Tourist population monitoring method and system based on agent map and block chain, and medium

Abstract

The embodiment of the invention provides a tourist population monitoring method and system based on an agent map and a block chain, and a medium. The method comprises the steps of collecting tourist population multi-source data in a business district through a preset device, constructing an intelligent agent map according to the tourist population multi-source data for dynamic updating, performing multi-dimensional monitoring according to the intelligent agent map, obtaining classified tourist population multi-source data, encrypting the classified tourist population multi-source data, obtaining an evidence storage data packet, and storing the evidence storage data packet in the business district. Differentiated distribution of data management authority and sharing of tourist population multi-source data are carried out according to participating nodes, verification processing is carried out according to an evidence storage data packet, verification passing proportion data are obtained and compared with a preset threshold value, a credible tourist population monitoring report is obtained, and evidence storage is carried out on calling. Through multi-source data acquisition, agent graph construction, a multi-dimensional monitoring algorithm, alliance block chain node construction and monitoring result verification, accurate demographic statistics and behavior analysis results are realized, and data support is provided for urban business district planning, tourism resource allocation and public service optimization.

Description

Technical Field

[0001] This application relates to the field of smart city monitoring technology, and more specifically, to a method, system, and medium for monitoring tourism population based on intelligent agent graphs and blockchain. Background Technology

[0002] With the rapid development of urban tourism, commercial districts, as the core carriers of tourism consumption, have crucial data on the quantity, structure, and behavioral characteristics of their tourist populations. This data is of great significance for the operation and management of commercial districts, the allocation of tourism resources, and the optimization of public services. Traditional methods for monitoring the tourist population in urban commercial districts mainly rely on manual statistics, gate counting, or single-device sensing. These methods suffer from problems such as narrow data coverage, poor real-time performance, susceptibility to data tampering, and difficulty in integrating data from different sources, thus failing to provide accurate and reliable support for decision-making.

[0003] Therefore, relevant technical solutions are urgently needed to address the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and medium for monitoring tourism population based on intelligent agent graphs and blockchain, which can achieve accurate population statistics and behavioral analysis results, and provide data support for urban business district planning, tourism resource allocation, and public service optimization.

[0005] This application also provides a method for monitoring tourism population based on intelligent agent graphs and blockchain, including the following steps: Multi-source data on tourist population within the business district are collected using pre-set devices; The multi-source tourism population data is processed to construct an intelligent agent map and dynamically update it. Multi-dimensional monitoring and processing are performed based on the aforementioned intelligent agent map to obtain multi-source data of classified tourist populations, which are then encrypted to obtain a data package for evidence storage. Data is processed based on participating nodes, and data management permissions are allocated differently to facilitate the sharing of multi-source tourism population data. The verification process is performed based on the stored evidence data to obtain the percentage of verified data and compare it with a preset threshold to obtain a reliable tourism population monitoring report and store the data for the call.

[0006] Optionally, in the tourism population monitoring method based on intelligent agent graph and blockchain described in the embodiments of this application, the step of collecting multi-source data on the tourism population within the business district through a preset device includes: Collect multi-source data on tourist populations using pre-set devices; The multi-source data on the tourist population includes identity feature data, behavioral feature data, spatiotemporal trajectory feature data, and related consumption feature data.

[0007] Optionally, in the tourism population monitoring method based on intelligent agent graph and blockchain described in the embodiments of this application, the step of processing the multi-source tourism population data, constructing an intelligent agent graph, and dynamically updating it includes: The multi-source tourism population data is preprocessed and standardized to obtain standardized multi-source tourism population data. A graph structure is constructed based on the nodes and boundary relationships of the standardized multi-source tourism population data to obtain an agent graph. The intelligent agent map is dynamically updated by processing the standardized multi-source tourism population data.

[0008] Optionally, in the tourism population monitoring method based on intelligent agent graph and blockchain described in the embodiments of this application, the step of performing multi-dimensional monitoring processing based on the intelligent agent graph to obtain classified multi-source tourism population data and performing encryption processing to obtain a data package for evidence storage includes: By combining the smart agent graph with a preset algorithm model, the standardized multi-source tourism population data is processed and classified to obtain classified multi-source tourism population data. The multi-source data of the classified tourist population is encrypted using a preset algorithm model to obtain a data package for evidence storage.

[0009] Optionally, in the tourism population monitoring method based on intelligent agent graphs and blockchain described in this application embodiment, the step of processing data according to participating nodes, differentially allocating data management permissions, and sharing multi-source tourism population data includes: To distribute identity certificates and key pairs to participating nodes, and to allocate data management permissions in a differentiated manner based on the identity certificates; Access is provided through a pre-defined protocol interface, and multi-source tourism population data is shared according to management permissions.

[0010] Optionally, in the tourism population monitoring method based on intelligent agent graph and blockchain described in the embodiments of this application, the step of performing verification processing based on the stored evidence data, obtaining the verification pass rate data and comparing it with a preset threshold to obtain a credible tourism population monitoring report and storing the call evidence includes: The verification process is performed based on the stored evidence data packet to obtain the verification pass rate data. The verified proportion data is compared with a preset threshold to obtain a reliable tourism population monitoring report; The reliable tourism population monitoring report is retrieved, and the retrieval request is documented.

