Full-life-cycle intelligent management method and system for rail transit assets

Abstract

The invention discloses a full-life-cycle intelligent management method and system for rail transit assets, and the method comprises the steps: obtaining standing book data and operation data of the rail transit assets, combining the standing book data with the operation data, and converting the combined data into a basic vector; obtaining economic data corresponding to the rail transit asset data, wherein the economic data is used for describing the economic state of the rail transit assets; converting the economic data into economic vectors; according to the basic vector and the economic vector, a basic score and an economic score are obtained through calculation; and calculating the ratio of the basic score to the economic score, obtaining a reference coefficient, and executing intelligent management according to the reference coefficient. According to the scheme, any asset is evaluated through integration of the basic score and the economic score, the cost performance of the asset is reflected, actions are implemented at any node of full life cycle management of the asset based on quantitative data, and the scheme is an accurate and efficient intelligent management scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of rail transit, in particular to a full life cycle intelligent management method and system for rail transit assets. BACKGROUND

[0002] Global rail transit, especially subway, continues to develop rapidly and has become a key support for the transportation system and public services of large urban agglomerations. According to statistics from the International Public Transport Association, there are currently more than 200 cities with over 240 subway systems in the world, and China is the core force of global subway expansion. As of the end of 2024, there are 54 cities and 325 lines in operation nationwide, with a total mileage of nearly 11,000 kilometers, and an annual passenger volume of 3.22 billion person-times, an increase of nearly 10% year-on-year.

[0003] With the rapid development of subways, a network, large-scale and multi-center pattern has emerged, with huge asset volume, complex structure and long life cycle, covering multiple levels such as vehicles, stations and interval buildings. However, the most direct purpose of traditional subway asset management is to account for fixed assets and financial accounting, and to meet the compliance requirements of asset supervision, emphasizing "whether the assets exist, how many assets, and whether the accounts and assets are consistent", which is a static and account-based management approach.

[0004] Therefore, the traditional subway asset management is a management approach centered on "things", but lacks dynamic control of asset status, life cycle and operation performance, and cannot bring intelligent and efficient management to subway assets, fully realizing the value of assets, which is a difficult problem to be solved. SUMMARY

[0005] One object of the present application is to solve the technical problem of intelligent and efficient asset management of rail transit, so as to achieve the purpose of dynamic intelligent and efficient management of assets held by the rail transit system.

[0006] According to one aspect of an embodiment of the present application, a full life cycle intelligent management method for rail transit assets is disclosed, the method comprising: Obtaining account data of rail transit assets, the account data being used to describe static data of the account of the rail transit assets; obtaining operation data adapted to the rail transit asset data, the operation data being used to describe dynamic data of the operation state of the rail transit assets; the account data and the operation data are combined and converted into a basic vector; Obtaining economic data corresponding to the rail transit asset data, the economic data being used to describe the economic state of the rail transit assets; the economic data is converted into an economic vector; Calculating a basic score and an economic score respectively according to the basic vector and the economic vector; The reference coefficient is obtained by calculating a ratio of the basic score and the economic score, and intelligent management is performed according to the reference coefficient.

[0007] According to an aspect of an embodiment of the present application, a full life cycle intelligent management system for rail transit assets is disclosed, comprising: A basic vector module is configured to acquire account data of rail transit assets, the account data being used to describe static data of an account of the rail transit assets; acquire operation data adapted to the rail transit asset data, the operation data being used to describe dynamic data of an operation state of the rail transit assets; and combine and convert the account data and the operation data into a basic vector; An economic vector module is configured to acquire economic data corresponding to the rail transit asset data, the economic data being used to describe an economic state of the rail transit assets; and convert the economic data into an economic vector; A score calculation module is configured to calculate a basic score and an economic score respectively according to the basic vector and the economic vector; An intelligent management module is configured to obtain a reference coefficient by calculating a ratio of the basic score and the economic score, and perform intelligent management according to the reference coefficient.

