Intelligent card data processing method based on Internet of Things

Abstract

The invention discloses a smart card data processing method based on the Internet of Things, and relates to the technical field of data processing, the use data set of each smart card user in a management area is acquired through an Internet of Things cloud server, the confidence of each smart card user is obtained through analysis, and the data of each smart card user is acquired according to the confidence of each smart card user. The method comprises the following steps: determining and locking each confidential smart card user according to the information of the confidential smart card user, processing and analyzing to obtain a specified perception read-write distance of each confidential smart card user, and finally carrying out perception read-write adjustment control of an access control reader-writer on each confidential smart card user, and realizing acquisition and analysis of smart card data through the Internet of Things technology. And the sensing read-write distance of the access control reader-writer is automatically adjusted according to the analysis result of the intelligent card data, so that the safety and the convenience of a management area are improved, and the use experience feeling of an intelligent card user is fully guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically to an intelligent card data processing method based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, as a convenient information transmission and storage carrier, intelligent cards have gradually become an indispensable part of people's lives. With continuous technological progress and the expansion of application scenarios, intelligent card data processing will play an increasingly important role in aspects such as health management, intelligent payment, and security management. Therefore, the intelligent card data processing technology based on the Internet of Things has emerged, bringing more application scenarios and function expansions to intelligent cards.

[0003] The prior art, such as the invention patent application with the publication number: CN104346298A, discloses a data processing method, device, and intelligent card based on an intelligent card. Among them, the method includes: receiving a data processing request from a target application program; obtaining a storage location corresponding to the data processing request; performing data processing on first volatile data at the storage location, where the storage location is in a fixed section of physical memory in the intelligent card, and the first volatile data is the data requested to be processed by the target application program.

[0004] The prior art, such as the invention patent application with the publication number: CN112799885A, discloses an intelligent card data processing method that can recover and import data from a remote end. It includes the following steps: identifying and collecting the internal data of the original intelligent card through remote radio frequency identification technology, performing fault diagnosis on the collected data in a diagnostic processor using a multivariate projection dimensionality reduction algorithm, decoding and transmitting the results of the fault diagnosis; preprocessing the data using a multi-source download backup algorithm, performing remote recovery of the damaged data in a remote processor using a node recovery algorithm, monitoring the data recovery results, marking the successfully recovered data using a ciphertext setting technology, and wirelessly integrating and importing the successfully recovered data into another intelligent card.

[0005] Combining the above solutions, it is found that currently in intelligent cards and data processing, there is a lack of centralized management of intelligent card data through in-depth data analysis algorithms using a cloud server, and there is little further personalized analysis of the actual operation data of user intelligent cards and adjustment and control operations on the reader-writer. This may lead to a decrease in the accuracy of data analysis, thus unable to meet the growing data processing requirements. At the same time, there is a lack of adaptive perception and adjustment of the reader-writer based on intelligent card data, which cannot effectively meet the usage experience of intelligent card users and is also not conducive to the effective use and management of intelligent cards. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent card data processing method based on the Internet of Things, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent card data processing method based on the Internet of Things, including S1, collecting and managing the usage data sets of each smart card user in the management area through the Internet of Things cloud server, and analyzing to obtain the confidence levels of each smart card user in the management area.

[0008] S2. According to the confidence levels of each smart card user in the management area, lock to obtain each confirmed smart card user.

[0009] S3. According to each confirmed smart card user, and statistically calculate the current set sensing read / write distance of the access control reader / writer in the management area, obtain the specified sensing read / write distance of each confirmed smart card user through processing and analysis, and perform sensing read / write adjustment control on the access control reader / writer for each confirmed smart card user.

[0010] Further, the usage data sets of each smart card user in the management area are collected through the Internet of Things cloud server, where the usage data sets include first element data, second element data, and third element data.

[0011] Further, for the first element data, the specific collection process is: set an activity monitoring period, monitor and count the number of identity verifications of each smart card user in the management area and the cumulative stay time in the management area during the activity monitoring period, and obtain the activity frequency of each smart card user in the management area through processing.

[0012] During the activity monitoring period, monitor and extract the maximum single-day stay duration and the minimum single-day stay duration of each smart card user in the management area.

