Method and device for determining health degree of train automatic control system

Abstract

The invention provides a method and a device for determining the health degree of an automatic train control system, which are applied to the technical field of rail transit, and the method comprises the following steps: obtaining hierarchical division information of each device in the automatic train control system, and obtaining health degree index data of each device; inputting the health degree index data of each device into a preset health degree calculation model to obtain a health degree score of each device; based on the hierarchy division information of each device, determining the hierarchy weight of each device; and according to the hierarchical weight of each device, based on the health degree score of each device, determining the total health degree score of the train automatic control system. According to the invention, the accuracy and practicability of health degree evaluation in the train automatic control system can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit technology, and in particular to a health degree determination method and device for a train automatic control system. BACKGROUND

[0002] In modern rail transit operation, the automatic train supervision (ATS) system plays a crucial role. It is responsible for monitoring train operation status, scheduling train operation plans, and ensuring efficient and safe operation of the entire rail transit network.

[0003] Traditional ATS system maintenance modes focus on passive response after failure, which often leads to long repair time and significant impact on operation. In recent years, with the rise of intelligent operation and maintenance concept, the evaluation of ATS system health degree has gradually become a research hotspot. However, existing health degree calculation methods have many limitations. Some methods do not fully consider the hierarchical relationship of different devices in the ATS system. The ATS system includes numerous devices, from bottom-level data acquisition devices and communication devices to upper-level servers and workstations, each with different importance and functional levels in the system, and different impacts on the overall system health degree.

[0004] Therefore, the related art health degree determination method for the train automatic control system has the technical problem of great limitation. SUMMARY

[0005] The present application provides a health degree determination method and device for a train automatic control system to solve the defect of great limitation of the health degree determination method for the train automatic control system in the prior art, and improve the accuracy and practicality of health degree evaluation in the train automatic control system.

[0006] The present application provides a health degree determination method for a train automatic control system, comprising the following steps. Hierarchical division information of each device in the train automatic control system is obtained, and health degree index data of the each device is obtained; the health degree index data of the each device is input into a preset health degree calculation model to obtain a health degree score of the each device; based on the hierarchical division information of the each device, a hierarchical weight of the each device is determined; based on the health degree score of the each device, a health degree total score of the train automatic control system is determined according to the hierarchical weight of the each device.

[0007] According to the present invention, a method for determining the health status of a train automatic control system includes obtaining hierarchical classification information of each device in the train automatic control system, which includes classifying each device into center-level devices and station-level devices based on the functional importance level and logical relationship of each device in the train automatic control system.

[0008] According to the present invention, a method for determining the health status of a train automatic control system includes determining the hierarchical weight of each device based on the hierarchical division information of each device. This includes configuring a corresponding hierarchical weight for each device according to the hierarchical division information of each device, wherein devices at different levels correspond to different weight values, and the magnitude of the weight value is positively correlated with the functional importance level of the device's level in the train automatic control system.

[0009] According to the present invention, a method for determining the health status of a train automatic control system includes obtaining health status index data of each device, which comprises: determining at least one operating status parameter of each device based on the device type of each device; and collecting real-time data of the operating status parameters of each device as health status index data of each device.

[0010] According to a method for determining the health status of a train automatic control system provided by the present invention, after determining the total health status score of the train automatic control system based on the health status scores of each device according to the hierarchical weights of each device, the method further includes: comparing the total health status score of the train automatic control system with a preset health status threshold; and generating system health warning information when the total health status score is lower than the health status threshold.

[0011] According to the present invention, a method for determining the health status of a train automatic control system includes determining the total health status score of the train automatic control system based on the health status scores of each device according to the hierarchical weights of each device. The total health score of the train automatic control system is determined according to the following formula. in, This represents the total health score of the train automatic control system. This indicates the total number of devices in the train automatic control system. This indicates the equipment index in the train automatic control system. Indicates the first The hierarchical weight of each device Indicates the first The health score of each device.

[0012] The application further provides a health degree determination device of a train automatic control system, comprising the following modules: an acquisition module, configured to acquire hierarchical division information of each device in the train automatic control system and acquire health degree index data of the each device; a score module, configured to input the health degree index data of the each device into a preset health degree calculation model to obtain a health degree score of the each device; a weight module, configured to determine a hierarchical weight of the each device based on the hierarchical division information of the each device; and a health degree module, configured to determine a health degree total score of the train automatic control system based on the health degree score of the each device according to the hierarchical weight of the each device.

