Automated Generation Method, System and Map Data Cloud Platform for High-Precision Map Data

Abstract

The present disclosure discloses an automated generation method, system and map data cloud platform for high-precision map data, belonging to the field of high-precision map production. The method includes: determining approximate coordinates according to the data collection planned route, and determining at least one virtual reference station required for collection according to the approximate coordinates; in the first period, obtaining the first-period global navigation satellite system (GNSS) data from at least one virtual reference station according to preset information, and preprocessing the first-period GNSS data; in the second period, collecting the second-period GNSS data and inertial navigation data according to the data collection planned route; the first period covers the second period; differentiating the preprocessed first-period GNSS data from the second-period GNSS data to obtain differential GNSS data; fusing and resolving the inertial navigation data with the differential GNSS data to correct the GNSS data and obtain high-precision trajectory coordinate data. Implementing the present disclosure can achieve the removal of base stations in the field collection of high-precision maps, and the integration of online mapping and output.

Description

Technical Field

[0001] The present disclosure relates to high-precision map production and services, and particularly to an automated generation method, system, and map data cloud platform for high-precision map data. Background Art

[0002] Currently, in the production process of advanced driver assistance system (ADAS) maps and high-precision maps, high-precision field data collection often becomes the only data source. Among them, the rationality of base station erection and the quality of base station data are crucial factors affecting the quality of high-precision map data collected in the field. Currently, it is basically through manual site selection and erection of physical base stations to receive GNSS (Global Navigation Satellite System) observation data. This method currently has the following problems:

[0003] 1) The erection conditions of base stations are relatively strict

[0004] It is usually required that the erection area is open and unobstructed, without other signal sources (radio, high-voltage lines, etc.), without rivers, without reflective buildings, and there should be no power outage during the observation process, otherwise it will lead to rework in high-precision field data collection.

[0005] 2) The cost of artificial equipment is high

[0006] Each high-precision map data collection vehicle needs to be equipped with at least one GNSS receiving device and a caregiver. With the rapid development of high-precision maps today, the market has higher expectations for the coverage and accuracy of high-precision maps.

[0007] Therefore, on the premise of ensuring accuracy, how to reduce labor and equipment costs has become a technical problem that map providers urgently need to solve. Summary of the Invention

[0008] In view of this, the present disclosure discloses an automated generation method, system, and map data cloud platform for high-precision map data, which can achieve the removal of base stations in high-precision map field data collection, and the integration of online mapping and output.

[0009] To achieve the above object, the technical solution adopted by the present disclosure is to provide an automated generation method for high-precision map data, the method comprising:

[0010] According to the data acquisition planned route, approximate coordinates are determined, and at least one virtual reference station required for acquisition is determined based on the approximate coordinates; within the first time period, according to preset information, first-time period Global Navigation Satellite System (GNSS) data is obtained from the at least one virtual reference station, and the first-time period GNSS data is preprocessed; within the second time period, according to the data acquisition planned route, second-time period GNSS data and inertial navigation data are acquired; wherein, the first time period covers the second time period; the preprocessed first-time period GNSS data is differenced from the second-time period GNSS data to obtain differenced GNSS data; the inertial navigation data and the differenced GNSS data are fused and resolved to correct the GNSS data to obtain high-precision trajectory coordinate data.

[0011] Correspondingly, to implement the above method, the present disclosure also discloses an automated generation system for high-precision map data, which system includes:

[0012] A base station replacement device, configured to obtain first-time period GNSS data from the at least one virtual reference station according to preset information, and preprocess the first-time period GNSS data;

[0013] A mobile acquisition device, equipped with a GNSS receiver and an inertial navigation device, configured to acquire second-time period GNSS data and inertial navigation data within the second time period according to the data acquisition planned route; wherein, the first time period covers the second time period, and the virtual reference station and the mobile acquisition device are time-synchronized;

[0014] A difference fusion module, configured to difference the preprocessed first-time period GNSS data from the second-time period GNSS data to obtain differenced GNSS data; and configured to fuse and resolve the inertial navigation data and the differenced GNSS data to correct the GNSS data to obtain high-precision trajectory coordinate data.

[0015] In addition, the present disclosure also discloses a map data cloud platform, which map data cloud platform includes: any one of the above automated generation systems for high-precision map data and a data customization and output system; wherein, the data customization and output system is equipped with a data product customization module, a visualization output module, an API interface module, and an API gateway module, configured to provide an access interface to authorized users, provide a visual product customization operation interface, output according to the high-precision map data products customized by the authorized users, and monitor the network and access status; wherein, the API gateway module further includes an operation and maintenance monitoring unit, a log management unit, and an identity authentication unit.

[0016] In particular, the present disclosure also discloses a lightweight map data cloud platform, which lightweight map data cloud platform includes the following components:

[0017] A base station replacement device, configured to obtain GNSS data of a first period from the at least one virtual reference station according to preset information, and preprocess the GNSS data of the first period;

[0018] A differential fusion module, configured to perform difference on the preprocessed GNSS data of the first period and the GNSS data of the second period to obtain differential GNSS data; and configured to perform fusion calculation on the inertial navigation data and the differential GNSS data to correct the GNSS data and obtain high-precision trajectory coordinate data;

[0019] A point cloud data calculation module, configured to automatically analyze the point cloud data and extract the road information; the road information at least includes road lines, signs and other road-related data; wherein, the point cloud data is collected by a sensing device configured on the mobile acquisition device;

[0020] A data production platform, configured to generate high-precision map data reflecting real ground objects in a geographic coordinate system based on the high-precision trajectory coordinate data and in combination with the road information; and configured to compile the high-precision map data according to requirements and output it in a preset format;

[0021] A map data warehouse, configured to store and manage customizable productized high-precision map data, including full-volume data and / or incremental data of the current version of the map;

[0022] A data customization and output system, configured with a data product customization module, a visualization output module, an API interface module and an API gateway module, configured to provide an access interface to authorized users, provide a visual product customization operation interface, output according to the high-precision map data product customized by the authorized user, and monitor the network and access status; wherein, the API gateway module further includes an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

