Trackside signal equipment data full life cycle processing method

Abstract

The invention discloses a trackside signal equipment data full life cycle processing method. The method comprises the following steps: S1) acquiring static data and dynamic data of trackside equipment; S2) encoding the static data by using an equipment grading standardization encoding rule and forming a static data tree model according to the full life cycle of the trackside equipment; s3) optimizing the static data tree model by using a standardized semantic description rule and forming an optimized static data tree model; s4) fusing the dynamic data to the optimized static data tree model and forming a full data model of the trackside equipment; s5) importing an equipment state prediction management rule and judging the security level of the trackside equipment according to the equipment state prediction management rule and the full data model, wherein the trackside equipment is divided into a plurality of levels by the equipment state prediction management rule; and S6) the safety level is used for maintenance operation of the trackside equipment.

Description

Technical Field

[0001] The present invention relates to the field of rail transit, and in particular to a method for processing data of trackside signal equipment throughout its entire life cycle. Background Art

[0002] With the continuous acceleration of urbanization, regional economic interaction and the continuous construction of half-hour economic circles, rail transit plays a vital role in the urban transportation system.

[0003] Since the current trackside signal equipment data is scattered, it is mainly distributed in the design trackside signal equipment life cycle is mainly divided into survey and design, manufacturing and installation, and operation and maintenance management stages. In the survey and design stage, designers form basic demand data such as design and manufacturing specifications based on social evaluation, economic benefits, and field environment assessment indicators, and further decompose the basic demand data into functional requirements and structural requirements in combination with experience and knowledge; in the manufacturing and installation stage, the equipment manufacturer will produce the equipment according to the design drawings. The general contractor, construction contractor, and installation engineer need to ensure the matching relationship between civil engineering and equipment, and the precise matching relationship between equipment and equipment to ensure that the equipment can operate well. In this process, the type, quantity, and installation location of the equipment may be changed within the scope allowed by the design due to considerations such as the natural environment and mechanical effects. The changed process data also needs to be recorded; in the operation and maintenance management stage, various sensors collect data and equipment failure repair data in real time, and store and manage the operation data through the information system to provide reliable and complete operation data support for subsequent data analysis.

[0004] Therefore, trackside signal equipment has the characteristics of multi-stage, multi-scenario, and strict safety. Therefore, trackside signal equipment is an important equipment to ensure the safe operation of rail transit. The data of trackside signal equipment has the problems of large volume, wide sources, inconsistent standards, difficult to obtain, easy to lose, and low degree of collaborative use.

[0005] Trackside signal equipment is rich in types and quantity, and the actual working conditions are complex. The difficulties and characteristics of traditional trackside signal equipment management are as follows: First, there is a problem of out-of-control of cross-domain data representation, and it is difficult to ensure the consistency of cross-domain mapping and coupling of multi-source heterogeneous data. Secondly, there will be a loss of control over cross-departmental safety traceability. This cross-departmental quality formation and control characteristics lead to unclear trackside signal safety characteristics, fuzzy evolution of quality status, and difficulty in tracing back quality defects. Thirdly, there is a loss of control over cross-scenario safety management. The working conditions of rail transit lines are complex and involve multi-scenario alternating operation and maintenance. There are multiple signal modal perceptions such as mechanical vibration and electromagnetic sensing during the operation of trackside signal equipment. From engineering construction installation and testing to operation, maintenance and overhaul, there is a multi-scenario spatiotemporal coupling, and the state linkage of multiple devices such as switch machines "commanding" signal machines. These characteristics make it difficult to accurately monitor and predict the operating status of trackside signals. Summary of the invention

[0006] In order to solve the above problems, the purpose of this application is to provide a full life cycle processing method that can manage trackside signal equipment.

[0007] The present application provides a method for processing trackside signal equipment data throughout its life cycle, including:

[0008] S1) acquiring static data and dynamic data of trackside equipment, wherein the static data includes design stage data, manufacturing stage data and installation stage data, and the dynamic data includes equipment failure data, equipment work order data, equipment point inspection data and integrated operation and maintenance platform data;

[0009] S2) encoding the static data using equipment hierarchical standardized coding rules and forming a static data tree model according to the full life cycle of the trackside equipment;

[0010] S3) optimizing the static data tree model using standardized semantic description rules and forming an optimized static data tree model, wherein the standardized semantic description rules unify the state language description of the trackside equipment by engineers in different fields;

[0011] S4) fusing the dynamic data into the optimized static data tree model to form a full data model of the trackside equipment;

[0012] S5) importing equipment state prediction management rules and judging the safety level of the trackside equipment according to the equipment state prediction management rules and the full data model, wherein the equipment state prediction management rules divide the trackside equipment into multiple levels;

[0013] S6) Using the safety level for maintenance work of the trackside equipment.

