Track equipment state management method and device

Abstract

The invention provides a track equipment state management method and device. The method comprises the following steps: acquiring basic data of a plurality of equipment units in a track; analyzing the basic data by using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score of each equipment unit; inputting the total score into a system dispatching model to generate a maintenance task list; sending notification information to the operation and maintenance personnel according to the maintenance task, so that the operation and maintenance personnel carry out field maintenance according to the notification information; and receiving rectification information fed back by the operation and maintenance personnel based on field maintenance, and storing the rectification information, the total score of the plurality of equipment units and the basic data into the equipment medical record. According to the invention, through integration of multiple detection means, the health state of the urban rail transit line equipment is comprehensively monitored. Intelligent analysis is performed based on a dynamic and static analysis algorithm and a neural network analysis algorithm, a maintenance task list is automatically generated, and accurate dispatching is realized. The manual intervention link is reduced, the maintenance response time is shortened, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a method and device for managing the status of rail equipment. Background Art

[0002] With the rapid development of urban rail transit, the health of its line equipment is directly related to driving safety and service quality. The operation of urban rail transit not only involves a large number of passenger transportation, but also involves complex equipment maintenance and management.

[0003] Traditional manual inspection methods play an important role in this process, but with the continuous expansion of the scale of urban rail transit operations, this method has gradually exposed many shortcomings: inspectors are prone to missed inspections and misjudgments in complex environments; technical personnel are limited and cannot fully cover the equipment, which may cause delays in equipment maintenance; detection tools are backward and cannot fully evaluate the status of equipment; data recording methods are backward, making it difficult to update in real time and analyze efficiently, affecting decision-making; fault information is slow to transmit, affecting the maintenance response speed, and may increase the difficulty and cost of maintenance.

[0004] Therefore, how to improve data collection efficiency and analysis accuracy is an urgent problem that needs to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for managing the status of rail equipment to solve the problems of easy missed detection, misjudgment and high maintenance cost in manual inspection.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of the present invention discloses a method for managing the status of a track device, the method comprising:

[0008] Collecting basic data of multiple equipment units in the track; wherein each of the equipment units corresponds to a mileage interval in the track, and the basic data is basic data of multiple equipment in the mileage interval;

[0009] Analyzing the basic data using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each of the equipment units;

[0010] Input the total score into the system dispatching model to generate a maintenance task order;

[0011] Sending notification information to the operation and maintenance personnel according to the maintenance task order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information;

[0012] Receive rectification information from the operation and maintenance personnel based on on-site maintenance feedback, and store the rectification information, the total scores of the plurality of equipment units and the basic data in the equipment medical record book.

[0013] Preferably, the basic data is analyzed using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each of the equipment units, including:

[0014] Preprocessing the basic data to obtain target basic data;

[0015] For each of the equipment units, a dynamic and static analysis algorithm is used to analyze the target basic data corresponding to the equipment unit according to a preset analysis method to obtain a dynamic and static analysis algorithm score, wherein the analysis method includes mean analysis or peak analysis;

[0016] Analyzing the target basic data corresponding to the equipment unit using a model corresponding to the neural network analysis algorithm to obtain a neural network analysis algorithm score;

[0017] Based on the dynamic and static analysis algorithm scores, preset dynamic and static analysis weights, the neural network analysis algorithm scores and preset neural network analysis weights, the total scores of the equipment units are calculated to obtain the total scores of each of the equipment units.

[0018] Preferably, for each of the equipment units, using a dynamic and static analysis algorithm to analyze the target basic data corresponding to the equipment unit according to a preset analysis method to obtain a dynamic and static analysis algorithm score includes:

[0019] Get the preset analysis method;

[0020] For each of the equipment units, analyzing the manual static line inspection data in the target basic data corresponding to the equipment unit according to the analysis method to obtain a manual static unit score;

[0021] Analyze the static detection data of the track inspection instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain a static unit score of the track inspection instrument;

[0022] Analyze the dynamic detection data of the instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the dynamic unit score of the instrument;

[0023] Analyze the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit to obtain the track inspection vehicle dynamic unit score;

[0024] The manual static unit score, the track inspection instrument static unit score, the train-adding instrument dynamic unit score and the track inspection vehicle dynamic unit score are summed to obtain the dynamic and static analysis algorithm score of the equipment unit.

[0025] Preferably, the step of inputting the total score into a system dispatching model to generate a maintenance task order includes:

[0026] Receiving the total score of each of the equipment units through the system dispatching model, and obtaining the speed grade corresponding to the track;

[0027] Determine the type of each of the equipment units through the system dispatch model, and determine the evaluation level rule corresponding to each of the equipment units according to the type and the speed level;

[0028] Analyzing the comprehensive evaluation level of each of the equipment units based on the evaluation level rules through the system dispatching model;

[0029] A maintenance task order is generated through the system dispatching model according to the priority of the comprehensive evaluation level, the comprehensive evaluation level and the equipment unit.

[0030] Preferably, storing the rectification information, the total scores of the plurality of equipment units and the basic data in the equipment medical record book comprises:

[0031] Performing format verification on the rectification information;

[0032] If the rectification information is verified, extracting the device unit ID of each device unit from the rectification information;

[0033] Obtain the device medical record ID corresponding to each device unit ID, and obtain the corresponding device medical record based on each device medical record ID; wherein each device medical record stores historical basic data and historical rectification information of each device in the corresponding device unit;

[0034] Storing the rectification information, the total score of the equipment unit and the basic data in the corresponding equipment medical record book;

[0035] If the rectification information verification fails, a prompt message is output.

[0036] A second aspect of the present invention discloses a device for managing the status of track equipment, the device comprising:

[0037] A collection unit, used for collecting basic data of a plurality of equipment units in the track; wherein each of the equipment units corresponds to a mileage interval in the track, and the basic data is basic data of a plurality of equipment in the mileage interval;

[0038] An analysis unit, used to analyze the basic data using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each of the equipment units;

[0039] A generating unit, used for inputting the total score into a system dispatching model to generate a maintenance task order;

[0040] A notification unit, used to send notification information to the operation and maintenance personnel according to the maintenance task order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information;

[0041] The storage unit is used to receive the rectification information of the operation and maintenance personnel based on the on-site maintenance feedback, and store the rectification information, the total scores of the multiple equipment units and the basic data in the equipment medical record book.

