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

Abstract

The invention provides a travel recommendation method and device based on map information points, equipment and a medium, and the method comprises the steps: responding to a target user trigger instruction, and obtaining and analyzing the travel information of the target user trigger instruction to determine a target travel area; screening high-heat information points to generate second map information points based on actual travel points marked by other users in the area and spatial heat of the actual travel points; determining a target map information point in combination with the matching degree of the target user portrait and the second map information point; and finally, in response to the display instruction, presenting a first recommended travel composed of target map information points and connecting lines thereof in the regional map, the travel including a macroscopic level and a specific level, and dynamically adjusting the display content according to the map display precision. According to the invention, the planning efficiency and the experience credibility can be improved.

Description

Technical Field

[0001] The present application relates to the field of route planning technology, and in particular to a method, device, equipment and medium for recommending a route based on map information points. Background Art

[0002] In recent years, location-based service (LBS) travel recommendation systems have gradually become a key technology for smart cities and the cultural tourism industry. The existing foundation suffers from the following drawbacks: the traditional POI update mechanism lags behind real-world changes and cannot adapt to dynamic events such as temporary closures and price fluctuations; the recommendation dimension is limited, lacking comprehensive modeling of spatiotemporal coupling relationships (such as the correlation between visitor flows between attractions) and user multimodal preferences (budget, time of day, and physical fitness); the anti-interference ability is weak, and the identification of false content relies on manual review, with a processing time of more than 24 hours; and the ecosystem is closed, with user-generated content contributors unable to receive value, resulting in insufficient production of high-quality content. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a method, apparatus, device and medium for itinerary recommendation based on map information points, which can improve planning efficiency and experience credibility.

[0004] The technical solution of the embodiment of the present application is implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for recommending itineraries based on map information points, wherein a map point itinerary recommendation module is displayed on a graphical user interface provided by a terminal device, the method comprising:

[0006] In response to a first trigger instruction from a target user to the itinerary recommendation module, obtaining itinerary information of the target user, determining node levels and itinerary areas of each node level included in the itinerary information by analyzing the itinerary information, and determining a target itinerary area from the itinerary areas according to a target location specified by the target user;

[0007] obtaining a plurality of first map information points located within the target travel area, and determining at least one second map information point 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 another user within the target travel area; and the first recommendation priority represents the spatial popularity of each of the first map information points;

[0008] determining a second recommendation priority for each of the at least one second map information point based on the actual user profile of the target user, and determining 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 a degree of match between each second map information point and the actual user profile;

[0009] In response to a second trigger instruction from the target user to the itinerary display module, a regional map of the target itinerary area is displayed on the graphical user interface, and a first recommended itinerary is displayed in the regional map; wherein the first recommended itinerary is represented by the target map information points and the lines between the target map information points, the first recommended itinerary includes a macro itinerary and a specific itinerary, and the macro itinerary or the specific itinerary is displayed separately based on the display accuracy of the regional map.

[0010] In a second aspect, the embodiment of the present application further provides a device for recommending itineraries based on map information points.

[0011] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the itinerary recommendation method based on map information points as described in any one of the first aspects.

[0012] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the itinerary recommendation method based on map information points as described in any one of the first aspects is executed.

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

[0014] (1) Accurate recommendations based on real user trajectories:

[0015] The system uses the actual travel points marked by other users in the target itinerary area as its core data source (first map information point), rather than relying on a traditional static POI database. This real-world behavior-based recommendation model dynamically reflects popular routes and hidden attractions, avoiding homogenized recommendations and providing users with more valuable itinerary suggestions.

[0016] (2) Journey optimization driven by collective intelligence:

[0017] By analyzing the spatial distribution and popularity of a large number of users' actual itineraries (the first priority for recommendations), the system can identify high-value routes and must-visit locations. Compared to existing technologies, this collective intelligence aggregation mechanism overcomes the limitations of a single user's perspective, making recommended itineraries more realistic and reducing user trial and error.

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

[0019] Users can directly manipulate actual travel points marked by other users on the map (e.g., connecting routes, viewing details), creating a real-time feedback loop of "exploration-verification-optimization." Compared to the traditional one-way recommendation model, this interactive design allows users to adjust their itineraries based on real-world examples, enhancing planning flexibility and reliability.

[0020] (4) Decentralized personalized matching system:

[0021] This approach combines the matching degree between user profiles and actual itinerary points (the second priority for recommendations) to achieve decentralized personalized recommendations. Unlike existing technologies that rely on matching with preset tags, this solution dynamically calibrates recommendation logic using real user behavior data, making itineraries more tailored to individual preferences and avoiding the pitfall of "one-size-fits-all" recommendations.

