Community old people activity safety detection and early warning management method and system based on AI intelligent security system

Abstract

The application provides a community old person activity safety detection and early warning management method and system based on an AI intelligent security system, relates to the technical field of early warning management, continuously collects and positionally analyze the old person activity images of the authorized space of the community through the security device group, analyzes the intention level behavior semantics of the current action type of the old person through the posture image sequence and the space coordinate track, compares and analyzes the deviation of each item through the fine activity analysis data and the activity rule baseline of the old person in the historical period, obtains the risk tendency analysis data and the corresponding pre-judgment hazard type, generates the hierarchical response strategy and matches the disposal scheme of the potential safety event through the risk tendency analysis data, the pre-judgment hazard type, the community environment state and the historical response record of the old person, obtains the full-chain safety guarantee execution instruction, and significantly improves the intelligent level and response efficiency of the community old-age security management.

Description

Technical Field

[0001] This invention proposes a method and system for monitoring and managing the safety of elderly people's activities in residential communities based on an AI-powered intelligent security system. It relates to the field of early warning management technology, specifically the field of monitoring and managing the safety of elderly people's activities in residential communities. Background Technology

[0002] With the continued acceleration of population aging in my country, community-based elderly care has become the mainstream model. A large number of elderly people face safety risks such as falls, sudden illnesses, and getting lost while active in their communities. Currently, the video surveillance systems commonly deployed in communities mainly rely on manual monitoring or post-event review, which suffers from shortcomings such as delayed response, high false negative rates, and inability to proactively issue warnings. While existing intelligent monitoring products have motion detection functions, they can only identify preset, simple behavioral patterns and cannot understand the true intentions behind the elderly's actions. It is difficult to distinguish whether an elderly person is actively bending down to pick up an object, falling due to dizziness, or lying down to rest normally or collapsing unconscious. Furthermore, the vast differences in lifestyle habits among elderly people mean that using a uniform standard for anomaly assessment leads to frequent false alarms for active elderly individuals while the true risks of frail elderly individuals are overlooked. In addition, existing systems lack a comprehensive response and handling mechanism after issuing warnings. Management personnel are unclear about who to notify, how to notify, and what to do on-site, rendering warning information ineffective in translating into effective safety guarantees. Summary of the Invention

[0003] This invention provides a method and system for detecting, warning, and managing the activities of elderly people in residential communities based on an AI-powered intelligent security system, in order to solve the above-mentioned problems: The present invention proposes a method and system for monitoring and managing the safety of elderly residents' activities in residential communities based on an AI-powered intelligent security system. The method includes: S1. Continuously collect and analyze the activity images of the elderly in the authorized space of the community through a security equipment group to obtain the posture image sequence and the corresponding spatial coordinate trajectory. S2. By analyzing the posture image sequence and spatial coordinate trajectory, perform intention-level behavioral semantic analysis on the current action type of the elderly to obtain refined activity analysis data; S3. By comparing the refined activity analysis data with the baseline of the elderly’s own activity patterns in historical periods, a deviation analysis is conducted to obtain risk tendency analysis data and corresponding predicted hazard types. S4. By analyzing risk propensity data, predicting potential hazard types, and combining the community environment and the elderly's historical response records, a graded response strategy is generated and a handling plan is matched for potential safety incidents to obtain full-chain safety assurance execution instructions.

[0004] Furthermore, the system includes: The positioning analysis module is used to continuously collect and analyze the activity images of the elderly in the authorized space of the community through a security equipment group, and obtain the posture image sequence and the corresponding spatial coordinate trajectory. The semantic parsing module is used to perform intent-level behavioral semantic parsing of the elderly’s current action type through posture image sequences and spatial coordinate trajectories, and obtain refined activity parsing data; The deviation analysis module is used to compare the refined activity analysis data with the baseline of the elderly’s own activity patterns in historical periods to obtain risk tendency analysis data and corresponding predicted hazard types. The matching module is used to generate graded response strategies and match disposal plans for potential safety events by combining risk tendency analysis data, predicting the type of hidden dangers, and combining the community environment status and the elderly’s historical response records, so as to obtain full-chain safety assurance execution instructions.

[0005] The beneficial effects of this invention are as follows: This method effectively solves the limitation of traditional video surveillance systems, which can only provide video recordings but cannot actively understand the meaning of behavior. Through intent-level semantic parsing technology, the system can accurately distinguish whether an elderly person is actively bending down to pick up an item or has fallen due to loss of control, thereby significantly reducing the false alarm rate. Simultaneously, by establishing individualized activity pattern baselines, the system can accurately identify behavioral changes in each elderly person that deviate from their normal lifestyle, avoiding the problem of frequent false alarms for active elderly people and underestimation of the true risk for frail elderly people when using a uniform template to measure all elderly people. In the early warning response stage, this method solves the problem of early warning information remaining on the screen and unable to be translated into effective action by automatically generating execution instructions containing complete handling elements. This enables community management personnel and family members to clearly know what to do, who should do it, how to do it, and when to do it when receiving an early warning, constructing a complete closed loop from risk perception to safety assurance, significantly improving the intelligence level and response efficiency of community elderly care safety management. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of a community elderly activity safety detection and early warning management method based on an AI-powered intelligent security system. Detailed Implementation

[0007] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0008] In one embodiment of the present invention, the present invention proposes a method and system for monitoring and managing the safety of elderly residents' activities in a community based on an AI-powered intelligent security system. The method includes: S1. Continuously collect and analyze the activity images of the elderly in the authorized space of the community through a security equipment group to obtain the posture image sequence and the corresponding spatial coordinate trajectory. S2. By analyzing the current action type of the elderly through posture image sequences and spatial coordinate trajectories, intention-level behavioral semantic analysis is performed to obtain refined activity analysis data. Behavioral meaning labels distinguish between active normal activities, physiologically necessary actions, and abnormal risk actions. S3. By comparing the refined activity analysis data with the baseline of the elderly’s own activity patterns in historical periods, a deviation analysis is conducted to obtain risk tendency analysis data and corresponding predicted hazard types. S4. By analyzing risk propensity data, predicting hazard types, and combining the community environment with the elderly's historical response records, a tiered response strategy is generated for potential safety incidents, and a corresponding handling plan is matched to obtain full-chain safety assurance execution instructions, such as... Figure 1 As shown.

[0009] The above technical solution solves the technical problems of large blind spots of single cameras, lack of spatial correlation, and inability to distinguish between the elderly slowly getting up and actually falling, the use of a uniform standard to measure all elderly people and the problem of false alarms caused by ignoring individual differences, and the lack of knowledge of what to do and when to do it after the warning is issued.

[0010] Security equipment clusters refer to a collection of various sensing devices deployed in public areas of a residential community (roads, squares, fitness areas), unit entrances and exits, and indoor spaces authorized by the owners. These devices include, but are not limited to, visible light cameras, infrared thermal imaging cameras, millimeter-wave radar, lidar, and pyroelectric sensors. These devices, through a unified time synchronization protocol (such as the PTP precise time protocol) and spatial coordinate system calibration, form a comprehensive sensing network covering the target area, enabling the synchronous capture of target activity states from multiple physical dimensions.

[0011] Each device achieves microsecond-level clock synchronization through the NTP / PTP protocol, and uses a calibration board to complete the calibration of camera intrinsic and extrinsic parameters (Zhang Zhengyou calibration method), unifying all devices under the global coordinate system of the cell.

[0012] Different types of sensing devices generate data with different structures and formats. Visible light cameras output RGB frame sequences (24 bits / pixel), infrared thermal imaging outputs temperature grayscale matrices (16 bits / pixel, temperature resolution ≤0.05℃), and millimeter-wave radar outputs point cloud data (including distance-velocity-angle information) and micro-motion Doppler spectra. These data streams are synchronized and aligned frame-by-frame on the time axis, forming a multi-dimensional raw information input.

[0013] By using ROS (Robot Operating System) or similar message middleware, data from each device is encapsulated into a unified message format according to timestamps and cached in a circular buffer for subsequent processing.

[0014] Cross-device spatiotemporal synchronization is the process of mapping data collected by devices at different locations at the same time to a unified spatiotemporal reference, solving the problem of data misalignment caused by differences in installation location and collection frequency. Target deduplication matching solves the problem of repeated identification when the same elderly person enters an area covered by multiple overlapping devices. It performs multi-view target association by extracting the target's color histogram, texture features (HOG / LBP), gait features, and position prediction (Kalman filtering).

[0015] By employing joint probabilistic data association (JPDA) or a data association strategy based on the Hungarian algorithm in multi-target tracking, and combining the positional consistency of targets in the global coordinate system with the similarity of appearance features (cosine distance), it is determined whether they belong to the same target, thus achieving continuous identity binding across cameras.

[0016] Cross-camera continuous binding, also known as Re-ID (Person Re-identification) technology, refers to the ability of the system to correctly identify the same elderly person as the same person when they leave the field of view of one camera and enter the field of view of another camera, by means of appearance features, gait features and trajectory spatiotemporal constraints, thus maintaining the continuity of identity ID in time and space.

[0017] A deep learning-based Re-ID model (such as PCB, MGN, and other network structures) is used to extract global and local features of the target and establish a feature vector library. When a new target enters, the similarity with the known target feature library is calculated by Euclidean distance / Mahathano distance, and a joint determination is made by combining spatiotemporal constraints (it is impossible for two distant locations to appear at the same time).

[0018] Skeletal keypoint extraction refers to the process of locating the pixel coordinates or normalized coordinates of major human joints (such as left and right shoulders, elbows, wrists, hips, knees, ankles, and the center of the torso, typically 17-25 keypoints) from an image frame. Commonly used deep learning models include OpenPose (bottom-up approach), HRNet (High Resolution Preservation Network), and MediaPipe Pose, which output the position and confidence score of each keypoint.

[0019] HRNet-W32 or MobileNetV3 is used as the backbone network. After pre-training on the COCO or MPII human pose dataset, it is fine-tuned (transfer learning) on ​​a self-built elderly dataset to adapt to the physical characteristics of the elderly (bending, hunching, limited range of limb movement, etc.). The output is a (x, y, confidence) triplet of each key point in the image coordinate system.

