Intelligent building access control automatic control system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent entrance guard automatic control, and discloses a building intelligent entrance guard automatic control system based on Internet of Things, which comprises the following steps: executing a peer judgment strategy, judging whether a resident i and a resident j pass through an entrance guard in peer, and forming a group by the residents passing through the entrance guard in peer; executing a group passing time calculation strategy, calculating the time required by the group where the first passing person is located to pass through the access control, and recording the time as group passing time; executing a real-time dynamic adjustment strategy, calculating the total number of residents of the group where the first pass is located, and dynamically adjusting the passing time of the group according to the actual number of people; executing an association overlapping detection strategy, and detecting whether there are unfamiliar residents in the group; when detecting that the unfamiliar residents exist in the group, executing a track analysis strategy, analyzing the motion tracks of the unfamiliar residents after passing through the access control, and re-confirming whether the unfamiliar residents exist in the group; when strange residents are confirmed to appear in the group, an early warning strategy is executed, and intelligent management of access control is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent access control automatic control, and in particular to an Internet of Things-based building intelligent access control automatic control system. Background Art

[0002] Smart access control systems are a key application of the Internet of Things (IoT). As people's demands for quality of life and safety continue to rise, traditional access control systems are no longer able to meet the demands of modern building management. By integrating sensors, facial recognition, fingerprint recognition, and card swiping technologies, smart access control systems enable real-time monitoring and management of access. First, they enhance security by detecting and recording the information of all entrants and exiters in real time, minimizing the risk of unauthorized entry. Second, through data analysis, smart access control systems intelligently determine resident access status, reducing false alarms and missed alerts. Furthermore, the system features remote control and automated management capabilities, allowing residents or managers to monitor access status anytime, anywhere, enabling unmanned intelligent management.

[0003] Many existing systems lack intelligent judgment of group traffic behavior, especially during peak hours, when multiple people pass through the access control system at the same time. The system is often unable to adjust the response time in real time, resulting in low or unstable traffic efficiency. Secondly, most traditional smart access control systems have certain blind spots in identifying unfamiliar residents, especially without the support of sufficient facial recognition or dynamic detection technology. Strange residents may be mistaken for normal residents, which in turn poses a safety hazard. In addition, existing smart access control systems are relatively simple in terms of dynamically adjusting the response time. They often adopt a fixed time opening strategy and cannot be flexibly adjusted according to the traffic conditions of residents or changes in the number of people. As a result, the access control opening time is too long or too short, affecting the user experience. In general, traditional smart access control systems lack sufficient intelligence and flexibility to meet increasingly complex security needs and efficient traffic management.

[0004] This solution proposes a building intelligent access control automatic control system based on Internet of Things technology, which solves the shortcomings of the existing access control system and provides a more intelligent and efficient access management method. Summary of the Invention

[0005] The present invention provides a building intelligent access control automatic control system based on the Internet of Things, which is used to promote the solution of the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solutions: an Internet of Things-based building intelligent access control system. When a resident passes through the building access control: Get the timestamp of resident i passing the access control at time t ; Get the timestamp of resident j passing the access control at time t ; Execute the peer judgment strategy to determine whether residents i and j pass through the access control together, and form a group of residents who pass through the access control together; The first resident in the group to pass through the gate is recorded as the first passer; Execute the group travel time calculation strategy and calculate the time required for the first passer-by in the group to pass through the access control, which is recorded as the group travel time; Get the actual number of people who passed through the access control at time t ; Set the delay time, which is the time it takes for the access control system to respond and adjust the door opening time; Implement real-time dynamic adjustment strategies, calculate the total number of households in the group where the first passer-by belongs, and dynamically adjust the group's travel time based on the actual number of people; When the first passer is identified and enters the access control system, the access control system sets the door opening time to the group passage time; Implement the association overlap detection strategy to detect whether there are unfamiliar residents in the group; When a stranger is detected in the group, the trajectory analysis strategy is executed to analyze the movement trajectory of the stranger after passing through the access control system, and to confirm whether there is a stranger in the group again; When it is confirmed that there are unfamiliar residents in the group, the early warning strategy is implemented and an early warning is issued.

