Smart home security monitoring method and system under cloud computing environment

Abstract

The present invention relates to the field of intelligent security, and discloses a method and system for intelligent home security monitoring in a cloud computing environment, including: firstly obtaining the environment layout, clarifying the key monitoring area, selecting the adapted cloud monitoring node and initializing the parameters, thereby collecting the equipment operation status in real time, analyzing the abnormal characteristics of the data flow, generating the risk warning curve and the potential risk point, calculating the security urgency value, determining the linkage trigger device according to the urgency value, generating the security response instruction, analyzing the execution effect data, identifying the security vulnerability characteristics, calculating the security risk value, and finally adjusting the system in real time according to the risk value and the intelligent adjustment mechanism, generating the adjustment feedback record, extracting the core adjustment elements, and forming the security monitoring report. The present invention improves the security monitoring effect of the intelligent home.

Description

Technical Field

[0001] The present invention relates to a smart home security monitoring method and system in a cloud computing environment, belonging to the field of smart security. Background Art

[0002] In today's information technology field, with the continuous improvement of people's living standards and the rapid development of Internet of Things technology, home security monitoring has become an important means to protect family property and family members' safety. Cloud computing, as a powerful computing and storage technology, has brought new opportunities and challenges to home security monitoring.

[0003] At present, traditional home security monitoring methods mainly include simple door locks, cameras, alarms and other devices. The functions of these devices are relatively simple and can only provide basic security protection. For example, door locks can only prevent illegal intrusions, cameras can only perform video monitoring, and alarms can only sound alarms when abnormal situations occur. Intelligent security analysis and early warning cannot be achieved. On the other hand, some existing intelligent monitoring devices, although they have certain data analysis and processing capabilities, are limited by local computing resources and storage capacity and cannot process a large amount of monitoring data. It is also difficult to achieve real-time security monitoring and remote control. In addition, these devices often lack effective coordination and integration, and cannot form a complete home security monitoring system, resulting in obvious deficiencies in the effect of home security monitoring. Therefore, a smart home security monitoring method based on a cloud computing environment is needed to make full use of the advantages of cloud computing to realize the intelligence, real-time and remoteness of home security monitoring, thereby improving the security monitoring effect of smart homes. Summary of the invention

[0004] To achieve the above object, the present invention provides a smart home security monitoring method in a cloud computing environment, comprising:

[0005] Acquire the environment layout of the smart home, analyze the key monitoring areas in the environment layout, determine the cloud monitoring nodes adapted to the key monitoring areas, perform parameter initialization configuration on the cloud monitoring nodes, and obtain initial configuration parameters;

[0006] Based on the initial configuration parameters, the operating status of the devices in the smart home is collected in real time, the real-time data stream under the operating status of the devices is analyzed, and the abnormal characteristic parameters in the real-time data stream are parsed, and based on the abnormal characteristic parameters, a risk warning curve corresponding to the smart home is generated, and potential risk points in the risk warning curve are extracted, and based on the potential risk points, a security urgency value corresponding to the smart home is calculated;

[0007] Based on the security urgency value, analyzing the linkage trigger device in the preset smart home security system, detecting the trigger feedback signal corresponding to the linkage trigger device, and generating the security response instruction corresponding to the smart home based on the trigger feedback signal;

[0008] Analyzing the execution effect data corresponding to the security response instruction, identifying the security vulnerability features in the execution effect data, and calculating the security risk value corresponding to the smart home security system based on the security vulnerability features;

[0009] Based on the security risk value and the preset intelligent adjustment mechanism, the preset smart home security system is adjusted in real time, an adjustment feedback record is generated, the core adjustment elements in the adjustment feedback record are extracted, and based on the core adjustment elements, a security monitoring report corresponding to the smart home is generated.

[0010] Optionally, the performing parameter initialization configuration on the cloud monitoring node to obtain initial configuration parameters includes:

[0011] Analyze the node requirements corresponding to the cloud monitoring nodes;

[0012] Based on the node requirements, determine the monitoring priority corresponding to the cloud monitoring node;

[0013] Querying the basic configuration template corresponding to the monitoring priority;

[0014] Based on the basic configuration template, the cloud monitoring node is initialized and configured with parameters to obtain initial configuration parameters.

[0015] Optionally, the real-time acquisition of the operating status of the devices in the smart home based on the initial configuration parameters includes:

[0016] Query the parameter collection frequency in the initial configuration parameters;

[0017] Based on the parameter collection frequency, determining a sampling period corresponding to the device in the smart home;

[0018] Based on the sampling period, activating a communication link between devices in the smart home;

[0019] Marking the key link points corresponding to the communication link;

[0020] Based on the key points of the link, the operating status of the devices in the smart home is collected in real time.

[0021] Optionally, the calculating, based on the potential risk point, a security urgency value corresponding to the smart home includes:

[0022] The security urgency value corresponding to the smart home is calculated using the following formula:

[0023] ;

[0024] in, represents the security emergency value corresponding to the smart home, Indicates the total number of potential risk points. Indicates the quantity index corresponding to the potential risk point, Indicates The probability of risk occurrence of a potential risk point, Representative The accident loss cost corresponding to each potential risk point is: Indicates that from The abnormal accident time corresponding to each potential risk point.

[0025] Optionally, analyzing a linkage trigger device in a preset smart home security system based on the security urgency value includes:

[0026] Real-time monitoring of the numerical fluctuations corresponding to the security emergency value;

[0027] Based on the fluctuation of the value, the security urgency value is divided into intervals to obtain urgency division intervals;

[0028] Based on the urgent division interval, determining the device linkage state corresponding to the device in the preset smart home security system;

[0029] Based on the linkage status of the device, query the linkage trigger combination corresponding to the device in the preset smart home security system;

[0030] Based on the linkage trigger combination, the linkage trigger devices in the preset smart home security system are analyzed.

[0031] Optionally, generating a security response instruction corresponding to the smart home based on the trigger feedback signal includes:

[0032] Extracting key information elements from the trigger feedback signal;

[0033] Associating the key information element with a device identifier in the smart home to obtain a device association identifier;

[0034] Based on the device association identifier, construct a device topology map corresponding to the smart home;

[0035] Extracting core topological points from the device topology map;

[0036] Based on the core topological point, a security response instruction corresponding to the smart home is generated.

[0037] Optionally, analyzing the execution effect data corresponding to the security response instruction includes:

[0038] Sort out the command task list corresponding to the security response command;

[0039] Query the task planning process corresponding to the subtask in the instruction task list;

[0040] Extracting key execution elements corresponding to the task planning process;

[0041] Based on the key execution elements, the execution effect data corresponding to the security response instruction is analyzed.

[0042] Optionally, the calculating, based on the security vulnerability feature, a security risk value corresponding to the smart home security system includes:

[0043] ;

[0044] in, represents the security risk value corresponding to the smart home security system, Indicates the total number of features corresponding to the security vulnerability features, Indicates the quantity index corresponding to the security vulnerability feature, Indicates The harm degree value corresponding to each security vulnerability feature, Indicates The frequency of vulnerabilities corresponding to the security vulnerability features, represents the total number of security devices in the smart home security system, Indicates the number index of security equipment. Indicates The security equipment is subject to The correlation impact of the security vulnerability characteristics.

[0045] Optionally, extracting the core adjustment elements in the adjustment feedback record includes:

[0046] Analyze the key adjustment scenarios covered by the adjustment feedback records;

[0047] Query the main regulation subject associated with the key regulation scenario;

[0048] Based on the main adjustment subject, determining the main adjustment process corresponding to the adjustment feedback record;

[0049] Locate the core elements in the main adjustment process;

[0050] Based on the core element points, the core adjustment elements in the adjustment feedback record are extracted.

