Park safety management and control method, system and equipment based on industrial Internet of Things, and medium

Abstract

The invention discloses a park safety management and control method, system and device based on an industrial internet of things, and a medium, and relates to the technical field of park safety management and control. The invention provides a park safety management and control method based on an industrial Internet of Things, and the method comprises the steps: obtaining an intrusion detection region for carrying out the safety early warning of a first region according to the first region for placing a preset article, and determining a first detection boundary of the intrusion detection region; adjusting a first detection boundary of an intrusion detection area to be a second detection boundary based on a risk factor of a preset article in the first area; monitoring the intrusion detection area in real time, and obtaining intrusion information in a preset time period; and according to the intrusion information, evaluating a safety risk coefficient of the park, and based on the safety risk coefficient, determining whether alarm information is sent out or not.

Description

Technical Field

[0001] The present application relates to the field of industrial park security management and control technology, and in particular to a method, system, equipment and medium for industrial park security management and control based on the Industrial Internet of Things. Background Art

[0002] Industrial Internet of Things (IIoT) technology is being increasingly applied across various industrial sectors, becoming a key driver of smart manufacturing and the development of smart industrial parks. By integrating advanced technologies such as sensors, network communications, cloud computing, and big data analytics, the IIoT enables interoperability between devices, real-time data collection and transmission, and data-driven intelligent decision-making and optimization. In industrial park management, the application of IIoT technology enables park managers to more accurately understand the usage of various resources, the operating status of equipment, and the movement of personnel within the park, effectively improving operational efficiency and management levels.

[0003] As a complex integrating industrial production, logistics and warehousing, R&D, and innovation, the safety of industrial parks is directly related to the normal operation of enterprises, the safety of employees and property, and the stability of the surrounding environment. Especially in industrial parks involving high-risk items such as hazardous chemicals and flammable and explosive materials, the consequences of a safety accident would be disastrous. Dynamic safety management and control of industrial parks to promptly identify and eliminate potential safety hazards are crucial measures to ensure the safe and stable operation of industrial parks. Summary of the Invention

[0004] The main purpose of this application is to provide a park security management method, system, equipment and medium based on the industrial Internet of Things, aiming to solve the technical problem of delayed park security management in the existing technology.

[0005] To achieve the above objectives, in a first aspect, the present application provides a campus security management and control method based on the Industrial Internet of Things, comprising: According to a first area for placing preset objects, an intrusion detection area for issuing a security warning for the first area is obtained, and a first detection boundary of the intrusion detection area is determined; Based on the risk factor of the preset objects in the first area, adjusting the first detection boundary of the intrusion detection area to the second detection boundary; Monitor the intrusion detection area in real time and obtain intrusion information within a preset time period; According to the intrusion information, the security risk factor of the park is evaluated, and based on the security risk factor, it is determined whether to issue an alarm information.

[0006] Optionally, the step of obtaining an intrusion detection area for issuing a security warning for the first area based on the first area for placing the preset object, and determining a first detection boundary of the intrusion detection area includes: Acquire, according to the location information of the first area, at least one second area associated with the first area; Determining whether the actual distance between the first area and the second area is greater than a preset distance; If not, merging the first area and the second area into a third area, and setting the first detection boundary based on the third area; If so, a first detection boundary is set for the first area and the second area respectively.

[0007] Optionally, the step of adjusting the first detection boundary of the intrusion detection area to the second detection boundary based on the risk factor of the preset object in the first area includes: The second detection boundary displacement expression is:

[0008] in, Indicates the radius of the first detection boundary, n indicates the number of preset item types, represents the spatial allocation weight of the i-th category preset items, represents the hazardous potential energy of the i-th type of preset items, and represents the exponential parameter, represents the critical danger threshold, represents the environmental coupling factor, represents the time response function.

[0009] Optionally, the hazardous potential energy expression of the i-th category preset item is:

[0010] Where, Indicates the hazard level coefficient, represents the quality of the i-th type of preset items, represents the reference mass, represents the nonlinear development coefficient, represents the reference temperature, Indicates the current temperature. Indicates temperature sensitivity; The spatial allocation weight expression of the i-th category preset item is:

[0011] Where, Indicates the initial distance between the i-th type of preset object and the detection boundary, represents the weight parameter, Indicates the initial distance of the j-th type of preset object from the detection boundary.

[0012] Alternatively, the expression for the environmental coupling factor is:

[0013] Where, Indicates the current wind speed. represents the reference wind speed, represents the angle between the wind direction and the boundary normal vector, represents the elevation difference between regions, represents the terrain attenuation coefficient, Indicates the real-time relative humidity. Indicates the reference humidity value, Represents the standard deviation of the humidity effect.

