Brake health prediction method and device, electronic equipment and storage medium

Abstract

The invention provides a brake health prediction method and device, electronic equipment and a storage medium, and belongs to the technical field of detection, and the method comprises the steps: determining measured values of a plurality of first operation parameters based on a plurality of pieces of monitoring sample data of the plurality of first operation parameters of a brake; determining a corresponding evaluation value based on the measured value of each first operation parameter to obtain a plurality of evaluation values; and carrying out weighted summation on the plurality of evaluation values to obtain a brake health prediction result. According to the brake health prediction method and device, the electronic equipment and the storage medium, the accuracy of elevator brake health prediction can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of detection technology, and more specifically, relates to a brake health prediction method and device, electronic equipment, and storage medium. Background Art

[0002] As a key component to ensure the safe operation of elevators, the performance and reliability of elevator brakes are crucial. However, existing elevator brake health prediction methods have many defects and shortcomings. On the one hand, the detection methods are limited and mostly remain at the qualitative detection stage, relying on manual experience and regular tests. It is impossible to monitor the performance changes of the brakes in real time and detect potential faults in time. On the other hand, these methods are greatly interfered by external factors, the evaluation results are subjective and inconsistent, and the fault prediction ability is insufficient. Therefore, it is particularly important to develop a method that can accurately predict the health status of elevator brakes. Summary of the invention

[0003] The purpose of the present disclosure is to provide a brake health prediction method and device, electronic equipment, and storage medium to improve the accuracy of elevator brake health prediction.

[0004] According to a first aspect of an embodiment of the present disclosure, a brake health prediction method is provided, comprising: Determining measured values ​​of a plurality of first operating parameters based on a plurality of monitoring sample data of a plurality of first operating parameters of the brake; Determine corresponding evaluation values ​​based on the measured values ​​of each first operating parameter to obtain multiple evaluation values; The multiple evaluation values ​​are weighted and summed to obtain a brake health prediction result.

[0005] A second aspect of the embodiments of the present disclosure provides a brake health prediction device, comprising: A first calculation module, configured to determine measured values ​​of a plurality of first operating parameters of the brake based on a plurality of monitoring sample data of the plurality of first operating parameters; A second calculation module, used to determine a corresponding evaluation value based on the measured value of each first operating parameter to obtain a plurality of evaluation values; The health prediction module is used to perform weighted summation of the multiple evaluation values ​​to obtain a brake health prediction result.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned brake health prediction method when executing the computer program.

[0007] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned brake health prediction method are implemented.

[0008] The beneficial effects of a brake health prediction method and device, electronic device, and storage medium provided by the embodiments of the present disclosure are: The disclosed embodiment collects monitoring sample data of multiple first operating parameters and predicts the health status of the brake based on the monitoring sample data of multiple first operating parameters, so as to more comprehensively understand the operating status of the brake. Compared with judging by only relying on a single parameter, the evaluation results of multiple parameters can more accurately predict the health status of the brake. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a brake health prediction method provided by an embodiment of the present disclosure; Figure 2 A schematic flow chart of a brake health prediction method provided by another embodiment of the present disclosure; Figure 3 A schematic diagram of a sensor acquisition system provided by an embodiment of the present disclosure; Figure 4 A structural block diagram of a brake health prediction device provided by an embodiment of the present disclosure; Figure 5 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0013] Please refer to Figure 1-Figure 2 , Figure 1 A schematic flow chart of a brake health prediction method provided in one embodiment of the present disclosure, the method comprising: S101: Determine the actual measured value of each first operating parameter based on a plurality of monitoring sample data of a plurality of first operating parameters of the brake.

[0014] In this embodiment, the first operating parameters of the brake may include pressure (corresponding to the car load), vibration amplitude, temperature, humidity and brake clearance, etc. The brake clearance refers to the spacing distance between the brake components when the brake is in a non-braking state.

[0015] Please refer to Figure 3 , pressure and vibration sensors can be installed in the car, connected to the car data storage via 485 communication, the car data storage transmits data to the gateway via LORA wireless communication, the temperature and humidity and brake clearance measurement sensors (i.e., distance sensors) installed in the elevator transmit data to the gateway via 485 communication, and finally the gateway integrates and uploads the data to the cloud. Through the above process, multiple monitoring sample data of multiple first operating parameters of the brake can be obtained.

