Method, apparatus, electronic device and storage medium for intersection matching

Abstract

The present disclosure provides a method, device, electronic device, and storage medium for intersection matching, relating to the field of computer image technology, and in particular, to the field of electronic maps. A specific implementation scheme comprises: obtaining attribute information of intersections in a first map and a second map, respectively; screening out an intersection pair that meets a first preset condition based on the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map; calculating a confidence level based on the attribute information of the intersection pair; and determining the first intersection and the second intersection in the intersection pair as matching intersections if the confidence level meets a second preset condition. The present disclosure fully utilizes the characteristics of intersections to quickly and accurately automatically extract matching intersections from two sets of maps, laying a good foundation for subsequent map merging.

Description

Technical Field

[0001] The present disclosure relates to the field of computer image technology, in particular to the field of electronic maps, and more specifically to a method, device, electronic device, and storage medium for intersection matching. Background Art

[0002] Before merging two sets of maps (or electronic maps, traffic road networks) containing overlapping areas, georeferencing is required. Before georeferencing, it is necessary to find several actually identical locations (also called homonymous points) in the two sets of maps with positional deviations as registration points.

[0003] Existing techniques require manually searching for alignment points by overlaying the two maps. However, this manual method is time-consuming and labor-intensive, requiring the naked eye to search for matching locations among a vast number of roads. This is inefficient and costly. Furthermore, manually searching for matching locations is prone to errors. If erroneous points of the same name are mistaken for actual identical locations, this can lead to significant errors in subsequent alignment. Summary of the Invention

[0004] The present disclosure provides a method, device, electronic device and storage medium for intersection registration, which can quickly and accurately automatically extract matching intersections from two sets of maps for subsequent map overlay registration.

[0005] According to one aspect of the present disclosure, a method for intersection matching is provided, which may include the following steps:

[0006] Obtain attribute information of intersections in the first map and the second map respectively;

[0007] Filtering an intersection pair that meets a first preset condition according to the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map;

[0008] Calculating confidence based on the attribute information of the intersection pair;

[0009] In a case where the confidence level meets a second preset condition, the first intersection and the second intersection in the intersection pair are determined as matching intersections.

[0010] According to another aspect of the present disclosure, a road intersection matching device is provided, the device comprising:

[0011] An acquisition module, configured to acquire attribute information of intersections in the first map and the second map respectively;

[0012] a first screening module, configured to screen out an intersection pair that meets a first preset condition according to the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map;

[0013] A calculation module, used for calculating the confidence level based on the attribute information of the intersection pair;

[0014] The second screening module is configured to determine the first intersection and the second intersection in the intersection pair as matching intersections if the confidence meets a second preset condition.

[0015] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method in any embodiment of the present disclosure.

[0019] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to cause a computer to execute the method in any embodiment of the present disclosure.

[0020] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instruction, wherein the computer program / instruction implements the method in any embodiment of the present disclosure when executed by a processor.

[0021] The disclosed technology first obtains attribute information for intersections in both maps. Based on this attribute information, it then selects intersection pairs that meet a first pre-set condition. A confidence score is then calculated based on the attribute information for these intersection pairs, and matching intersections are selected based on the confidence score. This technology can quickly and accurately find matching intersections in both maps, making it less likely to miss any, and can be automated without manual effort. The matching intersections found can then be used for subsequent map registration and merging.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0024] Figure 1 is a flowchart of a method for intersection matching according to an embodiment of the present disclosure;

[0025] Figure 2 is a schematic diagram of an intersection according to an embodiment of the present disclosure;

[0026] Figure 3 is a schematic diagram of an intersection according to another embodiment of the present disclosure;

[0027] Figure 4 is a schematic diagram of road angle calculation according to an embodiment of the present disclosure;

[0028] Figure 5 is a schematic diagram of an intersection according to yet another embodiment of the present disclosure;

[0029] Figure 6 is a flowchart of a method for intersection matching according to another embodiment of the present disclosure;

