Geographical information compliance evaluation method, device, storage medium and program product

Abstract

The application discloses a geographic information compliance evaluation method and device, a storage medium and a program product, and belongs to the technical field of data compliance. The method comprises the following steps: matching the to-be-inspected trajectory information with the compliance trajectory information in the compliance information library; if the matching result indicates that there is no compliance trajectory information matched with the to-be-inspected trajectory information in the compliance information library, storing the to-be-inspected trajectory information as sensitive trajectory information in the sensitive information library in association with the device identifier information of the target device, and determining the first sensitive trajectory information in the sensitive information library based on the to-be-inspected trajectory information; wherein each first sensitive trajectory information at least comprises part of the trajectory information in the to-be-inspected trajectory information, and the device identifier information associated with the first sensitive trajectory information is different from the device identifier information associated with the to-be-inspected trajectory information; and determining the compliance evaluation result of the to-be-inspected trajectory information based on the first sensitive trajectory information. The application can improve the efficiency of geographic information compliance evaluation.

Description

Technical Field

[0001] This application belongs to the field of data compliance technology, and in particular relates to a geographic information compliance assessment method, device, storage medium and program product. Background Technology

[0002] With the development of intelligent vehicles, vehicles can collect geographic information while driving, which can be used for map updates, algorithm iterations, testing and simulation, and other needs.

[0003] Currently, the collection of geographic information by vehicles may pose significant geographic information security risks. For example, vehicles might unknowingly enter military or classified areas and collect geographic information involving military or classified information. Therefore, to improve geographic information security, it is necessary to conduct compliance assessments on the collected geographic information. However, existing compliance assessment methods for geographic information rely on manual review, which is inefficient. Summary of the Invention

[0004] This application provides a geographic information compliance assessment method, device, storage medium, and program product, which can improve the efficiency of geographic information compliance assessment.

[0005] In a first aspect, embodiments of this application provide a geographic information compliance assessment method, the method comprising:

[0006] The trajectory information to be inspected is matched with the compliant trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected; the trajectory information to be inspected is the trajectory information in the geographic information collected by the target device.

[0007] If the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, then the trajectory information to be inspected is treated as sensitive trajectory information, associated with the device identification information of the target device, and stored in the sensitive information database. Based on the trajectory information to be inspected, the first sensitive trajectory information in the sensitive information database is determined. Each first sensitive trajectory information includes at least a portion of the trajectory information to be inspected, and the device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the trajectory information to be inspected.

[0008] Based on the first sensitive trajectory information, the compliance assessment result of the trajectory information to be inspected is determined.

[0009] Secondly, embodiments of this application provide a geographic information compliance assessment device, the device comprising:

[0010] The matching module is used to match the trajectory information to be inspected with the compliant trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected; the trajectory information to be inspected is the trajectory information in the geographic information collected by the target device.

[0011] The operation module is used to, if the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, store the trajectory information to be inspected as sensitive trajectory information in the sensitive information database, associate it with the device identification information of the target device, and determine the first sensitive trajectory information in the sensitive information database based on the trajectory information to be inspected; wherein each first sensitive trajectory information includes at least a part of the trajectory information in the trajectory information to be inspected, and the device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the trajectory information to be inspected;

[0012] The determination module is used to determine the compliance assessment result of the trajectory information to be inspected based on the first sensitive trajectory information.

[0013] Thirdly, embodiments of this application provide a geographic information compliance assessment device, the device comprising:

[0014] A processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the geographic information compliance assessment method as described in the first aspect.

[0015] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the geographic information compliance assessment method as described in the first aspect.

[0016] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by the processor of a geographic information compliance assessment device, cause the geographic information compliance assessment device to perform the geographic information compliance assessment method as described in the first aspect.

[0017] In this embodiment, after receiving the geographic information collected by the target device, the trajectory information to be inspected, which includes this information, can first be matched with the compliant trajectory information in the compliance information database. If the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, the trajectory information to be inspected can be stored as sensitive trajectory information, associated with the device identification information of the target device, in the sensitive database. Then, based on the trajectory information to be inspected, sensitive trajectory information in the sensitive information database that "includes part of the trajectory information to be inspected" and "is associated with different device identification information" is found. These sensitive trajectory information are all referred to as first sensitive trajectory information. Then, based on the first sensitive trajectory information, the compliance assessment result of the trajectory information to be inspected is determined. It can be seen that the compliance assessment of the trajectory information to be inspected in this embodiment does not require manual intervention, thereby improving the efficiency of the compliance assessment of the trajectory information to be inspected. In addition, by combining the compliance information database and the sensitive database to perform dual compliance assessment of the trajectory information to be inspected, the accuracy of the compliance assessment of the trajectory information to be inspected can be improved. Attached Figure Description

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

[0019] Figure 1(a) illustrates a geographic information compliance assessment system provided in an embodiment of this application.

[0020] Figure 1(b) shows another geographic information compliance assessment system provided in the embodiments of this application;

[0021] Figure 2 This is one of the flowcharts illustrating the geographic information compliance assessment method provided in the embodiments of this application;

[0022] Figure 3 This is the second flowchart illustrating the geographic information compliance assessment method provided in the embodiments of this application;

[0023] Figure 4 This is the third flowchart illustrating the geographic information compliance assessment method provided in the embodiments of this application;

[0024] Figure 5 This is a schematic diagram of the structure of the geographic information compliance assessment device provided in the embodiments of this application;

[0025] Figure 6 This is a schematic diagram of the structure of the geographic information compliance assessment device provided in the embodiments of this application. Detailed Implementation

[0026] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0028] As shown in Figure 1(a), this application embodiment provides a geographic information compliance assessment system, which may include a target device, a geographic information compliance assessment device, and a cloud service device, wherein:

[0029] The target device can collect geographic information and upload the collected geographic information to the geographic information compliance assessment device. Therefore, the target device can also be called a geographic information collection device. This application embodiment does not limit the form of the target device. It can be, but is not limited to, vehicles, robot dogs, robots, drones, etc. Furthermore, the vehicle can be a test vehicle or a private vehicle, etc.

[0030] Geographic information compliance assessment equipment can perform compliance assessment (also known as compliance check, compliance verification, compliance validation, etc.) on geographic information collected by the target device, and upload the compliant geographic information to the cloud service device;

[0031] Cloud service devices can use compliant geographic information, such as for map updates, algorithm iterations, and test simulations.

[0032] As shown in Figure 1(b), this application embodiment provides another geographic information compliance assessment system, which may include a target device and a geographic information compliance assessment device, wherein:

[0033] The target device is the same as the target device shown in Figure 1(a), and the details are as described above, so they will not be repeated here.

[0034] Geographic information compliance assessment equipment can conduct compliance assessments on geographic information collected by vehicles and use compliant geographic information, such as for map updates, algorithm iterations, and test simulations.

[0035] It can be understood that the geographic information compliance assessment device in Figure 1(b) integrates the functions of the geographic information compliance assessment device and the cloud service device in Figure 1(a). That is, the geographic information compliance assessment device in Figure 1(b) can be understood as an integration of the geographic information compliance assessment device and the cloud service device in Figure 1(a).

[0036] The geographic information compliance assessment method of this application embodiment can be applied to geographic information compliance assessment equipment, and further, it can be applied to the geographic information compliance assessment equipment shown in Figure 1(a) or Figure 1(b). The specific application can be determined according to the actual situation, and this application embodiment does not limit it.

[0037] In practice, this method can be executed by a geographic information compliance assessment device, or by a component of the geographic information compliance assessment device, such as the processor, chip, or chip system of the geographic information compliance assessment device, or by a logic module or software that implements all or part of the functions of the geographic information compliance assessment device.

[0038] In practical applications, geographic information compliance assessment equipment can be terminals, servers, service platforms, cloud computing, distributed systems, Internet of Things (IoT), vehicle-to-everything (V2X) systems, etc. Furthermore, terminals can be smartphones, tablets, laptops, desktop computers, etc.

[0039] It is understood that if the target device is a vehicle, the geographic information compliance assessment method in this application embodiment can be understood as a geographic information compliance check method collected by the vehicle.

[0040] The geographic information compliance assessment method provided in this application will be described in detail below with reference to the accompanying drawings and through some embodiments and application scenarios.

[0041] See Figure 2 , Figure 2 This is one of the flowcharts illustrating the geographic information compliance assessment method provided in the embodiments of this application. For example... Figure 2 As shown, the geographic information compliance assessment method may include the following steps:

[0042] Step 201: Match the trajectory information to be inspected with the compliant trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected; the trajectory information to be inspected is the trajectory information in the geographic information collected by the target device.

[0043] In practice, as the target device travels along the driving trajectory, it can collect geographic information through at least one or more sensors, including cameras, navigation and positioning modules, and radar sensors.

[0044] The format of the geographic information collected by the target device may include, but is not limited to, image format, text format, etc., and can be determined based on the sensor that collects the geographic information. This application does not limit this.

[0045] The geographic information collected by the target device may include trajectory information and / or non-trajectory information. The trajectory information may include information related to the target device's driving trajectory, such as images of the driving trajectory and at least two trajectory points within the driving trajectory. Trajectory points may be determined by coordinates and / or geographic location information. The aforementioned coordinates may be, but are not limited to, two-dimensional or three-dimensional coordinates. Geographic location information may be represented by longitude and latitude, and may also be represented by landmarks along the driving trajectory. Landmarks may include, but are not limited to, traffic lights and / or road intersections, or other objects on the road that indicate location. This application does not impose excessive restrictions on the method of collecting and / or selecting at least two trajectory points in the aforementioned driving trajectory; those skilled in the art can set it according to actual needs.

