Smart park safety management method and system based on Internet of Things

Abstract

The invention relates to the technical field of park safety management, in particular to a smart park safety management method and system based on the Internet of Things, and the system comprises a park safety management module, a fault scheme analysis module and an equipment safety check module. Safety key positions in the park are regularly monitored through multiple types of Internet of Things sensors, the positions of potential safety hazards are efficiently and accurately recognized, and the environment of the positions of the potential safety hazards is simulated in combination with the simulation technology. Data acquisition conditions of various Internet of Things sensors under different hidden danger conditions and fault conditions in safe doctor-patient positions are simulated, fault consistency schemes conforming to actual data acquisition conditions of the various Internet of Things sensors are efficiently determined, and a safety check sequence of each device in the potential safety hazard positions is determined by combining all the fault consistency schemes; the fault equipment can be quickly locked, the fault reason can be quickly determined, the inspection time is effectively shortened, and the overall safety management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of park security management, and more specifically, it relates to an intelligent park security management method and system based on the Internet of Things. Background Art

[0002] In today's society, with the acceleration of the urbanization process and the continuous expansion of the scale of parks, park security management is facing unprecedented challenges. As a complex integrating multiple functions such as production, office, and living, there are numerous internal devices in the park, and the potential safety hazards also increase accordingly.

[0003] With the rapid development of Internet of Things technology, more and more sensors are being applied in various fields, providing new ideas for park security management. However, there are still some problems in the application of current Internet of Things technology in park security management. On the one hand, although the computer rooms, power distribution rooms, and warehouses in the park can be monitored through sensors, how to efficiently and accurately identify the location and cause of potential safety hazards and formulate effective countermeasures according to the actual situation is still an urgent problem to be solved. For example, when the temperature in the computer room is abnormal, the sensor can only perform the function of detecting abnormal temperature, but it is difficult to accurately identify the cause of this phenomenon.

[0004] Traditional methods for detecting potential hazards often rely on the experience and judgment of technical personnel, which is not only time-consuming and laborious, but also may lead to misjudgment or missed judgment due to human factors. In addition, for complex potential safety hazards, it is often difficult to accurately judge their nature and severity based on the data of a single sensor, and it is necessary to comprehensively consider the data of multiple sensors and environmental factors.

[0005] Therefore, there is an urgent need for an intelligent park security management method and system based on the Internet of Things. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide an intelligent park security management method and system based on the Internet of Things.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: An intelligent park security management system based on the Internet of Things, including a park security management module, a fault solution analysis module, and an equipment safety inspection module; The park security management module: determines the key security locations in the park, regularly determines the security feedback values of each key security location, and determines whether there is a potential safety hazard location based on the comparison result between the security feedback value of the key security location and the security feedback boundary value; The fault solution analysis module: When a potential safety hazard location appears, a random simulation model for the potential safety hazard location is constructed. The random simulation model for potential safety hazards generates i fault assignment solutions, and a fault-consistent solution is selected from the fault assignment solutions. The equipment safety inspection module: Determines the safety inspection order of the equipment based on the fault-consistent solution, and conducts a safety inspection on the equipment based on the safety inspection order.

[0008] Furthermore, the safety feedback value of a safety-critical location is determined based on the following method: Select a safety-critical location, determine various types of Internet of Things sensors within this safety-critical location, obtain the acquisition data characteristics of various types of Internet of Things sensors, combine the acquisition data characteristics of various types of Internet of Things sensors into a key location feature set in the form of a feature set, obtain the safety feedback model corresponding to this safety-critical location, use the key location feature set as the input data of the safety feedback model, and the safety feedback model outputs the safety feedback value of this safety-critical location.

[0009] Furthermore, the acquisition data characteristics of the Internet of Things sensors are obtained based on the following method: Select a type of Internet of Things sensor, collect the acquisition data of this Internet of Things sensor within a period, perform feature extraction on the collected acquisition data, and extract the acquisition data characteristics of this Internet of Things sensor.