[0007] Preferably, the executing of the same-person judgment strategy to judge whether the residents i and j pass through the door together and group the residents who pass through the door together includes: Select any point on the ground as the origin to establish a two-dimensional coordinate system: Get the resident i at time spatial location ; Get the resident j at time spatial location ; Set time window ; Compute time-weighted Euclidean distance : , where t is the current time, is the attenuation factor, For the past Time has come The time point of the moment, Used to control the influence of each time point on the time-weighted Euclidean distance; Calculate the time interval between resident i and resident j passing through the access control ; Setting time interval thresholds and distance threshold ; like and , then it is determined that resident i and resident j pass through the access control together; Group residents who pass through the gate together ; , represents the number of all fellow residents of household i at time t.

[0008] Through the same-person judgment strategy, the system can accurately identify whether multiple residents are passing through at the same time, thereby optimizing the access control response speed, reducing unnecessary access control switch operations, and improving the system's intelligence level. At the same time, this strategy effectively improves the residents' passage experience and avoids the overall passage efficiency being affected by individual residents' brief stops or slow walking. In addition, through the calculation of time-weighted Euclidean distance, this strategy can accurately determine whether residents are traveling together. Even if there is a certain time difference or slight path deviation when the residents pass through, the group can still be accurately identified. The criteria for judging the same-person relationship are based on the dual judgment of time and spatial location, allowing the system to maintain a high level of recognition accuracy even in the face of short time lags or slight spatial deviations. In addition, this strategy helps to improve security because, through the same-person judgment, residents who are not passing alone can be identified, providing basic data support for subsequent adjustments to the group passage time and detection of unfamiliar residents.

[0009] Preferably, the execution of the group passage time calculation strategy to calculate the time required for the first passer-by in the group to pass through the gate includes: Setting indicator function ,in, are all groups that enter the access control at time t; Calculate the total number of times household i is the first passer-by : , where T is the total number of historical moments; Calculate the total time it takes for resident i, the first passer-by in the group, to pass through the access control at each historical moment : ,in, The last resident in the group to pass through the gate; Calculate the group passage time of resident i as the first passer-by in the group passing through the access control ; .

[0010] Through the group travel time calculation strategy, the system can dynamically analyze group travel time to ensure that the access control response speed matches the actual travel demand, thereby improving travel efficiency. This strategy uses historical data for calculation, allowing the access control to make intelligent predictions based on past travel data, reducing congestion or safety hazards caused by travel times that are too short or too long. In addition, the strategy uses mathematical models to reasonably calculate group travel time to ensure that groups of different sizes can pass through the access control smoothly, without the access control being closed prematurely or opened for too long due to unreasonable time settings, thereby preventing unauthorized personnel from taking the opportunity to enter. This strategy can also improve the flexibility and intelligence of the access control system, enabling it to adapt to changes in different building environments and residents' travel habits, and improve the overall travel experience and safety.

[0011] Preferably, the real-time dynamic adjustment strategy is executed to calculate the total number of households in the group where the first passerby belongs, and dynamically adjust the group passage time according to the actual number of people, including: Calculate the total number of households in the group where household i is the first passerby : ; Calculate the adjustment time for group travel based on the actual number of people and the total number of households ; ,in, is the dynamic adjustment coefficient, is the benchmark adjustment factor, is the time period adjustment factor; Set access efficiency of access control ; Set the delay time to indicate access control response efficiency ; Update delay time based on traffic efficiency , is the adjustment factor; Adjust group travel time: .

[0012] By dynamically adjusting the strategy in real time, the system can dynamically optimize the passage time based on the actual number of people in the group, ensuring that the access control opening time is neither too long nor too short, thereby improving access control efficiency. In addition, by comprehensively considering factors such as passage efficiency, access control response time, and dynamic adjustment coefficient, the strategy enables the system to make intelligent adjustments in different situations, improving flexibility and adaptability. Especially in cases where there are a large number of residents or the access control is used frequently, this strategy can effectively avoid residents being unable to pass smoothly due to insufficient access control opening time, or safety hazards caused by excessive opening time. This strategy can also optimize the access control response speed based on the continuous adaptation to residents' passage habits, reduce the inconvenience caused by long waiting times, and thus improve the user experience.

