first aspect, in a possible design, the underground driving condition attributes corresponding to the braking action of the rack vehicle are obtained in real time, including: According to the rate of change of working conditions and the data accuracy requirements, set the collection frequency for sensors in different locations; According to the set collection frequency, each sensor collects working condition attribute data in real time; Conduct preliminary verification on the collected working condition attribute data and eliminate abnormal data; Convert the verified and processed data into a unified format that can be recognized and processed.

[0012] In combination with the first aspect, in a possible design, the underground driving condition attributes acquired in real time are taken as the target, and the braking condition related influence matrix is ​​traversed to obtain the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed, including: The underground driving condition attributes are used as target attributes to be searched and matched in the braking condition related influence matrix; Establish an index based on the range division of different underground driving condition attributes, and locate the matrix area that may be related to the target attribute based on the index; In the located matrix area, the underground driving condition attribute corresponding to each element in the matrix is ​​compared with the target attribute one by one; For each compared matrix element, the similarity between the underground driving condition attribute and the target attribute is evaluated according to a preset similarity evaluation standard; if the similarity reaches a preset threshold, it is considered that the theoretical attenuation amplitude of the braking performance corresponding to the matrix element may be related to the current braking action; Find the matrix element with the highest similarity between the underground driving condition attribute and the target attribute, and determine the theoretical attenuation amplitude of the braking performance corresponding to the element as the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed.

[0013] In combination with the first aspect, in a possible design, based on the braking performance prediction score after the braking action to be performed, a brake pressure adjustment strategy corresponding to the braking action to be performed is generated, including: Predict and score the braking performance in different ranges in advance, formulate brake pressure adjustment strategies, and form a strategy library; Compare the predicted braking performance score with the score range in the brake pressure regulation strategy library; According to the interval of the braking performance prediction score, find the corresponding braking pressure adjustment strategy; The brake pressure adjustment amount is calculated based on the matched brake pressure adjustment strategy and the current brake pressure value.

[0014] In a second aspect, the present application also provides an adaptive brake pressure adjustment system for an underground rack rail vehicle, the system comprising: A matrix construction module is used to construct a braking condition-related influence matrix, in which different underground driving condition attributes correspond to different theoretical attenuation amplitudes of braking performance; A working condition attribute acquisition module, used to obtain the underground driving working condition attributes corresponding to the braking action of the rack rail vehicle in real time; The attenuation amplitude acquisition module takes the underground driving condition attributes acquired in real time as the target, traverses the preset braking condition related influence matrix, and obtains the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed; The performance score calculation module is used to obtain the braking performance score after the last braking action, and calculate the predicted braking performance score after the braking action in combination with the theoretical attenuation amplitude of the braking performance; The strategy generation module generates a brake pressure adjustment strategy corresponding to the braking action to be performed based on the brake performance prediction score after the braking action to be performed.

[0015] In conjunction with the second aspect, in one possible design, the matrix building module is further configured as follows: Collecting the braking characteristic parameter set and the corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action; The preset braking performance evaluation model is used to quantitatively evaluate the braking characteristic parameter set to obtain the braking performance score of the underground rack rail vehicle after each braking; For each braking performance score, a performance decay analysis is performed with the previous and most recent braking performance score to obtain the braking performance decay amplitude corresponding to the braking action of the underground rack rail vehicle; The correlation between the braking performance attenuation amplitude and the corresponding underground driving condition attributes is analyzed, and the braking condition related influence matrix is ​​constructed based on the analysis results.

[0016] In conjunction with the second aspect, in a possible design, the operating condition attribute acquisition module is further configured as follows: According to the rate of change of working conditions and the data accuracy requirements, set the collection frequency for sensors in different locations; According to the set collection frequency, each sensor collects working condition attribute data in real time; Conduct preliminary verification on the collected working condition attribute data and eliminate abnormal data; Convert the verified and processed data into a unified format that can be recognized and processed.

[0017] Compared with the prior art, the present invention has the following beneficial effects: By constructing a braking condition-related influence matrix, the relationship between underground driving condition attributes and the theoretical attenuation amplitude of braking performance is established, providing a comprehensive data framework for subsequent analysis; by acquiring condition attributes in real time, the actual operating status of the rack rail vehicle is closely matched to ensure that subsequent analysis and adjustment are based on real scenarios; by traversing the real-time condition attributes in the matrix to obtain the theoretical attenuation amplitude of braking performance, accurate mapping of operating conditions and braking performance changes is achieved; by obtaining the last braking performance score and combining it with the theoretical attenuation amplitude to calculate the predicted score, taking into account the historical braking conditions and the impact of current conditions on braking performance, a dynamic and comprehensive perspective is provided for the evaluation of braking performance; by generating a brake pressure adjustment strategy based on the predicted score, accurate adaptive adjustment of brake pressure is achieved.

[0018] In terms of accurately responding to complex working conditions, by constructing a braking condition-related influence matrix and obtaining the working condition attributes in real time to determine the theoretical attenuation amplitude, it can fully and dynamically adapt to the complex and changeable working conditions underground, taking into account the impact of the system's historical state on current braking; in terms of optimizing the performance of the braking system, it can not only accurately adjust the braking pressure according to different working conditions to improve safety, but also avoid excessive wear of components and unnecessary consumption of energy, extend component life and save energy; in terms of improving overall operating efficiency, stable brake pressure regulation ensures the stable operation of the rack rail vehicle, maintains a smooth transportation process, and can also adapt to the complex working conditions brought about by the expansion of mine mining scale, thereby improving operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1is a flow chart of the present invention; Figure 2 is a flow chart of constructing a brake condition-related influence matrix in an embodiment; Figure 3 It is a structural diagram of the brake pressure adaptive regulation system of an underground rack rail vehicle. DETAILED DESCRIPTION

[0020] The present application is described below in conjunction with the drawings in the present application.