[0051] In order to solve the above problems, the present invention also provides a smart home security monitoring system in a cloud computing environment, the system comprising:

[0052] An initialization module is used to obtain the environment layout of the smart home, analyze the key monitoring areas in the environment layout, determine the cloud monitoring nodes adapted to the key monitoring areas, perform parameter initialization configuration on the cloud monitoring nodes, and obtain initial configuration parameters;

[0053] An urgency value calculation module is used to collect the operating status of the devices in the smart home in real time based on the initial configuration parameters, analyze the real-time data stream under the operating status of the devices, and parse the abnormal characteristic parameters in the real-time data stream, generate a risk warning curve corresponding to the smart home based on the abnormal characteristic parameters, extract potential risk points in the risk warning curve, and calculate the security urgency value corresponding to the smart home based on the potential risk points;

[0054] An instruction generation module is used to analyze the linkage trigger device in the preset smart home security system based on the security urgency value, detect the trigger feedback signal corresponding to the linkage trigger device, and generate a security response instruction corresponding to the smart home based on the trigger feedback signal;

[0055] a risk value calculation module, used to analyze the execution effect data corresponding to the security response instruction, identify the security vulnerability features in the execution effect data, and calculate the security risk value corresponding to the smart home security system based on the security vulnerability features;

[0056] A report generation module is used to perform real-time adjustments to a preset smart home security system based on the security risk value and a preset intelligent adjustment mechanism, generate an adjustment feedback record, extract core adjustment elements from the adjustment feedback record, and generate a security monitoring report corresponding to the smart home based on the core adjustment elements.

[0057] Compared with the problems described in the background technology, the present invention obtains the environmental layout of the smart home and analyzes the key monitoring areas in the environmental layout, so that security resources can be accurately deployed and blind deployment of equipment can be avoided. For example, key deployment is carried out on doors, windows, and valuables storage areas to improve the targeted protection. Based on the initial configuration parameters, the present invention collects the operating status of the equipment in the smart home in real time, and can accurately control the home security situation. According to the parameter settings adapted to each area, the status of equipment such as door and window sensors and cameras is captured in a targeted manner, abnormalities are detected at the first time, and potential risks are prevented. Furthermore, based on the security urgency value, the present invention analyzes the linkage trigger devices in the preset smart home security system, which can be accurately matched, and the linkage devices in the corresponding areas with high values ​​can respond in time according to the high urgency value. For example, when the urgency value is high, the entire system is automatically turned on. House monitoring and alarm, optimize resource utilization, avoid unnecessary equipment linkage, save energy and equipment loss. Furthermore, the present invention analyzes the execution effect data corresponding to the security response instruction to assist in precise optimization, and accurately adjusts the subsequent security instructions based on the feedback of the instruction execution. If the alarm duration does not reach the expected deterrent effect, the setting can be optimized to ensure the reliability of the system. By understanding the implementation of the instructions by the equipment, the links with poor execution are promptly checked and the hidden dangers of faults are repaired. Finally, based on the security risk value and the preset intelligent adjustment mechanism, the present invention adjusts the preset smart home security system in real time, generates adjustment feedback records, and can optimize the security performance in real time. It is dynamically adjusted according to the risk value, so that the system is always in an efficient protection state, which is convenient for tracing the adjustment process. The feedback record provides a basis for subsequent analysis and is conducive to continuous improvement. Therefore, the smart home security monitoring method and system under the cloud computing environment provided by the embodiment of the present invention improve the security monitoring effect of the smart home. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a process flow of a smart home security monitoring method in a cloud computing environment provided by an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of modules for implementing the smart home security monitoring system in the cloud computing environment provided by one embodiment of the present invention.

[0060] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0061] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0062] The embodiment of the present application provides a smart home security monitoring method under a cloud computing environment. The execution subject of the smart home security monitoring method under the cloud computing environment includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the smart home security monitoring method under the cloud computing environment can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0063] Embodiment 1:

[0064] Reference Figure 1 FIG. 1 is a flow chart of a smart home security monitoring method in a cloud computing environment provided by an embodiment of the present invention. In this embodiment, the smart home security monitoring method in a cloud computing environment includes:

[0065] S1. Obtain the environment layout of the smart home, analyze the key monitoring areas in the environment layout, determine the cloud monitoring nodes adapted for the key monitoring areas, perform parameter initialization configuration on the cloud monitoring nodes, and obtain initial configuration parameters.

[0066] By acquiring the environmental layout of the smart home and analyzing the key monitoring areas in the environmental layout, the present invention can enable the precise deployment of security resources and avoid blind deployment of equipment. For example, key deployment can be carried out on doors, windows, and places where valuables are stored to improve the targeted nature of protection.

[0067] Among them, the smart home refers to a modern living space that has integrated advanced technologies such as the Internet of Things, artificial intelligence, and automated control, and is equipped with smart home appliances, smart sensors, smart security equipment, etc., which can achieve intelligent interaction of home appliances, remote control, and autonomous optimization of life scenarios, thereby comprehensively improving living comfort, convenience, and safety; the environmental layout refers to the structural distribution of the internal space of a smart home, covering elements such as room layout, furniture placement, door and window positions, and aisle direction. These elements comprehensively reflect the physical form of the home space, and play a key guiding role in accurately planning the scope of security monitoring, determining potential risk points, and rationally deploying security equipment; the key monitoring area refers to the key areas selected based on the smart home environment layout, according to factors such as the probability of risk occurrence, the importance of property, and the frequency of personnel activities. Parts, for example, the vicinity of doors and windows close to the outdoors, as high-incidence areas for illegal intrusion; rooms or storage areas for storing valuables and important documents; functional spaces such as kitchens and bathrooms where accidents are prone to occur, as well as exclusive areas where the elderly and children in the home often move around, etc. Optionally, the acquisition of the environmental layout of the smart home can be achieved through laser scanning technology, such as: using the principle of laser ranging, such as a common indoor laser scanner, which can quickly emit a laser beam and measure the time delay of the reflected light, so as to accurately calculate the distance to the surface of surrounding objects, and finally obtain the environmental layout; the analysis of the key monitoring areas in the environmental layout can be achieved through a risk assessment matrix algorithm, such as: scoring each area in the home environment according to factors such as property value, personnel vulnerability, and past accident history, and the area with a high score is the key monitoring area.

[0068] Furthermore, by determining the cloud monitoring nodes adapted to the key monitoring areas, the present invention can optimize resource allocation, ensure that computing and storage resources flow accurately to key security locations, avoid unnecessary waste, and maximize monitoring efficiency.

[0069] Among them, the cloud monitoring node refers to a virtual or physical unit with data collection, transmission, and preliminary processing functions that is distributed and deployed in the cloud based on the cloud computing technology architecture. It can establish connections with various security devices and sensors (such as cameras, infrared detectors, smoke alarms, etc.) in the smart home, receive real-time data generated from these terminal devices, use the powerful computing power of the cloud to quickly analyze and integrate, and then feed back the processing results to the user terminal or security management system. Optionally, the determination of the cloud monitoring node suitable for the key monitoring area can be achieved through a load balancing-based allocation algorithm, such as: using a polling algorithm to distribute data requests from numerous security devices in the smart home to each cloud monitoring node in turn, to ensure that the workload of each node is relatively balanced.

[0070] Furthermore, the present invention obtains initial configuration parameters by initializing parameters of the cloud monitoring node, thereby ensuring stable operation of the node, and accurately setting parameters such as data transmission rate and sampling frequency according to the characteristics of the key monitoring area, thereby avoiding node jams or overloads due to improper parameters, and ensuring smooth transmission of security data.

[0071] Among them, the initial configuration parameters refer to a series of parameter values ​​that are finally determined after the demand analysis, priority judgment and optimization and adjustment of the cloud monitoring node, which are used to start the formal operation of the cloud monitoring node. These parameters specifically set the operational details such as how the node collects smart home security data, at what rate it is transmitted, where it is stored, and how to initially process the data, to ensure that the node is accurately adapted to the security tasks of the corresponding monitoring area.