[0013] Preferably, the execution of the association overlap detection strategy to detect whether there are unfamiliar residents in the group includes: Extract facial recognition information of resident i ; The group The facial recognition information of each resident in ; Determine whether there are unfamiliar residents in the group ; ,in, For unfamiliar residents; Set the overlap between the figures of resident i and resident j passing through the access control ; Get the occupied area of household i in the space ; Get the occupied area of host j in space ; calculate ,in, is the overlapping area between households i and j, is the total area of the two households i and j after merging; Set overlap threshold ; like and There are unfamiliar residents in the group.

[0014] By associating overlapping detection strategies, the system can accurately identify whether there are unfamiliar residents in a group of people traveling together, thereby improving the security of the access control system. This strategy uses facial recognition information and body overlap calculations to effectively detect unauthorized personnel. Even if an unfamiliar resident attempts to enter the building by walking with a legitimate resident, they can still be identified by the system. In addition, this strategy combines space occupancy calculations, so that even if an unfamiliar resident walks with a legitimate resident but is not accurately captured by the facial recognition system, they can still be detected through abnormal values of body overlap, further improving the accuracy of recognition. This strategy also enhances the system's ability to prevent illegal intrusions, preventing strangers from following residents into the building, and improving the safety and protection level of the building.

[0015] Preferably, when detecting the presence of a strange resident in the group, executing a trajectory analysis strategy to analyze the movement trajectory of the strange resident after passing through the access control, and reconfirming whether there is a strange resident in the group, including: Get the movement status of the stranger at time t after passing the access control , ,in, For location, for speed; Get the acceleration of the stranger at time t ; Calculate the position at time t+1 and speed ; ; ,in, is the time step; The location of the stranger at time t+1 is calculated as and speed is Probability : ,in, Indicates that the location of the stranger at time t+1 is and speed is The prior probability of is the transition probability of the stranger's motion state from time t to time t+1, The acceleration at time t+1 is probability; Setting probability thresholds , get Location ; Get the location of household i in the group at time t+1 ; Calculate the separation distance ; Setting trajectory deviation threshold ; like , it is determined that there are unfamiliar residents in the group.

[0016] Using a trajectory analysis strategy, the system can further track the movement of strangers after detecting them, ensuring accurate identification results. This approach not only mitigates potential misidentification issues with single-shot facial recognition but also reaffirms the identity of strangers through trajectory deviation calculation. By analyzing the movement of strangers, including changes in position, velocity, and acceleration, this strategy enables a more comprehensive assessment of their movement patterns, avoiding unnecessary security alerts caused by misidentification. Furthermore, this strategy employs probabilistic calculations to predict the movement of strangers over multiple time steps, enabling the system to proactively identify potential anomalous behavior and enhance its ability to detect security threats.

[0017] Preferably, when a strange resident is confirmed to be present in the group, executing a warning strategy and issuing a warning includes: Set warning levels, which are low risk, medium risk and high risk: like , the warning level is low risk, and the facial recognition information of unfamiliar residents is recorded; like , then the warning level is medium risk, and the facial recognition information of the unfamiliar resident will be sent to the administrator; like , then the warning level is high risk and the alarm is immediately issued.

[0018] Through early warning strategies, the system can take appropriate measures based on the risk level of unfamiliar residents, improving the intelligent level of security prevention. This strategy categorizes unfamiliar residents according to their risk level, allowing the system to take appropriate responses to different situations. For example, in low-risk situations, only the unfamiliar resident's facial information is recorded, while in medium- and high-risk situations, this information is sent to the administrator or an alarm is directly triggered, ensuring that security threats are responded to promptly. This hierarchical management approach of the strategy avoids unnecessary alarms caused by misjudgments, while ensuring that swift action can be taken when real security threats occur. In this way, the system not only enhances the security of building access control, but also improves management efficiency, allowing residents to live in a safer environment.

