Tourism strategy generation method and system based on personality traits

Abstract

The invention relates to the technical field of smart tourism, and discloses a tourism strategy generation method and system based on personality traits, and the method comprises the steps: obtaining the personality traits information of a user; mapping the personality characteristic information to a preset travel strategy label library to obtain a mapping result; generating a candidate travel element set in combination with the mapping result based on the geographic position, travel time and budget constraint conditions of the user; and performing priority ranking on the candidate elements through a dynamic weight algorithm to generate a travel strategy of the user. According to the method, the personality characteristic information of the user is combined with the dynamic weight algorithm, the personalized travel strategy accurately matched with the requirements of the user is generated, the travel strategy is automatically generated, the decision-making time when the user selects travel elements is shortened, the efficiency of travel planning is improved, the user experience is improved, and the market competitiveness of travel enterprises is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart tourism, and particularly to a method and system for generating travel guides based on personality traits. Background Art

[0002] With the continuous progress of technology, the tourism industry is also developing towards the direction of intelligence and digitization. And tourism has also become one of the important ways for people to relax and entertain. Modern tourists are no longer satisfied with traditional group tours and standardized tourism products. They are more pursuing personalized and differentiated tourism experiences. Different tourists have different interests, cultural backgrounds and lifestyles, so their demands for tourist destinations, tourist activities and tourist services are also different. However, traditional methods for generating travel guides often lack personalization and cannot meet the needs of different users.

[0003] In view of this, there is a need for a method and system that can generate personalized travel guides according to the personality traits of users. Summary of the Invention

[0004] Aiming at the problem that traditional travel guide generation in the prior art lacks personalization and cannot meet the needs of different users, the present invention provides a method and system for generating travel guides based on personality traits, which can generate personalized travel guides based on personality traits, improve the quality of tourism services, and promote the transformation and upgrading of the tourism industry. The specific technical solutions are as follows:

[0005] A method for generating a travel guide based on personality traits, comprising:

[0006] Obtaining the personality trait information of a user;

[0007] Mapping the personality trait information to a preset travel guide tag library to obtain a mapping result;

[0008] Based on the geographical location, travel time and budget constraints of the user, combining the mapping result to generate a candidate travel element set;

[0009] Performing priority sorting on the candidate elements through a dynamic weight algorithm to generate a travel guide for the user.

[0010] Preferably, the obtaining the personality trait information of the user includes:

[0011] Obtaining the test results of the user's test on a psychological test platform corresponding to a preset standardized psychological scale;

[0012] Combining the user's test results and the standard scoring method of the preset standardized psychological scale to calculate the user's score;

[0013] Obtaining the personality trait information of the user according to the user's score situation.

[0014] Preferably, mapping the personality trait information to a preset travel guide tag library, the obtained mapping result includes:

[0015] Extract key features from the personality trait information;

[0016] According to the preset matching rules, map the extracted key features to the corresponding tags in the preset travel guide tag library;

[0017] Associate the mapped tags with the personality trait information to generate a mapping result.

[0018] Preferably, the travel guide tag library includes multiple tags related to personality traits, and each tag corresponds to one or more travel guide features.

[0019] Preferably, the tags in the travel guide tag library include scenic spot type tags, activity intensity tags, social attribute tags, and environmental preference tags;

[0020] The travel guide features corresponding to the scenic spot type tags include natural landscape type, human history type, adventure challenge type, and leisure and entertainment type;

[0021] The travel guide features corresponding to the activity intensity tags include high-intensity sports type, medium-intensity sightseeing type, and low-intensity recuperation type; the travel guide features corresponding to the social attribute tags include team cooperation type, independent exploration type, and stranger social type;

[0022] The travel guide features corresponding to the environmental preference tags include tolerance to crowded areas, noise sensitivity, and climate adaptability threshold.

[0023] Preferably, generating a candidate travel element set based on the user's geographical location, travel time, and budget constraint conditions, combined with the mapping result includes:

[0024] Obtain the constraint conditions for the user's travel, and the constraint conditions include the user's departure place and expected travel destination, travel date and travel duration, and the total travel budget;

[0025] According to the user's travel constraint conditions and the personality trait mapping result, screen out the scenic spots, itinerary arrangements, activity recommendations, accommodation suggestions, and dining suggestions that the user is interested in, and generate a candidate travel element set.