[0020] Based on the skeletal key points, posture parameter analysis further calculates the posture parameterization features such as the angle between each limb segment (upper arm, forearm, thigh, lower leg) and the reference axis (such as the vertical axis, trunk axis), joint flexion angle, and limb extension degree, which are used for subsequent action classification and anomaly detection.

[0021] The vector formed by adjacent key points is calculated using the coordinates of key points. The joint angle is obtained by dot product and norm (e.g., elbow joint angle = arccos((upper arm vector·forearm vector) / (|upper arm|·|forearm|))). The deviation value is then compared with the reference angle under the standard standing posture.

[0022] Multi-view triangulation utilizes two or more calibrated cameras to simultaneously observe the same target. Based on the target's projected coordinates on each imaging plane, combined with the camera projection matrix (essential matrix / fundamental matrix), the actual position (X, Y, Z) of the target in three-dimensional space is calculated using the principle of triangulation. Accuracy depends on the baseline distance, camera resolution, and calibration accuracy.

[0023] The three-dimensional coordinates are solved using DLT (Direct Linear Transform) or nonlinear least squares optimization (Levenberg-Marquardt). For the fusion localization of millimeter-wave radar and vision, UKF (Unscented Kalman Filter) is used to optimally fuse radar ranging information with visual triangulation results.

[0024] Spatial coordinate trajectories are time-series trajectory data formed by arranging the elderly person's three-dimensional spatial position in chronological order. They record the elderly person's movement path, velocity, acceleration, and dwell time and posture in specific areas (such as slippery surfaces, step edges, and near equipment) within the global coordinate system. Optimization typically employs cubic spline interpolation and Kalman filtering.

[0025] Implementation: Stored as a timestamped sequence structure in the format {timestamp, x, y, z, vx, vy, vz, label}, where v is the velocity component calculated by difference, and a sliding window mid-value filter is used to remove non-physical jump points.

[0026] Intention-level behavioral semantic parsing differs from simply classifying actions (such as walking or squatting). Intention-level parsing places action sequences within a temporal-spatial context to infer the purpose and motivation behind an elderly person's behavior (such as walking to a medical point to get medication, attempting to stand up but failing). This process involves mapping from the physical action space to the intention semantic space, requiring the integration of comprehensive information such as physical characteristics of the action, spatial location semantics, and temporal context.

[0027] Hierarchical Hidden Markov Model (HHMM) or temporal action detection network (such as SlowFast, TSN) is used to input the sequence of continuous action frames into the temporal action recognition model to obtain the action category, and then combine the location semantic information to infer the posterior probability of intent through a Bayesian network.

[0028] Involuntary characteristics refer to physical signal components in the elderly’s movements that are not actively controlled or driven by their own will. Typical examples include: (1) rapid falling characteristics – rapid descent of the trunk due to loss of balance or insufficient support, manifested as a vertical acceleration that is significantly greater than that of normal squatting or sitting; (2) tremor characteristics – high-frequency, low-amplitude swaying or irregular shaking of the limbs caused by Parkinson’s disease or senile tremor. Identifying involuntary characteristics is a key basis for distinguishing between normal movements and dangerous events.

[0029] Fourier transform / short-time Fourier transform were performed on the joint angle time series, and the energy ratio of the 4~8Hz frequency band was extracted as a quantification index of tremor. Sliding window variance analysis was performed on the vertical velocity of the center of mass. When the peak vertical velocity exceeded 0.8m / s and was accompanied by a peak acceleration >1.5g, it was judged as an abnormal fall.

[0030] Action physical feature vectors quantify and combine various physical parameters of an action (joint angle timing, center of mass displacement, velocity curve, peak acceleration, jitter spectrum energy, impact kinetic energy, recovery stabilization time, etc.) into numerical values ​​to form a multi-dimensional vector. This serves as a structured intermediate representation that maps from physical space to semantic space, and the dimensions are generally between 30 and 100.

[0031] Implementation method: Use PCA (Principal Component Analysis) or autoencoder to reduce the dimensionality of the original high-dimensional features to the dimensions that retain the cumulative contribution rate of principal components ≥95%, and then perform max-min normalization or Z-score standardization to unify the value range of each dimension to [0,1] or a distribution with a mean of 0 and a variance of 1.

[0032] The behavioral semantic knowledge mapping rule base is a pre-built set of inference rules used to convert physical feature vectors of actions into high-level behavioral semantics. The rule format is as follows: IF (Impact kinetic energy > threshold T1 AND Active control < threshold T2 AND Spatial location == stairwell area) THEN Behavior = Stairwell fall risk. The rules are derived from prior knowledge of human kinematics, statistical patterns of historical events in the community, and accumulated expert knowledge.

[0033] The fuzzy boundary of the rule is handled by a fuzzy inference system (Mamdani type or Takagi-Sugeno type). The precise physical feature value of the input is fuzzified by the membership function. The rule triggering strength is calculated by T-norm operation (min or product). Finally, the probability distribution of the behavior category is defuzzified by the centroid method.

[0034] Location semantic information refers to the actual place meaning assigned after spatial coordinates are projected onto the community's geographic information system. For example, coordinates (235.6, 89.2, 0.0) correspond to the semantics of the entrance to the cobblestone path in the central garden, the accessible ramp at the entrance of Unit 2, Building 6, or next to the waist twister in the fitness equipment area. Different locations have different inherent risk factors and functional attributes, which are important contexts for modifying behavioral semantics.

[0035] Create a GIS layer for the community, delineate the boundaries of each area using polygons (GeoJSON format), and assign attribute fields {area name, function type, basic hazard level, ground material, slope coefficient}. Use the ray casting method or Winding Number algorithm to determine which polygon area a coordinate point falls into.

[0036] The personalized activity pattern baseline is a behavioral benchmark obtained by statistical modeling of behavioral data from multiple days (generally ≥14 days) of each elderly person's historical normal life period (excluding dates of abnormal events). It includes: (1) typical activity types and their probability distribution in each time period (each 30-minute statistical interval); (2) activity intensity curve (number of steps, active duration); (3) spatial heat map (resident frequency and residence duration in each area); and (4) circadian rhythm (time range of waking up, afternoon nap, and returning home in the evening). This baseline reflects the elderly person's life habits and behavioral norms.

[0037] A Gaussian mixture model (GMM) was used to fit the behavioral characteristics distribution of each elderly person within the same time window, and the mean ± 1.5 times the standard deviation was taken as the normal fluctuation range; or the Holt-Winters time series prediction model was used to perform periodic decomposition on the activity intensity sequence to generate baseline prediction values ​​and confidence intervals.

[0038] Multidimensional deviation measures are quantified results of the deviation between actual observed values ​​and baseline expected values ​​across multiple dimensions, including time (same time period), space (same region), behavior type (same action category), and intensity (amplitude / frequency). The deviation for each dimension needs to be normalized to a comparable unit of measurement, such as time deviation in minutes, spatial deviation in Euclidean distance, and type deviation in semantic similarity.

[0039] For numerical features (activity intensity, speed, etc.), the relative deviation rate is calculated as |observed value - baseline value| / (baseline value + ε); for categorical features (activity type), the difference is measured by Jaccard distance or cross-entropy loss; for trajectory path deviation, the difference is measured by Hausdorff distance or DTW (Dynamic Time Warping) distance.

[0040] The comprehensive deviation score is a single quantitative index (usually ranging from 0 to 100 or 0 to 1) obtained by weighted summation of deviation measures from multiple dimensions or through a nonlinear fusion function, reflecting the severity of the elderly person's current activity status relative to their normal pattern. The weight allocation can be set according to the importance of each dimension to safety impact, such as spatial deviation (high-risk areas) having a greater weight than temporal deviation.

[0041] The weight coefficients for each dimension are determined using the Analytic Hierarchy Process (AHP) combined with expert evaluation. The final comprehensive score is calculated as Σ(wi × normalized deviation i), where Σwi = 1. Alternatively, the Dempster-Shafer evidence theory can be used to fuse evidence from different dimensions and output a comprehensive reliability value.

[0042] The time-series risk weighting coefficient is an additional weight assigned based on historical risk statistics for the current time period. For example, the weighting coefficient is 1.8 for the period from 0:00 to 5:00 AM, which is a period of deep sleep (high risk of falls when elderly people get up at night); and 0.7 for the period from 9:00 to 11:00 AM, which is a peak period of routine activities (high alertness and strong availability of external assistance). This coefficient modulates the overall risk quantification score along the time dimension.

[0043] The time distribution histogram of historical security incidents in the community is statistically analyzed. The basic risk probability for each time period is calculated using the formula: Risk Rate = Number of Incidents / Total Number of Residents · Duration of Time Period. After normalization, the probability is mapped to a coefficient and updated via a sliding window as the latest incident data is generated.

[0044] The spatial risk weighting coefficient is assigned a weight based on the basic hazard level of the specific geographical location where the deviation occurs and the real-time environmental conditions (such as ground humidity and current lighting conditions). For example, the basic hazard level of a staircase corner is 2.0, which is adjusted to 2.8 in real time when there is water accumulation after rain; the basic hazard level of a synthetic running track is 0.4.

[0045] The basic spatial risk coefficient (scale 1~5) is preset in the GIS attribute table, and the final spatial weight is obtained by dynamically multiplying the real-time status reported by the environmental sensors (coefficient × 1.3 when humidity ≥ 80%, coefficient × 1.2 when illuminance ≤ 50 lux).

[0046] The Hazard Type Behavioral Feature Association Mapping Table is a pre-defined rule mapping table that associates specific action deviation combinations with potential hazard types.

[0047] Rules are automatically derived from historically labeled events using decision trees or rule learning algorithms (such as RIPPER), and then reviewed and corrected by security experts. During matching, majority voting or confidence thresholds are used to determine the best match for the vulnerability type.

[0048] The urgency level of an incident is determined by a comprehensive assessment of risk profile and predictable hazard types, classifying the timeframe required for a response to prevent substantial harm (typically 3-5 levels). Examples include Level 1 Emergency (immediate intervention required, <30 seconds), Level 2 Emergency (intervention within 5 minutes), and Level 3 Warning (close observation, follow-up within 30 minutes). Assessment criteria include: quantified risk score, rate of hazard escalation (e.g., the risk of suffocation increases dramatically if no one assists after a fall within 2 minutes), and the individual vulnerability of the elderly (advanced age / underlying medical conditions).

