Cloud network end collaborative security management and control method
By acquiring dynamic and static perception data to generate real-time risk characteristics and using preset analysis algorithms to generate risk warning reports, the problem of low flexibility in the monitoring range of traditional security systems is solved, and efficient security management is achieved.
Patent Information
- Application Number
- CN202512010237.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-05-01
AI Technical Summary
Traditional security systems are limited by the fixed location and cost of camera installations, resulting in low flexibility in monitoring range, missing data, and low security management efficiency.
By acquiring dynamic and static sensing data, real-time risk characteristics are generated, risk warning reports are generated using preset analysis algorithms, and security dispatch data is generated based on the reports, thus realizing cloud-network-terminal collaborative security management.
It improves the efficiency and accuracy of monitoring and management, and can accurately classify regional risks by combining different types of data, thereby achieving precise security scheduling.
Smart Images
Figure CN121966955A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of security monitoring technology, and in particular to a cloud-network-device collaborative security management method. Background Technology
[0002] With urban development, public safety in cities and key areas faces increasingly complex challenges. Traditional security systems generally monitor areas only through networking. However, due to the fixed location and cost of camera equipment, not all areas can be monitored, resulting in data gaps and low flexibility in monitoring scope. Even security personnel generally follow fixed routes, leading to fixed monitoring gaps and inefficient management.
[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention
[0004] The main objective of this invention is to provide a cloud-network-device collaborative security management method, aiming to improve the efficiency of monitoring and management. To achieve the above objective, this invention provides a cloud-network-device collaborative security management method, which includes the following steps: Acquire dynamic sensing data and static sensing data, wherein the dynamic sensing data is data collected by mobile sensing devices and the static sensing data is data collected by fixedly installed sensing devices; Real-time risk features are generated based on the dynamic sensing data, the static sensing data, and the regional data of the target area. A risk alert report is generated based on the real-time risk characteristics and the preset analysis algorithm; Security dispatch data is generated based on the aforementioned risk warning report.
[0005] Optionally, the step of generating real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area includes: The target area is divided according to a preset scale to obtain multiple grid areas; Based on the static sensing data, all the grid regions are grouped to obtain a first type of grid region and a second type of grid region. The first type of grid region is equipped with a fixed sensing device, while the second type of grid region is not equipped with a fixed sensing device. The first risk characteristic corresponding to the first type of grid area is determined based on the static sensing data; The second risk feature corresponding to the second type of grid area is determined based on the dynamic sensing data, the first risk feature, and the regional data. The real-time risk characteristics are determined based on the first risk characteristic and the second risk characteristic.
[0006] Optionally, the regional data includes: adjacent grids of each second type of grid region, and the step of determining the second risk feature corresponding to the second type of grid region based on the dynamic sensing data, the first risk feature, and the regional data includes: Acquire the detection time and detection data of the second type of grid region using the dynamic sensing data; The second risk feature is determined based on the detection time, the detection data, and the risk features corresponding to adjacent grids in the second type of grid region.
[0007] Optionally, the step of determining the second risk characteristic based on the detection time, the detection data, and the risk characteristics corresponding to adjacent grids in the second type of grid region includes: When the difference between the detection time and the current time is less than a first preset duration, the second risk characteristic is determined based on the detection data; When the difference between the detection time and the current time is greater than or equal to the first preset duration, and the difference between the detection time and the current time is less than the second preset duration, the second risk feature is determined based on the detection data and the risk features corresponding to the adjacent grids of the second type of grid region. When the difference between the detection time and the current time is greater than or equal to the second preset duration, the second risk feature is determined based on the risk features corresponding to the adjacent grids of the second type of grid region; Wherein, the first preset duration is less than the second preset duration.
[0008] Optionally, the preset analysis algorithm is a risk assessment algorithm, and the step of generating a risk warning report based on the real-time risk characteristics and the preset analysis algorithm includes: Each of the real-time risk features is used to generate a corresponding real-time risk score, resulting in multiple real-time risk scores. Based on the risk assessment algorithm and multiple real-time risk scores, risk assessment results corresponding to different dimensions are calculated to obtain multiple different types of risk assessment results. A risk warning report is generated based on the results of multiple risk assessments.