[0016] By analyzing and processing a plurality of monitoring sample data of a plurality of first operating parameters of the brake, such as taking an average value, a median, or other statistical methods, the actual measured value of each first operating parameter can be determined.

[0017] S102: Determine corresponding evaluation values ​​based on the measured values ​​of each first operating parameter to obtain a plurality of evaluation values.

[0018] In this embodiment, the normal range of each first operating parameter can be pre-set based on historical operating data, and the measured value of each first operating parameter can be compared with the upper limit value to obtain an evaluation value. For example, the larger the ratio of the measured value to the upper limit value, the larger the evaluation value.

[0019] Each evaluation value can be used to characterize the performance of the brake in the corresponding aspect. The smaller the evaluation value, the better the performance. For example, if the ratio of the pressure value monitored at the first moment to the pressure upper limit is 0.6, and the ratio of the pressure value monitored at the second moment to the pressure upper limit is 0.7, it is considered that the brake pressure performance at the first moment is good.

[0020] S103: performing weighted summation of multiple evaluation values ​​to obtain a brake health prediction result.

[0021] In this embodiment, a weighted sum of multiple evaluation values ​​can be used to obtain a brake health prediction result, wherein the weight corresponding to each evaluation value can be determined according to the importance of each parameter to the brake health condition.

[0022] For example, in high-rise buildings, elevators are frequently used, and the weight of the people and cargo they carry varies greatly. At this time, the importance of pressure (corresponding to the car load) and brake clearance is relatively high, and a higher weight can be assigned to pressure and brake clearance; while parameters such as brake temperature have relatively little impact and can be assigned a lower weight. In rail transit, rail transit vehicles run at high speeds and vibrate greatly. Long-term vibration may cause the brake components to loosen or fatigue damage; at the same time, frequent braking will cause the brake temperature to rise sharply, and close attention needs to be paid to prevent overheating damage; brake clearance directly affects the braking effect and the safety of train operation. The importance of pressure (corresponding to passenger capacity or cargo weight) is relatively low, because the load changes of rail transit vehicles are relatively small, and the impact of humidity is also relatively small. Therefore, a higher weight can be assigned to vibration amplitude, temperature, and brake clearance.

[0023] The influence of various parameters can be comprehensively considered through weighted summation to obtain a comprehensive brake health prediction result.

[0024] It can be concluded from the above that the embodiment of the present disclosure can more comprehensively understand the operating status of the brake by collecting monitoring sample data of multiple first operating parameters. Compared with relying on only a single parameter for judgment, the evaluation results of multiple parameters can more accurately predict the health status of the brake.

[0025] In one embodiment of the present disclosure, determining measured values ​​of a plurality of first operating parameters based on a plurality of monitoring sample data of a plurality of first operating parameters of the brake includes: For any first operating parameter, Calculate the proportion of the first time period; the first time period is the time period when the corresponding multiple monitoring sample data are greater than the parameter threshold; In response to the proportion of the first time period being greater than the first threshold, determining an average value of a plurality of monitoring sample data in the first time period as a measured value of any first operating parameter; In response to the proportion of the first time period being less than or equal to the first threshold, an average value of multiple monitoring sample data in all time periods is determined as the measured value of any first operating parameter.

[0026] In this embodiment, taking any first operating parameter as an example, the corresponding multiple monitoring sample data are obtained by collecting sensor data multiple times within a set monitoring period, and each monitoring sample data includes monitoring data and corresponding monitoring time. The multiple monitoring sample data are arranged in chronological order, and the period between the latest time point and the earliest time point is the total monitoring period, that is, the set monitoring period.

[0027] The parameter threshold of each first operating parameter can be set in advance based on experience, and the time period (i.e., the first time period) in which multiple monitoring sample data of each first operating parameter is greater than the parameter threshold can be counted. Then, the ratio of the first time period to the set monitoring time period is calculated to obtain the proportion of the first time period. For example, if the monitoring period is set to 10 hours, and the monitoring sample data within 3 hours is greater than the parameter threshold, then the proportion of the first time period is 30%.

[0028] If the proportion of the first time period corresponding to a certain first operating parameter is greater than the first threshold, it indicates that the brake operates for a relatively long time with a larger first operating parameter and the working conditions of the brake are relatively harsh. At this time, the average value of multiple monitoring sample data in the first time period can be determined as the measured value of the first operating parameter, with the focus on the monitoring sample data of the first time period.

