A smart rail transit subway operation and maintenance management method, system and electronic equipment

Abstract

The present invention discloses a method, system, and electronic equipment for intelligent rail transit subway operation and maintenance management, relating to the field of operation and maintenance management technology. The method comprises: determining service forms and establishing multidimensional management targets for target scenarios based on an operation and maintenance checklist; constructing a subway full-factor data acquisition network to collect operation data and station data in real time and mark the data sources; establishing a mapping relationship between the operation data and station data and target line network management nodes, and importing the data into a digital model for construction; performing operation and maintenance analysis in the digital model based on the multidimensional management targets, identifying key management nodes, and obtaining node state vector sets; matching target operation and maintenance strategies based on the analysis results, and performing operation and maintenance management based on the strategies. This achieves the technical effects of online monitoring, complete monitoring, and good early warning capabilities.

Description

Technical Field

[0001] The present invention relates to the field of operation and maintenance management technology, and in particular to a method, system and electronic equipment for operation and maintenance management of a smart rail transit subway. Background Art

[0002] Existing operations and maintenance management often relies on a single data dimension, analyzing and making decisions based on isolated subsystems. This lacks a comprehensive and in-depth understanding of the overall operational status of the subway, and lacks integrated analysis of the adaptability of different stations or lines, resulting in poor integration. Technical issues include low scenario integration, a lack of targetedness and refinement, and inaccurate operations and maintenance analysis. Summary of the Invention

[0003] The present invention provides a smart rail transit subway operation and maintenance management method, system and electronic equipment to solve the technical problems in the existing technology such as low scene integration, lack of specificity and refinement, and inaccurate operation and maintenance analysis, and achieve the technical effect of improving the integration of operation and maintenance decision analysis, enhancing the accuracy of understanding of rail transit status, and improving the accuracy of management.

[0004] In a first aspect, the present invention provides a method for smart rail transit subway operation and maintenance management, wherein the method comprises:

[0005] According to the operation and maintenance list, determine the service form of operation and maintenance management, and establish multi-dimensional management goals for the target scenario corresponding to the service form.

[0006] A subway full-factor data collection network is constructed to collect operation data and station data of the target line network in real time, wherein the operation data and the station data have data source tags.

[0007] According to the data source tag, a mapping relationship between the operation data and the site data and the target line network management node is established, and the operation data and the site data are imported into the construction digital model of the target line network according to the mapping relationship.

[0008] Based on the multi-dimensional management target, digital operation and maintenance analysis is performed on the construction digital model to identify key management nodes in the target line network management nodes, and obtain a set of key management nodes and a corresponding set of node status vectors.

[0009] The key management node set and the corresponding node state vector set are used as analysis results, a target operation and maintenance strategy is obtained by matching, and operation and maintenance management is performed based on the target operation and maintenance strategy.

[0010] In a second aspect, the present invention further provides a smart rail transit subway operation and maintenance management system, wherein the system includes:

[0011] The management target determination module is used to determine the service form of operation and maintenance management based on the operation and maintenance list, and establish multi-dimensional management targets for target scenarios corresponding to the service form.

[0012] The IoT data acquisition module is used to build a subway full-factor data acquisition network to collect real-time operation data and station data of the target line network, wherein the operation data and the station data have data source tags.

[0013] The data mapping and importing module is used to establish a mapping relationship between the operation data and the site data and the target line network management node according to the data source tag, and import the operation data and the site data into the construction digital model of the target line network according to the mapping relationship.

[0014] The key node identification module is used to perform digital operation and maintenance analysis on the construction digital model based on the multi-dimensional management target, identify the key management nodes in the target line network management nodes, and obtain the key management node set and the corresponding node state vector set.

[0015] The strategy matching and management module is used to match and obtain the target operation and maintenance strategy based on the analysis results of the key management node set and the corresponding node state vector set, and perform operation and maintenance management based on the target operation and maintenance strategy.

[0016] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement a smart rail transit subway operation and maintenance management method provided by the present invention.

[0017] The present invention discloses a method, system and electronic equipment for subway operation and maintenance management for smart rail transit, including: determining the service form of operation and maintenance management according to the operation and maintenance list, and establishing multi-dimensional management goals of target scenarios corresponding to the service form; constructing a subway full-factor data acquisition network, and collecting real-time operation data and station data of the target line network, wherein the operation data and station data have data source tags; establishing a mapping relationship between the operation data and station data and the target line network management node according to the data source tags, and importing the operation data and station data into the construction digital model of the target line network according to the mapping relationship; based on the multi-dimensional management goals, Conduct digital operation and maintenance analysis, identify key management nodes in the target line network management nodes, obtain key management node sets and corresponding node state vector sets; use key management node sets and corresponding node state vector sets as analysis results, match and obtain target operation and maintenance strategies, and conduct operation and maintenance management based on the target operation and maintenance strategies. The present invention discloses a smart rail transit subway operation and maintenance management method, system and electronic equipment that solve the technical problems of low scene integration, lack of pertinence and refinement, and inaccurate operation and maintenance analysis, and achieves the technical effect of improving the integration of operation and maintenance decision analysis, enhancing the accuracy of understanding of rail transit status, and improving the accuracy of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a smart rail transit subway operation and maintenance management method according to the present invention;

[0019] Figure 2 This is a schematic diagram of the structure of a smart rail transit subway operation and maintenance management system of the present invention;

[0020] Figure 3 Schematic diagram of the structure of an exemplary electronic device of the present invention.

