Smart park access control management method and system based on Internet of Things

Abstract

The embodiment of the invention provides a smart park access control management method and system based on Internet of Things. Dynamic environment parameters of a target area in a park and identity verification data of a target object are collected in real time through Internet of Things equipment. Firstly, collected dynamic environment parameters are matched with a preset safety threshold range in real time, and an environment safety assessment result is generated. And then, extracting an identity feature vector of the target object based on the identity verification data, and performing multi-dimensional security verification in combination with an environmental security assessment result. When the multi-dimensional safety verification is passed, a dynamic access control strategy is intelligently generated according to the historical passing record of the target object and the current environment parameters. Finally, the dynamic access control strategy is issued to the target access control terminal through the cloud server, and intelligent control over the access permission and the physical locking state of the access control terminal is achieved. According to the invention, the safety management level of the smart park is improved, and the intelligence and high efficiency of access control management are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of the Internet of Things, and more particularly, to an intelligent park access control management method and system based on the Internet of Things. Background Art

[0002] With the continuous expansion of the scale of the park and the increasing demand for intelligent management, the security management of the park faces many challenges. The traditional access control system is only based on a single authentication method, such as simple card recognition or password verification. This method has many security vulnerabilities and is easy to be misused or cracked. At the same time, the traditional access control system lacks comprehensive consideration of the surrounding environment and cannot adjust the access control strategy according to the dynamic changes of the environment.

[0003] In the modern park environment, environmental factors such as temperature, humidity, and smoke concentration may have a significant impact on park security. For example, abnormal temperature fluctuations may indicate fire hazards or equipment failures, and a sudden increase in smoke concentration is a direct signal of a fire. However, the traditional access control system cannot combine these environmental factors with access control and cannot respond in a timely manner to environmental changes.

[0004] In addition, the authentication of target objects also needs to be more accurate and multi-dimensional. Relying solely on single biometric recognition or electronic identity credentials can no longer meet the security requirements because various forgery technologies are constantly developing. Multimodal biometric recognition and identity credential verification combined with blockchain technology can greatly improve the accuracy and security of identity verification.

[0005] Furthermore, with the development of the Internet of Things technology, a large number of Internet of Things devices in the park can provide rich data sources. However, how to integrate these data and conduct effective security management is an urgent problem to be solved. In the case of unstable network communication, such as the access control terminal being offline or having communication delays, how to ensure the normal operation of the access control system is also an important aspect to be considered. Finally, the traditional access control system lacks effective analysis and utilization of historical data and cannot mine potential security risks from historical data and give early warnings. Summary of the Invention

[0006] In view of this, the purpose of the present application is to provide an intelligent park access control management method and system based on the Internet of Things.

[0007] Combined with the first aspect of the present application, there is provided an intelligent park access control management method based on the Internet of Things, which is applied to an intelligent park access control management system based on the Internet of Things. The method includes:

[0008] Collecting dynamic environmental parameters of a target area and identity verification data of a target object through Internet of Things devices deployed in the park;

[0009] Match the dynamic environment parameters with a preset safety threshold range in real time to generate an environmental safety assessment result;

[0010] Extract the identity feature vector of the target object based on the authentication data, and perform multi-dimensional security verification in combination with the environmental safety assessment result;

[0011] When the multi-dimensional security verification passes, generate a dynamic access control policy according to the historical access records of the target object and the current environmental parameters;

[0012] Send the dynamic access control policy to the target access control terminal through the cloud server to control the access permission and physical locking state of the access control terminal.

[0013] In a possible implementation manner of the first aspect, the acquisition of the dynamic environment parameters of the target area by the Internet of Things devices deployed in the park includes:

[0014] Obtain real-time monitoring values of temperature, humidity, and smoke concentration through distributed environmental sensors;

[0015] Capture dynamic image data of the target area through a video acquisition device, and extract the trajectory and density distribution characteristics of moving objects;

[0016] Collect voiceprint feature data through a voice sensor and compare it with a preset abnormal voiceprint database to generate an acoustic environment assessment index.

[0017] In a possible implementation manner of the first aspect, the real-time matching of the dynamic environment parameters with a preset safety threshold range to generate an environmental safety assessment result includes:

[0018] Perform time series analysis on the temperature monitoring value to detect abnormal temperature fluctuation patterns;

[0019] Perform spatial matching of the moving object trajectory with a preset park electronic map to identify unauthorized area intrusion events;

[0020] Fuse the acoustic environment assessment index and the smoke concentration change rate to calculate the comprehensive environmental risk coefficient;

[0021] When the comprehensive environmental risk coefficient exceeds the dynamically adjusted threshold, trigger an environmental safety warning signal.

[0022] In a possible implementation manner of the first aspect, the acquisition steps of the authentication data include:

[0023] An encrypted data packet of an electronic identity credential read by a near-field communication device;

[0024] The multimodal biometric features collected by the biometric device include 3D face feature point cloud data and palm vein topologies;

[0025] The real-time positioning information and historical access authorization records uploaded through the mobile terminal.

[0026] In a possible implementation manner of the first aspect, extracting the identity feature vector of the target object based on the authentication data and performing multi-dimensional security verification in combination with the environmental security assessment result includes:

[0027] Performing blockchain node verification on the encrypted data packet of the electronic identity credential to confirm the validity period and permission level of the identity credential;

[0028] Performing hierarchical comparison between the multimodal biometric features and the pre-stored feature templates to generate a biometric feature matching degree matrix;

[0029] Calculating a spatial proximity parameter according to the real-time positioning information and the geographical coordinates of the access control terminal;

[0030] When the biometric feature matching degree matrix meets the dynamic verification threshold and the spatial proximity parameter is within the allowable range, generating an identity verification pass instruction.

[0031] In a possible implementation manner of the first aspect, generating a dynamic access control policy according to the historical access records of the target object and the current environmental parameters includes:

[0032] Analyzing the time distribution characteristics and access frequency patterns in the historical access records to establish an object behavior portrait;

[0033] Combining the visibility index and light intensity parameter in the current environmental parameters to adjust the biometric recognition sensitivity level of the access control terminal;

[0034] According to the real-time crowd density data of each area in the park, dynamically allocating the access priority weights of the access control terminals;

[0035] Generating a dynamic authorization instruction set including time window restrictions, area access permissions, and emergency escape routes.

