Motor vehicle environmental inspection data management information interconnection processing system

Abstract

The invention discloses a motor vehicle environmental inspection data management information interconnection processing system, and relates to the field of Internet of Things operating systems. The system comprises a data acquisition and multi-source fusion module for collecting ring detection data through a plurality of heterogeneous data sources and performing weighted fusion on the data; the data preprocessing and cleaning module is used for processing and cleaning the environmental inspection data; the data governance and standardization modeling module is used for obtaining structured data according to the preprocessed ring inspection data and obtaining an optimal data mapping scheme; the data storage and distributed management module is used for reducing query time by optimizing an index structure; the data sharing and opening interconnection module is used for protecting data privacy; and the intelligent analysis and supervision decision support module analyzes the multi-source motor vehicle environment detection data and identifies abnormal conditions. Through multi-source data fusion, knowledge graph optimization and intelligent analysis decision support, the accuracy, standardization degree and supervision efficiency of motor vehicle environmental inspection data are improved, and the scientificity of data management and environmental protection policy decision is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things operating systems, and in particular to a motor vehicle environmental inspection data management information interconnection processing system. Background Art

[0002] With the continuous improvement of vehicle emission standards, the collection, management, and analysis of environmental inspection data are playing an increasingly important role in vehicle environmental protection supervision. Currently, the collection of vehicle environmental inspection data relies on a variety of sensors and testing equipment, generating a wide variety of data, including emissions, temperature, humidity, pressure, and other multi-dimensional data. How to effectively manage and analyze this data to ensure emissions compliance has become a pressing technical challenge in the field of environmental protection supervision.

[0003] Therefore, there is an urgent need for a new data governance and information processing system that can provide systematic solutions for data collection, preprocessing, standardized modeling, data storage management, privacy protection, and intelligent analysis. This system can not only improve data quality and ensure data privacy, but also enhance regulatory efficiency through intelligent analysis, providing strong support for environmental regulatory decision-making.

[0004] To this end, the present invention proposes a motor vehicle environmental inspection data governance information interconnection and processing system. The system aims to improve the management and analysis efficiency of motor vehicle environmental inspection data through technical means such as multi-source data fusion, data preprocessing and cleaning, intelligent analysis and decision support, thereby achieving precise supervision and ensuring the effective implementation of environmental protection policies. Summary of the Invention

[0005] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a motor vehicle environmental inspection data management information interconnection processing system and system to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: a vehicle environmental inspection data management information interconnection processing system, comprising: The data collection and multi-source fusion module collects vehicle environmental inspection data from multiple heterogeneous data sources and performs weighted fusion on the data. Data preprocessing and cleaning module, which processes and cleans motor vehicle environmental inspection data based on wavelet threshold denoising algorithm and local outlier factor method; The data governance and standardized modeling module obtains structured data based on pre-processed multi-source vehicle environmental inspection data and obtains the optimal data mapping solution; The data storage and distributed management module reduces query time and controls storage costs by optimizing the index structure; Data sharing and open interconnection module, based on zero-knowledge proof technology, to protect data privacy; The intelligent analysis and supervision decision-making support module analyzes multi-source motor vehicle environmental inspection data to identify abnormal situations.

[0007] The present invention is further configured such that the weighted fusion of the motor vehicle environmental inspection data has the following fusion formula: , , where , respectively represent the basic probability assignments of the data sources, is the conflict factor, measuring the inconsistency between the data sources.

[0008] The present invention is further configured such that the steps for processing and cleaning the motor vehicle environmental inspection data are as follows: Denoise the data through the wavelet threshold denoising algorithm, and its formula is: , where is the wavelet coefficient of the th layer, is the threshold; Calculate the local outlier factor, and its formula is: , where is the local reachability density, is the nearest neighbor set, is the local outlier factor, used to detect abnormal emission data and improve the effectiveness of data cleaning.

