Algorithm detection method, device, equipment and program product

Abstract

Embodiments of the present disclosure relate to an algorithm detection method, device, equipment and program product. A first map data is processed by a to-be-detected algorithm to obtain second map data containing an algorithm target. The second map data is segmented based on the position of the algorithm target to obtain data around the position corresponding to the algorithm target, and then the accuracy of the to-be-detected algorithm is determined based on the data around the position corresponding to the algorithm target. Since the present embodiment divides a large amount of data into a small amount of data, and then detects the to-be-detected algorithm based on the small amount of data, the data can be loaded online for processing and detection, without manual offline operation, thereby improving the detection efficiency and the accuracy of algorithm detection.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of data processing, and in particular, to an algorithm detection method, device, equipment and program product. BACKGROUND

[0002] The related art can process high-precision map data by using an algorithm instead of manual work. The effect of the algorithm has an influence on the accuracy of data processing. Therefore, how to detect the effect of the algorithm and ensure the accuracy of the algorithm is a technical problem to be solved urgently.

[0003] However, the present inventors have found that the detection of the algorithm is mainly completed by manual work at present, which is low in efficiency and prone to errors, and an efficient algorithm detection method is urgently needed. SUMMARY

[0004] To solve the above technical problem, the present disclosure provides an algorithm detection method, device, equipment and program product.

[0005] The first aspect of the embodiments of the present disclosure provides an algorithm detection method, which comprises: processing first map data based on a to-be-detected algorithm to obtain second map data, the second map data comprising at least one algorithm mark; performing segmentation processing on the second map data based on the position of the algorithm mark to obtain data around the position corresponding to the algorithm mark; and determining the accuracy of the to-be-detected algorithm based on the data around the position corresponding to the algorithm mark.

[0006] The second aspect of the embodiments of the present disclosure provides an algorithm detection device, which comprises:

[0007] An algorithm processing module, configured to process first map data based on a to-be-detected algorithm to obtain second map data, the second map data comprising at least one algorithm mark;

[0008] A data segmentation module, configured to perform segmentation processing on the second map data based on the position of the algorithm mark to obtain data around the position corresponding to the algorithm mark;

[0009] A determination module, configured to determine the accuracy of the to-be-detected algorithm based on the data around the position corresponding to the algorithm mark.

[0010] The third aspect of the embodiments of the present disclosure provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method of the first aspect can be implemented.

[0011] The fourth aspect of the embodiments of the present disclosure provides a computer program product stored in a storage medium, and when the program product is executed by a processor of a computer device, the processor executes the method of the first aspect.

[0012] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0013] In the embodiments of the present disclosure, the first map data is processed by the to-be-tested algorithm to obtain second map data containing an algorithm target, the second map data is segmented based on the position of the algorithm target to obtain data around the position corresponding to the algorithm target, and then the accuracy of the to-be-tested algorithm is determined based on the data around the position corresponding to the algorithm target. Since the large amount of data is segmented into a small amount of data, and then the to-be-tested algorithm is detected based on the small amount of data, the data can be loaded online for processing and detection, without manual offline operation, thereby improving the detection efficiency and the accuracy of algorithm detection. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present disclosure and, together with the specification, serve to explain the principles of the present disclosure.

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0016] Figure 1 is a schematic diagram of an algorithm detection scene provided by the embodiments of the present disclosure;

[0017] Figure 2 is a flowchart of an algorithm detection method provided by the embodiments of the present disclosure;

[0018] Figure 3A and Figure 3B is a schematic diagram of a traffic scene at a crossroad provided by the embodiments of the present disclosure;

[0019] Figure 4 is a flowchart of a map data segmentation method provided by the embodiments of the present disclosure;

[0020] Figure 5 is a schematic diagram of a segmentation scene provided by the embodiments of the present disclosure;

[0021] Figure 6 is a structural schematic diagram of an algorithm detection device provided by the embodiments of the present disclosure;

[0022] Figure 7 is a structural schematic diagram of a computer device in the embodiments of the present disclosure. DETAILED DESCRIPTION

[0023] In order to enable a more clear understanding of the above-mentioned purposes, features and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0024] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present disclosure, but the present disclosure can also be implemented in other manners different from those described herein; obviously, the embodiments described in the specification are only a part of the embodiments of the present disclosure, and not all the embodiments.

