Road quality detection system and method based on block chain

Abstract

The invention discloses a road quality detection system and method based on a block chain. The system comprises a safety grade division module, a different parameter influence factor reference table module during road leveling, an influence factor calculation module during quality detection, a fluctuation range module of a road traffic efficiency influence factor, a quality detection core position determination module and a quality detection maximum value change curve module. Data detection positions corresponding to various types of compaction degree, water content, compactness, structural strength and flatness parameters of roads with different purposes are divided, and a respective longitudinal comparison mode is adopted; and finally, matching the road bearing capacity, the quality detection roadbed parameters and the maximum values of the crack width, the drainage capacity and the water seepage condition of the corresponding matched zoning positions during quality detection, so that the data detection positions which possibly have abnormal acquisition are repaired in advance, and the subsequent data analysis process is not participated. And the accuracy of final road traffic quality detection is ensured.

Description

Technical Field

[0001] The present invention relates to the field of highway quality inspection, and in particular to a highway quality inspection system and method based on blockchain. Background Art

[0002] The reliability of highway quality is closely related to driving safety, and quality inspection and monitoring after highway construction is the key to ensuring road quality. Traditional highway quality inspection methods usually rely on manual inspection, regular inspection and data report recording. These methods have problems such as insufficient real-time performance, easy data tampering and difficult traceability. With the development of technologies such as the Internet of Things and artificial intelligence, many inspection equipment and sensors have been applied to highway quality inspection, but there are still problems such as data isolation and difficulty in integration, which limits information sharing and data transparency. Blockchain, as a decentralized and tamper-proof data recording technology, has distributed storage and consensus mechanisms, which can ensure the authenticity and sharing of data, and provide new technical support for highway quality inspection. By combining blockchain with smart contracts, the highway quality inspection system can automatically record, verify and trigger warnings for inspection data without human supervision, providing a basis for efficient and transparent highway quality management.

[0003] At present, most highway quality inspections still use traditional methods, such as manual inspections, sampling inspections, measurement and analysis, etc. These methods are time-consuming and costly, and the data is easily interfered by human factors, making it difficult to ensure the accuracy and consistency of the data. Especially in long-term monitoring, traditional methods are difficult to continuously provide real-time data, and the inspection cycle is long and the frequency is low, resulting in the inability to timely discover potential quality problems. In addition, data management and sharing are also difficulties in the existing system. Multiple departments and units often find it difficult to coordinate and share inspection data, and data records are susceptible to tampering. There is a lack of unified standards, and the distribution of responsibilities is unclear. In terms of data management, traditional methods often lack a traceability mechanism, which makes it difficult to trace the specific responsible party when accident investigations or quality accountability are required. Summary of the invention

[0004] The purpose of the present invention is to provide a highway quality detection system and method based on blockchain. The method and system calculate the two detection objects at each data detection position separately, and then compare the influencing factor distribution according to the respective calculation results. According to the influencing factor distribution comparison results, it is judged whether there is a large error at the data detection position, and the data collected at the data detection position is repaired in advance to reduce the errors of subsequent calculations, thereby ensuring the accuracy of highway traffic quality detection.

[0005] The embodiment of the present invention is achieved as follows:

[0006] First, the road quality inspection system based on blockchain includes:

[0007] Safety level classification module, which classifies the traffic safety levels of roads with different uses through blockchain, obtains the bearing capacity and traffic efficiency analysis steps and the quality inspection blockchain data cloud. The bearing capacity and traffic efficiency analysis steps are used to analyze the threshold values ​​of climate impact, hydrological conditions, pavement structure layer, cumulative damage and pavement elastic modulus on the bearing capacity of highway traffic, and the quality inspection blockchain data cloud is used to classify the detailed data of highway traffic from road traffic interruption to quality inspection completion;

[0008] A reference table module for influencing factors of different parameters during highway leveling. This module obtains all highway bearing capacity and quality inspection roadbed parameters in the bearing capacity and traffic efficiency analysis step, and determines the compaction, moisture content, density, structural strength and flatness parameters of all highway bearing capacity and quality inspection roadbed parameters to form a parameter group of compaction, moisture content, density, structural strength and flatness during highway leveling, and calculates the influence factors of each compaction, moisture content, density, structural strength and flatness parameter in the parameter group of compaction, moisture content, density, structural strength and flatness during highway leveling on highway traffic efficiency, and obtains a reference table for influencing factors of different parameters during highway leveling;

[0009] The module for calculating the impact factors during quality inspection obtains the maximum values ​​of all crack widths, drainage capacity, and water seepage conditions in the quality inspection blockchain data cloud during quality inspection, and determines the maximum fluctuation range of all crack widths, drainage capacity, and water seepage conditions during quality inspection, thereby forming compliance parameter groups at different altitudes during quality inspection, and calculates the impact factors of the maximum fluctuation range of each crack width, drainage capacity, and water seepage condition during quality inspection in the compliance parameter groups at different altitudes during quality inspection on highway traffic efficiency, and obtains a reference table of impact factors of different parameters during quality inspection;

[0010] The module of fluctuation range of factors affecting highway traffic efficiency, which matches the maximum values ​​of highway bearing capacity, quality inspection roadbed parameters and crack width, drainage capacity and water seepage during quality inspection through the safety level parameter control database, determines the respective influencing factors on highway traffic efficiency based on the matched highway bearing capacity, quality inspection roadbed parameters and crack width, drainage capacity and water seepage during quality inspection and calculates the fluctuation range of the influencing factors;

[0011] The module for determining the core position of quality inspection is implemented by judging: if the influence factor values ​​of highway bearing capacity and quality inspection subgrade parameters in the reference table of different parameter influence factors during highway leveling are the same as the maximum values ​​of crack width, drainage capacity and water seepage during quality inspection in the reference table of different parameter influence factors during quality inspection, then the highway bearing capacity, quality inspection subgrade parameters and the maximum values ​​of crack width, drainage capacity and water seepage during quality inspection are used as the core position of quality inspection.