[0030] Figure 7 is a schematic diagram of an intersection according to an embodiment of the present disclosure;

[0031] Figure 8 is a schematic diagram of an intersection pair according to another embodiment of the present disclosure;

[0032] Figure 9 is a schematic diagram of an intersection according to yet another embodiment of the present disclosure;

[0033] Figure 10 is a schematic diagram of an apparatus for intersection matching according to an embodiment of the present disclosure;

[0034] Figure 11 is a schematic diagram of an apparatus for intersection matching according to another embodiment of the present disclosure;

[0035] Figure 12 It is a block diagram of an electronic device used to implement the intersection matching method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0037] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The term "at least one" in this article means any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C, can mean including any one or more elements selected from the set consisting of A, B, and C. The terms "first" and "second" in this article refer to multiple similar technical terms and distinguish them, and do not mean to limit the order or to limit to only two. For example, the first feature and the second feature refer to two categories / two features. The first feature can be one or more, and the second feature can also be one or more.

[0038] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0039] When registering multiple images, it's necessary to find corresponding registration points. When performing map registration, intersections are often chosen as registration points because they are easily identifiable and accurately located. During map registration, it's necessary to find identical intersections across multiple maps—in other words, matching intersections.

[0040] According to an embodiment of the present disclosure, a method for intersection matching is provided. Figure 1 FIG. 1 is a flow chart of a method for intersection matching according to an embodiment of the present disclosure, which specifically includes:

[0041] S101, respectively obtaining attribute information of intersections in a first map and a second map;

[0042] In one example, the first map and the second map are two maps to be geo-referenced. All intersections (also called junctions) in the first map and the second map are traversed, and attribute information of the intersections is recorded.

[0043] In one example, the attribute information includes at least one of structure attribute information, location attribute information, and name attribute information. Specifically, the name attribute information is the name of the intersection or the road that constitutes the intersection, such as Figure 2 As shown, the names of the roads that make up the intersection include: "Y154" or "Mayu Road", and "Y161" or "Maqin Road"; the location attribute is the specific geographical location of the intersection, such as the specific longitude and latitude; the structural attribute information is the number and angle of the roads that make up the intersection, such as Figure 2At the intersection shown, there are three road branches at the intersection. The angles between the three roads and the east direction are 10°, 103°, and 230°, respectively. In the specific calculation process, the angle between the road and a fixed direction is generally calculated, such as the angle with the east direction or the north direction. However, it is not recommended to directly calculate the angle between several roads, such as Figure 3 As shown, the road angles involved in intersection A and intersection B are both 90°, but intersection A and intersection B are obviously not actually the same matching intersections. Therefore, when reading the road angles, it is not recommended to obtain the angles between several roads.

[0044] In one example, Figure 4 As shown in the figure, when the road forming the intersection is curved, the shape points on the road are generally used to segment the road, and the corresponding angle is calculated based on the segment. In this way, only line segments are used when calculating the angle, rather than curves, making the calculated road angle more accurate. Figure 4 The curved road is divided into small segments, and the angle between each segment and the north direction is calculated. Road 1 corresponds to angle 1, road 2 corresponds to angle 2, and road 3 corresponds to angle 3. In the above embodiment, the name and location of the intersection, as well as the number or angle of the roads that make up the intersection, are obtained to provide a comprehensive and comprehensive description of the nature of the intersection from multiple perspectives. If any of these attributes is unavailable, the intersection can be described using the remaining attributes, minimizing the impact of the lack of any attribute information on the subsequent determination of intersection similarity based on the attribute information.

[0045] S102: Filtering out an intersection pair that meets a first preset condition based on the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map;

[0046] In one example, an intersection is arbitrarily selected from the first map as the first intersection, and then compared with all the intersections in the second map in turn to see whether the first preset condition is met, and the first intersection that meets the first preset condition and the second intersection from the second map are determined as an intersection pair; after one round of comparison, another intersection is selected from the first map to be compared with all the intersections in the second map, and this cycle is repeated until all the intersections in the first map are compared with all the intersections in the second map; it should be emphasized that there can be multiple intersection pairs that meet the first preset condition, and there can be some duplication. For example, as long as the first preset condition is met, intersection A1 in the first map and intersection B1 in the second map can form an intersection pair. At the same time, A1 in the first intersection and B2 in the second map can also form another intersection pair.