[0009] Optionally, the preset analysis algorithm is a risk extrapolation algorithm, and the step of generating a risk warning report based on the real-time risk characteristics and the preset analysis algorithm includes: The real-time risk features are standardized to obtain the real-time risk vector; The real-time risk vector and the projection time are input into the risk projection algorithm to obtain the risk change characteristics after the current moment; The risk warning report is determined based on the characteristics of the risk changes.
[0010] Optionally, the information types of the dynamic sensing data and static sensing data include: image information, sound information, and point cloud information.
[0011] Furthermore, to achieve the above objectives, the present invention also provides a cloud-network-device collaborative security management system, which includes: The acquisition module is used to acquire dynamic sensing data and static sensing data. The dynamic sensing data is data collected by mobile sensing devices, and the static sensing data is data collected by fixedly installed sensing devices. The feature extraction module is used to generate real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area; The analysis module is used to generate a risk warning report based on the real-time risk characteristics and a preset analysis algorithm; The scheduling module is used to generate security scheduling data based on the risk warning report.
[0012] Furthermore, to achieve the above objectives, the present invention also provides a cloud-network-device collaborative security management device, the cloud-network-device collaborative security management device comprising: a memory, a processor, and a cloud-network-device collaborative security management program stored on the memory and executable on the processor, the cloud-network-device collaborative security management program being configured to implement the steps of the cloud-network-device collaborative security management method described above.
[0013] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a cloud-network-device collaborative security management program, wherein when the cloud-network-device collaborative security management program is executed by a processor, it implements the steps of the cloud-network-device collaborative security management method described above.
[0014] This invention proposes a cloud-network-device collaborative security management method. This method acquires dynamic and static sensing data, generates real-time risk features based on the dynamic and static sensing data and regional data of the target area, generates risk warning reports based on the real-time risk features and a preset analysis algorithm, and generates security scheduling data based on the risk warning reports. This allows for the precise division of different areas into different risks by combining different types of data, thereby enabling accurate security scheduling. Attached Figure Description
[0015] Figure 1 This is a schematic diagram of the structure of a cloud-network-device collaborative security management and control device in the hardware operating environment involved in the embodiments of the present invention. Figure 2This is a flowchart illustrating the first embodiment of the cloud-network-device collaborative security management method of the present invention. Figure 3 This is a flowchart illustrating the second embodiment of the cloud-network-device collaborative security management method of the present invention.
[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0018] Reference Figure 1 , Figure 1 This is a schematic diagram of the cloud-network-device collaborative security management equipment structure of the hardware operating environment involved in the embodiments of the present invention.
[0019] like Figure 1 As shown, the cloud-network-device collaborative security management device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interactive device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interactive device 1003 may also connect to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.
[0020] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on cloud-network-edge collaborative security management equipment, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0021] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a cloud-network-device collaborative security management program.
[0022] exist Figure 1 In the cloud-network-device collaborative security management device shown, the network interface 1004 is mainly used for data communication with other devices; the interaction device 1003 is mainly used for data interaction with users; the processor 1001 and memory 1005 in the cloud-network-device collaborative security management device of the present invention can be set in the cloud-network-device collaborative security management device. The cloud-network-device collaborative security management device calls the cloud-network-device collaborative security management program stored in the memory 1005 through the processor 1001 and executes the cloud-network-device collaborative security management method provided in the embodiment of the present invention.
[0023] This invention provides a cloud-network-device collaborative security management method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a cloud-network-device collaborative security management method according to the present invention.
[0024] In this embodiment, the cloud-network-device collaborative security management method includes: Step S1: Obtain dynamic sensing data and static sensing data. The dynamic sensing data is data collected by a mobile sensing device, and the static sensing data is data collected by a fixedly installed sensing device. In this embodiment, the dynamic sensing data can be location information carried by security personnel and image or video data collected by image acquisition devices. Furthermore, the dynamic sensing data can also be image data and point cloud data collected by unmanned vehicles. In this embodiment, the difference between dynamic and static sensing data lies in whether the data acquisition device is mobile, i.e., whether the acquisition device is fixedly installed for a single area. The fixed sensing device can be a camera device fixed in one location. Preferably, if a monitoring device with a pan-tilt unit is fixed in an immovable position and only used to collect data from one area, the data collected by this monitoring device is static sensing data. If a monitoring device with a pan-tilt unit is installed on a mobile vehicle, and the vehicle moves to different areas to collect data, the collected data is dynamic sensing data.
