An intelligent monitoring method and system for privacy-sensitive area security based on AI semantic analysis and a memory

Abstract

This application discloses an intelligent monitoring method, system, and memory for privacy-sensitive areas based on AI semantic analysis, relating to the field of security monitoring technology. The method includes: synchronously collecting audio data and environmental feature data of privacy-sensitive areas through a distributed audio acquisition unit and an environmental perception unit, achieving millisecond-level alignment based on a timestamp synchronization protocol; performing collaborative preprocessing on the collected data to output valid speech segments and standardized environmental feature vectors; converting the valid speech segments into text, followed by multi-dimensional semantic analysis by an AI semantic analysis module to output quantitative scores indicating the probability of safety, suspected assistance requests, and irrelevant information; performing a three-level progressive calibration based on the scores, environmental feature vectors, and behavioral feedback data; and outputting graded alarm signals based on the calibration results. This application reduces the false alarm rate and effectively enhances the security capabilities of privacy-sensitive areas through multi-source data fusion, deep AI semantic analysis, and multi-level false alarm calibration.

Description

TECHNICAL FIELD

[0001] The described invention relates to the technical field of security monitoring, such as intelligent security monitoring for privacy-sensitive areas, such as special enclosed areas in people-dense places like shopping malls, schools, office buildings, train stations, etc., and more specifically to a multi-source data fusion intelligent audio monitoring method, system and computer readable memory based on AI semantic analysis, which solves the problem of high false alarm rate of traditional keyword recognition technology through the collaborative design of hardware multi-source data acquisition and software AI semantic analysis and false alarm calibration, adapts to low-power terminal devices, cloud processing platforms and security management terminals, and meets the rigid demand for privacy protection, real-time response and high-accuracy monitoring. BACKGROUND

[0002] For privacy-sensitive areas, mainly closed or semi-closed spaces in people-dense places like shopping mall cubicles, school restrooms, office building restrooms, train station waiting area special areas, etc., due to the high demand for privacy protection, the installation of cameras is a serious ethical and privacy controversy, therefore audio analysis has become the mainstream technical path for security monitoring in such areas. Preferably, the prior art matches the collected audio signals with pre-set sensitive keywords, and triggers an alarm when a matching keyword is detected, to achieve preliminary warning of security incidents. Considering an example of a shopping mall restroom: audio collection devices are deployed near each cubicle, with built-in keyword recognition algorithms, when the pre-set keywords such as "help" appear in the collected audio, the system automatically sends an alarm information to the security personnel. In some embodiments, these audio collection devices may integrate basic noise reduction modules to reduce the interference of environmental noise on keyword recognition. However, due to the complexity of the environment of privacy-sensitive areas (such as water flow sound, corridor footsteps, normal conversation of personnel) and the diversity of voice scenes (such as movie dialogue quotes, children playing simulated shouting, descriptive mention of sensitive words), the keyword matching mode of the prior art is difficult to achieve accurate judgment.

[0003] In the prior art, audio acquisition devices are deployed in centralized monitoring nodes in public areas, each audio acquisition device generates an independent audio monitoring channel, and the collected audio data is transmitted to the centralized monitoring node through the network for keyword matching analysis. In this environment, the monitoring system of the prior art usually only relies on the keyword detection result of a single audio channel to make alarm judgment, without combining multi-dimensional information such as environmental characteristics and personnel behavior. If only the signals of all audio channels are processed by keyword matching and an alarm is triggered, the security equipment receiving the alarm information will face a large amount of false information, which not only increases the workload of security personnel, but also easily leads to neglect of real security events; at the same time, single keyword matching cannot distinguish semantic scenes, further exacerbating the false alarm problem. The difficulty lies in that the prior art only relies on a single dimension of keyword features, lacks semantic depth understanding and multi-source data fusion judgment, resulting in a high false alarm rate, which cannot meet the reliability and practicality requirements of privacy-sensitive area security monitoring. SUMMARY

[0004] The purpose of the present application is to provide an AI semantic analysis-based intelligent monitoring method and system for privacy-sensitive area security, which can significantly reduce the false alarm rate, improve monitoring accuracy and reliability, and enhance privacy-sensitive area security.