[0023] Compared with the prior art, the technical solution disclosed in the present disclosure has the following technical effects:

[0024] The automated generation method and system for high-precision map data disclosed in the present disclosure adopt a base station replacement device that can replace physical base stations during high-precision map data collection. By setting up a Virtual Reference Station (VRS), through operations such as network transmission and format conversion for parsing and quality inspection, high-precision and high-quality virtual base station data is obtained, thus realizing the base station-free field collection of high-precision maps. It can avoid large-scale investment in manpower and material resources. On the premise of ensuring the accuracy of high-precision map data, labor productivity is released as much as possible and equipment cost consumption is reduced, and the collection of high-precision map data is completed quickly. Without the need for manual and surveying equipment investment, the mobile collection device performs carrier phase differential correction with the virtual reference station to achieve real-time RTK. Through the technical solution disclosed in the present disclosure, the efficiency of map collection and production can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 FIG. is a schematic flow chart of an automated generation method for high-precision map data disclosed in an embodiment of the present disclosure;

[0026] Figure 2 FIG. is a schematic diagram of the composition of an automated generation system for high-precision map data disclosed in an embodiment of the present disclosure;

[0027] Figure 3 FIG. is a schematic diagram of data interaction in the process of obtaining, producing, and outputting high-precision map data in an embodiment of the present disclosure;

[0028] Figure 4 FIG. is a schematic diagram of the composition of a map data cloud platform disclosed in an embodiment of the present disclosure;

[0029] Figure 5 FIG. is a schematic diagram of the composition of a lightweight map data cloud platform disclosed in an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The following elaborates on the preferred embodiments of the present disclosure in conjunction with the accompanying drawings, so that the advantages and features of the present disclosure are more easily understood by those skilled in the art, thereby making a clearer definition of the protection scope of the present disclosure.

[0031] It should be noted that in this article, relationships such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these actual operations. Moreover, the terms "comprising", "including" or any other variant are intended to cover non-exclusive inclusion, so as to include the inherent elements of a process, method, article or device that includes a series of elements. Without more limitations, the elements defined by the statement "including..." do not exclude the existence of additional identical elements in the process, method, article or device that includes the said elements.

[0032] Method embodiment:

[0033] This embodiment discloses an automated generation method for high-precision map data, and this method includes the following steps:

[0034] S100: According to the data collection planned route, determine the approximate coordinates, and determine at least one virtual reference station required for collection according to the approximate coordinates.

[0035] S200: During the first period, obtain the global navigation satellite system (GNSS) data of the first period from at least one virtual reference station according to the preset information.

[0036] S300: Preprocess the GNSS data of the first period.

[0037] S400: During the second period, collect the GNSS data and inertial navigation data of the second period according to the data collection planned route. Among them, the first period covers the second period.

[0038] S500: Differentiate the preprocessed GNSS data of the first period and the GNSS data of the second period to obtain the differential GNSS data.

[0039] S600: Perform fusion calculation on the inertial navigation data and the differential GNSS data, correct the GNSS data, and obtain the high-precision trajectory coordinate data.

[0040] ; In this embodiment, a base station replacement device that can replace the physical base station in high-precision map data collection can obtain base station data by obtaining data from a VRS (Virtual Reference Station), through network transmission, format conversion and other parsing and quality inspection operations, and then obtain high-precision and high-quality virtual base station data without the need for manual and surveying equipment investment. The mobile collection device performs carrier phase differential correction with the virtual reference station to achieve real-time RTK. Through this embodiment, on the premise of meeting the current high-precision map accuracy requirements, the labor productivity can be released as much as possible, the equipment cost consumption can be reduced, and the operation efficiency can be improved.

[0041] As an optional implementation manner, based on the above embodiments, the method for automatically generating high-precision map data may further include the following steps:

[0042] S700: During the second period, collect point cloud data and automatically analyze the point cloud data to extract road information. The road information at least includes road lines, signs and other road-related data.

[0043] S800: Based on the high-precision trajectory coordinate data and combined with the road information, generate high-precision map data reflecting real-world features in the geographic coordinate system.

[0044] Optionally, in the above embodiment S300, the preprocessing of the GNSS data in the first period further includes: parsing the GNSS data in the first period, performing quality detection, and screening data that meets preset conditions.

[0045] Among them, the parsing of the GNSS data in the first period may further include: converting the transmission data format of the GNSS data in the first period into the GNSS standard data format. During the collection process, according to the determined position coordinates of the virtual reference station, virtual observation data near the position of the virtual reference station, that is, the GNSS data in the first period, can be applied to the VRS server (CORS system) through NMEA (National Marine Electronics Association, a standard format formulated for marine electronic equipment). The GNSS data in the first period is transmitted through a network protocol and adopts the RTCM (Radio Technical Commission for Maritime services, GNSS standard protocol) format. For example: the output type is the NIRIP Client network protocol, the receiving type is the File file form, and the transmission standard format is RTCM3. The RTCM format is a differential service binary general format. Before differential fusion, this format needs to be converted into the decimal RINEX format (Receiver Independent Exchange Format, GNSS standard data format).

[0046] Optionally, two or more virtual reference stations may be adopted in the above embodiments to obtain the base station GNSS data at different distances from the mobile collection device. In the subsequent solution process, the base station data of multiple virtual reference stations can be loaded simultaneously, and the data of the mobile collection device in the same time period can be solved. The base station data is automatically weighted and allocated according to the distance between the base station and the mobile collection device, and the multi-base station is used to improve the adjustment stability and solution accuracy of the mobile collection device.

[0047] As an optional implementation manner, based on the above embodiments, the quality detection of the GNSS data in the first period further includes: transmission network monitoring and / or GNSS data quality detection. Among them:

[0048] 1) The transmission network monitoring may further include: performing real-time transmission monitoring on the network transmission signal on the side of the virtual reference station, and feeding back the monitored data packet loss information to the virtual reference station, and the virtual reference station re-transmits the corresponding GNSS data according to the received feedback information.

[0049] 2) The transmission network monitoring may further include: adopting network exception log monitoring and alarm to monitor the network transmission signal on the side of the virtual reference station, and giving an alarm when an exception log is detected, feeding back the monitored data packet loss information to the virtual reference station, and the virtual reference station re-transmits the corresponding GNSS data according to the received feedback information.