[0025] Step S2: Generate real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area; In this embodiment, the information types of the dynamic and static perception data include: image information, sound information, and point cloud information. Specifically, through image recognition algorithms, personnel feature information and environmental feature information are extracted from the images, which respectively reflect the situation of personnel and the environment in the scene. Personnel feature information can specifically include: the number of personnel; additionally, personnel feature information can also include at least one of: personnel movement status, personnel behavior and posture, personnel identity and association status, and personnel emotions and clothing characteristics. Environmental feature information specifically includes at least one of: spatial structure features, environmental physical state, key object status, abnormal event features, and natural state changes. Among these, the key object status can include: the presence status of fire-fighting facilities, the open / closed status of doors and windows, and the status of equipment operation indicator lights, etc.
[0026] Step S3: Generate a risk warning report based on the real-time risk characteristics and the preset analysis algorithm; Specifically, the preset analysis algorithm can be one, which comprehensively analyzes the real-time risk characteristics to obtain multiple risk indicators, thereby generating the risk warning report. In this embodiment, a risk warning report is generated using multiple real-time risk characteristics and the preset analysis algorithm, providing feedback to the cloud user.
[0027] Step S4: Generate security dispatch data based on the risk warning report.
[0028] In this embodiment, the risk situation in different areas is determined based on the risk warning report, such as crowd gatherings. Security dispatch data is generated based on the crowd gathering method, and the security dispatch data is used to control various management personnel to manage the required areas or locations.
[0029] In this embodiment, dynamic and static sensing data are acquired, and real-time risk features are generated based on the dynamic and static sensing data and the regional data of the target area. A risk warning report is generated based on the real-time risk features and a preset analysis algorithm, and security dispatch data is generated based on the risk warning report. Thus, interference can accurately classify different areas into different risks by combining different types of data, thereby enabling precise security dispatch.
[0030] Furthermore, based on the first embodiment, a second embodiment of the cloud-network-device collaborative security management method of the present invention is proposed. In this embodiment, reference is made to... Figure 3 The step of generating real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area includes: Step S21: Divide the target area according to a preset scale to obtain multiple grid areas; In this embodiment, the target area is divided according to a preset scale, commonly using a grid-like structure, typically with a grid area of 500 meters in length. During the division of the target area, the size and extent of the grid can be adaptively adjusted.
[0031] Step S22: Group all the grid regions according to the static sensing data to obtain a first type of grid region and a second type of grid region. The first type of grid region is equipped with a fixed sensing device, while the second type of grid region is not equipped with a fixed sensing device. Specifically, based on whether the grid area includes a device for collecting static sensing data, multiple grid areas are grouped into first-class grid areas and second-class grid areas. In particular, each grid area is marked according to the grouping results, and the marking content is either first-class grid area or second-class grid area.
[0032] Step S23: Determine the first risk feature corresponding to the first type of grid area based on the static perception data; In this embodiment, the first risk characteristic corresponding to the first type of grid area is determined through the podium sensing data. This first risk characteristic may include at least one of: number of people, personnel movement status, personnel behavior and posture, personnel identity and association status, and personnel emotion and clothing characteristics. Furthermore, the first risk characteristic may also include at least one of: spatial structure characteristics, environmental physical state, key object state, abnormal event characteristics, and natural state changes. The first risk characteristic of each grid area is used to express the current situation of that grid area. Since there are multiple grid areas, there are also multiple first risk characteristics.
[0033] Step S24: Determine the second risk feature corresponding to the second type of grid area based on the dynamic sensing data, the first risk feature, and the area data; It should be noted that, compared to the first type of grid area, the second type of grid area is not continuously monitored. Therefore, the real-time performance of the second risk feature in the second type of grid area will fluctuate compared to the first type of grid area. When the dynamic sensing data detects the current second type of grid area, the real-time performance of the second risk feature is high; when the dynamic sensing data detects the first type of grid area, the real-time performance of the second risk feature in the second type of grid area is low. This is because it requires a certain degree of estimation from the dynamic sensing data, the first risk feature, and the area data. Of course, since it is an estimation, it often relies on the real-time first risk feature of the neighborhood for real-time estimation.
[0034] Step S25: Determine the real-time risk characteristics based on the first risk characteristics and the second risk characteristics.