[0014] Furthermore, in the method for processing the trackside signal equipment data throughout its life cycle, the static data is collected through an open interface, database extraction, and file import.

[0015] Furthermore, in the method for processing trackside signal equipment data throughout its life cycle, the equipment classification and standardized coding rules further include line-level coding rules, stations and coding rules, equipment types and coding rules, and equipment-level coding rules.

[0016] Furthermore, in the method for processing the trackside signal equipment data throughout its life cycle, the equipment fault data further includes the fault location, fault cause, fault duration, fault handling time, fault handling subject and fault event flow status.

[0017] Furthermore, in the method for processing trackside signal equipment data throughout its life cycle, the equipment work order data is acquired through an external work order system.

[0018] Furthermore, in the method for processing trackside signal equipment data throughout its life cycle, the equipment work order data adopts the JSON data exchange format.

[0019] Furthermore, in the method for processing trackside signal equipment data throughout its life cycle, the equipment point inspection data is acquired through an external point inspection system.

[0020] Furthermore, in the method for processing the full life cycle of the trackside signal equipment data, the full data model includes the working time and action frequency of the trackside equipment, and judging the safety level of the trackside equipment further includes:

[0021] S41) obtaining the multiple levels of the equipment state prediction management rule;

[0022] S42) Determine the level of the trackside equipment according to the working time and the action frequency.

[0023] Furthermore, in the method for processing trackside signal equipment data throughout its life cycle, the multiple levels further include healthy status, good status, general status, fault alarm, and safety hazard.

[0024] Furthermore, in the method for processing the data of the trackside signal equipment throughout its life cycle, the maintenance operation further comprises:

[0025] When the level of the trackside equipment is in the healthy state, reducing inspections;

[0026] When the trackside equipment is in good condition, a routine inspection is performed;

[0027] When the level of the trackside equipment is the general status, the inspection is strengthened;

[0028] When the level of the trackside equipment is the fault alarm, replacing components of the trackside equipment;

[0029] When the level of the trackside equipment is the safety hazard, the trackside equipment is replaced with a new one.

[0030] The technical solution provided by the embodiment of the present application has the following advantages:

[0031] 1. Due to the use of static and dynamic data, the safety level of trackside equipment can be better and more completely monitored;

[0032] 2. Due to the use of equipment classification and standardized coding rules, trackside signal equipment can be managed by standardized coding and stored in the database;

[0033] 3. Since the equipment status prediction management rules have been imported, the equipment status safety level can be conveniently divided according to the full data model. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a preferred method for processing trackside equipment data throughout its life cycle in an embodiment of the present invention;

[0035] Figure 2 This is a flowchart of a specific application example of a method for processing trackside signal equipment data throughout its life cycle, which is preferred in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical scheme and advantages of the implementation of this application clearer, the technical scheme in the embodiment of this application will be described in more detail below in conjunction with the drawings in the embodiment of this application. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions. The described embodiments are part of the embodiments of this application, not all of them. The embodiments described below with reference to the drawings are exemplary and are intended to be used to explain this application, and should not be construed as limitations on this application. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0037] In addition, it should be noted that, unless otherwise clearly specified and limited, the words "installed", "connected", "connected" and similar terms used in the description of this application should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, an indirect connection through an intermediate medium, or the internal connection of two components. Technical personnel in the field can understand their specific meanings in this application according to the specific circumstances.