[0042] Preferably, the analysis unit comprises:

[0043] A preprocessing module, used for preprocessing the basic data to obtain target basic data;

[0044] A first analysis module is used to analyze the target basic data corresponding to each of the equipment units using a dynamic and static analysis algorithm according to a preset analysis method to obtain a dynamic and static analysis algorithm score, wherein the analysis method includes mean analysis or peak analysis;

[0045] The second analysis module is used to analyze the target basic data corresponding to the equipment unit using a model corresponding to the neural network analysis algorithm to obtain a neural network analysis algorithm score;

[0046] A calculation module is used to calculate the total score of the equipment unit based on the dynamic and static analysis algorithm score, the preset dynamic and static analysis weights, the neural network analysis algorithm score and the preset neural network analysis weights to obtain the total score of each equipment unit.

[0047] Preferably, the first analysis module includes:

[0048] Get submodule, used to get preset analysis method;

[0049] A first analysis submodule is used to analyze the manual static line inspection data in the target basic data corresponding to each of the equipment units according to the analysis method to obtain a manual static unit score;

[0050] A second analysis submodule is used to analyze the static detection data of the track inspection instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain a static unit score of the track inspection instrument;

[0051] The third analysis submodule is used to analyze the dynamic detection data of the instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the dynamic unit score of the instrument;

[0052] A fourth analysis submodule is used to analyze the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit to obtain a track inspection vehicle dynamic unit score;

[0053] The summing submodule is used to sum the manual static unit score, the track inspection instrument static unit score, the train-adding instrument dynamic unit score and the track inspection vehicle dynamic unit score to obtain the dynamic and static analysis algorithm score of the equipment unit.

[0054] Preferably, the generating unit comprises:

[0055] A receiving module, used for receiving the total score of each of the equipment units through a system dispatching model, and obtaining the speed grade corresponding to the track;

[0056] A determination module, used to determine the type of each of the equipment units through the system dispatch model, and determine the evaluation level rule corresponding to each of the equipment units according to the type and the speed level;

[0057] An analysis grade module, used for analyzing the comprehensive evaluation grade of each of the equipment units based on the evaluation grade rule through the system dispatch model;

[0058] A generation module is used to generate a maintenance task order according to the priority of the comprehensive evaluation level, the comprehensive evaluation level and the equipment unit through the system dispatching model.

[0059] Preferably, the storage unit comprises:

[0060] A format verification module, used to perform format verification on the rectification information;

[0061] an extraction module, configured to extract a device unit ID of each device unit from the rectification information if the rectification information is verified;

[0062] An acquisition module, used to acquire the device medical record ID corresponding to each device unit ID, and acquire the corresponding device medical record based on each device medical record ID; wherein each device medical record stores historical basic data and historical rectification information of each device in the corresponding device unit;

[0063] A storage module, used for storing the rectification information, the total score of the equipment unit and the basic data in the corresponding equipment medical record book;

[0064] The output module is used to output a prompt message if the rectification information verification fails.

[0065] Based on the above-mentioned embodiment of the present invention, a method and device for managing the status of track equipment is provided, which collects basic data of multiple equipment units in the track; uses dynamic and static analysis algorithms and neural network analysis algorithms to analyze the basic data to obtain the total score of each equipment unit; inputs the total score into the system dispatch model to generate a maintenance task list; sends notification information to the operation and maintenance personnel according to the maintenance task list, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information; receives rectification information from the operation and maintenance personnel based on on-site maintenance feedback, and stores the rectification information, the total scores of multiple equipment units and the basic data in the equipment medical record book. The present invention comprehensively monitors the health status of urban rail transit line equipment by integrating multiple detection methods. Based on dynamic and static analysis algorithms and neural network analysis algorithms, intelligent analysis is performed to automatically generate maintenance task lists and achieve accurate dispatch. This method reduces the manual intervention link, shortens the maintenance response time, and improves maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0067] Figure 1 A flow chart of a method for managing track equipment status provided by an embodiment of the present invention;

[0068] Figure 2 A flowchart of calculating the total scores of multiple equipment units using a dynamic and static analysis algorithm and a neural network analysis algorithm provided by an embodiment of the present invention;

[0069] Figure 3 A flow chart of analyzing the dynamic and static analysis algorithm scores of a device unit by using a dynamic and static analysis algorithm provided by an embodiment of the present invention;

[0070] Figure 4 A flowchart of generating a maintenance task list provided by an embodiment of the present invention;

[0071] Figure 5 A flowchart of updating a device medical record provided by an embodiment of the present invention;

[0072] Figure 6 A schematic diagram of a method for managing track equipment status provided by an embodiment of the present invention;

[0073] Figure 7A structural block diagram of a track equipment status management device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0076] From the background technology, we can see that, firstly, inspection personnel are prone to missed detections and misjudgments in complex environments; the number of technical personnel is limited and cannot fully cover the equipment, which may cause delays in equipment maintenance; secondly, the detection tools are backward and cannot fully evaluate the equipment status; the data recording method is backward and difficult to update in real time and analyze efficiently, affecting decision-making; finally, the fault information is transmitted slowly, which affects the maintenance response speed and may increase the difficulty and cost of maintenance.

[0077] Therefore, an embodiment of the present invention provides a method and device for managing the status of track equipment, which collects basic data of multiple equipment units in the track; uses dynamic and static analysis algorithms and neural network analysis algorithms to analyze the basic data to obtain the total score of each equipment unit; inputs the total score into the system dispatch model to generate a maintenance task order; sends notification information to the operation and maintenance personnel according to the maintenance task order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information; receives rectification information from the operation and maintenance personnel based on on-site maintenance feedback, and stores the rectification information, the total scores of multiple equipment units and the basic data in the equipment medical record book. The present invention comprehensively monitors the health status of urban rail transit line equipment by integrating multiple detection methods. Based on dynamic and static analysis algorithms and neural network analysis algorithms, intelligent analysis is performed to automatically generate maintenance task orders to achieve accurate dispatch. This method reduces the manual intervention link, shortens the maintenance response time, and improves maintenance efficiency.

