Information interaction service big data management method and system, and storage medium

Abstract

The application discloses an information interaction service big data management method and system and a storage medium, and relates to the technical field of big data management. The method comprises the following steps: obtaining personnel information interaction records and personnel information change records generated by information interaction terminals; calculating a behavior rule deviation degree based on the distribution of interaction time and the distribution of interaction terminals of each information interaction terminal within a preset statistical period based on an identity identifier; when personnel information changes, calculating the information change propagation priority of each information interaction terminal, determining the synchronization order of the changed information; calculating the terminal change redundancy based on the information update interval and the storage amount information; calculating the information time sensitivity based on the type preset time limit and the existing time length of the information interaction record, and determining the information interaction record retention strategy in combination with the behavior rule deviation degree and the terminal change redundancy. The application solves the technical problem that the existing information cleaning method cannot consider both the retention of low-frequency active personnel information and the optimization of storage resources.

Description

Technical Field

[0001] This invention relates to the field of big data management technology, specifically to a method, system, and storage medium for managing big data in information interaction services. Background Technology

[0002] In communities, industrial parks, and campuses, access control systems, visitor appointment systems, and attendance systems generate a large number of personnel information exchange records every day. With the long-term operation of these systems, a large amount of expired personnel information, such as canceled access card records, information on tenants who have moved out, and information on graduated students, continues to accumulate in the database, consuming storage resources and affecting query efficiency.

[0003] Existing data cleanup methods typically perform batch cleanup of expired data based on a preset fixed retention period, deleting data directly when its existence time exceeds a threshold. However, this one-size-fits-all cleanup strategy has significant shortcomings: some users, even without recent interaction records, still have valid permissions. For example, students who leave campus during winter and summer vacations have dormant access permissions that should not be cleared. Premature cleanup may mistakenly delete information of such users who are still using infrequently used permissions, preventing legitimate users from accessing services like access control upon their return to campus. Furthermore, existing methods do not comprehensively consider multiple dimensions such as the timeliness of information interaction records, the regularity of user behavior, and the storage redundancy of terminals. The cleanup strategy lacks specificity and fails to effectively release storage resources while ensuring data availability. Summary of the Invention

[0004] This invention provides a method, system, and storage medium for big data management of information interaction services, which solves the technical problem that existing information cleaning methods cannot simultaneously retain information of low-frequency active users and optimize storage resources.

[0005] In a first aspect, the present invention provides a method for managing big data in information interaction services, the method comprising: The system acquires personnel information interaction records and personnel information change records generated by the information interaction terminal, wherein the personnel information interaction records include at least an identity identifier; Based on the distribution of interaction times and interaction terminals between the identity identifier and each information interaction terminal within a preset statistical period, the behavioral pattern deviation of the identity identifier is calculated. When personnel information changes, the information change propagation priority of each information interaction terminal is calculated based on the recent interaction frequency and the time since the last interaction of the personnel on each of the information interaction terminals, and the synchronization order of the change information is determined according to the propagation priority. Based on the information update interval and storage capacity information of each information interaction terminal, the terminal change redundancy is calculated. Based on the type of information interaction record, the timeliness and duration of the record are preset, the information timeliness sensitivity is calculated, and combined with the behavioral pattern deviation and the terminal change redundancy, the information interaction record retention strategy of each information interaction terminal is determined.

[0006] Secondly, the present invention also provides an information interaction service big data management system, the system comprising: The data acquisition module is used to acquire personnel information interaction records and personnel information change records generated by the information interaction terminal, wherein the personnel information interaction records include at least an identity identifier; The behavior deviation calculation module is used to calculate the behavior pattern deviation of the identity identifier based on the distribution of interaction time and interaction terminal of the identity identifier with each information interaction terminal within a preset statistical period. The propagation priority calculation module is used to calculate the propagation priority of information change on each information interaction terminal based on the recent interaction frequency and the time since the last interaction of the personnel on each information interaction terminal when personnel information changes, and to determine the synchronization order of the changed information according to the propagation priority. The terminal redundancy calculation module is used to calculate the terminal change redundancy based on the information update interval and storage information of each of the information interaction terminals. The record retention strategy module is used to calculate the information timeliness sensitivity based on the type of information interaction record, preset the timeliness and the duration of existence, and combine the behavioral pattern deviation degree and the terminal change redundancy to determine the information interaction record retention strategy for each information interaction terminal.

[0007] Thirdly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the information interaction service big data management method as described in any one of claims 1 to 8.

[0008] One or more technical solutions provided in this invention have at least the following technical effects or advantages: First, this invention quantifies the regularity of human behavior from two dimensions: the dispersion of interaction time and the dispersion of interaction terminal, by calculating the behavioral pattern deviation of identity identifiers. Compared with existing methods that rely on fixed retention time for cleaning, it can identify personnel with regular interaction behavior but who have been in a low-frequency active state recently, thus avoiding the accidental cleaning of such personnel information who still have valid permissions.

[0009] Second, this invention calculates the timeliness sensitivity of information and uses the type of information interaction record, preset timeliness and existing duration as the basis for judging whether the record has expired. At the same time, it combines the deviation degree of behavioral patterns and the redundancy of terminal changes to jointly determine the retention strategy of information interaction records. Compared with the existing methods that only rely on a single timeliness factor for cleaning, this invention realizes differentiated record retention and cleaning decisions under multi-dimensional comprehensive evaluation. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the big data management method for information interaction services provided in an embodiment of the present invention; Figure 2 This is a logic diagram of the information interaction service big data management method provided in the embodiments of the present invention; Figure 3 This is a table diagram illustrating information interaction record data in the big data management method for information interaction services provided in this embodiment of the invention. Figure 4 This is a schematic diagram of the structure of the information interaction service big data management system provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the non-transitory computer-readable storage medium provided by the present invention.

[0012] The diagram is labeled as follows: Data acquisition module 11, Behavior deviation calculation module 12, Propagation priority calculation module 13, Terminal redundancy calculation module 14, Record retention strategy module 15, Computer-readable storage medium 600, Computer program 611. Detailed Implementation

[0013] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0014] Example 1, as Figure 1 , Figure 2 As shown, the present invention provides a big data management method for information interaction services, the method comprising: S100: Obtain personnel information interaction records and personnel information change records generated by the information interaction terminal, wherein the personnel information interaction records include at least an identity identifier; Step S100 provided in this embodiment of the invention includes: Collect personnel information interaction records within a preset time range from the event logs of each information interaction terminal. Each personnel information interaction record includes an identity identifier, information interaction type, interaction time, and information interaction terminal identifier. The identity identifier includes at least one of facial feature code, ID card number, mobile phone number, and access card number. The information interaction type includes at least one of access control access, visitor appointment, attendance check-in, and identity verification. Collect personnel information change records within a preset time range from the change log of the personnel information management system. Each personnel information change record must include at least an identity identifier and the time of the change.

[0015] The specific implementation method is as follows: The system collects records of personnel information interactions within a preset time range from the event logs of various information interaction terminals. Specifically, the information interaction terminals are intelligent devices deployed in communities, parks, campuses, and other locations, including access control gates, visitor appointment kiosks, attendance machines, and identity verification terminals. Each information interaction terminal automatically generates an event log for each personnel interaction, recording detailed information about that interaction.

[0016] The preset time range is a fixed-length time interval that traces backward from the current moment. The preset time range is determined by obtaining the longest valid period of personnel permissions within the locations served by the information interaction terminal, and using this longest valid period as the length of the preset time range. The longest valid period of personnel permissions is determined based on the type of location: for example, the academic cycle for students in a school campus is 4 years, the longest lease term for residents in a community is 3 years, and the longest employment contract term for employees in an industrial park is 5 years. For example, if the longest valid period in a school campus is 4 years, then the preset time range is 4 years.