[0050] As an optional implementation manner, based on the above embodiments, the GNSS data quality detection may further include: judging whether the GNSS data in the first period is qualified by the number of satellites per epoch, the satellite spatial distribution factor, the signal-to-noise ratio of each frequency band, and the multipath noise of each frequency band, and calculating the qualified rate. When the qualified rate of the GNSS data in the first period is lower than the preset threshold, the GNSS data in the first period of the corresponding virtual reference station is discarded. The GNSS data in the first period with a qualified rate reaching or higher than the preset threshold is the data qualified for quality detection. For example, the preset threshold of the qualified rate may be between 99% and 99.9%.

[0051] As an optional implementation manner, based on the above embodiments, fusing and resolving the inertial navigation data and the differential GNSS data to correct the GNSS data further includes:

[0052] For the GNSS data missing due to the satellite signal quality, using the inertial navigation data, through track extrapolation, calculate the missing GNSS data. For example, when the satellite signal is poor, after accurately determining the starting coordinates, through the inertial navigation accelerometer and track extrapolation, obtain the missing GNSS data coordinates

[0053] As an optional implementation manner, based on the above embodiments, in the process of fusion resolution, load the GNSS data of two or more virtual reference stations, and each virtual reference station is configured with a weight set based on the GNSS data quality, and fuse the virtual reference station data resolution accuracy according to the weight.

[0054] Before data collection, the position of the base station can be determined according to the planned route for field data collection. For example, according to the collection plan for the day in the field, the base station can be roughly determined at the middle position of the route to obtain approximate coordinates. Based on these approximate coordinates, a virtual base station (i.e., virtual reference station) is determined, and the data collection route is within the radiation range of the virtual base station. For example, within a radius of 20 kilometers centered on the base station, if it exceeds 20 kilometers, the number of virtual base stations can be appropriately increased. During the data collection process,

[0055] the virtual reference station and the mobile data collection device need to be time-synchronized and corresponding to the same satellite in order to share the base station data for differential processing. Multiple satellite data can be obtained during the same period. The base station and the mobile data collection device form a group of differential pairs for the same satellite at the same time, and multiple groups of differential pairs can be formed for multiple satellites during the same period.

[0056] In addition, the present disclosure also discloses an example of an automated method for generating high-precision map data. Referring to Figure 1 as shown, the automated method for generating high-precision map data includes process S101, process S102, process S103, and process S104.

[0057] Figure 1 The process S101 shown represents the process of determining the position of at least one virtual reference station required for field data collection according to the field data collection plan of the mobile data collection device. Replacing the base station for data collection in the prior art with a virtual reference station can conveniently and quickly complete the prior preparation work for data collection on the premise of avoiding large-scale investment in manpower and material resources, so as to further obtain the Global Navigation Satellite System (GNSS) observation data corresponding to the determined virtual reference station.

[0058] In an optional embodiment of the present disclosure, the process of determining the position of at least one virtual reference station required for field data collection according to the field data collection plan of the mobile data collection device includes planning the data collection route before field data collection and determining the position of the virtual reference station such that the planned field data collection route is within the radiation range of the virtual reference station, so as to facilitate further application for obtaining the GNSS observation data corresponding to the determined position of at least one virtual reference station.

[0059] In an optional embodiment of the present disclosure, the process of planning a collection route and determining the position of a virtual reference station before field collection includes roughly determining the virtual reference station at the middle position of the collection route so that the planned field collection route is within the radiation range of the virtual reference station, facilitating further application for obtaining the Global Navigation Satellite System (GNSS) observation data corresponding to the determined position of at least one virtual reference station. In an optional embodiment of the present disclosure, the process of planning a collection route and determining the position of a virtual reference station before field collection includes roughly determining the virtual reference station at the middle position of the collection route so that the planned field collection route is within the radiation range with a radius of 20 kilometers centered on the base station of the virtual reference station, facilitating further application for obtaining the GNSS observation data corresponding to the determined position of at least one virtual reference station.

[0060] Figure 1 The process S102 shown represents the process of applying to the virtual reference station server for the GNSS observation data corresponding to at least one virtual reference station according to the position of at least one virtual reference station, in order to further obtain the GNSS observation data corresponding to the position of the above-mentioned at least one virtual reference station.

[0061] In an optional embodiment of the present disclosure, the process of applying to the virtual reference station server for the GNSS observation data corresponding to at least one virtual reference station according to the position of at least one virtual reference station includes applying to an operator with the service ability of a virtual reference station (Virtual Reference Station, a type of CORS application) for the GNSS observation data corresponding to the above-mentioned at least one virtual reference station, in order to further obtain the GNSS observation data corresponding to the position of the above-mentioned at least one virtual reference station.

[0062] In an optional embodiment of the present disclosure, the process of applying to the virtual reference station server for the GNSS observation data corresponding to at least one virtual reference station according to the position of at least one virtual reference station includes that the virtual reference station server uses the existing Continuously Operating Reference Station (CORS) at the above-mentioned position as a Virtual Reference Station (VRS) according to the position of the above-mentioned at least one virtual reference station, to collect and provide the GNSS observation data required for field collection, in order to further obtain the GNSS observation data within the collection area planned for field collection.

[0063] In an optional embodiment of the present disclosure, the process of applying to the virtual reference station server for the global navigation satellite system observation data corresponding to at least one virtual reference station according to the position of at least one virtual reference station includes that the virtual reference station server adds and sets at least one continuously operating reference station used as a virtual reference station according to the position of the at least one virtual reference station, so as to further obtain the global navigation satellite system observation data within the acquisition area planned for field operation.

[0064] In an optional embodiment of the present disclosure, the process that the virtual reference station server adds and sets at least one continuously operating reference station used as a virtual reference station according to the position of the at least one virtual reference station includes that when there is no continuously operating reference station at the position, the virtual reference station server uses the existing continuously operating reference stations at the position of the at least one virtual reference station to virtually add and set at least one continuously operating reference station used as a virtual reference station, so that the acquisition area planned for field operation is within the radiation range of the virtual reference station, so as to further obtain the global navigation satellite system observation data within the acquisition area planned for field operation.

