Community access control data management method and device

Abstract

The invention discloses a community access control data management method. The method comprises the following steps: determining current access control data corresponding to an access control system by using a state monitoring module corresponding to a target community; the target community comprises an access control system and a linkage system; determining historical access control data corresponding to the access control system; fusing the current access control data and the historical access control data by using a preset data fusion algorithm to obtain comprehensive access control data; analyzing and processing the comprehensive access control data by utilizing a preset analysis model, so that the comprehensive access control data determines target linkage data corresponding to each linkage system according to a preset rule; and according to a preset transmission rule, sending the target linkage data to linkage systems, so that the linkage systems perform corresponding actions according to the target linkage data. According to the invention, integration and analysis of access control data are realized, and efficient linkage between the access control system and other linkage systems is realized, so that the utilization efficiency of community resources is improved, and the overall management level of a smart community is enhanced.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a method and device for managing community access control data. Background Art

[0002] Community access control data refers to various types of information and parameters collected, processed and analyzed in the smart community access control system. These data usually come from multiple modules of the access control system, including but not limited to face recognition cameras, QR code scanners, smart card readers, etc. The main purpose of access control data is to be used for identity authentication, access control and security monitoring to ensure the safety of the community and improve the convenience of residents.

[0003] In the context of smart communities, access control data are not just simple entry and exit records, they may also include residents’ identity information, access time, pass frequency, etc. Through intelligent analysis of these data, community managers can manage community resources more effectively, optimize energy use, and even predict and respond to residents’ needs.

[0004] Although community access control data contains huge value, many of them have not yet been fully explored and utilized. Access control systems usually exist as independent entities without forming an effective data sharing and linkage mechanism with other systems in the community.

[0005] Application Contents

[0006] The present application provides a method and device for managing community access control data to overcome the above-mentioned problems or at least partially solve the above-mentioned problems of community access control data isolation, application limitations, and lack of linkage.

[0007] In a first aspect, the present application provides a method for managing community access control data, including:

[0008] The current access control data corresponding to the access control system is determined by using the status monitoring module corresponding to the target community; the target community includes the access control system and the linkage system;

[0009] Determine the historical access control data corresponding to the access control system;

[0010] Using the preset data fusion algorithm, the current access control data and the historical access control data are fused to obtain comprehensive access control data;

[0011] Analyze and process the comprehensive access control data using a preset analysis model, so that the comprehensive access control data can determine the target linkage data corresponding to each linkage system according to a predetermined rule;

[0012] According to the preset transmission rules, the target linkage data is sent to the linkage system, so that each linkage system performs corresponding actions according to the target linkage data.

[0013] In a second aspect, the present application provides a management device for community access control data, including:

[0014] The current access control data determination module is configured to determine the current access control data corresponding to the access control system using the status monitoring module corresponding to the target community; the target community includes the access control system and the linkage system;

[0015] A historical access control data determination module is configured to determine historical access control data corresponding to the access control system;

[0016] The comprehensive access control data determination module is configured to fuse the current access control data with the historical access control data using a preset data fusion algorithm to obtain comprehensive access control data;

[0017] The target linkage data determination module is configured to analyze and process the comprehensive access control data using a preset analysis model so that the comprehensive access control data determines the target linkage data corresponding to each linkage system according to a predetermined rule;

[0018] The execution module is configured to send the target linkage data to the linkage system according to a preset transmission rule, so that each linkage system performs corresponding actions according to the target linkage data.

[0019] In a third aspect, the present application provides a readable medium comprising execution instructions. When a processor of an electronic device executes the execution instructions, the electronic device executes any method described in the first aspect.

[0020] In a fourth aspect, the present application provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor executes any method described in the first aspect.

[0021] The present application provides a method for managing community access control data, by using the status monitoring module corresponding to the target community to determine the current access control data corresponding to the access control system; the target community includes an access control system and a linkage system; the historical access control data corresponding to the access control system is determined; the current access control data and the historical access control data are fused using a preset data fusion algorithm to obtain comprehensive access control data; the comprehensive access control data is analyzed and processed using a preset analysis model so that the comprehensive access control data determines the target linkage data corresponding to each linkage system according to a predetermined rule; the target linkage data is sent to the linkage system according to a preset transmission rule so that each linkage system performs corresponding actions according to the target linkage data. The present application realizes the integration and analysis of access control data, and by realizing the efficient linkage between the access control system and other linkage systems, the utilization efficiency of community resources is improved, and the overall management level of the smart community is enhanced.

[0022] The further effects of the above-mentioned non-conventional preferred manner will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the existing technical solutions, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 A flowchart of a method for managing community access control data provided in one embodiment of the present application;

[0025] Figure 2 A flowchart of another method for managing community access control data provided in one embodiment of the present application;

[0026] Figure 3 A flowchart of another method for managing community access control data provided in one embodiment of the present application;

[0027] Figure 4 It is a structural diagram of a community access control data management device provided by an embodiment of the present application;

[0028] Figure 5 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0030] Community access control data refers to various types of information and parameters collected, processed and analyzed by the access control system in smart communities. These data come from multiple modules, such as face recognition cameras, QR code scanners, smart card readers, etc. The core functions of access control data are identity authentication, access control and security monitoring to ensure the safety of the community and improve the convenience of residents.