[0017] Each of the collected personnel information interaction records contains four fields: Identifier, which is a unique identifier for the interacting personnel, including at least one of facial recognition code, ID card number, mobile phone number, and access card number; Information Interaction Type, which is the type of operation for this interaction, including at least one of access control, visitor appointment, attendance check-in, and identity verification; Interaction Time, which is the precise time when this interaction occurred; and Information Interaction Terminal Identifier, which is the terminal device number that generated this interaction record.

[0018] For example, a personnel information interaction record collected by a campus access control gate includes: the identity identifier is student ID 20210101, the information interaction type is access control, the interaction time is 08:32:15 on July 15, 2025, and the information interaction terminal identifier is GateA03. For example, Figure 3This is a data example table of information interaction records provided by the big data management method for information interaction services in an embodiment of the present invention. It shows some of the original interaction records generated by different identity identifiers on various information interaction terminals in a campus.

[0019] The personnel information change records within a preset time range are collected from the change log of the personnel information management system. Specifically, the personnel information management system is a basic information platform used to manage personnel files and permissions. When events such as tenant move-out, student graduation, or employee resignation occur, the personnel information management system writes a change record to the change log. Each collected personnel information change record contains at least an identity identifier and the time of change. For example, a personnel information change record includes: identity identifier as student ID 20210101, change time as July 1, 2025, 10:00:00, and change type as graduation / departure.

[0020] The following technical effects were achieved through this step: The system synchronously collects personnel information interaction records and personnel information change records from two data sources: the information interaction terminal and the personnel information management system. It then correlates personnel behavior data with status change data, providing a complete data foundation for subsequent behavioral pattern deviation analysis and information timeliness sensitivity assessment.

[0021] S200: Based on the distribution of interaction times and interaction terminals between the identity identifier and each information interaction terminal within a preset statistical period, calculate the behavioral pattern deviation of the identity identifier. The interactive behavior patterns corresponding to different identity identifiers differ significantly: the interactive behavior of normally active individuals shows regularity in both time and space dimensions. For example, students mainly move regularly between dormitory access control and teaching building access control during breaks and commuting times. In contrast, the interactive behavior of inactive individuals typically manifests as a prolonged period without any interaction records, or an unpredictable historical interaction pattern. If only the time since the most recent interaction is used as a criterion, students who leave school during winter and summer vacations, with no interaction records during the holidays, are easily misjudged as inactive. The behavioral pattern deviation degree comprehensively characterizes the regularity of individual interactive behavior from two dimensions: the distribution of interaction time periods and the distribution of interaction terminals. The stronger the regularity, the lower the deviation degree, and the more inclined to retain the behavior; the weaker or absent the regularity, the higher the deviation degree, and the more inclined to remove the behavior.

[0022] Step S200 provided in this embodiment of the invention includes: Based on the type of information interaction terminal, multiple interaction time periods are divided, and the interaction frequency of each interaction time period is calculated based on the interaction time of the identity identifier. Based on the interaction frequency of each interaction period, the interaction period dispersion of the identity identifier is calculated and obtained. The historical number of interactions between the identity identifier and each interactive terminal identifier is counted, and the daily interaction frequency of each information interaction terminal is calculated. Based on the daily interaction frequency of each of the aforementioned information interaction terminals, the dispersion of the interaction terminals for obtaining the identity identifier is calculated. The behavioral pattern deviation of the identity identifier is obtained by weighting the dispersion of the interaction time period and the dispersion of the interaction terminal.

[0023] The specific implementation method is as follows: Based on the type of information interaction terminal, multiple interaction time periods are defined. Specifically, the types of information interaction terminals include at least access control gates, visitor reservation kiosks, attendance machines, and identity verification terminals. Different types of terminals have different peak usage periods throughout the day. The interaction time periods are divided as follows: a 24-hour day is divided into preset fixed-duration time segments. The preset fixed duration is determined by taking the minimum daily peak interaction duration for each type of information interaction terminal in its historical interaction records. For example, the daily peak interaction duration for access control gates is approximately 2 hours, with approximately 1 hour each in the morning and evening peak hours; the daily peak interaction duration for visitor reservation kiosks is approximately 4 hours, with approximately 2 hours each in the morning and afternoon; the daily peak interaction duration for attendance machines is approximately 1 hour; and the daily peak interaction duration for identity verification terminals is approximately 3 hours. Taking the minimum value of 1 hour, each 1-hour period is considered an interaction time period, resulting in a total of 24 interaction time periods throughout the day.

[0024] Based on the interaction time of the identity identifier, the interaction frequency for each interaction period is calculated. Interaction frequency = number of personnel information interaction records generated by that identity identifier within that interaction period ÷ number of weeks within the preset statistical period. The preset statistical period is the validity period of the permissions corresponding to that identity identifier, i.e., the start and end time range during which the personnel identity remains valid in the information interaction service. The validity period of permissions is determined by querying the permission activation time and permission expiration time corresponding to that identity identifier from the personnel information management system; the time interval between the permission activation time and the permission expiration time is the validity period of permissions. If the permissions of that identity identifier are always valid, i.e., no expiration time is found, then the current time is taken as the end time of the validity period of permissions.

[0025] For example, a student with student ID 20210101 has their permissions activated on September 1, 2021, and expire on July 1, 2025 (after graduation). The permissions are valid for approximately 4 years, totaling 208 weeks. Within this period, the student generated 416 interaction records during the 8:00 AM to 9:00 AM interaction time, resulting in an interaction frequency of 416 ÷ 208 = 2.0 times per week.

[0026] Iterate through all 24 interaction periods and count the interaction frequency of the identifier in each period to form a 24-dimensional interaction period frequency vector. For example, the 24-dimensional interaction period frequency vector of student ID 20210101 is [0.04,0,0,0,0.10,0.36,2.0,1.70,0.24,0.16,0.12,0.08,0.06,0.14,0.30,1.84,1.56,0.20,0.10,0.06,0.04,0.04,0.02,0.02], where the frequency is highest between 8:00 and 9:00 AM (2.0 times / week) and the frequency is 1.84 times / week between 4:00 and 5:00 PM (17:00).

[0027] Based on the interaction frequency of each interaction period, the interaction period dispersion of the identity identifier is calculated. Specifically, the interaction period dispersion measures the degree of concentration of the identity identifier's interaction behavior over time. The interaction period dispersion is calculated as follows: Interaction period dispersion = 1 - Standard deviation of interaction frequency for each interaction period ÷ Mean of interaction frequency for each interaction period. When interaction behavior is highly concentrated in a few periods, the standard deviation of frequency is much greater than the mean, and the interaction period dispersion is close to 0, indicating strong behavioral regularity; when interaction behavior is evenly distributed across various periods, the standard deviation of frequency is close to 0, and the interaction period dispersion is close to 1, indicating irregular behavior.

[0028] For example, the mean frequency of interaction across the 24 interaction periods is 0.306, and the standard deviation is 0.664. The dispersion of interaction periods = 1 - 0.664 ÷ 0.306 = 1 - 2.170, which is negative and is therefore set to 0. A dispersion of 0 indicates that the student's interaction behavior is highly concentrated in a few specific periods, and the behavior exhibits strong regularity.

