Tourism fact generation method and system based on real experience

Abstract

The invention discloses a tourism fact generation method and system based on real experience, and the method is executed by a computer system, and comprises the steps: collecting experience behavior data of a natural person in a tourism scene, verifying whether the natural person has the real experience qualification of the corresponding tourism scene, and generating an experience evidence chain, performing exception and manipulation immune filtering on the experience evidence chain, generating a forward experience evaluation factor and a correction factor, dynamically adjusting the effectiveness of the evaluation factor and the correction factor, and calculating and deciding a conflict experience result; and storing the judged evaluation factor, the rectification factor and the corresponding experience evidence chain into a traceable evidence storage module to form a tourism experience fact. Therefore, the system takes real experience as a unique legal data source, takes an experience evidence chain as a calculation foundation stone, realizes dynamic adjustment through an immune mechanism and time sequence credibility, adheres to a background line principle of individual experience priority, and finally forms a tourism experience fact with traceable evidence storage.

Description

Technical Field

[0001] This application relates to the fields of tourism information processing, public evaluation infrastructure, trusted data computing and intelligent decision input technology, and in particular to a method and system for generating tourism facts based on real experiences. Background Technology

[0002] Existing tourism evaluation systems (including review platforms, OTA ratings, short video recommendations, and public opinion monitoring systems) have the following shortcomings: (1) The existing system cannot verify whether the reviewer has actually arrived at or experienced the target scene; the real victimized tourists and the fraudsters cannot be distinguished. (2) Because ratings, negative reviews, and complaints lack binding with real behaviors such as location, trajectory, stay, and transactions, they cannot form verifiable "experience facts"; (3) Complaints and negative reviews are only used as customer service work orders or star ratings and are not used for risk warning, regulatory correction, index adjustment or financial risk control; (4) Old evaluations have a long-term impact on current scores, and the system lacks a time immunity mechanism, making it unable to reflect the dynamic changes in the scenario; (5) The existing platform’s ratings and rankings are untraceable, unauditable, and unverifiable, and cannot be used as a reliable data source for regulatory, industry decision-making, or artificial intelligence systems. Summary of the Invention

[0003] This application aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, one objective of this application is to propose a method for generating tourism facts based on real experiences. This system uses real experiences as the only legitimate data source, takes the chain of experience evidence as the computational foundation, achieves dynamic adjustment through immune mechanisms and temporal credibility, and adheres to the bottom-line principle of prioritizing individual experiences, ultimately forming traceable and verifiable tourism experience facts.

[0005] To achieve the above objectives, the first aspect of this application proposes a method for generating tourism facts based on real experiences, executed by a computer system, comprising: S1: collecting experiential behavior data of natural persons in tourism scenarios, wherein the experiential behavior data includes one or more of location information, duration of stay, trajectory data, transaction behavior, or device interaction behavior; S2: verifying whether the natural person possesses the qualifications for a real experience in the corresponding tourism scenario based on the experiential behavior data, and allowing them to submit evaluations, suggestions, or complaints only when the qualifications are met; S3: binding the evaluations, suggestions, or complaints with the corresponding experiential behavior data to generate a corresponding chain of experiential evidence; S4: performing anomaly and manipulation immune filtering on the chain of experiential evidence to identify and exclude experiential evidence formed by forgery, data manipulation, attacks, or non-real experiences; S5: generating positive experiential evaluation factors and based on the immune-filtered chain of experiential evidence. S6: Calculate the evidence weight for each experience evidence chain and generate a temporal credibility parameter based on the experience occurrence time; S7: Dynamically adjust the effectiveness of the evaluation factor and correction factor based on the evidence weight and temporal credibility parameter; S8: When different experience evidence chains produce differences or conflicts for the same experience object, the system calculates the evidence weight and temporal credibility of each experience evidence chain that has passed the real experience qualification verification and immune filtering, and prohibits setting the weight of any single experience evidence chain to zero based on the number of evidence; S9: Without violating the individual experience priority constraint mechanism, calculate and adjudicate the conflict experience results based on the evidence weight and temporal credibility to determine the valid experience results entering the experience fact layer; S10: Store the adjudicated evaluation factor, correction factor and their corresponding experience evidence chain in the traceable evidence storage module to form tourism experience facts.

[0006] In addition, the tourism fact generation method based on real experience proposed above in this application may also have the following additional technical features: In one embodiment of this application, the qualification for authentic experience includes a natural person's actual visit, continuous stay, or genuine transaction behavior in the target tourism scenario.