[0029] If the proportion of the first time period is less than or equal to the first threshold, it means that the first operating parameter is at a relatively normal level for most of the time. At this time, the average value of multiple monitoring sample data in all time periods is determined as the measured value of the first operating parameter to comprehensively consider the situation during the entire monitoring period.

[0030] Taking the pressure parameter among the multiple first operating parameters as an example, a pressure threshold value (such as 0.6 of the rated load) can be pre-set, and the first time period in which the pressure is greater than the pressure threshold value can be counted. If the proportion of the first time period is greater than the first threshold value, it indicates that the brake has been running under heavy load for a long time and the working conditions of the brake are relatively harsh. At this time, the average value of the multiple pressure monitoring sample data in the first time period can be determined as the measured value of the pressure, and the pressure monitoring sample data in the first time period can be focused on.

[0031] It can be concluded from the above that the present embodiment specifically determines the measured value according to the distribution characteristics of the parameter. When the parameter has a higher value in certain time periods, the situation in these time periods can be focused on to more accurately evaluate the performance and health of the brake under specific working conditions.

[0032] In one embodiment of the present disclosure, determining the corresponding evaluation value based on the measured value of each first operating parameter includes: The measured values ​​of the first operating parameters are normalized to obtain evaluation values ​​corresponding to the first operating parameters.

[0033] In this embodiment, since the convergence speed, accuracy, and measurement of the measured values ​​corresponding to the first operating parameters are not uniform, in order to facilitate the weighted calculation of multiple evaluation values, the measured values ​​corresponding to the first operating parameters can be calculated. Unified quantization processing. is the measured value of a first operating parameter, , They are The minimum and maximum values ​​of . The unified quantization process is as follows: .

[0034] In one embodiment of the present disclosure, a plurality of evaluation values ​​are weighted and summed to obtain a brake health prediction result, including: Determining a weight of each first operating parameter based on the number of multiple monitoring sample data of each first operating parameter; The brake health prediction result is obtained by weighted summing up the multiple evaluation values ​​based on the weights of the first operating parameters.

[0035] In this embodiment, if the number of monitoring sample data of a first operating parameter is large, it means that the monitoring of this first operating parameter is more sufficient and its importance in brake health prediction is relatively high, so it can be given a larger weight.

[0036] For example, among the five first operating parameters, namely, pressure, vibration amplitude, temperature, humidity, and brake clearance, there are 100 monitoring sample data of the pressure parameter, 80 monitoring sample data of the vibration amplitude, 60 monitoring sample data of the temperature parameter, 60 monitoring sample data of the humidity parameter, and 50 monitoring sample data of the brake clearance. The weight of each parameter can be determined according to the proportional relationship of the above sample quantities, such as the weight of the pressure parameter is 0.3, the weight of the vibration amplitude is 0.25, the weight of the temperature parameter is 0.2, the weight of the humidity parameter is 0.2, and the weight of the temperature parameter is 0.15.

[0037] After the weights of the first operating parameters are determined, the multiple evaluation values ​​are weighted and summed. Each evaluation value is multiplied by its corresponding parameter weight, and then all the products are added together to obtain a brake health prediction result.

[0038] From the above, it can be concluded that this embodiment determines the weights based on the number of monitoring sample data, which makes the calculation of weights more objective and scientific, avoids the deviation that may be caused by subjective determination of weights, and can better reflect the importance of each first operating parameter in actual operation.

[0039] In one embodiment of the present disclosure, determining the weight of each first operating parameter based on the number of multiple monitoring sample data of each first operating parameter includes: Calculating a first ratio of the number of the plurality of monitoring sample data of each first operating parameter to the number of all monitoring sample data; Calculate the information entropy redundancy corresponding to each first operating parameter based on the first ratio; The weight of each first operating parameter is calculated based on the corresponding information entropy redundancy of each first operating parameter.

[0040] In this embodiment, a specific implementation method of determining the weight of each first operating parameter based on the number of multiple monitoring sample data is provided.

[0041] First, calculate a first ratio of the number of multiple monitoring sample data of each first operating parameter to the number of all monitoring sample data : ; in, represents a first ratio corresponding to any first operating parameter, Indicates the number of a plurality of monitoring sample data of any first operating parameter; Indicates the number of all monitoring sample data.

