Rail transit AFC fault reporting processing system and method

Abstract

The application discloses a kind of rail transit AFC fault report processing system and method, it is related to rail transit fault processing technical field;The application is by calling the equipment operation data of AFC equipment, whether equipment is fault according to preset trigger rule is judged automatically, equipment fault identification is generated when fault occurs;Again with the fault trigger time in equipment fault identification as limit extraction equipment time series data, then according to equipment time series data automatically output equipment fault information, realize the automation processing of fault identification throughout;The application realizes fault determination and information extraction relying on equipment operation data, avoids artificial judgment deviation and information transmission error from source, greatly reduces AFC fault false alarm, wrong report probability;Simultaneously by fault identification accurate locking time series data range, reduce invalid data processing amount, improve fault identification speed and diagnostic accuracy, guarantee equipment fault information real and reliable.

Description

Technical Field

[0001] This invention belongs to the field of rail transit fault handling technology, specifically a rail transit AFC fault reporting system and method. Background Technology

[0002] AFC (Automatic Fare Collection) failure in rail transit refers to an abnormal state in which the automatic fare collection (AFC) system, such as ticket vending machines (TVMs), turnstiles (AGMs), and semi-automatic ticket vending machines (BOMs), cannot normally perform functions such as ticket purchase, ticket checking, fare calculation, and data transmission due to hardware, software, network communication, or power supply problems. Based on the scope of impact, it can be divided into three categories: Category A (directly affecting train operation or severely impacting service), Category B (indirectly affecting train operation or large-scale passenger transport), and Category C (general equipment failure). Common causes include equipment aging, high-frequency losses, network interruptions, failed software upgrades, and unstable power supply.

[0003] Due to the interplay of hardware and software faults in AFC equipment, station staff struggle to accurately distinguish between single-unit / local / system-level faults, leading to false alarms or missed alarms. Verbal relaying of information up the chain of command results in the loss of crucial information (fault time, affected area, equipment number), delaying crucial response time. Furthermore, the AFC system integrates mechanical, electronic, software, and network components, with multiple causes for the same phenomenon, making it difficult for on-site personnel to quickly pinpoint the root cause and ensuring efficient emergency repairs.

[0004] This invention provides a system and method for processing AFC (Automatic Facilitation Detection) fault reports in rail transit to solve the above-mentioned technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a processing system and method for reporting AFC faults in rail transit.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for processing AFC (Automatic Facilitation Detection) fault reports in rail transit, comprising: Equipment fault identifiers are generated from the equipment operation data of the AFC equipment, and equipment time-series data is extracted based on the equipment fault identifiers; among them, the equipment operation data is used to evaluate the working status of the AFC equipment, and the equipment time-series data is the equipment operation data within a preset time period; Equipment fault information is obtained by analyzing equipment time-series data, and the fault cluster type is determined based on the equipment fault information. The equipment fault information includes fault type, fault trigger time, and fault root cause. The fault cluster types include single-point equipment fault, equipment cluster fault, site cluster fault, and line cluster fault. After classifying the AFC fault based on the equipment fault information and the fault cluster type, the report is then processed.

[0007] In one possible implementation, a device fault identifier is generated using the device operation data of the AFC device, including: Retrieve the device operation data of the AFC device; the device operation data includes operation status data, device working data, and transaction status data; Analyze equipment operation data to determine if an equipment failure has occurred; if yes, generate an equipment failure identifier; otherwise, do not generate an equipment failure identifier; the equipment failure identifier includes a failure identifier code, AFC equipment number, failure trigger time, and failure trigger type.

[0008] In one possible implementation, equipment timing data is extracted based on equipment fault identifiers, including: The timing period of the AFC device is preset; the timing period includes the time period before the fault and the time period after the fault. Using the fault trigger time in the equipment fault identifier as the time limit, the equipment time sequence data is extracted from the equipment operation data of the AFC equipment according to the time sequence time period.

[0009] In one possible implementation, equipment fault information is obtained by analyzing equipment timing data, including: Invoke a pre-trained fault diagnosis model; wherein the fault diagnosis model is trained based on an artificial intelligence model; The equipment timing data is input into the fault diagnosis model to generate equipment fault information, which includes fault type, fault trigger time and fault root cause.

[0010] In one possible implementation, the fault cluster type is determined based on device fault information, including: Retrieve equipment fault information; Based on equipment fault information, fault cluster types are determined through hierarchical cluster identification; the cluster identification order is as follows: equipment cluster, site cluster, and line cluster.

