A journey recommendation method and device based on map information points, equipment and medium

Abstract

The application provides a journey recommendation method and device based on map information points, equipment and medium, the method comprises: in response to the target user triggering instruction, obtaining and analyzing the journey information to determine the target journey area; based on the actual journey point marked by other users in the area and its space heat, the high heat information point is screened to generate the second map information point; combining the target user portrait with the matching degree of the second map information point, the target map information point is determined; finally, in response to the display instruction, the first recommended journey composed of the target map information point and its connection line is presented in the area map, the journey contains macro and specific levels, and the display content is dynamically adjusted according to the map display accuracy; the application can improve the planning efficiency and experience credibility.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of path planning, and in particular to a travel recommendation method and device based on map information points, equipment and a medium. BACKGROUND

[0002] In recent years, the location-based service (LBS) based travel recommendation system has gradually become a key technology for smart cities and the tourism industry. The existing foundation has the following disadvantages: the traditional POI update mechanism lags behind the changes in the real scene and cannot adapt to dynamic events such as temporary closure and price fluctuations; the recommendation dimension is single: there is a lack of comprehensive modeling of spatio-temporal coupling relationships (such as the correlation between the flow of people between scenic spots) and user multi-modal preferences (budget / time period / physical strength); the anti-interference ability is weak: false content identification relies on manual review, and the processing time is more than 24 hours; the ecological closedness: UGC contributors cannot obtain value feedback, resulting in insufficient productivity of high-quality content. SUMMARY

[0003] Therefore, the embodiments of the present application provide a travel recommendation method and device based on map information points, which can improve the planning efficiency and experience credibility.

[0004] The technical scheme of the embodiments of the present application is as follows:

[0005] In a first aspect, the embodiments of the present application provide a travel recommendation method based on map information points, which displays a map point travel recommendation module on a graphical user interface provided by a terminal device, and the method comprises:

[0006] In response to a first trigger instruction of a target user for the travel recommendation module, the travel information of the target user is obtained, and by analyzing the travel information, the node level and the travel area of each node level included in the travel information are determined, and the target travel area is determined from the travel area according to the target position specified by the target user;

[0007] A plurality of first map information points located in the target travel area are obtained, and at least one second map information point is determined from the plurality of first map information points based on the first recommendation priority of each first map information point in the plurality of first map information points; wherein each first map information point is an actual travel point marked by other users in the target travel area; the first recommendation priority represents the spatial heat of each first map information point;

[0008] determine a second recommended priority of each second map information point in the at least one second map information point based on the actual user portrait of the target user, and determine a target map information point from the at least one second map information point based on the second recommended priority, wherein the second recommended priority represents a matching degree between the each second map information point and the actual user portrait;

[0009] display a region map of the target travel area on the graphical user interface in response to a second trigger instruction of the target user for the travel display module, and display a first recommended travel in the region map, wherein the first recommended travel is represented by the target map information point and a connection line between the target map information points, and the first recommended travel includes a macroscopic travel and a specific travel, and the macroscopic travel or the specific travel is displayed based on a display precision of the region map.

[0010] In a second aspect, the embodiments of the present application further provide a travel recommendation device based on map information points,

[0011] In a third aspect, the embodiments of the present application further provide an electronic device, comprising a processor, a storage medium and a bus, the storage medium stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine readable instructions to execute the travel recommendation method based on map information points in any one of the first aspect.

[0012] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to execute the travel recommendation method based on map information points in any one of the first aspect.

[0013] The embodiments of the present application have the following beneficial effects:

[0014] (1) Accurate recommendation based on real user trajectory:

[0015] The system uses actual travel points marked by other users in the target travel area as a core data source (first map information point), rather than relying on traditional static POI databases. This recommendation mode based on real behavior trajectory can dynamically reflect popular routes and hidden scenic spots, avoid homogenization of recommended content, and provide users with more valuable travel suggestions.

[0016] (2) Group intelligence driven travel optimization:

[0017] By analyzing the spatial distribution and popularity (first recommendation priority) of a large number of actual travel points of users, the system can identify high-value paths and must-visit places. Compared with existing technologies, this crowd wisdom aggregation mechanism breaks through the limitations of a single user perspective, making the recommended itinerary closer to the real travel scenario and reducing the user's trial and error cost.

[0018] (3) Dynamic interaction and real-time feedback loop:

[0019] Users can directly operate actual travel points marked by other users on the map (such as connecting routes and viewing details), forming a real-time feedback loop of "exploration-verification-optimization". Compared with traditional one-way recommendation mode, this interactive design allows users to adjust the itinerary based on real cases, enhancing the flexibility and credibility of planning.

[0020] (4) Decentralized personalized matching system:

[0021] Combined with the matching degree of user portraits and actual travel points (second recommendation priority), a decentralized personalized recommendation is achieved. Unlike existing technologies that rely on pre-set labels for matching, this solution dynamically calibrates the recommendation logic based on real user behavior data, making the itinerary planning more in line with individual preferences and avoiding the "one-size-fits-all" recommendation trap.

[0022] (5) Time effectiveness and scenario-based experience upgrade:

[0023] Actual travel points have time attributes (such as seasonally limited scenic spots and time-limited activities), and the system can generate more time-effective recommendation content accordingly. Compared with traditional static recommendations, this solution can capture the dynamic changes of the target area (such as holiday activities and new store openings), providing users with a scenario-based and immersive itinerary planning experience. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0025] Figure 1 is a flowchart of steps S101-S104 provided by the embodiments of the present application;

[0026] Figure 2 is a flowchart of steps S201-S202 provided by the embodiments of the present application;

[0027] Figure 3 is a flowchart of steps S301-S302 provided by the embodiments of the present application;

[0028] Figure 4 FIG. 1 is a structural schematic diagram of a travel recommendation device based on map information points according to an embodiment of the present application;

[0029] Figure 5 FIG. 2 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the drawings in the present application serve only the purpose of description and illustration, and do not serve to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn according to the actual proportions. The flowcharts in the present application show the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can not be implemented in sequence, and the steps without logical context relationship can be reversed in sequence or implemented simultaneously. In addition, one or more other operations can be added to the flowcharts or one or more operations can be removed from the flowcharts under the guidance of the content of the present application.