[0026] Preferably, performing priority sorting on the candidate elements through a dynamic weight algorithm to generate the user's travel guide includes:

[0027] Obtain the time attribute, cost information, and interest attribute of the candidate elements, and combined with the user's travel constraint conditions, calculate the time score, budget score, and interest score;

[0028] Set corresponding time weights, budget weights, and interest weights according to the constraints of the user's travel.

[0029] Calculate the comprehensive weight of each candidate element based on the time score, budget score, interest score, time weight, budget weight, and interest weight.

[0030] Sort the candidate elements according to the comprehensive weight to obtain the priority sorting result of the candidate elements.

[0031] Generate a travel guide for the user based on the priority sorting result of the candidate elements.

[0032] Preferably, the calculation formula for the comprehensive weight of each candidate element is as follows:

[0033] ω cx = ω t ·T m + ω b ·B m + ω i ·I m

[0034] In the formula, ω cx is the comprehensive weight of the xth candidate element; T m , B m and I m represent the time score, budget score, and interest score respectively; ω t , ω b and ω i represent the time weight, budget weight, and interest weight respectively.

[0035] Preferably, the calculation formulas for the time score, budget score, and interest score are as follows:

[0036]

[0037] In the formula, T α is the user's estimated play time; T β is the user's disposable time;

[0038]

[0039] In the formula, B α is the user's budget; B β is the cost of the candidate element;

[0040] I m = C·I i

[0041] In the formula, C is the preset interest score; I i is the degree of interest matching.

[0042] A tourism strategy generation system based on personality traits, which is applied to the aforementioned tourism strategy generation method based on personality traits, includes:

[0043] An information acquisition unit for acquiring the personality trait information of the user;

[0044] A mapping and matching unit for mapping the personality trait information to a preset tourism strategy tag library to obtain a mapping result;

[0045] An element generation unit for generating a candidate tourism element set based on the user's geographical location, travel time, and budget constraint conditions, in combination with the mapping result;

[0046] A strategy generation unit for sorting the priorities of the candidate elements through a dynamic weight algorithm to generate the user's tourism strategy.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] The tourism strategy generation method based on personality traits of the present invention can accurately match the interests and preferences of the user by acquiring the personality trait information of the user and mapping it to a preset tourism strategy tag library, and generate a tourism strategy that conforms to the user's personality. By combining the personality trait information of the user with a dynamic weight algorithm, a personalized and accurately matched tourism strategy that meets the user's needs is generated, and the tourism strategy is automatically generated, reducing the decision-making time of the user when selecting tourism elements, improving the efficiency of tourism planning, enhancing the user experience, and improving the market competitiveness of tourism enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0050] Figure 1 It is a flowchart of the tourism strategy generation method based on personality traits of the present invention.

[0051] Figure 2 It is a flowchart of an embodiment of the tourism strategy generation method based on personality traits of the present invention.

[0052] Figure 3 It is a flowchart of another embodiment of the tourism strategy generation method based on personality traits of the present invention.

[0053] Figure 4 It is a flowchart of yet another embodiment of the tourism strategy generation method based on personality traits of the present invention.

[0054] Figure 5 This is the schematic diagram of the travel guide generation system based on personality traits of the present invention. Detailed implementation manners

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] It should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0057] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0058] It should be further understood that the term " / and / " as used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0059] The following embodiments refer to Figures 1 to 5 .

[0060] The embodiment of the present application provides a travel guide generation method based on personality traits, including:

[0061] Step S1, obtaining the personality trait information of the user;

[0062] By designing a questionnaire including content such as personality tests and preference selections, guiding the user to fill it out. For example, using standardized psychological test tools such as the Big Five Personality Test or MBTI (Myers-Briggs Type Indicator), according to the user's answers, the system scores according to the scoring criteria of the scale, and obtains the specific scores of the user in the five dimensions of openness, conscientiousness, extraversion, agreeableness, and neuroticism, so as to determine the personality trait information of the user.