[0012] In an optional implementation, it also includes a quality detection maximum value change curve module, which calibrates the highway bearing capacity and quality detection roadbed parameters used as the quality detection position as the data detection position when the highway is leveled, uses all the highway bearing capacity and quality detection roadbed parameters to establish a highway bearing capacity change curve when the highway is leveled, uses the highway bearing capacity change curve when the highway is leveled to determine the sag risk detection section of the data detection position when the highway is leveled, compares all the sag risk detection sections with the data detected at the data detection position when the highway is leveled, and determines whether to use the data detection position when the highway is leveled as the core position for quality detection based on the comparison result. ; The maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection at the quality inspection position are calibrated as the data inspection position during quality inspection, and a maximum value change curve during quality inspection is established using all the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection; the maximum value change curve during quality inspection is used to determine the sagging risk detection section during quality inspection at the data inspection position during quality inspection; all the sagging risk detection sections during quality inspection are compared with the data detected at the data inspection position during quality inspection, and whether to use the data inspection position during quality inspection as the core position for quality inspection is determined based on the comparison result.

[0013] Secondly, the highway quality inspection method based on blockchain includes the following steps: dividing highways of different uses into traffic safety levels through blockchain, obtaining the bearing capacity and traffic efficiency analysis steps and the quality inspection blockchain data cloud, the bearing capacity and traffic efficiency analysis steps are used to conduct threshold analysis of the bearing capacity of highway traffic in terms of climate impact, hydrological conditions, pavement structure layer, cumulative damage and pavement elastic modulus, and the quality inspection blockchain data cloud is used to conduct detailed data classification of highway traffic from road traffic interruption to completion of quality inspection; obtaining the bearing capacity and traffic efficiency analysis steps of all highways, quality inspection routes, and traffic safety data of all highways. The basic parameters are determined, and the compaction, moisture content, density, structural strength and flatness parameters of all highway bearing capacity and quality inspection roadbed parameters are determined to form a parameter group of compaction, moisture content, density, structural strength and flatness when the highway is leveled. The impact factors of each compaction, moisture content, density, structural strength and flatness parameter in the parameter group of the highway leveling on the highway traffic efficiency are calculated, and a reference table of the impact factors of different parameters when the highway is leveled is obtained; all crack widths, drainage capacity, and water seepage conditions in the quality inspection blockchain data cloud are obtained during quality inspection. The maximum value is obtained, and the maximum fluctuation range of all crack widths, drainage capacity, and water seepage conditions during quality inspection is determined to form compliance parameter groups at different altitudes during quality inspection, and the influence factors of the maximum fluctuation range of each crack width, drainage capacity, and water seepage condition during quality inspection in the compliance parameter groups at different altitudes during quality inspection on the highway traffic efficiency are calculated to obtain a reference table of influence factors of different parameters during quality inspection; the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of crack width, drainage capacity, and water seepage conditions during quality inspection are matched through the safety level parameter reference database, and the matched highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of crack width, drainage capacity, and water seepage conditions during quality inspection are used to determine their respective influence factors on highway traffic efficiency and calculate the fluctuation range of the influence factors; if the influence factor values ​​of highway bearing capacity and quality inspection roadbed parameters in the reference table of influence factors of different parameters during highway leveling are the same as the influence factor values ​​of the maximum values ​​of crack width, drainage capacity, and water seepage conditions during quality inspection in the reference table of influence factors of different parameters during quality inspection, then the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity, and water seepage conditions during quality inspection are used as the core position of quality inspection.

[0014] In an optional implementation manner, the highway bearing capacity and quality inspection roadbed parameters used as the quality inspection position are calibrated as the data inspection position when the highway is leveled, all highway bearing capacity and quality inspection roadbed parameters are used to establish a highway bearing capacity change curve when the highway is leveled, the highway bearing capacity change curve when the highway is leveled is used to determine the sag risk detection section of the data inspection position when the highway is leveled, all sag risk detection sections are compared with the data detected by the highway data inspection position when the highway is leveled, and based on the comparison result, it is determined whether the highway data inspection position when the highway is leveled is used as the core position for quality inspection; the maximum values ​​of the crack width, drainage capacity, and water seepage during quality inspection used as the quality inspection position are calibrated as the data inspection position during quality inspection, and a maximum value change curve during quality inspection is established using the maximum values ​​of all crack widths, drainage capacities, and water seepage during quality inspection; the sag risk detection section during quality inspection of the data inspection position during quality inspection is determined using the maximum value change curve during quality inspection; all sag risk detection sections during quality inspection are compared with the data detected by the data inspection position during quality inspection, and based on the comparison result, it is determined whether the data inspection position during quality inspection is used as the core position for quality inspection.

[0015] In an optional implementation, the comparison includes the following steps: determining a blockchain data integration method for compaction, moisture content, density, structural strength and flatness parameters, the blockchain data integration method including on-chain and off-chain data integration, API integration, smart contracts and external system integration; determining the area between potholes in the blockchain data integration method, calculating a three-dimensional heat map of the pothole area in the blockchain data integration method of the corresponding depression risk detection section and the pothole area in the blockchain data integration method of the corresponding data detection position, obtaining the depth change of all three-dimensional heat maps, and using the depth change to calculate the comparison result between the corresponding depression risk detection section and the corresponding data detection position.

[0016] In an optional implementation, before obtaining the depth change of all three-dimensional heat maps, the following steps are also included: clustering all three-dimensional heat maps according to the degree of depth change to obtain clustering results; setting traffic calibration parameters for the potholes in the clustering results, and then using the corresponding change curve to set traffic intersections on each traffic calibration parameter to obtain multiple traffic intersection results, and determining the three-dimensional heat maps corresponding to different traffic intersection results among the multiple traffic intersection results to proceed to subsequent steps.

[0017] In an optional implementation, when determining different passable fork road results, the passable fork road result corresponding to the abnormal three-dimensional heat map is obtained by performing road impassability analysis based on all three-dimensional heat maps.

[0018] In an optional implementation, after using the bearing capacity change curve when the highway is leveled to determine the sagging risk detection section of the data detection position when the highway is leveled, the following steps are also included: obtaining a correlation coefficient between the data detection position when the highway is leveled and the sagging risk detection section; and repairing the sagging risk detection section whose correlation coefficient is greater than a preset value.

[0019] In an optional implementation, repairing a sag risk detection section having a correlation coefficient greater than a preset value further includes the following steps: using a bearing capacity variation curve when the highway is leveled to determine the number of positions between the repaired sag risk detection section and the data detection position when the highway is leveled; if the number of positions is greater than an adjusted preset value, replacing the repaired sag risk detection section as a benchmark for comparison with the data detection position when the highway is leveled.