[0014] Optionally, the expression of the time response function is:

[0015] Where, represents the system memory time constant, represents the initial moment, Indicates the current moment, Indicates the detection delay time, represents the time standard deviation, s is the integral variable, represents the historical moment, represents the error function.

[0016] Optionally, the expression for evaluating the security risk coefficient of the park based on the intrusion information is:

[0017] Where, represents the g-th type intrusion parameter, represents the number of invasion types, represents a nonlinear mapping function, represents the weight of the g-th type intrusion parameter, represents the time sensitivity coefficient, represents the critical time threshold, Indicates the invasion residence time, represents the maximum normalized depth in the intrusion point, Indicates the dynamic geographic reference depth, represents the spatial sensitivity coefficient, Represents the nonlinear index.

[0018] In a second aspect, the present application provides a campus security management and control system based on the Industrial Internet of Things, including a user platform, a service platform, a management platform, a sensor network platform, and an object platform that establish communication in sequence; The sensor network platform is configured to: Acquire a first area for placing a preset object and set a first detection boundary of an intrusion detection area; Based on the risk factor of the preset objects in the first area, adjusting the first detection boundary of the intrusion detection area to the second detection boundary; Monitor the intrusion detection area in real time and obtain intrusion information within a preset time period; The management platform is configured to: Based on the intrusion information, the security risk factor of the park is assessed.

[0019] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the above method when executing the computer program.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, characterized in that it stores a computer program that can be loaded by a processor and execute the above method.

[0021] Beneficial effects that this application can achieve: Embodiments of the present application propose a campus security management and control method, system, device, and medium based on the Industrial Internet of Things. The proposed campus security management and control method based on the Industrial Internet of Things includes: obtaining an intrusion detection area for issuing a security warning for a first area for placing preset items, and determining a first detection boundary of the intrusion detection area; adjusting the first detection boundary of the intrusion detection area to a second detection boundary based on the risk factor of the preset items within the first area; monitoring the intrusion detection area in real time to obtain intrusion information within a preset time period; assessing the security risk factor of the campus based on the intrusion information, and determining whether to issue an alarm based on the security risk factor. By obtaining the first area for placing preset items and dynamically adjusting the boundaries of the intrusion detection area based on the risk factor of the preset items within the area, the risk assessment is more closely aligned with the actual risk situation. This dynamic adjustment mechanism ensures focused monitoring of high-risk areas, thereby improving the accuracy of risk assessment. Real-time monitoring of the intrusion detection area enables timely detection and response to potential security threats. By obtaining intrusion information within the preset time period, campus managers can quickly understand the security status of the campus and provide strong support for taking appropriate measures. The real-time monitoring and dynamic adjustment mechanism enables risk assessment to reflect dynamic changes in the campus in real time, avoiding the lag associated with traditional methods. It helps park managers to identify and solve problems in a timely manner, thereby effectively reducing the probability of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the process of the campus security management and control method according to an embodiment of the present application; Figure 2 A schematic diagram of the framework of the service platform involved in the embodiments of this application; Figure 3 A schematic diagram of the framework of the management platform involved in the embodiments of this application; Figure 4 This is a schematic diagram of the framework of the sensor network platform involved in the embodiments of this application.

[0023] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0026] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0027] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0028] Example 1 Reference Figure 1 The first embodiment of the present application provides a campus security management and control method based on the Industrial Internet of Things, including the following steps: S10. According to a first area for placing preset objects, an intrusion detection area for issuing a security warning for the first area is obtained, and a first detection boundary of the intrusion detection area is determined.

[0029] Optionally, the first area is a specific space within the park for storing hazardous materials, such as a hazardous chemical warehouse or gas storage tank area. For example, the pre-defined types of items include explosives such as ammonium nitrate and trinitrotoluene; flammable gases and liquids such as liquefied petroleum gas and gasoline; and toxic chemicals such as chlorine and sodium cyanide. Digital modeling is performed using the following methods: The three-dimensional coordinate boundaries of the area are delineated using a geographic information system (GIS). UWB positioning base stations (model DW3000) are deployed, and positioning tags are placed at the four corners of the area to create an electronic fence. The specific scope of the first area is determined by combining the park's floor plan, building structure information, and the characteristics of the pre-defined items. Information on the items within the first area is entered into a database, including: hazard category (based on GHS classification, such as explosives, flammable liquids, and toxic gases); storage parameters (mass, container pressure, and stacking height); and emergency response plans (leakage disposal procedures and fire extinguishing agent types). LiDAR sensors (Velodyne VLP-16) are installed every 5 meters around the perimeter of the first area, such as along the interior walls of a warehouse, to form a 3D point cloud monitoring network. Deploy an audible and visual alarm system (120dB buzzer + strobe light) and link it to the access control system. The intrusion detection zone is used to provide security warnings for the primary zone. Typically, the intrusion detection zone is located a certain distance outside the primary zone.

