Travel route planning method based on computing power of intelligent computing center and related equipment

Abstract

The invention provides an itinerary planning method based on computing power of an intelligent computing center and related equipment, and relates to the technical field of computing power infrastructure, the method comprises the following steps: S1, under the condition that a travel request of a user is received, obtaining a database of the user, the database comprising a point of interest POI of the user; step S2, retrieving from the database based on the travel request to obtain a plurality of intermediate POIs (Point of Interest), the plurality of intermediate POIs being POIs of the user; s3, the multiple middle POIs are clustered, N target POIs are obtained through screening from the multiple clustered middle POIs, the N target POIs are POIs with the distance between the clustered POIs smaller than a preset distance threshold value, and N is an integer larger than 1; and S4, generating a first itinerary plan based on the N target POIs, wherein the first itinerary plan comprises the N target POIs arranged in sequence. The travel experience of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computing power infrastructure, and in particular to a trip planning method and related equipment based on the computing power of an intelligent computing center. Background Art

[0002] "Intelligent computing center" refers to a facility that provides the required computing power, data and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training and model reasoning) by using large-scale heterogeneous computing resources, including general computing power and intelligent computing power. The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from bottom-level computing power to top-level application enablement.

[0003] City travel or scenic spot travel is one of the ways for users to travel and play. When users need to travel, they can choose different travel methods according to their personal needs. For example, if users want to get close to nature, they can choose scenic spot travel; or if users need to go shopping, they can choose city travel.

[0004] Currently, users usually plan their travel itineraries manually or choose existing itineraries based on recommendations from travel agencies. However, planning itineraries manually is too cumbersome and takes up a lot of time, while existing itineraries recommended by travel agencies usually fail to meet individual needs, resulting in a poor travel experience for users.

[0005] It can be seen that there are problems in the relevant technologies such as complicated itinerary planning or itinerary failure to meet personal needs, resulting in a poor travel experience for users. Summary of the invention

[0006] The embodiments of the present invention provide a trip planning method and related equipment based on the computing power of an intelligent computing center to solve the problems in the prior art that itinerary planning is cumbersome or the itinerary cannot meet personal needs, resulting in a poor travel experience for users.

[0007] To solve the above problems, the present invention is achieved as follows:

[0008] In a first aspect, an embodiment of the present invention provides a trip planning method based on the computing power of an intelligent computing center, including:

[0009] Step S1: upon receiving a travel request from a user, obtaining a database of the user, wherein the database includes points of interest (POIs) of the user;

[0010] Step S2: searching from the database based on the travel request to obtain a plurality of intermediate POIs, where the plurality of intermediate POIs are the POIs of the user;

[0011] Step S3, clustering the multiple intermediate POIs, and screening out N target POIs from the clustered multiple intermediate POIs, wherein the N target POIs are POIs whose distances between clustered POIs are less than a preset distance threshold, and N is an integer greater than 1;

[0012] Step S4: Generate a first itinerary plan based on the N target POIs, wherein the first itinerary plan includes the N target POIs arranged in sequence.

[0013] In one embodiment, step S2 includes:

[0014] Step S21, decomposing the travel request based on preset category parameters to obtain multiple sub-requests and preference parameters corresponding to each sub-request;

[0015] Step S22: searching from the database based on the preference parameters corresponding to all sub-requests to obtain the plurality of intermediate POIs.

[0016] In one embodiment, step S4 includes:

[0017] Step S41, obtaining a first number of the first trip plans generated within a preset time period, and a preset constraint condition corresponding to the preset time period, wherein the preset constraint condition includes a preference constraint condition and a planning constraint condition, wherein the preference constraint condition is used to characterize the preference constraint for generating the first trip plan, and the planning constraint condition is used to characterize the quantity constraint for generating the first trip plan;

[0018] Step S42: When the preference parameters corresponding to each sub-request satisfy the preference constraint condition and the first number satisfies the planning constraint condition, the N target POIs are sorted based on a simulated annealing algorithm and the preference constraint condition to obtain the first itinerary plan.