[0025] In view of the problems in the related art, the embodiments of the present disclosure provide an algorithm detection method. The method can be implemented in a computer program product. When the computer program product runs on a computer device, the computer device executes the algorithm detection method provided by the embodiments of the present disclosure. Wherein, the computer device in the embodiments of the present disclosure can be embodied as any kind of device with computing and data processing capabilities, such as a computer, a server, etc., but is not limited to a computer and a server.

[0026] Figure 1 is a schematic diagram of an algorithm detection scenario provided by the embodiments of the present disclosure. As shown in Figure 1 The storage device 11 stores the map data to be processed (hereinafter referred to as first map data for convenience of distinction), which exemplarily can include at least one of the following: map data of a country, map data of a province, map data of a region, map data of multiple sheets constituting an electronic map, and historical map data reported as errors. Wherein, the sheet in the embodiments of the present disclosure can be understood as a mapping of a certain geographical area on an electronic map, and one electronic map can be obtained by splicing multiple sheets.

[0027] The computer device 12 is equipped with an algorithm to be detected (referred to as a to-be-detected algorithm for short) and a computer program product capable of implementing the method of the embodiments of the present disclosure. Wherein, the to-be-detected algorithm can be understood as an algorithm that can replace manual processing of map data, such as an algorithm for adding a stop line data near a traffic light intersection, etc., but is not limited to the algorithms listed here.

[0028] Computer device 12 can obtain first map data from storage device 11, and then call the algorithm under test by running the aforementioned computer program product. The algorithm under test processes the first map data. When processing the first map data, the algorithm under test marks the processed locations with algorithm labels. That is, the map data obtained by the algorithm under test (hereinafter referred to as the second map data) includes at least one algorithm label. The computer program product can segment the second map data based on the location of the algorithm label, and then segment the larger second map data to obtain smaller data around the location of the algorithm label. The accuracy of the algorithm under test can then be tested based on the smaller data. Since small data can be processed online using online tools without the need for offline manual processing, the detection efficiency and accuracy of algorithm detection are improved, and labor costs are saved.

[0029] The methods provided in this disclosure will be described below with reference to exemplary embodiments.

[0030] Figure 2 This is a flowchart of an algorithm detection method provided in an embodiment of this disclosure. This method can be executed by a computer device as described above. Figure 2 As shown, the algorithm detection method provided in this disclosure embodiment may include the following steps:

[0031] Step 201: Process the first map data based on the algorithm to be tested to obtain the second map data, which includes at least one algorithm marker.

[0032] The algorithm markers in this embodiment are used to mark the locations that have been processed by the algorithm.

[0033] In this embodiment of the disclosure, the first map data can be understood as the map data that needs to be processed by the algorithm under test, and the second map data can be understood as the map data obtained after the algorithm under test processes the first map data. The algorithm under test can add, delete, or modify data in the first map data, but is not limited to operations such as adding, deleting, and modifying. Locations processed by the algorithm under test will have algorithm markers inserted. For example, Figure 3A and Figure 3B This is a schematic diagram of a traffic scene at an intersection provided in an embodiment of this disclosure. Figure 3A The map data corresponding to the intersection (i.e., the first map data) does not include the data for the stop lines. In this scenario, the test algorithm described in this embodiment can be used to add stop lines to the intersection according to preset rules. The data for the stop lines (such as location, length, width, etc.) will be inserted into the map data of the intersection, and an algorithm marker will be inserted at the insertion location, thereby obtaining new map data, i.e., the second map data. The traffic scenario corresponding to the second map data is...Figure 3B The above is only an example of the to-be-tested algorithm, and is not the only limitation.