[0020] In an optional implementation manner, a relevant detection error is set for the replaced sag risk detection section, and the relevant detection error is used as a calculation basis for substitution into the sag risk detection section for comparison.

[0021] Beneficial effects:

[0022] The blockchain-based highway quality inspection system and method provided by the embodiment of the present invention divides the data inspection positions corresponding to various types of compaction, water content, density, structural strength and flatness parameters of highways for different purposes, such as the main bearing capacity and the data from road traffic interruption to quality inspection completion, to form a bearing capacity traffic efficiency analysis step and a quality inspection blockchain data cloud. A longitudinal comparative analysis is performed on each type of compaction, water content, density, structural strength and flatness parameter. In particular, the probability that only one of the bearing capacity data and the data from road traffic interruption to quality inspection completion at the same data inspection position is abnormal is very low. Either the same or similar degree of abnormality occurs, or it is a normal situation. By using the above-mentioned longitudinal comparison method, the highway bearing capacity, quality inspection roadbed parameters and crack width, drainage capacity, and water seepage conditions at the corresponding matching zoning positions are finally matched at the time of quality inspection. It is determined whether the degree of abnormality of the two is the same or tends to be the same to judge whether the data collection function of the data detection position is normal, thereby pre-repairing the data detection position where abnormal collection may occur, and not participating in the subsequent data analysis process, to ensure the accuracy of the final highway traffic quality inspection. The present invention utilizes blockchain technology to directly record the real-time data collected by different sensors on the chain. Each piece of data carries a timestamp, geographic location, and identification of the detection equipment to ensure that the data cannot be tampered with and is traceable. At the same time, the smart contract can automatically execute the detection process, such as triggering an alarm mechanism, assigning tasks, and even directly generating a test report when the predetermined conditions are met, to achieve a quality inspection process without human intervention. With the multi-party participation feature of the blockchain, all relevant units can share and verify the data, reduce the phenomenon of information islands, enable different departments to obtain a unified data source in real time, and improve information transparency. This method can not only reduce detection costs and improve detection efficiency, but also effectively solve the problems of difficult data tracing and unclear responsibilities, thereby ensuring the reliability and transparency of highway quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a module composition diagram of the system of the present invention;

[0025] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] This embodiment also provides a road quality detection system based on blockchain, please refer to Figure 1 The modular schematic diagram of the highway quality inspection system based on blockchain is mainly used to divide the functional modules of the highway quality inspection system based on blockchain according to the embodiment of the above method. For example, each functional module can be divided, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the present invention is schematic and is only a logical functional division. There may be other division methods in actual implementation. For example, in the case of dividing each functional module according to each function, such as Figure 1 The figure is a schematic diagram of the system module composition. The highway quality inspection system based on blockchain can include a safety level classification module, a reference table module for different parameter influencing factors during highway leveling, a module for calculating influencing factors during quality inspection, a module for the fluctuation range of factors affecting highway traffic efficiency, and a module for determining the core position of quality inspection. The functions of each module are described below.

[0028] The safety level classification module, which classifies the traffic safety levels of roads with different uses through blockchain, obtains the bearing capacity and traffic efficiency analysis steps and the quality inspection blockchain data cloud. The bearing capacity and traffic efficiency analysis steps are used to analyze the threshold values ​​of climate impact, hydrological conditions, pavement structure layer, cumulative damage and pavement elastic modulus on the bearing capacity of the road. The quality inspection blockchain data cloud is used to classify the detailed data of the road traffic from the interruption of pavement traffic to the completion of quality inspection;

[0029] A reference table module for influencing factors of different parameters during highway leveling, which integrates all highway bearing capacity and quality inspection roadbed parameters in the bearing capacity and traffic efficiency analysis step, and determines the compaction, moisture content, density, structural strength and flatness parameters of all the highway bearing capacity and quality inspection roadbed parameters to form a parameter group of compaction, moisture content, density, structural strength and flatness during highway leveling, and calculates the influence factors of each compaction, moisture content, density, structural strength and flatness parameter in the parameter group of compaction, moisture content, density, structural strength and flatness during highway leveling on highway traffic efficiency, and obtains a reference table for influencing factors of different parameters during highway leveling;

[0030] A module for calculating influencing factors during quality inspection, which integrates the maximum values ​​of all crack widths, drainage capacity, and water seepage conditions during quality inspection in the quality inspection blockchain data cloud, and determines the maximum value fluctuation range of all the crack widths, drainage capacity, and water seepage conditions during quality inspection, to form compliance parameter groups at different altitudes during quality inspection, and calculates the influence factors of the maximum value fluctuation range of each crack width, drainage capacity, and water seepage condition during quality inspection in the compliance parameter groups at different altitudes during quality inspection on highway traffic efficiency, and obtains a reference table of influencing factors of different parameters during quality inspection;

[0031] A fluctuation range module of the factors affecting highway traffic efficiency, which matches the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection through a safety level parameter reference database, determines the respective influencing factors on highway traffic efficiency based on the matched highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection, and calculates the fluctuation range of the influencing factors;

[0032] A module for determining the core position of quality inspection, which takes the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection as the core position of quality inspection if the influence factor values ​​in the reference table of different parameter influence factors during highway leveling are the same as the influence factor values ​​in the reference table of different parameter influence factors during quality inspection.

[0033] In some embodiments, the blockchain-based highway quality inspection system also includes a quality inspection maximum value change curve module, which calibrates the highway bearing capacity and quality inspection roadbed parameters used as the quality inspection position as the data inspection position when the highway is leveled, uses all the highway bearing capacity and quality inspection roadbed parameters to establish a highway bearing capacity change curve when the highway is leveled, uses the highway bearing capacity change curve when the highway is leveled to determine the sag risk detection section of the data detection position when the highway is leveled, compares all the sag risk detection sections with the data detected at the data detection position when the highway is leveled, and determines whether to use the highway data detection position when the highway is leveled as the quality inspection position according to the comparison result. The core position of quality inspection; the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection of the quality inspection position are calibrated as the data inspection position during quality inspection, and a maximum value change curve of quality inspection is established by using all the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection; the maximum value change curve of quality inspection is used to determine the sagging risk detection section during quality inspection of the data inspection position during quality inspection; all the sagging risk detection sections during quality inspection are compared with the data detected by the data inspection position during quality inspection, and whether to use the data inspection position during quality inspection as the core position of quality inspection is determined according to the comparison result.

