Path planning method and device, computer equipment and storage medium

Abstract

The invention discloses a path planning method and device, equipment and a storage medium, and belongs to the technical field of big data and the field of financial science and technology. The method comprises the steps of generating a first planning path based on target path planning information, determining a target violation high-incidence place, generating a second planning path based on the target path planning information and the target violation high-incidence place, and calculating the trajectory similarity between the first planning path and the second planning path. And comparing the trajectory similarity with a preset similarity threshold, when the trajectory similarity is greater than the threshold, determining that the first planning path does not pass through the target violation high-incidence place, and determining the first planning path which does not pass through the target violation high-incidence place as a target planning path. The invention further relates to the technical field of block chains, and the target path planning information can be stored in block chain nodes. Compared with a method of directly using violation point marking, the method of the invention identifies whether the planned path passes through a violation high-incidence place by calculating the trajectory similarity, and improves the accuracy of violation identification.

Description

Technical Field

[0001] The present application belongs to the field of big data technology and financial technology, and specifically relates to a path planning method, device, equipment and storage medium. Background Art

[0002] With the rapid development of the automobile industry and the improvement of people's living standards, cars have become the preferred means of transportation for people to travel. In order to ensure traffic safety, drivers must strictly abide by traffic rules during driving. However, some drivers have bad driving habits, and it is common for them to violate traffic rules multiple times in the same place. Driving violations are more likely to cause traffic accidents, thereby increasing the probability of auto insurance claims. In order to prevent this from happening, automatic reminders for high-incidence areas of driving violations can effectively prevent car owners from driving illegally, maintain road traffic order, and reduce the probability of auto insurance claims, which will produce good social effects and reduce the economic losses of car owners caused by violations. Therefore, in order to maintain road traffic order and help drivers avoid violations in illegal areas, it is necessary to warn drivers of violations.

[0003] In the prior art, the navigation system can generally only issue violation warnings based on the monitoring probes installed in the driving path, and cannot implement violation warnings in advance. Or the violation warning information provided by some navigation devices is obtained by planning the driving path, identifying the violation points of the driving path, and finally judging whether the violation points fall on the driving path. For example, the starting point or end point of the driving path is obtained, and the violation points within the specified distance range are identified with the starting point or end point of the driving path as the center of the circle, and then the violation points are marked on the map to judge whether the violation points fall on the driving path, so as to determine whether the driving path passes through the violation points. However, the path planning scheme generated by the navigation system is generally a collection of longitude and latitude coordinate point sequences, that is, the driving path is not actually a continuous line segment, but is composed of several points. Therefore, the use of the violation point marking method to judge whether the driving path passes through the violation point is prone to deviation, affecting the accuracy of the violation warning. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a path planning method, device, computer equipment and storage medium to solve the technical problem that the existing navigation system uses violation point markings to determine whether the driving path passes through a violation point, which is prone to deviation and affects the accuracy of violation warnings.

[0005] In order to solve the above technical problems, the present application embodiment provides a path planning method, which adopts the following technical solution:

[0006] A path planning method, comprising:

[0007] Acquire target path planning information, and perform first path planning based on the target path planning information to generate a first planned path;

[0008] Determining a target high-incidence location of traffic violations from a preset high-incidence location set of traffic violations based on the target path planning information;

[0009] Perform a second path planning based on the target path planning information and the target high-incidence location of traffic violations to generate a second planned path;

[0010] Calculating trajectory similarity between the first planned path and the second planned path;

[0011] Comparing the trajectory similarity with a preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determining that the first planned path does not pass through the target high-incidence location of traffic violations;

[0012] The first planned path that does not pass through the target high-incidence location of traffic violations is determined as a target planned path, and the target planned path is output.

[0013] Further, the target path planning information includes a path starting point and a path end point, and determining the target high-incidence location of traffic violations in the set of high-incidence locations of traffic violations based on the target path planning information specifically includes:

[0014] Calculating the straight-line distance between the starting point of the path and the end point of the path;

[0015] Determine a first high-incidence location for traffic violations based on the set of high-incidence locations for traffic violations, the starting point of the path, and the straight-line distance;

[0016] Determine a second high-incidence location for traffic violations based on the set of high-incidence locations for traffic violations, the end point of the path, and the straight-line distance;

[0017] The first high-incidence location of traffic violations and the second high-incidence location of traffic violations are combined to obtain the target high-incidence location of traffic violations.

[0018] Further, the determining of the first high-incidence location of traffic violations based on the high-incidence location set of traffic violations, the starting point of the path and the straight-line distance specifically includes:

[0019] Calculating the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the starting point of the path to obtain a first distance;

[0020] Comparing the first distance with the straight-line distance, determining a high-incidence location of traffic violations where the first distance is smaller than the straight-line distance as the first high-incidence location of traffic violations;

[0021] The determining of the second high-incidence location of traffic violations based on the high-incidence location set of traffic violations, the path endpoint and the straight-line distance specifically includes:

[0022] Calculating the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the end point of the path to obtain a second distance;

[0023] The second distance is compared with the straight-line distance, and a high-incidence location of traffic violations where the second distance is smaller than the straight-line distance is determined as the second high-incidence location of traffic violations.