[0042] Then calculate the index entropy value and information entropy redundancy The calculation formulas are:

[0043]

[0044] On this basis, the weight The calculation formula is: .

[0045] Among them, the calculation of information entropy takes into account the distribution of the monitoring sample data of each first operating parameter in the population. If the number of monitoring sample data of a certain first operating parameter is large, its information entropy is relatively small, the information entropy redundancy is large, and the corresponding weight is larger; if the number of monitoring sample data of a certain first operating parameter is small, its information entropy is relatively large, the information entropy redundancy is small, and the corresponding weight is smaller.

[0046] It can be concluded from the above that this embodiment can objectively quantify the importance of each first operating parameter in the brake health prediction through the calculation of information entropy and information entropy redundancy, avoid the deviation that may be caused by subjective judgment of weights, and make the determination of weights more scientific and reasonable.

[0047] In one embodiment of the present disclosure, the brake health prediction method further includes: Performing simulation based on multiple monitoring sample data of multiple second operating parameters of the brake to obtain fatigue data of the brake; Calculating a fatigue state prediction value of the brake based on the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters; A first model is constructed based on the fatigue state prediction value of the brake, the historical data of the brake clearance and the fatigue data of the brake; the first model is used to characterize the corresponding relationship between the brake clearance and the fatigue state prediction value; The first model is optimized, and the brake clearance value corresponding to the optimal optimization result is determined as the predicted value of the brake clearance.

[0048] In this embodiment, the brake clearance is one of the first operating parameters of the brake, and its specific value is one of the important indicators for measuring the performance of the brake, which has an important impact on the braking effect and operating stability of the elevator. Therefore, on the basis of more comprehensive prediction of the brake health state based on the monitoring sample data of multiple first operating parameters, the brake clearance can be predicted separately to assist in the judgment of the brake health state.

[0049] The plurality of second operating parameters mainly include the operating vibration frequency f of the brake, the braking response time tresp, the braking current I, the number of operations N, and the elevator operating speed v. The prediction steps of the brake clearance mainly include: Step 1: Simulation calculation of fatigue data In view of the operating state of the elevator brake under complex working conditions, five key indicators (i.e., the second operating parameters) are introduced using the ANSYS simulation platform: operating vibration frequency f, brake response time tresp, brake current I, operating times N, and elevator operating speed v. These indicators are used as input parameters to simulate in the ANSYS simulation environment and calculate the fatigue data of the brake under different working conditions.

[0050] Step 2: Calculate the fatigue state prediction value Based on the fatigue data, a mapping relationship between fatigue status and fatigue data and multiple monitoring sample data of second operating parameters corresponding to multiple fatigue data and historical data of brake fatigue status can be constructed.

[0051] On this basis, the fatigue state prediction value of the brake can be calculated based on the fatigue data of the brake and multiple monitoring sample data of multiple second operating parameters.

[0052] Step 3: Construct a model of the relationship between brake clearance and fatigue (also known as the first model) Based on the mapping relationship obtained in step 2, combined with ASME structural stress and main SN curve data, a model of the relationship between brake clearance and fatigue is constructed. This model can be expressed as: Fatigue=G(Gap,Stress,SN Curve) Among them, Fatigue is the fatigue state of the brake, Gap is the brake clearance, Stress is the structural stress, and S-NCurve is the main SN curve data. The model takes the brake clearance as input and the fatigue state prediction value as output, and establishes a mathematical relationship between the two.

[0053] Step 4: Evaluation of brake clearance The particle swarm optimization (PSO) algorithm is used to optimize the model of the relationship between brake clearance and fatigue constructed in step 3 to evaluate the brake clearance. The PSO algorithm optimizes the problem solution by simulating the predation behavior of bird flocks, and its mathematical expression is: ; ; in, and are the speed and position of particle i at time t, w is the inertia weight, c1 and c2 are learning factors, r1 and r2 are random numbers, pi is the historical optimal position of particle i, and pg is the global optimal position of all particles. The PSO algorithm is used to iteratively update the speed and position of the particle, and find the brake clearance value that minimizes the model prediction error as the predicted value of the brake clearance.

[0054] It can be concluded from the above that this embodiment achieves accurate prediction of brake clearance by constructing a relationship model between brake clearance and fatigue state and using particle swarm optimization algorithm to evaluate brake clearance. This embodiment not only improves the accuracy and efficiency of brake clearance adjustment, but also effectively prolongs the service life of the brake and reduces safety hazards during elevator operation.