[0036] In a possible implementation manner of the first aspect, the method further includes:

[0037] When it is detected that the access control terminal is offline or the communication delay exceeds the set threshold, starting the local decision-making mode of the edge computing node;

[0038] Executing an offline identity verification algorithm through the edge computing node and caching the verification result data;

[0039] Automatically synchronizing the cached data to the cloud server after the communication is restored to update the access control policy version information;

[0040] Adjust the encryption communication protocol level of the access control terminal according to the data synchronization status.

[0041] In a possible implementation manner of the first aspect, the method further includes:

[0042] Construct an access control event knowledge graph, and associate and store historical verification records, device status data, and environmental parameter changes;

[0043] Analyze the event association patterns in the knowledge graph through a deep learning model to predict potential security risks;

[0044] When an abnormal access behavior pattern is detected, automatically upgrade the verification security level of the access control terminal;

[0045] Generate a risk warning report and push it to the associated security management terminal.

[0046] Combined with the second aspect of the present application, there is provided an Internet of Things-based intelligent campus access control management system. The Internet of Things-based intelligent campus access control management system includes a machine-readable storage medium and a processor. The machine-readable storage medium stores machine-executable instructions. When the processor executes the machine-executable instructions, the Internet of Things-based intelligent campus access control management system implements the foregoing Internet of Things-based intelligent campus access control management method.

[0047] Combined with the third aspect of the present application, there is provided a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed, the foregoing Internet of Things-based intelligent campus access control management method is implemented.

[0048] Combined with any of the foregoing aspects, the dynamic environmental parameters of the target area in the campus and the identity verification data of the target object are collected in real time through Internet of Things devices. First, the collected dynamic environmental parameters are matched in real time with a preset security threshold range to generate an environmental security assessment result. Then, based on the identity verification data, the identity feature vector of the target object is extracted, and multi-dimensional security verification is performed in combination with the environmental security assessment result. When the multi-dimensional security verification passes, based on the historical access records of the target object and the current environmental parameters, a dynamic access control policy is intelligently generated. Finally, the dynamic access control policy is sent to the target access control terminal through the cloud server to realize intelligent control of the access permission and physical locking state of the access control terminal. The present invention improves the security management level of the intelligent campus and ensures the intelligence and efficiency of access control management. Description of the Drawings

[0049] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained in combination with these drawings.

[0050] Figure 1 Schematic flowchart of the method for managing access control in an intelligent park based on the Internet of Things provided by the embodiments of the present application. Detailed implementation manners

[0051] To enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0052] The terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or terminal comprising a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or terminals.

[0053] Referring to "embodiments" herein means that a specific feature, structure or characteristic described in connection with the embodiments can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0054] Figure 1 The schematic flowchart of the method for managing access control in an intelligent park based on the Internet of Things provided by the embodiments of the present application is shown. It should be understood that in other embodiments, the order of some steps of the method for managing access control in an intelligent park based on the Internet of Things in this embodiment can be shared based on actual needs, or some of the steps can also be omitted or maintained. The details of the method for managing access control in an intelligent park based on the Internet of Things include:

[0055] Step S110, collect the dynamic environmental parameters of the target area and the identity authentication data of the target object through the Internet of Things devices deployed in the park.

[0056] In this embodiment, for the collection of dynamic environmental parameters, environmental sensors are distributed at various key positions in the park. For example, environmental sensors are installed in the production workshops in the park, the passageways on each floor of the office building, around the warehouses in the park, and the public activity areas in the park. These environmental sensors can accurately obtain the real-time monitoring values of temperature, humidity, and smoke concentration. Taking the production workshop as an example, the operation of the production equipment in the workshop may generate heat, and the environmental sensors continuously monitor the temperature. During normal production periods, the temperature may remain within a relatively stable range, such as between 20 and 25 degrees Celsius. In terms of humidity, due to factors such as the ventilation system in the workshop, the humidity may be maintained at about 40%-60%. If there is an electrical equipment failure or a fire and smoke in the workshop, the smoke concentration sensor will immediately detect the increase in smoke concentration.

[0057] Video acquisition devices are also distributed in important areas of the park. Video acquisition devices are set at positions such as the entrances and exits of the park, parking lots, and road intersections inside the park. These devices continuously capture the dynamic image data of the target area. For example, at the entrance and exit of the park, the video acquisition device can clearly capture the entry and exit of personnel and vehicles, and can extract the trajectory and density distribution characteristics of moving objects. When a vehicle enters the park, the video acquisition device can track the driving trajectory of the vehicle, from entering the park gate to the driving path inside the park, and can also count the density distribution of vehicles in a specific area, such as the vehicle parking density at different times in the parking lot.

[0058] Sound sensors also play a role in the park. Sound sensors are installed in areas where important equipment is located, such as the computer rooms and power distribution rooms in the park, and some quiet office areas in the park. These sound sensors collect voiceprint feature data and compare it with a preset abnormal voiceprint database to generate an acoustic environment evaluation index. For example, in the computer room, if the equipment is operating normally, the sound sensors collect the sound characteristics of the normal operation of the equipment, which match the preset normal voiceprint data. Once there is an abnormal sound, such as the abnormal vibration sound generated by the loosening of equipment components or the abnormal sound made by illegal intruders, the sound sensors can detect it in time, and through comparison with the abnormal voiceprint database, generate the corresponding acoustic environment evaluation index to prompt possible security risks.

[0059] For the collection of authentication data of the target object, the park has set up multiple methods. Near-field communication devices are installed at each access control entrance of the park. When an employee or visitor approaches the access control with a card or mobile device with an electronic identity credential, the near-field communication device reads the encrypted data packet of the electronic identity credential. For example, the employee's work permit is built with an encrypted identity recognition chip. When approaching the access control card reader, the card reader can read the encrypted data packet containing the employee's identity information, affiliated department, permission level, etc.

[0060] Biometric devices are also widely used. At the access control of some important office areas or the finance office, devices capable of collecting multi-modal biometric features are installed. These devices can collect the 3D face feature point cloud data and palm vein topological structure of employees. When an employee needs to enter these areas, standing in front of the biometric device, the device will accurately collect the 3D face feature point cloud data of the employee, including the three-dimensional structure features of each key part of the face, and at the same time collect the topological structure of the palm vein, such as the branching direction and intersection points of the veins, etc., as an important basis for identity verification.

