CN117422363A

CN117422363A - Route planning method, device, equipment and storage medium

Info

Publication number: CN117422363A
Application number: CN202311426164.6A
Authority: CN
Inventors: 李佩; 宁永恒; 张瀚之
Original assignee: Jiangsu Yunmanman Tongcheng Information Technology Co ltd; Jiangsu Manyun Software Technology Co Ltd
Current assignee: Jiangsu Yunmanman Tongcheng Information Technology Co ltd; Jiangsu Manyun Software Technology Co Ltd
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2024-01-19

Abstract

The invention discloses a route planning method, a route planning device, route planning equipment and a storage medium. The method comprises the following steps: in response to a route planning request issued by the target demand driver, comparing attribute information of the candidate experience driver and the target demand driver to determine the target experience driver from the candidate experience drivers; according to the planned driving information of the target demand driver, determining a historical driving route of the target experience driver; and combining the historical driving routes according to the similarity between the historical driving routes to obtain a target route, and indicating a target demand driver to drive according to the target route. According to the technical scheme, an accurate and efficient driving route can be planned for a driver according to the attribute information of the driver and by combining the history driving route of the experience driver, so that the cost of the driver is reduced, the efficiency is improved, and unnecessary expense on the way is reduced.

Description

Route planning method, device, equipment and storage medium

Technical Field

The present invention relates to the field of vehicle technologies, and in particular, to a route planning method, apparatus, device, and storage medium.

Background

In the long-haul highway freight scenario, truck operation involves a number of costs, including fuel, high-speed traffic, vehicle maintenance, and driver wages. These costs play a critical role in the overall life cycle of the truck. In a specific transportation route, fuel cost, ETC (Electronic Toll Collection ) cost and vehicle consumption are directly affected by factors such as distance travelled, route selection, vehicle speed and load. This means that the driver's routing has a decisive influence on the costs of the entire transport process.

However, current commercial freight navigation products typically focus only on road connectivity and traffic congestion conditions, ignoring the special needs of different types of trucks and the impact of local traffic policies. This results in unnecessary detour mileage that the driver may travel, paying additional ETC fees, resulting in inefficient transportation.

Therefore, an accurate and efficient driving route is planned for a driver, so that the driver is helped to reduce the cost, improve the efficiency and reduce unnecessary expenditure on the way, and the method is a problem to be solved urgently.

Disclosure of Invention

The invention provides a route planning method, a device, equipment and a storage medium, which are used for planning an accurate and efficient driving route for a driver, helping the driver reduce the cost, improve the efficiency and reduce unnecessary expense on the way.

According to an aspect of the present invention, there is provided a route planning method including:

in response to a route planning request issued by the target demand driver, comparing attribute information of the candidate experience driver and the target demand driver to determine the target experience driver from the candidate experience drivers;

according to the planned driving information of the target demand driver, determining a historical driving route of the target experience driver;

and combining the historical driving routes according to the similarity between the historical driving routes to obtain a target route, and indicating a target demand driver to drive according to the target route.

According to another aspect of the present invention, there is provided a route planning apparatus comprising:

the first determining module is used for responding to the route planning request sent by the target demand driver and comparing attribute information of the candidate experience driver and the target demand driver so as to determine the target experience driver from the candidate experience drivers;

the second determining module is used for determining a historical driving route of the target experience driver according to the planned driving information of the target demand driver;

and the indicating module is used for merging the historical driving routes according to the similarity between the historical driving routes so as to obtain a target route and indicating a driver to drive according to the target route.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the route planning method according to any one of the embodiments of the invention.

According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a route planning method according to any embodiment of the present invention.

According to the technical scheme, attribute information of a candidate experience driver and attribute information of a target demand driver are compared in response to a route planning request sent by the target demand driver, so that the target experience driver is determined from the candidate experience drivers; according to the planned driving information of the target demand driver, determining a historical driving route of the target experience driver; and combining the historical driving routes according to the similarity between the historical driving routes to obtain a target route, and indicating a target demand driver to drive according to the target route. By the method, an accurate and efficient driving route can be planned for a driver according to the attribute information of the driver and by combining the history driving route of the experience driver, so that the driver is helped to reduce the cost, improve the efficiency and reduce unnecessary expense in the road.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a route planning method according to a first embodiment of the present invention;

