A system state evaluation method and system for rail transit

Abstract

This invention relates to a system state assessment method and system for rail transit, belonging to the field of rail transit technology. The method includes: acquiring equipment failure times; acquiring multiple pre-established initial degradation models, where the independent variable of each initial degradation model is failure time and the dependent variable is absolute failure rate; constructing a corresponding reliability calculation model for each initial degradation model, and constructing a parameter estimation model based on the reliability calculation model; solving for the parameters of each initial degradation model based on the parameter estimation model to obtain a basic degradation model; acquiring a verification model, verifying each basic degradation model using the verification model, and determining the optimal degradation model; calculating the absolute failure rate of the system based on the optimal degradation model to obtain the state assessment result. This method constructs quantitative indicators of system state, providing data support for the overall or partial modification of rail transit equipment. It is applicable to different system levels and different equipment types, exhibiting high applicability.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, and more specifically, to a system and method for assessing the status of rail transit systems. Background Technology

[0002] Rail transit is a massive equipment system. The condition of the equipment not only determines the safety and stability of transportation, but also incurs huge financial expenditures for maintenance and upgrades. Currently, there are no quantitative indicators for the deterioration status and trends of rail transit equipment. Whether the deterioration trend of lines and network equipment is within the normal range, and which parts or the whole need to be upgraded, relies mainly on expert experience or qualitative assessment methods.

[0003] Existing technologies are mainly designed for specific equipment. For similar but different structures or different types of equipment, the methods are no longer applicable. However, there are many types and models of rail transit equipment. Using specific technical methods for each model would be costly. Even if existing technologies are used to evaluate different types of equipment, the basic scoring values ​​and condition assessment conclusions are still not universally applicable due to differences in the environment and operating load of different lines, as well as the different structures and compositions of different equipment. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for evaluating the state of rail transit systems, thereby improving the aforementioned problems. To achieve this purpose, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a system state assessment method for rail transit, including:

[0006] The failure time of the device is obtained, which is the time difference from the start of use of the device to the occurrence of the failure.

[0007] Multiple pre-established initial degradation models are obtained, wherein the independent variable of the initial degradation model is the failure time and the dependent variable is the absolute failure rate;

[0008] Construct a corresponding reliability calculation model for each initial degradation model, and construct a parameter estimation model based on the reliability calculation model.

[0009] The parameters of each initial degradation model are solved based on the parameter estimation model to obtain the basic degradation model;

[0010] Obtain the testing model, test each basic degradation model using the testing model, and determine the optimal degradation model;

[0011] The absolute failure rate of the system is calculated based on the optimal degradation model, and the state assessment results are obtained.

[0012] Secondly, this application also provides a system state assessment system for rail transit, comprising:

[0013] The first acquisition module is used to acquire the fault time of the device, wherein the fault time is the time difference from the start of use of the device to the occurrence of the fault.

[0014] The second acquisition module is used to acquire multiple pre-established initial degradation models, wherein the independent variable of the initial degradation model is the failure time and the dependent variable is the absolute failure rate.

[0015] The first construction module is used to construct the corresponding reliability calculation model based on each initial degradation model, and to construct the parameter estimation model based on the reliability calculation model.

[0016] The first calculation module is used to solve the parameters of each initial degradation model based on the parameter estimation model to obtain the basic degradation model;

[0017] The verification module is used to acquire verification models, verify each basic degradation model through verification models, and determine the optimal degradation model.

[0018] The second calculation module is used to calculate the absolute failure rate of the system based on the optimal degradation model, and obtain the state assessment results.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention determines the equipment's degradation model and parameters by combining a built-in basic degradation model, a reliability calculation model, and actual operational data, thereby achieving a quantitative assessment of the equipment's status and degradation trend. This assessment method is not affected by different system structures or equipment types, and the assessment results for different lines are intuitive, understandable, and comparable. It is universally applicable to different rail transit lines, systems, and equipment, and has a low overall investment cost in practical applications.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a system state assessment method for rail transit in an embodiment;

[0024] Figure 2 The following is the result of evaluating the signaling systems of 13 urban rail lines using the method of this application in the embodiments;

[0025] Figure 3 This is a schematic diagram of the system status assessment device for rail transit, as shown in the embodiment.

[0026] The diagram is labeled as follows: 800 - System status assessment equipment for rail transit; 801 - Processor; 802 - Memory; 803 - Multimedia component; 804 - I / O interface; 805 - Communication component. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0029] Example 1

[0030] See Figure 1 This embodiment provides a system state assessment method for rail transit, including steps S100, S200, S300, S400, S500 and S600.