[0046] The aforementioned non-trajectory information may include information other than the aforementioned travel trajectory collected during the target device's journey along the travel trajectory. Non-trajectory information may include, but is not limited to, at least one of the following: Point of Interest (POI) information and / or non-POI information. POI may refer to a location object with business attributes and may include, but is not limited to, at least one of the following: map point of interest objects, i.e., any point element that can be uniquely identified on an electronic map and has coordinates and business attributes; store / service facility objects; reachable destination objects, which may include, but are not limited to, gas stations, parking lots, highway entrances and exits, receiving warehouses, etc.; tourism resource objects, which may include, but are not limited to, scenic spots, viewing platforms, toilets, restaurants, accommodations, cultural relics, etc.; and geographic entity objects.

[0047] POI information may include, but is not limited to, descriptive information and / or geographic location information of the POI. Furthermore, descriptive information may include, but is not limited to, at least one of text information, point cloud data, image information, etc. Non-POI information refers to information other than POI information, such as, but is not limited to, at least one of speed limits, weight limits, height limits, toll point information, etc., along the roads traversed by the driving trajectory.

[0048] After collecting geographic information, the target device can upload it to a geographic information compliance assessment device for compliance assessment, which can then be used for map updates, algorithm iterations, and test simulations. In some embodiments, the target device can periodically send the collected geographic information-related data to the geographic information compliance assessment device, or it can send the collected geographic information-related data to the geographic information compliance assessment device when a trigger condition indicated in a control message received from the geographic information compliance assessment device is met, or it can send the collected geographic information-related data to the geographic information compliance assessment device upon receiving a control message from the geographic information compliance assessment device instructing the transmission of geographic information. This application does not limit this. Further, trigger conditions may include, but are not limited to, events such as: the occurrence of an emergency (e.g., sudden braking, collision), entering or approaching certain designated areas (e.g., an intersection, a fenced area), etc.

[0049] After receiving geographic information collected by the target device, the geographic information compliance assessment device can perform a compliance assessment on this geographic information, that is, assess whether the geographic information is compliant. In the embodiments of this application, assessing whether geographic information is compliant can be understood as: assessing whether the geographic information complies with geographic information security regulatory laws, regulatory requirements, or compliance requirements, etc. In other words, assessing whether geographic information is compliant is assessing whether the geographic information meets (or complies with) legal rules and / or industry rules.

[0050] The received geographic information needs to be subject to compliance assessment. Therefore, if the received geographic information includes trajectory information, this trajectory information can be referred to as trajectory information to be inspected. That is, the trajectory information to be inspected is the trajectory information in the received geographic information. It can be understood that the geographic information may include one or more trajectory information to be inspected. If the received geographic information includes non-trajectory information, this non-trajectory information can be referred to as non-trajectory information to be inspected. That is, the non-trajectory information to be inspected is the non-trajectory information in the received geographic information. It can be understood that the geographic information may include one or more non-trajectory information to be inspected.

[0051] In this application embodiment, different methods can be used to evaluate whether the trajectory information to be inspected and the non-trajectory information to be inspected are compliant. Among them, the compliance assessment of the trajectory information to be inspected can be carried out through steps 201 to 203.

[0052] In step 201, for each trajectory information to be inspected included in the geographic information, a preliminary compliance assessment can be conducted on the trajectory information to be inspected through the compliance information database. The compliance information database stores one or more compliant trajectory information, meaning that all trajectory information in the compliance information database is compliant trajectory information.

[0053] In some embodiments, the compliance information database can be an open road network map database of a map platform or a standard electronic map. Further, the standard electronic map can be an electronic map that conforms to legal rules and / or industry rules, and can be a Standard Definition (SD) map database or a High Definition (HD) map database. The SD map database can also be referred to as SD data road network or whitelisted SD. In some embodiments, the compliance information database may include one or more standard electronic maps, and the different standard electronic maps in the compliance information database may be open-sourced from one or more map vendors. In some embodiments, the compliance trajectory information may include SD data road network and trajectory information of designated autonomous driving test areas.

[0054] In practice, the trajectory information to be inspected can be matched with the compliance trajectory information in the compliance information database to determine whether there is compliance trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0055] It is understandable that, in practical applications, the matching result between the trajectory information to be inspected and the compliance trajectory information in the compliance information database may be: there is compliance trajectory information in the compliance information database that matches the trajectory information to be inspected; or, there is no compliance trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0056] If a compliant trajectory matching the trajectory to be inspected exists in the compliance information database, it can be determined that the driving trajectory corresponding to the trajectory to be inspected is a driving trajectory in a public area. The use of the trajectory to be inspected will not cause geographic information security issues. Therefore, the trajectory to be inspected can be determined to be compliant and can be used for subsequent purposes. For the geographic information compliance assessment device in Figure 1(a), this trajectory information can be uploaded to the cloud service device; for the geographic information compliance assessment device in Figure 1(b), this trajectory information can be used for map updates, algorithm iterations, test simulations, etc.

[0057] If there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, there is reason to suspect that the driving trajectory corresponding to the trajectory information to be inspected is a driving trajectory in a non-public area. However, considering that the compliance information database may have limitations, in this embodiment of the application, a second compliance assessment of the trajectory information to be inspected can be further performed through step 202 to improve the accuracy of the compliance assessment of the trajectory information to be inspected.

[0058] Step 202: If the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, then the trajectory information to be inspected is regarded as sensitive trajectory information, associated with the device identification information of the target device and stored in the sensitive information database, and the first sensitive trajectory information in the sensitive information database is determined based on the trajectory information to be inspected; wherein, each first sensitive trajectory information includes at least part of the trajectory information in the trajectory information to be inspected, and the device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the trajectory information to be inspected.

[0059] In this embodiment of the application, if there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, the trajectory information to be inspected can be identified as sensitive trajectory information and then associated with the device identification information of the target device and stored in the sensitive information database.

[0060] It is understood that the sensitive information database is used to store at least one sensitive trajectory information, as well as the device identification information associated with each sensitive trajectory information. The device identification information associated with the sensitive trajectory information is the device identification information of the device that collected the sensitive trajectory information. The device identification information of the collecting device can uniquely identify the device. For vehicles, the device identification information can be, but is not limited to, an encrypted Vehicle Identification Number (VIN) or Vehicle Registration ID, etc.

[0061] After storing the trajectory information to be inspected in the sensitive information database, it is possible to further find sensitive trajectory information in the sensitive information database that "includes part of the trajectory information to be inspected" and "is associated with different device identification information" (hereinafter referred to as the first sensitive trajectory information).

[0062] Since the first sensitive trajectory information includes a portion of the trajectory information to be inspected, it can be considered as the trajectory information corresponding to the same trajectory. Furthermore, the device identification information associated with the first sensitive trajectory information differs from the device identification information associated with the trajectory information to be inspected, indicating that the first sensitive trajectory information and the trajectory information to be inspected were collected by different devices. Therefore, the number of first sensitive trajectory information entries included in the sensitive information database can characterize how many devices collected the driving trajectory corresponding to the trajectory information to be inspected before it was collected by the target device. In practical applications, the sensitive information database may or may not contain any first sensitive trajectory information, or it may contain one or more first sensitive trajectory information entries.

[0063] It is understandable that driving trajectories in public areas are publicly available, and various data collection devices can drive on them. Therefore, they can be collected by different devices, and their trajectory information can be made public. On the other hand, driving trajectories in non-public areas are not publicly available, and they are generally not collected by devices, so their trajectory information cannot be made public.

[0064] Based on this, the number of first-sensitivity trajectory information included in the sensitive information database can be used to determine whether the driving trajectory corresponding to the trajectory information to be inspected is a driving trajectory in a public area, thereby determining the compliance assessment and inspection result of the trajectory information to be inspected. It can be understood that if this driving trajectory is collected by multiple devices, it indicates that this driving trajectory is more likely to be located in a public area where the vehicle can freely enter and exit; conversely, the likelihood of this driving trajectory being located in a public area where the devices can freely enter and exit is less.

[0065] The embodiments of this application do not limit the method of determining the first sensitive trajectory information in the sensitive information database.

[0066] In some implementations, one can first find all sensitive trajectory information in the sensitive information database that includes at least a portion of the trajectory information to be inspected, excluding the trajectory information to be inspected. For ease of description, these sensitive trajectory information are referred to as target sensitive trajectory information. Then, the device identification information associated with each sensitive trajectory information in the target sensitive trajectory information can be compared. If two sensitive trajectory information are associated with the same device identification information, one of the sensitive trajectory information can be deleted. In this way, only the sensitive trajectory information associated with different device identification information in the target sensitive trajectory information is retained, resulting in the first sensitive trajectory information. Thus, the number of sensitive trajectory information included in the first sensitive trajectory information can accurately reflect how many devices have collected the driving trajectory corresponding to the trajectory information to be inspected before it was collected by the target device, thereby improving the accuracy of determining the compliance assessment result of the trajectory information to be inspected based on the first sensitive trajectory information.