[0021] like Figure 1 As shown, the method for adaptively adjusting the braking pressure of an underground rack vehicle of the present invention specifically comprises the following steps: Step S1, constructing a braking condition-related influence matrix; in the braking condition-related influence matrix, different underground driving condition attributes correspond to different braking performance theoretical attenuation amplitudes; Step S2, obtaining in real time the underground driving condition attributes corresponding to the braking action of the rack rail vehicle; Step S3, taking the underground driving working condition attributes acquired in real time as the target, traversing the preset braking working condition related influence matrix to obtain the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed; Step S4, obtaining the braking performance score after the last braking action, and combining the braking performance theoretical attenuation amplitude to calculate the braking performance prediction score after the braking action; Step S5: generating a brake pressure adjustment strategy corresponding to the braking action to be performed based on the braking performance prediction score after the braking action to be performed.

[0022] In this embodiment, by constructing a braking condition-related influence matrix, the relationship between the underground driving condition attributes and the theoretical attenuation amplitude of the braking performance is comprehensively sorted out to provide a data basis for subsequent adjustments; by acquiring the condition attributes in real time, the actual operating state of the vehicle is closely matched to make the adjustment more targeted and timely; the theoretical attenuation amplitude of the braking performance is traversed in the matrix, and the predicted score is calculated in combination with the historical score, and the accuracy of the braking performance evaluation is improved by taking into account the changes in the working conditions and the historical braking conditions; the braking pressure adjustment strategy is generated based on the predicted score to achieve accurate adaptive adjustment of the braking pressure, which can provide sufficient braking force under dangerous conditions such as heavy-load downhill driving to ensure driving safety, and avoid excessive braking pressure when driving without load or on flat roads, thereby reducing wear of braking components and energy consumption; this method significantly improves the safety, reliability and economy of the braking system of the underground rack rail vehicle.

[0023] In some embodiments of the present invention, for step S1, Figure 2 As shown in the figure, the construction of the braking condition related impact matrix includes: Step S11, collecting a braking characteristic parameter set and corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action; Step S12: using a preset braking performance evaluation model to quantitatively evaluate the braking characteristic parameter set, and obtaining a braking performance score of the underground rack rail vehicle after each braking; Step S13: for each braking performance score, a performance decay analysis is performed with the previous and most recent braking performance score to obtain a braking performance decay amplitude corresponding to the braking action of the underground rack rail vehicle; Step S14: performing a correlation analysis on the braking performance attenuation amplitude and the corresponding underground driving condition attributes, and constructing a braking condition related influence matrix according to the analysis results.

[0024] Specifically, step S11 is intended to collect key state parameters reflecting the braking situation of the rack rail vehicle and the external working conditions of the vehicle when it is driving, wherein the braking characteristic parameter set includes: a) Brake cylinder pressure: Brake cylinder pressure directly reflects the force applied during braking. By installing a pressure sensor on the brake cylinder, the pressure changes in the brake cylinder during the braking process are monitored in real time. After each braking action, the pressure value is recorded for subsequent analysis of the working status of the brake system and the braking effect; b) Brake pad wear: The wear degree of the brake pad is related to the reliability and service life of the brake system. Wear sensors are used to obtain the wear of the brake pad. After each braking, the thickness change of the brake pad is measured by the sensor to obtain the wear data of the brake pad; c) Brake oil temperature: Heat is generated during braking, causing the brake oil temperature to rise. Excessively high oil temperature can affect the performance and stability of the brake system. A temperature sensor is installed in the brake oil circuit to monitor the changes in the brake oil temperature in real time. After each braking operation, the brake oil temperature value is recorded to analyze the heat dissipation and working status of the brake system. d) Brake line pressure: The brake line is responsible for transmitting brake fluid and transferring the pressure of the brake cylinder to each brake component. A pressure sensor is installed in the brake line to monitor the pressure changes in the brake line in real time. After each braking action, the brake line pressure value is recorded to evaluate the sealing of the brake line and the transmission of brake fluid. e) Braking response time: Braking response time refers to the time interval from the driver issuing a braking command to the braking system starting to generate braking force. By installing a time sensor in the braking control circuit, the braking response time can be accurately measured. After each braking, the time data is recorded to analyze the response speed and sensitivity of the braking system.

[0025] Furthermore, the underground driving condition attributes include: f) Vehicle load: By installing a load cell on the chassis or suspension system of the underground rack rail vehicle, the vehicle load is measured in real time. Each time the vehicle brakes, the vehicle load data is recorded to facilitate subsequent analysis of the braking performance changes under different load conditions; g) Driving slope: The slope of underground tunnels varies greatly, which has a significant impact on braking performance. The driving slope of the vehicle is measured by installing a slope sensor or using an inertial measurement unit (IMU). Each time the vehicle brakes, the current driving slope data is recorded to provide a basis for analyzing the impact of the slope on braking performance; h) Driving speed: by installing speed sensors on the wheels or drive shafts of the rack rail vehicle, the driving speed of the vehicle is monitored in real time. Each time the vehicle brakes, the driving speed data before braking is recorded to facilitate subsequent analysis of the braking performance at different speeds; i) Curve curvature: There are many curves in underground tunnels, and the curvature of the curve will affect the stability of the vehicle during braking. The curvature of the curve is measured by installing an angle sensor or using a vehicle-mounted camera combined with image recognition technology. Each time the vehicle brakes, the curvature data of the curve is recorded to analyze the impact of the curve on the braking performance.