[0009] The present invention is further configured such that the formula for obtaining the structured data based on the preprocessed data for embedding update is: , where is the embedding representation of node at the th layer, is the embedding representation of node at the th layer, is the neighbor set of node , is the weight matrix of the graph neural network at the th layer, is the relationship strength between node and node , is the credibility value of the data associated with node , is a normalization factor, is the feature representation of the current node at the th layer.

[0010] The present invention is further configured such that the formula for obtaining the optimal data mapping scheme based on the standardized data set is: , is the state and action value function of, is the learning rate, is the reward function, is the discount factor, is in the new state under, select the optimal action corresponding value function, output the optimal data mapping scheme to ensure the optimal conversion process of data from the original format to the unified model.

[0011] The present invention is further configured such that the adaptive index is used to optimize data query, and the optimization objective is: , where is the query in the index under the execution time of, , represents the query the number of relevant indexes and data blocks, represents the index query time, represents the query load, is the index storage cost weight of, is the data block access latency of, is the data block cache hit rate of, is the data block required decompression time.

[0012] The present invention is further configured such that the zero - knowledge proof technology is used to protect data privacy, and the calculation formula is: , is the zero - knowledge proof value, , is the number of data items, is the data item credibility weight of, is the data item privacy protection strength of, is the challenge value of the verifier for the data item of, is the data item hidden complexity of, is the data item computing resource consumption of, is the hash function mapped to the verifiable proof.

[0013] The present invention is further configured such that the analysis of policy impact, and the calculation formula is: , is the average impact of policy intervention on emissions, is the number of policy impact factors and pollution factors, is the intensity of the th policy variable, is the implementation coverage rate of the th policy, is the direct impact of the th policy on emissions, is the weight of the th pollution factor, is the degree of dependence of the

[0014] This invention is further configured such that the formula for identifying abnormal situations is: , , are the number of real data points and the number of generated data points, is the weight of the th real data point, is the credibility of the th data point, is the noise level of data point , is the degree of outlier of data point , is the quality coefficient of the th generated data point, is the degree of abnormality of the th data point, , is the regularization factor.

[0015] This invention provides a vehicle environmental inspection data governance information interconnection processing system. The system includes a data acquisition and multi-source fusion module that collects vehicle environmental inspection data from multiple heterogeneous data sources and performs weighted fusion on the vehicle environmental inspection data; a data preprocessing and cleaning module that processes and cleans the vehicle environmental inspection data based on the wavelet threshold denoising algorithm and the local outlier factor method; a data governance and standardization modeling module that obtains structured data and gets the optimal data mapping scheme according to the preprocessed multi-source vehicle environmental inspection data; a data storage and distributed management module that reduces query time and controls storage costs by optimizing the index structure; a data sharing and open interconnection module that protects data privacy based on zero-knowledge proof technology; an intelligent analysis and supervision decision support module that analyzes the multi-source vehicle environmental inspection data, identifies abnormal situations, and the beneficial effects generated include: 1. Effective integration of multi-source data and improvement of data quality: Effectively integrate the motor vehicle inspection data from multiple heterogeneous data sources. Considering the credibility of each data source, reduce the conflict between data through weighted integration. This method can handle the inconsistencies between different data sources, improve the accuracy and reliability of the data, and provide a more stable and accurate basis for subsequent data analysis; 2. Optimized data governance and standardized modeling: Conduct association optimization of the knowledge graph. The system can construct a structured knowledge graph based on the cleaned data, realize the deep association and relationship modeling between data, and improve the usability and relevance of the data. In addition, the dynamic data mapping optimization method based on reinforcement learning can ensure the optimal conversion process from the original data to the unified data model, improving the data governance efficiency and the level of standardized processing; 3. Intelligent analysis and decision support to enhance supervision efficiency: Through quantitative analysis of emission impacts, the system can quantify the actual impacts of policy interventions on emissions, assisting decision-makers to make accurate decisions based on specific data analysis results when formulating and adjusting environmental protection policies. In addition, the emission data anomaly detection method can automatically identify and generate abnormal situations inconsistent with the generated emission data, improving the intelligent level of data quality monitoring and ensuring more efficient and accurate supervision decisions.

