Map data quality inspection method and device and electronic equipment

Abstract

The present disclosure provides a map data quality inspection method and device and electronic equipment, relates to the technical field of artificial intelligence, and in particular to map technology. The method comprises the following steps: acquiring map data to be inspected and a target quality inspection rule; determining an object that has been modified in the map data to be inspected; extracting target data from the map data to be inspected according to the object that has been modified and the type of the target quality inspection rule; and performing quality inspection on the target data by using the target quality inspection rule. The method improves the quality inspection efficiency of map data.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the map technology in the field of artificial intelligence technology, in particular to a map data quality inspection method and device and electronic equipment. BACKGROUND

[0002] In order to ensure the accuracy of map data, after using the collected data to generate or update the map data, the map data needs to be quality inspected so as to correct the errors therein.

[0003] At present, the quality inspection method for map data is to analyze the specifications and production standards of map data to obtain quality inspection rules, and based on these quality inspection rules, the map data is automatically quality inspected by using programs, and the quality inspection results are used for output to correct the map data.

[0004] In the current quality inspection scheme, after completing a map data processing process, all rules are used to automatically quality inspect the full amount of map data. With the increase of the amount of map data and the number of quality inspection rules, the quality inspection efficiency is getting lower and lower. SUMMARY

[0005] The present disclosure provides a map data quality inspection method, device and electronic equipment which improves the quality inspection efficiency of map data.

[0006] According to a first aspect of the present disclosure, a map data quality inspection method is provided, comprising:

[0007] acquiring map data to be quality inspected and target quality inspection rules;

[0008] determining an object modified in the map data to be quality inspected;

[0009] extracting target data from the map data to be quality inspected according to the modified object and the type of the target quality inspection rules;

[0010] quality inspecting the target data by using the target quality inspection rules.

[0011] According to a second aspect of the present disclosure, a map data quality inspection device is provided, comprising:

[0012] an acquisition module configured to acquire map data to be quality inspected and target quality inspection rules;

[0013] a determination module configured to determine an object modified in the map data to be quality inspected;

[0014] an extraction module configured to extract target data from the map data to be quality inspected according to the modified object and the type of the target quality inspection rules;

[0015] A quality inspection module is configured to perform quality inspection on the target data according to the target quality inspection rule.

[0016] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory connected with the at least one processor; wherein

[0019] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0020] According to a fourth aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, the computer instructions being used to enable the computer to perform the method of the first aspect.

[0021] According to a fifth aspect of the present disclosure, a computer program product is provided, the program product comprising: a computer program stored in a readable storage medium, the computer program being readable by at least one processor of an electronic device, and the at least one processor executes the computer program to enable the electronic device to perform the method of the first aspect.

[0022] According to a sixth aspect of the present disclosure, an autonomous vehicle is provided, comprising the electronic device of the third aspect.

[0023] According to the technical solution of the present disclosure, the quality inspection efficiency of map data is improved.

[0024] It should be understood that the contents described in this part are not intended to identify key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0025] The accompanying drawings are used to better understand the present solution and do not limit the present disclosure. Among them:

[0026] Figure 1 is a flowchart of a map data quality inspection method according to an embodiment of the present disclosure;

[0027] Figure 2 is a schematic diagram of an intersection according to an embodiment of the present disclosure;

[0028] Figure 3 is a schematic diagram of a road section according to an embodiment of the present disclosure;

[0029] Figure 4 is a range diagram of target data provided according to an embodiment of the present disclosure;

[0030] Figure 5 is a structural diagram of a map data quality inspection device provided according to an embodiment of the present disclosure;

[0031] Figure 6 is a schematic block diagram of an electronic device for implementing a map data quality inspection method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] Exemplary embodiments of the present disclosure are described herein with reference to the accompanying drawings, which are provided to assist in a comprehensive understanding of the present disclosure, and should be considered as merely exemplary. Accordingly, those skilled in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, in the following description, descriptions of well-known functions and constructions are omitted for clarity and conciseness.

[0033] In the map generation process, the map data unit issued for manual operation is a map sheet. According to the collected data, when there is data that needs to be repaired, the entire map sheet will be issued for operation, that is, the manual operation range is the collection of map sheets. After manual operation, the map data of the entire map sheet is inspected, wherein the inspection rule is an empirical rule formulated according to the specifications and standards of map data, and the inspection is automatically implemented through a program, and then the problems recorded in the inspection results are repaired. For example, during manual operation, the collected data needs to be maintained at 3 locations, and the 3 locations are scattered in 3 map sheets. At this time, 3 map sheet data are issued for manual operation. After manual maintenance of the data, all map data of the 3 map sheets will be inspected. Since most of the map data of the 3 map sheets has not been modified, such an inspection process is redundant, causing unnecessary time loss and low efficiency.