[0008] The full life cycle intelligent management method and system for rail transit assets of the present application, the basic score reflects product performance attributes of the rail transit assets, and the economic score reflects monetary attributes of the rail transit assets. For any asset, its value is not determined by only its product performance or price. The core logic of the full life cycle intelligent management of the rail transit assets is "performance-price ratio". Meanwhile, technical means are used to convert text data, static / dynamic data into index vectors, and the index vectors are calculated to obtain quantified data that can be compared horizontally. Then, based on the quantified data, decision actions are implemented at any node of the full life cycle management of the assets, such as various management modes of scrapping, replacing, purchasing new, selling, and continuous use, and the value of the assets at different time points is dynamically monitored. Intelligent decision making can fully exert the use value of the assets, rather than artificial subjective decision making, which is a precise and efficient intelligent management scheme. Other features and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0009] It should be understood that the general description above and the following detailed description are only exemplary and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0010] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which:

[0011] Figure 1 is a flow chart of a full life cycle intelligent management method for rail transit assets according to an embodiment.

[0012] Figure 2 is a data deployment architecture diagram of the full life cycle intelligent management for rail transit assets according to an embodiment.

[0013] Figure 3 is a schematic diagram of a background page of an asset management system according to an embodiment.

[0014] Figure 4 is a schematic diagram of a three-dimensional model object matched with a rail transit asset according to an embodiment.

[0015] Figure 5 is a schematic diagram of a three-dimensional model object constituting a rail transit asset space according to an embodiment.

[0016] Figure 6 is a display diagram of a three-dimensional model object rendered based on a rendering engine according to an embodiment.

[0017] Figure 7 is a logic block diagram of intelligent management of a three-dimensional model object combined with rendering according to an embodiment.

[0018] Figure 8 is a schematic diagram of a full life cycle intelligent management system for rail transit assets according to an embodiment. DETAILED DESCRIPTION

[0019] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of example implementations to those skilled in the art. Like reference numerals refer to like elements throughout the description. Repeated use of illustrations indicates reusability of a drawing figure across one or more examples.

[0020] Moreover, described features, structures, or characteristics can be combined in any suitable manner in one or more example implementations. In the following description, numerous specific details are provided to give a thorough understanding of example implementations. One skilled in relevant art will recognize, however, that the

[0021] Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. These functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0022] Referring to Figure 1 , Figure 1 is a flowchart of a full life cycle intelligent management method for rail transit assets according to an embodiment. The full life cycle intelligent management method for rail transit assets provided by the embodiment includes: Step S100: acquiring account data of rail transit assets, the account data being used to describe static data of an account of the rail transit assets; acquiring operation data adapted to the rail transit asset data, the operation data being used to describe dynamic data of an operation state of the rail transit assets; combining and converting the account data and the operation data into a basic vector; Step S200: acquiring economic data corresponding to the rail transit asset data, the economic data being used to describe an economic state of the rail transit assets; converting the economic data into an economic vector; Step S300: respectively calculating a basic score and an economic score according to the basic vector and the economic vector; Step S400: calculating a ratio of the basic score and the economic score to obtain a reference coefficient, and performing intelligent management according to the reference coefficient.

[0023] The above steps are described in detail as follows: The assets of rail transit are very large, involving various types of equipment required for rail transit facility operation and maintenance, such as vehicles, signal equipment, power supply equipment, communication equipment, automatic ticketing equipment, ventilation and air conditioning, platform doors, etc. The management of rail transit assets is managed by the finance department and / or the asset management department according to different attributes and management authorities (the corresponding department naming will be slightly different according to the situation of different cities), and then the rail transit asset data is isolated and complementary between departments, but some rail transit asset data can be obtained through a third party dimension, such as operation data, which will reflect the core assets related to operation. These assets will be recorded in the asset management department or the finance department, and are also the most important management objects of rail transit and the objects that need to be managed throughout the life cycle.

[0024] The rail transit asset, as an asset attribute, has static account data, i.e. recording asset equipment such as escalators, platform screen doors, shutter doors, lighting lamps, vehicles, power supply transformers, etc. The operation of the asset generates dynamic operation data such as running time, running speed, usage time, fault error times, etc. The "static + dynamic" combined data collectively describe the same rail transit asset object, which is combined and converted into a basic vector, i.e. vectorization of data, by account data and operation data.