[0049] Construct an urgency decision tree or random forest classifier based on real historical events. Input the comprehensive risk quantification score, the type of hazard, and the age / medical history label of the elderly person, and output the urgency level and confidence level.

[0050] The tiered response strategy library is a set of standardized action templates corresponding to different predefined urgency levels. Level 1 emergency actions include: immediately calling 120 (emergency services), automatically unlocking the unit's access control system to allow rescue personnel to enter, activating a high-powered voice call to wake the elderly, and notifying the nearest security personnel to arrive with an AED. Level 2 responses include: notifying family members, confirming the status via remote intercom, and dispatching property management personnel to the scene. The strategy library supports dynamic addition, deletion, and modification based on the actual situation of the community.

[0051] Implementation: The policy table structure stored in the database includes {Level ID, Action Type, Execution Role, Maximum Response Time, Standard Notification Template ID, Parallel / Serial Execution Flag}. Actions are atomic operations, and a policy consists of multiple actions arranged in a logical order to form a directed acyclic graph (DAG).

[0052] The role type refers to the category of responsible entity required to handle the situation, including but not limited to: first responder (nearest property security guard), medical coordinator (designated first aid qualified personnel), family contact person (social worker / customer service), area manager (security captain), and external rescue (120 / 119). Different roles have different scopes of authority and capability boundaries.

[0053] Role types are defined using enumeration, and the system role permission table is bound to the specific employee ID. The on-duty personnel ID can be obtained by querying the scheduling system in real time.

[0054] Available personnel matching and real-time availability confirmation: Based on the needs of the execution role, qualified on-duty personnel are selected from the community's human resources pool, and their current availability is confirmed through status awareness (such as personnel GPS badge location, work order system occupancy status, and walkie-talkie online status). If the preferred personnel are engaged in other emergency tasks, the process is automatically deferred to the backup personnel, ensuring that there is only one responsible entity for each action.

[0055] The system employs either weighted round-robin scheduling or a greedy allocation algorithm based on the nearest available and idle task; personnel status is synchronized in real time via WebSocket, and the Escalation (upgrade) mechanism is automatically triggered when a task times out for 5 minutes without response.

[0056] The system provides personalized contact method preferences for each assigned person (such as mobile phone calls, WeChat / WeChat Work push notifications, SMS, walkie-talkie call signs, etc.). The system records historical performance metrics for each channel: connection rate (e.g., 80% click-through rate for WeChat push notifications, 95% call connection rate) and average response time (e.g., 8 seconds average call answering time, 62 seconds average SMS reading time). These metrics are used for intelligent selection of the optimal notification channel and primary / backup switching strategies.

[0057] Statistical data is stored as a timeliness matrix, and a weighted scoring formula is used: Channel Score = 0.5 × Connection Rate + 0.3 × (1 / Normalized Average Response Time) + 0.2 × (User Preference Coefficient). The channel with the highest score is selected as the primary channel.

[0058] After a personalized treatment plan is generated, dynamic fine-tuning and optimization are performed based on historical feedback records of similar events involving the elderly. This includes adjustments to the timing of intervention (e.g., delayed / early notification to family members), tone of voice (e.g., gentle reminder vs. emergency warning), and allocation of auxiliary resources (e.g., whether a stretcher is needed vs. only assistance). For example, if an elderly person reacts nervously to voice alarms in past events, the adjustment will be to use a vibration-silent alert mode.

[0059] Establish a feedback-response parameter table for each elderly person, and continuously optimize each treatment parameter using a Multi-Armed Bandit or online learning strategy, with the objective function of minimizing the elderly person's discomfort and maximizing rescue efficiency.

[0060] The working principle and technical effects of the above-mentioned technical solution are as follows: This invention uses a group of multiple types of security devices deployed in various areas of the community to continuously collect images of elderly people's activities in authorized spaces around the clock, and completes information processing at four progressive levels. The first level integrates scattered two-dimensional image information into a sequence of posture images with three-dimensional spatial coordinates through multi-device collaborative acquisition and spatial positioning, achieving synchronous perception of the elderly person's spatial location and body posture. The second level performs deep semantic analysis of the elderly person's current actions, not only identifying the apparent form of the action but also further judging the behavioral intention behind it, classifying it as active normal activity, physiologically necessary action, or abnormal risk action, thus establishing a cognitive bridge from what is seen to what is being done. The third level compares the currently analyzed behavioral information with the personalized activity pattern baseline accumulated by the system for the elderly person, calculating the degree of deviation of various behavioral indicators, and predicting the types of potential hazards. The fourth level, based on the comprehensive risk analysis results, current community environmental conditions, and the elderly person's historical response records, automatically generates a graded response strategy, forming a complete execution instruction chain from early warning triggering to completion of handling.

[0061] This method effectively addresses the limitations of traditional video surveillance systems, which can only record images but cannot actively interpret the meaning of actions. Through intent-level semantic parsing technology, the system can accurately distinguish whether an elderly person is actively bending down to pick up an item or has fallen due to loss of control, thus significantly reducing the false alarm rate. Simultaneously, by establishing individualized activity pattern baselines, the system can accurately identify behavioral changes in each elderly person that deviate from their normal lifestyle, avoiding the problem of frequent false alarms for active elderly individuals and underestimation of the true risk for frail elderly individuals when using a uniform template to measure all elderly people. In the early warning response phase, this method automatically generates execution instructions containing complete handling elements, solving the problem of early warning information remaining on the screen and unable to be translated into effective action. This allows community management personnel and family members to clearly understand what to do, who should do it, how to do it, and when to do it when receiving an early warning, constructing a complete closed loop from risk perception to safety assurance, significantly improving the intelligence level and response efficiency of community elderly care safety management.

[0062] In one embodiment of the present invention, S1 includes: By pre-installing various types of security equipment (including high-definition cameras, infrared detectors, and millimeter-wave radar) in the community's public areas, unit entrances and authorized rooms, all active targets within the coverage area are collected synchronously around the clock to obtain multi-source heterogeneous raw sensing data streams; By performing cross-device spatiotemporal synchronization and target deduplication matching on the raw sensing data stream, the identity of the same elderly person is continuously bound across mirrors to obtain continuous target detection results. Real-time skeletal key point extraction and pose parameter analysis of elderly targets in video frame sequences are performed using continuous target detection results to obtain pose image sequences. By using posture image sequences, camera spatial calibration parameters, and multi-view triangulation methods, the position of the elderly in the three-dimensional space of the community is continuously calculated and mapped to obtain the spatial coordinate trajectory.

[0063] The technical problems addressed by the above-mentioned technical solutions are: targets are easily obscured from a single perspective, data from multiple cameras are isolated and cannot form a continuous trajectory, and traditional detection methods cannot distinguish between the elderly and other groups of people.

[0064] The working principle and technical effects of the above-mentioned technical solution are as follows: This method achieves full-coverage perception of the community's public areas, unit entrances and exits, and authorized indoor spaces by coordinating the deployment of multiple types of sensors, such as high-definition cameras, infrared detectors, and millimeter-wave radar. High-definition cameras provide texture and color information in the visible light band, infrared detectors supplement thermal radiation distribution information under nighttime or low-light conditions, and millimeter-wave radar provides micro-motion Doppler information and distance information unaffected by light and obstruction. The system aligns the data collected by each sensor under a unified time reference and spatial coordinate system to form a multi-source heterogeneous raw perception data stream. Through cross-device spatiotemporal synchronous processing, the system performs identity matching and deduplication binding on images of the same elderly person appearing in the field of view of different cameras, ensuring that each target has a unique and continuous identity identifier in the entire space. The system extracts skeletal key points and calculates posture parameters for continuously detected targets. At the same time, by utilizing the spatial geometric relationship between multiple cameras, combined with camera calibration parameters and triangulation principles, the system continuously solves and maps the elderly person's position in three-dimensional space, ultimately obtaining a posture image sequence describing the body configuration and a motion trajectory describing the spatial position.

[0065] This method effectively solves the technical challenges of target occlusion under a single viewpoint and the inability to form continuous trajectories due to data isolation between multiple cameras. Through collaborative perception of multiple types of sensors, the system can stably acquire key information about the elderly target even in complex environments such as nighttime, rain, fog, and backlighting, overcoming the severe performance degradation of a single visible light camera in insufficient light. By cross-device spatiotemporal synchronization and target deduplication binding, the elderly person's activity trajectory can remain continuous and consistent when crossing the coverage area of ​​multiple cameras, without losing the target or generating duplicate counts due to viewpoint switching. Through multi-view triangulation, the system elevates two-dimensional image information to three-dimensional spatial coordinates, enabling subsequent behavior analysis to not only rely on posture changes but also to make comprehensive judgments based on precise location information, providing a richer and more reliable data foundation for behavioral semantic analysis.

[0066] In one embodiment of the present invention, the step of extracting skeletal key points and analyzing pose parameters of an elderly target in a video frame sequence in real time using continuous target detection results to obtain a pose image sequence includes: The target region of the elderly in the current video frame is cropped and normalized by the continuous target detection results. At the same time, the target thermal radiation distribution data and micro-motion Doppler spectrum data collected in the same spatiotemporal coordinate system are read to obtain the fused target data after multimodal alignment. By jointly statistically analyzing the visible light band texture information and infrared band thermal radiation information in the fused target data, the illumination sufficiency and contrast evaluation index of the current imaging environment are calculated. When the ambient illumination is lower than the preset switching threshold or the contrast between the target and the background is lower than the preset standard, the infrared dominant channel is automatically switched to extract features, and the target edge is contoured and completed based on the millimeter-wave radar echo signal to obtain the target contour information. Using the enhanced target contour information through environmental adaptation, a multi-branch parallel regression network is used to simultaneously locate and verify multiple key points of the human skeleton, thereby obtaining a set of skeleton key point coordinates. By analyzing the spatial relationships of key points in the skeletal keypoint coordinate set, the deviation information of the torso's center of mass relative to the baseline standing posture is calculated frame by frame. Median filtering and smoothing are then performed on the time series to obtain the posture image sequence.