[0019] The present invention has the following beneficial effects: This IoT-based intelligent building access control system enhances access control intelligence, enabling automated management, reducing manual intervention, and improving access efficiency. The system accurately captures resident entry and exit times and, based on timestamps, implements a group identification strategy to identify whether residents are traveling together, further enhancing access control management. Furthermore, the system automatically identifies the first person to enter a group and calculates the group's access time. This ensures that access control opening times are tailored to the needs of different groups, preventing occupants from being blocked due to short access times or compromising access security due to long access times. Furthermore, by monitoring the actual number of people passing through the access control system, the system can dynamically adjust group access times to better reflect actual conditions and improve access efficiency. The system also detects unfamiliar residents, using trajectory analysis to determine whether they are suspicious individuals. This triggers an early warning mechanism when anomalies are detected, enhancing building security. Overall, the system not only optimizes the convenience of access control management, but also improves the flexibility and safety of passage, providing residents with a more efficient and safe entry and exit experience.

[0020] 2. This IoT-based intelligent building access control system uses a peer-to-peer identification strategy to accurately identify whether multiple residents are traveling together, effectively improving access control efficiency. This strategy calculates the spatial location of residents based on a two-dimensional coordinate system and, combined with time-weighted Euclidean distance, accurately measures the changing distance trends between residents, ensuring the reliability of the identification results. Setting time windows and thresholds ensures the system's adaptability to different access scenarios, thereby improving access control's adaptability to various situations. Furthermore, this strategy can quickly identify and group residents traveling together into groups, facilitating subsequent adjustments to access time and improving overall access efficiency. By applying a time-weighted factor, the system effectively reduces noise interference, improves calculation accuracy, and makes peer identification more accurate. This strategy not only optimizes the access control system's intelligence level but also reduces the number of invalid access control switches, improving the system's energy efficiency, and mitigating security risks.

[0021] 3. This IoT-based intelligent building access control system utilizes a group travel time calculation strategy. Based on the historical travel data of the first passer, the system accurately calculates the time required for a group to pass through the access control system. This optimizes the access control opening time, preventing residents from being unable to pass smoothly due to a short time limit or compromising access control security due to a long time limit. This strategy uses an indicator function to define all groups entering the access control system and counts the number of times a resident has passed through the system as the first passer. This makes the calculation more statistically significant, effectively reducing single-time errors and improving the accuracy of time predictions. The system also calculates the travel time of the last resident in a group to ensure that the group travel time meets the needs of all members, enhancing the access experience. Furthermore, this strategy optimizes the access control system's response speed, making the access control opening time more consistent with actual conditions, thereby reducing unnecessary waiting time and improving resident convenience.

[0022] 4. This IoT-based intelligent building access control system uses real-time dynamic adjustment strategies to dynamically adjust access control opening times based on the actual number of residents in a group, ensuring they better meet actual access needs and improve access efficiency. This strategy calculates the adjustment time for group access times based on the total number of residents in the group of the first passerby, and introduces a dynamic adjustment coefficient to make adjustments more precise and flexible. By setting access control access efficiency and response efficiency, the system optimizes the overall operating status of the access control system, reducing unnecessary energy consumption while increasing access speed. Furthermore, the strategy can set different adjustment parameters based on different time periods to adapt to different building usage patterns, improving the system's adaptability and intelligence. The implementation of this strategy makes access control management more intelligent and efficient, automatically adapting to the access conditions of residents of different sizes and time periods, thereby improving the overall quality of building management.

[0023] 5. This IoT-based building intelligent access control system utilizes an overlapping detection strategy to effectively identify unfamiliar residents within a group, enhancing access control security. This strategy utilizes facial recognition technology to verify resident identities and, by calculating body overlap, detects any strangers attempting to impersonate a member of a group, thereby reducing security vulnerabilities. Furthermore, the system calculates occupied area and incorporates an overlap threshold to ensure detection accuracy and reduce false positives. This strategy not only prevents strangers from following into access control areas, improving building security, but also reduces the impact of false positives on residents, optimizing the user experience. Overall, this strategy provides additional security protection for intelligent access control, effectively reducing security risks and providing residents with more reliable access and exit security.

