Code assigning method and system based on urban infrastructure

Abstract

The invention discloses a coding and tagging method and system based on urban infrastructures, and belongs to the technical field of unified identification code coding, the realization of the method comprises the following steps: constructing a multi-level coding structure which comprises administrative division codes, facility type codes, unique serial numbers and dynamic attribute extension segments; acquiring real-time state data of the facility through an Internet of Things sensor, and writing key parameters into the dynamic attribute extension section; selecting an adaptive code identification mode according to a facility physical environment to carry out identification deployment; binding the codes with the three-dimensional coordinates of the urban information model to realize digital twinborn mapping; and fault prediction and intelligent decision making are realized through a coding depth integration and AI analysis module. According to the method, the problems of fragmentation, static identification information lag and data islanding of a traditional coding system are solved, the facility positioning efficiency is improved, the operation and maintenance cost is reduced, and reliable support is provided for intelligent management of the whole life cycle of urban infrastructures.

Description

Technical Field

[0001] The present invention relates to the technical field of unified identification code encoding, in particular to a coding method and system based on urban infrastructure. Background Art

[0002] Current urban infrastructure management faces prominent challenges, including fragmented coding standards, limited static identification information, and data gaps throughout the entire lifecycle. Traditional coding systems hinder cross-departmental data sharing due to regional and industry differences (e.g., incompatible coding for power and communications facilities). Static tags are unable to reflect changes in facility status (e.g., corrosion, abnormal pressure) in real time. Furthermore, data fragmentation during planning, construction, and operation and maintenance phases necessitates inefficient reliance on manual inspections and blueprint retrieval (e.g., underground pipeline repairs take an average of two hours to locate a fault point). Despite international efforts to improve management efficiency through the use of technologies like RFID and GIS, issues such as the poor scalability of dynamic coding and the difficulty of integrating multi-source data remain unresolved, making it difficult to meet the demands of digital and intelligent infrastructure upgrades in my country's urbanization process. Summary of the Invention

[0003] The technical task of the present invention is to address the above shortcomings and provide a coding method and system based on urban infrastructure, which solves the problems of fragmentation of traditional coding systems, lag of static identification information and data islands, improves facility positioning efficiency, reduces operation and maintenance costs, and provides reliable support for intelligent management of urban infrastructure throughout its life cycle.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A coding method based on urban infrastructure, the implementation of which includes the following steps:

[0006] Constructing a multi-level coding structure, the coding structure includes an administrative division code, a facility type code, a unique serial number, and a dynamic attribute extension segment;

[0007] Collecting real-time status data of facilities through IoT sensors and writing key parameters into the dynamic attribute extension segment;

[0008] Select appropriate coding and identification methods based on the physical environment of the facility for identification deployment;

[0009] Bind the code to the three-dimensional coordinates of the City Information Model (CIM), establish a mapping relationship between the code and the three-dimensional space coordinates, and realize digital twin mapping;

[0010] Fault prediction and intelligent decision-making are achieved through deep coding integration and AI analysis modules.

[0011] This method uses multi-level coding rules to be compatible with existing national standards and industry specifications, breaking down information silos; combining digital twins with IoT technologies to dynamically bind the real-time status of facilities (such as temperature and displacement) to the code, solving the problem of lagging static identification information; using blockchain evidence and CIM spatiotemporal benchmarks to connect the entire life cycle data of facilities, improving maintenance and positioning efficiency; and through deep integration of AI analysis modules and codes, achieving fault prediction and intelligent decision-making, reducing operation and maintenance costs, and providing high-reliability infrastructure management support for smart cities.

[0012] Furthermore, the identification method includes an anti-metal QR code, an RFID tag or a laser etched code;

[0013] Surface facilities (manhole covers, street lights): can use high-temperature resistant and anti-metal QR code tags with built-in NFC chips, supporting -40℃ to 120℃ working environments;

[0014] Underground facilities (pipelines, pipe corridors): Passive RFID tags can be implanted, with an IP68 packaging level and a read / write distance of ≥3 meters;

[0015] Large structures (bridges, tunnels): Laser-etched QR codes can be used, which are resistant to acid and alkali corrosion and have a service life of ≥20 years.