[0005] In a first aspect, the present application provides an AI semantic analysis-based intelligent monitoring method for privacy-sensitive area security, comprising the following steps:

[0006] Synchronously collecting audio data and environmental characteristic data of the privacy-sensitive area through the distributed audio acquisition unit and the environmental perception unit of the hardware terminal module, and realizing millisecond-level alignment of the collected data based on a timestamp synchronization protocol;

[0007] Performing cooperative preprocessing on the audio data and the environmental characteristic data, the cooperative preprocessing including noise suppression, effective signal extraction, and feature structuring, and outputting effective speech segments and standardized environmental characteristic vectors marked with timestamps;

[0008] Converting the effective speech segments into structured text through an end-to-end speech-to-text model, extracting semantic feature vectors of the text by an AI semantic analysis module, performing multi-dimensional semantic research and judgment in combination with context dependency, and outputting quantized scores corresponding to safety probability, suspected help-seeking probability, and irrelevant probability;

[0009] Based on the quantized scores of the suspected help-seeking probability, the standardized environmental characteristic vectors, and behavior feedback data, performing three-level progressive calibration through a multi-dimensional false alarm calibration module; the behavior feedback data is associated data of personnel interactive responses and regional personnel flow data collected in real time during the calibration process;

[0010] According to the calibration result, a hierarchical alarm signal is output through an alarm decision module, and the hierarchical alarm signal and associated monitoring data are synchronized to a terminal management module.

[0011] Further, the distributed audio acquisition unit adopts a master-slave architecture design, including one master control device and one to eight array microphones, the master control device establishes a communication link with each array microphone through an enhanced ASN bus, realizes synchronous data transmission and clock calibration; the master control device integrates a high signal-to-noise ratio differential amplification circuit and an adaptive noise reduction chip, adopts an adaptive beamforming algorithm to focus on the target voice direction and suppresses the sidelobe interference; the distributed audio acquisition unit supports a 16kHz-32kHz configuration sampling rate, and the bit depth is adaptively adjusted to 16bit-32bit.

[0012] Further, the environmental perception unit adopts a multi-sensor fusion architecture, integrating a three-axis acceleration sensor, a high-precision decibel sensor, a human infrared sensor, and an ambient light sensor.

[0013] The three-axis acceleration sensor adopts a ±16g range, a sampling rate of 100Hz, and is used to detect abnormal vibration signals in the region and output vibration frequency and amplitude characteristics; the high-precision decibel sensor measures a range of 30dB-130dB with an accuracy of ±0.5dB, and distinguishes normal conversation from abnormal shouting sound intensity through a pre-set double threshold mechanism; the human infrared sensor has a detection distance of 0.1m-10m and a response time of ≤200ms, combined with ambient light sensor data to exclude non-person heat source interference and confirm the personnel staying state in the monitoring area; the environmental feature data collected by the environmental perception unit is fused and denoised by Kalman filtering algorithm, and a standardized environmental feature vector is output.

[0014] Further, the collaborative preprocessing includes:

[0015] An AI noise reduction model based on LSTM is used to eliminate environmental noise, and the AI noise reduction model is trained with typical noise samples (such as air conditioner noise, footsteps, and device operation noise) in the privacy area;

[0016] A double-threshold endpoint detection algorithm is used to segment the audio data, distinguishing between speech segments and non-speech segments, with a speech segment start threshold of -35dB-25dB and an end threshold of -45dB-35dB;

[0017] Based on UTC timestamp, the audio data and environmental feature data are aligned, with a time deviation of ≤10ms; the environmental feature data is subjected to min-max standardization processing to generate a standardized feature vector in the interval of 0-1, and is marked with a risk level according to a pre-set rule.

[0018] Further, the AI semantic analysis module adopts a BERT model architecture, including:

[0019] The model was optimized using INT8 quantization and structured pruning, with the pruning rate controlled between 30% and 50%.

[0020] A domain-adaptive pre-training mechanism is introduced, and the semantic feature extraction layer is fine-tuned based on the privacy region security monitoring scenario corpus (including help requests, conflict statements, and daily communication statements).

[0021] During the context association analysis, a bidirectional attention mechanism is used to strengthen the feature weights of key semantics (such as "help", "ask for help", "danger"), and semantic similarity is calculated and matched with the scene semantic database to output quantitative scores (0-1 range) for safety probability, suspected request for help probability and irrelevant probability.

[0022] Furthermore, the three-stage progressive calibration includes:

[0023] Level 1 Calibration: Set the first threshold range for the quantification score of suspected help request probability to [0.3, 0.5], the second threshold range to [0.5, 0.8], and the third threshold range to [0.8, 1.0]. If the quantification score of suspected help request probability is within the first threshold range, and the decibel sensor detects a sound intensity < 60dB and the accelerometer does not detect abnormal vibration, it is judged as a false alarm and marked. If the quantification score of suspected help request probability is within the second threshold range, and the decibel sensor detects a sound intensity ≥ 85dB, or the accelerometer detects abnormal vibration, it proceeds to Level 2 Calibration. If the quantification score of suspected help request probability is within the third threshold range, it directly proceeds to Level 3 Calibration.

[0024] Secondary calibration involves calling a scenario-tuned BERT model to perform semantic parsing on the speech text, extracting the sentiment features and intent keywords of the text, and performing intent matching in conjunction with a dynamically updated scenario semantic library. If the matching degree is ≥80%, it is judged as a suspected real event; otherwise, it is judged as a false alarm.