[0063] Set up a dedicated input box or option in the user interface of the travel platform, allowing users to directly input or select their personality trait information.

[0064] Step S2: Map the personality trait information to a preset travel guide tag library to obtain a mapping result.

[0065] The mapping can be performed by a pre-trained deep learning model, which establishes a non-linear mapping rule based on the correlation between the personality vectors of historical users and travel behavior data.

[0066] Step S3: Generate a candidate travel element set based on the user's geographical location, travel time, and budget constraints, in combination with the mapping result.

[0067] By obtaining the constraints of the user's travel, the constraints include the user's departure location and desired travel destination, travel date and duration, and the total travel budget; according to the constraints of the user's travel and the personality trait mapping result, filter out the scenic spots, itinerary arrangements, activity recommendations, accommodation suggestions, and dining suggestions that the user is interested in, and generate a candidate travel element set.

[0068] Step S4: Perform priority ranking on the candidate elements through a dynamic weight algorithm to generate the user's travel guide.

[0069] Rank the candidate elements according to the comprehensive weight to obtain the priority ranking result of the candidate elements. According to the priority ranking result of the candidate elements, generate the user's travel guide. According to the priority ranking result, reasonably arrange the itinerary for each day to ensure that all recommended activities are completed within the travel time. According to the budget constraints, reasonably allocate the costs of transportation, accommodation, dining, and activities to ensure that the total expenditure does not exceed the budget. At the same time, integrate the ranked candidate elements into a detailed travel guide text, including scenic spot introductions, activity arrangements, accommodation, and dining recommendations.

[0070] A travel guide generation method based on personality traits of the present invention can accurately match the interests and preferences of users by obtaining the personality trait information of users and mapping it to a preset travel guide tag library, and generate a travel guide that conforms to the user's personality. By combining the personality trait information of users with a dynamic weight algorithm, a personalized and accurately matched travel guide that meets the user's needs is generated, automatically generating a travel guide, reducing the decision-making time of users when selecting travel elements, improving the efficiency of travel planning, enhancing the user experience, and improving the market competitiveness of travel enterprises.

[0071] Specifically, in a preferred embodiment of the present application, the obtaining of the personality trait information of the user includes:

[0072] Step S11: Obtain the test results of the user's test on the psychological test platform corresponding to the preset standardized psychological scale.

[0073] By using the developed web page or mobile application as the psychological test platform. Users can register an account on it. After logging in, they can start the psychological test. The platform will display the questions in the order of the scale questions and provide corresponding options for users to choose. During the process of users answering questions, the platform will record each choice of the user in real time. When the user finishes answering all the questions, click the submit button. The platform will organize all the user's choice information into a test result data packet. The test result data packet will be transmitted to the server side through the network to obtain the test results.

[0074] Step S12: Combine the user's test results with the standard scoring method of the preset standardized psychological scale to calculate the user's score.

[0075] For the questions in each dimension, there will be positive scoring and reverse scoring situations. For example, in a positive scoring question in the openness dimension, choosing "very consistent" is scored 5 points, "relatively consistent" is scored 4 points, and so on; while for reverse scoring questions, it is the opposite.

[0076] Add up the scores of all the questions of the user in the openness dimension to obtain the raw score of this dimension. Then, perform standardization processing according to the norm data of the scale to obtain the final score of the openness dimension. The other four dimensions (conscientiousness, extraversion, agreeableness, neuroticism) are also scored in the same way.

[0077] Step S13: Obtain the user's personality trait information based on the user's score situation.

[0078] Interpretation of the scale results of the user's personality trait information:

[0079] Openness: Users with high scores usually have traits such as strong curiosity, creativity, and an open attitude towards new ideas and new experiences; users with low scores are relatively traditional and conservative and prefer familiar things.

[0080] Conscientiousness: High scorers show self-discipline, organization, planning, and reliability; low scorers may be more casual and lack organization.

[0081] Extraversion: People with high scores are extroverted, enthusiastic, and good at socializing; low scorers are more introverted, quiet, and prefer to be alone.