[0061] In addition, the park also utilizes the information uploaded by the mobile terminals of employees and visitors. For example, the security management system in the park requires employees to turn on the positioning function of their mobile terminals within the park, and the mobile terminals will upload the real-time positioning information of the employees in real time. At the same time, the system will record the historical access authorization records of employees, including information such as which areas they have entered and the stay time in each area. These information are all part of the identity verification data.

[0062] Step S120, perform real-time matching between the dynamic environmental parameters and a preset safety threshold range to generate an environmental safety assessment result.

[0063] Continuing with the previous example of the enterprise park, the time series analysis of the temperature monitoring value is an important assessment link. In the greenhouse area of the park, some plants that are more sensitive to temperature are planted. Under normal circumstances, the temperature during the day should be between 15-25 degrees Celsius, and at night between 10-15 degrees Celsius. The environmental sensor continuously collects temperature data to form a time series. If within a short period of time, such as within one hour, the temperature suddenly rises from 20 degrees Celsius to 30 degrees Celsius, this constitutes an abnormal temperature fluctuation pattern. This abnormal fluctuation may be due to a failure of the greenhouse temperature control system or external environmental factors. The system will determine that this situation may pose a threat to plant growth, thus affecting the environmental safety of the greenhouse area in the park.

[0064] Spatial matching of moving object trajectories with preset electronic maps of the park also plays a key role in security assessment. In the warehouse area of ​​the park, the preset electronic map of the park clearly divides the scope of each storage area and the storage areas of different items. The video acquisition device captures the trajectory of a moving object. If this trajectory shows that the object has entered the high-value item storage area from an unauthorized channel, this identifies an unauthorized area intrusion event. For example, an illegal person attempts to bypass the normal warehouse entrance and enter the area where valuable raw materials are stored from an unguarded channel on the side of the warehouse. The system can detect this abnormal intrusion behavior in a timely manner by matching the moving object trajectory with the electronic map.

[0065] Integrating acoustic environment assessment indicators with the rate of change of smoke concentration to calculate the comprehensive environmental risk coefficient is also an important means of environmental safety assessment. In the power distribution room of the park, the smoke concentration is extremely low under normal circumstances, and the acoustic environment is mainly the hum of normal operation of the equipment. If the smoke concentration suddenly tends to increase, and the sound sensor detects abnormal electric spark sounds, this indicates that there may be a risk of fire caused by electrical equipment failure. The system calculates the comprehensive environmental risk coefficient based on the rate of change of smoke concentration and the acoustic environment assessment indicators. When this coefficient exceeds the dynamically adjusted threshold, for example, the set threshold is 0.8, and the calculated coefficient is 0.9, an environmental safety alarm signal will be triggered. This alarm signal will be sent to the security monitoring center of the park to notify relevant personnel to conduct inspections and processing in a timely manner.

[0066] Step S130, extracting the identity feature vector of the target object based on the identity authentication data, and performing multi-dimensional security verification in combination with the environmental security assessment result.

[0067] Taking the access control of the advanced R&D area in the park as an example, when an employee tries to enter the area, the encrypted data packet of his electronic identity credential is first verified by the blockchain node. Assuming that the identity authentication system of the park is built based on blockchain technology, the employee's electronic identity credential information is stored on the node of the blockchain. The system verifies the validity period of the identity credential. For example, the employee's work card identity credential is valid for one year, starting from the date of issuance. At the same time, its permission level is verified. The R&D area is a high-level confidential area, and only R&D personnel with high permissions can enter. If the employee's identity credential is within the validity period and the permission level meets the requirements, this is an important aspect of multi-dimensional security verification.

[0068] For the verification of biometric features, the multimodal biometric features of employees are compared with the pre-stored feature templates in layers. Still taking the employees entering the advanced R & D area as an example, when an employee stands in front of the biometric device, the 3D face feature point cloud data and palm vein topology structure collected by the device are compared with the pre-stored feature template of this employee. First, the overall structure is compared, and then the key feature points are compared in detail in layers to generate a biometric matching degree matrix. If all the matching degree indicators in this matrix meet the dynamic verification threshold, for example, the face feature matching degree reaches more than 95%, and the palm vein feature matching degree reaches more than 90%, it indicates that the biometric matching degree meets the requirements.

[0069] At the same time, the spatial proximity parameter is calculated based on the real-time positioning information uploaded by the employee's mobile terminal and the geographical coordinates of the access control terminal. If the employee is near the access control and the distance is within the set allowable range, for example, within 5 meters, and the biometric matching degree matrix meets the dynamic verification threshold and the spatial proximity parameter is within the allowable range, the system will generate an identity verification passed instruction. In addition, combined with the previous environmental safety assessment results, if the current environmental safety assessment results show that the surrounding environment of the R & D area is normal, without abnormal temperature fluctuations, unauthorized intrusions or fire risks, etc., then the multi-dimensional security verification can pass smoothly.

[0070] Step S140, when the multi-dimensional security verification passes, generate a dynamic access control policy according to the historical access records of the target object and the current environmental parameters.

[0071] For employees who often move around in the park, analyze the time distribution characteristics and access frequency patterns in their historical access records to establish an object behavior portrait. For example, there is an employee in the marketing department. His historical access records show that he often moves around in the office building from 9 am to 6 pm on weekdays, occasionally goes to the meeting room to attend meetings around 2 pm, and goes to the park's exhibition center one to two times a week. Based on this information, the system establishes the behavior portrait of this employee.

[0072] Combined with the visibility index and light intensity parameter in the current environmental parameters, adjust the biometric recognition sensitivity level of the access control terminal. In the outdoor access control area of the park, if it is early morning or evening, the visibility is low and the light intensity is weak, the system will appropriately reduce the sensitivity level of the biometric device. For example, the original face feature matching degree requirement is more than 95%. In this case of low visibility and low light intensity, it may be adjusted to more than 90% to ensure that employees can pass the access control normally while ensuring a certain level of security.

[0073] Dynamically allocate the access priority weights of access control terminals according to the real-time pedestrian flow density data in each area of the park. In the cafeteria area of the park, the pedestrian flow density is relatively high during lunchtime. When employees go from other areas to the cafeteria, the access control terminals near the cafeteria will allocate higher access priority weights to the employees going to the cafeteria according to the pedestrian flow density data. This can ensure that employees can quickly pass through the access control and avoid congestion at the access control.