[0021] Explanation of the accompanying symbols: management target determination module 11, Internet of Things data acquisition module 12, data mapping and import module 13, key node identification module 14, strategy matching and management module 15, processor 31, memory 32, input device 33, output device 34. DETAILED DESCRIPTION

[0022] The technical solutions provided in the embodiments of the present invention are designed to address the technical problems of the prior art, such as low level of scenario integration, lack of specificity and refinement, and inaccurate operation and maintenance analysis. The overall approach adopted is as follows:

[0023] First, based on the O&M checklist, the service forms of O&M management are determined, and multidimensional management objectives for target scenarios are established for each service form. Next, a comprehensive subway data collection network is constructed to collect real-time operational and station data from the target network. These data are labeled with data sources. Based on the data source labels, a mapping relationship is established between the operational and station data and the target network's management nodes. This data is then imported into the target network's digital construction model. Next, based on the multidimensional management objectives, digital O&M analysis is performed on the construction digital model to identify key management nodes within the target network's management nodes. The set of key management nodes and their corresponding node state vectors are then obtained. Furthermore, using the key management node set and its corresponding node state vector set as the analysis results, the target O&M strategy is matched and derived. Finally, O&M management is executed based on the target O&M strategy.

[0024] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0025] Example 1: Figure 1 The figure is a flow chart of a smart rail transit subway operation and maintenance management method according to the present invention, wherein the method comprises:

[0026] According to the operation and maintenance list, determine the service form of operation and maintenance management, and establish multi-dimensional management goals for the target scenario corresponding to the service form.

[0027] Specifically, the operation and maintenance list refers to a list that lists in detail various types of equipment and facilities in the subway system and the corresponding operation and maintenance requirements, such as operation and maintenance task allocation, task orders, operation and maintenance project lists, etc. It covers all the maintenance contents required for subway operations and is the basic basis for determining the form of operation and maintenance management services.

[0028] Specifically, service forms in subway operation and maintenance management mainly include different types such as preventive maintenance services and responsive maintenance services. Different service forms correspond to different operation and maintenance strategies and management priorities. For example, for some key equipment, such as the core components of subway trains, preventive maintenance services may be more suitable to ensure their safe and stable operation; while for some non-critical equipment, responsive maintenance services may be considered to reduce costs.

[0029] Specifically, the multidimensional management goal is to quantify and standardize operation and maintenance management from multiple dimensions, such as safety indicators (used to measure the degree of protection of subway operation safety during the operation and maintenance process), efficiency indicators (reflecting the efficiency of operation and maintenance work), and economic indicators (involving the control of operation and maintenance costs, etc.). By establishing a multidimensional management goal system that includes these indicators, subway operation and maintenance management can be evaluated and guided more comprehensively and systematically.

[0030] In some embodiments, based on the operation and maintenance list, a service form of operation and maintenance management is determined, and a multi-dimensional management goal of a target scenario is established corresponding to the service form, including:

[0031] Determine the service form of operation and maintenance management based on the operation and maintenance list, and the service form includes preventive maintenance service and responsive maintenance service; determine the management target set including safety indicators, efficiency indicators and economic indicators based on the service form; fuse and deduplicate the management target set to reduce redundancy of management targets and form the multi-dimensional management target.

[0032] Specifically, preventive maintenance service refers to the maintenance of equipment at predetermined time intervals or operating cycles to prevent equipment failure; responsive maintenance service refers to the repair service performed after equipment failure occurs.

[0033] Specifically, the management goal set is a series of quantitative goals determined based on the service form, including safety indicators (used to measure the degree to which operation and maintenance management ensures the safety of subway operations), efficiency indicators (reflecting the efficiency of operation and maintenance work) and economic indicators (involving the control of operation and maintenance costs, etc.). By integrating and deduplicating these indicators, the redundancy of management goals can be reduced, forming more refined and effective multi-dimensional management goals.

[0034] Specifically, by analyzing the O&M requirements of each piece of equipment and facility on the O&M checklist, we can determine which equipment is suitable for preventive maintenance and which for responsive maintenance. For example, for critical subway equipment like train braking systems and signaling systems, which are crucial to subway operational safety, a combination of preventive and responsive maintenance is typically employed. Inspections and maintenance are performed at predetermined intervals, with intervention when risks arise to ensure normal operation. Meanwhile, for non-critical equipment like station lighting, responsive maintenance may be employed, with repairs carried out only after a failure occurs, reducing O&M costs.