[0016] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are given below. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings: Figure 1 It is a flowchart of a motor vehicle inspection data governance information interconnection processing system shown in an exemplary embodiment of the present invention. Detailed Description of the Embodiments

[0018] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0019] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0020] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0021] Embodiment 1 A motor vehicle environmental inspection data governance information interconnection processing system, as Figure 1 shown, includes: A data collection and multi-source fusion module that collects motor vehicle environmental inspection data through multiple heterogeneous data sources and performs weighted fusion on the motor vehicle environmental inspection data; A data preprocessing and cleaning module that processes and cleans the motor vehicle environmental inspection data based on the wavelet threshold denoising algorithm and the local outlier factor method; A data governance and standardization modeling module that obtains structured data according to the preprocessed multi-source motor vehicle environmental inspection data and obtains the optimal data mapping scheme; A data storage and distributed management module that reduces query time and controls storage costs by optimizing the index structure; A data sharing and open interconnection module that protects data privacy based on zero-knowledge proof technology; An intelligent analysis and supervision decision support module that analyzes the multi-source motor vehicle environmental inspection data and identifies abnormal situations.

[0022] The present invention is further configured such that the weighted fusion of the motor vehicle environmental inspection data has the following fusion formula: , , where , respectively represent the basic probability assignments of the data sources, is a conflict factor that measures the inconsistency between data sources. Specifically, the fusion formula fuses the basic probability assignments from different data sources, aiming to eliminate the conflicts between different data sources, improve the reliability and accuracy of data fusion, and finally generate a fused result with a relatively high comprehensive credibility. By calculating the conflict factor , the reliability and consistency of different data sources can be measured. When encountering data source conflicts, a normalization factor is used for adjustment to ensure the stability and efficiency of data fusion.

[0023] The present invention is further configured such that the steps for processing and cleaning the motor vehicle environmental inspection data are as follows: Denoise the data through the wavelet threshold denoising algorithm, and its formula is: , where is the -layer wavelet coefficient, is the threshold. Specifically, the core idea of the wavelet threshold denoising algorithm is to decompose the frequency components of the signal, retain the low-frequency part of the signal, and remove the high-frequency part. By setting the threshold η, the degree of noise removal can be controlled to avoid signal loss caused by excessive denoising; Calculate the local outlier factor, and its formula is: , where is the local reachability density, which is used to measure the local density of the data point . Calculate the distance from to its neighbors and calculate its density based on this information. Its formula is: , where is the distance between the data point and its neighbor , is the set of k nearest neighbors, representing the k neighbor data points that are closer to , is the local outlier factor, which is used to detect abnormal emission data and improve the effectiveness of data cleaning. Specifically, measures whether the data point is an outlier. The higher the value, the more likely is an outlier, and the lower the value, the more likely is a normal point. The abnormality of the data point is evaluated through the difference between the local density and the density of its neighbors. The algorithm determines whether the data point It is likely to be an outlier. On the contrary, if the density difference is small, then This method is very suitable for outlier detection in high-dimensional data, and can find outliers that are not easy to detect in the global dataset but are significantly different in the local area.