[0082] Agreeableness: High scorers are friendly, cooperative, and compassionate; low scorers may be more competitive and skeptical.

[0083] Neuroticism: People with high scores are emotionally unstable and prone to anxiety and depression; those with low scores are more emotionally stable and have a peaceful mindset.

[0084] In this embodiment, tests are conducted through standardized psychological scales, and scores and interpretation results are calculated in combination with standard scoring methods, which can relatively accurately obtain the user's personality trait information. At the same time, based on the score situations in multiple dimensions (openness, conscientiousness, extraversion, agreeableness, neuroticism), the user's personality trait information is comprehensively determined, covering all the main aspects of personality. This multi-dimensional assessment method avoids the one-sidedness of single-dimensional assessment, ensuring that the obtained personality trait information can relatively completely and truly reflect the user's personality characteristics, providing a reliable basis for subsequent applications such as the generation of travel guides.

[0085] Specifically, in a preferred implementation manner of the present application, the mapping of the personality trait information to the preset travel guide tag library to obtain the mapping result includes:

[0086] Step S21: Extract key features from the personality trait information;

[0087] Before mapping the personality trait information, it is necessary to preprocess the obtained personality trait information. And after processing, key features are extracted from the personality trait information. For example, scores of dimensions such as "extraversion" and "openness" are extracted from the Big Five personality test.

[0088] Step S22: Map the extracted key features to the corresponding tags in the preset travel guide tag library according to the preset matching rules;

[0089] Among them, the travel guide tag library includes multiple tags related to personality traits, and each tag corresponds to one or more travel guide features.

[0090] By defining different tag categories according to different dimensions, the tags in the travel guide tag library are classified into scenic spot type tags, activity intensity tags, social attribute tags, and environmental preference tags.

[0091] Each tag is refined: the travel guide features corresponding to the scenic spot type tag include natural landscape type, cultural and historical type, adventure challenge type, and leisure and entertainment type; the travel guide features corresponding to the activity intensity tag include high-intensity sports type, medium-intensity sightseeing type, and low-intensity recuperation type; the travel guide features corresponding to the social attribute tag include teamwork type, independent exploration type, and stranger social type; the travel guide features corresponding to the environmental preference tag include tolerance to crowded areas, noise sensitivity, and climate adaptability threshold.

[0092] Through multi-dimensional matching, comprehensively consider multiple personality trait dimensions for comprehensive matching. For example, when considering the scores of "openness" and "extroversion" simultaneously, since the user has high openness and extroversion and likes to try new things, the unique landforms, rare animals and plants in natural landscape attractions can meet their need to explore the novel, and the key features of this user can be mapped to the "natural landscape type" label. The user has good physical fitness and high openness and is willing to accept new challenges, which can be mapped to the "adventure challenge type" label. The user has strong sports ability and can withstand high-intensity activities, and can be mapped to the "high-intensity sports type" label. The user has high extroversion and is keen on social activities. Team collaboration-based tourism activities can allow them to give full play to their social skills and enjoy them. It can be mapped to the "team collaboration type" label. Similarly, based on the user's extroverted personality, communicating and interacting with strangers during the trip is where their fun lies. The user's characteristics can be mapped to the "stranger social type" label. The user is not very sensitive to noise and likes a lively atmosphere, which means they have a high tolerance for crowded areas, and can be mapped to the label corresponding to the characteristic of high "tolerance for crowded areas". Because the user is not very sensitive to noise, activities with relatively high noise levels such as rock music festivals can also allow them to enjoy themselves to the fullest, and can be mapped to the label corresponding to low "noise sensitivity".

[0093] Step S23: Associate the mapped labels with the personality trait information to generate a mapping result.

[0094] According to the defined matching rules, map the personality trait information to the tourism strategy label library to obtain a mapping result.