[0038] Figure 1 This is a preferred method for processing trackside equipment data throughout its life cycle in an embodiment of the present invention. Figure 1 As shown in the figure, the full life cycle processing method of trackside signal equipment data includes:

[0039] S1) acquiring static data and dynamic data of trackside equipment, wherein the static data includes design stage data, manufacturing stage data and installation stage data, and the dynamic data includes equipment failure data, equipment work order data, equipment point inspection data and integrated operation and maintenance platform data;

[0040] S2) encoding the static data using equipment hierarchical standardized coding rules and forming a static data tree model according to the full life cycle of the trackside equipment;

[0041] S3) optimizing the static data tree model using standardized semantic description rules and forming an optimized static data tree model, wherein the standardized semantic description rules unify the state language description of the trackside equipment by engineers in different fields;

[0042] S4) fusing the dynamic data into the optimized static data tree model to form a full data model of the trackside equipment;

[0043] S5) importing equipment state prediction management rules and judging the safety level of the trackside equipment according to the equipment state prediction management rules and the full data model, wherein the equipment state prediction management rules divide the trackside equipment into multiple levels;

[0044] S6) Using the safety level for maintenance work of the trackside equipment.

[0045] The specific steps of the method for processing trackside signal equipment data throughout its life cycle are further described below in conjunction with the accompanying drawings.

[0046] Step S1) Obtain static data and dynamic data of trackside equipment, wherein the static data includes design phase data, manufacturing phase data and installation phase data, and the dynamic data includes equipment failure data, equipment work order data, equipment point inspection data and integrated operation and maintenance platform data.

[0047] Specifically, static data refers to data that has been clearly defined and will not change during the entire life cycle of trackside signal equipment management. Static data mainly includes: data such as manufacturer name, mechanical drawings, assembly drawings and electrical working principle drawings in the design stage; equipment material, shape and color data in the manufacturing stage; purchase and inventory data in the procurement stage; line location, spatial location, installation work instructions and other data in the installation stage; debugging time and number, operation status of each equipment, linkage fault points, causes of linkage faults and other data in the linkage debugging stage.

[0048] Specifically, static data is collected through open interfaces, database extraction and file import. It also includes manual filling and collection, and conversion of paper files into digital files.

[0049] Step S2) Encode the static data using equipment hierarchical standardized coding rules and form a static data tree model based on the entire life cycle of the trackside equipment.

[0050] Specifically, the equipment classification standardization coding rules further include line-level coding rules, station and coding rules, equipment class and coding rules, and equipment-level coding rules. Among them, the equipment classification standardization coding rules are used for standardized coding management of trackside signal equipment and stored in the database. In this embodiment, the equipment class and coding rules are preferably used for classification management of equipment, and line-level-station-equipment class-equipment level coding rules are created step by step. For example, line level (Line) L1, L2, L3... represent Line 1, Line 2, Line 3...; station level, the stations of a line are named 01, 02, 03... from the bottom left to the top right... represent Station 1, Station 2, Station 3..., then L1403, represents the third station from left to right on Line 14, which is Lintao Road Station; the equipment class level uses C to represent the turnout switch machine class, X represents the signal machine class, J represents the axle counter class, and Y represents the transponder class; equipment level: each specific device has a line number, which is fixed. In general, for example, L1406C02 means: L14 (Line 14) -06 (Zhenxinxincun Station) -C means the turnout machine -02 means the second turnout machine.

[0051] Specifically, the static data of the entire life cycle of a certain equipment is also divided into stages such as design, manufacturing, installation, operation and maintenance, and scrapping, forming an equipment static data tree model.

[0052] Step S3) using standardized semantic description rules to optimize the static data tree model and form an optimized static data tree model, wherein the standardized semantic description rules unify the state language descriptions of the trackside equipment by engineers in different fields.

[0053] Specifically, standardized semantic description rules are used to regulate the language description of equipment status by engineers in different fields during the entire life cycle of trackside signal equipment and to unify the language scenarios used to represent the equipment maintenance process.

[0054] Step S4) The dynamic data is integrated into the optimized static data tree model to form a full data model of the trackside equipment.

[0055] Specifically, dynamic data refers to the data dynamically updated during the operation and use of trackside signal equipment, mainly including: equipment failure data, equipment work order data, equipment inspection data and comprehensive intelligent operation and maintenance platform data. After the static data model of the equipment is built, the current equipment dynamic data is obtained from each platform based on the unique equipment code, and the data obtained from each platform is integrated to retain the main information, remove duplicate and redundant information, and form multiple data into one data to form a full data model. This step can avoid data dispersion and multi-format storage.

[0056] Specifically, the equipment fault data further includes the fault location, fault cause, fault duration, fault handling time, fault handling subject and fault event flow status. Among them, the fault data can be managed, integrated into the numbered equipment and synchronized into the fault database.

