A smart community Internet of Things access control operation system

Abstract

The present invention provides a smart community Internet of Things access control operation system, which relates to the field of data processing technology. The system comprises: a data acquisition module, which is used to obtain a standard identity data set; a preliminary verification module, which is used to match the standard identity data set with a preset resident database to generate preliminary verification result data; a distributed computing node module, which is used to perform in-depth verification on the preliminary verification result data through computing nodes to obtain final verification result data; a task allocation module, which is used to generate node allocation data; a load balancing module, which is used to adjust the node allocation data; a fault-tolerant processing module, which is used to reallocate the verification tasks of the faulty nodes to other computing nodes; a dynamic authority management module, which is used to generate access control authority information for users according to the final verification result data; and an access control control module, which is used to control a target access control device to perform a door opening operation. The present invention improves the efficiency of the system in processing data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a smart community Internet of Things access control operation system. Background Art

[0002] The existing smart community IoT access control operation system usually adopts a centralized management method, using network cameras, access control controllers and background servers to form an intelligent access control management system. Residents verify their identities through access control cards, QR codes, or face recognition. After the system transmits the data to the server for comparison, it feeds back the door opening command to the access control controller to complete the operation. This method realizes the digital management of community access control and improves security and convenience. However, the existing system generally relies on a fixed server architecture and a single-point verification mode, which can easily affect the actual user experience when the server load is too high or the response delay increases.

[0003] In actual applications, especially during peak hours, such as rush hour, the access control system faces great concurrent access pressure. For example, when multiple residents use the face recognition function at the same time, the system needs to process a large amount of image data in real time and compare it with the data in the server. Once the server resources are tight or the network bandwidth is insufficient, the system response time will be significantly extended, which may cause residents to queue for a long time at the door. This problem exposes the defect of insufficient real-time processing capability in existing technologies, especially in large communities, where the existing architecture is difficult to meet the seamless service requirements under high concurrency conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a smart community Internet of Things access control operation system, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A smart community Internet of Things access control operation system, the system comprising:

[0007] The data collection module is used to obtain the user's identity data, including face images, QR code information and access card information; pre-process the obtained identity data to generate a standard identity data set;

[0008] A preliminary verification module is used to match the standard identity data set with the preset resident database to generate preliminary verification result data; wherein the preset resident database supports dynamic updates, including the addition, deletion or permission adjustment of resident information;

[0009] The distributed computing node module is used to perform in-depth verification of the preliminary verification result data through computing nodes to obtain the final verification result data; each computing node independently performs part of the verification task, the computing nodes are interconnected through the Internet of Things devices, and the computing capacity of each node is determined by its physical hardware performance;

[0010] A task allocation module is used to allocate the verification task of the preliminary verification result data to the optimal edge computing node according to the user access location and the load of the computing node, and generate node allocation data;

[0011] The load balancing module is used to monitor the load and task processing status of the computing nodes in real time, and adjust the node allocation data of the task allocation module according to the monitoring results;

[0012] The fault-tolerant processing module is used to monitor the operating status of computing nodes in real time. When a computing node failure is detected, the verification task of the failed node is reallocated to other computing nodes.

[0013] The dynamic permission management module is used to generate access control permission information for the user based on the final verification result data. The access control permission information includes the user identity, permission time range and target access control device identification;

[0014] The access control module is used to analyze the target access control device identification according to the access control authority information, and control the target access control device to perform the door opening operation, and record the operation data at the same time.

[0015] Preferably, the data acquisition module comprises:

[0016] An identity collection unit, used to obtain user identity data;

[0017] A feature separation unit is used to classify and extract multiple features in the user identity data to generate identity feature data, where the identity feature data includes a face image, QR code information, and access card information;

[0018] A data processing unit, used to perform standardization operations on identity feature data to generate a standard identity data set, wherein the standardization operation includes unifying data format and adjusting feature range;

[0019] The standard identity data set is stored in association with the user's identity data to form a data index.

[0020] Preferably, the preliminary verification module includes:

[0021] An identity matching unit, used to match the standard identity data set with the preset household database, calculate the matching score, and generate preliminary verification result data;

[0022] The matching accuracy adjustment unit is used to dynamically update the preset resident database and adjust the matching parameters of the identity matching unit.