[0034] In step 202, the second map data is segmented based on the position of the algorithm mark to obtain data around the position corresponding to the algorithm mark.

[0035] In some embodiments of the present disclosure, the position of the algorithm mark can be understood as the position of the algorithm mark in the second map data. In this case, the data within the preset range around the algorithm mark can be segmented from the second map data based on the position of the algorithm mark. For example, the second map data consists of 100 rows of data, and there is an algorithm mark on the 50th row. The data of the 50th row, as well as the data of the first 20 rows and the last 20 rows of the 50th row, can be segmented from the second map data to obtain the data around the position of the algorithm mark on the 50th row. Of course, this is only an example and is not the only limitation.

[0036] In some embodiments of the present disclosure, the position of the algorithm mark can also be understood as the geographical position corresponding to the algorithm mark. The geographical position corresponding to the algorithm mark can be understood as the geographical position corresponding to the data marked by the algorithm mark (such as the data of the stop line), or the algorithm mark itself can also be a special map data, which also has a position attribute. In this case, the geographical position corresponding to the algorithm mark can also be understood as the geographical position corresponding to the algorithm mark itself.

[0037] When the position of the algorithm mark is understood as the geographical position corresponding to the algorithm mark, the method of segmenting the second map data based on the position of the algorithm mark can include segmenting the map data within the preset range around the geographical position corresponding to the algorithm mark from the second map data. For example, in FIG. 3, it is assumed that the geographical position corresponding to the algorithm mark is the position of the stop line. The map data within 1000 meters around the stop line can be obtained from the second map data based on the position of the stop line, that is, the data within the preset range around the position corresponding to the algorithm mark. Of course, this is only an example and is not the only limitation. In fact, the distances of the algorithm mark corresponding to the geographical position spreading in each direction can also be uneven, that is, the outwardly expanding area can not be a regular circular area, but can be an irregular area.

[0038] In the present disclosure, the data segmented based on different algorithm marks can be stored in different layers. This facilitates the management and viewing of data.

[0039] The second map data is segmented based on the positions of the algorithm markers in the embodiments of the present disclosure, and the large amount of map data can be segmented into small amount of local map data, so that the processing speed of the data is improved. Meanwhile, the small amount of data can be transmitted and processed online, without the need of offline artificial processing, so that the labor cost is saved and the problem of instability and error in artificial processing can be avoided. Moreover, the small amount of local map data includes the data processed by the to-be-tested algorithm, and the accuracy of the to-be-tested algorithm can be accurately detected through the small amount of local map data.

[0040] In step 203, the accuracy of the to-be-tested algorithm is determined based on the data around the position corresponding to the algorithm marker.

[0041] In practice, the web editor is a front-end visualization platform of map data. The web editor is an online tool, which can be used to load and display map data with small amount of data. Compared with offline data visualization tools such as QGIS, the web editor has the characteristics of low threshold and easy operation. In the embodiments of the present disclosure, the data around the position corresponding to the algorithm marker is small amount of map data segmented from the second map data with large amount of data, which can be loaded and processed through the web editor. Therefore, in one implementation of the embodiments of the present disclosure, the data around the position corresponding to the algorithm marker can be loaded into the web editor, and the corresponding object (such as a person or a machine) can mark the accuracy of the data around the position corresponding to the algorithm marker in the web editor. For example, the data processed by the to-be-tested algorithm is correct, which can be marked by a label, and the incorrect data can be marked by another label. The marking information of the marking object is recorded by the web editor, and then the accuracy of the to-be-tested algorithm can be determined based on the marking information. For example, the to-be-tested algorithm generates 100 algorithm markers, and the data around 90 algorithm markers are marked as correct, so the accuracy of the to-be-tested algorithm is 90%. Of course, this is only an example and not the only limitation.