[0024] Further, the calculating the trajectory similarity between the first planned path and the second planned path specifically includes:

[0025] Calculating the Euclidean distance between the first planned path and the second planned path based on a preset Euclidean distance algorithm;

[0026] The Euclidean distance is used as the trajectory similarity between the first planned path and the second planned path.

[0027] Further, the calculating the Euclidean distance between the first planned path and the second planned path based on a preset Euclidean distance algorithm specifically includes:

[0028] Identify a sequence of longitude and latitude coordinate points in the first planned path, and represent the sequence of longitude and latitude coordinate points in the first planned path in vector form to obtain a first path vector set;

[0029] Identify a sequence of longitude and latitude coordinate points in the second planned path, and represent the sequence of longitude and latitude coordinate points in the second planned path in vector form to obtain a second path vector set;

[0030] The Euclidean distance algorithm is used to calculate the Euclidean distance between the first path vector set and the second path vector set to obtain the Euclidean distance between the first planned path and the second planned path.

[0031] Further, using the Euclidean distance algorithm to calculate the Euclidean distance between the first path vector set and the second path vector set to obtain the Euclidean distance between the first planned path and the second planned path specifically includes:

[0032] Calculating the longitude information and latitude information of each first path vector in sequence according to the sequence of longitude and latitude coordinate points in the first planned path, wherein the first path vector is any one of the first path vector set;

[0033] Calculating the longitude information and latitude information of each second path vector in sequence according to the sequence of longitude and latitude coordinate points in the second planned path, wherein the second path vector is any one of the second path vector set;

[0034] The Euclidean distance between the first path vector set and the second path vector set is calculated using the Euclidean distance algorithm based on the longitude information of the first path vector, the dimension information of the first path vector, the longitude information of the second path vector, and the dimension information of the second path vector.

[0035] Furthermore, after comparing the trajectory similarity with a preset similarity threshold, the method further includes:

[0036] When the trajectory similarity is less than or equal to the similarity threshold, determining that the first planned path passes through the target high-incidence location of traffic violations;

[0037] Determine the first planned path passing through the target high-incidence location of traffic violations as a dangerous path;

[0038] Delete the dangerous path.

[0039] In order to solve the above technical problems, the embodiment of the present application further provides a path planning device, which adopts the following technical solution:

[0040] A path planning device, comprising:

[0041] A first planning module, used to obtain target path planning information, and perform a first path planning based on the target path planning information to generate a first planned path;

[0042] A violation point identification module, used to determine a target violation high-incidence location in a preset violation high-incidence location set based on the target path planning information;

[0043] A second planning module, configured to perform a second path planning based on the target path planning information and the target high-incidence location of traffic violations, and generate a second planned path;

[0044] A similarity calculation module, used to calculate the trajectory similarity between the first planned path and the second planned path;

[0045] A similarity comparison module, used to compare the trajectory similarity with a preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determine that the first planned path does not pass through the target high-incidence location of traffic violations;

[0046] The path planning module is used to determine the first planned path that does not pass through the target violation high-incidence location as a target planned path, and output the target planned path.

[0047] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0048] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of any of the above-mentioned path planning methods when executing the computer-readable instructions.

[0049] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0050] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of any one of the path planning methods described above.

[0051] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0052] The present application discloses a path planning method, device, equipment and storage medium, which belongs to the field of big data technology and financial technology. The present application obtains target path planning information, performs a first path planning based on the target path planning information, generates a first planning path, determines a target high-incidence location of traffic violations in a preset high-incidence location set based on the target path planning information, performs a second path planning based on the target path planning information and the target high-incidence location of traffic violations, generates a second planning path, calculates the trajectory similarity between the first planning path and the second planning path, compares the trajectory similarity with the preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determines that the first planning path does not pass through the target high-incidence location of traffic violations, determines the first planning path that does not pass through the target high-incidence location of traffic violations as the target planning path, and outputs the target planning path. The present application utilizes the target path planning information and the target high-incidence violation location to generate an additional planned path, and evaluates whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity. If the calculated trajectory similarity is greater than the preset similarity threshold, it means that the planned path does not pass through the high-incidence violation location. The system will select the planned path that does not pass through the high-incidence violation location as the final target planned path. Compared with the method of directly using violation point markings, the present application identifies whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity, thereby improving the accuracy of violation identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 An exemplary system architecture diagram is shown in which the present application can be applied;

[0055] Figure 2 A flow chart showing an embodiment of a path planning method according to the present application is shown;

[0056] Figure 3 A schematic structural diagram of an embodiment of a path planning device according to the present application is shown;

[0057] Figure 4 A schematic structural diagram of an embodiment of a computer device according to the present application is shown. DETAILED DESCRIPTION

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0059] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0060] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0061] like Figure 1As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0062] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0063] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers and desktop computers, etc.

[0064] Server 105 can be a server that provides various services, such as a background server that provides support for pages displayed on terminal devices 101, 102, and 103. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0065] It should be noted that the path planning method provided in the embodiment of the present application is generally executed by a server, and accordingly, the path planning device is generally arranged in the server.