[0023] Preferably, the distributed computing node module includes:

[0024] A verification task generating unit, used to generate verification task data according to the characteristics of the preliminary verification result data and the verification requirements, and send it to the task allocation module;

[0025] A verification data processing unit is used to distribute data according to nodes, verify the verification task data through computing nodes, calculate intermediate verification values, and generate intermediate verification result data;

[0026] The data cross-validation unit is used to cross-validate the intermediate validation result data and calculate the final validation value, where:

[0027] When the final verification value meets the preset verification value, the final verification result data is generated;

[0028] When the final verification value does not meet the preset verification value, the node allocation data is adjusted, and the verification data processing unit and the data cross-validation unit are cyclically run until the preset fitness is met; the calculation formula of the verification value is:

[0029] ,

[0030] in, is the final verification value, is the total number of computing nodes participating in the verification, and Respectively Nodes and Each node calculates the intermediate verification value of the verification task. , For the and The verification task of each node takes time. , , and Respectively Nodes and The complexity of the verification task received by each node, and Respectively Nodes and Nodes receive matching scores for verification tasks. , and is the weight coefficient, , , and Respectively Nodes and The number of data features received by each node in the verification task. and Respectively Nodes and The computing power of each node, and Respectively Nodes and The number of concurrent requests for verification tasks received by a node, , , and is the adjustment coefficient.

[0031] Preferably, the task allocation module includes:

[0032] A task priority rating determination unit, used to generate task priority rating information according to the complexity of the preliminary verification result data;

[0033] The node adaptation unit is used to calculate the first fitness weight of the node according to the user access location, task priority score information, the load status of the calculation node and the interconnection delay, and generate node allocation data, the node allocation data includes the node allocation identifier of the verification task and the execution order of the verification task; the calculation formula of the first fitness weight is:

[0034] ,

[0035] in, is the first fitness weight, Prioritize tasks. is the current load ratio of the computing node, is the data transmission distance, which indicates the path transmission distance from the user access location to the target computing node, and is calculated from the network topology. is the remaining resource ratio of the computing node, The node response delay represents the delay from receiving the request to feeding back the response. , , , and is the weight coefficient.

[0036] Preferably, the load balancing module includes:

[0037] The load dynamic detection unit is used to monitor the load and task processing status of the computing node in real time. The task processing status includes task processing rate, response delay and resource occupancy;

[0038] The task primary allocation unit is used to calculate the second fitness weight of the node according to the node load, task processing status and task priority score information, and adjust the node allocation data; the calculation formula of the second fitness weight is:

[0039] ,in, is the second fitness weight, is the task processing rate of the node, , , and is the weight coefficient.

[0040] Preferably, the fault-tolerant processing module includes:

[0041] The fault detection unit is used to monitor the running status of the computing node. When an abnormality occurs in a node, a fault node identification is generated and the fault type and occurrence time of the node are recorded.

[0042] A data recovery unit, used for loading the data of the unfinished verification task according to the fault node identifier to obtain the unfinished verification task data;

[0043] The task secondary distribution unit is used to distribute the unfinished verification task data to other nodes according to the second fitness weight.

[0044] Preferably, the dynamic rights management module includes:

[0045] The permission update unit is used to dynamically adjust the permission time range and the target access control device identification by analyzing the user's access time, frequency and device usage records;

[0046] The permission constraint unit is used to make restrictive adjustments to the permission information when there is a significant deviation between the user's behavior data and the preset community constraint conditions.

[0047] Preferably, the access control module includes:

[0048] The device status feedback unit is used to monitor the operating parameters of the target access control device in real time and generate device operating status data. The operating parameters include device response time and execution accuracy;

[0049] A device status adjustment unit, used to adjust the operating status of a target access control device according to the device operating status data;

[0050] The data recording unit is used to record the operating parameters of the target access control device and generate a device status log.

[0051] Preferably, the system further comprises:

[0052] The exception handling module is used to analyze the abnormal situation of the target access control device according to the device status log, perform emergency processing, and generate exception processing result data; wherein:

[0053] Anomalies include unauthorized access attempts or equipment failures;

[0054] The exception handling result data includes notifying relevant managers or forcibly locking the target access control device.

[0055] The above solution of the present invention includes at least the following beneficial effects:

[0056] First, the system adopts a distributed collaboration mode of data acquisition module, preliminary verification module, distributed computing node module and task allocation module, replacing the traditional single-point verification mode. The data acquisition module can obtain the user's facial image, QR code information and access card data in real time, and generate a standard identity data set through preprocessing to provide standardized data input for subsequent modules. This data preprocessing mechanism reduces the computational burden of subsequent verification modules and improves data flow efficiency from the source.

[0057] Secondly, the distributed computing node module of the system shares the processing pressure of the server through multi-node parallel computing. Each computing node independently performs part of the verification task, and the nodes are interconnected through IoT devices, forming a distributed verification architecture with high fault tolerance and scalability. During peak hours, such as when multiple residents use the access control system at the same time, the task allocation module dynamically allocates the verification task to the optimal node based on the user's access location and node load status. This module combines the real-time load and response capability of the node, and ensures the rationality of task allocation through the first fitness weight calculation. The load balancing module further optimizes the node allocation data through real-time monitoring and adjustment, so that the system can quickly respond to concurrent verification requests.