[0034] Therefore, in the embodiments of the present disclosure, for the map data to be inspected, the modified objects are determined, and then the target data is extracted from the map data to be inspected based on the type of the target inspection rule to be executed this time. The target data includes the modified objects, and the range of the target data is related to the type of the target inspection rule. That is, the map data to be inspected is trimmed according to the type of the inspection rule in this way, so that the target inspection rule is used to inspect only the trimmed target data, avoiding the inspection of unnecessary data, reducing the amount of data to be inspected, and thus improving the inspection efficiency.

[0035] The present disclosure provides a map data quality inspection method and device and an electronic device, which are applied to the map technology field in the field of artificial intelligence technology, and can be particularly applied to the quality inspection process of map data to improve the quality inspection efficiency.

[0036] Below, the map data quality inspection method provided by the present disclosure will be described in detail through specific embodiments. It can be understood that the following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0037] Figure 1 is a flowchart of a map data quality inspection method according to an embodiment of the present disclosure. The execution subject of the method is a map data quality inspection device, which can be implemented in software and / or hardware. As shown in the figure, the method comprises: Figure 1

[0038] S101, obtaining map data to be inspected and target quality inspection rules.

[0039] The map data to be inspected is map data that has been manually processed, that is, the map data to be inspected has been updated, for example, modified, added, deleted, etc. in the objects therein, relative to the previous version of the map data. The objects in the map data can be links, road chains, or elements therein, such as road signs, traffic lights, etc. A link can be a section or all of an actual road, and a road chain can include multiple links.

[0040] The target quality inspection rules are the quality inspection rules that need to be performed on the map data to be inspected this time, and the target quality inspection rules can be set as needed. Since the total amount of quality inspection rules can be large, in the application process, the target quality inspection rules to be performed can be determined according to the specific circumstances of the manual update of the map data. The number of target quality inspection rules can be one or more.

[0041] S102, determining the modified objects in the map data to be inspected.

[0042] In order to execute the data volume of the target quality inspection rules and achieve accurate quality inspection, it is necessary to determine the modified objects in the map data to be inspected. For example, when determining the modified objects in the map data to be inspected, the map data to be inspected can be compared with the previous version of the map data to determine the modified objects therein, or the modified objects therein can also be determined according to the modification identifier in the map data to be inspected.

[0043] S103, extracting target data from the map data to be inspected according to the modified objects and the type of the target quality inspection rules.

[0044] ​The type of the target quality inspection rule can be used to represent a data range required by the target quality inspection rule, for example, in the execution of one type of target quality inspection rule, the quality inspection of an object at a position only requires data of the object itself, while in the execution of another type of target quality inspection rule, the quality inspection of an object at a position requires data of the object itself and data around the object. Therefore, in this step, target data is extracted from the map data to be inspected based on the type of the target quality inspection rule, the target data includes data of the modified object itself and may also include data around the modified object, and the range of the target data is related to the type of the target quality inspection rule.

[0045] It should be noted that when the number of target quality inspection rules is more than one, the type of the target quality inspection rule can also be one or more, and when the type of the target quality inspection rule is more than one, target data can be extracted for each type of target quality inspection rule, and the target data corresponding to different types of target quality inspection rules can be different.

[0046] S104, performing quality inspection on the target data by using the target quality inspection rule.

[0047] The map data to be inspected is cropped by S103, that is, the target data is cropped from the map data to be inspected, so that only the target data is subjected to quality inspection by using the target quality inspection rule, thereby reducing the data amount of quality inspection. When the type of the target quality inspection rule is more than one, the target data corresponding to different types of target quality inspection rules can be different, and each type of target quality inspection rule can be used to perform quality inspection on the target data corresponding to the type of target quality inspection rule.

[0048] The method provided by the embodiments of the present disclosure determines the modified object in the map data to be inspected, and then extracts target data from the map data to be inspected based on the type of the target quality inspection rule to be executed, so as to crop the map data to be inspected according to the type of the quality inspection rule, and only perform the target quality inspection rule on the cropped target data, thereby avoiding performing quality inspection on unnecessary data, reducing the data amount of quality inspection, and improving the quality inspection efficiency.

[0049] On the basis of the above-mentioned embodiments, the type of the target quality inspection rule is described. Optionally, the type of the target quality inspection rule is determined according to a keyword in the target quality inspection rule, and the type is an attribute inspection rule, a single road segment space inspection rule or a road chain space inspection rule.