[0031] S100. Obtain the equipment failure time, wherein the failure time is the time difference from the start of equipment use to the occurrence of failure.

[0032] First, a hierarchical equipment tree structure for the rail transit system is established, along with recording basic equipment information. The highest level is the track system; the second level consists of specialized systems, such as signaling systems, rolling stock systems, power supply systems, and electromechanical systems; the third level is the subsystem layer, where the signaling system includes ATC subsystems, interlocking subsystems, and ATS subsystems; the rolling stock system includes braking systems, traction systems, and air conditioning systems; and the fourth level is the equipment layer, where the signaling system's ATC subsystem includes area controllers, onboard controllers, and trackside transponders; and the rolling stock traction system includes pantographs, traction converters, traction control units, and traction motors.

[0033] Basic equipment information includes equipment identification information, equipment fault information, fault time t, fault impact, fault cause, and fault handling measures; fault time t is the time difference from when the equipment is first used to when the fault occurs.

[0034] S200. Obtain multiple pre-established initial degradation models, wherein the independent variable of the initial degradation model is the failure time and the dependent variable is the absolute failure rate;

[0035] Pre-built initial degradation models are stored in the database, including linear models, log-linear models, etc.

[0036] Each initial degradation model is a two-parameter monotonic function, denoted as ω(t)=αf(t;β) (t>0), where α is a general parameter, β is the degradation coefficient, which determines the shape of the function; ω(t) is the absolute failure rate, which is the state evaluation index that needs to be established in this application.

[0037] S300. Construct a corresponding reliability calculation model for each initial degradation model, and construct a parameter estimation model based on the reliability calculation model; specifically including:

[0038] The cumulative hazard rate function is constructed by calculating the integral of the initial degradation model over the failure time.

[0039] Cumulative hazard rate function:

[0040] ;

[0041] Based on the cumulative hazard rate function and the total number of failures, a probability quality function for each failure is constructed.

[0042] Probability mass function:

[0043] ;

[0044] Where n represents the nth failure; each failure has a probability mass function, which contains information about all previous failures;

[0045] A parameter estimation model is constructed by performing maximum likelihood estimation on all probability mass functions.

[0046] The likelihood function in this embodiment is:

[0047] ;

[0048] Where s1, s2......s n This represents the probabilistic mass function corresponding to each failure; i = 0, 1, 2, ..., n;

[0049] The constructed likelihood function is the parameter estimation model.

[0050] S400. Solve for the parameters of each initial degradation model based on the parameter estimation model to obtain the basic degradation model;

[0051] Solving the likelihood function yields the values ​​of α and β, thus obtaining the basic degradation model;

[0052] In this embodiment, the Particle Swarm Optimization (PSO) algorithm is used for maximum likelihood estimation, including the following steps:

[0053] A swarm of particles is randomly generated in the parameter space, where each particle represents a combination of parameters of the initial deterioration model, namely α and β.

[0054] The fitness function is calculated based on the position of each particle using the parameter estimation model.

[0055] The particle positions are optimized and updated with the goal of maximizing fitness to obtain the optimal parameters.

[0056] S500. Obtain the verification model, verify each basic degradation model through the verification model, and determine the optimal degradation model.

[0057] The model validation library includes validation models, a database of validation critical values, and validation principles. Different validation models correspond to different validation critical values ​​and validation principles. Validation models include Cramer-Von-Mises (CVM) test, least squares analysis, chi-square goodness-of-fit test, etc. This application preferably adopts the CVM test, which is more targeted to this scheme.

[0058] Call the CVM test model and calculate the statistic for each basic deterioration model based on the CVM test model;

[0059] Taking the basic degradation model as a log-linear model as an example, that is The CVM test model is as follows:

[0060] ;

[0061] Where n is the total number of failures, s i Let be the failure time of the i-th failure, T be the total running time, β be the estimated value obtained from the solution, and C be the statistic.

[0062] Taking the basic degradation model as a linear model as an example, that is The CVM test model is as follows:

[0063] ;

[0064] Where n is the total number of failures, si is the relative time of the i-th failure, T is the total running time, β is the estimated value obtained from the solution, and C is the statistic.

[0065] The statistic is compared with the preset critical value to obtain the first set of models;

[0066] Based on the amount of equipment failure time data and the type of equipment, either the Bayesian information criterion or the Akark information criterion is selected, and the optimal degradation model is selected from the first model set according to the selected criterion.