[0024] 6. This IoT-based intelligent building access control system uses a trajectory analysis strategy to further confirm the identity of unfamiliar residents, enhancing access control security. This strategy analyzes the movement trajectory of unfamiliar residents, calculates their position, velocity, and acceleration, and determines their behavior patterns to ensure accurate detection. By introducing prior probabilities and trajectory deviation thresholds, the system can filter out false positives and focus on identifying suspicious individuals, improving the system's intelligence. Furthermore, this strategy can dynamically track the movement paths of unfamiliar residents, making security management more comprehensive and enhancing the access control system's risk prevention and control capabilities. Overall, this strategy provides an additional layer of security for the access control system, effectively reducing unauthorized intrusions and improving the overall security of building management.

[0025] 7. This IoT-based intelligent building access control system uses an early warning strategy to ensure building safety by taking timely action upon detecting unfamiliar residents. This strategy employs different response measures based on risk levels, such as recording the unfamiliar resident's information, notifying administrators, or even directly alerting the police, thereby minimizing potential safety hazards. Furthermore, this strategy dynamically adjusts risk thresholds to ensure appropriate security management solutions in diverse environments, improving the system's adaptability. Through the implementation of this strategy, the access control system can more accurately respond to potential risks, providing residents with a safer living environment while reducing unnecessary false alarms and optimizing the accuracy of security management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the process of the present invention.

[0027] Figure 2 The figure is a flow chart of calculating the group travel time according to the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] Example 1, refer to Figure 1 , an Internet of Things-based building intelligent access control automatic control system, including: When a resident passes through the building access control: Get the timestamp of resident i passing the access control at time t ; Get the timestamp of resident j passing the access control at time t ; Execute the peer judgment strategy to determine whether residents i and j pass through the access control together, and form a group of residents who pass through the access control together; The first resident in the group to pass through the gate is recorded as the first passer; Execute the group travel time calculation strategy and calculate the time required for the first passer-by in the group to pass through the access control, which is recorded as the group travel time; Get the actual number of people who passed through the access control at time t ; Set the delay time, which is the time it takes for the access control system to respond and adjust the door opening time; Implement real-time dynamic adjustment strategies, calculate the total number of households in the group where the first passer-by belongs, and dynamically adjust the group's travel time based on the actual number of people; When the first passer is identified and enters the access control system, the access control system sets the door opening time to the group passage time; Implement the association overlap detection strategy to detect whether there are unfamiliar residents in the group; When a stranger is detected in the group, the trajectory analysis strategy is executed to analyze the movement trajectory of the stranger after passing through the access control system, and to confirm whether there is a stranger in the group again; When it is confirmed that there are unfamiliar residents in the group, the early warning strategy is implemented and an early warning is issued.

[0030] The design of an IoT-based intelligent building access control system effectively enhances the intelligence and security of access control management. First, the system captures timestamps of residents' access through the access control system, enabling real-time monitoring and analysis of resident access behavior. By implementing a group identification strategy, it accurately determines whether residents enter together at the same time, ensuring accurate identification of residents within a group. This precise identification helps improve access control efficiency and security, avoiding the inconvenience caused by misidentification or delays. The calculation and dynamic adjustment of group access time further optimizes access control response time. By adjusting access time in real time based on changes in the number of residents passing through, it effectively avoids excessive or short delays, making the system more flexible and efficient. Furthermore, the detection and early warning strategy for unfamiliar residents, combining trajectory analysis with facial recognition technology, ensures the timely identification of potential security threats, providing residents with a safer and more intelligent living environment.

[0031] The execution of the same-person judgment strategy to judge whether resident i and resident j pass through the door together and form the residents who pass through the door together into a group includes: Select any point on the ground as the origin to establish a two-dimensional coordinate system: Get the resident i at time spatial location ; Get the resident j at time spatial location ; Set time window ; Compute time-weighted Euclidean distance : , where t is the current time, is the attenuation factor, For the past Time has come The time point of the moment, Used to control the influence of each time point on the time-weighted Euclidean distance; Calculate the time interval between resident i and resident j passing through the access control ; Setting time interval thresholds and distance threshold ; like and , then it is determined that resident i and resident j pass through the access control together; Group residents who pass through the gate together ; , represents the number of all fellow residents of household i at time t.