[0031] In the context of smart communities, the application of access control data is far more than a simple entry and exit record. They also contain multi-dimensional data such as residents' identity information, access time, and pass frequency. Through intelligent analysis of this information, community managers can manage community resources more efficiently. For example, costs can be reduced by optimizing energy use, or residents' needs can be predicted through data analysis, thereby improving the community's service quality and residents' satisfaction.

[0032] Although community access control data has great potential value, many communities have not yet fully explored and utilized the potential of this data. The access control system usually exists as an independent unit and fails to form an effective data sharing and linkage mechanism with other systems in the community (such as security systems, property management systems, etc.). This isolated state limits the application scope of the data and weakens its ability to support intelligent community management.

[0033] In order to solve this problem, it is necessary to link the access control system with other management systems in the community through data fusion and intelligent analysis, so as to improve the utilization efficiency of data and realize all-round smart community management. Therefore, the embodiment of the present application proposes a method for managing community access control data, aiming to overcome the problems of isolated access control data, application limitations and lack of linkage mechanism. In this embodiment, a method for managing community access control data includes:

[0034] Step 101: Use the status monitoring module corresponding to the target community to determine the current access control data corresponding to the access control system; the target community includes the access control system and the linkage system.

[0035] The status monitoring module can monitor and collect various data of the access control system in real time, such as the entry and exit records of residents, the operating status of access control equipment, etc. This data can be collected in a variety of ways, including different sensing devices such as face recognition cameras, QR code scanners, smart card readers, etc. In this way, the system can ensure that the latest real-time data related to community safety and management is obtained.

[0036] Current access control data refers to the real-time or near-real-time data collected from the access control system at a certain moment or within a specific time range. This data can be specific to a single entry and exit record of a certain resident, or it can cover the entry and exit activity information of multiple residents in the entire community over a period of time.

[0037] For the current data of a single resident, the system will record the resident's identity, entry and exit time, access control device passed, etc. For example, when a resident enters the community by swiping a card, the system will immediately record the resident's identity information, card swiping time, entrance location, etc.

[0038] On the other hand, the current access control data can also be the comprehensive data of multiple users in the recent period. For example, the system can summarize the entry and exit records of all residents in the community in the past 24 hours to generate an overall picture of group access control activities. In this way, community managers can understand the peak entry and exit in a certain period of time, the frequency of use of each entrance, and other information, providing a reference for community management optimization.

[0039] The target community includes not only the access control system, but also multiple other linkage systems associated with it. These linkage systems may involve the community's security system, lighting system, property management system, etc. By connecting the data of the access control system with other systems, the status monitoring module is not only a simple data collection tool, but also an important medium for achieving efficient collaboration between various systems. When the access control system records the entry and exit activities of residents, the status monitoring module can quickly capture and transmit this information, providing a basis for the subsequent linkage system response.

[0040] In this way, the access control system can not only complete the function of access control independently, but also provide real-time data support for other systems in the community through the status monitoring module, and ultimately realize the collaborative management and automated control of the entire smart community.

[0041] Step 102: Determine the historical access control data corresponding to the access control system.

[0042] In addition to the current access control data, the system also needs to determine the historical access control data corresponding to the access control system. Historical access control data refers to the resident access information, equipment operation status, etc. recorded by the access control system in the past period of time. These data usually include detailed information such as the time, frequency, and access path of residents entering and leaving the community, which may cover a time range of several days, weeks, or even months.

[0043] The determination of historical access control data is usually done by retrieving and summarizing the data stored in the access control system. The system can set the time range of the data according to different needs to ensure that the acquired historical data can complement the current access control data, providing a basis for subsequent data fusion and intelligent analysis. Through in-depth mining and analysis of these historical data, the system can formulate more forward-looking management strategies and achieve refined management of the community.

[0044] Step 103: Use a preset data fusion algorithm to fuse the current access control data and the historical access control data to obtain comprehensive access control data.

[0045] The purpose of data fusion is to integrate data from different time points, analyze current and historical behavior patterns, form a deeper understanding of community access, and provide support for subsequent decision-making and actions.

[0046] The data fusion algorithm first extracts features from the current access control data and historical access control data. For example, the current access control data may involve the latest entry and exit records of residents, while the historical data includes long-term entry and exit time, frequency, and behavioral habits. Through feature extraction, the system can associate each piece of data with its corresponding behavioral pattern, providing a targeted basis for fusion.

