Urban rail transit signal fault early warning method and system

Abstract

The invention discloses an urban rail transit signal fault early warning method and system, and belongs to the technical field of urban rail transit signals, and the method comprises the following steps: S1, collecting and processing the real-time data of urban rail transit signals; s2, training an urban rail transit signal fault prediction model, and predicting an urban rail transit signal fault based on the urban rail transit signal fault prediction model; and S3, when the urban rail transit signal has a fault behavior, automatically carrying out early warning and alarming, and notifying a manager to carry out timely maintenance management on the urban rail transit signal fault behavior. According to the invention, the problem of poor urban rail transit signal management effect caused by the fact that urban rail transit signal faults cannot be effectively predicted and early warned in the prior art is solved. According to the invention, effective prediction and early warning can be carried out on urban rail transit signal faults, the urban rail transit signal management effect can be improved, and the operation safety and reliability of urban rails are high.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rail transit signals, and specifically to an urban rail transit signal fault early warning method and system. Background Art

[0002] Due to various factors, such as equipment aging and human errors, the safe operation of rail transit is affected. Rail transit signal failures occur frequently in actual operations; these failures not only affect the normal operation of rail transit but also may pose safety hazards to passengers and the surrounding environment.

[0003] Chinese Patent with publication number CN113513963B discloses a measuring device for urban rail transit signal equipment, which can simply and effectively perform ranging inspections between two adjacent tracks and reduce the intervention of manual labor; however, this patent has the following defects:

[0004] Existing technologies cannot effectively predict and early warn urban rail transit signal failures, resulting in poor management effects of urban rail transit signals and low safety and reliability of urban rail operation. Summary of the Invention

[0005] The purpose of the present invention is to provide an urban rail transit signal fault early warning method and system, which can effectively predict and early warn urban rail transit signal failures, improve the management effect of urban rail transit signals, and make the safety and reliability of urban rail operation high, solving the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An urban rail transit signal fault early warning method includes the following steps:

[0008] S1. Data collection and processing: Based on intelligent collection devices, real-time monitoring and continuous collection of train operation data, signal lamp status data, line facility data, and station environment data are carried out to determine the real-time data of urban rail transit signals. The real-time data of urban rail transit signals is cleaned and converted, and the real-time data of urban rail transit signals is stored.

[0009] S2. Model training and fault prediction: Train an urban rail transit signal fault prediction model, deploy the urban rail transit signal fault prediction model in an actual urban rail transit signal fault prediction application, analyze the real-time data of urban rail transit signals based on the urban rail transit signal fault prediction model, predict whether there are fault behaviors in the urban rail transit signals, and determine the urban rail transit signal fault prediction result.

[0010] S3, Fault Warning Management: When there is a fault in the urban rail transit signal, an early warning alarm is automatically issued, and the management personnel are notified to promptly maintain and manage the urban rail transit signal fault, and the urban rail transit signal fault is promptly eliminated.

[0011] Preferably, in S1, collecting real-time data of urban rail transit signals includes:

[0012] Based on sensors, the departure time, expected arrival time, actual arrival time, traveling distance, traveling speed, traveling position, and passenger capacity of urban rail trains are monitored in real time to obtain train operation data;

[0013] Based on sensors, the on-off state, display state, and indication direction of urban rail transit signal machines are monitored in real time to obtain signal machine state data;

[0014] Based on sensors, the positions, states, and capacities of urban rail sections, turnouts, tunnels, and bridge facilities are monitored in real time to obtain line facility data;

[0015] Based on sensors, the temperature, humidity, and noise parameters in urban rail stations are monitored in real time to obtain station environment data;

[0016] Among them, based on the train operation data, signal machine state data, line facility data, and station environment data, the real-time data of urban rail transit signals is determined.

[0017] Preferably, in S1, processing the real-time data of urban rail transit signals includes:

[0018] Cleaning the real-time data of urban rail transit signals;

[0019] Checking whether there are duplicate values, missing values, and abnormal values in the real-time data of urban rail transit signals, and processing the duplicate values, missing values, and abnormal values existing in the real-time data of urban rail transit signals;

[0020] Among them, for duplicate values, the duplicate values existing in the real-time data of urban rail transit signals are deleted, and only unique data records are retained;

[0021] Among them, for missing values, if the missing values have little overall impact on the fault warning of urban rail transit signals, the missing values are directly deleted. If the missing values have a large overall impact on the fault warning of urban rail transit signals, filling or interpolation methods are used to fill or interpolate the missing values to make the missing values complete;

[0022] Among them, for outliers, if the overall impact of the outliers on the fault warning of urban rail transit signals is small, the outliers are directly deleted. If the overall impact of the outliers on the fault warning of urban rail transit signals is large, replacement or correction methods are used to replace or correct the outliers to make them normal.

[0023] Preferably, in the step S1, when processing the real-time data of urban rail transit signals, it further includes:

[0024] Converting the format and type of the real-time data of urban rail transit signals, removing the dimension differences between the real-time data of urban rail transit signals, and determining the standardized real-time data of urban rail transit signals;

[0025] Integrating the standardized real-time data of urban rail transit signals, integrating the standardized real-time data of urban rail transit signals into a unified view, and verifying the integrated real-time data of urban rail transit signals to check the accuracy of the integration of the real-time data of urban rail transit signals;

[0026] Storing the integrated real-time data of urban rail transit signals so that the real-time data of urban rail transit signals are stored in the database.