[0030] S20: Based on the risk factor of the preset object in the first area, adjust the first detection boundary of the intrusion detection area to a second detection boundary.

[0031] Optionally, the detection boundary can be adjusted based on risk factors. Risk factors may change with the increase or decrease of stored goods. Constantly monitoring the primary detection boundary can hinder accurate risk assessments when the quantity or type of goods changes. A risk factor quantification model dynamically estimates the risk factor for the primary area. During boundary adjustments, new coordinates are transmitted via the LoRaWAN protocol to smart security posts (with built-in GPS / Beidou dual-mode positioning, accuracy of ±10cm). A movable explosion-proof wall (with a pressure resistance of 1.5MPa and automatic deployment within 30 seconds) is deployed in the expanded area.

[0032] S30: Monitor the intrusion detection area in real time to obtain intrusion information within a preset time period.

[0033] Optional, real-time monitoring of intrusion areas and acquisition of intrusion information in intrusion areas, through a thermal imaging camera (FLIRA700), whose temperature sensitivity is ≤50mK, to identify human heat sources at night. Use a multi-spectral camera (wavelength 400-1700nm) to distinguish metal tools from ordinary objects. Use vibrating optical fiber (BOTDA technology), laid along the border, with a positioning accuracy of ±0.5 meters, to identify climbing and shearing behaviors. Use millimeter wave radar (77GHz), penetrate rain and fog, and detect the speed of moving targets (0.1-30m / s). Run the YOLOv5 model to classify the target type (person / vehicle / animal) in real time, S40: Evaluate the security risk factor of the park based on the intrusion information, and determine whether to issue an alarm based on the security risk factor.

[0034] Optionally, assess the park's security risk factor and, based on the resulting risk factors, set a multi-level risk rating. This risk rating is pushed to patrol personnel's AR glasses (HoloLens 2) via the MQTT protocol, overlaid with digital twin trajectory predictions for real-time feedback. A park security risk assessment indicator system is constructed based on the type and characteristics of intrusion information. Indicators may include intrusion frequency, intrusion severity, and target sensitivity. Based on the security risk factor assessment results, for example, when the security risk factor exceeds a set threshold, a risk warning is issued promptly. Park managers should take appropriate countermeasures based on the warning information, such as strengthening monitoring, deploying additional security personnel, and activating emergency response plans, to mitigate security risks. Fixed boundaries may provide insufficient coverage in high-risk areas and excessive coverage in low-risk areas. During low-risk periods, the monitoring range can be narrowed to reduce equipment energy consumption (for example, disabling some sensors at night).

[0035] Example 2 Based on Example 1, this example provides a campus security management and control method based on the Industrial Internet of Things, including the following steps: S10. According to a first area for placing preset objects, an intrusion detection area for issuing a security warning for the first area is obtained, and a first detection boundary of the intrusion detection area is determined.

[0036] Optionally, the step of obtaining an intrusion detection area for issuing a security warning for the first area based on the first area for placing the preset object, and determining a first detection boundary of the intrusion detection area includes: S101. Acquire at least one second area associated with the first area according to location information of the first area; Specifically, the park's geographic information system (GIS) or IoT positioning technology is used to accurately analyze the location information of the first area, including its latitude and longitude coordinates, its specific location in the park's floor plan, etc. Combined with the park's building structure diagram, functional zoning map, etc., the boundaries and surrounding environment of the first area are further confirmed. Based on the location information of the first area, at least one second area that is spatially adjacent, functionally related, or has potential safety implications is identified. The second area may include storage areas, passageways, equipment rooms, office areas, etc. adjacent to the first area. The relevant information of the first and second areas, including area functions, types of stored items, frequency of personnel activities, etc., is integrated to provide a basis for subsequent distance determination and boundary setting.

[0037] S102: Determine whether the actual distance between the first area and the second area is greater than a preset distance; Specifically, using GIS or IoT positioning technology, calculate the actual distance between the first and second areas. This distance calculation should take into account factors such as the park's actual topography and building layout to ensure accuracy. A reasonable preset distance should be set based on the park's safety management requirements, the hazardous nature of the pre-set items, and the configuration of monitoring resources. This preset distance should ensure that the potential impact range is effectively covered in the event of a safety incident while avoiding waste of monitoring resources. The calculated actual distance is compared with the preset distance to determine the spatial relationship between the first and second areas.