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

[0023] Actual itinerary points have inherent temporal attributes (such as seasonal attractions and limited-time events), allowing the system to generate more timely recommendations. Compared to traditional static recommendations, this solution can capture dynamic changes in the target area (such as holiday events and new store openings), providing users with a scenario-based, immersive itinerary planning experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 10 is a flow chart of steps S101-S104 provided in an embodiment of the present application;

[0026] Figure 2 It is a flowchart of steps S201-S202 provided in an embodiment of the present application;

[0027] Figure 3 Schematic diagram of the process of steps S301-S302 provided in the embodiment of the present application;

[0028] Figure 4 This is a structural diagram of a device for recommending itineraries based on map information points provided by an embodiment of the present application;

[0029] Figure 5 It is a schematic diagram of the composition structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0031] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0032] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various 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 application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0033] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, 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 existence 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 those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0036] See also Figure 1 , Figure 1 This is a flow chart of steps S101-S104 of the itinerary recommendation method based on map information points provided in an embodiment of the present application, which will be combined with Figure 1 Steps S101-S104 are shown for explanation.

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

[0038] Here, the user initiates a request (the first trigger instruction) through the itinerary recommendation module of the graphical user interface (GUI). The system collects the user's itinerary information (such as departure point, destination, and duration of stay), breaks down the itinerary into multiple levels of nodes (such as city → business district → specific location), divides the candidate areas based on the node hierarchy (such as "city center business district"), and combines it with the user's specified target location (such as "city A district B") to lock in the final recommendation range (target itinerary area).

[0039] In step S102, a plurality of first map information points located within 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 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; and the first recommendation priority represents the spatial popularity of each of the first map information points.

[0040] Here, multiple first map information points (such as restaurants and attractions) within the target itinerary area are obtained. These points are marked as actual itinerary points by other users. The first recommendation priority is used to sort the information points based on spatial popularity (such as number of visits, length of stay, user ratings, etc.), and at least one second map information point with high popularity is selected.

[0041] In step S103, a second recommendation priority of each of 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 recommendation priority; wherein the second recommendation priority represents the degree of matching between each second map information point and the actual user portrait.

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

[0043] In step S104, in response to the target user's second trigger instruction for the itinerary display module, a regional map of the target itinerary area is displayed on the graphical user interface, and a first recommended itinerary is displayed in the regional map; wherein the first recommended itinerary is represented by the target map information points and the connecting lines between the target map information points, the first recommended itinerary includes a macro itinerary and a specific itinerary, and the macro itinerary or the specific itinerary is displayed separately based on the display accuracy of the regional map.

[0044] Here, the user initiates a request (a second trigger command) through the itinerary display module, which displays a regional map of the target itinerary area in the GUI. The first recommended itinerary consists of target map information points and their connections, divided into macro (such as inter-city routes) and specific (such as tourist routes within a tourist attraction) levels. The display resolution automatically switches based on the map zoom level (for example, displaying detailed routes when zoomed in).

[0045] In some embodiments, see Figure 2 , Figure 2 It is a flow chart of steps S201-S202 provided in an embodiment of the present application. The method also includes steps S201-S202, which will be described in combination with each step.

[0046] In step S201, in response to the accuracy adjustment operation for the regional map, the current map accuracy information is determined, and the map information points to be displayed are determined from the target map information points based on the current map accuracy information; wherein the number of the map information points to be displayed increases as the current map accuracy information increases.

[0047] In step S202, 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 between the map information points to be displayed.

[0048] Here, based on the above embodiment, a new map accuracy dynamic adjustment function is added. By responding to the user's zoom or accuracy adjustment operations on the regional map, the target map information points adapted to the current accuracy are dynamically filtered and displayed, optimizing the level of detail of the itinerary visualization and the user experience.

[0049] Users can adjust the display accuracy of regional maps through gestures (such as pinch-to-zoom and swipe) or interface controls (such as the "+" / "-" buttons). The system captures the current map zoom ratio (such as 1:1000, 1:5000) or display level (such as "city level" or "block level") as the current map accuracy information. Based on the current map accuracy information, points to be displayed are selected from the target map information points. The number of points to be displayed is positively correlated with map accuracy (the higher the accuracy, the more detailed the display).

[0050] In some embodiments, see Figure 3 , Figure 3 It is a flow chart of steps S301-S302 provided in an embodiment of the present application. The method also includes steps S301-S302, which will be described in combination with each step.

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

[0052] In step S302, if a first specific map information point matching the screening feature exists in the target map information point, the first specific map information point is displayed with a first display effect, and second specific map information points matching the screening feature are 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 itinerary, and the first display effect is different from the second display effect.

[0053] Here, based on the above embodiment, a new function for filtering specific map information points is added, allowing users to specify filtering features (such as "cultural places" and "parent-child friendly") through interactive operations. The system dynamically adjusts the display effects of map information points according to the filtering features, highlights matching points and expands related recommendations, thereby improving the efficiency of users in discovering places of interest.

[0054] The user initiates a filtering operation through the interface control (such as a filter button or voice command) and specifies the filtering features (such as "food" or "art exhibition"). The system parses the filtering features entered by the user and generates a feature vector (for example, filtering features = {category: "art", rating: ≥4.5, price: ≤200 yuan}

[0055] Supports multi-feature combination filtering (such as "art + parenting"). Among the target map information points, find the point that fully matches the filtering characteristics and mark it as the first specific map information point. Example: A user filters for "art exhibitions" and matches "798 Art District Gallery".