[0010] Furthermore, to select a fault-consistent solution from the fault assignment solutions: Determine the fault sensing consistency value of each fault assignment solution, set a fault sensing consistency threshold. When the fault sensing consistency value of a fault assignment solution is greater than the fault sensing consistency threshold, mark this fault assignment solution as a fault-consistent solution.

[0011] Furthermore, the fault sensing consistency value of the fault assignment solution is determined based on the following method: The random simulation model for potential safety hazards conducts a one-period simulation on the fault assignment solution. During the simulation process, each Internet of Things sensor entity within the random simulation model for potential safety hazards performs real-time data acquisition. After the simulation ends, determine the sensing feature difference values of various types of Internet of Things sensors, set a sensing feature difference threshold. When the sensing feature difference value of a type of Internet of Things sensor is less than the sensing feature difference threshold, increase the number of sensing feature consistencies by one, and mark the number of sensing feature consistencies as Sum and average the sensing feature difference values of various types of Internet of Things sensors to calculate the sensing feature difference average , through Calculate to obtain the fault sensing consistency value of this fault assignment solution , where a1 is the first coefficient.

[0012] Furthermore, the sensing feature gap value of a type of IoT sensor is determined based on the following method: Determine an IoT sensor of a type in the potential safety hazard location, obtain the acquisition data features of this type of IoT sensor, synchronously obtain the acquisition data features of the IoT sensor entity of this type in the potential hazard random simulation model, combine the two acquisition data features into a feature comparison group, obtain the sensing feature gap model of this type of IoT sensor, use the feature comparison group as the input data of the sensing feature gap model, and the sensing feature gap model outputs the sensing feature gap value of this type of IoT sensor.

[0013] Furthermore, determine the safety inspection order of the devices based on the fault consistency scheme, and conduct safety inspections on the devices based on the safety inspection order: Determine the fault consistency times of each device entity based on the fault consistency scheme, sort all device entities in descending order according to the values of the fault consistency times, and conduct safety inspections on the corresponding devices of the device entities in the sorted order.

[0014] Furthermore, the fault consistency times of the device entity are determined based on the following method: Select a device entity, obtain all fault consistency schemes, and when a fault consistency scheme targets this device entity, increase the fault consistency times by one.

[0015] Furthermore, a smart park safety management method based on the Internet of Things includes the following steps: Step 1: Determine the safety critical locations in the park and regularly determine the safety feedback values of each safety critical location; Step 2: Based on the comparison result between the safety feedback value of the safety critical location and the safety feedback boundary value, determine whether a potential safety hazard location appears; Step 3: When a potential safety hazard location appears, construct a potential hazard random simulation model for the potential safety hazard location; Step 4: The potential hazard random simulation model generates i fault assignment schemes, and select the fault consistency schemes from the fault assignment schemes; Step 5: Determine the safety inspection order of the devices based on the fault consistency scheme, and conduct safety inspections on the devices based on the safety inspection order.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The method of the present invention combines simulation technology to simulate the environment of the potential safety hazard location, and simulates the data acquisition of various IoT sensors under different potential hazard conditions and fault conditions in the potential safety hazard location, and efficiently determines the fault consistency schemes that conform to the actual data acquisition of various IoT sensors. Combining all the fault consistency schemes, it effectively shortens the inspection time, improves the overall safety management efficiency, ensures that the park can quickly return to the normal operation state, and reduces the adverse effects such as production stagnation and safety accidents caused by equipment failures; 2. The system of the present invention sets up a park safety management module, a fault solution analysis module, and an equipment safety inspection module. Through various types of Internet of Things sensors, regular safety monitoring is carried out on key safety locations in the park, and potential safety hazard locations are efficiently and accurately identified. Combining simulation technology, the environment of potential safety hazard locations is simulated, and the safety inspection order of each device in the potential safety hazard locations is determined to ensure quick locking of faulty devices and determination of the cause of the fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a system module diagram of a smart park safety management system based on the Internet of Things; Figure 2 It is a flowchart for determining the safety feedback value of key safety locations; Figure 3 It is a flowchart for determining the sensing feature gap value of Internet of Things sensors; Figure 4 It is a method flowchart of a smart park safety management method based on the Internet of Things. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Embodiment 1: Refer to Figures 1 to 3 , a smart park safety management system based on the Internet of Things, including a park safety management module, a fault solution analysis module, and an equipment safety inspection module.