[0031] In the following description, “some embodiments” are described, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0032] In addition, the described embodiments are only some of the embodiments of the present application, not all of the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative labor are within the scope of protection of the present application.

[0033] In the following description, the terms “first\second\third” are only to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that “first\second\third” can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0034] It should be noted that the term “comprising” will be used in the embodiments of the present application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to be limiting of this application.

[0036] Referring to Figure 1 , Figure 1 is a flowchart of steps S101-S104 of the route recommendation method based on map information points provided by embodiments of the application, which will be described in conjunction with Figure 1 steps S101-S104 shown in the flowchart.

[0037] In step S101, in response to a first trigger instruction of a target user to a route recommendation module, the route information of the target user is acquired, and by analyzing the route information, the node level and the route area of each node level included in the route information are determined, and the target route area is determined from the route area according to the target location specified by the target user.

[0038] Here, the user initiates a request (first trigger instruction) through the route recommendation module of the graphical user interface (GUI). The system collects user route information (such as departure location, destination, stay time, etc.), decomposes the route into multiple levels of nodes (such as city → business district → specific location), divides candidate areas according to node level (such as "city center business district"), and locks the final recommended range (target route area) in combination with the target location specified by the user (such as "A city B district").

[0039] In step S102, a plurality of first map information points located in the target route area are acquired, and at least one second map information point is determined from the plurality of first map information points based on a first recommendation priority of each first map information point in the plurality of first map information points; wherein each first map information point is an actual route point marked by other users in the target route area; the first recommendation priority represents the spatial heat of each first map information point.

[0040] Here, a plurality of first map information points (such as restaurants, scenic spots, etc.) in the target route area are acquired, which are marked as actual route points by other users. The first recommendation priority is based on the spatial heat (such as access volume, stay time, user rating, etc.) to sort the information points, and at least one second map information point with higher heat is selected.

[0041] In step S103, a second recommended priority of each second map information point in the at least one second map information point is determined based on the actual user portrait of the target user, and a target map information point is determined from the at least one second map information point based on the second recommended priority; wherein the second recommended priority represents the matching degree of the each second map information point and the actual user portrait.

[0042] Here, the actual features of the target user (such as interest tags, consumption habits, historical behaviors, etc.) are extracted, and then the matching degree of each second map information point and the user portrait (such as "sightseeing spots suitable for family travel") is evaluated to determine the final recommended target map information point.

[0043] In step S104, in response to a second trigger instruction of the target user to the travel display module, a region map of the target travel region is displayed on the graphical user interface, and a first recommended travel is displayed in the region map; wherein the first recommended travel is represented by the target map information point and the connection line between the target map information points, and the first recommended travel includes a macroscopic travel and a specific travel, and the macroscopic travel or the specific travel is displayed based on the display precision of the region map.

[0044] Here, the user initiates a request (second trigger instruction) through the travel display module to display the region map of the target travel region in the GUI. The first recommended travel is composed of the target map information points and their connection lines, and is divided into macroscopic (such as inter-city routes) and specific (such as in-sightseeing path) levels, and the display precision is automatically switched according to the map zoom ratio (such as displaying detailed paths when zooming in).

[0045] In some embodiments, referring to Figure 2 , Figure 2 is a flowchart of steps S201-S202 provided by the embodiments of the present application, and the method further includes steps S201-S202, which will be described in conjunction with each step.

[0046] In step S201, in response to an accuracy adjustment operation of the region map, current map accuracy information is determined, and based on the current map accuracy information, a to-be-displayed map information point is determined from the target map information points; wherein the number of to-be-displayed map information points increases with the increase of the current map accuracy information.

[0047] In step S202, the first recommended travel is displayed in the region map; wherein the first recommended travel is represented by the to-be-displayed map information points and the connection lines between the to-be-displayed map information points.

[0048] Here, on the basis of the above-mentioned embodiments, a new map accuracy dynamic adjustment function is added, which dynamically filters and displays target map information points that adapt to the current accuracy by responding to the user's zooming or accuracy adjustment operation on the regional map, optimizes the detail level and user experience of the trip visualization.

[0049] The user can adjust the display accuracy of the regional map through gestures (such as double-finger zooming, sliding) or interface controls (such as "+" / "-" buttons), and the system captures the zoom ratio (such as 1:1000, 1:5000) or display level (such as "city level" "block level") of the current map as the current map accuracy information. According to the current map accuracy information, filter the to-be-displayed points from the target map information points, and the number of to-be-displayed points is positively correlated with the map accuracy (the higher the accuracy, the more detailed the display).

[0050] In some embodiments, referring to Figure 3 , Figure 3 is a flowchart of steps S301-S302 provided by the embodiments of the present application, and the method further includes steps S301-S302, which will be described in conjunction with each step.

[0051] In step S301, a filtering feature is determined in response to a filtering operation of a target user on a specific map information point.

[0052] In step S302, if the target map information point has a first specific map information point matching the filtering feature, the first specific map information point is displayed with a first display effect, and a second specific map information point matching the filtering feature is filtered within a specific range, and the second specific map information point is displayed with a second display effect; wherein the specific range is determined based on the first specific map information point and / or the recommended trip, and the first display effect is different from the second display effect.

[0053] Here, on the basis of the above-mentioned embodiments, a new specific map information point filtering function is added, which allows the user to specify a filtering feature (such as "cultural site" "child-friendly") through an interactive operation, and the system dynamically adjusts the display effect of the map information point according to the filtering feature, highlights the matching points and expands the related recommendations, and improves the efficiency of the user to find interesting places.

[0054] The user initiates a filtering operation through an interface control (such as a filtering button, a voice instruction) to specify a filtering feature (such as "food" "art exhibition"), and the system analyzes the filtering feature input by the user to generate a feature vector (such as: filtering feature = {category: "art", rating: >=4.5, price: <=200 yuan}

[0055] Support multi-feature combination filtering (e.g. "art + parent-child"). In the target map information point, find the point that matches the filtering feature completely, and mark it as the first specific map information point. Example: the user filters "art exhibition", and matches "798 Art Zone Gallery".

[0056] The second specific map information point filtering is based on the first specific map information point, for example, within a radius of 500 meters centered on the point; it can also be based on the recommended route, within a range of 200 meters along the recommended route. Within the specific range, filter the points that partially match the filtering feature (e.g. "art related but slightly lower score"), and mark them as the second specific map information point, for example, around "798 Art Zone Gallery", match "art studio".