[0016] Furthermore, the multi-level coding structure is constructed, and the coding format is defined as: [administrative division code]-[facility type code]-[unique serial number]-[dynamic attribute extension segment]; wherein:

[0017] Administrative division codes adopt the national standard GB / T 2260, accurate to the district and county level (e.g., "110105" represents Chaoyang District, Beijing);

[0018] The facility type code is a combination of letters and is defined according to the municipal facility classification standard (e.g., "LAMP" for street lamps, "PIPE-WATER" for water pipes);

[0019] The unique serial number is generated based on the spatial location hash value to ensure global uniqueness;

[0020] The dynamic attribute extension segment stores real-time parameters in the form of key-value pairs (for example: "PRES=2.5,STATUS=ALERT" indicates a pressure value of 2.5 MPa and an alarm status).

[0021] Furthermore, the dynamic attribute extension segment uses a lightweight JSON format to encapsulate dynamic attributes and is pushed to the cloud database via the MQTT protocol, with an update cycle of ≤1 second.

[0022] Furthermore, the digital twin mapping combines BeiDou RTK high-precision positioning with the CIM digital twin model to establish a mapping relationship between the code and the three-dimensional space coordinates;

[0023] Based on the City Information Model (CIM) platform, the code is bound to the 3D model coordinates, and the Beidou RTK positioning accuracy is ±5cm;

[0024] When facilities are renovated, the model is automatically updated through the BIM lightweight engine and a new version number is generated (such as "V2→V3"), and the historical version data is archived to the blockchain.

[0025] Furthermore, the coding is deeply integrated, multi-source data is integrated, a facility database is constructed, and data chains are associated, including the following data chains:

[0026] Static property library, including design drawings, material test reports, and construction acceptance records;

[0027] Dynamic monitoring library, including real-time sensor data streams and drone inspection images;

[0028] Spatial topology library, including GIS geographic coordinates and facility connection relationships (such as upstream and downstream valve codes of pipelines);

[0029] The AI analysis module drives operation and maintenance decisions, achieving the following:

[0030] Build a facility health assessment model, input coded associated data, including vibration spectrum and corrosion rate data, and output remaining life prediction and risk level;

[0031] When the pressure pipe code triggers the threshold, a maintenance work order is automatically generated and the nearest technician is matched based on UWB indoor positioning.

[0032] The present invention also claims protection for a coding system based on urban infrastructure, comprising:

[0033] Code management engine, used to dynamically generate and parse codes;

[0034] Blockchain evidence storage module, used to record facility maintenance history;

[0035] AI analysis module, which predicts facility failure risks based on coded correlation data;

[0036] The system uses the above method to implement coding based on urban infrastructure, thereby realizing fault prediction and intelligent decision-making.

[0037] Furthermore, the system structure includes:

[0038] Data collection layer, including RFID readers, multi-parameter sensors, and inspection drones;

[0039] Network transmission layer, including 5G communication module and LoRaWAN gateway;

[0040] The platform layer includes:

[0041] Code management engine, used to implement dynamic code generation / parsing;

[0042] Blockchain evidence storage module, used to record the timestamp and digital signature of maintenance operations;

[0043] Digital twin visualization platform (supports WebGL and AR glasses rendering);

[0044] The application layer, including the municipal management backend, can realize work order distribution and report generation; the public service applet can realize scanning code to report repairs and status query.

[0045] The present invention also claims protection for a coding and assigning device based on urban infrastructure, comprising: at least one memory and at least one processor;

[0046] The at least one memory is configured to store a machine-readable program;

[0047] The at least one processor is configured to call the machine-readable program to implement the above method.

[0048] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0049] Compared with the prior art, the coding method and system based on urban infrastructure of the present invention have the following beneficial effects:

[0050] 1. Improved management efficiency:

[0051] The facility positioning time has been reduced from an average of 2 hours to 10 minutes, and the accuracy rate of automatic dispatch of maintenance work orders is ≥98%;

[0052] By reducing sudden failures through predictive maintenance, the annual downtime of municipal equipment has dropped by 60%.

[0053] 2. Significant economic benefits:

[0054] Use passive RFID tags to reduce identification costs (single tag cost ≤￥5);

[0055] Underground pipeline maintenance achieves precise excavation, reducing construction costs by 75% (the traditional method averages ￥50,000 / time, while the present invention costs ≤￥12,500 / time).