[0066] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0067] Continue to refer Figure 2, shows a flow chart of an embodiment of the path planning method according to the present application. The embodiment of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0068] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0069] In the prior art, the method of marking violation points is usually used to determine whether the driving path passes through the violation points, but this method is prone to recognition bias, affecting the accuracy of violation warning. The present application discloses a path planning method, device, equipment and storage medium, belonging to the field of big data technology and financial technology. The present application uses the target path planning information and the target violation high-incidence location to generate an additional planned path, and evaluates whether the planned path passes through the violation high-incidence location by calculating the trajectory similarity. If the calculated trajectory similarity is greater than the preset similarity threshold, it means that the planned path does not pass through the violation high-incidence location. The system will select the planned path that does not pass through the violation high-incidence location as the final target planned path. Compared with the method of directly using the violation point marking method, the present application calculates the trajectory similarity to identify whether the planned path passes through the violation high-incidence location, thereby improving the accuracy of violation identification.

[0070] The path planning method comprises the following steps:

[0071] S201, obtaining target path planning information, and performing a first path planning based on the target path planning information to generate a first planned path.

[0072] In this embodiment, target path planning information is obtained, wherein the target path planning information includes a path starting point and a path end point. The path starting point and path end point information may be provided by a user or obtained through other means. The target path planning information is the basis for path planning.

[0073] The navigation system uses a mature integrated map service and calls the API interface of the service to generate the first planned path based on the path start point and path end point information. The integrated map service is a service that provides path planning functions. The integrated map service usually has high-quality path planning algorithms and real-time traffic information, which can generate accurate and optimized paths. At the same time, the API interface can be used to easily integrate and interact with the integrated map service. The integrated map service usually considers traffic conditions, road networks and other related factors to generate the optimal or most suitable path. By calling the API interface of the service, the system can obtain the generated first planned path.

[0074] By utilizing mature integrated map services and their API interfaces, the present application can generate a first planned path based on target path planning information, providing a basis for subsequent path planning processes.

[0075] S202: Determine a target high-incidence traffic violation location in a preset high-incidence traffic violation location set based on the target path planning information.

[0076] In this embodiment, the navigation system determines the target high-incidence location of traffic violations from the preset set of high-incidence locations of traffic violations based on the target route planning information. The purpose is to identify locations where traffic violations are more common in the past or in statistical data, which may include traffic congestion areas, high-accident locations, or other locations with records of traffic violations. By determining the target high-incidence locations of traffic violations, the navigation system can further optimize the route planning to avoid these locations or adopt other strategies to reduce the risk of traffic violations.

[0077] Furthermore, the target path planning information includes a path starting point and a path end point. Based on the target path planning information, the target high-incidence location of traffic violations is determined in the set of high-incidence locations of traffic violations, specifically including:

[0078] Calculate the straight-line distance between the path start point and the path end point;

[0079] Determine the first high-incidence location of traffic violations based on the set of high-incidence locations of traffic violations, the starting point of the path, and the straight-line distance;

[0080] Determine the second high-incidence location of traffic violations based on the set of high-incidence locations of traffic violations, the end point of the path, and the straight-line distance;

[0081] The first high-incidence location for traffic violations and the second high-incidence location for traffic violations are combined to obtain a target high-incidence location for traffic violations.

[0082] In this embodiment, the straight-line distance between the starting point and the end point of the path is obtained. The straight-line distance is the shortest distance between two points. The straight-line distance between the two points can be obtained by calculating the longitude and latitude coordinates of the two points. The first distance is obtained by calculating the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the starting point of the path, and then the first distance and the straight-line distance are compared to determine the high-incidence location of traffic violations whose first distance is less than the straight-line distance as the first high-incidence location of traffic violations; the second distance is obtained by calculating the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the end point of the path, and then the second distance and the straight-line distance are compared to determine the high-incidence location of traffic violations whose second distance is less than the straight-line distance as the second high-incidence location of traffic violations. The first high-incidence location of traffic violations and the second high-incidence location of traffic violations are combined to obtain the target high-incidence location of traffic violations.

[0083] In a specific embodiment of the present application, the set of high-incidence locations of traffic violations includes m high-incidence locations of traffic violations, and the set of high-incidence locations of traffic violations is expressed as:

[0084] L = {(x i ,y i )|i<<m}

[0085] Among them, (x i ,y i ) is the coordinate of the ith high-incidence location of traffic violations in L, m is the number of all high-incidence locations of traffic violations in L, assuming that a certain path planning starts from S and ends at T, then the distance set obtained by calculating the starting point S, the end point T and the m high-incidence locations of traffic violations is:

[0086] L′={{D(S,(x i ,y i )),D(T,(x i ,y i ))}|i<<m}

[0087] Where D represents the distance function, D(S,(x i ,y i )) represents the set of first distances, D(T,(x i ,y i )) represents the set of the second distance. The distance set L′ is filtered to find all the high-incidence locations of traffic violations whose first distance or second distance is less than the straight-line distance between the starting point and the end point, namely, the target high-incidence locations of traffic violations. The target high-incidence locations of traffic violations are represented as follows:

[0088] L″={(x i ,y i )|i<<n,L′ i <D(S,T)}

[0089] Where D(S,T) is the straight-line distance between the starting point S and the end point T.