[0026] Through the above methods, the braking characteristic parameter set and the corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action are comprehensively collected, providing a rich and accurate data basis for the subsequent construction of the braking condition-related influence matrix.

[0027] Step S12 visualizes the complex braking performance so that different braking conditions have quantifiable and comparable standards. The quantitative evaluation method is as follows: Step S121, input the collected braking characteristic parameter set after each braking into a preset braking performance evaluation model; the braking characteristic parameter set is used as an input variable of the braking performance evaluation model to provide a data basis for the braking performance evaluation model to evaluate the braking performance; for example, the size of the brake cylinder pressure reflects the power output during braking, and a lower brake cylinder pressure may mean insufficient braking force; excessive wear of the brake pad may lead to a decrease in braking effect, etc. Each parameter reflects the working state of the braking system from different aspects; Step S122: Analyze and calculate the input braking characteristic parameters according to the algorithm and weight coefficients set inside the braking performance evaluation model; use weighted average, hierarchical analysis, neural network and other methods to comprehensively evaluate various parameters; for example, give higher weights to parameters such as brake cylinder pressure and brake line pressure that directly affect the braking force, while parameters such as brake pad wear and brake oil temperature will not immediately cause brake failure, but will have an important impact on braking performance in the long run, so they are given appropriate weights; the model integrates and calculates various parameters through these algorithms and weights to obtain a value that can comprehensively reflect the braking performance.

[0028] Step S123, after the model is operated and processed, a specific value is output, that is, the braking performance score of the underground rack rail vehicle after each braking. The braking performance score can intuitively reflect the performance status of the rack rail vehicle braking system during this braking. The scoring range and standard are determined according to actual needs and experience when establishing the model. For example, a scoring range of 0-100 points can be set. The higher the score, the better the braking performance. A score below 60 points indicates that there are certain problems with the braking performance and inspection and maintenance are required.

[0029] Through the above method, with the comprehensively collected braking characteristic parameter set as input, the braking characteristic parameter set is quantitatively evaluated using the preset braking performance evaluation model, which can transform the complex braking system working state into an intuitive braking performance score, and provide data support for subsequent analysis of the braking performance attenuation range and construction of the braking condition-related impact matrix.

[0030] Step S13 compares and analyzes each braking performance score with the most recent score before, accurately presenting the dynamic changes of braking performance. The specific contents are as follows: Step S131: for each obtained braking performance score, find its previous and most recent braking performance score as a comparison object; analyze the change of braking performance in two adjacent braking processes, because adjacent braking processes have a strong correlation in time and working conditions, which can more accurately reflect the real-time change trend of braking performance; Step S132: numerically compare the current braking performance score with the selected last most recent braking performance score, and calculate the difference between the two; for example, if the current braking performance score is 85 points and the last most recent braking performance score is 90 points, then the difference is 85-90=-5 points, and the difference preliminarily reflects whether the braking performance is improved or reduced and the approximate degree of change; Step S133, combining the braking characteristic parameter set and the corresponding underground driving condition attribute to analyze the reason for the difference, for example, if it is found that the wear of the brake pad increases significantly during this braking, and the driving slope is large and the vehicle is heavy, then it can be inferred that the reduction in braking performance may be due to the large load and slope causing the brake pad to wear more severely, thereby affecting the braking performance; Step S134, taking all the above factors into consideration, comprehensively evaluating the attenuation of braking performance, and finally determining a value that can accurately reflect the degree of braking performance attenuation, namely, the braking performance attenuation amplitude; the braking performance attenuation amplitude is not just a simple score difference, but a more accurate measure of the actual degree of change in braking performance after comprehensive consideration of various influencing factors; for example, although the score difference is 5 points, considering the special working conditions of this braking and the changes in the braking characteristic parameters, after comprehensive analysis, it is determined that the braking performance attenuation amplitude is 8%, which can more accurately reflect the performance changes of the braking system between the two brakings, and provide more valuable data support for the subsequent construction of the braking condition-related impact matrix and other work.

[0031] Through the above method, adjacent scores are selected for comparison, and their strong correlation with time and working conditions is used to accurately present the real-time change trend of braking performance, providing an intuitive basis for braking system status monitoring; calculating the score difference can preliminarily judge the rise and fall and degree of change of braking performance, the operation is simple and intuitive, and performance fluctuations can be quickly located; combining the braking characteristic parameters and the attributes of underground driving conditions to analyze the reasons for the difference, deeply explore the internal factors of performance changes, and provide clues for fault diagnosis and performance optimization; comprehensively evaluate and determine the attenuation amplitude of braking performance, get rid of the limitation of simple numerical difference, comprehensively consider various influencing factors, and obtain more accurate measurement values, so as to provide high-quality data for subsequent work such as constructing the braking condition-related influence matrix, and help the scientific management and safe operation of the underground rack vehicle braking system.