[0013] The activity frequency, maximum stay duration, and minimum single-day stay duration of each smart card user in the management area are jointly used as the first element data.

[0014] Further, for the second element data, the specific collection process is: monitor and count the number of card loss reports and the number of valid access control identifications of each smart card user in the management area during the activity monitoring period, and jointly use the number of card loss reports and the number of valid access control identifications of each smart card user in the management area as the second element data.

[0015] Further, for the third element data, the specific collection process is: monitor and count the number of times each smart card user uses the smart card to unlock public facilities and the number of times of smart card parking fee payments of each smart card user during the activity monitoring period.

[0016] Arrange a number of monitoring time points during the activity monitoring period. At each monitoring time point, monitor and extract the communication transmission signal strength between the smart card of each smart card user and the identity verification device in the area, and extract the reference signal strength stored in the cloud database, and import it into the preset distance signal attenuation model to calculate the straight-line distance between each smart card user in the management area and the identity verification device in the area at each monitoring time point.

[0017] Monitor and extract the single average identity verification usage time of each smart card user in the management area during the activity monitoring period.

[0018] Jointly use the number of times of unlocking public facilities by the smart cards of each smart card user in the management area, the number of times of paying smart card parking fees, the straight-line distance between each smart card user in the management area and the identity verification device in the area at each monitoring time point, and the single average identity verification usage time of each smart card user in the management area as the third element data.

[0019] Furthermore, obtain the confidence level of each smart card user in the management area. The specific analysis process is as follows: Statistically analyze the first element data of each smart card user in the management area, and extract the first element verification data from the cloud database, including the deviation of the defined stay duration and the reference activity frequency, and analyze and obtain the first abnormal characterization value of each smart card user in the management area. The first abnormal characterization value of each smart card user in the management area represents the quantization result obtained by performing data analysis and processing on the first element data of each smart card user, and is used to analyze the active abnormal degree of each smart card user in the management area, and is used as the basis for analyzing the confidence level of each smart card user in the management area.

[0020] Statistically analyze the second element data of each smart card user in the management area, and extract the second element verification data from the cloud database, including the defined number of card loss reports and the defined number of valid access control identifications, and analyze and obtain the second abnormal characterization value of each smart card user in the management area. The second abnormal characterization value of each smart card user in the management area represents the quantization result obtained by performing data analysis and processing on the second element data of each smart card user in the management area, and is used to analyze the abnormal degree of card usage of each smart card user in the management area, and is used as the basis for analyzing the confidence level of each smart card user in the management area.

[0021] Statistically manage the third element data of each smart card user in the management area, and extract the third element verification data from the cloud database, including the confidence factor for single - time smart card parking fee payment, the confidence factor for single - time public facility use, the authentication reference distance, and the reference consumption time for authentication use, and analyze to obtain the third abnormal characterization value of each smart card user in the management area. The third abnormal characterization value of each smart card user in the management area represents a quantitative result for analyzing the abnormal degree of card use of each smart card user in the management area obtained by performing data analysis processing on the third element data of each smart card user in the management area.

[0022] According to the first abnormal characterization value of each smart card user in the management area, the second abnormal characterization value of each smart card user in the management area, and the third abnormal characterization value of each smart card user in the management area, comprehensively calculate the confidence characterization value of each smart card user in the management area.

[0023] According to the confidence characterization value of each smart card user in the management area, and match it with the confidence level corresponding to each set confidence characterization value interval, to obtain the confidence level of each smart card user in the management area.

[0024] Furthermore, the confidence level of each smart card user in the management area represents a numerical basis for quantitatively analyzing the confidence level of each smart card user in the management area obtained by processing the first abnormal characterization value of each smart card user in the management area, the second abnormal characterization value of each smart card user in the management area, and the third abnormal characterization value of each smart card user in the management area.

[0025] Furthermore, the specific process of locking to obtain each confirmed smart card user is as follows: Compare the confidence level of each smart card user in the management area with the confidence level threshold stored in the cloud database. If the confidence level of a certain smart card user in the management area is higher than the confidence level threshold, then mark this smart card user in the management area as a confirmed smart card user, and thus lock to obtain each confirmed smart card user.