[0090] In the above embodiment, the straight-line distance between the starting point and the end point of the path is used to determine the places where traffic violations are frequent. This is equivalent to determining the first area with the starting point of the path as the center and the straight-line distance between the starting point and the end point of the path as the radius. The places where traffic violations are frequent in the first area are the first places where traffic violations are frequent. The second area is determined with the end point of the path as the center and the straight-line distance between the starting point and the end point of the path as the radius. The places where traffic violations are frequent in the second area are the second places where traffic violations are frequent. All places where traffic violations are frequent involved in path planning can be quickly determined through the starting point of the path, the end point of the path, and the straight-line distance between the starting point and the end point of the path.

[0091] Further, the first high-incidence location of traffic violations is determined based on the set of high-incidence locations of traffic violations, the starting point of the path and the straight-line distance, specifically including:

[0092] Calculate the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the starting point of the path to obtain the first distance;

[0093] Compare the first distance and the straight-line distance, and determine the high-incidence location of traffic violations where the first distance is smaller than the straight-line distance as the first high-incidence location of traffic violations;

[0094] The second high-incidence location of traffic violations is determined based on the set of high-incidence locations of traffic violations, the end point of the path, and the straight-line distance, including:

[0095] Calculate the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the end point of the path to obtain the second distance;

[0096] The second distance is compared with the straight-line distance, and the high-incidence location of traffic violations whose second distance is smaller than the straight-line distance is determined as the second high-incidence location of traffic violations.

[0097] In this embodiment, the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the starting point of the path is calculated to obtain a first distance, and the first distance is compared with the straight-line distance between the starting point of the path and the end point of the path. If the first distance is less than the straight-line distance, the high-incidence location of traffic violations is determined as the first high-incidence location of traffic violations. The distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the end point of the path is calculated to obtain a second distance, and the second distance is compared with the straight-line distance between the starting point of the path and the end point of the path. If the second distance is less than the straight-line distance, the high-incidence location of traffic violations is determined as the second high-incidence location of traffic violations.

[0098] By calculating the distance, we can determine which high-incidence violation location is closer to the path start / end point, and thus is more likely to be a high-incidence violation location. At the same time, by comparing the straight-line distance, we can exclude some high-incidence violation locations that are farther away, thus improving the accuracy of determining high-incidence violation locations.

[0099] S203, performing a second path planning based on the target path planning information and the target high-incidence location of traffic violations to generate a second planned path.

[0100] In this embodiment, the navigation system generates a second planned path based on the path start point and path end point in the target path planning information and the second path planning. The target high-incidence locations of traffic violations are taken as an important consideration for path planning, and the methods adopted include avoiding these locations, giving priority to bypassing these locations, or taking other strategies to reduce the risk of traffic violations. The navigation system adds the target high-incidence locations of traffic violations and optimizes the path in the second path planning to reduce the risk of traffic violations passing through these locations.

[0101] S204: Calculate the trajectory similarity between the first planned path and the second planned path.

[0102] In this embodiment, the navigation system calculates the trajectory similarity between the first planned path and the second planned path by a preset Euclidean distance algorithm. The Euclidean distance of a path refers to the sum of the straight-line distances between adjacent nodes on the path. In two-dimensional space, the Euclidean distance is determined by calculating the straight-line distance between two nodes. Specifically, for consecutive nodes (x1, y1) and (x2, y2) on the path, the Euclidean distance of the path can be calculated as sqrt((x2-x1)^2+(y2-y1)^2).

[0103] For example, consider a simple path, node 1 is located at coordinates (0,0), node 2 is located at coordinates (3,4), and node 3 is located at coordinates (7,2). When calculating the Euclidean distance of the path, first calculate the distance from node 1 to node 2, that is, sqrt((3-0)^2+(4-0)^2)=5, then calculate the distance from node 2 to node 3, that is, sqrt((7-3)^2+(2-4)^2)=4.472, and finally add these two distances to get the Euclidean distance of the path as 5+4.472=9.472.

[0104] Further, calculating the trajectory similarity between the first planned path and the second planned path specifically includes:

[0105] Calculate the Euclidean distance between the first planned path and the second planned path based on a preset Euclidean distance algorithm;

[0106] The Euclidean distance is taken as the trajectory similarity between the first planned path and the second planned path.

[0107] In this embodiment, a preset Euclidean distance algorithm is used to calculate the Euclidean distance between the first planned path and the second planned path, and this distance is used as the trajectory similarity between the two paths. Euclidean distance is a commonly used distance measurement method for measuring the distance between two points. Euclidean distance is used to measure the similarity between two paths, that is, the smaller the distance between the paths, the higher the similarity.

[0108] Further, the Euclidean distance between the first planned path and the second planned path is calculated based on a preset Euclidean distance algorithm, specifically including:

[0109] Identify a sequence of longitude and latitude coordinate points in a first planned path, and represent the sequence of longitude and latitude coordinate points in the first planned path in a vector form to obtain a first path vector set;

[0110] Identify a sequence of longitude and latitude coordinate points in the second planned path, and represent the sequence of longitude and latitude coordinate points in the second planned path in vector form to obtain a second path vector set;

[0111] The Euclidean distance algorithm is used to calculate the Euclidean distance between the first path vector set and the second path vector set to obtain the Euclidean distance between the first planned path and the second planned path.