[0042] In the embodiments of the present disclosure, the first map data is processed by the to-be-tested algorithm to obtain the second map data containing algorithm markers, the second map data is segmented based on the positions of the algorithm markers to obtain the data around the position corresponding to the algorithm marker, and then the accuracy of the to-be-tested algorithm is determined based on the data around the position corresponding to the algorithm marker. Since the large amount of data is segmented into small amount of data in the embodiments of the present disclosure, and then the to-be-tested algorithm is detected based on the small amount of data, the data can be loaded online for processing and detection, without the need of offline artificial operation, so that the detection efficiency and the accuracy of algorithm detection are improved.

[0043] Figure 4 is a flowchart of a map data segmentation method provided by the embodiments of the present disclosure, as shown in Figure 4As shown, in an implementation of the embodiment of the present disclosure, the second map data can be segmented by the following method:

[0044] In step 401, a preset number of algorithm tags are determined from the second map data as target algorithm tags.

[0045] In the embodiment of the present disclosure, the preset number of algorithm tags can be randomly selected, or can be selected according to a preset rule, such as selecting algorithm tags in a preset scene (such as a crossroads, a curved road, a fork, etc.) according to a preset rule. It should be noted that when the algorithm tags in the preset scene are selected according to the preset rule, if the number of algorithm tags in the preset scene is less than the preset number, the actual number of selected algorithm tags is used, and if the number of algorithm tags in the preset scene exceeds the preset number, only the preset number of algorithm tags are selected.

[0046] Compared with the traditional manual extraction of algorithm tags, the embodiment of the present disclosure can automatically determine a preset number of algorithm tags from the second map data, preventing the problem of unstable data caused by manual extraction.

[0047] In step 402, based on the position of the target algorithm tag, data around the position corresponding to the target algorithm tag is segmented from the second map data.

[0048] In the embodiment of the present disclosure, the position of the target algorithm tag can be understood as the position of the target algorithm tag in the second map data, or can also be understood as the geographical position corresponding to the target algorithm tag. Taking the geographical position corresponding to the target algorithm tag as an example, in an implementation of the embodiment of the present disclosure, the geographical position corresponding to the target algorithm tag can be taken as the center to segment data within a preset range (such as 1000 meters) around the geographical position from the second map data.

[0049] By expanding outward from the geographical position corresponding to the target algorithm tag, the map data in the expanded range is obtained, which can ensure the comprehensiveness of the data in the processing scene corresponding to the to-be-tested algorithm, and is beneficial to improve the accuracy of algorithm detection. For example, in the scene shown in FIG. 3, the expanded range obtained by expanding outward from the position of the stop line can not only include the data of the stop line, but also include the data of at least part of the intersection around the stop line, and then the position of the stop line added can be accurately judged according to the intersection data, thereby improving the accuracy of algorithm detection.

[0050] In some other embodiments, considering that the expanded range can split the data of a complete object (such as a lane line, a lane arrow, etc.) into two or more parts, destroying the integrity of the data, the data located outside the expanded range (i.e., outside the preset range around the corresponding geographical position of the target algorithm mark) and having relevance with the data inside the expanded range can also be segmented from the second map data to form a data set with the data inside the expanded range, wherein in the embodiments of the present disclosure, the data of the same object can be considered to have relevance, such as the data on the same road arrow, the data on the same road line, the data on the same stop line, etc.

[0051] For example, Figure 5 is a schematic diagram of a scene provided by an embodiment of the present disclosure, as Figure 5 shown, the preset range around the corresponding geographical position of the target algorithm mark a contains part of a road arrow, and another part of the road arrow is located outside the preset range. In this case, the data of the part of the road arrow located outside the preset range is also segmented to form a data set with the map data inside the preset range.