[0056] The second specific map information point expansion filter is based on the first specific map information point, for example, within a 500-meter radius around the first specific map information point; it can also be based on the recommended itinerary, within a 200-meter radius around the recommended route. Within the specified range, points that partially match the filter characteristics (such as "art-related but slightly lower-rated") are selected and marked as the second specific map information point, for example, "art studio" is matched around "798 Art District Gallery."

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

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

[0059]

[0060] Wherein, 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. α is the negative review suppression coefficient and α≥1. W user is the user's credit weight, Among them, N valid is the number of valid markings by the user, t is the time decay factor, which characterizes 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.

[0061] Here, the embodiment of the present application is through e -λt Simulate the natural decline of popularity over time. For example, with a rapid initial decline, a new check-in point will quickly lose priority in the first few hours or days to prevent old content from dominating the screen for a long time; with a slow long-term decline, the decline rate gradually slows over time to preserve the long-tail effect of historical high-quality content.

[0062] The cumulative value of negative feedback (D) can be understood as the number of negative reviews, which directly reduces the priority and prevents the spread of false or low-quality content (for example, if a check-in point has 10 likes but 5 negative reviews, its priority will be significantly suppressed).

[0063] The area radius factor (R) is used to adjust the area density to prevent too many check-in points from being displayed in the same area (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 (in hours / days) from the creation of the check-in point to the current moment, or the time interval since the last interaction. For example, if t = 0 (new creation), e -λ·0 =1, 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) controls the steepness of time decay. It can be adjusted dynamically. For example, increasing λ in high-traffic areas (e.g., λ = 0.2 in scenic areas) accelerates the elimination of old content. Reducing λ in low-traffic areas (e.g., λ = 0.05 in remote scenic spots) preserves historical content. Machine learning models can dynamically optimize the λ value based on data such as regional traffic density and user duration.

[0067] The area radius factor (R) is used to adjust spatial density. For example, if the R value is larger in the city center, the denominator is increased and the priority is lowered (to avoid displaying too many street signs in the same block). Natural scenic areas have smaller R values ​​and their priority is increased (to encourage high-quality content in sparse areas). In this embodiment of the application, R can be adjusted in real time based on the user device GPS accuracy (e.g., 10-meter accuracy range) or the map zoom level.

[0068] As an example of a real-world application scenario, see the following scenario:

[0069] Scenario 1: Amusement park project recommendation.

[0070] Parameter settings:

[0071] λ = 0.15, R is set according to the distance between the facilities (e.g., if the distance between the roller coaster and the carousel is 50 meters, then R = 50).

[0072] Effect:

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

[0074] Scenario 2: Mountain climbing path planning

[0075] Parameter settings:

[0076] λ = 0.05 (retain historical path information), R = 100 (the mountain road area is large).

[0077] Effect:

[0078] A "hidden trail" check-in point marked 3 days ago. Because L=50 and D=1, it can still maintain a high P value and continues to be recommended to subsequent climbers.

[0079] The differences between the embodiment of the present application and the traditional LBS platform are shown in Table 1.

[0080]

[0081] Table 1

[0082] The above method realizes adaptive adjustment in both time and space dimensions through λ and R. The denominator D·R effectively suppresses fake likes / malicious bad reviews (for example, if a merchant fakes 10 likes, the corresponding area R value must be extremely large or the bad review D must be extremely small to increase the priority). Optionally, D α In (α>1), α can be flexibly set to make the number of negative reviews have a stronger inhibitory effect on the priority. Through the embodiments of this application, the system can accurately quantify the spatiotemporal value of user-generated content, achieving dynamic and interference-resistant intelligent recommendations, which is significantly different from the static labeling system of existing social platforms.

[0083] In some embodiments, the second recommendation priority is calculated as follows:

[0084]

[0085] Wherein, P2 represents the second recommendation priority of the target map information point; S pref is the 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 the time period matching coefficient, C time Indicates the overlap between the target user's preferred time period and the second map information point's active time period, B budget is the budget adaptation factor, B budget Indicates the matching degree between user consumption level and price range, P user Indicates the target user's consumption capacity benchmark value, P spot Indicates the normalized price index of the second map information point; U crowdis the congestion penalty, U crowd It represents the ratio of crowd density to comfort threshold, N current is the crowd density, N threshold is the comfortable carrying capacity, k is the sensitivity coefficient; F price is the price fluctuation factor, F price represents the dynamic pricing impact coefficient, ΔP is the price fluctuation value, P base is the base 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 It can be derived from real-time counting of scenic area gates, heat maps of mobile phone base station density, and Bluetooth probe scanning of user devices. For example, when N current >1.2N threshold When , an exponential penalty can be triggered:

[0087] As an example, the comfortable carrying capacity of a pavilion is N threshold For 200 people, if real-time N current If the number of people is 300, then U crowd for e 0.5(1.5-1) =1.28, the priority is reduced by 28%.