[0035] Furthermore, after determining the service model, a management objective set consisting of safety, efficiency, and economic indicators is determined based on the service model. For example, for preventive maintenance services, safety indicators may include the coverage rate of regular inspections and the effectiveness of fault prevention; efficiency indicators may include the completion time of maintenance work and the work efficiency of maintenance personnel; and economic indicators involve controlling maintenance costs, such as the procurement cost of spare parts and the cost of using maintenance tools. Finally, the management objective set is fused and deduplicated to reduce redundancy. For example, if both safety and efficiency indicators involve monitoring the operating status of equipment, these two indicators can be fused (e.g., selecting the sampling rate of the one with the higher sampling requirement) to avoid duplicate monitoring, thereby forming a more refined and effective multi-dimensional management objective.

[0036] The above-mentioned method and steps, by determining the service form according to the operation and maintenance list, can make the operation and maintenance management more in line with the actual needs of the subway system and improve the pertinence and effectiveness of operation and maintenance management; determining the management goal set including safety, efficiency and economic indicators can evaluate and guide operation and maintenance management from multiple dimensions, which helps to improve the overall level of operation and maintenance management; by fusing and deduplicating the management goal set, the redundancy of management goals can be reduced, making the management goals more refined and effective, reducing the complexity and cost of operation and maintenance management, and improving the efficiency of operation and maintenance management.

[0037] A subway full-factor data collection network is constructed to collect operation data and station data of the target line network in real time, wherein the operation data and the station data have data source tags.

[0038] Specifically, the subway full-factor data collection network refers to a network that can collect various data in the subway system through sensors and monitoring equipment installed at various locations in the subway system. For example, the track status monitoring terminal installed on the subway track can monitor the wear and stress conditions of the track in real time; the vehicle operation data collection terminal installed on the subway vehicle can collect data such as the vehicle speed, acceleration, and braking system status.

[0039] Specifically, operation data includes operating status data of subway vehicles and equipment, such as speed, acceleration, energy consumption, etc., as well as operating status data of track, signal and other systems (such as current, voltage, wind speed, wind pressure); station data covers various information within the station, such as passenger flow, equipment operating status (such as the working conditions of gates, escalators, etc.), and environmental data (such as temperature, humidity, air quality, etc.).

[0040] Specifically, data source labeling refers to the annotation of collected data to indicate the source of the data, such as which sensor, subway station or subway car the data comes from, so that the data can be accurately analyzed and processed later.

[0041] In some embodiments, a subway full-factor data collection network is constructed to collect operation data and station data of a target line network in real time, wherein the operation data and the station data have data source tags, including:

[0042] Interact with the digital model of the construction to extract the current status collection network; combine the multi-dimensional management objectives to perform blind spot analysis on the current status collection network and determine the difference collection network; combine the difference collection network to access the subway vehicle operation data collection terminal, the track status monitoring terminal and the site equipment monitoring terminal through the Internet of Things communication terminal to form the subway full-factor data collection network; configure the monitoring granularity parameters of the subway full-factor data collection network to perform real-time collection of the operation data and the site data.

[0043] Specifically, the construction of a digital model refers to a virtual model of the subway system constructed using digital technology, such as a BIM-based and civil engineering Revit model, which contains detailed information on various aspects of the subway lines, stations, equipment, etc., and is a true reflection of the subway system.

[0044] Specifically, by interacting with the construction digital model, information about the existing data collection network is obtained, including the location of the collection point, the type of collection equipment, the type of collected data, etc.; then, the existing collection network is analyzed to find out the data collection blind spots, that is, the data areas or data types that have not been collected, and determine the difference collection network. In other words, the difference collection network refers to the collection network that needs to be added based on the results of the blind spot analysis to supplement the deficiencies of the existing collection network.

[0045] Specifically, the IoT communication terminal connects various collection terminals wirelessly or wired, thereby achieving interconnection between devices and forming a complete data collection network; the monitoring granularity parameters refer to the parameters for setting the accuracy and frequency of data collection, such as the time interval for collecting data, the accuracy of collecting data, etc. The setting of the monitoring granularity parameters is adjusted according to actual needs.

[0046] Specifically, based on the difference collection network, the subway vehicle operation data collection terminal, track status monitoring terminal and station equipment monitoring terminal are connected through the Internet of Things communication terminal to form a subway full-factor data collection network, including the replacement, reset, maintenance and new addition of existing collection equipment or sensors.

[0047] The above method steps, by interacting with the construction digital model and extracting information from the current data collection network, can fully understand the current data collection situation in the subway system and provide accurate basic data for subsequent blind spot analysis. Secondly, combining blind spot analysis with multi-dimensional management objectives can ensure that the construction of the data collection network is more scientific and reasonable, meeting the various needs of subway operation and maintenance management. By determining the difference collection network, the deficiencies of the current collection network can be targeted to improve the comprehensiveness and accuracy of data collection. Thirdly, by connecting various collection terminals through IoT communication terminals, a full-factor subway data collection network can be formed, which can realize real-time data transmission and sharing, and improve the efficiency and reliability of data collection. Finally, by configuring monitoring granularity parameters, the accuracy and frequency of data collection can be flexibly adjusted to meet the needs of different data types and application scenarios, further improving the quality and effectiveness of data collection.