[0067] In other implementations, the number of first sensitive trajectory information in the sensitive information database can be counted based on the shortest trajectory path of the driving trajectory corresponding to the trajectory information to be checked in the sensitive information database. For details, please refer to the relevant description below, which will not be described in detail here.

[0068] Step 203: Based on the first sensitive trajectory information, determine the compliance assessment result of the trajectory information to be inspected.

[0069] In practice, the compliance assessment result of the trajectory information to be inspected can be determined based on the number of first sensitive trajectory information included in the sensitive information database.

[0070] In this embodiment of the application, a first value can be set to evaluate whether the trajectory information to be inspected is compliant. The first value can be set based on actual needs. In some embodiments, the first value can be set based on the historical number of times the driving trajectory in the non-public area has been collected. For example, the first value can be set to the maximum value of the number of times the driving trajectory in the non-public area has been collected in the historical statistics. In one example, the first value can be 10, but it is not limited to this.

[0071] In practice, the number of first sensitive trajectory information included in the sensitive information database can be compared with a first value, and then the compliance assessment result of the trajectory information to be inspected can be determined based on the comparison result.

[0072] In some embodiments, determining the compliance assessment result of the trajectory information to be inspected based on the first sensitive trajectory information may include:

[0073] If the number of first sensitive trajectory information is greater than the first value, the compliance assessment result of the trajectory information to be inspected is determined to be compliant.

[0074] If the number of first-sensitive trajectory information entries is greater than the first value, it indicates that the driving trajectory corresponding to the trajectory information to be inspected has been collected a large number of times by different devices. This suggests that the driving trajectory corresponding to the trajectory information to be inspected is likely located in a public area that the devices can freely enter and exit. Therefore, the trajectory information to be inspected can be publicly disclosed, and thus it can be determined that the trajectory information to be inspected is compliant trajectory information. This ensures the accuracy of the compliance assessment of the trajectory information to be inspected and eliminates the need for manual intervention, thereby improving the efficiency of the compliance assessment. Furthermore, it can be added to the compliance information database to enrich the compliant trajectory information in the database, thereby improving the accuracy of preliminary compliance assessments based on the compliance information database.

[0075] If the number of first-sensitive trajectory information is less than or equal to the first value, it indicates that the driving trajectory corresponding to the trajectory information to be inspected has been collected by different devices less frequently. This suggests that the driving trajectory corresponding to the trajectory information to be inspected is likely located in a non-public area, and the trajectory information to be inspected cannot be made public. Therefore, the trajectory information to be inspected can be determined to be non-compliant trajectory information. For the geographic information compliance assessment device in Figure 1(a), uploading this trajectory information to the cloud service device can be abandoned; for the geographic information compliance assessment device in Figure 1(b), this trajectory information can be abandoned.

[0076] The geographic information compliance assessment method of this application embodiment, after receiving geographic information collected by the target device, first matches the trajectory information to be inspected, which includes it, with the compliant trajectory information in the compliance information database. If the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, the trajectory information to be inspected can be stored as sensitive trajectory information, associated with the device identification information of the target device, in a sensitive database. Then, based on the trajectory information to be inspected, sensitive trajectory information in the sensitive information database that "includes part of the trajectory information to be inspected" and "is associated with different device identification information" is found. These sensitive trajectory information are all referred to as first sensitive trajectory information. Then, based on the first sensitive trajectory information, the compliance assessment result of the trajectory information to be inspected is determined. It can be seen that the compliance assessment of the trajectory information to be inspected in this application embodiment does not require manual intervention, thereby improving the efficiency of the compliance assessment of the trajectory information to be inspected. In addition, by combining the compliance information database and the sensitive database for dual compliance assessment of the trajectory information to be inspected, the accuracy of the compliance assessment of the trajectory information to be inspected can be improved.

[0077] The following is a detailed explanation of how to obtain the first sensitive trajectory information.

[0078] In some embodiments, determining the first sensitive trajectory information in the sensitive information database based on the trajectory information to be inspected may include:

[0079] A second sensitive trajectory information matching the trajectory information to be inspected is determined from the sensitive information database; the second sensitive trajectory information is sensitive trajectory information other than the trajectory information to be inspected in the sensitive information database.

[0080] Based on the trajectory length, the third sensitive trajectory information is determined from the second sensitive trajectory information and the trajectory information to be inspected;

[0081] Based on the third sensitive trajectory information, the first sensitive trajectory information is determined from the sensitive information database.

[0082] In these embodiments, a sensitive trajectory that matches the trajectory to be checked from among the sensitive trajectory information in the sensitive information database (excluding the trajectory information to be checked) can be found first. This sensitive trajectory information is referred to as the second sensitive trajectory information. That is, the second sensitive trajectory information can be any sensitive trajectory information in the sensitive information database (excluding the trajectory information to be checked) that matches the trajectory information to be checked. It is worth noting that "matching" here means that it contains at least some of the same trajectory information. In other words, the second sensitive trajectory information can be any sensitive trajectory information in the sensitive information database (excluding the trajectory information to be checked) that includes at least some of the trajectory information in the trajectory information to be checked.

[0083] Then, based on the trajectory length, the second sensitive trajectory information or the trajectory information to be inspected can be identified as the third sensitive trajectory information. Afterwards, based on the third sensitive trajectory information, all the first sensitive trajectory information in the sensitive information database can be found. In this way, the first sensitive trajectory information in the sensitive information database can be accurately obtained.

[0084] This application does not limit the method of obtaining the second sensitive trajectory information. In some embodiments, the trajectory information to be checked can be randomly matched with other sensitive trajectory information in the sensitive information database to determine the second sensitive trajectory information.

[0085] In other embodiments, determining second sensitive trajectory information that matches the trajectory information to be inspected from a sensitive information database may include:

[0086] Sensitive trajectory information in the sensitive information database, excluding the trajectory information to be inspected, is matched sequentially with the trajectory information to be inspected according to the preset arrangement order of the first trajectory features to determine whether it includes part of the trajectory information to be inspected, until the first sensitive trajectory information that matches the trajectory information to be inspected is found, that is, the first sensitive trajectory information that includes part of the trajectory information to be inspected, and the first sensitive trajectory information is determined as the second sensitive trajectory information.

[0087] The first trajectory feature may include, but is not limited to, any one or more of the following: trajectory length, geometric trend (i.e., line shape), and sequence number. Furthermore, the trajectory sequence number may be determined based on the trajectory length and / or the trajectory line shape. For example, the trajectory sequence number may be positively correlated with the trajectory length, i.e., the longer the trajectory, the larger the sequence number, and vice versa; the trajectory sequence number may also be positively correlated with the completeness of the trajectory line shape, i.e., the higher the completeness of the trajectory line shape, the larger the trajectory sequence number, and vice versa.

[0088] The preset arrangement order of the first trajectory features with different manifestations can be different, and can be set according to actual needs. This application does not limit this.

[0089] In some implementations, the preset order of trajectory lengths can be ascending, but it is not limited to this. In these implementations, sensitive trajectory information in the sensitive information database (excluding the trajectory to be inspected) can be matched sequentially with the trajectory to be inspected, according to the ascending order of trajectory length. The first sensitive trajectory to match the trajectory to be inspected is designated as the second sensitive trajectory. In this case, the second sensitive trajectory is the one in the sensitive information database that matches the trajectory to be inspected and has the shortest trajectory path. It can be understood that for each trajectory of the same type, the trajectory containing the shortest trajectory path has a higher weight. Therefore, determining the first sensitive trajectory in the sensitive information database based on the second sensitive trajectory can maximize the finding of sensitive trajectory information in the database that corresponds to the trajectory to be inspected, thereby improving the reliability of trajectory information compliance assessment.

[0090] In other implementations, the preset arrangement order of trajectory line shapes can be an ascending order of trajectory line shape completeness, but it is not limited to this. In these implementations, sensitive trajectory information in the sensitive information database, excluding the trajectory information to be inspected, can be matched sequentially with the trajectory information to be inspected according to the ascending order of trajectory line shape completeness. The first sensitive trajectory information found to match the trajectory information to be inspected is determined as the second sensitive trajectory information. In this case, the second sensitive trajectory information is the sensitive trajectory information in the sensitive information database that matches the trajectory information to be inspected but has the least complete trajectory line shape. It can be understood that for each trajectory information of the same trajectory, the trajectory information containing incomplete trajectory line shapes has a higher weight. Thus, determining the first sensitive trajectory information in the sensitive information database based on the second sensitive trajectory information can maximize the finding of sensitive trajectory information in the sensitive information database that corresponds to the trajectory information to be inspected, thereby improving the reliability of trajectory information compliance assessment.

[0091] In some other implementations, if the trajectory number is positively correlated with the trajectory length or line shape, the preset arrangement order of the trajectory numbers can be an ascending order. Based on the above, it can be seen that determining the first sensitive trajectory information in the sensitive information database based on the second sensitive trajectory information determined in this way can maximize the finding of sensitive trajectory information in the sensitive information database that corresponds to the trajectory information to be inspected, thereby improving the reliability of trajectory information compliance assessment.

[0092] It is evident that using the above method to determine the second sensitive trajectory information can improve the reliability of trajectory information compliance assessment.