[0027] Preferably, in the step S2, training the urban rail transit signal fault prediction model includes:

[0028] Collecting the historical data of urban rail transit signals, and dividing the collected historical data of urban rail transit signals into a training set and a test set;

[0029] Based on the training set, training the deep learning model so that the deep learning model autonomously learns the fault prediction of urban rail transit signals, and training a fault prediction model of urban rail transit signals based on deep learning technology;

[0030] Based on the test set, performing a performance test on the fault prediction model of urban rail transit signals based on deep learning technology, judging whether the fault prediction model of urban rail transit signals based on deep learning technology can achieve the expected effect, and determining the optimal fault prediction model of urban rail transit signals.

[0031] Preferably, in the step S2, predicting the urban rail transit signal faults includes:

[0032] Obtaining the optimal fault prediction model of urban rail transit signals, and deploying the optimal fault prediction model of urban rail transit signals in the actual fault prediction application of urban rail transit signals;

[0033] Input the real-time data of urban rail transit signals into the optimal urban rail transit signal fault prediction model, analyze the real-time data of urban rail transit signals according to the optimal urban rail transit signal fault prediction model, predict whether there is a fault behavior in the urban rail transit signals, and determine the urban rail transit signal fault prediction result;

[0034] Among them, the urban rail transit signal fault prediction result is that there is a fault behavior in the urban rail transit signal or there is no fault behavior in the urban rail transit signal.

[0035] Preferably, in S3, early warning management for urban rail transit signal faults includes:

[0036] When there is a fault behavior in the urban rail transit signal, automatically trigger the early warning mechanism, conduct automatic alarm, and transmit the fault early warning information to the management personnel in a timely manner by means of text messages or push notifications, so that the management personnel can take corresponding measures according to the urban rail transit signal fault behavior and adjust and optimize the urban rail transit signal;

[0037] Track the adjustment and optimization situation of urban rail transit signals in real time, online monitor the adjustment and optimization effect of urban rail transit signals, and provide a visual user interaction interface to display the adjustment and optimization situation and effect of urban rail transit signals in a visual form.

[0038] Preferably, when there is a fault behavior in the urban rail transit signal, automatically give an early warning alarm and notify the management personnel to carry out timely maintenance management on the urban rail transit signal fault behavior, including:

[0039] Obtain the early warning alarm result of the urban rail transit signal, extract keywords from the early warning alarm result to obtain the corresponding key information;

[0040] Cluster the early warning alarm results based on the key information, and obtain the category set corresponding to the early warning alarm results based on the clustering results;

[0041] Determine the fault characteristics of each category set based on the key information of each category set, generate a fault summary for each category set based on the fault characteristics, and explain the faults for each category set based on the fault summary;

[0042] Make a first record of the category set and the corresponding fault explanation result based on a preset report. At the same time, based on the early warning alarm notification result to the management personnel, open the uplink communication permission of the management personnel terminal, and receive the fault behavior data and fault maintenance data returned by the management personnel terminal in real time based on the opening result;

[0043] The fault behavior data and fault maintenance data are used as source data for the second record. Meanwhile, when the fault behavior data is an equipment fault, the identity tag of the faulty equipment is extracted, and the historical fault data of the faulty equipment is retrieved from the historical fault data record library based on the identity tag;

[0044] The historical fault data, current fault behavior data, and fault maintenance data are analyzed to evaluate the remaining service life of the faulty equipment, and the remaining service life is used as the first derivative result of the source data for the third record;

[0045] Meanwhile, the early warning alarm is eliminated based on the feedback reception result of the fault maintenance data, and the time span value from notifying the management personnel to receiving the feedback of the fault maintenance data is extracted based on the elimination result;

[0046] The emergency response efficiency for the urban rail transit signal fault behavior is determined based on the time span value, and the emergency response efficiency is used as the second derivative result for the fourth record;

[0047] A record maintenance report for the urban rail transit signal fault is generated based on the first record, second record, third record, and fourth record, and the maintenance report is recorded and filed.

[0048] Preferably, a record maintenance report for the urban rail transit signal fault is generated based on the first record, second record, third record, and fourth record, including:

[0049] The obtained historical fault data, current fault behavior data, and fault maintenance data, as well as the time span value from notifying the management personnel to receiving the feedback of the fault maintenance data, and the remaining service life of the faulty equipment is evaluated based on the historical fault data, current fault behavior data, and fault maintenance data respectively, and the emergency response efficiency for the urban rail transit signal fault behavior is calculated based on the time span value from notifying the management personnel to receiving the feedback of the fault maintenance data. The specific steps are as follows;

[0050] The remaining service life of the faulty equipment is calculated according to the following formula:

[0051]

[0052] Among them, T represents the remaining service life of the faulty equipment; t represents the total service life of the faulty equipment at the time of factory; represents the natural loss factor when the faulty equipment is in normal use and without faults, and the value range is (0.1, 0.15); i represents the serial number value of the number of faults occurred by the faulty equipment, and the value range is [1, n]; n represents the total number of faults occurred by the faulty equipment; j represents the serial number value of the fault type occurred by the faulty equipment, and the value range is [1, m]; m represents the total number of fault types occurred by the faulty equipment; α ijIt represents the reduction in the service life of the faulty device under the j-th fault type of the i-th fault; k represents the error coefficient, and its value range is (0.01, 0.015); e represents the natural constant, and its value is 2.71828; ln(·) represents the logarithmic function with base e;