[0035] In this embodiment, the risk features corresponding to all grid areas are used as the real-time risk features. These risk features include: the first risk feature and the second risk feature.
[0036] In this embodiment, the target area is divided according to a preset scale to obtain multiple grid areas. All grid areas are grouped according to the static sensing data to obtain a first type of grid area and a second type of grid area. A first risk feature corresponding to the first type of grid area is determined according to the static sensing data. A second risk feature corresponding to the second type of grid area is determined according to the dynamic sensing data, the first risk feature, and the area data. The real-time risk feature is determined according to the first risk feature and the second risk feature, thereby obtaining accurate and complete real-time risk features and improving the accuracy of generating risk warning reports.
[0037] Furthermore, based on the first or second embodiment, a third embodiment of the cloud-network-device collaborative security management method of the present invention is proposed. In this embodiment, the regional data includes: adjacent grids of each second type of grid region, and the step of determining the second risk feature corresponding to the second type of grid region based on the dynamic sensing data, the first risk feature, and the regional data includes: Acquire the detection time and detection data of the second type of grid region using the dynamic sensing data; The second risk feature is determined based on the detection time, the detection data, and the risk features corresponding to adjacent grids in the second type of grid region.
[0038] In this embodiment, the dynamic sensing data actually detects both the first type of grid region and the second type of grid region. The detection time and data for the second type of grid region are obtained from the dynamic sensing data. For each grid region, the real-time performance is determined based on the detection time and data corresponding to that grid region. Here, real-time performance is determined by the difference between the detection time and the current time. If the difference is within a certain range, the detection data can be directly used as the data for that grid region, and the risk feature can be directly extracted as the second risk feature. Of course, when the difference between the detection time and the current time is large, the risk features of neighboring grids are needed to determine the second risk feature. Here, the neighboring grids can be adjacent to the second type of grid region. Commonly, the average value of each risk feature in the neighboring grid is calculated as the second risk feature.
[0039] Furthermore, the step of determining the second risk characteristic based on the detection time, the detection data, and the risk characteristics corresponding to adjacent grids in the second type of grid region includes: When the difference between the detection time and the current time is less than a first preset duration, the second risk characteristic is determined based on the detection data; When the difference between the detection time and the current time is greater than or equal to the first preset duration, and the difference between the detection time and the current time is less than the second preset duration, the second risk feature is determined based on the detection data and the risk features corresponding to the adjacent grids of the second type of grid region. When the difference between the detection time and the current time is greater than or equal to the second preset duration, the second risk feature is determined based on the risk features corresponding to the adjacent grids of the second type of grid region; Specifically, the second risk feature determined based on the detection data can be obtained by extracting the detection data using an image recognition algorithm. No restrictions are placed on the image recognition algorithm used.
[0040] In this embodiment, preferably, when the difference between the detection time and the current time is greater than or equal to a first preset duration, and the difference between the detection time and the current time is less than a second preset duration, a calculation formula can be set, which includes: a first weight of the detection data and a second weight of the adjacent grids of the second type of grid region. The second risk feature is calculated by comprehensively considering the detection data, the first weight, the risk features of the adjacent grids of the second type of grid region, and the corresponding second weights according to the calculation formula.
[0041] In this embodiment, when the difference between the detection time and the current time is less than a first preset duration, the second risk feature is determined based on the detection data. When the difference between the detection time and the current time is greater than or equal to the first preset duration, and the difference between the detection time and the current time is less than a second preset duration, the second risk feature is determined based on the detection data and the risk features corresponding to adjacent grids in the second type of grid region. When the difference between the detection time and the current time is greater than or equal to the second preset duration, the second risk feature is determined based on the risk features corresponding to adjacent grids in the second type of grid region, thereby improving the accuracy of the second risk feature.
[0042] Furthermore, based on any of the above embodiments, a fourth embodiment of the cloud-network-device collaborative security management method of the present invention is proposed, wherein the preset analysis algorithm is a risk assessment algorithm, and the step of generating a risk warning report based on the real-time risk characteristics and the preset analysis algorithm includes: Each of the real-time risk features is used to generate a corresponding real-time risk score, resulting in multiple real-time risk scores. Based on the risk assessment algorithm and multiple real-time risk scores, risk assessment results corresponding to different dimensions are calculated to obtain multiple different types of risk assessment results. A risk warning report is generated based on the results of multiple risk assessments.