[0019] In one embodiment, step S4 further includes:

[0020] Step S43: Acquire the plan view information of the area corresponding to the travel request, wherein the plan view information includes a plurality of locations and a condition parameter of whether each two locations are directly accessible, and each of the plurality of locations corresponds to a POI;

[0021] Step S44: constructing a location constraint condition based on the multiple locations and a condition parameter of whether direct access is allowed between each two locations;

[0022] Step S45: sorting the N target POIs based on the simulated annealing algorithm and the position constraint condition to obtain a second itinerary plan, in which two adjacent target POIs are allowed to be directly reached.

[0023] In one embodiment, step S4 further includes:

[0024] Step S46: When the preference parameter corresponding to each sub-request satisfies the preference constraint but the first number does not satisfy the planning constraint, the N target POIs are sorted based on a simulated annealing algorithm, the preference constraint and the planning constraint to obtain a third itinerary plan.

[0025] In one embodiment, the preference constraint condition is that the preference parameter corresponding to each sub-request meets the preset preference parameter;

[0026] The planning constraint parameter is that the number of the first trip plans generated based on the computing power of the intelligent computing center received within the preset time period is less than or equal to a preset number threshold.

[0027] In a second aspect, an embodiment of the present invention further provides a trip planning device based on the computing power of an intelligent computing center, comprising:

[0028] An acquisition module, configured to acquire a database of the user upon receiving a travel request from the user, wherein the database includes points of interest (POIs) of the user;

[0029] A retrieval module, configured to retrieve from the database based on the travel request to obtain a plurality of intermediate POIs, wherein the plurality of intermediate POIs are the POIs of the user;

[0030] A clustering module, used to cluster the multiple intermediate POIs, and screen out N target POIs from the clustered multiple intermediate POIs, wherein the N target POIs are POIs whose distances between the clustered POIs are less than a preset distance threshold, and N is an integer greater than 1;

[0031] A sorting module is used to generate a first itinerary plan based on the N target POIs, wherein the first itinerary plan includes the N target POIs arranged in sequence.

[0032] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the itinerary planning method based on the computing power of an intelligent computing center as described in the first aspect above are implemented.

[0033] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the itinerary planning method based on the computing power of an intelligent computing center as described in the first aspect above are implemented.

[0034] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps in the itinerary planning method based on the computing power of an intelligent computing center as described in the first aspect above.

[0035] In an embodiment of the present invention, when a user's travel request is received, a database of the user is obtained, the database includes the user's points of interest POI; based on the travel request, multiple intermediate POIs are retrieved from the database, and the multiple intermediate POIs are the user's POIs; the multiple intermediate POIs are clustered, and N target POIs are screened from the multiple intermediate POIs after clustering to obtain N target POIs, the N target POIs are POIs whose distances between the clustered POIs are less than a preset distance threshold, and N is an integer greater than 1; a first itinerary plan is generated based on the N target POIs, and the first itinerary plan includes N target POIs arranged in sequence. In this way, through the multiple intermediate POIs retrieved from the database, each intermediate POI corresponds to a travel location that the user is interested in; then, N target POIs are screened from the multiple intermediate POIs through clustering, and a first itinerary plan is generated, so that the first itinerary plan meets the user's personal needs, and at the same time, the first itinerary plan can be automatically generated, thereby improving the user's travel experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0037] Figure 1 It is a flow chart of a trip planning method based on the computing power of an intelligent computing center provided by an embodiment of the present invention;

[0038] Figure 2 It is a schematic diagram of different locations of a scenic spot that cannot be directly reached provided by an embodiment of the present invention;

[0039] Figure 3 It is a structural diagram of a trip planning device based on the computing power of an intelligent computing center provided by an embodiment of the present invention;

[0040] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The "computing power" mentioned in the present invention is the ability of computer equipment or computing / data center to process information. It is the ability of computer hardware and software to work together to execute certain computing requirements. It is the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity. It mainly provides services to the society through computing power infrastructure.