[0057] The first specific map information point is highlighted with a first display effect (e.g. red icon, pulsing animation). The second specific map information point is displayed with a second display effect (e.g. blue icon, semi-transparent highlight). The ordinary point remains the default display effect (e.g. gray icon).

[0058] In some embodiments, the first recommendation priority is calculated as follows:

[0059]

[0060] Where P1 represents the first recommendation priority of the first map information point, the larger the P1 value, the higher the recommendation weight; L represents the cumulative value of positive feedback; D represents the cumulative value of negative feedback, and a is the negative review suppression coefficient and a ≥ 1; W user is the user credit weight, and the user credit weight Where N valid is the effective marking frequency of the user, t is the time decay factor, which represents the length of time the first map information point has existed or the time interval from the latest interaction; λ is the decay rate coefficient, which is used to control the intensity of time decay; R is the area radius factor, which is determined based on the location information of the first map information point, and is used to adjust the spatial density of the plurality of first map information points.

[0061] Here, the embodiments of the present application simulate the natural law of heat decay over time by e -λt Simulate the natural law of heat decay over time. For example: initial rapid decay, new check-in points will quickly reduce priority in the first few hours / days to avoid old content dominating the screen for a long time; slow decay over time, the decay rate gradually slows down over time, preserving the long tail effect of historical high-quality content.

[0062] The accumulated value of negative feedback (D) can be understood as a difference in evaluation, which directly reduces the priority and prevents the spread of false or low-quality content (for example: a certain check-in point has 10 likes but 5 negative evaluations, the priority will be significantly inhibited).

[0063] The area radius factor (R) is used to adjust the area density to prevent the same area from displaying too many check-in points (for example, a larger R value can be set in the city center, and a smaller R value can be set in a sparse area).

[0064]

[0065] The time decay factor (t) is the time difference (unit: hours / days) from the creation of the check-in point to the current time, or the time interval of the last interaction. For example, if t=0 (newly created), e -λ·0 =1, the time decay has no effect, if t=7 days, λ=0.1, then e -0.1·7 ≈0.496, the priority is halved.

[0066] The decay rate coefficient (λ) is used to control the "steepness" of time decay. It can be dynamically adjusted, for example, increasing λ in high-traffic areas (such as λ=0.2 in scenic areas) to accelerate the elimination of old content. Decrease λ in low-traffic areas (such as λ=0.05 in remote scenic spots) to preserve historical content. The λ value can be dynamically optimized by machine learning models based on data such as area passenger flow density and user stay time.

[0067] The area radius factor (R) is used for spatial density adjustment, for example, a larger R value in the city center to expand the denominator and reduce the priority (to avoid displaying too many signs in the same neighborhood). A smaller R value in natural scenic areas to increase the priority (to encourage high-quality content in sparse areas). In the embodiments of the present application, R can be adjusted in real time based on the GPS accuracy of the user device (such as a 10-meter accuracy range) or the map zoom level.

[0068] As an example of an actual application scenario, please refer to the following scenarios:

[0069] Scenario 1: Amusement park project recommendation.

[0070] Parameter settings:

[0071] λ=0.15, R is set according to the distance between facilities (such as a distance of 50 meters between a roller coaster and a carousel, then R=50).

[0072] Effect:

[0073] The newly released "roller coaster queue only 5 minutes" check-in point has a high initial P value, which is quickly recommended to users; if the queue time becomes longer after 2 hours (negative evaluations D increase), the P value decreases rapidly and stops being recommended.

[0074] Scenario 2: Hiking path planning

[0075] Parameter settings:

[0076] λ = 0.05 (preserve historical route information), R = 100 (mountainous areas are relatively large).

[0077] Effect:

[0078] A "hidden trail" checkpoint marked 3 days ago still maintains a high P value due to L=50 and D=1, and is continuously recommended to subsequent hikers.

[0079] The differences between the embodiments of this application and traditional LBS platforms are shown in Table 1.

[0080]

[0081] Table 1

[0082] The above method achieves adaptive adjustment in both spatiotemporal dimensions through λ and R. The denominator D·R effectively suppresses fake likes / malicious negative reviews (e.g., for a merchant to get 10 fake likes, the corresponding R value in the area must be extremely large or the negative review D must be extremely small to increase the priority). Optionally, D... α In (α>1), α can be flexibly set to make the negative reviews have a stronger inhibitory effect on priority. Through the embodiments of this application, the system can accurately quantify the spatiotemporal value of user-generated content, realize dynamic and interference-resistant intelligent recommendations, and form a significant difference from the static tagging system of existing social platforms.

[0083] In some embodiments, the second recommendation priority is calculated in the following manner:

[0084]

[0085] Wherein, P2 represents the second recommendation priority of the target map information point; S pref For interest matching degree, S pref S represents the similarity between the actual user profile and the labels of the second map information points. pref =cos(θ), where θ is the angle between the TF-IDF vectors of the second map information point and the user profile; C time C is the time period matching coefficient. time This indicates the degree of overlap between the target user's preferred time period and the active time period of the second map information point. B budget B is the budget fit factor. budget This indicates the degree of matching between a user's spending level and the price range. P user P represents the baseline value of the target user's spending power. spot U represents the standardized price index for the second map information point; crowdU is the congestion penalty, N crowd represents the ratio of crowd density to comfort threshold, N current is the crowd density, N threshold is the comfort carrying capacity, k is the sensitivity coefficient; F price is the price fluctuation factor, F price represents the dynamic pricing influence coefficient, ΔP is the price fluctuation value, P base is the basic price, γ is the price sensitivity; the user portrait, the user consumption level, the comfort threshold ratio and the price sensitivity are determined based on demand information.

[0086] U crowd may be derived from real-time counting of scenic spot gates, mobile base station density heat map, user equipment Bluetooth probe scanning and other ways, for example, when N current > 1.2N threshold , an exponential penalty can be triggered:

[0087] As an example, the comfort carrying capacity N threshold of a certain exhibition hall is 200 people, and if the real-time N current is 300 people, then U crowd is e 0.5(1.5-1) = 1.28, the priority is reduced by 28%.