[0024] The present invention is further configured such that, according to the preprocessed data, structured data is obtained, and its embedding update formula is: ,in, For nodes In the The embedding representation of the layer represents the node The latest status or characteristics of For nodes In the The embedding representation of the layer represents the neighbor nodes status, For nodes The neighbor set of For the The weight matrix of the layer graph neural network, For nodes and nodes The strength of the relationship between For nodes The credibility value of the associated data, is a normalization factor, For the current node In the The feature representation of the layer, specifically, its calculation process is from node Neighbor nodes and its own characteristics Collect information through the weight matrix and the strength of the relationship between nodes The information of neighbor nodes is weighted. The strength of information transmission is determined by the weight matrix and relationship strength Control, Node and neighbor nodes During the transmission of information, the credibility value of the data must be Adjustments are made to more accurately convey valid information by normalizing the factor , adjust the influence of different neighbor nodes to avoid the embedding of some neighbor nodes having too much impact on the update process. At the same time, the node The self-information is passed through the weight matrix Adjustments are made so that the influence of self-information is not ignored. Finally, through the nonlinear function Activate the updated information and generate nodes In the The embedded representation of the layer. Through the embedding update method, structured information can be efficiently extracted from the preprocessed data. The complex relationships and associations between nodes are effectively captured, providing a reliable basis for subsequent analysis, prediction, and decision support. This method can dynamically adjust the embedded representation of nodes according to the information of neighbor nodes, the strength of the relationships between nodes, and the credibility of the data, making the state update of nodes more accurate and avoiding the limitations brought by static models. Through the process of node embedding update, the features of nodes can be comprehensively calculated in combination with the information of their neighbors. Therefore, the representation of nodes is more comprehensive and can reflect the relative importance and role of nodes in the overall graph.

[0025] The present invention is further configured such that, according to the standardized data set, an optimal data mapping scheme is obtained, and its formula is: , is the state and the action value function of, is the learning rate, is the reward function, is the discount factor, is the value function corresponding to the optimal action selected under the new state , and the optimal data mapping scheme is output to ensure the optimal conversion process of data from the original format to the unified model. Specifically, through this learning algorithm, the data mapping process can be automatically optimized to find the optimal mapping path from the original data format to the unified model format, improving the efficiency and accuracy of data standardization. Based on the feedback mechanism, as the learning process progresses, the system can automatically adapt to the complexity and changes of data mapping, dynamically adjust the mapping strategy, and ensure optimal performance. By introducing the reward mechanism in reinforcement learning, the system can make intelligent decisions based on real-time feedback, providing support for subsequent data processing, conversion, and analysis, reducing manual intervention, and improving the intelligent level of data governance.

[0026] The present invention is further configured such that the adaptive index is used to optimize data query, and the optimization objective is: , where is the execution time of the query under the index , , represents the number of indexes and data blocks related to the query , represents the index query time, represents the query load, is the storage cost weight of the index , is the access latency of the data block . is the cache hit rate of the data block , is the decompression time required for the data block . Specifically reflects the relationship among the index query time, query load, and storage cost during the index query process. For each index , calculate the weighted relationship of its query time, load, and storage cost to obtain the contribution of the index to the query time , which measures the processing efficiency of the data block during the query process. The access latency, cache hit rate, and decompression time of the data block jointly affect the processing time of the data block. For each data block , calculate the comprehensive impact of its latency, cache hit rate, and decompression time to obtain the contribution of the data block to the query time. By adaptively optimizing the access strategies of the index and data block, the query execution time can be significantly reduced, thereby improving the response speed and overall performance of the system. Through the dynamic optimization of various factors such as the index query time, load, storage cost, data block access latency, and cache hit rate during the query process, the system can adaptively adjust the allocation of query resources according to the real-time query situation, optimize the cache hit rate and the processing method of the data block, which helps to improve the utilization efficiency of the cache, thereby reducing the frequency and response time of data access, and further improving the overall query efficiency of the system.

