Road geographic information safety risk assessment method for open test of intelligent network connection automobile

Abstract

The invention discloses an intelligent connected vehicle open test road geographic information safety risk assessment method, and relates to the technical field of road safety assessment, and the method comprises the steps: obtaining open test road geographic environment data, carrying out the preprocessing of the open test road geographic environment data, and obtaining the preprocessed region data; performing security risk factor identification on the preprocessed regional data to obtain data security risk factors; calculating a security risk score of each data security risk element; and performing security risk assessment through the security risk score to obtain an assessment result. According to the evaluation method, various potential safety risk factors in the open test road geographic environment can be comprehensively considered, and scientific and reliable geographic information safety guarantee is provided for the open test of the intelligent connected automobile through accurate calculation and evaluation. According to the method, the safety of the test road is improved, and powerful support is provided for research, development and popularization of the intelligent networked automobile.

Description

Technical Field

[0001] This application relates to the field of road safety assessment technology, and in particular to a method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles. Background Technology

[0002] In the development of intelligent connected vehicles, safety assessments of open test roads are crucial. However, existing assessment methods often limit themselves to traditional traffic safety factors, such as road conditions and traffic flow, while neglecting potential security risks in geographic environmental data. This geographic environmental data may contain sensitive information, such as the location of military facilities and the distribution of land resources. If leaked or maliciously used, it could seriously impact national security and social stability. Therefore, how to comprehensively and accurately assess the security risks in the geographic environment of open test roads has become an urgent problem to be solved in the field of intelligent connected vehicles. Summary of the Invention

[0003] The main purpose of this application is to provide a method for assessing the security risks of open test roads for intelligent connected vehicles based on geographic information, aiming to solve the technical problem of how to comprehensively and accurately assess the security risks in the geographic environment of open test roads.

[0004] To achieve the above objectives, this application proposes a method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles. The method includes: Obtain open test road geographic environment data, preprocess the open test road geographic environment data, and obtain preprocessed regional data; Security risk factors are identified in the preprocessed regional data to obtain data security risk factors. Calculate the security risk score for each data security risk element; The security risk score is used to conduct a security risk assessment, and the assessment result is obtained.

[0005] In one embodiment, the steps of acquiring open test road geographic environment data and preprocessing the open test road geographic environment data to obtain preprocessed regional data include: Obtain geographic environment data for open test roads, including traffic infrastructure data, high-precision map data, satellite imagery data, aerial imagery data, traffic participant data, and intelligent communication network data; Extract image patch information containing sensitive keywords from existing data using text retrieval methods; Preprocessing is performed based on the open test road geographic environment data and the patch information to obtain preprocessed regional data.

[0006] In one embodiment, the step of preprocessing the open test road geographic environment data and the patch information to obtain preprocessed regional data includes: The open test road geographic environment data and the patch information are converted to a preset coordinate system, and the projection error is eliminated by control point matching to obtain the open test road geographic environment data and patch information after the projection error is eliminated. Under the preset coordinate system, the corresponding ground features of the open test road geographic environment data and patch information after the projection error is eliminated are identified by feature point extraction, and geometric transformation parameters are calculated based on the corresponding ground features; Based on the geometric transformation parameters, the spatial positions of the open test road geographic environment data and patch information after eliminating projection errors are aligned, and patches with offsets exceeding the threshold are corrected. The corrected map features are fused with the open test road geographic environment data after projection error elimination to obtain preprocessed regional data.

[0007] In one embodiment, the step of identifying security risk factors in the preprocessed regional data to obtain data security risk factors includes: Acquire data throughout its entire lifecycle; Based on the entire data lifecycle, the preprocessed regional data is identified for security risk factors, resulting in data security risk factors for each data lifecycle. These data security risk factors include military security factors, territorial security factors, resource security factors, social security factors, economic security factors, and general security factors.

[0008] In one embodiment, the step of calculating the security risk score of each data security risk element includes: Obtain the severity score of each data security risk element and the probability score of each data security risk element occurring; The weight values ​​of each data security risk element are calculated using the network analytic hierarchy process. The security risk score of each data security risk element is calculated based on the severity score, the probability score, and the weight value.

[0009] In one embodiment, the step of conducting a security risk assessment using the security risk score to obtain the assessment result includes: Obtain a risk assessment model; The security risk score is input into the risk assessment model to conduct a security risk assessment and obtain the geographic information risk level. The assessment results are obtained based on the risk level of the geographic information.

[0010] In one embodiment, after the step of conducting a security risk assessment using the security risk score and obtaining the assessment result, the method further includes... The geographic information risk level is obtained based on the assessment results. The corresponding buffer zone is determined based on the risk level of the geographic information. Based on the buffer zone, passable roads are identified and classified to obtain passable roads and their levels. The open roads are updated based on the accessible roads and the accessible road level.

[0011] Furthermore, to achieve the above objectives, this application also proposes a geographic information security risk assessment device for open testing roads of intelligent connected vehicles, the device comprising: The acquisition module is used to acquire open test road geographic environment data, preprocess the open test road geographic environment data, and obtain preprocessed regional data. The identification module is used to identify security risk elements in the preprocessed regional data to obtain data security risk elements. The calculation module is used to calculate the security risk score of each data security risk element; The assessment module is used to conduct a security risk assessment based on the security risk score and obtain the assessment result.