[0074] Finally, generate a dynamic authorization instruction set including time window restrictions, area access permissions, and emergency escape routes. For example, for a newly recruited employee during their probation period, they may be restricted to activities in the office area and training area and can only enter the park between 9:00 am and 5:00 pm on weekdays. At the same time, the access control authorization instructions for each area also include the emergency escape route information for that area. In case of an emergency, employees can quickly evacuate according to the escape routes in the authorization instructions.

[0075] Step S150, send the dynamic access control policy to the target access control terminal through the cloud server to control the access permission and physical locking state of the access control terminal.

[0076] In the management system of the enterprise park, when the dynamic access control policy is generated, the cloud server will send these policies to the target access control terminal. For example, for the access control of the advanced R & D area mentioned above, the cloud server sends the dynamic access control policy including time window restrictions, area access permissions, etc. to the access control terminal. If the employee's identity verification passes and factors such as the current environmental parameters and historical access records meet the requirements, the cloud server will send an instruction to the access control terminal to allow the employee to pass. At this time, the physical locking state of the access control terminal will be released, and the employee can enter the R & D area normally.

[0077] Another example is in the visitor management of the park. If the multi-dimensional security verification of the visitor passes, the cloud server will send the dynamic access control policy to the corresponding access control terminal according to information such as the visitor's access area and time limit. If the visitor is authorized to visit the exhibition center of the park between 10:00 am and 11:00 am, when the time reaches 10:00 am, the cloud server will send an instruction to the access control terminal of the exhibition center to allow the visitor to pass. At the same time, at 11:00 am, according to the policy requirements, the access control terminal may restrict the visitor from continuing to pass or re-verify, and adjust the physical locking state according to the situation to ensure the orderly progress of the park's security management.

[0078] Based on the above steps, the dynamic environmental parameters of the target area in the park and the identity verification data of the target object are collected in real time through Internet of Things devices. First, the collected dynamic environmental parameters are matched with the preset security threshold range in real time to generate an environmental security assessment result. Then, based on the identity verification data, the identity feature vector of the target object is extracted, and multi-dimensional security verification is performed in combination with the environmental security assessment result. When the multi-dimensional security verification passes, based on the historical access records of the target object and the current environmental parameters, a dynamic access control policy is intelligently generated. Finally, the dynamic access control policy is sent to the target access control terminal through the cloud server to realize the intelligent control of the access permission and physical locking state of the access control terminal. The present invention improves the security management level of the smart park and ensures the intelligence and efficiency of access control management.

[0079] In a possible implementation manner, collecting the dynamic environmental parameters of the target area through the Internet of Things devices deployed in the park includes:

[0080] Obtaining real-time monitoring values of temperature, humidity, and smoke concentration through distributed environmental sensors.

[0081] Capturing dynamic image data of the target area through a video acquisition device, and extracting the trajectory and density distribution characteristics of moving objects.

[0082] Collecting voiceprint feature data through a voice sensor and comparing it with a preset abnormal voiceprint database to generate an acoustic environment assessment index.

[0083] In this embodiment, first, real-time monitoring values of temperature, humidity, and smoke concentration are obtained through distributed environmental sensors. In the layout of the park, environmental sensors are widely installed in various areas. For example, in the production workshop of the park, numerous production equipment operates continuously, generating heat dissipation and humidity changes, and the environmental sensors can accurately capture the real-time situation of temperature and humidity. In some precision electronics equipment production workshops, the temperature needs to be strictly controlled between 20 - 22 degrees Celsius, and the humidity is maintained at 40% - 50% to ensure the normal operation of production equipment and product quality. At the same time, in the storage warehouse of the park, especially in the warehouse area storing flammable items, the smoke concentration sensor continuously monitors the smoke concentration in the environment. Once smoke is generated, even the slightest change in concentration can be detected by the sensor. For example, when a certain cargo in the warehouse starts to smoke due to moisture or other reasons, the smoke concentration sensor can quickly reflect this situation, providing a basis for subsequent safety handling.

[0084] Secondly, dynamic image data of the target area is captured by a video acquisition device, and the moving object trajectory and density distribution characteristics are extracted. In the traffic arteries of the park, such as main roads, parking lot entrances and exits, etc., the video acquisition device plays an important role. Taking the main road of the park as an example, the video acquisition device continuously collects image data for 24 hours. During the morning rush hour, the traffic volume of vehicles and pedestrians is large, and the video acquisition device can accurately track the movement trajectories of each vehicle and each pedestrian. By analyzing these trajectories, the traffic flow direction and the formation of congestion points can be understood. For example, it is found that the vehicle density at a certain intersection is too high during a specific time period, which is likely to cause congestion, providing a decision-making basis for the park's traffic management. In the parking lot of the park, the video acquisition device can count the vehicle parking density in different areas. When a certain area is approaching saturation, it can guide newly entering vehicles to other areas with available parking spaces.

[0085] Furthermore, voiceprint feature data is collected by a voice sensor and compared with a preset abnormal voiceprint database to generate an acoustic environment evaluation index. In the equipment machine room area of the park, the voice sensor is installed near key equipment. Under normal circumstances, the equipment operation will emit sounds with specific frequencies and intensities. The voice sensor collects these normal voiceprint feature data and stores them as normal reference data. Once the equipment fails, such as the bearing of the motor wears or the fan blade becomes loose, the sound emitted by the equipment will change. After the voice sensor collects the voiceprint feature data of these abnormal sounds, it is compared with the preset abnormal voiceprint database. If the match is successful, it means that there may be a safety hazard in the equipment. For example, the database stores the high-frequency harsh voiceprint features emitted when the motor is overloaded. When the voice sensor collects similar voiceprints, it will generate corresponding acoustic environment evaluation indexes, indicating that there may be a fault risk in the equipment in this area and timely inspection and maintenance are required.

[0086] In a possible implementation manner, the real-time matching of the dynamic environment parameters with a preset safety threshold range to generate an environmental safety evaluation result includes:

[0087] Perform time series analysis on the temperature monitoring value to detect abnormal temperature fluctuation patterns.

[0088] Perform spatial matching of the moving object trajectory with a preset park electronic map to identify unauthorized area intrusion events.

[0089] Fuse the acoustic environment evaluation index and the smoke concentration change rate to calculate the comprehensive environmental risk coefficient.