[0067] Bayesian information criterion is more suitable for situations with large samples, and it tends to choose simpler models; Akark information criterion is more suitable for situations with small samples, and it tends to choose more complex models; if the amount of existing sample data is small, such as less than a certain threshold, Akark information can be chosen.

[0068] At the same time, the type of equipment being evaluated also needs to be considered. When the equipment or system being evaluated is directly related to driving safety (such as the braking system), the model is required to be more reliable, stable, and interpretable. In this case, the Bayesian information criterion is directly adopted, which has a lower risk of overfitting and is more stable in its performance on unseen data. It is far more suitable for safety-critical systems than a complex model with high accuracy but which occasionally issues serious error warnings.

[0069] S600: The absolute failure rate of the system is calculated based on the optimal degradation model, and the state assessment result is obtained.

[0070] Degradation curves can be plotted based on the optimal degradation model to determine the degradation trend, and the severity of degradation can be assessed based on the degradation coefficient.

[0071] Figure 2 To compare and analyze the degradation status of the signaling systems of 13 urban rail lines in a certain year, the horizontal axis represents time, and the vertical axis represents the degradation status. The trend of the curves in the graph is determined by the degradation coefficient. As can be seen from the graph, the degradation trends of lines A and I are the most severe, with line I still showing a significant deterioration trend. These analytical conclusions are completely consistent with the actual operational situation.

[0072] Furthermore, this method also includes step S700:

[0073] Construct an equipment lineage diagram for a rail transit system; it can display the dynamic interaction relationships, energy flow, information flow, control flow, and logical dependencies between various equipment and components in the system.

[0074] Sampling is performed based on the lineage path of the equipment lineage map to construct different equipment sets;

[0075] Each time, random sampling can be used, that is, randomly or with a certain probability, select a device node, and extract a number of connected related nodes on the path before and after the node according to a random number (a certain range can be preset), and form a set of the extracted devices;

[0076] Each set of equipment is treated as a system to be evaluated. The optimal degradation model for each system to be evaluated is calculated and the state is evaluated to obtain the evaluation results for each set of equipment.

[0077] Based on the assessment results, key deterioration nodes in the equipment lineage diagram are analyzed, and recommendations for equipment upgrades and maintenance in rail transit are generated based on these key deterioration nodes.

[0078] For example, in 100 random assessments based on equipment lineage diagrams, the degradation coefficient or absolute failure rate in 70 of these assessments exceeded a preset threshold. Furthermore, the equipment sets in these 70 assessments all included traction converters, indicating that the traction converters are the main factor limiting the overall condition, i.e., critical degradation nodes. Based on this approach, critical degradation nodes in the equipment lineage diagram can be marked, or a degradation value can be generated for each degradation node.

[0079] The equipment upgrade and maintenance strategy for rail transit needs to be determined by considering the deterioration of the nodes and the economic and time costs of upgrades and maintenance.

[0080] For example, if the degradation value of a traction converter node is high, while the degradation values ​​of its upstream and downstream nodes are very low, and the replacement cost of the traction converter is low, it indicates that replacing the traction converter can achieve a significant improvement in the health of the traction system at a low cost, thus increasing the priority of this equipment replacement.

[0081] For example, if several related device nodes have deteriorated to some extent, but the deterioration value is not high, and updating or maintaining these devices requires high costs, then their priority for updating or maintenance can be reduced.

[0082] Based on this method, reasonable operation and maintenance management suggestions and decisions can be given, providing guidance for the transformation of rail transit.

[0083] The optimal degradation model constructed will be different when the devices in the device set are different. In other words, the optimal degradation model constructed is the most suitable for each device set. Compared with using the same model to evaluate different device combinations, the evaluation results of this method will obviously be more accurate and more in line with the actual situation.

[0084] Furthermore, this method also includes step S800:

[0085] The faults are sorted by the time they occurred;

[0086] The sorted faults are grouped according to the preset group interval and step size; the group interval is the number of faults in each group, and the step size is the difference between the starting fault numbers of two adjacent groups.

[0087] Based on extensive data analysis, taking a signal system as an example, the optimal group interval and step size are 10 and 5 respectively. The grouping is as follows: Group 1 {1,2,3,4,5,6,7,8,9,10}, Group 2 {6,7,8,9,10,11,12,12,14,15}, and so on. The data within each group represents the sequence number of the system failure.