[0048] According to the data source tag, a mapping relationship between the operation data and the site data and the target line network management node is established, and the operation data and the site data are imported into the construction digital model of the target line network according to the mapping relationship.

[0049] Specifically, the target network management node refers to the specific node that needs to be managed in the subway network, such as stations, sections, equipment, line locations, etc.

[0050] Specifically, first, based on the data source tag, a mapping relationship between operation data and site data and the target network management node is established, and the collected operation data and site data are corresponded to the specific nodes in the subway network so that these data can be accurately imported into the construction digital model; for example, a sensor collects the cable temperature at a certain location in a certain station, and it is necessary to establish a mapping relationship between this data and the management node of the station, so that the passenger flow data can be accurately matched with the station in the construction digital model; then, based on the established mapping relationship, the operation data and site data are imported into the construction digital model, so that the construction digital model can reflect the operation status and data status of each node in the subway network in real time, providing a basis for subsequent digital operation and maintenance analysis.

[0051] Based on the multi-dimensional management target, digital operation and maintenance analysis is performed on the construction digital model to identify key management nodes in the target line network management nodes, and obtain a set of key management nodes and a corresponding set of node status vectors.

[0052] Specifically, key management nodes refer to nodes that are determined through analysis to play a key role in subway operation and maintenance management and require operation and maintenance disposal; node state vectors refer to vectorized representations of node status. By analyzing the detailed data of key management nodes, their node state vector sets can be obtained to comprehensively reflect the operating status.

[0053] By identifying key management nodes, we can more accurately locate the parts of the subway system that require focus and maintenance, thereby optimizing the allocation of operation and maintenance resources and improving operation and maintenance efficiency. Obtaining the node status vector set of key management nodes provides a detailed basis for the subsequent formulation of operation and maintenance strategies, enabling operation and maintenance personnel to take corresponding measures based on the specific status of the nodes.

[0054] In some embodiments, based on the multi-dimensional management objectives, digital operation and maintenance analysis is performed on the construction digital model to identify key management nodes among the target line network management nodes, including:

[0055] By performing cumulative analysis on the accumulated load and usage frequency of the target line network management nodes, fatigue nodes are determined; by performing conflict analysis on the neighborhood consistency of the comparison data, sensing deviation nodes are determined; by performing transient analysis on the real-time status changes of the target line network management nodes, actual abnormal nodes are determined; by performing concentration analysis on the risk concentration of the nodes, high-risk nodes are determined.

[0056] Specifically, cumulative load refers to the total workload borne by a device or node within a certain period of time, such as the sum of the traction and braking forces borne by the traction motor of a subway vehicle during operation; usage frequency refers to the number of times a device or node is used or triggered within a unit of time, such as the number of times a ticket vending machine is used per day. By counting these data, the fatigue level of the device or node can be assessed, that is, long-term high load and high-frequency use may lead to device performance degradation or failure.

[0057] Specifically, first, the load data and usage frequency data of the target line network management nodes are extracted from the construction digital model. For example, for the traction motors of subway vehicles, their daily traction and braking force data are collected; for the ticket vending machines, their daily usage times are collected; then, the cumulative load and usage frequency of each node are accumulated and calculated. For example, the cumulative load of the traction motor can be accumulated by adding up the daily traction and braking force data to obtain the total load for one month or one year; the usage frequency of the ticket vending machine can be accumulated by adding up the daily usage times to obtain the total usage times for one month or one year; then, based on the accumulation results, a threshold is set to identify fatigue nodes. For example, if the cumulative load of the traction motor exceeds a certain set threshold (such as 1,000 tons·kilometers), or the usage frequency of the ticket vending machine exceeds a certain threshold (such as 1,000 times per day), these nodes are identified as fatigue nodes; in summary, through cumulative analysis, the fatigue level of the equipment can be discovered in advance, maintenance can be arranged in time, and sudden failure of the equipment due to excessive fatigue can be avoided.

[0058] Specifically, neighborhood consistency means that the data of adjacent nodes should be consistent or similar under normal circumstances; for example, two adjacent temperature sensors should measure similar temperature values ​​under normal circumstances. If the data of a node is significantly different from the data of other adjacent nodes, there may be sensing bias.

[0059] Specifically, first, sensor data from the target network management node is extracted from the digital construction model, including data from adjacent nodes. For example, for temperature sensors in cables or cabinets, the measured temperature data is collected, along with data from adjacent sensors. The data from each node is then compared with that of its adjacent nodes, the difference is calculated, and a threshold is set based on the difference to identify sensor deviation nodes. For example, if the temperature measured by a temperature sensor differs from the average temperature of other adjacent sensors by more than a set threshold (e.g., ±2°C), the node is identified as a sensor deviation node. Through conflict analysis, deviations in sensor data can be detected promptly, ensuring data accuracy and promptly troubleshooting sensor failures or data transmission issues to prevent erroneous data from affecting operation and maintenance decisions.