[0043] The "computing power" (Computational Power, CP) described in the present invention is the ability of a data center server to process data and output results. It is a comprehensive indicator to measure the computing power of a data center, including general computing power, super computing power and intelligent computing power. The commonly used unit of measurement is the number of floating point operations performed per second (FLOPS: Floating Point Operations Per Second, 1EFLOPS = 10^18FLOPS). The larger the value, the stronger the comprehensive computing power. According to calculations, 1EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream notebooks. The calculation formula is: CP = CP 通用 +CP 智能 +CP 超级 .

[0044] The "Network Power" (NP) described in the present invention is a manifestation of the data transmission capability of computing facilities, including comprehensive capabilities such as network architecture, network bandwidth, transmission latency, intelligent management and scheduling, etc. The carrying capacity involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling capabilities. In the embodiment of the present invention, the carrying capacity uses the memory bandwidth.

[0045] The "Storage Power" (SP) described in the present invention is the comprehensive ability of a data center in terms of data storage capacity, performance, safety and reliability, and green and low-carbon. It is a comprehensive indicator for measuring the data storage capacity of a data center, including external storage devices such as storage arrays and server built-in storage devices. The commonly used unit of measurement for storage capacity is exabyte (EB, 1EB = 2^60bytes), and the commonly used unit of measurement for performance is the number of reads and writes per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB). The disaster recovery ratio is an important manifestation of safety and reliability.

[0046] The "computing power infrastructure" described in the present invention is a new type of information infrastructure that integrates information computing power, network carrying capacity, and data storage capacity. It can realize centralized computing, storage, transmission and application of information, and presents characteristics such as diversity and ubiquity, intelligence and agility, safety and reliability, green and low-carbon. It is of great significance to promote industrial transformation and upgrading, enable my country's scientific and technological innovation, meet people's better life and realize high-efficiency social governance.

[0047] The “computing power” mentioned in the present invention includes general computing power, intelligent computing power and super computing power.

[0048] The “general computing power” mentioned in the present invention refers to the computing power provided by a server based on a central processing unit (CPU) chip, which is used to support basic general computing such as cloud computing and edge computing.

[0049] The "intelligent computing power" described in the present invention is a computing platform for large-scale deployment of special chips such as graphics processing units (GPUs), field programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) for various innovative applications of artificial intelligence, such as natural language processing and machine vision.

[0050] The "super computing power" mentioned in the present invention is mainly the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and uses a dedicated operating system to handle extremely complex or data-intensive problems. It is mainly used for calculations in cutting-edge scientific fields, such as planetary simulation, drug molecule design, and gene analysis.

[0051] The "intelligent computing center" mentioned in the present invention refers to a facility that provides the required computing power, data and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training and model reasoning) by using large-scale heterogeneous computing resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, etc.). The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from bottom-level computing power to top-level application enablement.

[0052] The "computing resources" mentioned in the present invention refer to the technologies and facilities with information calculation, transmission, storage and application capabilities required for the development of the digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and supporting and guarantee resources such as wind, fire, water and electricity.

[0053] The "Point of Interest (POI)" mentioned in the present invention refers to a place or feature or landscape with special significance that can attract people's attention on the map, such as a house, a shop, a bus stop, a sunrise, a sunset, a sea of ​​clouds or aurora, etc. POI usually includes four aspects of information: name, category, longitude and latitude. POI can remind users of detailed information about the branches of road conditions and surrounding buildings, and can also facilitate the search for required location information during navigation, so as to select the most convenient and unobstructed road for route planning.

[0054] The "clustering" described in the present invention is spatial clustering, which calculates the similarity between objects, classifies objects with high similarity into one class, and objects with low similarity into different classes, thereby realizing the rapid determination of the user's travel location (i.e., target POI) through the existing content in the database. Among them, the distance between the clustered POIs is used to represent the similarity between the POIs.