[0011] In one possible implementation, the type of faulty cluster is determined through a step-by-step cluster assessment, including: Determine if there is an AFC device in the device cluster that matches the device fault information; if yes, associate the device fault information with the device cluster and perform site cluster determination; otherwise, determine it as a single point of failure. Determine if there are two device clusters in the site cluster with the same device fault information; if yes, associate the device fault information with the site cluster and determine the line cluster; otherwise, determine it as a device cluster fault. Determine if there are two site clusters with identical equipment fault information in the line cluster; if yes, associate the equipment fault information with the line cluster and determine it as a line cluster fault; otherwise, determine it as a site cluster fault.

[0012] In one possible implementation, the criteria for determining the consistency of equipment fault information include the fault type and the fault trigger time.

[0013] In one possible implementation, the AFC fault classification and reporting process is performed based on device fault information and fault cluster type, including: AFC fault classification is performed based on equipment fault information and fault cluster type to obtain AFC fault level; whereby AFC fault level is determined according to the scope of AFC fault impact. Report equipment fault information, fault cluster type, and AFC fault level.

[0014] A second aspect of the present invention provides a processing system for reporting faults in rail transit AFC (Automatic Fare Collection) systems, including a fault reporting module and several reporting sub-modules connected thereto. Several reporting sub-modules: used to generate equipment fault identifiers based on equipment operation data, extract equipment time-series data based on equipment fault identifiers; analyze equipment time-series data to obtain equipment fault information, and identify fault cluster types based on equipment fault information; and, Used for reporting and processing AFC fault classification based on equipment fault information and fault cluster type; Fault reporting module: Used to determine whether the fault cluster type is a line cluster fault based on the received reporting results; if yes, it updates the fault cluster type and AFC fault classification and reports it; if no, it reports it directly.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention retrieves the equipment operation data of AFC devices and automatically determines whether the equipment is faulty based on preset triggering rules. When a fault occurs, a fault identifier is generated. Then, the equipment time-series data is extracted based on the fault trigger time in the fault identifier, and the equipment fault information is automatically output based on the time-series data, realizing fully automated fault identification. This invention relies on equipment operation data to achieve fault determination and information extraction, eliminating the need for manual observation by station staff, passenger feedback, or passive alarms. It avoids human judgment bias and information transmission errors from the source, significantly reducing the probability of false alarms and misreports of AFC faults. At the same time, the fault identifier accurately locks the time-series data range, reducing the amount of invalid data processing, improving the speed and accuracy of fault identification, ensuring the authenticity and reliability of equipment fault information, providing accurate basis for subsequent fault handling, solving the industry pain points of difficulty in distinguishing between true and false alarms and easy false alarms and missed alarms in the traditional mode, and comprehensively improving the accuracy and stability of AFC fault identification.

[0016] 2. This invention first determines the fault cluster type step by step based on equipment fault information; then, it combines equipment fault information and cluster type to complete AFC fault classification. The site-level classification and reporting are completed by the reporting submodule within the site. After the fault reporting module summarizes the data from the entire line, it re-verifies whether it is a line-level fault. If it is a line-level fault, it updates the fault cluster type and AFC fault level and reports it again. If it does not meet the requirements, it reports directly, realizing two-level classification and dynamic calibration. This invention changes the traditional mode of direct reporting of site classification, avoids the deviation in fault level judgment caused by the local perspective of the site, prevents the overestimation of small-scale faults and the underestimation of large-scale system faults, and eliminates the misleading of maintenance personnel in resource allocation and priority judgment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the method steps for handling AFC fault reporting in rail transit according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the steps of a method for obtaining equipment fault information based on equipment fault identifiers in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the method steps for determining the type of fault cluster based on equipment fault information in an embodiment of the present invention; Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] AFC (Automatic Fare Collection) malfunctions directly impact passenger transport order, operational efficiency, and ticketing security: From the passenger's perspective, ticket purchase / entry / exit is obstructed, long queues occur, and congestion can easily lead to overcrowding and complaints during peak hours, severely affecting the travel experience; From the operational perspective, stations are forced to use manual ticketing / side gate access, resulting in a surge in labor costs, and gate congestion may spread to the platform, causing train delays and irregular train intervals; From the ticketing perspective, failed transactions and data loss / errors affect ticket revenue and reconciliation.

[0021] The existing AFC (Automatic Fare Collection) fault reporting and handling process follows a closed-loop workflow: discovery → tiered reporting → emergency response → repair → restoration → review. Discovery: Station personnel (station staff / train operation duty officers) discover faults through monitoring, equipment alarms, or passenger feedback, and immediately report to the duty station manager, simultaneously recording the time, location, equipment, phenomenon, and scope of impact. Tiered Reporting: After verification by the duty station manager, Category C (general) faults are directly reported to maintenance; Category B (large-scale) faults are reported to the OCC (Operations Control Center) and the AFC maintenance center; Category A (severe / system-level) faults immediately activate the emergency plan and report to the OCC, the responsible leader, and relevant departments. Emergency Response: The station activates downgrade mode, opening side doors, manual ticket checking, and selling pre-paid tickets; broadcasting announcements to guide passengers and maintain order; the OCC monitors the entire line, coordinating support and train operation adjustments. Repair: Maintenance personnel conduct remote diagnosis or on-site repair, prioritizing the restoration of core equipment / functions; after fault resolution, testing and verification are performed, the system is restored to normal mode, and manual ticketing data is simultaneously updated. Closed-loop review: Fault archiving, cause analysis, development of preventive measures, and report generation to optimize processes and contingency plans.