[0032] By introducing a time-weighted Euclidean distance strategy, it's possible to more accurately determine whether residents are traveling together. This approach, combining spatial location, time windows, and a time-weighted mechanism, enables precise calculation of resident movement behavior. By setting appropriate time interval and distance thresholds, the system can determine in real time whether residents i and j are traveling together through the access control system at the same time. This allows for not only temporal factors but also spatial factors to be considered, further improving the accuracy and reliability of judgments. This refined judgment method allows for more accurate group identification, avoiding misjudgments or missed detections, and thus optimizing the management effectiveness of the access control system.

[0033] The group travel time calculation strategy is executed to calculate the time required for the first passer-by in the group to pass through the access control, including: Setting indicator function ,in, are all groups that enter the access control at time t; Calculate the total number of times household i is the first passer-by : , where T is the total number of historical moments; Calculate the total time it takes for resident i, the first passer-by in the group, to pass through the access control at each historical moment : ,in, The last resident in the group to pass through the gate; Calculate the group passage time of resident i as the first passer-by in the group passing through the access control ; .

[0034] Through the group passage time calculation strategy, the system can more accurately predict and adjust the access control response time. By calculating historical data and group passage time, the system can derive the behavioral patterns of residents when they are the first passers-by. This calculation method based on historical data can provide reliable data support for dynamic adjustments during the actual passage process, thereby improving the response speed and efficiency of the access control. The group passage time when the resident is the first passer-by can help the system predict the time it takes for the entire group to pass through the access control, effectively avoiding the access control response being too fast or too slow due to too many or too few people. Through these dynamic adjustment strategies, the system can provide each resident with a smoother and smarter passage experience, reducing waiting time and improving the overall user experience.

[0035] The real-time dynamic adjustment strategy is implemented to calculate the total number of households in the group where the first passerby is located, and dynamically adjust the group passage time based on the actual number of people, including: Calculate the total number of households in the group where household i is the first passerby : ; Calculate the adjustment time for group travel based on the actual number of people and the total number of households ; ,in, is the dynamic adjustment coefficient, is the benchmark adjustment factor, is the time period adjustment factor; Set access efficiency of access control ; Set the delay time to indicate access control response efficiency ; Update delay time based on traffic efficiency , is the adjustment factor; In this embodiment, the flowchart reference for adjusting the group passage time is Figure 2 : .

[0036] Real-time dynamic adjustment strategies help automatically adjust the access control response time under different numbers of people passing through, thereby improving access efficiency. By dynamically adjusting the group passage time, the system can accurately adjust the access control opening time based on real-time data. The number of residents and the size of the group will directly affect the access control efficiency. Dynamic adjustment can optimize the response time based on the actual number of people and access efficiency, making the access control work more flexible and efficient. At the same time, by introducing a delay time adjustment mechanism, detailed adjustments can be made based on the access control response efficiency, avoiding the problem of slow or too fast response due to rigid settings. Overall, this strategy effectively improves the adaptability of the access control and optimizes the user experience of residents.

[0037] The execution of the association overlap detection strategy to detect whether there are unfamiliar residents in the group includes: Extract facial recognition information of resident i ; The group The facial recognition information of each resident in ; Determine whether there are unfamiliar residents in the group ; ,in, For unfamiliar residents; Set the overlap between the figures of resident i and resident j passing through the access control ; Get the occupied area of household i in the space ; Get the occupied area of host j in space ; calculate ,in, is the overlapping area between households i and j, is the total area of the two households i and j after merging; Set overlap threshold ; like and There are unfamiliar residents in the group.