[0007] In one embodiment of this application, the experience evidence chain includes at least one or more of time identifiers, spatial identifiers, device identifiers, or event identifiers.

[0008] In one embodiment of this application, the immune filtering includes abnormal frequency detection, trajectory anomaly detection, device anomaly detection, and behavioral consistency detection.

[0009] In one embodiment of this application, the chain of experience evidence identified as abnormal or manipulated shall not enter the experience fact layer.

[0010] In one embodiment of this application, the evidence weight is determined based on the completeness of the experience behavior, the duration of stay, the credibility of the transaction, or the credibility of the history.

[0011] In one embodiment of this application, the timing reliability decays over time.

[0012] In one embodiment of this application, each chain of experience evidence that has passed real-world qualification verification and immune filtering has a non-zero weight lower bound.

[0013] In one embodiment of this application, any single chain of experience evidence that passes real experience qualification verification and immune filtering shall not be algorithmically rejected due to inconsistency in the number of other chains of experience evidence.

[0014] In one embodiment of this application, the group experience results are used to statistically verify the anomaly, consistency, or stability of a single experience evidence chain, and are not used to directly negate a single experience evidence chain.

[0015] In one embodiment of this application, when there is a conflict among multiple chains of experience evidence, the system determines the valid result to enter the experience fact layer based on evidence weight and temporal credibility under the individual experience priority constraint mechanism.

[0016] In one embodiment of this application, the experiential facts are accompanied by corresponding evidence chain identifiers, weight status, and adjudication status for traceability and auditability.

[0017] In one embodiment of this application, the experience facts are provided to a tourism experience index system, anomaly handling system, ranking system, or cultural and tourism supervision system via an interface.

[0018] In one embodiment of this application, data that fails to pass the real experience qualification verification, immune filtering, or adjudication mechanism shall not be allowed to enter the experience fact layer.

[0019] In one embodiment of this application, the facts of the tourism experience are used as the sole or fundamental source of factual input when an artificial intelligence model or automated decision-making system generates tourism evaluations, risk assessments, or recommendations.

[0020] In one embodiment of this application, the authentic experience evidence is applicable to newly added experience behavior types and collection methods in the future. In specific implementation, the authentic experience evidence can be processed and verified through one or a combination of the following methods: (1) anonymizing or de-identifying the evidence fields; (2) using summary information, feature vectors, or consistency identifiers; (3) completing the authenticity judgment through hash comparison or consistency verification results. The system judges the authenticity of the experience based on the above processing results, without directly acquiring, storing, or disclosing the complete original data of the personal terminal.

[0021] In one embodiment of this application, the evidence weight and temporal credibility are used to determine the final validity of experiential evidence in the experiential fact system.

[0022] The second objective of this application is to propose a tourism fact generation system based on real-world experiences.

[0023] To achieve the above objectives, the second aspect of this application proposes a tourism fact generation system based on real experience, comprising: an experience behavior collection module, an evaluation subject verification module, an evidence chain generation module, an immune filtering module, an evidence weight and temporal credibility calculation module, an experience factor generation module, a conflict determination and adjudication module, and a traceable evidence storage module, which are connected in sequence.

[0024] In addition, the tourism fact generation system based on real experience proposed in this application may also have the following additional technical features: In one embodiment of this application, the conflict determination and adjudication module follows an individual experience priority constraint mechanism during the adjudication process.

[0025] In one embodiment of this application, the evidence storage module supports third-party auditing and regulatory verification.

[0026] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0027] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for generating tourism facts based on real-world experiences, as described in this application. Figure 2 This is a block diagram of a tourism fact generation system based on real experience, as described in this application. Figure 3 Flowchart for immune filtering and weight of evidence calculation; Figure 4 To experience the fact calculation flowchart; Figure 5 A flowchart for traceable evidence preservation; Figure 6 Multi-system fact call structure diagram Figure 7 A schematic diagram illustrating the structure of tourism experience facts as an external factual input source for artificial intelligence and automated decision-making systems.

[0028] Figure 8A structural diagram illustrating how a single tourist's real experience is transformed into calculable, adjudicable, and verifiable facts about their tourism experience. Detailed Implementation

[0029] Embodiments of this application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. Rather, embodiments of this application include all variations, modifications, and equivalents falling within the spirit and scope of the appended claims.