[0047] The system can weight current data and historical data according to the preset weight rules. Usually, current data will be given a higher weight in the fusion process due to its timeliness, while historical data also has a certain reference value because of its reflection of long-term trends. Through this weighted fusion, the system can generate comprehensive access control data that not only reflects the current real-time situation, but also takes into account the impact and trend of historical behavior.

[0048] The generation of comprehensive access control data allows the system to not only handle short-term immediate situations, but also analyze the access patterns of residents over a wider time dimension. This provides the system with a more comprehensive analysis basis to help formulate more refined management strategies, such as optimizing access control configuration, adjusting security measures or predicting future lighting needs.

[0049] Step 104: Analyze and process the comprehensive access control data using a preset analysis model, so that the comprehensive access control data can determine the target linkage data corresponding to each linkage system according to a predetermined rule.

[0050] The system analyzes and processes the integrated access control data through a preset analysis model, thereby determining the target linkage data required by each linkage system according to the predetermined rules. The role of the analysis model is to convert large amounts of complex access control data into actionable information, so that different linkage systems in the community can perform corresponding operations or adjust configurations based on this data.

[0051] The analysis model will conduct in-depth mining of the comprehensive access control data to extract key features. Comprehensive access control data not only includes the residents' entry and exit records, but also may involve multi-dimensional information such as entry and exit time, frequency, and path. Through these features, the analysis model can identify the behavior patterns of community residents, such as peak entry and exit hours, abnormal activities, etc. In this way, the system can better predict the community's security needs and resource allocation needs based on the processing results of the analysis model.

[0052] The system matches the features processed by the analysis model with the functional requirements of each linkage system according to predetermined rules. For example, the security system may need to adjust the monitoring range of the camera or the strictness of the access control verification according to the access data; the lighting system can automatically adjust the lighting intensity of the public area according to the peak access times of the residents. The analysis model ensures that each linkage system can obtain valid information related to its own operation from the comprehensive access control data, thereby generating the corresponding target linkage data.

[0053] In this way, the system can achieve highly automated and intelligent management. The target linkage data not only helps each linkage system to achieve self-adjustment, but also can respond flexibly according to the real-time behavior patterns of community residents, ensuring efficient use of resources and overall community safety.

[0054] Step 105: According to a preset transmission rule, the target linkage data is sent to the linkage system, so that each linkage system performs corresponding actions according to the target linkage data.

[0055] The system sends the generated target linkage data to each linkage system according to the preset transmission rules to ensure that these systems can take corresponding actions based on the data. The transmission rules are formulated to ensure the efficient, secure and accurate transmission of data between different systems, thereby realizing the intelligent management of the community.

[0056] The transmission rules must first take into account the characteristics and requirements of different linkage systems. Each linkage system, such as security, lighting, property management, etc., has its own specific operating environment and response mechanism. The system will set the corresponding transmission priority, data format and communication protocol based on these characteristics to ensure that the target linkage data can be transmitted to the corresponding system at the right time and in the right way. For example, the security system may need to respond to access control data in real time, so the transmission rules will prioritize the immediacy of security data, while the lighting system may not have high requirements for latency, so data transmission can be done in batches.

[0057] In addition, the system can also generate a transmission queue based on the preset network environment. The transmission queue can help the system manage the order in which data is sent, ensure that important data is transmitted first, and avoid the impact of network congestion on the system response speed. When the target linkage data is generated, the system will gradually send the data to each linkage system according to the transmission queue to ensure the stability and efficiency of data transmission.

[0058] After the target linkage data is transmitted to each linkage system, these systems will take corresponding actions based on the received data. For example, the security system may initiate additional monitoring measures, and the lighting system will automatically adjust the light brightness or turn on the lights in specific areas based on the data. This process realizes the linkage of various systems, making the access control data no longer a single entry and exit record, but a core data source that can trigger a variety of community management actions.

[0059] Through such a transmission mechanism, various systems in the entire community can work together. Relying on accurate and efficient data transmission, the linkage system can respond in the first time to achieve intelligent scheduling of community resources and improve overall security.

[0060] Through the above technical solution, it can be seen that the beneficial effects of this embodiment are:

[0061] The embodiment of the present application provides a method for managing community access control data, which determines the current access control data corresponding to the access control system by using the status monitoring module corresponding to the target community; the target community includes an access control system and a linkage system; determines the historical access control data corresponding to the access control system; uses a preset data fusion algorithm to fuse the current access control data and the historical access control data to obtain comprehensive access control data; uses a preset analysis model to analyze and process the comprehensive access control data so that the comprehensive access control data determines the target linkage data corresponding to each linkage system according to a predetermined rule; according to a preset transmission rule, the target linkage data is sent to the linkage system so that each linkage system performs corresponding actions according to the target linkage data. The present application realizes the integration and analysis of access control data, and by realizing the efficient linkage between the access control system and other linkage systems, it improves the utilization efficiency of community resources and enhances the overall management level of the smart community.

[0062] Figure 1 What is shown is only a basic embodiment of a method for managing community access control data of the present application. By performing certain optimization and expansion on this basis, other preferred embodiments of a method for managing community access control data can be obtained.