[0072] As an embodiment of the present invention, the cloud monitoring node is initialized and configured with parameters to obtain initial configuration parameters, including: analyzing the node requirements corresponding to the cloud monitoring node; determining the monitoring priority corresponding to the cloud monitoring node based on the node requirements; querying the basic configuration template corresponding to the monitoring priority; and initializing and configuring the cloud monitoring node with parameters based on the basic configuration template to obtain initial configuration parameters.

[0073] Among them, the node demand refers to the functional demands put forward by the cloud monitoring node in terms of data processing capability, transmission speed, storage capacity, etc. in order to meet the security tasks of the key monitoring areas of the smart home. For example, for the living room area where people are active and real-time high-definition video monitoring is required, the cloud monitoring node connected to it needs to have high-speed data reception and processing capabilities to cope with the continuous influx of large amounts of video stream data. This is the node demand generated by the node based on regional characteristics; the monitoring priority refers to the priority of different cloud monitoring nodes based on factors such as the urgency of the risks faced by the key monitoring areas of the smart home, the importance of property, and the sensitivity of personnel safety. The priority processing levels divided by the nodes, such as the secret room area where valuables are stored, which requires immediate response once an abnormality occurs, the corresponding cloud monitoring node has a high monitoring priority; while the storage room, which is relatively safe and has less personnel flow, has a lower priority; the basic configuration template refers to a set of standardized parameter setting frameworks pre-developed in the cloud computing platform based on common monitoring priorities, different security scenario requirements and general technical specifications of cloud monitoring nodes, which covers basic settings such as data collection frequency, transmission protocol, and storage format. After determining the monitoring priority of a cloud monitoring node, you can quickly query and obtain the basic configuration template that matches it.

[0074] Furthermore, the analysis of the node requirements corresponding to the cloud monitoring nodes can be achieved through a cluster analysis method, such as: through cluster analysis, it is known that the cloud monitoring nodes in the area need to have a certain data storage capacity to record environmental changes, and at the same time have a medium data transmission rate to cope with sudden human intrusion detection information upload, thereby determining the node requirements; the determination of the monitoring priority corresponding to the cloud monitoring node can be achieved through a hierarchical analysis method, such as: multiplying the weight of each factor with the score of the monitoring area under the factor and summing them up to obtain the monitoring priority score of the cloud monitoring node corresponding to each monitoring area, and determining the priority according to the score; the query corresponding to the monitoring priority The basic configuration template can be implemented through a matching query based on a rule engine, such as: setting a series of query rules based on monitoring priority and security scenarios, and the rule engine performs reasoning and matching according to the input monitoring priority and the pre-set rule logic to obtain the basic configuration template; the parameter initialization configuration of the cloud monitoring node can be implemented through an adaptive dynamic configuration algorithm, such as: in the entrance door area of ​​a smart home, the cloud monitoring node parameters are initially set according to the basic configuration template. As the peak period of personnel entry and exit arrives, data congestion is monitored, and the adaptive dynamic configuration algorithm automatically increases the transmission rate and optimizes the storage allocation to ensure smooth monitoring and obtain the optimized initial configuration parameters.

[0075] S2. Based on the initial configuration parameters, the operating status of the equipment in the smart home is collected in real time, the real-time data stream under the operating status of the equipment is analyzed, and the abnormal characteristic parameters in the real-time data stream are parsed. Based on the abnormal characteristic parameters, a risk warning curve corresponding to the smart home is generated, and potential risk points in the risk warning curve are extracted. Based on the potential risk points, a security urgency value corresponding to the smart home is calculated.

[0076] Based on the initial configuration parameters, the present invention collects the operating status of the equipment in the smart home in real time, can accurately control the home security situation, and according to the parameter settings adapted to each area, specifically captures the status of equipment such as door and window sensors and cameras, detects abnormalities at the first time, and prevents potential risks.

[0077] Among them, the device operating status refers to the working conditions and parameter performance of the security equipment in the smart home at a certain moment, including the camera's picture clarity, shooting angle, sensor trigger status, detection value, smart door lock switch status, power information, etc.

[0078] As an embodiment of the present invention, the real-time collection of the operating status of the devices in the smart home based on the initial configuration parameters includes: querying the parameter collection frequency in the initial configuration parameters; determining the sampling period corresponding to the devices in the smart home based on the parameter collection frequency; activating the communication link between the devices in the smart home based on the sampling period; marking the link key points corresponding to the communication link; and collecting the operating status of the devices in the smart home in real time based on the link key points.

[0079] The parameter collection frequency refers to the frequency of collecting the operating data of smart home devices, which is set in advance in the initial configuration parameters according to the security requirements of each key monitoring area of ​​the smart home, the characteristics of the equipment and the processing capacity of the cloud monitoring node. For example, for the key monitored valuable storage area, in order to capture potential risks in a timely manner, it may be set to collect data every 5 seconds. This "every 5 seconds" is the parameter collection frequency of the equipment corresponding to the area; the sampling period refers to the time interval actually used to extract sample data from the smart home devices, which is calculated based on the parameter collection frequency. If the parameter collection frequency is every 10 seconds, Once, then the corresponding sampling period is 10 seconds, and the operating status data is obtained from the device once as planned during this period; the communication link refers to the physical or logical connection channel for transmitting data established between the cloud monitoring node and various security devices in the smart home (such as cameras, sensors, smart door locks, etc.), which covers wired connections (such as Ethernet cable connections) and wireless connections (such as Wi-Fi, Bluetooth, ZigBee, etc.); the key point of the link refers to the node parts in the communication link that play a key supporting role in the stable transmission of data and require special attention and maintenance, such as wireless access points, router ports, signal repeaters, etc.

[0080] Furthermore, the query of the parameter collection frequency in the initial configuration parameters can be implemented through a cloud computing configuration management platform, such as Ansible Tower and other platforms; the determination of the sampling period corresponding to the device in the smart home can be implemented through a scripting programming language, such as Python, Bash and other languages; the activation of the communication link between the devices in the smart home can be implemented through a network management tool, such as OpenWrt, Cisco Packet Tracer and other tools; the marking of the link key points corresponding to the communication link can be implemented through a network monitoring tool, such as Wireshark, Nagios and other tools; the real-time collection of the operating status of the devices in the smart home can be implemented through a real-time monitoring system, such as Prometheus, Grafana and other systems.

[0081] The present invention can quickly detect potential risks by analyzing the real-time data stream under the operating state of the device and parsing the abnormal characteristic parameters in the real-time data stream. Through in-depth analysis of the data stream, it can accurately locate anomalies such as sudden changes in sensor values ​​and interruption of device connection, and provide early warning of possible safety hazards.

[0082] The real-time data stream refers to a series of data information sequences reflecting the current operating status of the device, which are continuously transmitted to the cloud monitoring node by various security devices in the smart home according to the set sampling period, including video picture data collected by the camera in real time, environmental values ​​(such as temperature, humidity, smoke concentration, human infrared sensing, etc.) fed back by the sensor in real time, the switch state and power data of the smart door lock, etc.; the abnormal characteristic parameter refers to the key data indicator with risk indication significance presented by the deviation from the normal operating mode of the device or the preset safety threshold in the real-time data stream. For example, under normal circumstances, the smoke concentration value of the indoor smoke alarm should be close to zero. If the value in the data stream suddenly rises sharply and exceeds the set danger threshold, this smoke concentration value is the abnormal characteristic parameter; for example, the power of the smart door lock drops sharply to close to the shutdown threshold in a short time, and this power change value also belongs to the abnormal characteristic parameter. Optionally, the analysis of the real-time data stream under the operating state of the device can be implemented by data visualization tools, such as Grafana and other tools; the analysis of the abnormal characteristic parameters in the real-time data stream can be implemented by parameter analysis tools, such as MATLAB, Wireshark and other tools.