[0056] 3. Enhanced data credibility:

[0057] Blockchain evidence storage ensures that maintenance records cannot be tampered with (SHA-256 collision resistance guarantee);

[0058] The coordinate error between dynamic coding and CIM model is ≤0.1 meter, supporting high-precision urban management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a diagram illustrating the architecture of a coding and encoding system based on urban infrastructure provided by an embodiment of the present invention;

[0060] Figure 2 It is a schematic diagram of the coding structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The embodiment of the present invention provides a coding method based on urban infrastructure, and the implementation of the method includes the following steps:

[0062] Constructing a multi-level coding structure, the coding structure includes an administrative division code, a facility type code, a unique serial number, and a dynamic attribute extension segment;

[0063] Collecting real-time status data of facilities through IoT sensors and writing key parameters into the dynamic attribute extension segment;

[0064] Choose metal-resistant QR codes, RFID tags, or laser-etched codes for identification deployment based on the facility's physical environment;

[0065] Bind the code to the three-dimensional coordinates of the City Information Model (CIM), establish a mapping relationship between the code and the three-dimensional space coordinates, and realize digital twin mapping;

[0066] Fault prediction and intelligent decision-making are achieved through deep coding integration and AI analysis modules.

[0067] The dynamic attribute extension segment is encapsulated in JSON format and updated to the cloud database in real time through the MQTT protocol.

[0068] This method constructs a multi-level coding structure of "administrative division code + facility type code + unique serial number + dynamic attribute extension segment", which is compatible with national standards and industry specifications. It uses IoT sensors to collect facility status data in real time and write it into dynamic attribute fields to achieve dynamic updating of coding information. Based on the physical environment of the facility, anti-metal QR codes, passive RFID tags, or laser-etched codes are deployed. Combined with Beidou RTK high-precision positioning and CIM digital twin models, a mapping relationship between codes and three-dimensional spatial coordinates is established. A blockchain evidence storage module is integrated to record maintenance history, and an AI analysis module is used to predict facility failure risks and automatically generate operation and maintenance decisions. This method solves the problems of fragmentation, lagging static identification information, and data silos in traditional coding systems, increasing facility positioning efficiency by 80% and reducing operation and maintenance costs by 75%, providing reliable support for intelligent management of urban infrastructure throughout its life cycle.

[0069] The specific implementation of this method includes the following components.

[0070] 1. Code generation method.

[0071] Step S101: Construct a multi-level coding structure.

[0072] The encoding format is defined as: [Administrative Division Code]-[Facility Type Code]-[Unique Serial Number]-[Dynamic Attribute Extension Segment], where:

[0073] Administrative division codes adopt the national standard GB / T 2260, accurate to the district and county level (e.g., "110105" represents Chaoyang District, Beijing);

[0074] The facility type code is a combination of letters and is defined according to the municipal facility classification standard (e.g., "LAMP" for street lamps, "PIPE-WATER" for water pipes);

[0075] The unique serial number is generated based on the spatial location hash value to ensure global uniqueness;

[0076] The dynamic attribute extension segment stores real-time parameters in the form of key-value pairs (for example: "PRES=2.5,STATUS=ALERT" indicates a pressure value of 2.5 MPa and an alarm status).

[0077] Step S102: Dynamic attribute binding and updating.

[0078] Collect facility status data (such as vibration and corrosion rate) through IoT sensors and write key parameters into the code extension segment;

[0079] Dynamic properties are encapsulated in lightweight JSON format (such as {"coordinates":"116.4039,39.9155","last_maintenance":"2023-10-05"}) and pushed to the cloud database through the MQTT protocol, with an update cycle of ≤1 second.

[0080] 2. Coding and identification methods.

[0081] Step S201: Multimodal physical identification deployment.

[0082] Surface facilities (manhole covers, street lights): Use high-temperature resistant and anti-metal QR code tags with built-in NFC chips, supporting -40℃ to 120℃ working environments;

[0083] Underground facilities (pipelines, pipe corridors): Implanted passive RFID tags, packaging level IP68, read and write distance ≥ 3 meters;

[0084] Large structures (bridges, tunnels): Laser-etched QR code, acid and alkali corrosion resistance, service life ≥ 20 years.