[0025] Specifically, the account data and operation data are numerically converted, then merged and constructed into a one-dimensional vector, i.e. a basic vector. Among them, for non-numeric indicators such as brand, equipment type, elevator / handrail elevator, air conditioner, etc., can be converted into numerical vector elements through methods such as one-hot encoding, category embedding, label encoding or text vectorization; for numerical indicators such as service life, running time, usage time, etc., the numerical values can be directly extracted as vector elements. Now, a specific embodiment is combined for description as follows: The account data and operation data are merged into a vector, i.e. a basic vector A = [1, 0, 0, 1, 0, 16, 7]. Based on the basic vector A, a numerical value can be directly calculated, which is a basic score.

[0026] In addition, the whole life cycle management of rail transit assets not only involves equipment and operation data, which are all mapped to monetary value, i.e. the core goal of management is "cost reduction and efficiency improvement". Therefore, economic data corresponding to rail transit asset data need to be obtained, such as purchase price 100,000, maintenance fee 13,000 / year, equipment depreciation rate 10% per year, etc. Some of these data are dynamic data and some are static data, because some rail transit asset data are static and fixed, and some are dynamically changing. Moreover, the numerical values can be directly extracted as vector elements and as economic vectors. A specific embodiment is combined for description as follows: The numerical values in the economic data are extracted and converted into an economic vector B = [10, 1.3, 0.1]. Based on the economic vector B, a numerical value can be directly calculated, which is an economic score.

[0027] The basic score reflects the product performance attribute of the rail transit asset, and the economic score reflects the monetary attribute of the rail transit asset. For any asset, its value is not determined by its product performance or price alone. The core logic of intelligent management of the whole life cycle of the rail transit asset is "cost performance", and the text data, static / dynamic data are converted into an index vector by technical means, and the index vector is calculated to obtain quantified data that can be compared horizontally. Then, based on the quantified data, decision actions such as scrapping, replacement, new purchase, sale, and continuous use are implemented at any node of the whole life cycle management of the asset, and the value of the asset at different time points is dynamically monitored. Intelligent decision-making can fully utilize the use value of the asset, rather than subjective decision-making, which is a precise and efficient intelligent management solution.

[0028] In addition, in other embodiments, in the framework of the basic vector, different rail transit asset categories and data of different operating states are selected, and the basic vector composed of different data is constructed according to different management targets, which has the characteristics of independent and flexible function design, and can meet the needs of different users, and has higher management friendliness.

[0029] Specifically, in an embodiment, according to the different physical characteristics of the asset (such as rail vehicles are high-speed mobile equipment, signals are network equipment, escalators are mechanical action equipment, etc.), different operating data (such as rail vehicles are mechanical fatigue data, signals are network instability data, and escalators are overload / wear data), and different management targets (such as rail vehicle safety, signal real-time redundancy, and reliable machine), the combination of the constructed basic vector is also different. Based on this, different target basic vector combination methods are constructed for different asset categories, and the specific application examples are as follows: (1) Rail transit asset: mechanical and electrical equipment Rail transit assets such as escalators, vertical elevators, lighting, air conditioning, and fresh air machines; the core management targets include: predictive maintenance, energy optimization, and spare part replacement cycle; the key data involved includes load, power, temperature, etc.; based on this, the basic vector formed can be: A1=[age, brand level, rated power, actual power load, failure frequency, downtime length].

[0030] (2) Rail transit asset: vehicle equipment Rail transit assets such as trains, bogies, and braking equipment; the core management targets include: key component life, maintenance strategy evaluation, and safety risk; the key data involved includes mechanical load, vibration, temperature, etc.; based on this, the basic vector formed can be: A2=[running mileage, vibration mean square value, temperature rise, braking times, power].