[0030] The following description, in conjunction with the accompanying drawings, illustrates a method and system for generating tourism facts based on real-world experiences, according to embodiments of this application.

[0031] like Figure 1 As shown in the figure, a method for generating tourism facts based on real experiences according to an embodiment of this application may include: S1: Collect data on the experiential behavior of natural persons in tourism scenarios. The experiential behavior data includes one or more of the following: location information, duration of stay, trajectory data, transaction behavior, or device interaction behavior.

[0032] S2: Verify whether a natural person has the real experience qualification for the corresponding tourism scenario based on experience behavior data, and allow them to submit evaluation, suggestions or complaints only when they meet the qualification.

[0033] In the embodiments of this application, the qualification for genuine experience includes a natural person's actual visit, continuous stay or genuine transaction behavior to the target tourism scene, thereby ensuring that the data on tourists' genuine experience behavior is taken as the starting point.

[0034] S3: Link evaluations, suggestions, or complaints with corresponding experience behavior data to generate a corresponding experience evidence chain.

[0035] In one embodiment of this application, the experience evidence chain includes at least one or more of time identifiers, spatial identifiers, device identifiers, or event identifiers.

[0036] It should be noted that this chain of evidence is a genuine experience chain, used to verify whether the subject of the evaluation or complaint possesses the qualifications for a genuine tourism experience. Its acquisition and use follow the principles of minimum necessity and consistency verification. Furthermore, the genuine experience chain in this application does not require the disclosure or collection of all information from the personal terminal, nor does it require a complete record of the entire experience process. Rather, it only requires the acquisition of necessary evidence fields directly related to the specific tourism experience event for the purpose of authenticity verification.

[0037] Furthermore, the evidence chain of a real experience can be composed of one or more of the following types of evidence: (1) temporal consistency evidence related to the experience event; (2) spatial consistency evidence related to the experience scenario; (3) behavioral occurrence evidence related to the experience process; and (4) consumption event or interaction event evidence related to the experience result. Different types of evidence can be selectively combined according to the specific implementation scenario, without having to satisfy all types of evidence at the same time.

[0038] Furthermore, when obtaining evidence of genuine experiences, the system does not collect personal terminal information unrelated to the tourism experience event, does not require continuous or full monitoring of all behaviors of personal terminals, and does not require long-term storage of original data that can directly identify an individual. The evidence chain is only used to determine whether the evaluation or complaint originates from genuine experience behavior.

[0039] The technical purpose of the real experience evidence chain in this invention is to: (1) limit the real experience qualification of the subject of evaluation or complaint; (2) prevent false evaluation, brushing or malicious complaint; (3) provide a credible input basis for subsequent rating fact generation, linkage control and responsibility settlement. The evidence chain is not used for personal profile construction, behavior prediction or privacy data analysis.

[0040] S4: Performs anomaly and manipulation immunity filtering on the experience evidence chain to identify and exclude experience evidence formed by fake, inflated, attacked, or non-genuine experiences.

[0041] In the embodiments of this application, immune filtering includes abnormal frequency detection, trajectory abnormality detection, device abnormality detection, and behavior consistency detection. Furthermore, experience evidence chains identified as abnormal or manipulated are not allowed to enter the experience fact layer. This can eliminate experience evidence formed by forgery, traffic fraud, attacks, or non-genuine experiences, ensuring the authenticity of experience evidence. Moreover, genuine experience evidence is applicable to newly added experience behavior types and collection methods in the future.

[0042] S5: Generate positive experience evaluation factors based on the experience evidence chain after immune filtering and corrective factors based on complaints.

[0043] S6: Calculate the evidence weight for each experience evidence chain and generate a temporal credibility parameter based on the time when the experience occurred.

[0044] In the embodiments of this application, the evidence weight is determined based on the completeness of the experience behavior, the duration of stay, the credibility of the transaction, or the credibility of the history.

[0045] Furthermore, temporal reliability decays over time, so that recent experiential evidence has a greater impact on experiential facts.

[0046] S7: Based on the evidence weight and time series credibility parameters, the effectiveness of the evaluation factor and the correction factor is dynamically adjusted.

[0047] S8: When different experience evidence chains produce differences or conflicts for the same experience object, the system calculates the evidence weight and temporal credibility of each experience evidence chain that has passed the real experience qualification verification and immune filtering, and prohibits setting the weight of any single experience evidence chain to zero based on the number of evidence.