[0078] See also Figure 1 , shows a flow chart of a method for managing the status of a track device provided by an embodiment of the present invention, the method comprising:

[0079] Step S101: Collect basic data of multiple equipment units in the track.

[0080] The track is divided into mileage intervals (for example, every 25 meters) to obtain multiple mileage intervals, and each mileage interval corresponds to an equipment unit.

[0081] It should be noted that there are multiple devices in the track of each mileage section, so each equipment unit includes multiple devices.

[0082] In the process of implementing step S101, the geometrical dimension data of each device in the multiple equipment units in the track and the data detected in real time during the train running, such as vibration, speed, load, etc., are collected to obtain basic data. The basic data includes but is not limited to the manual static line inspection data of multiple equipment units in the track collected by manual inspection; the static inspection data of multiple equipment units in the track collected by track inspection instrument detection; the dynamic inspection data of multiple equipment units in the track collected by train inspection instrument detection; and the dynamic inspection data of multiple equipment units in the track collected by track inspection vehicle detection.

[0083] It is understandable that the basic data also includes basic information such as the time and location of the test.

[0084] It should be noted that the dynamic inspection data of the track inspection vehicle includes the track inspection vehicle overrun data and the track inspection vehicle TQI data.

[0085] It is understandable that the manual inspection method collects data on track gauge and level; the track inspection instrument inspection method collects data on track gauge, level and triangular pit; the train-adding instrument inspection method collects data on mileage, speed and vehicle model; and the track inspection vehicle inspection method collects data such as the allowable value difference management value of the dynamic geometric dimensions of the track and the TQI summary report.

[0086] It should be noted that when providing basic data, the data of the track inspection vehicle, track inspection vehicle overrun and track inspection vehicle TQI not only show the detection values, but also indicate the overrun level of these values. The biggest difference between this and manual line inspection data and track inspection vehicle data is that the latter usually only provide detection values ​​without indicating whether these values ​​exceed safety standards or limits.

[0087] Step S102: Analyze the basic data using dynamic and static analysis algorithms and neural network analysis algorithms to obtain a total score for each equipment unit.

[0088] In the specific implementation of step S102, the basic data is analyzed using a dynamic and static analysis algorithm to obtain a dynamic and static analysis algorithm score, and the basic data is analyzed using a neural network analysis algorithm to obtain a neural network analysis algorithm score. Based on the dynamic and static analysis algorithm score and the neural network analysis algorithm score, a weighted sum is taken to obtain the total score of multiple equipment units. For the specific calculation process, see Figure 2 The content shown includes:

[0089] Step S201: pre-process the basic data to obtain target basic data.

[0090] In the specific implementation of step S201, before analyzing the basic data using the dynamic and static analysis algorithms and the neural network analysis algorithm, the basic data is preprocessed, including preprocessing operations such as cleaning, denoising, and normalization, to obtain target basic data.

[0091] It can be understood that preprocessing the basic data can ensure that the data quality of the target basic data meets the input requirements of the neural network analysis model.

[0092] Step S202: for each equipment unit, the target basic data corresponding to the equipment unit is analyzed using a dynamic and static analysis algorithm according to a preset analysis method to obtain a dynamic and static analysis algorithm score.

[0093] It is understandable that the target basic data includes static data and dynamic data. Specifically, manual static line inspection data and track inspection instrument static inspection data are static data; and the train instrument dynamic inspection data and track inspection vehicle dynamic inspection data are dynamic data.

[0094] It should be noted that the preset analysis method is specifically mean analysis or peak analysis. Mean analysis refers to summing the absolute values ​​of the measured point data with values, then dividing it by the number of measured points to obtain the mean, and then calculating the scores of each geometric dimension according to the preset weight table, and finally summing them up to obtain the equipment unit score. Peak analysis refers to selecting the maximum value of the measured points with values ​​in each geometric dimension, calculating the scores according to the weight table and summing them.

[0095] In the specific implementation of step S202, for each device unit, the target basic data corresponding to the device unit is analyzed by the dynamic and static analysis algorithm according to the preset analysis method to obtain the dynamic and static analysis algorithm score of the device unit, thereby obtaining the dynamic and static analysis algorithm score of each device unit. Figure 3 The content shown includes:

[0096] Step S301: Obtain a preset analysis method.

[0097] It is understandable that the analysis method includes mean analysis or peak analysis. Therefore, when the preset analysis method is mean analysis, the analysis in the following analysis process is based on the mean analysis method; when the preset analysis method is peak analysis, the analysis in the following analysis process is based on the peak analysis method.

[0098] Step S302: for each equipment unit, the manual static line inspection data in the target basic data corresponding to the equipment unit is analyzed according to the analysis method to obtain a manual static unit score.

[0099] In the process of specifically implementing step S302, for each equipment unit, the artificial static line inspection data in the target basic data corresponding to the equipment unit is obtained; the scores of each geometric dimension in the artificial static line inspection data are analyzed according to the analysis method; the scores of all geometric dimensions are summed up to obtain the artificial static unit score of the equipment unit, thereby obtaining the artificial static unit scores of all equipment units.

[0100] Step S303: Analyze the track inspection instrument static detection data in the target basic data corresponding to the equipment unit according to the analysis method to obtain the track inspection instrument static unit score.

[0101] In the process of specifically implementing step S303, for each equipment unit, the track inspection instrument static detection data in the target basic data corresponding to the equipment unit is obtained; the scores of each geometric dimension in the track inspection instrument static detection data are analyzed according to the analysis method; the scores of all geometric dimensions are summed up to obtain the track inspection instrument static unit score of the equipment unit, thereby obtaining the track inspection instrument static unit scores of all equipment units.

[0102] Step S304: Analyze the dynamic detection data of the instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the dynamic unit score of the instrument.