[0032] Step S14 reveals the correlation between the braking performance attenuation amplitude and the underground driving condition attributes through correlation analysis, and the specific contents are as follows: Step S141, sorting out the obtained braking performance attenuation amplitude data and the corresponding underground driving condition attribute data; the underground driving condition attributes include information such as vehicle load, driving slope, driving speed and curve curvature, ensuring that these data are accurate, complete and one-to-one corresponding; Step S142, select a suitable correlation analysis method to perform correlation analysis; for example, the Pearson correlation coefficient analysis method can be used to measure the linear correlation between the braking performance attenuation amplitude and each underground driving condition attribute; other more complex analysis methods, such as multivariate linear regression analysis, etc., can also be used to consider the comprehensive impact of the interaction between multiple condition attributes on the braking performance attenuation amplitude; for the attribute of the braking performance attenuation amplitude and the vehicle load, the correlation coefficient between the two is obtained by analysis and calculation; for example, if the calculated correlation coefficient is 0.7, it means that the braking performance attenuation amplitude and the vehicle load are strongly positively correlated, that is, the greater the vehicle load, the greater the braking performance attenuation amplitude may be; similarly, the braking performance attenuation amplitude and other condition attributes such as driving slope, driving speed, curve curvature, etc. are respectively correlated and analyzed to obtain corresponding correlation coefficients or regression equations and other analysis results to clarify the relationship between them; Step S143, construct a braking condition related influence matrix based on the results of the correlation analysis; the rows and columns of the matrix can correspond to different underground driving condition attributes and braking performance attenuation amplitude ranges respectively; in the matrix, the relationship between each condition attribute and the braking performance attenuation amplitude is expressed in the form of a numerical value or symbol; for example, if the vehicle load has a strong correlation with a certain braking performance attenuation amplitude range, mark a larger numerical value or specific symbol, such as "++", at the corresponding position of the matrix; if the correlation between the two is weak, mark a smaller numerical value or symbol, such as "+" or "±"; for other condition attributes such as driving slope, driving speed and curve curvature, they are also marked in the matrix in the same way, so as to form a braking condition related influence matrix that comprehensively reflects the relationship between the braking performance attenuation amplitude and the underground driving condition attributes; the braking condition related influence matrix can intuitively show the changing trend of braking performance under different conditions, and provide an important reference basis for the subsequent adjustment of the braking pressure according to the real-time conditions.

[0033] Through the above methods, the data on the attenuation amplitude of braking performance and the attributes of underground driving conditions are collated to ensure that the data is accurate and complete, laying a solid foundation for subsequent analysis; appropriate analysis methods are selected to comprehensively consider the linear and comprehensive relationship between the attributes of each working condition and the attenuation amplitude of braking performance, accurately revealing the intrinsic connection between them; the braking condition-related influence matrix constructed based on the analysis results clearly presents the changing trend of braking performance under different working conditions in an intuitive row and column representation form, providing a key reference for underground rack rail vehicles to achieve precise adjustment of braking pressure according to real-time working conditions, improving the adaptability and safety of the braking system to complex working conditions, and ensuring efficient mine operations.

[0034] In some embodiments of the present invention, step S2 provides real-time data support for subsequent precise control with the help of reasonably selected and accurately deployed sensors. The specific implementation steps are as follows: Step S21, according to the rate of change of working conditions and the data accuracy requirements, set the acquisition frequency for sensors at different positions; for working condition parameters that change rapidly, such as driving speed and curve curvature, set a higher acquisition frequency, such as setting the acquisition frequency of the corresponding sensor to 100Hz; for working condition parameters that change relatively slowly, such as vehicle load and driving slope, set a relatively low acquisition frequency, such as setting the acquisition frequency of the corresponding sensor to 10Hz; Step S22, each sensor collects working condition attribute data in real time at a predetermined frequency; the sensor converts the collected physical quantity into an electrical signal and transmits it; Step S23: Perform preliminary verification on the collected working condition attribute data and remove abnormal data; use a data filtering algorithm, such as median filtering, to process the signal output by the sensor to remove spike noise and abnormal values ​​caused by factors such as electromagnetic interference. For example, for driving speed data, if the speed value collected at a certain moment is far beyond the normal driving speed range of the vehicle, replace it with a reasonable value through a median filtering algorithm; Step S24, converting the verified and processed data into a unified format that can be recognized and processed; for example, converting analog or digital data output by different sensors into a binary encoding format to prepare for subsequent query in the braking condition-related influence matrix and braking performance analysis; In this embodiment, the collection frequency is set according to the rate of change of the operating condition and the data accuracy requirements to achieve precise control of the operating parameters with different changing characteristics. High-frequency collection is set for the rapidly changing operating parameters to ensure the real-time nature of the data, and low-frequency collection is set for the slowly changing operating parameters to reduce the burden of data processing while taking into account data quality and efficiency. The real-time collection and transmission of the sensor ensures that the system can obtain the vehicle operating status information in a timely manner and provide real-time data for brake pressure adjustment. The algorithm is used to verify the data to effectively eliminate abnormal values, improve data accuracy, and avoid misjudgment caused by erroneous data. The data is unified in format to eliminate format differences, which is convenient for identification and processing, thereby improving the scientificity and reliability of the adaptive adjustment of the brake pressure.

[0035] The sensors include weighing sensors, acceleration sensors, speed sensors and angle sensors, specifically: a) Load cell: installed at the key load-bearing part of the rack rail vehicle chassis; for example, a resistance strain gauge load cell is used to convert the pressure generated by the vehicle load into an electrical signal output through the pressure-strain principle; b) Acceleration sensor: installed on the longitudinal center line of the rack rail vehicle, close to the center of gravity of the vehicle; for example, a dual-axis acceleration sensor is used to accurately calculate the vehicle's driving slope by detecting the components of gravity acceleration in different axial directions; c) Speed ​​sensor: installed near the driving wheel of the rack vehicle; for example, an electromagnetic induction speed sensor is used. When the wheel rotates, the sensor's sensing element cuts the magnetic lines of force and generates a pulse signal proportional to the wheel speed. By measuring the number of pulses per unit time and combining it with the wheel circumference, the vehicle's speed can be accurately calculated. d) Angle sensor: installed on the steering mechanism of the rack rail vehicle; for example, a potentiometer-type angle sensor is used. As the steering mechanism rotates, the resistance value of the sensor changes, and the output is a voltage signal proportional to the steering angle. By monitoring the rate and duration of change of the steering angle, combined with parameters such as the vehicle wheelbase, the curvature of the curve is calculated using a geometric algorithm, providing the braking system with information on the driving conditions on the curve.