[0034] In some embodiments, the quality detection maximum value change curve module is also used to determine the blockchain data integration method of compaction, moisture content, density, structural strength and flatness parameters, and the blockchain data integration method includes on-chain and off-chain data integration, API integration, smart contract and external system integration; determine the area between potholes in the blockchain data integration method, and calculate the three-dimensional heat map of the pothole area in the blockchain data integration method of the corresponding sag risk detection section and the pothole area in the blockchain data integration method of the corresponding data detection position; cluster all three-dimensional heat maps according to the degree of depth change to obtain clustering results; set traffic calibration parameters for the potholes in the clustering results, and then use the corresponding change curve to set traffic forks on each of the traffic calibration parameters to obtain multiple traffic fork results, and determine the three-dimensional heat maps corresponding to different traffic fork results in the multiple traffic fork results to perform subsequent steps; obtain the depth change of all three-dimensional heat maps, and use the depth change to calculate the comparison result between the corresponding sag risk detection section and the corresponding data detection position. And obtaining the correlation coefficient between the data detection position when the highway is leveled and the sag risk detection section; and repairing the sag risk detection section with a correlation coefficient greater than a preset value.

[0035] In some embodiments, in a highway quality inspection system based on blockchain, a graph neural network (GNN) can be used to model the complex relationships in the highway network and the interactions between influencing factors. The following is a formula for describing each module using a graph neural network.

[0036] 1. Establishment of graph neural network model: The highway network is abstracted into a graph G = (V, E), where V represents a set of nodes, each node represents a detection location or parameter (such as highway bearing capacity, crack width, etc.). E represents a set of edges, connecting nodes with correlation (such as the relationship between highway bearing capacity and climate conditions, drainage capacity, etc.).

[0037] 2. Node feature initialization: The features of each node v∈V are initialized to parameters related to road quality detection. For example, the node feature vector h v It can be expressed as: v =[Purpose, frequency of traffic, bearing capacity, climate impact, hydrological conditions, pavement structure layer, cumulative damage, elastic modulus, crack width, drainage capacity, water seepage].

[0038] 3. Graph convolution layer: In each layer of graph convolution, the node feature vector is updated according to the information of neighboring nodes. The update formula is:

[0039]

[0040] in: is the feature vector of node v in the k+1th layer, W (k) is the weight matrix of the kth layer, Indicates the neighbor nodes of node v, -d v and d u are the degrees of node v and its neighbor node u, respectively, and σ is the activation function (e.g. ReLU).

[0041] 4. Safety level classification module: Use the output node embedding of the graph neural network to calculate the safety level of the highway through a classification layer:

[0042]

[0043] Among them, W 安全 and b 安全 are the weights and biases of the classification layer.

[0044] 5. Reference table module for factors affecting different parameters during road leveling: The graph neural network can learn the impact of each node on the traffic efficiency during road leveling. For each parameter i, the impact factor is calculated as follows:

[0045]

[0046] By combining the influencing factors of different parameters, a reference table of influencing factors for highway leveling is constructed.

[0047] 6. Module for calculating influencing factors during quality inspection: For crack width, drainage capacity and water seepage during quality inspection, the graph neural network can analyze the maximum fluctuation range of these features. The formula is as follows:

[0048]

[0049] In different altitude parameter groups, the impact factor is calculated for the maximum value fluctuation range of each feature.

[0050] 7. Fluctuation range module of factors affecting highway traffic efficiency: match highway bearing capacity, quality inspection parameters and crack width according to safety level parameters. Fluctuation of traffic efficiency

[0051] The range is calculated as follows:

[0052]

[0053] Among them, variance calculation is used to evaluate the fluctuation range.

[0054] 8. Quality detection core position determination module: Using the attention mechanism of the graph neural network, it is possible to determine which nodes have higher influence weights. Attention weight α uv Indicates the importance of node u to node v:

[0055]

[0056] If the impact factors are the same, this node is considered as the core position of quality detection.

[0057] 9. Quality detection maximum value change curve module: By constructing the bearing capacity change curve of the quality detection location, the sagging risk detection section is predicted. Using time series models, such as RNN (recurrent neural network), the changes of node features over time can be tracked:

[0058]

[0059] in, It represents the node characteristics at time t, and the sag risk is determined through curve analysis.

[0060] 10. 3D heat map generation and cluster analysis: Generate 3D heat maps and use cluster analysis to identify traffic forks and potential anomalies:

[0061] Heat map = f(pothole area, depth change)

[0062] The heat map was clustered to identify the clustering patterns of abnormal areas and traffic diversions.

[0063] The present invention utilizes blockchain technology to directly record the real-time data collected by different sensors on the chain. Each piece of data carries a timestamp, geographic location, and identification of the detection equipment to ensure that the data cannot be tampered with and is traceable. At the same time, the smart contract can automatically execute the detection process, such as triggering an alarm mechanism, assigning tasks, and even directly generating a test report when the predetermined conditions are met, to achieve a quality inspection process without human intervention. With the multi-party participation feature of the blockchain, all relevant units can share and verify the data, reduce the phenomenon of information islands, enable different departments to obtain a unified data source in real time, and improve information transparency. This method can not only reduce detection costs and improve detection efficiency, but also effectively solve the problems of difficult data tracing and unclear responsibilities, thereby ensuring the reliability and transparency of highway quality detection.

[0064] like Figure 2 As shown, the road quality detection method based on blockchain provided in this embodiment includes the following steps:

[0065] S101. Highways for different purposes are divided into different traffic safety levels through blockchain, and the bearing capacity and traffic efficiency analysis steps and the quality inspection blockchain data cloud are obtained. The bearing capacity and traffic efficiency analysis steps are used to conduct threshold analysis of climate impact, hydrological conditions, pavement structure layers, cumulative damage and pavement elastic modulus on the bearing capacity of highway traffic. The quality inspection blockchain data cloud is used to conduct detailed data classification of highway traffic from road traffic interruption to completion of quality inspection.