[0088] For each group of faults, calculate the corresponding optimal degradation model and obtain the corresponding degradation coefficient;

[0089] Determine if the degradation coefficient is greater than the preset alarm threshold; if so, generate an alarm message.

[0090] The degradation trend is compared with a set threshold, and an alarm is output when it exceeds the set threshold. Setting the alarm threshold requires a certain amount of accumulated data. This data can come from similar lines or from previous assessments of the current line. For example, if this method is used to assess the overall degradation trend of the signal system of more than ten lines in a city, and it is found that during the mid-term operation, the degradation coefficient of most relatively stable lines is below 1.2, then the threshold setting can refer to this data.

[0091] Example 2

[0092] This embodiment provides a system status assessment system for rail transit, including:

[0093] The first acquisition module is used to acquire the fault time of the device, wherein the fault time is the time difference from the start of use of the device to the occurrence of the fault.

[0094] The second acquisition module is used to acquire multiple pre-established initial degradation models, wherein the independent variable of the initial degradation model is the failure time and the dependent variable is the absolute failure rate.

[0095] The first construction module is used to construct the corresponding reliability calculation model based on each initial degradation model, and to construct the parameter estimation model based on the reliability calculation model.

[0096] The first calculation module is used to solve the parameters of each initial degradation model based on the parameter estimation model to obtain the basic degradation model;

[0097] The verification module is used to acquire verification models, verify each basic degradation model through verification models, and determine the optimal degradation model.

[0098] The second calculation module is used to calculate the absolute failure rate of the system based on the optimal degradation model, and obtain the state assessment results.

[0099] As an optional implementation, the first building module includes:

[0100] The first calculation unit is used to calculate the integral of the initial degradation model with respect to the failure time and construct the cumulative hazard rate function.

[0101] The first building unit is used to construct the probability quality function for each failure based on the cumulative hazard rate function and the total number of failures.

[0102] The second building block is used to construct a parameter estimation model by performing maximum likelihood estimation on all probability mass functions.

[0103] As an optional implementation, the testing module includes:

[0104] The second calculation unit is used to call the CVM test model and calculate the statistics of each basic deterioration model based on the CVM test model.

[0105] The first screening unit is used to compare the statistic with the preset test critical value to screen and obtain the first model set;

[0106] The second screening unit is used to select either the Bayesian information criterion or the Akark information criterion based on the amount of data on the equipment's failure time and the type of equipment, and to select the optimal degradation model from the first model set according to the selected criterion.

[0107] As an optional implementation, the system further includes:

[0108] The second building module is used to construct the equipment lineage diagram of the rail transit system;

[0109] The sampling module is used to sample based on the lineage path of the equipment lineage map to construct different equipment sets;

[0110] The evaluation module is used to treat each set of equipment as a system to be evaluated, calculate the optimal degradation model for each system to be evaluated, perform state evaluation, and obtain the evaluation results for each set of equipment.

[0111] The generation module is used to analyze key deterioration nodes in the equipment bloodline diagram based on the evaluation results, and generate equipment update and maintenance recommendations for rail transit based on the key deterioration nodes.

[0112] Example 3

[0113] Corresponding to the above method embodiments, this embodiment also provides a system state assessment device for rail transit. The system state assessment device for rail transit described below and the system state assessment method for rail transit described above can be referred to in correspondence.

[0114] Figure 3 This is a block diagram illustrating a system condition assessment device 800 for rail transportation, according to an exemplary embodiment. Figure 3 As shown, the system status assessment device 800 for rail transit includes a processor 801 and a memory 802. The system status assessment device 800 for rail transit may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805. The processor 801 controls the overall operation of the system status assessment device 800 for rail transit to complete all or part of the steps in the aforementioned system status assessment method for rail transit. The memory 802 stores various types of data to support the operation of the system status assessment device 800 for rail transit. This data may include, for example, commands for any application or method operating on the system status assessment device 800 for rail transit, and application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0115] Multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals.

[0116] The received audio signals can be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the system status assessment device 800 for rail transit and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof, is possible; therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0117] Example 4

[0118] Corresponding to the above-described method embodiment for system state assessment of rail transit, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the above-described method for system state assessment of rail transit.

[0119] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described embodiment of the system state assessment method for rail transit.