[0057] Furthermore, the dynamic data of the equipment work order data comes from the external work order system. The work order system is connected using the JSON data exchange format. The interface content includes the interface address, interface protocol, storage data, data content and method definition. The main contents of the work order data include: work order number, work team, equipment code, equipment name, work content, work type, work type description, work person in charge, work approver, actual start time, actual completion time, work order completion description and work order status.

[0058] The equipment point inspection data comes from the external point inspection system, and the equipment point inspection data is obtained through the external point inspection system. The JSON data exchange format is used for the point inspection system, and the interface content includes the interface address, interface protocol, storage data, data content and method definition. The main contents of the point inspection data: plan number, task number, equipment code, equipment name, inspection serial number, inspection items, normal and recorded value.

[0059] Step S5) importing equipment status prediction management rules and judging the safety level of the trackside equipment according to the equipment status prediction management rules and the full data model, wherein the equipment status prediction management rules divide the trackside equipment into multiple levels.

[0060] Specifically, the equipment status safety level is divided based on the analysis of static and dynamic data of the equipment through the equipment status prediction management rules, and then the current equipment health status level is determined, and work guidance is provided to maintenance personnel according to different safety levels. The multiple levels further include healthy status, good status, general status, fault alarm and safety hazard.

[0061] Furthermore, the full data model includes the working time and action frequency of the trackside equipment, and judging the safety level of the trackside equipment further includes:

[0062] S41) obtaining the multiple levels of the equipment state prediction management rule;

[0063] S42) Determine the level of the trackside equipment according to the working time and the action frequency.

[0064] Specifically, the equipment status prediction management rules can classify the equipment status into five levels: healthy, good, general, fault alarm, and safety hazard based on the massive data, historical experience, and expert system judgment rules in the entire life cycle of a type of equipment. Among them, the working time and action frequency of the trackside equipment can be used to comprehensively determine the status level of the equipment. The following is an example:

[0065] Assuming that the basic estimated service life of trackside signal equipment is 15 years, there are different calculation methods for turnouts and switch machines. Turnout switch machines can be divided into two-level turnout switch machines and three-level turnout switch machines. The calculation formula for the estimated service life of a three-level turnout is:

[0066] T=T 0 +H+3 (1)

[0067] in,

[0068] T is the estimated time for the equipment to go down the turnout, in days;

[0069] T 0 The time when the device was online, in days;

[0070] H is the estimated remaining useful life of the equipment. Taking leap years into account, 3d is added, where d represents days.

[0071] The maximum number of operations per month of a secondary turnout can differ by ten times or even thirty times from that of a tertiary turnout. Therefore, for a secondary turnout, its downtime should be calculated from the ratio of the current number of equipment operations to the maximum allowable number of operations. The calculation formula is:

[0072]

[0073] Where:

[0074] M n is the number of equipment actions in a single month, Indicates the maximum number of times the device operates in a single month, in times;

[0075] P M The maximum number of actions allowed for the device, in times.

[0076] According to formula (2), the service life of the secondary turnout equipment can be estimated. The equipment service life estimation is of great significance to equipment status detection, maintenance time arrangement and equipment inventory management.

[0077] Specifically, the state level of the equipment is comprehensively determined based on the service time of the equipment and the action frequency of the metal structure parts.

[0078] Maintenance operation guidance opinions are provided according to the equipment state level, and the maintenance operation guidance opinions further include:

[0079] When the level of the trackside equipment is the healthy state, the inspection is reduced; when the level of the trackside equipment is the good state, the regular inspection is carried out; when the level of the trackside equipment is the general state, the inspection is strengthened; when the level of the trackside equipment is the fault alarm, the components of the trackside equipment are replaced; when the level of the trackside equipment is the safety hazard, the trackside equipment is replaced with a new one.

[0080] Figure 2 This is a specific application example flowchart of the full life cycle processing method for trackside signal equipment data preferred in the embodiments of the present invention. As Figure 2 shown, taking the full life cycle data management of the switch machine in the trackside signal equipment as an example, first, all data in the design, manufacturing, installation, operation and maintenance, and retirement stages of the switch are collected, and then the equipment classification standard coding rules and the standardized semantic description rules are used to complete the description. Then the dynamic data is incorporated to complete the management of all data, and finally the state of the predicted equipment is obtained.