[0053] Calculate the emergency response efficiency for the urban rail transit signal fault behavior according to the following formula:

[0054]

[0055] Among them, η represents the emergency response efficiency for the urban rail transit signal fault behavior, and its value range is (0, 1); α represents the time length from detecting the urban rail transit signal fault behavior to sending a notice to the management personnel; ω represents the time length from the management personnel receiving the notice to resolving the urban rail transit signal fault behavior; ρ represents the benchmark time length required to resolve the urban rail transit signal fault behavior, and its value is less than or equal to α + ω;

[0056] Compare the calculated remaining service life and emergency response efficiency with the corresponding preset thresholds respectively.

[0057] When there is a remaining service life or emergency response efficiency less than the corresponding preset threshold, generate an optimization warning signal and send a reminder to the management terminal.

[0058] If the remaining service life is abnormal, send a device replacement notice to the management terminal.

[0059] If the emergency response efficiency is abnormal, determine an optimization plan for the emergency response based on the management terminal, and optimize the execution steps of the emergency response based on the optimization plan until it is greater than or equal to the corresponding preset threshold.

[0060] Otherwise, continuously evaluate and monitor the remaining service life and emergency response efficiency respectively, and update the evaluation and monitoring results in the maintenance inspection report.

[0061] According to another aspect of the present invention, there is provided an urban rail transit signal fault warning system for implementing the urban rail transit signal fault warning method as described above, including:

[0062] A data acquisition module for acquiring real-time data of urban rail transit signals;

[0063] A data processing module for processing the acquired real-time data of urban rail transit signals;

[0064] A model training module for training an urban rail transit signal fault prediction model;

[0065] A fault prediction module for predicting urban rail transit signal faults;

[0066] An early warning management module for managing early warnings of urban rail transit signal faults.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] 1. Based on intelligent acquisition devices, the present invention monitors and continuously collects train operation data, signal machine status data, line facility data, and station environment data in real time to determine the real-time data of urban rail transit signals. Moreover, it cleans and converts the real-time data of urban rail transit signals, stores the real-time data of urban rail transit signals, trains an urban rail transit signal fault prediction model, deploys the urban rail transit signal fault prediction model in actual urban rail transit signal fault prediction applications, analyzes the real-time data of urban rail transit signals based on the urban rail transit signal fault prediction model, predicts whether there are fault behaviors in urban rail transit signals, and determines the urban rail transit signal fault prediction results, effectively predicting urban rail transit signal faults.

[0069] 2. When there are fault behaviors in the urban rail transit signals of the present invention, the early warning mechanism is automatically triggered for automatic alarm, timely discovering the fault behaviors of urban rail transit signals. Moreover, the fault warning information is transmitted to the management personnel in a timely manner by means of text messages or push notifications, enabling the management personnel to take corresponding measures according to the fault behaviors of urban rail transit signals, timely maintaining and managing the fault behaviors of urban rail transit signals, and timely eliminating the fault behaviors of urban rail transit signals. It can effectively predict and early warn urban rail transit signal faults, improve the management effect of urban rail transit signals, and make the operation safety and reliability of urban rail transit high.

[0070] 3. By obtaining the early warning and alarm results of urban rail transit signals, extracting keywords, then determining the category set through clustering, determining the fault characteristics and generating a fault summary based on the key information, recording the category set and the fault description, simultaneously sending early warning and alarm notifications to the management personnel and enabling the uplink communication permission to receive the backhaul data, recording this data and retrieving the historical fault data when the equipment fails, then analyzing the data to evaluate and record the remaining service life of the faulty equipment, eliminating the early warning and alarm according to the backhaul result of the fault repair data and calculating the emergency response efficiency, and finally generating and filing a record maintenance report by integrating various records, the effective analysis and classification of the early warning and alarm results are realized, which helps to quickly and accurately understand the fault situation. The generated fault summary and description can help relevant personnel quickly grasp the essence of the fault. The timely notification and data backhaul to the management personnel improve the efficiency and collaboration of fault handling. The evaluation of the remaining service life of the faulty equipment helps to plan maintenance and replacement in advance. The determination of the emergency response efficiency helps to continuously optimize the emergency handling process and mechanism, so as to facilitate the comprehensive and effective management of urban rail transit signals according to the finally obtained record maintenance report, and improve the effect of urban rail transit signal fault early warning and problem solving.