[0067] Specifically, by using the environmentally adaptive enhanced target contour information, a multi-branch parallel regression network is employed to simultaneously locate and verify multiple key points of the human skeleton, obtaining a set of skeletal key point coordinates, including: By using the enhanced target contour information after environmental adaptation, the target human body region is input into the backbone feature extraction layer of a multi-branch parallel regression network. The backbone network performs multi-scale feature encoding on the input image to generate a comprehensive feature map containing shallow spatial details and deep semantic information, thus obtaining a multi-scale feature representation. Multi-scale feature representations are input into three parallel regression subnetworks: head keypoint branch, upper limb keypoint branch, and lower limb keypoint branch. Each subnetwork independently performs heatmap regression prediction and offset refinement on its responsible subset of skeletal keypoints. Information sharing and consistency constraints are achieved between the subnetworks through a cross-branch feature interaction module to obtain the initial localization coordinates and initial confidence scores of each keypoint. By using the initial positioning coordinates and initial confidence scores of each key point, low-confidence key points with confidence scores below a preset confidence threshold are marked, and the local feature response distribution of the key point in the adjacent spatial region is extracted. The presence of multi-peak ambiguity or response drift in the local response map is calculated to determine the positioning ambiguity type and obtain the ambiguous attribute label of the low-confidence key point. By marking ambiguous attributes of low-confidence keypoints, when the multi-peak blur is determined to be caused by occlusion, the skeletal keypoint localization results of the same target at the same moment under the view of other cameras are called to perform cross-view consistency verification and triangulation correction; when the response drift is determined to be caused by image blur, the localization coordinates of the keypoint in multiple adjacent frames are called to perform Kalman filtering recursion and motion trajectory smoothing extrapolation to obtain the corrected keypoint coordinates with confidence that meet the preset confidence threshold. By combining the coordinates of all the key points after the above correction with the kinematic constraints of the human skeleton structure (including the constraint of consistent length of symmetrical limbs, the constraint of the range of angle change of adjacent joints and the rigidity constraint of the trunk), the coordinate set is optimized globally and outliers are removed to obtain the coordinate set of skeletal key points.

[0068] The working principle and technical effects of the above-mentioned solution are as follows: Based on the target contour information after environmental adaptive enhancement, this method achieves accurate localization of key points of the human skeleton through a multi-branch parallel regression network. This network uses a backbone feature extraction layer to encode multi-scale features of the input image, generating a comprehensive feature map that contains both shallow spatial details and deep semantic information. These feature maps are input to three parallel regression sub-networks: head key point branch, upper limb key point branch, and lower limb key point branch. Each sub-network is independently responsible for key point localization within its respective region. Simultaneously, information sharing and consistency constraints are achieved between different branches through a cross-branch feature interaction module, ensuring the overall skeleton's coordination. Each branch sub-network outputs the initial localization coordinates and confidence score of each key point through two steps: heatmap regression prediction and offset refinement. For key points with low confidence scores, the system further analyzes the local feature response distribution within spatially adjacent regions to determine the specific type of localization ambiguity—whether it is multi-peak blurring caused by occlusion or response drift caused by image blurring. To address multi-peak blurring caused by occlusion, the system uses the skeletal keypoint localization results of the same target from different camera perspectives at the same instant to perform cross-view consistency verification and triangulation constraint correction. To address response drift caused by image blurring, the system uses the localization coordinates of the keypoint in multiple adjacent frames to perform Kalman filtering recursion and motion trajectory smoothing extrapolation. The system uses the kinematic constraints of the human skeletal structure as the global optimization criterion, performing consistency verification and outlier removal on all keypoint coordinates.

[0069] This method effectively addresses the technical challenge of missing or erroneous skeletal keypoint localization in complex environments due to occlusion and image quality degradation. Through a multi-branch parallel regression structure, the keypoint localization task for different body regions is decomposed into specialized sub-networks for processing. Each sub-network can be optimized for the characteristics of its region, improving localization accuracy. Cross-branch feature interaction allows branches to share complementary information, ensuring consistency in spatial relationships between the upper limbs, lower limbs, and head, avoiding localization results that violate human physiological structures. A scene-specific correction mechanism based on ambiguity types enables the system to adopt differentiated remedial strategies based on the specific reasons for localization failures. Cross-view correction leverages the spatial redundancy of multi-camera coverage, while temporal filtering correction utilizes the temporal continuity of actions. The combination of these two approaches ensures that even under severe occlusion or image quality degradation, the system can still obtain a complete and accurate set of skeletal keypoint coordinates.

[0070] In one embodiment of the present invention, S2 includes: By using a sequence of posture images and spatial coordinate trajectories within a continuous time window, the continuous temporal changes of the elderly's movements are dynamically tracked and segmented into stages to obtain complete process segments. By performing spatiotemporal correlation analysis on the posture change patterns and spatial position movement laws in the complete process segment, it is determined whether the action is accompanied by involuntary characteristics (such as sudden speed change, sudden height drop, irregular shaking) and the physical feature vector of the action is obtained. By combining the physical feature vectors of the action with a pre-set behavioral semantic knowledge mapping rule base, the concept of the elderly’s current behavior is transformed at the intention level (the body tilt + arm support is interpreted as active standing up, and the body tilt + no support + rapid falling is interpreted as suspected falling), and preliminary semantic analysis results are obtained. By combining the preliminary semantic analysis results with the location semantic information in the spatial coordinate trajectory (such as the scene of occurring at the bathroom door, next to the kitchen stove, or at the stairwell), the meaning of the behavior is corrected and ambiguity is resolved through scene context, and refined activity analysis data is obtained. The data clearly marks whether the behavior belongs to an active, normal physiological need action or an abnormal risk action.

[0071] The technical problem solved by the above-mentioned technical solution is that traditional solutions can only recognize the posture of the action but cannot determine the intention of the behavior. For example, they cannot distinguish whether the elderly person is squatting down to pick up an object on their own initiative or is a sign of falling due to dizziness, nor can they distinguish whether the elderly person is lying down to rest on their own initiative or has fainted and fallen to the ground.

[0072] The working principle and technical effects of the above-mentioned technical solution are as follows: This method takes posture image sequences and spatial coordinate trajectories as inputs, and performs dynamic temporal tracking and stage division of the elderly's movements within a continuous time window. A complete movement process is divided into three stages: start, duration, and end, obtaining a complete process segment. Spatiotemporal correlation analysis is performed on this process segment, extracting physical feature parameters of the movement from posture change patterns and trajectory feature parameters from spatial position movement patterns. A comprehensive determination is made as to whether the movement is accompanied by involuntary characteristics, i.e., whether there are physical signs indicating possible loss of control or involuntary movement, such as sudden speed changes, sudden drops in altitude, or irregular shaking. Based on the physical analysis, the system inputs the physical feature vector of the movement into a preset behavioral semantic knowledge mapping rule base. This rule base defines the behavioral meaning corresponding to different combinations of physical features in the form of structured rules, realizing the intention-level conversion from posture parameters to behavioral concepts. The system jointly analyzes the behavioral concepts obtained from the initial analysis with the positional semantic information in the spatial coordinate trajectory. Contextual correction and ambiguity resolution are performed on the analysis results according to the specific scenario in which the behavior occurs, ultimately outputting refined activity analysis data clearly labeled as active normal activity, physiologically necessary movement, or abnormal risk movement.

[0073] This method effectively addresses the fundamental limitation of traditional behavior recognition schemes, which can only identify postures but cannot understand behavioral intent. Traditional schemes rely solely on posture matching to identify actions; for example, detecting a body tilt and a decrease in height is automatically considered a fall, failing to distinguish whether an elderly person is actively crouching to pick up an object, slowly sitting down, or actually falling, leading to numerous false alarms. This method introduces intent-level semantic parsing, adding a cognitive layer of intent understanding on top of physical feature analysis. It detects whether actions are consciously controlled through involuntary feature detection and corrects the interpretation of behavioral meaning through scene context, enabling the system to accurately distinguish between voluntary behavior and abnormal events. For example, when an elderly person slowly bends over, the system can determine it as a voluntary action based on the control characteristics of the movement, avoiding false alarms; when an elderly person suddenly tilts and lacks arm support, the system can perceive involuntary features and accurately identify it as a potential fall. This cognitive upgrade from posture to intent transforms the security system from a passive recorder of images to an active interpreter of behavior, significantly improving the accuracy and reliability of security detection.

[0074] In one embodiment of the present invention, the step of performing spatiotemporal correlation analysis on the action segment by analyzing the posture change patterns and spatial position movement patterns in the complete process segment, determining whether the action is accompanied by involuntary characteristics (such as sudden speed changes, sudden drops in altitude, irregular shaking), and obtaining the action physical feature vector includes: By using the coordinate set of skeletal key points in each frame of the complete process segment, the displacement difference of the torso center of mass in the vertical direction and the rate of change of velocity per unit time between adjacent frames are calculated, and the absolute value of the rate of change of velocity is accumulated along the action time axis to obtain the characteristic value of vertical impact kinetic energy. By using the trajectory curves of each key point in the complete process segment, the angular swing amplitude and swing regularity score of the limb end relative to the trunk coordinate system during the movement are calculated. At the same time, the positions of the velocity change points at the beginning and end of the movement and the velocity ratio before and after the change are extracted to obtain active control feature values. Cross-comparison is performed between the vertical impact kinetic energy characteristic value and the active control characteristic value; When the impact kinetic energy characteristic value exceeds the preset first threshold and the active control characteristic value is lower than the preset second threshold, the action is determined to be accompanied by involuntary rapid falling characteristics. When there are irregular jitter spectrum components in the action and the jitter amplitude exceeds the preset third threshold, the action is determined to be accompanied by involuntary tremor characteristics, and the physical attribute determination result including involuntary fall flag and involuntary tremor flag is obtained. By combining the physical attribute judgment results with the total duration of the complete process segment, the horizontal drift distance of the center of mass, and the recovery stabilization time after the action ends, the overall controllability of the action is comprehensively quantified and scored. All the above physical characteristic parameters are combined into a multi-dimensional numerical vector to obtain the action physical characteristic vector.