[0060] Specifically, transient analysis refers to monitoring and analyzing the real-time status data of nodes to identify abnormal fluctuations in the data. For example, when the braking system of a subway vehicle is operating normally, the braking pressure should be maintained within a certain range. If there is a sudden and large fluctuation, it may indicate that there is an abnormality in the braking system.

[0061] Specifically, real-time status data of target network management nodes is extracted from the digital model. For example, for the braking system of a subway vehicle, real-time data on brake pressure is collected. This real-time status data is then monitored, and the real-time rate of change of brake pressure is calculated. Then, abnormal nodes are identified based on a set threshold. For example, if the real-time rate of change of brake pressure exceeds a set threshold (e.g., ±10%), the node is identified as an abnormal node. Transient analysis allows for real-time monitoring of equipment operating status, timely detection of abnormal fluctuations, and timely implementation of preventive measures to prevent equipment failures.

[0062] Specifically, risk concentration refers to the concentration of risk factors at a node. For example, if a certain location on a cable rack or a cabinet in an equipment room has a high density (in terms of type, frequency, or frequency) of risks and anomalies, the concentration of these factors will result in a high risk concentration in that area.

[0063] Specifically, first, the risk factor data of the target line network management nodes are extracted from the construction digital model, including risk factor data of multiple dimensions, such as equipment aging, historical failure rate, local high temperature and other data; then, based on the above-mentioned risk factor data obtained, the risk factors of each node are comprehensively analyzed to calculate the risk concentration; illustratively, the risk concentration of each node is calculated by the weighted average method, and a threshold is set according to the risk concentration to identify high-risk nodes; for example, if the risk concentration of a station exceeds the set threshold (such as 0.8), the station is identified as a high-risk node; through concentration analysis, a comprehensive multi-dimensional analysis can be provided, high-risk nodes can be identified in advance, and timely measures can be taken to reduce risks.

[0064] In some embodiments, obtaining a set of key management nodes and a corresponding set of node status vectors includes:

[0065] The cumulative analysis results, conflict analysis results, transient analysis results and centralization analysis results corresponding to the fatigue nodes, the sensor deviation nodes, the actual abnormal nodes and the high-risk nodes are obtained, and output as an initial analysis result set; the initial analysis result set is dimensionlessly processed, and based on the dimensionlessly processed initial analysis result set, a corresponding multidimensional vector is constructed and stored in the node state vector set corresponding to the key management node set.

[0066] Specifically, the key management node set refers to a set of nodes that play a key role in subway operation and maintenance management, such as fatigue nodes, sensor deviation nodes, real-time abnormal nodes, and high-risk nodes; the node state vector set refers to a set of vectorized representations that describe the status of these key management nodes. Each vector contains a set of parameters that can reflect the status characteristics of the node in different aspects, such as the operating temperature, pressure, vibration, etc. of the equipment.

[0067] Specifically, first, the analysis results corresponding to the key management nodes are obtained, including the cumulative analysis results of fatigue nodes, the conflict analysis results of sensor deviation nodes, the transient analysis results of actual abnormal nodes, and the centralized analysis results of high-risk nodes. These results are combined to form an initial analysis result set. For example, for a fatigue node, its cumulative analysis results include the specific values ​​of the cumulative load and usage frequency; for a sensor deviation node, its conflict analysis results include the data difference values ​​with other adjacent nodes; then, the initial analysis result set is dimensionlessly processed, that is, data of different dimensions are converted into dimensionless data through certain mathematical transformations to facilitate comparison and analysis. For example, data such as cumulative load and usage frequency are divided by their respective reference values ​​to obtain dimensionless index values; finally, based on the initial analysis result set after dimensionless processing, a corresponding multidimensional vector is constructed and stored in the node state vector set corresponding to the key management node set.

[0068] The above method steps, by obtaining the analysis results of key management nodes and forming an initial analysis result set, can provide comprehensive data support for subsequent operation and maintenance decisions; dimensionless processing of the initial analysis result set can eliminate the influence of different dimensions, making the data more comparable and facilitating comprehensive analysis and evaluation.

[0069] The key management node set and the corresponding node state vector set are used as analysis results, a target operation and maintenance strategy is obtained by matching, and operation and maintenance management is performed based on the target operation and maintenance strategy.

[0070] Specifically, the target operation and maintenance strategy refers to the preset operation and maintenance plan that is optimal for the node, obtained by matching the state vector set of key management nodes, including preventive maintenance, emergency repairs, optimization and adjustment measures; the new operation and maintenance work order refers to the specific operation and maintenance task order generated according to the target operation and maintenance strategy, which is used to guide operation and maintenance personnel to perform actual operations.

[0071] Specifically, the importance and urgency of new operation and maintenance work orders are evaluated to determine their order in the pending work order list; the pending work order list refers to a collection of operation and maintenance work orders waiting to be executed, sorted by priority, and used to reasonably arrange operation and maintenance tasks.