[0038] Facial recognition and body overlap analysis can effectively identify and eliminate potential threats from unfamiliar residents. During group traffic, the presence of unfamiliar residents can pose a safety hazard. By incorporating facial recognition technology and body overlap calculation, the system can monitor each resident's identity in real time and identify the presence of strangers based on the overlap. If an unfamiliar resident appears in a group, the system can accurately detect them through facial information and overlap area analysis, preventing unauthorized entry. This mechanism significantly improves the security of the access control system and reduces the risk of human negligence. Furthermore, the technology's real-time and high accuracy ensure that residents enjoy convenient access while ensuring their safety.

[0039] When a stranger is detected in the group, a trajectory analysis strategy is executed to analyze the movement trajectory of the stranger after passing through the access control system, and to confirm whether there is a stranger in the group again, including: Get the movement status of the stranger at time t after passing the access control , ,in, For location, for speed; Get the acceleration of the stranger at time t ; Calculate the position at time t+1 and speed ; ; ,in, is the time step; The location of the stranger at time t+1 is calculated as and speed is Probability : ,in, Indicates that the location of the stranger at time t+1 is and speed is The prior probability of is the transition probability of the stranger's motion state from time t to time t+1, The acceleration at time t+1 is probability; Setting probability thresholds , get Location ; Get the location of household i in the group at time t+1 ; Calculate the separation distance ; Setting trajectory deviation threshold ; like , it is determined that there are unfamiliar residents in the group.

[0040] The trajectory analysis strategy further improves the accuracy and response speed of identifying unfamiliar residents by tracking their movements in real time. By analyzing the movement of unfamiliar residents behind the access control system in real time, the system can gain a more comprehensive understanding of their behavior patterns and further confirm their presence. This strategy not only enhances security protection but also effectively reduces false alarms. By tracking and analyzing motion data such as position, velocity, and acceleration, the system can accurately predict the behavior of unfamiliar residents, further improving its ability to identify potential threats. Furthermore, the system dynamically adjusts its analysis results based on real-time motion changes, ensuring the safety of every resident.

[0041] When it is confirmed that a strange resident appears in the group, the early warning strategy is executed to issue an early warning, including: Set warning levels, which are low risk, medium risk and high risk: like , the warning level is low risk, and the facial recognition information of unfamiliar residents is recorded; like , then the warning level is medium risk, and the facial recognition information of the unfamiliar resident will be sent to the administrator; like , then the warning level is high risk and the alarm is immediately issued.

[0042] By introducing early warning strategies, the system can promptly issue alerts when unfamiliar residents are detected, enhancing safety. When an unfamiliar resident is identified in a group, the system issues an alert based on the risk level and implements different response measures depending on the risk level. This hierarchical early warning mechanism ensures system flexibility while also enabling a responsive response tailored to the actual threat level. Facial information is recorded for low-risk situations, administrators are notified for medium-risk situations, and an immediate alarm is triggered for high-risk situations, effectively avoiding safety hazards caused by slow or overreaction. This precise and hierarchical early warning mechanism provides a solid foundation for building security management.

[0043] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0044] The above is only 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 technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A building intelligent access control system based on the Internet of Things, characterized in that: include: When a resident passes through the building access control: Get the timestamp of resident i passing the access control at time t ; Get the timestamp of resident j passing the access control at time t ; Execute the peer judgment strategy to determine whether residents i and j pass through the access control together, and form a group of residents who pass through the access control together; The first resident in the group to pass through the gate is recorded as the first passer; Execute the group travel time calculation strategy and calculate the time required for the first passer-by in the group to pass through the access control, which is recorded as the group travel time. Get the actual number of people who passed the access control at time t ; Set the delay time, which is the time it takes for the access control system to respond and adjust the door opening time; Implement real-time dynamic adjustment strategies, calculate the total number of households in the group where the first passer-by belongs, and dynamically adjust the group's travel time based on the actual number of people; When the first passer is identified and enters the access control system, the access control system sets the door opening time to the group passage time; Implement the association overlap detection strategy to detect whether there are unfamiliar residents in the group; When a stranger is detected in the group, the trajectory analysis strategy is executed to analyze the movement trajectory of the stranger after passing through the access control system, and to confirm whether there is a stranger in the group again; When it is confirmed that there are unfamiliar residents in the group, the early warning strategy is implemented and an early warning is issued.