[0012] Furthermore, to achieve the above objectives, this application also proposes a geographic information security risk assessment device for open test roads of intelligent connected vehicles. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the geographic information security risk assessment method for open test roads of intelligent connected vehicles as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-described method for assessing the geographic information security risks of open test roads for intelligent connected vehicles.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the above-described method for assessing the geographic information security risks of open test roads for intelligent connected vehicles.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: 1) By acquiring and preprocessing road geographic environment data, potential safety risk factors in open test roads can be accurately identified, thus providing a safer and more reliable environment for the testing of intelligent connected vehicles. This helps reduce safety issues caused by road environment factors during testing and ensures that testing activities are not affected by unforeseen geographic information problems. Through the identification and scoring of data security risk factors, the method can objectively quantify safety risks in the road environment, forming a refined risk assessment system. This not only helps ensure the security of road information but also provides necessary safety guarantees for the autonomous driving system of intelligent connected vehicles, reducing potential risks of autonomous driving systems in actual road testing. By dynamically assessing safety risks, test plans and risk control strategies can be adjusted in a timely manner according to changes in the road environment. This continuous safety management not only enhances the adaptability of intelligent connected vehicles on open test roads but also provides a reference for similar tests in the future, promoting the standardization and normalization of the intelligent connected vehicle field. This assessment method can comprehensively consider various potential safety risk factors in the geographic environment of open test roads, and through accurate calculation and assessment, provides scientific and reliable geographic information security guarantees for the open testing of intelligent connected vehicles. This method not only improves the safety of test roads, but also provides strong support for the research and development and promotion of intelligent connected vehicles.

[0016] 2) By acquiring traffic infrastructure data, high-precision map data, satellite imagery data, aerial imagery data, traffic participant data, and intelligent communication network data, the method can cover multi-dimensional information sources and comprehensively understand the road environment. This data integration makes testing more accurate, providing a richer and more diverse information foundation for the open testing of intelligent connected vehicles, and helping to better simulate real road testing environments. Extracting map feature information containing sensitive keywords from existing data through text retrieval can help identify and screen potentially high-risk or sensitive areas, ensuring that testing is not conducted in areas with potential safety hazards. This not only reduces potential safety issues but also reduces manual intervention and improves efficiency through intelligent screening. By extracting sensitive keyword information from existing data, the method can effectively identify and process geographic information that may involve privacy or sensitivity, reducing the risk of data leakage. This method meets the needs of modern data privacy protection, ensuring that geographic data processing does not violate privacy-related regulations and policies; preprocessing based on open testing road geographic environment data and map feature information can effectively integrate and optimize the raw data, remove redundant information, and highlight key areas. This makes subsequent data analysis and security risk assessment processes more efficient, avoids reducing the accuracy of analysis due to data redundancy, and helps improve the accuracy of assessment results and decision-making efficiency. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles in this application. Figure 3 This is a flowchart illustrating Embodiment 3 of the method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles in this application. Figure 4 This is a schematic diagram of the module structure of the geographic information security risk assessment device for open testing roads of intelligent connected vehicles, as described in this application embodiment. Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is: to acquire open test road geographic environment data, to preprocess the open test road geographic environment data, and to obtain preprocessed regional data; Security risk factors are identified in the preprocessed regional data to obtain data security risk factors. Calculate the security risk score for each data security risk element; The security risk score is used to conduct a security risk assessment, and the assessment result is obtained.

[0024] Because existing technologies are often limited to traditional traffic safety factors, such as road conditions and traffic flow, they often overlook the potential security risks in geographic environmental data. This geographic environmental data may contain sensitive information, such as the location of military facilities and the distribution of land resources. If leaked or maliciously used, it could have a serious impact on national security and social stability.

[0025] This application provides a solution that aims to identify potential safety risk factors and calculate corresponding safety risk scores by comprehensively analyzing open test road geographical environment data, thereby providing a more scientific and reliable safety assessment for the open testing of intelligent connected vehicles.

[0026] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a geographic information security risk assessment device for open testing roads of intelligent connected vehicles. The following description uses a geographic information security risk assessment device for open testing roads of intelligent connected vehicles as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, embodiments of this application provide a method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for assessing the security risks of geographic information on open testing roads for intelligent connected vehicles in this application.

[0028] In this embodiment, the method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles includes steps S10 to S40: Step S10: Obtain open test road geographic environment data, preprocess the open test road geographic environment data to obtain preprocessed regional data.

[0029] It's important to note that the first step is to collect geographic environmental data about the open test road. This data may include high-definition map data, satellite imagery data, and traffic infrastructure data. After data collection, preprocessing is necessary. Preprocessing may include data cleaning, such as removing redundant or invalid data, standardizing data formats, and coordinate system transformation, to ensure consistency and accuracy in subsequent analyses. For example, if the collected high-definition map data and satellite imagery data use different coordinate systems, they need to be transformed to the same coordinate system for overlay analysis and comparison.

[0030] In practice, the geographic environment data for the open test roads is a comprehensive dataset extracted from multiple data sources, including but not limited to traffic infrastructure databases, data provided by high-precision map service providers, satellite remote sensing imagery, and aerial imagery. The selection of these data sources aims to comprehensively cover the geographic, traffic, and environmental information of the area where the open test roads are located, ensuring the comprehensiveness and accuracy of the risk assessment. Specifically, traffic infrastructure data provides key information such as road networks, intersection layouts, and traffic signs and markings; high-precision map data contains details such as the precise geometry of roads, lane information, and traffic restrictions; and satellite remote sensing and aerial imagery provide macro-level information such as the natural landscape along the roads, building distribution, and potentially sensitive areas. Integrating this information provides a solid data foundation for subsequent identification of safety risk factors.