[0048] It should be noted that the weight of evidence and the credibility of time sequence are used to determine the final validity of experiential evidence in the experiential fact system.

[0049] Furthermore, each chain of experience evidence that has passed the real-world experience qualification verification and immune filtering has a non-zero weight lower bound, thus ensuring that it always has the minimum influence weight in the experience fact adjudication.

[0050] Furthermore, any single chain of experience evidence that passes real-world experience qualification verification and immune filtering shall not be algorithmically rejected due to inconsistency in the number of other chains of experience evidence.

[0051] In one embodiment of this application, the group experience results are used to statistically verify the anomaly, consistency, or stability of a single experience evidence chain, and are not used to directly negate a single experience evidence chain.

[0052] S9: Without violating the individual experience priority constraint mechanism, calculate and adjudicate the conflict experience results based on evidence weight and temporal credibility to determine the valid experience results that enter the experience fact layer.

[0053] In the embodiments of this application, when there is a conflict between multiple experience evidence chains, the system determines the valid results to enter the experience fact layer based on evidence weight and temporal credibility under the individual experience priority constraint mechanism, while data that has not passed the real experience qualification verification, immune filtering or adjudication mechanism shall not enter the experience fact layer.

[0054] Furthermore, the experiential facts are provided to tourism experience index systems, anomaly handling systems, ranking systems, or cultural and tourism supervision systems through interfaces.

[0055] S10: Store the adjudicated evaluation factors, correction factors and their corresponding experience evidence chains in the traceable evidence storage module to form tourism experience facts.

[0056] In one embodiment of this application, facts about the tourism experience are used as the sole or fundamental source of factual input when an artificial intelligence model or automated decision-making system generates tourism evaluations, risk assessments, or recommendations.

[0057] like Figure 2As shown in the figure, a tourism fact generation system based on real experience according to an embodiment of this application may include: an experience behavior collection module, an evaluation subject verification module, an evidence chain generation module, an immune filtering module, an evidence weight and temporal credibility calculation module, an experience factor generation module, a conflict determination and adjudication module, and a traceable evidence storage module, which are connected in sequence.

[0058] It should be noted that the conflict determination and adjudication module follows the individual experience priority constraint mechanism in the adjudication process, the evidence storage module supports third-party audit and regulatory verification, and the system provides factual sources to the tourism experience index system, compensation system, ranking system or regulatory system.

[0059] Specifically, such as Figure 2 As shown, the system starts with real tourist experience behavior data and constructs a complete link from the real world to calculable experience facts. Specifically, it includes: experience behavior collection module, evaluation subject verification module, evidence chain generation module, immune filtering module, evidence weight and temporal credibility calculation module, experience factor generation module, conflict determination and adjudication module, and traceable evidence storage module. Among them, real tourist experience behavior data includes visits, stays, trajectories, transactions or device interaction behaviors. After being collected by the experience behavior collection module, it is input into the real experience qualification verification module to determine whether a natural person has the real experience qualification for the corresponding tourism scenario.

[0060] Once an individual passes the real-world experience qualification verification, their submitted evaluations, suggestions, or complaints, along with the corresponding experience behavior data, are bound together by the experience evidence chain generation module to form an experience evidence chain that includes time identifiers, spatial identifiers, behavioral identifiers, and device identifiers.

[0061] The experience evidence chain enters the immune filtering module, which identifies and filters non-genuine experience data generated by anomalies, fraudulent traffic, attacks, or forgery, and only allows experience evidence chains that pass the immune verification to enter the subsequent calculation process.

[0062] The experience evidence chain, filtered by the immune system, is input into the evidence weight and temporal credibility calculation module. The module calculates the evidence weight for each experience evidence chain and generates the corresponding temporal credibility parameter based on the time of the experience, so that recent real experiences have a greater impact on the system.

[0063] In the experience factor calculation layer, the system generates positive experience evaluation factors and corrective factors based on complaints, based on evidence weight and temporal credibility parameters.

[0064] When there are multiple chains of evidence for the same experience object, the experience fact adjudication module, under the premise of adhering to the individual experience priority constraint mechanism, combines evidence weight and temporal credibility to calculate and adjudicate different experience results, and determines the valid experience results that enter the experience fact layer.