[0112] In this embodiment, a sequence of longitude and latitude coordinate points in the first planned path and the second planned path is identified and represented in vector form, thereby obtaining a first path vector set and a second path vector set. Representing the sequence of longitude and latitude coordinate points in vector form can facilitate subsequent distance calculation and similarity comparison.

[0113] A path vector set refers to a form in which a path is represented as a set of vectors, where each vector represents a subpath in the path, and the first path vector set and the second path vector set represent vector sets of two paths, respectively. The Euclidean distance algorithm is used to calculate the Euclidean distance between the path vector sets. The Euclidean distance algorithm is suitable for calculating the Euclidean distance between vectors. Through the Euclidean distance algorithm, the Euclidean distance between the corresponding vectors in the first path vector set and the second path vector set can be calculated, and these distances are accumulated to obtain the Euclidean distance between the two paths. Finally, the Euclidean distance is used to measure the similarity between the two paths. Since the smaller the Euclidean distance, the smaller the distance between the path vector sets, the higher the similarity between the two paths.

[0114] Further, the Euclidean distance between the first path vector set and the second path vector set is calculated using the Euclidean distance algorithm to obtain the Euclidean distance between the first planned path and the second planned path, specifically including:

[0115] Calculating the longitude information and latitude information of each first path vector in sequence according to the sequence of longitude and latitude coordinate points in the first planned path, wherein the first path vector is any path vector in the first path vector set;

[0116] Calculating the longitude information and latitude information of each second path vector in sequence according to the sequence of longitude and latitude coordinate points in the second planned path, wherein the second path vector is any one of the second path vector set;

[0117] The Euclidean distance between the first path vector set and the second path vector set is calculated using the Euclidean distance algorithm based on the longitude information of the first path vector, the dimension information of the first path vector, the longitude information of the second path vector, and the dimension information of the second path vector.

[0118] Among them, the calculation formula of the Euclidean distance algorithm is:

[0119]

[0120] Among them, tr1 is the first path vector set, tr1 is expressed as tr2 is the second path vector set, tr2 is expressed as n is the number of path vectors contained in the path vector set, d E (tr1,tr2) represents the Euclidean distance between the first planned path and the second planned path. is the kth path vector in the first path vector set, is the kth path vector in the second path vector set, lat represents latitude, lng represents longitude, lat represents the dimension information of the kth path vector in the first path vector set. lat represents the dimension information of the kth path vector in the second path vector set. lng represents the longitude information of the kth path vector in the first path vector set, lng represents the longitude information of the kth path vector in the second path vector set.

[0121] S205, comparing the trajectory similarity with a preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determining that the first planned path does not pass through the target high-incidence location of traffic violations.

[0122] The Euclidean distance is used as a standard to measure the similarity between two paths. The trajectory similarity here refers to the similarity between two paths obtained by calculating the Euclidean distance. The preset similarity threshold is a pre-set path similarity threshold used to determine whether the two paths are similar enough.

[0123] In this embodiment, the calculated trajectory similarity is compared with the preset similarity threshold. If the trajectory similarity is greater than the similarity threshold, it means that the similarity between the two paths exceeds the preset threshold. When the trajectory similarity is greater than the similarity threshold, it means that there is a large difference between the first planned path and the second planned path generated based on the target high-incidence location of traffic violations. Therefore, it can be concluded that the first planned path does not pass through the target high-incidence location of traffic violations.

[0124] S206: Determine the first planned path that does not pass through the target high-incidence location of traffic violations as the target planned path, and output the target planned path.

[0125] In this embodiment, after comparing the trajectory similarity with the similarity threshold, when the trajectory similarity is greater than the similarity threshold, it is determined that the first planned path does not pass through the target high-incidence location of traffic violations, and the first planned path that does not pass through the target high-incidence location of traffic violations is determined as the target planned path, that is, the path is considered to be a safe planned path that meets the requirements. The target planned path is output, usually presented in a certain format or data structure, so that users or other systems can further use or process it.

[0126] In the above embodiment, the present application discloses a path planning method, which belongs to the field of big data technology and financial technology. The present application obtains target path planning information, performs a first path planning based on the target path planning information, generates a first planning path, determines a target high-incidence location of traffic violations in a preset high-incidence location set based on the target path planning information, performs a second path planning based on the target path planning information and the target high-incidence location of traffic violations, generates a second planning path, calculates the trajectory similarity between the first planning path and the second planning path, compares the trajectory similarity with the preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determines that the first planning path does not pass through the target high-incidence location of traffic violations, determines the first planning path that does not pass through the target high-incidence location of traffic violations as the target planning path, and outputs the target planning path. The present application utilizes the target path planning information and the target high-incidence violation location to generate an additional planned path, and evaluates whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity. If the calculated trajectory similarity is greater than the preset similarity threshold, it means that the planned path does not pass through the high-incidence violation location. The system will select the planned path that does not pass through the high-incidence violation location as the final target planned path. Compared with the method of directly using violation point markings, the present application identifies whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity, thereby improving the accuracy of violation identification.