[0052] In the embodiments of the present disclosure, the data in the preset range around the corresponding geographical position of the target algorithm mark and the data outside the preset range and having relevance with the data inside the preset range are segmented as a data set, which can ensure the data integrity of the scene processed by the to-be-tested algorithm and improve the accuracy of algorithm detection.

[0053] Figure 6 is a structural schematic diagram of an algorithm detection device provided by an embodiment of the present disclosure. The algorithm detection device can be understood as the computer device or part of the functional modules in the computer device in the above embodiments. As Figure 6 shown, the algorithm detection device 60 includes:

[0054] An algorithm processing module 61 is configured to process the first map data based on the to-be-tested algorithm to obtain second map data, wherein the second map data includes at least one algorithm mark.

[0055] A data segmentation module 62 is configured to segment the second map data based on the position of the algorithm mark to obtain data around the position.

[0056] A determination module 63 is configured to determine the accuracy of the to-be-tested algorithm based on the data around the position.

[0057] In a feasible implementation, the data segmentation module 62 includes:

[0058] A determination subunit is configured to determine a preset number of algorithm marks from the second map data as target algorithm marks.

[0059] The segmentation subunit is configured to segment data around the position from the second map data based on the target algorithm.

[0060] In an implementation, the segmentation subunit is configured to segment data within a preset range around the geographical position corresponding to the target algorithm from the second map data.

[0061] In an implementation, the segmentation subunit is further configured to segment data outside the preset range and having a correlation with the data within the preset range from the second map data based on the correlation between the data.

[0062] In an implementation, the determining module 63 is configured to load the data around the position into a web editor, obtain annotation information of the data from the target algorithm based on the web editor, and determine the accuracy of the target algorithm based on the annotation information.

[0063] In an implementation, the first map data includes at least one of map data of at least one map sheet in a map and historical error reporting map data.

[0064] The apparatus provided by the embodiments of the present disclosure can perform the method in any of the embodiments of the present disclosure. Figures 1-5 The apparatus provided by the embodiments of the present disclosure can perform the method in any of the embodiments of the present disclosure.

[0065] The embodiments of the present disclosure further provide a computer device including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method in any of the above method embodiments can be implemented.

[0066] An example of the computer device is shown in FIG. 14. Figure 7 FIG. 14 is a structural schematic diagram of a computer device according to an embodiment of the present disclosure. Figure 7 FIG. 14 is a structural schematic diagram of a computer device according to an embodiment of the present disclosure. Figure 7 The computer device shown in FIG. 14 is merely an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0067] As shown in FIG. 14, the computer device 1400 can include a processor 1401, a memory 1402, a communication interface 1403, and a communication bus 1404. Figure 7As shown, the computer device 1400 can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1401 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1402 or loaded into a random access memory (RAM) 1403 from a storage device 1408. Various programs and data required by the computer device 1400 for operation are also stored in the RAM 1403. The processing device 1401, the ROM 1402, and the RAM 1403 are connected to each other through a bus 1404. An input / output (I / O) interface 1405 is also connected to the bus 1404.

[0068] Generally, the following devices can be connected to the I / O interface 1405: input devices 1406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 1407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1408 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1409. The communication devices 1409 can allow the computer device 1400 to communicate with other devices wirelessly or through wires to exchange data. Although Figure 7 The computer device 1400 is shown with various devices, but it should be understood that not all of the shown devices are required to be implemented or present. More or fewer devices can alternatively be implemented or present.

[0069] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication devices 14014, or installed from the storage devices 1408, or installed from the ROM 1402. When the computer program is executed by the processing device 1401, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0070] It should be noted that the computer readable medium in the above disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0071] The computer readable medium described above can be contained in the computer device described above; or can exist separately and not be assembled into the computer device.

[0072] The computer readable medium described above carries one or more programs, when the one or more programs are executed by the computer device, the computer device: processes the first map data based on the to-be-tested algorithm to obtain second map data, the second map data includes at least one algorithm mark; based on the position of the algorithm mark, the second map data is segmented to obtain data around the position corresponding to the algorithm mark; based on the data around the position corresponding to the algorithm mark, the accuracy of the to-be-tested algorithm is determined.