[0055] In one embodiment of the present disclosure, calculating a fatigue state prediction value of the brake based on fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters includes: Inputting the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters into a fatigue state inverse model to obtain a fatigue state prediction value of the brake; The fatigue state inverse model is as follows:

[0056] Among them, y(t) is the fatigue state prediction value of the brake, x(t) is a vector composed of the fatigue data of the brake and multiple monitoring sample data of multiple second operating parameters, and n and m are the orders of the model.

[0057] In this embodiment, the data-driven technology NARX (nonlinear autoregressive network model) and inversion iteration ART (algebraic reconstruction algorithm) can be used to construct a fatigue state inversion model, which can characterize the mapping relationship between the fatigue state and fatigue data and multiple monitoring sample data of the second operating parameter.

[0058] Before inputting fatigue data into the fatigue state inverse model, data preprocessing and feature extraction are required, including noise removal, missing value processing, data standardization, etc. In addition, fatigue-related features such as stress amplitude, stress mean, number of stress cycles, etc. can also be extracted.

[0059] Data preprocessing can use various statistical methods and filtering techniques, such as mean filtering, median filtering, etc., and feature extraction can use principal component analysis (PCA), wavelet transform and other methods.

[0060] It can be concluded from the above that this embodiment uses NARX (nonlinear autoregressive network model) and inversion iterative algorithm ART (algebraic reconstruction algorithm) in data-driven technology to construct a fatigue state inversion model, which can achieve accurate tracing of the fatigue state of the brake.

[0061] Corresponding to a brake health prediction method in the above embodiment, Figure 4 This is a structural block diagram of a brake health prediction device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 4 The brake health prediction device 20 includes: a first calculation module 21, a second calculation module 22 and a health prediction module 23. Wherein, the first calculation module 21 is used to determine the measured values ​​of the plurality of first operating parameters based on the plurality of monitoring sample data of the plurality of first operating parameters of the brake; A second calculation module 22, configured to determine a corresponding evaluation value based on the measured value of each first operating parameter, to obtain a plurality of evaluation values; The health prediction module 23 is used to perform weighted summation of multiple evaluation values ​​to obtain a brake health prediction result.

[0062] In one embodiment of the present disclosure, the first calculation module 21 is specifically used to: For any first operating parameter, Calculate the proportion of the first time period; the first time period is the time period when the corresponding multiple monitoring sample data are greater than the parameter threshold; In response to the proportion of the first time period being greater than the first threshold, determining an average value of a plurality of monitoring sample data in the first time period as a measured value of any first operating parameter; In response to the proportion of the first time period being less than or equal to the first threshold, an average value of multiple monitoring sample data in all time periods is determined as the measured value of any first operating parameter.

[0063] In one embodiment of the present disclosure, the second calculation module 22 is specifically used for: The measured values ​​of the first operating parameters are normalized to obtain evaluation values ​​corresponding to the first operating parameters.

[0064] In one embodiment of the present disclosure, the health prediction module 23 is specifically used to: Determining a weight of each first operating parameter based on the number of multiple monitoring sample data of each first operating parameter; The brake health prediction result is obtained by weighted summing up the multiple evaluation values ​​based on the weights of the first operating parameters.

[0065] In one embodiment of the present disclosure, the health prediction module 23 is specifically used to: Calculating a first ratio of the number of the plurality of monitoring sample data of each first operating parameter to the number of all monitoring sample data; Calculate the information entropy redundancy corresponding to each first operating parameter based on the first ratio; The weight of each first operating parameter is calculated based on the information entropy redundancy corresponding to each first operating parameter.

[0066] In one embodiment of the present disclosure, the health prediction module 23 is specifically used to: Performing simulation based on multiple monitoring sample data of multiple second operating parameters of the brake to obtain fatigue data of the brake; Calculating a fatigue state prediction value of the brake based on the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters; A first model is constructed based on the fatigue state prediction value of the brake, the historical data of the brake clearance and the fatigue data of the brake; the first model is used to characterize the corresponding relationship between the brake clearance and the fatigue state prediction value; The first model is optimized, and the brake clearance value corresponding to the optimization result is determined as the predicted value of the brake clearance.