[0083] Furthermore, the present invention generates a risk warning curve corresponding to the smart home based on the abnormal characteristic parameters, extracts potential risk points in the risk warning curve, visualizes the risk situation, and converts abstract data into an intuitive curve, allowing users to see the fluctuations of risks at a glance, such as the abnormal increase in smoke concentration, to enhance risk perception.

[0084] Among them, the risk warning curve refers to a continuous curve drawn with time as the horizontal axis and abnormal characteristic parameters parsed from the real-time data stream under the operation state of the smart home device as the vertical axis. It intuitively presents the changing trajectory of the abnormal parameters over time. For example, the smoke concentration value of the smoke alarm is plotted into a curve at a time interval of every 5 minutes, so as to clearly show whether the smoke concentration is stable, slowly rising or rising sharply over a period of time; the potential risk point refers to the key nodes on the risk warning curve that characterize the intensification of abnormal conditions and the large degree of deviation from the normal range, indicating that safety problems are about to arise. For example, in the risk warning curve of the smart meter power, when the power value suddenly exceeds the normal power consumption range by a large margin and lasts for a period of time, the corresponding time point and power value are potential risk points. Optionally, the generation of the risk warning curve corresponding to the smart home can be achieved through a curve drawing tool, such as: OriginPro, Tableau and other tools; the extraction of potential risk points in the risk warning curve can be achieved through a curve extraction tool, such as: Alteryx and other tools.

[0085] Based on the potential risk points, the present invention calculates the security urgency value corresponding to the smart home to achieve accurate resource allocation. According to the urgency value, the severity of the risk can be quickly judged, and more manpower and material resources can be allocated to high-urgency areas on a priority basis. For example, fire extinguishers can be allocated to kitchens with high potential risks of smoke alarms on a priority basis.

[0086] Among them, the security urgency value refers to a quantitative indicator used to measure the urgency of the overall security situation of a smart home. It comprehensively considers the relevant factors of multiple potential risk points in the home. The larger the value, the more urgent the security risk situation faced by the smart home, and the higher the degree of need to take immediate security measures; the smaller the value, the relatively stable current security situation and the lower the urgency of security.

[0087] As an embodiment of the present invention, the calculating the security urgency value corresponding to the smart home based on the potential risk point includes:

[0088] The security urgency value corresponding to the smart home is calculated using the following formula:

[0089] ;

[0090] in, represents the security emergency value corresponding to the smart home, Indicates the total number of potential risk points. Indicates the quantity index corresponding to the potential risk point, Indicates The probability of risk occurrence of a potential risk point, Representative The accident loss cost corresponding to each potential risk point is: Indicates that from The abnormal accident time corresponding to each potential risk point.

[0091] Specifically, the risk occurrence probability refers to the probability of the first The probability of a risk event actually occurring at each potential risk point; the accident loss cost refers to the probability of a risk event actually occurring at each potential risk point. The monetary quantified value of various losses that the smart home will suffer if an accident occurs at a potential risk point. These losses include but are not limited to property losses (such as the value of damage to furniture, electrical appliances, decoration, etc.), possible medical costs for personal injuries (such as the cost of treatment for personal injuries caused by fire, leakage, etc.), equipment repair costs (such as the cost of replacing damaged smart device parts or a whole set of equipment), indirect costs caused by inconvenience or interruption of family life caused by the accident (such as the cost of renting a house outside due to a water leakage accident that makes the house uninhabitable), etc.; the abnormal accident time refers to the time from the first The time interval between the occurrence of an abnormality at a potential risk point (i.e., it begins to deviate from the normal operating state or shows signs that may lead to a risk event) and the occurrence of an actual accident (i.e., the risk event causes obvious losses or harm). It is a value in time units (such as hours, minutes, etc.), reflecting the speed or urgency of the potential risk point developing into an actual accident.

[0092] S3. Based on the security urgency value, analyze the linkage trigger device in the preset smart home security system, detect the trigger feedback signal corresponding to the linkage trigger device, and generate a security response instruction corresponding to the smart home based on the trigger feedback signal.

[0093] The present invention analyzes the linkage triggering devices in the preset smart home security system based on the security urgency value, and can accurately match them. According to the urgency value, the linkage devices in the high-value corresponding area can respond in time. For example, when the urgency value is high, the whole house monitoring and alarm can be automatically turned on, thereby optimizing resource utilization, avoiding unnecessary equipment linkage, and saving energy and equipment loss.

[0094] Among them, the preset smart home security system refers to a comprehensive security protection system that is pre-set and configured in a smart home environment, which integrates a variety of security equipment and technologies, such as smart cameras, door and window sensors, smoke alarms, intrusion detectors, etc. These devices are interconnected through the network and communicate with the central control system. The system has the functions of automatic monitoring, early warning, alarm and linkage with other smart home devices. It can perceive abnormal situations in the home environment in real time, such as illegal intrusion, fire hazards, gas leaks, etc., and automatically take corresponding security measures according to preset rules and strategies, such as sending alarm notifications, starting video recording, closing doors and windows, cutting off power, etc.; the linkage trigger device refers to each specific smart home security device that constitutes the linkage trigger combination. These devices are the execution units of the entire security system. According to the requirements of the linkage trigger combination, corresponding actions are performed in specific security emergency situations. For example, the smart cameras, door and window sensors, smart light bulbs, etc. in the above-mentioned medium urgency interval linkage trigger combination, and the smoke alarms, gas leak detectors, etc. in the high urgency interval linkage trigger combination are all linkage trigger devices.

[0095] As an embodiment of the present invention, the analysis of the linkage trigger devices in the preset smart home security system based on the security urgency value includes: real-time monitoring of the numerical fluctuation corresponding to the security urgency value; based on the numerical fluctuation, dividing the security urgency value into intervals to obtain urgency division intervals; based on the urgency division intervals, determining the device linkage status corresponding to the device in the preset smart home security system; based on the device linkage status, querying the linkage trigger combination corresponding to the device in the preset smart home security system; based on the linkage trigger combination, analyzing the linkage trigger devices in the preset smart home security system.

[0096] The numerical fluctuation refers to the numerical variation characteristics and trends of the security urgency value (U) as time or other factors change, including the specific values ​​of the security urgency value at different times, the amplitude of the increase or decrease of the value, the frequency of change, and whether there is periodicity. For example, the security urgency value may fluctuate within a day due to the activities of family members, environmental factors (such as temperature changes affecting the operation of electrical equipment), external interference (such as nearby construction that may affect door and window sensors), etc.; the urgency division interval refers to dividing the security urgency value into several different interval ranges according to its numerical fluctuation. For example, the security urgency value can be divided into a low urgency interval (such as 0-30), a medium urgency interval (31-60) and a high urgency interval (61-100), etc.; the device linkage status refers to the status of each device in the preset smart home security system. The working state or operation mode that the equipment should be in under different security urgency levels. For example, in the low urgency interval, some non-critical security equipment can be in a dormant or low-power state and only perform basic monitoring; in the high urgency interval, all security equipment may need to enter a full working state, such as the camera starts high-definition recording, the alarm is in a state that can be triggered at any time, and the doors and windows are automatically locked. The linkage trigger combination refers to a specific device set formed by combining multiple security devices together according to different device linkage states. For example, a linkage trigger combination in the medium urgency interval may include the coordinated work of devices such as smart cameras starting recording, door and window sensors increasing the monitoring frequency, and smart light bulbs flashing warnings; and the linkage trigger combination in the high urgency interval will increase the comprehensive detection of smoke alarms and gas leak detectors and automatic alarm connections with external security agencies.