[0047] In one example, in the process of determining an intersection pair, the first preset condition is whether the attribute information of the two intersections meets the preset range, which can be specifically whether the structural attribute information, location attribute information or name attribute information meets the preset range. The preset range specifically includes: whether the names of the first intersection and the second intersection or the names of related roads are consistent, whether the number of branches is the same, and whether the angles are similar. Generally, in operation, the attribute information of the intersection contains multiple attribute items. As long as there is an attribute item that meets the preset range, the corresponding first intersection and second intersection are determined to be an intersection pair that meets the first preset condition. For example, Figure 2 The first intersection found in the first map is shown, and the second intersection found in the second map is shown. Figure 5 As shown, we can see that the road names that make up this intersection are "Y154" and "Mayu Road", and the angles are 11°, 105°, and 235°, respectively. It is obvious that the second intersection and Figure 2 Compared with the first intersection in , if the number of forks is the same, the road name is the same, and the angle is similar, then the two are determined to be an intersection pair that meets the first preset condition.

[0048] S103: Calculate the confidence level based on the attribute information of the intersection pair.

[0049] In one example, after selecting intersection pairs that meet a first preset condition, a confidence level is calculated based on the attribute information of the first and second intersections in the intersection pair. In actual operation, if the attribute information includes multiple attribute items, the confidence level is calculated based on the number of attribute items that meet the first preset condition. The number of attribute items that meet the first preset condition is proportional to the confidence level, i.e., the higher the degree of match, the higher the confidence level. For example, an intersection pair that matches both the road name and angle attribute items has a higher confidence level, while an intersection pair that only matches the road name or the angle has a lower confidence level. Furthermore, parameter values corresponding to each attribute item in the attribute information can be preset to characterize the similarity between the two intersections. The sum of all parameter values is the confidence level. For example, if the road names or number of intersections are the same, each is scored 1 point; if the intersection positions or angles differ within a predetermined range, each is scored 1 point; if the two intersections in a pair have the same road name, the same number of intersections, and the position difference is within a predetermined range, then 3 points are awarded; if the two intersections in another pair only have the same number of intersections, and all other differences are different, then 1 point is awarded; compared to the previous pair, the confidence level of the latter pair is lower. The confidence level can be flexibly set according to the specific application environment and is not specifically limited here. Using the above embodiment, the confidence level can be calculated based on multiple attribute items in the attribute information. The confidence level can well reflect the degree of match between the two intersections, helping to quickly screen out paired intersections with a high degree of match.

[0050] S104: If the confidence level meets a second preset condition, determine the first intersection and the second intersection in the intersection pair as matching intersections.

[0051] In one example, the calculated confidence levels can be sorted from high to low, and a percentage can be preset as a second preset condition. The top intersection pairs that meet the preset percentage are then selected, for example, the top 20% of the ranking. Alternatively, a preset confidence level can be set to select only those intersection pairs with a confidence level greater than the preset value. Finally, the intersections on the first map and the intersections on the second map in the selected intersection pairs are determined as matching intersections.

[0052] Using the above embodiment, all intersections in both maps are first identified and their corresponding attribute information is recorded. Then, based on this attribute information, roughly matching intersection pairs are screened. A confidence score is calculated based on the attribute information of these intersection pairs, and finally matching intersection pairs are screened based on the confidence score. This technology can quickly and accurately find matching intersection pairs in both maps, making it less likely to miss any, and it can be automated without manual intervention.