[0063] like Figure 2 As shown, another specific embodiment of a method for managing community access control data of the present application.

[0064] In this embodiment, a method for managing community access control data includes the following steps:

[0065] Step 201: Use the status monitoring module corresponding to the target community to determine the current access control data corresponding to the access control system; the target community includes the access control system and the linkage system.

[0066] Step 202: Determine the historical access control data corresponding to the access control system.

[0067] Step 203: Use a preset data fusion algorithm to fuse the current access control data and the historical access control data to obtain comprehensive access control data.

[0068] Step 204: Using a feature extraction algorithm, determine the current data features corresponding to the current access control data, and determine the historical data features corresponding to the historical access control data.

[0069] The role of the feature extraction algorithm is to extract representative information from complex raw data, so that the system can process this data more efficiently and accurately. The feature extraction algorithm will analyze the current access control data and identify important features related to the entry and exit of residents. These features may include but are not limited to the time of entry and exit, the access control verification method used (such as face recognition, QR code scanning, smart card swiping, etc.), the type of device (such as which access control device is used), and the location of entry and exit (such as unit door, underground garage entrance, etc.). By extracting these features, the system can capture the residents' real-time behavior information and provide accurate current access control data.

[0070] At the same time, the feature extraction algorithm also processes the historical access control data to extract features related to the long-term entry and exit habits of residents. Historical data features may include the frequency of residents' entry and exit, peak hours in specific time periods, periodic activities (such as entry and exit patterns every morning or evening), etc. These features help identify residents' behavior patterns and provide a reference for future behavior prediction and risk assessment.

[0071] The feature extraction process is not limited to identifying information in a single dimension, but also takes multiple dimensions into consideration. For example, the system may combine multiple factors such as the identity information of the resident, the frequency of device use, community events, etc. to form a multi-dimensional feature combination. This feature extraction not only helps to accurately analyze the current access control data, but also can dig out the behavior patterns hidden in the historical data.

[0072] Step 205: Determine the current data weight corresponding to the current data feature according to the preset weight determination rule, and determine the historical data weight corresponding to the historical data feature.

[0073] The setting of weights determines the respective influence of current access control data and historical access control data when they are integrated, thereby ensuring that the generated comprehensive access control data can reflect the residents' immediate behavior while taking into account long-term behavior patterns.

[0074] Current data features are usually highly time-sensitive, reflecting the entry and exit of residents at a specific time point, so their weight is usually high. In order to ensure that the system can respond quickly to current community activities, the current data weight can ensure that the system gives priority to the real-time behavior of residents. For example, during the peak period of residents entering and leaving, the system may need to make immediate responses based on current data features, such as time, location, verification method, etc., to adjust the allocation of community resources or improve security measures.

[0075] Historical data features reflect the long-term behavior patterns and regularity of residents, which is of great significance for predicting future behavior and identifying potential safety hazards. Although the weight of historical data is relatively low, it cannot be ignored. By giving historical data features a certain weight, the system can make a more comprehensive analysis based on the long-term behavior habits of residents without relying entirely on current data. For example, if a resident usually enters and exits at fixed time periods, but suddenly enters and exits abnormally at abnormal times, the reference of historical data can help the system quickly detect this anomaly.

[0076] The weight determination will also be dynamically adjusted according to preset rules. For example, the system may set different weight allocation strategies according to the specific scenarios of the community. For key security areas or emergency situations, the weight of current data features may be further increased, while in more stable time periods, the weight of historical data features may account for a larger proportion. This dynamic weight adjustment mechanism ensures that the system can adapt to changes in different scenarios and make the best response.

[0077] The current data weight and the historical data weight may be determined by determining a preset time weight table; based on the time weight table, determining the current data weight corresponding to the current data feature, and determining the historical data weight corresponding to the historical data feature.

[0078] The time weight table is a tool that quantifies the importance of data based on the time dimension. It helps the system distinguish the importance of data in different time periods, thereby achieving more accurate weight allocation when integrating current access control data with historical access control data.

[0079] The system will pre-set a time weight table, which is usually customized according to the specific situation of the community. The values ​​in the time weight table represent the importance of data at different time points. For example, access control data within the last 24 hours may be given a higher weight because these data directly reflect the current activities of residents and have high timeliness and practical significance. Although the historical data from a week ago, a month ago or longer is relatively less important, it cannot be ignored. They can provide long-term patterns of resident behavior.

[0080] Based on the time weight table, the system assigns corresponding weights to each current data feature and historical data feature. For current data features, such as the entry and exit records of a resident who has just passed the access control system, they are usually given higher weights due to their strong timeliness, so that the system can respond to changes in the resident's behavior immediately. At the same time, historical data features, such as the frequency of entry and exit of residents in the past month, habitual time periods, etc., although less timely, can still provide a reference for long-term behavior patterns, so they will also be given certain weights. These weight values ​​are dynamically adjusted through the time weight table to ensure that data from different time periods can reasonably participate in the fusion calculation.