[0088] C time is used to perform time window constraints, and is matched at multiple granularities, such as macro seasonal matching or micro time period matching. Examples of macro seasonal matching are: C time = 2 in winter for northern ski resorts, and C time = 1 in summer; examples of micro time period matching are: if the user's preferred time period is morning, then the priority of the morning route is increased, and if the user's preferred time period is evening, then the recommendation weight of the night market is increased.

[0089] B budget is used to perform consumption level constraints, and a hierarchical matching strategy is adopted, and a budget adaptation factor B budget is determined based on the relationship between user budget registration and merchant price interval. When B budget = 1, it is a complete adaptation, B budget = 0 is filtered out and not displayed, and 0 < B budget < 1 is a partial adaptation.

[0090] As an example of an actual application scenario, see the following scenarios:

[0091] Scenario: amusement park planning.

[0092] User Profile: Family with a child (age 6), budget ￥2000, prefers amusement facilities.

[0093] Real-time parameters:

[0094] U crowd : E project has 300 people waiting (threshold 200 people) -> U crowd = 1.5;

[0095] C time : Current time is 14:00, which is the lunch peak -> C time = 0.8;

[0096] S pref : Family tag matching degree is 0.92.

[0097] Based on the above formula, we get:

[0098]

[0099] The system determines that P2 is lower than 0.7, and automatically recommends other projects with higher scores, such as F project (P2 value of F project is 0.8).

[0100] In some embodiments, the method further comprises:

[0101] In response to a trigger operation for any target map information point, or in response to the target user being within a preset range of the target map information point, display the second map information points within a specified range centered on the target map information point; wherein the area radius factor is only displayed in the second map information points with the highest second recommendation priority P2, and the other second map information points are folded.

[0102] For example, when the user actively clicks on the graphical identification of the target map information point, or the user device GPS coordinates remain within the radius R1 range (R1 = 50-200 meters adjustable) centered on the target point for T seconds, an expanded display area is dynamically defined with the target point as the center and a radius R2 (R2 = 3R1), and all second map information points within this area are quickly retrieved through a spatial indexing algorithm. For the second map information points in the overlapping area, perform deduplication and optimization. When the coverage areas of two information points have an intersection, if the intersection area is greater than a threshold A (A = π(R2 / 5) 2 ), it is determined that there is an overlap; only the information point with the highest P2 value is retained in the overlapping area, and the remaining points enter the folding queue.

[0103] Then, a hierarchical visualization interface is generated, rendering the AR identification of the optimized information point and the associated route, as well as a set of expandable thumbnail icons generated at the edge of the screen, arranged in descending order of P2; R2 can be dynamically adjusted, for example:

[0104]

[0105] wherein R base is the reference radius (default 300 meters), N points is the total number of current visible area information points.

[0106] The AR identifier in the above embodiment can be a 3D holographic signboard, the material transparency of which is dynamically adjusted according to the distance, and the signboard is provided with a built-in direction light effect to guide the user's line of sight to the target direction.

[0107] In some scenarios, for example, the user clicks the "viewing platform" information point, the system triggers the surrounding recommendation:

[0108] Taking the viewing platform as the center, R2=450 meters (because the map zoom level Z=1:800), a total of 32 second map information points are found, including restaurants, photo points, and toilets; in the intersection area, the coverage ranges of 4 restaurant information points overlap; by calculating the P2 values of the points, restaurant A (P2=0.82), restaurant B (P2=0.79), restaurant C (P2=0.65), and restaurant D (P2=0.58) are obtained; at this time, the AR signboard of restaurant A is displayed on the main interface, and the other three are folded into edge icons, which can be viewed in detail after being clicked.

[0109] In some embodiments, the first map information point comprises an effective time information, the effective time information representing a period during which the first map information point can be acquired, and the first map information point being invalid after the period ends, the period of the first map information point being associated with the first recommendation priority P1.

[0110] The effective time information T can be calculated in the following way:

[0111]

[0112] wherein T base is the basic effective period (default 24 hours), P threshold is the priority threshold (system dynamically adjusts, initial value is 0.5), N interact is the number of effective interactions (such as likes, collections, and other positive operations).

[0113] The dynamic adjustment follows the following rules:

[0114] Priority-driven: when P1>P threshold , for every 0.1 increase in priority, the effective period is extended by 10%;

[0115] Interaction gain: for every 1 additional effective interaction, the effective period is extended by 2%;

[0116] Bad review penalty: shorten 15% of the validity period and reset the interaction gain count for every 1 bad review received.

[0117] When T≤0, then hard fail stop recommendation; when P1<0.3P threshold Soft fail can be triggered, i.e. demote to fold layer even if not expired. For failed points can be transferred to cold storage and keep field, through periodic analysis of failed point data, R threshold can be optimized:

[0118]

[0119] where η is the learning rate, N useful_expired is the number of failed points marked "still valuable" by historical users, N total_expired is the total number of failed points.

[0120] Priority and validity period exist two-way influence, the influence of priority on the validity period is embodied in gain channel:

[0121]

[0122] Decay channel is automatically deducted time per hour:

[0123] The formula indicates that when P1 threshold , the time elapse speed is accelerated by 20%, T remaining represents the remaining time.

[0124] The influence of validity period on priority is embodied in urgency incentive:

[0125] For example, when T remaining <2h, trigger emergency state, increase the time factor weight in P1 calculation.

[0126] In a specific scenario application, the user at 12:00 map information point "Tangbao store", the initial parameters:

[0127] T base = 24h, P1=0.6, P threshold =0.5;

[0128] Then T=24×(1+ln(1+0.6 / 0.5))=24×1.18=28.3h;

[0129] After the following event flow occurs:

[0130] (1) 13:00: received five likes, N interact =5, update the validity period: T=28.3×(1+5 / 10)=42.45h;

[0131] (2) 15:00: P1 drops to 0.4 due to negative reviews, triggering soft failure and weight reduction;

[0132] (3) 18:00: Other users renew, P1 returns to 0.44, but P1 < 0.5 threshold , time elapses accelerates 20%.

[0133] (4) Next day 10:00: T remaining is exhausted, and the map information point is disabled.

[0134] The system will automatically hide the check-in point and push a prompt to the creator: "Your'soup shop information point' has expired, with 32 views and 5 likes received."