[0011] Secondly, embodiments of this application provide a tourism population monitoring system based on intelligent agent graphs and blockchain. This system includes a memory and a processor. The memory includes a program for a tourism population monitoring method based on intelligent agent graphs and blockchain. When executed by the processor, the program for the tourism population monitoring method based on intelligent agent graphs and blockchain performs the following steps: Multi-source data on tourist population within the business district are collected using pre-set devices; The multi-source tourism population data is processed to construct an intelligent agent map and dynamically update it. Multi-dimensional monitoring and processing are performed based on the aforementioned intelligent agent map to obtain multi-source data of classified tourist populations, which are then encrypted to obtain a data package for evidence storage. Data is processed based on participating nodes, and data management permissions are allocated differently to facilitate the sharing of multi-source tourism population data. The verification process is performed based on the stored evidence data to obtain the percentage of verified data and compare it with a preset threshold to obtain a reliable tourism population monitoring report and store the data for the call.

[0012] Optionally, in the tourism population monitoring system based on intelligent agent graphs and blockchain described in this application embodiment, the step of collecting multi-source data on the tourism population within the business district through a preset device includes: Collect multi-source data on tourist populations using pre-set devices; The multi-source data on the tourist population includes identity feature data, behavioral feature data, spatiotemporal trajectory feature data, and related consumption feature data.

[0013] Optionally, in the tourism population monitoring system based on intelligent agent graph and blockchain described in this application embodiment, the step of processing the multi-source tourism population data, constructing an intelligent agent graph, and dynamically updating it includes: The multi-source tourism population data is preprocessed and standardized to obtain standardized multi-source tourism population data. A graph structure is constructed based on the nodes and boundary relationships of the standardized multi-source tourism population data to obtain an agent graph. The intelligent agent map is dynamically updated by processing the standardized multi-source tourism population data.

[0014] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a program for a tourism population monitoring method based on intelligent agent graphs and blockchain. When the program for a tourism population monitoring method based on intelligent agent graphs and blockchain is executed by a processor, it implements the steps of the tourism population monitoring method based on intelligent agent graphs and blockchain as described in any of the above claims.

[0015] As can be seen from the above, the tourism population monitoring method, system, and medium based on intelligent agent graphs and blockchain provided in this application collect multi-source data of the tourism population within a business district through a preset device, process the multi-source data of the tourism population, construct an intelligent agent graph and update it dynamically, perform multi-dimensional monitoring processing based on the intelligent agent graph, obtain classified multi-source data of the tourism population and encrypt it to obtain a data storage data package, process it according to the participating nodes, allocate data management permissions differently, share multi-source data of the tourism population, perform verification processing based on the data storage data package, obtain the verification pass rate data and compare it with a preset threshold, obtain a credible tourism population monitoring report and store the call evidence. Through multi-source data collection, intelligent agent graph construction, multi-dimensional monitoring algorithms, consortium blockchain node construction and monitoring result verification, accurate population statistics and behavioral analysis results are achieved, providing data support for urban business district planning, tourism resource allocation and public service optimization.

[0016] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a tourism population monitoring method based on intelligent agent graphs and blockchain provided in this application embodiment.

[0019] Figure 2 A flowchart illustrating the multi-source data acquisition process of the tourism population monitoring method based on intelligent agent graphs and blockchain provided in this application embodiment.

[0020] Figure 3 A high-level flowchart of the tourism population monitoring method based on intelligent agent graph and blockchain provided in the embodiments of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating tourism population monitoring based on intelligent agent graphs and blockchain in some embodiments of this application. This tourism population monitoring method based on intelligent agent graphs and blockchain is used in terminal devices, such as mobile phones and computers. The method includes the following steps: S11. Collect multi-source data on tourist population within the business district using preset devices; S12. Process the multi-source tourism population data to construct an intelligent agent map and update it dynamically. S13. Perform multi-dimensional monitoring and processing based on the intelligent agent map to obtain multi-source data of classified tourist population and encrypt it to obtain a data package for evidence storage. S14. Process data according to participating nodes, allocate data management permissions differently, and share multi-source tourism population data. S15. Perform verification processing based on the evidence data packet to obtain the verification pass rate data and compare it with a preset threshold to obtain a reliable tourism population monitoring report and perform evidence storage on the call.

[0024] The system collects multi-source data on the tourist population within the business district through pre-set devices, processes this data to construct and dynamically update an intelligent agent graph, performs multi-dimensional monitoring based on the graph, obtains categorized multi-source tourist population data, encrypts it, and generates a data package for evidence storage. This data is then processed according to participating nodes, with differentiated data management permissions assigned, and shared among the multi-source tourist population data. The data package is then verified to obtain the percentage of verified data, which is compared with a preset threshold to generate a reliable tourist population monitoring report, which is then stored as evidence. Through multi-source data collection, intelligent agent graph construction, multi-dimensional monitoring algorithms, consortium blockchain node setup, and monitoring result verification, accurate population statistics and behavioral analysis results are achieved, providing data support for urban business district planning, tourism resource allocation, and public service optimization.

[0025] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the multi-source data collection process for tourism population monitoring based on intelligent agent graphs and blockchain in some embodiments of this application. According to an embodiment of the present invention, the step of collecting multi-source tourism population data within a business district using a preset device specifically involves: S21. Collect multi-source data on tourist population through a preset device; S22. The multi-source data of the tourist population includes identity feature data, behavioral feature data, spatiotemporal trajectory feature data, and related consumption feature data.