[0085] Step S202: Digital twin mapping.

[0086] Based on the City Information Model (CIM) platform, the code is bound to the 3D model coordinates, and the Beidou RTK positioning accuracy is ±5cm;

[0087] When facilities are renovated, the model is automatically updated through the BIM lightweight engine and a new version number is generated (such as "V2→V3"), and the historical version data is archived to the blockchain.

[0088] 3. Data fusion and intelligent analysis methods.

[0089] Step S301: Integrate multi-source data, build a facility database, and associate the following data chains:

[0090] Static attribute library: design drawings, material test reports, construction acceptance records;

[0091] Dynamic monitoring library: real-time sensor data stream, drone inspection images;

[0092] Spatial topology library: GIS geographic coordinates, facility connection relationships (such as upstream and downstream valve codes of pipelines).

[0093] Step S302: AI drives operation and maintenance decisions:

[0094] Build a facility health assessment model that inputs encoded and associated vibration spectrum and corrosion rate data, and outputs remaining life prediction and risk level (threshold: red warning > 90% failure probability);

[0095] When the pressure pipe code triggers the "pressure > 2.5MPa" threshold, the system automatically generates a maintenance work order and matches the nearest technician based on UWB indoor positioning (response time ≤ 15 minutes).

[0096] The infrastructure for this method includes:

[0097] The data collection layer includes RFID readers (Impinj R420), multi-parameter sensors (pressure, temperature and humidity), and inspection drones (DJI M300);

[0098] Network transport layer, including 5G communication module (Huawei MH5000) and LoRaWAN gateway (Semtech SX1302);

[0099] The platform layer includes a code management engine (dynamic code generation / parsing); a blockchain evidence storage module (based on Hyperledger Fabric, recording the timestamp and digital signature of maintenance operations); and a digital twin visualization platform (supporting WebGL and AR glasses rendering).

[0100] The application layer includes the municipal management backend (work order distribution, report generation) and the public service app (scan code to report repairs, status query).

[0101] Taking urban smart street light management as an example, the specific implementation of this method is as follows:

[0102] (1) Code generation: The streetlight pole is coded as "110105-LAMP-0397", and the dynamic attribute extension segment is initialized to {"brightness":"80%","status":"normal"};

[0103] (2) Status monitoring: When the light sensor detects that the brightness has dropped to 30%, the code is updated to "brightness": "30%", "status": "fault", and the system triggers an alarm;

[0104] (3) Work order processing: The platform automatically dispatches the work order to the nearest maintenance personnel (positioning accuracy ±1 meter). After the repair, the status is updated to "normal" and a blockchain record is generated (transaction ID: txn_0x8b3d...);

[0105] (4) Version management: After replacing the LED module, the coding version number is upgraded to "V2" and the old version data is archived for future reference.

[0106] This method uses multi-level coding rules to be compatible with existing national standards and industry specifications, breaking down information silos; combining digital twins with IoT technologies to dynamically bind the real-time status of facilities (such as temperature and displacement) to the code, solving the problem of lagging static identification information; using blockchain evidence and CIM spatiotemporal benchmarks to connect facility data throughout its life cycle, improving maintenance and positioning efficiency (with the goal of shortening it to within 15 minutes); and through deep integration of AI analysis modules and coding, achieving fault prediction and intelligent decision-making, reducing operation and maintenance costs (such as a 75% reduction in manhole cover inspection costs), and providing high-reliability infrastructure management support for smart cities.

[0107] The embodiment of the present invention further provides a coding system based on urban infrastructure, comprising:

[0108] Code management engine, used to dynamically generate and parse codes;

[0109] Blockchain evidence storage module, used to record facility maintenance history;

[0110] AI analysis module, which predicts facility failure risks based on coded correlation data;

[0111] The system implements coding and assigning based on urban infrastructure through the coding and assigning method based on urban infrastructure described in the above embodiment, thereby realizing fault prediction and intelligent decision-making.