[0065] In an optional embodiment of the present disclosure, the process that the virtual reference station server uses the existing continuously operating reference stations at the position of the at least one virtual reference station to virtually add and set at least one continuously operating reference station used as a virtual reference station includes that the virtual reference station server adds and sets at least one continuously operating reference station used as a virtual reference station in an interpolation manner, so that the acquisition area planned for field operation is within the radiation range of the virtual reference station, so as to further obtain the global navigation satellite system observation data within the acquisition area planned for field operation.

[0066] In an optional embodiment of the present disclosure, the process of applying to the virtual reference station server for the global navigation satellite system observation data corresponding to at least one virtual reference station according to the position of at least one virtual reference station includes applying to the virtual reference station server for obtaining the global navigation satellite system observation data corresponding to the virtual reference station through the standard format (NMEA) formulated by marine electronic equipment, so as to smoothly obtain the required global navigation satellite system observation data corresponding to the virtual reference station.

[0067] In an optional embodiment of the present disclosure, applying to the virtual reference station server for the global navigation satellite system observation data corresponding to at least one virtual reference station is the global navigation satellite system observation data near the approximate coordinates of the position of at least one virtual reference station determined in step S101, so as to further obtain the global navigation satellite system observation data corresponding to the position of the at least one virtual reference station.

[0068] In an optional embodiment of the present disclosure, the Global Navigation Satellite System (GNSS) observation data near the approximate coordinates of the at least one virtual reference station is the radiation range of the virtual reference station, that is, the GNSS observation data within 10 - 20 kilometers, so as to further obtain the GNSS observation data within the radiation range of the position of the at least one virtual reference station.

[0069] Figure 1 The process S103 shown represents the process of obtaining GNSS observation data from the virtual reference station server and performing inspection and processing on the GNSS observation data, so as to conveniently obtain the GNSS observation data and make the data inspectable, further ensuring that the received GNSS observation data is complete and reliable, and facilitating the subsequent calculation of the GNSS observation data.

[0070] In an optional embodiment of the present disclosure, in obtaining the global satellite navigation observation data from the virtual reference station server, the background server is used to receive and obtain the above-mentioned GNSS observation data for data inspection and processing, further ensuring that the received GNSS observation data is complete and reliable.

[0071] In an optional embodiment of the present disclosure, the process of obtaining GNSS observation data from the virtual reference station server includes transmitting and obtaining data in the standard protocol differential message (RTCM) format through a network protocol, so as to further perform inspection and processing on the observation data.

[0072] In an optional embodiment of the present disclosure, the process of inspecting and processing the GNSS observation data includes converting the format of the GNSS observation data so that the data after the format conversion can be inspected subsequently.

[0073] In an optional embodiment of the present disclosure, the process of converting the format of the GNSS observation data includes converting the GNSS observation data obtained in step S102 from the binary standard protocol differential message format (RTCM) to the decimal standard data format (RINEX), so as to further inspect the data after the format conversion.

[0074] In an optional embodiment of the present disclosure, the process of inspecting and processing the GNSS observation data includes checking whether there is signal loss in the GNSS observation data and / or performing real-time inspection on the quality of the GNSS observation data, facilitating the immediate reporting of problems as they occur, to ensure that the received GNSS observation data is complete and reliable.

[0075] In an optional embodiment of the present disclosure, the process of checking and processing the global navigation satellite system observation data includes checking for packet loss during the network transmission of the global navigation satellite system observation data, avoiding signal loss caused by network signals or server problems, facilitating the immediate reporting of signal loss problems, so as to ensure that the received global navigation satellite system observation data is complete.

[0076] In an optional embodiment of the present disclosure, the process of checking for packet loss during the network transmission of the global navigation satellite system observation data includes monitoring the network transmission situation of the global navigation satellite system observation data, avoiding signal loss caused by network signals or server problems, facilitating the immediate reporting of network signal and server problems, so as to ensure that the received global navigation satellite system observation data is complete.

[0077] Optionally, when a network anomaly occurs during the process of monitoring the network transmission situation of the global navigation satellite system observation data, an alarm is issued to achieve the immediate reporting of network signal or server problems, so as to ensure that the received global navigation satellite system observation data is complete.

[0078] In an optional embodiment of the present disclosure, the process of checking and processing the global navigation satellite system observation data includes checking the quality of the global navigation satellite system observation data, facilitating the immediate reporting of quality problems of the global navigation satellite system observation data, so as to ensure that the received global navigation satellite system observation data is reliable.

[0079] In an optional embodiment of the present disclosure, the process of checking the quality of the global navigation satellite system observation data includes checking the number of satellites in each epoch of the global navigation satellite system observation data, facilitating the immediate reporting of problems with the number of satellites in each epoch of the global navigation satellite system observation data, so as to ensure that the received global navigation satellite system observation data is reliable.

[0080] Optionally, the process of checking the quality of the global navigation satellite system observation data includes checking whether the number of observed satellites in each epoch of each navigation system in the corresponding navigation system of the global navigation satellite system observation data meets the requirement of at least 4 satellites, facilitating the immediate reporting of the problem of insufficient number of satellites in each epoch of the global navigation satellite system observation data, so as to ensure that the received global navigation satellite system observation data is reliable.

[0081] In an optional embodiment of the present disclosure, the process of checking the quality of the global navigation satellite system observation data includes checking the satellite space distribution factor of the global navigation satellite system observation data, facilitating the immediate reporting of any issues with the satellite space distribution factor of the global navigation satellite system observation data as they occur, so as to ensure that the received global navigation satellite system observation data is reliable.

[0082] Optionally, the process of checking the quality of the global navigation satellite system observation data includes checking whether the satellite space distribution factor of the global navigation satellite system observation data meets the ideal situation of being less than 6, facilitating the immediate reporting of any issues with the overly large satellite space distribution factor of the global navigation satellite system observation data as they occur, so as to ensure that the received global navigation satellite system observation data is reliable.