[0075] Specifically, by analyzing the trajectory curves of key points in the complete process segment, the angular swing amplitude and swing regularity scores of the limb's extremities relative to the torso coordinate system during the movement are calculated. Simultaneously, the locations of velocity abrupt change points at the beginning and end of the movement, as well as the velocity ratio before and after the abrupt change, are extracted to obtain active control feature values, including: By using the trajectory curves of each key point in the complete process segment, a follow-up torso coordinate system is established with the torso center of mass as the origin and the longitudinal axis of the torso as the reference. The absolute spatial coordinates of the key points at the limb ends (including the left and right wrists and left and right ankles) are transformed to the relative coordinates under this follow-up torso coordinate system, eliminating the influence of the overall torso translation on the limb swing analysis and obtaining the relative motion trajectory of the limb ends. By analyzing the relative motion trajectories of the limbs, Fourier spectrum analysis is performed on the angular swing sequence of each limb end within a complete process segment. The amplitude of the fundamental frequency component and the proportion of harmonic components are extracted, and the peak value and decay rate of the autocorrelation function of the swing sequence are calculated. When the autocorrelation function shows obvious periodicity and slow decay, the swing is judged to be highly regular. When the proportion of high-frequency harmonics in the spectrum is too high, the swing is judged to be irregular. The swing regularity score of each limb end is obtained. The overall movement regularity score is calculated by weighting and summing the swing regularity scores of each limb end (where the weight of the upper limb end is α and the weight of the lower limb end is β, and α+β=1, dynamically adjusted according to the movement type), thus obtaining the first dimension feature of movement control. By extracting the zero-crossing points of the first derivative of the velocity change curves from the velocity change curves in the complete process segment, the velocity abrupt change points are identified. The first velocity abrupt change point in the beginning stage of the action and the last velocity abrupt change point in the end stage of the action are marked. The ratio of the peak velocity to the starting velocity, the ratio of the peak velocity to the ending velocity, and the total number and distribution density of velocity abrupt change points in the entire segment are calculated to obtain a velocity abrupt change feature vector including the peak velocity ratio, the starting and ending velocity transition ratio, and the abrupt change frequency. Feature fusion is performed by combining the regularity comprehensive score and the velocity mutation feature vector. When the regularity comprehensive score is higher than the preset regularity threshold and all mutation parameters in the velocity mutation feature vector fall within the preset active control parameter range, a high active control score is assigned. When the regularity comprehensive score is lower than the preset regularity threshold or mutation parameters in the velocity mutation feature vector exceed the preset active control parameter range, the active control score is reduced. The fusion result is quantized into a continuous value in the range of 0 to 1 to obtain the active control feature value.

[0076] The working principle and technical effects of the above-mentioned technical solution are as follows: This method achieves accurate analysis of limb swing behavior by constructing a follow-up torso coordinate system. The system establishes a follow-up coordinate system with the torso's center of mass as the origin and the torso's longitudinal axis as the reference. The absolute spatial coordinates of key points at the limb ends are transformed to this relative coordinate system, thereby eliminating the interference of overall torso translation on the swing analysis and obtaining a pure relative limb motion trajectory. The system performs Fourier spectrum analysis on the angular swing sequence of each limb end during the movement process, extracting the fundamental frequency component, harmonic components, and their proportions of the swing signal. Simultaneously, it calculates the peak value and decay rate of the autocorrelation function of the swing sequence. When the autocorrelation function exhibits obvious periodicity and slow decay, it indicates that the swing is regular; when the proportion of high-frequency harmonics in the spectrum is too high, it indicates that the swing is irregular. Based on this, the system generates a swing regularity score for each limb end and forms the first dimension of the overall movement's controllable characteristics through weighted summation. Simultaneously, the system extracts velocity abrupt change points from the velocity change curve, analyzes the abrupt change characteristics at the beginning and end of the movement, including the velocity peak multiple, the beginning-to-end velocity transition ratio, and the abrupt change frequency, forming a velocity abrupt change feature vector. The system integrates the regularity comprehensive score with the velocity mutation feature vector, and comprehensively judges the active control score based on whether the regularity is higher than the preset threshold and whether the mutation parameter falls within the active control parameter range.

[0077] This method effectively solves the technical challenge of quantifying whether an action is under conscious control. Active controllability is the core difference between autonomous human behavior and passive abnormal events, but effective calculable methods have long been lacking. This method transforms the intuitively perceptible regularity of actions into calculable mathematical indicators through spectral analysis and autocorrelation analysis in a moving coordinate system; and quantifies the smoothness and controllability of actions into measurable parameters through velocity mutation feature analysis. The fusion of these two methods allows the system to continuously quantify and score the degree of active control over actions, thereby constructing an intrinsic criterion at the physical level to distinguish between active behavior and abnormal events. When an elderly person's active control score shows a continuous downward trend, it may indicate a decline in their physical functions, thus enabling early detection of changes in the elderly person's health status.

[0078] In one embodiment of the present invention, S3 includes: Data on the normal daily routines of each elderly person within a preset number of days is obtained, and statistical analysis is performed on the intensity of the elderly person's daily activity paths and behaviors to obtain a personalized baseline profile of activity patterns. By comparing refined activity analysis data with personalized activity pattern baseline files item by item under the same time dimension, the deviation magnitude and duration between actual behavior and expected baseline are calculated to obtain multi-dimensional deviation measurement values. By combining multi-dimensional deviation measures with correlation analysis between deviation items (such as the risk superposition when deviation in outing time and decrease in activity intensity occur simultaneously), a weighted comprehensive assessment of each deviation is conducted to obtain a comprehensive deviation score. By combining the deviation score with the spatiotemporal context of the deviation (such as going out at night, going out during wet and slippery weather, or spending a long time in the kitchen), and comparing it with the preset hazard type judgment rules, risk tendency classification and hazard type matching are carried out to obtain risk tendency analysis data and corresponding predicted hazard types (including fall risk, getting lost risk, sudden illness risk, behavioral regression risk, etc.).

[0079] The technical problem addressed by the above solution is that the living habits of different elderly people vary greatly. Using a uniform template to judge abnormalities will lead to frequent false alarms for active elderly people, while the real risks of frail elderly people will be overlooked. The system cannot establish dynamic safety assessment standards that vary from person to person.

[0080] The working principle and technical effects of the above-mentioned technical solution are as follows: This method systematically records and statistically analyzes the activity patterns of each elderly person during normal living periods through the accumulation of data over a continuously preset number of days. This includes dimensions such as the daily activity time distribution, routine activity paths, and behavioral intensity, forming a personalized activity pattern baseline file unique to each elderly person. This baseline file is not fixed but dynamically updated as data accumulates and the elderly person's lifestyle habits evolve naturally. After the baseline is established, the system compares the refined activity analysis data of the current moment with the expected values ​​for the same time period and week type in the baseline file item by item, calculating the deviation magnitude and duration between the actual behavior and the expected baseline, obtaining deviation measurements in multiple dimensions. The system analyzes the correlation between various deviations; for example, when a shift in outing time and a decrease in activity intensity occur simultaneously, their cumulative effect has a higher risk connotation than a single deviation. Based on this, a weighted comprehensive assessment is performed to obtain a comprehensive deviation score. The system combines this comprehensive score with the spatiotemporal context information of the deviation, including whether the time of the deviation occurs during a dangerous period and whether the spatial location of the deviation is in a high-risk area. It then compares this with preset hazard type judgment rules to complete the risk tendency classification and matching with specific hazard types.

[0081] This method effectively addresses the dual dilemma of frequent false alarms and underreporting of genuine risks caused by using a uniform standard to measure all elderly individuals. Each elderly person's lifestyle, activity level, and behavioral habits vary significantly. An active elderly person's occasional late-night return home might be considered normal social activity, while a frail elderly person's deviation from a routine morning walk might indicate early cognitive decline. This method establishes a unique activity pattern baseline for each elderly person, ensuring that risk assessment is based on a personalized benchmark. This allows the system to accurately identify behavioral changes that are truly meaningful to that individual, rather than simply comparing them to population averages. Through multi-dimensional deviation measurement and correlation analysis, the system can capture potential risks that are difficult to detect from a single dimension. For example, a decrease in activity intensity itself may be insignificant, but if it is accompanied by a shift in the time of day or abnormal lingering in a specific area, it may constitute a warning signal requiring attention. By weighting the risk within spatiotemporal context, the system further improves the accuracy of risk identification, making the assessment results more consistent with actual safety logic. The final risk propensity classification and hazard type matching transform abstract behavioral deviations into clear and understandable risk warnings.

[0082] In one embodiment of the present invention, the step of comprehensively considering the deviation degree score and the spatiotemporal context information of the deviation (such as going out at night, going out during wet and slippery weather, or spending a long time in the kitchen), and comparing it with preset hazard type judgment rules, to perform risk tendency classification and hazard type matching, thereby obtaining risk tendency analysis data and corresponding predicted hazard types, includes: By combining the deviation score and the time label information of the deviation, the deviation time is compared with the preset sensitive period classification rules to determine whether the current deviation occurs during high-risk periods such as early morning / late night, midday high temperature periods, or slippery periods of rain or snow, and obtain the time-series risk weighting coefficient. By using the spatial coordinate trajectory information of the deviation, the deviation location is spatially overlaid with the preset area safety level map to determine whether the current deviation occurs in preset high-risk areas such as stairwells, kitchen stove areas, wet and slippery bathroom floors, community entrances and exits, and vehicle passageways. Based on the basic hazard level of the deviation area, a corresponding spatial risk weight is assigned to obtain the spatial risk weighting coefficient. By performing multidimensional weighted product calculations using the comprehensive deviation score, time-series risk weighting coefficient, and spatial risk weighting coefficient, a comprehensive risk quantification score is obtained. This comprehensive risk quantification score is then mapped according to preset multi-level risk threshold intervals (including safe interval, attention interval, warning interval, and emergency interval) to obtain risk tendency classification results. By combining the risk tendency classification results with the specific type labels of deviation actions (such as prolonged stillness, sudden drop in walking speed, surge in outing frequency, abnormal stay in a specific area, etc.), a many-to-one matching decision is made by referring to a preset hazard type behavior characteristic association mapping table. The mapping table pre-stores typical behavioral deviation combination patterns corresponding to various hazard types. When the combination of deviation action types meets the triggering conditions of any hazard type, the corresponding hazard type identifier is matched and output, thus obtaining risk tendency analysis data containing risk level and hazard type label.