[0066] This step indicates the determination of highways for different purposes. The highways for different purposes are mainly equipped with two detection functions, from road traffic interruption to quality inspection completion and bearing capacity, which are realized by multiple data detection positions arranged in the highways for different purposes. Each data detection position will perform synchronous detection from road traffic interruption to quality inspection completion and bearing capacity at the corresponding point. Considering that the probability of normal or abnormal detection from road traffic interruption to quality inspection completion and bearing capacity detection at the same data detection position is greater, and the probability of abnormality in only one of them is smaller, the compaction degree, moisture content, density, structural strength and flatness parameters of the two functions corresponding to each data detection position are separately extracted for longitudinal comparison, which can further determine whether there is a possibility of abnormal detection at the data detection position. That is, the traffic safety level of highways for different purposes is divided into the bearing capacity traffic efficiency analysis step and the quality inspection blockchain data cloud for subsequent longitudinal comparison.

[0067] S102, integrating all highway bearing capacity and quality inspection roadbed parameters in the bearing capacity and traffic efficiency analysis step, and determining the compaction, moisture content, density, structural strength and smoothness parameters of all highway bearing capacity and quality inspection roadbed parameters, forming a parameter group of compaction, moisture content, density, structural strength and smoothness when the highway is leveled, and calculating the influence factors of each compaction, moisture content, density, structural strength and smoothness parameter in the parameter group of compaction, moisture content, density, structural strength and smoothness when the highway is leveled on the highway traffic efficiency, and obtaining a reference table of influence factors of different parameters when the highway is leveled.

[0068] This step means using the factors affecting the highway traffic efficiency of compaction, moisture content, density, structural strength and flatness parameters to achieve the above-mentioned longitudinal comparison. Since this step does not know whether there is a possibility of abnormal detection at the data detection position, it is necessary to present and analyze the compaction, moisture content, density, structural strength and flatness parameters of all highway bearing capacity and quality inspection roadbed parameters. Similarly, the same treatment measures are taken for the maximum values ​​of crack width, drainage capacity and water seepage during quality inspection;

[0069] S103. Integrate the maximum values ​​of all crack widths, drainage capacities, and water seepage conditions during quality inspection in the quality inspection blockchain data cloud, and determine the maximum value fluctuation range of all crack widths, drainage capacities, and water seepage conditions during quality inspection to form compliance parameter groups at different altitudes during quality inspection, and calculate the influence factors of the maximum value fluctuation range of each crack width, drainage capacity, and water seepage condition in the compliance parameter groups at different altitudes during quality inspection on highway traffic efficiency, and obtain a reference table of influence factors of different parameters during quality inspection; present and analyze the maximum value fluctuation range of all crack widths, drainage capacity, and water seepage conditions during quality inspection.

[0070] This step presents a reference table of influencing factors of different parameters during highway leveling and a reference table of influencing factors of different parameters during quality inspection, that is, it is possible to find the comparison between the data of the two functional inspections at the same data inspection position and the data of the two functional inspections at other data inspection positions. If the distribution trends of the influencing factors of the two functional compaction, water content, density, structural strength and flatness parameters at the same data inspection position are the same, it means that the compaction, water content, density, structural strength and flatness parameters of the two functions are accurate or correct from the perspective of the data inspection position;

[0071] S104. Match the highway bearing capacity, quality inspection subgrade parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection through the safety level parameter reference database, and determine their respective influencing factors on highway traffic efficiency by using the matched highway bearing capacity, quality inspection subgrade parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection, and calculate the fluctuation range of the influencing factors.

[0072] The safety level parameter comparison database in this step mainly refers to the situation that belongs to the same configuration and responsible entity. In practice, the bearing capacity and the inspection from road traffic interruption to quality inspection completion may be carried out at different inspection sections, but the different inspection sections are all supervised by the same entity. It is necessary to combine the corresponding separate highway bearing capacity, quality inspection roadbed parameters with the maximum values ​​of crack width, drainage capacity, and water seepage during quality inspection to carry out the above-mentioned longitudinal comparison, especially based on the influencing factors of highway traffic efficiency and calculate the fluctuation range of the influencing factors.

[0073] S105. If the influence factor values ​​of the highway bearing capacity and quality inspection subgrade parameters in the reference table of different parameter influence factors during highway leveling are the same as the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection in the reference table of different parameter influence factors during quality inspection, then the highway bearing capacity, quality inspection subgrade parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection are used as the core position of quality inspection.

[0074] This step means that if the maximum values ​​of the highway bearing capacity, quality inspection roadbed parameters and crack width, drainage capacity and water seepage during quality inspection that are matched to the same inspection section have the same or approximately the same influencing factor distribution trends in their respective influencing factor distribution trend diagrams, then it can be determined that it is preliminarily judged to be a point that may belong to normal and accurately collected data. If the respective influencing factor distribution trends are very different, it can be judged to be an abnormal inspection section, and the uploaded data will be repaired to further reduce data errors.

[0075] Through the above technical scheme, only the compaction, water content, density, structural strength and flatness parameters of the detection section with a high degree of abnormality are repaired from the perspective of preliminary screening, and the retained detection section may also have abnormal detection. However, compared with the method of applying the compaction, water content, density, structural strength and flatness parameters of all detection sections as average values, the error is smaller and the data acquisition is more reliable. In some embodiments, in order to further analyze the retained detection section to further determine whether the detection section is abnormal. The analysis method provided in this embodiment also includes step A6 and its sub-steps.

[0076] S106. By establishing a change curve, a sag risk detection section is found for comparison, so as to screen the maximum values ​​of highway bearing capacity, quality detection roadbed parameters, crack width, drainage capacity, and water seepage during quality detection.

[0077] Specifically: the highway bearing capacity and quality inspection roadbed parameters used as the quality inspection position are calibrated as the data inspection position when the highway is leveled, all the highway bearing capacity and quality inspection roadbed parameters are used to establish a highway bearing capacity change curve when the highway is leveled, the sag risk detection section of the data inspection position when the highway is leveled is determined by using the highway bearing capacity change curve when the highway is leveled, all sag risk detection sections are compared with the data detected by the data inspection position when the highway is leveled, and it is determined whether the data inspection position when the highway is leveled is used as the core position of quality inspection according to the comparison result; the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection used as the quality inspection position are calibrated as the data inspection position when the highway is leveled, and a maximum value change curve during quality inspection is established by using the maximum values ​​of all the crack width, drainage capacity and water seepage during quality inspection; the sag risk detection section of the data inspection position when the quality inspection is determined by using the maximum value change curve during quality inspection; all sag risk detection sections during quality inspection are compared with the data detected by the data inspection position when the quality inspection is, and it is determined whether the data inspection position when the quality inspection is used as the core position of quality inspection according to the comparison result.