fig. 2 is a flowchart of a route planning method according to a second embodiment of the present invention;

fig. 3 is a block diagram of a route planning device according to a third embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," "target," "candidate," "alternative," and the like in the description and claims of the invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a route planning method according to a first embodiment of the present invention; the method is suitable for responding to a route planning request sent by a target demand driver and combining the related history information of an experienced driver to plan an accurate and effective target route for the target demand driver, and can be implemented by a route planning device which can be implemented in a software and/or hardware mode and can be integrated into electronic equipment with a route planning function, such as a vehicle. As shown in fig. 1, the route planning method includes:

s101, responding to a route planning request sent by a target demand driver, and comparing attribute information of the candidate experience driver and the target demand driver to determine the target experience driver from the candidate experience drivers.

The target demand driver can be a novice driver with less transportation experience, an experienced driver unfamiliar with a new route, or other drivers with recommended demands. The route planning request refers to a request sent by a target demand driver for planning a route, and the route planning request can include attribute information of the target demand driver and also can include driving information of the target demand driver. The attribute information may include at least one of: age, sex, driving age, driving habit, and driving length, the driving information may include at least one of: start point, end point, and route characteristics (route characteristics may be road type, traffic condition, and time).

Alternatively, the attribute information of the candidate experience drivers and the target demand drivers may be compared based on a preset similarity comparison rule, so as to determine a group of candidate experience drivers most similar to the target demand drivers as the target experience drivers.

Optionally, comparing the attribute information of the candidate empirical driver and the target demand driver to determine the target empirical driver from the candidate empirical drivers includes: adopting a collaborative filtering algorithm based on a user, and respectively determining the similarity of the attribute information between each candidate experience driver and the target demand driver according to the attribute information of the candidate experience drivers and the target demand drivers; and if the similarity is greater than a preset similarity threshold, determining the candidate experience driver as a target experience driver.

Wherein the user-based collaborative filtering algorithm (Collaborative Filtering) can determine the association of candidate experienced drivers and target demand drivers by measuring the similarity between the attribute information of the two. The similarity may be, for example, a cosine similarity between the candidate empirical driver and the target demand driver attribute information correspondence matrix.

It should be noted that, by matching a group of experience drivers most similar to the attribute information of each target demand driver according to the attribute information, and planning a route for the demand driver according to the relevant route of the experience driver, the effectiveness of planning the route can be effectively improved.

Optionally, a collaborative filtering algorithm based on a user is adopted, and according to attribute information of candidate experience drivers and target demand drivers, similarity of the attribute information between each candidate experience driver and the target demand driver is respectively determined, including: respectively carrying out standardization processing on attribute information of candidate experience drivers and target demand drivers to obtain a candidate vector matrix and a target demand matrix; and determining the magnitude of a vector included angle between the candidate vector matrix and the target demand matrix by adopting a collaborative filtering algorithm based on a user so as to obtain cosine similarity.

The normalization process refers to a process mode of quantifying attribute information into a mathematical calculation or model training, and the normalization can scale numerical characteristics to similar scales. For example, the age segmentation and the road type can be subjected to single-heat coding by adopting a single-heat coding (One-Hot Encoding) technology for standardized processing. The one-hot encoding may convert data (e.g., gender) into binary vectors.

Alternatively, non-digital data, such as text data, in the attribute information of the candidate experience driver and the target demand driver may be converted into binary vectors, each of which is represented as a binary bit, based on the one-hot encoding technique, and the digital data in the attribute information of the candidate experience driver and the target demand driver may be normalized so that the data has similar scales, i.e., normalized, to obtain a candidate vector matrix and a target demand matrix.

Illustratively, the cosine similarity sim (i, j) can be derived based on the following formula:

wherein i refers to a candidate vector matrix corresponding to candidate empirical driver attribute information, and j refers to a target demand matrix corresponding to target demand driver attribute information.

It should be noted that, by the above method, for each novice driver, a group of old drivers most similar to them can be found according to their driver attribute characteristics. The group of old drivers are experienced drivers similar in attribute to novice drivers, i.e., target experienced drivers.

S102, determining a historical driving route of the target experience driver according to the planned driving information of the target demand driver.