[0093] This application does not limit the method of obtaining the third sensitive trajectory information. In some embodiments, determining the third sensitive trajectory information from the second sensitive trajectory information and the trajectory information to be inspected based on the trajectory length includes: determining the smaller trajectory length between the second sensitive trajectory information and the trajectory information to be inspected as the third sensitive trajectory information.

[0094] In these embodiments, the trajectory with the shorter length between the second sensitive trajectory information and the trajectory information to be inspected can be directly used as the third sensitive trajectory information. Then, the first sensitive trajectory information is determined from the sensitive information database based on the third sensitive trajectory information. Since trajectory information containing incomplete trajectory line shapes has a higher weight for each trajectory information of the same trajectory, determining the first sensitive trajectory information from the sensitive information database based on the shorter trajectory length between the second sensitive trajectory information and the trajectory information to be inspected can maximize the finding of sensitive trajectory information in the sensitive information database that corresponds to the trajectory information to be inspected, thereby improving the reliability of trajectory information compliance assessment.

[0095] In other embodiments, the second sensitive trajectory information and the trajectory information to be inspected can be scored by combining trajectory length and trajectory line shape, and the one with the higher score can be used as the third sensitive trajectory information. Specifically, a first score can be determined based on the trajectory length of the trajectory information; the first score can be negatively correlated with the trajectory length, i.e., the shorter the trajectory length, the higher the first score. A second score can be determined based on the trajectory line shape of the trajectory information; the second score can be negatively correlated with the trajectory line shape, i.e., the higher the second score, the better. Then, the first score and the second score can be weighted and averaged to obtain the final score of the trajectory information. Thus, determining the third sensitive trajectory information through trajectory length and trajectory line shape can further improve the reliability of trajectory information compliance assessment.

[0096] As stated above, the first sensitive trajectory information is sensitive trajectory information in the sensitive information database that includes at least a portion of the trajectory information to be inspected, and whose associated device identification information differs from the device identification information associated with the trajectory information to be inspected. Based on this, it can be understood that the first sensitive trajectory information can satisfy the following:

[0097] The first sensitive trajectory information is the sensitive trajectory information in the sensitive information database other than the trajectory information to be inspected;

[0098] The first sensitive trajectory information is matched with the third sensitive trajectory information, that is, the first sensitive trajectory information includes at least some of the trajectory information in the third sensitive trajectory information;

[0099] The device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the third sensitive trajectory information.

[0100] This application does not limit the specific implementation method of determining the first sensitive trajectory information from the sensitive information database based on the third sensitive trajectory information.

[0101] In some implementations, the third sensitive trajectory information can be matched with other sensitive trajectory information in the sensitive information database to find all sensitive trajectory information corresponding to the driving trajectory information to be checked in the sensitive information database, i.e., the aforementioned target sensitive trajectory information. Then, the device identification information associated with each sensitive trajectory information in the target sensitive trajectory information can be compared. If two sensitive trajectory information are associated with the same device identification information, one of the sensitive trajectory information can be deleted. In this way, only the sensitive trajectory information associated with different device identification information in the target sensitive trajectory information is retained, thus obtaining the first sensitive trajectory information.

[0102] In other implementations, the third sensitive trajectory information can be matched with other sensitive trajectory information in the sensitive information database. The first sensitive trajectory information that matches the third sensitive trajectory information can be directly identified as the first sensitive trajectory information. Subsequently, for each sensitive trajectory information in the sensitive information database that matches the third sensitive trajectory information, its associated device identification information can be compared with the associated device identification information that has already been identified as the first sensitive trajectory information. If the associated device identification information is different from the associated device identification information that has already been identified as the first sensitive trajectory information, it can be identified as the first sensitive trajectory information; otherwise, it can be identified as not being the first sensitive trajectory information.

[0103] In this way, the number of sensitive trajectory information included in the first sensitive trajectory information can accurately reflect how many devices have collected the driving trajectory corresponding to the trajectory information to be inspected before it is collected by the target device, thereby improving the accuracy of judging whether the trajectory information to be inspected is compliant based on the number of first sensitive trajectory information.

[0104] The following section provides a detailed explanation of how to obtain the matching results between the inspection trajectory information and the compliance trajectory information in the compliance information database.

[0105] This application does not limit the matching method between the trajectory information to be inspected and the compliance trajectory information in the compliance information database.

[0106] In some embodiments, a match can be determined by calculating the degree of matching between the trajectory information to be inspected and the compliance trajectory information in the compliance information database, and then comparing it with a pre-set matching threshold. Specifically, if the degree of matching between the trajectory information to be inspected and a certain compliance trajectory information in the compliance information database is greater than the matching threshold, it can be determined that the compliance trajectory information matches the trajectory information to be inspected; if the degree of matching between the trajectory information to be inspected and a certain compliance trajectory information in the compliance information database is less than or equal to the matching threshold, it can be determined that the compliance trajectory information does not match the trajectory information to be inspected.

[0107] In other embodiments, matching the trajectory information to be inspected with compliance trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected may include:

[0108] Based on the second trajectory features of the trajectory information to be inspected and the compliance trajectory information in the compliance information database, the best matching trajectory information corresponding to the trajectory information to be inspected is determined from the compliance information database; wherein, the second trajectory features include the geometric features and / or road attributes of the trajectory corresponding to each trajectory information;

[0109] Based on the trajectory matching degree between the best matching trajectory information and the trajectory information to be inspected, it is determined whether there is any compliance trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0110] In these embodiments, the best matching trajectory information corresponding to the trajectory information to be inspected can be found in the compliance information database based on the second trajectory features of the trajectory information to be inspected and the compliance trajectory information in the compliance information database. For the specific acquisition method, please refer to the relevant content, which will not be described here.

[0111] Subsequently, the trajectory matching degree between this best matching trajectory information and the trajectory information to be inspected can be obtained. Based on the trajectory matching degree, it can be finally determined whether the two match, and then it can be concluded whether there is a matching result of compliance trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0112] In some implementations, trajectory matching degree can be expressed as a trajectory matching ratio. The trajectory matching ratio of two trajectory pieces can characterize the spatial overlap ratio of the trajectories corresponding to the two trajectory pieces, i.e., the trajectory length matching degree. In specific implementations, the trajectory matching ratio can be calculated based on the trajectory lengths corresponding to the best-matching trajectory and the trajectory to be checked. In these implementations, the trajectory matching ratio is directly proportional to the matching degree of the trajectory lengths of the two; that is, the closer the trajectory lengths are, the higher the matching degree, and vice versa.

[0113] In other implementations, trajectory matching can be expressed as trajectory similarity. The trajectory similarity between two trajectory pieces can characterize the degree of similarity between the corresponding trajectories in one or more dimensions, such as spatial shape, temporal dynamics, and semantic behavior. Specifically, the trajectory matching degree can be obtained by calculating the similarity between the best-matching trajectory and the trajectory to be checked in one or more dimensions. In these implementations, the trajectory matching degree is directly proportional to the similarity between the two trajectory pieces; that is, the higher the similarity, the higher the trajectory matching degree, and vice versa.

[0114] Furthermore, based on the trajectory matching degree between the best matching trajectory information and the trajectory information to be inspected, it is determined whether there is compliance trajectory information in the compliance information database that matches the trajectory information to be inspected. This can include:

[0115] In response to a trajectory matching degree greater than the second value, the best matching trajectory is determined to be the compliant trajectory information that matches the trajectory information to be checked;

[0116] If the trajectory matching degree is less than or equal to the second value, it is determined that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0117] In these embodiments, a second value can be preset to characterize the maximum trajectory matching degree of mismatch between two trajectory information, so as to evaluate the matching degree of the two trajectory information. The specific setting of the second value is related to the specific form of trajectory matching degree. That is, the corresponding second value can be different for different forms of trajectory matching degree. In one example, if the trajectory matching degree is expressed as a trajectory matching ratio, the second value can be set to 98% or 99%, but it is not limited to this.

[0118] After obtaining the trajectory matching degree between the best matching trajectory information and the trajectory information to be inspected, the trajectory matching degree can be compared with the second value to obtain the result of whether the best matching trajectory information and the trajectory information to be inspected match, and then to obtain the matching result of whether there is a compliant trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0119] Specifically, if the trajectory matching degree is greater than the second value, it can be determined that the best matching trajectory information matches the trajectory information to be inspected, indicating that there is compliant trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0120] If the trajectory matching degree is less than or equal to the second value, it can be determined that the best matching trajectory information does not match the trajectory information to be inspected, and it can be determined that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected.

[0121] In this way, it is possible to accurately determine whether there is compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, thereby improving the accuracy of the compliance assessment of trajectory geographic information.

[0122] This application does not limit the method of determining the best matching trajectory information. In some embodiments, determining the best matching trajectory information corresponding to the trajectory information to be inspected from the compliance information database based on the second trajectory features of the trajectory information to be inspected and the compliance trajectory information in the compliance information database may include:

[0123] Based on the coordinate points included in the trajectory information to be inspected, an observation set and a hidden state set are generated; where each observation in the observation set corresponds to a coordinate point in the trajectory information to be inspected; and each hidden state in the hidden state set corresponds to the mapping point of each coordinate point in the road network.

[0124] Based on the set of hidden states and the set of observations, the parameters of the hidden Markov model are generated according to the second trajectory features of the trajectory information to be inspected and the compliance trajectory information in the compliance information database.