[0031] Suppose a connected intelligent vehicle will be tested on a newly built highway in a city. To assess the road's geospatial security risks, autonomous driving requirements, and other geospatial security factors, it's necessary to collect high-resolution map data, satellite imagery, and surrounding traffic infrastructure data. After collecting this data, we find that the high-resolution map data uses the WGS84 coordinate system, while the satellite imagery uses a local coordinate system. Therefore, we need to transform these two sets of data to the same coordinate system, such as the UTM coordinate system, for subsequent risk assessment.

[0032] Step S20: Identify security risk factors in the preprocessed regional data to obtain data security risk factors.

[0033] In practice, in-depth analysis of the preprocessed regional data is required to identify potential security risk factors. These factors may include the location of military facilities, sensitive areas of land resources, and traffic congestion points. The identification process may require the assistance of specialized geographic information systems such as GIS software or algorithms to improve the accuracy and efficiency of the identification.

[0034] For example, we overlaid and analyzed high-precision map data and satellite imagery data converted to the same coordinate system. Through comparison and analysis, we discovered a military base near the road, and the base's entrance was not far from the test road. Therefore, we identified this military base as a potential security risk factor.

[0035] Data security risk factors can include military security factors, national security factors, resource security factors, social security factors, economic security factors, and general security factors. When identifying these security risk factors, the timeliness of the data must also be considered to ensure that the data used accurately reflects the current road environment and potential risks. For example, if a geological disaster has recently occurred in a certain area, the relevant data needs to be updated in a timely manner so that this newly emerging risk factor can be accurately considered in subsequent risk assessments.

[0036] In one feasible implementation, step S20 may include steps A11-A12: Step A11: Obtain the entire data lifecycle; It should be noted that the entire data lifecycle includes multiple data lifecycles such as data collection, data storage, data usage, data transmission, and data sharing.

[0037] Step A12: Identify security risk factors for the preprocessed regional data based on the entire data lifecycle to obtain data security risk factors under each data lifecycle. The data security risk factors include military security factors, territorial security factors, resource security factors, social security factors, economic security factors, and general security factors.

[0038] Understandably, analyzing the factors that may affect data at each stage of its lifecycle can reveal data security risk elements. For example, in the data acquisition stage, inaccurate acquisition equipment or improper operation may lead to data errors, introducing security risks. In the data storage stage, security vulnerabilities in the storage medium or insufficient protection measures may result in data leakage or tampering. In the data usage stage, unauthorized users or improper operation may also pose security risks. In the data transmission stage, insecure transmission paths and methods may lead to data interception or tampering. In the data sharing stage, failure to properly anonymize or encrypt data may result in the leakage of sensitive information. A comprehensive analysis of these factors allows for a more complete identification of potential security risk elements.

[0039] For example, during the data collection phase, illegal or irregular data or data collected beyond the scope may be obtained. During the data usage phase, there is a risk of data tampering. By summarizing and analyzing the above factors, we can obtain military security factors, territorial security factors, resource security factors, social security factors, economic security factors, and general security factors other than the above factors.

[0040] In practice, military security elements include military restricted areas and military facilities; territorial security elements include national borders, nautical charts, and territorial boundaries; resource security elements include mineral resources, energy facilities, and food; social security elements include key territorial security departments, hazardous materials facilities, and important public safety facilities; economic security elements include transportation, electricity, communications, pipelines, industrial and mining facilities, and other important facilities for economic development; and general security elements include other security elements not listed above.

[0041] By comprehensively analyzing the geographic environment data of open test roads, potential safety risk factors are identified, and corresponding safety risk scores are calculated, providing a more scientific and reliable safety assessment for the open testing of intelligent connected vehicles. This method not only improves the safety of test roads but also provides strong support for the research, development, and promotion of intelligent connected vehicles. Simultaneously, through data integration and intelligent screening, manual intervention is reduced, efficiency is improved, and it ensures that geographic data processing does not violate privacy-related regulations and policies, meeting the needs of modern data privacy protection.

[0042] Step S30: Calculate the security risk score of each data security risk element.

[0043] In practice, each identified safety risk element is quantitatively assessed to determine its corresponding safety risk score. This may require considering multiple factors, such as the distance between the risk element and the test road, the type of risk element, and its importance. The assessment process may require the use of specialized risk assessment models or algorithms to ensure the accuracy and objectivity of the assessment.

[0044] For example, a security risk score is calculated for identified military bases. Considering the importance of the military base and its distance from the test road (assuming it's within 1 kilometer), we assign a high security risk score to this risk element, such as 80 points. This means that special attention and protection are needed for this area during subsequent testing.

[0045] Security risk scores can be used to evaluate the security risk of each data security risk element. The value range is [0,1]. The larger the value, the higher the security risk of each data security risk element.

[0046] In one feasible implementation, step S30 may include steps B11 to B13: Step B11: Obtain the severity score of each data security risk element and the probability score of each data security risk element occurring; It should be noted that the security risk score of each data security risk element is related to the data hazard severity score, the probability score of the data risk element occurring, and the data risk weight value. Therefore, the hazard severity score and the probability score of the data security risk element occurring can be obtained.

[0047] The severity score refers to the potential harm caused by a data security risk element if it triggers a risk event. Determining this score typically requires a quantitative assessment of different types of security risk elements based on professional risk assessment standards and historical data. For example, military bases, as security risk elements, generally have a high severity score because leaks or malicious use could pose a serious threat to national security.