[0078] The corresponding data detection position of the quality detection position may have two detection functions that are normal or abnormal at the same time. In order to further identify, further longitudinal comparison is used. Taking the highway bearing capacity and quality detection roadbed parameters as the quality detection position as an example, it is calibrated as the data detection position when the highway is leveled, and the topological relationship between the data detection position when the highway is leveled and the other highway bearing capacity and quality detection roadbed parameters is established to obtain the bearing capacity change curve when the highway is leveled. The bearing capacity change curve when the highway is leveled is used to find the other highway bearing capacity and quality detection roadbed parameters directly associated with the data detection position when the highway is leveled and use them as the sag risk detection section. The bearing capacity data of one or more sag risk detection sections are compared with the bearing capacity data of the data detection position when the highway is leveled. The data comparison is performed by using the direct association, that is, the two detection sections that are directly connected in the actual underground environment. The principle of connectivity principle that the bearing capacity data theoretically remains the same further identifies whether the data collection of the data detection position when the highway is leveled is normal. The data detection position during quality detection is processed in the same way, which will not be repeated here.

[0079] By performing the above-mentioned longitudinal comparison of the data of the data detection position during highway leveling or the data detection position during quality inspection separately or simultaneously, it is possible to further identify whether the data detection position during highway leveling or the data detection position during quality inspection has detected abnormalities, thereby determining whether it can be used as the core position for quality inspection. For example, if it is found that the data similarity between one of the data detection positions during highway leveling and multiple sagging risk detection sections is low, the compaction degree, moisture content, density, structural strength and flatness parameters of the data detection position during highway leveling will be excluded to ensure that the data source for subsequent analysis has further reliability.

[0080] In this embodiment, the comparison is mainly performed through a differential analysis of smaller or finer indicators, and the comparison includes the following steps:

[0081] S201: Determine the blockchain data integration method for compaction, moisture content, density, structural strength and flatness parameters, and the blockchain data integration method includes on-chain and off-chain data integration, API integration, and smart contract and external system integration; this step indicates the method for obtaining the compaction, moisture content, density, structural strength and flatness parameters of the corresponding sag risk detection section (sag risk detection section or sag risk detection section during quality inspection) and the corresponding data detection position (data detection position during highway flatness or data detection position during quality inspection), and there are mainly three related methods: on-chain and off-chain data integration, on-chain and off-chain data storage: store key data (such as transaction records, verification information, metadata, etc.) on the blockchain, and store large files or unstructured data (such as images, videos) in the distributed storage system off-chain (such as IPFS, Swarm), and index the off-chain data through the hash value recorded on the chain to achieve integrity verification. Off-chain calculation and on-chain verification: Perform complex data calculation and processing off-chain, upload the calculation results and verification information to the blockchain, ensure the credibility and transparency of the data, and reduce the storage pressure on the chain. API integration, REST API and GraphQL API: Integrate blockchain data with external systems through standardized API interfaces (such as REST or GraphQL). API allows external applications to read blockchain data, query transaction records, and trigger smart contract operations. Data callback mechanism: External systems can set callback functions (webhooks) to trigger callbacks when certain events occur on the blockchain (such as smart contract execution completion, transaction completion), and synchronize blockchain data in real time. Smart contract integration with external systems, Oracles: Connect blockchain with external data sources through oracle services (such as Chainlink), and smart contracts can call off-chain data, such as weather data, financial market prices, etc., for conditional execution and dynamic verification. Event-driven: Smart contracts can be integrated with external systems through event triggering. When an on-chain event occurs, the external system receives an event notification to achieve cross-system data synchronization.

[0082] S202: Determine the area between the potholes in the blockchain data integration method, and calculate the three-dimensional heat map of the pothole area in the blockchain data integration method of the corresponding sag risk detection section and the pothole area in the blockchain data integration method of the corresponding data detection position; this step means using the compaction, moisture content, density, structural strength and flatness parameter sub-items between the corresponding sag risk detection section and the corresponding data detection position to compare, and further find the specific similarity detection index comparison, in which the principle of expressing the similarity in the form of the distance between the corresponding data is used to further obtain the judgment result of whether there is an abnormal detection at the corresponding data detection position. Thus, step B4 is performed: obtain the depth change of all three-dimensional heat maps, and use the depth change to calculate the comparison result between the corresponding sag risk detection section and the corresponding data detection position. This step means observing and analyzing all three-dimensional heat maps, selecting the median based on the concentration, and then using the median selected by the concentration to judge the similarity. For example, if the three-dimensional heat map represented by the median is greater than 1, it means that the similarity is not high, otherwise it means that the similarity is acceptable, so that the corresponding data detection position is used as the quality detection position.

[0083] Based on the above technical solution, the following steps are also included before obtaining the depth changes of all three-dimensional thermal maps:

[0084] S203: Cluster all three-dimensional heat maps according to the depth change degree to obtain clustering results; set traffic calibration parameters for the potholes in the clustering results, and then use the corresponding change curve to set traffic forks on each of the traffic calibration parameters, which means that it is necessary to perform comprehensive assignment based on the distance between the positions of the change curves to obtain multiple traffic fork results, and determine the three-dimensional heat maps corresponding to different traffic fork results in the multiple traffic fork results for subsequent steps. This step means that when calculating the concentration of the three-dimensional heat map, it is necessary to consider the number of positions between the corresponding sag risk detection section and the data detection position. The number of positions is obtained through the corresponding change curve. If the number of positions is larger, the traffic fork is larger, and the traffic fork is placed on the traffic calibration parameters, so that the traffic fork result obtained is closer to the actual situation. The relationship between the traffic fork and the traffic calibration parameters can be obtained through a theoretical model formed by using historical data, or it can be set through experience. However, if a large three-dimensional heat map appears in the final traffic fork result, it will be excluded as an abnormal point to ensure a higher accuracy of the concentration calculation.