[0125] Based on the parameters of the Hidden Markov Model, the optimal hidden state sequence corresponding to the set of observations is calculated using the Viterbi algorithm.

[0126] Based on the target compliance trajectory information that matches the optimal hidden state sequence in the compliance information database, the best matching trajectory information is determined.

[0127] In these embodiments, the best matching trajectory information corresponding to the trajectory information to be inspected in the compliance information database can be obtained by using a Hidden Markov Model (HMM) and the Viterbi algorithm (also known as a dynamic programming algorithm).

[0128] In practice, an observation set and a hidden state set can be generated based on the coordinate points (such as GPS points) in the trajectory information to be inspected.

[0129] In this embodiment, the observed value is the coordinate point in the trajectory information to be inspected; therefore, the set of observed values ​​is the set of coordinate points in the trajectory information to be inspected. The hidden state is the mapping point (also called a candidate point or projection point) of the coordinate point in the trajectory information to be inspected onto the road network. Therefore, the set of hidden states may include the mapping point of the coordinate point in the trajectory information to be inspected onto the road network. Further, the road network can be the road network corresponding to the compliance information database, but is not limited to this. In these embodiments, for ease of subsequent description and understanding, it is assumed that the set of observed values ​​includes M observed values, and the set of hidden states includes N hidden states, where N and M are both integers greater than 1.

[0130] Subsequently, based on the hidden state set and the observation set, and the second trajectory feature of the trajectory information to be inspected and the compliant trajectory information in the compliance information database, HMM parameters can be generated. The HMM parameters may include a single-state generation probability matrix B (also known as an emission probability matrix), a transition probability matrix A (also known as a state transition probability matrix), and an initial state probability vector π. For specific implementation details, please refer to the relevant instructions below, which will not be described in detail here.

[0131] Then, based on the HMM parameters, the optimal hidden state sequence corresponding to the observation set can be calculated using the Viterbi algorithm. Then, the compliance trajectory information in the compliance information database that has the highest matching degree with the optimal hidden state sequence is determined as the best matching trajectory information in the compliance information database that corresponds to the trajectory information to be inspected.

[0132] The Viterbi algorithm, as a dynamic programming solver, can recursively calculate the maximum probability path for each candidate point and eventually backtrack to obtain the optimal hidden state sequence corresponding to the globally optimal set of observations.

[0133] Then, the target compliance trajectory information that matches the optimal hidden state sequence in the compliance information database can be determined. The target compliance trajectory information may include one or more compliance trajectory information.

[0134] If the target compliance trajectory information includes only one compliance trajectory information, the target compliance trajectory information can be directly used as the best matching trajectory information in the compliance information database corresponding to the trajectory information to be inspected.

[0135] If the target compliance trajectory information includes multiple compliance trajectory information, a compliance inspection information can be randomly selected from the target compliance trajectory information, or the compliance trajectory information with the highest matching degree with the optimal hidden state sequence in the target compliance trajectory information can be used as the best matching trajectory information corresponding to the trajectory information to be inspected in the compliance information database.

[0136] Therefore, by using the above method to obtain the best matching trajectory information through HMM and Viterbi algorithm, it is possible to accurately determine whether there is compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, thereby improving the accuracy of trajectory geographic information compliance assessment.

[0137] In some embodiments, the set of observations includes M observations, where M is an integer greater than 1; the set of hidden states includes N hidden states, where N is an integer greater than 1.

[0138] Based on the set of hidden states and the set of observations, and using the second trajectory features of the trajectory to be inspected and the compliance trajectory information in the compliance information database, parameters for the hidden Markov model are generated, which may include:

[0139] Based on the set of hidden states and the set of observed values, an N×M singlet generation probability matrix is ​​generated using geometric features.

[0140] Based on the set of hidden states and road attributes, generate an N×N transition probability matrix;

[0141] The N×M single-state generation probability matrix, the N×N transition probability matrix, and the N-dimensional initial state probability vector are determined as the parameters of the Hidden Markov Model.

[0142] In specific implementation, for a singlet generation probability matrix B, the element B in the i-th row and k-th column of the singlet generation probability matrix B... ik Let B represent the probability that hidden state i generates observation k in a singlet state. Therefore, the singlet generation probability matrix B is an N×M matrix.

[0143] The embodiments in this application do not limit B. ik In some embodiments, the calculation method for B is as follows: ik It can be determined based on the geometric features between the hidden state i and the observed value k. These geometric features can be, but are not limited to, straight-line distance, azimuth angle, etc. In some implementations, B... ik B can be determined based on the straight-line distance between the hidden state i and the observed value k. Furthermore, B... ik It can be negatively correlated with the straight-line distance between the two, that is, the smaller the straight-line distance between the two, the better B. ik The larger, the lower B ik The smaller, the better, B ik It can be inversely proportional to the straight-line distance between the two. Thus, it makes B... ik It can accurately represent the probability of generating observation value k in the single state of hidden state i, thereby improving the accuracy of obtaining the best matching trajectory information.

[0144] For the transition probability matrix A, A ij The probability matrix A represents the probability of transitioning from hidden state i to hidden state j, which measures the rationality of the vehicle moving between candidate points at adjacent time points. Therefore, the transition probability matrix A is an N×N matrix.

[0145] The embodiments in this application do not limit A. ij In some embodiments, the calculation method involves the element A in the i-th row and j-th column of the transition probability matrix A. ij The distance can be determined based on the distance between hidden state i and hidden state j. This distance can include, but is not limited to, the shortest trajectory distance (also known as the shortest path distance in the road network, i.e., the shortest distance a vehicle actually travels from hidden state i to hidden state j in the road network), straight-line distance, etc. Furthermore, A ijIt can also be determined by combining road attributes, which can include, but are not limited to, road class. Road class can include highways, urban expressways, national highways, etc.

[0146] In some implementations, A can be determined based on the shortest trajectory distance (hereinafter referred to as the first distance) or the straight-line distance (hereinafter referred to as the second distance) between hidden state i and hidden state j. ij A ij It can be negatively correlated with the first or second distance, and further, it can be inversely proportional.

[0147] In other implementations, A can be determined based on the difference between the first distance and the second distance. ij To determine the plausibility of hidden state i and hidden state j being on the same driving trajectory, we obtain A. ij In practical implementation, a third value can be preset to measure the likelihood of two hidden states sharing the same travel trajectory. If the difference between the first and second distances is greater than the third value, the probability of hidden state i and hidden state j sharing the same travel trajectory is relatively low. The A value is determined based on the difference between the first and second distances. ij The difference is relatively small; if the difference between the first and second distances is less than or equal to the third value, the probability that hidden state i and hidden state j are on the same driving trajectory is relatively high. A is determined based on the difference between the first and second distances. ij Relatively large.

[0148] In other implementations, A can be determined based on the first distance, the second distance, and the target road attributes. ij The target road attributes include the road level of the driving trajectory at the first distance. Optionally, an N×M emission probability matrix A is generated based on the hidden state set, which may include:

[0149] Calculate the difference between the first distance and the second distance;

[0150] Based on the difference, determine the initial probability of hidden state i transitioning to hidden state j; where the initial probability is inversely proportional to the difference.

[0151] Based on the relationship between the shortest trajectory distance and the compliance information database, the target weight corresponding to the target road attribute is obtained; where, if the compliance information database includes the target compliance trajectory information corresponding to the shortest trajectory distance, the target weight is directly proportional to the road level of the target compliance trajectory information; if the compliance information database does not include the compliance trajectory information corresponding to the shortest trajectory distance, the target weight is zero.

[0152] The product of the initial probability and the target weight is defined as Aij.

[0153] In these embodiments, the initial probability of hidden state i transitioning to hidden state j can be determined first by the difference between the first distance and the second distance. The initial probability is inversely proportional to this difference. That is, the larger the difference between the first distance and the second distance, the less likely it is that hidden state i will transition to hidden state j on the same driving trajectory. Therefore, the transition probability of the two is smaller. Conversely, the smaller the difference, the greater the transition probability.

[0154] Subsequently, a target weight can be obtained based on the target road attribute to determine the probability of hidden state i transitioning to hidden state j. Specifically, the target weight corresponding to the target road attribute can be determined based on whether the compliance information database contains compliant trajectory information corresponding to the shortest trajectory distance. If it does, the target weight can be determined based on the road level of the compliant trajectory information. In some implementations, the target weight can be directly proportional to the road level, i.e., the higher the road level, the greater the target weight. If it does not, the target weight can be set to 0. This ensures that the matching results are strictly limited to the certified trusted road network, eliminating interference from invalid roads.

[0155] Then, the product of the initial probability and the target weight can be determined as Aij.

[0156] In this way, determining Aij in the above manner can ensure that the matching results are strictly limited to the certified trusted road network, eliminating the interference of invalid roads; it can also match roads of higher road level, thereby improving the accuracy of matching and thus improving the accuracy of compliance assessment.

[0157] For the initial state probability vector π, π i Let π represent the initial probability of hidden state i, which can be set according to actual needs. Therefore, the initial state probability vector π is an N-dimensional vector.

[0158] It is evident that the HMM parameters determined through the above method can improve the accuracy of obtaining the best matching trajectory information, thereby improving the accuracy of compliance assessment.