[0081] The use of a time weight table allows the system to flexibly adjust the influence of data within different time frames. For example, when responding to an emergency, the weight of current data may be greatly increased to ensure that the system can immediately process the latest community dynamics. In regular community monitoring scenarios, the weight of historical data will be appropriately increased to provide longer-term behavioral pattern analysis support.

[0082] Through this weight allocation method based on the time weight table, the system can more accurately balance the influence of short-term and long-term data when integrating current data and historical data. Ultimately, the comprehensive access control data generated by the system has both the real-time nature of current behavior and the regularity of historical behavior.

[0083] Step 206: Using the current data weight and the historical data weight, the current access control data and the historical access control data are integrated to determine the comprehensive access control data.

[0084] After feature extraction, the current access control data contains the in and out information of residents in a specific time period, such as time, location, and identity verification method. These data reflect the immediate behavior of residents and have important reference value when dealing with emergencies or real-time management in the community. Therefore, the system will assign a higher weight to the current access control data according to the preset weight rules. This weight gives the current data a greater influence, making it more prominent in the fusion process, to ensure that the system has agile response capabilities to the current community dynamics.

[0085] Historical access control data provides a reference for residents' long-term behavior patterns. Although these data are not as timely as current access control data, they can reveal in-depth information such as residents' entry and exit patterns and activity frequency. By giving historical access control data a certain weight, the system can avoid relying solely on short-term fluctuations in current data and balance the analysis of immediate behavior with long-term trends. This balance ensures that when the system makes decisions, it can not only reflect the current activities of residents, but also predict future trends based on historical behavior, thereby improving the predictability of management.

[0086] During the fusion process, the current access control data can be weighted by using the current data weight to determine the first weighted data; the historical access control data can be weighted by using the historical data weight to determine the second weighted data; the first weighted data and the second weighted data are added to determine the comprehensive access control data.

[0087] The system weights the current access control data and historical access control data separately and adds them together to generate comprehensive access control data. This process ensures that the system can capture the residents' immediate behavior and combine long-term historical data, making the access control data more accurate and practical.

[0088] According to the preset time weight table, the system assigns a certain weight to the current access control data, which is the first weighted data. The system also assigns a certain weight to the historical access control data, which is the second weighted data. The system adds the first weighted data and the second weighted data to generate comprehensive access control data. This comprehensive data includes both the current resident's immediate behavior and its long-term historical behavior pattern, thus achieving a balance between the short-term and the long-term. This fusion process can help the community management system understand the residents' entry and exit more accurately, providing a basis for subsequent decision-making and management. For example, when the resident's current entry and exit behavior deviates greatly from its historical pattern, the comprehensive access control data can automatically trigger anomaly detection or security alarm.

[0089] Step 207: Analyze and process the comprehensive access control data using a preset analysis model, so that the comprehensive access control data can determine the target linkage data corresponding to each linkage system according to a predetermined rule.

[0090] Step 208: Send the target linkage data to the linkage system according to the preset transmission rule, so that each linkage system performs corresponding actions according to the target linkage data.

[0091] Through the above technical solutions, it can be seen that the beneficial effects of this embodiment are: by using the feature extraction algorithm, the key information in the current access control data and the historical access control data can be effectively extracted, thereby improving the accuracy of data analysis; the preset weight determination rules ensure the reasonable integration of current data and historical data, so that the system can not only respond to the real-time dynamics in the community in a timely manner, but also make a comprehensive analysis based on long-term behavior patterns. Finally, through data fusion, the generated comprehensive access control data can provide more intelligent and refined decision-making support for community management, improving community security and management efficiency.

[0092] like Figure 3 FIG. 1 is another specific embodiment of a method for managing community access control data of the present application. This embodiment is further described on the basis of the above-mentioned embodiment.

[0093] In this embodiment, a method for managing community access control data includes the following steps:

[0094] Step 301: Determine the current access control data corresponding to the access control system using the status monitoring module corresponding to the target community; the target community includes the access control system and the linkage system.

[0095] Step 302: Determine the historical access control data corresponding to the access control system.

[0096] Step 303: Use a preset data fusion algorithm to fuse the current access control data and the historical access control data to obtain comprehensive access control data.

[0097] Step 304: Analyze and process the comprehensive access control data using a preset analysis model, so that the comprehensive access control data can determine the target linkage data corresponding to each linkage system according to a predetermined rule.

[0098] Step 305: Determine the linkage characteristics corresponding to each linkage system.

[0099] The linkage feature refers to the basis for triggering the linkage system to perform specific operations under specific conditions. It can reflect the interaction relationship and response strategy between the systems. Each linkage system has different functions and roles, and its linkage features are also different, which are usually closely related to the application scenarios of the system and community needs.

