Passenger flow data storage method and system

Abstract

The invention discloses a passenger flow data storage method and system, and belongs to the field of big data processing analysis, and the system comprises a data repetition judgment module which is used for comparing new data with existing data, checking the uniqueness of the data, and avoiding repeated recording; the algorithm configuration judgment module is used for verifying the rationality of an algorithm and the validity of configuration, and adjusting algorithm parameters in real time according to data feedback of different service requirements; the data processing module is used for converting data from different sources into a unified passenger flow data structure; the client storage module is used for persistently storing client data, supporting multiple databases and having a data backup and recovery mechanism; the sales visiting track module is used for analyzing the visiting track of the customer and providing data support for marketing decision making; and the time axis video processing module is used for analyzing the activities of the customers in the store in real time and providing a dynamic passenger flow analysis report and a visual customer behavior map. According to the invention, efficient, intelligent and accurate management of passenger flow data is realized.

Description

Technical Field

[0001] The present invention relates to the field of big data processing and analysis, and more specifically, to a passenger flow data storage method and system. Background Art

[0002] With the increasing number of commercial activities, especially in the retail, real estate and service industries, the collection and analysis of customer flow data has become particularly important. Modern enterprises need to gain insights into customer behavior through customer flow data, optimize the shopping experience, and improve customer satisfaction and loyalty. However, existing technologies generally have the following deficiencies in the storage and processing of customer flow data:

[0003] 1) Data duplication: During data processing, duplicate records are likely to appear, which reduces data quality and affects the accuracy of analysis results. Duplicate data not only wastes storage resources, but may also lead to incorrect business decisions.

[0004] 2) Low processing efficiency: In the face of massive amounts of data, traditional data processing methods often cannot respond quickly, affecting timely decision-making. Delays in data processing may cause companies to miss market opportunities.

[0005] 3) Customers and sales trajectories cannot be effectively linked: There is a lack of effective mechanisms to link customer activities with sales staff performance, resulting in loss of sales potential. The correlation between customer behavior and sales results is not strong, which affects the formulation of sales strategies.

[0006] 4) Lack of real-time analysis capabilities: Many existing systems are unable to provide real-time customer behavior monitoring, making companies slow to respond to rapidly changing market environments. The lack of real-time data analysis capabilities makes it difficult for companies to quickly adjust their strategies to respond to market changes.

[0007] Therefore, developing an efficient and intelligent passenger flow data storage and processing system has important practical significance. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a passenger flow data storage method and system, which can achieve accurate, efficient and intelligent management of passenger flow data through multi-level rule links and components, improve the operational efficiency of the enterprise, and provide strong support for market decision-making.

[0009] The object of the present invention is achieved through the following solutions:

[0010] A passenger flow data storage system, comprising:

[0011] The data duplication judgment module is used to compare new data with existing data through algorithms and data indexes, check the uniqueness of data, and avoid duplicate records;

[0012] The algorithm configuration judgment module is used to verify the rationality of the algorithm and the effectiveness of the configuration, and adjust the algorithm parameters in real time according to the data feedback of different business needs;

[0013] Data processing module, used to convert data from different sources into a unified passenger flow data structure;

[0014] The customer saving module is used to persistently store customer data, supports multiple databases and has a data backup and recovery mechanism;

[0015] Sales visit track module, used to analyze customer visit tracks and provide data support for marketing decisions;

[0016] The timeline video processing module is used to analyze customer activities in the store in real time, providing dynamic customer flow analysis reports and visual customer behavior maps.

[0017] Furthermore, the rationality of the verification algorithm and the validity of the configuration are verified, specifically for data that has passed the repeatability check, the configuration of the verification algorithm and its mapping relationship with the project.

[0018] Furthermore, in the customer saving module, it specifically includes a sales consultant track saving component, a visitor group information saving component and a subsequent processing callback component;

[0019] The sales consultant track saving component is used to traverse all sales consultants through the first iterator and save customer visit track information related to each consultant;

[0020] The visiting group information storage component is used to traverse all visiting groups through the second iterator and save relevant information of the visiting groups;

[0021] The subsequent processing callback component is used to call the subsequent processing callback component after the data storage is completed to perform subsequent processing and analysis of the data.