[0055] See also Figure 1 , Figure 1 is a flow chart of a method for itinerary planning based on the computing power of an intelligent computing center provided by an embodiment of the present invention. Figure 1 As shown, the following steps are included:

[0056] Step S1: upon receiving a travel request from a user, obtaining a database of the user, wherein the database includes points of interest (POIs) of the user;

[0057] Step S2: searching from the database based on the travel request to obtain a plurality of intermediate POIs, where the plurality of intermediate POIs are the POIs of the user;

[0058] Step S3, clustering the multiple intermediate POIs, and screening out N target POIs from the clustered multiple intermediate POIs, wherein the N target POIs are POIs whose distances between clustered POIs are less than a preset distance threshold, and N is an integer greater than 1;

[0059] Step S4: Generate a first itinerary plan based on the N target POIs, wherein the first itinerary plan includes the N target POIs arranged in sequence.

[0060] In an embodiment of the present invention, when a user's travel request is received, a database of the user is obtained, the database includes the user's points of interest POI; based on the travel request, multiple intermediate POIs are retrieved from the database, and the multiple intermediate POIs are the user's POIs; the multiple intermediate POIs are clustered, and N target POIs are screened from the multiple intermediate POIs after clustering to obtain N target POIs, the N target POIs are POIs whose distances between the clustered POIs are less than a preset distance threshold, and N is an integer greater than 1; a first itinerary plan is generated based on the N target POIs, and the first itinerary plan includes N target POIs arranged in sequence. In this way, through the multiple intermediate POIs retrieved from the database, each intermediate POI corresponds to a travel location that the user is interested in; then, N target POIs are screened from the multiple intermediate POIs through clustering, and a first itinerary plan is generated, so that the first itinerary plan meets the user's personal needs, and at the same time, the first itinerary plan can be automatically generated, thereby improving the user's travel experience.

[0061] The preset distance threshold is used to measure the similarity between POIs. When the distance between the POIs is greater than the preset distance threshold, it is considered that the similarity between the two POIs is low, and the possibility that the user goes to the corresponding locations of the two POIs at the same time is low, so the two POIs are not used as target POIs.

[0062] In one embodiment, step S2 includes:

[0063] Step S21, decomposing the travel request based on preset category parameters to obtain multiple sub-requests and preference parameters corresponding to each sub-request;

[0064] Step S22: searching from the database based on the preference parameters corresponding to all sub-requests to obtain the plurality of intermediate POIs.

[0065] It is understandable that the user's travel request is usually the user's colloquial expression, and the travel request needs to be analyzed to obtain the user's real needs. In the embodiment of the present invention, the travel request is decomposed based on the preset category parameters to obtain multiple sub-requests and preference parameters corresponding to each sub-request, and the user's travel needs can be determined through the preference parameters.

[0066] For example, the preference parameters may be preference parameters in three aspects: granularity, specificity and attitude. The sub-requests in three aspects: granularity, specificity and attitude are obtained by decomposing the travel request. Granularity is used to characterize whether the travel request is a request for a single location (i.e., POI) or a request for overall itinerary planning. Specificity is used to characterize whether the travel request is a clear request or a vague request. Attitude is used to characterize whether the travel request is a positive request or a negative request.

[0067] For example, if the user's travel request is "I want to go to Guangdong for three days", the user's travel request is decomposed into three sub-requests, namely, a request for overall itinerary planning (i.e. "go to Guangdong for three days"), a clear request, and a positive request. At this time, multiple intermediate POIs are retrieved according to the three sub-requests, and multiple intermediate POIs are POIs that meet the three preferences (i.e., POIs belonging to Guangdong are satisfied at the same time, and the user likes or is positive about the POI), which meets the user's travel request; at the same time, the intermediate POI is the POI retrieved from the user's corresponding database, which also meets the user's personal needs.