[0022] Due to the interplay of hardware and software faults in AFC equipment, station staff find it difficult to identify and accurately distinguish between single-unit / local / system-level faults. Verbal relaying of information up and down the chain of command can easily lead to the loss of key information, resulting in false alarms or missed alarms. Moreover, the source of the fault mainly depends on passive identification, which can also cause the omission of fault information, thereby affecting the efficiency of fault handling.

[0023] This invention utilizes the equipment operation data of AFC devices to automatically identify equipment fault information, then determines the fault cluster type based on the equipment fault information, and performs AFC fault classification and automated reporting based on the equipment fault information and fault cluster type. This invention improves the accuracy of AFC fault reporting and the efficiency of fault handling by automating the identification of equipment fault information and the generation of AFC fault classification.

[0024] Please see Figure 1 The first aspect of this invention provides a method for processing AFC (Automatic Facilitation Detection) fault reporting in rail transit, the implementation process of which is as follows: A01: Generate equipment fault identifiers from the equipment operation data of the AFC equipment, and extract equipment timing data based on the equipment fault identifiers; among which, the equipment operation data is used to evaluate the working status of the AFC equipment; The equipment fault identifier is generated by judging the equipment fault of AFC equipment (such as ticket vending machine (TVM), turnstile (AGM), semi-automatic ticket vending machine (BOM) etc.) through equipment operation data. The equipment fault identifier is used to extract equipment timing data to determine the time base.

[0025] Compared to existing methods of obtaining fault information through station monitoring, equipment alarms, or passenger feedback, this invention utilizes the equipment operation data of AFC devices to automatically determine whether a fault has occurred, which can effectively reduce the probability of false alarms or misreports. Moreover, when an AFC device malfunctions, it extracts the device's time sequence data based on the device fault identifier, avoiding full analysis of the AFC device's operation data, reducing the amount of data processing, and thus improving the efficiency of AFC fault reporting.

[0026] A02: Obtain equipment fault information by analyzing equipment time-series data, and determine the fault cluster type based on the equipment fault information; wherein, the equipment fault information includes fault type, fault trigger time, and fault root cause, and the fault cluster type includes single-point equipment fault, equipment cluster fault, site cluster fault, and line cluster fault. The analysis of equipment timing data can be achieved through artificial intelligence models. Equipment fault information includes fault type and corresponding root cause. The root cause is a possible fault identified by matching the equipment timing data, and it is mainly provided as a reference for maintenance personnel.

[0027] Fault cluster types include single-point device failure, equipment cluster failure, site cluster failure, and line cluster failure. A single-point device failure refers to a failure that occurs only on a single AFC device. An equipment cluster failure refers to a failure that occurs on multiple AFC devices within the same equipment cluster. A site cluster failure refers to a failure that occurs on multiple equipment clusters within the same site cluster. A line cluster failure refers to a failure that occurs on multiple site clusters within the same line cluster.

[0028] This invention automatically identifies equipment fault information based on equipment time-series data, accurately identifying fault types and matching their possible root causes; moreover, it identifies fault cluster types based on equipment fault information and determines the scope of fault occurrence based on equipment fault information, so as to ensure the accuracy of AFC fault rating.

[0029] A03: After classifying the AFC fault based on the equipment fault information and fault cluster type, report and process the fault.

[0030] Based on equipment fault information and fault cluster type, the impact of the fault can be comprehensively assessed to complete the AFC fault classification. This invention considers not only equipment fault information but also the scope of the fault's impact when classifying AFC faults, ensuring accuracy and improving fault handling efficiency.

[0031] The reporting process in this invention includes multiple levels, at least site-level and line-level reporting. Both site-level and line-level reporting require AFC fault classification. When an AFC device fault occurs at a site, AFC classification is performed within that site based on the device fault information and fault cluster type before reporting. The highest fault cluster type determined within that site is a site cluster fault, which constitutes a site-level report. After each site's reporting process, the device fault information reported by each site is used to determine whether the AFC fault is a line cluster fault. If it is a line cluster fault, AFC fault classification needs to be performed again and reported. If the AFC fault is not a line-level cluster fault, the AFC fault reported by the site is directly reported.