[0085] It should be noted that, based on the above scheme, there may be a real and correct three-dimensional heat map in the repaired abnormal three-dimensional heat map, which needs to be replaced in the concentration measurement process to obtain a more objective and reasonable benchmark for constructing data comparison, that is, to determine the passable fork results corresponding to the abnormal three-dimensional heat map when determining the different passable fork results. The abnormal three-dimensional heat map is obtained by analyzing the road impassability based on all three-dimensional heat maps. By analyzing all three-dimensional heat maps by road impassability, the value of the three-dimensional heat map is used as the horizontal coordinate, and the corresponding position distance position is used as the vertical coordinate to perform road impassability analysis to find the abnormal three-dimensional heat map. If the value of the abnormal three-dimensional heat map is large, but the position, that is, the coordinates and the corresponding data detection position are close, the position is determined by the road impassability analysis curve, and then it can be used as a replacement object.

[0086] In actual comparative calculations, there will be situations where the benchmark environments are different between adjacent locations, especially for bearing capacity data. In a geographical environment with certain faults or related errors between the two locations, the bearing capacity data of the two cannot be calculated through the connectivity principle to calculate the similarity of compaction, moisture content, density, structural strength and flatness parameters. At this time, the following steps need to be performed, that is, after using the bearing capacity change curve when the highway is leveled to determine the sag risk detection section of the data detection position when the highway is leveled, the following steps are also included: obtaining the correlation coefficient between the data detection position when the highway is leveled and the sag risk detection section; repairing the sag risk detection section with a correlation coefficient greater than a preset value. This step represents repairing the sag risk detection section with a relatively large correlation error between the sag risk detection section and the data detection position when the highway is leveled, and not using it as a benchmark for subsequent comparisons to ensure the rationality of data analysis. The preset value is predetermined and can be sufficiently small while ensuring the calculation accuracy.

[0087] On the basis of the above technical solution, considering that once all the sag risk detection sections are greater than the preset value, there will be too few samples of sag risk detection sections, which is not conducive to comprehensively obtaining more reasonable data comparison results. That is, the sag risk detection sections that may cause larger errors in compaction, water content, density, structural strength and flatness parameters due to related errors can be repaired, and the rest can be replaced. Then, the repairing of the sag risk detection sections with correlation coefficients greater than the preset value also includes the following steps: using the load-bearing capacity change curve when the highway is leveled to determine the number of positions between the repaired sag risk detection section and the data detection position when the highway is leveled, if the number of positions is less than or equal to the adjusted preset value, the repaired sag risk detection section is replaced as a benchmark for comparison with the data detection position when the highway is leveled.

[0088] This step means replacing the sag risk detection section that is far away but has reasonable correlation errors, and keeping the sag risk detection section that is close but has serious relative correlation errors. The preset adjustment value is also predetermined, and can be sufficiently large while ensuring that the calculation meets the limit. On the basis of this scheme, in order to further obtain a more accurate data calculation benchmark, the replaced sag risk detection section is set with a relevant detection error, and the relevant detection error is used as a calculation benchmark for comparison with the sag risk detection section. That is, the reasonable correlation error (such as the relative correlation error caused by the difference in terrain) is converted into the relevant detection error, which is substituted as an influencing factor and set in the three-dimensional thermal map, so that when performing comparative calculations, a more reliable and reasonable data calculation benchmark is available.

[0089] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. The highway quality inspection system based on blockchain is characterized by: The system includes: a safety level classification module, a reference table module for factors affecting different parameters during road leveling, a module for calculating factors affecting quality inspection, a module for the fluctuation range of factors affecting road traffic efficiency, and a module for determining the core position of quality inspection; The safety level classification module classifies the traffic safety levels of roads with different uses through blockchain, and obtains the bearing capacity traffic efficiency analysis step and the quality inspection blockchain data cloud. The bearing capacity traffic efficiency analysis step is used to analyze the threshold values ​​of climate impact, hydrological conditions, pavement structure layer, cumulative damage and pavement elastic modulus on the bearing capacity of the road. The quality inspection blockchain data cloud is used to classify the detailed data of the road traffic from the interruption of pavement traffic to the completion of quality inspection. The reference table module for influencing factors of different parameters during highway leveling integrates all highway bearing capacity and quality inspection roadbed parameters in the bearing capacity and traffic efficiency analysis step, and determines the compaction, moisture content, density, structural strength and flatness parameters of all highway bearing capacity and quality inspection roadbed parameters to form a parameter group of compaction, moisture content, density, structural strength and flatness during highway leveling, and calculates the influence factors of each compaction, moisture content, density, structural strength and flatness parameter in the parameter group of compaction, moisture content, density, structural strength and flatness during highway leveling on highway traffic efficiency, and obtains a reference table for influencing factors of different parameters during highway leveling; The quality inspection influencing factor calculation module integrates the maximum values ​​of all crack widths, drainage capacity, and water seepage conditions in the quality inspection blockchain data cloud during quality inspection, and determines the maximum value fluctuation range of all crack widths, drainage capacity, and water seepage conditions during quality inspection, to form compliance parameter groups at different altitudes during quality inspection, and calculates the influence factors of the maximum value fluctuation range of each crack width, drainage capacity, and water seepage condition in the compliance parameter groups at different altitudes during quality inspection on highway traffic efficiency, and obtains a reference table of influencing factors of different parameters during quality inspection; The fluctuation range module of the highway traffic efficiency influencing factor matches the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection through the safety level parameter reference database, and determines the respective influencing factors on highway traffic efficiency based on the matched highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection, and calculates the fluctuation range of the influencing factor; The quality inspection core position determination module determines that if the influence factor values ​​of the highway bearing capacity and quality inspection roadbed parameters in the reference table of different parameter influence factors during highway leveling are the same as the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection in the reference table of different parameter influence factors during quality inspection, then the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection are used as the core position of quality inspection.

2. The road quality detection system based on blockchain according to claim 1 is characterized in that: The system also includes a quality detection maximum value change curve module, which calibrates the highway bearing capacity and quality detection roadbed parameters as the data detection position when the highway is leveled, uses all the highway bearing capacity and quality detection roadbed parameters to establish a highway bearing capacity change curve when the highway is leveled, uses the highway bearing capacity change curve when the highway is leveled to determine the sag risk detection section of the data detection position when the highway is leveled, compares all the sag risk detection sections with the data detected by the data detection position when the highway is leveled, and determines whether to use the data detection position when the highway is leveled as the core position for quality detection based on the comparison results; The maximum values ​​of the crack width, drainage capacity and water seepage at the quality inspection position during quality inspection are calibrated as the data inspection position during quality inspection, and a maximum value change curve during quality inspection is established using all the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection; the maximum value change curve during quality inspection is used to determine the sagging risk detection section during quality inspection at the data inspection position during quality inspection; all the sagging risk detection sections during quality inspection are compared with the data detected at the data inspection position during quality inspection, and whether to use the data inspection position during quality inspection as the core position for quality inspection is determined according to the comparison result.