[0072] In some embodiments, the key management node set and the corresponding node state vector set are used as analysis results to match and obtain a target operation and maintenance strategy, and operation and maintenance management is performed based on the target operation and maintenance strategy, further comprising:

[0073] Based on the target operation and maintenance strategy, a corresponding new operation and maintenance work order is generated; a priority evaluation is performed on the new operation and maintenance work order, and according to the priority evaluation result, the new operation and maintenance work order is inserted into the list of pending work orders; and task issuance and operation and maintenance processing are performed according to the list of pending work orders.

[0074] Specifically, first, the target operation and maintenance strategy is matched based on the analysis results of the key management node set and the corresponding node state vector set. For example, if the node state vector of a key management node shows that its equipment temperature has risen abnormally and the vibration amplitude exceeds the normal range, then the matched target operation and maintenance strategy may be to immediately inspect and repair the equipment; then, based on the target operation and maintenance strategy, the corresponding new operation and maintenance work order is generated, and the content, requirements, time nodes and other information of the operation and maintenance task are recorded in detail; then, the priority of the new operation and maintenance work order is evaluated, that is, the priority of the work order is determined according to its importance and urgency. For example, a fault repair work order that may affect the safe operation of the subway will be given a higher priority, while some routine preventive maintenance work orders will have a relatively low priority; finally, based on the priority evaluation results, the new operation and maintenance work order is inserted into the list of pending work orders, and tasks are issued and operation and maintenance are handled in order of priority, so as to ensure that the operation and maintenance work is carried out efficiently and orderly.

[0075] In some embodiments, the target operation and maintenance strategy includes: a preventive maintenance plan based on cumulative analysis, a monitoring maintenance plan based on conflict analysis, an emergency maintenance plan based on transient analysis, and a configuration optimization maintenance plan based on centralization analysis.

[0076] Specifically, a preventive maintenance plan is a scheduled maintenance schedule for fatigue nodes based on an analysis of their cumulative load and usage frequency. For example, if the cumulative load on a traction motor exceeds a set threshold, a deep inspection and maintenance is scheduled for the following month to prevent equipment failure due to fatigue.

[0077] Specifically, the monitoring and maintenance plan involves developing a monitoring and calibration plan for sensor deviation nodes based on conflict analysis results with data from other nodes. For example, if a temperature sensor's measurement value deviates significantly from that of other adjacent sensors, a sensor calibration and data monitoring plan is planned for this week.

[0078] Specifically, emergency maintenance plans are designed to address abnormal nodes based on transient analysis of their real-time state changes, and to formulate emergency maintenance measures. For example, if the pressure in a braking system suddenly drops, the emergency plan is immediately activated, and technicians are dispatched to conduct on-site inspections and repairs.

[0079] Specifically, configuration optimization and maintenance plans are developed for high-risk nodes based on risk concentration analysis. For example, if a certain equipment cabinet or cable rack has a high risk concentration, the frequency of equipment inspections can be increased, and equipment or cable configurations can be optimized to reduce risk.

[0080] By using the above methods and steps, corresponding operation and maintenance strategies can be formulated for different types of node states, which can accurately solve various problems in the subway system and improve the pertinence and effectiveness of operation and maintenance management.

[0081] In summary, the intelligent rail transit subway operation and maintenance management method provided by the present invention has the following technical effects:

[0082] According to the operation and maintenance list, the service form of operation and maintenance management is determined, and multi-dimensional management goals of the target scenario are established corresponding to the service form; a subway full-factor data collection network is constructed to collect operation data and station data of the target line network in real time, wherein the operation data and station data have data source tags; according to the data source tags, a mapping relationship between operation data and station data and target line network management nodes is established, and the operation data and station data are imported into the construction digital model of the target line network according to the mapping relationship; based on the multi-dimensional management goals, digital operation and maintenance analysis is performed on the construction digital model, key management nodes in the target line network management nodes are identified, and the key management node set and the corresponding node state vector set are obtained; with the key management node set and the corresponding node state vector set as the analysis results, the target operation and maintenance strategy is matched and obtained, and operation and maintenance management is performed based on the target operation and maintenance strategy, thereby achieving the technical effect of improving the integration of operation and maintenance decision analysis, strengthening the accuracy of understanding of rail transit status, and improving the accuracy of management.

[0083] Example 2: Figure 2 This is a schematic diagram of the structure of a smart rail transit subway operation and maintenance management system of the present invention. For example, Figure 1 The flow chart of a smart rail transit subway operation and maintenance management method of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0084] Based on the same concept as the smart rail transit subway operation and maintenance management method in the embodiment, the present invention also provides a smart rail transit subway operation and maintenance management system including:

[0085] The management target determination module 11 is used to determine the service form of operation and maintenance management according to the operation and maintenance list, and establish multi-dimensional management targets of target scenarios corresponding to the service form.

[0086] The IoT data acquisition module 12 is used to build a subway full-factor data acquisition network to collect operation data and station data of the target line network in real time, wherein the operation data and the station data have data source tags.

[0087] The data mapping and importing module 13 is used to establish a mapping relationship between the operation data and the site data and the target line network management node according to the data source tag, and import the operation data and the site data into the construction digital model of the target line network according to the mapping relationship.