[0083] In an optional embodiment of the present disclosure, the process of checking the quality of the global navigation satellite system observation data includes checking the signal-to-noise ratio of each frequency band of the global navigation satellite system observation data, facilitating the immediate reporting of any issues with the signal-to-noise ratio of the global navigation satellite system observation data as they occur, so as to ensure that the received global navigation satellite system observation data is reliable.

[0084] Optionally, the process of checking the quality of the global navigation satellite system observation data includes checking whether the L1 carrier signal-to-noise ratio meets the ideal situation of 40 dbhz, and / or whether the L2 carrier signal-to-noise ratio meets the ideal situation of being greater than 20 dbhz among the signal-to-noise ratios of each frequency band of the global navigation satellite system observation data, facilitating the immediate reporting of any issues with the signal-to-noise ratio of the global navigation satellite system observation data not meeting the ideal situation as they occur, so as to ensure that the received global navigation satellite system observation data is reliable.

[0085] In an optional embodiment of the present disclosure, the process of checking the quality of the global navigation satellite system observation data includes checking whether the L1 carrier multipath meets the ideal situation of being less than 0.6 meters, and / or whether the L2 carrier multipath meets the ideal situation of being less than 0.6 meters among the multipath noises of each frequency band of the global navigation satellite system observation data, facilitating the immediate reporting of any issues with the multipath noises of each frequency band of the global navigation satellite system observation data not meeting the ideal situation as they occur, so as to ensure that the received global navigation satellite system observation data is reliable.

[0086] In an optional embodiment of the present disclosure, the process of checking whether the multipath noise of each frequency band in the global navigation satellite system observation data meets the ideal situation where the L1 carrier multipath is less than 0.6 meters and / or the L2 carrier multipath is less than 0.6 meters includes checking the data paths of rivers and / or glass reflections in the environment around the receiving end mobile station, so as to further report immediately any problems in the multipath noise of each frequency band in the global navigation satellite system observation data that is affected by the environment around the mobile station and does not meet the ideal situation, to ensure the reliability of the received global navigation satellite system observation data.

[0087] In an optional embodiment of the present disclosure, after checking and processing the global navigation satellite system observation data, the above data is downloaded and stored in the form of a file in the local path for further differential processing and solution calculation of the global navigation satellite system observation data.

[0088] Figure 1 The process S104 shown represents the process of using the checked and processed global navigation satellite system observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution calculation to obtain high-precision trajectory coordinate points, so as to finally obtain the high-precision trajectory coordinate points required for a high-precision map.

[0089] In an optional embodiment of the present disclosure, the mobile acquisition device data collected by the above mobile acquisition device includes inertial combined navigation data collected by the mobile acquisition device using an inertial combined navigation system (GNSS / INS combined navigation system), which is used for fusion solution calculation with the checked and processed global navigation satellite system observation data, so as to finally obtain the high-precision trajectory coordinate points required for a high-precision map.

[0090] In an optional embodiment of the present disclosure, the process of using the checked and processed global navigation satellite system observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution calculation to obtain high-precision trajectory coordinate points includes using the background server to receive and obtain the above mobile acquisition device data for further fusion solution calculation with the global navigation satellite system observation data. In an optional embodiment of the present disclosure, the process of using the checked and processed global navigation satellite system observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution calculation to obtain high-precision trajectory coordinate points includes performing differential processing on the checked and processed global navigation satellite system observation data and the mobile acquisition device data at the same time, and performing fusion solution calculation to obtain high-precision trajectory coordinate points.

[0091] In an optional embodiment of the present disclosure, the process of differencing the processed global navigation satellite system (GNSS) observation data with the mobile acquisition device data at the same time includes: for each satellite's data in the processed GNSS observation data, forming a difference group with the data of the same satellite of the mobile acquisition device at the same time, and then performing fusion solution to obtain high-precision trajectory coordinate points.

[0092] In an optional embodiment of the present disclosure, the process of using the processed GNSS observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution to obtain high-precision trajectory coordinate points includes: using multiple groups of GNSS observation data corresponding to multiple virtual reference stations obtained from the virtual reference station server after inspection and processing, and the mobile acquisition device data collected by the mobile acquisition device, to perform fusion solution to obtain high-precision trajectory coordinate points; performing fusion solution on the GNSS observation data corresponding to multiple different virtual reference stations and the data of the mobile acquisition device. In this way, in the case where one group of data has packet loss or quality problems, other groups of data can be used for supplementary replacement, which can improve the solution accuracy.

[0093] In an optional embodiment of the present disclosure, the process of using the processed GNSS observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution to obtain high-precision trajectory coordinate points includes: using multiple groups of mobile acquisition device data collected by multiple mobile acquisition devices to perform fusion solution to obtain high-precision trajectory coordinate points, and performing fusion solution on the mobile acquisition device data of the same target obtained by different mobile acquisition devices, which can improve the adjustment stability and solution accuracy of the mobile acquisition device.

[0094] In an optional embodiment of the present disclosure, the process of using the processed GNSS observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution to obtain high-precision trajectory coordinate points includes: using multiple groups of GNSS observation data corresponding to multiple virtual reference stations obtained from the virtual reference station server after inspection and processing, and a group of mobile acquisition device data collected by one mobile acquisition device, to perform fusion solution to obtain high-precision trajectory coordinate points. In the case where one group of GNSS observation data has packet loss or quality problems, other groups of data can be used for supplementary replacement, which can improve the adjustment stability and solution accuracy of the mobile acquisition device.

[0095] In an optional embodiment of the present disclosure, after the inspection and processing in step S103, the multi-group Global Navigation Satellite System (GNSS) observation data obtained by a mobile acquisition device can be used for the solution of the GNSS observation data of multiple mobile acquisition devices in the same area during the same period. Reusing the data obtained for one acquisition point can save resources and improve the field acquisition efficiency.

[0096] For example, the GNSS observation data corresponding to three to four virtual reference stations obtained from the virtual reference station server can cover the solution of the GNSS observation data of mobile acquisition devices in the same period within the entire administrative region, thus saving resources and improving the field acquisition efficiency.