[0026] The system collects multi-source data on tourist populations within the business district using pre-set devices such as video surveillance cameras, WiFi probes, Bluetooth beacons, and consumer terminals. It deploys these devices across a three-dimensional network in the city's core business district, dividing the district into four functional areas: a core consumption area, a transportation hub, a leisure and sightseeing area, and a densely populated merchant area. The deployment density of the devices is determined based on the population density in each area. The multi-source tourist population data includes identity characteristics such as tourist origin, identity type, age range, travel companion attributes, and mode of transportation; behavioral characteristics covering consumption type, tour route nodes, interactive behaviors, and information acquisition channels; spatiotemporal trajectory characteristics such as entry time, duration of stay, activity area, and trajectory continuity; and related consumption characteristics such as single-purchase amount, purchase frequency, merchant type, payment method, and consumption correlation.

[0027] According to an embodiment of the present invention, the step of processing the multi-source tourism population data to construct an intelligent agent map and dynamically updating it specifically includes: The multi-source tourism population data is preprocessed and standardized to obtain standardized multi-source tourism population data. A graph structure is constructed based on the nodes and boundary relationships of the standardized multi-source tourism population data to obtain an agent graph. The intelligent agent map is dynamically updated by processing the standardized multi-source tourism population data.

[0028] This solution employs a pre-defined data processing model to perform redundancy removal, correction, cleaning, and standardization preprocessing on multi-source tourist population data. In this embodiment, a redundancy identification model calculates the spatiotemporal similarity of location data. When the time difference and spatial distance between two location data points are both less than a preset threshold, they are identified as duplicate data and removed to prevent interference from duplicate data in subsequent analysis. An outlier correction model uses the quartile method to identify outliers in consumption data, such as data significantly higher or lower than the normal consumption range. The average consumption data of the tourist in the adjacent time period is used to correct these outliers, ensuring the accuracy of the consumption data. Finally, the cleaned location and consumption data are processed using a preset data standard format. Standardization processes are used to obtain standardized, structured multi-source tourism population data. For example, location data is uniformly converted to latitude and longitude format, and consumption data is uniformly converted to a structured format of tourist identifier-consumption time-consumption amount-facility identifier. A graph structure is constructed based on the node and boundary relationships of the standardized multi-source tourism population data. Each tourist is treated as an individual intelligent agent node, and a unique tourist identifier is assigned to each intelligent agent node. Simultaneously, basic tourist attribute information such as gender and age group, and spatiotemporal trajectory characteristics such as entry time into the business district, exit time from the business district, duration of stay at each facility node, and movement path are recorded, forming a complete attribute library of tourist intelligent agent nodes. Merchants, attractions, and other facilities within the business district are extracted, and a unique facility identifier is assigned to each facility. The system constructs facility nodes, whose attributes include facility type, spatial location, and business scope. By analyzing the spatiotemporal trajectory characteristics of tourists, it matches the activity trajectories of tourist agent nodes within the business district with the spatial locations of facility nodes. When a tourist's activity trajectory covers the spatial range of a facility node, an initial association edge is established between that tourist agent node and the facility node. Weights are calculated based on the frequency of tourist spending at the corresponding facility node, resulting in weighted association edges. Higher weight values ​​indicate a stronger association between the tourist and the facility. The system integrates tourist agent nodes, facility nodes, and weighted association edges, storing them according to graph database specifications to construct an initial business district tourist population agent graph, thus achieving [the goal of...]. The visualization and storage of points and associated edges are processed based on standardized multi-source data. When the amount of newly added standardized multi-source data is less than a preset ratio, such as 8%, an incremental update mode is adopted to add and update the weights of newly added tourist agent nodes, facility nodes, and associated edges. When the amount of newly added standardized multi-source data is greater than or equal to the preset ratio, a full update mode is adopted to re-execute the entire process of data preprocessing, node and associated edge construction, and dynamically update the agent graph to ensure the accuracy and timeliness of the graph data. A graph query interface is provided, allowing users to query the distribution of tourist nodes in the business district and the relationship between tourists and facility nodes in real time by inputting parameters for any time period, providing data support for business district operation decisions.

[0029] According to an embodiment of the present invention, the step of performing multi-dimensional monitoring processing based on the intelligent agent map to obtain classified multi-source tourist population data and performing encryption processing to obtain a data packet for evidence storage specifically includes: By combining the smart agent graph with a preset algorithm model, the standardized multi-source tourism population data is processed and classified to obtain classified multi-source tourism population data. The multi-source data of the classified tourist population is encrypted using a preset algorithm model to obtain a data package for evidence storage.