[0053] Figure 6 FIG. 1 is a flow chart of a method for intersection matching according to another embodiment of the present disclosure, which specifically includes:

[0054] S601, respectively obtaining attribute information of intersections in the first map and the second map;

[0055] S602: Filtering out an intersection pair that meets a first preset condition based on the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map;

[0056] S603, calculating the confidence level based on the attribute information of the intersection pair;

[0057] The above steps S601-S603 are the same as steps S101-S103 and will not be repeated here.

[0058] S604: If the confidence level meets the second preset condition, calculate the number of intersection pairs;

[0059] In one example, the confidence level of each intersection pair is determined to determine whether it meets the second preset condition. The specific process is as described in step S104 and will not be repeated here. The number of intersection pairs that meet the second preset condition is calculated. Due to the influence of road and street name accuracy, the intersection pairs that meet the second preset condition may not be the actual matching intersection pairs. If there are multiple intersection pairs that meet the second preset condition, statistical trends can be used for further screening.

[0060] S605: When there are multiple intersection pairs, obtain an offset rule based on the attribute information of the intersection pairs;

[0061] In one example, when there are multiple intersection pairs, the offset pattern of the area where the intersection pairs are located is obtained based on the attribute information of the intersection pairs, wherein the area where the intersection pairs are located can be an area within a fixed radius with the intersections included in the intersection pairs as the center. The offset pattern refers to the difference between the two intersections in the intersection pair, generally referring to the distance and angle between the two intersection pairs. Figure 7 In the area shown, the dotted line represents the first map, and the solid line represents the second map. The displayed area includes four intersection pairs: A1-B1, A2-B2, A3-B3, and A4-B4. It can be seen that the offsets of these four intersection pairs are similar. The offset pattern in the area is represented by numerical values, specifically including: calculating the difference based on the attribute information of the intersection pair, generally calculating the distance and angle at the intersection position, for example Figure 7 The distances and angles of the four intersection pairs are shown in the following table:

[0062]

[0063] Next, the distribution range of the difference is analyzed to obtain the offset pattern, wherein the offset pattern includes the difference range and the ratio of the number of intersection pairs falling within the difference range to the number of intersection pairs whose confidence meets the preset value. For example, if the distance difference range 1 is 48-53 meters and the distance difference range 2 is 53-58 meters, then there are 3 intersections falling within the distance difference range 1, accounting for 75%; there is 1 intersection falling within the distance difference range 2, accounting for 25%. The angle-based difference range and proportion are similar to the distance-based method and will not be repeated here. The obtained range and corresponding proportion are the offset pattern. Using the above embodiment, the information between the intersection pairs that have been preliminarily matched is used to obtain the offset pattern between the two maps. The offset pattern can be described by the distance difference and the angle difference. Through the distance difference and the angle difference, the offset pattern between the two maps can be better reflected, and preparations can be made for the subsequent screening of actually identical intersection pairs based on the pattern.

[0064] S606: Determine the first intersection and the second intersection in the intersection pair selected based on the offset rule as matching intersections.

[0065] In one example, if the offset of a certain intersection pair is found to be significantly different from that of the other intersection pairs, the intersection pair with the large offset will be deleted. Figure 8 The intersection pair A5-B5 shown has a distance difference of 110 meters and an angle of 45°, which is much larger than the other intersection pairs. Therefore, this intersection pair is deleted.

[0066] In one example, based on the offset pattern, a corresponding first difference range can be obtained when the number ratio meets a preset value. For example, the preset value is set to 75%, and then the offset pattern that any 75% of the intersection pairs meet is calculated. It is found that the offset distances of 75% of the intersection pairs are mainly concentrated between 45 and 55 meters, and this distance is used as the first difference range. The first intersection and the second intersection in the intersection pair that fall within the first difference range are then determined as matching intersections. The distance differences of all intersection pairs are then counted, and the two intersections included in the intersection pair that fall between 45 and 55 meters are determined as the final matching intersections. Using the above embodiment, the offset pattern of a preset percentage of intersection pairs can be accurately counted, and then the approximate deformation pattern between the two maps can be obtained based on the offset pattern, which is used to further accurately screen matching intersections.