[0029] The historical interaction counts between the identity identifier and each interactive terminal identifier are statistically analyzed, and the daily interaction frequency of each information interaction terminal is calculated. Specifically, the daily interaction frequency = the total historical interaction count of the identity identifier on that information interaction terminal ÷ the number of weeks in the preset statistical period. For example, student ID 20210101 has 3120 interaction records at dormitory access control Gate A03 within a 4-year validity period, with a daily interaction frequency of 3120 ÷ 208 ≈ 15.0 times / week; 1248 interaction records at library access control Gate B02, with a daily interaction frequency of 1248 ÷ 208 ≈ 6.0 times / week; 832 interaction records at laboratory access control Gate C01, with a daily interaction frequency of 832 ÷ 208 ≈ 4.0 times / week; and 2080 interaction records at cafeteria access control Gate D05, with a daily interaction frequency of 2080 ÷ 208 ≈ 10.0 times / week. There are no interaction records at other access control points, and the daily interaction frequency is 0.

[0030] Based on the daily interaction frequency of each information interaction terminal, the interaction terminal dispersion of the identity identifier is calculated. Interaction terminal dispersion measures the spatial concentration of the interaction behavior of that identity identifier. The calculation method for interaction terminal dispersion is the same as that for interaction time period dispersion: Interaction terminal dispersion = 1 - Standard deviation of daily interaction frequency of each information interaction terminal ÷ Mean of daily interaction frequency of each information interaction terminal. When interaction behavior is highly concentrated on a few terminals, the standard deviation of daily interaction frequency is large, and the interaction terminal dispersion is close to 0, indicating strong behavioral regularity; when interaction behavior is evenly distributed across multiple terminals, the standard deviation of daily interaction frequency is small, and the interaction terminal dispersion is close to 1, indicating irregular behavior.

[0031] For example, taking the daily interaction frequency of the above 5 terminals as an example, the mean is (15.0+6.0+4.0+10.0+0)÷5=35.0÷5=7.0 times / week, and the standard deviation is 5.24. The interaction terminal dispersion = 1-5.24÷7.0=1-0.749≈0.251. The interaction terminal dispersion of 0.251 indicates that the student's interaction behavior is relatively concentrated on a few terminals, showing a certain spatial regularity.

[0032] The behavioral pattern deviation of the identifier is obtained by weighting the dispersion of interaction time periods and interaction terminals. Behavioral pattern deviation = Time period weight coefficient × Interaction time period dispersion + Terminal weight coefficient × Interaction terminal dispersion. The time period weight coefficient and terminal weight coefficient are determined as follows: Samples of regularly behaving individuals and randomly failing individuals, verified manually from all historical records of the location, are obtained, and the interaction time period dispersion and interaction terminal dispersion of the two types of samples are calculated separately. The prediction accuracy of the interaction time period dispersion in distinguishing the two types of samples, and the prediction accuracy of the interaction terminal dispersion in distinguishing the two types of samples, are calculated. The prediction accuracy is calculated as follows: using the median of the interaction time period dispersion as a threshold, samples below the threshold are predicted as regularly behaving individuals, and samples above the threshold are predicted as randomly failing individuals. The proportion of samples whose prediction results match the manual verification results is counted as the prediction accuracy of the interaction time period dispersion. The prediction accuracy of the interaction terminal dispersion is calculated similarly. Time period weight coefficient = Prediction accuracy of interaction time period dispersion ÷ (Prediction accuracy of interaction time period dispersion + Prediction accuracy of interaction terminal dispersion). Terminal weight coefficient = 1 - time period weight coefficient. For example, if the prediction accuracy of the interaction time period dispersion is 0.72 and the prediction accuracy of the interaction terminal dispersion is 0.68, the time period weight coefficient = 0.72 ÷ (0.72 + 0.68) = 0.72 ÷ 1.40 ≈ 0.514, and the terminal weight coefficient = 1 - 0.514 = 0.486. When historical data is insufficient, both the time period weight coefficient and the terminal weight coefficient are set to 0.5. The closer the behavioral pattern deviation is to zero, the stronger the regularity; the closer it is to one, the weaker the regularity of the behavioral pattern.

[0033] For example, the interaction time dispersion of student ID 20210101 is 0, and the interaction terminal dispersion is 0.251. The behavioral pattern deviation is calculated as 0.5 × 0 + 0.5 × 0.251 ≈ 0.126. This value of 0.126 is close to 0, indicating that the student's interaction behavior has strong regularity, and its associated information interaction records should be retained. If a student's interaction behavior is irregular, it may mean that the access card or facial information corresponding to that identity identifier is at risk of being stolen or misused by others, i.e., someone else using the card or facial recognition to pass through. Such misuse is usually manifested as inconsistent interaction time and inconsistent interaction terminals, showing a significant deviation from the normal behavior pattern of the person whose identity was stolen, and its associated information interaction records should be deleted.

[0034] The following technical effects were achieved through this step: The behavioral pattern deviation is obtained by weighted fusion of the dispersion of interaction time period and the dispersion of interaction terminal. The stronger the regularity, the lower the two dispersions, and the closer the behavioral pattern deviation is to zero; the weaker the regularity or the absence of regularity, the higher the dispersion, and the closer the behavioral pattern deviation is to one. Compared with using only the time since the most recent interaction as the judgment standard, this behavioral pattern deviation can effectively distinguish between individuals with regular but recently infrequently active behavior and individuals with irregular but historically inactive behavior, providing a quantitative basis for behavioral dimension differentiation for subsequent information interaction record retention strategies.

[0035] S300: When personnel information changes, the information change propagation priority of each information interaction terminal is calculated based on the recent interaction frequency of the personnel on each of the information interaction terminals and the time since the last interaction, and the synchronization order of the change information is determined according to the propagation priority. When personnel information changes (such as student graduation, tenant move-out, or employee resignation), the changed information needs to be synchronized to all information interaction terminals to ensure that the personnel's permission status on each terminal is updated in a timely manner. Different terminals have varying degrees of association with the same person. For example, a student may primarily use dormitory and library access control systems and rarely use laboratory access control systems. If the changed information is synchronized to all terminals in a uniform order, terminals closely associated with the student may receive the change notification late, allowing the deactivated personnel to still access the system during this period, creating a security risk. Simultaneously, pushing the changed information to terminals with very low association also wastes communication resources and terminal storage space. Therefore, it is necessary to calculate the information change propagation priority for each terminal based on the recent interaction frequency and the time since the last interaction, and synchronize the changed information sequentially from highest to lowest priority.

[0036] Step S300 provided in this embodiment of the invention includes: The number of recent interactions of the changed personnel's identity identifier on each information interaction terminal within a preset statistical window is obtained, and the number of recent interactions is divided by the duration of the preset statistical window to obtain the recent interaction frequency of the information interaction terminal. Obtain the time of the most recent information interaction of the identity identifier of the changed personnel on each of the information interaction terminals, and calculate the difference between the current time and the time of the most recent information interaction as the time since the last interaction; Obtain the change time from the personnel information change record of the changed personnel, calculate the difference between the current time and the change time, and use it as the time elapsed since the change occurred; Based on the time elapsed since the last interaction and the time elapsed since the change occurred, and combined with the preset information synchronization cycle of each information interaction terminal, the synchronization urgency is calculated and obtained. Divide the recent interaction frequency by the maximum value among the recent interaction frequencies of all the information interaction terminals, and use the resulting quotient as the interaction frequency normalization value. Based on the normalized value of the interaction frequency and the urgency of synchronization, the information change propagation priority of the information interaction terminal is calculated and obtained. The information interaction terminals are sorted in descending order of information change propagation priority to obtain the information change propagation priority of each information interaction terminal.