[0026] Furthermore, the specific process of processing and analyzing to obtain the specified perceived reading and writing distance of each confirmed smart card user is as follows: Statistically calculate the confidence level of each confirmed smart card user, and perform a difference processing with the confidence level threshold stored in the cloud database, mark the obtained difference as the reading and writing adjustment indication value, and statistically obtain the reading and writing adjustment indication value of each confirmed smart card user.

[0027] Extract the currently set perceived reading and writing distance of the access control reader, and according to the reading and writing adjustment indication value of each confirmed smart card user and the currently set perceived reading and writing distance of the access control reader, and extract the supplementary perceived reading and writing distance corresponding to the reading and writing adjustment unit indication value in the cloud database, and comprehensively analyze to obtain the specified perceived reading and writing distance of each confirmed smart card user in the management area.

[0028] Furthermore, the specified perceived read / write distance of each confirmed smart card user in the management area is a numerical result obtained by processing the read / write adjustment indication value of each confirmed smart card user and the currently set perceived read / write distance of the access control reader / writer, and is used to quantitatively evaluate the perceived read / write ideal distance of each confirmed smart card user.

[0029] The present invention has the following beneficial effects:

[0030] (1) A smart card data processing method based on a cloud server provided by the present invention collects usage data sets of each smart card user in the management area through an Internet of Things cloud server, analyzes to obtain the confidence levels of each smart card user, determines and locks each confirmed smart card user according to the confidence levels of each smart card user, then processes and analyzes according to the information of the confirmed smart card users to obtain the specified perceived read / write distances of each confirmed smart card user, and finally performs perceived read / write adjustment control on the access control reader / writer for each confirmed smart card user. By means of Internet of Things technology, the collection and analysis of smart card data are realized, and the perceived read / write distance of the access control reader / writer is automatically adjusted according to the analysis result of the smart card data, which helps to improve the security and convenience of the management area and fully guarantees the usage experience of smart card users.

[0031] (2) By analyzing to obtain the confidence levels of each smart card user in the management area, the present invention comprehensively analyzes the behavior data of each smart card user, comprehensively considers the activity conditions and behavior characteristics of each smart card user in the management area from multiple aspects, can more accurately understand the behavior patterns and preferences of users, and thus improves the accuracy of user identity recognition and the security of the system. It can not only provide more accurate user recognition capabilities for smart cards, but also provide more personalized services and experiences for users, thereby enhancing the application value of smart cards in the Internet of Things environment.

[0032] (3) By locking each confirmed smart card user, the present invention can improve the accuracy and security of the smart card system's perception and recognition of user identities, and can also provide a more convenient and secure identity verification experience for users. Further, personalized analysis and monitoring evaluation are carried out for each confirmed smart card user, and perceived read / write adjustment control is performed on the access control reader / writer for each confirmed smart card user, increasing the accuracy of data analysis.

[0033] (4) The present invention processes and analyzes to obtain the specified perceived reading and writing distance of each confident smart card user, which can realize the dynamic adjustment of user identity recognition in the smart card system, improve the accuracy and flexibility of user identity, and through real-time monitoring and adjustment, can more effectively handle abnormal situations in user identity recognition, thereby enhancing the security and reliability of smart card data processing, providing a more stable and reliable data processing solution for smart card applications, and making the use of smart cards more flexible and intelligent.

[0034] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inner", "periphery", etc. indicating the orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0038] Please refer to Figure 1 As shown, the embodiment of the present invention provides an Internet of Things-based smart card data processing method: including S1, collecting the usage data sets of each smart card user in the management area through the Internet of Things cloud server, and analyzing to obtain the confidence levels of each smart card user in the management area.

[0039] It should be noted that the Internet of Things cloud server refers to the server in the cloud computing platform used to support Internet of Things devices and systems. It provides resources such as storage, computing, and networking, is used to receive, store, process, and analyze the data generated by Internet of Things devices, and provides services and support for Internet of Things applications.

[0040] S2, locking each confident smart card user according to the confidence levels of each smart card user in the management area.