[0097] Furthermore, the real-time monitoring of the numerical fluctuation corresponding to the security urgency value can be achieved through data monitoring tools, such as: Prometheus and other tools; the interval division of the security urgency value can be achieved through a fixed threshold method, such as: dividing the security urgency value into corresponding intervals through a fixed threshold, thereby obtaining the urgency division interval; the determination of the device linkage status corresponding to the device in the preset smart home security system can be achieved through a state machine model, such as: finite state machine, hierarchical state machine and other models; the query of the linkage trigger combination corresponding to the device in the preset smart home security system can be achieved through a key-value pair storage system, such as: when a query is required, according to the current security urgency interval as the key, the value of the corresponding linkage trigger combination is obtained from Redis to obtain the linkage trigger combination; the analysis of the linkage trigger device in the preset smart home security system can be achieved through a device analysis tool, such as: Matplotlib, Seaborn and other tools.

[0098] The present invention can confirm the execution status of the device action in real time by detecting the trigger feedback signal corresponding to the linkage trigger device. If no feedback is received after the alarm is triggered, the fault can be promptly checked to ensure that the security measures are effectively implemented, facilitate precise control, know the device response time according to the feedback signal, optimize the subsequent linkage logic, and improve the overall efficiency of the security system.

[0099] Among them, the trigger feedback signal refers to the electrical signal or data information that is transmitted back to the control system when the linkage trigger device in the preset smart home security system receives the trigger command and executes the corresponding security action, which is used to indicate the execution status of its own action and related parameters. For example, after the smart camera receives the trigger command to start high-definition recording and upload it to the cloud in real time, it will send a feedback signal to the central control system containing information such as recording has been turned on, current frame rate, resolution, and upload progress; for example, after the door and window sensor triggers the automatic locking of doors and windows, it will feedback a signal indicating that the doors and windows have been successfully locked, the locking time, and the lock status. Optionally, the detection of the trigger feedback signal corresponding to the linkage trigger device can be implemented through a communication protocol, such as MQTT, HTTP and other protocols, to perform data transmission between the device and the control system to obtain a trigger feedback signal.

[0100] Furthermore, the present invention generates security response instructions corresponding to the smart home based on the trigger feedback signal, can achieve dynamic optimization, and timely adjust the instructions according to device feedback. If the camera feedback image is blurry, the shooting parameter instructions can be immediately optimized to improve security quality.

[0101] Among them, the security response instruction refers to the specific operation command finally generated by the system and issued to the smart home security device based on the above series of analysis results. It clearly instructs each device on the tasks to be performed under the current trigger feedback scenario, including the start, stop, parameter adjustment of the device action, and coordinated linkage with other devices. For example, in the case of the above-mentioned kitchen smoke alarm being triggered, the security response instruction may be a command to send an immediate valve closing command to the gas valve closing device.

[0102] As an embodiment of the present invention, generating a security response instruction corresponding to the smart home based on the trigger feedback signal includes: extracting a key information element in the trigger feedback signal; associating the key information element with a device identifier in the smart home to obtain a device association identifier; constructing a device topology map corresponding to the smart home based on the device association identifier; extracting core topology points in the device topology map; and generating a security response instruction corresponding to the smart home based on the core topology points.

[0103] Among them, the key information element refers to the key indicative element extracted from the trigger feedback signal, which covers the basic description of the device's execution action, the key points of the state change, and some abnormal characteristics that may be included. For example, for the trigger signal fed back by the smart camera, the key information element may include the camera's current recording status (recording, paused recording, recording failure), picture resolution, frame rate, and whether an abnormal moving target is detected. Details; for the feedback signal of the smart door lock, the key information element is the door lock's open and closed state, unlocking method (password, fingerprint, Bluetooth, etc.), whether there are multiple incorrect unlocking attempts, etc. ; The device association identifier refers to a unique identifier formed by correspondingly associating the key information element extracted from the trigger feedback signal with the specific device in the smart home. It accurately locates the individual device that sends the feedback signal through a specific code, name or attribute combination. For example, based on the MAC address of the device, combined with the device type code (such as 01 for camera, 02 for door lock, etc.) and the room area code (such as 001 for living room, 002 for bedroom), a device association identifier such as "01-001-MAC123456" is constructed; The device topology map refers to the physical location of all security devices in the smart home. A graphical presentation of the logical position relationship, logical connection relationship and mutual cooperation mode. It uses nodes to represent various security devices, and the lines between nodes represent the communication links, linkage trigger relationships or spatial proximity relationships between devices. For example, in the device topology diagram, if there is a linkage relationship between the smart camera node and the smart light node, when the camera detects an abnormality, it can trigger the light flashing warning, then there will be a connection with a specific logo (such as "linkage warning") between the two. At the same time, the position layout of the nodes in the diagram also tries to reflect the actual room location of the device. For example, the living room area focuses on displaying the cameras in the living room, the door and window sensors in the living room, and the smart light sensors in the living room. The core topological points refer to the key areas, key equipment and their associated important nodes focused on by the current trigger feedback signal in the equipment topology diagram. These core topological points carry key decision-making information. When the security equipment in a certain area triggers feedback, the topological points corresponding to the area where it is located and the equipment that closely cooperates with it become the core. For example, if the smoke alarm in the kitchen sends a trigger feedback signal, the smoke alarm node in the kitchen area, the gas valve closing device node connected to it, the ventilation fan node leading to the outside, etc., because they are directly related to the fire prevention processing process, constitute the core topological points.

[0104] Furthermore, the extraction of key information elements in the trigger feedback signal can be achieved through a data analysis algorithm, such as: parsing out the information represented by a specific byte bit representing the open or closed state of the lock in the feedback of the smart door lock as the key information element; the association of the key information element with the device identifier in the smart home can be achieved through a database association query, such as: using the device type and the regional characteristics contained as association conditions, querying the corresponding device identifier and key information element combination to form a device association identifier; the construction of the device topology map corresponding to the smart home can be achieved through a graph database technology, such as: identifying the network IP address of the security device connection information, import these devices as nodes into Neo4j, so as to obtain the device topology map; the extraction of the core topology points in the device topology map can be achieved through the centrality measurement algorithm, such as: if the smoke alarm node degree centrality is high and its own risk weight is 0.8, the nodes connected to it, such as the ventilation fan, gas valve closing device, etc., constitute the core topology point with it; the generation of the security response instructions corresponding to the smart home can be achieved through the rule engine, such as: when the core topology point is triggered by the kitchen smoke alarm and the key information element shows that the smoke concentration exceeds the standard, the rule stipulates that a closing instruction should be sent to the gas valve closing device and a full-speed operation instruction should be issued to the ventilation fan.

[0105] S4. Analyze the execution effect data corresponding to the security response instruction, identify the security vulnerability features in the execution effect data, and calculate the security risk value corresponding to the smart home security system based on the security vulnerability features.

[0106] The present invention assists in precise optimization by analyzing the execution effect data corresponding to the security response instruction, and accurately adjusts subsequent security instructions based on the feedback of instruction execution. If the alarm duration does not achieve the expected deterrent effect, the setting can be optimized to ensure system reliability. By understanding the implementation of the instructions by the equipment, the links with poor execution can be checked in time to repair potential faults.

[0107] Among them, the execution effect data refers to a data set that reflects the performance of various aspects of the security response command during the actual execution process, such as the actual video resolution data, frame rate fluctuation curve, local storage capacity change data and cloud upload log (including the number of successful uploads, the number of failed uploads, the time taken for each upload, etc.) collected from the smart camera; the lock status feedback (whether it is normally locked or not locked due to a malfunction) and the lock timestamp obtained from the smart door lock; the actual sound decibel value of the sound and light alarm and the alarm duration; and the time when the family member’s mobile phone received the alarm notification and whether it was successfully received.