[0030] This process involves collecting standardized data and dynamic update logs of the intelligent agent graph used in the construction of the tourism population intelligent agent graph for the business district. A pre-defined algorithm model is used in conjunction with the intelligent agent graph to process and classify the standardized multi-source tourism population data. For example, a real-time tourism population statistics algorithm scans all tourist nodes in the intelligent agent graph, removing invalid nodes with a stay duration less than a preset threshold, and counting the number of valid nodes to obtain the real-time number of tourists in the business district. A source area distribution analysis algorithm extracts source area attribute information such as place of residence and departure location stored in tourist identity feature nodes, and uses a proportion statistics model to calculate the proportion of tourists from each region to the total number of tourists, obtaining the source area distribution results. A stay duration clustering algorithm uses the entry and exit time fields of tourist nodes to calculate the stay duration of a single tourist. The elbow rule is used to determine the initial value of the cluster centers in the K-means clustering algorithm, dividing tourists into three categories: short-term visitors, moderate stayers, and deep consumers, obtaining the number and proportion of different types of tourists. A consumption behavior association algorithm converts the movement trajectory of tourists within the business district into a tour path sequence and converts consumption records into... To construct a correlation matrix between the consumption behavior sequence and the two, the Pearson correlation coefficient is calculated. The closer the coefficient is to 1, the stronger the correlation between the tour route and the consumption behavior. For example, tourists on a certain tour route have a significantly higher frequency of consumption at catering merchants than those on other routes. Consumption behavior correlation analysis data is obtained. Intermediate calculation data during the operation of four types of algorithms are collected, such as the iteration process data of the clustering algorithm, the intermediate matrix of correlation calculation, the initial value of the algorithm parameters, and the parameter update record including update time, values ​​before and after the update, update reason, operator, and calculation result verification information. Intermediate calculation data, initial value of algorithm parameters, parameter update record, and calculation result verification information during the operation of the four types of algorithm models are collected in real time and classified and labeled according to the rule of algorithm type-run time-data type to obtain multi-source data of classified tourist population. The multi-source data of classified tourist population is encrypted by a preset algorithm model such as the SHA-256 hash algorithm model to generate a unique and irreversible hash value, ensuring that the data content can be quickly identified once it is tampered with. According to the order of timestamps, the original data with hash value and classification label and the update log are packaged to obtain the evidence storage data package.

[0031] According to an embodiment of the present invention, the step of processing data based on participating nodes, differentially allocating data management permissions, and sharing multi-source tourism population data specifically includes: To distribute identity certificates and key pairs to participating nodes, and to allocate data management permissions in a differentiated manner based on the identity certificates; Access is provided through a pre-defined protocol interface, and multi-source tourism population data is shared according to management permissions.

[0032] The alliance blockchain identifies the Business District Management Committee, the Municipal Tourism Bureau, a third-party auditing agency, and a data collection service provider as core participating nodes. A pre-defined certificate authorization center node within the alliance distributes a unique identity certificate and key pair to each participating node. Node authentication is performed via the identity certificate, and data transmission is encrypted using the key pair to ensure secure communication between nodes. Through the alliance blockchain smart contract, data management permissions are allocated differentiatedly based on the identity certificate. These permissions are written to the blockchain and cannot be tampered with. The Business District Management Committee node has full access rights, allowing access to data from all time periods for adjustments to the business district's layout, optimization of merchant recruitment strategies, and formulation of visitor flow management plans. The Municipal Tourism Bureau node has regulatory-level access rights. Data from all business districts within the jurisdiction is retrieved for tourism resource allocation, cultural and tourism policy formulation, and tourism market operation analysis. Third-party auditing agency nodes have audit-level access qualifications and can retrieve full monitoring data and verification process data for issuing third-party data audit reports. Data collection service provider nodes have limited access qualifications and can only retrieve monitoring result fragments corresponding to the data collected by their own nodes for optimizing data collection strategies. Access is provided through a preset protocol interface, and tourism population data is shared according to management permissions. Authorized nodes can quickly query the on-chain time, data source node, and subsequent update records of the corresponding multi-source tourism population data by inputting the data hash value or timestamp, realizing full lifecycle traceability of multi-source tourism population data.

[0033] According to an embodiment of the present invention, the step of performing verification processing based on the stored evidence data, obtaining the verification pass rate data and comparing it with a preset threshold to obtain a reliable tourism population monitoring report and storing the call as evidence specifically involves: The verification process is performed based on the stored evidence data packet to obtain the verification pass rate data. The verified proportion data is compared with a preset threshold to obtain a reliable tourism population monitoring report; The reliable tourism population monitoring report is retrieved, and the retrieval request is documented.