[0088] C time Used to perform time window constraints and match at multiple granularities, such as macro season matching or micro period matching. An example of macro season matching is: winter C in a northern ski resort time =2, summer C time =1; an example of micro-time period matching is: if the user's preferred time period is morning, the priority of the morning market route is increased; if the user's preferred time period is evening, the recommendation weight of the night market is increased.

[0089] B budget It is used to constrain consumption levels and adopts a hierarchical matching strategy to determine the budget adaptation factor B based on the relationship between the user's budget registration and the merchant's price range. budget , when B budget =1 is fully adapted, B budget =0 when filtering is not displayed, 0 budget When <1, it is partial adaptation.

[0090] As an example of a real-world application scenario, see the following scenario:

[0091] Scenario: Amusement park planning.

[0092] ​User profile: Parent-child family (children aged 6, budget ￥2000, preference for amusement facilities).

[0093] Real-time parameters:

[0094] U crowd :The number of people waiting for project E is 300 (threshold is 200) → U crowd =1.5;

[0095] C time :Currently 14:00 is the noon peak → C time =0.8;

[0096] S pref : Parent-child label matching degree is 0.92.

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

[0098]

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

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

[0101] In response to a trigger operation on 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 whose area radius factors overlap, only the second map information point with the highest second recommendation priority P2 is displayed, and the other second map information points are displayed in a folded manner.

[0102] For example, when the user actively clicks on the graphic icon of the target map information point, or the GPS coordinates of the user device are within the radius R1 (R1 = 50 to 200 meters adjustable) centered on the target point for T seconds, an extended display area with a radius R2 (R2 = 3R1) centered on the target point is dynamically delineated, and all second map information points in the area are quickly retrieved through a spatial index algorithm. Deduplication optimization is performed on the second map information points in the overlapping area. When the coverage areas of two information points intersect, if the intersection area is greater than the threshold A (A = π(R2 / 5) 2 ), it is determined to be overlapping; only the information point with the highest P2 value is retained in the overlapping area, and the remaining points enter the folding queue.

[0103] Afterwards, a layered visualization interface is generated, rendering the AR logos of the preferred information points and the associated routes, and generating a set of expandable thumbnail icons at the edge of the screen, sorted in descending order of P2; R2 can be dynamically adjusted, for example:

[0104]

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

[0106] The AR sign in the above embodiment can be a 3D holographic road sign, the transparency of the material is dynamically adjusted with the distance, and the road sign has built-in directional light effects to guide the user's sight towards the target direction.

[0107] In some scenarios, for example, when a user clicks on the "viewing platform" information point, the system triggers nearby recommendations:

[0108] With the observation deck as the center, R2 = 450 meters (due to the map zoom level Z = 1:800), and a total of 32 secondary map information points were discovered, including restaurants, photo spots, and restrooms. At the intersection, the coverage of four restaurant information points overlapped. By calculating the P2 value of each point, the following were obtained: Restaurant A (P2 = 0.82), Restaurant B (P2 = 0.79), Restaurant C (P2 = 0.65), and Restaurant D (P2 = 0.58). At this time, the main interface displays the AR road sign for Restaurant A, and the other three are folded into edge icons, which can be clicked to view details.

[0109] In some embodiments, the first map information point includes valid time information, which represents the period during which the first map information point can be obtained. When the period ends, the first map information point becomes invalid. The period of the first map information point is associated with the first recommendation priority P1.

[0110] The effective time information T can be calculated as follows:

[0111]

[0112] Among them, T base The default validity period is 24 hours. threshold is the priority threshold (dynamically adjusted by the system, with an initial value of 0.5), N interact It is the number of effective interactions (positive operations such as likes and favorites).

[0113] Dynamic adjustment follows the following rules:

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

[0115] Interaction gain: Each additional effective interaction will extend the validity period by 2%;

[0116] Negative review penalty: For each negative review received, the validity period will be shortened by 15% and the interaction gain count will be reset.

[0117] When T≤0, hard failure stop recommendation; when P1<0.3P threshold , can trigger soft failure, even if it has not expired, it will be downgraded to the folding layer. For failure points, they can be transferred to cold storage and the fields can be retained. By periodically analyzing the failure point data, R threshold To optimize:

[0118]

[0119] Among them, η is the learning rate, N useful_expired is the number of invalid points marked as “still valuable” by historical users, N total_expired is the total number of failure points.

[0120] Priority and validity period have a bidirectional impact. The impact of priority on validity period is reflected in the gain channel:

[0121]

[0122] The decay channel automatically deducts time every hour:

[0123] This formula means that when P1 <P threshold When the time passes, the speed of time increases by 20%. remaining Indicates the remaining time.

[0124] The effect of validity period on priority is reflected in the urgency incentive:

[0125] For example, when T remaining When the time is less than 2 hours, an emergency state is triggered and the weight of the time factor in the P1 calculation is increased.

[0126] In a specific scenario, the user visits the "Tangbao Shop" map information point at 12:00. The initial parameters are:

[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] The following flow of events then occurs:

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

[0131] (2) 15:00: Due to negative reviews, P1 dropped to 0.4, triggering a soft failure demotion;

[0132] (3) 18:00: Other users renew their subscriptions, and P1 recovers to 0.44, but P1 <P threshold , time passes 20% faster.