[0032] This invention reassesses the fault cluster type of AFC faults after reporting at the station level. If the fault cluster type is inconsistent with the station-level reporting result, the AFC fault is reclassified and reported again. Analyzing AFC faults from the perspective of rail transit lines can provide maintenance personnel with a global view and help improve the efficiency of AFC fault handling.

[0033] It should be explained that a fault type may be classified as a Class B fault at a single site, but if the same fault type occurs at multiple points along the entire line, its impact range is relatively large, and it may be classified as a Class A fault. This invention further analyzes the reported data after site-level reporting to determine if the fault cluster type has changed. If it has changed, the AFC fault classification is re-performed, and the reported data is processed based on the updated data. This helps maintenance personnel accurately grasp the extent of the AFC fault's impact, thereby accelerating the efficiency of AFC fault handling.

[0034] In existing solutions, AFC equipment fault information mostly comes from station staff, passengers, or equipment alarms. However, station staff have difficulty determining whether an AFC equipment fault is a real fault, and they are also unable to determine the root cause and scope of the AFC equipment fault, which leads to false alarms or missed alarms.

[0035] This invention uses the equipment operation data of AFC equipment as a basis to automatically analyze the equipment fault identifier of AFC equipment, and uses the equipment operation data before and after the equipment fault identifier to obtain equipment fault information. This invention uses the fault trigger time of the equipment fault identifier as the time limit to obtain equipment time sequence data, which can ensure the accuracy of fault identification while reducing the amount of data processing, thereby improving the data processing efficiency.

[0036] The implementation process for generating equipment fault identifiers from the equipment operation data of the AFC device in this invention is as follows: B01: Retrieve the device operation data of the AFC device; The AFC equipment has a built-in data acquisition device that collects equipment operation data at a preset acquisition frequency (e.g., 1-5 seconds / time) and uploads the equipment operation data in real time through the dedicated rail transit network for AFC fault analysis.

[0037] Equipment operation data includes operational status data, equipment working data, and transaction status data. Operational status data includes gate opening status, ticket vending machine transaction status, and read / write module working status. Equipment working data includes voltage, current, and network bandwidth. Transaction status data includes transaction success rate and transaction failure codes. It may also include environmental data such as equipment operating temperature and humidity.

[0038] B02: Analyze equipment operation data to determine whether an equipment failure has occurred; if yes, generate an equipment failure identifier; if no, do not generate an equipment failure identifier.

[0039] The generation of equipment fault identifiers is achieved through preset triggering rules. Once the equipment operation data meets the preset triggering rules, it is determined that an equipment fault has occurred, and an equipment fault identifier is generated. If the preset triggering rules are not met, the equipment operation data is continuously analyzed.

[0040] Triggering rules include transaction anomaly rules, status anomaly rules, and heartbeat anomaly rules. Transaction anomaly rules include three consecutive failed transactions (such as failed ticket purchase or ticket check), or a single failed transaction lasting more than 10 seconds. Status anomaly rules refer to abnormalities displayed in device operation fields, such as gate ticketing issues, reader / writer module malfunctions, or power outages. Heartbeat anomaly rules refer to an AFC device's communication heartbeat interruption lasting more than 15 seconds, which is considered an offline condition. The above triggering rules are for illustrative purposes only and are not intended to be limiting; specific settings can be implemented based on actual circumstances.

[0041] The equipment fault identifier includes a fault identifier code (composed of the AFC device number and the fault trigger timestamp), the AFC device number, the fault trigger time, and the fault trigger type. The fault trigger type includes transaction anomaly, status anomaly, and heartbeat anomaly.

[0042] For example, an AGM gate (device number: AGM-005) at a certain station has a built-in data acquisition module that collects equipment operation data every 3 seconds. The station's reporting submodule analyzes the transaction status data in the equipment operation data and detects that the AGM gate failed to check tickets three times in a row at 10:10:03, 10:10:06, and 10:10:10 (with the same transaction failure code), which meets the transaction anomaly triggering rules. The station immediately generates a device fault identifier, with the fault identifier code being AGM-005-20000101101010, the AFC device number being AGM-005, the fault triggering time being January 1, 2000 at 10:10:10, and the fault triggering type being transaction anomaly. The fault identifier generation is then completed.

[0043] This invention automatically analyzes equipment operating data through preset triggering rules to determine whether equipment failure has occurred. If a failure occurs, an equipment failure identifier is generated. This eliminates the need for manual judgment by station staff or on-duty personnel, thereby improving identification efficiency and accuracy, and increasing the efficiency of AFC failure identification and reporting.