[0100] In smart communities, common linkage systems include security systems, lighting systems, environmental control systems, emergency response systems, etc. Taking the security system as an example, its linkage features may be closely related to abnormal activities of the access control system. When the access control system detects that an unauthorized person is trying to enter, the linkage features of the security system may require the camera to start recording, the alarm to sound, or an alarm signal to the security guard. The linkage features of the lighting system may be associated with the entry and exit times of residents. For example, when residents enter the community at night, the linkage features may require the public lighting system to automatically turn on to ensure the safety and comfort of the residents.

[0101] In order to determine these linkage features, the system first needs to analyze the specific functions of each linkage system and its triggering conditions in different scenarios. This is usually done by collecting historical data, observing system operation, and combining the actual needs of the community. For example, the linkage feature of the lighting system can determine the best time to turn on or off by analyzing the travel patterns of residents; while the linkage feature of the security system needs to adjust the trigger sensitivity based on historical security events and access control abnormality data.

[0102] When determining linkage features, the system also needs to consider the coordination between different linkage systems. For example, when the access control system detects a large number of people entering, the lighting system and environmental control system may need to be synchronized to adjust the indoor temperature and light brightness to provide a comfortable environment for residents. At the same time, the emergency response system may also automatically identify potential security risks through linkage features and take preventive measures in advance.

[0103] Step 306: Analyze linkage features from the comprehensive access control data using a machine learning algorithm.

[0104] Through training and analysis of large amounts of historical data, machine learning algorithms can automatically extract regular and predictive linkage features from complex access control data, providing an intelligent decision-making basis for community management.

[0105] The sources of access control data are very diverse, including residents' entry and exit records, visitor access information, access control system abnormal events, etc. These data not only cover the identity and behavior patterns of residents, but may also contain dynamic information such as specific time periods and entry and exit frequencies. Due to the large amount of data and complex features, it is difficult for manual analysis to quickly and accurately identify the hidden patterns in the data. At this time, machine learning algorithms can extract potential linkage features from these complex comprehensive data through their powerful self-learning capabilities.

[0106] During implementation, machine learning algorithms usually pre-process the comprehensive access control data first, including data cleaning, noise reduction, normalization and other steps to ensure that the data input into the model is consistent and high quality. Next, the algorithm will perform feature extraction and pattern recognition based on pre-set models, such as decision trees, support vector machines, neural networks, etc. By learning the behavior patterns of residents, the algorithm can parse out linkage features such as "peak entry and exit time periods", "abnormal entry and exit frequencies" or "behavioral habits of specific residents".

[0107] Machine learning algorithms can also dynamically update and optimize linkage features. As access control data continues to accumulate, the algorithm can adjust and improve the recognition accuracy of linkage features by continuously learning new data. For example, the system can adjust peak entry and exit times or optimize the trigger conditions of the security system based on the latest data, making community management more flexible and efficient.

[0108] Step 307: According to a predetermined rule, the linkage feature is combined with the configuration parameters corresponding to each linkage system to determine the target linkage data.

[0109] The system can generate precise linkage instructions based on the analyzed linkage features and the specific configuration requirements of each linkage system to ensure that the systems work in a coordinated manner. The predetermined rules can be based on the community's management strategy, the system's working logic, and the actual needs of the residents. For example, a rule may stipulate that when the access control system detects that a resident enters the community at night, the brightness of the lighting system should be adjusted to a medium level and automatically turned off within five minutes. At the same time, the rules can also adjust the trigger conditions of the security system based on the abnormal conditions of the access control data, such as automatically activating the camera and increasing the sensitivity of monitoring when an unauthorized person attempts to enter.

[0110] Once the linkage characteristics and configuration parameters of each system are determined, the system will generate target linkage data based on these rules. Target linkage data can be regarded as a specific set of instructions for the linkage system, covering information such as the actions that each system needs to perform, the time of execution, and the conditional triggering. For example, when a resident enters the community, the target linkage data may instruct the lighting system to light up the lights in the relevant area according to the configuration parameters, the elevator system automatically adjusts the elevator to the floor where the resident is located, and the security system remains in monitoring mode to ensure safety.

[0111] Step 308: According to a preset transmission rule, the target linkage data is sent to the linkage system, so that each linkage system performs corresponding actions according to the target linkage data.

[0112] Determine the linkage time corresponding to the linkage system based on the configuration parameters; determine the network environment corresponding to the target community; generate a transmission queue corresponding to the target linkage data based on the network environment and the linkage time; and send the target linkage data to each linkage system according to the transmission queue.

[0113] According to the preset transmission rules, sending the target linkage data to each linkage system is in the process of ensuring the linkage systems within the smart community. The system must not only ensure the accuracy and timeliness of data transmission, but also reasonably schedule the transmission order and priority of the data according to the specific requirements of each linkage system and the actual conditions of the network environment.