[0159] In the above, the optimal matching trajectory information is obtained through a trajectory matching method based on Hidden Markov Models (HMM). In other embodiments, the optimal matching trajectory information can also be obtained using trajectory matching methods based on geometric shapes such as Dynamic Time Warping (DTW) or Longest Common Subsequence (LCSS), depending on the actual needs. This application does not limit this approach.

[0160] The following provides specific instructions on the compliance handling of non-track information to be inspected.

[0161] In some embodiments, the method may further include:

[0162] Based on the blacklist and / or whitelist, the non-trajectory information to be inspected is processed for compliance to obtain compliant non-trajectory information corresponding to the non-trajectory information to be inspected; wherein, the non-trajectory information to be inspected is the non-trajectory information in the geographic information collected by the target device.

[0163] The blacklist contains sensitive words, while the whitelist contains compliant words.

[0164] In this embodiment, the geographic information compliance assessment device can pre-load a blacklist and a whitelist of words. It can be understood that words in the whitelist are compliant words (also known as safe words) and can be used. Words in the blacklist are all sensitive words and cannot be used.

[0165] In some embodiments, the blacklist may contain various sensitive words related to machines, and may include, but is not limited to, at least one of the following:

[0166] a) Military command organs, command engineering projects, combat engineering projects, military airports, ports, wharves, barracks, training grounds, test sites, military caves and warehouses, military information infrastructure, military reconnaissance, navigation and observation stations, military surveying, navigation and navigation aids, military highways and dedicated railway lines, military power transmission lines, military oil, water and gas pipelines, border defense and coastal defense control facilities and other military facilities directly used for military purposes;

[0167] b) Military restricted areas, military-controlled areas, and the buildings, structures, and roads within them;

[0168] c) Important facilities at joint military-civilian airports, ports, and wharves;

[0169] d) Key departments such as national security.

[0170] The whitelist can include some POI names that contain sensitive words, but the POI itself is not confidential.

[0171] If the received geographic information includes non-track information to be inspected, compliance assessments can be performed on each piece of non-track information based on the blacklist and whitelist word databases.

[0172] If non-compliant words are detected, they can be further processed for compliance. In practice, compliance processing may include, but is not limited to, any of the following: encrypting sensitive words in non-compliant words, replacing sensitive words in non-compliant words, etc.

[0173] In this way, compliant non-trajectory information corresponding to the non-trajectory information to be inspected can be obtained for subsequent use, thereby improving the reliability of the use of non-trajectory information.

[0174] In some embodiments, compliance processing is performed on the non-trajectory information to be inspected based on a blacklist and / or a whitelist to obtain compliant non-trajectory information corresponding to the non-trajectory information to be inspected, which may include:

[0175] The text information corresponding to the non-trajectory information to be checked is segmented into words to obtain the first list of words to be checked.

[0176] Based on the first list of words to be checked and the blacklist of words, obtain the second list of words to be checked; wherein, the second list of words to be checked includes: all words to be checked that contain sensitive words from the blacklist of words in the first list of words to be checked;

[0177] Based on the second list of words to be checked and the whitelist word library, obtain the third list of words to be checked for non-trajectory information; wherein, the third list of words to be checked includes: all words to be checked that contain compliant words in the whitelist word library in the second list of words to be checked;

[0178] Based on the second and third lists of words to be checked, compliance processing is performed on the non-trajectory information to be checked to obtain compliant non-trajectory information corresponding to the non-trajectory information to be checked.

[0179] In these embodiments, the text information corresponding to the non-trajectory information to be checked can be segmented first to obtain all the words to be checked for the non-trajectory information to be checked, and then the first list of words to be checked L1 can be generated.

[0180] It is worth noting that, in the embodiments of this application, the non-trajectory information to be inspected may include text information and / or non-text information. For the non-text information it contains, it can be converted into text information first and then segmented.

[0181] In some embodiments, the first list of words to be checked, L1, can be obtained by word segmentation using methods such as spaces, punctuation marks, HuggingFace, and Tokenizers, but is not limited to these methods.

[0182] In some embodiments, the word to be checked may include Point of Interest (POI) information and / or non-POI information, as detailed in the above descriptions, which will not be repeated here.

[0183] After obtaining the first list of words to be checked, L1, we can first use the blacklist word library to detect whether each word in the first list of words to be checked contains sensitive words, so as to obtain all the words to be checked that contain sensitive words in the first list of words to be checked, and then generate the second list of words to be checked, L2.

[0184] In practice, the following words in the first list of words to be checked, L1, can all be considered as words containing sensitive words:

[0185] It exactly matches a sensitive word in the blacklist, meaning the two are equivalent;

[0186] It can completely contain a sensitive word from the blacklist, but it doesn't match it exactly. That is, the word to be checked completely contains the sensitive word, but it may also include other words.

[0187] For words that are included in the first list of words to be checked (L1) but not in the second list of words to be checked (L2), they can be identified as compliant words and no further processing is required.

[0188] For words to be checked that are included in the second list of words to be checked L2, we can detect whether each word in the second list of words to be checked L2 contains compliant words based on the compliant words in the whitelist word library, so as to obtain all words to be checked that contain compliant words in the second list of words to be checked L2, and then generate the third list of words to be checked L3.

[0189] In practice, the following words in the second list of words to be checked (L2) can all be considered as compliance-sensitive words to be checked:

[0190] It exactly matches a compliant term in the whitelist, meaning the two are equivalent;

[0191] It can completely contain a compliant word from the whitelist, but not exactly match it. That is, the word to be checked can completely contain compliant words, but may also include other words.

[0192] Then, based on the second list of words to be checked (L2) and the third list of words to be checked (L3), compliance processing can be performed on the non-trajectory information to be checked to obtain compliant non-trajectory information corresponding to the non-trajectory information to be checked.

[0193] In this way, by leveraging blacklist and whitelist databases, accurate compliance assessment and processing of non-trajectory information to be inspected can be achieved, thereby improving the reliability of compliance assessment of non-trajectory information and thus enhancing the reliability of its use.

[0194] In some embodiments, compliance processing is performed on the non-trajectory information to be inspected based on a second list of words to be inspected and a third list of words to be inspected to obtain compliant non-trajectory information corresponding to the non-trajectory information to be inspected, which may include:

[0195] Non-compliant words in the second list of words to be checked are processed to meet compliance requirements; non-compliant words are those in the second list of words to be checked but not in the third list of words to be checked.

[0196] Based on the compliant terms in the non-trajectory information to be inspected and the non-compliant terms after compliance processing, the compliant non-trajectory information corresponding to the non-trajectory information to be inspected is obtained.

[0197] In these embodiments, non-compliant words can be determined first based on the second list of words to be checked (L2) and the third list of words to be checked (L3). Specifically, words in the second list of words to be checked, excluding those in the third list, can be identified as non-compliant words. It is understood that non-compliant words are words outside the compliant words in the whitelist, and that contain sensitive words in the blacklist, potentially including the following situations:

[0198] 1) Words to be checked that are included in the second list of words to be checked (L2) but not in the third list of words to be checked (L3);

[0199] 2) A word to be checked that is included in the second list of words to be checked (L2) and the third list of words to be checked (L3), but this word to be checked does not completely match the compliant words in the whitelist word library.

[0200] In other words, words that are included in the second list of words to be checked (L2) but not in the third list of words to be checked (L3) can be identified as non-compliant words and can be processed to comply with regulations.

[0201] For words to be checked that are included in the third list of words to be checked (L3), the processing method can be determined based on whether they completely match the compliant words in the whitelist. Specifically, if they completely match the compliant words, they can be determined to be compliant words and no processing is required; if they do not completely match the compliant words, they can be determined to be non-compliant words and compliance processing can be performed on them.

[0202] It is worth noting that the compliance processing methods differ for the two scenarios mentioned above (1) and (2). Specifically, for the word to be checked in scenario 1), compliance processing such as encryption or replacement can be performed on the entire word to be checked. For the word to be checked in scenario 2), it can be split into two parts: a first part that completely matches the compliant words in the whitelist, and a second part excluding the first part. Specifically, the first part can be retained, and then the sensitive words contained in the second part can be encrypted or replaced for compliance processing. This yields the compliant words corresponding to the word to be checked.

[0203] For ease of understanding, the following example is provided:

[0204] Assume the first list of words to be checked, L1, includes: Security Department, Security Components Company, and Security Department of Security Components Company. The blacklist includes: Security Department; the whitelist includes: Security Components Company.

[0205] Based on the above logic, the second list of words to be checked, L2, includes: security department, security component company, and security department of the security component company.

[0206] The third list of words to be checked, L3, includes: Safety Components Company, and the Safety Department of Safety Components Company.

[0207] Therefore, the term "security department" to be checked can be directly identified as a non-compliant term, and it can be replaced with "***".

[0208] Since the term "security component company" is a perfect match for "security component company" in the whitelist, it can be directly identified as a compliant term and will not be processed.

[0209] The term "Security Department of Security Components Company" to be checked can be identified as a non-compliant term because it does not completely match "Security Components Company" in the whitelist. It can then be replaced with "*** of Security Components Company".

[0210] After compliance assessment and processing of each word in the first list of words to be checked L1, the processed words in the first list of words to be checked L1 can be restored according to their original format to obtain the compliant non-track information corresponding to the non-track information to be checked.