[0120] Specifically, the readable storage medium can be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0121] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0122] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A system state assessment method for rail transit, characterized in that, include: The failure time of the device is obtained, which is the time difference from the start of use of the device to the occurrence of the failure. Multiple pre-established initial degradation models are obtained, wherein the independent variable of the initial degradation model is the failure time and the dependent variable is the absolute failure rate; For each initial degradation model, a corresponding reliability calculation model is constructed. Based on the reliability calculation model, a parameter estimation model is constructed, including: The cumulative hazard rate function is constructed by calculating the integral of the initial degradation model over the failure time. Based on the cumulative hazard rate function and the total number of failures, a probability quality function for each failure is constructed. A parameter estimation model is constructed by performing maximum likelihood estimation on all probability quality functions; The parameters of each initial degradation model are solved based on the parameter estimation model to obtain the basic degradation model, including: A swarm of particles is randomly generated in the parameter space, with each particle representing a combination of parameters of the initial degradation model. The fitness function is calculated based on the position of each particle using the parameter estimation model. The particle positions are optimized and updated with the goal of maximizing fitness to obtain the optimal parameters; Obtain the testing model, test each basic degradation model using the testing model, and determine the optimal degradation model, including: Call the CVM test model and calculate the statistic for each basic deterioration model based on the CVM test model; The statistic is compared with the preset critical value to obtain the first set of models; Based on the amount of equipment failure time data and the type of equipment, either the Bayesian information criterion or the Akark information criterion is selected, and the optimal degradation model is selected from the first model set according to the selected criterion. The absolute failure rate of the system is calculated based on the optimal degradation model, and the state assessment results are obtained.

2. The system state assessment method for rail transit according to claim 1, characterized in that, The method further includes: Constructing the equipment lineage diagram for a rail transit system; Sampling is performed based on the lineage path of the equipment lineage map to construct different equipment sets; Each set of equipment is treated as a system to be evaluated. The optimal degradation model for each system to be evaluated is calculated and the state is evaluated to obtain the evaluation results for each set of equipment. Based on the assessment results, key deterioration nodes in the equipment lineage diagram are analyzed, and recommendations for equipment upgrades and maintenance in rail transit are generated based on these key deterioration nodes.

3. The system state assessment method for rail transit according to claim 1, characterized in that, The method further includes: The faults are sorted by the time they occurred; The sorted faults are grouped according to the preset group interval and step size; For each group of faults, calculate the corresponding optimal degradation model and obtain the corresponding degradation coefficient; Determine if the degradation coefficient is greater than the preset alarm threshold; if so, generate an alarm message.

4. A system state assessment system for rail transit, characterized in that, include: The first acquisition module is used to acquire the device's failure time, which is the time difference between the start of use of the device and the occurrence of the failure. The first building module includes: The first calculation unit is used to calculate the integral of the initial degradation model with respect to the failure time and construct the cumulative hazard rate function. The first building unit is used to construct the probability quality function for each failure based on the cumulative hazard rate function and the total number of failures. The second building unit is used to construct a parameter estimation model by performing maximum likelihood estimation on all probability mass functions; The second acquisition module is used to acquire multiple pre-established initial degradation models, wherein the independent variable of the initial degradation model is the failure time and the dependent variable is the absolute failure rate. The first construction module is used to construct the corresponding reliability calculation model based on each initial degradation model, and to construct the parameter estimation model based on the reliability calculation model. The first calculation module is used to solve the parameters of each initial degradation model based on the parameter estimation model to obtain the basic degradation model; The verification module is used to acquire verification models, verify each basic degradation model using these models, and determine the optimal degradation model. The verification module includes: The second calculation unit is used to call the CVM test model and calculate the statistics of each basic deterioration model based on the CVM test model. The first screening unit is used to compare the statistic with the preset test critical value to screen and obtain the first model set; The second screening unit is used to select either the Bayesian information criterion or the Akark information criterion based on the amount of data on the equipment's failure time and the type of equipment, and select the optimal degradation model from the first model set according to the selected criterion. The second calculation module is used to calculate the absolute failure rate of the system based on the optimal degradation model, and obtain the state assessment results.

5. A system state assessment system for rail transit according to claim 4, characterized in that, The system also includes: The second building module is used to construct the equipment lineage diagram of the rail transit system; The sampling module is used to sample based on the lineage path of the equipment lineage map to construct different equipment sets; The evaluation module is used to treat each set of equipment as a system to be evaluated, calculate the optimal degradation model for each system to be evaluated, perform state evaluation, and obtain the evaluation results for each set of equipment. The generation module is used to analyze key deterioration nodes in the equipment bloodline diagram based on the evaluation results, and generate equipment update and maintenance recommendations for rail transit based on the key deterioration nodes.

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