[0083] The comprehensive risk quantification score is obtained by multi-dimensional weighted product calculation using a comprehensive deviation score, a time-series risk weighting coefficient, and a spatial risk weighting coefficient. This includes: By using the comprehensive deviation score as the basic risk factor, the score value is mapped to a preset normalized risk intensity range to obtain the normalized basic risk intensity value. By using temporal risk weighting coefficients and spatial risk weighting coefficients, the normalized basic risk intensity value is subjected to a first nonlinear amplification and a second nonlinear amplification, respectively. The temporal risk weighting coefficient adopts a piecewise exponential function: the temporal coefficient corresponding to the normal daytime period is assigned the first benchmark value, the temporal coefficient corresponding to the sensitive period increases exponentially with the increase of sensitivity, and the temporal coefficient corresponding to the high-risk nighttime period rises to the highest saturation value; the spatial risk weighting coefficient adopts a step function: the spatial coefficient corresponding to the safe area is assigned the second benchmark value, the spatial coefficient corresponding to the generally concerned area is assigned the third intermediate value, and the spatial coefficient corresponding to the high-risk area rises to the highest limit value, thus obtaining the intermediate risk value amplified by time and the intermediate risk value amplified by time and space. By using the intermediate risk value amplified by time series and the intermediate risk value amplified by spatiotemporal joint, a coupling amplification factor is introduced between deviation types. The coupling amplification factor is determined by looking up a table based on whether two conditions are met: the deviation duration exceeds a preset duration and the risk level of the deviation action type is greater than or equal to a preset level. When both conditions are met, the coupling amplification factor takes the preset maximum value; when only one condition is met, the preset intermediate value is taken; and when neither condition is met, the preset minimum value is taken. The time series amplification result and the spatiotemporal amplification result are multiplied by the coupling amplification factor to obtain the comprehensive risk quantification score. The risk quantification score is mapped according to a preset multi-level risk threshold range. Specifically, when the comprehensive risk quantification score falls into the first preset range, it is mapped to a safety level; when it falls into the second preset range, it is mapped to a concern level; when it falls into the third preset range, it is mapped to a warning level; and when it falls into the fourth preset range, it is mapped to an emergency level, thus obtaining the risk tendency classification result.

[0084] The working principle and technical effects of the above technical solution are as follows: This method uses a multi-dimensional weighted product operation to convert multi-dimensional deviation measurements into a comprehensive risk quantification score. The core concept of this method is that different risk factors are not simply linearly superimposed, but rather have a synergistic amplification coupling effect. Actual safety risks often depend on the most unfavorable factor rather than the average level of each factor. Specifically, the system uses the comprehensive deviation score as the basic risk factor and maps it to a normalized risk intensity range. The time-series risk weighting coefficient uses a piecewise exponential function to assign differentiated weights to different time periods. The daytime regular period is assigned a baseline value, sensitive periods increase exponentially according to sensitivity, and the high-risk period at night rises to the highest saturation value, reflecting the exponential influence of time factors. The spatial risk weighting coefficient uses a step function to assign differentiated weights to different regions. The safe region is the baseline value, and the high-risk region rises to the highest limit value, reflecting the inherent differences in personal safety risks in different spaces. After these two nonlinear amplifications, the system further introduces a coupling amplification factor between deviation types. This factor is determined by looking up a table based on the degree to which two conditions are met: whether the deviation duration exceeds the preset duration and whether the risk level of the deviation action type reaches the preset level. When both conditions are met, the maximum value is taken, thus achieving a synergistic amplification effect when risk factors are superimposed.

[0085] This method effectively addresses the technical deficiency of traditional linear weighted risk assessment methods, which tend to underestimate complex risks in safety scenarios. In actual safety assessments, the risk of a single factor is often limited, but when multiple adverse factors coexist, such as an elderly person lingering near a stairwell late at night on a rainy day, the overall risk is far greater than the sum of individual risk factors. This method uses an exponential function to nonlinearly map time-series risks, reflecting the safety logic that the risk amplification effect during high-risk periods does not increase uniformly but accelerates; it uses a step function to classify and assign values ​​to spatial risks, accurately expressing the inherent risk levels of different areas; and most importantly, by introducing a coupling amplification factor, it captures the synergistic enhancement effect when multiple risk factors coexist, enabling the comprehensive risk quantification score to truly reflect the severity of actual safety hazards. This risk assessment method is more in line with the bottleneck effect and coupling amplification law in the field of safety management, making the risk classification results more accurate and reliable.

[0086] In one embodiment of the present invention, S4 includes: By combining risk propensity analysis data, predicting the types of potential hazards, and current community environmental information (including weather conditions, ground slipperiness, pedestrian density in public areas, and elevator operation status), a comprehensive assessment is made of the urgency and feasible scope of potential safety incidents to obtain the incident urgency level and recommended intervention time window. The event urgency level is matched with a pre-defined tiered response strategy library to determine the applicable response level (including four levels: red, orange, yellow, and blue). The standard handling action set for the corresponding level is retrieved from the strategy library to obtain the tiered response strategy framework. By combining the hierarchical response strategy framework with the pre-set distribution of rescue resources for the corresponding elderly, the responsible parties and execution methods are assigned for each action in the strategy framework, resulting in a personalized handling plan that includes a list of notification recipients, priority of notification channels, notification content templates, and on-site handling guidelines. By combining personalized handling plans with historical response feedback records of similar incidents involving the elderly (such as whether family members answered the phone in a timely manner and whether property management arrived within the agreed time), the timing of notifications and intervention methods in the plans are dynamically fine-tuned and optimized to obtain full-chain safety assurance execution instructions. These instructions cover the complete closed-loop process of early warning triggering, information push, personnel dispatch, on-site handling, result confirmation, and event archiving.

[0087] The technical problem addressed by the above solution is that after the existing system issues an early warning, it does not know who to notify, how to notify, when to intervene for the most effective time, or what to do on-site, resulting in the early warning information remaining on the screen and failing to be transformed into effective action.

[0088] The working principle and technical effects of the above-mentioned technical solution are as follows: This method processes the input risk tendency analysis data and predicted hazard types, and simultaneously incorporates the current community environmental status information, including outdoor weather conditions, ground slipperiness, public area pedestrian density, and elevator operation status, etc., to comprehensively assess the urgency of potential safety incidents and the scope of possible harm, thereby determining the incident urgency level and recommended intervention time window. Based on the determined urgency level, the system matches it with a pre-set graded response strategy library, selects the applicable level from four response levels (red, orange, yellow, and blue), and retrieves the standardized set of handling actions corresponding to that level from the strategy library to form a graded response strategy framework. Under the guidance of the strategy framework, the system, based on the elderly person's pre-entered rescue resource distribution information, including emergency contacts, family contact information, medical records, etc., assigns specific responsible parties and allocates execution methods for each handling action in the strategy framework, generating a personalized handling plan that includes a list of notification recipients, the priority of notification channels for each recipient, differentiated notification content templates for different recipients, and on-site handling guidelines. The system retrieves historical response feedback records from similar incidents involving the elderly person, and dynamically fine-tunes and optimizes the notification timing and intervention methods in the plan accordingly. Finally, it outputs a complete safety assurance execution instruction covering the entire closed loop from early warning triggering, information push, personnel dispatch, on-site handling, result confirmation to event archiving.

[0089] This method effectively addresses the industry pain point where warnings remain on screens and fail to translate into actual safety assurance because no one knows what to do, when to do it, or who should do it after an alert is issued. Traditional warning systems typically only push a notification containing abnormal information to management personnel, leaving subsequent decision-making entirely dependent on their personal experience and on-site judgment. This can easily lead to hesitation, missed notifications, and disorganized resource allocation in emergencies. This method, through the predefined hierarchical response strategy library, solidifies standardized handling procedures corresponding to different types and levels of safety incidents into the system, ensuring the standardization and comprehensiveness of handling decisions. By combining individual rescue resource information for the elderly for personalized matching, the system can accurately determine who should be notified for each incident, what the most effective notification method is, which personnel need to be dispatched, and what operational guidelines should be followed on-site, giving the warning information complete behavioral instruction attributes. By introducing historical response feedback records for dynamic fine-tuning and optimization, the system can continuously learn and adapt to the response habits of different responsible parties, continuously improving the success rate of notification delivery and response speed. The final output of the full-chain safety assurance execution instructions makes the warning a truly effective signal driving actual action, building a complete closed-loop management capability from perception to response.

[0090] In one embodiment of the present invention, S4 further includes: By combining the handling plan in the execution instructions of the whole chain safety guarantee with the real-time feedback status of the on-site responsible person, the dynamic physical conditions of the environment where the elderly are located are fused with multi-dimensional situational awareness to obtain a real-time set of on-site environmental parameters, which includes at least the ground humidity coefficient, light illuminance value, environmental noise decibel value and personnel density index. By using real-time parameters of the on-site environment, the dynamic evolution path of the danger corresponding to the predicted type of hazard is dynamically extrapolated. Combined with the interaction between the elderly's current posture stability margin and the environmental friction coefficient, the expected deterioration time window and key turning point of the hazard under no-intervention conditions are calculated to obtain risk evolution trend prediction data. By analyzing the spatiotemporal overlap between the risk evolution trend prediction data and the coverage and effectiveness of the current implemented actions, when the predicted deterioration time window is less than the preset safety margin threshold and the expected arrival time of the existing actions cannot cover the window, an adaptive control command for the on-site environment is triggered to coordinate and control the ground dehumidification equipment, auxiliary lighting equipment and voice guidance equipment within a set range around the elderly, and obtain the environmental active intervention execution parameters. By prioritizing and dynamically inserting unexecuted backup actions in the environmental proactive intervention execution parameters and hierarchical response strategy framework, the expected environmental parameters after intervention are used as new boundary conditions and re-substituted into the risk evolution deduction loop until the spatiotemporal coverage of the remaining action sequence can completely encompass the entire risk evolution path, thus obtaining a closed-loop optimized dynamic action instruction set.