[0071] 4. By calculating the remaining service life of the faulty equipment and the emergency response efficiency of the urban rail transit signal fault behavior respectively, the management of urban rail transit signals is realized from two aspects, and it is also convenient to send corresponding reminders to the management terminal in time when the remaining service life and the emergency response efficiency are abnormal, thus improving the effect of urban rail transit signal fault early warning management. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 is a flowchart of the urban rail transit signal fault early warning method of the present invention;

[0073] Figure 2 is a module diagram of the urban rail transit signal fault early warning system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0075] In order to solve the problems that the existing technology cannot effectively predict and early warn the urban rail transit signal faults, resulting in poor management effect of urban rail transit signals and low safety and reliability of urban rail operation, please refer to Figure 1 - Figure 2 , the following technical solutions are provided in this embodiment:

[0076] Urban rail transit signal fault warning method, comprising the following steps:

[0077] S1. Data acquisition and processing: Based on intelligent acquisition devices, real-time monitoring and continuous acquisition of train operation data, signal machine status data, line facility data and station environment data are carried out to determine the real-time data of urban rail transit signals, clean and convert the real-time data of urban rail transit signals, and store the real-time data of urban rail transit signals;

[0078] In this embodiment, the acquisition of real-time data of urban rail transit signals includes:

[0079] Based on sensors, real-time monitoring of the departure time, expected arrival time, actual arrival time, travel distance, travel speed, travel position and carrying capacity of urban rail trains is carried out to obtain train operation data;

[0080] Based on sensors, real-time monitoring of the on / off state, display state and indication direction of urban rail transit signal machines is carried out to obtain signal machine status data;

[0081] Based on sensors, real-time monitoring of the positions, states and capacities of urban rail sections, turnouts, tunnels and bridge facilities is carried out to obtain line facility data;

[0082] Based on sensors, real-time monitoring of the temperature, humidity and noise parameters in urban rail stations is carried out to obtain station environment data;

[0083] Among them, based on the train operation data, signal machine status data, line facility data and station environment data, the real-time data of urban rail transit signals is determined.

[0084] In this embodiment, the processing of the real-time data of urban rail transit signals includes:

[0085] Clean the real-time data of urban rail transit signals;

[0086] Check whether there are duplicate values, missing values and abnormal values in the real-time data of urban rail transit signals, and process the duplicate values, missing values and abnormal values existing in the real-time data of urban rail transit signals;

[0087] Among them, for duplicate values, the duplicate values existing in the real-time data of urban rail transit signals are deleted, and only unique data records are retained;

[0088] Among them, for missing values, if the missing values have little overall impact on the urban rail transit signal fault warning, the missing values are directly deleted. If the missing values have a large overall impact on the urban rail transit signal fault warning, filling or interpolation methods are used to fill or interpolate the missing values to make the missing values complete;

[0089] Among them, for outliers, if the outliers have little overall impact on the urban rail transit signal fault warning, the outliers are directly deleted. If the outliers have a large overall impact on the urban rail transit signal fault warning, replacement or correction methods are used to replace or correct the outliers to make the outliers normal.

[0090] In this embodiment, the processing of the urban rail transit signal real-time data further includes:

[0091] Converting the format and type of the urban rail transit signal real-time data, removing the dimensional difference between the urban rail transit signal real-time data, and determining the standardized urban rail transit signal real-time data;

[0092] Integrating the standardized urban rail transit signal real-time data, integrating the standardized urban rail transit signal real-time data into a unified view, and verifying the integrated urban rail transit signal real-time data to check the accuracy of the integration of the urban rail transit signal real-time data;

[0093] Storing the integrated urban rail transit signal real-time data so that the urban rail transit signal real-time data is stored in the database.

[0094] S2. Model training and fault prediction: Training the urban rail transit signal fault prediction model, deploying the urban rail transit signal fault prediction model in the actual urban rail transit signal fault prediction application, analyzing the urban rail transit signal real-time data based on the urban rail transit signal fault prediction model, predicting whether there is a fault behavior in the urban rail transit signal, and determining the urban rail transit signal fault prediction result;

[0095] In this embodiment, training the urban rail transit signal fault prediction model includes:

[0096] Collecting the urban rail transit signal historical data, and dividing the collected urban rail transit signal historical data into a training set and a test set;

[0097] Based on the training set, training the deep learning model so that the deep learning model autonomously learns the urban rail transit signal fault prediction, and training the urban rail transit signal fault prediction model based on the deep learning technology;

[0098] Based on the test set, perform performance testing on the urban rail transit signal fault prediction model based on deep learning technology, determine whether the urban rail transit signal fault prediction model based on deep learning technology can achieve the expected effect, and determine the optimal urban rail transit signal fault prediction model.

[0099] In this embodiment, predicting urban rail transit signal faults includes:

[0100] Obtain the optimal urban rail transit signal fault prediction model, and deploy the optimal urban rail transit signal fault prediction model in the actual urban rail transit signal fault prediction application;

[0101] Input the real-time urban rail transit signal data into the optimal urban rail transit signal fault prediction model, analyze the real-time urban rail transit signal data according to the optimal urban rail transit signal fault prediction model, predict whether there is a fault behavior in the urban rail transit signal, and determine the urban rail transit signal fault prediction result;

[0102] Among them, the urban rail transit signal fault prediction result is that there is a fault behavior in the urban rail transit signal or there is no fault behavior in the urban rail transit signal.

[0103] S3. Fault warning management: When there is a fault behavior in the urban rail transit signal, automatically give an early warning alarm, and notify the management personnel to carry out timely maintenance management on the urban rail transit signal fault behavior, and lift the urban rail transit signal fault behavior in time.