[0068] In one embodiment, step S4 includes:

[0069] Step S41, obtaining a first number of the first trip plans generated within a preset time period, and a preset constraint condition corresponding to the preset time period, wherein the preset constraint condition includes a preference constraint condition and a planning constraint condition, wherein the preference constraint condition is used to characterize the preference constraint for generating the first trip plan, and the planning constraint condition is used to characterize the quantity constraint for generating the first trip plan;

[0070] Step S42: When the preference parameters corresponding to each sub-request satisfy the preference constraint condition and the first number satisfies the planning constraint condition, the N target POIs are sorted based on a simulated annealing algorithm and the preference constraint condition to obtain the first itinerary plan.

[0071] The preset time period is the time period for the travel request to plan the trip after the intelligent computing center receives the user's travel request, which is used to measure the density of the request. If the travel request is to set a time period for the planned trip, the time period is set to a fixed time period, such as today and tomorrow.

[0072] The above-mentioned preset constraints are used to restrict when sorting the N target POIs, so that the generated itinerary planning satisfies the preset constraints. In some optional implementations, the preset constraints also include at least one of the constraints such as time constraints and type constraints. Among them, the time constraint can be a constraint on the opening hours of the POI. For example, if the POI corresponds to a park that is only open during working hours, then a time constraint is set to limit the access time of the POI so that the time to visit the POI in the generated itinerary planning is working hours. In addition, the type constraint can be a constraint between adjacent POIs in the plan, that is, the types of two consecutive POIs need to be different to avoid users being in the same type of place for a long time when traveling.

[0073] In the embodiment of the present invention, the preset constraint conditions include preference constraint conditions and planning constraint conditions. Among them, the preference constraint conditions are used to characterize the preference constraint of generating the first trip plan, and are combined with multiple sub-requests obtained by decomposing the user's travel request to determine whether to generate the first trip plan. The planning constraint conditions are used to characterize the quantity constraint of generating the first trip plan, so as to avoid generating too many first trip plans within the preset time period, resulting in excessive passenger flow and poor user experience.

[0074] In an embodiment of the present invention, the first number of first itinerary plans generated within a preset time period and the preset constraints corresponding to the preset time period are obtained, the preset constraints include preference constraints and planning constraints, the preference constraints are used to characterize the preference constraints for generating the first itinerary plan, and the planning constraints are used to characterize the quantity constraints for generating the first itinerary plan; when the preference parameters corresponding to each sub-request meet the preference constraints, and the first number meets the planning constraints, the N target POIs are sorted based on the simulated annealing algorithm and the preference constraints to obtain the first itinerary plan. In this way, itinerary planning is performed by the constraints of the preference constraints and the planning constraints, so that the order of the N target POIs in the first itinerary plan can meet the travel needs of the user.

[0075] In one embodiment, step S4 further includes:

[0076] Step S43: Acquire the plan view information of the area corresponding to the travel request, wherein the plan view information includes a plurality of locations and a condition parameter of whether each two locations are directly accessible, and each of the plurality of locations corresponds to a POI;

[0077] Step S44: constructing a location constraint condition based on the multiple locations and a condition parameter of whether direct access is allowed between each two locations;

[0078] Step S45: sorting the N target POIs based on the simulated annealing algorithm and the position constraint condition to obtain a second itinerary plan, in which two adjacent target POIs are allowed to be directly reached.

[0079] It should be noted that in many scenic spots, especially those with mountainous landscapes, if multiple tourist attractions in the scenic spot are projected onto a plane, there will be a situation where the straight-line distance between some attractions is very close, but in fact there is no direct access between the attractions. For example, there are peaks, valleys or canyons between the attractions. In this case, the two attractions cannot be reached directly and need to be detoured. In the process of detour, other attractions will be passed, which need to be planned differently.

[0080] In an embodiment of the present invention, the floor plan information of the area corresponding to the travel request is obtained, and the floor plan information includes multiple locations and conditional parameters of whether each two locations can be directly reached, and each of the multiple locations corresponds to a POI; the location constraint condition is constructed based on the multiple locations and the conditional parameters of whether each two locations can be directly reached; the N target POIs are sorted based on the simulated annealing algorithm and the location constraint condition to obtain the second itinerary plan, and two adjacent target POIs in the second itinerary plan are allowed to be directly reached. In this way, by setting the location constraint condition, there are no two target POIs that cannot be directly reached in the generated second itinerary plan, so that the generated itinerary plan is more in line with the actual situation of the travel location, and the user's travel experience is improved.