[0095] In this embodiment, extracting key features from the personality trait information and then mapping them to the tourism strategy label library based on multi-dimensional matching rules can comprehensively consider various factors of the user's personality characteristics, interest preferences, physical fitness, etc. Through comprehensive matching, such as considering multiple dimensions such as "openness" and "extroversion" simultaneously, rather than simple matching in a single dimension, the tourism strategy highly conforms to the user's real needs. At the same time, the tourism strategy label library is classified according to dimensions such as scenic spot type, activity intensity, social attributes, and environmental preferences, and the refined label categories cover all key aspects of tourism activities. Through this multi-dimensional and precise mapping method, the generated tourism strategy can provide users with tourism suggestions and plans that more conform to their own characteristics, reduce discomfort or regret caused by the mismatch between tourism arrangements and personal traits, and thus greatly improve the user's tourism experience and satisfaction.

[0096] Specifically, in a preferred implementation manner of the present application, the generating the user's tourism strategy by prioritizing candidate elements through a dynamic weight algorithm includes:

[0097] Step S41: Obtain the time attribute, cost information, and interest attribute of the candidate elements, and calculate the time score, budget score, and interest score in combination with the constraints of the user's travel.

[0098] The time score represents the accessibility and time efficiency of the candidate elements during the user's travel time. The calculation process of the time score is as follows:

[0099] Obtain the time attribute of the candidate elements, including obtaining the opening hours of the candidate scenic spots or activities to ensure their accessibility during the user's travel time, and obtaining the estimated playing time of the candidate scenic spots or activities for evaluating their time efficiency.

[0100] Calculate the time score according to the ratio of the estimated playing time to the user's available time. The time score is calculated as follows:

[0101]

[0102] In the formula, T α is the user's estimated playing time; T β is the user's disposable time.

[0103] The budget score represents the matching degree between the cost of the candidate elements and the user's budget. Obtain the cost information of the candidate elements, including obtaining the ticket price of the candidate scenic spots, obtaining the cost of the candidate activities, obtaining the cost of the candidate accommodation, and obtaining the cost of the candidate catering, etc.

[0104] Calculate the budget score according to the ratio of the cost of the candidate elements to the user's budget. The calculation formula of the budget score is as follows:

[0105]

[0106] In the formula, B α is the user's budget; B β is the cost of the candidate elements.

[0107] The interest score represents the matching degree between the candidate elements and the user's personality traits. Obtain the interest attribute of the candidate elements, including obtaining the type of the candidate scenic spots, such as culture, nature, adventure, etc., and obtaining the type of the candidate activities, such as social, adventure, leisure, etc.

[0108] Calculate the interest score according to the matching degree between the type of the candidate elements and the user's personality traits. The calculation formula of the interest score is as follows:

[0109] I m = C · I i

[0110] In the formula, C is the preset interest score value; I i is the interest matching degree.

[0111] For example, if the user's personality trait is "extroversion" and a scenic spot is of the "natural landscape" type, then the interest matching degree I i ≥1, the preset interest score C takes a fixed value of 1, and the interest score I m = C·I i ≥1.

[0112] Step S42: Set the corresponding time weight, budget weight, and interest weight according to the travel constraints of the user;

[0113] The time weight assigns time weights to scenic spots and activities according to the urgency of the user's travel time. For example, in a short trip, scenic spots that are short and efficient are recommended first.

[0114] The budget weight assigns weights to travel elements at different cost levels according to the budget constraint. For example, when the budget is tight, scenic spots and activities with high cost performance are recommended first.

[0115] The interest weight assigns higher weights to travel elements that the user is interested in according to the personality trait mapping result.

[0116] At the same time, when setting the time weight, budget weight, and interest weight, it satisfies ω t + ω b + ω i = 1.

[0117] Step S43: Calculate the comprehensive weight of each candidate element according to the time score, budget score, interest score, time weight, budget weight, and interest weight; the calculation formula for calculating the comprehensive weight of each candidate element is as follows:

[0118] ω cx = ω t ·T m + ω b ·T m + ω i ·T m

[0119] In the formula, ω cx is the comprehensive weight of the xth candidate element; T m , B m and I m represent the time score, budget score, and interest score respectively; ω t , ω b and ω i represent the time weight, budget weight, and interest weight respectively.