[0067] In one embodiment of the present disclosure, the health prediction module 23 is further used to: Inputting the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters into a fatigue state inverse model to obtain a fatigue state prediction value of the brake; The fatigue state inverse model is as follows:

[0068] Among them, y(t) is the fatigue state prediction value of the brake, x(t) is a vector composed of the fatigue data of the brake and multiple monitoring sample data of multiple second operating parameters, and n and m are the orders of the model.

[0069] See also Figure 5 , Figure 5 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 5 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 4 The functions of modules 21 to 23 are shown.

[0070] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0071] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0072] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0073] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of a brake health prediction method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0074] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0075] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0076] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0077] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0078] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0080] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0081] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A brake health prediction method, characterized in that: include: Determining measured values ​​of a plurality of first operating parameters based on a plurality of monitoring sample data of a plurality of first operating parameters of the brake; Determine corresponding evaluation values ​​based on the measured values ​​of each first operating parameter to obtain multiple evaluation values; The multiple evaluation values ​​are weighted and summed to obtain a brake health prediction result.

2. A brake health prediction method according to claim 1, characterized in that: The determining of the measured values ​​of the plurality of first operating parameters based on the plurality of monitoring sample data of the plurality of first operating parameters of the brake comprises: For any first operating parameter, Calculate the proportion of the first time period; the first time period is a time period in which the corresponding plurality of monitoring sample data is greater than the parameter threshold; In response to the proportion of the first time period being greater than the first threshold, determining an average value of the plurality of monitoring sample data in the first time period as the measured value of any one of the first operating parameters; In response to the proportion of the first time period being less than or equal to the first threshold, an average value of multiple monitoring sample data in all time periods is determined as the measured value of any one of the first operating parameters.

3. A brake health prediction method according to claim 1, characterized in that: The determining a corresponding evaluation value based on the measured value of the first operating parameter includes: The measured values ​​of the first operating parameters are normalized to obtain evaluation values ​​corresponding to the first operating parameters.

4. A brake health prediction method as claimed in claim 1, characterized in that: The weighted summing of the multiple evaluation values ​​to obtain a brake health prediction result includes: Determining a weight of each first operating parameter based on the number of multiple monitoring sample data of each first operating parameter; The plurality of evaluation values ​​are weightedly summed based on the weights of the first operating parameters to obtain a brake health prediction result.

5. A brake health prediction method as claimed in claim 4, characterized in that: The determining the weight of each first operating parameter based on the number of the plurality of monitoring sample data of each first operating parameter comprises: Calculating a first ratio of the number of the plurality of monitoring sample data of each first operating parameter to the number of all monitoring sample data; Calculate the information entropy redundancy corresponding to each first operating parameter based on the first ratio; The weight of each first operating parameter is calculated based on the information entropy redundancy corresponding to each first operating parameter.

6. A brake health prediction method as claimed in claim 1, characterized in that: Also includes: Performing simulation based on multiple monitoring sample data of multiple second operating parameters of the brake to obtain fatigue data of the brake; Calculating a fatigue state prediction value of the brake based on the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters; Building a first model based on the fatigue state prediction value of the brake, the historical data of the brake clearance and the fatigue data of the brake; the first model is used to characterize the corresponding relationship between the brake clearance and the fatigue state prediction value; The first model is optimized, and a brake clearance value corresponding to the optimization result is determined as a predicted value of the brake clearance.

7. A brake health prediction method as claimed in claim 6, characterized in that: The calculating the fatigue state prediction value of the brake based on the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters comprises: Inputting the fatigue data of the brake and a plurality of monitoring sample data of a plurality of second operating parameters into a fatigue state inverse model to obtain a fatigue state prediction value of the brake; The fatigue state inverse model is specifically: Among them, y(t) is the fatigue state prediction value of the brake, x(t) is a vector composed of the fatigue data of the brake and multiple monitoring sample data of multiple second operating parameters, and n and m are the orders of the model.

8. A brake health prediction device, characterized in that: include: A first calculation module, configured to determine measured values ​​of a plurality of first operating parameters of the brake based on a plurality of monitoring sample data of the plurality of first operating parameters; A second calculation module, used to determine a corresponding evaluation value based on the measured value of each first operating parameter to obtain a plurality of evaluation values; The health prediction module is used to perform weighted summation of the multiple evaluation values ​​to obtain a brake health prediction result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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