[0037] The specific implementation method is as follows: The system retrieves the identity identifier of the changed personnel and the number of recent interactions on various information interaction terminals within a preset statistical window. Specifically, the preset statistical window is a fixed-length time interval tracing back from the current moment, used to measure the level of "recent" interaction activity. The method for determining the length of the preset statistical window is as follows: taking the normal activity cycle of the personnel, such as the weekly course cycle of a student on campus or the weekly work cycle of a park employee, the preset statistical window length is taken as 4 complete activity cycles to ensure that sufficient behavioral samples are included to assess the closeness of the association between the terminal and the personnel. For example, if the activity cycle is 1 week, the preset statistical window length = 7 × 4 = 28 days.

[0038] Extract all personnel information interaction records generated by the identity identifier within a preset statistical window, group them according to the identifier of the information interaction terminal, and obtain the recent interaction count of the identity identifier on each information interaction terminal. Divide the recent interaction count by the duration of the preset statistical window to obtain the recent interaction frequency of the information interaction terminal. That is, recent interaction frequency = recent interaction count ÷ preset statistical window duration. For example, a student generated 56 interaction records on dormitory access control Gate A03 in the past 28 days, with a recent interaction frequency of 56 ÷ 28 = 2.0 times / day; generated 28 interaction records on library access control Gate B02, with a recent interaction frequency of 28 ÷ 28 = 1.0 times / day; generated 42 interaction records on cafeteria access control Gate D05, with a recent interaction frequency of 42 ÷ 28 = 1.5 times / day; and generated 4 interaction records each on laboratory access control Gate C01 and teaching building access control Gate E06, with a recent interaction frequency of 4 ÷ 28 ≈ 0.14 times / day.

[0039] The system retrieves the most recent information interaction time of the changed personnel's identity identifier on each information interaction terminal, and calculates the difference between the current time and the most recent information interaction time as the time since the last interaction. Specifically, the time since the last interaction reflects the timeliness of the association between the terminal and the personnel. The shorter the time since the last interaction, the more recently the personnel have been using the terminal, and the closer the association is; the longer the time since the last interaction, the longer the time since the last interaction, the longer the personnel have not used the terminal. For example, the student's most recent interaction on the dormitory access control Gate A03 was 1 day ago, and the time since the last interaction is 1 day; the most recent interaction on the library access control Gate B02 was 2 days ago, and the time since the last interaction is 2 days; the most recent interaction on the cafeteria access control Gate D05 was 1 day ago, and the time since the last interaction is 1 day; the most recent interaction on the laboratory access control Gate C01 was 15 days ago, and the time since the last interaction is 15 days; and the most recent interaction on the teaching building access control Gate E06 was 3 days ago, and the time since the last interaction is 3 days.

[0040] Obtain the change time from the personnel information change record of the changed personnel, and calculate the difference between the current time and the change time as the elapsed time since the change occurred. For example, if the student's graduation and departure change occurred 4 days ago, the elapsed time since the change is 4 days.

[0041] Based on the time elapsed since the last interaction and the time elapsed since the change occurred, combined with the preset information synchronization cycle of each information interaction terminal, the synchronization urgency is calculated. Specifically, the information synchronization cycle is a fixed time interval between each information interaction terminal receiving and updating local personnel information from the backend, set by the communication scheduling strategy of the terminal device, and the synchronization cycle may differ between different terminals. The synchronization urgency is used to measure the urgency of pushing change information to the terminal, and its calculation method is: Synchronization Urgency = max(Time elapsed since last interaction ÷ Information synchronization cycle, Time elapsed since change occurred ÷ Information synchronization cycle). The maximum value of the ratios of the time elapsed since the last interaction and the time elapsed since the change occurred to the synchronization cycle is taken. When the calculated result is greater than 1, it is taken as 1. The reason for the truncation is that when the time elapsed since the change occurred or the time elapsed since the last interaction exceeds the information synchronization cycle of the terminal, it indicates that the change information is in an overdue and unsynchronized state. For the terminal, at least one synchronization window has been missed. At this time, the synchronization urgency reaches the maximum critical value, and further increases are not meaningful. This truncation process keeps the synchronization urgency within the normalized range of 0 to 1, making it easier to perform weighted calculations on the same dimension as the normalized value of the interaction frequency.

[0042] For example, the information synchronization cycle for dormitory access control Gate A03 is once every 2 days, and the synchronization urgency is max(1÷2,4÷2)=max(0.5,2.0)=2.0, which is set to 1. The information synchronization cycle for library access control Gate B02 is once every 3 days, and the synchronization urgency is max(2÷3,4÷3)=max(0.67,1.33)=1.33, which is set to 1. The information synchronization cycle for cafeteria access control Gate D05 is once every day, and the synchronization urgency is max(1÷1,4÷1)=max(1.0,4.0)=4.0, which is set to 1. The information synchronization cycle for laboratory access control Gate C01 is once every 5 days, and the synchronization urgency is max(15÷5,4÷5)=max(3.0,0.8)=3.0, which is set to 1. The information synchronization cycle of the teaching building access control GateE06 is once every 7 days, and the synchronization urgency is max(3÷7,4÷7)=max(0.43,0.57)=0.57.

[0043] Based on the interaction frequency normalization value and synchronization urgency, the information change propagation priority of the information interaction terminal is calculated. Specifically, the information change propagation priority = frequency weighting coefficient × interaction frequency normalization value + urgency weighting coefficient × synchronization urgency. The frequency weighting coefficient and urgency weighting coefficient are determined as follows: The proportion of times the changed information on the terminal was hit after synchronization according to priority in the historical information change propagation record (i.e., the user immediately tried to use the terminal after the changed information arrived at the terminal) is used. The frequency weighting coefficient is the complement of this proportion, and the urgency weighting coefficient is the proportion itself. For example, if the proportion of times the changed information on the terminal was hit after synchronization according to priority in the historical record is 0.35, the frequency weighting coefficient = 1 - 0.35 = 0.65, and the urgency weighting coefficient = 0.35.

[0044] For example, the information change propagation priority of dormitory access control Gate A03 is 0.65×1.0+0.35×1=1.0; the information change propagation priority of library access control Gate B02 is 0.65×0.5+0.35×1=0.675; the information change propagation priority of canteen access control Gate D05 is 0.65×0.75+0.35×1≈0.838; the information change propagation priority of laboratory access control Gate C01 is 0.65×0.07+0.35×1≈0.396; and the information change propagation priority of teaching building access control Gate E06 is 0.65×0.07+0.35×0.57≈0.0455+0.1995≈0.245.

[0045] The information interaction terminals are sorted according to their information change propagation priority from highest to lowest, and the sorting results are obtained for each terminal. For example, the sorting results are: Dormitory Access Gate A03 (1.0) > Cafeteria Access Gate D05 (0.838) > Library Access Gate B02 (0.675) > Laboratory Access Gate C01 (0.396) > Teaching Building Access Gate E06 (0.245). Change information is synchronized to each terminal in this order, prioritizing the dormitory access gate (which the student uses most frequently and has the highest urgency), followed by the cafeteria and library access gates, and finally the teaching building and laboratory access gates (which have lower relevance).

[0046] The following technical effects were achieved through this step: The priority of information change propagation is calculated by weighting the recent interaction frequency normalized value and the synchronization urgency. The recent interaction frequency normalized value measures the long-term closeness of the relationship between the terminal and the person, while the synchronization urgency measures the urgency of pushing the current change event to the terminal. The two are combined and the synchronization order is determined in descending order. Prioritizing the push of changes to terminals with frequent interactions and urgent updates improves the efficiency of change information propagation and the utilization of terminal storage space, while reducing the storage of invalid information on terminals with low correlation.