[0041] S3. Based on each identified smart card user, statistically manage the currently set sensing read / write distance of the access control reader / writer in the management area, obtain the specified sensing read / write distance of each identified smart card user through processing and analysis, and perform sensing read / write adjustment control on each identified smart card user for the access control reader / writer.

[0042] It should be noted that the access control reader / writer is a device used to control the access control system. It is usually installed at a fixed position at the door or access control passage, used to read the information on the smart card and communicate with the access control system. Its main functions include reading the information on the smart card, verifying the user's identity, opening or closing the access control device, etc.

[0043] Specifically, collect the usage data sets of each smart card user in the management area through the Internet of Things cloud server, where the usage data sets include first element data, second element data, and third element data.

[0044] Specifically, for the first element data, the specific collection process is as follows: Set the activity monitoring period, monitor and statistically count the number of identity verifications of each smart card user in the management area and the cumulative stay time in the management area during the activity monitoring period, and obtain the activity frequency of each smart card user in the management area through processing.

[0045] It should be noted that the activity frequency of each smart card user in the management area represents the quantitative result obtained by analyzing the number of identity verifications of each smart card user in the management area and the cumulative stay time in the management area, and is used as the basis for analyzing the confidence level of each smart card user in the management area. The activity frequency of each smart card user in the management area can not only be realized through a positioning system based on Internet of Things technology, such as using positioning technologies such as GPS, Wi-Fi, or Bluetooth to obtain the user's location information and infer the user's activity frequency accordingly, but also can be obtained through the following calculation method. The specific calculation method is as follows: , where represents the activity frequency of the i-th smart card user in the management area, represents the number of identity verifications of the i-th smart card user in the management area, represents the cumulative stay time of the i-th smart card user in the management area in the management area. i is the number of each smart card user in the management area, , and n represents the number of smart card users.

[0046] During the activity monitoring period, monitor and extract the single-day maximum stay duration and single-day minimum stay duration of each smart card user in the management area.

[0047] Combine the activity frequency, maximum stay duration, and minimum stay duration per day of each smart card user in the management area as the first element data.

[0048] Specifically, for the second element data, the specific collection process is as follows: During the activity monitoring period, monitor and count the card loss reporting times and the number of valid access control identifications of each smart card user in the management area, and combine the card loss reporting times and the number of valid access control identifications of each smart card user in the management area as the second element data.

[0049] Specifically, for the third element data, the specific collection process is as follows: During the activity monitoring period, monitor and count the usage times of public facilities unlocked by the smart cards of each smart card user in the management area and the parking fee payment times of each smart card user's card.

[0050] Arrange several monitoring time points during the activity monitoring period. At each monitoring time point, monitor and extract the communication transmission signal strength between the card of each smart card user and the identity verification device in the area, and extract the reference signal strength stored in the cloud database, and import it into the preset distance signal attenuation model to calculate the straight-line distance between each smart card user in the management area and the identity verification device in the area at each monitoring time point.

[0051] It should be explained that the signal strength is usually monitored using the received signal strength indication. RSSI is a quantitative indicator of the strength of the signal received by the receiving device, usually expressed in decibels. The higher the RSSI value, the stronger the signal strength.

[0052] It should be explained that the distance signal attenuation model means that in wireless communication, the signal will be attenuated to a certain extent during the transmission process, resulting in the signal strength weakening as the distance increases. The distance signal attenuation model is used to describe the attenuation situation of the signal during the transmission process. The process of calculating the signal attenuation model usually includes collecting experimental data within a certain range, including the signal strength values (RSSI) at different distances and the corresponding distances. Using the collected experimental data, through a fitting algorithm, such as the least squares method, a distance signal attenuation model suitable for the actual situation is fitted. Then, the verified data is used to verify the fitted model, evaluate the accuracy and applicability of the model, and apply the fitted model to the actual scenario. According to the measured signal strength value, the distance is deduced or the communication quality is evaluated.