[0114] The system determines the linkage time of the linkage system based on the configuration parameters. The linkage time refers to the specific time point or time period when each system needs to perform its function. For different linkage systems, the linkage time may vary due to their different tasks. For example, the linkage time of the lighting system may only take a few seconds, while the security system may need to be monitored continuously throughout the night. Therefore, the system needs to arrange the transmission time of the target linkage data according to these different time requirements to ensure that each system can receive accurate data at the appropriate time point and perform corresponding operations.

[0115] The system will determine the network environment of the target community. The network environment includes network bandwidth, latency, signal strength, and device connectivity. For smart communities, data transmission usually relies on the local area network or wireless network within the community, so the stability of the network and the transmission rate will directly affect the response speed and overall efficiency of each linkage system. If the network environment is poor, delays or data loss may occur during the transmission process, which will affect the timely response of the linkage system. Therefore, the system needs to dynamically monitor the community's network status and make corresponding adjustments based on the actual network environment.

[0116] After determining the linkage time and network environment, the system will generate a transmission queue for the target linkage data based on this information. The transmission queue refers to the transmission order and priority of the target linkage data between different systems. For time-sensitive tasks, such as real-time response of the alarm system, the system will place the relevant data in a priority position in the queue to ensure that the data can be transmitted first. For less urgent tasks, such as the adjustment of the environmental control system, they can be arranged for transmission at a later time. By reasonably arranging the data transmission sequence, the system can effectively avoid data transmission congestion and maximize the use of network bandwidth resources.

[0117] When the linkage system receives the target linkage data, it parses the target linkage data according to the configuration parameters to determine the linkage instructions and execution conditions corresponding to the target linkage data; when the linkage system meets the execution conditions, it executes the target linkage data according to the linkage instructions to perform corresponding actions.

[0118] The linkage system will conduct in-depth analysis and processing of the received target linkage data according to its built-in configuration parameters. These configuration parameters usually contain information such as the linkage system's functions, operating conditions, and response rules, and are an important basis for ensuring that the linkage system can accurately execute instructions. By parsing the target linkage data, the system can determine the linkage instructions contained therein and the required execution conditions.

[0119] The linkage instructions are usually specific operation commands, for example, the lighting system may receive an instruction to turn on or adjust the brightness. The execution conditions refer to whether the linkage system meets specific prerequisites, such as whether it is a peak time at a certain moment, whether there are residents approaching the door, or whether the security system detects an abnormal situation. After parsing the data, the linkage system will further execute the corresponding operation instructions when it confirms that its environment meets these execution conditions.

[0120] This mechanism ensures the flexibility and intelligence of the linkage system. Each system will not blindly execute instructions, but will make the most reasonable response under the premise of meeting the execution conditions. For example, when the lighting system receives data that the resident is about to return home, the surrounding street lights will only be turned on if it detects that the resident is approaching the gate and there is insufficient light at night. If the system is in daytime or the resident is not approaching, it can choose to ignore the instruction to reduce energy consumption. This intelligent judgment based on execution conditions can not only improve the response speed of the system, but also avoid unnecessary waste of resources.

[0121] It can be seen from the above technical solutions that the beneficial effects of this embodiment are: by introducing data fusion algorithms and machine learning technologies, it is possible to make full use of current and historical access control data, conduct comprehensive analysis and processing of the data, and improve data utilization and analysis accuracy. In particular, by extracting and analyzing the features of the linkage system, the efficient collaboration of various systems in the community is ensured, and the fully automated management of the smart community is realized. In addition, based on the preset transmission rules and linkage conditions, it ensures that the linkage system can respond and execute in a timely manner after receiving the instructions, which greatly improves the level of intelligence in community management, reduces human intervention, and achieves efficient resource management and security.

[0122] like Figure 4 The figure shows a specific embodiment of a community access control data management device of the present application. This embodiment is a community access control data management device, that is, for executing Figures 1 to 3 A physical device of a method for managing community access control data. Its technical solution is essentially consistent with the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. In this embodiment, a management device for community access control data includes:

[0123] The current access control data determination module 401 is configured to determine the current access control data corresponding to the access control system using the status monitoring module corresponding to the target community; the target community includes the access control system and the linkage system;

[0124] The historical access control data determination module 402 is configured to determine the historical access control data corresponding to the access control system;

[0125] The comprehensive access control data determination module 403 is configured to fuse the current access control data and the historical access control data using a preset data fusion algorithm to obtain comprehensive access control data;

[0126] The target linkage data determination module 404 is configured to analyze and process the comprehensive access control data using a preset analysis model, so that the comprehensive access control data determines the target linkage data corresponding to each linkage system according to a predetermined rule;

[0127] The execution module 405 is configured to send the target linkage data to the linkage system according to a preset transmission rule, so that each linkage system performs corresponding actions according to the target linkage data.

[0128] Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the present application. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0129] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0130] The memory is used to store execution instructions. Specifically, the execution instructions are computer programs that can be executed. The memory may include internal memory and non-volatile memory, and provides execution instructions and data to the processor.