[0058] In addition, the fault-tolerant processing module of this system solves the potential impact of node failure on task processing. When a node anomaly is detected, the system can quickly recover unfinished tasks and reallocate them to other nodes to ensure the continuity and integrity of the verification task. This design significantly improves the reliability and stability of the system, and it can maintain normal operation even under extreme conditions.

[0059] The dynamic permission management module generates real-time access control permission information for each user to meet the complex management needs of the community. Combined with the final verification result data, the system supports dynamic update of permissions based on time range, target device and other dimensions to ensure the accuracy and flexibility of permission management.

[0060] Through the access control module, the system immediately opens the door after completing user identity authentication, and records the device status and operation log, providing traceable operation records for community security management. This approach not only improves user convenience, but also enhances the overall security of the community.

[0061] In summary, the present invention successfully overcomes the performance bottleneck of the existing smart community IoT access control system under the centralized management mode through the organic combination of modular design and distributed architecture. Compared with the prior art, the present invention has significantly improved the concurrent processing capability, task scheduling strategy, system reliability, authority management flexibility and user experience, and provides an efficient, safe and reliable technical solution for the intelligent access control management of large communities. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is an architecture diagram of a smart community Internet of Things access control operation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0064] like Figure 1 As shown, an embodiment of the present invention proposes a smart community Internet of Things access control operation system, the system comprising:

[0065] The data collection module is used to obtain the user's identity data, including face images, QR code information and access card information; pre-process the obtained identity data to generate a standard identity data set;

[0066] A preliminary verification module is used to match the standard identity data set with the preset resident database to generate preliminary verification result data; wherein the preset resident database supports dynamic updates, including the addition, deletion or permission adjustment of resident information;

[0067] The distributed computing node module is used to perform in-depth verification of the preliminary verification result data through computing nodes to obtain the final verification result data; each computing node independently performs part of the verification task, the computing nodes are interconnected through the Internet of Things devices, and the computing capacity of each node is determined by its physical hardware performance;

[0068] A task allocation module is used to allocate the verification task of the preliminary verification result data to the optimal edge computing node according to the user access location and the load of the computing node, and generate node allocation data;

[0069] The load balancing module is used to monitor the load and task processing status of the computing nodes in real time, and adjust the node allocation data of the task allocation module according to the monitoring results;

[0070] The fault-tolerant processing module is used to monitor the operating status of computing nodes in real time. When a computing node failure is detected, the verification task of the failed node is reallocated to other computing nodes.

[0071] The dynamic permission management module is used to generate access control permission information for the user based on the final verification result data. The access control permission information includes the user identity, permission time range and target access control device identification;

[0072] The access control module is used to analyze the target access control device identification according to the access control authority information, and control the target access control device to perform the door opening operation, and record the operation data at the same time.

[0073] In the embodiment of the present invention, the data acquisition module can flexibly collect the user's facial image, QR code information and access card information, providing the system with a variety of identity input methods. By preprocessing these data, a standard identity data set is generated to ensure the standardization and consistency of subsequent verification. The preliminary verification module quickly screens out qualified users by matching with the preset resident database, and supports the dynamic update of the preset resident database, so that the system can adapt to the scenario where the resident information changes frequently. The distributed computing node module further verifies the preliminary verification results in depth through multi-node collaboration. This architecture not only improves the verification efficiency, but also reduces the risk of single point failure through independent execution of tasks between nodes. The task allocation module allocates the verification task to the optimal edge node according to the user's access location and the load of the computing node, ensuring the accuracy and fairness of task allocation. The load balancing module monitors the node load and task processing status in real time, and dynamically adjusts the task allocation strategy, thereby ensuring the efficient operation of the entire system. The fault-tolerant processing module enhances the robustness of the system through fault detection and task redistribution mechanism. The dynamic authority management module dynamically generates access control authority information according to the verification results. The access control module directly controls the switch status of the target device according to the permission information, and records the operation data, providing traceability for the system. This modular design makes the system highly flexible, reliable and scalable, and can meet the needs of smart communities for efficient and secure access control management.

[0074] In a preferred embodiment of the present invention, the data acquisition module includes:

[0075] An identity collection unit, used to obtain user identity data;

[0076] A feature separation unit is used to classify and extract multiple features in the user identity data to generate identity feature data, where the identity feature data includes a face image, QR code information, and access card information;

[0077] A data processing unit, used to perform standardization operations on identity feature data to generate a standard identity data set, wherein the standardization operation includes unifying data format and adjusting feature range;

[0078] The standard identity data set is stored in association with the user's identity data to form a data index.