[0013] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the health degree determination method of the train automatic control system according to any one of the above when executing the program.

[0014] The application further provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the health degree determination method of the train automatic control system according to any one of the above.

[0015] The application further provides a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the health degree determination method of the train automatic control system according to any one of the above.

[0016] The health degree determination method and device of the train automatic control system provided by the application firstly acquire hierarchical division information and health degree index data of each device, thereby providing a structured basis for subsequent analysis. Secondly, the health degree index data is input into a preset health degree calculation model to calculate a health degree score of the device, thereby realizing conversion from complex indexes to objective quantitative scores. Thirdly, the weight of the device is determined based on the hierarchical division information, thereby introducing an importance difference mechanism to make the evaluation more in line with the actual structure of the system. Finally, the health degree total score of the train automatic control system is obtained by weighted summation, thereby providing a comprehensive monitoring index and improving the accuracy and practicability of the evaluation of the train automatic control system and supporting efficient operation and maintenance of the train automatic control system. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1A flowchart of a method for determining the health degree of a train automatic control system is provided.

[0019] Figure 2 A module diagram of a device for determining the health degree of a train automatic control system is provided.

[0020] Figure 3 An entity structure diagram of an electronic device is provided. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0022] Traditional train automatic monitoring system maintenance modes mostly focus on passive response after failure, which often leads to long maintenance time and great impact on operation. In recent years, with the rise of intelligent operation and maintenance concept, the evaluation of the health degree of the train automatic monitoring system has gradually become a research hotspot. However, the existing health degree calculation methods have many limitations.

[0023] On the one hand, some methods do not fully consider the hierarchical relationship of different devices in the train automatic monitoring system. The train automatic monitoring system includes numerous devices, from the bottom layer data acquisition devices, communication devices to the upper layer servers, workstations, etc. Their importance and functional hierarchy in the system are different, and the influence degree on the overall health degree of the system also differs.

[0024] On the other hand, there is a lack of scientific and reasonable weight distribution mechanism. Without accurately assigning corresponding weights according to the criticality of the device to the system operation, the health degree calculation result cannot accurately reflect the real state of the train automatic monitoring system. For example, some simple average calculation or weighted calculation methods without distinguishing the hierarchy, when facing the complex device system of the train automatic monitoring system, are prone to misjudgment or cannot effectively identify potential risks. Therefore, there is an urgent need for a rail transit train automatic monitoring system health degree calculation method that can comprehensively consider the device hierarchy and weight, to realize accurate evaluation of the health state of the train automatic monitoring system, early warning of potential failures, optimization of operation and maintenance strategies, improvement of the safety and reliability of rail transit operation, reduction of operation and maintenance costs, and improvement of operation efficiency.

[0025] The rail transit ATS system contains a large number of different types of equipment, from the hardware level of servers, workstations, communication equipment, signal acquisition equipment, to the software level of dispatching software, data processing programs, etc. The association between these devices is complex, and accurately dividing the device hierarchy is a complex process. For example, some devices may be in multiple functional subsystems at the same time, and their hierarchical relationship in different subsystems may change, which brings challenges to the health degree calculation based on hierarchy.

[0026] The determination of the weight depends largely on expert experience and human judgment. Different experts may give different evaluations of the importance of the equipment according to their professional background, work experience, etc., resulting in strong subjectivity in weight determination. For example, for the weight of communication equipment and data processing equipment in the ATS system health degree, some experts may emphasize the real-time importance of communication equipment, while others may focus on the accuracy of data processing equipment, which will cause differences in weight allocation.

[0027] According to the train automatic control system health degree determination method provided by the application, the equipment in the field is distinguished according to the importance and level according to the actual situation of the train automatic control system. Then a health degree calculation model is constructed to calculate the health degree of the equipment.

[0028] To achieve the above-mentioned goal, the train automatic control system health degree calculation method based on device hierarchy and weight provided by the application includes the following steps: Device hierarchy division: classify the equipment according to different importance levels, such as center level, station level, etc.

[0029] Health degree calculation model construction: select appropriate health degree indicators for different types of equipment. For server equipment, you can select CPU usage, memory usage, disk space remaining, system response time, etc.

[0030] According to the defined device hierarchy weight and health degree calculation model, the health degree score of the system is calculated.

[0031] Optionally, the train automatic control system health degree determination method of the application can be executed by a server, a terminal device, or a combination of the two. Taking the train automatic control system health degree determination method of the application as an example, the server executes the method.