[0048] The probability score refers to the likelihood of a specific data security risk factor occurring. Determining this score also requires professional assessment models and algorithms, involving statistical analysis of historical data to derive the probability of different security risk factors occurring. For example, the probability of a military base located near a test road being compromised during intelligent connected vehicle testing needs to be quantitatively assessed.

[0049] Step B12: Calculate the weight values ​​of each data security risk element using the network hierarchy analysis method; The Analytic Network Process (ANP) is a multi-criteria decision analysis method based on network structure. In this step, the ANP method is used to calculate the weights of each data security risk element, primarily considering the potential mutual influence and dependencies between these elements. By constructing a network structure model, pairwise comparisons are made between security risk elements to derive their relative importance, i.e., their weight values. This step helps to more accurately reflect the actual impact of each security risk element in subsequent risk assessments.

[0050] Specifically, by constructing a hierarchical model and building a judgment matrix, the weight values ​​of each element can be calculated. Taking data security risk elements as an example, the possible hierarchical structure is as follows: Target layer: Data security risk assessment; Criterion layer: Individual data security risk elements; Sub-criteria layer: Specific sub-elements for each data security risk element. In this hierarchical structure, a judgment matrix is ​​constructed to compare each data security risk element pairwise to determine their relative importance. Assuming there are n data security risk elements, an n×n judgment matrix is ​​constructed, where element a... ijThe importance of the i-th element relative to the j-th element is represented by a score from 1 to 9: 1: equally important, 3: slightly important, 5: important, 7: very important, 9: extremely important, and 2, 4, 6, 8, etc., represent relative importance between these scores. The judgment matrix is ​​then normalized to obtain the relative weight of each data security risk element. The specific steps are as follows: calculate the sum of each column of the judgment matrix; divide each matrix element by the sum of its corresponding column to obtain the standardized matrix; calculate the average of each row of the standardized matrix to obtain the weight of the data security risk element corresponding to that row.

[0051] Let the judgment matrix be A: ; Calculate the sum of each column: ; Standardized matrix : ; Calculate the average of each row as follows: ; It is understandable that the average value is the weight of each data security risk element, thus obtaining the weight of each data security risk element.

[0052] Step B13: Calculate the security risk score of each data security risk element based on the severity score, the probability score, and the weight value.

[0053] In this step, the severity score and probability score obtained in step B11, as well as the weight value obtained in step B12, are comprehensively considered to calculate the security risk score of each data security risk element as follows: ; In the above formula, i is the data element number, and j is the data risk element number. The security risk score represents the security risk value of each data security risk element. The severity score is the number of points that determine the degree of harm. For weight values, This represents the probability score.

[0054] In practice, after obtaining the safety risk scores, the highest and lowest scores among all safety scores can be used as references to normalize the other safety risk scores, as shown in the following formula: ; In the above formula, The normalized security risk score, The minimum value of the safety risk score. This represents the maximum value of the safety risk score.

[0055] By quantitatively assessing the severity, likelihood, and weight of each data security risk element, a more accurate and scientific security risk score can be obtained, providing a more reliable guarantee for the open testing of intelligent connected vehicles.

[0056] Step S40: Conduct a security risk assessment using the security risk score to obtain the assessment results.

[0057] It should be noted that a comprehensive assessment can be conducted using professional risk assessment models or algorithms to arrive at the final assessment result. The assessment result may include information such as the geographic information security risk level and potential security vulnerabilities, providing a scientific and reliable basis for the open testing of intelligent connected vehicles.

[0058] For example, all identified security risk factors, such as the security risk scores of military bases, are input into the risk assessment model. The model performs comprehensive calculations and analysis based on these scores, ultimately determining the geographic information security risk level of the test road to be "high risk." This means that in subsequent testing, more stringent security measures are needed to protect the test vehicles and data. Simultaneously, the assessment results can also identify specific security risks, such as those near military bases, allowing the testing team to implement targeted prevention and response measures.

[0059] This embodiment provides a method for assessing geographic information security risks on open test roads for intelligent connected vehicles. By acquiring and preprocessing road geographic environment data, it can accurately identify potential safety risk factors in open test roads, thereby providing a safer and more reliable environment for intelligent connected vehicle testing. This helps reduce safety issues caused by road environment factors during testing and ensures that testing activities are not affected by unforeseen geographic information problems. Through the identification and scoring of data security risk factors, the method can objectively quantify safety risks in the road environment, forming a refined risk assessment system. This not only helps ensure the security of road information but also provides necessary safety guarantees for the autonomous driving system of intelligent connected vehicles, reducing potential risks in actual road testing. By dynamically assessing safety risks, test plans and risk control strategies can be adjusted in a timely manner according to changes in the road environment. This continuous safety management not only enhances the adaptability of intelligent connected vehicles on open test roads but also provides a reference for similar tests in the future, promoting the standardization and normalization of the intelligent connected vehicle field. This assessment method can comprehensively consider various potential safety risk factors in the geographic environment of open test roads, and through accurate calculation and assessment, provides scientific and reliable geographic information security guarantees for the open testing of intelligent connected vehicles. This method not only improves the safety of test roads, but also provides strong support for the research and development and promotion of intelligent connected vehicles.

[0060] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S10 includes steps S101 to S103: Step S101: Obtain geographical environment data of the open test road, including traffic infrastructure data, high-precision map data, satellite imagery data, aerial imagery data, traffic participant data, and intelligent communication network data.