[0081] For example, Figure 2 In the schematic diagram of the scenic spot shown in the figure, the distance between point A and point F projected onto the plane is relatively close. If the plan is based on the plane route, it may be planned to be reached by a direct line. However, in the actual scenic spot, point A and point F are located on two different peaks, with a canyon in the middle that cannot be directly crossed. It is necessary to detour A→B→C→D→E→F to reach it, and the location corresponding to a POI in the middle cannot be skipped. In this case, the plane map information of the scenic spot is obtained, and the generated second itinerary plan is constrained by whether any two locations from A to F can be directly reached, so that the second itinerary plan meets the following requirements: Figure 2 The situation shown.

[0082] In one embodiment, step S4 further includes:

[0083] Step S46: When the preference parameter corresponding to each sub-request satisfies the preference constraint but the first number does not satisfy the planning constraint, the N target POIs are sorted based on a simulated annealing algorithm, the preference constraint and the planning constraint to obtain a third itinerary plan.

[0084] It should be noted that if a large number of requests for the same type of preference appear at the same scenic destination (i.e., the preference parameters of the sub-requests corresponding to the travel requests are the same), if planning is still done according to a single request, it will inevitably cause congestion during the user's actual travel, affecting the user experience and posing a challenge to the operation of the scenic spot. When planning itineraries based on the computing power of the intelligent computing center, preference constraints and planning constraints are added to the constraints. If there are many similar planning demands, multiple planning paths are used for diversion, which can not only meet the user's reasonable travel itinerary, but also effectively avoid congestion in the itinerary.

[0085] In an embodiment of the present invention, when the preference parameter corresponding to each sub-request satisfies the preference constraint condition, but the first number does not satisfy the planning constraint condition, the N target POIs are sorted based on a simulated annealing algorithm, the preference constraint condition and the planning constraint condition to obtain a third itinerary plan. In this way, it is avoided that the same travel plan is generated when a large number of identical travel requests are received, so as to avoid generating too many first itinerary plans within a preset time period, resulting in excessive passenger flow and poor user experience.

[0086] In one embodiment, the preference constraint condition is that the preference parameter corresponding to each sub-request meets the preset preference parameter;

[0087] The planning constraint parameter is that the number of the first trip plans generated based on the computing power of the intelligent computing center received within the preset time period is less than or equal to a preset number threshold.

[0088] See also Figure 3 , Figure 3 is a structural diagram of a trip planning device based on the computing power of an intelligent computing center provided by an embodiment of the present invention, such as Figure 3 As shown, the itinerary planning device 300 based on the computing power of the intelligent computing center includes:

[0089] An acquisition module 301 is used to acquire a database of the user when receiving a travel request from the user, wherein the database includes points of interest (POI) of the user;

[0090] A retrieval module 302 is used to retrieve from the database based on the travel request to obtain a plurality of intermediate POIs, where the plurality of intermediate POIs are the POIs of the user;

[0091] A clustering module 303 is used to cluster the multiple intermediate POIs, and screen out N target POIs from the clustered multiple intermediate POIs, wherein the N target POIs are POIs whose distances between the clustered POIs are less than a preset distance threshold, and N is an integer greater than 1;

[0092] The sorting module 304 is configured to generate a first itinerary plan based on the N target POIs, wherein the first itinerary plan includes the N target POIs arranged in sequence.

[0093] In one embodiment, the retrieval module 302 includes:

[0094] A decomposition unit, configured to decompose the travel request based on a preset category parameter to obtain a plurality of sub-requests and a preference parameter corresponding to each sub-request;

[0095] The retrieval unit is used to retrieve the plurality of intermediate POIs from the database based on the preference parameters corresponding to all the sub-requests.