[0104] In this embodiment, the early warning management of urban rail transit signal faults includes:

[0105] When there is a fault behavior in the urban rail transit signal, automatically trigger the early warning mechanism, give an automatic alarm, and transmit the fault early warning information to the management personnel in a timely manner by means of text messages or push notifications, so that the management personnel can take corresponding measures in time according to the urban rail transit signal fault behavior, and adjust and optimize the urban rail transit signal;

[0106] Track the adjustment and optimization situation of the urban rail transit signal in real time, online monitor the adjustment and optimization effect of the urban rail transit signal, and provide a visual user interaction interface to display the adjustment and optimization situation and effect of the urban rail transit signal in a visual form.

[0107] To better show the principle of urban rail transit signal fault early warning, this embodiment provides an urban rail transit signal fault early warning system for implementing the urban rail transit signal fault early warning method as described above, including:

[0108] A data acquisition module for collecting real-time urban rail transit signal data;

[0109] Among them, based on sensors, real-time monitoring and continuous collection of train operation data, signal lamp status data, line facility data, and station environment data are carried out to determine the real-time data of urban rail transit signals.

[0110] A data processing module is used to process the real-time data of urban rail transit signals collected.

[0111] Among them, the real-time data of urban rail transit signals is cleaned to remove duplicate values, missing values, and abnormal values, and the real-time data of urban rail transit signals is transformed and integrated, and the real-time data of urban rail transit signals is stored.

[0112] A model training module is used to train a fault prediction model for urban rail transit signals.

[0113] Among them, historical data of urban rail transit signals is collected, and based on deep learning technology, a fault prediction model for urban rail transit signals used to predict faults in urban rail transit signals is trained.

[0114] A fault prediction module is used to predict faults in urban rail transit signals.

[0115] Among them, based on the fault prediction model for urban rail transit signals, the real-time data of urban rail transit signals is analyzed to predict whether there are fault behaviors in urban rail transit signals, and the fault prediction results of urban rail transit signals are determined.

[0116] An early warning management module is used to manage the early warning of faults in urban rail transit signals.

[0117] Among them, when there are fault behaviors in urban rail transit signals, early warning alarms are automatically issued, and managers are notified to timely maintain and manage the fault behaviors of urban rail transit signals, and the fault behaviors of urban rail transit signals are timely eliminated.

[0118] This embodiment provides a method for early warning of faults in urban rail transit signals. When there are fault behaviors in urban rail transit signals, early warning alarms are automatically issued, and managers are notified to timely maintain and manage the fault behaviors of urban rail transit signals, including:

[0119] Obtain the early warning alarm result of urban rail transit signals, extract keywords from the early warning alarm result to obtain the corresponding key information;

[0120] Cluster the early warning alarm result based on the key information, and obtain the category set corresponding to the early warning alarm result based on the clustering result;

[0121] Determine the fault characterization of each category set based on the key information of each category set, generate a fault summary for each category set from the fault characterization, and provide a fault description for each category set based on the fault summary;

[0122] Make a first record of the category set and the corresponding fault description result based on a preset report. At the same time, based on the warning and alarm notification result for the management personnel, enable the uplink communication permission of the management personnel's terminal, and receive the fault behavior data and fault repair data transmitted back by the management personnel's terminal in real time based on the enabling result;

[0123] Make a second record of the fault behavior data and the fault repair data as source data. At the same time, when the fault behavior data is an equipment fault, extract the identity tag of the faulty equipment, and retrieve the historical fault data of the faulty equipment from the historical fault data record library based on the identity tag;

[0124] Analyze the historical fault data, the current fault behavior data, and the fault repair data, evaluate the remaining service life of the faulty equipment, and make a third record of the remaining service life as the first derivative result of the source data;

[0125] At the same time, cancel the warning and alarm based on the feedback reception result of the fault repair data, and extract the time span value from the notification of the management personnel to the reception of the transmitted back fault repair data based on the cancellation result;

[0126] Determine the emergency response efficiency for the fault behavior of urban rail transit signals based on the time span value, and make a fourth record of the emergency response efficiency as the second derivative result;

[0127] Generate a record and repair report for urban rail transit signal faults based on the first record, the second record, the third record, and the fourth record, and file the record and repair report.

[0128] In this embodiment, the keyword refers to the data in the warning and alarm result that can reflect the alarm type, i.e., the warning and alarm location, etc., which is the finally extracted key information.

[0129] In this embodiment, the fault characterization refers to the fault manifestation corresponding to the warning and alarm result of each category set determined according to the key information. For example, it can be that there are potential hazard points in the line facilities, etc. The purpose is to determine the fault situation corresponding to each category set.

[0130] In this embodiment, the fault summary refers to the data information that can explain the fault of each category set, including the type of the fault and the location of the fault, etc.

[0131] In this embodiment, the preset report is a data report constructed in advance for recording the warning and alarm result.

[0132] In this embodiment, the uplink communication permission refers to the permission to enable a user to send data to the management terminal.

[0133] In this embodiment, the fault behavior data refers to the specific data about faults transmitted back by the management personnel, including data such as the cause of the fault and the severity of the current fault.

[0134] In this embodiment, the fault repair and inspection data refers to the current situation of the faulty equipment after the management personnel have detected and repaired the fault.

[0135] In this embodiment, an equipment fault means that a hardware fault has occurred.

[0136] In this embodiment, the identity tag refers to a marking symbol that can distinguish different devices.