[0061] In practice, the open test road geographic environment data includes multiple subcategories, designed to provide detailed information about the test road and its surrounding environment. Its main data types include: Traffic infrastructure data: such as the location, type, and function of facilities like roads, bridges, tunnels, traffic signals, and signs.

[0062] High-precision map data: High-precision digital map data, which typically includes information such as road networks, geographic features, and buildings. It has a high resolution and can be accurate to the meter level.

[0063] Satellite imagery data: Remote sensing images acquired by satellites can provide extensive geographic information and are suitable for analysis of large areas.

[0064] Aerial imagery data: Images acquired through drones or other flight platforms typically have high spatial resolution, making them suitable for detailed analysis.

[0065] Traffic participant data includes information on traffic subjects such as vehicles, pedestrians, and bicycles, typically involving traffic flow, vehicle speed, and traffic density.

[0066] Intelligent communication network data: such as data from vehicle-to-everything (V2X) and road information transmission systems, involving real-time traffic dynamics, signal control, etc.

[0067] Step S102: Extract image patch information containing sensitive keywords from existing data through text retrieval.

[0068] Text retrieval is used to extract map features containing sensitive keywords from existing data. Specifically, text retrieval technology can be used to filter map features containing sensitive terms such as military facilities, government agencies, and classified units from existing data including the Third National Land Survey, 1:500 large-scale topographic maps, and real estate data. This existing data records some sensitive geographic information elements within the Guangzhou area, providing a foundation for subsequent work.

[0069] Map patch information refers to geographic units that represent ground entities or regions, extracted from remote sensing imagery, maps, or other geographic information system data. Map patches are typically spatial representations of ground features such as roads, buildings, trees, and rivers.

[0070] Map feature information can include attributes related to ground features, such as location, shape, size, and type.

[0071] Step S103: Based on the open test road geographic environment data and the patch information, perform preprocessing to obtain preprocessed regional data.

[0072] In practice, preprocessing can be performed based on the open test road geographic environment data and patch information. The main goal of preprocessing is to ensure the accuracy and consistency of the data and eliminate potential spatial errors by processing the open test road geographic environment data and extracted patch information. This is an important step to ensure the accuracy and reliability of the data in subsequent use.

[0073] In one feasible implementation, step S103 may include: The open test road geographic environment data and the patch information are converted to a preset coordinate system, and the projection error is eliminated by control point matching to obtain the open test road geographic environment data and patch information after the projection error is eliminated. Under the preset coordinate system, the corresponding ground features of the open test road geographic environment data and patch information after the projection error is eliminated are identified by feature point extraction, and geometric transformation parameters are calculated based on the corresponding ground features; Based on the geometric transformation parameters, the spatial positions of the open test road geographic environment data and patch information after eliminating projection errors are aligned, and patches with offsets exceeding the threshold are corrected. The corrected map features are fused with the open test road geographic environment data after projection error elimination to obtain preprocessed regional data.

[0074] It should be noted that the open test road geographic environment data and patch information are converted to a unified preset coordinate system. Common coordinate systems include geographic coordinate systems, such as WGS84, and projected coordinate systems, such as UTM. This operation is to ensure that data from different sources can be aligned within the same spatial reference frame. Coordinate system conversion usually requires the use of professional GIS software or coordinate transformation tools to ensure that the converted data does not lose information or introduce deviations.

[0075] Control points are points whose locations are known and correspond to the same geographical location in different data sources. By matching control points, projection errors in different data sources can be corrected. Different data sources use different projection methods, which can easily lead to deviations in spatial location.

[0076] The process of control point matching includes: selecting appropriate control points, such as feature points or landmarks on the map, matching and adjusting them, and finally aligning the image precisely using the control points to eliminate projection errors.

[0077] Feature points are clearly identifiable points in a dataset, such as intersections or building corners. Extracting and matching these feature points allows us to identify portions of the same feature from different data sources—these are known as "homonymous features." The identification and matching of homonymous features is fundamental to data spatial alignment, ensuring that the locations of identical features across different data sources are completely consistent.

[0078] After matching features with the same name, geometric transformations are needed to adjust the spatial positions between different data sources to ensure alignment. This process typically involves calculating geometric transformation parameters, such as rotation angles, translation amounts, and scaling ratios. The data is then spatially aligned based on these parameters to eliminate spatial discrepancies caused by different data sources.

[0079] If, after alignment, some polygons still exhibit spatial misalignment exceeding a set threshold, these polygons need to be corrected. Correction methods may include fine-tuning the polygon positions or supplementing missing information through other means.

[0080] Finally, the corrected patch information and the geographic environment data after projection error elimination will be merged to generate a new, unified preprocessed regional data.

[0081] The final preprocessed regional data is a dataset that has undergone coordinate system transformation, projection error correction, geometric transformation, offset correction, and data fusion.

[0082] Specifically, the selected patches are fused using the latest satellite and aerial imagery. Adjacent patches with consistent ownership are merged through image comparison and spatial analysis techniques to form preprocessed regional data. For example, for sensitive units enclosed by multiple building facade patches, a larger area is delineated using satellite remote sensing imagery based on their spatial location and ownership characteristics, resulting in a more regular geographic information range and facilitating subsequent boundary delineation.