[0127] Furthermore, after comparing the trajectory similarity with a preset similarity threshold, the method further includes:

[0128] When the trajectory similarity is less than or equal to the similarity threshold, it is determined that the first planned path passes through the target violation high-incidence location;

[0129] The first planned route passing through the target high-incidence location of traffic violations is determined as a dangerous route;

[0130] Remove dangerous paths.

[0131] In this embodiment, if the trajectory similarity is less than or equal to the similarity threshold, it means that the similarity between the two paths is lower than the preset threshold. When the trajectory similarity is less than or equal to the similarity threshold, it means that the first planned path is less different from the second planned path generated based on the target high-violation location, that is, the first planned path passes through the target high-violation location. Therefore, it can be concluded that the first planned path passes through the target high-violation location, and the first planned path passing through the target high-violation location is determined as a dangerous path, and the dangerous path is deleted.

[0132] This application determines whether the first planned path passes through the target high-violation location based on trajectory similarity and similarity threshold. If the similarity is lower than the similarity threshold, that is, the path difference is small, it indicates that the first planned path passes through the target high-violation location. Therefore, it is marked as a dangerous path and deleted from the path planning results, thereby improving the accuracy of violation identification.

[0133] In this embodiment, the path planning method is executed on the electronic device (eg Figure 1 The server shown in the figure) can receive instructions or obtain data through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0134] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned target path planning information, the above-mentioned target path planning information can also be stored in a node of a blockchain.

[0135] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0136] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0137] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0138] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a path planning device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0139] like Figure 3 As shown, the path planning device 300 described in this embodiment includes:

[0140] The first planning module 301 is used to obtain target path planning information, and perform a first path planning based on the target path planning information to generate a first planned path;

[0141] A violation point identification module 302 is used to determine a target violation high-incidence location in a preset violation high-incidence location set based on the target path planning information;

[0142] The second planning module 303 is used to perform a second path planning based on the target path planning information and the target violation high-incidence location to generate a second planned path;

[0143] A similarity calculation module 304 is used to calculate the trajectory similarity between the first planned path and the second planned path;

[0144] A similarity comparison module 305 is used to compare the trajectory similarity with a preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, it is determined that the first planned path does not pass through the target violation high-incidence location;

[0145] The path planning module 306 is used to determine the first planned path that does not pass through the target high-incidence location of traffic violations as the target planned path, and output the target planned path.

[0146] Furthermore, the target path planning information includes a path starting point and a path end point, and the violation point identification module 302 specifically includes:

[0147] The start-end point distance calculation submodule is used to calculate the straight-line distance between the path start point and the path end point;

[0148] A first traffic violation identification submodule, for determining a first traffic violation high incidence location based on a set of traffic violation high incidence locations, a path starting point, and a straight-line distance;

[0149] A second traffic violation identification submodule is used to determine a second traffic violation high-incidence location based on the set of traffic violation high-incidence locations, the end point of the path, and the straight-line distance;

[0150] The traffic violation location combination submodule is used to combine the first traffic violation high-incidence location and the second traffic violation high-incidence location to obtain a target traffic violation high-incidence location.

[0151] Furthermore, the first violation identification submodule specifically includes:

[0152] A first distance calculation unit is used to calculate the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the starting point of the path to obtain a first distance;

[0153] A first traffic violation identification unit is used to compare the first distance with the straight-line distance, and determine a high-incidence traffic violation location where the first distance is smaller than the straight-line distance as a first high-incidence traffic violation location;

[0154] The second violation identification submodule specifically includes:

[0155] A second distance calculation unit is used to calculate the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the end point of the path to obtain a second distance;

[0156] The second traffic violation identification unit is used to compare the second distance with the straight-line distance, and determine the high-incidence traffic violation location where the second distance is smaller than the straight-line distance as the second high-incidence traffic violation location.

[0157] Furthermore, the similarity calculation module 304 specifically includes:

[0158] The Euclidean distance calculation submodule is used to calculate the Euclidean distance between the first planned path and the second planned path based on a preset Euclidean distance algorithm;

[0159] The trajectory similarity submodule is used to use the Euclidean distance as the trajectory similarity between the first planned path and the second planned path.

[0160] Furthermore, the Euclidean distance calculation submodule specifically includes:

[0161] A first path vector unit is used to identify a sequence of longitude and latitude coordinate points in a first planned path, and to represent the sequence of longitude and latitude coordinate points in the first planned path in a vector form to obtain a first path vector set;

[0162] A second path vector unit is used to identify a sequence of longitude and latitude coordinate points in a second planned path, and to represent the sequence of longitude and latitude coordinate points in the second planned path in a vector form to obtain a second path vector set;

[0163] The Euclidean distance calculation unit is used to calculate the Euclidean distance between the first path vector set and the second path vector set using the Euclidean distance algorithm to obtain the Euclidean distance between the first planned path and the second planned path.