[0043] Optionally, the risk assessment algorithm includes multiple different indicator calculation formulas. This allows for the determination of different risk indicators and risk assessment results across different dimensions based on these formulas and real-time risk characteristics. During the generation of a risk alert report based on these assessment results, tables and images can be generated visually. Risk indicators may include: crowd density indicators, behavioral anomaly indices, spatiotemporal anomaly coefficients, and spatial conflict risk values. For example, if delivery drivers gather in specific areas and at specific times, it will significantly cause anomalies in crowd density indicators, behavioral anomaly indices, spatiotemporal anomaly coefficients, and spatial conflict risk values, thus directly identifying the anomaly and generating a highly real-time risk alert report.
[0044] In this embodiment, a corresponding real-time risk score is generated for each of the real-time risk features, resulting in multiple real-time risk scores. Based on the risk assessment algorithm and the multiple real-time risk scores, risk evaluation results corresponding to different dimensions are calculated, resulting in multiple different types of risk evaluation results. A risk warning report is generated based on the multiple risk evaluation results, thereby enabling analysis from multiple dimensions and allowing for the subsequent dispatch of security personnel with corresponding capabilities, thus improving the accuracy of dispatch.
[0045] Furthermore, based on any of the above embodiments, a fifth embodiment of the cloud-network-device collaborative security management method of the present invention is proposed, wherein the preset analysis algorithm is a risk inference algorithm, and the step of generating a risk warning report based on the real-time risk characteristics and the preset analysis algorithm includes: The real-time risk features are standardized to obtain the real-time risk vector; The real-time risk vector and the projection time are input into the risk projection algorithm to obtain the risk change characteristics after the current moment; The risk warning report is determined based on the characteristics of the risk changes.
[0046] In this embodiment, based on the fourth embodiment, the pre-set analysis algorithm can include two algorithms: a risk assessment algorithm and a risk inference algorithm. The corresponding analysis results are obtained respectively, generating Wang Zheng's risk warning report. Furthermore, based on the risk change characteristics obtained in this embodiment, the risk assessment algorithm can be executed to evaluate future risk changes, thereby achieving risk identification in advance. Specifically, Min-Max or Z-Score methods are used as standardization methods to map real-time risk characteristics of different dimensions into a real-time risk vector of a unified dimension. This vector, along with a set inference time threshold, is input into the core model of the risk inference algorithm. The algorithm here can be based on cellular automata, LSTM time series prediction, or agent simulation, combined with a historical event database and physical laws, to iteratively infer future trends. Of course, this embodiment does not limit the type of risk inference algorithm.
[0047] Furthermore, in other embodiments, the personnel characteristic information includes: the number of personnel, and the environmental characteristic information includes: noise level, weather type, and scene type, at least one of these.
[0048] Furthermore, this invention also proposes a cloud-network-device collaborative security management system, characterized in that the cloud-network-device collaborative security management system includes: The acquisition module is used to acquire dynamic sensing data and static sensing data. The dynamic sensing data is data collected by mobile sensing devices, and the static sensing data is data collected by fixedly installed sensing devices. The feature extraction module is used to generate real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area; The analysis module is used to generate a risk warning report based on the real-time risk characteristics and a preset analysis algorithm; The scheduling module is used to generate security scheduling data based on the risk warning report.
[0049] Furthermore, this invention also proposes a cloud-network-device collaborative security management device, which includes: a memory, a processor, and a cloud-network-device collaborative security management program stored in the memory and executable on the processor. The cloud-network-device collaborative security management program is configured to implement the steps of the cloud-network-device collaborative security management method described above.
[0050] Furthermore, this embodiment of the invention also proposes a storage medium storing a cloud-network-device collaborative security management program, which, when executed by a processor, implements the steps of the cloud-network-device collaborative security management method described above.
[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0052] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0053] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.
[0054] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.
Claims
1. A cloud-network-device collaborative security management method, characterized in that, The cloud-network-device collaborative security management method includes the following steps: Acquire dynamic sensing data and static sensing data, wherein the dynamic sensing data is data collected by mobile sensing devices and the static sensing data is data collected by fixedly installed sensing devices; Real-time risk features are generated based on the dynamic sensing data, the static sensing data, and the regional data of the target area. A risk alert report is generated based on the real-time risk characteristics and the preset analysis algorithm; Security dispatch data is generated based on the aforementioned risk warning report.