[0036] In some embodiments of the present invention, step S3 accurately matches the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be braked by traversing the real-time underground driving condition attributes in a preset matrix, and is specifically implemented as follows: Step S31: using the underground driving condition attributes as target attributes to be searched and matched in the braking condition related influence matrix; namely, vehicle load, driving slope, driving speed and curve curvature information, reflecting the actual operating condition of the rack vehicle when it is currently waiting to brake; Step S32: Establish an index based on the range of different working condition attributes, and locate the matrix area that may be related to the target attribute based on the index; for example, according to the numerical range of vehicle load, quickly determine the corresponding row or column range in the matrix to narrow the search space; Step S33: in the initially located matrix area, compare the underground driving condition attributes corresponding to each element in the matrix with the target attributes one by one; by comparing specific values ​​such as vehicle load, driving slope, driving speed and curve curvature, determine the similarity between them; for example, calculate the difference between the target driving speed and the driving speed corresponding to each element in the matrix, or use a more complex similarity calculation method (such as Euclidean distance, cosine similarity, etc.) to measure the similarity of the overall working condition attributes; Step S34: for each compared matrix element, determine its similarity with the target underground driving condition attribute according to a preset similarity evaluation standard; if the similarity reaches a certain threshold, it is considered that the theoretical attenuation amplitude of the braking performance corresponding to the matrix element may be related to the current braking action; Step S35, after traversing the matrix area and evaluating the similarity of all elements, find the matrix element with the highest similarity to the target underground driving condition attribute, and determine the theoretical attenuation amplitude of the braking performance corresponding to the element as the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be taken; the theoretical attenuation amplitude of the braking performance reflects the possible attenuation degree of the braking performance predicted according to historical data and analysis under the current underground driving condition, which provides an important basis for the subsequent calculation of the braking performance prediction score after the braking action to be taken.

[0037] In this embodiment, through the operation of the above step S3, the real-time underground driving condition attributes are clearly taken as the search target, which is closely aligned with the actual operating conditions of the rack rail vehicle and provides direction for subsequent operations; by establishing an index to locate the relevant matrix area, the search range is narrowed, the search efficiency is improved, and computing resources are saved; one-by-one comparison and complex similarity calculation methods are used to comprehensively measure the similarity of the condition attributes to ensure the accuracy of the evaluation; the similarity is determined according to the preset standard, and the theoretical attenuation amplitude of the braking performance related to the current braking action can be accurately screened out; the attenuation amplitude corresponding to the element with the highest similarity is found to provide a reliable basis for the calculation of the braking performance prediction score, so that the entire brake pressure adaptive adjustment mechanism can be based on accurate prediction of braking performance changes, better adapt to complex conditions, and ensure the safe and efficient operation of the underground rack rail vehicle.

[0038] In some embodiments of the present invention, step S4 accurately calculates the predicted braking performance score after the braking action by combining the last braking performance score with the theoretical attenuation amplitude of the current braking performance, so as to provide core data support for the subsequent formulation of a reasonable braking pressure adjustment strategy, which is specifically implemented as follows: Step S41, obtaining a braking performance score after the last braking action; querying and extracting the braking performance score recorded after the last braking action from the storage device, the score is obtained by quantitatively evaluating the braking characteristic parameter set after the last braking action through a preset braking performance evaluation model, reflecting the comprehensive performance state of the rack rail vehicle braking system during the last braking; for example, after the last braking action, the braking performance score obtained by the braking performance evaluation model is 80 points; Step S42, determining the theoretical attenuation amplitude of the braking performance; the theoretical attenuation amplitude of the braking performance is obtained by traversing the preset braking condition-related influence matrix with the underground driving condition attributes obtained in real time as the target, wherein different underground driving condition attributes correspond to different theoretical attenuation amplitudes of the braking performance; for example, under the current real-time working condition, by matching in the matrix, it is determined that the corresponding theoretical attenuation amplitude of the braking performance is 10%, which means that under the current working condition, the braking performance will theoretically decay by 10%; Step S43, calculate the predicted braking performance score after the braking action according to the obtained braking performance score after the last braking action and the determined theoretical attenuation amplitude of the braking performance; a common calculation method is to calculate according to a certain formula, for example: predicted braking performance score after the braking action = braking performance score after the last braking action × (1-theoretical attenuation amplitude of the braking performance), taking the data of the above example as an example, if the last braking performance score is 80 points and the theoretical attenuation amplitude of the braking performance is 10%, then the predicted braking performance score after the braking action = 80 × (1-0.1) = 72 points, and the predicted score of 72 points reflects the predicted braking performance level after this braking action under the current working conditions, taking into account the theoretical attenuation of the braking performance; Step S44, perform a rationality check on the calculated braking performance prediction score; compare the braking performance scores under similar working conditions in historical data, or make a comprehensive judgment based on the actual operating status of the current vehicle (such as whether there is a braking system fault warning, etc.); if it is found that the predicted score may deviate significantly from the actual situation, further check the data source in the calculation process (such as the accuracy of the last braking performance score, the rationality of the theoretical attenuation amplitude of the braking performance, etc.), and make corresponding adjustments as needed to ensure that the final braking performance prediction score can truly reflect the braking performance after the braking action.