[0097] In an optional embodiment of the present disclosure, the process of using the inspected and processed GNSS observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution to obtain high-precision trajectory coordinate points includes using commercial solution software to simultaneously load multiple groups of GNSS observation data corresponding to virtual reference base stations obtained from the virtual reference station server after inspection and processing, and performing fusion solution with the mobile acquisition device data in the same period, which can improve the solution accuracy.

[0098] In an optional embodiment of the present disclosure, the process of using the inspected and processed GNSS observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution to obtain high-precision trajectory coordinate points includes using commercial solution software to simultaneously load the mobile acquisition device data obtained by different mobile acquisition devices for fusion solution, which can improve the adjustment stability and solution accuracy of the mobile acquisition device.

[0099] In an optional embodiment of the present disclosure, the process of using the inspected and processed GNSS observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion solution to obtain high-precision trajectory coordinate points includes using commercial solution software to simultaneously load multiple groups of GNSS observation data corresponding to multiple virtual reference stations and a set of mobile acquisition device data collected by one mobile acquisition device for fusion solution. In the case where one group of GNSS observation data has packet loss or quality problems, other groups of data can be used for supplementary replacement, which can improve the adjustment stability and solution accuracy of the mobile acquisition device.

[0100] In an optional embodiment of the present disclosure, the above-mentioned process of fusing and solving the multiple sets of global navigation satellite system observation data after inspection and processing with the data of the mobile collection device in the same time period includes weighting and allocating each set of data in the multiple sets of global navigation satellite system observation data after inspection and processing according to the distance between the virtual reference station corresponding to the multiple sets of global navigation satellite system observation data and a mobile collection device, and then fusing and solving the global navigation satellite system observation data after inspection and processing and the mobile collection device data in the same time period, thereby improving the adjustment stability and solution accuracy of the mobile collection device.

[0101] The above-mentioned process of using multiple sets of global navigation satellite system observation data after inspection and processing and fusing and solving with mobile collection device data in the same time period includes weighting and distributing each set of mobile collection device data in the multiple sets of mobile collection device data according to the distance between the mobile collection device corresponding to the multiple sets of mobile collection device data and a virtual reference station, and then using the global navigation satellite system observation data and mobile collection device data after inspection and processing in the same time period for fusion and solution, thereby improving the adjustment stability and solution accuracy of the mobile collection device.

[0102] In an optional embodiment of the present disclosure, the above-mentioned process of fusing and solving the multiple sets of global navigation satellite system observation data after inspection and processing with the mobile collection device data of the same time period includes weighted distribution of each set of data in the multiple sets of global navigation satellite system observation data after inspection and processing and the multiple sets of mobile collection device data according to the distance between the virtual reference station corresponding to the multiple sets of global navigation satellite system observation data and the mobile collection device corresponding to the multiple sets of mobile collection device data, and then fusing and solving the global navigation satellite system observation data after inspection and processing and the mobile collection device data of the same time period, thereby improving the adjustment stability and solution accuracy of the mobile collection device.

[0103] Product embodiment:

[0104] The above is an explanation of the embodiment of the method for automatically generating high-precision map data disclosed in the present invention. Figures 2 to 5 , the automatic generation system of high-precision map data disclosed in this disclosure is explained:

[0105] To implement the above method, Figure 3 As shown, the automatic generation system of high-precision map data disclosed in this disclosure includes:

[0106] A base station replacement device, configured to obtain GNSS data for a first period of time from at least one virtual reference station according to preset information, and pre-process the GNSS data for the first period of time;

[0107] A mobile acquisition device, configured with a GNSS receiver and an inertial navigation device, is used to collect GNSS data and inertial navigation data in the second period according to a data acquisition planned route; wherein, the first period covers the second period, and the virtual reference station and the mobile acquisition device are time-synchronized.

[0108] A differential fusion module is used to perform differential processing on the preprocessed GNSS data in the first period and the GNSS data in the second period to obtain differential GNSS data; and is used to perform fusion calculation on the inertial navigation data and the differential GNSS data to correct the GNSS data and obtain high-precision trajectory coordinate data.

[0109] The automatic generation of high-precision map data disclosed in this embodiment is the same. A base station replacement device that can replace the physical base station is designed. By obtaining GNSS data from the virtual reference station, it realizes the non-base station operation of high-precision map field acquisition, can avoid large-scale investment in manpower and material resources, and on the premise of ensuring the accuracy of high-precision map data, releases labor productivity as much as possible and reduces equipment cost consumption, quickly completes the acquisition of high-precision map data, and performs carrier phase differential correction between the mobile acquisition device and the virtual reference station without the need for manual and surveying equipment investment to achieve real-time RTK. Through the technical solution disclosed in this disclosure, the efficiency of map acquisition and production can be effectively improved.

[0110] In an optional embodiment, the base station replacement device further includes:

[0111] An acquisition preset module is used to set preset information for acquisition, including a data acquisition planned route, the approximate coordinates determined according to the implementation of the acquisition planned route, and at least one virtual reference station required for acquisition;

[0112] A data configuration module is used to set preset information for obtaining GNSS data in the first period according to the acquisition planned route. The preset information includes network output and reception types, and the data formats of output and reception;

[0113] A data transceiver module is used to obtain GNSS data in the first period from at least one virtual reference station, and receive GNSS data and inertial navigation data in the second period;

[0114] A data parsing module is used to parse and convert the format of the GNSS data in the first period;

[0115] A quality detection module is used to monitor the quality of the GNSS data in the first period, including transmission network monitoring and / or GNSS data quality detection.

[0116] As an optional implementation manner, the above-mentioned automatic generation system of high-precision map data may further include: a point cloud data calculation module and a data production platform. Among them:

[0117] The point cloud data resolution module is used to automatically analyze point cloud data and extract road information; the road information at least includes road lines, signs and other road-related data; among them, the mobile acquisition device is equipped with a sensing device for collecting point cloud data;

[0118] The data production platform is used to generate high-precision map data reflecting real-world features in the geographic coordinate system based on high-precision trajectory coordinate data and in combination with road information; and is used to compile the high-precision map data according to requirements and output it in a preset format.

