Municipal road damage intelligent measurement method and system based on digital twinborn technology

Abstract

The invention discloses a municipal road damage intelligent measurement method and system based on a digital twin technology, and relates to the technical field of municipal roads. The method comprises the steps of collecting municipal road damage data through a multi-mode sensor deployed on a municipal road, calculating a municipal road damage fusion value through a damage data fusion algorithm, and calculating a municipal road damage fusion tuning value according to the municipal road damage fusion value and municipal road historical damage data, optimizing a pre-trained damage intelligent measurement model, outputting a municipal road damage measurement result, constructing a municipal road damage virtual three-dimensional architecture through a digital twin technology, performing damage change simulation analysis on the municipal road, calculating a municipal road damage risk value, and generating a municipal road damage prediction report. According to the invention, through deep fusion of multi-modal sensor cooperation and a digital twinning technology, intelligent accurate measurement and risk assessment of municipal road damage are realized.

Description

Technical Field

[0001] The present invention relates to the field of municipal road technology, and in particular to a municipal road damage intelligent measurement method and system based on digital twin technology. Background Art

[0002] Municipal roads, as a vital vehicle for urban transportation, are constantly exposed to factors such as vehicle loads, environmental erosion, and material aging, making them susceptible to damage such as cracks, potholes, and subsidence. Traditional detection methods rely on manual inspections or single sensors, resulting in low efficiency, fragmented data, and poor real-time performance. Existing technologies lack multimodal sensor data fusion, making it difficult to accurately quantify the extent of damage. Furthermore, the lack of dynamic collaborative analysis between historical data and real-time measurements results in delayed risk assessments and an inability to effectively support preventative maintenance. An intelligent, high-precision method for measuring municipal road damage is urgently needed. Summary of the Invention

[0003] The present invention provides an intelligent measurement method for municipal road damage based on digital twin technology, comprising:

[0004] Step S1: collecting municipal road damage data through multimodal sensors deployed on municipal roads;

[0005] Step S2: Calculate the municipal road damage fusion value using a damage data fusion algorithm based on the municipal road damage data;

[0006] Step S3: Calculate the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimize the pre-trained damage intelligent measurement model, and output the municipal road damage measurement results;

[0007] Step S4: constructing a virtual three-dimensional architecture of municipal road damage using digital twin technology based on the municipal road damage measurement results;

[0008] Step S5: simulate and analyze damage changes on municipal roads using a virtual three-dimensional architecture for municipal road damage, calculate municipal road damage risk values, and generate a municipal road damage prediction report.

[0009] The above-mentioned intelligent municipal road damage measurement method based on digital twin technology includes the following sub-steps: calculating the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimizing the pre-trained intelligent damage measurement model, and outputting the municipal road damage measurement results:

[0010] Step S31: training a damage intelligent measurement model using machine learning technology based on historical municipal road damage data and damage measurement requirements;

[0011] Step S32: Calculate the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data;

[0012] Step S33: Optimize the damage intelligent measurement model according to the municipal road damage fusion tuning value and output the municipal road damage measurement results.

[0013] The above-mentioned intelligent measurement method for municipal road damage based on digital twin technology, wherein constructing a virtual three-dimensional architecture of municipal road damage using digital twin technology based on municipal road damage measurement results includes the following sub-steps:

[0014] Step S41: constructing a virtual three-dimensional architecture of municipal roads using digital twin technology based on the municipal road layout;

[0015] Step S42: performing damage mapping on the municipal road virtual three-dimensional framework according to the municipal road damage measurement results to construct a municipal road damage virtual three-dimensional framework.

[0016] The above-mentioned intelligent measurement method for municipal road damage based on digital twin technology includes the following sub-steps: simulating and analyzing damage changes on municipal roads through a virtual three-dimensional architecture of municipal road damage, calculating municipal road damage risk values, and generating a municipal road damage prediction report.

[0017] Step S51: Perform damage change simulation analysis on municipal roads using a virtual 3D architecture for municipal road damage to generate municipal road damage prediction data;

[0018] Step S52: Calculate the municipal road damage risk value using a damage risk prediction algorithm based on the municipal road damage prediction data;

[0019] Step S53: Generate a municipal road damage prediction report based on the municipal road damage prediction data and the municipal road damage risk value.