[0022] A passenger flow data processing method comprises the following steps:

[0023] In the preliminary data processing flow, data storage is completed using the passenger flow data storage system based on the above-mentioned method;

[0024] After completing the preliminary processing flow, enter the main data processing flow to further analyze and process the visitor group data;

[0025] After completing the main data processing flow, calculate the customer area trajectory.

[0026] Furthermore, in the data preliminary processing flow, data storage is completed using the passenger flow data storage system according to claim 3, which specifically includes the following sub-steps:

[0027] S101, using a data duplication judgment component to check the uniqueness of input data to ensure that duplicate records are avoided during data storage; if duplicate data is found, subsequent processing is stopped and an error message is returned;

[0028] S102, if the data passes the duplication check, the algorithm configuration judgment component is used to verify the configuration of the algorithm and its mapping relationship with the project to ensure the rationality and consistency of the data processing process;

[0029] S103, after confirming that the data and configuration are valid, calling the data processing component to convert the three-party data into a usable passenger flow data structure;

[0030] S104, after the data processing is completed, the customer saving component is called to store the processed customer data into the database.

[0031] Furthermore, in the main process of entering data processing, further analyzing and processing the visitor group data specifically includes the following sub-steps:

[0032] S201, obtaining preset rule configuration and corresponding visitor group information to provide basic data for subsequent processing;

[0033] S202, according to the obtained rules, setting the type of visiting customers for subsequent customer grouping and analysis;

[0034] S203, recording the customer's visit time information to provide data support in the time dimension for analyzing customer behavior;

[0035] S204, selecting a first main decision maker through a main decision maker selection component for tracking and management in subsequent customer processing;

[0036] S205, merging information of multiple visitor groups using a merge visitor group component to ensure the integrity and completeness of the data;

[0037] S206, selecting a second main decision maker through a reselect main decision maker component to ensure that the main decision maker of the customer can be accurately identified after the merger;

[0038] S207, using the regional trajectory link to process the activity trajectory of the customer in different regions, and provide regional data support for subsequent analysis;

[0039] S208, using a customer group processing component to group customers by type to facilitate subsequent management and analysis;

[0040] S209, using the visitor group validity component to check the validity of the visitor group to ensure that only valid visitor records are retained and processed;

[0041] S210, using the merged visitor group deletion component to delete the merged visitor group information that is no longer valid, to ensure the accuracy and neatness of the data;

[0042] S211, using a visitor group matching component to match visitor group information with customer records for subsequent analysis and decision making;

[0043] S212, using a visitor registration matching component to match the customer's visitor registration information with the customer record in the database to ensure the accuracy of the information;

[0044] S213, if some visitor groups no longer meet the definition of customers, then use the visitor group data preparation component that is changed to non-customer to mark these data as non-customer data;

[0045] S214, processing the change of the visiting group through the visiting group change link to ensure the update and accuracy of all data.

[0046] Furthermore, the calculation of the customer area trajectory specifically includes the following sub-steps:

[0047] S301, calling the sales area track component to analyze the customer activity track within the sales area and provide basic data for subsequent regional analysis;

[0048] S302, merging data of different regional intervals using a merge regional interval data component, so as to better understand the movement pattern of customers between various regions;

[0049] S303, merging data of adjacent areas using a merge adjacent data component to ensure that the activities of the customer in the adjacent areas can be effectively integrated and analyzed;

[0050] S304, using the area track saving component to save the merged sales area track information into the database to ensure data persistence;

[0051] S305, using the customer area track component to analyze the customer's activity track in different areas, providing in-depth insights into customer behavior;

[0052] S306, using a merge interval data component to merge the time interval data of the customer between the areas, so as to analyze the moving speed and residence time of the customer between the areas;

[0053] S307, merging the regional interval data using a re-merging regional interval data component to ensure the accuracy and consistency of the data;

[0054] S308, merging adjacent data using a merge adjacent data component to ensure that all related data are effectively integrated;

[0055] S309, using the customer area track saving component to save the customer's area track information into the database to ensure data persistence and traceability;

[0056] S310, using a timeline video processing component to process video data related to the timeline to provide support for dynamic analysis of customer behavior;

[0057] S311, calling the reception area component to analyze the customer's activities in the reception area and provide a more detailed customer behavior analysis.