[0211] In this way, the compliant non-track information corresponding to the non-track information to be inspected can be obtained without containing non-compliant words, thereby improving the reliability of the use of non-track information.

[0212] In other embodiments, after obtaining the second list of words to be checked L2 and the third list of words to be checked L3, the subject of the trajectory information to be checked can be directly modified and adjusted based on the second list of words to be checked L2 and the third list of words to be checked L3 to handle non-compliant words in the trajectory information to be checked in compliance. In this way, it is not necessary to use the method of restoring based on compliant words to obtain the compliant non-trajectory information corresponding to the non-trajectory information to be checked, thereby improving the accuracy of the compliant non-trajectory information corresponding to the non-trajectory information to be checked.

[0213] In other embodiments, after obtaining the first list of words to be checked L1, it can be directly compared with the blacklist and whitelist respectively. Words to be checked that completely match the sensitive words in the blacklist are identified as non-compliant words and processed. Words to be checked that completely match the compliant words in the whitelist are identified as compliant words. The remaining words are sent for manual review.

[0214] It should be noted that the various embodiments described in this application can be combined with each other or implemented individually without conflict, and this application does not limit this.

[0215] In this embodiment, "information" can be replaced with "data," such as "information database" (as mentioned above) being called "database," "trajectory information" being called "trajectory data," and "non-trajectory information" being called "non-trajectory data," etc. "Compliance assessment" can be replaced with "compliance verification" or "compliance validation," etc. Correspondingly, "x to be inspected" can be replaced with "x to be verified" or "x to be validated," etc. "Trajectory" can be replaced with "road" or "path," etc.

[0216] For ease of understanding, a specific embodiment will be used as an example:

[0217] In this embodiment of the application, for the collected trajectory data, the trajectory data is first judged by the whitelist (i.e., the aforementioned compliance information database) to check whether the trajectory is compliant; secondly, the non-trajectory information involved in the collected data is judged for sensitive words. If there are sensitive words, the sensitive words are blocked, that is, compliance processing is performed.

[0218] In one specific embodiment, road data (i.e., the aforementioned geographic information) is acquired during the process of a test vehicle / private car traveling along a preset driving trajectory. The road data includes the vehicle's trajectory and non-trajectory information. The non-trajectory information includes the POI description information - structured text information collected by the vehicle during the process of traveling along the driving trajectory, hereinafter referred to as POI text description information. Multiple POI text description information may be collected during the trajectory travel process.

[0219] Therefore, in this specific embodiment, the trajectory information in the road survey information is subjected to the desensitization process of step 1, and the structured text information in the road survey information is subjected to the desensitization process of step 2.

[0220] Step 1: Verify the whitelist of route acquisition trajectories (i.e., the aforementioned trajectory information).

[0221] Considering that the SD data released by map platforms is usually compliant and verified, and has undergone anonymization, SD data itself is a whitelist data set. Therefore, it can be used as the basic data for the whitelist. If the road data collection trajectory matches, it means that the data has been included in the whitelist. The overall process can be found in [link to documentation]. Figure 3 It can include the following:

[0222] 1. Use the acquired third-party SD data to construct the whitelist draft data (i.e., the aforementioned compliance information database).

[0223] 2. The road survey vehicle uploads the data collection trajectory, and the compliance is verified by the server (i.e., the aforementioned geographic information compliance assessment equipment);

[0224] 3. Using the HMM+Viterbi algorithm, the geometric features and road attributes (also known as road grade attributes) of the collected trajectory are extracted and compared with the whitelist SD data; then the matching ratio (i.e., the aforementioned trajectory matching ratio) is calculated based on the path length.

[0225] HMM: A probabilistic graphical model used to model time series data. Its core idea is to assume that the system is driven by a sequence of hidden states and that the state changes are indirectly reflected through the observable output sequence.

[0226] The Viterbi algorithm is a dynamic programming-based sequence decoding algorithm used to find the most probable sequence of hidden states in a Hidden Markov Model (HMM). Its core idea is to optimize the exponentially complex exhaustive search to polynomial time complexity by recursively calculating the optimal path probability and backtracking the path.

[0227] HMM + Viterbi matching is a technical framework that combines the probabilistic modeling capabilities of Hidden Markov Models (HMMs) with the optimal path search capabilities of the Viterbi algorithm to solve the problem of optimal state sequence matching for time series data. Its core logic is: modeling state transitions and observation generation patterns using HMMs → using the Viterbi algorithm to infer the most probable state sequence from the observation sequence.

[0228] 4. If the matching rate is greater than 98% (the rate can be flexibly configured, i.e., the second value mentioned above), it means that the collected trajectory is compliant and falls within the whitelist range. The collected data can be uploaded to the cloud (i.e., the cloud service device mentioned above) for circulation and use.

[0229] 5. If the matching rate is less than 98% (the rate can be flexibly configured), it means that the collection trajectory is not within the whitelist range and the collected data may involve sensitive data (such as military or classified information). Therefore, the server can initially determine that it is non-compliant, return an error message, and temporarily restrict the upload of the collection data packet based on the unique ID of the collection data packet. However, the final compliance assessment result of the collection trajectory can be further determined in conjunction with the sensitive information database.

[0230] 6. In the later stages, the system can continuously perform statistics on the trajectory data based on the sensitive dataset (i.e., the aforementioned sensitive information database), record the trajectory data and vehicle information such as encrypted VIN codes. When multiple different vehicles have collected non-whitelist data for the same road segment, it indicates that the road segment is a publicly available road segment. The road segment data can be automatically added to the whitelist SD for updates and supplements, so as to achieve the closed-loop operation of the whitelist data.

[0231] exist Figure 3The input includes road trajectory data, the input trajectory (i.e., the trajectory information to be checked mentioned above), whitelist path data (i.e., the compliance information database mentioned above), matching ratio (i.e., the trajectory matching ratio mentioned above), vehicle and equipment information (i.e., the equipment identification information mentioned above), shortest path (i.e., the third sensitive trajectory information mentioned above), and set number of times (i.e., the first value mentioned above).

[0232] Step 2, Sensitive word verification.

[0233] The system receives a segment of structured text information (i.e., the aforementioned non-track information) from the data collected by the vehicle traveling along the trajectory. This structured text information is then segmented using methods such as spaces, punctuation, HuggingFace, and Tokenizers to obtain a list of words to be verified, L1. L1 contains one or more POI text descriptions. A POI text description can be a long text description of a POI, such as: "parking lot of the security department of xx university x campus"; a POI text description can also contain both a POI description and other information besides the POI description, such as: "speed limit 40".

[0234] like Figure 4 As shown, it may include the following:

[0235] 1. Load the blacklist data from the dictionary into memory to obtain the blacklist of words LB (i.e., the aforementioned blacklist of words).

[0236] 2. Load the whitelist data into memory to obtain the whitelist of words LW (i.e., the aforementioned whitelist of words).

[0237] 3. For each POI text description in the list of words to be verified L1, treat it as the current word W (i.e., each POI text description may be a long text description, or it may contain other non-POI information along with POI information). Sequentially determine whether the current word W contains sensitive words. If the current word W contains sensitive words, then perform desensitization processing on the sensitive words; if the current word W does not contain sensitive words, then do not perform any processing.

[0238] The process of identifying and desensitizing the current word W includes the following steps:

[0239] Example of process data:

[0240] Blacklist of terms LB: Security Department;

[0241] LW (a pseudonym): Security Components Company;

[0242] Input content: Component company, safety component company, safety department of safety component company, safety department.

[0243] Process description:

[0244] a. Detect whether the current word W contains some words from LB, record the contained words, and sort them by word length from largest to smallest to obtain list L2; for example: Security Department, Security Components Company, Security Department of Security Components Company.

[0245] b. If L2 is empty, then the current word W is a non-sensitive word, such as "company parts". In this case, no processing is required. At the same time, the next POI text description information in L1 is used as the current word W, and the process returns to step a. Otherwise, proceed to step c.

[0246] c. Detect whether the current word W contains some words in LW, record the contained words, and sort them by word length from largest to smallest to obtain list L3; for example: Safety Department of Safety Components Company, Safety Components Company.

[0247] d. For each word W2 in L2 (a blacklisted word in the POI text description information), if W3 exists in L3, it means that the blacklisted word of the POI belongs to the whitelist. For example, the word "Security Department" exists in Security Components Company and no processing is required.

[0248] e. For each word W2 in L2, if W3 does not exist in L3, it means that W2 is a sensitive word and needs to be replaced with ***; for example, the word "security department" exists in the security department of the security component company, and the latter security department needs to be replaced, and the output is: security component company's ***;

[0249] f. After all word segments are detected, compared, and replaced, the word segments are restored to their original format. The text data with hidden sensitive information and restored format is returned to the input party. According to the example above, the final output data is: Component Company, Security Component Company, Security Component Company's ***, ***.

[0250] exist Figure 4 In this context, the input text data refers to the aforementioned non-trajectory information to be checked; the blacklist words refer to the aforementioned sensitive words.

[0251] Based on the geographic information compliance assessment method provided in the above embodiments, this application also provides specific implementation methods of the geographic information compliance assessment device. Please refer to the following embodiments.

[0252] See Figure 5 The geographic information compliance assessment device provided in this application embodiment may include:

[0253] The matching module 501 is used to match the trajectory information to be inspected with the compliant trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected; the trajectory information to be inspected is the trajectory information in the geographic information collected by the target device.