[0053] It should be noted that the straight-line distance between each smart card user and the authentication device in the management area represents the quantitative result obtained by analyzing and processing the signal strength between each smart card user and the authentication device in the management area for analyzing the distance between each smart card user and the authentication device in the management area. The straight-line distance between each smart card user and the authentication device in the management area can not only be calculated by placing multiple readers in the access control system, using the signal strength between the smart card user and each reader to calculate the user's location, and then using the triangulation method to estimate the straight-line distance between the user and the authentication device, but also be obtained through the following calculation method. The specific calculation method is as follows:

[0054] , where represents the straight-line distance between the i-th smart card user and the authentication device in the management area at the j-th monitoring time point, represents the communication transmission signal strength between the smart card of each smart card user and the authentication device in the area at the j-th monitoring time point, represents the reference signal strength, represents the set path loss exponent, and j is the number of each monitoring time point, , and m represents the number of monitoring time points.

[0055] During the activity monitoring period, monitor and extract the single average authentication usage time of each smart card user in the management area.

[0056] Jointly use the number of times of unlocking public facilities by the smart card of each smart card user in the management area, the number of times of paying smart card parking fees, the straight-line distance between each smart card user and the authentication device in the area at each monitoring time point, and the single average authentication usage time of each smart card user in the management area as the third element data.

[0057] It should be noted that the single average authentication usage time of the user refers to the sum of the durations of each user's verification, divided by the number of user authentications, and the average is obtained.

[0058] Specifically, count the first element data of each smart card user in the management area, extract the first element verification data from the cloud database, including the deviation of the stay duration definition and the reference activity frequency, and analyze to obtain the first abnormal characterization value of each smart card user in the management area. The first abnormal characterization value of each smart card user in the management area represents the quantitative result obtained by analyzing and processing the first element data of each smart card user for analyzing the active abnormal degree of each smart card user in the management area, and is used as the analysis basis for the confidence level of each smart card user in the management area.

[0059] It should be noted that the cumulative stay time of each smart card user in the management area can be obtained not only by connecting or communicating the smart card with other devices and recording the connection and communication time of the devices to understand the cumulative stay time of the user in the management area, so as to evaluate the first abnormal characterization value of each smart card user in the management area, but also by the following calculation method. The specific calculation method is as follows: ; In the formula, is the first abnormal characterization value of the i-th smart card user in the management area, represents the maximum stay duration of the i-th smart card user in the management area, represents the minimum daily stay duration of the i-th smart card user in the management area, represents the activity frequency of the i-th smart card user in the management area, represents the reference activity frequency, represents the defined deviation of the stay duration, represents the compensation factor corresponding to the set activity duration, represents the compensation factor corresponding to the set activity frequency. i is the number of each smart card user in the management area, , and n represents the number of smart card users.

[0060] Statistical second element data of each smart card user in the management area is collected, and second element verification data is extracted from the cloud database, including defining the number of card loss reports and the number of valid access control identifications. Then, the second abnormal characterization value of each smart card user in the management area is analyzed. The second abnormal characterization value of each smart card user in the management area represents a quantitative result obtained by analyzing the second element data of each smart card user in the management area, which is used to analyze the abnormal degree of card use of each smart card user in the management area and serves as the basis for analyzing the confidence level of each smart card user in the management area.

[0061] It should be noted that the second abnormal characterization value of each smart card user in the management area can be obtained not only by analyzing the access frequency or time of the user, but also by the following calculation method. The specific calculation method is as follows: ; In the formula, is the second abnormal characterization value of the i-th smart card user in the management area, represents the number of card loss reports of the i-th smart card user in the management area, represents the defined number of card loss reports, represents the number of valid access control identifications of the i-th smart card user in the management area, represents the defined number of valid access control identifications, represents the compensation factor corresponding to the set number of card loss reports, represents the compensation factor corresponding to the set effective recognition times of the access control, and \(e\) represents the natural constant.

[0062] Statistically manage the third-element data of each smart card user in the area, and extract the third-element verification data from the cloud database, including the confidence factor for single smart card parking fee payment, the confidence factor for single public facility use, the reference distance for identity verification, and the reference consumption time for identity verification. Then analyze to obtain the third abnormal characterization value of each smart card user in the management area. The third abnormal characterization value of each smart card user in the management area represents a quantitative result for analyzing the abnormal degree of card use of each smart card user in the management area obtained by performing data analysis and processing on the third-element data of each smart card user in the management area.