[0103] In the process of specifically implementing step S304, for each equipment unit, the dynamic detection data of the adding-on instrument in the target basic data corresponding to the equipment unit is obtained, and the water addition score and the vertical addition score corresponding to the dynamic detection data of the adding-on instrument are analyzed according to the analysis method; the water addition score and the vertical addition score are summed to obtain the dynamic unit score of the adding-on instrument of the equipment unit, thereby obtaining the dynamic unit scores of the adding-on instrument of all equipment units.

[0104] Step S305: Analyze the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit to obtain the track inspection vehicle dynamic unit score.

[0105] It should be noted that the dynamic inspection data of the track inspection vehicle includes the over-limit data of the track inspection vehicle and the TQI data of the track inspection vehicle, specifically the track inspection report, such as the TQI summary report. Among them, the TQI summary report mainly includes the detection values ​​of the equipment units (such as horizontal geometric dimension values ​​and TQI values, etc.) and the conclusion on whether the TQI exceeds the standard. Therefore, regardless of whether the analysis method is mean analysis or peak analysis, it needs to be analyzed based on the detection value, and the dynamic unit score of the track inspection vehicle is the same.

[0106] In the process of specifically implementing step S305, for each equipment unit, the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit is obtained; all detection values ​​in the track inspection vehicle dynamic inspection data are analyzed to obtain the scores of all detection values; the sum of the scores of all detection values ​​is calculated to obtain the track inspection vehicle dynamic unit score of the equipment unit, thereby obtaining the track inspection vehicle dynamic unit scores of all equipment units.

[0107] Step S306: sum up the manual static unit score, the track inspection instrument static unit score, the train instrument dynamic unit score and the track inspection vehicle dynamic unit score to obtain the dynamic and static analysis algorithm score of the equipment unit.

[0108] In the process of implementing step S306, for each equipment unit, the sum of the manual static unit score, the track inspection instrument static unit score, the added instrument dynamic unit score and the track inspection vehicle dynamic unit score is calculated to obtain the dynamic and static analysis algorithm score of the equipment unit, thereby obtaining the dynamic and static analysis algorithm score of each equipment unit.

[0109] Step S203: Analyze the target basic data corresponding to the equipment unit using the model corresponding to the neural network analysis algorithm to obtain the neural network analysis algorithm score.

[0110] It should be noted that the corresponding model of the neural network analysis algorithm is a neural network analysis model built based on the classic recurrent neural network model (RNN) and the long short-term memory network model (LSTM), as well as the naive Bayes classifier.

[0111] It is understandable that the neural network analysis model sets multiple input channels according to the data type of the basic data (such as gauge, level, triangular pit, direction, height, etc.) for receiving the target basic data corresponding to the equipment unit. The neural network analysis model is pre-trained based on historical basic data.

[0112] In the process of specifically implementing step S203, for each device unit, the target basic data input value corresponding to the device unit is analyzed in the corresponding neural network analysis model of the neural network analysis algorithm to obtain the neural network analysis algorithm score of the device unit, thereby obtaining the neural network analysis algorithm scores of multiple device units.

[0113] Step S204: Based on the dynamic and static analysis algorithm scores, the preset dynamic and static analysis weights, the neural network analysis algorithm scores and the preset neural network analysis weights, the total scores of the equipment units are calculated to obtain the total scores of each equipment unit.

[0114] It should be noted that after obtaining the dynamic and static analysis algorithm scores and the neural network analysis algorithm scores, in order to further ensure that the unit quality evaluation of the equipment unit is more accurate, the preset dynamic and static analysis weights and the preset neural network analysis weights in the weight model are used to assist in determining the unit health status of the equipment unit (that is, expressed as the total score of the equipment unit).

[0115] In the specific implementation process of step S204, the implementation formula of the weight model is shown in formula (1). Based on the dynamic and static analysis algorithm scores, the preset dynamic and static analysis weights, the neural network analysis algorithm scores and the preset neural network analysis weights, the total score of the equipment unit is calculated to obtain the total score of each equipment unit.

[0116] S 总得分 =S1×K1+S2×K2(1)

[0117] Among them, S 总得分 is the total score of the equipment unit; S1 is the dynamic and static analysis algorithm score; K1 is the preset dynamic and static analysis weight; S2 is the neural network analysis algorithm score; K2 is the preset neural network analysis weight.

[0118] Step S103: input the total score into the system dispatching model to generate a maintenance task order.

[0119] In the specific implementation of step S103, the total score of each equipment unit is input into the system dispatching model, and the system dispatching model analyzes the comprehensive evaluation level of each equipment unit based on the total score of each equipment unit, thereby generating a maintenance task order. The specific analysis process is as follows: Figure 4 As shown, including:

[0120] Step S401: Receive the total score of each equipment unit through the system dispatch model, and obtain the speed level corresponding to the track.

[0121] It is understandable that at the completion stage of a track, its corresponding speed level is fixed. That is to say, the speed levels corresponding to each equipment unit on this track are the same. For example: the maximum speed designed for high-speed railway tracks is usually between 230 km / h and 320 km / h; the speed that EMU tracks usually support is between 150 km / h and 220 km / h; the designed speed of green train tracks is generally around 80 km / h.

[0122] In the process of specifically implementing step S401, the total score of each equipment unit is received through the system dispatching model, and at the same time, the speed level corresponding to the track is obtained.

[0123] Among them, the speed levels are, for example: 0 < V ≤ 80, 80 < V ≤ 120, 120 < V ≤ 160, 160 < V ≤ 200, a total of four speed levels.

[0124] Step S402: Determine the type of each equipment unit through the system dispatching model, and determine the evaluation level rule corresponding to each equipment unit according to the type and speed level.

[0125] It is understandable that different speed levels have different quality requirements for equipment units. The higher the speed level, the higher the quality requirements for equipment units, and the lower the speed level, the lower the quality requirements for equipment units. For example: the quality requirements for equipment units at the speed level of 0 < V ≤ 80 are lower than those at the speed level of 80 < V ≤ 120.

[0126] In the process of specifically implementing step S402, the type of each equipment unit (such as types of rail, curve, vertical curve, turnout, etc.) is determined through the system dispatching model, and the evaluation level rule corresponding to each equipment unit is determined according to the type and speed level.