[0133] (4) 10:00 the next day: T remaining Exhausted, map information points become invalid.

[0134] The system will automatically hide the check-in point and push a prompt to the creator: "Your 'Soup Dumpling Shop Information Point' has expired and received 32 views and 5 likes."

[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, sequentially connecting at least one other second map information point with the first selected second map information point as a starting point to obtain a connection route; wherein each second map information point within the specified range corresponds to a type identifier, and the type identifier represents a scene type of the second map information point;

[0137] determining a second recommended itinerary based on the connecting route;

[0138] Alternatively, in response to a trigger operation on any second map information point within the specified range, detailed information of the 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 the first recommendation priority P1;

[0139] In response to a confirmation operation on the second map information point, the second recommended itinerary is determined with the second map information point as a destination.

[0140] Here, the user selects at least one secondary map information point within a specified range and generates a connection route through a connection operation (such as dragging a line or clicking to select sequentially). The connection operation starts with the first selected secondary map information point and connects the other selected information points in sequence. Each secondary map information point is associated with a type identifier, which indicates the scene type of the information point (such as scenic spot, restaurant, hotel, etc.). Based on the user-generated connection route, the system automatically determines a second recommended itinerary.

[0141] Alternatively, if a user clicks or triggers any second map information point within the specified range, the system will display detailed information about that information point, including other users' reviews of that location mark. Reviews from different users are sorted based on the first recommendation priority, P1, with reviews with higher priority displayed first. If a user confirms a second map information point (e.g., by clicking the "Confirm" button), the system will automatically generate a second recommended itinerary with that information point as the destination.

[0142] In the above embodiment, users can create recommended itineraries in two ways: connecting multiple information points to form a route or directly selecting a single information point as a destination. This design balances the user's needs for flexible itinerary planning and rapid itinerary creation. Users can create personalized travel routes by connecting information points such as attractions, restaurants, and hotels. Users can also quickly create exploration itineraries by selecting interesting locations in unfamiliar cities.

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

[0144] (1) Accurate recommendations based on real user trajectories:

[0145] The system uses the actual travel points marked by other users in the target itinerary area as its core data source (first map information point), rather than relying on a traditional static POI database. This real-world behavior-based recommendation model dynamically reflects popular routes and hidden attractions, avoiding homogenized recommendations and providing users with more valuable itinerary suggestions.

[0146] (2) Journey optimization driven by collective intelligence:

[0147] By analyzing the spatial distribution and popularity of a large number of users' actual itineraries (the first priority for recommendations), the system can identify high-value routes and must-visit locations. Compared to existing technologies, this collective intelligence aggregation mechanism overcomes the limitations of a single user's perspective, making recommended itineraries more realistic and reducing user trial and error.

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

[0149] Users can directly manipulate actual travel points marked by other users on the map (e.g., connecting routes, viewing details), creating a real-time feedback loop of "exploration-verification-optimization." Compared to the traditional one-way recommendation model, this interactive design allows users to adjust their itineraries based on real-world examples, enhancing planning flexibility and reliability.

[0150] (4) Decentralized personalized matching system:

[0151] This approach combines the matching degree between user profiles and actual itinerary points (the second priority for recommendations) to achieve decentralized personalized recommendations. Unlike existing technologies that rely on matching with preset tags, this solution dynamically calibrates recommendation logic using real user behavior data, making itineraries more tailored to individual preferences and avoiding the pitfall of "one-size-fits-all" recommendations.

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

[0153] Actual itinerary points have inherent temporal attributes (such as seasonal attractions and limited-time events), allowing the system to generate more timely recommendations. Compared to traditional static recommendations, this solution can capture dynamic changes in the target area (such as holiday events and new store openings), providing users with a scenario-based, immersive itinerary planning experience.

[0154] Based on the same inventive concept, an embodiment of the present application also provides an itinerary recommendation device based on map information points corresponding to the itinerary recommendation method based on map information points in the first embodiment. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned itinerary recommendation method based on map information points, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0155] like Figure 4 As shown, Figure 4 : is a schematic diagram of the structure of a trip recommendation device 400 based on map information points provided in an embodiment of the present application. The trip recommendation device 400 based on map information points includes:

[0156] A first determining module 401 is configured to respond to a first trigger instruction from a target user to the itinerary recommendation module, obtain itinerary information of the target user, analyze the itinerary information, determine the node levels and itinerary areas of each node level included in the itinerary information, and determine a target itinerary area from the itinerary areas according to a target location specified by the target user;

[0157] A second determining module 402 is configured to obtain a plurality of first map information points located within 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 of the plurality of first map information points; wherein each of the first map information points is an actual travel point marked by another user within the target travel area; and the first recommendation priority represents the spatial popularity of each of the first map information points;

[0158] a third determining module 403, configured to determine a second recommendation priority for each of the at least one second map information point based on the actual user profile of the target user, and 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 a degree of match between each second map information point and the actual user profile;

[0159] The display module 404 is configured to respond to a second trigger instruction from the target user to the itinerary display module, display a regional map of the target itinerary area on the graphical user interface, and display a first recommended itinerary in the regional map; wherein the first recommended itinerary is represented by the target map information points and the lines between the target map information points, and the first recommended itinerary includes a macro itinerary and a specific itinerary, and the macro itinerary or the specific itinerary is displayed separately based on the display accuracy of the regional map.