[0044] The equipment operation data of AFC devices includes various types of data. Performing full analysis of the equipment operation data would not only reduce the accuracy of fault identification but also significantly increase the amount of data processing. This invention extracts the equipment time-series data by pre-setting the time period of the AFC device and using the fault trigger time as the time boundary. This allows for accurate acquisition of equipment operation data that characterizes the fault state, thereby improving fault identification efficiency.

[0045] Please see Figure 2 The implementation process for extracting equipment timing data based on equipment fault identifiers in this invention is as follows: C01: Preset timing period for AFC devices; the timing period includes the time period before the fault and the time period after the fault. The time series period refers to the time range corresponding to the equipment's time series data. It is used to narrow the scope of data analysis to reduce the amount of data processing. The pre-fault time period and post-fault time period in the time series period are set based on historical data statistics. That is, the amount of equipment operation data extracted before and after the fault occurs can meet the requirements for fault identification, especially the accuracy of fault identification. The pre-fault time period in the time series period is set to 30 seconds by default, and the post-fault time period is set to 90 seconds by default.

[0046] C02: Using the fault trigger time in the equipment fault identifier as the time limit, extract the equipment timing data from the equipment operation data of the AFC equipment according to the timing time period.

[0047] The fault trigger time in the equipment fault identifier is used as the time limit. Before the time limit, the equipment operation data is extracted according to the time period before the fault. After the time limit, the equipment operation data is extracted according to the time period after the fault. The equipment operation data of the two time periods are concatenated to obtain the equipment time sequence data.

[0048] For example, the fault trigger time in the equipment fault identifier of a certain TVM ticket vending machine is 10:10:10 on January 1, 2000. Assume that the time period before the fault and the time period after the fault are 30s and 90s respectively. Taking 10:10:10 on January 1, 2000 as the time boundary, the equipment operation data from 10:09:40 to 10:10:10 is extracted as the time series data before the fault, and the equipment operation data from 10:10:10 to 10:11:40 is extracted as the time series data after the fault. The time series data before the fault and the time series data after the fault are concatenated to obtain the equipment time series data of the TVM ticket vending machine.

[0049] This invention extracts equipment time-series data from equipment operation data by using time-series time periods and equipment fault identifiers. It extracts only the equipment operation data within a preset time range before and after the equipment fault, and identifies equipment fault information through this equipment operation data. This avoids full analysis of equipment operation data, reducing the amount of data processing while ensuring the accuracy of fault identification.

[0050] The identification of existing AFC faults mainly relies on experience-based judgment, and fault diagnosis is limited to the fault type, failing to determine the root cause of the fault type. This leads to insufficient accuracy in fault diagnosis and fails to provide maintenance personnel with sufficient reliable information, thus affecting the efficiency of fault handling.

[0051] This invention analyzes equipment time-series data using a pre-trained fault diagnosis model to obtain the corresponding fault type and root cause, thereby improving fault diagnosis accuracy and fault handling efficiency. The implementation process for obtaining equipment fault information by analyzing equipment time-series data in this invention is as follows: D01: Call the pre-trained fault diagnosis model; where the fault diagnosis model is trained based on an artificial intelligence model; The artificial intelligence model is trained using a training sample set, which includes several sample data. Each sample data includes sample input data and its corresponding sample output data. The sample input data has the same content attributes as the device timing data of the AFC device. The sample output data is the fault type of the AFC device corresponding to the sample input data, the root cause of the fault type, and the fault trigger time.

[0052] The training sample set can be extracted from historical data or simulated through a digital twin model. The root causes of failures in the sample output data are obtained from historical data, that is, by statistically analyzing historical data, the root causes that may lead to the type of failure are obtained. When training an artificial intelligence model using the training sample set, it is necessary to standardize the training sample set, such as through normalization or one-hot encoding. Alternatively, a standardization layer can be set in the artificial intelligence model. The specific training process will not be elaborated here.

[0053] Artificial intelligence models are used to establish nonlinear mapping relationships between sample input data and sample output data. BP neural network models or RBF neural network models can be selected. Existing mature structures of artificial intelligence models can be chosen without modifying their structures.

[0054] D02: Input the equipment timing data into the fault diagnosis model to generate equipment fault information; the equipment fault information includes fault type, fault trigger time and fault root cause.

[0055] After extracting the device timing data of the AFC device, it is standardized and then input into the fault diagnosis model to obtain the device fault information corresponding to the device timing data. The device fault information includes the fault type, the root cause of the fault, and the fault trigger time.

[0056] In some other preferred embodiments, the fault diagnosis model may output only the fault type and the root cause of the fault, and integrate the output data with the fault trigger time in the equipment fault identifier to form equipment fault information.

[0057] Fault types include hardware faults, software faults, network faults, etc., and the root cause is the possible reason for these fault types. Since the fault cause is used to help maintenance personnel troubleshoot equipment faults as quickly as possible, it is only for reference. In some equipment fault information, the root cause corresponding to the fault type may be empty.