[0079] In the embodiment of the present invention, the optimized design of the data acquisition module significantly improves the accuracy and efficiency of identity data acquisition. The identity acquisition unit can capture a variety of identity features, including facial images, QR code information and access card information, providing users with a variety of verification methods. The feature separation unit classifies and extracts the features in these identity data to generate identity feature data, ensuring the pertinence and efficiency of feature processing. The data processing unit unifies the data format and adjusts the feature range through standardized operations on the identity feature data, so that subsequent modules can efficiently process the standardized data set. By associating and storing the identity data with the standard identity data set, a data index is formed, which further improves the data access speed and system response efficiency. The modular design not only improves the quality of the collected data, but also ensures seamless connection with subsequent modules, providing an important guarantee for the overall performance of the system.

[0080] More specifically, the unified data format refers to normalizing different types of identity data, such as face images, QR code information, and access card information, so that they conform to a unified data storage and processing format. For example: standardize the image format to JPEG or PNG, and adjust the image resolution to a fixed size supported by the system processing model, such as 224×224 pixels. Store the decoded string data in JSON or table format to ensure that the parsed content can be directly matched to the database. Convert the card number or identification code into a unified encoding format, such as hexadecimal representation.

[0081] More specifically, feature range adjustment performs unified normalization processing on the range of each data feature. For example, the feature vector extracted by the deep learning model for facial features is normalized to the range of 0 to 1 for subsequent comparison. QR codes and access control cards ensure the consistency of data length and eliminate invalid characters or redundant information to ensure connection with the database.

[0082] In a preferred embodiment of the present invention, the preliminary verification module includes:

[0083] An identity matching unit, used to match the standard identity data set with the preset household database, calculate the matching score, and generate preliminary verification result data;

[0084] The matching accuracy adjustment unit is used to dynamically update the preset resident database and adjust the matching parameters of the identity matching unit.

[0085] In the embodiment of the present invention, the preliminary verification module greatly improves the verification accuracy and flexibility through the collaborative work of the identity matching unit and the matching accuracy adjustment unit. The identity matching unit uses the standard identity data set to match the preset household database, calculates the matching score, and thus generates preliminary verification result data. This matching score-based mechanism provides reliable basic data for subsequent modules. The matching accuracy adjustment unit supports the dynamic update of the preset household database and adjusts the matching parameters in real time, so that the system can flexibly respond to changes in the database and ensure the stability of the matching results. The overall design ensures that the preliminary verification module not only ensures accuracy, but also takes into account the adaptability to complex data processing requirements.

[0086] More specifically, the matching score is calculated differently depending on the verification device. For face recognition and fingerprint recognition, the feature vector of the input data is extracted through a deep learning model and matched with the feature vector stored in the database. The cosine similarity or Euclidean distance of the feature vector is calculated to generate a matching score. For access card verification, the matching score can be obtained by the correspondence between the card ID and the system record, with a corresponding value of 1 and a non-corresponding value of 0.

[0087] More specifically, dynamically updating the preset resident database and adjusting the matching parameters of the identity matching unit include:

[0088] The preset resident database stores the identity information of all residents in the community, such as name, room number, and identity verification data. Dynamic updates are implemented by: the system administrator adds new resident information through the management backend or the resident self-service registration device, and automatically updates the database at the same time. For residents whose leases have expired or visitors whose permissions have expired, the system regularly cleans up their data to ensure the accuracy of the database. When residents apply for permission changes, such as adding access devices or adjusting time ranges, the corresponding permission fields in the database are modified in real time.

[0089] According to the status of database updates, the parameters of the identity matching unit are adjusted in real time. For example, after adding new resident data, the system optimizes the matching threshold so that it can more accurately match the new residents. When the database scales up, the matching fault tolerance rate is improved to accommodate more diverse identity data.

[0090] In a preferred embodiment of the present invention, the distributed computing node module includes:

[0091] A verification task generating unit, used to generate verification task data according to the characteristics of the preliminary verification result data and the verification requirements, and send it to the task allocation module;

[0092] A verification data processing unit is used to distribute data according to nodes, verify the verification task data through computing nodes, calculate intermediate verification values, and generate intermediate verification result data;

[0093] The data cross-validation unit is used to cross-validate the intermediate validation result data and calculate the final validation value, where:

[0094] When the final verification value meets the preset verification value, the final verification result data is generated;

[0095] When the final verification value does not meet the preset verification value, the node allocation data is adjusted, and the verification data processing unit and the data cross-validation unit are cyclically run until the preset fitness is met; the calculation formula of the verification value is:

[0096] ,

[0097] in, is the final verification value, is the total number of computing nodes participating in the verification, and Respectively Nodes and Each node calculates the intermediate verification value of the verification task. , For the and The verification task of each node takes time. , , and Respectively Nodes and The complexity of the verification task received by each node, and Respectively Nodes and Nodes receive matching scores for verification tasks. , and is the weight coefficient, , , and Respectively Nodes and The number of data features received by each node in the verification task. and Respectively Nodes and The computing power of each node, and Respectively Nodes and The number of concurrent requests for verification tasks received by a node, , , and is the adjustment coefficient.