[0058] Furthermore, in step S103, the passenger flow data structure includes visitors, customer groups, visitor trajectories, visitor graphs, and trajectory association relationships.

[0059] Further, in step S104, after the data processing is completed, the customer saving component is called to store the processed customer data in the database, which specifically includes the following parallel iterative operations:

[0060] Sales consultant track preservation: traverse all sales consultants through iterators and save customer visit track information related to each consultant.

[0061] Saving visitor group information: traverse all visitor groups through another iterator and save relevant information of the visitor groups.

[0062] Subsequent processing callback component: After the data storage is completed, the system will call the subsequent processing callback component to perform subsequent processing and analysis of the data.

[0063] Furthermore, step S304 is performed twice to ensure data integrity and redundant backup.

[0064] The beneficial effects of the present invention include:

[0065] (1) The present invention can improve data processing efficiency: by establishing multi-level rules and component links, data processing efficiency is greatly improved, ensuring that enterprises can respond quickly to market changes.

[0066] (2) The present invention can reduce the data duplication rate: realize the uniqueness judgment of data and the effective elimination of duplicate records, ensure data quality, and improve the accuracy of analysis results.

[0067] (3) The present invention can enhance data relevance: optimize the relationship between customer behavior data and sales personnel activities, improve the accuracy of sales and market decisions, and help companies develop more effective marketing strategies.

[0068] (4) The present invention can provide real-time data analysis capabilities: through timeline video processing and dynamic analysis, it can provide real-time customer behavior monitoring and help enterprises quickly adjust strategies to respond to market changes.

[0069] (5) The present invention is flexible and scalable: the system design takes into account business environments of different sizes and types, and can be flexibly configured and expanded according to enterprise needs to adapt to future development. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0071] Figure 1 A passenger flow data storage flow chart of the system according to an embodiment of the present invention;

[0072] Figure 2 A customer rule judgment flow chart of the rule engine structure in the system of an embodiment of the present invention;

[0073] Figure 3 A flow chart of the processing of a visiting group by a rule engine structure in a system according to an embodiment of the present invention;

[0074] Figure 4 A flow chart of validity rule determination of a rule engine structure in a system according to an embodiment of the present invention;

[0075] Figure 5 This is a flow chart of regional trajectory processing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0076] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0077] In view of the current situation in the background, the inventor of this application further believes that after creative thinking, it is further clarified that the prior art has the following technical problems:

[0078] 1) Complexity management: Developers need to manually manage workflows, which is complex and may make the code difficult to maintain and extend.

[0079] 2) Inefficient processing capabilities: Traditional solutions that do not support parallel processing may result in slow data processing, especially when processing large-scale data, which may cause performance bottlenecks.

[0080] 3) Inadequate error handling: Traditional solutions manually implement error handling and retry mechanisms, which may cause the system to be less robust when encountering problems, increasing the risk of system crashes or data loss.

[0081] 4) Integration difficulties: Traditional solutions may have difficulty integrating with other systems or data sources, limiting the flexibility and adaptability of the system.

[0082] 5) Complex testing and debugging: Due to the lack of component-based design, the testing and debugging process may become complicated, increasing the cost of development and maintenance.

[0083] Therefore, in the overall technical concept of the present invention, the passenger flow data storage and processing system, especially for the commercial field, specifically combines the rule engine and the data analysis model, aiming to improve the accuracy and efficiency of passenger flow management, and can be widely used in data analysis and decision support in industries such as retail, real estate, catering, and exhibitions. Through in-depth analysis of passenger flow data, enterprises can better understand customer needs, optimize operational strategies, and enhance market competitiveness.