[0034] The process involves processing the initial evidence storage data package and sending it to all nodes within the consortium blockchain via a blockchain data interaction interface. This triggers a cross-validation process using differentiated verification logic. A core verification node, comprised of nodes from the business district management committee, the municipal tourism bureau, and a third-party auditing agency, is responsible for full verification of the data integrity, logical rationality, and algorithmic compliance of the initial evidence storage data package. This includes verifying the correlation between the initial evidence storage data package and the original data in the intelligent agent graph, checking the compliance of parameter application in clustering and association algorithms, and verifying the correctness of the calculation logic for statistical results such as the proportion of customer origin areas. Nodes from various data collection service providers form ordinary verification nodes, responsible for sampling and verifying the local data consistency of the initial evidence storage data package, focusing on verifying the accuracy of the monitoring results corresponding to the data collected by their respective nodes. After completing verification, each node submits verification result feedback to the consortium blockchain master node. The feedback includes a verification pass / fail conclusion, details of verification difference data, and the node's digital signature. The master node statistically analyzes all feedback results to obtain the pass / fail percentage data and compares it with a preset threshold. The values ​​are compared, and if they exceed a preset threshold, the initial evidence data packet is deemed to have passed verification and is stored on the blockchain. The master node automatically integrates the initial evidence data packet, verification feedback from each node, and verification pass rate information to obtain a trusted tourism population monitoring report stamped with a blockchain timestamp and node signature. If the percentage of data that passes verification does not reach the preset threshold, the master node sends the verification difference data details to the off-chain algorithm server, triggering the algorithm to recalculate and correct the results. After correction, the cross-verification process is initiated again. Each node submits a trusted tourism population monitoring report retrieval request to the consortium blockchain master node. The request includes the time period for retrieval, the purpose of retrieval, and the node's digital signature. The smart contract automatically verifies the permission level of the requesting node. If the permissions match, the retrieval is authorized and a retrieval operation record is generated in real time. The record includes the retrieval node identifier, retrieval time, retrieval report number, and purpose of retrieval. After the retrieval operation record is confirmed by the master node, it is written to the blockchain for evidence storage, forming an immutable operation traceability ledger. After obtaining the trusted tourism population monitoring report, the authorized node can only use the data within its authorized scope and must not disclose the report content to unauthorized entities outside the consortium.

[0035] Please refer to Figure 3 , Figure 3 This is a high-level flowchart of a tourism population monitoring method based on intelligent agent graphs and blockchain in some embodiments of this application.

[0036] This invention also discloses a tourism population monitoring system based on intelligent agent graphs and blockchain, including a memory and a processor. The memory includes a method program for monitoring tourism population based on intelligent agent graphs and blockchain. When the processor executes the method program for monitoring tourism population based on intelligent agent graphs and blockchain, it performs the following steps: Multi-source data on tourist population within the business district are collected using pre-set devices; The multi-source tourism population data is processed to construct an intelligent agent map and dynamically update it. Multi-dimensional monitoring and processing are performed based on the aforementioned intelligent agent map to obtain multi-source data of classified tourist populations, which are then encrypted to obtain a data package for evidence storage. Data is processed based on participating nodes, and data management permissions are allocated differently to facilitate the sharing of multi-source tourism population data. The verification process is performed based on the stored evidence data to obtain the percentage of verified data and compare it with a preset threshold to obtain a reliable tourism population monitoring report and store the data for the call.

[0037] The system collects multi-source data on the tourist population within the business district through pre-set devices, processes this data to construct and dynamically update an intelligent agent graph, performs multi-dimensional monitoring based on the graph, obtains categorized multi-source tourist population data, encrypts it, and generates a data package for evidence storage. This data is then processed according to participating nodes, with differentiated data management permissions assigned, and shared among the multi-source tourist population data. The data package is then verified to obtain the percentage of verified data, which is compared with a preset threshold to generate a reliable tourist population monitoring report, which is then stored as evidence. Through multi-source data collection, intelligent agent graph construction, multi-dimensional monitoring algorithms, consortium blockchain node setup, and monitoring result verification, accurate population statistics and behavioral analysis results are achieved, providing data support for urban business district planning, tourism resource allocation, and public service optimization.

[0038] According to an embodiment of the present invention, the step of collecting multi-source data on tourist population within the business district through a preset device specifically includes: Collect multi-source data on tourist populations using pre-set devices; The multi-source data on the tourist population includes identity feature data, behavioral feature data, spatiotemporal trajectory feature data, and related consumption feature data.

[0039] The system collects multi-source data on tourist populations within the business district using pre-set devices such as video surveillance cameras, WiFi probes, Bluetooth beacons, and consumer terminals. It deploys these devices across a three-dimensional network in the city's core business district, dividing the district into four functional areas: a core consumption area, a transportation hub, a leisure and sightseeing area, and a densely populated merchant area. The deployment density of the devices is determined based on the population density in each area. The multi-source tourist population data includes identity characteristics such as tourist origin, identity type, age range, travel companion attributes, and mode of transportation; behavioral characteristics covering consumption type, tour route nodes, interactive behaviors, and information acquisition channels; spatiotemporal trajectory characteristics such as entry time, duration of stay, activity area, and trajectory continuity; and related consumption characteristics such as single-purchase amount, purchase frequency, merchant type, payment method, and consumption correlation.

[0040] According to an embodiment of the present invention, the step of processing the multi-source tourism population data to construct an intelligent agent map and dynamically updating it specifically includes: The multi-source tourism population data is preprocessed and standardized to obtain standardized multi-source tourism population data. A graph structure is constructed based on the nodes and boundary relationships of the standardized multi-source tourism population data to obtain an agent graph. The intelligent agent map is dynamically updated by processing the standardized multi-source tourism population data.