2. The building intelligent access control automatic control system based on the Internet of Things according to claim 1 is characterized in that: The execution of the same-person judgment strategy to judge whether resident i and resident j pass through the door together and form the residents who pass through the door together into a group includes: Select any point on the ground as the origin to establish a two-dimensional coordinate system: Get the resident i at time spatial location ; Get the resident j at time spatial location ; Set time window ; Compute time-weighted Euclidean distance : , where t is the current time, is the attenuation factor, For the past Time has come The time point of the moment, Used to control the influence of each time point on the time-weighted Euclidean distance; Calculate the time interval between resident i and resident j passing through the access control ; Setting time interval thresholds and distance threshold ; like and , then it is determined that resident i and resident j pass through the access control together; Group residents who pass through the gate together ; , represents the number of all fellow residents of household i at time t.

3. The building intelligent access control automatic control system based on the Internet of Things according to claim 2 is characterized in that: The group travel time calculation strategy is executed to calculate the time required for the first passer-by in the group to pass through the access control, including: Setting indicator function ,in, is the group that enters the access control at time t; Calculate the total number of times household i is the first passer-by : , where T is the total number of historical moments; Calculate the total time it takes for resident i, the first passer-by in the group, to pass through the access control at each historical moment : ,in, The last resident in the group to pass through the gate; Calculate the group passage time of resident i as the first passer-by in the group passing through the access control ; 。 4. The building intelligent access control system based on the Internet of Things according to claim 3 is characterized in that: The real-time dynamic adjustment strategy is implemented to calculate the total number of households in the group where the first passerby is located, and dynamically adjust the group passage time based on the actual number of people, including: Calculate the total number of households in the group where household i is the first passerby : ； Calculate the adjustment time for group travel based on the actual number of people and the total number of households ; ,in, is the dynamic adjustment coefficient, is the benchmark adjustment factor, is the time period adjustment factor; Set access efficiency of access control ; Update delay time based on traffic efficiency , is the adjustment factor; Adjust group travel time: .

5. The building intelligent access control system based on the Internet of Things according to claim 3 is characterized in that: The execution of the association overlap detection strategy to detect whether there are unfamiliar residents in the group includes: Extract facial recognition information of resident i ; The group The facial recognition information of each resident in ; Determine whether there are unfamiliar residents in the group ; ,in, For unfamiliar residents; Set the overlap between the figures of resident i and resident j passing through the access control ; Get the occupied area of household i in the space ; Get the occupied area of host j in space ; calculate ,in, is the overlapping area between households i and j, is the total area of the two households i and j after merging; Set overlap threshold ; like and There are unfamiliar residents in the group.

6. The building intelligent access control system based on the Internet of Things according to claim 4 is characterized in that: When a stranger is detected in the group, a trajectory analysis strategy is executed to analyze the movement trajectory of the stranger after passing through the access control system, and to confirm whether there is a stranger in the group again, including: Get the movement status of the stranger at time t after passing the access control , ,in, For location, for speed; Get the acceleration of the stranger at time t ; Calculate the position at time t+1 and speed ; ； ,in, is the time step; The location of the stranger at time t+1 is calculated as and speed is Probability : ,in, Indicates that the location of the stranger at time t+1 is and speed is The prior probability of is the transition probability of the stranger's motion state from time t to time t+1, The acceleration at time t+1 is probability; Setting probability thresholds , get Location ; Get the location of household i in the group at time t+1 ; Calculate the separation distance ; Setting trajectory deviation threshold ; like , it is determined that there are unfamiliar residents in the group.

7. The building intelligent access control system based on the Internet of Things according to claim 6 is characterized in that: When it is confirmed that a strange resident appears in the group, the early warning strategy is executed to issue an early warning, including: Set warning levels, which are low risk, medium risk and high risk: like , the warning level is low risk, and the facial recognition information of unfamiliar residents is recorded; like , then the warning level is medium risk, and the facial recognition information of the unfamiliar resident will be sent to the administrator; like , then the warning level is high risk and the alarm is immediately issued.

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