[0039] In this embodiment, the last braking performance score is obtained through the operation of the above step S4. This score can truly reflect the comprehensive performance of the last braking system after quantitative evaluation, providing a historical data basis for this calculation; the theoretical attenuation amplitude of the braking performance is determined by traversing the preset matrix, and the real-time working conditions are closely related to make the prediction fit the actual operating status; the braking performance prediction score is calculated using a scientific formula to clearly and intuitively present the expected braking performance level under the current working conditions; the prediction score is checked for rationality, and a comprehensive judgment is made based on historical data and the actual operating status of the vehicle, data deviations are promptly checked, and the calculation process is adjusted to ensure that the score truly reflects the braking performance after the current braking action, and to provide solid and reliable data support for the subsequent formulation of accurate and effective brake pressure adjustment strategies, which effectively guarantees the safe and stable operation of underground rack rail vehicles.

[0040] In some embodiments of the present invention, step S5 can dynamically adjust the brake pressure according to the prediction result of the braking performance of the rack rail vehicle under real-time working conditions to ensure that the brake system can maintain a stable and efficient working state under different working conditions. The specific implementation is as follows: Step S51, establishing a brake pressure adjustment strategy library; based on a large amount of experimental data, theoretical analysis and actual operation experience, a series of detailed brake pressure adjustment strategies are formulated for different ranges of brake performance prediction scores to form a strategy library; for example, the brake performance prediction score is divided into multiple intervals, such as a score of 0-40 points, which represents extremely poor braking performance, and a strategy of substantially increasing the brake pressure and issuing an alarm is set; a score of 40-60 points represents poor braking performance, and a strategy of appropriately increasing the brake pressure and performing a system inspection prompt is set; a score of 60-80 points represents average braking performance, and a strategy of maintaining the current brake pressure and continuously monitoring is set; a score of 80-100 points represents good braking performance, and a strategy of appropriately reducing the brake pressure to save energy is set; the above strategies not only take into account the braking performance, but also comprehensively take into account multiple factors such as the actual operation safety of the underground rack vehicle, the wear of the brake components, and energy consumption; Step S52: Compare the calculated predicted score of the braking performance after the braking action with the score interval in the brake pressure adjustment strategy library; find the corresponding brake pressure adjustment strategy according to the score interval; for example, if the calculated predicted score of the braking performance is 50 points, by searching the strategy library, it is determined that it is in the interval of 40-60 points, so as to match the strategy of "appropriately increase the brake pressure and perform a system inspection prompt"; Step S53: Calculate the brake pressure adjustment amount based on the matched brake pressure adjustment strategy and the current brake pressure value; for example, if the strategy is to increase the brake pressure appropriately, calculate the brake pressure value to be increased by a specific formula based on factors such as the distance between the score and the interval boundary, the current vehicle load, and the driving slope, such as increasing the brake pressure by a certain percentage (such as 1%) for every 1 point increase in the score difference; if the strategy involves other operations, such as issuing an alarm or a system inspection prompt, the corresponding operation parameters also need to be clarified, such as the alarm level, the content and method of the prompt, etc.; Step S54: Generate a corresponding brake pressure adjustment control instruction based on the calculated brake pressure adjustment amount; the instruction includes the direction of brake pressure adjustment (increase or decrease), the specific adjustment value, the execution time requirement and other relevant operation information (such as alarm triggering, etc.); for example, the generated instruction may be "Increase the brake pressure by 10% in the next 5 seconds and issue a level 2 alarm at the same time; Step S55: After receiving the instruction, the actuator performs corresponding operations according to the instruction requirements to achieve the adjustment of the brake pressure and other related operations; the actuator includes but is not limited to the brake pump, brake valve, etc.; Step S56, verify whether the actual adjusted pressure meets the expectations, and make corresponding adjustments if there is any deviation; each sensor monitors the actual changes in the brake pressure in real time. After the brake pressure adjustment actuator executes the instruction, the actual brake pressure value is compared with the adjustment target required in the instruction. If it is found that there is a deviation between the actual brake pressure and the target pressure, and the deviation exceeds a certain allowable range, the brake pressure adjustment strategy is adjusted according to the size and direction of the deviation, and a new adjustment instruction is regenerated and sent to ensure that the brake pressure can be accurately adjusted to the expected target value, thereby achieving precise control of the brake pressure.

[0041] In this embodiment, through the operation of the above step S5, a brake pressure adjustment strategy library is established, and comprehensive and scientific strategies are formulated for different brake performance prediction score intervals by integrating experimental data, theoretical analysis and practical experience, taking into full consideration various aspects such as operation safety, component wear and energy consumption; the predicted score is compared with the strategy library, and accurate strategies can be quickly matched, and then the adjustment amount is calculated in combination with the current brake pressure value, and a control instruction containing detailed operation information is generated, so that the actuator can execute accurately; the adjusted pressure is verified in real time, and once the deviation exceeds the allowable range, the strategy and instruction are adjusted in time to achieve precise control of the brake pressure, ensuring that the brake system always maintains stable and efficient operation under complex and changeable working conditions, thereby improving the safety and economy of the operation of the underground rack rail vehicle.