3. The road quality detection method based on blockchain is characterized by: The method includes: Highways for different purposes are divided into different traffic safety levels through blockchain, and the bearing capacity and traffic efficiency analysis steps and the quality inspection blockchain data cloud are obtained. The bearing capacity and traffic efficiency analysis steps are used to analyze the threshold values ​​of climate impact, hydrological conditions, pavement structure layer, cumulative damage and pavement elastic modulus on the bearing capacity of highway traffic. The quality inspection blockchain data cloud is used to classify the detailed data of highway traffic from road traffic interruption to quality inspection completion; Integrate all highway bearing capacity and quality inspection roadbed parameters in the bearing capacity and traffic efficiency analysis step, and determine the compaction, moisture content, density, structural strength and flatness parameters of all highway bearing capacity and quality inspection roadbed parameters to form a compaction, moisture content, density, structural strength and flatness parameter group for highway flatness, and calculate the influence factors of each compaction, moisture content, density, structural strength and flatness parameter in the compaction, moisture content, density, structural strength and flatness parameter group for highway flatness on highway traffic efficiency, and obtain a reference table of influence factors of different parameters for highway flatness; Integrate the maximum values ​​of all crack widths, drainage capacity, and water seepage conditions during quality inspection in the quality inspection blockchain data cloud, and determine the maximum fluctuation range of all crack widths, drainage capacity, and water seepage conditions during quality inspection to form compliance parameter groups at different altitudes during quality inspection, and calculate the influence factors of the maximum fluctuation range of each crack width, drainage capacity, and water seepage condition during quality inspection in the compliance parameter groups at different altitudes during quality inspection on highway traffic efficiency, and obtain a reference table of influence factors of different parameters during quality inspection; Match the highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection through the safety level parameter reference database, determine the respective influencing factors on highway traffic efficiency by the matched highway bearing capacity, quality inspection roadbed parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection, and calculate the fluctuation range of the influencing factors; If the influence factor values ​​of the highway bearing capacity and quality inspection subgrade parameters in the reference table of different parameter influence factors during highway leveling are the same as the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection in the reference table of different parameter influence factors during quality inspection, then the highway bearing capacity, quality inspection subgrade parameters and the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection shall be used as the core position of quality inspection.

4. The road quality detection method based on blockchain according to claim 3 is characterized in that: The highway bearing capacity and quality inspection roadbed parameters used as the quality inspection position are calibrated as the data inspection position when the highway is leveled, and a bearing capacity change curve when the highway is leveled is established by using all the highway bearing capacity and quality inspection roadbed parameters. The bearing capacity change curve when the highway is leveled is used to determine the sag risk detection section of the data inspection position when the highway is leveled, and all the sag risk detection sections are compared with the data detected by the data inspection position when the highway is leveled, and it is determined whether to use the data inspection position when the highway is leveled as the core position for quality inspection according to the comparison result; The maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection at the quality inspection position are calibrated as the data inspection position during quality inspection, and a maximum value change curve during quality inspection is established using all the maximum values ​​of the crack width, drainage capacity and water seepage during quality inspection; the maximum value change curve during quality inspection is used to determine the sagging risk detection section during quality inspection at the data inspection position during quality inspection; all the sagging risk detection sections during quality inspection are compared with the data detected at the data inspection position during quality inspection, and whether to use the data inspection position during quality inspection as the core position for quality inspection is determined based on the comparison result.

5. The road quality detection method based on blockchain according to claim 4 is characterized in that: The comparison comprises the following steps: Determine the blockchain data integration method for compaction, moisture content, density, structural strength and flatness parameters, the blockchain data integration method including on-chain and off-chain data integration, API integration, smart contract and external system integration; determine the area between potholes in the blockchain data integration method, and calculate the three-dimensional heat map of the pothole area in the blockchain data integration method of the corresponding sag risk detection section and the pothole area in the blockchain data integration method of the corresponding data detection position; The depth changes of all three-dimensional heat maps are obtained, and the depth changes are used to calculate the comparison results between the corresponding sag risk detection section and the corresponding data detection position.

6. The road quality detection method based on blockchain according to claim 5 is characterized in that: The step of obtaining the depth change of all three-dimensional heat maps also includes the following steps: Cluster all three-dimensional heat maps according to the degree of depth change to obtain clustering results; set traffic calibration parameters for the potholes in the clustering results, and then use the corresponding change curve to set traffic intersections on each of the traffic calibration parameters to obtain multiple traffic intersection results, and determine the three-dimensional heat maps corresponding to different traffic intersection results among the multiple traffic intersection results to proceed to subsequent steps.

7. The road quality detection method based on blockchain according to claim 6 is characterized in that: When determining the different passable fork road results, the passable fork road result corresponding to the abnormal three-dimensional heat map is obtained by performing road impassability analysis based on all three-dimensional heat maps.

8. The road quality detection method based on blockchain according to claim 4 is characterized in that: After determining the sag risk detection section of the data detection position when the highway is leveled using the load-bearing capacity change curve when the highway is leveled, the following steps are also included: obtaining a correlation coefficient between the data detection position when the highway is leveled and the sag risk detection section; and repairing the sag risk detection section whose correlation coefficient is greater than a preset value.

9. The road quality detection method based on blockchain according to claim 8 is characterized in that: The method of repairing the sag risk detection section having a correlation coefficient greater than a preset value also includes the following steps: using the load-bearing capacity change curve when the highway is leveled to determine the number of positions between the repaired sag risk detection section and the data detection position when the highway is leveled; if the number of positions is greater than the adjustment preset value, replacing the repaired sag risk detection section as a benchmark for comparison with the data detection position when the highway is leveled; setting a relevant detection error for the replaced sag risk detection section, and using the relevant detection error as a calculation benchmark for substituting the sag risk detection section for comparison.

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