[0096] In one embodiment, the sorting module 304 includes:

[0097] a first acquisition unit, configured to acquire a first number of the first trip plans generated within a preset time period, and a preset constraint condition corresponding to the preset time period, wherein the preset constraint condition includes a preference constraint condition and a planning constraint condition, wherein the preference constraint condition is used to characterize a preference constraint for generating the first trip plan, and the planning constraint condition is used to characterize a quantity constraint for generating the first trip plan;

[0098] The first sorting unit is used to sort the N target POIs based on a simulated annealing algorithm and the preference constraint condition to obtain the first itinerary plan when the preference parameter corresponding to each sub-request satisfies the preference constraint condition and the first number satisfies the planning constraint condition.

[0099] In one embodiment, the sorting module 304 further includes:

[0100] A second acquisition unit is used to acquire the plan view information of the area corresponding to the travel request, wherein the plan view information includes a plurality of locations and a condition parameter of whether each two locations are directly accessible, and each of the plurality of locations corresponds to a POI;

[0101] A construction unit, configured to construct a location constraint condition based on the multiple locations and a condition parameter of whether direct access is allowed between each two locations;

[0102] The second sorting unit is used to sort the N target POIs based on a simulated annealing algorithm and the position constraint condition to obtain a second itinerary plan, in which two adjacent target POIs are allowed to be directly reached.

[0103] In one embodiment, the sorting module 304 further includes:

[0104] The third sorting unit is used to sort the N target POIs based on a simulated annealing algorithm, the preference constraint and the planning constraint to obtain a third itinerary plan when the preference parameter corresponding to each sub-request satisfies the preference constraint but the first number does not satisfy the planning constraint.

[0105] In one embodiment, the preference constraint condition is that the preference parameter corresponding to each sub-request meets the preset preference parameter;

[0106] The planning constraint parameter is that the number of the first trip plans generated based on the computing power of the intelligent computing center received within the preset time period is less than or equal to a preset number threshold.

[0107] The itinerary planning device based on the computing power of an intelligent computing center provided in an embodiment of the present invention is capable of implementing each process of each embodiment of the above-mentioned itinerary planning method based on the computing power of an intelligent computing center. The technical features correspond one to one and can achieve the same technical effect. To avoid repetition, they will not be described here.

[0108] It should be noted that the itinerary planning device based on the computing power of the intelligent computing center in the embodiment of the present invention can be a device, or a component, integrated circuit, or chip in an electronic device.

[0109] The embodiment of the present invention further provides an electronic device, see Figure 4 , Figure 4 4 is a schematic diagram of a structure of an electronic device provided by the present invention, the electronic device includes a memory 401, a processor 402 and a program or instruction stored in the memory 401 and running thereon, and when the program or instruction is executed by the processor 402, it can be realized Figure 1 Any steps in the corresponding embodiment of the itinerary planning method based on the computing power of the intelligent computing center and the same beneficial effects are achieved will not be repeated here.

[0110] The processor 402 may be a CPU, an ASIC, an FPGA or a GPU.

[0111] A person skilled in the art can understand that all or part of the steps of implementing the above-mentioned itinerary planning method embodiment based on the computing power of an intelligent computing center can be completed by hardware related to program instructions, and the program can be stored in a readable medium.

[0112] The embodiment of the present invention further provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above Figure 1The corresponding steps in the embodiment of the itinerary planning method based on the computing power of the intelligent computing center can achieve the same technical effect, and will not be repeated here to avoid repetition. The storage medium is such as a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0113] The terms "first", "second" etc. in the embodiments of the present invention are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. In addition, the terms "include" and "have" and any variation thereof are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. In addition, "and / or" is used in the present application to represent at least one of the connected objects, such as A and / or B and / or C, indicating that A alone, B alone, C alone, and A and B all exist, B and C all exist, A and C all exist, and 7 situations in which A, B and C all exist.

[0114] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0115] 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 disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a second terminal device, etc.) to execute the methods of each embodiment of the present application.