[0042] In some schemes, multiple embodiments of the present application can be combined, and the combined scheme can be implemented. Optionally, some operations in the process of each method embodiment are optionally combined, and / or the order of some operations is optionally changed. In addition, the execution order between the steps of each process is only exemplary and does not constitute a restriction on the execution order between the steps. There can also be other execution orders between the steps. It is not intended to indicate that the execution order is the only order in which these operations can be performed. A person of ordinary skill in the art will think of a variety of ways to reorder the operations described herein. In addition, it should be noted that the process details involved in a certain embodiment of this article are also applicable to other embodiments in a similar manner, or different embodiments can be used in combination.

[0043] In addition, some steps in the method embodiment may be equivalently replaced by other possible steps. Alternatively, some steps in the method embodiment may be optional and may be deleted in certain usage scenarios. Alternatively, other possible steps may be added to the method embodiment.

[0044] Furthermore, the various method embodiments may be implemented separately or in combination.

[0045] like Figure 3 As shown, the present invention also provides an adaptive adjustment system for brake pressure of an underground rack rail vehicle, which specifically includes the following modules: A matrix construction module is used to construct a braking condition-related influence matrix, in which different underground driving condition attributes correspond to different theoretical attenuation amplitudes of braking performance; A working condition attribute acquisition module, used to obtain the underground driving working condition attributes corresponding to the braking action of the rack rail vehicle in real time; The attenuation amplitude acquisition module takes the underground driving condition attributes acquired in real time as the target, traverses the preset braking condition related influence matrix, and obtains the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed; The performance score calculation module is used to obtain the braking performance score after the last braking action, and calculate the predicted braking performance score after the braking action in combination with the theoretical attenuation amplitude of the braking performance; The strategy generation module generates a brake pressure adjustment strategy corresponding to the braking action to be performed based on the brake performance prediction score after the braking action to be performed.

[0046] In this embodiment, the matrix construction module sorts out the relationship between the working condition attributes and the braking performance attenuation amplitude, providing data support for subsequent analysis; the working condition attribute acquisition module collects data in real time to ensure that the system can be adjusted according to the actual working conditions; the attenuation amplitude acquisition module traverses the matrix to accurately match the braking performance attenuation amplitude under the current working conditions, providing a basis for performance evaluation; the performance score calculation module combines the historical score with the theoretical attenuation amplitude to calculate the predicted score after the braking action, and comprehensively considers the changes in braking performance; the strategy generation module generates an adjustment strategy based on the predicted score to achieve precise control of the braking pressure; the modules work together to adjust the braking pressure in real time according to the complex working conditions underground, which not only ensures driving safety, but also avoids excessive wear of braking components and energy waste, and improves the safety, reliability and economy of the underground rack vehicle braking system.

[0047] In a specific implementation, as an embodiment, the matrix construction module is further configured to: The braking characteristic parameter set and the corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action are collected.

[0048] The braking characteristic parameter set is quantitatively evaluated using a preset braking performance evaluation model to obtain a braking performance score of the underground rack rail vehicle after each braking; For each braking performance score, a performance decay analysis is performed with the previous and most recent braking performance score to obtain the braking performance decay amplitude corresponding to the braking action of the underground rack rail vehicle; A correlation analysis is performed on the braking performance attenuation amplitude and the corresponding underground driving condition attributes, and a braking condition related influence matrix is ​​constructed according to the analysis results.

[0049] The matrix construction module constructs a braking condition-related influence matrix by collecting, analyzing and processing the braking characteristic parameters and underground driving condition attributes, providing data support for the entire underground rack vehicle brake pressure adaptive adjustment system, so that the system can accurately predict the attenuation of braking performance based on the real-time underground driving condition attributes, thereby achieving precise adjustment of braking pressure, ensuring driving safety, and improving the reliability and economy of the braking system.

[0050] In a specific implementation, as an embodiment, the operating condition attribute acquisition module is further configured to: According to the rate of change of working conditions and the data accuracy requirements, set the collection frequency for sensors in different locations; According to the set collection frequency, each sensor collects working condition attribute data in real time; Performing preliminary verification on the collected working condition attribute data and eliminating abnormal data; Convert the verified and processed data into a unified format that can be recognized and processed.

[0051] The working condition attribute acquisition module provides real-time, accurate and reliable underground driving condition attribute data for the underground rack rail vehicle brake pressure adaptive adjustment system by reasonably setting the sensor acquisition frequency, accurately collecting data, strictly verifying data and unifying the data format. The system can accurately adjust the brake pressure according to the actual working conditions, thereby ensuring driving safety, avoiding excessive wear of brake components and energy waste, and improving the safety, reliability and economy of the underground rack rail vehicle braking system.

[0052] This embodiment divides the functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0053] The various variations and specific embodiments of the method for adaptively adjusting the braking pressure of an underground rack rail vehicle in the aforementioned embodiment 1 are also applicable to the adaptively adjusting the braking pressure of an underground rack rail vehicle in the present embodiment. Through the aforementioned detailed description of the method for adaptively adjusting the braking pressure of an underground rack rail vehicle, those skilled in the art can clearly know the implementation method of the adaptively adjusting the braking pressure of an underground rack rail vehicle in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0054] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for adaptively adjusting brake pressure of an underground rack rail vehicle, characterized in that: The method comprises: Constructing a braking condition-related influence matrix; in the braking condition-related influence matrix, different underground driving condition attributes correspond to different braking performance theoretical attenuation amplitudes; Real-time acquisition of underground driving condition attributes corresponding to the braking action of the rack vehicle; Taking the underground driving working condition attributes acquired in real time as the target, traversing the braking working condition related influence matrix to obtain the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed; Obtaining the braking performance score after the last braking action, and combining the braking performance theoretical attenuation amplitude, calculating the braking performance prediction score after the braking action; Based on the braking performance prediction score after the braking action to be performed, a brake pressure adjustment strategy corresponding to the braking action to be performed is generated.