[0050] Optionally, the tables, fields, relationships, and algorithms used in the target quality inspection rules are analyzed to determine whether they are attribute inspection rules or spatial inspection rules. Attribute inspection rules are used to check the attribute information of objects in map data, such as the speed limit value of a speed limit sign. Spatial inspection rules are further divided into single-segment spatial inspection rules and road chain spatial inspection rules. For example, target quality inspection rules containing coordinate geometry processing algorithms are spatial inspection rules, while others are attribute inspection rules. Spatial inspection rules that use road chain algorithms are road chain spatial inspection rules. For example, if the target quality inspection rule is an SQL rule, SQL lexical analysis is used to extract the tables, fields, and relationships used in the target quality inspection rule for judgment. For example, if the target quality inspection rule is a C++ rule, the corresponding code exists in a specific SDK, and the tables, fields, and relationships used are also extracted based on SDK keywords for judgment.

[0051] It should be noted that since the quality inspection rules for map data are pre-determined empirical rules, all quality inspection rules can be analyzed in advance to determine the type of each rule. This allows for quick identification of the target quality inspection rule's type when acquiring it. Based on determining the type of the quality inspection rule, the distance range for extracting target data corresponding to different types of quality inspection rules can be further predetermined when cropping map data. Specifically, for attribute inspection rules and single-segment spatial inspection rules, it is not necessary to pre-mark the distance range. However, for road chain spatial inspection rules, the maximum value of the extracted road chain range from the historical execution records of the rule can be determined as a preset distance for that rule. Then, for that road chain spatial inspection rule, target data can be extracted based on this preset distance.

[0052] Based on the above embodiments, the processing procedure after obtaining the map data to be inspected and the target inspection rules is described.

[0053] Optionally, the map data to be inspected can be differentially analyzed with the previous version of the map data to be inspected to identify modified objects in the map data to be inspected. Therefore, when extracting target data, extraction is performed based on the modified objects, reducing data volume and improving quality inspection efficiency. The map data to be inspected can be map data after manual processing, while its previous version is the map data from the manual quality inspection.

[0054] Optionally, the data extraction range corresponding to the target quality inspection rule is determined based on the type of the target quality inspection rule; target data is extracted from the map data to be inspected based on the modified object and the data extraction range corresponding to the target quality inspection rule. Different types of target quality inspection rules have different corresponding data extraction ranges. By extracting target data for different types of target quality inspection rules, map data can be cropped and inspected according to the classification of target quality inspection rules, improving efficiency. For example, target quality inspection rule A is an attribute inspection rule, target quality inspection rule B is a single road segment spatial inspection rule, and target quality inspection rule C is a road chain spatial inspection rule. For example, the preset distance corresponding to target quality inspection rule C is 300 meters. The corresponding data extraction ranges are explained below.

[0055] Optionally, if the target quality inspection rule is of the type of attribute inspection rule, then the data extraction scope corresponding to the target quality inspection rule is determined to be the modified object.

[0056] For attribute inspection rules, the quality inspection process for modified objects only needs to determine the attribute information of the object. Therefore, the data extraction scope only includes the modified object. In other words, when extracting target data, only the information of the modified object needs to be extracted, including its attribute information.

[0057] Optionally, if the target quality inspection rule is a single-segment spatial inspection rule, the data extraction range corresponding to the target quality inspection rule is determined based on the location of the modified object. For single-segment spatial inspection rules, the quality inspection process for the modified object may require the use of information from other road segments surrounding the modified object; this can be explained on a case-by-case basis.

[0058] Optionally, if the modified object is located at an intersection, such as... Figure 2 As shown, the data extraction scope corresponding to the target quality inspection rule is determined to be all road segments at the intersection. That is, when extracting target data, it is necessary to extract information from all road segments at the intersection where the modified object is located, including its attribute information. If the modified object is located in the middle of the road, then the data extraction scope corresponding to the target quality inspection rule is determined to be the road segment where the modified object is located, as well as the preceding and succeeding road segments. The preceding and succeeding road segments are the preceding and succeeding road segments, respectively. Figure 3 As shown, the dashed line represents the road segment where the modified object is located, and the solid lines before and after it indicate the preceding and succeeding road segments. This means that when extracting target data, it is necessary to extract information about the road segment where the modified object is located, as well as the preceding and succeeding road segments, including their attribute information. It can be understood that if the road segment only has a preceding or only a succeeding segment, then only the information of that road segment and its preceding or succeeding segments will be extracted.