[0032] Figure 1 is the flowchart of the train automatic control system health degree determination method provided by the application, as Figure 1 shown, the method includes the following steps.

[0033] In step 101, the hierarchical classification information of each device in the train automatic control system is obtained, and the health degree index data of each device is obtained.

[0034] In the embodiment of the present application, all devices are systematically classified according to the actual physical architecture and functional logic of the ATS system.

[0035] The core devices directly responsible for the train dispatching command, plan preparation and data summary processing of the whole line, such as the central server, the database server, the network core switch and the like, are classified as the center-level devices. The station server, the station operator workstation, the communication front-end machine, the regional network switch and the field signal acquisition device (such as the RTU, the PLC) and the like deployed in each station and responsible for the train monitoring and data acquisition of the station and the adjacent section are classified as the station-level devices.

[0036] The hierarchical classification information of each device in the train automatic control system can be pre-configured and stored in a device information database, which records the unique identifier (such as the device ID, the name) of each device and the corresponding hierarchical attribute (such as the center-level or the station-level). In the actual application process, the hierarchical classification information of all devices can be obtained by querying the database.

[0037] According to the health degree determination method of the train automatic control system provided by the present application, the hierarchical classification information of each device in the train automatic control system comprises: Based on the functional importance level and the logical relationship of each device in the train automatic control system, the device is classified as a center-level device and a station-level device.

[0038] In the embodiment of the present application, the center-level device refers to the device that undertakes the global, core command and processing function in the train automatic control system, and its typical characteristics include: being responsible for the dispatching command of the whole line, the management and issuance of the operation plan, the summary, storage and processing of the whole network operation data, and the communication management as the nerve center of the whole system.

[0039] The station-level device refers to the device deployed in each station and mainly responsible for the local or regional monitoring, execution and data acquisition function, and its functional range is usually limited within the station and the adjacent physical or logical section.

[0040] For example, according to the technical specification, the network topology diagram and the system architecture design document of the device, the central server, the database server, the core network switch, the dispatcher workstation and the like are determined as the center-level devices. The station server, the station operator workstation, the communication front-end machine, the regional network switch and the field data acquisition and control device such as the remote terminal unit are classified as the station-level devices.

[0041] Through the embodiment of the present application, the hierarchical division information of each device in the train automatic control system based on the functional importance level and the logical relationship is obtained, and the devices are divided into center level and station level devices, thereby providing an ordered and reasonable device classification basis for determining the health degree of the train automatic control system.

[0042] According to the health degree determination method of the train automatic control system provided by the present application, the health degree index data of each device is obtained, including: According to the device type of each device, at least one running state parameter of each device is determined. Real-time data of the running state parameter of each device is collected as the health degree index data of each device.

[0043] In the embodiment of the present application, according to the device type of each device, at least one running state parameter corresponding to each device is determined from a pre-defined index mapping relationship. The pre-defined index mapping relationship is established based on the correspondence between the device type and the key running state.

[0044] For example, for server and workstation type computing devices, the determined running state parameters include the central processing unit usage rate, the memory usage rate, the disk remaining storage capacity and the system response time; for network communication devices, the determined running state parameters include the port state, the bandwidth utilization rate, the data packet error rate and the packet loss rate; for field data acquisition devices, the determined running state parameters include the power supply state, the communication connection state and the input / output module state.

[0045] Real-time data of the running state parameter of each device is collected. At least one of the following modes is used to obtain the current value of the running state parameter with a preset collection period: a polling mechanism, a query interface based on the Windows management specification and an agent program deployed locally on the device.

[0046] After the collected real-time data is processed by format standardization, the health degree index data of each device is output, and a device identifier and a timestamp information are attached to each data record.

[0047] Through the embodiment of the present application, at least one running state parameter of each device is determined according to the device type, and real-time data of these parameters is collected as the health degree index data. The key parameters closely related to the health of the device can be accurately focused on, and the collected data has high pertinence and representativeness, thereby providing a reliable basis for accurately evaluating the health degree of each device of the train automatic control system.

[0048] In step 102, the health degree index data of each device is input into a preset health degree calculation model to obtain the health degree score of each device.

[0049] The health degree calculation model pre-establishes a corresponding calculation rule library for each device type, which contains the weight coefficient of each running state parameter, the threshold of the scoring interval, and the corresponding score mapping relationship.