[0041] This solution employs a pre-defined data processing model to perform redundancy removal, correction, cleaning, and standardization preprocessing on multi-source tourist population data. In this embodiment, a redundancy identification model calculates the spatiotemporal similarity of location data. When the time difference and spatial distance between two location data points are both less than a preset threshold, they are identified as duplicate data and removed to prevent interference from duplicate data in subsequent analysis. An outlier correction model uses the quartile method to identify outliers in consumption data, such as data significantly higher or lower than the normal consumption range. The average consumption data of the tourist in the adjacent time period is used to correct these outliers, ensuring the accuracy of the consumption data. Finally, the cleaned location and consumption data are processed using a preset data standard format. Standardization processes are used to obtain standardized, structured multi-source tourism population data. For example, location data is uniformly converted to latitude and longitude format, and consumption data is uniformly converted to a structured format of tourist identifier-consumption time-consumption amount-facility identifier. A graph structure is constructed based on the node and boundary relationships of the standardized multi-source tourism population data. Each tourist is treated as an individual intelligent agent node, and a unique tourist identifier is assigned to each intelligent agent node. Simultaneously, basic tourist attribute information such as gender and age group, and spatiotemporal trajectory characteristics such as entry time into the business district, exit time from the business district, duration of stay at each facility node, and movement path are recorded, forming a complete attribute library of tourist intelligent agent nodes. Merchants, attractions, and other facilities within the business district are extracted, and a unique facility identifier is assigned to each facility. The system constructs facility nodes, whose attributes include facility type, spatial location, and business scope. By analyzing the spatiotemporal trajectory characteristics of tourists, it matches the activity trajectories of tourist agent nodes within the business district with the spatial locations of facility nodes. When a tourist's activity trajectory covers the spatial range of a facility node, an initial association edge is established between that tourist agent node and the facility node. Weights are calculated based on the frequency of tourist spending at the corresponding facility node, resulting in weighted association edges. Higher weight values ​​indicate a stronger association between the tourist and the facility. The system integrates tourist agent nodes, facility nodes, and weighted association edges, storing them according to graph database specifications to construct an initial business district tourist population agent graph, thus achieving [the goal of...]. The visualization and storage of points and associated edges are processed based on standardized multi-source data. When the amount of newly added standardized multi-source data is less than a preset ratio, such as 8%, an incremental update mode is adopted to add and update the weights of newly added tourist agent nodes, facility nodes, and associated edges. When the amount of newly added standardized multi-source data is greater than or equal to the preset ratio, a full update mode is adopted to re-execute the entire process of data preprocessing, node and associated edge construction, and dynamically update the agent graph to ensure the accuracy and timeliness of the graph data. A graph query interface is provided, allowing users to query the distribution of tourist nodes in the business district and the relationship between tourists and facility nodes in real time by inputting parameters for any time period, providing data support for business district operation decisions.

[0042] According to an embodiment of the present invention, the step of performing multi-dimensional monitoring processing based on the intelligent agent map to obtain classified multi-source tourist population data and performing encryption processing to obtain a data packet for evidence storage specifically includes: By combining the smart agent graph with a preset algorithm model, the standardized multi-source tourism population data is processed and classified to obtain classified multi-source tourism population data. The multi-source data of the classified tourist population is encrypted using a preset algorithm model to obtain a data package for evidence storage.

[0043] This process involves collecting standardized data and dynamic update logs of the intelligent agent graph used in the construction of the tourism population intelligent agent graph for the business district. A pre-defined algorithm model is used in conjunction with the intelligent agent graph to process and classify the standardized multi-source tourism population data. For example, a real-time tourism population statistics algorithm scans all tourist nodes in the intelligent agent graph, removing invalid nodes with a stay duration less than a preset threshold, and counting the number of valid nodes to obtain the real-time number of tourists in the business district. A source area distribution analysis algorithm extracts source area attribute information such as place of residence and departure location stored in tourist identity feature nodes, and uses a proportion statistics model to calculate the proportion of tourists from each region to the total number of tourists, obtaining the source area distribution results. A stay duration clustering algorithm uses the entry and exit time fields of tourist nodes to calculate the stay duration of a single tourist. The elbow rule is used to determine the initial value of the cluster centers in the K-means clustering algorithm, dividing tourists into three categories: short-term visitors, moderate stayers, and deep consumers, obtaining the number and proportion of different types of tourists. A consumption behavior association algorithm converts the movement trajectory of tourists within the business district into a tour path sequence and converts consumption records into... To construct a correlation matrix between the consumption behavior sequence and the two, the Pearson correlation coefficient is calculated. The closer the coefficient is to 1, the stronger the correlation between the tour route and the consumption behavior. For example, tourists on a certain tour route have a significantly higher frequency of consumption at catering merchants than those on other routes. Consumption behavior correlation analysis data is obtained. Intermediate calculation data during the operation of four types of algorithms are collected, such as the iteration process data of the clustering algorithm, the intermediate matrix of correlation calculation, the initial value of the algorithm parameters, and the parameter update record including update time, values ​​before and after the update, update reason, operator, and calculation result verification information. Intermediate calculation data, initial value of algorithm parameters, parameter update record, and calculation result verification information during the operation of the four types of algorithm models are collected in real time and classified and labeled according to the rule of algorithm type-run time-data type to obtain multi-source data of classified tourist population. The multi-source data of classified tourist population is encrypted by a preset algorithm model such as the SHA-256 hash algorithm model to generate a unique and irreversible hash value, ensuring that the data content can be quickly identified once it is tampered with. According to the order of timestamps, the original data with hash value and classification label and the update log are packaged to obtain the evidence storage data package.

[0044] According to an embodiment of the present invention, the step of processing data based on participating nodes, differentially allocating data management permissions, and sharing multi-source tourism population data specifically includes: To distribute identity certificates and key pairs to participating nodes, and to allocate data management permissions in a differentiated manner based on the identity certificates; Access is provided through a pre-defined protocol interface, and multi-source tourism population data is shared according to management permissions.