[0031] (3) Rail transit assets: platform equipment The platform equipment of the rail transit assets, such as the gate machine, the shield door and the like; the core management target thereof has: reducing the failure, optimizing the spare part replacement strategy and the like; the key data involved includes the switching frequency, the wear rate and the like; based on this, the basic vector constituted can be: A3=[switching times, failure rate, passenger throughput, failure event frequency].

[0032] For different management targets, the basic vector has flexibility and can be autonomously set in multiple modes, serving the needs of different management users, such as the users of the asset statistics, the operation and maintenance units and the like, having precise, efficient and user-friendly intelligent management experience.

[0033] In an embodiment, refer to Figure 2 , Figure 2 is a data deployment architecture diagram for the whole life cycle intelligent management of rail transit assets according to an embodiment.

[0034] For the data of the whole life cycle intelligent management of rail transit assets, it needs to be obtained across departments and platforms, some data involves core secrets and needs to be deployed locally, and some data distributed in different areas needs to be deployed in the cloud. Therefore, based on the special scene of rail transit, a special scheme needs to be designed for the source and collection of data. Specifically as follows: The ledger data is obtained through the fixed asset management platform and stored locally; the list of company assets recorded by the ledger data is generally recorded through similar tools or forms such as the fixed asset management platform, and as important asset details, it is generally saved locally. The operation data is obtained through the Internet of Things platform (IOT) and stored in the cloud; the operation data (including real-time data or stage collected data, etc.) of each terminal operation equipment is obtained through similar tools such as the Internet of Things platform, and since the terminal equipment may be distributed in different subway stations, subway lines, etc., the related operation data is stored in the cloud. The economic data is obtained through the financial management platform and stored locally; for the core financial data, it must be stored locally.

[0035] The above-mentioned ledger data, operation data and economic data are collected and stored locally, and according to the local computing power resources and storage resources, the reference coefficients calculated are set locally and physically isolated from the ledger data and economic data stored locally. Such a setting can not only protect the data, but also guarantee the whole life cycle intelligent management of rail transit assets.

[0036] In an embodiment, refer to Figures 3-4is a schematic diagram of a background page of an asset management system and a three-dimensional model object matched with a rail transit asset according to an embodiment. In which, Figure 3 The asset page shows a category level navigation of equipment types (annotated with a red box on the left), supports input of a keyword to search for an equipment type (annotated with a red box on the top), and the middle is information of a data equipment asset. Specifically, when a specific equipment type is selected, a right list shows detailed record data of the equipment account of the corresponding equipment type. In addition, the three-dimensional model object (such as an escalator) in Figure 4 is a matching display of the asset and the model, and visualizes the specific position and related state of the asset in the station.

[0037] Through vector numerical processing of the asset, basic scores and economic scores and other data are obtained, and visualization of these data, especially in combination with the three-dimensional model, provides more abundant and friendly management for intelligent management of the whole life cycle of the rail transit asset, and makes the management more humanized.

[0038] Based on this, the scheme has a three-dimensional model material library, extracts a three-dimensional model object matched with the rail transit asset from the three-dimensional model material library, and the data format includes IFC, RVT, OBJ, etc. Further, the three-dimensional model object includes: a geometric model describing a geometric structure corresponding to the rail transit asset; and attribute parameter data that can write basic scores and economic scores.

[0039] In the embodiment, the BIM technical scheme is used to express the three-dimensional model object, including geometric structure representation of the asset, and the attribute parameter data that can be written is used for semantic expression and can be used to write basic scores and economic scores. If limited by the size of the data, the attribute parameter data is in an extensible data format, which can satisfy the bytes of writing basic scores and economic scores. Based on this, the data of the three-dimensional model object is integrated with the basic scores, the economic scores, and the reference coefficient, that is, the three-dimensional model object is integrated with the quantitative data of the asset, the value of the asset is expressed by the three-dimensional model object, and the quantitative visualization in the whole life cycle of the asset is realized, so that the intelligent management is accurate and can be referenced, and friendly and executable.