[0081] Using the preferred method of the present invention, static data management, dynamic data management, and equipment state prediction management can be realized. Through the data optimization of the equipment classification standard coding rules and the standardized semantic description rules for static data, and the optimization of the equipment fault data and the data obtained from the equipment external interfaces for dynamic data, and then the static data and the dynamic data are fused into a full data model to perform prediction management on the equipment state.

[0082] In view of the wide sources of trackside signal data, many professional fields, and complex equipment usage scenarios of the present invention, starting from the management concept of the full life cycle, it is distinguished and managed from the perspectives of static data and dynamic data. In addition, based on the data, the state of the in-service equipment can also be predicted.

[0083] The present invention effectively solves the problem of out-of-control in the full life cycle management center of trackside signal equipment, and helps the coordinated management and positive collaborative use among various business departments.

[0084] Those skilled in the art will understand that information, signals, and data can be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips described throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0085] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, boxes, modules, circuits, and steps are generally described above in the form of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician can implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present application.

[0086] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0087] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0088] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0089] The above embodiments are provided for persons familiar with the art to implement or use the present application. Personnel familiar with the art can make various modifications or changes to the above embodiments without departing from the application concept of the present application. Therefore, the protection scope of the present application is not limited to the above embodiments, but should be the maximum scope of the innovative features mentioned in the claims.

Claims

1. A method for processing trackside signal equipment data throughout its life cycle. include: S1) acquiring static data and dynamic data of trackside equipment, wherein the static data includes design stage data, manufacturing stage data and installation stage data, and the dynamic data includes equipment failure data, equipment work order data, equipment point inspection data and integrated operation and maintenance platform data; S2) encoding the static data using equipment hierarchical standardized coding rules and forming a static data tree model according to the full life cycle of the trackside equipment; S3) optimizing the static data tree model using standardized semantic description rules and forming an optimized static data tree model, wherein the standardized semantic description rules unify the state language description of the trackside equipment by engineers in different fields; S4) fusing the dynamic data into the optimized static data tree model to form a full data model of the trackside equipment; S5) importing equipment state prediction management rules and judging the safety level of the trackside equipment according to the equipment state prediction management rules and the full data model, wherein the equipment state prediction management rules divide the trackside equipment into multiple levels; S6) Using the safety level for maintenance work of the trackside equipment.

2. According to the method for processing trackside signal equipment data throughout its life cycle according to claim 1, It is characterized in that The static data is collected through open interfaces, database extraction and file import.

3. According to the method for processing trackside signal equipment data throughout its life cycle as described in claim 1, It is characterized in that The equipment classification standardization coding rules further include line-level coding rules, station and coding rules, equipment class and coding rules, and equipment-level coding rules.

4. The method for processing trackside signal equipment data throughout its life cycle according to claim 1, It is characterized in that The equipment fault data further includes fault location, fault cause, fault duration, fault handling time, fault handling subject and fault event flow status.

5. According to the method for processing trackside signal equipment data throughout its life cycle as described in claim 1, It is characterized in that The equipment work order data is obtained through an external work order system.

6. The method for processing trackside signal equipment data throughout its life cycle according to claim 1, It is characterized in that The equipment work order data adopts the JSON data exchange format.

7. The method for processing trackside signal equipment data throughout its life cycle according to claim 1, It is characterized in that The equipment point inspection data is obtained through an external point inspection system.

8. The method for processing trackside signal equipment data throughout its life cycle according to claim 1, It is characterized in that The full data model includes the working time and action frequency of the trackside equipment, and judging the safety level of the trackside equipment further includes: S41) obtaining the multiple levels of the equipment state prediction management rule; S42) Determine the level of the trackside equipment according to the working time and the action frequency.

9. The method for processing trackside signal equipment data throughout its life cycle according to claim 1, It is characterized in that The multiple levels further include status healthy, status good, status general, fault warning and safety hazard.

10. The method for processing trackside signal equipment data throughout its life cycle according to claim 9, It is characterized in that The maintenance operation further includes: When the level of the trackside equipment is in the healthy state, reducing inspections; When the trackside equipment is in good condition, a routine inspection is performed; When the level of the trackside equipment is the general status, the inspection is strengthened; When the level of the trackside equipment is the fault alarm, replacing components of the trackside equipment; When the level of the trackside equipment is the safety hazard, the trackside equipment is replaced with a new one.

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