[0090] When the comprehensive environmental risk coefficient exceeds the dynamically adjusted threshold, trigger an environmental safety warning signal.

[0091] In the park's security management system, real-time matching of the collected dynamic environmental parameters with the preset security threshold range is a crucial link.

[0092] Perform time series analysis on the temperature monitoring values ​​to detect abnormal temperature fluctuation patterns. Take the greenhouse planting area of ​​the park as an example, where various precious flowers and rare plants are planted. Different plants have different growth requirements for temperature and are very sensitive to temperature fluctuations. Under normal circumstances, the daytime temperature should be between 18-25 degrees Celsius. Environmental sensors continuously collect temperature data to form a time series. If the temperature suddenly drops sharply from 22 degrees Celsius to 15 degrees Celsius within a certain period of time, such as for several consecutive hours, this constitutes an abnormal temperature fluctuation pattern. This fluctuation may be due to a failure in the greenhouse's temperature control system, or the external bad weather affects the greenhouse's insulation effect. By detecting this abnormal temperature fluctuation pattern, the system can promptly detect the risk of plant frost damage that may be faced in the greenhouse planting area, and take corresponding measures, such as starting backup heating equipment or checking the sealing of the greenhouse.

[0093] The trajectory of moving objects is spatially matched with the preset electronic map of the park to identify unauthorized area intrusion events. In the confidential R&D area of ​​the park, the entry of personnel and vehicles is strictly restricted. The preset electronic map of the park clearly identifies the boundaries of the R&D area and the various functional divisions inside. The video acquisition device monitors the dynamic image data around the R&D area at all times and extracts the trajectory of moving objects. If an unauthorized person or vehicle approaches the R&D area and its movement trajectory shows that it has entered the boundary of the R&D area, the system will identify this as an unauthorized area intrusion event. For example, if an outsider tries to break through the security line and enter the R&D area to steal confidential information, once their movement trajectory is captured by the system and spatially matched with the electronic map, the system will immediately issue an alarm to notify the park's security personnel to intercept and handle it.

[0094] Integrate the acoustic environment assessment index and the change rate of smoke concentration to calculate the comprehensive environmental risk coefficient. In the office building of the park, especially on the floors where electrical equipment is concentrated, such as computer rooms and power distribution rooms. During normal operation, the acoustic environment is mainly the humming sound of normal equipment operation, and the smoke concentration is extremely low. If at a certain moment, the sound sensor detects abnormal electrical discharge sounds, the acoustic environment assessment index shows an abnormal situation, and at the same time, the smoke concentration sensor detects a slight upward trend in the smoke concentration, the system will integrate these two parameters to calculate the comprehensive environmental risk coefficient. Suppose the abnormal degree of the acoustic environment assessment index is 0.3 (according to the predefined assessment criteria), and the change rate of smoke concentration is 0.2 (also according to the predefined criteria). Through a specific calculation formula (such as weighted summation), the comprehensive environmental risk coefficient is obtained as 0.5. When this comprehensive environmental risk coefficient exceeds the dynamically adjusted threshold (for example, set to 0.4), an environmental safety warning signal will be triggered. This warning signal will be sent to the security monitoring center of the park, prompting relevant personnel to inspect the equipment and environment in the office building to eliminate potential fire or electrical fault risks.

[0095] In a possible implementation manner, the step of collecting the authentication data includes:

[0096] The encrypted data packet of the electronic identity credential read by the near-field communication device.

[0097] The multi-modal biometric features collected by the biometric device, including three-dimensional face feature point cloud data and palm vein topology.

[0098] The real-time positioning information and historical access authorization records uploaded by the mobile terminal.

[0099] The encrypted data packet of the electronic identity credential read by the near-field communication device. Near-field communication devices are equipped at each access control entrance of the park. When employees and visitors enter the park, they need to use a card or mobile device with an electronic identity credential. For example, the employee's work permit contains an encrypted chip with the employee's identity information. When the employee brings the work permit close to the near-field communication card reader at the access control, the card reader can read the encrypted data packet. This encrypted data packet contains important identity information such as the employee's name, department, position, and permission level. This information is encrypted to ensure security during transmission and reading, preventing the identity information from being stolen or tampered with.

[0100] The multimodal biometric features collected by the biometric device include 3D face feature point cloud data and palm vein topological structure. Biometric devices are installed at key entrances such as the access control for important office areas and the access control for the finance room within the park. When an employee needs to enter these areas, they stand in front of the biometric device. The biometric device will first collect the 3D face feature point cloud data of the employee. During this process, the device will accurately capture the three-dimensional structural features of each key part of the employee's face, such as the three-dimensional coordinates and shape features of the eyes, nose, mouth, etc., to form a complete face feature point cloud data. At the same time, the device will also collect the palm vein topological structure of the employee, including unique structural features such as the branching direction and intersection points of the palm veins. For example, for the access control of the finance room, only authorized finance personnel can enter. When a finance personnel stands in front of the biometric device, the face feature point cloud data and palm vein topological structure collected by the device will serve as important bases for their identity verification.

[0101] The real-time positioning information and historical access authorization records uploaded through the mobile terminal. The park requires employees to turn on the positioning function of the mobile terminal within the park, and the mobile terminal will regularly upload the real-time positioning information of the employees to the park's security management system. For example, when an employee moves within the park, from the office building to the park's cafeteria, the mobile terminal will send the employee's location change information to the system in real time. At the same time, the system will record the historical access authorization records of the employees. These records contain information such as which areas the employees have entered, the stay time in each area, and the timestamps of entry and exit. Taking a sales employee who often shuttles between different areas within the park for business negotiations as an example, the system will record the detailed information of his each entry into areas such as the customer reception area and the meeting room. Together with the real-time positioning information, these information provide data support for the identity verification and security management of the employees.

[0102] In a possible implementation manner, extracting the identity feature vector of the target object based on the identity verification data and performing multi-dimensional security verification in combination with the environmental security assessment result includes:

[0103] Performing blockchain node verification on the encrypted data packet of the electronic identity credential to confirm the validity period and permission level of the identity credential.

[0104] Performing hierarchical comparison between the multimodal biometric features and the pre-stored feature templates to generate a biometric feature matching degree matrix.

[0105] Calculating the spatial proximity parameter according to the real-time positioning information and the geographical coordinates of the access control terminal.