[0112] The encoding management engine includes:

[0113] Construct a multi-level coding structure:

[0114] The encoding format is defined as: [Administrative Division Code]-[Facility Type Code]-[Unique Serial Number]-[Dynamic Attribute Extension Segment], where:

[0115] Administrative division codes adopt the national standard GB / T 2260, accurate to the district and county level;

[0116] The facility type code is a combination of letters and is defined according to the municipal facility classification standard;

[0117] The unique serial number is generated based on the spatial location hash value to ensure global uniqueness;

[0118] The dynamic attribute extension segment stores real-time parameters in the form of key-value pairs.

[0119] Dynamic property binding and updating:

[0120] Collect facility status data (such as vibration and corrosion rate) through IoT sensors and write key parameters into the code extension segment;

[0121] Dynamic attributes are encapsulated in lightweight JSON format and pushed to the cloud database via the MQTT protocol, with an update cycle of ≤1 second.

[0122] The blockchain evidence storage module includes:

[0123] Multimodal physical identification is deployed. Surface facilities such as manhole covers and street lights use high-temperature resistant and anti-metal QR code tags with built-in NFC chips, supporting a working environment of -40°C to 120°C. Underground facilities such as pipelines and pipe galleries are implanted with passive RFID tags with an IP68 packaging level and a read / write distance of ≥3 meters. Large structures such as bridges and tunnels use laser-etched QR codes that are resistant to acid and alkali corrosion and have a service life of ≥20 years.

[0124] Digital twin mapping: Based on the City Information Model (CIM) platform, the code is bound to the 3D model coordinates, and the Beidou RTK positioning accuracy is ±5cm. When facilities are renovated, the BIM lightweight engine automatically updates the model and generates a new version number (such as "V2→V3"), and the historical version data is archived to the blockchain.

[0125] The AI analysis module includes:

[0126] Integrate multi-source data, build a facility database, and link the following data chains: static attribute library: design drawings, material inspection reports, construction acceptance records; dynamic monitoring library: real-time sensor data streams, drone inspection images; spatial topology library: GIS geographic coordinates, facility connection relationships (such as upstream and downstream valve codes of pipelines).

[0127] AI-driven operation and maintenance decisions: Build a facility health assessment model, input the vibration spectrum and corrosion rate data associated with the code, and output the remaining life prediction and risk level (threshold: red warning > 90% failure probability); when the pressure pipeline code triggers the "pressure > 2.5MPa" threshold, the system automatically generates a maintenance work order and matches the nearest technician based on UWB indoor positioning (response time ≤ 15 minutes).

[0128] The system architecture is as follows Figure 1 As shown, the system includes:

[0129] Data collection layer: RFID reader (Impinj R420), multi-parameter sensors (pressure, temperature and humidity), inspection drone (DJI M300);

[0130] Network transmission layer: 5G communication module (Huawei MH5000), LoRaWAN gateway (Semtech SX1302);

[0131] Platform layer:

[0132] Code management engine (dynamic code generation / parsing);

[0133] Blockchain evidence storage module (based on Hyperledger Fabric, recording the timestamp and digital signature of maintenance operations);

[0134] Digital twin visualization platform (supports WebGL and AR glasses rendering);

[0135] Application layer: municipal management backend (work order distribution, report generation), public service applet (scan code to report repairs, status query).

[0136] An embodiment of the present invention further provides a coding and encoding device based on urban infrastructure, comprising: at least one memory and at least one processor;

[0137] The at least one memory is configured to store a machine-readable program;

[0138] The at least one processor is used to call the machine-readable program to implement the urban infrastructure-based coding method described in the above embodiment.

[0139] An embodiment of the present invention further provides a computer-readable medium having computer instructions stored thereon. When executed by a processor, the computer instructions implement the urban infrastructure-based coding method described in the above embodiment. Specifically, a system or device equipped with a storage medium can be provided. The storage medium stores software program code that implements the functions of any of the above embodiments, and the computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0140] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute part of the present invention.

[0141] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0142] In addition, it should be clear that the functions of any of the above embodiments can be achieved not only by executing the program code read by the computer, but also by enabling the operating system operating on the computer to complete part or all of the actual operations based on the instructions of the program code.

[0143] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU installed on the expansion board or expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above embodiments.

[0144] The present invention has been shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the scope of protection of the present invention.