[0047] S400: Calculate the terminal change redundancy based on the information update interval and storage capacity information of each of the information interaction terminals; Each information interaction terminal not only needs to receive personnel information changes in a timely manner, but also faces constraints on local storage space. Different terminals have varying storage capacities and update frequencies: some terminals have small storage capacities and have not been updated for a long time, resulting in near-saturation of storage space. A large amount of outdated personnel information is consuming terminal storage resources, affecting the information retrieval efficiency for normal users; other terminals have ample storage capacity and are updated promptly, thus requiring less cleanup of accumulated information. Therefore, it is necessary to quantify the redundancy of changes based on the update status and storage condition of individual terminals, providing terminal-level evaluation indicators for subsequent differentiated cleanup strategies.

[0048] Step S400 provided in this embodiment of the invention includes: Obtain the preset information update interval of each of the information interaction terminals as the standard terminal update interval, and obtain the number of personnel information entries currently stored in each of the information interaction terminals as the terminal storage capacity. Obtain the time interval between the current moment and the last information update as the update interval duration, and calculate the ratio of the update interval duration to the standard terminal update interval to obtain the information update interval ratio; Divide the terminal storage amount by the upper limit of the storage capacity of the information interaction terminal to obtain the terminal storage occupancy rate of the information interaction terminal; Multiply the information update interval ratio by the terminal storage occupancy rate to obtain the terminal change redundancy.

[0049] The specific implementation method is as follows: The preset information update intervals of each information interaction terminal are obtained as the standard terminal update interval. Specifically, the information update interval is a predetermined time interval for the information interaction terminal to receive and update local personnel information data from the backend. It is set by the terminal's device specifications and communication scheduling strategy. Different models and uses of terminals have different standard update intervals. For example, the standard terminal update interval for dormitory access control Gate A03 is once every 2 days, for cafeteria access control Gate D05 it is once every 1 day, for library access control Gate B02 it is once every 3 days, for laboratory access control Gate C01 it is once every 5 days, and for teaching building access control Gate E06 it is once every 7 days.

[0050] The number of personnel information entries currently stored on each information interaction terminal is obtained as the terminal's storage capacity. The terminal's storage capacity is recorded by the terminal's local storage module, representing the total number of personnel information entries currently stored on that terminal. For example, the cafeteria access control GateD05 currently stores 18,520 personnel information entries.

[0051] The time interval between the current moment and the last information update is obtained as the update interval. Specifically, the last information update time is extracted from the terminal's information update log. The update interval reflects the lag of the terminal since the last information synchronization. The longer the update interval, the longer the terminal has been since it last received changed information, and the higher the possibility that the local personnel information is outdated. For example, the last information update of the cafeteria access control GateD05 occurred 0.5 days ago, and the update interval is 0.5 days.

[0052] Calculate the ratio of the update interval duration to the standard terminal update interval to obtain the information update interval ratio. Specifically, the information update interval ratio = update interval duration ÷ standard terminal update interval. The closer the information update interval ratio is to 0, the more timely the terminal update is, with ample time remaining before the standard update interval; the closer the information update interval ratio is to or exceeds 1, the more time the terminal has approached or exceeded the standard update interval without updating, indicating a lag in updates. For example, the information update interval ratio of the cafeteria access control GateD05 is 0.5 ÷ 1 = 0.5.

[0053] Divide the terminal's storage capacity by the terminal's maximum storage capacity to obtain the terminal's storage occupancy rate. Specifically, the maximum storage capacity is the maximum number of personnel information entries that can be stored as set in the terminal's hardware specifications. Terminal storage occupancy rate = Terminal storage capacity ÷ Maximum storage capacity. The closer the terminal storage occupancy rate is to 1, the more saturated the terminal's storage space is, and the more urgent the need to clear accumulated information. For example, the maximum storage capacity of the cafeteria access control GateD05 is 20,000 entries, so the terminal storage occupancy rate = 18520 ÷ 20000 = 0.926.

[0054] The terminal change redundancy is obtained by multiplying the information update interval ratio by the terminal storage occupancy rate. Specifically, terminal change redundancy = information update interval ratio × terminal storage occupancy rate. Terminal change redundancy combines information from two dimensions: the lag in receiving change information and the scarcity of its own storage space. A value closer to 1 indicates that the terminal has been out of stock for a long time and its storage space is nearing saturation; in subsequent record retention strategies, redundant information records on this terminal should be cleared to free up storage space. A value closer to 0 indicates that the terminal updates promptly and has ample storage space; record retention has a higher priority. For example, the terminal change redundancy of the cafeteria access control GateD05 is 0.5 × 0.926 = 0.463.

[0055] The following technical effects were achieved through this step: By quantifying the urgency of information cleanup for individual terminals from two dimensions—the update status and storage status—it is possible to identify terminals with lagging updates and limited storage space, assigning them higher cleanup priorities, while applying more conservative cleanup strategies to terminals with timely updates and ample storage space, thus achieving differentiated cleanup decisions at the terminal level.

[0056] S500: Based on the type of information interaction record, preset time limit and existing duration, calculate the information time sensitivity, and combine the behavioral pattern deviation degree and the terminal change redundancy to determine the information interaction record retention strategy for each information interaction terminal.

[0057] Different types of interaction records have different business validity periods. For example, the validity period of access control records is usually shorter than that of visitor appointment records. The longer a record has existed since its creation, the lower its timeliness value. Retaining historical records that are well past their expiration date simply because of the behavioral patterns of the individuals associated with those records will waste terminal storage space; conversely, deleting important records that are still within their expiration period simply because of high terminal storage pressure will affect information traceability. Therefore, it is necessary to comprehensively evaluate each record pending processing on each terminal based on three dimensions: information timeliness, behavioral patterns, and terminal redundancy, to generate differentiated retention or deletion decisions.

[0058] Step S500 provided in this embodiment of the invention includes: From the preset information type and effective duration mapping table, query and obtain the preset effective duration and initial sensitivity value corresponding to the information interaction type; Obtain the pending interaction time of the pending information interaction record, calculate the difference between the current time and the pending interaction time, and obtain the duration of the existing data. Calculate the ratio of the existing duration to the preset effective duration to obtain the time decay ratio of the information interaction record to be processed; The initial sensitivity value is corrected using the time-sensitivity attenuation ratio to obtain the information time-sensitivity, wherein the information time-sensitivity and the time-sensitivity attenuation ratio are negatively correlated.

[0059] The specific implementation method is as follows: The preset information type and validity period mapping table is used to retrieve the preset validity period and initial sensitivity value corresponding to the information interaction type. Specifically, the preset information type and validity period mapping table is a lookup table indexed by information interaction type, with each information interaction type corresponding to a preset validity period and an initial sensitivity value. The preset validity period indicates the minimum number of valid days that the information interaction record of this type should be retained for business purposes, as stipulated by the information management specifications of the venue. The initial sensitivity value indicates the business importance of the information interaction record of this type when it is generated, and its value ranges from 0 to 1. For example, access control records serve as proof of personnel's daily movement, with a validity period of 90 days and an initial sensitivity value of 0.7; visitor appointment records serve as proof of visitor security registration, with a validity period of 180 days and an initial sensitivity value of 0.9; attendance records serve as proof of attendance statistics, with a validity period of 365 days and an initial sensitivity value of 0.8; and identity verification records serve as proof of identity verification, with a validity period of 30 days and an initial sensitivity value of 0.6. For example, the information interaction type of the information interaction record to be processed is access control, and the preset validity period is 90 days, with an initial sensitivity value of 0.7, is obtained from the mapping table.