[0065] Ultimately, the evaluation factors, correction factors, and their corresponding experience evidence chains, after being adjudicated, are written into the traceable experience fact storage module, forming tourism experience facts containing fact values, weight status, time stamps, and evidence chain IDs, which are used for subsequent index calculations, anomaly handling, supervision, and artificial intelligence system calls.

[0066] Figure 8 It demonstrates how a single tourist's authentic experience is systematically transformed into calculable, adjudicable, and verifiable facts about the tourism experience, with its structure including the following levels: The first layer is the real experience input layer, which uses tourists' real experience behaviors in tourism scenarios as input, including but not limited to: arrival behavior, stay behavior, movement trajectory, consumption transactions, scanning codes or using services and interacting with devices. These behaviors constitute the objective factual basis for the occurrence of the experience.

[0067] The second layer: Experience Identifier and Evidence Element Layer. The system automatically generates the following identifier elements for each experience: time identifier (time of experience occurrence), spatial identifier (GPS, scene ID or geographical location), behavioral identifier (stay, consumption, use, complaint, etc.), and device identifier (terminal, ticket or account). The above elements together constitute the basic requirements for experience evidence.

[0068] The third layer is the experience evidence chain object. The system binds the experience behavior with the identification elements to form an experience evidence chain object = time + space + behavior + device + experience content. Each experience evidence chain object corresponds to a unique evidence chain ID, which is used for subsequent tracing and auditing.

[0069] The fourth layer is the evidence weight and temporal credibility layer. The system calculates the evidence weight parameters (based on dwell time, behavior completeness, transaction credibility, etc.) and temporal credibility parameters (new evidence has higher weight, and old evidence automatically decays). This layer is used to quantify the effectiveness of each real experience in fact-based adjudication.

[0070] The fifth layer: the fact-determination state layer. When there are multiple chains of evidence for the same experience object, the system determines the validity of each chain of evidence based on evidence weight, temporal credibility, and individual experience priority constraint mechanism, thus forming a fact-determination state.

[0071] The sixth layer: Experience Fact Object. The experience evidence obtained through adjudication is encapsulated as: Experience Fact Object = Fact Value + Weight State + Time Sequence State + Evidence Chain ID. This object is no longer an "opinion" but an auditable tourism experience fact.

[0072] The seventh layer: Experience factor output. The experience fact object further generates positive experience factors and correction factors (complaints and abnormal experiences), which are used for index calculation, abnormal warning, compensation triggering and regulatory system calls.

[0073] like Figure 6 As shown, the traceable experience fact storage module of this application serves as a unified source of facts, providing experience fact data to multiple different types of systems, including tourism experience index systems, ranking and recommendation systems, anomaly handling and compensation systems, and government regulatory systems.

[0074] The tourism experience index system is used to calculate the comprehensive experience index of tourism scenarios, tourism objects, or cities based on positive experience factors, corrective factors, evidence weights, and time series credibility in the experience facts.

[0075] The ranking and recommendation system is used to sort, display, expose, or recommend tourist attractions based on experiential facts, ensuring that the generated rankings and recommendations are based on verifiable experiential facts rather than subjective platform rules.

[0076] The anomaly handling and compensation system is used to automatically trigger handling processes, including refunds, compensation, service repairs, or liability tracing, when there are high-weight negative experiences, corrective factors, or anomalies in the experience facts.

[0077] The government regulatory system is used to obtain experiential facts, corresponding evidence chains, adjudication results and their weight status through interfaces for administrative supervision, law enforcement evidence collection, industry credit evaluation or risk warning.

[0078] Through this structure, this application makes the facts of the tourism experience the sole credible data source for all evaluation, index, compensation, and regulatory systems, preventing each system from making independent judgments based on unverifiable raw review data.

[0079] Figure 7 This is a schematic diagram illustrating the structure of tourism experience facts as an external fact input source for the artificial intelligence and automated decision-making system in this invention.

[0080] The traceable experience fact storage module of this application serves as a unified source of tourism experience facts, providing verified, immune-filtered, and adjudicated tourism experience fact data to artificial intelligence models, automated recommendation systems, risk warning systems, and intelligent decision-making systems.

[0081] Artificial intelligence models obtain experiential fact objects, including experiential fact values, evidence chain identifiers, evidence weights, temporal credibility, evaluation factors, and correction factors, through fact interfaces, which are then used for model training, inference, prediction, or decision analysis.