Wherein the planned travel information includes at least one of: a target origin, a target destination, a desired road type, and a desired traffic condition. The historical driving route may refer to a route that is the same as or similar to the planned driving information of the target demand driver during the historical driving of the target experienced driver.

Optionally, the route planning request sent by the target demand driver may be parsed to determine the planned driving information of the target demand driver.

It should be noted that, by determining the historical driving route of the target empirical driver, the historical route of the most selected driving of the experienced driver can be obtained, so as to help determine a more accurate and preferable driving route for the target demand driver.

Optionally, determining the historical driving route of the target experience driver according to the planned driving information of the target demand driver includes: according to a target starting point and a target ending point of a target demand driver, determining a first historical route which is consistent with a starting point and a stopping point of the target demand driver from candidate historical driving routes of target experience drivers; matching a second historical route from the first historical route according to the expected road type and the expected traffic condition of the target demand driver; and sorting the second historical routes according to the running time of each second historical route, and screening the historical running routes with the specified route number from the second historical routes.

Alternatively, the historical driving route which best matches the demand of the target experience driver can be selected from the historical driving routes of the target experience driver according to the current cargo transportation demand of the target demand driver.

S103, combining the historical driving routes according to the similarity between the historical driving routes to obtain a target route, and indicating a driver to drive according to the target route.

The similarity between the historical driving routes refers to the similarity of the distribution condition of the track points on the historical driving routes, and a collaborative filtering algorithm based on articles can be adopted to measure the similarity between the historical driving routes.

Optionally, for each historical driving route, if a corresponding similar route exists, the historical driving route and the corresponding similar route are combined to generate an alternative route, if no corresponding similar route exists, the historical driving route is directly used as an alternative route, and after each historical driving route is traversed, the alternative route set can only comprise the combined route, can only comprise the historical driving route, and can also comprise the combined route and the historical driving route.

Optionally, the set of alternative routes may be used as a target route, and if the number of target routes is at least one, the final route may be determined from the target routes based on a preset screening rule to indicate the target demand driver to drive, or at least two target routes may be sent to the target demand driver for the target demand driver to select.

Optionally, according to the similarity between the historical driving routes, merging the historical driving routes to obtain a target route, including: for each historical driving route, determining a similar route corresponding to each historical driving route according to the association relation of the track points between the historical driving route and other historical routes; determining the similarity between each historical driving route and the corresponding similar route based on a preset similarity calculation rule; if the similarity is greater than a preset similarity threshold, combining the historical driving route with the similar route to obtain a target route.

The preset similarity threshold may be, for example, 0.7.

For example, the similarity ratio simi-rate between each historical driving route and the corresponding similar route may be determined based on the following formula:

wherein C is _i The simi-route indicates the number of track points of the similar route corresponding to the history travel route, and route indicates the number of track points of other history travel routes except the current history travel route. len () represents the number of sequences in which the travel route matrix is taken.

It should be noted that when two routes are combined, the relative positions and time sequence of the track points generally need to be considered. If the first trajectory points of route 1 and route 2 are (1, 2) and (3, 4), respectively, the first trajectory point of the merged route may be determined considering the following method: one of the two trajectory points is selected as a starting point, typically the trajectory point that appears earlier. In this case, (1, 2) may be selected as the starting point. Ensure that the merged route remains chronological order. If the trace points (3, 4) occur after the trace points (1, 2), they need to be arranged in time order. The combined track points can be determined by interpolation or other methods to ensure that the combined track points are positioned at the middle position of the two routes before combination, and can be calculated according to the relative positions and time of the track points.

Optionally, determining a similar route corresponding to each historical driving route includes: for each historical driving route, respectively determining the curved surface distance between each track point on the historical driving route and track points on other historical routes; and determining a similar route corresponding to the historical driving route according to the relation between the curved surface distance and the preset distance threshold value. The preset distance threshold may be, for example, 5.