[0040] In one example embodiment, refer to Figure 5 , Figure 5 is a schematic diagram of a three-dimensional model object constituting a rail transit asset space according to an embodiment. Since the data sources of the rail transit asset are different, some come from account data, operation data, economic data or other data, and the asset categories of the rail transit, such as vehicles and mechanical and electrical information, are recorded in these data, which will cause the respective data information to be unable to be aligned, and brings inconvenience to the management of the asset.

[0041] Therefore, the object data is obtained from the Internet of Things platform, the object data is compared with the account data, the data that cannot be matched is obtained, and the missing object data and / or the missing asset data are generated. Specifically, the data of the field device actually operated obtained from the Internet of Things platform (IOT) should be aligned with the data of the rail transit operating company. However, in some specific cases, some data accessed to the IOT platform is not recorded in the account data, or some devices that have been eliminated or damaged or lost in the field have data recorded in the account but not in the IOT data, so there will be missing and unmatchable data in the cross data matching. Based on this, for the device data missing in the IOT data, the missing device data is marked as missing object data; for the device data missing in the account, the missing device data is marked as asset missing model; if both the account and the IOT device data are missing, both are marked.

[0042] In addition, in combination with the accompanying drawings Figure 5 illustrated application example, the missing object data and the missing asset data are extracted from the three-dimensional model material library to correspond to the model, and are marked as object missing model and asset missing model respectively. Specifically, by comparing the missing object data and the missing asset data obtained from the IOT data and the account data, the corresponding model is extracted from the three-dimensional model material library and marked.

[0043] Based on this, the object missing model, the asset missing model and the three-dimensional model object constitute a rail transit asset space, which is a new multi-angle comprehensive display of the three-dimensional modeling of the rail transit asset.

[0044] Further, in an embodiment, the missing object data and the missing asset data are compared with the target content of the economic data respectively, if there is unmatchable target data, an alarm data is generated; the alarm data is mapped to the object missing model or the asset missing model (such as the yellow part of the asset in Figure 5 the accompanying drawings).

[0045] Specifically, for the asset management department of rail transit, all asset data is subject to financial data, and economic data comes from the financial management platform, so the economic data is the core basis. Based on this, various data are compared with the target content (i.e. asset category) of the economic data, if there is unmatchable target data (including data missing in the economic data or not recorded in the IOT), an alarm data is directly generated, i.e. the missing alarm data is mapped to the object missing model or the asset missing model.

[0046] In an exemplary embodiment, referring to Figure 6 , Figure 6is a display diagram of a three-dimensional model object rendered based on a rendering engine according to an embodiment.

[0047] The scheme of the present application is provided with a rendering engine; the rendering engine generates different colors according to the three-dimensional model mapped by the basic score, and the model color connected with the three-dimensional model object is different.

[0048] Specifically, the rendering engine calls the data of the attribute parameters in the three-dimensional model, and renders the three-dimensional model object to different colors according to the basic score recorded in the attribute parameters, but ensures that the colors of the three-dimensional model objects connected with each other are different, and the color difference can be colored by "map coloring method" (to ensure that the adjacent colors are different). Of course, different color expressions can be exhausted.

[0049] Further, in other embodiments, the rendering engine adjusts the color brightness of the three-dimensional model mapped by the economic score according to the economic score. Specifically, the color brightness of the three-dimensional model object corresponding to the economic score is graded according to different economic scores, and the differentiated brightness plays a visual value guide for the economy of the same type of three-dimensional model object.

[0050] On this basis, reference is made to Figure 7 , Figure 7 is a logic block diagram of intelligent management of a three-dimensional model object combined with rendering according to an embodiment.

[0051] According to the judgment of whether the reference coefficient exceeds the preset value, the management work order is triggered and the specified target is displayed or sent.

[0052] Specifically, the reference coefficient is essentially an expression of "asset cost performance", and for different types of assets or asset values at different time nodes, the asset cost performance coefficients (i.e. reference coefficients) are also different.

[0053] In the present embodiment, according to the corresponding instructions triggered by the reference coefficient exceeding the preset value, a specific map can be generated in a specified color (such as red / yellow, etc.), and the map is directly matched with the three-dimensional model object. The design of the scheme is not realized by directly rendering the model through the rendering engine, but by realizing different display of the model through the map.