[0082] Automated recommendation systems rank, recommend, or match tourism scenarios, service providers, or products based on experiential facts, thereby avoiding decision-making biases caused by false reviews, fake reviews, or marketing data.

[0083] The risk warning and automated response system automatically triggers warning, compensation, regulatory linkage, or risk control actions based on high-weight correction factors, abnormal experience facts, or rapidly changing experience status.

[0084] In the structure of this invention, the artificial intelligence system, automated recommendation system, and decision-making system must not directly use the original evaluation text, star rating, or complaint data. Instead, they must be trained and make decisions based on tourism experience facts that have been verified through real experience eligibility, immune filtering, temporal credibility correction, and factual adjudication. This ensures that the input data of the AI ​​is authentic, verifiable, and legally traceable.

[0085] The following examples illustrate this in detail.

[0086] Example 1: Generation of Evidence Chain of Tourists' Real Experiences Tourist A arrived at the "Xiyuan Road·Daguanlou Area" of a scenic spot in Kunming at 12:03 on May 1, 2026. The system collected the following experience behavior data: GPS location: 25.0341N, 102.6845E, stay duration: 45 minutes, transaction behavior: purchase of 1 ticket, QR code scanning behavior: scanned the scenic spot entrance QR code, device identifier: mobile device ID=D123456. Tourist A submitted a complaint at 12:47: "The toilet is too dirty, affecting the experience." The system executes claims S1–S3: binds the complaint to the following: time: 12:47, space: toilet scene IDT-903, behavior: stay + QR code scanning, device: D123456, and generates: experience evidence chain ID=EVID-20260501-0001. This evidence chain is sent to the immune filtering module.

[0087] Example 2: Immune Filtering and Abnormal Removal (corresponding) Figure 3 ) The system detected that the tourist submitted only one complaint at this scenic spot that day, and their travel history matched the location of the restroom. There was no evidence of fake reviews or device malfunctions, therefore this chain of evidence passed the immune filter and was marked as: legitimate experience evidence.

[0088] Example 3: Calculation of Temporal Credibility and Weight of Evidence The system calculates the dwell time as 45 minutes, with a weight of 0.9. Since there is a real transaction, a weight bonus is introduced. The time sequence credibility is 0.98, which is 3 minutes from the current time. Finally, the weight of this evidence chain is W=0.92.

[0089] Example 4: Prioritizing Individual Experience (corresponding to S8–S10) The system also received positive reviews from five other tourists: "The toilet is okay." However, these reviews were short-lived, lacked QR code scanning, and did not describe specific events. Therefore, according to claim S8: quantity cannot negate a single genuine experience, this high-weighted complaint evidence is included in the experience fact layer.

[0090] Example 5: Experience Fact Generation (corresponding to) Figure 4 ) The system generates a positive experience factor of 0.75 and a correction factor (complaints) of -0.92. After combining the weights and time, the calculated toilet experience fact value is 0.43 (leaning towards negative), along with the evidence chain ID, weight, timestamp, and adjudication status.

[0091] Example 6: Evidence Preservation and Traceability (corresponding to) Figure 5 ) The system encapsulates this experience fact as: Fact ID: FACT-20260501-TOILET-01.

[0092] The content includes: fact value, weight, evidence chain ID, rule version, and calculation time, which are written into the traceable evidence storage module.

[0093] Example 7: External System Call (corresponding to) Figure 6 , Figure 7 ) The government's regulatory system retrieved this fact and found that the experience was below the threshold, triggering a scenic area rectification notice and risk labeling. After the AI ​​tour guide system retrieved this fact, it marked the toilet as "not recommended at this time".

[0094] Thus, as can be seen from the above embodiments, this application realizes the capabilities of real tourists, evidence, immunity, weighting, individual priority, facts, evidence preservation, government and AI use, which is something that no existing review system can achieve.

[0095] In summary, the tourism fact generation method and system based on real experience in this application uses real experience as the only legitimate data source, takes the experience evidence chain as the computational foundation, achieves dynamic adjustment through immune mechanisms and temporal reliability, and adheres to the bottom-line principle of prioritizing individual experience, ultimately forming traceable and verifiable tourism experience facts.