[0038] S103: If the actual distance is less than or equal to the preset distance, merge the first area and the second area into a third area, and set the first detection boundary based on the third area; Specifically, when the actual distance between the first area and the second area is less than or equal to the preset distance, the two areas are merged into a new third area. The fusion process should take into account factors such as the functional characteristics and security requirements of the area to ensure that the merged third area has reasonable boundaries and monitoring range. Based on the merged third area, the first detection boundary of the intrusion detection area is set. The first detection boundary should ensure coverage of the third area and its surrounding areas that may be affected, while taking into account the actual configuration of monitoring resources and monitoring effects. Based on factors such as the risk factors of preset items in the third area and the frequency of human activities, the first detection boundary is dynamically adjusted and optimized to improve the accuracy and efficiency of monitoring.

[0039] S104: If the actual distance is greater than the preset distance, set a first detection boundary for the first area and the second area respectively.

[0040] Specifically, when the actual distance between the first and second areas is greater than the preset distance, independent first detection boundaries are set for each of these areas. Boundary setting should take into account factors such as the functional characteristics and safety requirements of each area, as well as the risk factors of pre-set items. When setting independent boundaries, ensure that the boundaries between adjacent areas are coordinated and consistent to avoid monitoring blind spots or overlapping areas. Effective connection and coordinated monitoring between adjacent areas can be achieved by adjusting boundary positions and adding monitoring equipment. The established first detection boundaries are monitored in real time to ensure their integrity and effectiveness. Once an anomaly or intrusion is detected at the boundary, a warning message should be issued promptly and appropriate countermeasures should be implemented.

[0041] S20: Based on the risk factor of the preset object in the first area, adjust the first detection boundary of the intrusion detection area to a second detection boundary.

[0042] Optionally, the step of adjusting the first detection boundary of the intrusion detection area to the second detection boundary based on the risk factor of the preset object in the first area includes: The second detection boundary displacement expression is:

[0043] in, Indicates the radius of the first detection boundary, n indicates the number of preset item types, represents the spatial allocation weight of the i-th category preset items, represents the hazardous potential energy of the i-th type of preset items, and represents the exponential parameter, represents the critical danger threshold, represents the environmental coupling factor, represents the time response function.

[0044] This represents the initial detection boundary radius (the first detection boundary), which is calculated based on the inherent properties of the hazardous material (mass, pressure, toxicity, etc.). This serves as a baseline reference value, and all dynamic adjustments are based on this initial value for expansion or contraction. Ensure that boundary adjustments are physically interpretable.

[0045] represents the dangerous potential energy aggregation term, It represents the dangerous potential energy of the i-th type of preset items. There may be multiple types of preset items placed in the first area. Represents the spatial weight, which is distributed through Softmax. The threat intensity of multiple dangerous goods categories is mapped to a scalar value based on the item's hazard level, quality, and temperature sensitivity, reflecting the need for larger perimeters for close-quarter storage of high-risk items.

[0046] represents the normalization and logarithmic smoothing terms, This represents the critical hazard threshold, corresponding to international safety standards (such as the NFPA Hazardous Materials Storage Code). A logarithmic function applies nonlinear compression to the total hazard level. The total hazard level is compared to the standard threshold to determine whether boundary adjustments are necessary. This diminishing marginal effect prevents excessive boundary expansion caused by high hazard values. This ensures that boundary adjustments remain stable as the hazard level increases, enhancing system robustness.

[0047] represents the nonlinear sensitivity correction term, This is an adjustable index parameter used to control risk sensitivity. When the total risk exceeds the threshold, the index increases by an adjustment amount to amplify high-risk scenarios. As the total risk approaches the threshold, the adjustment amount changes gradually to suppress low-risk fluctuations, enhance the response to extreme risk events, and reduce false triggering of low-risk disturbances.

[0048] It represents the environmental coupling factor, quantifies the enhancement or weakening effect of the environment on hazard propagation, and realizes the dynamic correction of physical field coupling.

[0049] It represents the time response function, reflects the time-varying characteristics of risk, and ensures the cumulative response of the system to continuous threats.