[0084] Furthermore, if Figures 1 to 5 As shown, in a preferred embodiment, a passenger flow data storage system is provided, specifically comprising:

[0085] Data duplication judgment module: By comparing new data with existing data, the uniqueness of data is checked to effectively avoid duplicate records; this component uses efficient hashing algorithms and data indexing technology to ensure rapid identification of duplicates;

[0086] Algorithm configuration judgment module: verifies the rationality of the algorithm and the effectiveness of the configuration, and supports flexible adjustment of algorithm parameters based on different business needs; this component can automatically optimize the algorithm configuration based on real-time data feedback to improve processing efficiency;

[0087] Data processing module: converts data from different sources into a unified passenger flow data structure to ensure the accuracy of subsequent analysis; this component supports input of multiple data formats, including structured and unstructured data;

[0088] Customer Saving Module: Persistently stores customer data and supports multiple databases to meet the needs of enterprises of different sizes; this component provides data backup and recovery mechanisms to ensure data security;

[0089] Sales visit track module: In-depth analysis of customer visit tracks to provide data support for marketing decisions; through in-depth analysis of customer behavior, help companies optimize sales strategies and resource allocation

[0090] Timeline video processing module: Analyzes customer activities in the store in real time and provides the company with dynamic customer flow analysis reports; this component can generate visual customer behavior maps to help management quickly understand customer behavior patterns.

[0091] Furthermore, in a preferred embodiment, a method for storing passenger flow data is provided, which specifically includes:

[0092] S1, data storage process, specifically includes the following sub-steps:

[0093] First, the system uses the data duplication judgment component to check the uniqueness of the input data to ensure that duplicate records are avoided during data storage. If duplicate data is found, the system will stop subsequent processing and return an error message.

[0094] If the data passes the duplication check, the system then uses the algorithm configuration judgment component to verify the configuration of the algorithm and its mapping relationship with the project. This component ensures the rationality and consistency of the data processing process.

[0095] After confirming that the data and configuration are valid, the system will call the data processing component to convert the three-party data into a usable passenger flow data structure, including visitors, customer groups, visit trajectories, visit maps, trajectory associations, etc.

[0096] After the data processing is completed, the system will call the customer save component to store the processed customer data in the database. This step includes two parallel iterative operations:

[0097] Sales consultant track preservation: traverse all sales consultants through iterators and save customer visit track information related to each consultant.

[0098] Saving visitor group information: traverse all visitor groups through another iterator and save relevant information of the visitor groups.

[0099] Subsequent processing callback component: After the data storage is completed, the system will call the subsequent processing callback component to perform subsequent processing and analysis of the data.

[0100] S2, the main process of data processing, specifically includes the following sub-steps:

[0101] The system first obtains the preset rule configuration and corresponding visitor group information to provide basic data for subsequent processing.

[0102] Based on the acquired rules, the system sets the type of visiting customers for subsequent customer grouping and analysis.

[0103] The customer time component is used to record the customer's visit time information, providing data support in the time dimension for analyzing customer behavior.

[0104] The system selects the first main decision maker through the main decision maker selection component so as to track and manage the decision maker in the subsequent customer processing process.

[0105] Use the Merge Visitor Group component to merge information from multiple visitor groups to ensure data integrity and completeness.

[0106] Reselect the main decision maker component: The system selects the main decision maker for the second time through this component to ensure that the customer's main decision maker can be accurately identified after the merger.

[0107] Regional trajectory links are used to process customers’ activity trajectories in different regions, providing regional data support for subsequent analysis.

[0108] Use the customer group processing component to group customers by type to facilitate subsequent management and analysis.

[0109] The system uses the visitor group validity component to check the validity of the visitor group to ensure that only valid visitor records are retained and processed.

[0110] Use the merged visitor group deletion component to delete merged visitor group information that is no longer valid to ensure the accuracy and neatness of the data.

[0111] Use the visitor group matching component to match visitor group information with customer records to facilitate subsequent analysis and decision-making.

[0112] The system uses the visitor registration matching component to match the customer's visitor registration information with the customer record in the database to ensure the accuracy of the information.

[0113] If some visitor groups no longer meet the definition of customers, the visitor group data preparation component using the change to non-customer data will prepare these data to be marked as non-customer data.

[0114] Finally, changes to the visiting group are processed through the visiting group change link to ensure that all data is updated and accurate.

[0115] S3, data storage process, specifically includes the following sub-steps:

[0116] The system first calls the sales area track component to analyze the customer activity track in the sales area and provide basic data for subsequent regional analysis.

[0117] Use the Merge Zone Interval Data component to combine data from different zone intervals to better understand customer movement patterns between zones.

[0118] Merge adjacent data components to merge data from adjacent areas, ensuring that customer activities in adjacent areas can be effectively integrated and analyzed.