[0088] The key node identification module 14 is used to perform digital operation and maintenance analysis on the construction digital model based on the multi-dimensional management target, identify the key management nodes in the target line network management nodes, and obtain the key management node set and the corresponding node state vector set.

[0089] The strategy matching and management module 15 is used to match and obtain a target operation and maintenance strategy based on the analysis results of the key management node set and the corresponding node state vector set, and perform operation and maintenance management based on the target operation and maintenance strategy.

[0090] In some embodiments, the management target determination module 11 includes:

[0091] The service form determination unit is used to determine the service form of operation and maintenance management according to the operation and maintenance list, and the service form includes preventive maintenance service and responsive maintenance service.

[0092] The management objective set determining unit is used to determine a management objective set including security indicators, efficiency indicators and economic indicators according to the service form.

[0093] The multi-dimensional management target forming unit is used to perform fusion and deduplication processing on the management target set to reduce management target redundancy and form the multi-dimensional management target.

[0094] In some embodiments, the IoT data acquisition module 12 includes:

[0095] The current situation acquisition network extraction unit is used to interact with the construction digital model and extract the current situation acquisition network.

[0096] The difference collection network determining unit is used to perform blind spot analysis of the current collection network in combination with the multi-dimensional management target, and determine the difference collection network.

[0097] The subway full-factor data collection network forming unit is used to combine the above-mentioned difference collection network and access the subway vehicle operation data collection terminal, the track status monitoring terminal and the station equipment monitoring terminal through the Internet of Things communication terminal to form the above-mentioned subway full-factor data collection network.

[0098] The monitoring granularity parameter configuration unit is used to configure the monitoring granularity parameters of the subway full-factor data collection network to perform real-time collection of the operation data and the station data.

[0099] In some embodiments, the key node identification module 14 includes:

[0100] The fatigue node determination unit is used to determine the fatigue node by performing cumulative analysis on the accumulated load and usage frequency of the target line network management node.

[0101] The sensing deviation node determination unit is used to perform conflict analysis by comparing the neighborhood consistency of data and determine the sensing deviation node.

[0102] The real-time abnormal node determination unit is used to perform transient analysis by monitoring the real-time state changes of the target line network management node to determine the real-time abnormal node.

[0103] The high-risk node determination unit is used to perform concentration analysis by analyzing the risk concentration of the nodes and determine the high-risk nodes.

[0104] In some embodiments, the key node identification module 14 further includes:

[0105] The initial analysis result set acquisition unit is used to obtain the cumulative analysis results, conflict analysis results, transient analysis results and concentration analysis results corresponding to the fatigue node, the sensor deviation node, the actual abnormal node and the high-risk node, and output them as the initial analysis result set.

[0106] The node state vector set construction unit is used to perform dimensionless processing on the initial analysis result set, and construct a corresponding multidimensional vector based on the dimensionless initial analysis result set, and store it in the node state vector set corresponding to the key management node set.

[0107] In some embodiments, the policy matching and management module 15 includes:

[0108] A new operation and maintenance work order generation unit is added, which is used to generate a corresponding new operation and maintenance work order based on the target operation and maintenance strategy.

[0109] The priority evaluation and work order insertion unit is used to perform priority evaluation on the newly added operation and maintenance work order and insert the newly added operation and maintenance work order into the list of pending work orders according to the priority evaluation result.

[0110] The task issuing and operation and maintenance processing unit is used to issue tasks and perform operation and maintenance processing according to the list of work orders to be processed.

[0111] In some implementations, the target operation and maintenance strategy includes: a preventive maintenance plan based on cumulative analysis, a monitoring maintenance plan based on conflict analysis, an emergency maintenance plan based on transient analysis, and a configuration optimization maintenance plan based on centralization analysis.

[0112] Example 3, Figure 3 The schematic structural diagram of an exemplary electronic device provided for the present invention shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3As shown, the electronic device includes a processor 31, a memory 32, an input device 33 and an output device 34; the number of processors 31 in the electronic device can be one or more. Figure 3 Taking a processor 31 as an example, the processor 31, memory 32, input device 33 and output device 34 in the electronic device can be connected through a bus or other means. Figure 3 The bus connection is taken as an example.

[0113] Memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the intelligent rail transit subway operation and maintenance management method in an embodiment of the present invention. Processor 31 executes the software programs, instructions, and modules stored in memory 32 to perform various functional applications and data processing of the computer device, thereby implementing the intelligent rail transit subway operation and maintenance management method.

[0114] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the intelligent rail transit subway operation and maintenance management system described in embodiment two. For the sake of brevity of the specification, no further elaboration will be given here.