[0127] It should be noted that the evaluation level rules are as follows:

[0128] (1) When the type of the equipment unit is rail, the evaluation level rules are as follows:

[0129] ① Speed level (0 < V ≤ 80); 0 ≤ S 总得分 <18 corresponding comprehensive evaluation level = excellent; 18 ≤ S 总得分 <20 corresponding comprehensive evaluation level = qualified; 20 ≤ S 总得分 <99999 corresponding comprehensive evaluation level = unqualified.

[0130] ② Speed level (80 < V ≤ 120); 0 ≤ S 总得分 <15 corresponding comprehensive evaluation level = excellent; 15≤S 总得分 <20 corresponds to comprehensive evaluation level = qualified; 20≤S 总得分 Corresponding comprehensive evaluation level <99999 = disqualified.

[0131] ③Speed ​​level (120 <V≤160); 0≤S 总得分 <15 corresponds to comprehensive evaluation level = excellent; 15≤S 总得分 <20 corresponds to comprehensive evaluation level = qualified; 20≤S 总得分 Corresponding comprehensive evaluation level <99999 = disqualified.

[0132] ④Speed ​​level (160 <V≤200); 0≤S 总得分 <3 corresponds to comprehensive evaluation level = excellent; 3≤S 总得分 <4 corresponds to comprehensive evaluation level = qualified; 4≤S 总得分 Corresponding comprehensive evaluation level <99999 = disqualified.

[0133] (2) When the equipment unit type is curve, vertical curve, or turnout, the evaluation grade rules are as follows:

[0134] ①Speed ​​level (0 <V≤80); 0≤S 总得分 <15 corresponds to comprehensive evaluation level = excellent; 15≤S 总得分 <20 corresponds to comprehensive evaluation level = qualified; 20≤S 总得分 Corresponding comprehensive evaluation level <99999 = disqualified.

[0135] ②Speed ​​level (80 <V≤120); 0≤S 总得分 <15 corresponds to comprehensive evaluation level = excellent; 15≤S 总得分 <20 corresponds to comprehensive evaluation level = qualified; 20≤S 总得分 Corresponding comprehensive evaluation level <99999 = disqualified.

[0136] ③Speed ​​level (120 <V≤160); 0≤S 总得分 <15 corresponds to comprehensive evaluation level = excellent; 15≤S 总得分 <20 corresponds to comprehensive evaluation level = qualified; 20 ≤ S 总得分 <99999 corresponds to the comprehensive evaluation level = unqualified.

[0137] ④ Speed level (160 < V ≤ 200); 0 ≤ S 总得分 <15 corresponds to the comprehensive evaluation level = excellent; 15 ≤ S 总得分 <20 corresponds to the comprehensive evaluation level = qualified; 20 ≤ S 总得分 <99999 corresponds to the comprehensive evaluation level = unqualified.

[0138] Step S403: Analyze the comprehensive evaluation level of each equipment unit through the system dispatching model based on the evaluation level rules.

[0139] In the process of specifically implementing step S403, analyze the comprehensive evaluation level of each equipment unit through the system dispatching model according to the evaluation level rules corresponding to each equipment unit.

[0140] For example: The type of equipment unit A is rail, corresponding to the speed level (120 < V ≤ 160); the S of equipment unit A 总得分 = 14, then the comprehensive evaluation level of equipment unit A is excellent.

[0141] Another example: The type of equipment unit B is curve, corresponding to the speed level (80 < V ≤ 120); the S of equipment unit B 总得分 = 80, then the comprehensive evaluation level of equipment unit B is unqualified.

[0142] Step S404: Generate a maintenance work order through the system dispatching model according to the priority of the comprehensive evaluation level, the comprehensive evaluation level, and the equipment unit.

[0143] It should be noted that in the comprehensive evaluation level, the priority of unqualified is higher than that of qualified, and the priority of qualified is higher than that of excellent.

[0144] In the process of specifically implementing step S404, generate a maintenance work order through the system dispatching model according to the priority of the comprehensive evaluation level of each equipment unit, the comprehensive evaluation level of the equipment unit, and the basic data of the equipment unit.

[0145] It can be understood that the maintenance work order includes but is not limited to the location of the equipment unit and the rectification requirements of the equipment unit, etc. Among them, the rectification requirements can be understood as repairing the equipment unit with an unqualified comprehensive evaluation level to a qualified or excellent comprehensive evaluation level.

[0146] Step S104: Send a notification message to the operation and maintenance personnel according to the maintenance work order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification message.

[0147] In the specific implementation of step S104, notification information is sent to the operation and maintenance personnel according to the maintenance task list, that is, the maintenance task list is sent to the operation and maintenance personnel, and the operation and maintenance personnel are organized to perform on-site maintenance work.

[0148] It should be noted that the notification information can be sent to the operation and maintenance personnel through the mobile terminal device, wherein the notification information can be in the form of charts and data.

[0149] It is understandable that sending notification information to operation and maintenance personnel through mobile devices can ensure that operation and maintenance personnel can respond to emergencies quickly and improve work efficiency in real time. In addition, timely notification reduces response time, allows for quick on-site maintenance operations, and optimizes the problem-solving process. At the same time, notification information in the form of charts and data can help operation and maintenance personnel perform maintenance better.

[0150] Step S105: receiving rectification information from the operation and maintenance personnel based on the on-site maintenance feedback, and storing the rectification information, the total scores of the multiple equipment units and the basic data in the equipment medical record book.

[0151] It is understandable that after the operation and maintenance personnel carry out on-site maintenance work, they will fill in the rectification information based on the on-site maintenance situation, including but not limited to the mileage range of the rectification, the geometric dimensions after the rectification, the rectification workload, the rail temperature before and after the rectification, and the material conditions, etc.

[0152] It should be noted that the operation and maintenance personnel can specifically add rectification information through the mobile terminal device. The system receives the rectification information fed back by the mobile terminal device through a customized standardized data interface (interface API).

[0153] In the specific implementation of step S105, after receiving the rectification information based on the on-site maintenance feedback from the operation and maintenance personnel, the equipment medical record of each equipment unit is updated in combination with the rectification information, the total scores of the multiple equipment units and the basic data.