[0083] To improve the accuracy of the data, joint supplementary mapping can also be carried out. Specifically, (1) Multi-party collaboration mechanism: The planning and natural resources authorities take the lead, and the national security department and relevant military forces jointly form a professional supplementary mapping team. Each department gives full play to its own advantages, the national security department relies on its intelligence information resources, and the military relies on its understanding of military areas to establish a multi-department joint mechanism. (2) On-site supplementary mapping: The mapping team conducts supplementary mapping of the entire Guangzhou area based on the regional results formed in the early stage. Through existing data of the national security and military, image comparison, on-site mapping and other methods, sensitive areas that were not identified in the early stage are supplemented and marked to ensure that all sensitive areas in Guangzhou can be accurately marked, and to improve the integrity and accuracy of sensitive geographic information data.

[0084] This embodiment acquires traffic infrastructure data, high-precision map data, satellite imagery data, aerial imagery data, traffic participant data, and intelligent communication network data. This method covers multiple information sources, providing a comprehensive understanding of the road environment. This data integration makes testing more accurate, providing a richer and more diverse information foundation for open testing of intelligent connected vehicles, and helping to better simulate real road testing environments. Extracting map features containing sensitive keywords from existing data through text retrieval helps identify and filter potentially high-risk or sensitive areas, ensuring that testing is not conducted in areas with potential safety hazards. This not only reduces potential safety issues but also reduces manual intervention and improves efficiency through intelligent screening. By extracting sensitive keyword information from existing data, the method can effectively identify and process geographic information that may involve privacy or sensitivity, reducing the risk of data leakage. This method meets the needs of modern data privacy protection, ensuring that geographic data processing does not violate privacy-related regulations and policies. Preprocessing based on open testing road geographic environment data and map feature information can effectively integrate and optimize the raw data, removing redundant information and highlighting key areas. This makes subsequent data analysis and security risk assessment processes more efficient, avoids reducing the accuracy of analysis due to data redundancy, and helps improve the accuracy of assessment results and decision-making efficiency.

[0085] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S40 includes steps S401 to S403: Step S401: Obtain the risk assessment model.

[0086] It should be noted that the risk assessment model is used to assess the risks of geographic information. A risk assessment model for geographic information can be constructed in advance. Specifically, for different types of facilities, such as military facilities, energy facilities, and communication facilities, security classifications and weighting coefficient functions for geographic features can be designed based on their importance, sensitivity, and potential risk level. For example, military facilities are given the highest weight.

[0087] As shown in Table 1, Table 1 is a schematic table of geographic element security classification: Table 1

[0088] Step S402: Input the security risk score into the risk assessment model to conduct a security risk assessment and obtain the geographic information risk level.

[0089] In practice, the safety risk score can be input into the risk assessment model, and then the risk assessment model can be used to evaluate the risk level of each geographic information, such as high risk, medium risk, and low risk, by combining the weight of facility type.

[0090] Step S403: Obtain the assessment result based on the geographic information risk level.

[0091] In practice, the geographic information risk level can be used as the final assessment result, thereby providing a quantitative basis for subsequent scope delineation.

[0092] In one feasible implementation, after obtaining the evaluation results, the method further includes: The geographic information risk level is obtained based on the assessment results. The corresponding buffer zone is determined based on the risk level of the geographic information. Based on the buffer zone, passable roads are identified and classified to obtain passable roads and their levels. The open roads are updated based on the accessible roads and the accessible road level.

[0093] It should be noted that a specific geographic information risk level can be obtained based on the assessment results. For example, the level can be divided into multiple tiers, thus obtaining the level size represented by numbers.

[0094] Specifically, the buffer zone can be determined based on the maximum range of data collection by crowdsourced vehicles and the geographic information risk level of different facilities. A wider buffer zone is set for high-risk areas to enhance protection; the buffer zone for low-risk areas is relatively narrower. For example, the buffer zone for high-risk military facilities is set at 1000 meters, while the buffer zone for low-risk communication facilities is set at 50 meters.

[0095] If an area is assessed as high-risk, such as due to frequent traffic accidents or severe road damage, its buffer zone is usually large to ensure effective coverage of potentially dangerous areas.

[0096] Within the buffer zone, GIS network analysis and overlay analysis can be used to identify passable roads, providing a basis for subsequent comprehensive consideration of the delineation of the multi-source area. Furthermore, the intersection areas of passable roads within different impact zones are identified as key risk prevention areas. Simultaneously, passable roads are classified into levels, and further classified according to their risk level. For example: Level A roads: safe and unimpeded passage; Level B roads: minor obstacles exist, requiring monitoring; Level C roads: high-risk sections, recommended to avoid passage or subject to special management, such as speed limits and warning signs.

[0097] It should be noted that collaboration with national security and defense departments is also possible. This involves comprehensively discussing and evaluating identified accessible roads, considering factors such as road boundaries, accessibility, autonomous driving requirements, and geographic information security. This process updates open roads and ultimately defines the scope of open, crowdsourced updated roads. This allows for dynamic management and intelligent optimization of open road data. After obtaining the evaluation results, through grading, buffer zone analysis, road identification, and level classification, effective management of road safety can be ensured, providing a more efficient, flexible, and safe operating mode for the transportation system. These operations help improve the intelligence level of urban traffic management, enhance emergency response capabilities, and provide precise geographic data support for autonomous driving and intelligent transportation.