[0054] Because the reference coefficient is a cost-effective expression of the entity device mapped by the three-dimensional model object, it is the core basis for intelligent management of the entity device, such as scrapping, replacement, and new purchase decision. The final result of this decision is a "dynamic" expression (rendering engine) based on the previous basis score and economic score, and finally presented in a "static" way (i.e. through the reference coefficient to realize the display of the map), without the need for dynamic color changes. In addition, in other embodiments, the map is pre-set map information stored in a designated database, and based on the map information corresponding to different reference coefficients, in addition to color, text, picture and other information can be called and displayed together.

[0055] The cleverness of this design is to avoid the rendering engine being constrained by different model colors, resulting in chaotic color display between different models. More importantly, fixed map information is not easy to be tampered with, because the map information only sets the content related to the decision, and is stored in a third-party database, which realizes physical isolation of data and intelligent management of the whole life cycle of rail transit assets.

[0056] In one example embodiment, in combination with the accompanying drawings Figure 8 is a schematic diagram of a whole life cycle intelligent management system for rail transit assets according to an embodiment.

[0057] A whole life cycle intelligent management system for rail transit assets, comprising: a basis vector module for obtaining account data of rail transit assets, the account data being used to describe static data of the rail transit asset account; obtaining operation data adapted to the rail transit asset data, the operation data being used to describe dynamic data of the rail transit asset operation state; combining and converting the account data and the operation data into a basis vector; an economic vector module for obtaining economic data corresponding to the rail transit asset data, the economic data being used to describe the economic state of the rail transit asset; converting the economic data into an economic vector; a score calculation module for calculating a basis score and an economic score respectively according to the basis vector and the economic vector; an intelligent management module for calculating a reference coefficient by calculating the ratio of the basis score and the economic score, and performing intelligent management according to the reference coefficient.

[0058] The whole life cycle intelligent management system for rail transit assets of the present application, the basis vector and the economic vector obtained by the basis vector module and the economic vector module respectively, the basis score and the economic score calculated by the score calculation module, and finally the reference coefficient based on the basis score and the economic score obtained by the intelligent management module to realize intelligent management.

[0059] Specifically, the basic score reflects the product performance attribute of the rail transit asset, and the economic score reflects the monetary attribute of the rail transit asset. For any asset, its value can be fully exerted not by its product performance or price level alone. The core logic of the intelligent management of the whole life cycle of the rail transit asset is the "performance-price ratio". Meanwhile, the text data, static / dynamic data are converted into an index vector by technical means, and the index vector is calculated to obtain quantified data that can be compared horizontally. Then, based on the quantified data, decision actions such as scrapping, replacement, new purchase, sale, continuous use and various management modes are implemented at any node of the whole life cycle management of the asset, and the value of the asset at different time points is dynamically monitored. Intelligent decision making can fully exert the use value of the asset, rather than artificial subjective decision making, which is a precise and efficient intelligent management scheme.

[0060] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present application.

[0061] In the example embodiments of the present application, a computer program medium is also provided, which stores computer readable instructions. When the computer readable instructions are executed by the processor of the computer, the computer executes the method described in the method embodiment part.

[0062] According to one embodiment of the present application, a program product for implementing the method in the above method embodiment is also provided, which can be in the form of a portable compact disc read-only memory (CD-ROM) and includes program codes, and can run on a terminal device such as a personal computer. However, the program product of the present application is not limited to this, and in this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used or combined with an instruction execution system, device or apparatus.

[0063] The program product can take any combination of one or more computer-readable media. The computer-readable media can be a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0064] The computer-readable signal medium can include a computer-readable storage medium that is configured to store and deliver a computer-readable program code. The computer-readable program code can be propagated as a computer-readable signal medium.

[0065] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the foregoing.

[0066] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider. The present application can be implemented as a computer program product, which can include a computer-readable medium having stored computer program code.

[0067] It should be noted that, although several modules or units of the device for action execution are mentioned in the foregoing detailed description, such a division is not mandatory. Indeed, according to an embodiment of the present application, features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, features and functionalities of one module or unit described above can be further divided into a plurality of modules or units.