[0019] Park safety management module: Determine the key safety locations in the park (the key safety locations can be the computer room of the park, or the power distribution room of the park, or the warehouse of the park). Deploy various types of Internet of Things sensors inside each key safety location (the types of Internet of Things sensors include gas sensors, smoke sensors, temperature sensors, and humidity sensors). Regularly determine the safety feedback value of each key safety location, and set a safety feedback boundary value (the safety feedback boundary value is a preset value used to compare with the safety feedback value). When the safety feedback value of a key safety location is greater than the safety feedback boundary value, mark this key safety location as a potential safety hazard location (when the safety feedback value of a key safety location is less than or equal to the safety feedback boundary value, no marking is performed).

[0020] The safety feedback value of the key safety location is determined based on the following method: Select a key safety location, determine the various types of Internet of Things sensors inside this key safety location, obtain the acquisition data features of the various types of Internet of Things sensors, combine the acquisition data features of the various types of Internet of Things sensors into a key location feature set in the form of a feature set, obtain the safety feedback model corresponding to this key safety location, use the key location feature set as the input data of the safety feedback model, and the safety feedback model outputs the safety feedback value of this key safety location.

[0021] The acquisition data features of the Internet of Things sensors are obtained based on the following method: Select a type of Internet of Things sensor, collect the acquisition data of the Internet of Things sensor within a cycle, perform feature extraction on the collected acquisition data (the methods of feature extraction include time-domain feature extraction and frequency-domain feature extraction), and extract the acquisition data features of the Internet of Things sensor.

[0022] Different safety-critical locations correspond to different safety feedback models. For example, each of the computer room and the power distribution room corresponds to a safety feedback model, and each safety feedback model is constructed based on a deep learning model. In this embodiment, taking the computer room and the power distribution room as examples, the construction process of the safety feedback model is disclosed.

[0023] Safety feedback model for the computer room: Construct a deep learning model, collect the m key location feature sets corresponding to the computer room, use the key location feature sets of the computer room as training data to train the deep learning model, assign a safety feedback value to each training data, and the value range of the safety feedback value is (1.0~50.0). The closer the value of the safety feedback value is to 50, the greater the potential safety hazard inside the computer room. Divide the training data into a training set, a validation set, and a test set, with a ratio of 70%:15%:15%. Train the training set, the validation set, and the validation set. After training is completed, the safety feedback model of the computer room is obtained.

[0024] Safety feedback model for the power distribution room: Construct a deep learning model, collect the m key location feature sets corresponding to the power distribution room, use the key location feature sets of the power distribution room as training data to train the deep learning model, assign a safety feedback value to each training data, and the value range of the safety feedback value is (1.0~50.0). The closer the value of the safety feedback value is to 50, the greater the potential safety hazard inside the power distribution room. Divide the training data into a training set, a validation set, and a test set, with a ratio of 70%:15%:15%. Train the training set, the validation set, and the validation set. After training is completed, the safety feedback model of the power distribution room is obtained.

[0025] Fault solution analysis module: When a safety hazard location appears, construct a hazard random simulation model for the safety hazard location. The hazard random simulation model generates i fault assignment solutions, determine the fault sensing consistency value of each fault assignment solution, set a fault sensing consistency threshold (the fault sensing consistency threshold is a preset value used to compare with the fault sensing consistency value). When the fault sensing consistency value of the fault assignment solution is greater than the fault sensing consistency threshold, mark the fault assignment solution as a fault-consistent solution (when the fault sensing consistency value of the fault assignment solution is less than or equal to the fault sensing consistency threshold, no marking is performed).