[0119] In an optional embodiment, the above-mentioned high-precision map data automatic generation system may further include a map data warehouse, which is used to store and manage customizable productized high-precision map data, including the full amount of map data and / or incremental data of the current version.

[0120] Refer to Figure 3 and Figure 4 As shown, the embodiment of the present disclosure also discloses a map data cloud platform, which includes: the high-precision map data automatic generation system disclosed in any of the foregoing embodiments, and a data customization and output system;

[0121] Among them, the data customization and output system is configured with a data product customization module, a visualization output module, an API interface module and an API gateway module, which are used to provide access interfaces to authorized users, provide a visual product customization operation interface, output according to the high-precision map data products customized by authorized users, and monitor the network and access status; among them, the API gateway module further includes an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

[0122] In this embodiment, the base station replacement device can be set on the map data cloud platform. The data interaction between the map data cloud platform, the virtual reference station and the mobile acquisition device can be specifically referred to Figure 3 As shown, which shows the key data interaction in the process of obtaining, producing and outputting high-precision map data.

[0123] Refer to Figure 5 As shown, the embodiment of the present disclosure also discloses a lightweight map data cloud platform, which includes the following components:

[0124] A base station replacement device, which is used to obtain the first-period GNSS data from at least one virtual reference station according to preset information and preprocess the first-period GNSS data;

[0125] The differential fusion module is used to perform differential processing on the pre - processed GNSS data of the first period and the GNSS data of the second period to obtain differential GNSS data; and is used to perform fusion calculation on the inertial navigation data and the differential GNSS data to correct the GNSS data and obtain high - precision trajectory coordinate data;

[0126] The point cloud data calculation module is used to automatically analyze the point cloud data and extract road information; the road information at least includes road lines, signs and other road - related data; among which, the point cloud data is collected by the sensing device configured on the mobile acquisition device;

[0127] The data production platform is used to generate high - precision map data reflecting real - world features in the geographic coordinate system based on the high - precision trajectory coordinate data and in combination with the road information; and is used to compile the high - precision map data according to requirements and output it in a preset format;

[0128] The map data warehouse is used to store and manage customizable product - level high - precision map data, including the full - volume data and / or incremental data of the current version of the map;

[0129] The data customization and output system is configured with a data product customization module, a visualization output module, an API interface module and an API gateway module, and is used to provide access interfaces to authorized users, provide a visual product customization operation interface, output according to the high - precision map data products customized by authorized users, and monitor the network and access status; among which, the API gateway module further includes an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

[0130] In an optional embodiment of the present disclosure, the above - mentioned module for performing fusion calculation to obtain high - precision trajectory coordinate points by using the processed global navigation satellite system observation data and the mobile acquisition device data collected by the mobile acquisition device can use the processed multi - group global navigation satellite system observation data corresponding to multiple virtual reference stations obtained from the virtual reference station server and the mobile acquisition device data collected by the mobile acquisition device to perform fusion calculation to obtain high - precision trajectory coordinate points. In this way, in the case where one group of data has packet loss or quality problems, other groups of data can be used for supplementary replacement, which can improve the adjustment stability and calculation accuracy of the mobile acquisition device.

[0131] In an optional embodiment of the present disclosure, the module for using the processed global navigation satellite system observation data and the mobile acquisition device data collected by the mobile acquisition device to perform fusion calculation to obtain high-precision trajectory coordinate points uses multiple sets of mobile acquisition device data collected by multiple mobile acquisition devices to perform fusion calculation to obtain high-precision trajectory coordinate points, and uses the mobile acquisition device data of the same target obtained by different mobile acquisition devices for fusion calculation, which can improve the adjustment stability and calculation accuracy of the mobile acquisition device.

[0132] In an optional embodiment of the present disclosure, each module of the high-precision map data automatic generation system of the present disclosure can be directly in hardware, in a software module executed by a processor, or in a combination of both.

[0133] The software module may reside in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. The exemplary storage medium is coupled to the processor such that the processor can read information from and write information to the storage medium.

[0134] The processor may be a central processing unit (CPU for short), or may also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), field programmable gate arrays (FPGA for short), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In an alternative, the storage medium may be integral with the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in the user terminal as discrete components.

[0135] In an optional embodiment of the present disclosure, a computer-readable storage medium stores computer instructions, and the computer instructions are operated to execute the high-precision map data automatic generation method described in any embodiment.

[0136] In an optional embodiment, the map cloud platform may further include the following components:

[0137] A data acquisition device, configured to acquire high-precision trajectory coordinate point cloud data based on a virtual reference station and upload it to the data update module. By replacing the base station for data acquisition in the prior art with a virtual reference station, it is possible to conveniently and quickly obtain the high-precision trajectory coordinate point cloud data corresponding to the virtual reference station on the premise of avoiding large-scale investment in manpower and material resources.

[0138] A data update module, configured to automatically extract the data in the area where the high-precision trajectory coordinate point cloud data has a large difference from the achievement library according to the data uploaded by the data acquisition device, and send it to the update operation module for update and improvement, and store the high-precision trajectory coordinate point cloud data before and after the update and improvement.

[0139] In an optional embodiment of the present disclosure, the above data update module stores the high-precision trajectory coordinate point cloud data collected and uploaded by the module and / or the high-precision trajectory coordinate point cloud data updated and improved by the update operation module through a solving program.

[0140] An update operation module, configured to accurately locate the suspected area according to the data in the area where the high-precision trajectory coordinate point cloud data has a large difference from the achievement library sent by the data update module, and after updating and improving the suspected area, send the data in the area where the updated and improved high-precision trajectory coordinate point cloud data has a large difference from the achievement library back to the data update module.

[0141] In the embodiments provided by the present disclosure, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be omitted or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in a typical, mechanical or other form.

[0142] The units described as separate units may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] The above are only embodiments of the present disclosure, and thus do not limit the patent scope of the present disclosure. Any equivalent structural transformation made by using the content of the specification and drawings of the present disclosure, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present disclosure.