[0068] Furthermore, although individual steps of the methods in the present application are described in a particular order in the drawings, this is not required or implied as to the order in which the steps are performed, nor is it required that all of the steps shown be performed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, combined into a single step, broken into multiple steps, and / or the like.

[0069] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software and / or by software in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the methods according to the embodiments of the present application.

[0070] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the present application cover any and all variations of the application that come within the scope of the general inventive concepts described herein and including all such variations as fall within the scope of the claims. The specification and examples are illustrative of the application and are not intended to be limiting.

Claims

1. A method for intelligent management of the entire lifecycle of rail transit assets, characterized in that, The method includes: Obtain ledger data of rail transit assets, the ledger data being used to describe the static data of the rail transit asset accounts; obtain operational data adapted to the rail transit asset data, the operational data being used to describe the dynamic data of the operational status of the rail transit assets; combine the ledger data and the operational data and convert them into a basic vector; Obtain economic data corresponding to the rail transit asset data, the economic data being used to describe the economic status of the rail transit assets; convert the economic data into an economic vector. The basic score and the economic score are calculated based on the basic vector and the economic vector, respectively. The ratio of the base score to the economic score is calculated to obtain a reference coefficient, and intelligent management is performed based on the reference coefficient.

2. The method according to claim 1, characterized in that, The ledger data is obtained through the fixed asset management platform and stored locally; the operational data is obtained through the Internet of Things platform and stored in the cloud; the economic data is obtained through the financial management platform and stored locally. The calculation and storage of the reference coefficients are set up locally and are physically isolated from the ledger data and economic data stored locally.

3. The method according to claim 1 or 2, characterized in that, A 3D model resource library is provided; 3D model objects matching the rail transit assets are extracted from the 3D model resource library; The three-dimensional model object includes: a geometric model, which describes the geometric structure corresponding to the rail transit asset; The attribute parameter data can be written into the basic score and the economic score.

4. The method according to claim 3, characterized in that, The IoT platform is used to obtain object data, which is compared with the ledger data to obtain data that cannot be matched and generate missing object data and / or missing asset data. The missing object data and the missing asset data are respectively extracted from the 3D model material library and marked as missing object model and missing asset model, respectively. The missing object model, the missing asset model, and the three-dimensional model object constitute the rail transit asset space.

5. The method according to claim 4, characterized in that, The missing object data and the missing asset data are compared with the target content of the economic data. If there is a target data that cannot be matched, alarm data is generated. The alarm data is mapped to the object missing model or the asset missing model.

6. The method according to claim 5, characterized in that, A texture is generated based on the alarm data, and the texture is attached to the object missing model or the asset missing model mapped by the alarm data.

7. The method according to claim 3, characterized in that, A rendering engine is provided; the rendering engine generates different colors based on the three-dimensional model object mapped by the basic score, and the model colors connected to the three-dimensional model object are different.

8. The method according to claim 7, characterized in that, The rendering engine adjusts the color brightness of the 3D model object mapped by the economic score based on the economic score.

9. The method according to claim 8, characterized in that, Based on the reference coefficient, determine whether the value exceeds the preset value, trigger a management work order, and display or send the specified target.

10. A full lifecycle intelligent management system for rail transit assets, comprising: The basic vector module is used to acquire the ledger data of rail transit assets, and the ledger data is used to describe the static data of the rail transit asset accounts; Obtain operational data adapted to the rail transit asset data, the operational data being used to describe the dynamic data of the operational status of the rail transit assets; combine the ledger data with the operational data and convert it into a basic vector; The economic vector module is used to acquire economic data corresponding to the rail transit asset data, the economic data being used to describe the economic status of the rail transit asset; the economic data is converted into an economic vector. The score calculation module is used to calculate the basic score and the economic score based on the basic vector and the economic vector, respectively. The intelligent management module is used to calculate the ratio of the basic score to the economic score and obtain a reference coefficient, and to perform intelligent management based on the reference coefficient.

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