[0096] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0097] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0098] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating tourism facts based on real-world experiences, characterized in that, Executed by a computer system, including: S1: Collect data on the experiential behavior of natural persons in tourism scenarios, including one or more of the following: location information, duration of stay, trajectory data, transaction behavior, or device interaction behavior; S2: Based on the aforementioned experience behavior data, verify whether the natural person has the real experience qualification for the corresponding tourism scenario. Only when the qualification is met, allow them to submit evaluation, suggestions or complaints. S3: Bind the evaluation, suggestion or complaint content with the corresponding experience behavior data to generate a corresponding experience evidence chain; S4: Perform anomaly and manipulation immune filtering on the chain of experience evidence to identify and exclude experience evidence formed by fake, fraudulent, attack, or non-genuine experiences. S5: Generate positive experience evaluation factors and correction factors based on the experience evidence chain after immune filtering; S6: Calculate the evidence weight for each experience evidence chain and generate a temporal credibility parameter based on the experience occurrence time; S7: Based on the evidence weight and time-series credibility parameters, dynamically adjust the effectiveness of the evaluation factor and the correction factor; S8: When different experience evidence chains produce differences or conflicts for the same experience object, the system calculates the evidence weight and temporal credibility of each experience evidence chain that has passed the real experience qualification verification and passed the immune filter, and prohibits setting the weight of any single experience evidence chain to zero based on the number of evidence. S9: Without violating the individual experience priority constraint mechanism, calculate and adjudicate the conflict experience results based on evidence weight and temporal credibility to determine the valid experience results that enter the experience fact layer; S10: Store the adjudicated evaluation factors, correction factors and their corresponding experience evidence chains in the traceable evidence storage module to form tourism experience facts.

2. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The eligibility for a genuine experience includes an individual's actual visit to, continuous stay in, or genuine transaction activities at the target tourism destination.

3. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The chain of evidence for the experience includes at least one or more of the following: time identifier, space identifier, device identifier, or event identifier.

4. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The immune filtering includes abnormal frequency detection, trajectory anomaly detection, device anomaly detection, and behavioral consistency detection.

5. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, Experience evidence chains that are identified as abnormal or manipulated must not be included in the experience fact layer.

6. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The weight of evidence is determined based on the completeness of the experience behavior, the duration of stay, the credibility of the transaction, or the credibility of the history.

7. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The reliability of the time sequence decays over time.

8. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, Each chain of experience evidence that has passed real-world qualification verification and immune filtering has a non-zero weight lower bound.

9. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, Any single chain of experience evidence that passes real-world qualification verification and immune filtering shall not be algorithmically rejected due to inconsistency in the number of other chains of experience evidence.

10. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The results of group experiences are used to statistically verify the anomalies, consistency, or stability of individual experience evidence chains, and are not used to directly negate individual experience evidence chains.

11. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, When there is a conflict between multiple chains of experiential evidence, the system determines the valid result to enter the experiential fact layer based on evidence weight and temporal credibility, under the individual experience priority constraint mechanism.

12. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The experiential facts are accompanied by corresponding evidence chain identifiers, weight status, and adjudication status for traceability and auditability.

13. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The experiential facts are provided to the tourism experience index system, anomaly handling system, ranking system, or cultural and tourism supervision system through an interface.

14. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, Data that fails to pass the real-world experience qualification verification, immune filtering, or adjudication mechanism shall not be allowed to enter the experience fact layer.

15. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The facts of the tourism experience are used as the sole or fundamental source of factual input for artificial intelligence models or automated decision-making systems when generating tourism evaluations, risk assessments, or recommendations.

16. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The aforementioned authentic experience evidence is applicable to any new types of experiential behaviors and collection methods added in the future.

17. The method for generating tourism facts based on real experiences according to claim 1, characterized in that, The evidence weight and temporal credibility are used to determine the final validity of experiential evidence in the experiential fact system.

18. A tourism fact generation system based on real-world experiences, characterized in that, include: The modules are sequentially connected: experience behavior collection module, evaluation subject verification module, evidence chain generation module, immune filtering module, evidence weight and temporal credibility calculation module, experience factor generation module, conflict determination and adjudication module, and traceable evidence storage module.

19. A tourism fact generation system based on real experience according to claim 18, characterized in that, The conflict determination and adjudication module follows an individual experience priority constraint mechanism during the adjudication process.

20. A tourism fact generation system based on real experience according to claim 18, characterized in that, The evidence storage module supports third-party auditing and regulatory verification.

21. A tourism fact generation system based on real experience according to claim 18, characterized in that, The system provides fact sources to tourism experience index systems, compensation systems, ranking systems, or regulatory systems.

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