[0120] Step S44: Sort the candidate elements according to the comprehensive weight to obtain the priority sorting result of the candidate elements;

[0121] Sort the candidate elements according to the comprehensive weight to generate a priority list. Suppose there are three scenic spots in the set of candidate elements, namely the first scenic spot (t = 8, b = 5, i = 9), the second scenic spot (t = 7, b = 6, i = 8), and the third scenic spot (t = 6, b = 7, i = 7). Suppose the time weight ω t = 0.5, the budget weight ω b = 0.3, and the interest weight ω i = 0.2. Then the comprehensive weight is calculated as follows:

[0122] The comprehensive weight of the first scenic spot ω c1 :

[0123] ω c1 = 0.5×8 + 0.3×5 + 0.2×9 = 7.7

[0124] The comprehensive weight of the second scenic spot ω c2 :

[0125] ω c2 = 0.5×7 + 0.3×6 + 0.2×8 = 7.1

[0126] The comprehensive weight of the third scenic spot ω c3 :

[0127] ω c3 = 0.5×6 + 0.3×7 + 0.2×7 = 6.7

[0128] According to the comprehensive weight, the priority order is: the first scenic spot > the second scenic spot > the third scenic spot.

[0129] Step S45: Generate a travel guide for the user according to the priority sorting result of the candidate elements.

[0130] Integrate the sorted candidate elements into a detailed travel guide text, including scenic spot introductions, activity arrangements, accommodation and dining recommendations. And it can dynamically adjust the guide according to real-time data (such as scenic spot opening hours, weather conditions, etc.) to ensure the practicality and flexibility of the guide.

[0131] In this embodiment, by obtaining the interest attributes of the candidate elements and calculating the interest scores, the user's interest preferences are fully considered. The calculation method can assign a suitable interest score to each candidate element based on the user's unique interest tendency, so that the generated travel guide accurately matches the user's interest needs, greatly improving the user's satisfaction with the travel content. In the process of generating the guide, combined with the time and budget constraints of the user's travel, it can ensure that the recommended travel elements are reasonably compact in time arrangement, and select candidate elements that meet the price range according to the user's budget constraints to prevent overspending or budget waste. And by setting the time weight, budget weight and interest weight, the importance of each factor can be flexibly adjusted according to the different needs and preferences of the user. For example, for users with limited budgets but ample time, the budget weight can be appropriately increased, the time weight can be reduced, and the budget constraint can be met first; for users who pursue the ultimate travel experience and are not very sensitive to the budget, the interest weight can be increased, highlighting the dominant position of interest in the guide generation, so that the travel guide can adapt to the diverse user needs. Finally, the candidate elements are sorted according to the comprehensive weight to ensure that the priority of each element in the travel guide is clear and reasonable. Elements with high priority are included in the guide first, ensuring the optimal configuration of tourism resources, allowing users to experience the tourism projects that best suit their needs within a limited time and budget, and improving travel efficiency and experience quality.

[0132] The embodiment of the present application further provides a travel strategy generation system based on personality traits, which is applied to the aforementioned travel strategy generation method based on personality traits, including:

[0133] An information acquisition unit, used to acquire the user's personality trait information;

[0134] A mapping and matching unit, used to map the personality trait information to a preset travel strategy tag library to obtain a mapping result;

[0135] An element generation unit, used to generate a set of candidate travel elements based on the user's geographical location, travel time and budget constraints and in combination with the mapping results;

[0136] The strategy generation unit is used to prioritize candidate elements through a dynamic weight algorithm and generate a user's travel strategy.

[0137] The functional explanation of each unit in this embodiment is the same as that of a travel guide generation method based on personality traits, and the technical effect is the same, so it will not be repeated here.

[0138] Those of ordinary skill in the art can realize that the units of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0139] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0140] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0141] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of each embodiment of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A method for generating a travel strategy based on personality traits, characterized in that: include: Obtaining user’s personality traits information; Mapping the personality trait information to a preset travel strategy tag library to obtain a mapping result; Based on the user's geographic location, travel time and budget constraints, a set of candidate travel elements is generated by combining the mapping results; The candidate elements are prioritized through a dynamic weight algorithm to generate a travel guide for users.