[0026] A random simulation model for potential hazards at the location of potential safety hazards is constructed based on the following method: Select a simulation software, obtain the design drawings of the location of potential safety hazards, create a model of the location of potential safety hazards in the simulation software based on the design drawings of the location of potential safety hazards, determine all the equipment within the location of potential safety hazards (the equipment can be distribution cabinets, server cabinets, ventilation equipment, etc.), as well as the corresponding models and locations of the equipment, add the corresponding equipment entities to the model of the location of potential safety hazards, and add the corresponding attribute parameters to each equipment entity. Determine various types of Internet of Things sensors within the location of potential safety hazards, as well as the corresponding locations of the Internet of Things sensors, add the corresponding Internet of Things sensor entities to the model of the location of potential safety hazards, and complete the construction of the random simulation model for potential hazards at the location of potential safety hazards. The random simulation model for potential hazards can randomly generate a fault assignment plan. The fault assignment plan randomly assigns fault parameters to random equipment entities (it is necessary to collect a large amount of fault data and technical information of equipment, establish a detailed fault parameter database. The fault assignment plan can randomly assign a fault parameter to a single equipment entity, such as assigning the parameter of damaged fan blades to the ventilation equipment entity. The fault assignment plan can also randomly assign multiple fault parameters to multiple equipment, such as assigning the parameter of damaged fan blades and the parameter of damaged motor to the ventilation equipment entity, and at the same time assigning the parameter of overheated CPU to the server cabinet entity). After adjustment, the Internet of Things sensor entities perform real-time data collection.

[0027] The fault sensing consistency value of the fault assignment plan is determined based on the following method: The random simulation model for potential hazards conducts a cycle of simulation on the fault assignment plan (for example, if the fault assignment plan is to assign the parameter of damaged fan blades to the ventilation equipment entity, then the random simulation model for potential hazards conducts a cycle of simulation under the parameter of damaged fan blades of the ventilation equipment entity. The cycle duration of the simulation is the same as the cycle duration for regularly determining the safety feedback values of each safety critical location). During the simulation process, each Internet of Things sensor entity in the random simulation model for potential hazards performs real-time data collection. After the simulation is completed, determine the sensing feature gap values of various types of Internet of Things sensors, set a sensing feature gap threshold (the sensing feature gap threshold is a preset value used to compare with the sensing feature gap value). When the sensing feature gap value of a type of Internet of Things sensor is less than the sensing feature gap threshold, increase the number of sensing features in agreement by one, mark the number of sensing features in agreement as Sum and calculate the average value of the sensing feature gap values of various types of Internet of Things sensors to obtain the average sensing feature gap Through Calculate to obtain the fault sensing consistency value of this fault assignment plan where a1 is the first coefficient, and the value of a1 is 1.98.

[0028] The sensing feature gap value of a type of Internet of Things (IoT) sensor is determined in the following manner: Determine an IoT sensor of a certain type in the location of potential safety hazards, obtain the acquisition data features of this type of IoT sensor, synchronously obtain the acquisition data features of the entity of this type of IoT sensor in the random simulation model of potential hazards, combine the two sets of acquisition data features into a feature comparison group, obtain the sensing feature gap model of this type of IoT sensor, use the feature comparison group as the input data of the sensing feature gap model, and the sensing feature gap model outputs the sensing feature gap value of this type of IoT sensor.

[0029] Different types of IoT sensors correspond to different sensing feature gap models. For example, a gas sensor and a smoke sensor each correspond to a sensing feature gap model. Each sensing feature gap model is constructed based on a deep learning model. In this embodiment, taking the gas sensor and the smoke sensor as examples, the construction process of the sensing feature gap model is disclosed.

[0030] Sensing feature gap model of gas sensor: Construct a deep learning model, collect n feature comparison groups of the gas sensor. Each feature comparison group contains the acquisition data features of a gas sensor and the acquisition data features of the entity of the gas sensor. Use the feature comparison groups of the gas sensor as the training data to train the deep learning model, and assign a sensing feature gap value to each training data. The value range of the sensing feature gap value is (0.0~10.0). The closer the sensing feature gap value is to 0, the more similar the acquisition data features of the gas sensor and the acquisition data features of the entity of the gas sensor are. Divide the training data into a training set, a validation set, and a test set in the ratio of 60%:20%:20%, and train the training set, the validation set, and the test set. After training is completed, the sensing feature gap model of the gas sensor is obtained.