[0135] In some embodiments, the method further comprises:

[0136] In response to a connection operation for a second map information point within the specified range, at least one other second map information point is connected in sequence starting from the first selected second map information point to obtain a connection route; wherein the second map information point within the specified range corresponds to a type identifier, and the type identifier represents the scene type of the second map information point;

[0137] Determine a second recommended route based on the connection route;

[0138] Alternatively, in response to a trigger operation for any second map information point within the specified range, display the detail information of the second map information point; wherein the detail information includes evaluation information marked by other users for the same location information, and different evaluation information corresponding to the location is determined based on the first recommended priority P1 to determine the display order;

[0139] In response to a confirmation operation of the second map information point, determine the second recommended route with the second map information point as the destination.

[0140] Here, the user selects at least one second map information point within the specified range and generates a connection route through a connection operation (such as dragging a connection or clicking a sequence selection). The connection operation starts from the first selected second map information point and connects other selected information points in sequence. Each second map information point corresponds to a type identifier, which represents the scene type of the information point (such as scenic spots, restaurants, hotels, etc.). The system automatically determines a second recommended route based on the connection route generated by the user.

[0141] Alternatively, the user clicks or triggers any second map information point within a specified range, and the system displays the detailed information of the information point, including the evaluation information marked by other users for the location. The evaluation information of different users is sorted based on the first recommendation priority PI, and the evaluation with high priority is displayed preferentially. The user confirms a certain second map information point (such as clicking the "confirm" button), and the system automatically generates a second recommended route with the information point as the destination.

[0142] In the above embodiment, the user can generate a recommended route in two ways: connecting multiple information points to form a route or directly selecting a single information point as the destination. This design takes into account the user's need for freedom to plan a route and the need for quick route generation. The user can connect information points such as scenic spots, restaurants, and hotels to generate a personalized travel route, or the user can select a place of interest in an unfamiliar city to quickly generate an exploration route.

[0143] In summary, the embodiments of the present application have the following beneficial effects:

[0144] (1) Precise recommendation based on real user trajectories:

[0145] The system uses actual route points marked by other users in the target route area as the core data source (first map information point), rather than relying on traditional static POI databases. This recommendation mode based on real behavior trajectories can dynamically reflect popular routes and hidden scenic spots, avoiding homogenization of recommended content and providing users with more valuable route suggestions.

[0146] (2) Group intelligence driven route optimization:

[0147] By analyzing the spatial distribution and heat of a large number of actual route points of users (first recommendation priority), the system can identify high-value paths and must-visit locations. Compared with existing technologies, this group intelligence aggregation mechanism breaks through the limitations of a single user perspective, making the recommended route more close to the real travel scenario and reducing the user's trial and error cost.

[0148] (3) Dynamic interaction and real-time feedback loop:

[0149] Users can directly manipulate actual route points marked by other users on the map (such as connecting routes and viewing details), forming a real-time feedback loop of "exploration-verification-optimization". Compared with traditional one-way recommendation mode, this interactive design allows users to adjust the route based on real cases, enhancing the flexibility and credibility of planning.

[0150] (4) Decentralized personalized matching system:

[0151] The matching degree of the user portrait and the actual travel point (second recommendation priority) is combined to realize decentralized personalized recommendation. Unlike the matching method relying on preset labels in the prior art, the scheme dynamically calibrates the recommendation logic through real user behavior data, so that the travel planning is more in line with individual preferences, and the "one-size-fits-all" recommendation trap is avoided.

[0152] (5) Timeliness and scenario-based experience upgrade:

[0153] The actual travel point has a time attribute (such as a season-limited scenic spot or a time-limited activity), and the system can generate more time-sensitive recommendation content accordingly. Compared with traditional static recommendations, the scheme can capture the dynamic changes of the target area (such as holiday activities and new store openings), and provide users with scenario-based and immersive travel planning experiences.

[0154] Based on the same inventive concept, the embodiments of the present application also provide a travel recommendation device based on map information points corresponding to the travel recommendation method based on map information points in the first embodiment. Since the principle of solving problems in the device of the embodiments of the present application is similar to the above-mentioned travel recommendation method based on map information points, the implementation of the device can be referred to the implementation of the method, and the repeated parts will not be described here.

[0155] As shown in Figure 4 , the structure schematic diagram of the travel recommendation device 400 based on map information points provided by the embodiments of the present application is shown in Figure 4 . The travel recommendation device 400 based on map information points comprises:

[0156] A first determination module 401 is configured to, in response to a first trigger instruction of a travel recommendation module by a target user, acquire travel information of the target user, and determine a node level included in the travel information and a travel area of each node level by analyzing the travel information, and determine a target travel area from the travel area according to a target position specified by the target user;

[0157] A second determination module 402 is configured to acquire a plurality of first map information points located in the target travel area, and determine at least one second map information point from the plurality of first map information points based on a first recommendation priority of each first map information point in the plurality of first map information points; wherein the each first map information point is an actual travel point marked by other users in the target travel area; and the first recommendation priority represents the spatial heat of the each first map information point.

[0158] The third determining module 403 is configured to determine a second recommended priority of each second map information point in the at least one second map information point based on the actual user portrait of the target user, and determine a target map information point from the at least one second map information point based on the second recommended priority, wherein the second recommended priority represents a matching degree between the each second map information point and the actual user portrait.

[0159] The display module 404 is configured to display a region map of the target region on the graphical user interface and display a first recommended route in the region map in response to a second triggering instruction of the target user for the route display module, wherein the first recommended route is represented by the target map information point and a connection line between the target map information points, and the first recommended route includes a macro route and a specific route, and the macro route or the specific route is displayed based on a display precision of the region map.

[0160] Those skilled in the art should understand that, Figure 4 The implementation functions of each unit in the map information point-based route recommendation apparatus 400 shown can be understood with reference to the foregoing related descriptions of the map information point-based route recommendation method. Figure 4 The functions of each unit in the map information point-based route recommendation apparatus 400 shown can be implemented by a program running on a processor, or by a specific logic circuit.

[0161] In a possible implementation, the display module 404 further includes:

[0162] In response to an accuracy adjustment operation for the region map, current map accuracy information is determined, and a to-be-displayed map information point is determined from the target map information point based on the current map accuracy information, wherein the number of the to-be-displayed map information points increases with the increase of the current map accuracy information.