[0067] In one example, Figure 9 As shown, intersection A from the first map forms an intersection pair with intersections B1 and B2 on the second map. Measurements show that the distance between A and B1 is 500 meters, with an angle of 75° relative to due east, and the distance between A and B2 is 350 meters, with an angle of 40° relative to due east. Statistics of the remaining intersection pairs in the area reveal that 80% of these pairs have distance differences between 480 and 520 meters, with angles between 70° and 80°. This offset pattern indicates that intersection B1, not B2, actually matches intersection A.

[0068] Using the above embodiment, after obtaining preliminary matching intersections that meet the preset criteria, in order to further and more accurately screen for actual matching intersections, a statistical trend-based screening can be performed within a fixed area. Specifically, based on the matching information found in the current area, roughly similar offset patterns, including offset direction and distance, are statistically analyzed. Intersection pairs that do not conform to these patterns are then discarded. This fully utilizes all the attributes of the intersection pairs, automatically extracting matching intersections and ensuring more accurate extraction results.

[0069] like Figure 10 As shown, an embodiment of the present disclosure provides a device 1000 for intersection matching, which includes:

[0070] An acquisition module 1001 is configured to acquire attribute information of intersections in the first map and the second map respectively;

[0071] A first screening module 1002 is configured to screen out an intersection pair that meets a first preset condition based on the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map;

[0072] A calculation module 1003 is used to calculate the confidence level based on the attribute information of the intersection pair;

[0073] The second screening module 1004 is configured to determine the first intersection and the second intersection in the intersection pair as matching intersections if the confidence level meets a second preset condition.

[0074] like Figure 11 As shown, in the embodiment of the present disclosure, the second screening module 1004 includes:

[0075] The calculation unit 1101 is configured to calculate the number of the intersection pairs when the confidence level meets the second preset condition;

[0076] The offset rule unit 1102 is configured to obtain an offset rule based on attribute information of the intersection pairs when there are multiple intersection pairs;

[0077] The screening unit 1103 is configured to determine the first intersection and the second intersection in the intersection pair selected based on the offset rule as matching intersections.

[0078] In one example, the offset rule unit 1102 is used to:

[0079] Calculate the difference value based on the attribute information of the intersection pair;

[0080] The distribution range of the difference is analyzed to obtain a deviation rule, wherein the deviation rule includes the difference range and the ratio of the number of intersection pairs falling within the difference range to the number of intersection pairs whose confidence meets the second preset condition.

[0081] In one example, the screening unit 1103 is used to:

[0082] Based on the offset rule, when the number ratio meets the preset value, a corresponding first difference range is obtained;

[0083] The first intersection and the second intersection in the intersection pair that fall within the first difference range are determined as matching intersections.

[0084] In one example, the acquisition module 1001 is used to:

[0085] At least one of structure attribute information, location attribute information, and name attribute information of the intersection in the first map and the second map is obtained respectively.

[0086] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0087] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0088] In one example, the calculation module 1003 is used to:

[0089] In the case that the attribute information includes multiple attribute items, the confidence level is calculated according to the number of attribute items that meet the first preset condition, wherein the number of attribute items that meet the first preset condition is proportional to the confidence level.

[0090] Figure 12 A schematic block diagram of an example electronic device 1200 that can be used to implement embodiments of the present disclosure is shown. 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 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] like Figure 12 As shown, the device 1200 includes a computing unit 1201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1202 or a computer program loaded from a storage unit 1208 into a random access memory (RAM) 1203. Various programs and data required for the operation of the device 1200 can also be stored in the RAM 1203. The computing unit 1201, the ROM 1202, and the RAM 1203 are connected to each other via a bus 1204. An input / output (I / O) interface 1205 is also connected to the bus 1204.