For example, the curved surface distance Dtk between the trajectory points cit (x 1, y 1) and rjk (x 2, y 2) may be determined based on the following formula:

wherein R is a preset radius parameter, and arccos () represents the inverse cosine. R represents a surface distance matrix and Dtk represents a surface distance from the locus point cit to rjk. Specifically: r is a matrix for storing the curved surface distances between different trajectory points. It is a two-dimensional matrix in which rows represent one locus point cit and columns represent another locus point rjk. The element R [ i ] [ j ] of R represents the surface distance between the locus points cit and rjk. Dtk is a specific surface distance for measuring the distance between the locus points cit and rjk. Calculated is the curved surface distance between the locus point cit on any one target experienced driver Route and the locus point rjk on each other target experienced driver Route in the set of similar routes Route. The purpose of this is to calculate the similarity between the trajectory points in order to find the trajectory point of the target experienced driver that is most similar to the trajectory point cit of the target demand driver.

Optionally, according to the target route, indicating that the target needs the driver to drive, including: adopting a preset interpolation method to supplement track points around sparse track points in the target route to obtain a complete target route; and indicating the target to drive according to the complete target route.

Example two

Fig. 2 is a flowchart of a route planning method according to a second embodiment of the present invention; the present embodiment provides a preferred example of combining historic travel routes on the basis of the above-described embodiments.

As shown in fig. 2, the method may include the following process:

(1) Initializing an old driver Route set Route (i.e. a historical driving Route), and selecting a city-city order Route track C.

Wherein the city-city order path trajectory C represents a cargo transportation path that includes between a starting city and a destination city. This trajectory may be the route selected by the old driver in past shipping orders. The city-city order Route track C is used as part of initializing old driver Route set Route as one of the initial routes for constructing a similar Route set for new drivers to reference in subsequent path planning. The old drivers' historical routes will be used to match the needs of the new drivers and provide more accurate navigation advice.

(2) The curved surface distance D of the track point Ci (xi, yi) on the route C and the track point Rj (xj, yj) of the old driver route set route Rj is calculated.

(3) And (3) determining whether D is smaller than 5, if so, executing the step (4), and if not, returning to execute the step (2).

(4) The trajectory points Ci are added to the similar route simi-route.

(5) Judging whether all track points on the route C are calculated, if yes, executing the step (6), otherwise, returning to execute the step (2).

(6) The simi-route similarity rate is calculated.

(7) If the similarity rate is greater than 0.7, executing the step (8), otherwise, executing the step (9).

(8) Updating Rj, and fusing similar track points of Ci and Rj.

(9) Ci is added as a new old driver Route to the set Route, denoted Rj+1.

(10) Whether all routes have been calculated (i.e., whether all routes in the old driver's route set have been traversed). If yes, ending, otherwise, returning to the step (1).

It should be noted that, according to the driving behavior habit and personal characteristics of the experience driver and the attribute information such as the cargo type, the route planning of the experience driver with the highest similarity is matched for the driver with the target requirement, such as the large truck driver, which is not familiar with the route, based on the collaborative filtering algorithm, so that the cost reduction and efficiency enhancement can be effectively realized for the transportation of the target experience driver.

The technical scheme of the invention can be a supplement to the traditional navigation and path planning. The mining, filtering and aggregation of massive historical track data can use the experience of old drivers to make valuable supplements to the existing navigation and path planning software, and particularly the current situation is that truck navigation users are sparse, so that the mining and filtering are particularly valuable.

Based on collaborative filtering, personalized navigation advice is provided, and the method is particularly suitable for novice drivers, and is helpful for reducing driving pressure and improving safety. By utilizing the historical track data, the driving habit of the user can be better understood, and more accurate route selection is provided for the user, so that the defects of the traditional scheme are overcome, and the defect that the personalized requirements of the user cannot be considered in the traditional scheme is overcome. By improving the navigation accuracy of a novice driver, traffic accidents and congestion are hopefully reduced, and the safety and fluency of the whole road are improved. Therefore, the present invention is of great importance in improving navigation accuracy and user satisfaction, especially for drivers who lack driving experience. The method provides valuable supplement for the traditional navigation and path planning algorithm, and is expected to improve road traffic and driving experience.

Example III

Fig. 3 is a block diagram of a route planning device according to a third embodiment of the present invention; the present embodiment is applicable to a case where an accurate and effective target route is planned for a target demand driver in response to a route planning request issued by the target demand driver, in combination with relevant history information of an experienced driver, and the route planning apparatus may be implemented in the form of hardware and/or software and configured in a device having a route planning function, such as a vehicle. As shown in fig. 3, the route planning device specifically includes:

a first determining module 301, configured to compare attribute information of the candidate experience drivers and the target demand drivers in response to a route planning request issued by the target demand driver, so as to determine the target experience driver from the candidate experience drivers;

a second determining module 302, configured to determine a historical driving route of the target experienced driver according to the planned driving information of the target demand driver;

and the indicating module 303 is configured to combine the historical driving routes according to the similarity between the historical driving routes to obtain a target route, and indicate the target to the driver to drive according to the target route.