[0106] When the biometric feature matching degree matrix meets the dynamic verification threshold and the spatial proximity parameter is within the allowable range, generating an identity verification pass instruction.

[0107] Verify the encrypted data packet of the electronic identity credential through blockchain nodes to confirm the validity period and permission level of the identity credential. Assume that the park has adopted an identity verification system based on blockchain technology, and the electronic identity credential information of employees and visitors is stored on the blockchain nodes. When an employee attempts to enter a specific area of the park, the system will verify the encrypted data packet of their electronic identity credential through blockchain nodes. Taking the R & D laboratory in the park as an example, only R & D personnel with high-level permissions can enter this laboratory. The system first verifies whether the employee's identity credential is within the validity period. For example, the validity period of the employee's work permit identity credential is three years, starting from the issuance date. If the credential is within the validity period, the system then verifies its permission level. If the employee's permission level is high-level R & D permission and matches the access permission of the R & D laboratory, then the verification in this regard passes. This blockchain node verification method ensures the authenticity and immutability of the identity credential and improves the security of identity verification.

[0108] Perform hierarchical comparison between multi-modal biometric features and pre-stored feature templates to generate a biometric feature matching degree matrix. Taking the access control of the office of senior management in the park as an example, when a manager needs to enter the office, they stand in front of the biometric recognition device. The three-dimensional face feature point cloud data and palm vein topological structure collected by the biometric recognition device will be hierarchically compared with the pre-stored feature template of this manager. First, a preliminary comparison of the overall structure is carried out, such as the contour of the face and the general trend of the palm vein. Then, a more detailed hierarchical comparison is carried out. For example, the detailed features around the eyes in the face feature point cloud data and the tiny branch structure of the palm vein are compared. During this process, the system will generate a biometric feature matching degree matrix. This matrix contains the matching degree values of each comparison level of the face feature and the palm vein feature. For example, the overall structure matching degree of the face feature is 90%, the detailed feature matching degree around the eyes is 85%, the overall trend matching degree of the palm vein is 92%, and the tiny branch structure matching degree is 88%, etc. If all the matching degree indicators in this biometric feature matching degree matrix meet the dynamic verification threshold (for example, the set overall matching degree threshold is above 80%), then the verification in terms of biometric features passes.

[0109] Calculate the spatial proximity parameter based on the real-time positioning information and the geographical coordinates of the access control terminal. In the park, when an employee approaches a certain access control terminal, the system calculates the spatial proximity parameter according to the real-time positioning information uploaded by the employee's mobile terminal and the geographical coordinates of the access control terminal. For example, at the access control of the employee dormitory area in the park, the employee walks from inside the dormitory towards the access control to go out. The employee's mobile terminal sends its location information in real time, and the system calculates that the distance between the employee and the access control terminal is 3 meters (assuming this distance is the spatial proximity parameter). If the set allowable range is within 5 meters and the biometric matching degree matrix meets the dynamic verification threshold, then the verification in terms of spatial proximity passes. When all these verification conditions are met, the system generates an identity verification passed instruction to allow the employee to pass through the access control. At the same time, during this process, the previous environmental safety assessment results are also combined. If the environmental safety assessment results show that there are abnormal situations in the area around the access control, such as too high smoke concentration or unauthorized moving objects approaching, even if the identity verification passes, the system may also take additional security measures, such as issuing a warning notice or temporarily restricting the opening of the access control, to ensure the overall safety of the park.

[0110] In a possible implementation manner, the generating a dynamic access control policy according to the historical access records of the target object and the current environmental parameters includes:

[0111] Analyze the time distribution characteristics and access frequency patterns in the historical access records to establish an object behavior portrait.

[0112] Combine the visibility index and light intensity parameter in the current environmental parameters to adjust the biometric sensitivity level of the access control terminal.

[0113] Dynamically allocate the access priority weights of the access control terminals according to the real-time pedestrian flow density data in each area of the park.

[0114] Generate a dynamic authorization instruction set including time window restrictions, area access permissions, and emergency escape routes.

[0115] Taking a senior engineer in the park as an example, the system will analyze his historical access records in detail. From the perspective of time distribution characteristics, he usually moves within the park between 8:30 am and 5:30 pm on weekdays, and goes to the cafeteria for lunch from 12:00 to 1:00. From the perspective of access frequency patterns, he goes to the laboratory for experiments three times a week, goes to the meeting room for project meetings twice a week, and enters and exits his office floor multiple times a day. Based on this information, the system creates an object behavior portrait of this engineer. This behavior portrait not only reflects his daily work activity patterns but also provides an important basis for formulating access control strategies. For example, if he tries to enter the laboratory at a non-working time, such as 8:00 pm, since this does not match his normal behavior portrait, the system may conduct a more rigorous review of his entry request or directly reject his entry request unless he can provide a reasonable explanation and additional authorization.

[0116] Combined with the visibility index and light intensity parameters in the current environmental parameters, adjust the biometric sensitivity level of the access control terminal. In the outdoor access control areas of the park, such as the park gate and some access controls for the passages connecting the inside and outside of the park, the visibility index and light intensity parameters in the current environmental parameters have an important impact on the biometric accuracy of the access control. In the early morning or evening, the visibility is low and the light intensity is weak. For example, on a winter morning with heavy fog, the visibility may be only dozens of meters and the light intensity is also relatively low. In this case, the biometric device of the access control terminal may be affected. To ensure that employees can pass through the access control normally, the system will adjust the biometric sensitivity level according to the current visibility index and light intensity parameters. The originally set requirement for face feature matching was above 95%. In this case of low visibility and low light intensity, the system may adjust the matching requirement to above 90%. This can prevent employees from being unable to pass through the access control due to environmental factors and also ensure the security of the access control to a certain extent.

[0117] According to the real-time crowd density data of each area in the park, dynamically allocate the access priority weights of the access control terminals. In the public activity areas of the park, such as the cafeteria and gymnasium, the crowd density varies greatly at different times. Taking the cafeteria as an example, lunchtime is the peak crowd density period, and a large number of employees go to the cafeteria for lunch. During this period, the access control terminals near the cafeteria will allocate higher access priority weights to the employees going to the cafeteria according to the real-time crowd density data of each area in the park. Suppose there are other people in the park who are on their way to the cafeteria from other areas. When they arrive at the cafeteria access control, because their access priority weights are higher, the system will give priority to processing their access control verification requests to ensure that they can pass through the access control quickly and avoid congestion at the access control. For employees who go to the cafeteria during non-peak crowd density periods, their access priority weights are relatively low, and the access control verification will be carried out according to the normal process.