[0119] The system uses the regional track saving component to save the merged sales region track information to the database to ensure data persistence. This step is performed twice to ensure data integrity and redundant backup.

[0120] The customer region track component is used to analyze customer activity tracks in different regions, providing in-depth insights into customer behavior.

[0121] Use the Merge Interval Data component to merge the time interval data of customers between areas to facilitate analysis of customers' movement speed and stay time between areas.

[0122] The regional interval data is re-merged by re-merging the regional interval data component to ensure the accuracy and consistency of the data.

[0123] The adjacent data components are re-merged to ensure that all related data are effectively integrated.

[0124] The system uses the customer area trajectory preservation component to save the customer's area trajectory information into the database to ensure data persistence and traceability.

[0125] The timeline video processing component is used to process timeline-related video data to support dynamic analysis of customer behavior.

[0126] Finally, the system uses the reception area component to call the reception area component to analyze the customer's activities in the reception area and provide a more detailed customer behavior analysis.

[0127] The present invention has the following advantages:

[0128] 1) The present invention splits a complex workflow into multiple independent components, so that each component can focus on a specific task. This design improves the maintainability and reusability of the code.

[0129] 2) The present invention has high flexibility and scalability, allowing components to be dynamically added, deleted or modified to adapt to changing business needs. This flexibility enables the system to quickly respond to market changes.

[0130] 3) The present invention has strong parallel processing capabilities and supports the parallel execution of multiple components, which significantly improves the efficiency of data processing and can effectively shorten the processing time for scenarios that require processing large amounts of data.

[0131] 4) The present invention designs an error handling and retry mechanism, provides a built-in error handling and retry mechanism, ensures automatic recovery when a component fails to execute, reduces manual intervention, and improves system reliability.

[0132] 5) The present invention has good integration and compatibility, can be integrated with various data sources and external systems, supports various data formats and protocols, and enhances the compatibility and adaptability of the system.

[0133] 6) The present invention is easy to test and debug. Due to the independence of the components, unit testing and integration testing become simpler, and developers can more easily identify and fix problems.

[0134] According to one aspect of an embodiment of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program includes a computer instruction, and the computer instruction is stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method provided in the above various optional implementations.

[0135] As another aspect, an embodiment of the present invention further provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

Claims

1. A passenger flow data storage system, characterized in that: include: The data duplication judgment module is used to compare new data with existing data through algorithms and data indexes, check the uniqueness of data, and avoid duplicate records; The algorithm configuration judgment module is used to verify the rationality of the algorithm and the effectiveness of the configuration, and adjust the algorithm parameters in real time according to the data feedback of different business needs; Data processing module, used to convert data from different sources into a unified passenger flow data structure; The customer saving module is used to persistently store customer data, supports multiple databases and has a data backup and recovery mechanism; Sales visit track module, used to analyze customer visit tracks and provide data support for marketing decisions; The timeline video processing module is used to analyze customer activities in the store in real time, providing dynamic customer flow analysis reports and visual customer behavior maps.

2. The passenger flow data storage system according to claim 1, characterized in that: The rationality of the verification algorithm and the effectiveness of the configuration are verified, specifically for data that has passed the repeatability check, the configuration of the verification algorithm and its mapping relationship with the project.

3. The passenger flow data storage system according to claim 1, characterized in that: The customer saving module specifically includes a sales consultant track saving component, a visitor group information saving component, and a subsequent processing callback component; The sales consultant track saving component is used to traverse all sales consultants through the first iterator and save customer visit track information related to each consultant; The visiting group information storage component is used to traverse all visiting groups through the second iterator and save relevant information of the visiting groups; The subsequent processing callback component is used to call the subsequent processing callback component after the data storage is completed to perform subsequent processing and analysis of the data.

4. A passenger flow data processing method, characterized in that: The steps include: In the preliminary data processing flow, data storage is completed using the passenger flow data storage system according to claim 3; After completing the preliminary processing flow, enter the main data processing flow to further analyze and process the visitor group data; After completing the main data processing flow, calculate the customer area trajectory.