[0254] The operation module 502 is used to, if the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, store the trajectory information to be inspected as sensitive trajectory information in the sensitive information database, associate it with the device identification information of the target device, and determine the first sensitive trajectory information in the sensitive information database based on the trajectory information to be inspected; wherein each first sensitive trajectory information includes at least a part of the trajectory information in the trajectory information to be inspected, and the device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the trajectory information to be inspected;

[0255] The determination module 503 is used to determine the compliance assessment result of the trajectory information to be inspected based on the first sensitive trajectory information. The geographic information compliance assessment device provided in this application embodiment can implement the various processes in the method embodiment, and will not be described again here to avoid repetition.

[0256] Figure 6 A schematic diagram of the hardware structure for geographic information compliance assessment provided in an embodiment of this application is shown.

[0257] The geographic information compliance assessment device may include a processor 601 and a memory 602 storing computer program instructions.

[0258] Specifically, the processor 601 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0259] Memory 602 may include mass storage for data or instructions. For example, and not limitingly, memory 602 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 602 may include removable or non-removable (or fixed) media. Where appropriate, memory 602 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 602 is non-volatile solid-state memory.

[0260] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0261] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any of the geographic information compliance assessment methods in the above embodiments.

[0262] In one example, the geographic information compliance assessment device may also include a communication interface 606 and a bus 610. For example, Figure 6 As shown, the processor 601, memory 602, and communication interface 606 are connected through bus 610 and complete communication with each other.

[0263] The communication interface 606 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0264] Bus 610 includes hardware, software, or both, that couples the components of a geospatial compliance assessment device together. This is an example, not a limitation.

[0265] The bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 610 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0266] Furthermore, in conjunction with the geographic information compliance assessment methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the geographic information compliance assessment methods in the above embodiments.

[0267] This application embodiment may also provide a computer program product, wherein when the instructions in the computer program product are executed by the processor of the geographic information compliance assessment device, the geographic information compliance assessment device performs any of the geographic information compliance assessment methods in the above embodiments.

[0268] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0269] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. The programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM, floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0270] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0271] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0272] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A geographic information compliance assessment method, characterized in that, include: The trajectory information to be inspected is matched with the compliance trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected; The trajectory information to be inspected is the trajectory information in the geographic information collected by the target device; If the matching result indicates that there is no compliant trajectory information in the compliance information database that matches the trajectory information to be inspected, then the trajectory information to be inspected is stored as sensitive trajectory information and associated with the device identification information of the target device in the sensitive information database. A second sensitive trajectory information matching the trajectory information to be inspected is determined from the sensitive information database; The second sensitive trajectory information is the sensitive trajectory information in the sensitive information database other than the trajectory information to be inspected; Based on the trajectory length, the third sensitive trajectory information is determined from the second sensitive trajectory information and the trajectory information to be inspected; Based on the third sensitive trajectory information, first sensitive trajectory information is determined from the sensitive information database; wherein each first sensitive trajectory information includes at least a portion of the trajectory information to be inspected, and the device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the trajectory information to be inspected; Based on the first sensitive trajectory information, the compliance assessment result of the trajectory information to be inspected is determined.

2. The method according to claim 1, characterized in that, The step of determining the compliance assessment result of the trajectory information to be inspected based on the first sensitive trajectory information includes: In response to the fact that the quantity of the first sensitive trajectory information is greater than a first value, the compliance assessment result of the trajectory information to be inspected is determined to be compliant.

3. The method according to claim 1, characterized in that, The first sensitive trajectory information satisfies: The first sensitive trajectory information is sensitive trajectory information in the sensitive information database other than the trajectory information to be inspected; The first sensitive trajectory information matches the third sensitive trajectory information; The device identification information associated with the first sensitive trajectory information is different from the device identification information associated with the third sensitive trajectory information.

4. The method according to claim 1, characterized in that, The step of determining the second sensitive trajectory information that matches the trajectory information to be inspected from the sensitive information database includes: matching the sensitive trajectory information other than the trajectory information to be inspected in the sensitive information database with the trajectory information to be inspected in sequence according to the preset arrangement order of the first trajectory features, until the first sensitive trajectory information that matches the trajectory information to be inspected is found, and determining the first sensitive trajectory information as the second sensitive trajectory information; and / or The step of determining the third sensitive trajectory information from the second sensitive trajectory information and the trajectory information to be inspected based on the trajectory length includes: determining the smaller of the trajectory lengths of the second sensitive trajectory information and the trajectory information to be inspected as the third sensitive trajectory information.

5. The method according to claim 1, characterized in that, The step of matching the trajectory information to be inspected with the compliance trajectory information in the compliance information database to obtain the matching result of the trajectory information to be inspected includes: Based on the second trajectory features of the trajectory information to be inspected and the compliance trajectory information in the compliance information database, the best matching trajectory information corresponding to the trajectory information to be inspected is determined from the compliance information database; wherein, the second trajectory features include the geometric features and / or road attributes of the trajectory corresponding to each trajectory information; Based on the trajectory matching degree between the best matching trajectory information and the trajectory information to be inspected, it is determined whether there is any compliance trajectory information in the compliance information database that matches the trajectory information to be inspected.

6. The method according to claim 5, characterized in that, The step of determining whether there is compliant trajectory information matching the trajectory information to be inspected in the compliance information database based on the trajectory matching degree between the best matching trajectory information and the trajectory information to be inspected includes: in response to the trajectory matching degree being greater than a second value, determining that the best matching trajectory is compliant trajectory information matching the trajectory information to be inspected; in response to the trajectory matching degree being less than or equal to the second value, determining that there is no compliant trajectory information matching the trajectory information to be inspected in the compliance information database. and / or The second trajectory feature, based on the trajectory information to be inspected and the compliance trajectory information in the compliance information database, determines the best matching trajectory information corresponding to the trajectory information to be inspected from the compliance information database, including: Based on the coordinate points included in the trajectory information to be inspected, an observation set and a hidden state set are generated; wherein each observation in the observation set corresponds to a coordinate point in the trajectory information to be inspected; based on the hidden state set and the observation set, and using the second trajectory features of the trajectory information to be inspected and the compliant trajectory information in the compliance information database, hidden Markov model parameters are generated; based on the hidden Markov model parameters, the optimal hidden state sequence corresponding to the observation set is calculated using the Viterbi algorithm; based on the target compliant trajectory information in the compliance information database that matches the optimal hidden state sequence, the best matching trajectory information is determined; each hidden state included in the hidden state set corresponds to a mapping point of each coordinate point in the road network.

7. The method according to claim 1, characterized in that, The method further includes: Based on the blacklist and / or whitelist, compliance processing is performed on the non-trajectory information to be inspected to obtain compliant non-trajectory information corresponding to the non-trajectory information to be inspected; wherein, the non-trajectory information to be inspected is non-trajectory information in the geographic information collected by the target device; The blacklist contains sensitive words, while the whitelist contains compliant words.

8. The method according to claim 7, characterized in that, The step of performing compliance processing on the non-trajectory information to be inspected based on a blacklist and / or a whitelist to obtain compliant non-trajectory information corresponding to the non-trajectory information to be inspected includes: The text information corresponding to the non-trajectory information to be checked is segmented into words to obtain a first list of words to be checked; Based on the first list of words to be checked and the blacklist of words, a second list of words to be checked is obtained; wherein, the second list of words to be checked includes: all words to be checked that contain sensitive words in the blacklist of words in the first list of words to be checked; Based on the second list of words to be checked and the whitelist word library, a third list of words to be checked for the non-trajectory information to be checked is obtained; wherein, the third list of words to be checked includes: all words to be checked that contain compliant words in the whitelist word library in the second list of words to be checked; Based on the second list of words to be checked and the third list of words to be checked, the non-trajectory information to be checked is processed for compliance to obtain compliant non-trajectory information corresponding to the non-trajectory information to be checked.

9. The method according to claim 8, characterized in that, The step of performing compliance processing on the non-trajectory information to be inspected based on the second list of words to be inspected and the third list of words to be inspected, to obtain compliant non-trajectory information corresponding to the non-trajectory information to be inspected, includes: Non-compliant words in the second list of words to be checked are processed to meet compliance requirements; wherein, the non-compliant words are words in the second list of words to be checked that are not in the third list of words to be checked. Based on the compliant terms in the non-track information to be inspected and the non-compliant terms after compliance processing, compliant non-track information corresponding to the non-track information to be inspected is obtained.

10. The method according to claim 8, characterized in that, The term to be checked includes Point of Interest (POI) information and / or non-POI information; wherein, the POI information includes POI description information and / or location information; the non-POI information includes at least one of the following: speed limit information, weight limit information, height limit information, and toll point information of the roads traversed by the driving trajectory; and / or The compliance information database is a standard electronic map.

11. The method according to claim 10, characterized in that, The descriptive information includes at least one of text information, point cloud data, and image information.

12. A geographic information compliance assessment device, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the geographic information compliance assessment method as described in any one of claims 1 to 11.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the geographic information compliance assessment method as described in any one of claims 1 to 11.

14. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the geographic information compliance assessment device, the geographic information compliance assessment performs the geographic information compliance assessment method as described in any one of claims 1 to 11.

Images