[0027] The present invention is further configured such that the data privacy is protected based on the zero-knowledge proof technology, and the calculation formula is: , is the zero-knowledge proof value , is the number of data items is the credibility weight of the data item . Each data item has a credibility related to its authenticity. The higher the credibility, the more credible the data item is the privacy protection strength of the data item . The stronger the privacy protection, the stricter the privacy protection measures for the data item and the higher the protection level is the challenge value of the verifier for the data item , that is, the challenge strength proposed by the verifier during the verification. The higher the challenge value, the more complex the proof required from the verifier during the verification is the hiding complexity of the data item . The higher the complexity, the more difficult it is to decrypt or speculate on the data item, and the more difficult it is for data privacy to be leaked is the computational resource consumption of the data item , that is, the computational resources required for the verification process. The greater the computational resource consumption, the more time and computational power are required for the verification process The hash function maps to a verifiable proof. Through zero-knowledge proof technology, the privacy of the data is fully protected. The verifier can confirm the authenticity of the data without obtaining the actual content of the data. Parameters such as privacy protection strength and hiding complexity are used to strengthen the protection of the data, making it more difficult for the data to be leaked or attacked during transmission or verification. By comprehensively considering factors such as the credibility of data items, computational resource consumption, and the challenge value of the verifier, the data verification process is optimized to be both efficient and secure.

[0028] The present invention is further configured such that the analysis of policy impact, and the calculation formula is: , is the average impact of policy intervention on emissions, is the number of policy impact factors and pollution factors, is the th strength of the th policy variable, representing the intensity or strength of policy implementation. A larger value indicates a stronger policy measure, is the implementation coverage rate of the th policy, that is, the scope or popularity of policy implementation. A larger value indicates a wider area or group affected by the policy, is the direct impact of the th policy on emissions, that is, the degree to which the policy directly acts on emissions. A higher indicates a greater direct impact of the policy on emissions, is the weight of the th pollution factor, that is, the proportion of the pollution factor in the overall emissions. The higher the weight, the greater the contribution of the pollution factor to emissions, is the degree of dependence of the th pollution factor, that is, the sensitivity of emissions to changes in the pollution factor. A higher indicates that changes in the pollution factor will affect emissions to a greater extent. Specifically, represents the average impact of policy intervention on emissions, that is, the degree of change in environmental emissions after policy implementation. The larger the value, the more significant the role of the policy in emission control. Through the

[0029] value, the specific impact of policy intervention on environmental emissions can be quantified, providing data support for policy adjustment. This method combines policy strength, implementation coverage rate, the weight and dependence degree of pollution factors, ensuring a more comprehensive policy evaluation. , , is the number of real data points and the number of generated data points, is the weight of the th real data point, is the credibility of the nth data point, is the noise level of the data point , is the degree of outlier of the data point , is the quality coefficient of the nth generated data point, is the degree of abnormality of the nth data point, , is the regularization factor. Specifically, represents the anomaly assessment of the real data points, which evaluates the degree of anomaly of each real data point, taking into account the weight, credibility, noise level and degree of outlier of the data point, represents the anomaly assessment of the generated data points, which evaluates the degree of anomaly of each generated data point, taking into account the weight, quality coefficient, noise level and degree of abnormality of the data point, represents the degree of abnormality, that is, the abnormal situation in the data. A higher indicates that the system has detected more abnormal data points and may require further processing or verification. The larger it is, the more likely there are anomalies in the data. Through anomaly data detection, the system can accurately identify the abnormal situations in the data. By comprehensively considering multiple factors, the accuracy of data cleaning can be improved, ensuring the effectiveness and accuracy of subsequent analysis.

[0030] It should be noted that the information interconnection processing system for motor vehicle environmental inspection data governance provided in the above embodiment and the information interconnection processing system for motor vehicle environmental inspection data governance provided in the above embodiment belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiment and will not be repeated here. In actual application, the information interconnection processing system for motor vehicle environmental inspection data governance provided in the above embodiment can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0032] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be understood specifically by referring to the context before and after.