[0050] Optionally, the hazardous potential energy expression of the i-th category preset item is:

[0051] Where, Indicates the hazard level coefficient, represents the quality of the i-th type of preset items, represents the reference mass, represents the nonlinear development coefficient, represents the reference temperature, Indicates the current temperature. Indicates temperature sensitivity; Indicates the hazard level coefficient, which is a quantitative classification of the hazard level of an item according to international standards (such as GHS), usually an exponential growth value (such as 1-6). For example, explosives , corrosive liquids It directly determines the inherent hazard intensity of an item. High-hazard items contribute exponentially to potential energy. As a base value, it dominates the magnitude of hazard potential energy and ensures differentiation between items of different hazard categories.

[0052] It represents mass normalization, which is used to eliminate the influence of absolute mass dimension, unify storage capacity of different scales to a comparable scale, convert mass into relative ratio, and avoid numerical overflow (such as direct calculation of kiloton storage resulting in excessively large values).

[0053] represents the nonlinear amplification factor, and its expression is: It increases with the increase of the danger level. For example: ,but This is used to enhance the mass impact of high-risk items, reflecting the fact that "a small amount of high-risk items may be more dangerous than a large amount of low-risk items." This is achieved through a power function, which achieves nonlinear amplification, making the higher the hazard level, the more sensitive the mass change is to potential energy.

[0054] represents the temperature correction term, , is the reference temperature value, , represents the temperature sensitivity and controls the steepness of the response curve. , the overall correction term range is [0,2], when When , the correction term is 1 (no correction), when As the temperature approaches 2, the hazard potential increases. Quantify the impact of temperature increases on hazards (e.g., accelerating chemical reactions and increasing volatilization rates). Dynamically couple with ambient temperature to achieve thermodynamic risk correction.

[0055] The spatial allocation weight expression of the i-th category preset item is:

[0056] Where, Indicates the initial distance between the i-th type of preset items and the detection boundary, represents the weight parameter, Indicates the initial distance of the j-th type of preset object from the detection boundary.

[0057] The initial distance between the i-th category preset object and the detection boundary, The reciprocal of the distance converts the distance into "closeness". The closer the distance ( The smaller the value), count down The larger the value, the higher the weight distribution will be.

[0058] Represents the weight parameter, whose value is adjustable and controls the concentration of weight distribution. The larger it is, the more significant the weight advantage of close items is (the sharper the distribution); The smaller the value, the more evenly distributed the weights are (the distribution is smoother). It can flexibly adapt to different park layout requirements (such as high-risk areas requiring strong distance sensitivity). Softmax normalization, the denominator is the weight of all items. Sum and map the weights to probability distributions, satisfying , highlighting the importance of the nearest items while preserving global information. Through exponential functions and inverse operations, the weights of nearby items are significantly higher than those of distant items. When new items are added, the weights are automatically redistributed without manual intervention.

[0059] Optionally, the expression for the environmental coupling factor is:

[0060] Where, Indicates the current wind speed. represents the reference wind speed, represents the angle between the wind direction and the boundary normal vector, represents the elevation difference between regions, represents the terrain attenuation coefficient, Indicates the real-time relative humidity. Indicates the reference humidity value, Represents the standard deviation of the humidity effect.

[0061] represents the wind field effect term, Indicates the current wind speed (unit: m / s), Indicates the reference wind speed value (such as ), Indicates the angle between the wind direction and the boundary normal vector (0° for downwind direction and 180° for upwind direction). Approaching 1, the wind field accelerates the spread of hazardous substances and the boundary needs to be expanded. As the wind speed approaches -1, it hinders diffusion and may reduce the boundary. An exponent of 0.7 reduces the marginal effect of high wind speeds and avoids excessive expansion.

[0062] represents the terrain attenuation term, It indicates the elevation difference between the hazard source area and the expansion direction (unit: meter). A positive value indicates that the hazard source is at a higher elevation. It represents the terrain attenuation coefficient, which is used to control the intensity of the elevation effect. , the denominator increases, the terrain term decreases, and the boundary expansion is suppressed (high ground may hinder the downward diffusion of substances). If the hazard source is located in the lowlands, , the denominator decreases, the terrain term increases, and the boundary needs to be expanded (hazardous substances are easily accumulated in low-lying areas).

[0063] represents the humidity correction term, Indicates the standard deviation of humidity influence, which controls the sensitivity of humidity deviation. , the index is negative, the value is less than 1, and the boundary expansion is suppressed (such as high humidity reduces the risk of dust explosion). , the index is positive, the value is greater than 1, and the boundary needs to be expanded (the risk of flammable materials in dry environments increases).