2. The cloud-network-device collaborative security management method as described in claim 1, characterized in that, The step of generating real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area includes: The target area is divided according to a preset scale to obtain multiple grid areas; Based on the static sensing data, all the grid regions are grouped to obtain a first type of grid region and a second type of grid region. The first type of grid region is equipped with a fixed sensing device, while the second type of grid region is not equipped with a fixed sensing device. The first risk characteristic corresponding to the first type of grid area is determined based on the static sensing data; The second risk feature corresponding to the second type of grid area is determined based on the dynamic sensing data, the first risk feature, and the regional data. The real-time risk characteristics are determined based on the first risk characteristic and the second risk characteristic.
3. The cloud-network-device collaborative security management method as described in claim 2, characterized in that, The regional data includes: adjacent grids of each second type of grid region, and the step of determining the second risk feature corresponding to the second type of grid region based on the dynamic sensing data, the first risk feature, and the regional data includes: Acquire the detection time and detection data of the second type of grid region using the dynamic sensing data; The second risk feature is determined based on the detection time, the detection data, and the risk features corresponding to adjacent grids in the second type of grid region.
4. The cloud-network-device collaborative security management method as described in claim 3, characterized in that, The step of determining the second risk characteristic based on the detection time, the detection data, and the risk characteristics corresponding to adjacent grids in the second type of grid region includes: When the difference between the detection time and the current time is less than a first preset duration, the second risk characteristic is determined based on the detection data; When the difference between the detection time and the current time is greater than or equal to the first preset duration, and the difference between the detection time and the current time is less than the second preset duration, the second risk feature is determined based on the detection data and the risk features corresponding to the adjacent grids of the second type of grid region. When the difference between the detection time and the current time is greater than or equal to the second preset duration, the second risk feature is determined based on the risk features corresponding to the adjacent grids of the second type of grid region; Wherein, the first preset duration is less than the second preset duration.
5. The cloud-network-device collaborative security management method as described in claim 1, characterized in that, The preset analysis algorithm is a risk assessment algorithm, and the step of generating a risk warning report based on the real-time risk characteristics and the preset analysis algorithm includes: Each of the real-time risk features is used to generate a corresponding real-time risk score, resulting in multiple real-time risk scores. Based on the risk assessment algorithm and multiple real-time risk scores, risk assessment results corresponding to different dimensions are calculated to obtain multiple different types of risk assessment results. A risk warning report is generated based on the results of multiple risk assessments.
6. The cloud-network-device collaborative security management method as described in claim 1, characterized in that, The preset analysis algorithm is a risk extrapolation algorithm, and the step of generating a risk warning report based on the real-time risk characteristics and the preset analysis algorithm includes: The real-time risk features are standardized to obtain the real-time risk vector; The real-time risk vector and the projection time are input into the risk projection algorithm to obtain the risk change characteristics after the current moment; The risk warning report is determined based on the characteristics of the risk changes.
7. The cloud-network-device collaborative security management method as described in any one of claims 1 to 6, characterized in that, The information types of the dynamic and static sensing data include: image information, sound information, and point cloud information.
8. A cloud-network-device collaborative security management system, characterized in that, The cloud-network-device collaborative security management system includes: The acquisition module is used to acquire dynamic sensing data and static sensing data. The dynamic sensing data is data collected by mobile sensing devices, and the static sensing data is data collected by fixedly installed sensing devices. The feature extraction module is used to generate real-time risk features based on the dynamic sensing data, the static sensing data, and the regional data of the target area; The analysis module is used to generate a risk warning report based on the real-time risk characteristics and a preset analysis algorithm; The scheduling module is used to generate security scheduling data based on the risk warning report.
9. A cloud-network-terminal collaborative security management and control device, characterized in that, The cloud-network-device collaborative security management device includes: a memory, a processor, and a cloud-network-device collaborative security management program stored on the memory and executable on the processor. The cloud-network-device collaborative security management program is configured to implement the steps of the cloud-network-device collaborative security management method as described in any one of claims 1 to 7.
10. A storage medium, characterized in that, The storage medium stores a cloud-network-device collaborative security management program, which, when executed by a processor, implements the steps of the cloud-network-device collaborative security management method as described in any one of claims 1 to 7.