[0118] Generate a dynamic authorization instruction set that includes time window restrictions, area access permissions, and emergency escape routes. For newly hired employees in the park, the system will generate a specific dynamic authorization instruction set for them. In terms of time window restrictions, new employees may only be allowed to enter the park between 9:00 am and 6:00 pm on weekdays, which is based on their onboarding training and work arrangements. In terms of area access permissions, new employees may initially only be authorized to enter the office area and training area. As their working time in the park increases and their work requirements change, the area access permissions will gradually expand. At the same time, in the access authorization instructions for each area, the emergency escape route information for that area is also included. For example, in the access authorization instructions for the office building, employees will be clearly informed which staircase or passage to use to evacuate to a safe area in case of an emergency such as a fire. Such a dynamic authorization instruction set can not only ensure the normal activities of new employees in the park but also guarantee their life safety in case of an emergency.

[0119] In a possible implementation manner, the method further includes:

[0120] When it is detected that the access control terminal is offline or the communication delay exceeds the set threshold, start the local decision-making mode of the edge computing node.

[0121] Execute the offline authentication algorithm through the edge computing node and cache the verification result data.

[0122] Automatically synchronize the cached data to the cloud server after the communication is restored, and update the access control policy version information.

[0123] Adjust the encryption communication protocol level of the access control terminal according to the data synchronization status.

[0124] In the access control management system of the park, the normal communication of the access control terminal is crucial for security management. However, sometimes the access control terminal may be offline or the communication delay may exceed the set threshold.

[0125] For example, at the access control terminal in a remote area of the park, due to network line failures or external interference, the communication with the cloud server is interrupted or the communication delay exceeds the set threshold (for example, a delay threshold set to 5 seconds). At this time, the system will automatically activate the local decision-making mode of the edge computing node. The edge computing node is located near the access control terminal and stores some necessary identity verification data and algorithms. When entering the local decision-making mode, the edge computing node will execute the offline identity verification algorithm. Suppose an employee attempts to enter the park through this offline access control terminal. The edge computing node will read the encrypted data packet of the electronic identity credential in the employee's near-field communication device and perform local verification on it. At the same time, the biometric device collects the multi-modal biometric features of the employee, and the edge computing node performs hierarchical comparison of these biometric features with the pre-stored feature templates stored locally. If the employee's identity verification is passed, the edge computing node will cache the verification result data. This cached data will play an important role after the communication is restored.

[0126] When the communication between the access control terminal and the cloud server is restored, the verification result data cached at the edge computing node before will be automatically synchronized to the cloud server. For example, after the communication is restored in the previously mentioned offline situation of the access control terminal, the edge computing node will send the cached employee identity verification result data to the cloud server. After receiving these data, the cloud server will update the access control policy version information based on these data and the current park security policy and other relevant factors. If there are new security regulations or access control policy adjustments during the offline period, the cloud server will send the updated access control policy version information to the access control terminal. For example, due to the increased security level of a certain area in the park, the cloud server may adjust the access permission requirements of the access control terminal and send the new access control policy version information to the access control terminal so that the access control terminal can manage access control according to the new policy.

[0127] When the data synchronization status between the access control terminal and the cloud server is good, for example, the data is synchronized completely and accurately without data loss or errors, the encryption communication protocol level of the access control terminal may remain at the default level, such as the medium encryption level. This medium encryption level can ensure the security of data transmission while maintaining good communication efficiency. However, if there are some problems during the data synchronization process, such as partial data loss or data transmission errors, the system will consider that the data synchronization status is poor. In this case, in order to ensure the security of subsequent data transmission, the encryption communication protocol level of the access control terminal will be upgraded. For example, from the medium encryption level to the high encryption level. The high encryption level uses more complex encryption algorithms and longer encryption keys, which can provide higher data security protection but may have a certain impact on communication efficiency. This way of adjusting the encryption communication protocol level according to the data synchronization status can find a balance between ensuring data security and maintaining the normal operation of the access control management system.

[0128] In one possible implementation, the method further includes:

[0129] Construct an access control event knowledge graph, and associate and store historical verification records, device status data, and environmental parameter changes.

[0130] Analyze the event association patterns in the knowledge graph through a deep learning model to predict potential security risks.

[0131] When an abnormal access behavior pattern is detected, automatically upgrade the verification security level of the access control terminal.

[0132] Generate a risk warning report and push it to the associated security management terminal.

[0133] In the above embodiments, the Internet of Things-based intelligent campus access control management system for executing the above method embodiments has at least one processor, a control module (chipset) coupled to at least one of the (at least one) processors, a memory coupled to the control module, a non-volatile memory (NVM) / storage device coupled to the control module, at least one load / output device coupled to the control module, and a network interface coupled to the control module.

[0134] The processor may include at least one single-core or multi-core processor, and the processor may include any combination of general-purpose processors or dedicated processors (such as graphics processors, application processors, baseband processors, etc.). For some alternative embodiments, the Internet of Things-based intelligent campus access control management system can be used as an electronic device such as the gateway described in the embodiments of the present application.

[0135] For some alternative embodiments, the IoT-based smart campus access control management system may include at least one computer-readable medium (e.g., a memory or NVM / storage device) having instructions and at least one processor coupled to the at least one computer-readable medium and configured to execute the instructions to implement modules to perform the actions described in this disclosure.

[0136] For one embodiment, the control module may include any suitable interface controller to provide any suitable interface to at least one of the (at least one) processors and / or any suitable device or component communicating with the control module.

[0137] The control module may include a memory controller module to provide an interface to the memory. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0138] The memory may be used, for example, to load and store data and / or instructions for the IoT-based smart campus access control management system. For one embodiment, the memory may include any suitable volatile memory, e.g., suitable DRAM.

[0139] For one embodiment, the control module may include at least one load-to / output controller to provide an interface to the NVM / storage device and the (at least one) load-to / output device.

[0140] For example, the NVM / storage device may be used to store data and / or instructions. The NVM / storage device may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (at least one) non-volatile storage device (e.g., at least one hard disk drive (HDD), at least one compact disc (CD) drive, and / or at least one digital versatile disc (DVD) drive).