[0092] Various components in device 1200 are connected to I / O interface 1205, including an input unit 1206, such as a keyboard and mouse; an output unit 1207, such as various types of displays and speakers; a storage unit 1208, such as a magnetic disk and optical disk; and a communication unit 1209, such as a network card, a modem, a wireless communication transceiver, etc. Communication unit 1209 allows device 1200 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0093] The computing unit 1201 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 1201 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized 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 1201 performs the various methods and processes described above, such as the intersection matching method. For example, in some embodiments, the intersection matching method (including the steps of obtaining attribute information and screening intersection matches) can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1208. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 1200 via the ROM 1202 and / or the communication unit 1209. When the computer program is loaded into the RAM 1203 and executed by the computing unit 1201, one or more steps of the intersection matching method described above can be performed. Alternatively, in other embodiments, the computing unit 1201 may be configured to perform the intersection matching method in any other appropriate manner (eg, by means of firmware).

[0094] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A 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, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, 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 foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0098] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0099] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0100] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0101] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

[0102] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

Claims

1. A method for intersection matching, comprising: Obtain attribute information of intersections in the first map and the second map respectively; Filtering out an intersection pair that meets a first preset condition according to the attribute information, wherein the intersection pair consists of a first intersection in the first map and a second intersection in the second map; Calculating confidence based on the attribute information of the intersection pair; When the confidence level meets the second preset condition, calculating the number of the intersection pairs; In the case where there are multiple intersection pairs, calculating differences based on attribute information of the intersection pairs; analyzing a distribution range of the differences to obtain a deviation pattern, wherein the deviation pattern includes a difference range and a ratio of the number of intersection pairs falling within the difference range to the number of intersection pairs whose confidence meets a second preset condition; Based on the offset rule, when the number ratio meets the preset value, a corresponding first difference range is obtained; The first intersection and the second intersection in the intersection pair falling within the first difference range are determined as matching intersections.

2. The method according to claim 1, wherein The step of respectively obtaining the attribute information of the intersections in the first map and the second map includes: At least one of structure attribute information, location attribute information, and name attribute information of the intersection in the first map and the second map is obtained respectively.

3. The method according to claim 1, wherein The calculating of the confidence level according to the attribute information of the intersection pair includes: In the case that the attribute information includes multiple attribute items, the confidence level is calculated according to the number of attribute items that meet the first preset condition, wherein the number of attribute items that meet the first preset condition is proportional to the confidence level.

4. A device for intersection matching, comprising: An acquisition module, configured to acquire attribute information of intersections in the first map and the second map respectively; a first screening module, configured to screen out, based on the attribute information, a pair of intersections meeting a first preset condition, wherein the pair of intersections consists of a first intersection in the first map and a second intersection in the second map; A calculation module, configured to calculate a confidence level based on the attribute information of the intersection pair; a second screening module, configured to determine the first intersection and the second intersection in the intersection pair as matching intersections if the confidence level meets a second preset condition; Wherein, the second screening module includes: a calculation unit, configured to calculate the number of the intersection pairs when the confidence level meets a second preset condition; a deviation rule unit, configured to calculate, when there are multiple intersection pairs, differences based on attribute information of the intersection pairs; and analyze a distribution range of the differences to obtain a deviation rule, wherein the deviation rule includes a difference range and a ratio of the number of intersection pairs falling within the difference range to the number of intersection pairs whose confidence meets a second preset condition; A screening unit, configured to obtain a corresponding first difference range based on the offset rule when the number ratio meets a preset value; The first intersection and the second intersection in the pair of intersections that fall within the first difference range are determined as matching intersections.

5. The device according to claim 4, wherein The acquisition module is used to: At least one of structure attribute information, location attribute information, and name attribute information of the intersection in the first map and the second map is obtained respectively.

6. The device according to claim 4, wherein The calculation module is used for: In the case that the attribute information includes multiple attribute items, the confidence level is calculated according to the number of attribute items that meet the first preset condition, wherein the number of attribute items that meet the first preset condition is proportional to the confidence level.

7. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 according to any one of claims 1 to 3.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-3.

9. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 3.

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