[0020] The present invention also provides a municipal road damage intelligent measurement system based on digital twin technology, comprising:

[0021] The data acquisition module collects municipal road damage data through multimodal sensors deployed on municipal roads;

[0022] Data fusion module, which calculates municipal road damage fusion value based on municipal road damage data through damage data fusion algorithm;

[0023] The result generation module calculates the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimizes the pre-trained damage intelligent measurement model, and outputs the municipal road damage measurement results;

[0024] A virtual 3D architecture construction module, which uses digital twin technology to construct a virtual 3D architecture of municipal road damage based on municipal road damage measurement results;

[0025] The damage analysis module simulates and analyzes damage changes on municipal roads through a virtual three-dimensional architecture of municipal road damage, calculates municipal road damage risk values, and generates a municipal road damage prediction report.

[0026] In the above-mentioned intelligent municipal road damage measurement system based on digital twin technology, the result generation module specifically includes:

[0027] The model training submodule uses machine learning technology to train the intelligent damage measurement model based on historical damage data of municipal roads and damage measurement requirements;

[0028] The tuning calculation submodule calculates the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data;

[0029] The result acquisition submodule optimizes the damage intelligent measurement model according to the municipal road damage fusion tuning value and outputs the municipal road damage measurement results.

[0030] In the above-mentioned intelligent municipal road damage measurement system based on digital twin technology, the virtual three-dimensional architecture construction module specifically includes:

[0031] The virtual 3D architecture construction submodule builds a virtual 3D architecture of municipal roads based on the municipal road layout using digital twin technology;

[0032] The damage mapping submodule performs damage mapping on the virtual three-dimensional architecture of municipal roads based on the municipal road damage measurement results, and constructs a virtual three-dimensional architecture of municipal road damage.

[0033] In the above-mentioned intelligent municipal road damage measurement system based on digital twin technology, the damage analysis module specifically includes:

[0034] The prediction data acquisition submodule simulates and analyzes damage changes on municipal roads through a virtual 3D architecture of municipal road damage, generating municipal road damage prediction data.

[0035] The damage risk calculation submodule calculates the municipal road damage risk value based on the municipal road damage prediction data using the damage risk prediction algorithm;

[0036] The report acquisition submodule generates a municipal road damage prediction report based on the municipal road damage prediction data and the municipal road damage risk value.

[0037] The beneficial effects achieved by this invention are as follows: Through the deep integration of multimodal sensor collaboration and digital twin technology, this invention enables intelligent and precise measurement and risk assessment of municipal road damage. This fully automated process and highly scalable system provide an innovative solution for the intelligent operation and maintenance of smart city infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 This is a flow chart of an intelligent measurement method for municipal road damage based on digital twin technology provided in Example 1 of the present application;

[0040] Figure 2 This is a schematic diagram of an intelligent measurement system for municipal road damage based on digital twin technology provided in Example 2 of the present application. DETAILED DESCRIPTION

[0041] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1 As shown, the first embodiment of the present application provides an intelligent measurement method for municipal road damage based on digital twin technology, which includes the following steps:

[0044] Step S1: collecting municipal road damage data through multimodal sensors deployed on municipal roads;

[0045] Specifically, the collected original municipal road damage data will be unified in dimensions and scales of different types of data through data preprocessing technology, and multivariate data alignment will be achieved through timestamp matching and spatial coordinate calibration. The processed data will be integrated according to feature categories to generate municipal road damage data with standardized format and unified structure.

[0046] Step S2: Calculate the municipal road damage fusion value using a damage data fusion algorithm based on the municipal road damage data;

[0047] Specifically, according to the municipal road damage data through the damage data fusion algorithm Calculate the municipal road damage fusion value of various damage types in each area of ​​the municipal road, where SCR is the municipal road damage fusion value, n is the number of modes of damage data, i is in the range of [1, n], ψ i is the fusion weight vector of the i-th modal data, im is the number of damage data collected by the i-th modal sensor, the value range of ij is [1, im], cs ij is the jth damage data collected by the i-th modal sensor, yc ij is the abnormal discrimination factor of the jth damage data collected by the i-th modal sensor, gz i is the failure rate of the i-th modal sensor, js i is the precision degradation rate of the i-th modal sensor, yx i is the damage factor fusion factor of the i-th mode. The damage types of the municipal roads include but are not limited to cracks, deformation, surface wear, looseness, water damage, and special diseases.