[0050] For each device, the corresponding calculation rule is called according to its device type to standardize the collected health degree index data. The standardization process includes comparing the measured value of each running state parameter with the preset scoring interval threshold to determine the basic score of the parameter according to the interval; then, the basic scores of the parameters are weighted according to the predefined weight coefficients to obtain the health degree score of the device.

[0051] The health degree score of each device is output, which is a value between 0 and 100, and the higher the value, the better the health status of the device. The health degree score of each device is accompanied by a device identifier and a calculation timestamp for subsequent system health degree total score calculation.

[0052] Step 103, based on the hierarchical division information of each device, determine the hierarchical weight of each device.

[0053] According to the health degree determination method of the train automatic control system provided by the present application, the hierarchical weight of each device is determined based on the hierarchical division information of each device, which includes: According to the hierarchical division information of each device, the corresponding hierarchical weight of each device is configured, wherein different hierarchical devices correspond to different weight values, and the size of the weight value is positively correlated with the functional importance level of the device in the train automatic control system.

[0054] In the embodiment of the present application, the mapping relationship between the device hierarchy and the weight value is pre-established. The mapping relationship is set based on the functional importance level of different device hierarchies in the train automatic control system, wherein the device hierarchy with higher functional importance level is assigned a larger weight value.

[0055] Specifically, the weight value of the center level device is set to be higher than that of the station level device to accurately reflect the key influence of the center level device on the overall health status of the system.

[0056] According to the hierarchical division information of the respective devices, a corresponding hierarchical weight is automatically assigned to each device by querying a predefined device-hierarchical weight mapping table. The device-hierarchical weight mapping table takes the device hierarchy as the primary key and stores the standardized weight coefficients. For a device classified as a center level, the system automatically assigns it a first weight value; for a device classified as a station level, the system automatically assigns it a second weight value, wherein the first weight value is greater than the second weight value.

[0057] The respective devices and their corresponding hierarchical weight values are output to form a device-weight correspondence list. This list contains device identifiers, device hierarchies, and corresponding weight values, providing input data for subsequent weighted calculation of the health score.

[0058] According to the embodiment of the present application, the corresponding hierarchical weight is configured according to the device hierarchical division information, and the weight value is positively correlated with the functional importance level of the device in the train automatic control system, providing accurate basis for subsequent system resource allocation, fault handling priority determination, etc.

[0059] Step 104, according to the hierarchical weight of each device, based on the health score of each device, determine the health score of the train automatic control system.

[0060] In the embodiment of the present application, the health score of each device and its corresponding hierarchical weight are obtained, and the weighted sum algorithm is used to calculate the health score of the train automatic control system. In specific implementation, the health score of each device is multiplied by its corresponding hierarchical weight value to obtain the weighted contribution value of the device to the system total score; then, the weighted contribution values of all devices are added to obtain the health score of the system.

[0061] According to the health degree determination method of the train automatic control system provided by the present application, the health score of the train automatic control system is determined according to the hierarchical weight of each device and based on the health score of each device, which includes: The health score of the train automatic control system is determined according to the following formula, Wherein, represents the health score of the train automatic control system, represents the total number of devices in the train automatic control system, represents the device index in the train automatic control system, represents the hierarchical weight of the i-th device, represents the health score of the i-th device.

[0062] ​​In the embodiment of the present application, for each device, the health score thereof is multiplied by the corresponding hierarchical weight to obtain the weighted contribution value of the device to the total score of the system health. Then, the weighted contribution values of all devices are summed up to finally obtain the total score of the health of the train automatic control system.

[0063] In the specific implementation, to ensure the comparability and consistency of the calculation results, the sum of all weight coefficients is normalized to 1. The output value range of the total score of the health is 0 to 100, and the total score of the health comprehensively reflects the operating states of all devices, and the hierarchical weight reflects the relative importance of each device in the train automatic control system.

[0064] The final calculation result (i.e., the total score of the health of the train automatic control system) is attached with time stamp information and stored in the system database, which is used for historical trend analysis and operating state tracking of the system health state. The calculation process is automatically executed periodically to ensure the real-time and accuracy of the total score of the health of the system.

[0065] According to the embodiment of the present application, the total score of the health of the train automatic control system is calculated according to the hierarchical weight and the health score of each device, which can comprehensively consider the influence of different hierarchical devices on the overall health of the system and accurately reflect the actual health status of the system.