[0060] The system retrieves the pending interaction time from the pending information interaction record and calculates the difference between the current time and the pending interaction time to obtain the existing duration. Specifically, the pending interaction time is the time when the information interaction occurred recorded in the personnel information interaction record. Existing duration = current time - pending interaction time, in days. For example, if the interaction time of this access control record is 120 days ago, the existing duration is 120 days.

[0061] Calculate the ratio of the existing duration to the preset valid duration to obtain the time-depreciation ratio of the information interaction record to be processed. Specifically, the time-depreciation ratio = existing duration ÷ preset valid duration. The time-depreciation ratio reflects the degree of decay of the record's existing duration relative to the business validity period; a larger ratio indicates more severe decay. For example, the time-depreciation ratio of access control records = 120 ÷ 90 ≈ 1.333.

[0062] The initial sensitivity value is corrected using the timeliness decay ratio to obtain the information timeliness sensitivity. Specifically, information timeliness sensitivity = initial sensitivity value ÷ (1 + timeliness decay ratio). Information timeliness sensitivity is negatively correlated with the timeliness decay ratio. The initial sensitivity value gradually decreases under the influence of the denominator (1 + timeliness decay ratio), and the larger the decay ratio, the lower the sensitivity. The closer the information timeliness sensitivity is to one, the more likely the information interaction record is still in a high timeliness period and cannot be deleted; the closer it is to zero, the more likely the information interaction record has far exceeded its business validity period and can be safely deleted or archived. For example, the information timeliness sensitivity of access control records = 0.7 ÷ (1 + 1.333) = 0.7 ÷ 2.333 ≈ 0.300.

[0063] Step S500 provided in this embodiment of the invention further includes: Based on the information timeliness sensitivity, the information aging degree is calculated and obtained, and the information aging degree is multiplied by the first retention weight coefficient to obtain the aging cleanup component; Multiply the behavioral pattern deviation by the second retention weight coefficient, and the resulting product is recorded as the anomaly cleanup component. Multiply the terminal change redundancy by the third retention weight coefficient, and the resulting product is recorded as the redundancy cleanup component. The aging cleanup component, the abnormal cleanup component, and the redundant cleanup component are added together, and the sum is used as the comprehensive cleanup index of the information interaction record to be processed, wherein the sum of the first retention weight coefficient, the second retention weight coefficient, and the third retention weight coefficient is one. When the comprehensive cleanup index is greater than or equal to the preset cleanup execution threshold, the pending information interaction record is marked as a cleanupable record; When the comprehensive cleanup index is less than the cleanup execution threshold, the pending information interaction record is marked as a reserved record and continues to be stored in the information interaction terminal; The first retention weight coefficient, the second retention weight coefficient, and the third retention weight coefficient are obtained based on the type of the information interaction terminal and the terminal storage capacity.

[0064] The specific implementation method is as follows: Based on information timeliness sensitivity, the information aging degree is calculated. Specifically, information aging degree = 1 - information timeliness sensitivity. The information aging degree reflects the extent to which the record should be deleted due to expiration since its creation; the higher the aging degree, the more it should be deleted. For example, the information aging degree of this access control record = 1 - 0.300 = 0.700.

[0065] The aging degree is multiplied by the first retention weight coefficient to obtain the aging cleanup component. Specifically, the aging cleanup component = first retention weight coefficient × information aging degree. The behavioral pattern deviation degree is multiplied by the second retention weight coefficient, and the resulting product is recorded as the abnormal cleanup component. The abnormal cleanup component = second retention weight coefficient × behavioral pattern deviation degree. The behavioral pattern deviation degree is calculated in step S200. The larger the deviation degree, the more the interaction behavior of the identifier deviates from the normal pattern, the lower the credibility of its associated information interaction records, and the more it should be cleaned up. The terminal change redundancy degree is multiplied by the third retention weight coefficient, and the resulting product is recorded as the redundancy cleanup component. The redundancy cleanup component = third retention weight coefficient × terminal change redundancy degree. The terminal change redundancy degree is calculated in step S400. The larger the redundancy degree, the greater the storage pressure on the terminal, the more urgent the need to clean up the deposited information, and the more it should be cleaned up.

[0066] The sum of the first, second, and third retention weight coefficients is 1. These three weight coefficients are obtained based on the type and storage capacity of the information interaction terminal. The acquisition method is as follows: Among all pending information interaction records served by the terminal, the percentage of records with an information aging rate greater than 0.5 is recorded as the expiration rate; the percentage of records with a deviation in the behavioral patterns of associated personnel greater than the median of all terminals in the location is recorded as the abnormal user rate; and the terminal storage occupancy rate is also calculated. The expiration rate, abnormal user rate, and terminal storage occupancy rate are used as importance measures for three dimensions, and after normalization, they are used as the first, second, and third retention weight coefficients, respectively. A higher expiration rate indicates a larger number of expired records on the terminal, necessitating greater cleanup of records related to expiration timeliness, thus increasing the first retention weight coefficient. Similarly, a higher abnormal user rate indicates more records associated with individuals exhibiting abnormal behavior, requiring greater cleanup of records related to behavior, thus increasing the second retention weight coefficient. Furthermore, a higher terminal storage occupancy rate indicates tighter storage space, necessitating greater cleanup of redundant records, thus increasing the third retention weight coefficient. For example, if a terminal has an expiration rate of 0.55, an abnormal user rate of 0.30, and a storage occupancy rate of 0.15, after normalization, the first retention weight coefficient = 0.55 ÷ (0.55 + 0.30 + 0.15) = 0.55, the second retention weight coefficient = 0.30 ÷ 1.0 = 0.30, and the third retention weight coefficient = 0.15 ÷ 1.0 = 0.15.

[0067] For example, the information aging degree of this access control record is 0.700, the first retention weight coefficient is 0.55, and the aging cleanup component = 0.55 × 0.700 = 0.385. The behavioral pattern deviation of the record's associated identity identifier is 0.126, the second retention weight coefficient is 0.30, and the anomaly cleanup component = 0.30 × 0.126 ≈ 0.038. The terminal change redundancy of the terminal where this record is located is 0.463, the third retention weight coefficient is 0.15, and the redundancy cleanup component = 0.15 × 0.463 ≈ 0.069.

[0068] The aging cleanup component, the abnormal cleanup component, and the redundant cleanup component are added together, and the sum is used as the comprehensive cleanup index for the information interaction records to be processed. Specifically, the comprehensive cleanup index = aging cleanup component + abnormal cleanup component + redundant cleanup component. The comprehensive cleanup index integrates the cleanup needs assessment from three dimensions: information timeliness, behavioral patterns, and terminal redundancy. The higher the index, the more the record should be cleaned. For example, the comprehensive cleanup index = 0.385 + 0.038 + 0.069 = 0.492.

[0069] The overall cleanup index is compared with a preset cleanup execution threshold. Specifically, the preset cleanup execution threshold is determined as follows: The overall cleanup index of all historical cleanup records for the location, verified by manual review, is obtained. The 25th percentile of the overall cleanup index of all correctly cleaned records is taken as the cleanup execution threshold. The 25th percentile indicates that approximately 75% of correct cleanup decisions have an overall cleanup index higher than this value, and records with an index lower than this value are more likely to be cleaned correctly. For example, the cleanup execution threshold is set to 0.45. The overall cleanup index of this access control record is 0.492, which is greater than or equal to the cleanup execution threshold of 0.45, so the record is marked as a cleanable record. If the overall cleanup index is less than the cleanup execution threshold, the record is marked as a retained record and continues to be stored in the terminal.