[0137] In this embodiment, the first derivative result refers to the result obtained by analyzing the faulty equipment based on the fault behavior data and the fault repair and inspection data, and is a record parameter in the maintenance report.

[0138] The working principle and beneficial effects of the above technical solution are as follows: By obtaining the early warning and alarm results of urban rail transit signals and extracting keywords, then determining the category set through clustering, determining the fault characteristics and generating a fault summary based on the key information, recording the category set and the fault description, at the same time sending early warning and alarm notifications to the management personnel and enabling the uplink communication permission to receive the transmitted-back data, recording this data and retrieving the historical fault data when an equipment fault occurs, then analyzing the data to evaluate and record the remaining service life of the faulty equipment, eliminating the early warning and alarm according to the result of the fault repair and inspection data transmission and calculating the emergency response efficiency, and finally generating and filing a maintenance report by comprehensively various records, which realizes the effective analysis and classification of the early warning and alarm results, helps to quickly and accurately understand the fault situation, the generated fault summary and description can help relevant personnel quickly grasp the essence of the fault, the timely notification and data transmission to the management personnel improve the efficiency and coordination of fault handling, the evaluation of the remaining service life of the faulty equipment helps to plan maintenance and replacement in advance, and the determination of the emergency response efficiency helps to continuously optimize the emergency handling process and mechanism, so as to facilitate the comprehensive and effective management of urban rail transit signals according to the finally obtained maintenance report, and improve the effect of urban rail transit signal fault early warning and problem solving.

[0139] This embodiment provides a method for early warning of urban rail transit signal faults, generating a maintenance report for urban rail transit signal faults based on the first record, the second record, the third record and the fourth record, including:

[0140] Obtain the historical fault data, current fault behavior data, and fault repair data, as well as the time span value from notifying the management personnel to receiving the back-transmitted fault repair data. Then, respectively evaluate the remaining service life of the faulty equipment based on the historical fault data, current fault behavior data, and fault repair data, and calculate the emergency response efficiency of the urban rail transit signal fault behavior based on the time span value from notifying the management personnel to receiving the back-transmitted fault repair data. The specific steps are as follows;

[0141] Calculate the remaining service life of the faulty equipment according to the following formula:

[0142]

[0143] Where, T represents the remaining service life of the faulty equipment; t represents the total factory service life of the faulty equipment; represents the natural wear factor of the faulty equipment during normal use without faults, and its value range is (0.1, 0.15); i represents the serial number value of the number of faults occurred by the faulty equipment, and its value range is [1, n]; n represents the total number of faults occurred by the faulty equipment; j represents the serial number value of the fault type occurred by the faulty equipment, and its value range is [1, m]; m represents the total number of fault types occurred by the faulty equipment; α ij represents the reduction in service life of the faulty equipment under the j-th fault type of the i-th fault; k represents the error coefficient, and its value range is (0.01, 0.015); e represents the natural constant, and its value is 2.71828; ln(·) represents the logarithmic function with base e;

[0144] Calculate the emergency response efficiency of the urban rail transit signal fault behavior according to the following formula:

[0145]

[0146] Where, η represents the emergency response efficiency of the urban rail transit signal fault behavior, and its value range is (0, 1); α represents the time length from detecting the urban rail transit signal fault behavior to sending a notice to the management personnel; ω represents the time length from the management personnel receiving the notice to resolving the urban rail transit signal fault behavior; ρ represents the benchmark time required to resolve the urban rail transit signal fault behavior, and its value is less than or equal to α + ω;

[0147] Compare the calculated remaining service life and emergency response efficiency with the corresponding preset thresholds respectively;

[0148] When there is a remaining service life or emergency response efficiency less than the corresponding preset threshold, generate an optimization warning signal and send a reminder to the management terminal;

[0149] When the remaining service life is abnormal, send a device replacement notice to the management terminal;

[0150] When the emergency response efficiency is abnormal, determine an optimization plan for the emergency response based on the management terminal, and optimize the execution steps of the emergency response based on the optimization plan until it is greater than or equal to the corresponding preset threshold;

[0151] Otherwise, continuously evaluate and monitor the remaining service life and the emergency response efficiency respectively, and update and record the evaluation and monitoring results in the maintenance report.

[0152] In this embodiment, the reduction in the service life of the faulty device under different faults and different fault types is obtained through multiple experimental tests.

[0153] In this embodiment, the natural loss factor is used to characterize the natural impact degree of the device on the service life under normal use conditions, that is, the degree of shortening of the device's own life when no fault occurs.

[0154] In this embodiment, the error coefficient is set in advance and can be adjusted according to specific circumstances.

[0155] In this embodiment, the preset threshold is set in advance, which is used to measure whether the remaining service life and the emergency response efficiency meet the requirements respectively, and can be adjusted.

[0156] In this embodiment, generate an optimization warning signal and send a reminder to the management terminal, that is, send a device replacement reminder to the management terminal.

[0157] In this embodiment, the optimization plan refers to the specific measures for optimizing the processing methods or measures of the emergency response. For example, it can be to adjust the notification method for the management personnel, etc., with the purpose of shortening the notification time.