[0091] The working principle and technical effects of the above-mentioned technical solution are as follows: This method integrates the execution feedback of the disposal plan with on-site sensor data to achieve real-time situational awareness of multi-dimensional environmental parameters such as ground humidity, light intensity, noise, and personnel density. Based on the predicted physical evolution law of the hazard type, combined with the elderly person's current posture stability margin and environmental friction coefficient, a hazard evolution dynamic model is established to quantify and extrapolate the time window and critical turning point of hazard deterioration under no-intervention conditions. The extrapolated deterioration window is compared with the expected arrival time of existing disposal actions in terms of spatiotemporal overlap. When a coverage gap is found, an environmental adaptive control command is immediately triggered to coordinate the control of dehumidification, lighting, and voice guidance equipment to actively improve the environmental conditions around the elderly person. Finally, the expected parameters after environmental intervention are re-substituted into the evolution model to prioritize and dynamically insert backup disposal actions. This process is repeated through multiple rounds of iteration until the spatiotemporal coverage of the disposal sequence completely encompasses the entire risk evolution path.

[0092] This method breaks through the passive mode of traditional security systems that statically wait for rescue after alarms. It incorporates dynamic environmental factors into risk evolution prediction, significantly improving the accuracy of predicting the development of hidden dangers in complex scenarios. Through the proactive collaborative intervention of environmental equipment, it provides the elderly with extra safety buffer time and space before rescue arrives. Through a closed-loop iterative optimization mechanism, the response plan is continuously and adaptively adjusted according to environmental changes and the progress of action execution, avoiding response failure caused by rigid contingency plans.

[0093] In one embodiment of the present invention, the step of using a tiered response strategy framework combined with the pre-defined distribution of rescue resources for the corresponding elderly person to assign responsible parties and allocate execution methods for each action in the strategy framework, thereby obtaining a personalized handling plan that includes a list of notification recipients, notification channel priorities, notification content templates, and on-site handling guidelines, including: By extracting the required role type (including first contact person, second contact person, property patrol officer, property monitoring officer, emergency coordination officer, etc.) for each action according to the standard set of handling actions defined in the hierarchical response strategy framework, a list of role requirements is obtained. By combining the role requirement list with the pre-stored emergency contact information table for the elderly (including family member name, relationship with the elderly, contact information and preset contact priority) and the current property duty schedule (including the name of the staff on duty, current on-duty status and dispatchable indicator) in the system, the available personnel for each role are matched and the real-time availability is confirmed, and an execution role allocation table with specific responsible persons is obtained. By executing the role allocation table, for each assigned specific person, query their preset preferred notification channels (including APP push, SMS, telephone voice, WeChat message) and channel historical connection rate and response time data, sort the available channels according to response time from shortest to longest, determine the primary and backup notification channels for each person, and obtain the channel priority configuration results. By combining the channel priority configuration results with the standardized notification content templates corresponding to the response levels in the hierarchical response strategy framework (including event type description, basic information of the elderly, current location coordinates, on-site scene summary and suggested actions), and replacing the variable placeholders in the template one by one with the specific values ​​and text of the current event, differentiated notification content and on-site handling guidance texts for different responsible parties are generated to obtain personalized handling solutions.

[0094] Specifically, by executing the role allocation table, for each assigned specific person, the system queries their preset preferred notification channels (including APP push, SMS, telephone voice, and WeChat messages) and the historical connection rate and response time data of these channels. Available channels are then sorted from shortest to longest response time to determine each person's primary and backup notification channels, obtaining the channel priority configuration results, including: By executing the specific responsible person identifier bound in the role assignment table, the system queries the pre-registered list of available notification channels for that responsible person and the corresponding contact address (including APP account identifier, mobile phone number, WeChat number, etc.) in the system's preset personal information database, excluding channels that are currently under maintenance and unavailable, and obtains the set of valid available channels for that responsible person; By querying the historical notification delivery records of the same responsible person within the past preset number of days for each channel identifier in the effective and available channel set, the response delay (time difference from system sending to responsible person confirmation of receipt), connection success rate (number of successful delivery / total number of attempts) and the responsible person's proactive feedback rate to the channel (including reading confirmation, reply, etc.) for each delivery are extracted to obtain the three-dimensional data of the historical service quality of each channel. By using historical three-dimensional service quality data from each channel, response latency, connection success rate, and proactive feedback rate are normalized. The data are then weighted and summed using response latency weight γ1, connection success rate weight γ2, and proactive feedback rate weight γ3. The response latency weight γ1 is greater than the connection success rate weight γ2, and the connection success rate weight γ2 is greater than the proactive feedback rate weight γ3. This yields a comprehensive quality score for each channel, and all effective channels are ranked from highest to lowest based on their comprehensive quality scores. Based on the ranking results of effective channels, the channel with the highest comprehensive quality score is determined as the primary notification channel for the responsible person, and the channel with the second highest ranking is determined as the backup notification channel. Initial push waiting time thresholds and automatic switching conditions are set for the primary and backup channels respectively. If the primary channel does not receive a confirmation reply from the responsible person within the initial push waiting time threshold, it will automatically switch to the backup channel for a second push, and obtain the channel priority configuration result containing the primary and backup channel identifiers, push order and switching conditions.

[0095] The working principle and technical effect of the above technical solution are as follows: This method achieves optimal path selection for early warning notifications by dynamically evaluating and adaptively optimizing the multi-channel notification capabilities of each responsible person. Based on the responsible person identifier bound in the execution role allocation table, the system queries the personal information database for all available notification channels and corresponding contact addresses pre-registered by that responsible person, excluding channels currently under maintenance and unavailable, thus forming a set of effective available channels. For each channel in the set, the system queries the historical records for detailed delivery records of notifications sent by that channel to the same responsible person within a preset number of days. From these records, three key service quality indicators are extracted: response latency, connection success rate, and the responsible person's proactive feedback rate for each delivery. The system normalizes each of the three indicators and then performs a weighted sum, with response latency having the highest weight, connection success rate the second highest, and proactive feedback rate the third highest. A comprehensive quality score is calculated for each channel, and the channels are sorted from highest to lowest score. The system designates the channel with the highest score as the primary notification channel and the second highest score as the backup channel. It also sets an initial push waiting time threshold and automatic switching conditions for each channel. If the primary channel does not receive a confirmation reply from the responsible person within the set waiting time, the system automatically switches to the backup channel for a second push.

[0096] This method effectively solves the practical problems of warning notifications being sent but not received, or received but with slow responses. Different responsible parties exhibit significant differences in response efficiency to different notification channels; some respond quickly to mobile app push notifications, while others prefer answering phone calls, and these preferences may change over time. This method introduces a dynamic channel ranking mechanism based on historical service quality data, enabling the system to automatically learn and update the most effective contact methods for each responsible party, rather than relying on static preset priorities. By using response latency as the most weighted factor in the comprehensive scoring, the system directly integrates the core requirement of obtaining a rapid response into the channel selection decision logic. Through the primary and backup channel switching mechanism, the system can automatically activate backup channels for supplementary push notifications when the initial notification fails to receive confirmation, avoiding the security vulnerability of warning information not reaching the intended recipients due to the failure of a single channel. This entire mechanism continuously improves the delivery and response rates of warning notifications, ensuring that in truly urgent situations, every warning message can be delivered to the most needed people in the fastest way and receive timely confirmation.

[0097] In one embodiment of the present invention, the system includes: The positioning analysis module is used to continuously collect and analyze the activity images of the elderly in the authorized space of the community through a security equipment group, and obtain the posture image sequence and the corresponding spatial coordinate trajectory. The semantic parsing module is used to perform intention-level behavioral semantic parsing of the elderly’s current action type through posture image sequences and spatial coordinate trajectories, obtain refined activity parsing data, and distinguish between active normal activities, physiologically necessary actions and abnormal risk actions by behavioral meaning labels. The deviation analysis module is used to compare the refined activity analysis data with the baseline of the elderly’s own activity patterns in historical periods to obtain risk tendency analysis data and corresponding predicted hazard types. The matching module is used to generate graded response strategies and match disposal plans for potential safety events by combining risk tendency analysis data, predicting the type of hidden dangers, and combining the community environment status and the elderly’s historical response records, so as to obtain full-chain safety assurance execution instructions.

[0098] The working principle and technical effects of the above-mentioned technical solution are as follows: This invention uses a group of multiple types of security devices deployed in various areas of the community to continuously collect images of elderly people's activities in authorized spaces around the clock, and completes information processing at four progressive levels. The first level integrates scattered two-dimensional image information into a sequence of posture images with three-dimensional spatial coordinates through multi-device collaborative acquisition and spatial positioning, achieving synchronous perception of the elderly person's spatial location and body posture. The second level performs deep semantic analysis of the elderly person's current actions, not only identifying the apparent form of the action but also further judging the behavioral intention behind it, classifying it as active normal activity, physiologically necessary action, or abnormal risk action, thus establishing a cognitive bridge from what is seen to what is being done. The third level compares the currently analyzed behavioral information with the personalized activity pattern baseline accumulated by the system for the elderly person, calculating the degree of deviation of various behavioral indicators, and predicting the types of potential hazards. The fourth level, based on the comprehensive risk analysis results, current community environmental conditions, and the elderly person's historical response records, automatically generates a graded response strategy, forming a complete execution instruction chain from early warning triggering to completion of handling.

[0099] This system effectively addresses the limitations of traditional video surveillance systems, which can only record images but cannot actively interpret the meaning of actions. Through intent-level semantic parsing technology, the system can accurately distinguish whether an elderly person is actively bending down to pick up an item or has fallen due to loss of control, thus significantly reducing the false alarm rate. Simultaneously, by establishing individualized activity pattern baselines, the system can accurately identify behavioral changes in each elderly person that deviate from their normal lifestyle, avoiding the problem of frequent false alarms for active elderly individuals and underestimation of the true risk for frail elderly individuals when using a uniform template to measure all elderly people. In the early warning response phase, this method automatically generates execution instructions containing complete handling elements, solving the problem of early warning information remaining on the screen and unable to be translated into effective action. This allows community management personnel and family members to clearly understand what to do, who should do it, how to do it, and when to do it when receiving an early warning, constructing a complete closed loop from risk perception to safety assurance, significantly improving the intelligence level and response efficiency of community elderly care safety management.