[0164] Furthermore, the Euclidean distance calculation unit specifically includes:

[0165] A first longitude and latitude calculation subunit, configured to calculate the longitude information and latitude information of each first path vector in sequence according to the sequence of longitude and latitude coordinate points in the first planned path, wherein the first path vector is any path vector in the first path vector set;

[0166] A second longitude and latitude calculation subunit is used to calculate the longitude information and latitude information of each second path vector in turn according to the sequence of longitude and latitude coordinate points in the second planned path, wherein the second path vector is any path vector in the second path vector set;

[0167] The Euclidean distance calculation subunit is used to calculate the Euclidean distance between the first path vector set and the second path vector set based on the longitude information of the first path vector, the dimension information of the first path vector, the longitude information of the second path vector and the dimension information of the second path vector using the Euclidean distance algorithm.

[0168] Among them, the calculation formula of the Euclidean distance algorithm is:

[0169]

[0170] Among them, tr1 is the first path vector set, tr1 is expressed as tr2 is the second path vector set, tr2 is expressed as n is the number of path vectors contained in the path vector set, d E (tr1,tr2) represents the Euclidean distance between the first planned path and the second planned path. is the kth path vector in the first path vector set, is the kth path vector in the second path vector set, lat represents latitude, lng represents longitude, lat represents the dimension information of the kth path vector in the first path vector set. lat represents the dimension information of the kth path vector in the second path vector set. lng represents the longitude information of the kth path vector in the first path vector set, lng represents the longitude information of the kth path vector in the second path vector set.

[0171] Furthermore, the path planning device 300 further includes:

[0172] A path violation judgment module, used for determining that the first planned path passes through a target violation-prone location when the trajectory similarity is less than or equal to a similarity threshold;

[0173] A dangerous path determination module, used to determine the first planned path passing through the target high-incidence location of traffic violations as a dangerous path;

[0174] The dangerous path deletion module is used to delete the dangerous path.

[0175] In the above embodiment, the present application discloses a path planning device, which belongs to the field of big data technology and financial technology. The present application obtains target path planning information, performs a first path planning based on the target path planning information, generates a first planning path, determines a target high-incidence location of traffic violations in a preset high-incidence location set based on the target path planning information, performs a second path planning based on the target path planning information and the target high-incidence location of traffic violations, generates a second planning path, calculates the trajectory similarity between the first planning path and the second planning path, compares the trajectory similarity with the preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determines that the first planning path does not pass through the target high-incidence location of traffic violations, determines the first planning path that does not pass through the target high-incidence location of traffic violations as the target planning path, and outputs the target planning path. The present application utilizes the target path planning information and the target high-incidence violation location to generate an additional planned path, and evaluates whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity. If the calculated trajectory similarity is greater than the preset similarity threshold, it means that the planned path does not pass through the high-incidence violation location. The system will select the planned path that does not pass through the high-incidence violation location as the final target planned path. Compared with the method of directly using violation point markings, the present application identifies whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity, thereby improving the accuracy of violation identification.

[0176] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0177] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0178] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0179] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the path planning method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0180] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the path planning method.

[0181] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0182] In the above embodiment, the present application discloses a computer device, which belongs to the field of big data technology and financial technology. The present application obtains target path planning information, performs a first path planning based on the target path planning information, generates a first planning path, determines a target high-incidence location of traffic violations in a preset high-incidence location set based on the target path planning information, performs a second path planning based on the target path planning information and the target high-incidence location of traffic violations, generates a second planning path, calculates the trajectory similarity between the first planning path and the second planning path, compares the trajectory similarity with the preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determines that the first planning path does not pass through the target high-incidence location of traffic violations, determines the first planning path that does not pass through the target high-incidence location of traffic violations as the target planning path, and outputs the target planning path. The present application utilizes the target path planning information and the target high-incidence violation location to generate an additional planned path, and evaluates whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity. If the calculated trajectory similarity is greater than the preset similarity threshold, it means that the planned path does not pass through the high-incidence violation location. The system will select the planned path that does not pass through the high-incidence violation location as the final target planned path. Compared with the method of directly using violation point markings, the present application identifies whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity, thereby improving the accuracy of violation identification.

[0183] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the path planning method as described above.

[0184] In the above embodiment, the present application discloses a computer-readable storage medium, which belongs to the field of big data technology and financial technology. The present application obtains target path planning information, performs a first path planning based on the target path planning information, generates a first planning path, determines a target high-incidence location of traffic violations in a preset high-incidence location set based on the target path planning information, performs a second path planning based on the target path planning information and the target high-incidence location of traffic violations, generates a second planning path, calculates the trajectory similarity between the first planning path and the second planning path, compares the trajectory similarity with the preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determines that the first planning path does not pass through the target high-incidence location of traffic violations, determines the first planning path that does not pass through the target high-incidence location of traffic violations as the target planning path, and outputs the target planning path. The present application utilizes the target path planning information and the target high-incidence violation location to generate an additional planned path, and evaluates whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity. If the calculated trajectory similarity is greater than the preset similarity threshold, it means that the planned path does not pass through the high-incidence violation location. The system will select the planned path that does not pass through the high-incidence violation location as the final target planned path. Compared with the method of directly using violation point markings, the present application identifies whether the planned path passes through the high-incidence violation location by calculating the trajectory similarity, thereby improving the accuracy of violation identification.