[0048] Step S3: Calculate the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimize the pre-trained damage intelligent measurement model, and output the municipal road damage measurement results;

[0049] Furthermore, the municipal road damage fusion tuning value is calculated based on the municipal road damage fusion value and the municipal road historical damage data, and the pre-trained damage intelligent measurement model is optimized to output the municipal road damage measurement results, including the following sub-steps:

[0050] Step S31: training a damage intelligent measurement model using machine learning technology based on historical municipal road damage data and damage measurement requirements;

[0051] Specifically, damage index data is obtained according to the municipal road damage measurement requirements, and the damage intelligent measurement model is trained through machine learning technology based on the municipal road historical damage data and damage index data.

[0052] Step S32: Calculate the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data;

[0053] Specifically, according to the municipal road damage fusion value and municipal road historical damage data, the damage fusion data tuning algorithm is used. Fine-tune the municipal road damage fusion value of various damage types in various areas of municipal roads, where the value range of k is [1, K], K is the number of damage types of municipal roads, SZC k is the municipal road damage fusion tuning value of the kth damage type, kR is the number of historical damage factors of the kth damage type, δ kris the weight of the rth historical damage factor of the kth damage type, ls kr is the historical damage data of the rth historical damage factor of the kth damage type, pz k is the offset calibration value of the kth damage type, yh k is the tuning factor of the kth damage type.

[0054] Step S33: Optimize the intelligent damage measurement model according to the municipal road damage fusion tuning value and output the municipal road damage measurement results;

[0055] Specifically, the damage characteristic trends reflected by the municipal road damage fusion tuning value SZC are analyzed, and the correlation characteristic data of various damage types in various areas of municipal roads are obtained. Based on these correlation characteristic data, the damage intelligent measurement model is optimized to identify the thresholds and quantification standards of different types of damage such as cracks and potholes, and the municipal road damage measurement results containing damage elements such as damage location and type are output.

[0056] Step S4: constructing a virtual three-dimensional architecture of municipal road damage using digital twin technology based on the municipal road damage measurement results;

[0057] Furthermore, based on the municipal road damage measurement results, a virtual 3D architecture of municipal road damage is constructed using digital twin technology, including the following sub-steps:

[0058] Step S41: constructing a virtual three-dimensional architecture of municipal roads using digital twin technology based on the municipal road layout;

[0059] Specifically, the layout of municipal roads is obtained through the geographic information system, and the geometric shape of municipal roads is accurately reproduced in the virtual space through digital twin technology to construct a virtual three-dimensional architecture of municipal roads.

[0060] Step S42: performing damage mapping on the municipal road virtual three-dimensional framework according to the municipal road damage measurement results to construct the municipal road damage virtual three-dimensional framework;

[0061] Specifically, various damage conditions are mapped to corresponding positions of the virtual three-dimensional architecture in a visual form according to the municipal road damage measurement results, thereby constructing a virtual three-dimensional architecture of municipal road damage.

[0062] Step S5: simulate and analyze damage changes on municipal roads using a virtual 3D architecture for municipal road damage, calculate municipal road damage risk values, and generate a municipal road damage prediction report;

[0063] Furthermore, the damage change simulation analysis of municipal roads is performed through the municipal road damage virtual 3D architecture, the municipal road damage risk value is calculated, and the municipal road damage prediction report is generated, which includes the following sub-steps:

[0064] Step S51: Perform damage change simulation analysis on municipal roads using a virtual 3D architecture for municipal road damage to generate municipal road damage prediction data;

[0065] Specifically, damage evolution rules are pre-set within a virtual 3D damage framework based on historical damage data for municipal roads. Dynamic parameters such as traffic flow and climate conditions are then input into the framework to simulate the development of municipal road damage under different scenarios. Based on the pre-set damage evolution rules, the changing trends of various damage conditions are analyzed to generate municipal road damage prediction data.

[0066] Step S52: Calculate the municipal road damage risk value using a damage risk prediction algorithm based on the municipal road damage prediction data;

[0067] Specifically, according to the municipal road damage prediction results, the damage risk prediction algorithm Calculate the municipal road damage risk value, where DJ is the damage risk prediction value, T is the number of municipal road damage types, the value range of t is [1, T], μ t is the risk weight of the t-th municipal road damage type, qk t is the risk prediction value of the t-th municipal road damage type, tY is the number of degradation factors of the t-th municipal road damage type, and the value range of ty is [1, tY]. is the weight of the yth degradation factor of the tth municipal road damage type, yc ty is the damage prediction value of the yth deterioration factor of the tth municipal road damage type, pf ty is the damage risk scoring factor of the yth degradation factor of the tth municipal road damage type.