[0154] It should be noted that the equipment medical record book at least includes the medical record book ID, inspection time, inspection location (such as track mileage), inspection items, evaluation level (such as specific values ​​or ratings), inspection equipment information, etc.

[0155] It is understandable that updating the equipment medical record book of each equipment unit helps provide data support for subsequent operation and maintenance personnel when managing the equipment on the track, ensuring the traceability and accuracy of the data.

[0156] Specific stored procedures such as Figure 5 As shown, including:

[0157] Step S501: perform format verification on the rectification information; if the rectification information verification passes, execute step S502; if the rectification information verification fails, execute step S505.

[0158] It should be noted that a standardized data format is pre-customized in the system. Therefore, the rectification information needs to be formatted and verified. If the verification passes, further storage operations are performed. If the verification fails, the operation and maintenance personnel need to be informed to make modifications or other operations.

[0159] For example, if the format of a field is required to be in digital format, but the operation and maintenance personnel feedback is in Chinese characters, the verification will fail.

[0160] Step S502: If the rectification information is verified to be successful, then the device unit ID of each device unit is extracted from the rectification information.

[0161] In the specific implementation process of step S502 , if the rectification information is verified to be successful, the rectification information is analyzed, and the device unit ID of each device unit is extracted from the rectification information.

[0162] In some specific embodiments, after analyzing the rectification information, key information such as track mileage, equipment unit ID, and maintenance details are extracted from the rectification information.

[0163] Step S503: Obtain the equipment medical record ID corresponding to each equipment unit ID, and obtain the corresponding equipment medical record based on each equipment medical record ID.

[0164] Among them, each equipment medical record book stores the historical basic data and historical rectification information of each equipment in the corresponding equipment unit.

[0165] In the specific implementation of step S503, after extracting the device unit ID of each device unit, the device medical record ID corresponding to each device unit ID is obtained from the database, thereby obtaining the corresponding device medical record based on each device medical record ID.

[0166] Step S504: store the rectification information, the total score of the equipment unit and the basic data in the corresponding equipment medical record book.

[0167] In the specific implementation of step S504, the rectification information corresponding to each equipment unit, the total score of the equipment unit and the pre-processed basic data (ie, the target basic data) are correspondingly stored in the equipment medical record book corresponding to each equipment unit.

[0168] In the specific application process, if the rectification information includes the rectification information of the new equipment, a new record of the new equipment is created in the equipment medical record book (i.e., a new inspection record or maintenance record is added). Among them, the new equipment is the equipment that has no relevant records in the equipment medical record book. In other words, in this equipment unit, the current new equipment has never been inspected or repaired.

[0169] In some embodiments, the equipment medical record will also store the comprehensive evaluation level of the corresponding equipment unit, so that the life of the equipment unit can be predicted based on the equipment medical record. For example, if the comprehensive evaluation level of the equipment unit is found to have dropped from excellent to qualified based on the equipment medical record, it means that the life of the equipment unit is about to end. If the comprehensive evaluation level drops to disqualified, it indicates that the equipment unit needs to be repaired.

[0170] Step S505: If the rectification information verification fails, a prompt message is output.

[0171] In the specific implementation of step S505, if the rectification information fails to be verified, a prompt message is output to prompt which field in the rectification information fails to be verified, so that the operation and maintenance personnel can make modifications based on the prompt message.

[0172] In some specific embodiments, the operation log of the equipment medical history book is recorded and updated to achieve rapid integration of maintenance information and dynamic update of the health status of the equipment unit, providing a solid foundation for long-term maintenance and fault diagnosis of the equipment unit.

[0173] It is understandable that the "Equipment Medical Record Book" can intuitively reflect the health status of the equipment and historical inspection and maintenance records, which helps operation and maintenance personnel to more effectively perform daily equipment health diagnosis and track quality assessment. When a problem occurs, it can also accurately locate the specific equipment within the 25-meter mileage range, thereby significantly reducing the workload of operation and maintenance personnel in locating the disease. Based on this, the "Equipment Medical Record Book" effectively improves the efficiency of inspection and maintenance, saves costs, and improves the quality of results.

[0174] Combined with the above Figures 1 to 5 For the contents shown, see Figure 6 , showing a schematic diagram of a method for managing the status of track equipment provided by an embodiment of the present invention.

[0175] A method for managing the status of track equipment provided by an embodiment of the present invention can essentially be understood as a closed-loop quality management method for track lines. In this method, first, according to the detection cycle, various detection instruments and methods are used to detect the geometric dimensions and other safety parameters of each device in the track to obtain basic data. Then, through dynamic and static analysis algorithms and neural network analysis algorithms, targeted analysis results (i.e., the total score of each equipment unit) are obtained. Then, these analysis results and basic data are summarized and screened to form a corresponding maintenance task order, and finally implemented in daily maintenance operations. The effectiveness of the previous maintenance rectification can be verified through the basic data detected next time, and it can be used to guide subsequent maintenance work. At the same time, the "equipment medical record book" is used to feedback and register the test results and maintenance results to form a traceable line equipment history.

[0176] In an embodiment of the present invention, basic data of multiple equipment units in the track are collected; the basic data are analyzed using dynamic and static analysis algorithms and neural network analysis algorithms to obtain a total score for each equipment unit; a maintenance task order is generated based on the total score; rectification information based on on-site maintenance feedback from operation and maintenance personnel is received, and the rectification information, the total scores of multiple equipment units and the basic data are stored in the equipment medical record book. The present invention comprehensively monitors the health status of urban rail transit line equipment by integrating multiple detection methods. Intelligent analysis is performed based on dynamic and static analysis algorithms and neural network analysis algorithms, and maintenance task orders are automatically generated to achieve accurate dispatching. The manual intervention link is reduced, the maintenance response time is shortened, and the maintenance efficiency is improved.

[0177] Corresponding to the method for managing the track equipment status provided by the above embodiment of the present invention, see Figure 7 , shows a structural block diagram of a track equipment status management device provided by an embodiment of the present invention. The device includes: a collection unit 701, an analysis unit 702, a generation unit 703, a notification unit 704 and a storage unit 705.