[0141] The NVM / storage device may include storage resources that are physically part of the device on which the IoT-based smart campus access control management system is installed, or it may be accessible by the device without being part of the device. For example, the NVM / storage device may be accessed over a network via the (at least one) load-to / output device.

[0142] (At least one) loading / output device can provide an interface for an Internet of Things-based smart campus access control management system to communicate with any other suitable device. The loading / output device can include a communication component, a pinyin component, a sensor component, etc. The network interface can provide an interface for the Internet of Things-based smart campus access control management system to communicate based on at least one network. The Internet of Things-based smart campus access control management system can wirelessly communicate with at least one component of the wireless network based on any prior and / or protocol in at least one wireless network prior and / or protocol, such as accessing a wireless network based on a communication prior.

[0143] For one embodiment, at least one of the (at least one) processors can be logically loaded together with at least one controller of the control module (e.g., the memory controller module). For one embodiment, at least one of the (at least one) processors can be logically loaded together with at least one controller of the control module to form a system-level load. For one embodiment, at least one of the (at least one) processors can be logically integrated with at least one controller of the control module on the same die. For one embodiment, at least one of the (at least one) processors can be logically integrated with at least one controller of the control module on the same die to form a system-on-chip (SoC).

[0144] The embodiments of the present application have been introduced in detail above. Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, based on the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

[0145] An embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the Internet of Things-based smart campus access control management method described in the foregoing embodiments.

[0146] An embodiment of the present invention discloses a computer program product that includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute the steps in the Internet of Things-based smart campus access control management method described in the foregoing embodiments.

[0147] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative work.

[0148] Through the above specific descriptions of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, and the storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other medium that can be used for a computer to have or store data.

[0149] Finally, it should be noted that: the above-disclosed is only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart park access control management method based on the Internet of Things, characterized in that: The method comprises: The IoT devices deployed in the park collect dynamic environmental parameters of the target area and identity authentication data of the target object; The dynamic environmental parameters are matched with the preset safety threshold range in real time to generate an environmental safety assessment result; Extracting the identity feature vector of the target object based on the identity authentication data, and performing multi-dimensional security verification in combination with the environmental security assessment result; When the multi-dimensional security verification is passed, a dynamic access control strategy is generated according to the historical access records of the target object and the current environmental parameters; The dynamic access control strategy is sent to the target access control terminal through the cloud server to control the access authority and physical locking status of the access control terminal.

2. The method for managing access control of a smart park based on the Internet of Things according to claim 1 is characterized in that: The method of collecting dynamic environmental parameters of the target area through the IoT devices deployed in the park includes: Obtain real-time monitoring values ​​of temperature, humidity, and smoke concentration through distributed environmental sensors; Capture dynamic image data of the target area through a video acquisition device to extract the trajectory and density distribution characteristics of moving objects; Voiceprint feature data is collected through sound sensors and compared with the preset abnormal voiceprint database to generate acoustic environment assessment indicators.

3. The method for managing access control of a smart park based on the Internet of Things according to claim 2 is characterized in that: The step of matching the dynamic environmental parameters with a preset safety threshold range in real time to generate an environmental safety assessment result includes: Performing time series analysis on the temperature monitoring values ​​to detect abnormal temperature fluctuation patterns; Spatially matching the trajectory of the moving object with a preset electronic map of the park to identify unauthorized area intrusion events; The acoustic environment assessment index and the smoke concentration change rate are integrated to calculate the comprehensive environmental risk coefficient; When the comprehensive environmental risk coefficient exceeds the dynamically adjusted threshold, an environmental safety alarm signal is triggered.

4. The method for managing access control of a smart park based on the Internet of Things according to claim 1 is characterized in that: The step of collecting the identity verification data includes: An encrypted data packet of electronic identity credentials read by a near field communication device; Multimodal biometric features collected by biometric devices, including 3D facial feature point cloud data and palm vein topology; Real-time positioning information and historical access authorization records uploaded through mobile terminals.

5. The method for managing access control of a smart park based on the Internet of Things according to claim 4 is characterized in that: The extracting of the identity feature vector of the target object based on the identity authentication data and performing multi-dimensional security verification in combination with the environmental security assessment result includes: Performing blockchain node verification on the electronic identity credential encrypted data packet to confirm the validity period and authority level of the identity credential; Performing hierarchical comparison between the multimodal biometric features and pre-stored feature templates to generate a biometric matching matrix; Calculate spatial proximity parameters based on the real-time positioning information and the geographic coordinates of the access control terminal; When the biometric matching matrix meets the dynamic verification threshold and the spatial proximity parameter is within the allowable range, an identity verification pass instruction is generated.

6. The method for managing access control of a smart park based on the Internet of Things according to claim 1 is characterized in that: The generating of a dynamic access control strategy according to the historical passage record of the target object and the current environmental parameters includes: Analyze the time distribution characteristics and access frequency patterns in the historical access records to establish a behavior profile of the object; Adjust the biometric sensitivity level of the access control terminal based on the visibility index and light intensity parameters in the current environmental parameters; Dynamically allocate access priority weights to access control terminals based on real-time crowd density data in each area of ​​the park; Generate dynamic authorization instruction sets that include time window restrictions, area access rights, and emergency escape routes.

7. The method for managing access control of a smart park based on the Internet of Things according to claim 1 is characterized in that: The method further comprises: When it is detected that the access control terminal is offline or the communication delay exceeds the set threshold, the local decision-making mode of the edge computing node is started; Executing an offline identity authentication algorithm through the edge computing node and caching the verification result data; Automatically synchronize cached data to the cloud server after communication is restored and update access control policy version information; Adjust the encryption communication protocol level of the access control terminal according to the data synchronization status.

8. The method for managing access control of a smart park based on the Internet of Things according to claim 1 is characterized in that: The method further comprises: Build a knowledge graph of access control events to associate and store historical verification records, device status data, and environmental parameter changes; Analyze the event association patterns in the knowledge graph through a deep learning model to predict potential security risks; When abnormal access behavior patterns are detected, the verification security level of the access control terminal is automatically upgraded; Generate risk warning reports and push them to associated security management terminals.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a computer, the smart park access control management method based on the Internet of Things described in any one of claims 1-8 is implemented.

10. A smart park access control management system based on the Internet of Things, characterized in that: It includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a computer, the smart park access control management method based on the Internet of Things as described in any one of claims 1-8 is implemented.

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