[0045] The alliance blockchain identifies the Business District Management Committee, the Municipal Tourism Bureau, a third-party auditing agency, and a data collection service provider as core participating nodes. A pre-defined certificate authorization center node within the alliance distributes a unique identity certificate and key pair to each participating node. Node authentication is performed via the identity certificate, and data transmission is encrypted using the key pair to ensure secure communication between nodes. Through the alliance blockchain smart contract, data management permissions are allocated differentiatedly based on the identity certificate. These permissions are written to the blockchain and cannot be tampered with. The Business District Management Committee node has full access rights, allowing access to data from all time periods for adjustments to the business district's layout, optimization of merchant recruitment strategies, and formulation of visitor flow management plans. The Municipal Tourism Bureau node has regulatory-level access rights. Data from all business districts within the jurisdiction is retrieved for tourism resource allocation, cultural and tourism policy formulation, and tourism market operation analysis. Third-party auditing agency nodes have audit-level access qualifications and can retrieve full monitoring data and verification process data for issuing third-party data audit reports. Data collection service provider nodes have limited access qualifications and can only retrieve monitoring result fragments corresponding to the data collected by their own nodes for optimizing data collection strategies. Access is provided through a preset protocol interface, and tourism population data is shared according to management permissions. Authorized nodes can quickly query the on-chain time, data source node, and subsequent update records of the corresponding multi-source tourism population data by inputting the data hash value or timestamp, realizing full lifecycle traceability of multi-source tourism population data.

[0046] According to an embodiment of the present invention, the step of performing verification processing based on the stored evidence data, obtaining the verification pass rate data and comparing it with a preset threshold to obtain a reliable tourism population monitoring report and storing the call as evidence specifically involves: The verification process is performed based on the stored evidence data packet to obtain the verification pass rate data. The verified proportion data is compared with a preset threshold to obtain a reliable tourism population monitoring report; The reliable tourism population monitoring report is retrieved, and the retrieval request is documented.

[0047] The process involves processing the initial evidence storage data package and sending it to all nodes within the consortium blockchain via a blockchain data interaction interface. This triggers a cross-validation process using differentiated verification logic. A core verification node, comprised of nodes from the business district management committee, the municipal tourism bureau, and a third-party auditing agency, is responsible for full verification of the data integrity, logical rationality, and algorithmic compliance of the initial evidence storage data package. This includes verifying the correlation between the initial evidence storage data package and the original data in the intelligent agent graph, checking the compliance of parameter application in clustering and association algorithms, and verifying the correctness of the calculation logic for statistical results such as the proportion of customer origin areas. Nodes from various data collection service providers form ordinary verification nodes, responsible for sampling and verifying the local data consistency of the initial evidence storage data package, focusing on verifying the accuracy of the monitoring results corresponding to the data collected by their respective nodes. After completing verification, each node submits verification result feedback to the consortium blockchain master node. The feedback includes a verification pass / fail conclusion, details of verification difference data, and the node's digital signature. The master node statistically analyzes all feedback results to obtain the pass / fail percentage data and compares it with a preset threshold. The values ​​are compared, and if they exceed a preset threshold, the initial evidence data packet is deemed to have passed verification and is stored on the blockchain. The master node automatically integrates the initial evidence data packet, verification feedback from each node, and verification pass rate information to obtain a trusted tourism population monitoring report stamped with a blockchain timestamp and node signature. If the percentage of data that passes verification does not reach the preset threshold, the master node sends the verification difference data details to the off-chain algorithm server, triggering the algorithm to recalculate and correct the results. After correction, the cross-verification process is initiated again. Each node submits a trusted tourism population monitoring report retrieval request to the consortium blockchain master node. The request includes the time period for retrieval, the purpose of retrieval, and the node's digital signature. The smart contract automatically verifies the permission level of the requesting node. If the permissions match, the retrieval is authorized and a retrieval operation record is generated in real time. The record includes the retrieval node identifier, retrieval time, retrieval report number, and purpose of retrieval. After the retrieval operation record is confirmed by the master node, it is written to the blockchain for evidence storage, forming an immutable operation traceability ledger. After obtaining the trusted tourism population monitoring report, the authorized node can only use the data within its authorized scope and must not disclose the report content to unauthorized entities outside the consortium.

[0048] A third aspect of the present invention provides a readable storage medium comprising a program for a tourism population monitoring method based on an intelligent agent graph and blockchain, wherein when the program is executed by a processor, it implements the steps of the tourism population monitoring method based on an intelligent agent graph and blockchain as described in any of the preceding claims.

[0049] This invention discloses a tourism population monitoring method, system, and medium based on intelligent agent graphs and blockchain. It collects multi-source tourism population data within a business district using a pre-set device, processes this data to construct and dynamically update an intelligent agent graph, performs multi-dimensional monitoring based on the graph, obtains categorized multi-source tourism population data, encrypts it, and generates a data package for evidence storage. This data is then processed according to participating nodes, with differentiated data management permissions allocated, and shared among the multi-source tourism population data. The data package is then verified to obtain the percentage of verified data, which is compared with a pre-set threshold to generate a reliable tourism population monitoring report, and the report is stored as evidence. Through multi-source data collection, intelligent agent graph construction, multi-dimensional monitoring algorithms, consortium blockchain node construction, and monitoring result verification, accurate population statistics and behavioral analysis results are achieved, providing data support for urban business district planning, tourism resource allocation, and public service optimization.