[0058] In the existing AFC fault reporting process, the reporting process is carried out after the fault type of the AFC device is determined. There may be multiple AFC devices with the same fault type that are reported multiple times. When analyzing a single AFC device, it is not conducive to accurately identifying the cause of the fault. Moreover, a large number of similar fault reporting records will increase the difficulty of maintenance and reduce maintenance efficiency.

[0059] This invention identifies fault cluster types that cause AFC (Automatic Facing) faults based on equipment fault information, in order to accurately determine the impact range of AFC faults and thus ensure the accuracy of AFC fault classification. Please refer to [link / reference]. Figure 3 The implementation process for determining the fault cluster type based on equipment fault information in this invention is as follows: E01: Retrieve equipment fault information; The generated device fault information is retrieved, and the fault type is then used to determine which cluster type the fault occurred in, so as to reasonably assess the scope of the fault's impact.

[0060] E02: Based on equipment fault information, the fault cluster type is obtained by performing a step-by-step cluster determination; the cluster determination order is equipment cluster, site cluster and line cluster.

[0061] This invention pre-defines equipment clusters, station clusters, and line clusters based on the installation standards for AFC (Automatic Fare Collection) equipment in rail transit. Equipment clusters include several AFC devices of the same type, station clusters include AFC devices at the same rail transit station, and line clusters include AFC devices on the same rail transit line.

[0062] When determining the fault cluster type based on equipment fault information, a step-by-step cluster determination method is adopted. First, it checks if the fault cluster type is an equipment cluster; if not, it checks if it's a site cluster; and finally, it checks if it's a line cluster. This invention accurately identifies the coverage range of fault types through this step-by-step cluster determination method, enabling a reasonable assessment of the AFC fault impact range and ensuring the reliability of AFC fault classification.

[0063] The implementation process for determining the type of faulty cluster through step-by-step cluster identification in this invention is as follows: F01: Determine if there is an AFC device in the device cluster that matches the device fault information; if yes, associate the device fault information and the device cluster to determine the site cluster; if no, determine it as a single point of failure. A device cluster includes several AFC devices of the same type. If the fault information of another AFC device in the cluster matches the fault information of the faulty device (the AFC device corresponding to the invoked fault information), it indicates that multiple AFC devices in the cluster are affected. This fault information is then associated with the device cluster, but the cluster type cannot be determined at this point. If no other AFC device in the cluster matches the faulty device's information, it means the fault information exists only in the faulty device, and is therefore classified as a single point of failure.

[0064] For example, within the AGM (Automatic Guided Vehicle) turnstile cluster at a certain station, only AGM No. 1 experiences a ticket reading / writing failure. The other AGM turnstiles of the same type within the cluster are operating normally and do not exhibit the same failure information. This is determined to be a single point of failure and does not require further investigation into the station or line cluster. However, if AGM No. 3 experiences a ticket reading / writing failure simultaneously with the failure information of AGM No. 1, the failure information will be associated with the AGM turnstile's cluster and the process will proceed to the station cluster assessment.

[0065] F02: Determine if there are two device clusters with identical device fault information in the site cluster; if yes, associate the device fault information with the site cluster and perform line cluster determination; if no, determine it as a device cluster fault. When associating device fault information with a device cluster, at least one AFC device within the device cluster must be faulty. If the device fault information of two device clusters within a site cluster is identical, it indicates that the fault type appears in at least two device clusters within the site cluster. Associating the faulty device information with the site cluster is then performed, but the fault cluster type cannot be determined as a site cluster. If the device fault information of no two device clusters within a site cluster is identical, it indicates that the faulty device information appears in only one device cluster, and this is determined to be a device cluster fault.

[0066] For example, continuing from the previous example, no other turnstile equipment clusters or customer service terminal equipment clusters within this site cluster have similar fault information. Only the AGM turnstile equipment cluster shows the same fault type in the equipment fault information, which is determined to be a device cluster fault. If other clusters within the site show the same fault type in the equipment fault information, the equipment fault information is associated with the site cluster and the process is then entered into the line cluster determination.

[0067] F03: Determine if there are two site clusters with identical equipment fault information in the line cluster; if yes, associate the equipment fault information with the line cluster and determine it as a line cluster fault; otherwise, determine it as a site cluster fault.

[0068] When associating device fault information with a site cluster, at least one device cluster within the site cluster must be faulty. If two line clusters within a line cluster have identical device fault information, it indicates that the fault type occurs in at least two site clusters within the line cluster. Therefore, the device fault information can be associated with the line cluster, and the faulty cluster type can be determined as a line cluster. If no two site clusters within a line cluster have identical device fault information, it indicates that the faulty device information occurs in only one site cluster, and this is determined as a site cluster fault.