[0158] The working principle and beneficial effects of the above technical solution are: by calculating the remaining service life of the faulty device and the emergency response efficiency of the urban rail transit signal failure behavior respectively, the urban rail transit signal is managed from two aspects, and it is also convenient to send corresponding reminders to the management terminal in time when the remaining service life and the emergency response efficiency are abnormal, improving the effect of the urban rail transit signal failure early warning management.

[0159] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0160] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An urban rail transit signal fault early warning method, characterized in that: The following steps are involved: S1. Data collection and processing: Based on intelligent collection equipment, real-time monitoring and continuous collection of train operation data, signal status data, line facility data and station environment data are carried out to determine the real-time data of urban rail transit signals, clean and convert the real-time data of urban rail transit signals, and store the real-time data of urban rail transit signals; S2. Model training and fault prediction: Train the urban rail transit signal fault prediction model, deploy the urban rail transit signal fault prediction model in the actual urban rail transit signal fault prediction application, analyze the real-time data of the urban rail transit signal based on the urban rail transit signal fault prediction model, predict whether the urban rail transit signal has fault behavior, and determine the urban rail transit signal fault prediction result; S3. Fault warning management: When there is a fault in the urban rail transit signal, an early warning alarm will be automatically issued, and the management personnel will be notified to carry out timely maintenance and management of the urban rail transit signal fault behavior, and the urban rail transit signal fault behavior will be resolved in time.

2. The urban rail transit signal fault early warning method according to claim 1, characterized in that: In S1, real-time data of urban rail transit signals is collected, including: Based on sensors, the departure time, expected arrival time, actual arrival time, travel distance, travel speed, travel location and passenger load of urban rail trains are monitored in real time to obtain train operation data; Based on sensors, the switch status, display status and indication direction of urban rail transit signals are monitored in real time to obtain signal status data; Based on sensors, the location, status and capacity of urban rail sections, turnouts, tunnels and bridge facilities are monitored in real time to obtain line facility data; Based on sensors, the temperature, humidity and noise parameters in urban rail stations are monitored in real time to obtain station environment data; Among them, the real-time data of urban rail transit signals is determined based on train operation data, signal status data, line facility data and station environment data.

3. The urban rail transit signal fault early warning method according to claim 2, characterized in that: In S1, the real-time data of urban rail transit signals is processed, including: Cleaning of real-time data of urban rail transit signals; Check whether there are duplicate values, missing values ​​and abnormal values ​​in the real-time data of urban rail transit signals, and process the duplicate values, missing values ​​and abnormal values ​​in the real-time data of urban rail transit signals; Among them, for duplicate values, the duplicate values ​​existing in the real-time data of urban rail transit signals are deleted, and the unique data record is retained; Among them, for missing values, if the missing values ​​have a small overall impact on the urban rail transit signal fault warning, the missing values ​​will be directly deleted; if the missing values ​​have a large overall impact on the urban rail transit signal fault warning, the missing values ​​will be filled or interpolated by using filling or interpolation methods to make the missing values ​​complete; Among them, for outliers, if the outliers have a small overall impact on urban rail transit signal fault warning, the outliers are directly deleted. If the outliers have a large overall impact on urban rail transit signal fault warning, replacement or correction methods are used to replace or correct the outliers to make them normal.

4. The urban rail transit signal fault early warning method according to claim 3, characterized in that: In the above S1, the real-time data of urban rail transit signals is processed, and further includes: Convert the format and type of real-time data of urban rail transit signals, remove the dimension differences between real-time data of urban rail transit signals, and determine standardized real-time data of urban rail transit signals; Integrate the standardized real-time data of urban rail transit signals into a unified view, and verify the integrated real-time data of urban rail transit signals to check the accuracy of the integration of real-time data of urban rail transit signals; The integrated real-time data of urban rail transit signals are stored so that the real-time data of urban rail transit signals are stored in a database.

5. The urban rail transit signal fault early warning method according to claim 4, characterized in that: In S2, training the urban rail transit signal fault prediction model includes: Collecting historical data of urban rail transit signals, and dividing the collected historical data of urban rail transit signals into a training set and a test set; Based on the training set, the deep learning model is trained to enable the deep learning model to autonomously learn the prediction of urban rail transit signal faults, and an urban rail transit signal fault prediction model based on deep learning technology is trained; Based on the test set, a performance test is conducted on the urban rail transit signal fault prediction model based on deep learning technology to determine whether the urban rail transit signal fault prediction model based on deep learning technology can achieve the expected effect and to determine the optimal urban rail transit signal fault prediction model.

6. The urban rail transit signal fault early warning method according to claim 5, characterized in that: In S2, predicting urban rail transit signal failures includes: Obtain the optimal urban rail transit signal fault prediction model and deploy it in actual urban rail transit signal fault prediction applications; Inputting the real-time data of urban rail transit signals into the optimal urban rail transit signal fault prediction model, analyzing the real-time data of urban rail transit signals according to the optimal urban rail transit signal fault prediction model, predicting whether the urban rail transit signals have fault behavior, and determining the urban rail transit signal fault prediction result; Among them, the urban rail transit signal fault prediction result is that the urban rail transit signal has faulty behavior or the urban rail transit signal does not have faulty behavior.