[0160] Those skilled in the art should understand that Figure 4 The functions implemented by each unit in the itinerary recommendation device 400 based on map information points can be understood by referring to the related description of the itinerary recommendation method based on map information points. Figure 4 The functions of the various units in the apparatus 400 for recommending itineraries based on map information points may 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 the accuracy adjustment operation for the regional map, determining current map accuracy information, and determining to-be-displayed map information points from the target map information points based on the current map accuracy information; wherein the number of the to-be-displayed map information points increases as the current map accuracy information increases;

[0163] 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 between the map information points to be displayed.

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

[0165] In response to a target user's filtering operation for a specific map information point, determining a filtering feature;

[0166] If a first specific map information point matching the screening feature exists among the target map information points, the first specific map information point is displayed with a first display effect, and second specific map information points matching the screening feature are 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 itinerary, and the first display effect is different from the second display effect.

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

[0168]

[0169] Wherein, 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. α is the negative review suppression coefficient and α≥1. W user is the user's credit weight, Among them, N valid is the number of valid markings by the user, t is the time decay factor, which characterizes 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.

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

[0171]

[0172] Wherein, P2 represents the second recommendation priority of the target map information point; S pref is the 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 the time period matching coefficient, C time Indicates the overlap between the target user's preferred time period and the second map information point's active time period, B budget is the budget adaptation factor, B budget Indicates the matching degree between user consumption level and price range, P userIndicates the target user's consumption capacity benchmark value, P spot Indicates the normalized price index of the second map information point; U crowd is the congestion penalty, U crowd It represents the ratio of crowd density to comfort threshold, N current is the crowd density, N threshold is the comfortable carrying capacity, k is the sensitivity coefficient; F price is the price fluctuation factor, F price represents the dynamic pricing impact coefficient, ΔP is the price fluctuation value, P base is the base 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.

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

[0174] In response to a trigger operation on 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 whose area radius factors overlap, only the second map information point with the highest second recommendation priority P2 is displayed, and the other second map information points are displayed in a folded manner.

[0175] In a possible implementation, the first map information point includes validity time information, which represents the period during which the first map information point can be obtained. When the period expires, the first map information point becomes invalid. The period of the first map information point is 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 a second map information point within the specified range, sequentially connecting at least one other second map information point with the first selected second map information point as a starting point to obtain a connection route; wherein each second map information point within the specified range corresponds to a type identifier, and the type identifier represents a scene type of the second map information point;

[0178] determining a second recommended itinerary based on the connecting route;

[0179] Alternatively, in response to a trigger operation on any second map information point within the specified range, detailed information of the 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 the first recommendation priority P1;

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

[0181] The above-mentioned itinerary recommendation device based on map information points has the following beneficial effects:

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

[0183] The system uses the actual travel points marked by other users in the target itinerary area as its core data source (first map information point), rather than relying on a traditional static POI database. This real-world behavior-based recommendation model dynamically reflects popular routes and hidden attractions, avoiding homogenized recommendations and providing users with more valuable itinerary suggestions.

[0184] (2) Journey optimization driven by collective intelligence:

[0185] By analyzing the spatial distribution and popularity of a large number of users' actual itineraries (the first priority for recommendations), the system can identify high-value routes and must-visit locations. Compared to existing technologies, this collective intelligence aggregation mechanism overcomes the limitations of a single user's perspective, making recommended itineraries more realistic and reducing user trial and error.

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

[0187] Users can directly manipulate actual travel points marked by other users on the map (e.g., connecting routes, viewing details), creating a real-time feedback loop of "exploration-verification-optimization." Compared to the traditional one-way recommendation model, this interactive design allows users to adjust their itineraries based on real-world examples, enhancing planning flexibility and reliability.

[0188] (4) Decentralized personalized matching system:

[0189] This approach combines the matching degree between user profiles and actual itinerary points (the second priority for recommendations) to achieve decentralized personalized recommendations. Unlike existing technologies that rely on matching with preset tags, this solution dynamically calibrates recommendation logic using real user behavior data, making itineraries more tailored to individual preferences and avoiding the pitfall of "one-size-fits-all" recommendations.

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

[0191] Actual itinerary points have inherent temporal attributes (such as seasonal attractions and limited-time events), allowing the system to generate more timely recommendations. Compared to traditional static recommendations, this solution can capture dynamic changes in the target area (such as holiday events and new store openings), providing users with a scenario-based, immersive itinerary planning experience.