[0098] This embodiment establishes a risk assessment model that quantifies and systematically processes different types of safety risks. The safety risk score input to the model provides a specific risk indication for each geographic area, enabling risk identification to go beyond intuitive perception and be conducted through data-driven scientific assessment. The results help decision-makers quickly identify areas with high risks. The reasonable incorporation of various factors into the model enhances the comprehensiveness and accuracy of the risk assessment. Dynamically updating the geographic information risk level allows for rapid adjustments to traffic management measures or emergency response strategies, improving the flexibility and responsiveness of public safety management. The geographic information risk level calculated by the model can provide a scientific basis for decision-making in areas such as traffic management, urban planning, and disaster prevention.

[0099] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the geographic information security risk assessment method for open testing roads of intelligent connected vehicles in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0100] This application also provides a geographic information security risk assessment device for open testing roads of intelligent connected vehicles. Please refer to [link / reference]. Figure 4 The intelligent connected vehicle open test road geographic information security risk assessment device includes: The acquisition module 10 is used to acquire open test road geographic environment data, preprocess the open test road geographic environment data, and obtain preprocessed regional data.

[0101] The identification module 20 is used to identify security risk elements in the preprocessed regional data to obtain data security risk elements.

[0102] The calculation module 30 is used to calculate the security risk score of each data security risk element.

[0103] The assessment module 40 is used to conduct a security risk assessment based on the security risk score and obtain the assessment result.

[0104] The intelligent connected vehicle open test road geographic information security risk assessment device provided in this application adopts the intelligent connected vehicle open test road geographic information security risk assessment method in the above embodiments, which can solve the technical problem of how to comprehensively and accurately assess the security risks in the geographic environment of open test roads. Compared with the prior art, the beneficial effects of the intelligent connected vehicle open test road geographic information security risk assessment device provided in this application are the same as the beneficial effects of the intelligent connected vehicle open test road geographic information security risk assessment method provided in the above embodiments, and other technical features in the intelligent connected vehicle open test road geographic information security risk assessment device are the same as the features disclosed in the above embodiment methods, and will not be repeated here.

[0105] In one embodiment, the acquisition module 10 is further configured to acquire open test road geographic environment data, the data including traffic infrastructure data, high-precision map data, satellite image data, aerial image data, traffic participant data, and intelligent communication network data; Extract image patch information containing sensitive keywords from existing data using text retrieval methods; Preprocessing is performed based on the open test road geographic environment data and the patch information to obtain preprocessed regional data.

[0106] In one embodiment, the acquisition module 10 is further configured to convert the open test road geographic environment data and the patch information to a preset coordinate system, and eliminate projection errors by matching control points to obtain open test road geographic environment data and patch information after projection error elimination; Under the preset coordinate system, the corresponding ground features of the open test road geographic environment data and patch information after the projection error is eliminated are identified by feature point extraction, and geometric transformation parameters are calculated based on the corresponding ground features; Based on the geometric transformation parameters, the spatial positions of the open test road geographic environment data and patch information after eliminating projection errors are aligned, and patches with offsets exceeding the threshold are corrected. The corrected map features are fused with the open test road geographic environment data after projection error elimination to obtain preprocessed regional data.

[0107] In one embodiment, the identification module 20 is further configured to acquire the entire data lifecycle; Based on the entire data lifecycle, the preprocessed regional data is identified for security risk factors, resulting in data security risk factors for each data lifecycle. These data security risk factors include military security factors, territorial security factors, resource security factors, social security factors, economic security factors, and general security factors.

[0108] In one embodiment, the calculation module 30 is further configured to obtain the severity score of each data security risk element and the probability score of each data security risk element occurring. The weight values ​​of each data security risk element are calculated using the network analytic hierarchy process. The security risk score of each data security risk element is calculated based on the severity score, the probability score, and the weight value.

[0109] In one embodiment, the assessment module 40 is further configured to acquire a risk assessment model; The security risk score is input into the risk assessment model to conduct a security risk assessment and obtain the geographic information risk level. The assessment results are obtained based on the risk level of the geographic information.

[0110] In one embodiment, the assessment module 40 is further configured to obtain a geographic information risk level based on the assessment result; The corresponding buffer zone is determined based on the risk level of the geographic information. Based on the buffer zone, passable roads are identified and classified to obtain passable roads and their levels. The open roads are updated based on the accessible roads and the accessible road level.

[0111] This application provides a geographic information security risk assessment device for open test roads of intelligent connected vehicles. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the geographic information security risk assessment method for open test roads of intelligent connected vehicles in the above embodiment 1.

[0112] The following is for reference. Figure 5 This document illustrates a structural schematic diagram of a geographic information security risk assessment device suitable for implementing embodiments of this application for open testing roads of intelligent connected vehicles. The geographic information security risk assessment device for open testing roads of intelligent connected vehicles in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The illustrated intelligent connected vehicle open test road geographic information security risk assessment device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0113] like Figure 5As shown, the intelligent connected vehicle open test road geographic information security risk assessment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the intelligent connected vehicle open test road geographic information security risk assessment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the intelligent connected vehicle open test road geographic information security risk assessment equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an intelligent connected vehicle open test road geographic information security risk assessment equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0114] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0115] The intelligent connected vehicle open test road geographic information security risk assessment device provided in this application adopts the intelligent connected vehicle open test road geographic information security risk assessment method in the above embodiments, which can solve the technical problem of how to comprehensively and accurately assess the security risks in the geographic environment of open test roads. Compared with the prior art, the beneficial effects of the intelligent connected vehicle open test road geographic information security risk assessment device provided in this application are the same as the beneficial effects of the intelligent connected vehicle open test road geographic information security risk assessment method provided in the above embodiments, and other technical features in the intelligent connected vehicle open test road geographic information security risk assessment device are the same as the features disclosed in the previous embodiment method, and will not be repeated here.