[0066] According to the health determination method of the train automatic control system provided by the present application, after the total score of the health of the train automatic control system is determined according to the hierarchical weight of each device and based on the health score of each device, the method further comprises: comparing the total score of the health of the train automatic control system with a preset health threshold value; generating system health warning information when the total score of the health is lower than the health threshold value.

[0067] In the embodiment of the present application, at least one health threshold value is preset, which is determined based on historical operating data, expert experience and system reliability requirements. The health threshold value includes boundary values of different health states, for example, a first threshold value (such as 80 points) is set as a general warning line, and a second threshold value (such as 60 points) is set as a serious warning line. The threshold value data is stored in the system configuration file, which supports dynamic adjustment according to the actual operating requirements.

[0068] The total score of the health of the train automatic control system calculated is compared with the preset health threshold value in real time to determine the health level interval thereof. The comparison result is divided into three states: a healthy state (the total score is higher than all warning threshold values), a warning state (the total score is lower than the general warning line but higher than the serious warning line), and a dangerous state (the total score is lower than the serious warning line).

[0069] When the health score is lower than the set health threshold, an early warning generation mechanism is automatically triggered. According to the exceeding degree, a corresponding level of health early warning information is generated, including early warning level, current health score, value lower than the threshold, time stamp and other core information. The early warning information is displayed through at least one of the man-machine interface, short message notification and sound and light alarm device to alert the operation and maintenance personnel.

[0070] The generated early warning information is recorded in the system log database at the same time, including early warning time, early warning content, processing state and other information, forming a complete early warning event archive. The system also supports classification query and statistical analysis of early warning information, providing data support for operation and maintenance decision.

[0071] Through the embodiments of the present application, real-time monitoring and active early warning of the system health state are realized, effectively improving the reliability and maintenance efficiency of the system.

[0072] Through the above embodiments of the present application, by reasonably dividing the device level and scientifically determining the weight, the contribution and influence of each device in the ATS system on the overall operation condition can be comprehensively and meticulously considered. It is no longer limited to single device or general overall evaluation, but goes deep into each level of the system architecture, so that the health degree calculation result can accurately reflect the true health level of the ATS system. Whether it is a small performance fluctuation of the core server or an abnormality of the basic sensor, it can be properly reflected in the health degree value, providing system state information with high pertinence and accuracy for the operation and maintenance personnel.

[0073] Based on the accurate health degree calculation, potential fault hidden dangers in the system can be found in advance. Due to the full consideration of the device level and weight, the subtle abnormal changes of the devices at the key level and with high weight can be captured and amplified in the health degree index, so as to issue an early warning before the fault actually occurs.

[0074] With the help of the health degree calculation method, the operation and maintenance personnel can clearly understand the importance degree of different devices to the system health. In the case of limited resources, the operation and maintenance manpower, material and financial resources can be reasonably arranged according to the level and weight of the devices and the corresponding health degree condition. For the devices with low health degree and high level and weight, more resources are preferentially allocated for deep detection, repair and maintenance; and for the devices with acceptable health degree and low level, the maintenance frequency and resource investment can be appropriately reduced. This resource optimization configuration method based on accurate data not only ensures that the key parts of the system are always in good running state, but also effectively improves the operation and maintenance efficiency, reduces the overall operation and maintenance cost, and realizes the maximum utilization of operation and maintenance resources.

[0075] The health degree determination device of the train automatic control system provided by the present application is described below, and the health degree determination device of the train automatic control system described below can be correspondingly referred to the health degree determination method of the train automatic control system described above.

[0076] Reference Figure 2 , Figure 2 is a schematic diagram of a module of the health degree determination device of the train automatic control system provided by the present application.

[0077] The acquisition module 201 is configured to acquire the hierarchical division information of each device in the train automatic control system and acquire the health degree index data of the each device.

[0078] The score module 202 is configured to input the health degree index data of the each device into a preset health degree calculation model to obtain the health degree score of the each device.

[0079] The weight module 203 is configured to determine the hierarchical weight of the each device based on the hierarchical division information of the each device.

[0080] The health degree module 204 is configured to determine the total score of the health degree of the train automatic control system based on the health degree score of the each device according to the hierarchical weight of the each device.

[0081] Specifically, the health degree determination device of the train automatic control system provided by the present application can realize all the method steps realized by the health degree determination method embodiment of the train automatic control system, and can achieve the same technical effects. The same parts and beneficial effects in the present embodiment as the method embodiment are not described in detail here.