2. A method for generating a travel strategy based on personality traits according to claim 1, characterized in that: The obtaining of the user's personality trait information includes: Obtain the test results of the user taking the test on the psychological testing platform corresponding to the preset standardized psychological scale; Calculate the user's score by combining the user's test results with a standard scoring method of a preset standardized psychological scale; The user's personality trait information is obtained according to the user's score.

3. A method for generating a travel strategy based on personality traits according to claim 2, characterized in that: The mapping of the personality trait information to a preset travel strategy tag library to obtain a mapping result includes: Extract key features from personality trait information; Mapping the extracted key features to corresponding tags in a preset travel guide tag library according to preset matching rules; The mapped labels are associated with the personality trait information to generate mapping results.

4. A method for generating a travel strategy based on personality traits according to claim 3, characterized in that: The travel strategy tag library includes a plurality of tags related to personality traits, and each tag corresponds to one or more travel strategy features.

5. The method for generating a travel strategy based on personality traits according to claim 4, characterized in that: The labels of the travel guide label library include attraction type labels, activity intensity labels, social attribute labels and environmental preference labels; The travel strategy features corresponding to the attraction type labels include natural landscape, humanities and history, adventure and challenge, and leisure and entertainment; The travel strategy features corresponding to the activity intensity label include high-intensity sports type, medium-intensity sightseeing type, and low-intensity recuperation type; the travel strategy features corresponding to the social attribute label include teamwork type, independent exploration type, and stranger social type; The travel strategy features corresponding to the environmental preference labels include tolerance to densely populated areas, noise sensitivity, and climate adaptability thresholds.

6. The method for generating a travel strategy based on personality traits according to claim 3, characterized in that: The generating of a candidate travel element set based on the user's geographic location, travel time and budget constraints in combination with the mapping results includes: obtaining the user's travel constraints, the constraints including the user's departure place and desired travel destination, travel date and travel duration, and the total travel budget; According to the user's travel constraints and personality trait mapping results, the attractions, itineraries, activity recommendations, accommodation suggestions and dining suggestions that the user is interested in are screened out, and a set of candidate travel elements is generated.

7. A method for generating a travel strategy based on personality traits according to claim 6, characterized in that: The prioritization of candidate elements by a dynamic weight algorithm to generate a user's travel guide includes: Obtain the time attribute, cost information and interest attribute of the candidate element, and calculate the time score, budget score and interest score based on the user's travel constraints; Set the corresponding time weight, budget weight and interest weight according to the user's travel constraints; Calculate the comprehensive weight of each candidate element based on the time score, budget score, interest score, time weight, budget weight and interest weight; Sort the candidate elements according to the comprehensive weights to obtain the priority sorting results of the candidate elements; Generate a travel guide for the user based on the priority ranking results of the candidate elements.

8. The method for generating a travel strategy based on personality traits according to claim 7, characterized in that: The calculation formula for calculating the comprehensive weight of each candidate element is as follows: oh cx =ω t ·T m +oh b ·B m +oh i ·T m In the formula, ω cx is the comprehensive weight of the xth candidate element; T m , B m and I m Respectively represent time score, budget score and interest score; ω t ,ω b and ω i They represent time weight, budget weight and interest weight respectively.

9. A method for generating travel strategies based on personality traits according to claim 8, characterized in that: The calculation formulas for the time score, budget score and interest score are as follows: Where, T α Estimated play time for users; T β Time available to users; In the formula, B α Budget for users; B β Cost for candidate elements; I m =C·I i Where C is the preset interest score; I i The degree of interest matching.

10. A travel strategy generation system based on personality traits, characterized in that: A method for generating a travel strategy based on personality traits, applied to any one of claims 1 to 9, comprising: An information acquisition unit, used to acquire the user's personality trait information; A mapping and matching unit is used to map the personality trait information to a preset travel strategy tag library to obtain a mapping result; an element generation unit is used to generate a candidate travel element set based on the user's geographical location, travel time and budget constraints and in combination with the mapping result; The strategy generation unit is used to prioritize candidate elements through a dynamic weight algorithm and generate a user's travel strategy.

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