7. The urban rail transit signal fault early warning method according to claim 6, characterized in that: In S3, early warning management of urban rail transit signal failures is performed, including: When there is a fault in the urban rail transit signal, the early warning mechanism is automatically triggered, an automatic alarm is issued, and the fault warning information is promptly transmitted to the management personnel through SMS or push notification, so that the management personnel can take timely response measures according to the fault behavior of the urban rail transit signal and adjust and optimize the urban rail transit signal; It can track the adjustment and optimization of urban rail transit signals in real time, monitor the adjustment and optimization effects of urban rail transit signals online, and provide a visual user interaction interface to display the adjustment and optimization status and effects of urban rail transit signals in a visual form.

8. The urban rail transit signal fault early warning method according to claim 1, characterized in that: When there is a fault in the urban rail transit signal, an early warning alarm will be automatically issued, and the management personnel will be notified to carry out timely maintenance and management of the urban rail transit signal fault, including: Obtain the early warning alarm results of urban rail transit signals, and extract keywords from the early warning alarm results to obtain the corresponding key information; Cluster the early warning alarm results based on key information, and obtain a category set corresponding to the early warning alarm results based on the clustering results; Determine a fault representation of each category set based on key information of each category set, generate a fault summary of each category set from the fault representation, and provide a fault description for each category set based on the fault summary; Based on the preset report, the category set and the corresponding fault description results are first recorded. At the same time, based on the early warning alarm notification result for the management personnel, the uplink communication permission of the management personnel terminal is opened, and based on the opening result, the fault behavior data and fault repair data returned by the management personnel terminal are received in real time; The fault behavior data and the fault repair data are used as source data for a second record. Meanwhile, when the fault behavior data is a device fault, an identity tag of the faulty device is extracted, and historical fault data of the faulty device is retrieved from a historical fault data record library based on the identity tag. Analyze historical fault data, current fault behavior data, and fault repair data to evaluate the remaining service life of the faulty equipment, and record the remaining service life as a first derivative result of the source data for third-party recording; At the same time, the early warning alarm is eliminated based on the feedback reception result of the fault repair data, and the time span value from notifying the management personnel to receiving the feedback fault repair data is extracted based on the elimination result; determining an emergency response efficiency to the urban rail transit signal failure behavior based on the time span value, and fourthly recording the emergency response efficiency as a second derivative result; A record maintenance report of the urban rail transit signal failure is generated based on the first record, the second record, the third record and the fourth record, and the record maintenance report is archived.

9. The urban rail transit signal fault early warning method according to claim 8, characterized in that: A record inspection report of urban rail transit signal failure is generated based on the first record, the second record, the third record and the fourth record, including: The historical fault data, current fault behavior data and fault repair data as well as the time span from notifying the management personnel to receiving the returned fault repair data are obtained, and the remaining service life of the faulty equipment and the time span from notifying the management personnel to receiving the returned fault repair data are evaluated based on the historical fault data, current fault behavior data and fault repair data, respectively, and the emergency response efficiency to the fault behavior of the urban rail transit signal is calculated. The specific steps are as follows; The remaining service life of the faulty equipment is calculated according to the following formula: Where T represents the remaining service life of the faulty equipment; t represents the total service life of the faulty equipment before it leaves the factory; represents the natural loss factor of the faulty device when it is in normal use and has no faults, and its value range is (0.1, 0.15); i represents the number of faults that occur in the faulty device, and its value range is [1, n]; n represents the total number of faults that occur in the faulty device; j represents the number of fault types that occur in the faulty device, and its value range is [1, m]; m represents the total number of fault types that occur in the faulty device; α ij represents the reduction in service life of the faulty equipment under the jth fault type of the i-th fault; k represents the error coefficient, and its value range is (0.01, 0.015); e represents a natural constant, and its value is 2.71828; ln· represents a logarithmic function with e as the base; The emergency response efficiency to urban rail transit signal failure behavior is calculated according to the following formula: Among them, η represents the emergency response efficiency to urban rail transit signal failure behavior, and its value range is (0, 1); α represents the time length from detecting the urban rail transit signal failure behavior to sending a notification to the management personnel; ω represents the time length from the management personnel receiving the notification to solving the urban rail transit signal failure behavior; ρ represents the benchmark time required to solve the urban rail transit signal failure behavior, and its value is less than or equal to α+ω; The calculated remaining service life and emergency response efficiency are compared with the corresponding preset threshold values; When there is a remaining service life or the emergency response efficiency is less than the corresponding preset threshold, an optimization warning signal is generated and a reminder is sent to the management terminal; If the remaining service life is abnormal, a device replacement notification is sent to the management terminal; If the emergency response efficiency is abnormal, an optimization plan for the emergency response is determined based on the management terminal, and the execution steps of the emergency response are optimized based on the optimization plan until it is greater than or equal to the corresponding preset threshold; Otherwise, the remaining service life and emergency response efficiency shall be continuously evaluated and monitored, and the evaluation and monitoring results shall be updated and recorded in the record maintenance report.

10. An urban rail transit signal fault warning system, used to implement the urban rail transit signal fault warning method according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, used to collect real-time data of urban rail transit signals; A data processing module is used to process the collected real-time data of urban rail transit signals; Model training module, used to train urban rail transit signal fault prediction model; Fault prediction module, used to predict urban rail transit signal faults; The early warning management module is used for early warning management of urban rail transit signal failures.

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