[0178] The collection unit 701 is used to collect basic data of multiple equipment units in the track; wherein each equipment unit corresponds to a mileage interval in the track, and the basic data is the basic data of multiple equipment in the mileage interval.

[0179] The analysis unit 702 is used to analyze the basic data using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each equipment unit.

[0180] The generating unit 703 is used to input the total score into the system dispatching model to generate a maintenance task order.

[0181] The notification unit 704 is used to send notification information to the operation and maintenance personnel according to the maintenance task order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information.

[0182] The storage unit 705 is used to receive rectification information from the operation and maintenance personnel based on the on-site maintenance feedback, and store the rectification information, the total scores of multiple equipment units and basic data in the equipment medical record book.

[0183] In an embodiment of the present invention, basic data of multiple equipment units in the track are collected; the basic data are analyzed using dynamic and static analysis algorithms and neural network analysis algorithms to obtain a total score for each equipment unit; a maintenance task order is generated based on the total score; rectification information based on on-site maintenance feedback from operation and maintenance personnel is received, and the rectification information, the total scores of multiple equipment units and the basic data are stored in the equipment medical record book. The present invention comprehensively monitors the health status of urban rail transit line equipment by integrating multiple detection methods. Intelligent analysis is performed based on dynamic and static analysis algorithms and neural network analysis algorithms, and maintenance task orders are automatically generated to achieve accurate dispatching. The manual intervention link is reduced, the maintenance response time is shortened, and the maintenance efficiency is improved.

[0184] Combination Figure 7 The content shown, the analysis unit 702, includes: a preprocessing module, a first analysis module, a second analysis module and a calculation module.

[0185] The preprocessing module is used to preprocess the basic data to obtain target basic data.

[0186] The first analysis module is used to analyze the target basic data corresponding to each equipment unit using a dynamic and static analysis algorithm according to a preset analysis method to obtain a dynamic and static analysis algorithm score, wherein the analysis method includes mean analysis or peak analysis.

[0187] The second analysis module is used to analyze the target basic data corresponding to the equipment unit using the corresponding model of the neural network analysis algorithm to obtain the score of the neural network analysis algorithm.

[0188] The calculation module is used to calculate the total score of the equipment unit based on the dynamic and static analysis algorithm scores, preset dynamic and static analysis weights, the neural network analysis algorithm scores and preset neural network analysis weights to obtain the total score of each equipment unit.

[0189] Combination Figure 7 The content shown, the first analysis module, includes: an acquisition submodule, a first analysis submodule, a second analysis submodule, a third analysis submodule, a fourth analysis submodule and a summation submodule.

[0190] Get submodule, used to get preset analysis method.

[0191] The first analysis submodule is used to analyze the manual static line inspection data in the target basic data corresponding to each equipment unit according to the analysis method to obtain the manual static unit score.

[0192] The second analysis submodule is used to analyze the static detection data of the track inspection instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the static unit score of the track inspection instrument.

[0193] The third analysis submodule is used to analyze the dynamic detection data of the instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the dynamic unit score of the instrument.

[0194] The fourth analysis submodule is used to analyze the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit to obtain the track inspection vehicle dynamic unit score.

[0195] The summation submodule is used to sum the manual static unit score, the track inspection instrument static unit score, the train instrument dynamic unit score and the track inspection vehicle dynamic unit score to obtain the dynamic and static analysis algorithm score of the equipment unit.

[0196] Combination Figure 7 The content shown, the generating unit 703, includes: a receiving module, a determining module, an analyzing level module and a generating module.

[0197] The receiving module is used to receive the total score of each equipment unit through the system dispatching model and obtain the speed grade corresponding to the track.

[0198] The determination module is used to determine the type of each equipment unit through the system dispatch model, and determine the evaluation level rules corresponding to each equipment unit according to the type and speed level.

[0199] The analysis grade module is used to analyze the comprehensive evaluation grade of each equipment unit based on the evaluation grade rules through the system dispatch model.

[0200] The generation module is used to generate a maintenance task order according to the priority of the comprehensive evaluation level, the comprehensive evaluation level and the equipment unit through the system dispatching model.

[0201] Combination Figure 7 The content shown, the storage unit 705, includes: a format verification module, an extraction module, an acquisition module, a storage module and an output module.

[0202] The format verification module is used to verify the format of rectification information.

[0203] The extraction module is used to extract the device unit ID of each device unit from the rectification information if the rectification information is verified.

[0204] The acquisition module is used to obtain the equipment medical record book ID corresponding to each equipment unit ID, and obtain the corresponding equipment medical record book based on each equipment medical record book ID; wherein each equipment medical record book stores the historical basic data and historical rectification information of each equipment in the corresponding equipment unit.

[0205] The storage module is used to store the rectification information, the total score of the equipment unit and the basic data in the corresponding equipment medical record book.

[0206] The output module is used to output prompt information if the rectification information verification fails.

[0207] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0208] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0209] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing the status of track equipment, characterized in that: The method comprises: Collecting basic data of multiple equipment units in the track; wherein each of the equipment units corresponds to a mileage interval in the track, and the basic data is basic data of multiple equipment in the mileage interval; Analyzing the basic data using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each of the equipment units; Input the total score into the system dispatching model to generate a maintenance task order; Sending notification information to the operation and maintenance personnel according to the maintenance task order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information; Receive rectification information from the operation and maintenance personnel based on on-site maintenance feedback, and store the rectification information, the total scores of the plurality of equipment units and the basic data in the equipment medical record book.

2. The method according to claim 1, characterized in that The basic data is analyzed using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each of the equipment units, including: Preprocessing the basic data to obtain target basic data; For each of the equipment units, a dynamic and static analysis algorithm is used to analyze the target basic data corresponding to the equipment unit according to a preset analysis method to obtain a dynamic and static analysis algorithm score, wherein the analysis method includes mean analysis or peak analysis; Analyzing the target basic data corresponding to the equipment unit using a model corresponding to the neural network analysis algorithm to obtain a neural network analysis algorithm score; Based on the dynamic and static analysis algorithm scores, preset dynamic and static analysis weights, the neural network analysis algorithm scores and preset neural network analysis weights, the total scores of the equipment units are calculated to obtain the total scores of each of the equipment units.