[0108] As an embodiment of the present invention, the analysis of the execution effect data corresponding to the security response instruction includes: sorting out the instruction task list corresponding to the security response instruction; querying the task planning process corresponding to the subtask in the instruction task list; extracting the key execution elements corresponding to the task planning process; and analyzing the execution effect data corresponding to the security response instruction based on the key execution elements.

[0109] Among them, the command task list refers to a list formed by a detailed listing of specific work tasks covered by the security response command. For example, when the security system detects that an abnormal person breaks into the room, the command task list corresponding to the security response command issued may include: the smart camera turns on high-definition recording and uploads it to the cloud storage in real time to record the intruder's movement trajectory; the smart door lock immediately executes the locking operation to prevent illegal intruders from further entering the room; the sound and light alarm is activated, and a high-decibel alarm sound is issued to deter intruders; at the same time, the intrusion alarm notification is pushed to the mobile phones of family members to inform them of the emergency at home; the task planning process refers to the detailed step arrangement and time planning of each subtask in the execution process, which describes the various stages that the subtask goes through from start to completion, as well as the estimated start and end time, sequence and other information of each stage. Taking the high-definition recording and uploading tasks of smart cameras as an example, its task The task planning process may include: starting the high-definition recording function within 0.5 seconds after receiving the security response command to ensure that the picture is clear and captures the characteristics of the intruder; temporarily storing the recording data locally every 10 seconds to prevent data loss; establishing a connection with the cloud server within 1 minute and starting to upload the recording data in real time, and the upload speed must be maintained above a certain bandwidth to ensure timely data transmission; the key execution elements refer to the core points extracted from the task planning process that play a decisive role in the successful execution of the task. Taking the smart camera task as an example, the key execution elements include: whether the recording resolution meets the high-definition standard (such as 1080p), whether the frame rate is stable at the preset value (such as 25fps) to ensure smooth pictures, whether the local temporary storage successfully creates a folder and continuously writes data, and whether the connection with the cloud is established within the specified time and there is no interruption or packet loss during the upload process.

[0110] Furthermore, the combing of the instruction task list corresponding to the security response instruction can be achieved through a decision tree model, such as generating a corresponding instruction task list by analyzing specific trigger conditions (such as abnormal personnel intrusion); the query of the task planning process corresponding to the subtask in the instruction task list can be achieved through a process query tool, such as top / htop, ps and other tools in the Linux system; the extraction of the key execution elements corresponding to the task planning process can be achieved through data mining tools, such as Apache Nifi, RapidMiner and other tools; the analysis of the execution effect data corresponding to the security response instruction can be achieved through an effect analysis tool, such as SAS, KNIME and other tools.

[0111] The present invention can accurately prevent risks by identifying security vulnerability features in the execution effect data. By identifying vulnerabilities, such as blurred camera imaging at night and delayed door locking, it helps to optimize security layout, reallocate equipment resources according to vulnerability features, optimize linkage strategies, and improve overall security effectiveness.

[0112] Among them, the security vulnerability characteristics refer to various abnormal performances and potential hidden dangers presented in the process of analyzing the security response instruction execution effect data, which may cause weak links or failure risks in the smart home security system. For example, the device execution action does not meet the expected standard, and the picture clarity of the smart camera in a low-light environment is seriously insufficient, and it is impossible to effectively identify intruders; the response time is too long, and when an abnormal situation is triggered, the sound and light alarm is delayed for several seconds before starting, which misses the warning opportunity; the linkage coordination fails, and after the smoke alarm detects smoke, the ventilation equipment linked to it is not turned on according to the instruction, resulting in the smoke cannot be discharged in time; and the equipment stability is poor, such as the smart door lock frequently erroneously unlocked or unable to lock normally. These are all security vulnerability characteristics. Optionally, the identification of the security vulnerability characteristics in the execution effect data can be achieved by an anomaly detection algorithm, such as: using the isolation forest algorithm, constructing a random forest model, dividing the data points into different isolated tree branches, and those data points with a shallow depth in the tree are determined as abnormal points.

[0113] The present invention calculates the security risk value corresponding to the smart home security system based on the security vulnerability characteristics, can accurately quantify the security situation, facilitate the reasonable allocation of resources, give priority to high-risk vulnerabilities according to the risk value, deploy human and material resources for repair and optimization, and improve security efficiency.

[0114] Among them, the hazard level value refers to the quantitative value of the severity of the harm that each security vulnerability feature may cause to the smart home security system. The larger the value, the greater the threat to home security once the vulnerability feature is triggered. For example, the hazard level value of the vulnerability of illegally opening doors and windows may be high.

[0115] As an embodiment of the present invention, the calculating the security risk value corresponding to the smart home security system based on the security vulnerability feature includes:

[0116] ;

[0117] in, represents the security risk value corresponding to the smart home security system, Indicates the total number of features corresponding to the security vulnerability features, Indicates the quantity index corresponding to the security vulnerability feature, Indicates The harm degree value corresponding to each security vulnerability feature, Indicates The frequency of vulnerabilities corresponding to the security vulnerability features, represents the total number of security devices in the smart home security system, Indicates the number index of security equipment. Indicates The security equipment is subject to The correlation impact of the security vulnerability characteristics.

[0118] Specifically, the frequency of vulnerability occurrence refers to the ratio of the number of occurrences of a security vulnerability feature to the total number of observations within a certain period of time or under specific conditions, reflecting the frequency of occurrence of the vulnerability feature. For example, if a vulnerability of a smart camera that causes screen freezes due to unstable network occurs frequently, then the frequency of occurrence of the vulnerability is high. The degree of correlation impact refers to the The security equipment is A quantitative indicator of the degree of influence of a security vulnerability feature. The value range is usually between 0 and 1. The closer the value is to 1, the greater and closer the impact of the vulnerability feature on the corresponding security equipment. For example, the false alarm vulnerability of a smoke alarm may have a high degree of correlation with the linkage of ventilation equipment, because a false alarm may cause the ventilation equipment to start up unnecessary.

[0119] S5. Based on the security risk value and the preset intelligent adjustment mechanism, the preset smart home security system is adjusted in real time, an adjustment feedback record is generated, the core adjustment elements in the adjustment feedback record are extracted, and based on the core adjustment elements, a security monitoring report corresponding to the smart home is generated.

[0120] Based on the security risk value and the preset intelligent adjustment mechanism, the present invention adjusts the preset smart home security system in real time and generates adjustment feedback records, which can optimize the security performance in real time and dynamically adjust according to the risk value, so that the system is always in a highly efficient protection state, which is convenient for tracing the adjustment process. The feedback records provide a basis for subsequent analysis and are conducive to continuous improvement.

[0121] Among them, the preset intelligent adjustment mechanism refers to a set of pre-set rules and processes. When the security risk value of the smart home security system reaches a certain threshold or undergoes specific changes, the system can automatically adjust and optimize the parameters, working modes, linkage strategies, etc. of the security equipment according to these rules and processes, such as automatically increasing the monitoring frequency of the camera, adjusting the sensitivity of the sensor, etc., so as to reduce the risk value and improve the security effect; the adjustment feedback record refers to a document or data set formed by recording the relevant information of each adjustment during the real-time adjustment of the smart home security system according to the intelligent adjustment mechanism. This information includes but is not limited to the time of adjustment, the security risk value that triggers the adjustment, the specific adjustment content (such as changes in equipment parameters, switching of working modes, etc.), comparison of the system status before and after adjustment, etc. Optionally, the real-time adjustment of the preset smart home security system can be achieved through system adjustment tools, such as: CCleaner, Glary Utilities and other tools.

[0122] The present invention helps to quickly gain insight into the system change rules by extracting the core adjustment elements in the adjustment feedback records, and can clarify the system optimization path by focusing on key adjustment actions, such as equipment parameter adjustment and linkage mode switching.