[0098] In an embodiment of the present invention, the distributed computing node module realizes an efficient deep verification process through the division of labor and cooperation among the verification task generation unit, the verification data processing unit and the data cross-verification unit. The verification task generation unit generates task data according to the characteristics of the preliminary verification results and the verification requirements, and sends it to the task allocation module to ensure the reasonable scheduling of the verification task. The verification data processing unit independently executes the verification task through the computing node to generate intermediate verification result data. This parallel processing method significantly improves the verification efficiency. The data cross-verification unit performs consistency analysis on the intermediate verification results. When the final verification value does not meet the preset conditions, it triggers the process of re-adjusting the node allocation to ensure the reliability and consistency of the verification results. The module design not only improves the verification speed of the system, but also enhances the robustness and flexibility of the system through multi-node collaboration.

[0099] More specifically, based on the characteristics of the preliminary verification result data and the verification requirements, verification task data is generated, mainly including:

[0100] Analyze the complexity of the verification task based on the type and content of the preliminary verification result data, such as the matching score and verification time. For example, if the matching score is low, the system may require a deeper verification with higher accuracy. If the data complexity is high, the task needs to be split into multiple subtasks and processed in a distributed manner.

[0101] Generate task data based on verification requirements, where verification accuracy includes verification timeliness, giving priority to meeting rapid response requirements in high-concurrency scenarios.

[0102] Verification task data includes task type, required node resources, priority information, etc. For example, specify that a verification task requires the use of high computing power nodes and set a completion time limit for the task.

[0103] In a preferred embodiment of the present invention, the task allocation module includes:

[0104] A task priority rating determination unit, used to generate task priority rating information according to the complexity of the preliminary verification result data;

[0105] The node adaptation unit is used to calculate the first fitness weight of the node according to the user access location, task priority score information, the load status of the calculation node and the interconnection delay, and generate node allocation data, the node allocation data includes the node allocation identifier of the verification task and the execution order of the verification task; the calculation formula of the first fitness weight is:

[0106] ,

[0107] in, is the first fitness weight, Priority score for the task, the value range is 0-1, the larger the value, the more urgent the task. It is the current load ratio of the computing node, and its value range is 0-1. is the data transmission distance, which indicates the path transmission distance from the user access location to the target computing node, and is calculated from the network topology. is the remaining resource ratio of the computing node, The node response delay represents the delay from receiving the request to feeding back the response. , , , and is the weight coefficient.

[0108] In an embodiment of the present invention, the design of the task allocation module effectively solves the problem of task scheduling and resource allocation. The task priority score determination unit generates task priority score information by analyzing the complexity of the preliminary verification result data to ensure that verification tasks with high priority scores are allocated first. The node adaptation unit comprehensively considers the user access location, task priority score, calculation node load status and interconnection delay, calculates the first fitness weight of the node, and generates node allocation data. This allocation mechanism based on multi-dimensional parameters not only improves the rationality of task allocation, but also maximizes the use of system resources and improves the overall efficiency of verification tasks.

[0109] More specifically, task priority score information is generated based on the complexity of the preliminary verification result data, mainly through weighted calculation using the above-mentioned calculation formula for the complexity of the verification task, and the obtained result is used as the task priority score.

[0110] In a preferred embodiment of the present invention, the load balancing module includes:

[0111] The load dynamic detection unit is used to monitor the load and task processing status of the computing node in real time. The task processing status includes task processing rate, response delay and resource occupancy;

[0112] The task primary allocation unit is used to calculate the second fitness weight of the node according to the node load, task processing status and task priority score information, and adjust the node allocation data; the calculation formula of the second fitness weight is:

[0113] ,in, is the second fitness weight, is the task processing rate of the node, , , and is the weight coefficient.

[0114] In the embodiment of the present invention, the load balancing module ensures the smooth operation of the system through the coordinated operation of the load dynamic detection unit and the task one-time allocation unit. The load dynamic detection unit monitors the load and task processing status of the computing node in real time, collects the task processing rate, response delay and resource occupancy of the node, and provides a decision-making basis for task allocation. The task one-time allocation unit calculates the second fitness weight according to the node load and task priority scoring information, and dynamically adjusts the node allocation data to ensure the balance and efficiency of task allocation. The design of the module effectively alleviates the problem of uneven node load and enhances the processing capacity and response speed of the system.