[0064] Through the dynamic coupling mechanism, namely, downwind + low-lying + dry: the wind field promotes diffusion, the terrain is unobstructed, low humidity increases the risk, and the boundary is significantly expanded. Upwind + highland + high humidity: the wind field inhibits diffusion, the terrain is blocked, and high humidity reduces the risk, significantly reducing the boundary. Through the exponent (0.7) and the denominator This approach avoids the distortions inherent in linear assumptions and better reflects actual physical laws. It quantifies the synergistic effects of wind, terrain, and humidity on hazard diffusion, responding in real time to environmental changes such as sudden changes in wind speed or a sudden drop in humidity. Each parameter has a clear physical meaning, facilitating adjustments and optimizations by operations and maintenance personnel.

[0065] Optionally, the expression for the time response function is:

[0066] Where, represents the system memory time constant, represents the initial moment, Indicates the current moment, Indicates the detection delay time, represents the time standard deviation, s is the integral variable, represents the historical moment, represents the error function.

[0067] Indicates that from the initial moment To the current moment , integrating the contributions of all historical events to reflect the cumulative effect over time. This ensures that the model not only relies on the current state but also considers the ongoing impact of historical events to avoid instantaneous noise interference.

[0068] represents the exponential decay term, Represents the system memory time constant (unit: seconds), which controls the decay rate of the impact of historical events. Indicates the current time and historical moments The time difference. Approaching That is, the time difference is small, Approaching 1, the retention time is fully affected. Much greater than , As it approaches 0, the impact of the event is forgotten. This implements an exponential forgetting mechanism, making the system more sensitive to recent events and the impact of distant events gradually fades.

[0069] represents the error function term, Indicates the detection delay time (unit: seconds), which represents the time lag from event occurrence to system detection confirmation. Indicates the time standard deviation (unit: seconds), which controls the uncertainty range of the detection delay. is the S-type error function, whose value varies between -1 and 1, but we usually focus on its non-negative part. The value transitions smoothly from 0 to 1, simulating a gradual response to the detection delay. The larger it is, the smoother the transition (high uncertainty in detection time), The smaller it is, the steeper the transition (higher detection accuracy). Simulate detection delay and time uncertainty to avoid the unreality of instantaneous response.

[0070] Furthermore, the mathematical expression of the error function is: .

[0071] Represents the normalization factor to ensure that the integration result is consistent with the time constant It is irrelevant to maintain dimensional consistency and numerical stability. As it increases, the integral result is scaled down to compensate for the slow decline of exponential decay. When it decreases, the integral result is amplified to balance the effect of rapid forgetting. This makes the response amplitudes under different time constants comparable and avoids the scale distortion of the result caused by parameter adjustment.

[0072] For ease of understanding, the following examples are given. It should be noted that the following examples are for illustration only and do not represent actual conditions.

[0073] Suppose an industrial park stores two types of dangerous goods: Class A items: flammable liquids (hazard level coefficient ), storage quality kg, distance from the initial detection boundary , current temperature .

[0074] Category B items: explosives (hazard level coefficient ), storage quality kg, distance from the initial detection boundary , current temperature .

[0075] Environmental parameters: wind speed , the angle between the wind direction and the boundary normal vector , elevation difference ,humidity .

[0076] System parameters, initial detection boundary radius , critical danger threshold , spatial weight parameter , exponential parameter Reference quality kg, temperature sensitivity coefficient .

[0077] For Category A items: ; ; For Category B items: ; ; Calculate spatial weights: ; ; Calculate the dangerous potential energy aggregation term: ; Normalization and logarithmic smoothing terms: ; Nonlinear sensitivity correction: ; Environmental coupling factors: ; Wind field item: ; Terrain items: ; Humidity item: ; Time response function (assuming it has been integrated): ; Final displacement: ; Adjusted detection boundary radius .

[0078] S30. Evaluate the security risk factor of the park based on the intrusion information, and determine whether to issue an alarm based on the security risk factor.

[0079] The objects of intrusive information may include unauthorized vehicles, personnel or animals.

[0080] S40: Evaluate the security risk factor of the park based on the intrusion information.

[0081] Optionally, the expression for evaluating the security risk coefficient of the park based on the intrusion information is:

[0082] Where, represents the g-th type intrusion parameter, represents the number of invasion types, represents a nonlinear mapping function, represents the weight of the g-th type intrusion parameter, represents the time sensitivity coefficient, represents the critical time threshold, Indicates the invasion residence time, represents the maximum normalized depth in the intrusion point, Indicates the dynamic geographic reference depth, represents the spatial sensitivity coefficient, Represents the nonlinear index.

[0083] Indicates the g-type intrusion parameters, including: Breaking into the depths, .

[0084] Dwell time, .

[0085] Path Angle .

[0086] Risks of carrying items .