[0185] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0186] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0187] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A path planning method, characterized in that: include: Acquire target path planning information, and perform first path planning based on the target path planning information to generate a first planned path; Determining a target high-incidence location of traffic violations from a preset high-incidence location set of traffic violations based on the target path planning information; Perform a second path planning based on the target path planning information and the target high-incidence location of traffic violations to generate a second planned path; Calculating trajectory similarity between the first planned path and the second planned path; Comparing the trajectory similarity with a preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determining that the first planned path does not pass through the target high-incidence location of traffic violations; The first planned path that does not pass through the target high-incidence location of traffic violations is determined as a target planned path, and the target planned path is output.

2. The path planning method according to claim 1, characterized in that: The target path planning information includes a path starting point and a path end point, and determining a target high-incidence location of traffic violations in the set of high-incidence locations of traffic violations based on the target path planning information specifically includes: Calculating the straight-line distance between the starting point of the path and the end point of the path; Determine a first high-incidence location for traffic violations based on the set of high-incidence locations for traffic violations, the starting point of the path, and the straight-line distance; Determine a second high-incidence location for traffic violations based on the set of high-incidence locations for traffic violations, the end point of the path, and the straight-line distance; The first high-incidence location of traffic violations and the second high-incidence location of traffic violations are combined to obtain the target high-incidence location of traffic violations.

3. The path planning method according to claim 2, characterized in that: The determining of the first high-incidence location of traffic violations based on the high-incidence location set of traffic violations, the starting point of the path and the straight-line distance specifically includes: Calculating the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the starting point of the path to obtain a first distance; Comparing the first distance with the straight-line distance, determining a high-incidence location of traffic violations where the first distance is smaller than the straight-line distance as the first high-incidence location of traffic violations; The determining of the second high-incidence location of traffic violations based on the high-incidence location set of traffic violations, the path endpoint and the straight-line distance specifically includes: Calculating the distance between each high-incidence location of traffic violations in the set of high-incidence locations of traffic violations and the end point of the path to obtain a second distance; The second distance is compared with the straight-line distance, and a high-incidence location of traffic violations where the second distance is smaller than the straight-line distance is determined as the second high-incidence location of traffic violations.

4. The path planning method according to claim 1, characterized in that: The calculating the trajectory similarity between the first planned path and the second planned path specifically includes: Calculating the Euclidean distance between the first planned path and the second planned path based on a preset Euclidean distance algorithm; The Euclidean distance is used as the trajectory similarity between the first planned path and the second planned path.

5. The path planning method according to claim 4, characterized in that: The calculating the Euclidean distance between the first planned path and the second planned path based on a preset Euclidean distance algorithm specifically includes: Identify a sequence of longitude and latitude coordinate points in the first planned path, and represent the sequence of longitude and latitude coordinate points in the first planned path in vector form to obtain a first path vector set; Identify a sequence of longitude and latitude coordinate points in the second planned path, and represent the sequence of longitude and latitude coordinate points in the second planned path in vector form to obtain a second path vector set; The Euclidean distance algorithm is used to calculate the Euclidean distance between the first path vector set and the second path vector set to obtain the Euclidean distance between the first planned path and the second planned path.

6. The path planning method according to claim 5, characterized in that: Calculating the Euclidean distance between the first path vector set and the second path vector set using the Euclidean distance algorithm to obtain the Euclidean distance between the first planned path and the second planned path specifically includes: Calculating the longitude information and latitude information of each first path vector in sequence according to the sequence of longitude and latitude coordinate points in the first planned path, wherein the first path vector is any one of the first path vector set; Calculating the longitude information and latitude information of each second path vector in sequence according to the sequence of longitude and latitude coordinate points in the second planned path, wherein the second path vector is any one of the second path vector set; The Euclidean distance between the first path vector set and the second path vector set is calculated using the Euclidean distance algorithm based on the longitude information of the first path vector, the dimension information of the first path vector, the longitude information of the second path vector, and the dimension information of the second path vector.

7. The path planning method according to any one of claims 1 to 6, characterized in that: After comparing the trajectory similarity with a preset similarity threshold, the method further includes: When the trajectory similarity is less than or equal to the similarity threshold, determining that the first planned path passes through the target high-incidence location of traffic violations; Determine the first planned path passing through the target high-incidence location of traffic violations as a dangerous path; Delete the dangerous path.

8. A path planning device, characterized in that: include: A first planning module, used to obtain target path planning information, and perform a first path planning based on the target path planning information to generate a first planned path; A violation point identification module, used to determine a target violation high-incidence location in a preset violation high-incidence location set based on the target path planning information; A second planning module, configured to perform a second path planning based on the target path planning information and the target high-incidence location of traffic violations, and generate a second planned path; A similarity calculation module, used to calculate the trajectory similarity between the first planned path and the second planned path; A similarity comparison module, used to compare the trajectory similarity with a preset similarity threshold, and when the trajectory similarity is greater than the similarity threshold, determine that the first planned path does not pass through the target high-incidence location of traffic violations; The path planning module is used to determine the first planned path that does not pass through the target violation high-incidence location as a target planned path, and output the target planned path.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the path planning method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the path planning method according to any one of claims 1 to 7 are implemented.

Images