5. The passenger flow data processing method according to claim 4, characterized in that: In the data preliminary processing flow, data storage is completed using the passenger flow data storage system according to claim 3, which specifically includes the following sub-steps: S101, using a data duplication judgment component to check the uniqueness of input data to ensure that duplicate records are avoided during data storage; if duplicate data is found, subsequent processing is stopped and an error message is returned; S102, if the data passes the duplication check, the algorithm configuration judgment component is used to verify the configuration of the algorithm and its mapping relationship with the project to ensure the rationality and consistency of the data processing process; S103, after confirming that the data and configuration are valid, calling the data processing component to convert the three-party data into a usable passenger flow data structure; S104, after the data processing is completed, the customer saving component is called to store the processed customer data into the database.

6. The passenger flow data processing method according to claim 4, characterized in that: In the main process of entering data processing, further analyzing and processing the visitor group data specifically includes the following sub-steps: S201, obtaining preset rule configuration and corresponding visitor group information to provide basic data for subsequent processing; S202, according to the obtained rules, setting the type of visiting customers for subsequent customer grouping and analysis; S203, recording the customer's visit time information to provide data support in the time dimension for analyzing customer behavior; S204, selecting a first main decision maker through a main decision maker selection component for tracking and management in subsequent customer processing; S205, merging information of multiple visitor groups using a merge visitor group component to ensure the integrity and completeness of the data; S206, selecting a second main decision maker through a reselect main decision maker component to ensure that the main decision maker of the customer can be accurately identified after the merger; S207, using the regional trajectory link to process the activity trajectory of the customer in different regions, and provide regional data support for subsequent analysis; S208, using a customer group processing component to group customers by type to facilitate subsequent management and analysis; S209, using the visitor group validity component to check the validity of the visitor group to ensure that only valid visitor records are retained and processed; S210, using the merged visitor group deletion component to delete the merged visitor group information that is no longer valid, to ensure the accuracy and neatness of the data; S211, using a visitor group matching component to match visitor group information with customer records for subsequent analysis and decision making; S212, using a visitor registration matching component to match the customer's visitor registration information with the customer record in the database to ensure the accuracy of the information; S213, if some visitor groups no longer meet the definition of customers, then use the visitor group data preparation component that is changed to non-customer to mark these data as non-customer data; S214, processing the change of the visiting group through the visiting group change link to ensure the update and accuracy of all data.

7. The passenger flow data processing method according to claim 4, characterized in that: The calculation of the client area trajectory specifically includes the following sub-steps: S301, calling the sales area track component to analyze the customer activity track within the sales area and provide basic data for subsequent regional analysis; S302, merging data of different regional intervals using a merge regional interval data component, so as to better understand the movement pattern of customers between various regions; S303, merging data of adjacent areas using a merge adjacent data component to ensure that the activities of the customer in the adjacent areas can be effectively integrated and analyzed; S304, using the area track saving component to save the merged sales area track information into the database to ensure data persistence; S305, using the customer area track component to analyze the customer's activity track in different areas, providing in-depth insights into customer behavior; S306, using a merge interval data component to merge the time interval data of the customer between the areas, so as to analyze the moving speed and stay time of the customer between the areas; S307, merging the regional interval data using a re-merging regional interval data component to ensure the accuracy and consistency of the data; S308, merging adjacent data using a merge adjacent data component to ensure that all related data are effectively integrated; S309, using the customer area track saving component to save the customer's area track information into the database to ensure data persistence and traceability; S310, using a timeline video processing component to process video data related to the timeline to provide support for dynamic analysis of customer behavior; S311, calling the reception area component to analyze the customer's activities in the reception area and provide a more detailed customer behavior analysis.

8. The passenger flow data processing method according to claim 5, characterized in that: In step S103, the passenger flow data structure includes visitors, customer groups, visitor trajectories, visitor graphs and trajectory association relationships.

9. The passenger flow data processing method according to claim 5, characterized in that: In step S104, after the data processing is completed, the customer saving component is called to store the processed customer data in the database, which specifically includes the following parallel iterative operations: Sales consultant track preservation: traverse all sales consultants through iterators and save customer visit track information related to each consultant. Saving visitor group information: traverse all visitor groups through another iterator and save relevant information of the visitor groups. Subsequent processing callback component: After the data storage is completed, the system will call the subsequent processing callback component to perform subsequent processing and analysis of the data.

10. The passenger flow data processing method according to claim 7, characterized in that: Step S304 is performed twice to ensure data integrity and redundant backup.

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