[0069] For example, continuing from the previous example, if two different station clusters, station A and station B, in a rail transit line simultaneously experience the aforementioned equipment fault information, it is determined to be a line cluster fault, and the equipment fault information is associated with the flow restriction cluster fault; otherwise, it is determined to be a station cluster fault.

[0070] This invention determines the type of fault cluster by step-by-step cluster identification. First, it identifies AFC devices with the same fault information within the equipment cluster; then, it identifies equipment clusters with the same fault information within the site cluster; and finally, it identifies site clusters with the same fault information within the line cluster. This gradually narrows the scope of the fault impact and clarifies the fault level. It eliminates the need for manual on-site verification of the status of each device within the cluster, which not only shortens the fault classification and reporting time but also accurately distinguishes between single-point faults, equipment cluster faults, site cluster faults, and line cluster faults, avoiding ambiguity in the classification level.

[0071] Consistent equipment fault information means that the fault type and fault trigger time are substantially the same.

[0072] Since different data processing efficiencies may cause the same AFC fault to trigger at different AFC devices, different device clusters, or different site clusters, in some preferred embodiments, a time difference threshold (such as 1 second) can be preset. That is, when the time difference between the fault trigger times is less than the time difference threshold, the fault trigger times are also determined to be the same.

[0073] It should be noted that the determination of consistent equipment fault information does not include the determination of the same fault root cause. The fault root cause is mainly used to help maintenance personnel eliminate faults as soon as possible. Even if the AFC faults are the same, their matched fault root causes may not be the same.

[0074] The implementation process of AFC fault classification and reporting based on equipment fault information and fault cluster type in this invention is as follows: G01: Based on the equipment fault information and fault cluster type, AFC fault classification is performed to obtain the AFC fault level; As mentioned above, AFC fault levels are generally divided into Level A, Level B, and Level C. Level A indicates that the AFC fault directly affects train operation or seriously affects service. Level B indicates that the AFC fault indirectly affects train operation or has a large-scale impact on passenger transport. Level C indicates that the AFC fault is just a general equipment fault and will not have a significant impact on train operation or service.

[0075] When classifying AFC faults, a match can be made from a preset fault classification form based on equipment fault information and fault cluster type. The fault classification form has preset AFC fault levels corresponding to different fault types and fault cluster types.

[0076] G02: Report equipment fault information, fault cluster type, and AFC fault level.

[0077] After completing the AFC fault classification, the equipment fault information, fault cluster type, and AFC fault level are reported. During AFC fault reporting, the reporting submodule within the site first classifies the AFC fault based on the equipment fault information and the fault cluster type determined within the site (the highest being the site cluster type). Then, the equipment fault information, fault cluster type, and AFC fault level are reported to the fault reporting module. The fault reporting module performs a consistency check on the equipment fault information at each site. If it belongs to the line cluster type, the AFC fault classification is re-performed based on the equipment fault information and line cluster type, and the equipment fault information, line cluster type, and the re-determined AFC fault level are reported.

[0078] For example, a site reporting submodule identifies a device cluster failure occurring only at its own site. Based on the failure details, it initially classifies it as Level B and uploads the device failure details, device cluster failure type, and Level B along with the failure report module. After aggregating data from the entire line, the failure report module identifies two other sites experiencing the same type of failure with a time difference meeting the threshold. Confirming this as a line cluster failure, it overturns the initial site classification and reclassifies the site as an AFC (Automatic Fault Coordination) failure at Level A. Finally, it reports the failure information, line cluster failure type, and Level A.

[0079] A second aspect of the present invention provides a processing system for reporting AFC faults in rail transit, including a fault reporting module and several reporting sub-modules connected thereto; Several reporting sub-modules: used to generate equipment fault identifiers based on equipment operation data, extract equipment time-series data based on equipment fault identifiers; analyze equipment time-series data to obtain equipment fault information, and identify fault cluster types based on equipment fault information; and, Used for reporting and processing AFC fault classification based on equipment fault information and fault cluster type; Fault reporting module: Used to determine whether the fault cluster type is a line cluster fault based on the received reporting results; if yes, it updates the fault cluster type and AFC fault classification and reports it; if no, it reports it directly.

[0080] Several reporting sub-modules are located within rail transit stations. They analyze the equipment operation data of AFC (Automatic Facing) devices within the station to obtain equipment fault information and fault types. Based on the equipment fault information and fault cluster type, they classify the AFC fault and then report it to the fault reporting module; this is station-level reporting. Before being processed by the reporting sub-modules, the reported data can be manually verified by the station's shift supervisor.