[0070] The following technical effects were achieved through this step: By using information timeliness sensitivity, behavioral pattern deviation, and terminal change redundancy as three independent evaluation factors, and constructing a comprehensive cleanup index by weighting the information aging degree, abnormal cleanup component, and redundant cleanup component, the record retention strategy decision integrates information from three dimensions: the degree of information timeliness decay, the behavioral patterns of related personnel, and the storage status of the terminal. This approach can avoid mistakenly cleaning up the accumulated records of personnel with strong behavioral patterns even when the information has far exceeded its validity period, and prioritize cleaning up records with high comprehensive cleanup indices when terminal storage pressure is high. This achieves differentiated record retention and cleanup decisions under multi-dimensional comprehensive evaluation.

[0071] Example 2, as Figure 4As shown, based on the same inventive concept provided in Embodiment 1, this embodiment of the invention also provides that the system includes: The data acquisition module 11 is used to acquire personnel information interaction records and personnel information change records generated by the information interaction terminal, wherein the personnel information interaction records include at least an identity identifier; The behavior deviation calculation module 12 is used to calculate the behavior pattern deviation of the identity identifier based on the distribution of interaction time and interaction terminal of the identity identifier with each information interaction terminal within a preset statistical period. The propagation priority calculation module 13 is used to calculate the information change propagation priority of each information interaction terminal based on the recent interaction frequency and the time since the last interaction of the personnel on each information interaction terminal when the personnel information changes, and to determine the synchronization order of the changed information according to the propagation priority. The terminal redundancy calculation module 14 is used to calculate the terminal change redundancy based on the information update interval and storage information of each of the information interaction terminals. The record retention strategy module 15 is used to calculate the information timeliness sensitivity based on the type of information interaction record, preset the timeliness and the duration of existence, and combine the behavior pattern deviation degree and the terminal change redundancy to determine the information interaction record retention strategy for each information interaction terminal.

[0072] In one embodiment, the data acquisition module 11 is further configured to collect personnel information interaction records within a preset time range from the event logs of each information interaction terminal. Each personnel information interaction record includes an identity identifier, information interaction type, interaction time, and identification of the information interaction terminal. The identity identifier includes at least one of facial feature code, ID card number, mobile phone number, and access card number. The information interaction type includes at least one of access control access, visitor appointment, attendance check-in, and identity verification. Collect personnel information change records within a preset time range from the change log of the personnel information management system. Each personnel information change record must include at least an identity identifier and the time of the change.

[0073] In one embodiment, the behavior deviation calculation module 12 is further configured to divide multiple interaction periods based on the information interaction terminal type, and to count the interaction frequency of each interaction period based on the interaction time of the identity identifier. Based on the interaction frequency of each interaction period, the interaction period dispersion of the identity identifier is calculated and obtained. The historical number of interactions between the identity identifier and each interactive terminal identifier is counted, and the daily interaction frequency of each information interaction terminal is calculated. Based on the daily interaction frequency of each of the aforementioned information interaction terminals, the dispersion of the interaction terminals for obtaining the identity identifier is calculated. The behavioral pattern deviation of the identity identifier is obtained by weighting the dispersion of the interaction time period and the dispersion of the interaction terminal.

[0074] In one embodiment, the propagation priority calculation module 13 is further used to obtain the number of recent interactions of the identity identifier of the changed personnel on each information interaction terminal within a preset statistical window, and divide the number of recent interactions by the duration of the preset statistical window to obtain the recent interaction frequency of the information interaction terminal. Obtain the time of the most recent information interaction of the identity identifier of the changed personnel on each of the information interaction terminals, and calculate the difference between the current time and the time of the most recent information interaction as the time since the last interaction; Obtain the change time from the personnel information change record of the changed personnel, calculate the difference between the current time and the change time, and use it as the time elapsed since the change occurred; Based on the time elapsed since the last interaction and the time elapsed since the change occurred, and combined with the preset information synchronization cycle of each information interaction terminal, the synchronization urgency is calculated and obtained. Divide the recent interaction frequency by the maximum value among the recent interaction frequencies of all the information interaction terminals, and use the resulting quotient as the interaction frequency normalization value. Based on the normalized value of the interaction frequency and the urgency of synchronization, the information change propagation priority of the information interaction terminal is calculated and obtained. The information interaction terminals are sorted in descending order of information change propagation priority to obtain the information change propagation priority of each information interaction terminal.

[0075] In one embodiment, the terminal redundancy calculation module 14 is further used to obtain the preset information update interval of each of the information interaction terminals as the standard terminal update interval, and to obtain the number of personnel information entries currently stored in each of the information interaction terminals as the terminal storage capacity. Obtain the time interval between the current moment and the last information update as the update interval duration, and calculate the ratio of the update interval duration to the standard terminal update interval to obtain the information update interval ratio; Divide the terminal storage amount by the upper limit of the storage capacity of the information interaction terminal to obtain the terminal storage occupancy rate of the information interaction terminal; Multiply the information update interval ratio by the terminal storage occupancy rate to obtain the terminal change redundancy.

[0076] In one embodiment, the record retention strategy module 15 is also used to query and obtain the preset valid duration and initial sensitivity value corresponding to the information interaction type from the preset information type and valid duration mapping table. Obtain the pending interaction time of the pending information interaction record, calculate the difference between the current time and the pending interaction time, and obtain the duration of the existing data. Calculate the ratio of the existing duration to the preset effective duration to obtain the time decay ratio of the information interaction record to be processed; The initial sensitivity value is corrected using the time-sensitivity decay ratio to obtain the information time-sensitivity, wherein the information time-sensitivity and the time-sensitivity decay ratio are negatively correlated. Based on the information timeliness sensitivity, the information aging degree is calculated and obtained, and the information aging degree is multiplied by the first retention weight coefficient to obtain the aging cleanup component; Multiply the behavioral pattern deviation by the second retention weight coefficient, and the resulting product is recorded as the anomaly cleanup component. Multiply the terminal change redundancy by the third retention weight coefficient, and the resulting product is recorded as the redundancy cleanup component. The aging cleanup component, the abnormal cleanup component, and the redundant cleanup component are added together, and the sum is used as the comprehensive cleanup index of the information interaction record to be processed, wherein the sum of the first retention weight coefficient, the second retention weight coefficient, and the third retention weight coefficient is one. When the comprehensive cleanup index is greater than or equal to the preset cleanup execution threshold, the pending information interaction record is marked as a cleanupable record; When the comprehensive cleanup index is less than the cleanup execution threshold, the pending information interaction record is marked as a reserved record and continues to be stored in the information interaction terminal; The first retention weight coefficient, the second retention weight coefficient, and the third retention weight coefficient are obtained based on the type of the information interaction terminal and the terminal storage capacity.

[0077] Example 3, as Figure 4 As shown, the present invention also provides a non-transitory computer-readable storage medium 600, wherein a computer software program 611 is stored in the storage medium, and the computer software program implements the information interaction service big data management method disclosed in this application when executed by a processor.