3. The method according to claim 2, characterized in that For each of the equipment units, the target basic data corresponding to the equipment unit is analyzed using a dynamic and static analysis algorithm according to a preset analysis method to obtain a dynamic and static analysis algorithm score, including: Get the preset analysis method; For each of the equipment units, analyzing the manual static line inspection data in the target basic data corresponding to the equipment unit according to the analysis method to obtain a manual static unit score; Analyze the static detection data of the track inspection instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain a static unit score of the track inspection instrument; Analyze the dynamic detection data of the instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the dynamic unit score of the instrument; Analyze the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit to obtain the track inspection vehicle dynamic unit score; The manual static unit score, the track inspection instrument static unit score, the train-adding instrument dynamic unit score and the track inspection vehicle dynamic unit score are summed to obtain the dynamic and static analysis algorithm score of the equipment unit.

4. The method according to claim 1, characterized in that: The total score is input into the system dispatching model to generate a maintenance task order, including: Receiving the total score of each of the equipment units through the system dispatching model, and obtaining the speed grade corresponding to the track; Determine the type of each of the equipment units through the system dispatch model, and determine the evaluation level rule corresponding to each of the equipment units according to the type and the speed level; Analyzing the comprehensive evaluation level of each of the equipment units based on the evaluation level rules through the system dispatching model; A maintenance task order is generated through the system dispatching model according to the priority of the comprehensive evaluation level, the comprehensive evaluation level and the equipment unit.

5. The method according to claim 1, characterized in that The storing of the rectification information, the total scores of the plurality of equipment units and the basic data in the equipment medical record book comprises: Performing format verification on the rectification information; If the rectification information is verified, extracting the device unit ID of each device unit from the rectification information; Obtain the device medical record ID corresponding to each device unit ID, and obtain the corresponding device medical record based on each device medical record ID; wherein each device medical record stores historical basic data and historical rectification information of each device in the corresponding device unit; Storing the rectification information, the total score of the equipment unit and the basic data in the corresponding equipment medical record book; If the rectification information verification fails, a prompt message is output.

6. A track equipment status management device, characterized in that: The device comprises: A collection unit, used for collecting basic data of a plurality of equipment units in the track; wherein each of the equipment units corresponds to a mileage interval in the track, and the basic data is basic data of a plurality of equipment in the mileage interval; An analysis unit, used to analyze the basic data using a dynamic and static analysis algorithm and a neural network analysis algorithm to obtain a total score for each of the equipment units; A generating unit, used for inputting the total score into a system dispatching model to generate a maintenance task order; A notification unit, used to send notification information to the operation and maintenance personnel according to the maintenance task order, so that the operation and maintenance personnel can perform on-site maintenance according to the notification information; The storage unit is used to receive the rectification information of the operation and maintenance personnel based on the on-site maintenance feedback, and store the rectification information, the total scores of the multiple equipment units and the basic data in the equipment medical record book.

7. The device according to claim 6, characterized in that The analysis unit comprises: A preprocessing module, used for preprocessing the basic data to obtain target basic data; A first analysis module is used to analyze the target basic data corresponding to each of the equipment units using a dynamic and static analysis algorithm according to a preset analysis method to obtain a dynamic and static analysis algorithm score, wherein the analysis method includes mean analysis or peak analysis; The second analysis module is used to analyze the target basic data corresponding to the equipment unit using a model corresponding to the neural network analysis algorithm to obtain a neural network analysis algorithm score; A calculation module is used to calculate the total score of the equipment unit based on the dynamic and static analysis algorithm score, the preset dynamic and static analysis weights, the neural network analysis algorithm score and the preset neural network analysis weights to obtain the total score of each equipment unit.

8. The device according to claim 7, characterized in that The first analysis module comprises: Get submodule, used to get preset analysis method; A first analysis submodule is used to analyze the manual static line inspection data in the target basic data corresponding to each of the equipment units according to the analysis method to obtain a manual static unit score; A second analysis submodule is used to analyze the static detection data of the track inspection instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain a static unit score of the track inspection instrument; The third analysis submodule is used to analyze the dynamic detection data of the instrument in the target basic data corresponding to the equipment unit according to the analysis method to obtain the dynamic unit score of the instrument; A fourth analysis submodule is used to analyze the track inspection vehicle dynamic inspection data in the target basic data corresponding to the equipment unit to obtain a track inspection vehicle dynamic unit score; The summing submodule is used to sum the manual static unit score, the track inspection instrument static unit score, the train-adding instrument dynamic unit score and the track inspection vehicle dynamic unit score to obtain the dynamic and static analysis algorithm score of the equipment unit.

9. The device according to claim 6, characterized in that The generating unit comprises: A receiving module, used for receiving the total score of each of the equipment units through a system dispatching model, and obtaining the speed grade corresponding to the track; A determination module, used to determine the type of each of the equipment units through the system dispatch model, and determine the evaluation level rule corresponding to each of the equipment units according to the type and the speed level; An analysis grade module, used for analyzing the comprehensive evaluation grade of each of the equipment units based on the evaluation grade rule through the system dispatch model; A generation module is used to generate a maintenance task order according to the priority of the comprehensive evaluation level, the comprehensive evaluation level and the equipment unit through the system dispatching model.

10. The device according to claim 6, characterized in that The storage unit comprises: A format verification module, used to perform format verification on the rectification information; an extraction module, configured to extract a device unit ID of each device unit from the rectification information if the rectification information is verified; An acquisition module, used to acquire the device medical record ID corresponding to each device unit ID, and acquire the corresponding device medical record based on each device medical record ID; wherein each device medical record stores historical basic data and historical rectification information of each device in the corresponding device unit; A storage module, used for storing the rectification information, the total score of the equipment unit and the basic data in the corresponding equipment medical record book; The output module is used to output a prompt message if the rectification information verification fails.

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