[0192] like Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of an electronic device 500 provided in an embodiment of the present application. The electronic device 500 includes:

[0193] A processor 501, a storage medium 502 and a bus 503, wherein the storage medium 502 stores machine-readable instructions executable by the processor 501. When the electronic device 500 is running, the processor 501 communicates with the storage medium 502 via the bus 503, and the processor 501 executes the machine-readable instructions to perform the steps of the itinerary recommendation method based on map information points described in the embodiment of the present application.

[0194] In actual application, the various components in the electronic device 500 are coupled together via the bus 503. It is understood that the bus 503 is used to realize the connection and communication between these components. In addition to the data bus, the bus 503 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 5 Various buses are labeled as bus 503.

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

[0196] (1) Accurate recommendations based on real user trajectories:

[0197] The system uses the actual travel points marked by other users in the target itinerary area as its core data source (first map information point), rather than relying on a traditional static POI database. This real-world behavior-based recommendation model dynamically reflects popular routes and hidden attractions, avoiding homogenized recommendations and providing users with more valuable itinerary suggestions.

[0198] (2) Journey optimization driven by collective intelligence:

[0199] By analyzing the spatial distribution and popularity of a large number of users' actual itineraries (the first priority for recommendations), the system can identify high-value routes and must-visit locations. Compared to existing technologies, this collective intelligence aggregation mechanism overcomes the limitations of a single user's perspective, making recommended itineraries more realistic and reducing user trial and error.

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

[0201] Users can directly manipulate actual travel points marked by other users on the map (e.g., connecting routes, viewing details), creating a real-time feedback loop of "exploration-verification-optimization." Compared to the traditional one-way recommendation model, this interactive design allows users to adjust their itineraries based on real-world examples, enhancing planning flexibility and reliability.

[0202] (4) Decentralized personalized matching system:

[0203] This approach combines the matching degree between user profiles and actual itinerary points (the second priority for recommendations) to achieve decentralized personalized recommendations. Unlike existing technologies that rely on matching with preset tags, this solution dynamically calibrates recommendation logic using real user behavior data, making itineraries more tailored to individual preferences and avoiding the pitfall of "one-size-fits-all" recommendations.

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

[0205] Actual itinerary points have inherent temporal attributes (such as seasonal attractions and limited-time events), allowing the system to generate more timely recommendations. Compared to traditional static recommendations, this solution can capture dynamic changes in the target area (such as holiday events and new store openings), providing users with a scenario-based, immersive itinerary planning experience.

[0206] The embodiment of the present application further provides a computer-readable storage medium, which stores executable instructions. When the executable instructions are executed by at least one processor 501, the itinerary recommendation method based on map information points described in the embodiment of the present application is implemented.

[0207] In some embodiments, the storage medium can be a magnetic 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 storage, an optical disc, or a compact disc read-only memory (CD ROM); it can also be various devices including one or any combination of the above memories.

[0208] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may 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] As an example, executable instructions may, but do not necessarily, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (for example, files storing one or more modules, subroutines, or code portions).

[0210] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0211] The computer-readable storage medium has the following advantages:

[0212] (1) Accurate recommendations based on real user trajectories:

[0213] The system uses the actual travel points marked by other users in the target itinerary area as its core data source (first map information point), rather than relying on a traditional static POI database. This real-world behavior-based recommendation model dynamically reflects popular routes and hidden attractions, avoiding homogenized recommendations and providing users with more valuable itinerary suggestions.

[0214] (2) Journey optimization driven by collective intelligence:

[0215] By analyzing the spatial distribution and popularity of a large number of users' actual itineraries (the first priority for recommendations), the system can identify high-value routes and must-visit locations. Compared to existing technologies, this collective intelligence aggregation mechanism overcomes the limitations of a single user's perspective, making recommended itineraries more realistic and reducing user trial and error.

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

[0217] Users can directly manipulate actual travel points marked by other users on the map (e.g., connecting routes, viewing details), creating a real-time feedback loop of "exploration-verification-optimization." Compared to the traditional one-way recommendation model, this interactive design allows users to adjust their itineraries based on real-world examples, enhancing planning flexibility and reliability.

[0218] (4) Decentralized personalized matching system:

[0219] This approach combines the matching degree between user profiles and actual itinerary points (the second priority for recommendations) to achieve decentralized personalized recommendations. Unlike existing technologies that rely on matching with preset tags, this solution dynamically calibrates recommendation logic using real user behavior data, making itineraries more tailored to individual preferences and avoiding the pitfall of "one-size-fits-all" recommendations.

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

[0221] Actual itinerary points have inherent temporal attributes (such as seasonal attractions and limited-time events), allowing the system to generate more timely recommendations. Compared to traditional static recommendations, this solution can capture dynamic changes in the target area (such as holiday events and new store openings), providing users with a scenario-based, immersive itinerary planning experience.