[0078] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for managing big data in information interaction services, characterized in that: include: The system acquires personnel information interaction records and personnel information change records generated by the information interaction terminal, wherein the personnel information interaction records include at least an identity identifier; Based on the distribution of interaction times and interaction terminals between the identity identifier and each information interaction terminal within a preset statistical period, the behavioral pattern deviation of the identity identifier is calculated. When personnel information changes, the information change propagation priority of each information interaction terminal is calculated based on the recent interaction frequency of the personnel on each information interaction terminal and the time since the last interaction, and the synchronization order of the change information is determined according to the propagation priority. Based on the information update interval and storage capacity information of each information interaction terminal, the terminal change redundancy is calculated. Based on the type of information interaction record, the timeliness and duration of the record are preset, the information timeliness sensitivity is calculated, and combined with the behavioral pattern deviation and the terminal change redundancy, the information interaction record retention strategy of each information interaction terminal is determined.

2. The information interaction service big data management method according to claim 1, characterized in that, The personnel information interaction records and personnel information change records generated by the information interaction terminal include: Collect personnel information interaction records within a preset time range from the event logs of each information interaction terminal. Each personnel information interaction record includes an identity identifier, information interaction type, interaction time, and information interaction terminal identifier. The identity identifier includes at least one of facial feature code, ID card number, mobile phone number, and access card number. The information interaction type includes at least one of access control access, visitor appointment, attendance check-in, and identity verification. Collect personnel information change records within a preset time range from the change log of the personnel information management system. Each personnel information change record must include at least an identity identifier and the time of the change.

3. The information interaction service big data management method according to claim 1, characterized in that, The calculation of the behavioral pattern deviation of the identity identifier based on the distribution of interaction times and interaction terminals between the identity identifier and each information interaction terminal within a preset statistical period includes: Based on the type of information interaction terminal, multiple interaction time periods are divided, and the interaction frequency of each interaction time period is calculated based on the interaction time of the identity identifier. Based on the interaction frequency of each interaction period, the interaction period dispersion of the identity identifier is calculated and obtained. The historical number of interactions between the identity identifier and each interactive terminal identifier is counted, and the daily interaction frequency of each information interaction terminal is calculated. Based on the daily interaction frequency of each of the aforementioned information interaction terminals, the dispersion of the interaction terminals for obtaining the identity identifier is calculated. The behavioral pattern deviation of the identity identifier is obtained by weighting the dispersion of the interaction time period and the dispersion of the interaction terminal.

4. The information interaction service big data management method according to claim 1, characterized in that, When personnel information changes, the information change propagation priority of each information interaction terminal is calculated based on the recent interaction frequency and the time since the last interaction of the personnel on each of the information interaction terminals, and the synchronization order of the change information is determined according to the propagation priority, including: The number of recent interactions of the changed personnel's identity identifier on each information interaction terminal within a preset statistical window is obtained, and the number of recent interactions is divided by the duration of the preset statistical window to obtain the recent interaction frequency of the information interaction terminal. Obtain the time of the most recent information interaction of the identity identifier of the changed personnel on each of the information interaction terminals, and calculate the difference between the current time and the time of the most recent information interaction as the time since the last interaction; Obtain the change time from the personnel information change record of the changed personnel, calculate the difference between the current time and the change time, and use it as the time elapsed since the change occurred; Based on the time elapsed since the last interaction and the time elapsed since the change occurred, and combined with the preset information synchronization cycle of each information interaction terminal, the synchronization urgency is calculated and obtained. Divide the recent interaction frequency by the maximum value among the recent interaction frequencies of all the information interaction terminals, and use the resulting quotient as the interaction frequency normalization value. Based on the normalized value of the interaction frequency and the urgency of synchronization, the information change propagation priority of the information interaction terminal is calculated and obtained. The information interaction terminals are sorted in descending order of information change propagation priority to obtain the information change propagation priority of each information interaction terminal.

5. The information interaction service big data management method according to claim 1, characterized in that, The calculation of terminal change redundancy based on the information update interval and storage capacity information of each of the information interaction terminals includes: The preset information update interval of each of the information interaction terminals is obtained as the standard terminal update interval, and the number of personnel information entries currently stored in each of the information interaction terminals is obtained as the terminal storage capacity. Obtain the time interval between the current moment and the last information update as the update interval duration, and calculate the ratio of the update interval duration to the standard terminal update interval to obtain the information update interval ratio; Divide the terminal storage amount by the upper limit of the storage capacity of the information interaction terminal to obtain the terminal storage occupancy rate of the information interaction terminal; Multiply the information update interval ratio by the terminal storage occupancy rate to obtain the terminal change redundancy.

6. The information interaction service big data management method according to claim 1, characterized in that, The calculation of information timeliness sensitivity based on the type of information interaction record, preset timeliness, and existing duration includes: From the preset information type and effective duration mapping table, query and obtain the preset effective duration and initial sensitivity value corresponding to the information interaction type; Obtain the pending interaction time of the pending information interaction record, calculate the difference between the current time and the pending interaction time, and obtain the duration of the existing data. Calculate the ratio of the existing duration to the preset effective duration to obtain the time decay ratio of the information interaction record to be processed; The initial sensitivity value is corrected using the time-sensitivity attenuation ratio to obtain the information time-sensitivity, wherein the information time-sensitivity and the time-sensitivity attenuation ratio are negatively correlated.

7. The information interaction service big data management method according to claim 1, characterized in that, The method of determining the information interaction record retention strategy for each information interaction terminal by combining the behavioral pattern deviation degree and the terminal change redundancy degree includes: Based on the information timeliness sensitivity, the information aging degree is calculated and obtained, and the information aging degree is multiplied by the first retention weight coefficient to obtain the aging cleanup component; Multiply the behavioral pattern deviation by the second retention weight coefficient, and the resulting product is recorded as the anomaly cleanup component. Multiply the terminal change redundancy by the third retention weight coefficient, and the resulting product is recorded as the redundancy cleanup component. The aging cleanup component, the abnormal cleanup component, and the redundant cleanup component are added together, and the sum is used as the comprehensive cleanup index of the information interaction record to be processed, wherein the sum of the first retention weight coefficient, the second retention weight coefficient, and the third retention weight coefficient is one. When the comprehensive cleanup index is greater than or equal to the preset cleanup execution threshold, the pending information interaction record is marked as a cleanupable record; When the comprehensive cleanup index is less than the cleanup execution threshold, the pending information interaction record is marked as a reserved record and continues to be stored in the information interaction terminal.

8. The information interaction service big data management method according to claim 7, characterized in that, The first retention weight coefficient, the second retention weight coefficient, and the third retention weight coefficient are obtained based on the type of the information interaction terminal and the terminal storage capacity.

9. An information interaction service big data management system, characterized in that, The system is used to implement the information interaction service big data management method according to any one of claims 1 to 8, the system comprising: The data acquisition module is used to acquire personnel information interaction records and personnel information change records generated by the information interaction terminal, wherein the personnel information interaction records include at least an identity identifier; The behavior deviation calculation module is used to calculate the behavior pattern deviation of the identity identifier based on the distribution of interaction time and interaction terminal of the identity identifier with each information interaction terminal within a preset statistical period. The propagation priority calculation module is used to calculate the information change propagation priority of each information interaction terminal based on the recent interaction frequency and the time since the last interaction of the personnel on each information interaction terminal when personnel information changes, and to determine the synchronization order of the changed information according to the propagation priority. The terminal redundancy calculation module is used to calculate the terminal change redundancy based on the information update interval and storage information of each of the information interaction terminals. The record retention strategy module is used to calculate the information timeliness sensitivity based on the type of information interaction record, preset the timeliness and the duration of existence, and combine the behavioral pattern deviation degree and the terminal change redundancy to determine the information interaction record retention strategy for each information interaction terminal.

10. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores a computer software program, which, when executed by a processor, implements the information interaction service big data management method as described in any one of claims 1 to 8.

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