[0063] It should be noted that the third abnormal characterization value of each smart card user in the management area can not only use machine learning algorithms, such as regression analysis, decision tree, neural network, etc., to learn the abnormal characteristics of users from smart card data and establish a model to predict the abnormal characterization value of users, but also be obtained through the following calculation method. The specific calculation method is as follows: ; where is the third abnormal characterization value of the \(i\)-th smart card user in the management area, represents the number of times the \(i\)-th user in the smart card data uses public facilities, represents the number of times the \(i\)-th user in the smart card data pays the smart card parking fee, represents the straight-line distance between the \(i\)-th smart card user and the identity verification device at the \(j\)-th monitoring time point in the management area, represents the average consumption time for single identity verification of the \(i\)-th smart card user in the management area, represents the reference distance for identity verification, represents the reference consumption time for identity verification use, represents the confidence factor for single public facility use, represents the confidence factor for single smart card parking fee payment, represents the correction factor corresponding to the straight-line distance between the smart card user and the identity verification device, represents the correction factor corresponding to the consumption time for smart card user identity verification use.

[0064] According to the first abnormal characterization value of each smart card user in the management area, the second abnormal characterization value of each smart card user in the management area, and the third abnormal characterization value of each smart card user in the management area, comprehensively calculate the confidence characterization value of each smart card user in the management area.

[0065] Based on the confidence representation values of each smart card user in the management area and matching them with the confidence levels corresponding to the set intervals of confidence representation values, the confidence levels of each smart card user in the management area are obtained.

[0066] It should be noted that the confidence representation values of each smart card user in the management area represent the numerical basis for quantifying and analyzing the confidence level of each smart card user in the management area obtained by processing the first abnormal representation value of each smart card user in the management area, the second abnormal representation value of each smart card user in the management area, and the third abnormal representation value of each smart card user in the management area. It can be obtained not only through pattern recognition and analysis using historical data, but also through more accurate calculation methods. The specific calculation method is as follows: ; In the formula, is the confidence representation value of the i-th smart card user in the management area, is the first abnormal representation value of the i-th smart card user in the management area, is the second abnormal representation value of the i-th smart card user in the management area, is the third abnormal representation value of the i-th smart card user in the management area, represents the weight factor corresponding to the set first abnormal representation value, represents the weight factor corresponding to the set second abnormal representation value, represents the weight factor corresponding to the set third abnormal representation value.

[0067] Specifically, the confidence level of each smart card user in the management area represents the numerical basis for quantifying and analyzing the confidence level of each smart card user in the management area obtained by processing the first abnormal representation value of each smart card user in the management area, the second abnormal representation value of each smart card user in the management area, and the third abnormal representation value of each smart card user in the management area.

[0068] Specifically, to lock and obtain each confirmed smart card user, the specific process is as follows: Compare the confidence levels of each smart card user in the management area with the confidence level threshold stored in the cloud database. If the confidence level of a certain smart card user in the management area is higher than the confidence level threshold, then mark this smart card user in the management area as a confirmed smart card user, and thus lock and obtain each confirmed smart card user.

[0069] Specifically, to process and analyze to obtain the specified sensing read-write distance of each confirmed smart card user, the specific process is as follows: Statistically calculate the confidence levels of each confirmed smart card user, perform a difference processing with the confidence level threshold stored in the cloud database, mark the obtained difference as the read-write adjustment indication value, and statistically obtain the read-write adjustment indication values of each confirmed smart card user.

[0070] It should be noted that the read / write adjustment indication value of each confirmed smart card user represents a quantitative result obtained by analyzing and processing the confidence level of each confirmed smart card user and the confidence threshold, which is used to analyze the read / write adjustment degree of each confirmed smart card user in the management area, and serves as the analysis basis for the designated sensing read / write distance of each confirmed smart card user in the management area. In another embodiment, the read / write adjustment indication value of each confirmed smart card user can also be obtained through pattern recognition and analysis of historical data.