[0073] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0074] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0075] The units described in the embodiments of the present disclosure can be implemented by hardware, software, or a combination of hardware and software. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0076] The functions described in this specification can be implemented in part or in whole through one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0077] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0078] The embodiments of the present disclosure further provide a computer readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a processor, the method in any of the embodiments of the present disclosure is implemented. Figures 1-5 The embodiments of the present disclosure further provide a computer readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a processor, the method in any of the embodiments of the present disclosure is implemented.

[0079] The embodiments of the present disclosure further provide a computer program product, which is stored in a storage medium. When the program product is executed by a processor of a computer device, the processor executes the method in any of the embodiments of the present disclosure. Figures 1-5 The embodiments of the present disclosure further provide a computer program product, which is stored in a storage medium. When the program product is executed by a processor of a computer device, the processor executes the method in any of the embodiments of the present disclosure.

[0080] It should be noted that, in this document, the terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0081] The foregoing is merely illustrative of the various implementations of the present disclosure and the general principles thereof. Numerous modifications can be made to these illustrations, and equivalents can be substituted therefor, without departing from the scope of the present disclosure. The specific embodiments commensurate with the specific application are intended to be illustrative only and not limiting of the scope of the application as set forth in the following claims.

Claims

1. An algorithmic detection method, wherein, The method comprises: processing first map data based on a to-be-tested algorithm to obtain second map data, wherein the second map data comprises at least one algorithm mark, and the algorithm mark is used for marking a position processed by the algorithm; segmenting the second map data based on a position of the algorithm mark to obtain data around the position; and determining accuracy of the to-be-tested algorithm based on the data around the position.

2. The method of claim 1, wherein, The segmentation of the second map data based on the position of the algorithm mark to obtain the data around the position comprises: determining a preset number of algorithm marks from the second map data as target algorithm marks; and segmenting the second map data based on positions of the target algorithm marks to obtain data around the positions.

3. The method of claim 2, wherein, The segmentation of the second map data based on the positions of the target algorithm marks to obtain the data around the positions comprises: segmenting the second map data based on a geographical position corresponding to the target algorithm mark to obtain data within a preset range around the geographical position.

4. The method of claim 3, wherein, The segmentation of the second map data based on the positions of the target algorithm marks to obtain the data around the positions further comprises: segmenting the second map data based on an association relationship between data to obtain data outside the preset range and having an association relationship with the data within the preset range.

5. The method of claim 1, wherein, The determination of the accuracy of the to-be-tested algorithm based on the data around the position comprises: loading the data around the position into a web editor, obtaining annotation information of an annotation object on the data based on the web editor, and determining the accuracy of the to-be-tested algorithm based on the annotation information. The first map data comprises at least one of the following: map data of at least one sheet in a map, and historically reported error map data.

6. The method of any one of claims 1-5, wherein, The method comprises:

7. An algorithm detection apparatus, wherein, processing first map data based on a to-be-tested algorithm to obtain second map data, wherein the second map data comprises at least one algorithm mark, and the algorithm mark is used for marking a position processed by the algorithm; segmenting the second map data based on a position of the algorithm mark to obtain data around the position; and determining accuracy of the to-be-tested algorithm based on the data around the position. The data segmentation module comprises:

8. The apparatus of claim 7, wherein, a determining subunit configured to determine a preset number of algorithm marks from the second map data as target algorithm marks; and a segmentation subunit configured to segment the second map data based on positions of the target algorithm marks to obtain data around the positions. The method comprises:

9. A computer device, comprising: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method according to any one of claims 1-6 is implemented. The program product is stored in a storage medium, and when the program product is executed by a processor of a computer device, the processor executes the method according to any one of claims 1-6.

10. A computer program product, characterised in that, ​

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