[0087] Represents a nonlinear mapping function, for example: (Deep nonlinear saturation).

[0088] (Time increases gradually).

[0089] Represents the parameter weight, which is distributed through Softmax: ; Dedication level for parameters ( , ).

[0090] Mapping intrusion parameters of varying dimensions (depth, time, path, and cargo) into a unified risk value. Highly weighted parameters (such as cargo risk) dominate the risk value, reflecting priority. Quantifying multi-dimensional intrusion behavior avoids one-sided assessments based on a single parameter.

[0091] represents the time enhancement factor, Represents the time sensitivity coefficient, which controls the exponential growth rate of risk over time. Indicates the critical time threshold, after which the risk increases significantly. , the risk increases approximately linearly with the length of stay. , the risk growth rate rises sharply, reflecting the "time accumulation effect". It emphasizes the amplifying effect of retention time on risk and is suitable for chronic threat scenarios (such as leakage and latency).

[0092] Indicates the spatial threat factor: It represents the maximum normalized depth among all invasion points, reflecting the most serious spatial invasion.

[0093] Indicates the dynamic geographic datum depth, which is adjusted according to the hazard level.

[0094] Represents the spatial sensitivity coefficient, which controls the linear strength of the depth effect.

[0095] Represents a nonlinear exponent that amplifies the threat of depth exceeding the limit.

[0096] when , the factor is close to 1, and the risk is mainly determined by other parameters. , the factor increases in power law, reflecting the “crossing the boundary means high risk”. Quantify the severity of space invasion, especially for high-risk areas ( Smaller (more sensitive) security risks. The obtained security risk factor is compared with a preset threshold, and the comparison result determines whether to issue an alarm. The recipient of the alarm information can be a management personnel.

[0097] To facilitate understanding, the following examples are given: A liquid chlorine storage tank in a high-risk area of ​​an industrial park (hazard level coefficient k=4.5, critical depth ), an intrusion event occurs, the parameters are as follows: Depth of penetration: (normalized value, second detection boundary radius , actual penetration depth 42.5m).

[0098] Duration: Minutes, critical time threshold minute.

[0099] Path angle: The angle between the invasion path and the core area is 30°. .

[0100] Carrying risk, metal tools detected (hazard level coefficient ).

[0101] Parameter settings: , , , weight level , .

[0102] Obtained by calculation: Parameters: , , , .

[0103] Non-linear mapping: .

[0104] Weight calculation: .

[0105] Weighted sum calculation: .

[0106] Time enhancement factor: .

[0107] Space threat factors: .

[0108] Total risk factor .

[0109] Threshold setting, is green, is yellow, is red, When a red alert is triggered, the system automatically activates the sprinkler system to dilute hazardous materials. It shuts down ventilation in high-risk areas to prevent spread. It also pushes a response plan to emergency personnel or management personnel via AR glasses, displaying the intruder's real-time trajectory.

[0110] Example 3 Based on Example 1, this embodiment provides a campus security management and control system based on the Industrial Internet of Things, including a user platform, a service platform, a management platform, a sensor network platform, and an object platform that establish communication in sequence; The sensor network platform is configured to: According to a first area for placing preset objects, an intrusion detection area for issuing a security warning for the first area is obtained, and a first detection boundary of the intrusion detection area is determined; Based on the risk factor of the preset objects in the first area, adjusting the first detection boundary of the intrusion detection area to the second detection boundary; Monitor the intrusion detection area in real time and obtain intrusion information within a preset time period; According to the intrusion information, the security risk factor of the park is evaluated, and based on the security risk factor, it is determined whether to issue an alarm information.

[0111] The user platform is configured to provide front-end services to users; users obtain the required perception service information through the user platform, process the perception service information, and convert it into user perception information; users analyze the user perception information and make corresponding decisions based on their own wishes, and convert the user perception information into user control information through the corresponding information system and send it to the service platform, thereby expressing the user's corresponding service demand intention.

[0112] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., which realize user-end services through integration with user information system software.

[0113] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entity of the service platform includes various servers.

[0114] The management platform is configured to perform at least one of equipment operation status monitoring and management, data monitoring and management, equipment parameter management, and life cycle management; the management platform is the operation coordination platform of the Internet of Things, which may include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0115] The sensor network platform is configured to perform at least one of the following: network management, command management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides data communication, parsing, identification, and classification, preventing data from various object platforms from being directly aggregated on the management platform, which would result in data redundancy and inefficient data processing. The physical entities of the object platforms include various gateways, edge computing devices, and other devices.

[0116] The object platform is configured to perform specific production tasks such as production control, detection, and measurement; the physical entities in the object platform include various production equipment, sensors, etc.