[0116] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0118] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the method for assessing the geographic information security risks of open test roads for intelligent connected vehicles in the above embodiments.

[0119] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0120] The aforementioned computer-readable storage medium may be included in the geographic information security risk assessment equipment for open testing roads of intelligent connected vehicles; or it may exist independently and not be installed in the geographic information security risk assessment equipment for open testing roads of intelligent connected vehicles.

[0121] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the intelligent connected vehicle open test road geographic information security risk assessment device, the device performs the following actions: acquires open test road geographic environment data; preprocesses the open test road geographic environment data to obtain preprocessed regional data; identifies security risk elements in the preprocessed regional data to obtain data security risk elements; calculates the security risk score for each data security risk element; and performs a security risk assessment based on the security risk scores to obtain an assessment result.

[0122] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0124] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0125] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for assessing the geographic information security risks of open test roads for intelligent connected vehicles. This method can solve the technical problem of how to comprehensively and accurately assess security risks in the geographic environment of open test roads. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the method for assessing the geographic information security risks of open test roads for intelligent connected vehicles provided in the above embodiments, and will not be repeated here.

[0126] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for assessing the geographic information security risks of open test roads for intelligent connected vehicles.

[0127] The computer program product provided in this application can solve the technical problem of how to comprehensively and accurately assess the security risks in the geographic environment of open test roads. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent connected vehicle open test road geographic information security risk assessment method provided in the above embodiments, and will not be repeated here.

[0128] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles, characterized in that, The method for assessing the geographic information security risks of open testing roads for intelligent connected vehicles includes: Obtain open test road geographic environment data, preprocess the open test road geographic environment data, and obtain preprocessed regional data; Security risk factors are identified in the preprocessed regional data to obtain data security risk factors. Calculate the security risk score for each data security risk element; The security risk score is used to conduct a security risk assessment, and the assessment result is obtained.

2. The method as described in claim 1, characterized in that, The steps of acquiring open test road geographic environment data and preprocessing the open test road geographic environment data to obtain preprocessed regional data include: Obtain geographic environment data for open test roads, including traffic infrastructure data, high-precision map data, satellite imagery data, aerial imagery data, traffic participant data, and intelligent communication network data; Extract image patch information containing sensitive keywords from existing data using text retrieval methods; Preprocessing is performed based on the open test road geographic environment data and the patch information to obtain preprocessed regional data.

3. The method as described in claim 2, characterized in that, The step of preprocessing the open test road geographic environment data and the patch information to obtain preprocessed regional data includes: The open test road geographic environment data and the patch information are converted to a preset coordinate system, and the projection error is eliminated by control point matching to obtain the open test road geographic environment data and patch information after the projection error is eliminated. Under the preset coordinate system, the corresponding ground features of the open test road geographic environment data and patch information after the projection error is eliminated are identified by feature point extraction, and geometric transformation parameters are calculated based on the corresponding ground features; Based on the geometric transformation parameters, the spatial positions of the open test road geographic environment data and patch information after eliminating projection errors are aligned, and patches with offsets exceeding the threshold are corrected. The corrected map features are fused with the open test road geographic environment data after projection error elimination to obtain preprocessed regional data.

4. The method as described in claim 1, characterized in that, The step of identifying security risk factors in the preprocessed regional data to obtain data security risk factors includes: Acquire data throughout its entire lifecycle; Based on the entire data lifecycle, the preprocessed regional data is identified for security risk factors, resulting in data security risk factors for each data lifecycle. These data security risk factors include military security factors, territorial security factors, resource security factors, social security factors, economic security factors, and general security factors.

5. The method as described in claim 1, characterized in that, The steps for calculating the security risk score of each data security risk element include: Obtain the severity score of each data security risk element and the probability score of each data security risk element occurring; The weight values ​​of each data security risk element are calculated using the network analytic hierarchy process. The security risk score of each data security risk element is calculated based on the severity score, the probability score, and the weight value.

6. The method as described in claim 1, characterized in that, The steps for conducting a security risk assessment using the security risk score and obtaining the assessment result include: Obtain a risk assessment model; The security risk score is input into the risk assessment model to conduct a security risk assessment and obtain the geographic information risk level. The assessment results are obtained based on the risk level of the geographic information.

7. The method according to any one of claims 1 to 6, characterized in that, After the step of conducting a security risk assessment using the security risk score and obtaining the assessment result, it also includes... The geographic information risk level is obtained based on the assessment results. The corresponding buffer zone is determined based on the risk level of the geographic information. Based on the buffer zone, passable roads are identified and classified to obtain passable roads and their levels. The open roads are updated based on the accessible roads and the accessible road level.

8. A geographic information security risk assessment device for open testing roads of intelligent connected vehicles, characterized in that, The device includes: The acquisition module is used to acquire open test road geographic environment data, preprocess the open test road geographic environment data, and obtain preprocessed regional data. The identification module is used to identify security risk elements in the preprocessed regional data to obtain data security risk elements. The calculation module is used to calculate the security risk score of each data security risk element; The assessment module is used to conduct a security risk assessment based on the security risk score and obtain the assessment result.

9. A geographic information security risk assessment device for open testing roads of intelligent connected vehicles, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for assessing the geographic information security risks of open test roads for intelligent connected vehicles as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for assessing the geographic information security risks of open test roads for intelligent connected vehicles as described in any one of claims 1 to 7.

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