[0031] Sensing feature gap model of smoke sensor: Construct a deep learning model, collect n feature comparison groups of the smoke sensor. Each feature comparison group contains the acquisition data features of a smoke sensor and the acquisition data features of the entity of the smoke sensor. Use the feature comparison groups of the smoke sensor as the training data to train the deep learning model, and assign a sensing feature gap value to each training data. The value range of the sensing feature gap value is (0.0~10.0). The closer the sensing feature gap value is to 0, the more similar the acquisition data features of the smoke sensor and the acquisition data features of the entity of the smoke sensor are. Divide the training data into a training set, a validation set, and a test set in the ratio of 60%:20%:20%, and train the training set, the validation set, and the test set. After training is completed, the sensing feature gap model of the smoke sensor is obtained.

[0032] Device safety inspection module: Determine the number of consistent failures of each device entity based on the consistent failure scheme, sort all device entities in descending order according to the value of the number of consistent failures, and perform safety inspections on the corresponding devices of the device entities in the sorted order.

[0033] The number of consistent failures of a device entity is determined based on the following method: Select a device entity and obtain all consistent failure schemes. When a consistent failure scheme targets this device entity (for example, if the selected ventilation device entity and the consistent failure scheme is to assign the parameter of damaged fan blades to the ventilation device entity, then this consistent failure scheme targets the ventilation device entity), increase the number of consistent failures by one.

[0034] Set up a park safety management module, a failure scheme analysis module, and a device safety inspection module. Regularly monitor the safety-critical locations in the park through multi-type IoT sensors, efficiently and accurately identify the locations of potential safety hazards, simulate the environment of the locations of potential safety hazards using simulation technology, determine the safety inspection order of each device in the locations of potential safety hazards, and ensure quick locking of faulty devices and determination of the cause of the fault.

[0035] Example 2: Refer to Figure 4 , A smart park safety management method based on the Internet of Things, including the following steps: Step 1: Determine the safety-critical locations in the park and regularly determine the safety feedback values of each safety-critical location.

[0036] Step 2: Based on the comparison result between the safety feedback value of the safety-critical location and the safety feedback boundary value, determine whether there are locations of potential safety hazards.

[0037] Step 3: When there are locations of potential safety hazards, construct a random hazard simulation model for the locations of potential safety hazards.

[0038] Step 4: The random hazard simulation model generates i failure assignment schemes, and select the consistent failure schemes from the failure assignment schemes.

[0039] Step 5: Determine the safety inspection order of the devices based on the consistent failure schemes, and perform safety inspections on the devices based on the safety inspection order.

[0040] The above method combines simulation technology to simulate the environment of the locations of potential safety hazards, and simulates the data collection of various IoT sensors under different hazard conditions and fault conditions in the locations of potential safety hazards. It efficiently determines the consistent failure schemes that match the actual data collection of various IoT sensors. Combining all the consistent failure schemes can effectively shorten the inspection time, improve the overall safety management efficiency, ensure that the park can resume normal operation as soon as possible, and reduce the adverse effects such as production stagnation and safety accidents caused by equipment failures.

[0041] The above formulas are all dimensionless and take their numerical values for calculation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0042] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0043] It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0044] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0045] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0046] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0047] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0048] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent park security management system based on the Internet of Things, characterized in that It includes a park security management module, a fault scheme analysis module, and an equipment security inspection module; The park security management module: determines the security critical locations within the park, regularly determines the security feedback values of each security critical location, and determines whether there are security hazard locations based on the comparison result between the security feedback value of the security critical location and the security feedback boundary value; The fault scheme analysis module: when there are security hazard locations, constructs a hazard random simulation model for the security hazard locations, the hazard random simulation model generates i fault assignment schemes, and selects the fault consistent schemes from the fault assignment schemes; The equipment security inspection module: determines the security inspection order of the equipment based on the fault consistent schemes, and conducts security inspections on the equipment based on the security inspection order.