[0071] Extract the currently set sensing read / write distance of the access control reader / writer, and based on the read / write adjustment indication value of each confirmed smart card user and the currently set sensing read / write distance of the access control reader / writer, extract the supplementary sensing read / write distance corresponding to the read / write adjustment unit indication value, and comprehensively analyze to obtain the designated sensing read / write distance of each confirmed smart card user in the management area.

[0072] It should be noted that the designated sensing read / write distance of each confirmed smart card user in the management area is obtained by processing the read / write adjustment indication value of each confirmed smart card user and the currently set sensing read / write distance of the access control reader / writer. The designated sensing read / write distance of each confirmed smart card user in the management area can not only be obtained through pattern recognition and analysis using historical data, but also can be obtained through the following calculation method. The specific calculation method is as follows: ; where is the designated sensing read / write distance of the i-th confirmed smart card user in the management area, is the read / write adjustment indication value of the i-th confirmed smart card user in the management area, represents the currently set sensing read / write distance of the access control reader / writer, represents the supplementary sensing read / write distance corresponding to the read / write adjustment unit indication value.

[0073] Specifically, the designated sensing read / write distance of each confirmed smart card user in the management area represents a numerical result obtained by processing the read / write adjustment indication value of each confirmed smart card user and the currently set sensing read / write distance of the access control reader / writer, which is used to quantitatively evaluate the ideal sensing read / write distance of each confirmed smart card user.

[0074] In the embodiment, the control of the sensing read / write adjustment of the access control reader / writer for each confirmed smart card user can specifically control the read / write distance by adjusting the power. For users who need a longer read / write distance, the power can be increased to increase the read / write distance; for users who need a shorter read / write distance, the power can be decreased to reduce the read / write distance.

[0075] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0076] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A smart card data processing method based on the Internet of Things, characterized in that ,include: S1. Collect usage data sets of each smart card user in the management area through the IoT cloud server, and analyze them to obtain the confidence of each smart card user in the management area; S2. According to the confidence level of each smart card user in the management area, each confirmed smart card user is locked; S3. According to the confirmed smart card users, the currently set perception reading and writing distances of the access control readers in the management area are counted, and the specified perception reading and writing distances of the confirmed smart card users are obtained through processing and analysis, and the perception reading and writing adjustment control of the access control readers for the confirmed smart card users is performed.

2. The method for processing smart card data based on the Internet of Things according to claim 1, characterized in that: The usage data set of each smart card user in the management area is collected by the Internet of Things cloud server, wherein the usage data set includes first element data, second element data, and third element data.

3. The method for processing smart card data based on the Internet of Things according to claim 2, characterized in that: The specific collection process of the first element data is as follows: Setting an activity monitoring cycle, monitoring and counting the number of identity authentications of each smart card user in the management area and the cumulative stay time in the management area during the activity monitoring cycle, and obtaining the activity frequency of each smart card user in the management area through processing; During the activity monitoring cycle, the maximum and minimum daily stay duration of each smart card user in the management area is monitored and extracted; The activity frequency, maximum stay time and minimum stay time per day of each smart card user in the management area are combined as the first element data.

4. The method for processing smart card data based on the Internet of Things according to claim 2, characterized in that: The specific collection process of the second element data is as follows: During the activity monitoring cycle, the number of card loss reports and the number of effective access control identifications of each smart card user in the management area are monitored and counted, and the number of card loss reports and the number of effective access control identifications of each smart card user in the management area are combined as the second element data.

5. The method for processing smart card data based on the Internet of Things according to claim 2, characterized in that: The specific collection process of the third element data is as follows: During the activity monitoring cycle, the number of times each smart card user unlocks public facilities and pays parking fees with a smart card is monitored and counted in the management area; Arrange several monitoring time points in the activity monitoring cycle, monitor and extract the communication transmission signal strength between each smart card user's card and the identity verification device in the area at each monitoring time point, extract the reference signal strength stored in the cloud database, and import it into the preset distance signal attenuation model to calculate the straight-line distance between each smart card user in the management area and the identity verification device in the area at each monitoring time point; Monitor and extract the average authentication time consumed by each smart card user in the management area during the activity monitoring cycle; The number of times each smart card user in the management area uses a smart card to unlock public facilities, the number of times each smart card user pays for parking fees, the straight-line distance between each smart card user in the management area and the identity authentication device in the area at each monitoring time point, and the average single identity authentication time consumed by each smart card user in the management area are combined as the third element data.