[0059] Optionally, if the target quality inspection rule is a link space inspection rule, the data extraction range corresponding to the target quality inspection rule is determined to be within a preset distance around the modified object. The preset distance is the maximum value of the extracted link range in the historical execution record of the target quality inspection rule. That is, when extracting target data, all data within a preset distance range around the location of the modified object needs to be extracted. This preset distance can be predetermined; for example, the preset distance corresponding to the target quality inspection rule C in the aforementioned example is 300 meters.

[0060] By extracting target data using the above method, the map data to be inspected is cropped, reducing the amount of data to be inspected. In particular, different cropping of the map data to be inspected is performed for different types of target inspection rules, using the least amount of data to achieve quality inspection, thereby improving quality inspection efficiency.

[0061] Optionally, based on the above embodiments, for the attribute inspection rules and single-segment spatial inspection rules in the target quality inspection rules, since the range of the target data they extract is relatively small, the target data corresponding to these two types can be used together as the first target data, which can also be called small-range cropped data, such as... Figure 4 As shown, data within a small range (401) is extracted from the map data to be inspected as the first target data. For the road chain space inspection rule in the target quality inspection rules, since the range of the extracted target data is relatively large, this type of target data can be used as the second target data, also known as large-range cropped data, such as... Figure 4 As shown, data from a large area (402) in the map data to be inspected is extracted as the first target data. This results in two different sets of target data. For the first target data, attribute inspection rules and single-segment spatial inspection rules are executed; for the second target data, road link spatial inspection rules are executed. Other data undergoes quality inspection wirelessly, improving inspection efficiency.

[0062] By implementing target quality inspection rules, the cropped target data is inspected according to the rules, and a record of problems after quality inspection is generated, which is then provided for manual repair, thereby ensuring the accuracy of the map data.

[0063] Figure 5 This is a schematic diagram of a map data quality inspection device provided according to an embodiment of this disclosure. Figure 5 As shown, the map data quality inspection device 500 includes:

[0064] The acquisition module 501 is used to acquire the map data to be inspected and the target inspection rules.

[0065] The determination module 502 is used to determine the modified objects in the map data to be inspected.

[0066] Extraction module 503 is used to extract target data from the map data to be inspected based on the modified object and the type of target quality inspection rule.

[0067] The quality inspection module 504 is used to perform quality inspection on the target data using the target quality inspection rules.

[0068] In one embodiment, the extraction module 503 includes:

[0069] The first determining unit is used to determine the data extraction range corresponding to the target quality inspection rule based on the type of the target quality inspection rule.

[0070] The extraction unit is used to extract target data from the map data to be inspected based on the modified object and the data extraction range corresponding to the target quality inspection rule.

[0071] In one embodiment, the first determining unit includes:

[0072] The first determining subunit is used to determine the data extraction range corresponding to the target quality inspection rule as the modified object if the type of the target quality inspection rule is an attribute inspection rule.

[0073] In one embodiment, the first determining unit includes:

[0074] The second determining subunit is used to determine the data extraction range corresponding to the target quality inspection rule based on the location of the modified object if the target quality inspection rule is a single-segment spatial inspection rule.

[0075] In one implementation, the second determining subunit is used for:

[0076] If the modified object is located at an intersection, then the data extraction range corresponding to the target quality inspection rule is determined to be all road segments at the intersection;

[0077] If the modified object is located in the middle of the road, then the data extraction range corresponding to the target quality inspection rule is determined to be the road segment where the modified object is located and the road segments before and after it.

[0078] In one embodiment, the first determining unit includes:

[0079] The third determining subunit is used to determine the data extraction range corresponding to the target quality inspection rule as a preset distance around the modified object if the type of the target quality inspection rule is a link space inspection rule. The preset distance is the maximum value of the extracted link range in the historical execution record of the target quality inspection rule.

[0080] In one implementation, the determining module 502 includes:

[0081] The second determining unit is used to perform differential analysis between the map data to be inspected and the previous version of the map data to be inspected, and to determine the modified objects in the map data to be inspected.

[0082] In one implementation, it further includes:

[0083] The third determining unit is used to determine the type of the target quality inspection rule based on the keywords in the target quality inspection rule. The type is attribute inspection rule, single road segment space inspection rule, or road link space inspection rule.

[0084] The apparatus of this disclosure embodiment can be used to perform the quality inspection method for map data in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0085] According to embodiments of the present disclosure, the present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions.

[0086] According to embodiments of this disclosure, this disclosure also provides a computer program product, the program product comprising: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and the at least one processor executing the computer program causing the electronic device to perform the scheme provided in any of the above embodiments.