[0222] In the several embodiments provided in this application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0223] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

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

[0225] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, platform server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0226] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for itinerary recommendation based on map information points, characterized in that: Displaying a map point itinerary recommendation module on a graphical user interface provided by a terminal device, the method comprising: In response to a first trigger instruction from a target user to the itinerary recommendation module, obtaining itinerary information of the target user, determining node levels and itinerary areas of each node level included in the itinerary information by analyzing the itinerary information, and determining a target itinerary area from the itinerary areas according to a target location specified by the target user; obtaining a plurality of first map information points located within the target travel area, and determining at least one second map information point 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 another user within the target travel area; and the first recommendation priority represents the spatial popularity of each of the first map information points; determining a second recommendation priority for each of the at least one second map information point based on the actual user profile of the target user, and determining 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 a degree of match between each second map information point and the actual user profile; In response to a second trigger instruction from the target user to the itinerary display module, a regional map of the target itinerary area is displayed on the graphical user interface, and a first recommended itinerary is displayed in the regional map; wherein the first recommended itinerary is represented by the target map information points and the lines between the target map information points, the first recommended itinerary includes a macro itinerary and a specific itinerary, and the macro itinerary or the specific itinerary is displayed separately based on the display accuracy of the regional map.

2. The method according to claim 1, characterized in that The method further comprises: In response to the accuracy adjustment operation for the regional map, determining current map accuracy information, and determining to-be-displayed map information points from the target map information points based on the current map accuracy information; wherein the number of the to-be-displayed map information points increases as the current map accuracy 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 between the map information points to be displayed.

3. The method according to claim 1, characterized in that The method further comprises: In response to a target user's filtering operation for a specific map information point, determining a filtering feature; If a first specific map information point matching the screening feature exists among the target map information points, the first specific map information point is displayed with a first display effect, and second specific map information points matching the screening feature are 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 itinerary, and the first display effect is different from the second display effect.

4. The method according to claim 1, wherein The first recommendation priority is calculated as follows: Wherein, 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. α is the negative review suppression coefficient and α≥1. W user is the user's credit weight, Among them, N valid is the number of valid markings by the user, t is the time decay factor, which characterizes 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.

5. The method according to claim 4, characterized in that The second recommendation priority is calculated as follows: Wherein, P2 represents the second recommendation priority of the target map information point; S pref is the 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 the time period matching coefficient, C time Indicates the overlap between the target user's preferred time period and the second map information point's active time period, B budget is the budget adaptation factor, B budget Indicates the matching degree between user consumption level and price range, P user Indicates the target user's consumption capacity benchmark value, P spot Indicates the normalized price index of the second map information point; U crowd is the congestion penalty, U crowd It represents the ratio of crowd density to comfort threshold, N current is the crowd density, N threshold is the comfortable carrying capacity, k is the sensitivity coefficient; F price is the price fluctuation factor, F price represents the dynamic pricing impact coefficient, ΔP is the price fluctuation value, P base is the base 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.

6. The method according to claim 5, characterized in that The method further comprises: In response to a trigger operation on 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 whose area radius factors overlap, only the second map information point with the highest second recommendation priority P2 is displayed, and the other second map information points are displayed in a folded manner.

7. The method according to claim 4, characterized in that The first map information point includes valid time information, which represents the period during which the first map information point can be obtained. When the period ends, the first map information point becomes invalid. The period of the first map information point is associated with the first recommendation priority P1.

8. The method according to claim 6, characterized in that The method further comprises: In response to a connection operation for a second map information point within the specified range, sequentially connecting at least one other second map information point with the first selected second map information point as a starting point to obtain a connection route; wherein each second map information point within the specified range corresponds to a type identifier, and the type identifier represents a scene type of the second map information point; determining a second recommended itinerary based on the connecting route; Alternatively, in response to a trigger operation on any second map information point within the specified range, detailed information of the 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 the first recommendation priority P1; In response to a confirmation operation on the second map information point, the second recommended itinerary is determined with the second map information point as a destination.

9. A device for recommending itineraries based on map information points, characterized in that: A map point itinerary recommendation module is displayed on a graphical user interface provided by a terminal device, the device comprising: a first determining module, configured to respond to a first trigger instruction from a target user to the itinerary recommendation module, obtain itinerary information of the target user, determine the node levels and itinerary areas of each node level included in the itinerary information by analyzing the itinerary information, and determine a target itinerary area from the itinerary areas according to a target location specified by the target user; a second determining module configured to obtain a plurality of first map information points located within 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 of the plurality of first map information points; wherein each of the first map information points is an actual travel point marked by another user within the target travel area; and the first recommendation priority represents a spatial popularity of each of the first map information points; a third determining module, configured to determine a second recommendation priority for each of the at least one second map information point based on the actual user profile of the target user, and 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 a degree of match between each second map information point and the actual user profile; a display module, configured to respond to a second trigger instruction from a target user to the itinerary display module, display a regional map of the target itinerary area on the graphical user interface, and display a first recommended itinerary in the regional map; wherein the first recommended itinerary is represented by information points on the target map and lines between the information points on the target map, the first recommended itinerary includes a macro itinerary and a specific itinerary, and the macro itinerary or the specific itinerary is displayed separately based on the display accuracy of the regional map.

10. An electronic device, characterized in that: include: A processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the itinerary recommendation method based on map information points as described in any one of claims 1 to 8.

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