6. The method for processing smart card data based on the Internet of Things according to claim 2, characterized in that: The analysis obtains the confidence of each smart card user in the management area. The specific analysis process is as follows: Counting the first element data of each smart card user in the management area, and extracting the first element verification data from the cloud database, including the deviation of the stay time definition and the reference activity frequency, and analyzing to obtain the first abnormal characterization value of each smart card user in the management area, wherein the first abnormal characterization value of each smart card user in the management area represents the quantitative result obtained by performing data analysis and processing on the first element data of each smart card user, which is used to analyze the abnormal activity degree of each smart card user in the management area, and serves as the basis for analyzing the confidence of each smart card user in the management area; Counting the second element data of each smart card user in the management area, and extracting the second element verification data from the cloud database, including defining the number of card loss reports, defining the number of effective access control identifications, and analyzing to obtain the second abnormal characterization value of each smart card user in the management area, wherein the second abnormal characterization value of each smart card user in the management area represents a quantitative result obtained by performing data analysis and processing on the second element data of each smart card user in the management area, and is used as an analysis basis for the confidence of each smart card user in the management area; Counting the third element data of each smart card user in the management area, and extracting the third element verification data from the cloud database, including the confidence factor of a single smart card parking fee payment, the confidence factor of a single public facility use, the identity verification reference distance, and the identity verification reference consumption time, and analyzing to obtain the third abnormal characterization value of each smart card user in the management area, wherein the third abnormal characterization value of each smart card user in the management area represents a quantitative result obtained by performing data analysis and processing on the third element data of each smart card user in the management area, and is used to analyze the degree of abnormal card use of each smart card user in the management area; Comprehensively calculating the confidence characterization value of each smart card user in the management area according to the first abnormal characterization value of each smart card user in the management area, the second abnormal characterization value of each smart card user in the management area, and the third abnormal characterization value of each smart card user in the management area; According to the confidence characterization value of each smart card user in the management area, the confidence degree of each smart card user in the management area is matched with the confidence degree corresponding to each set confidence characterization value interval to obtain the confidence degree of each smart card user in the management area.

7. The method for processing smart card data based on the Internet of Things according to claim 6, characterized in that: The confidence level of each smart card user in the management area is represented by processing the first abnormal characterization value of each smart card user in the management area, the second abnormal characterization value of each smart card user in the management area, and the third abnormal characterization value of each smart card user in the management area, to obtain a numerical basis for quantitatively analyzing the confidence level of each smart card user in the management area.

8. The method for processing smart card data based on the Internet of Things according to claim 1, characterized in that: The locking process is as follows: The confidence of each smart card user in the management area is compared with the confidence threshold stored in the cloud database. If the confidence of a smart card user in the management area is higher than the confidence threshold, the smart card user in the management area is marked as a confirmed smart card user, thereby locking in each confirmed smart card user.

9. The method for processing smart card data based on the Internet of Things according to claim 5, characterized in that: The processing and analysis obtains the designated perceived reading and writing distance of each confirmed smart card user, and the specific process is: The confidence of each confirmed smart card user is counted, and the difference is processed with the confidence threshold stored in the cloud database, and the obtained difference is marked as the read-write adjustment indication value, and the read-write adjustment indication value of each confirmed smart card user is obtained by counting; The currently set perception read-write distance of the access control reader is extracted, and based on the read-write adjustment indicator value of each confirmed smart card user and the currently set perception read-write distance of the access control reader, the supplementary perception read-write distance corresponding to the read-write adjustment unit indicator value in the cloud database is extracted, and the specified perception read-write distance of each confirmed smart card user in the management area is obtained through comprehensive analysis.

10. The method for processing smart card data based on the Internet of Things according to claim 9, characterized in that: The specified perceived read-write distance of each confirmed smart card user in the management area represents a numerical result for quantitatively evaluating the perceived ideal read-write distance of each confirmed smart card user obtained by processing the read-write adjustment indicator value of each confirmed smart card user and the currently set perceived read-write distance of the access control reader / writer.

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