Claims

1. A method for automatically generating high-precision map data, characterized in that: include: Determining approximate coordinates according to a planned data collection route, and determining at least one virtual reference station required for collection based on the approximate coordinates, wherein carrier phase differential correction processing is performed on the mobile collection device and the virtual reference station; During a first time period, acquiring, from the at least one virtual reference station, global navigation satellite system (GNSS) data for a first time period according to preset information, and preprocessing the GNSS data for the first time period; During a second period, collecting GNSS data and inertial navigation data for the second period according to the data collection planned route; wherein the first period covers the second period; Differentiating the preprocessed GNSS data of the first period from the GNSS data of the second period to obtain differential GNSS data; Fusing and solving the inertial navigation data with the differential GNSS data, and correcting the GNSS data to obtain high-precision trajectory coordinate data; During the second period, point cloud data is collected and automatically parsed to extract road information; the road information includes at least road lines, signs and other road-related data; Based on the high-precision trajectory coordinate data and combined with the road information, high-precision map data reflecting real objects in a geographic coordinate system is generated.

2. The method for automatically generating high-precision map data according to claim 1, characterized in that: Using two or more virtual reference stations; and / or, Preprocessing the GNSS data for the first period further includes: parsing the GNSS data for the first period, performing quality inspection, and screening data that meets preset conditions; The parsing of the GNSS data for the first period includes converting a transmission data format of the GNSS data for the first period into a GNSS standard data format.

3. The method for automatically generating high-precision map data according to claim 2, characterized in that: Performing quality testing on the GNSS data in the first period further includes: transmission network monitoring and / or GNSS data quality testing; in: The transmission network monitoring further includes: performing real-time transmission monitoring of the network transmission signal on the virtual reference station side, and feeding back the monitored data packet loss information to the virtual reference station, and the virtual reference station re-issuing the corresponding GNSS data based on the received feedback information; and / or the transmission network monitoring further includes: using network abnormality log monitoring and alarm to monitor the network transmission signal on the virtual reference station side, and issuing an alarm when an abnormality log is detected, feeding back the monitored data packet loss information to the virtual reference station, and the virtual reference station re-issuing the corresponding GNSS data based on the received feedback information; and / or, The GNSS data quality detection further includes: judging whether the GNSS data of the first time period is qualified based on the number of satellites in each epoch, the satellite spatial distribution factor, the signal-to-noise ratio of each frequency band, and the multipath noise of each frequency band, and calculating the qualification rate; when the qualification rate of the GNSS data of the first time period is lower than a preset threshold, discarding the GNSS data of the first time period of the corresponding virtual reference station; the GNSS data of the first time period whose qualification rate reaches or exceeds the preset threshold is considered to be data that has passed the quality detection.

4. The method for automatically generating high-precision map data according to any one of claims 1 to 3, characterized in that: The fusing and solving the inertial navigation data with the differential GNSS data to correct the GNSS data further includes: For GNSS data missing due to satellite signal quality, using the inertial navigation data to calculate the missing GNSS data through dead reckoning; and / or, During the fusion solution process, GNSS data of two or more virtual reference stations are loaded. Each virtual reference station is configured with a weight set based on the GNSS data quality. The solution accuracy of the virtual reference station data is fused according to the weight.

5. An automatic generation system for high-precision map data, characterized in that: include: Determining approximate coordinates according to a data collection plan route, and determining at least one virtual reference station required for collection based on the approximate coordinates; a base station replacement device, configured to obtain GNSS data for a first period of time from the at least one virtual reference station according to preset information, and preprocess the GNSS data for the first period of time; a mobile collection device, configured with a GNSS receiver and an inertial navigation device, configured to collect GNSS data and inertial navigation data for a second period of time according to the data collection planned route during a second period of time; wherein the first period of time covers the second period of time, and the virtual reference station and the mobile collection device are time synchronized; a differential fusion module for performing a differential calculation on the pre-processed GNSS data of the first period and the GNSS data of the second period to obtain differential GNSS data; and for performing a fusion calculation on the inertial navigation data and the differential GNSS data to correct the GNSS data and obtain high-precision trajectory coordinate data; Wherein, carrier phase differential correction processing is performed on the mobile acquisition device and the virtual reference station; During the second period, point cloud data is collected and automatically parsed to extract road information; the road information includes at least road lines, signs and other road-related data; Based on the high-precision trajectory coordinate data and combined with the road information, high-precision map data reflecting real objects in a geographic coordinate system is generated.

6. The automatic generation system of high-precision map data according to claim 5, characterized in that: The base station replacement device further comprises: The collection preset module is used to set preset information for collection, including the data collection planning route, the approximate coordinates determined based on the implementation of the collection planning route, and at least one virtual reference station required for collection; a data configuration module, configured to set preset information for acquiring GNSS data for the first period according to the acquisition planning route, the preset information including network output and reception types, and output and received data formats; a data transceiver module, configured to obtain GNSS data for a first period from the at least one virtual reference station, and receive GNSS data for a second period and the inertial navigation data; A data analysis module, configured to analyze and convert the GNSS data of the first period into a new format; The quality detection module is used to perform quality monitoring on the GNSS data in the first period, including transmission network monitoring and / or GNSS data quality detection.

7. The automatic generation system of high-precision map data according to claim 5 or 6, characterized in that: The system also includes: a point cloud data solving module for automatically analyzing the point cloud data and extracting the road information; the road information at least includes road lines, signs and other road-related data; wherein the mobile collection device is equipped with a sensor device for collecting the point cloud data; A data production platform for generating high-precision map data representing real-world features in a geographic coordinate system based on the high-precision trajectory coordinate data and the road information; and for compiling the high-precision map data as required and outputting it in a preset format; and / or The map data warehouse is used to store and manage customizable productized high-precision map data, including the full data and / or incremental data of the current version of the map.

8. A map data cloud platform, characterized in that: include: The automatic generation system and data customization and output system of high-precision map data according to any one of claims 5 to 7; Among them, the data customization and output system is configured with a data product customization module, a visual output module, an API interface module and an API gateway module, which are used to provide an access interface to authorized users, provide a visual product customization operation interface, output high-precision map data products customized by the authorized users, and monitor the network and access status; among them, the API gateway module further includes an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

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