[0117] Optionally, the sensor network platform includes a master database in communication with the management platform, and at least two sensor network sub-platforms in communication with the master database; Optionally, each sensor network sub-platform corresponds to an API function or API server.

[0118] Example 4 This embodiment provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, any of the above methods is implemented.

[0119] Example 5 This embodiment provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the above method.

[0120] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A park security management and control method based on industrial Internet of Things, characterized in that: include: According to a first area for placing preset objects, an intrusion detection area for issuing a security warning for the first area is obtained, and a first detection boundary of the intrusion detection area is determined; Based on the risk factor of the preset objects in the first area, adjusting the first detection boundary of the intrusion detection area to the second detection boundary; Monitor the intrusion detection area in real time and obtain intrusion information within a preset time period; According to the intrusion information, the security risk factor of the park is evaluated, and based on the security risk factor, it is determined whether to issue an alarm information.

2. The industrial park security management and control method based on the industrial Internet of Things according to claim 1, characterized in that: The step of obtaining an intrusion detection area for issuing a security warning for the first area based on the first area for placing the preset object, and determining a first detection boundary of the intrusion detection area includes: Acquire, according to the location information of the first area, at least one second area associated with the first area; Determining whether the actual distance between the first area and the second area is greater than a preset distance; If not, merging the first area and the second area into a third area, and setting the first detection boundary based on the third area; If so, a first detection boundary is set for the first area and the second area respectively.

3. The industrial park security management and control method based on the industrial Internet of Things according to claim 1, characterized in that: The step of adjusting the first detection boundary of the intrusion detection area to the second detection boundary based on the risk factor of the preset object in the first area includes: The second detection boundary displacement expression is: in, Indicates the radius of the first detection boundary, n indicates the number of preset item types, represents the spatial allocation weight of the i-th category preset items, represents the hazardous potential energy of the i-th type of preset items, and represents the exponential parameter, represents the critical danger threshold, represents the environmental coupling factor, represents the time response function.

4. The industrial park security management and control method based on the industrial Internet of Things according to claim 3 is characterized in that: The expression of the hazardous potential energy of the i-th type of preset items is: Where, Indicates the hazard level coefficient, represents the quality of the i-th type of preset items, represents the reference mass, represents the nonlinear development coefficient, represents the reference temperature, Indicates the current temperature. Indicates temperature sensitivity; The spatial allocation weight expression of the i-th category preset item is: Where, Indicates the initial distance between the i-th type of preset object and the detection boundary, represents the weight parameter, Indicates the initial distance of the j-th type of preset object from the detection boundary.

5. The industrial park security management and control method based on the industrial Internet of Things according to claim 3 is characterized in that: The expression of the environmental coupling factor is: Where, Indicates the current wind speed. represents the reference wind speed, represents the angle between the wind direction and the boundary normal vector, represents the elevation difference between regions, represents the terrain attenuation coefficient, Indicates the real-time relative humidity. Indicates the reference humidity value, Represents the standard deviation of the humidity effect.

6. The industrial park security management and control method based on the industrial Internet of Things according to claim 3 is characterized in that: The expression of the time response function is: Where, represents the system memory time constant, represents the initial moment, Indicates the current moment, Indicates the detection delay time, represents the time standard deviation, s is the integral variable, represents the historical moment, represents the error function.

7. The industrial park security management and control method based on the industrial Internet of Things according to claim 1, characterized in that: The expression for evaluating the security risk coefficient of the park based on the intrusion information is: Where, represents the g-th type intrusion parameter, represents the number of invasion types, represents a nonlinear mapping function, represents the weight of the g-th type intrusion parameter, represents the time sensitivity coefficient, represents the critical time threshold, Indicates the invasion residence time, represents the maximum normalized depth in the intrusion point, Indicates the dynamic geographic reference depth, represents the spatial sensitivity coefficient, Represents the nonlinear index.

8. A park security management and control system based on industrial Internet of Things, characterized by: It includes establishing a management platform, a sensor network platform and an object platform for communication in sequence; The management platform is configured to: According to a first area for placing preset objects, an intrusion detection area for issuing a security warning for the first area is obtained, and a first detection boundary of the intrusion detection area is determined; Based on the risk factor of the preset objects in the first area, adjusting the first detection boundary of the intrusion detection area to the second detection boundary; Monitor the intrusion detection area in real time and obtain intrusion information within a preset time period; According to the intrusion information, the security risk factor of the park is evaluated, and based on the security risk factor, it is determined whether to issue an alarm information.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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