2. The intelligent park security management system based on the Internet of Things according to claim 1, characterized in that, The security feedback value of the security critical location is determined based on the following method: select a security critical location, determine various types of Internet of Things sensors within the security critical location, obtain the acquisition data characteristics of various types of Internet of Things sensors, combine the acquisition data characteristics of various types of Internet of Things sensors into a key location feature set in the form of a feature set, obtain the security feedback model corresponding to the security critical location, use the key location feature set as the input data of the security feedback model, and the security feedback model outputs the security feedback value of the security critical location.

3. The intelligent park security management system based on the Internet of Things according to claim 2, wherein The acquisition data characteristics of the Internet of Things sensors are obtained based on the following method: select a type of Internet of Things sensor, collect the acquisition data of the Internet of Things sensor within a period, perform feature extraction on the collected acquisition data, and extract the acquisition data characteristics of the Internet of Things sensor.

4. A smart campus security management system based on the Internet of Things according to claim 1, characterized in that, Select the fault consistent schemes from the fault assignment schemes: determine the fault sensing consistent value of each fault assignment scheme, set the fault sensing consistent threshold, and when the fault sensing consistent value of the fault assignment scheme is greater than the fault sensing consistent threshold, mark the fault assignment scheme as a fault consistent scheme.

5. The intelligent park security management system based on the Internet of Things according to claim 4, characterized in that, The fault sensing consistency value of the fault endowment scheme is determined based on the following method: The hidden danger random simulation model conducts a one-cycle simulation on the fault endowment scheme. During the simulation process, each Internet of Things sensor entity in the hidden danger random simulation model collects data in real time. After the simulation ends, the sensing feature gap values of various types of Internet of Things sensors are determined, and a sensing feature gap threshold is set. When the sensing feature gap value of a certain type of Internet of Things sensor is less than the sensing feature gap threshold, the number of sensing feature consistencies is increased by one, and the number of sensing feature consistencies is marked as , and the sum mean of the sensing feature gap values of various types of Internet of Things sensors is calculated to obtain the sensing feature gap mean , through calculate to obtain the fault sensing consistency value of this fault endowment scheme , where a1 is the first coefficient.

6. The intelligent park security management system based on the Internet of Things according to claim 5, characterized in that, The sensing feature gap value of a type of Internet of Things sensor is determined based on the following method: determine a type of Internet of Things sensor in the security hazard location, obtain the acquisition data characteristics of the type of Internet of Things sensor, synchronously obtain the acquisition data characteristics of the type of Internet of Things sensor entity in the hazard random simulation model, combine the two acquisition data characteristics into a feature comparison group, obtain the sensing feature gap model of the type of Internet of Things sensor, use the feature comparison group as the input data of the sensing feature gap model, and the sensing feature gap model outputs the sensing feature gap value of the type of Internet of Things sensor.

7. The intelligent park security management system based on the Internet of Things according to claim 1, characterized in that Determine the security inspection order of the equipment based on the fault consistent schemes, and conduct security inspections on the equipment based on the security inspection order: determine the fault consistent times of each equipment entity based on the fault consistent schemes, sort all the equipment entities in descending order according to the value of the fault consistent times, and conduct security inspections on the corresponding equipment of the equipment entities in sequence according to the sorting.

8. An intelligent park security management system based on the Internet of Things according to claim 7, characterized in that, The fault consistent times of the equipment entity are determined based on the following method: select an equipment entity, obtain all the fault consistent schemes, and when a fault consistent scheme is for the equipment entity, increase the fault consistent times by one.

9. A security management method for an intelligent park based on the Internet of Things, applied to the security management system for an intelligent park based on the Internet of Things according to any one of claims 1-8, characterized in that It includes the following steps: Step 1: Determine the safety-critical locations within the park and regularly determine the safety feedback values of each safety-critical location; Step 2: Based on the comparison result between the safety feedback value of the safety-critical location and the safety feedback boundary value, determine whether there is a safety hazard location; Step 3: When there is a safety hazard location, construct a random simulation model for the safety hazard location; Step 4: The random simulation model for hazards generates i fault assignment schemes and selects the fault-consistent scheme from the fault assignment schemes; Step 5: Determine the safety inspection order of the equipment based on the fault-consistent scheme and conduct a safety inspection on the equipment based on the safety inspection order.

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