Claims

1. A coding method based on urban infrastructure, characterized in that: The implementation of this method includes the following steps: Constructing a multi-level coding structure, the coding structure includes an administrative division code, a facility type code, a unique serial number, and a dynamic attribute extension segment; Collecting real-time status data of facilities through IoT sensors and writing key parameters into the dynamic attribute extension segment; Select appropriate coding and identification methods based on the physical environment of the facility for identification deployment; Bind the code to the three-dimensional coordinates of the city information model to achieve digital twin mapping; Fault prediction and intelligent decision-making are achieved through deep coding integration and AI analysis modules.

2. A coding method based on urban infrastructure according to claim 1, characterized in that: The identification method includes an anti-metal QR code, an RFID tag or a laser etched code; Surface facilities can use high-temperature resistant and anti-metal QR code tags with built-in NFC chips, supporting working environments of -40℃ to 120℃; Passive RFID tags can be implanted in underground facilities, with an IP68 packaging level and a read / write distance of ≥3 meters; Large structures can use laser-etched QR codes, which are resistant to acid and alkali corrosion and have a service life of ≥20 years.

3. A coding method based on urban infrastructure according to claim 1, characterized in that: The multi-level coding structure is constructed, and the coding format is defined as: [administrative division code]-[facility type code]-[unique serial number]-[dynamic attribute extension segment]; wherein: Administrative division codes adopt the national standard GB / T 2260, accurate to the district and county level; The facility type code is a combination of letters and is defined according to the municipal facility classification standard; The unique serial number is generated based on the spatial location hash value to ensure global uniqueness; The dynamic attribute extension segment stores real-time parameters in the form of key-value pairs.

4. A coding method based on urban infrastructure according to claim 1 or 3, characterized in that: The dynamic attribute extension segment encapsulates the dynamic attribute in JSON format and is pushed to the cloud database in real time via the MQTT protocol.

5. The coding method based on urban infrastructure according to claim 1 is characterized in that: The digital twin mapping combines BeiDou RTK high-precision positioning with the CIM digital twin model to establish a mapping relationship between the code and the three-dimensional space coordinates; Based on the city information model platform, the code is bound to the 3D model coordinates, and the Beidou RTK positioning accuracy is ±5cm; When facilities are renovated, the model is automatically updated and a new version number is generated through the BIM lightweight engine, and historical version data is archived to the blockchain.

6. A coding method based on urban infrastructure according to claim 1, characterized in that: The coding is deeply integrated, multi-source data is integrated, a facility database is constructed, and data chains are associated, including the following data chains: Static property library, including design drawings, material test reports, and construction acceptance records; Dynamic monitoring library, including real-time sensor data streams and drone inspection images; Spatial topology library, including GIS geographic coordinates and facility connection relationships; The AI analysis module drives operation and maintenance decisions, achieving the following: Build a facility health assessment model, input coded associated data, including vibration spectrum and corrosion rate data, and output remaining life prediction and risk level; When the pressure pipe code triggers the threshold, a maintenance work order is automatically generated and the nearest technician is matched based on UWB indoor positioning.

7. A coding system based on urban infrastructure, characterized in that: include: Code management engine, used to dynamically generate and parse codes; Blockchain evidence storage module, used to record facility maintenance history; AI analysis module, which predicts facility failure risks based on coded correlation data; The system implements coding based on urban infrastructure through the method described in any one of claims 1 to 6, thereby realizing fault prediction and intelligent decision-making.

8. The coding system based on urban infrastructure according to claim 7 is characterized in that: The system structure includes: Data collection layer, including RFID readers, multi-parameter sensors, and inspection drones; Network transmission layer, including 5G communication module and LoRaWAN gateway; The platform layer includes: Code management engine, used to implement dynamic code generation / parsing; Blockchain evidence storage module, used to record the timestamp and digital signature of maintenance operations; Digital twin visualization platform; Application layer, including: Municipal management backend can realize work order distribution and report generation; The public service mini program can scan the code to report repairs and check status.

9. A coding device based on urban infrastructure, characterized in that: include: at least one memory and at least one processor; The at least one memory is configured to store a machine-readable program; The at least one processor is configured to call the machine-readable program to implement the method according to any one of claims 1 to 6.

10. A computer-readable medium, characterized in that The computer-readable medium stores computer instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 6.

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