[0163] The first recommended route is displayed in the region map, wherein the first recommended route is represented by the to-be-displayed map information point and a connection line between the to-be-displayed map information points.

[0164] In a possible implementation, the display module 404 further includes:

[0165] In response to a filtering operation of the target user for a specific map information point, a filtering feature is determined.

[0166] If there is a first specific map information point in the target map information point that matches the screening feature, the first specific map information point is displayed with a first display effect, and a second specific map information point that matches the screening feature is screened within a specific range, and the second specific map information point is displayed with a second display effect; wherein the specific range is determined based on the first specific map information point and / or the recommended route, and the first display effect is different from the second display effect.

[0167] In a possible implementation, the first recommendation priority is calculated by the following manner:

[0168]

[0169] wherein P1 represents the first recommendation priority of the first map information point, the greater the value of P1, the higher the recommendation weight; L represents the cumulative value of positive feedback; D represents the cumulative value of negative feedback, a is a difference evaluation inhibition coefficient and a≥1; W user is a user credit weight, the user credit weight wherein N valid is the effective marking times of the user, t is a time decay factor, the time decay factor represents the length of time that the first map information point has existed or the time interval from the latest interaction of the first map information point; λ is a decay rate coefficient, the decay rate coefficient is used to control the strength of time decay; R is a region radius factor, the region radius factor is determined based on the position information of the first map information point, and the region radius factor is used to adjust the spatial density of the plurality of first map information points.

[0170] In a possible implementation, the second recommendation priority is calculated by the following manner:

[0171]

[0172] wherein P2 represents the second recommendation priority of the target map information point; S pref is an interest matching degree, S pref represents the similarity between the actual user portrait and the label of the second map information point, S pref =cos(θ), θ is the TF-IDF vector angle of the second map information point in the user portrait; C time is a time period matching coefficient, C time represents the coincidence degree between the preferred time period of the target user and the active time period of the second map information point, B budget is a budget adaptation factor, B budget represents the matching degree between the user consumption level and the price interval, P usera consumption ability benchmark value of the target user, P spot a standardized price index of the second map information point; U crowd a congestion penalty, U crowd a ratio of the crowd density to the comfort threshold, U N current a crowd density, N threshold a comfort carrying capacity, k is a sensitive coefficient; F price a price fluctuation factor, F price a dynamic pricing influence coefficient, U ΔP is a price fluctuation value, P base a basic price, γ is a price sensitivity; the user portrait, the user consumption level, the comfort threshold ratio and the price sensitivity are determined based on demand information.

[0173] In a possible implementation, the display module 404 further includes:

[0174] In response to a trigger operation for any target map information point, or in response to the target user being within a preset range of the target map information point, display the second map information points within a specified range centered on the target map information point; wherein in the second map information points with overlapping area radius factors, only the second map information point with the highest second recommendation priority P2 is displayed, and other second map information points are folded and displayed.

[0175] In a possible implementation, the first map information point includes a valid time information, the valid time information representing a period during which the first map information point can be acquired, and the first map information point being invalid after the period ends, the period of the first map information point being associated with the first recommendation priority P1.

[0176] In a possible implementation, the display module 404 further includes:

[0177] In response to a connection operation for the second map information points within the specified range, sequentially connect at least one other second map information point starting from the first selected second map information point to obtain a connection route; wherein the second map information points within the specified range correspond to a type identifier, the type identifier representing a scene type of the second map information point.

[0178] Determine a second recommended itinerary based on the connection route.

[0179] Or, in response to a trigger operation for any second map information point within the specified range, display the detail information of the second map information point; wherein the detail information includes evaluation information marked by other users for the same location information, and different evaluation information corresponding to the location is determined based on a first recommended priority P1 to display an order;

[0180] In response to a confirmation operation for the second map information point, a second recommended route is determined with the second map information point as the destination.

[0181] The above-mentioned map information point-based route recommendation device has the following beneficial effects:

[0182] (1) Accurate recommendation based on real user trajectories:

[0183] The system uses actual route points marked by other users in the target route area as the core data source (first map information point), rather than relying on traditional static POI databases. This recommendation mode based on real behavior trajectories can dynamically reflect popular routes and hidden scenic spots, avoid homogenization of recommended content, and provide users with more valuable route suggestions.

[0184] (2) Group intelligence-driven route optimization:

[0185] By analyzing the spatial distribution and heat of a large number of actual route points (first recommended priority), the system can identify high-value paths and must-visit places. Compared with existing technologies, this group intelligence aggregation mechanism breaks through the limitations of a single user perspective, making the recommended route more close to the real travel scenario and reducing the user's trial and error cost.

[0186] (3) Dynamic interaction and real-time feedback loop:

[0187] Users can directly operate actual route points marked by other users on the map (such as connecting routes and viewing details), forming a real-time feedback loop of "exploration-verification-optimization". Compared with traditional one-way recommendation mode, this interactive design allows users to adjust the route based on real cases, enhancing the flexibility and credibility of planning.

[0188] (4) Decentralized personalized matching system:

[0189] Combined with the matching degree of user portraits and actual route points (second recommended priority), a decentralized personalized recommendation is realized. Unlike the matching method of existing technologies relying on preset labels, this scheme dynamically calibrates the recommendation logic through real user behavior data, making the route planning more in line with individual preferences and avoiding the "one-size-fits-all" recommendation trap.

[0190] (5) Time effectiveness and scenario-based experience upgrade:

[0191] Actual travel point has time attribute (such as season limited scenic spot, time limited activity), the system can generate more time effective recommendation content according to this. Compared with traditional static recommendation, the scheme can capture the dynamic change of target area (such as holiday activity, new store opening), and provide users with scenario-based, immersive travel planning experience.

[0192] As Figure 5 shown, Figure 5 a constituent structure schematic diagram of an electronic device 500 provided by the embodiment of the application is provided, the electronic device 500 comprises:

[0193] a processor 501, a storage medium 502 and a bus 503, the storage medium 502 stores machine readable instructions executable by the processor 501, when the electronic device 500 runs, the processor 501 and the storage medium 502 communicate through the bus 503, the processor 501 executes the machine readable instructions to execute the steps of the travel recommendation method based on the map information point described in the embodiment of the application.