[0116] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A trip planning method based on the computing power of an intelligent computing center, characterized in that: include: Step S1: upon receiving a travel request from a user, obtaining a database of the user, wherein the database includes points of interest (POIs) of the user; Step S2: searching from the database based on the travel request to obtain a plurality of intermediate POIs, where the plurality of intermediate POIs are the POIs of the user; Step S3, clustering the multiple intermediate POIs, and screening out N target POIs from the clustered multiple intermediate POIs, wherein the N target POIs are POIs whose distances between clustered POIs are less than a preset distance threshold, and N is an integer greater than 1; Step S4: Generate a first itinerary plan based on the N target POIs, wherein the first itinerary plan includes the N target POIs arranged in sequence.

2. The method according to claim 1, characterized in that The step S2 comprises: Step S21, decomposing the travel request based on preset category parameters to obtain multiple sub-requests and preference parameters corresponding to each sub-request; Step S22: searching from the database based on the preference parameters corresponding to all sub-requests to obtain the plurality of intermediate POIs.

3. The method according to claim 2, characterized in that The step S4 comprises: Step S41, obtaining a first number of the first trip plans generated within a preset time period, and a preset constraint condition corresponding to the preset time period, wherein the preset constraint condition includes a preference constraint condition and a planning constraint condition, wherein the preference constraint condition is used to characterize the preference constraint for generating the first trip plan, and the planning constraint condition is used to characterize the quantity constraint for generating the first trip plan; Step S42: When the preference parameters corresponding to each sub-request satisfy the preference constraint condition and the first number satisfies the planning constraint condition, the N target POIs are sorted based on a simulated annealing algorithm and the preference constraint condition to obtain the first itinerary plan.

4. The method according to claim 3, characterized in that The step S4 further comprises: Step S43: Acquire the plan view information of the area corresponding to the travel request, wherein the plan view information includes a plurality of locations and a condition parameter of whether each two locations are directly accessible, and each of the plurality of locations corresponds to a POI; Step S44: constructing a location constraint condition based on the multiple locations and a condition parameter of whether direct access is allowed between each two locations; Step S45: sorting the N target POIs based on the simulated annealing algorithm and the position constraint condition to obtain a second itinerary plan, in which two adjacent target POIs are allowed to be directly reached.

5. The method according to claim 3, characterized in that The step S4 further comprises: Step S46: When the preference parameter corresponding to each sub-request satisfies the preference constraint but the first number does not satisfy the planning constraint, the N target POIs are sorted based on a simulated annealing algorithm, the preference constraint and the planning constraint to obtain a third itinerary plan.

6. The method according to claim 4 or 5, characterized in that The preference constraint condition is that the preference parameters corresponding to each sub-request meet the preset preference parameters; The planning constraint parameter is that the number of the first trip plans generated based on the computing power of the intelligent computing center received within the preset time period is less than or equal to a preset number threshold.

7. A trip planning device based on the computing power of an intelligent computing center, characterized in that: include: An acquisition module, configured to acquire a database of the user upon receiving a travel request from the user, wherein the database includes points of interest (POIs) of the user; A retrieval module, configured to retrieve from the database based on the travel request to obtain a plurality of intermediate POIs, wherein the plurality of intermediate POIs are the POIs of the user; A clustering module, used to cluster the multiple intermediate POIs, and screen out N target POIs from the clustered multiple intermediate POIs, wherein the N target POIs are POIs whose distances between the clustered POIs are less than a preset distance threshold, and N is an integer greater than 1; A sorting module is used to generate a first itinerary plan based on the N target POIs, wherein the first itinerary plan includes the N target POIs arranged in sequence.

8. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the itinerary planning method based on the computing power of an intelligent computing center are implemented as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the itinerary planning method based on the computing power of an intelligent computing center are implemented as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises computer instructions, which, when executed by a processor, implement the steps of the itinerary planning method based on the computing power of an intelligent computing center as described in any one of claims 1 to 6.

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