[0123] Among them, the core adjustment elements refer to the most valuable information set extracted from the adjustment feedback records that best reflects the essential characteristics and key changes of the system adjustment. Combining the above links, in the adjustment case triggered by severe weather, the data collection sensitivity adjustment range of the door and window sensors, the specific values ​​of the light brightness and flashing frequency of the intelligent lighting system, and the decision information of whether to initiate external notification based on the overall adjustment situation of the system, etc., are all core adjustment elements.

[0124] As an embodiment of the present invention, the extraction of the core adjustment elements in the adjustment feedback record includes: analyzing the key adjustment scenarios covered by the adjustment feedback record; querying the main adjustment subjects associated with the key adjustment scenarios; based on the main adjustment subjects, determining the main adjustment process corresponding to the adjustment feedback record; locating the core element points in the main adjustment process; based on the core element points, extracting the core adjustment elements in the adjustment feedback record.

[0125] Among them, the key adjustment scenario refers to the representative and iconic situational conditions faced by the smart home security system in the process of real-time adjustment according to the security risk value, which covers various reasons for triggering the adjustment and environmental background information. For example, when the security risk value rises suddenly at night, it may be due to the increase in false alarms of the smart camera caused by dim light, or the frequent triggering of the sound sensor due to the noise interference of the surrounding environment. Such a situation where the system adjustment demand is triggered by a specific time period and special environmental factors is a key adjustment scenario; the main adjustment subject refers to the security equipment, software module or smart component that directly participates in the execution of the adjustment action and plays a core role in promoting the change of the system state in a specific key adjustment scenario. For example, when the smart home triggers the security system adjustment due to bad weather (heavy rain, strong wind), the door and window sensors may have the risk of misjudgment due to rain erosion or strong wind vibration. Then the door and window sensors are one of the main adjustment subjects in this scenario, and the system will adjust its data collection frequency, error calibration algorithm and other aspects; at the same time, in order to cope with the low light environment in bad weather, the smart lighting system automatically switches to strong light lighting and flashing warning mode, and the smart lighting system becomes another main adjustment subject in this scenario. Adjustment subject; the main adjustment process refers to a series of orderly operation steps and logical sequences around the main adjustment subject, which is used to achieve precise adjustment of the smart home security system. Taking the above-mentioned bad weather scenario as an example, for the door and window sensor, which is the main adjustment subject, its main adjustment process may include: first, the system monitors the abnormal data fluctuations of the door and window sensor due to bad weather in real time; then, according to the preset algorithm, reduce its data collection sensitivity to avoid too many false alarms; then, start the backup data verification module to conduct a secondary check on the collected data; finally, transmit the verified valid data to the central control system, and decide whether to trigger further security linkage based on the analysis results, such as notifying the owner or starting the surrounding monitoring equipment to strengthen prevention; the core element point refers to the key node in the main adjustment process that has a decisive influence on the adjustment effect and carries key technical parameters or key decision information. In the adjustment process of the smart lighting system, when the system determines that it needs to switch to strong light lighting and flashing warning mode, the set value of the light brightness, the control parameters of the flashing frequency, the coverage range of the lighting area, etc., which are directly related to the balance between the warning effect and energy consumption, are the core elements.

[0126] Furthermore, the analysis of the key adjustment scenarios covered by the adjustment feedback records can be achieved through cluster analysis, such as: unsupervised learning of the adjustment feedback records through the K-means algorithm to find data sets with similar features, thereby identifying and extracting key adjustment scenarios; the query of the main adjustment subjects associated with the key adjustment scenarios can be achieved through a relationship graph analysis tool, such as: Neo4j and other tools, by modeling the relationship between the device and the key adjustment scenario, executing a graph query to find and list the relevant main adjustment subjects; the determination of the main adjustment process corresponding to the adjustment feedback record can be achieved through a business process modeling tool, such as: using BPMN to visually model the operation steps of the main adjustment subject, so as to clarify and record the main adjustment process; the positioning of the core elements in the main adjustment process can be achieved through a decision tree algorithm, such as: CART algorithm, to analyze the factors affecting the adjustment effect in the data, find the key nodes of these factors, and then locate the core elements; the extraction of the core adjustment elements in the adjustment feedback record can be achieved through feature extraction technology, such as: principal component analysis method, to extract the most representative core adjustment elements from the adjustment feedback record.

[0127] The present invention generates a security monitoring report corresponding to the smart home based on the core adjustment elements, which can provide users with clear and intuitive security situation insights and allow users to quickly know the dynamic adjustment status of the home security system by presenting key adjustment points.

[0128] Among them, the security monitoring report refers to a document that comprehensively and systematically presents the operating status of the smart home security system, the adjustments made based on the core adjustment elements and their effects in the form of text, charts or a combination of the two. It covers such contents as the time and reason for triggering the adjustment, the main security equipment involved and its initial state, the state change after the adjustment, the key parameter settings during the adjustment process (such as camera resolution adjustment, sensor sensitivity change), and the evaluation of the security performance of the system after adjustment. Optionally, the generation of the security monitoring report corresponding to the smart home can be achieved through a report generation tool, such as: Tableau, JasperReports and other tools.

[0129] Compared with the problems described in the background technology, the present invention obtains the environmental layout of the smart home and analyzes the key monitoring areas in the environmental layout, so that security resources can be accurately deployed and blind deployment of equipment can be avoided. For example, key deployment is carried out on doors, windows, and valuables storage areas to improve the targeted protection. Based on the initial configuration parameters, the present invention collects the operating status of the equipment in the smart home in real time, and can accurately control the home security situation. According to the parameter settings adapted to each area, the status of equipment such as door and window sensors and cameras is captured in a targeted manner, abnormalities are detected at the first time, and potential risks are prevented. Furthermore, based on the security urgency value, the present invention analyzes the linkage trigger devices in the preset smart home security system, which can be accurately matched, and the linkage devices in the corresponding areas with high values ​​can respond in time according to the high urgency value. For example, when the urgency value is high, the entire system is automatically turned on. House monitoring and alarm, optimize resource utilization, avoid unnecessary equipment linkage, save energy and equipment loss. Furthermore, the present invention analyzes the execution effect data corresponding to the security response instruction to assist in precise optimization, and accurately adjusts the subsequent security instructions based on the feedback of the instruction execution. If the alarm duration does not reach the expected deterrent effect, the setting can be optimized to ensure the reliability of the system. By understanding the implementation of the instructions by the equipment, the links with poor execution are promptly checked and the hidden dangers of faults are repaired. Finally, based on the security risk value and the preset intelligent adjustment mechanism, the present invention adjusts the preset smart home security system in real time, generates adjustment feedback records, and can optimize the security performance in real time. It is dynamically adjusted according to the risk value, so that the system is always in an efficient protection state, which is convenient for tracing the adjustment process. The feedback record provides a basis for subsequent analysis and is conducive to continuous improvement. Therefore, the smart home security monitoring method and system under the cloud computing environment provided by the embodiment of the present invention improve the security monitoring effect of the smart home.

[0130] Embodiment 2:

[0131] like Figure 2 As shown, it is a functional module diagram of a smart home security monitoring system in a cloud computing environment of the present invention.

[0132] The smart home security monitoring system 200 in a cloud computing environment of the present invention can be installed in an electronic device. According to the functions to be implemented, the smart home security monitoring system in the cloud computing environment may include an initialization module 201, an urgency value calculation module 202, an instruction generation module 203, a risk value calculation module 204, and a report generation module 205. The module of the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and is stored in the memory of the electronic device.