[0194] In actual application, each component in the electronic device 500 is coupled together through the bus 503. It can be understood that the bus 503 is used to realize the connection and communication between the components. The bus 503 includes not only the data bus, but also the power bus, the control bus and the state signal bus. However, in order to clearly illustrate, all kinds of buses are marked as the bus 503 in the Figure 5 .

[0195] The above electronic device has the following beneficial effects:

[0196] (1) accurate recommendation based on real user trajectory:

[0197] The system uses the actual travel points marked by other users in the target travel area as the core data source (first map information point), instead of relying on the traditional static POI database. This recommendation mode based on real behavior trajectory can dynamically reflect the popular routes and hidden scenic spots, avoid the homogenization of recommended content, and provide users with more valuable travel suggestions.

[0198] (2) group wisdom driven travel optimization:

[0199] By analyzing the spatial distribution and heat of a large number of actual travel points of users (first recommendation priority), the system can identify high value paths and must-see places. Compared with the prior art, this group wisdom aggregation mechanism breaks through the limitation of single user perspective, makes the recommended travel closer to the real travel scene, and reduces the trial and error cost of users.

[0200] (3) dynamic interaction and real-time feedback closed loop:

[0201] Users can directly operate other users' marked actual route points on the map (such as connecting routes, viewing details), forming a real-time feedback closed loop of "exploration-verification-optimization". Compared with the traditional one-way recommendation mode, this interactive design allows users to adjust the route based on real cases, enhancing the flexibility and credibility of planning.

[0202] (4) Decentralized personalized matching system:

[0203] Combined with the matching degree of user portraits and actual route points (the second recommendation priority), a decentralized personalized recommendation is realized. Unlike the matching method relying on preset labels in the prior art, the present scheme dynamically calibrates the recommendation logic through real user behavior data, so that the route planning is more in line with individual preferences, and the "one-size-fits-all" recommendation trap is avoided.

[0204] (5) Time effectiveness and scenario-based experience upgrade:

[0205] Actual route points have time attributes (such as seasonally limited scenic spots and time-limited activities), and the system can generate more time-effective recommendation content accordingly. Compared with traditional static recommendations, the present scheme can capture the dynamic changes of the target area (such as holiday activities and new store openings), and provide users with a scenario-based and immersive route planning experience.

[0206] The embodiment of the present application also provides a computer readable storage medium, the storage medium stores executable instructions, when the executable instructions are executed by at least one processor 501, the route recommendation method based on map information points is realized.

[0207] In some embodiments, the storage medium can be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD ROM), etc. It can also be various devices including one or any combination of the above memories.

[0208] In some embodiments, the executable instructions can take the form of a program, software, software modules, scripts, or code, written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and they can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0209] By way of example, the executable instructions can or can not correspond to a file in a file system, can be stored in a part of a file that holds other programs or data, e.g., one or more scripts stored in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub programs, or code portions.

[0210] By way of example, the executable instructions can be deployed to execute on one computer device, or on multiple computer devices that are located at one site, or that are distributed across multiple sites and interconnected by a communication network.

[0211] The above computer-readable storage medium has the following beneficial effects:

[0212] (1) Precise recommendation based on real user trajectory:

[0213] The system uses actual travel points marked by other users in the target travel area as the core data source (first map information point), rather than relying on traditional static POI databases. This recommendation mode based on real behavior trajectory can dynamically reflect popular routes and hidden scenic spots, avoid homogenization of recommended content, and provide users with more valuable travel suggestions.

[0214] (2) Travel optimization driven by collective wisdom:

[0215] By analyzing the spatial distribution and heat of a large number of actual travel points (first recommendation priority), the system can identify high-value paths and must-visit places. Compared with existing technologies, this collective wisdom aggregation mechanism breaks through the limitations of a single user perspective, making the recommended travel more close to the real travel scene and reducing the user's trial and error cost.

[0216] (3) Dynamic interaction and real-time feedback loop:

[0217] Users can directly manipulate actual travel points marked by other users on the map (such as connecting routes, viewing details), forming a real-time feedback loop of "exploration-verification-optimization". Compared with the traditional one-way recommendation mode, this interactive design allows users to adjust the travel based on real cases, enhancing the planning flexibility and credibility.

[0218] (4) Decentralized personalized matching system:

[0219] Combine the matching degree of user portrait and actual travel point (second recommendation priority) to realize decentralized personalized recommendation. Unlike the matching method relying on preset labels in existing technologies, the scheme dynamically calibrates the recommendation logic through real user behavior data, so that the travel planning is more in line with individual preferences, and the "one-size-fits-all" recommendation trap is avoided.

[0220] (5) Time effectiveness and scenario-based experience upgrade:

[0221] The actual travel point has a time attribute (such as season-limited scenic spots and time-limited activities), and the system can generate more time-effective recommendation content accordingly. Compared with traditional static recommendations, the scheme can capture the dynamic changes of the target area (such as holiday activities and new store openings), and provide users with scenario-based and immersive travel planning experience.

[0222] In several embodiments provided in the present application, it should be understood that the disclosed method and electronic device can be implemented by other ways. The device embodiments described above are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division way, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed components can be through some interfaces, indirect coupling or communication connection of the devices or units, which can be electrical, mechanical or other forms.

[0223] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the present embodiment scheme.