[0082] Figure 3 is a schematic diagram of the physical structure of the electronic device provided by the present application, such as Figure 3As shown, the electronic device can include a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communications bus 340. The processor 310 can invoke the logic instructions in the memory 330 to execute the health degree determination method of the train automatic control system, which includes: obtaining hierarchical division information of each device in the train automatic control system, and obtaining health degree index data of the each device; inputting the health degree index data of the each device into a preset health degree calculation model to obtain a health degree score of the each device; determining a hierarchical weight of the each device based on the hierarchical division information of the each device; and determining a health degree total score of the train automatic control system based on the health degree score of the each device according to the hierarchical weight of the each device.

[0083] In addition, the logic instructions in the memory 330 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0084] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the health degree determination method of the train automatic control system provided by the above-mentioned methods, which includes: obtaining hierarchical division information of each device in the train automatic control system, and obtaining health degree index data of the each device; inputting the health degree index data of the each device into a preset health degree calculation model to obtain a health degree score of the each device; determining a hierarchical weight of the each device based on the hierarchical division information of the each device; and determining a health degree total score of the train automatic control system based on the health degree score of the each device according to the hierarchical weight of the each device.

[0085] In another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements a health degree determination method of a train automatic control system provided by each of the above methods, the method comprising: obtaining hierarchical division information of each device in the train automatic control system, and obtaining health degree index data of the each device; inputting the health degree index data of the each device into a preset health degree calculation model to obtain a health degree score of the each device; determining a hierarchical weight of the each device based on the hierarchical division information of the each device; and determining a health degree total score of the train automatic control system based on the health degree score of the each device according to the hierarchical weight of the each device.

[0086] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0087] From the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software plus a necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0088] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining the health status of a train automatic control system, characterized in that, include: Obtain the hierarchical division information of each device in the train automatic control system, and obtain the health index data of each device; The health index data of each device is input into a preset health calculation model to obtain the health score of each device. Based on the hierarchical division information of each device, the hierarchical weight of each device is determined; Based on the hierarchical weights of each device and the health scores of each device, the total health score of the train automatic control system is determined.

2. The method for determining the health status of a train automatic control system according to claim 1, characterized in that, The acquisition of hierarchical division information of various devices in the train automatic control system includes: Based on the functional importance and logical relationship of each device in the train automatic control system, the devices are divided into center-level devices and station-level devices.

3. The method for determining the health status of a train automatic control system according to claim 1, characterized in that, The step of determining the hierarchical weight of each device based on the hierarchical division information of each device includes: Based on the hierarchical classification information of each device, a corresponding hierarchical weight is configured for each device. Different weight values ​​are assigned to devices at different levels, and the magnitude of the weight value is positively correlated with the functional importance level of the device's level in the train automatic control system.

4. The method for determining the health status of a train automatic control system according to claim 1, characterized in that, The acquisition of health index data for each device includes: Based on the equipment type of each device, at least one operating status parameter of each device is determined; Real-time data of the operating status parameters of each device are collected and used as health index data for each device.

5. The method for determining the health status of a train automatic control system according to claim 1, characterized in that, After determining the total health score of the train automatic control system based on the health score of each device according to its hierarchical weight, the method further includes: The total health score of the train automatic control system is compared with a preset health threshold. When the total health score is lower than the health threshold, a system health warning message is generated.

6. The method for determining the health status of a train automatic control system according to claim 1, characterized in that, The process of determining the total health score of the train automatic control system based on the health scores of each device, according to the hierarchical weights of each device, includes: The total health score of the train automatic control system is determined according to the following formula. in, This represents the total health score of the train automatic control system. This indicates the total number of devices in the train automatic control system. This indicates the equipment index in the train automatic control system. Indicates the first The hierarchical weight of each device Indicates the first The health score of each device.

7. A health determination device for an automatic train control system, characterized in that, include: The acquisition module is used to acquire the hierarchical division information of each device in the train automatic control system and to acquire the health index data of each device. The scoring module is used to input the health index data of each device into a preset health calculation model to obtain the health score of each device. The weighting module is used to determine the hierarchical weight of each device based on the hierarchical division information of each device; The health module is used to determine the total health score of the train automatic control system based on the health score of each device according to the hierarchical weight of each device.

8. 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, it implements the health determination method of the train automatic control system as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the health determination method of the train automatic control system as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the health determination method of the train automatic control system as described in any one of claims 1 to 6.

Images