[0115] In a preferred embodiment of the present invention, the fault-tolerant processing module includes:

[0116] The fault detection unit is used to monitor the running status of the computing node. When an abnormality occurs in a node, a fault node identification is generated and the fault type and occurrence time of the node are recorded.

[0117] A data recovery unit, used for loading the data of the unfinished verification task according to the fault node identifier to obtain the unfinished verification task data;

[0118] The task secondary distribution unit is used to distribute the unfinished verification task data to other nodes according to the second fitness weight.

[0119] In the embodiment of the present invention, the load balancing module ensures the smooth operation of the system through the coordinated operation of the load dynamic detection unit and the task one-time allocation unit. The load dynamic detection unit monitors the load and task processing status of the computing node in real time, collects the task processing rate, response delay and resource occupancy of the node, and provides a decision-making basis for task allocation. The task one-time allocation unit calculates the second fitness weight according to the node load and task priority scoring information, and dynamically adjusts the node allocation data to ensure the balance and efficiency of task allocation. The design of the module effectively alleviates the problem of uneven node load and enhances the processing capacity and response speed of the system.

[0120] In a preferred embodiment of the present invention, the dynamic rights management module includes:

[0121] The permission update unit is used to dynamically adjust the permission time range and the target access control device identification by analyzing the user's access time, frequency and device usage records;

[0122] The permission constraint unit is used to make restrictive adjustments to the permission information when there is a significant deviation between the user's behavior data and the preset community constraint conditions.

[0123] In an embodiment of the present invention, the dynamic permission management module realizes flexible adjustment of user permissions through the combination of the permission update unit and the permission constraint unit. The permission update unit dynamically adjusts the permission time range and the target access control device identification according to the user's access time, access frequency and device usage record, thereby improving the accuracy of permission allocation. The permission constraint unit makes constraint adjustments to the permission information according to the deviation between the user behavior data and the preset community constraint conditions to avoid the risk of permission abuse. The design of the module enhances the dynamic adaptability of permission management and provides a more intelligent permission allocation mechanism for the community access control system.

[0124] More specifically, by analyzing the user's access time, frequency, and device usage records, the permission time range and target access control device identification are dynamically adjusted, mainly including:

[0125] Analyze user access time: The system counts the time distribution of access control usage based on the user's access records. For example, if a user usually uses access control between 8:00-9:00 am and 17:00-18:00 pm on weekdays, the system will generate a corresponding permission time range for the user based on his or her behavior pattern. If the user's access time is too concentrated, the system will shorten the permission range and activate it only during the commonly used time period to reduce the security risk of the device. For special users, such as community administrators or emergency service personnel, the system will appropriately extend the permission time range according to actual needs.

[0126] Analyze user access frequency: Based on the frequency of access control use by users over a period of time, the system dynamically evaluates the activity of their permissions. For example, high-frequency users, such as residents who use access control every day, can maintain normal permission settings. Low-frequency users, such as temporary visitors, may be restricted to using access control devices within a shorter time frame to reduce resource usage. For users who have not used permissions for a long time, the system can remind them to update their permissions through notifications, or suspend their access control permissions to ensure safety.

[0127] Analyze device usage records: The system adjusts the scope of permissions for target devices based on the user's use of different access control devices. For example, if a user's permissions originally cover multiple access control devices in the entire community, but he only uses a certain device for a long time, the system can automatically optimize permissions and only activate commonly used devices for him. If the user occasionally uses other devices, such as parking lot access control, the system will temporarily authorize within a limited range.

[0128] Dynamic adjustment implementation method: The system monitors the user's behavior data in real time through the dynamic permission management module, and optimizes and updates the permission time range and target device identification based on preset community constraints.

[0129] More specifically, the preset community constraints include:

[0130] Manage occupants based on their identity, occupant category, such as owner, tenant, visitor, and specific scope of access rights, such as specific access control devices or specified time ranges. For example, a visitor's access rights have strict time limits, while an owner's rights can cover multiple devices.

[0131] When community management needs change, such as adding new residents or adjusting resident permissions, the system can update access control permissions in real time based on the new constraints, ensuring that the preset community constraints always meet actual needs.

[0132] In a preferred embodiment of the present invention, the access control module includes:

[0133] The device status feedback unit is used to monitor the operating parameters of the target access control device in real time and generate device operating status data. The operating parameters include device response time and execution accuracy;

[0134] A device status adjustment unit, used to adjust the operating status of a target access control device according to the device operating status data;

[0135] The data recording unit is used to record the operating parameters of the target access control device and generate a device status log.