[0068] Step S53: generating a municipal road damage prediction report based on the municipal road damage prediction data and the municipal road damage risk value;

[0069] Specifically, the municipal road damage risk level is set according to the municipal road damage risk value DJ. The municipal road damage risk level includes four levels: slight damage, mild damage, moderate damage, and severe damage.

[0070] Based on the municipal road damage prediction data, the municipal road damage prediction report presents the specific damage type, location, degree data of municipal roads, as well as visual report content such as data charts and map annotations. According to the damage risk level, the municipal road damage intelligent measurement report presents the repair urgency and repair priority of municipal roads, and combines the historical repair data of municipal roads to generate maintenance and repair recommendations.

[0071] Example 2

[0072] like Figure 2As shown, the second embodiment of the present application provides a municipal road damage intelligent measurement system based on digital twin technology, including:

[0073] The data collection module 21 collects municipal road damage data through multimodal sensors deployed on municipal roads;

[0074] Specifically, the collected original municipal road damage data will be unified in dimensions and scales of different types of data through data preprocessing technology, and multivariate data alignment will be achieved through timestamp matching and spatial coordinate calibration. The processed data will be integrated according to feature categories to generate municipal road damage data with standardized format and unified structure.

[0075] The data fusion module 22 calculates the municipal road damage fusion value based on the municipal road damage data using a damage data fusion algorithm;

[0076] Specifically, according to the municipal road damage data through the damage data fusion algorithm Calculate the municipal road damage fusion value of various damage types in each area of ​​the municipal road, where SCR is the municipal road damage fusion value, n is the number of modes of damage data, i is in the range of [1, n], ψ i is the fusion weight vector of the i-th modal data, im is the number of damage data collected by the i-th modal sensor, the value range of ij is [1, im], cs ij is the jth damage data collected by the i-th modal sensor, yc ij is the abnormal discrimination factor of the jth damage data collected by the i-th modal sensor, gz i is the failure rate of the i-th modal sensor, js i is the precision degradation rate of the i-th modal sensor, yx i is the damage factor fusion factor of the i-th mode. The damage types of the municipal roads include but are not limited to cracks, deformation, surface wear, looseness, water damage, and special diseases.

[0077] The result generation module 23 calculates the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimizes the pre-trained damage intelligent measurement model, and outputs the municipal road damage measurement results;

[0078] Furthermore, the result generation module 23 includes the following submodules:

[0079] The model training submodule uses machine learning technology to train the intelligent damage measurement model based on historical damage data of municipal roads and damage measurement requirements;

[0080] Specifically, damage index data is obtained according to the municipal road damage measurement requirements, and the damage intelligent measurement model is trained through machine learning technology based on the municipal road historical damage data and damage index data.

[0081] The tuning calculation submodule calculates the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data;

[0082] Specifically, according to the municipal road damage fusion value and municipal road historical damage data, the damage fusion data tuning algorithm is used. Fine-tune the municipal road damage fusion value of various damage types in various areas of municipal roads, where the value range of k is [1, K], K is the number of damage types of municipal roads, SZC k is the municipal road damage fusion tuning value of the kth damage type, kR is the number of historical damage factors of the kth damage type, δ kr is the weight of the rth historical damage factor of the kth damage type, ls kr is the historical damage data of the rth historical damage factor of the kth damage type, pz k is the offset calibration value of the kth damage type, yh k is the tuning factor of the kth damage type.

[0083] The result acquisition submodule optimizes the damage intelligent measurement model according to the municipal road damage fusion tuning value and outputs the municipal road damage measurement results;

[0084] Specifically, the damage characteristic trends reflected by the municipal road damage fusion tuning value SZC are analyzed, and the correlation characteristic data of various damage types in various areas of municipal roads are obtained. Based on these correlation characteristic data, the damage intelligent measurement model is optimized to identify the thresholds and quantification standards of different types of damage such as cracks and potholes, and the municipal road damage measurement results containing damage elements such as damage location and type are output.