[0131] In one possible implementation, the processor reads the corresponding execution instructions from the non-volatile memory into the memory and then runs them, and can also obtain the corresponding execution instructions from other devices to form a community access control data management device at the logical level. The processor executes the execution instructions stored in the memory to implement a community access control data management method provided in any embodiment of the present application through the executed execution instructions.

[0132] The above application Figure 4The method performed by a management device for community access control data provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0133] The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0134] The embodiment of the present application also proposes a readable medium, which stores an execution instruction. When the stored execution instruction is executed by a processor of an electronic device, the electronic device can execute a community access control data management method provided in any embodiment of the present application, and is specifically used to execute the following: Figure 1 or Figure 2 or Figure 3 The method shown.

[0135] The electronic device in each of the aforementioned embodiments may be a computer.

[0136] Those skilled in the art should understand that the embodiments of the present application can be provided as methods or computer program products. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware.

[0137] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0138] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0139] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for managing community access control data, characterized in that: include: Determine the current access control data corresponding to the access control system by using the status monitoring module corresponding to the target community; The target community includes the access control system and linkage system; Determining historical access control data corresponding to the access control system; Using a preset data fusion algorithm, the current access control data and the historical access control data are fused to obtain comprehensive access control data; Analyzing and processing the comprehensive access control data using a preset analysis model, so that the comprehensive access control data can determine the target linkage data corresponding to each linkage system according to a predetermined rule; According to a preset transmission rule, the target linkage data is sent to the linkage system, so that each linkage system performs corresponding actions according to the target linkage data.

2. The method according to claim 1, characterized in that The using of a preset data fusion algorithm to fuse the current access control data with the historical access control data to obtain comprehensive access control data includes: Using a feature extraction algorithm, determine the current data features corresponding to the current access control data, and determine the historical data features corresponding to the historical access control data; According to a preset weight determination rule, determine the current data weight corresponding to the current data feature, and determine the historical data weight corresponding to the historical data feature; The current access control data and the historical access control data are fused using the current data weight and the historical data weight to determine the comprehensive access control data.

3. The method according to claim 2, characterized in that The determining, according to a preset weight determination rule, the current data weight corresponding to the current data feature, and determining the historical data weight corresponding to the historical data feature includes: Determine a preset time weight table; Based on the time weight table, the current data weight corresponding to the current data feature is determined, and the historical data weight corresponding to the historical data feature is determined.

4. The method according to claim 2, characterized in that: The using the current data weight and the historical data weight to fuse the current access control data and the historical access control data to determine the comprehensive access control data includes: Performing weighted processing on the current access control data using the current data weight to determine first weighted data; Performing weighted processing on the historical access control data using the historical data weight to determine second weighted data; The first weighted data and the second weighted data are added to determine the comprehensive access control data.

5. The method according to claim 1, characterized in that The method of analyzing and processing the comprehensive access control data by using a preset analysis model so that the comprehensive access control data determines the target linkage data corresponding to each linkage system according to a predetermined rule includes: Determining linkage features corresponding to each of the linkage systems; Analyzing the linkage feature from the comprehensive access control data using a machine learning algorithm; According to the predetermined rule, the linkage feature is combined with the configuration parameters corresponding to each of the linkage systems to determine the target linkage data.

6. The method according to claim 5, characterized in that The step of sending the target linkage data to the linkage system according to a preset transmission rule includes: Determine a linkage time corresponding to the linkage system based on the configuration parameters; Determining a network environment corresponding to the target community; Based on the network environment and linkage time, generating a transmission queue corresponding to the target linkage data; The target linkage data is sent to each linkage system according to the transmission queue.

7. The method according to claim 6, characterized in that The method of causing each linkage system to perform corresponding actions according to the target linkage data includes: When the linkage system receives the target linkage data, the linkage system parses the target linkage data according to the configuration parameters to determine the linkage instruction and execution condition corresponding to the target linkage data; When the linkage system meets the execution condition, the target linkage data is executed according to the linkage instruction to perform corresponding actions.

8. A management device for community access control data, characterized in that: include: A current access control data determination module is configured to determine the current access control data corresponding to the access control system using a status monitoring module corresponding to the target community; The target community includes the access control system and linkage system; A historical access control data determination module, configured to determine historical access control data corresponding to the access control system; a comprehensive access control data determination module, configured to fuse the current access control data and the historical access control data using a preset data fusion algorithm to obtain comprehensive access control data; A target linkage data determination module is configured to analyze and process the comprehensive access control data using a preset analysis model, so that the comprehensive access control data determines the target linkage data corresponding to each linkage system according to a predetermined rule; The execution module is configured to send the target linkage data to the linkage system according to a preset transmission rule, so that each linkage system performs corresponding actions according to the target linkage data.

9. A computer-readable storage medium storing a computer program, wherein the computer program is used to execute a method for managing community access control data as described in any one of claims 1 to 7.

10. An electronic device, comprising: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the instructions to implement a community access control data management method as described in any one of claims 1-7.

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