[0136] In the embodiment of the present invention, the access control module improves the intelligence level of the access control device through the collaboration of the device status feedback unit, the device status adjustment unit and the data recording unit. The device status feedback unit monitors the operating parameters of the target access control device in real time to ensure the stability of the device operation. The device status adjustment unit dynamically adjusts the device operation mode according to the operating status data to optimize the device performance. The data recording unit records the device operating parameters and generates a status log to provide data support for abnormal analysis and system optimization. The module design ensures the safety and efficiency of the access control device.

[0137] More specifically, according to the device operation status data, the operation status of the target access control device is adjusted, mainly including:

[0138] Collection of equipment operation status data: The system collects the operating parameters of the target access control equipment in real time through the equipment status feedback unit, including:

[0139] Device response time: reflects the delay time from when the device receives the door opening command to when it executes it, and is used to evaluate the operating efficiency of the device.

[0140] Execution accuracy: refers to the accuracy of the access control device when performing door opening or closing operations, avoiding operation failures due to execution deviations.

[0141] Fault alarm data: alarm information of equipment hardware problems such as motor abnormality and power failure.

[0142] Operation status analysis: The system analyzes the operation status data to identify whether there are any abnormalities in the current status of the device. For example, if the response time is long, it may indicate that the device is overloaded or the signal transmission is delayed. A decrease in execution accuracy may be a precursor to aging or damage of the device hardware components. The analysis results will be quantified and output in the form of a status score for subsequent adjustment logic.

[0143] Dynamic adjustment of operating status: Based on the operating status data and analysis results, the system can take the following adjustment measures:

[0144] Adjust device operation mode: Switch the device to energy-saving mode or high-performance mode. For example, enable high-performance mode during peak hours to improve response speed, and switch to energy-saving mode during off-peak hours to reduce power consumption.

[0145] Adjust device load: If the device load is too high, the system can temporarily shut down non-essential functions or distribute some tasks to other devices through the load balancing module.

[0146] Trigger automatic maintenance process: When the equipment status score is lower than the set threshold, the system can automatically notify the operation and maintenance personnel to conduct inspection and maintenance.

[0147] Feedback after adjustment: After each adjustment of the operating status, the system will re-collect the equipment operating parameters. The adjustment records will be stored in the equipment status log for long-term performance evaluation of the system and optimization strategy update.

[0148] In a preferred embodiment of the present invention, the system further comprises:

[0149] The exception handling module is used to analyze the abnormal situation of the target access control device according to the device status log, perform emergency processing, and generate exception processing result data; wherein:

[0150] Anomalies include unauthorized access attempts or equipment failures;

[0151] The exception handling result data includes notifying relevant managers or forcibly locking the target access control device.

[0152] The acquisition equipment includes a face recognition device, a QR code scanning device and an access card reading device;

[0153] In the embodiment of the present invention, the exception handling module timely discovers and handles the abnormal situation of the target access control device by analyzing the device status log. The module can execute targeted emergency handling measures according to different abnormal types, such as notifying relevant managers or forcibly locking the target access control device, thereby reducing the impact of abnormalities on system operation. The module design improves the security and responsiveness of the system and provides reliable access control management guarantee for smart communities.

[0154] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A smart community Internet of Things access control operation system, characterized in that: The system comprises: The data collection module is used to obtain the user's identity data, including face images, QR code information and access card information; pre-process the obtained identity data to generate a standard identity data set; A preliminary verification module is used to match the standard identity data set with the preset resident database to generate preliminary verification result data; wherein the preset resident database supports dynamic updates, including the addition, deletion or permission adjustment of resident information; The distributed computing node module is used to perform in-depth verification of the preliminary verification result data through computing nodes to obtain the final verification result data; each computing node independently performs part of the verification task, the computing nodes are interconnected through the Internet of Things devices, and the computing capacity of each node is determined by its physical hardware performance; A task allocation module is used to allocate the verification task of the preliminary verification result data to the optimal edge computing node according to the user access location and the load of the computing node, and generate node allocation data; The load balancing module is used to monitor the load and task processing status of the computing nodes in real time, and adjust the node allocation data of the task allocation module according to the monitoring results; The fault-tolerant processing module is used to monitor the operating status of computing nodes in real time. When a computing node failure is detected, the verification task of the failed node is reallocated to other computing nodes. The dynamic permission management module is used to generate access control permission information for the user based on the final verification result data. The access control permission information includes the user identity, permission time range and target access control device identification; The access control module is used to analyze the target access control device identification according to the access control authority information, and control the target access control device to perform the door opening operation, and record the operation data at the same time; The distributed computing node module includes: A verification task generating unit, used to generate verification task data according to the characteristics of the preliminary verification result data and the verification requirements, and send it to the task allocation module; A verification data processing unit is used to distribute data according to nodes, verify the verification task data through computing nodes, calculate intermediate verification values, and generate intermediate verification result data; The data cross-validation unit is used to cross-validate the intermediate validation result data and calculate the final validation value, where: When the final verification value meets the preset verification value, the final verification result data is generated; When the final verification value does not meet the preset verification value, the node allocation data is adjusted, and the verification data processing unit and the data cross-validation unit are cyclically run until the preset fitness is met; the calculation formula of the verification value is: ， in, is the final verification value, is the total number of computing nodes participating in the verification, and Respectively Nodes and Each node calculates the intermediate verification value of the verification task. , For the and The verification task of each node takes time. , , and Respectively Nodes and The complexity of the verification task received by each node, and Respectively Nodes and Nodes receive matching scores for verification tasks. , and is the weight coefficient, , , and Respectively Nodes and The number of data features received by each node in the verification task. and Respectively Nodes and The computing power of each node, and Respectively Nodes and The number of concurrent requests for verification tasks received by a node, , and is the adjustment coefficient.