[0087] Figure 6 This is a schematic block diagram of an electronic device used to implement the map data quality inspection method of embodiments of this disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the disclosure described and / or claimed herein.

[0088] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0089] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of displays, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a map data quality inspection method. For example, in some embodiments, the map data quality inspection method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the map data quality inspection method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the map data quality inspection method by any other suitable means (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0095] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0096] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0097] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0098] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for quality inspection of map data, comprising: obtaining map data to be inspected and a target quality inspection rule; determining an object modified in the map data to be inspected; determining a data extraction range corresponding to the target quality inspection rule according to a type of the target quality inspection rule; extracting target data from the map data to be inspected according to the object modified and the data extraction range corresponding to the target quality inspection rule; the type of the target quality inspection rule is used to represent a data range required by the target quality inspection rule; the type is an attribute inspection rule, a single road segment spatial inspection rule or a road chain spatial inspection rule; if the type of the target quality inspection rule is the attribute inspection rule, determining that the data extraction range corresponding to the target quality inspection rule is the object modified; if the type of the target quality inspection rule is the single road segment spatial inspection rule, determining the data extraction range corresponding to the target quality inspection rule according to a position of the object modified; if the type of the target quality inspection rule is the road chain spatial inspection rule, determining that the data extraction range corresponding to the target quality inspection rule is a preset distance around the object modified, and the preset distance is a maximum value of extracted road chain ranges in historical execution records of the target quality inspection rule; and performing quality inspection on the target data by using the target quality inspection rule.

2. The method of claim 1, wherein, The determining of the data extraction range corresponding to the target quality inspection rule according to the position of the object modified comprises: if the position of the object modified is an intersection, determining that the data extraction range corresponding to the target quality inspection rule is all road segments of the intersection; if the position of the object modified is a road segment, determining that the data extraction range corresponding to the target quality inspection rule is the road segment and road segments before and after the road segment.

3. The method of claim 1 or 2, wherein, The determining of the object modified in the map data to be inspected comprises: performing difference between the map data to be inspected and a previous version of the map data to be inspected to determine the object modified in the map data to be inspected. 4.The method of claim 1 or 2, further comprising: determining the type of the target quality inspection rule according to a keyword in the target quality inspection rule. 5.An apparatus for quality inspection of map data, comprising: an obtaining module configured to obtain map data to be inspected and a target quality inspection rule; a determining module configured to determine an object modified in the map data to be inspected; an extracting module configured to extract target data from the map data to be inspected according to the object modified and a type of the target quality inspection rule; the type of the target quality inspection rule is used to represent a data range required by the target quality inspection rule; the type is an attribute inspection rule, a single road segment spatial inspection rule or a road chain spatial inspection rule; a quality inspection module configured to perform quality inspection on the target data by using the target quality inspection rule; the extracting module comprises: a first determining unit configured to determine a data extraction range corresponding to the target quality inspection rule according to the type of the target quality inspection rule. The extraction unit is configured to extract target data from the map data to be inspected according to the modified object and a data extraction range corresponding to the target quality inspection rule; The first determination unit comprises: The first determination sub-unit is configured to determine that the data extraction range corresponding to the target quality inspection rule is the modified object if the type of the target quality inspection rule is an attribute inspection rule; The second determination sub-unit is configured to determine the data extraction range corresponding to the target quality inspection rule according to a position of the modified object if the type of the target quality inspection rule is a single-section spatial inspection rule; The third determination sub-unit is configured to determine that the data extraction range corresponding to the target quality inspection rule is a preset distance around the modified object, the preset distance being a maximum value of the extracted link range in a historical execution record of the target quality inspection rule if the type of the target quality inspection rule is a link spatial inspection rule.

6. The apparatus of claim 5, wherein, The second determination sub-unit is configured to: determine that the data extraction range corresponding to the target quality inspection rule is all sections of the intersection if the position of the modified object is an intersection; and determine that the data extraction range corresponding to the target quality inspection rule is the section where the modified object is located and the sections before and after the section if the position of the modified object is a road section.

7. The apparatus of claim 5 or 6, wherein, The determination module comprises: The second determination unit is configured to determine a modified object in the map data to be inspected by differentiating the map data to be inspected and a previous version of the map data to be inspected.

8. The apparatus of claim 5 or 6, further comprising: The third determination unit is configured to determine the type of the target quality inspection rule according to a keyword in the target quality inspection rule.

9. An electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-4.

11. A computer program product comprising a computer program, the computer program being executed by a processor to implement the method of any one of claims 1-4.

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