[0224] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0225] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a nonvolatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0226] The above merely describes the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A trip recommendation method based on map information points, characterized in that, Displaying a map point travel recommendation module on a graphical user interface provided by a terminal device, the method includes: In response to the first trigger command from the target user for the trip recommendation module, the system obtains the target user's trip information, and by analyzing the trip information, determines the node levels and trip areas of each node level, and determines the target trip area from the trip area based on the target location specified by the target user. A plurality of first map information points located within the target travel area are acquired, and at least one second map information point is determined from the plurality of first map information points based on a first recommendation priority of each of the plurality of first map information points; wherein, each of the first map information points is an actual travel point marked by other users in the target travel area; the first recommendation priority represents the spatial popularity of each of the first map information points; Based on the actual user profile of the target user, a second recommendation priority is determined for each of the at least one second map information points, and a target map information point is determined from the at least one second map information point based on the second recommendation priority; wherein, the second recommendation priority represents the degree of matching between each second map information point and the actual user profile; In response to a second trigger command from a target user to the trip display module, a regional map of the target trip area is displayed on the graphical user interface, and a first recommended trip is displayed in the regional map; wherein, the first recommended trip is represented by the target map information points and the lines connecting the target map information points, and the first recommended trip includes a macro trip and a specific trip, and the macro trip or the specific trip is displayed based on the display precision of the regional map respectively; The first recommendation priority is calculated in the following way: ； Wherein, P1 represents the first recommendation priority of the first map information point, and the larger the P1 value, the higher the recommendation weight; L represents the cumulative value of positive feedback; D represents the cumulative value of negative feedback; and α is the negative review suppression coefficient and α≥1. The user credit weight, the user credit weight ,in, The valid number of times a user marks a point is defined as follows: t is a time decay factor, which represents the length of time the first map information point has existed, or the time interval between the first map information point and the latest interaction; λ is a decay rate coefficient, which is used to control the intensity of time decay; R is a region radius factor, which is determined based on the location information of the first map information point and is used to adjust the spatial density of the plurality of first map information points.

2. The method according to claim 1, characterized in that, The method further includes: In response to a precision adjustment operation on the map of the region, the system determines the current map precision information and, based on the current map precision information, determines map information points to be displayed from the target map information points; wherein, the number of map information points to be displayed increases as the current map precision information increases; The first recommended itinerary is displayed on the regional map; wherein the first recommended itinerary is represented by the map information points to be displayed and the lines connecting the map information points to be displayed.

3. The method according to claim 1, characterized in that, The method further includes: In response to a target user's filtering operation for specific map information points, determine the filtering features; If a first specific map information point exists among the target map information points that matches the filtering feature, the first specific map information point is displayed with a first display effect. Furthermore, a second specific map information point matching the filtering feature is filtered within a specific range, and the second specific map information point is displayed with a second display effect. The specific range is determined based on the first specific map information point and / or the recommended itinerary, and the first display effect differs from the second display effect.

4. The method according to claim 1, characterized in that, The second recommendation priority is calculated in the following way: ； Wherein, P2 represents the second recommendation priority of the target map information point; For interest matching degree, This indicates the similarity between the actual user profile and the labels of the second map information points. , θ The angle between the TF-IDF vectors of the second map information point and the user profile; The time period matching coefficient, This indicates the degree of overlap between the target user's preferred time period and the active time period of the second map information point. = ; For budget adaptation factors, This indicates the degree of matching between a user's spending level and the price range. , This represents the baseline value of the target user's spending power. A standardized price index representing the second map information point; As a penalty for overcrowding, This represents the ratio of pedestrian density to the comfort threshold. , For human traffic density, For comfortable load capacity, k Sensitivity coefficient; As a price volatility factor, This represents the dynamic pricing impact coefficient. , This represents the price fluctuation value. Based on the price, Price sensitivity; the user profile, the user's spending level, the comfort threshold ratio, and the price sensitivity are determined based on demand information.

5. The method according to claim 4, characterized in that, The method further includes: In response to a trigger operation targeting any target map information point, or in response to the target user being within a preset range of the target map information point, a second map information point within a specified range centered on the target map information point is displayed; wherein, among the second map information points with overlapping area radius factors, only the second map information point with the highest second recommendation priority P2 is displayed, and the other second map information points are collapsed.

6. The method according to claim 1, characterized in that, The first map information point includes a valid time information, which represents the period during which the first map information point can be acquired. When the period expires, the first map information point becomes invalid. The validity period of the first map information point is associated with the first recommendation priority P1.

7. The method according to claim 5, characterized in that, The method further includes: In response to a connection operation for a second map information point within the specified range, at least one other second map information point is sequentially connected starting from the first selected second map information point to obtain a connection route; wherein, the second map information point within the specified range corresponds to a type identifier, and the type identifier represents the scene type of the second map information point; A second recommended itinerary is determined based on the aforementioned connection route; Alternatively, in response to a trigger operation targeting any second map information point within the specified range, detailed information about that second map information point is displayed; wherein, the detailed information includes evaluation information marked by other users for the same location information, and the display order of different evaluation information corresponding to the location is determined based on a first recommendation priority P1; In response to the confirmation operation of the second map information point, the second recommended itinerary is determined with the second map information point as the destination.

8. A trip recommendation device based on map information points, characterized in that, The device displays a map point travel recommendation module on a graphical user interface provided by a terminal device, the device comprising: The first determining module is used to respond to the first trigger command of the target user for the trip recommendation module, obtain the trip information of the target user, and determine the node level and the trip area of ​​each node level by analyzing the trip information, and determine the target trip area from the trip area according to the target location specified by the target user. The second determining module is used to acquire multiple first map information points located within the target travel area, and determine at least one second map information point from the multiple first map information points based on a first recommendation priority of each first map information point; wherein, each first map information point is an actual travel point marked by other users in the target travel area; the first recommendation priority represents the spatial popularity of each first map information point; the first recommendation priority is calculated in the following way: Wherein, P1 represents the first recommendation priority of the first map information point, and the larger the P1 value, the higher the recommendation weight; L represents the cumulative value of positive feedback; D represents the cumulative value of negative feedback; α is the negative review suppression coefficient and α≥1; The user credit weight, the user credit weight ,in, The effective number of user tags is t, which is the time decay factor, representing the length of time the first map information point has existed or the time interval between the first map information point and the latest interaction; λ is the decay rate coefficient, which is used to control the intensity of time decay; R is the area radius factor, which is determined based on the location information of the first map information point and is used to adjust the spatial density of the multiple first map information points. The third determining module is used to determine a second recommendation priority for each of the at least one second map information points based on the actual user profile of the target user, and to determine a target map information point from the at least one second map information point based on the second recommendation priority; wherein, the second recommendation priority represents the degree of matching between each second map information point and the actual user profile; The display module is used to respond to a second trigger command from a target user to the trip display module, display a regional map of the target trip area on the graphical user interface, and display a first recommended trip in the regional map; wherein, the first recommended trip is represented by the target map information points and the lines connecting the target map information points, and the first recommended trip includes a macro trip and a specific trip, and the macro trip or the specific trip is displayed based on the display accuracy of the regional map respectively.

9. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the map point-based trip recommendation method as described in any one of claims 1 to 7.

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