Further, the first determining module 301 may include:

the first determining unit is used for respectively determining the similarity of the attribute information between each candidate experience driver and the target demand driver according to the attribute information of the candidate experience driver and the target demand driver by adopting a collaborative filtering algorithm based on a user;

and the second determining unit is used for determining the candidate experience driver as a target experience driver if the similarity is larger than a preset similarity threshold value.

Further, the first determining unit is specifically configured to:

respectively carrying out standardization processing on attribute information of candidate experience drivers and target demand drivers to obtain a candidate vector matrix and a target demand matrix;

and determining the magnitude of a vector included angle between the candidate vector matrix and the target demand matrix by adopting a collaborative filtering algorithm based on a user so as to obtain cosine similarity.

Further, the second determining module 302 is specifically configured to:

according to a target starting point and a target ending point of a target demand driver, determining a first historical route which is consistent with a starting point and a stopping point of the target demand driver from candidate historical driving routes of target experience drivers;

matching a second historical route from the first historical route according to the expected road type and the expected traffic condition of the target demand driver;

and sorting the second historical routes according to the running time of each second historical route, and screening the historical running routes with the specified route number from the second historical routes.

Further, the indicating module 303 may include:

the route determining unit is used for determining similar routes corresponding to each historical driving route according to the association relation of the track points between the historical driving route and other historical routes;

a similarity determining unit, configured to determine a similarity between each historical driving route and a corresponding similarity route based on a preset similarity calculation rule;

and the obtaining unit is used for combining the historical driving route and the similar route to obtain the target route if the similarity is greater than a preset similarity threshold.

Further, the route determination unit is specifically configured to:

for each historical driving route, respectively determining the curved surface distance between each track point on the historical driving route and track points on other historical routes;

and determining a similar route corresponding to the historical driving route according to the relation between the curved surface distance and the preset distance threshold value.

Further, the indication module 303 is further configured to:

adopting a preset interpolation method to supplement track points around sparse track points in the target route to obtain a complete target route;

and indicating the target to drive according to the complete target route.

Example IV

Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as route planning methods.

In some embodiments, the route planning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more of the steps of the route planning method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the route planning method in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

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

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, 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: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A method of route planning, comprising:

2. The method of claim 1, wherein comparing the attribute information of the candidate empirical drivers and the target demand drivers to determine the target empirical driver from the candidate empirical drivers comprises:

adopting a collaborative filtering algorithm based on a user, and respectively determining the similarity of the attribute information between each candidate experience driver and the target demand driver according to the attribute information of the candidate experience drivers and the target demand drivers;

and if the similarity is greater than a preset similarity threshold, determining the candidate experience driver as a target experience driver.

3. The method of claim 2, wherein determining the similarity of the attribute information between each candidate experienced driver and the target demand driver based on the attribute information of the candidate experienced driver and the target demand driver, respectively, using a user-based collaborative filtering algorithm, comprises:

4. The method of claim 1, wherein determining the historical travel route for the target experienced driver based on the planned travel information for the target demand driver comprises:

5. The method according to claim 1, wherein combining the historical travel routes to obtain the target route based on the similarity between the historical travel routes, comprises:

for each historical driving route, determining a similar route corresponding to each historical driving route according to the association relation of the track points between the historical driving route and other historical routes;

determining the similarity between each historical driving route and the corresponding similar route based on a preset similarity calculation rule;

if the similarity is greater than a preset similarity threshold, combining the historical driving route with the similar route to obtain a target route.

6. The method of claim 5, wherein determining a similar route for each historical travel route comprises:

7. The method of claim 1, wherein indicating a target demand driver to drive based on the target route comprises:

and indicating the target to drive according to the complete target route.

8. A route planning device, comprising:

9. An electronic device, the electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the route planning method of any one of claims 1-7.

10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the route planning method of any of claims 1-7 when executed.