2. The method for adaptively adjusting brake pressure of an underground rack rail vehicle according to claim 1, characterized in that: Construct the brake condition related impact matrix, including: Collecting the braking characteristic parameter set and the corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action; The braking characteristic parameter set is quantitatively evaluated using a preset braking performance evaluation model to obtain a braking performance score of the underground rack rail vehicle after each braking; For each braking performance score, a performance decay analysis is performed with the previous and most recent braking performance score to obtain the braking performance decay amplitude corresponding to the braking action of the underground rack rail vehicle; A correlation analysis is performed on the braking performance attenuation amplitude and the corresponding underground driving condition attributes, and a braking condition related influence matrix is ​​constructed according to the analysis results.

3. The method for adaptively adjusting brake pressure of an underground rack rail vehicle according to claim 2, characterized in that: The brake characteristic parameter set includes brake cylinder pressure, brake pad wear, brake oil temperature, brake line pressure and brake response time.

4. The method for adaptively adjusting brake pressure of an underground rack rail vehicle according to claim 2, characterized in that: The underground driving condition attributes include vehicle load, driving slope, driving speed and curve curvature.

5. The method for adaptively adjusting brake pressure of an underground rack rail vehicle according to any one of claims 1 to 4, characterized in that: Real-time acquisition of underground driving condition attributes corresponding to the rack vehicle's braking action, including: According to the rate of change of working conditions and the data accuracy requirements, set the collection frequency for sensors in different locations; According to the set collection frequency, each sensor collects working condition attribute data in real time; Performing preliminary verification on the collected working condition attribute data and eliminating abnormal data; Convert the verified and processed data into a unified format that can be recognized and processed.

6. The method for adaptively adjusting brake pressure of an underground rack rail vehicle according to any one of claims 1 to 4, characterized in that: Taking the underground driving condition attributes acquired in real time as the target, traverse the braking condition related influence matrix to obtain the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed, including: Using the underground driving condition attribute as a target attribute to be searched and matched in the braking condition related influence matrix; Establishing an index according to the range division of different underground driving working condition attributes, and locating a matrix area related to the target attribute according to the index; In the located matrix area, the underground driving condition attribute corresponding to each element in the matrix is ​​compared with the target attribute one by one; For each compared matrix element, the similarity between the underground driving condition attribute and the target attribute is evaluated according to a preset similarity evaluation standard; if the similarity reaches a preset threshold, it is considered that the theoretical attenuation amplitude of the braking performance corresponding to the matrix element is related to the current braking action to be performed; Find the matrix element with the highest similarity between the underground driving condition attribute and the target attribute, and determine the theoretical attenuation amplitude of the braking performance corresponding to the element as the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed.

7. The method for adaptively adjusting brake pressure of an underground rack rail vehicle according to any one of claims 1 to 4, characterized in that: Based on the braking performance prediction score after the braking action to be performed, a braking pressure adjustment strategy corresponding to the braking action to be performed is generated, including: Predict and score the braking performance in different ranges in advance, formulate brake pressure adjustment strategies, and form a strategy library; Comparing the braking performance prediction score with the score interval in the brake pressure regulation strategy library; Finding a corresponding brake pressure adjustment strategy according to the interval of the brake performance prediction score; The brake pressure adjustment amount is calculated based on the matched brake pressure adjustment strategy and the current brake pressure value.

8. An adaptive brake pressure adjustment system for an underground rack rail vehicle, characterized in that: The system comprises: A matrix construction module is used to construct a braking condition-related influence matrix, in which different underground driving condition attributes correspond to different theoretical attenuation amplitudes of braking performance; A working condition attribute acquisition module, used to obtain the underground driving working condition attributes corresponding to the braking action of the rack rail vehicle in real time; The attenuation amplitude acquisition module takes the underground driving condition attributes acquired in real time as the target, traverses the preset braking condition related influence matrix, and obtains the theoretical attenuation amplitude of the braking performance corresponding to the braking action to be performed; The performance score calculation module is used to obtain the braking performance score after the last braking action, and calculate the predicted braking performance score after the braking action in combination with the theoretical attenuation amplitude of the braking performance; The strategy generation module generates a brake pressure adjustment strategy corresponding to the braking action to be performed based on the brake performance prediction score after the braking action to be performed.

9. The underground rack vehicle brake pressure adaptive adjustment system according to claim 8, characterized in that: The matrix building module is further configured to: Collecting the braking characteristic parameter set and the corresponding underground driving condition attributes of the underground rack rail vehicle after each braking action; The braking characteristic parameter set is quantitatively evaluated using a preset braking performance evaluation model to obtain a braking performance score of the underground rack rail vehicle after each braking; For each braking performance score, a performance decay analysis is performed with the previous and most recent braking performance score to obtain the braking performance decay amplitude corresponding to the braking action of the underground rack rail vehicle; A correlation analysis is performed on the braking performance attenuation amplitude and the corresponding underground driving condition attributes, and a braking condition related influence matrix is ​​constructed according to the analysis results.

10. The brake pressure adaptive adjustment system for an underground rack vehicle according to any one of claims 8 and 9, characterized in that: The operating condition attribute acquisition module is further configured to: According to the rate of change of working conditions and the data accuracy requirements, set the collection frequency for sensors in different locations; According to the set collection frequency, each sensor collects working condition attribute data in real time; Performing preliminary verification on the collected working condition attribute data and eliminating abnormal data; Convert the verified and processed data into a unified format that can be recognized and processed.

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