[0100] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for safety detection, early warning, and management of elderly residents' activities in a community based on an AI-powered intelligent security system, characterized in that: The method includes: S1. Continuously collect and analyze the activity images of the elderly in the authorized space of the community through a security equipment group to obtain the posture image sequence and the corresponding spatial coordinate trajectory. S2. By analyzing the posture image sequence and spatial coordinate trajectory, perform intention-level behavioral semantic analysis on the current action type of the elderly to obtain refined activity analysis data; S3. By comparing the refined activity analysis data with the baseline of the elderly’s own activity patterns in historical periods, a deviation analysis is conducted to obtain risk tendency analysis data and corresponding predicted hazard types. S4. By analyzing risk propensity data, predicting potential hazard types, and combining the community environment and the elderly's historical response records, a graded response strategy is generated and a handling plan is matched for potential safety incidents to obtain full-chain safety assurance execution instructions.

2. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 1, characterized in that, S1 includes: By pre-installing various types of security devices in the community's public areas, unit entrances and exits, and authorized rooms, all active targets within the coverage area are collected synchronously around the clock to obtain multi-source heterogeneous raw sensing data streams; By performing cross-device spatiotemporal synchronization and target deduplication matching on the raw sensing data stream, the identity of the same elderly person is continuously bound across mirrors to obtain continuous target detection results. Real-time skeletal key point extraction and pose parameter analysis of elderly targets in video frame sequences are performed using continuous target detection results to obtain pose image sequences. By using posture image sequences, camera spatial calibration parameters, and multi-view triangulation methods, the position of the elderly in the three-dimensional space of the community is continuously calculated and mapped to obtain the spatial coordinate trajectory.

3. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 2, characterized in that, The process involves real-time skeletal keypoint extraction and pose parameter analysis of elderly targets in a video frame sequence based on continuous target detection results, to obtain a pose image sequence, including: The target region of the elderly in the current video frame is cropped and normalized by the continuous target detection results. At the same time, the target thermal radiation distribution data and micro-motion Doppler spectrum data collected in the same spatiotemporal coordinate system are read to obtain the fused target data after multimodal alignment. By jointly statistically analyzing the visible light band texture information and infrared band thermal radiation information in the fused target data, the illumination sufficiency and contrast evaluation index of the current imaging environment are calculated. When the ambient illumination is lower than the preset switching threshold or the contrast between the target and the background is lower than the preset standard, the infrared dominant channel is automatically switched to extract features, and the target edge is contoured and completed based on the millimeter-wave radar echo signal to obtain the target contour information. Using the enhanced target contour information through environmental adaptation, a multi-branch parallel regression network is used to simultaneously locate and verify multiple key points of the human skeleton, thereby obtaining a set of skeleton key point coordinates. By analyzing the spatial relationships of key points in the skeletal keypoint coordinate set, the deviation information of the torso's center of mass relative to the baseline standing posture is calculated frame by frame. Median filtering and smoothing are then performed on the time series to obtain the posture image sequence.

4. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 1, characterized in that, S2 includes: By using a sequence of posture images and spatial coordinate trajectories within a continuous time window, the continuous temporal changes of the elderly's movements are dynamically tracked and segmented into stages to obtain complete process segments. By analyzing the spatiotemporal correlation of the action segments through the posture change patterns and spatial position movement laws in the complete process segments, it is determined whether the action is accompanied by involuntary characteristics, and the physical feature vector of the action is obtained. By combining the physical feature vectors of actions with a pre-set behavioral semantic knowledge mapping rule base, the concept of the elderly’s current behavior is transformed at the intention level to obtain preliminary semantic analysis results. By combining the preliminary semantic analysis results with the positional semantic information in the spatial coordinate trajectory, the meaning of the behavior is corrected and ambiguity is resolved through scene context correction, resulting in refined activity analysis data.

5. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 4, characterized in that, The process involves performing spatiotemporal correlation analysis on the action segment by analyzing the posture change patterns and spatial movement patterns within the complete process segment, determining whether the action is accompanied by involuntary characteristics, and obtaining the action's physical feature vector, including: By using the coordinate set of skeletal key points in each frame of the complete process segment, the displacement difference of the torso center of mass in the vertical direction and the rate of change of velocity per unit time between adjacent frames are calculated, and the absolute value of the rate of change of velocity is accumulated along the action time axis to obtain the characteristic value of vertical impact kinetic energy. By using the trajectory curves of each key point in the complete process segment, the angular swing amplitude and swing regularity score of the limb end relative to the trunk coordinate system during the movement are calculated. At the same time, the positions of the velocity change points at the beginning and end of the movement and the velocity ratio before and after the change are extracted to obtain active control feature values. Cross-comparison is performed between the vertical impact kinetic energy characteristic value and the active control characteristic value; When the impact kinetic energy characteristic value exceeds the preset first threshold and the active control characteristic value is lower than the preset second threshold, the action is determined to be accompanied by involuntary rapid falling characteristics. When there are irregular jitter spectrum components in the action and the jitter amplitude exceeds the preset third threshold, the action is determined to be accompanied by involuntary tremor characteristics, and the physical attribute determination result is obtained. By combining the physical attribute judgment results with the total duration of the complete process segment, the horizontal drift distance of the center of mass, and the recovery stabilization time after the action ends, the overall controllability of the action is comprehensively quantified and scored. All the above physical characteristic parameters are combined into a multi-dimensional numerical vector to obtain the action physical characteristic vector.

6. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 1, characterized in that, S3 includes: Data on the normal daily routines of each elderly person within a preset number of days is obtained, and statistical analysis is performed on the intensity of the elderly person's daily activity paths and behaviors to obtain a personalized baseline profile of activity patterns. By comparing refined activity analysis data with personalized activity pattern baseline files item by item under the same time dimension, the deviation magnitude and duration between actual behavior and expected baseline are calculated to obtain multi-dimensional deviation measurement values. By combining multi-dimensional deviation measures with correlation analysis between deviation items, a weighted comprehensive evaluation of each deviation is conducted to obtain a comprehensive deviation score. By combining the deviation score with the spatiotemporal context of the deviation, and comparing it with the preset hazard type judgment rules, risk tendency classification and hazard type matching are carried out to obtain risk tendency analysis data and corresponding predicted hazard types.

7. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 6, characterized in that, The process involves comprehensively analyzing the deviation score and the spatiotemporal context of the deviation, comparing it against preset hazard type determination rules, performing risk tendency classification and hazard type matching, and obtaining risk tendency analysis data and corresponding predicted hazard types, including: By combining the deviation score and the time label information of the deviation, the deviation time is compared with the preset sensitive period classification rules to obtain the time-series risk weighting coefficient; By using the spatial coordinate trajectory information of the deviation, the deviation location is spatially overlaid with the preset area safety level map for analysis, and corresponding spatial risk weights are assigned according to the basic hazard level of the deviation area to obtain the spatial risk weighting coefficient. By performing multidimensional weighted product calculations using the comprehensive deviation score, time-series risk weighting coefficient, and spatial risk weighting coefficient, a comprehensive risk quantification score is obtained. This comprehensive risk quantification score is then mapped according to a preset multi-level risk threshold range to obtain a risk tendency classification result. By combining the risk tendency classification results with the specific type labels of the deviation actions, a many-to-one matching decision is made by referring to the preset hazard type behavior characteristic association mapping table. When the combination of deviation action types meets the triggering conditions of any hazard type, the corresponding hazard type identifier is matched and output to obtain risk tendency analysis data.

8. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 1, characterized in that, S4 includes: By combining risk propensity analysis data, predicting the types of potential hazards, and information on the community's environmental conditions, a comprehensive assessment is conducted on the urgency and feasible scope of potential safety incidents to obtain the incident urgency level and recommended intervention time window. The event urgency level is matched with a pre-defined tiered response strategy library to determine the applicable response level for this event. The standard handling action set for the corresponding level is retrieved from the strategy library to obtain the tiered response strategy framework. By combining the hierarchical response strategy framework with the pre-defined distribution of rescue resources for the corresponding elderly, the responsible parties and execution methods for each action in the strategy framework are assigned, and personalized response plans are obtained. By combining personalized handling plans with historical response feedback records of similar events involving the elderly, the timing of notifications and intervention methods in the plans are dynamically fine-tuned and optimized to obtain full-chain safety assurance execution instructions.

9. The community elderly activity safety detection and early warning management method based on AI intelligent security system according to claim 8, characterized in that, The process involves using a tiered response strategy framework combined with the pre-defined distribution of rescue resources for the corresponding elderly person. This framework assigns responsibility and allocation of execution methods for each action, resulting in personalized response plans, including: By extracting the execution role types one by one from the standard set of actions defined in the hierarchical response strategy framework, a list of role requirements is obtained. By combining the role requirement list with the pre-stored emergency contact information table for the elderly and the current property duty schedule, the available personnel for each role are matched and real-time availability is confirmed to obtain the execution role allocation table. By executing the role allocation table, for each assigned specific person, query their preset preferred notification channels and historical connection rate and response time data of the channels, sort the available channels according to the response time from shortest to longest, determine the primary notification channel and the backup notification channel, and obtain the channel priority configuration result; By combining the channel priority configuration results with the standardized notification content templates corresponding to the response levels in the hierarchical response strategy framework, and by replacing the variable placeholders in the templates one by one with the specific values ​​and text of the current event, differentiated notification content and on-site handling guidance text are generated to obtain personalized handling solutions.

10. A community elderly activity safety detection and early warning management system based on an AI intelligent security system, characterized in that: The system includes: The positioning analysis module is used to continuously collect and analyze the activity images of the elderly in the authorized space of the community through a security equipment group, and obtain the posture image sequence and the corresponding spatial coordinate trajectory. The semantic parsing module is used to perform intent-level behavioral semantic parsing of the elderly’s current action type through posture image sequences and spatial coordinate trajectories, and obtain refined activity parsing data. The deviation analysis module is used to compare the refined activity analysis data with the baseline of the elderly’s own activity patterns in historical periods to obtain risk tendency analysis data and corresponding predicted hazard types. The matching module is used to generate graded response strategies and match disposal plans for potential safety events by combining risk tendency analysis data, predicting the type of hidden dangers, and combining the community environment status and the elderly’s historical response records, so as to obtain full-chain safety assurance execution instructions.

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