[0085] A virtual three-dimensional architecture construction module 24 constructs a virtual three-dimensional architecture of municipal road damage using digital twin technology based on municipal road damage measurement results;

[0086] Furthermore, the virtual three-dimensional architecture construction module 24 includes the following submodules:

[0087] The virtual 3D architecture construction submodule builds a virtual 3D architecture of municipal roads based on the municipal road layout using digital twin technology;

[0088] Specifically, the layout of municipal roads is obtained through the geographic information system, and the geometric shape of municipal roads is accurately reproduced in the virtual space through digital twin technology to construct a virtual three-dimensional architecture of municipal roads.

[0089] The damage mapping submodule performs damage mapping on the virtual 3D architecture of municipal roads based on the municipal road damage measurement results, and constructs the virtual 3D architecture of municipal road damage;

[0090] Specifically, various damage conditions are mapped to corresponding positions of the virtual three-dimensional architecture in a visual form according to the municipal road damage measurement results, thereby constructing a virtual three-dimensional architecture of municipal road damage.

[0091] Damage analysis module 25, which simulates and analyzes damage changes on municipal roads through a virtual 3D architecture for municipal road damage, calculates municipal road damage risk values, and generates a municipal road damage prediction report;

[0092] Furthermore, the damage analysis module 25 includes the following submodules:

[0093] The prediction data acquisition submodule simulates and analyzes damage changes on municipal roads through a virtual 3D architecture of municipal road damage, generating municipal road damage prediction data.

[0094] Specifically, damage evolution rules are pre-set within a virtual 3D damage framework based on historical damage data for municipal roads. Dynamic parameters such as traffic flow and climate conditions are then input into the framework to simulate the development of municipal road damage under different scenarios. Based on the pre-set damage evolution rules, the changing trends of various damage conditions are analyzed to generate municipal road damage prediction data.

[0095] The damage risk calculation submodule calculates the municipal road damage risk value based on the municipal road damage prediction data using the damage risk prediction algorithm;

[0096] Specifically, according to the municipal road damage prediction results, the damage risk prediction algorithm Calculate the municipal road damage risk value, where DJ is the damage risk prediction value, T is the number of municipal road damage types, the value range of t is [1, T], μ t is the risk weight of the t-th municipal road damage type, qk t is the risk prediction value of the t-th municipal road damage type, tY is the number of degradation factors of the t-th municipal road damage type, and the value range of ty is [1, tY]. is the weight of the yth degradation factor of the tth municipal road damage type, yc ty is the damage prediction value of the yth deterioration factor of the tth municipal road damage type, pf ty is the damage risk scoring factor of the yth degradation factor of the tth municipal road damage type.

[0097] The report acquisition submodule generates a municipal road damage prediction report based on the municipal road damage prediction data and the municipal road damage risk value;

[0098] Specifically, the municipal road damage risk level is set according to the municipal road damage risk value DJ. The municipal road damage risk level includes four levels: slight damage, mild damage, moderate damage, and severe damage.

[0099] Based on the municipal road damage prediction data, the municipal road damage prediction report presents the specific damage type, location, degree data of municipal roads, as well as visual report content such as data charts and map annotations. According to the damage risk level, the municipal road damage intelligent measurement report presents the repair urgency and repair priority of municipal roads, and combines the historical repair data of municipal roads to generate maintenance and repair recommendations.

[0100] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, comprising: at least one memory and at least one processor;

[0101] The memory is used to store one or more program instructions;

[0102] The processor is used to run one or more program instructions to execute an intelligent measurement method for municipal road damage based on digital twin technology.

[0103] Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute an intelligent measurement method for municipal road damage based on digital twin technology.

[0104] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are run on a computer, the computer executes the above-mentioned intelligent measurement method for municipal road damage based on digital twin technology.

[0105] In the embodiments of the present invention, the processor may be an integrated circuit chip having signal processing capabilities. The processor may be 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 devices, discrete gate or transistor logic devices, or discrete hardware components.

[0106] The methods, steps, and logic diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The processor reads the information from the storage medium and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0107] The storage medium may be a memory and may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0108] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0109] Volatile memory may be random access memory (RAM), which is used as an external cache memory. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM).