[0133] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0134] The initialization module 201 is used to obtain the environment layout of the smart home, analyze the key monitoring area in the environment layout, determine the cloud monitoring node adapted to the key monitoring area, perform parameter initialization configuration on the cloud monitoring node, and obtain initial configuration parameters;

[0135] The urgency value calculation module 202 is used to collect the operating status of the devices in the smart home in real time based on the initial configuration parameters, analyze the real-time data stream under the operating status of the devices, and parse the abnormal characteristic parameters in the real-time data stream, generate a risk warning curve corresponding to the smart home based on the abnormal characteristic parameters, and extract potential risk points in the risk warning curve, and calculate the security urgency value corresponding to the smart home based on the potential risk points;

[0136] The instruction generation module 203 is used to analyze the linkage trigger device in the preset smart home security system based on the security urgency value, detect the trigger feedback signal corresponding to the linkage trigger device, and generate the security response instruction corresponding to the smart home based on the trigger feedback signal;

[0137] The risk value calculation module 204 is used to analyze the execution effect data corresponding to the security response instruction, identify the security vulnerability features in the execution effect data, and calculate the security risk value corresponding to the smart home security system based on the security vulnerability features;

[0138] The report generation module 205 is used to perform real-time adjustments to the preset smart home security system based on the security risk value and the preset intelligent adjustment mechanism, generate an adjustment feedback record, extract the core adjustment elements in the adjustment feedback record, and generate a security monitoring report corresponding to the smart home based on the core adjustment elements.

[0139] In detail, the modules in the smart home security monitoring system 200 under the cloud computing environment of the embodiment of the present invention are used in the same manner as described above. Figure 1 The same technical means are used as the smart home security monitoring method in the cloud computing environment described in , and can produce the same technical effects, so they will not be repeated here.

[0140] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A smart home security monitoring method in a cloud computing environment, characterized in that: The method comprises: Acquire the environment layout of the smart home, analyze the key monitoring areas in the environment layout, determine the cloud monitoring nodes adapted to the key monitoring areas, perform parameter initialization configuration on the cloud monitoring nodes, and obtain initial configuration parameters; Based on the initial configuration parameters, the operating status of the devices in the smart home is collected in real time, the real-time data stream under the operating status of the devices is analyzed, and the abnormal characteristic parameters in the real-time data stream are parsed, and based on the abnormal characteristic parameters, a risk warning curve corresponding to the smart home is generated, and potential risk points in the risk warning curve are extracted, and based on the potential risk points, a security urgency value corresponding to the smart home is calculated; Based on the security urgency value, analyzing the linkage trigger device in the preset smart home security system, detecting the trigger feedback signal corresponding to the linkage trigger device, and generating the security response instruction corresponding to the smart home based on the trigger feedback signal; Analyzing the execution effect data corresponding to the security response instruction, identifying the security vulnerability features in the execution effect data, and calculating the security risk value corresponding to the smart home security system based on the security vulnerability features; Based on the security risk value and the preset intelligent adjustment mechanism, the preset smart home security system is adjusted in real time, an adjustment feedback record is generated, the core adjustment elements in the adjustment feedback record are extracted, and based on the core adjustment elements, a security monitoring report corresponding to the smart home is generated.

2. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The initializing and configuring the cloud monitoring node parameters to obtain initial configuration parameters includes: Analyze the node requirements corresponding to the cloud monitoring nodes; Based on the node requirements, determine the monitoring priority corresponding to the cloud monitoring node; Querying the basic configuration template corresponding to the monitoring priority; Based on the basic configuration template, the cloud monitoring node is initialized and configured with parameters to obtain initial configuration parameters.

3. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The real-time collection of the operating status of the devices in the smart home based on the initial configuration parameters includes: Query the parameter collection frequency in the initial configuration parameters; Based on the parameter collection frequency, determining a sampling period corresponding to the device in the smart home; Based on the sampling period, activating a communication link between devices in the smart home; Marking the key link points corresponding to the communication link; Based on the key points of the link, the operating status of the devices in the smart home is collected in real time.

4. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The calculating, based on the potential risk point, the security urgency value corresponding to the smart home includes: The security urgency value corresponding to the smart home is calculated using the following formula: ； in, represents the security emergency value corresponding to the smart home, Indicates the total number of potential risk points. Indicates the quantity index corresponding to the potential risk point, Indicates The probability of risk occurrence of a potential risk point, Representative The accident loss cost corresponding to each potential risk point is: Indicates that from The abnormal accident time corresponding to each potential risk point.

5. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The step of analyzing the linkage triggering device in the preset smart home security system based on the security urgency value includes: Real-time monitoring of the numerical fluctuations corresponding to the security emergency value; Based on the fluctuation of the value, the security urgency value is divided into intervals to obtain urgency division intervals; Based on the urgent division interval, determining the device linkage state corresponding to the device in the preset smart home security system; Based on the linkage status of the device, query the linkage trigger combination corresponding to the device in the preset smart home security system; Based on the linkage trigger combination, the linkage trigger devices in the preset smart home security system are analyzed.

6. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The generating, based on the trigger feedback signal, a security response instruction corresponding to the smart home comprises: Extracting key information elements from the trigger feedback signal; Associating the key information element with a device identifier in the smart home to obtain a device association identifier; Based on the device association identifier, construct a device topology map corresponding to the smart home; Extracting core topological points from the device topology map; Based on the core topological point, a security response instruction corresponding to the smart home is generated.

7. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The analyzing the execution effect data corresponding to the security response instruction includes: Sort out the command task list corresponding to the security response command; Query the task planning process corresponding to the subtask in the instruction task list; Extracting key execution elements corresponding to the task planning process; Based on the key execution elements, the execution effect data corresponding to the security response instruction is analyzed.

8. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The calculating, based on the security vulnerability feature, a security risk value corresponding to the smart home security system, comprises: ； in, represents the security risk value corresponding to the smart home security system, Indicates the total number of features corresponding to the security vulnerability features, Indicates the quantity index corresponding to the security vulnerability feature, Indicates The harm degree value corresponding to each security vulnerability feature, Indicates The frequency of vulnerabilities corresponding to the security vulnerability features, represents the total number of security devices in the smart home security system, Indicates the number index of security equipment. Indicates The security equipment is subject to The correlation impact of the security vulnerability characteristics.

9. The smart home security monitoring method in a cloud computing environment as claimed in claim 1, characterized in that: The extracting the core adjustment elements in the adjustment feedback record includes: Analyze the key adjustment scenarios covered by the adjustment feedback records; Query the main regulation subject associated with the key regulation scenario; Based on the main adjustment subject, determining the main adjustment process corresponding to the adjustment feedback record; Locate the core elements in the main adjustment process; Based on the core element points, the core adjustment elements in the adjustment feedback record are extracted.

10. A smart home security monitoring system in a cloud computing environment, characterized in that: The system comprises: An initialization module is used to obtain the environment layout of the smart home, analyze the key monitoring areas in the environment layout, determine the cloud monitoring nodes adapted to the key monitoring areas, perform parameter initialization configuration on the cloud monitoring nodes, and obtain initial configuration parameters; An urgency value calculation module is used to collect the operating status of the devices in the smart home in real time based on the initial configuration parameters, analyze the real-time data stream under the operating status of the devices, and parse the abnormal characteristic parameters in the real-time data stream, generate a risk warning curve corresponding to the smart home based on the abnormal characteristic parameters, extract potential risk points in the risk warning curve, and calculate the security urgency value corresponding to the smart home based on the potential risk points; An instruction generation module is used to analyze the linkage trigger device in the preset smart home security system based on the security urgency value, detect the trigger feedback signal corresponding to the linkage trigger device, and generate a security response instruction corresponding to the smart home based on the trigger feedback signal; a risk value calculation module, used to analyze the execution effect data corresponding to the security response instruction, identify the security vulnerability features in the execution effect data, and calculate the security risk value corresponding to the smart home security system based on the security vulnerability features; A report generation module is used to perform real-time adjustments to a preset smart home security system based on the security risk value and a preset intelligent adjustment mechanism, generate an adjustment feedback record, extract core adjustment elements from the adjustment feedback record, and generate a security monitoring report corresponding to the smart home based on the core adjustment elements.

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