2. According to claim 1, a smart community Internet of Things access control operation system is characterized in that: The data acquisition module comprises: An identity collection unit, used to obtain user identity data; A feature separation unit is used to classify and extract multiple features in the user identity data to generate identity feature data, where the identity feature data includes a face image, QR code information, and access card information; A data processing unit, used to perform standardization operations on identity feature data to generate a standard identity data set, wherein the standardization operation includes unifying data format and adjusting feature range; The standard identity data set is stored in association with the user's identity data to form a data index.

3. According to claim 2, a smart community Internet of Things access control operation system is characterized in that: The preliminary verification module includes: An identity matching unit, used to match the standard identity data set with the preset household database, calculate the matching score, and generate preliminary verification result data; The matching accuracy adjustment unit is used to dynamically update the preset resident database and adjust the matching parameters of the identity matching unit.

4. According to claim 3, a smart community Internet of Things access control operation system is characterized in that: The task allocation module includes: A task priority rating determination unit, used to generate task priority rating information according to the complexity of the preliminary verification result data; The node adaptation unit is used to calculate the first fitness weight of the node according to the user access location, task priority score information, the load status of the calculation node and the interconnection delay, and generate node allocation data, the node allocation data includes the node allocation identifier of the verification task and the execution order of the verification task; the calculation formula of the first fitness weight is: ， in, is the first fitness weight, Prioritize tasks. is the current load ratio of the computing node, is the data transmission distance, which indicates the path transmission distance from the user access location to the target computing node, and is calculated from the network topology. is the remaining resource ratio of the computing node, The node response delay represents the delay from receiving the request to feeding back the response. , , , and is the weight coefficient.

5. According to claim 4, a smart community Internet of Things access control operation system is characterized in that: The load balancing module includes: The load dynamic detection unit is used to monitor the load and task processing status of the computing node in real time. The task processing status includes task processing rate, response delay and resource occupancy; The task primary allocation unit is used to calculate the second fitness weight of the node according to the node load, task processing status and task priority score information, and adjust the node allocation data; the calculation formula of the second fitness weight is: ,in, is the second fitness weight, is the task processing rate of the node, , , and is the weight coefficient.

6. A smart community Internet of Things access control operation system according to claim 5, characterized in that: The fault-tolerant processing module comprises: The fault detection unit is used to monitor the running status of the computing node. When an abnormality occurs in a node, a fault node identification is generated and the fault type and occurrence time of the node are recorded. A data recovery unit, used for loading the data of the unfinished verification task according to the fault node identifier to obtain the unfinished verification task data; The task secondary distribution unit is used to distribute the unfinished verification task data to other nodes according to the second fitness weight.

7. A smart community Internet of Things access control operation system according to claim 6, characterized in that: The dynamic rights management module includes: The permission update unit is used to dynamically adjust the permission time range and the target access control device identification by analyzing the user's access time, frequency and device usage records; The permission constraint unit is used to make restrictive adjustments to the permission information when there is a significant deviation between the user's behavior data and the preset community constraint conditions.

8. A smart community Internet of Things access control operation system according to claim 7, characterized in that: The access control module comprises: The device status feedback unit is used to monitor the operating parameters of the target access control device in real time and generate device operating status data. The operating parameters include device response time and execution accuracy; A device status adjustment unit, used to adjust the operating status of a target access control device according to the device operating status data; The data recording unit is used to record the operating parameters of the target access control device and generate a device status log.

9. A smart community Internet of Things access control operation system according to claim 8, characterized in that: The system further comprises: The exception handling module is used to analyze the abnormal situation of the target access control device according to the device status log, perform emergency processing, and generate exception processing result data; wherein: Anomalies include unauthorized access attempts or equipment failures; The exception handling result data includes notifying relevant managers or forcibly locking the target access control device.

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