[0110] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0111] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0112] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent measurement method for municipal road damage based on digital twin technology, characterized in that: include: Step S1: collecting municipal road damage data through multimodal sensors deployed on municipal roads; Step S2: Calculate the municipal road damage fusion value using a damage data fusion algorithm based on the municipal road damage data; Step S3: Calculate the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimize the pre-trained damage intelligent measurement model, and output the municipal road damage measurement results; Step S4: constructing a virtual three-dimensional architecture of municipal road damage using digital twin technology based on the municipal road damage measurement results; Step S5: simulate and analyze damage changes on municipal roads using a virtual three-dimensional architecture for municipal road damage, calculate municipal road damage risk values, and generate a municipal road damage prediction report.

2. The intelligent measurement method for municipal road damage based on digital twin technology according to claim 1, characterized in that: Calculating the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimizing the pre-trained damage intelligent measurement model, and outputting the municipal road damage measurement results include the following sub-steps: Step S31: training a damage intelligent measurement model using machine learning technology based on historical municipal road damage data and damage measurement requirements; Step S32: Calculate the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data; Step S33: Optimize the damage intelligent measurement model according to the municipal road damage fusion tuning value and output the municipal road damage measurement results.

3. The intelligent measurement method for municipal road damage based on digital twin technology according to claim 1, characterized in that: The construction of a virtual 3D architecture of municipal road damage using digital twin technology based on municipal road damage measurement results includes the following sub-steps: Step S41: constructing a virtual three-dimensional architecture of municipal roads using digital twin technology based on the municipal road layout; Step S42: performing damage mapping on the municipal road virtual three-dimensional framework according to the municipal road damage measurement results to construct a municipal road damage virtual three-dimensional framework.

4. The intelligent measurement method for municipal road damage based on digital twin technology according to claim 1, characterized in that: Using a virtual 3D architecture for municipal road damage to simulate and analyze damage changes on municipal roads, calculate municipal road damage risk values, and generate a municipal road damage prediction report includes the following sub-steps: Step S51: Perform damage change simulation analysis on municipal roads using a virtual 3D architecture for municipal road damage to generate municipal road damage prediction data; Step S52: Calculate the municipal road damage risk value using a damage risk prediction algorithm based on the municipal road damage prediction data; Step S53: Generate a municipal road damage prediction report based on the municipal road damage prediction data and the municipal road damage risk value.

5. An intelligent municipal road damage measurement system based on digital twin technology, characterized by: include: The data acquisition module collects municipal road damage data through multimodal sensors deployed on municipal roads; Data fusion module, which calculates municipal road damage fusion value based on municipal road damage data through damage data fusion algorithm; The result generation module calculates the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data, optimizes the pre-trained damage intelligent measurement model, and outputs the municipal road damage measurement results; A virtual 3D architecture construction module, which uses digital twin technology to construct a virtual 3D architecture of municipal road damage based on municipal road damage measurement results; The damage analysis module simulates and analyzes damage changes on municipal roads through a virtual three-dimensional architecture of municipal road damage, calculates municipal road damage risk values, and generates a municipal road damage prediction report.

6. The municipal road damage intelligent measurement system based on digital twin technology according to claim 5 is characterized in that: The result generation module includes: The model training submodule uses machine learning technology to train the intelligent damage measurement model based on historical damage data of municipal roads and damage measurement requirements; The tuning calculation submodule calculates the municipal road damage fusion tuning value based on the municipal road damage fusion value and the municipal road historical damage data; The result acquisition submodule optimizes the damage intelligent measurement model according to the municipal road damage fusion tuning value and outputs the municipal road damage measurement results.

7. The municipal road damage intelligent measurement system based on digital twin technology according to claim 5 is characterized in that: Virtual 3D architecture building blocks, including: The virtual 3D architecture construction submodule builds a virtual 3D architecture of municipal roads based on the municipal road layout using digital twin technology; The damage mapping submodule performs damage mapping on the virtual three-dimensional architecture of municipal roads based on the municipal road damage measurement results, and constructs a virtual three-dimensional architecture of municipal road damage.

8. The municipal road damage intelligent measurement system based on digital twin technology according to claim 5, characterized in that: Damage analysis module, including: The prediction data acquisition submodule simulates and analyzes damage changes on municipal roads through a virtual 3D architecture of municipal road damage, generating municipal road damage prediction data. The damage risk calculation submodule calculates the municipal road damage risk value based on the municipal road damage prediction data using the damage risk prediction algorithm; The report acquisition submodule generates a municipal road damage prediction report based on the municipal road damage prediction data and the municipal road damage risk value.

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