[0115] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A smart rail transit subway operation and maintenance management method, characterized in that: The method comprises: Determine the service form of operation and maintenance management based on the operation and maintenance list, and establish multi-dimensional management goals for target scenarios corresponding to the service form, including preventive maintenance services and responsive maintenance services; Build a subway full-factor data collection network to collect real-time operation data and station data for the target line network, where the operation data and station data are marked with data source tags. The operation data includes the operation status data of subway vehicles and equipment, and the station data includes various information such as passenger flow, equipment operation status, and environmental data within the station; Establishing a mapping relationship between the operation data and the station data and a target network management node according to the data source tag, and importing the operation data and the station data into a construction digital model of the target network according to the mapping relationship, wherein the target network management node refers to a specific node in the subway network that needs to be managed; Based on the multi-dimensional management goal, digital operation and maintenance analysis is performed on the construction digital model to identify key management nodes in the target line network management nodes, and obtain a set of key management nodes and a corresponding set of node state vectors; Taking the key management node set and the corresponding node state vector set as analysis results, matching and obtaining a target operation and maintenance strategy, and performing operation and maintenance management based on the target operation and maintenance strategy; Based on the multi-dimensional management objectives, digital operation and maintenance analysis is performed on the construction digital model to identify key management nodes among the target line network management nodes, including: Determine fatigue nodes by performing cumulative analysis on the accumulated load and usage frequency of the target network management nodes; By comparing the neighborhood consistency of the data, conflict analysis is performed to determine the sensing deviation node; By monitoring the real-time status changes of the target line network management node, transient analysis is performed to determine the actual abnormal node; Conduct concentration analysis by analyzing the risk concentration of nodes to identify high-risk nodes; The target operation and maintenance strategies include: preventive maintenance plans based on cumulative analysis, monitoring maintenance plans based on conflict analysis, emergency maintenance plans based on transient analysis, and configuration optimization maintenance plans based on centralized analysis.

2. The intelligent rail transit subway operation and maintenance management method according to claim 1, characterized in that: Based on the operation and maintenance checklist, determine the service form of operation and maintenance management, and establish multi-dimensional management goals for the target scenario corresponding to the service form, including: Determining a management objective set including safety indicators, efficiency indicators, and economic indicators based on the service form; The management objective set is subjected to fusion and deduplication processing to reduce redundancy of management objectives and form the multi-dimensional management objective.

3. The intelligent rail transit subway operation and maintenance management method according to claim 1, characterized in that: Construct a subway full-factor data collection network to collect real-time operation data and station data of the target line network, wherein the operation data and the station data have data source tags, including: Interact with the digital model to extract the current collection network; In combination with the multi-dimensional management objectives, a blind spot analysis of the current collection network is performed to determine a differential collection network; Combined with the difference collection network, the subway vehicle operation data collection terminal, the track status monitoring terminal and the station equipment monitoring terminal are connected through the Internet of Things communication terminal to form the subway full-factor data collection network; The monitoring granularity parameters of the subway full-factor data collection network are configured to collect the operation data and the station data in real time. The monitoring granularity parameters refer to parameters for setting the accuracy and frequency of data collection.

4. The intelligent rail transit subway operation and maintenance management method according to claim 1, characterized in that: Get the key management node set and the corresponding node status vector set, including: Obtaining cumulative analysis results, conflict analysis results, transient analysis results, and centrality analysis results corresponding to the fatigue node, the sensor deviation node, the actual abnormal node, and the high-risk node, and outputting them as an initial analysis result set; The initial analysis result set is dimensionally non-dimensionalized, and a corresponding multi-dimensional vector is constructed based on the dimensionally non-dimensionalized initial analysis result set, and stored in the node state vector set corresponding to the key management node set.

5. The intelligent rail transit subway operation and maintenance management method according to claim 1, characterized in that: The key management node set and the corresponding node state vector set are used as analysis results to match and obtain a target operation and maintenance strategy, and perform operation and maintenance management based on the target operation and maintenance strategy, further comprising: Based on the target operation and maintenance strategy, generate corresponding new operation and maintenance work orders; Performing a priority evaluation on the newly added operation and maintenance work order, and inserting the newly added operation and maintenance work order into a list of pending work orders based on the priority evaluation result; Issue tasks and perform maintenance based on the pending work order list.

6. A smart rail transit subway operation and maintenance management system, characterized in that: The system is used to execute the intelligent rail transit subway operation and maintenance management method according to any one of claims 1 to 5, and the system includes: A management target determination module is used to determine the service form of operation and maintenance management based on the operation and maintenance list, and establish multi-dimensional management targets for target scenarios corresponding to the service form; The IoT data collection module is used to build a subway full-factor data collection network and collect real-time operation data and station data of the target line network, wherein the operation data and the station data have data source tags; a data mapping and importing module, configured to establish a mapping relationship between the operation data and the site data and a target line network management node according to the data source tag, and import the operation data and the site data into a construction digital model of the target line network according to the mapping relationship; A key node identification module is used to perform digital operation and maintenance analysis on the construction digital model based on the multi-dimensional management target, identify key management nodes among the target line network management nodes, and obtain a set of key management nodes and a corresponding set of node state vectors; The strategy matching and management module is used to match and obtain the target operation and maintenance strategy based on the analysis results of the key management node set and the corresponding node state vector set, and perform operation and maintenance management based on the target operation and maintenance strategy.

7. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable instructions; The processor is configured to implement the intelligent rail transit subway operation and maintenance management method as described in any one of claims 1 to 5 when executing the executable instructions stored in the memory.

Images