[0033] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0034] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0035] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0036] Those skilled in the art can clearly understand that for the sake of convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0037] In several embodiments provided in this application, it should be understood that the disclosed systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0038] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0040] When the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0041] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A motor vehicle environmental inspection data governance information interconnection processing system, characterized in that, Including: A data collection and multi-source fusion module that collects motor vehicle environmental inspection data through multiple heterogeneous data sources and performs weighted fusion on the motor vehicle environmental inspection data; A data preprocessing and cleaning module that processes and cleans motor vehicle environmental inspection data based on the wavelet threshold denoising algorithm and the local outlier factor method; A data governance and standardization modeling module that obtains structured data from the preprocessed multi-source motor vehicle environmental inspection data and gets the optimal data mapping scheme; A data storage and distributed management module that reduces query time and controls storage costs by optimizing the index structure; A data sharing and open interconnection module that protects data privacy based on zero-knowledge proof technology; An intelligent analysis and supervision decision support module that analyzes multi-source motor vehicle environmental inspection data and identifies abnormal situations.

2. The information interconnection processing system for motor vehicle environmental inspection data governance according to claim 1, characterized in that, Weighted fusion is performed on the motor vehicle environmental inspection data, and its fusion formula is: , , where , respectively represent the basic probability assignments of the data sources, is the conflict factor, which measures the inconsistency between the data sources.

3. A motor vehicle environmental inspection data governance information interconnection processing system according to claim 1, characterized in that, The steps for processing and cleaning motor vehicle environmental inspection data are as follows: Denoise the data through the wavelet threshold denoising algorithm, and its formula is: , where is the -layer wavelet coefficient, is the threshold; Calculate the local outlier factor, and its formula is: , where is the local reachability density, is the set of k-nearest neighbors, is the local outlier factor, which is used to detect abnormal emission data and improve the effectiveness of data cleaning.

4. A motor vehicle environmental inspection data governance information interconnection processing system according to claim 2, characterized in that, Based on the preprocessed data, structured data is obtained, and its embedding update formula is as follows: , where is the embedding representation of node at the -th layer, is the embedding representation of node at the -th layer, is the neighbor set of node , is the weight matrix of the graph neural network at the -th layer, is the relationship strength between node and node , is the credibility value of the data associated with node , is a normalization factor, is the feature representation of the current node at the -th layer.

5. The information interconnection processing system for motor vehicle environmental inspection data governance according to claim 4, wherein Obtain the optimal data mapping scheme based on the standardized dataset, and its formula is: , is the state and the action value function, is the learning rate, is the reward function, is the discount factor, is the value function corresponding to the optimal action selected under the new state , and output the optimal data mapping scheme to ensure the optimal conversion process of data from the original format to the unified model.

6. The information interconnection processing system for motor vehicle environmental inspection data governance according to claim 1, wherein Adopt adaptive indexing to optimize data query, and the optimization objective is: , where is the execution time of query under the index ; , represents the number of indexes and data blocks related to query ; represents the index query time ; is the storage cost weight of index ; is the access latency of data block ; is the cache hit rate of data block ; is the decompression time required for data block .

7. The information interconnection processing system for motor vehicle environmental inspection data governance according to claim 1, characterized in that Protect data privacy based on zero - knowledge proof technology, and the calculation formula is: , is the zero - knowledge proof value, , is the number of data items, is the data item 's credibility weight, is the data item 's privacy protection intensity, is the challenge value of the verifier for the data item , is the data item 's hiding complexity, is the data item 's computing resource consumption, is the hash function mapped to a verifiable proof.

8. The information interconnection processing system for motor vehicle environmental inspection data governance according to claim 1, wherein Analyze the policy impact, and the calculation formula is as follows: , is the average impact of policy intervention on emissions, is the number of policy impact factors and pollution factors, is the intensity of the th policy variable, is the implementation coverage rate of the th policy, is the direct impact of the th policy on emissions, is the weight of the th pollution factor, is the degree of dependence of the 9. The information interconnection processing system for motor vehicle environmental inspection data governance according to claim 1, wherein, Identify abnormal situations, and its formula is: , , are the number of real data points and the number of generated data points, is the weight of the -th real data point, is the credibility of the -th data point, is the noise level of the data point , is the outlier degree of the data point , is the quality coefficient of the -th generated data point, is the abnormality degree of the -th data point, , is the regularization factor.

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