[0081] After receiving data from several reporting sub-modules, the fault reporting module analyzes the equipment fault information in the reported data to determine whether it is a line cluster fault. If it is a line cluster fault, it re-classifies the AFC fault based on the equipment fault information and the line cluster fault, and reports the updated data. If it is not a line cluster fault, it reports directly according to the data received from the reporting sub-modules.

[0082] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments.

[0083] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any other combination thereof. When implemented using a software program, it can be implemented entirely or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0084] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for processing AFC (Automatic Facilitation Detection) fault reports in rail transit, characterized in that, include: Equipment fault identifiers are generated from the equipment operation data of the AFC equipment, and equipment time-series data is extracted based on the equipment fault identifiers; among them, the equipment operation data is used to evaluate the working status of the AFC equipment, and the equipment time-series data is the equipment operation data within a preset time period; Equipment fault information is obtained by analyzing equipment time-series data, and the fault cluster type is determined based on the equipment fault information. The equipment fault information includes fault type, fault trigger time, and fault root cause. The fault cluster types include single-point equipment fault, equipment cluster fault, site cluster fault, and line cluster fault. After classifying the AFC fault based on the equipment fault information and the fault cluster type, the report is then processed.

2. The method for processing AFC fault reporting in rail transit according to claim 1, characterized in that, Equipment fault identifiers are generated from the equipment operation data of the AFC device, including: Retrieve the device operation data of the AFC device; the device operation data includes operation status data, device working data, and transaction status data; Analyze equipment operation data to determine if an equipment failure has occurred; if yes, generate an equipment failure identifier; otherwise, do not generate an equipment failure identifier; the equipment failure identifier includes a failure identifier code, AFC equipment number, failure trigger time, and failure trigger type.

3. The method for handling AFC fault reporting in rail transit according to claim 1, characterized in that, Extract equipment timing data based on equipment fault identifiers, including: The timing period for the AFC device is preset; the timing period includes the time period before the fault and the time period after the fault. Using the fault trigger time in the equipment fault identifier as the time limit, the equipment time sequence data is extracted from the equipment operation data of the AFC equipment according to the time sequence time period.

4. The method for processing AFC fault reporting in rail transit according to claim 1, characterized in that, Equipment fault information is obtained by analyzing equipment timing data, including: Invoke a pre-trained fault diagnosis model; wherein the fault diagnosis model is trained based on an artificial intelligence model; The equipment timing data is input into the fault diagnosis model to generate equipment fault information, which includes fault type, fault trigger time and fault root cause.

5. The method for processing AFC fault reporting in rail transit according to claim 1, characterized in that, The type of fault cluster is determined based on equipment fault information, including: Retrieve equipment fault information; Based on equipment fault information, fault cluster types are determined through hierarchical cluster identification; the cluster identification order is as follows: equipment cluster, site cluster, and line cluster.

6. The method for processing AFC fault reporting in rail transit according to claim 5, characterized in that, The faulty cluster type is determined by performing a step-by-step cluster identification process, including: Determine if there is an AFC device in the device cluster that matches the device fault information; if yes, associate the device fault information with the device cluster and perform site cluster determination; otherwise, determine it as a single point of failure. Determine if there are two device clusters in the site cluster with the same device fault information; if yes, associate the device fault information with the site cluster and determine the line cluster; otherwise, determine it as a device cluster fault. Determine if there are two site clusters with identical equipment fault information in the line cluster; if yes, associate the equipment fault information with the line cluster and determine it as a line cluster fault; otherwise, determine it as a site cluster fault.

7. A method for processing AFC fault reporting in rail transit according to claim 6, characterized in that, The criteria for determining whether equipment fault information is consistent include the fault type and the fault trigger time.

8. The method for processing AFC fault reporting in rail transit according to claim 1, characterized in that, After classifying and reporting AFC faults based on equipment fault information and fault cluster type, the process includes: AFC fault classification is performed based on equipment fault information and fault cluster type to obtain AFC fault level; whereby AFC fault level is determined according to the scope of AFC fault impact. Report equipment fault information, fault cluster type, and AFC fault level.

9. A processing system for reporting AFC faults in rail transit, used to execute the processing method for reporting AFC faults in rail transit as described in any one of claims 1 to 1, characterized in that, It includes a fault reporting module and several reporting sub-modules connected to it; Several reporting sub-modules: used to generate equipment fault identifiers based on equipment operation data, and extract equipment timing data based on equipment fault identifiers; Equipment fault information is obtained by analyzing equipment time-series data, and the fault cluster type is identified based on the equipment fault information. as well as, Used for reporting and processing AFC fault classification based on equipment fault information and fault cluster type; Fault reporting module: Used to determine whether the fault cluster type is a line cluster fault based on the received reporting results; If yes, then update and report the fault cluster type and AFC fault rating; No, report it directly.

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