[0050] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0051] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0052] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0053] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0054] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A tourism population monitoring method based on intelligent agent graphs and blockchain, characterized in that, Includes the following steps: Multi-source data on tourist population within the business district are collected using pre-set devices; The multi-source tourism population data is processed to construct an intelligent agent map and dynamically update it. Multi-dimensional monitoring and processing are performed based on the aforementioned intelligent agent map to obtain multi-source data of classified tourist populations, which are then encrypted to obtain a data package for evidence storage. Data is processed based on participating nodes, and data management permissions are allocated differently to facilitate the sharing of multi-source tourism population data. The verification process is performed based on the stored evidence data to obtain the percentage of verified data and compare it with a preset threshold to obtain a reliable tourism population monitoring report and store the data for the call.

2. The tourism population monitoring method based on intelligent agent graph and blockchain according to claim 1, characterized in that, The process of collecting multi-source data on tourist population within the business district through a preset device includes: Collect multi-source data on tourist populations using pre-set devices; The multi-source data on the tourist population includes identity feature data, behavioral feature data, spatiotemporal trajectory feature data, and related consumption feature data.

3. The tourism population monitoring method based on intelligent agent graph and blockchain according to claim 2, characterized in that, The step of processing the multi-source tourism population data to construct an intelligent agent map and dynamically updating it includes: The multi-source tourism population data is preprocessed and standardized to obtain standardized multi-source tourism population data. A graph structure is constructed based on the nodes and boundary relationships of the standardized multi-source tourism population data to obtain an agent graph. The intelligent agent map is dynamically updated by processing the standardized multi-source tourism population data.

4. The tourism population monitoring method based on intelligent agent graph and blockchain according to claim 1, characterized in that, The process of performing multi-dimensional monitoring and processing based on the intelligent agent map to obtain multi-source data on categorized tourist populations and encrypting it to obtain a data package for evidence storage includes: By combining the smart agent graph with a preset algorithm model, the standardized multi-source tourism population data is processed and classified to obtain classified multi-source tourism population data. The multi-source data of the classified tourist population is encrypted using a preset algorithm model to obtain a data package for evidence storage.

5. The tourism population monitoring method based on intelligent agent graph and blockchain according to claim 1, characterized in that, The process of processing data based on participating nodes, differentially allocating data management permissions, and sharing multi-source tourism population data includes: To distribute identity certificates and key pairs to participating nodes, and to allocate data management permissions in a differentiated manner based on the identity certificates; Access is provided through a pre-defined protocol interface, and multi-source tourism population data is shared according to management permissions.

6. The tourism population monitoring method based on intelligent agent graph and blockchain according to claim 1, characterized in that, The step of performing verification processing based on the stored evidence data, obtaining the verification pass rate data and comparing it with a preset threshold to obtain a reliable tourism population monitoring report and storing the call evidence includes: The verification process is performed based on the stored evidence data packet to obtain the verification pass rate data. The verified proportion data is compared with a preset threshold to obtain a reliable tourism population monitoring report; The reliable tourism population monitoring report is retrieved, and the retrieval request is documented.

7. A tourism population monitoring system based on intelligent agent graphs and blockchain, characterized in that: The system includes a memory and a processor. The memory contains a program based on an intelligent agent graph and a blockchain-based tourism population monitoring method. When the program is executed by the processor, it performs the following steps: Multi-source data on tourist population within the business district are collected using pre-set devices; The multi-source tourism population data is processed to construct an intelligent agent map and dynamically update it. Multi-dimensional monitoring and processing are performed based on the aforementioned intelligent agent map to obtain multi-source data of classified tourist populations, which are then encrypted to obtain a data package for evidence storage. Data is processed based on participating nodes, and data management permissions are allocated differently to facilitate the sharing of multi-source tourism population data. The verification process is performed based on the stored evidence data to obtain the percentage of verified data and compare it with a preset threshold to obtain a reliable tourism population monitoring report and store the data for the call.

8. The tourism population monitoring system based on intelligent agent graph and blockchain according to claim 7, characterized in that, The process of collecting multi-source data on tourist population within the business district through a preset device includes: Collect multi-source data on tourist populations using pre-set devices; The multi-source data on the tourist population includes identity feature data, behavioral feature data, spatiotemporal trajectory feature data, and related consumption feature data.

9. The tourism population monitoring system based on intelligent agent graphs and blockchain according to claim 8, characterized in that, The step of processing the multi-source tourism population data to construct an intelligent agent map and dynamically updating it includes: The multi-source tourism population data is preprocessed and standardized to obtain standardized multi-source tourism population data. A graph structure is constructed based on the nodes and boundary relationships of the standardized multi-source tourism population data to obtain an agent graph. The intelligent agent map is dynamically updated by processing the standardized multi-source tourism population data.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for a tourism population monitoring method based on intelligent agent graphs and blockchain. When the program is executed by a processor, it implements the steps of the tourism population monitoring method based on intelligent agent graphs and blockchain as described in any one of claims 1 to 6.

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