[0025] The three-level calibration process involves sending a low-power alert tone via a hardware terminal module if the first two levels of calibration do not yield clear results. If a voice response or a one-click alarm trigger signal is detected within a preset time of 3-10 seconds, the event is considered real. If no response is detected and the infrared sensor detects that the person has left the area, the event is considered a false alarm. If no response is detected but the person remains, a second audio acquisition and semantic analysis process is initiated.

[0026] Furthermore, the AI ​​semantic analysis module employs a multi-classifier ensemble learning architecture to output quantitative scores corresponding to safety probability, suspected request for help probability, and irrelevant probability, including:

[0027] The AI ​​semantic analysis module integrates five base classifiers: logistic regression, random forest, support vector machine, lightweight neural network, and XGBoost. Each base classifier performs parallel inference based on the same semantic feature vector.

[0028] An improved AdaBoost algorithm is used to weight and fuse the inference results of each base classifier, and the fusion weights are dynamically optimized through cross-validation.

[0029] Every 24 hours, the model is incrementally fine-tuned using 500-1000 newly collected labeled text data points. The fine-tuning process employs a gradient accumulation strategy, and the learning rate is dynamically adjusted to [5×10]. −6 1×10 −5 Set batchsize to 16.

[0030] Furthermore, the graded alarm signals are divided into three levels:

[0031] A Level 1 alarm signal corresponds to a suspected request for help probability score of [0.5, 0.7) and calibration confirms there is no emergency risk; only a prompt message is sent to the terminal management module.

[0032] A level 2 alarm signal corresponds to a suspected request for help probability score of [0.7, 0.9) and calibration confirms the existence of potential risks. An alarm message is sent to the terminal management module and regional video linkage acquisition is initiated.

[0033] A Level 3 alarm signal corresponds to a suspected assistance probability score ≥ 0.9 or a confirmed emergency event. Simultaneously, an alarm signal is sent to the terminal management module, activating the audible and visual alarm device and pushing real-time monitoring data from the site.

[0034] Secondly, this application also provides an intelligent monitoring system for the security of privacy-sensitive areas based on AI semantic analysis, which, when running the aforementioned intelligent monitoring method for the security of privacy-sensitive areas based on AI semantic analysis, includes:

[0035] The acquisition module is used to synchronously acquire audio data and environmental feature data of privacy-sensitive areas through the distributed audio acquisition unit and environmental perception unit of the hardware terminal module, and to achieve millisecond-level alignment of the acquired data based on the timestamp synchronization protocol.

[0036] The preprocessing module is used to perform collaborative preprocessing on audio data and environmental feature data. Collaborative preprocessing includes noise suppression, effective signal extraction and feature structuring, and outputs effective speech segments with timestamps and standardized environmental feature vectors.

[0037] The analysis module is used to convert effective speech segments into structured text through an end-to-end speech-to-text model. The AI ​​semantic analysis module extracts the semantic feature vector of the text, performs multi-dimensional semantic judgment in combination with contextual dependencies, and outputs quantitative scores corresponding to safety probability, suspected request for help probability and irrelevant probability.

[0038] The calibration module is used for quantitative scoring based on the probability of suspected assistance, standardized environmental feature vectors, and real-time behavioral feedback data. It performs a three-level progressive calibration through a multi-dimensional false alarm calibration module. The behavioral feedback data consists of real-time collected data on personnel interaction responses and regional personnel flow during the calibration process.

[0039] The alarm module is used to output graded alarm signals through the alarm decision module based on the calibration results, and to synchronize the graded alarm signals and associated monitoring data to the terminal management module.

[0040] Thirdly, this application also provides a computer-readable storage device that tangibly stores computer program instructions, which, when executed by one or more processors, cause the processors to perform the aforementioned intelligent monitoring method for privacy-sensitive area security based on AI semantic analysis.

[0041] Compared with the prior art, this application has the following beneficial effects:

[0042] This application provides an intelligent monitoring method, system, and computer-readable storage device for privacy-sensitive areas based on AI semantic analysis. By synchronously collecting audio and environmental data, performing collaborative preprocessing, AI semantic analysis, multi-dimensional calibration, and hierarchical alarms, it effectively solves the problems of high false alarm rate and lack of semantic understanding in the prior art. It can significantly reduce the false alarm rate, improve the accuracy and reliability of monitoring, and enhance the security of privacy-sensitive areas. Attached Figure Description

[0043] Figure 1 A flowchart illustrating the intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis provided in this application embodiment;

[0044] Figure 2 This is a schematic diagram of the structure of an intelligent monitoring system for privacy-sensitive areas based on AI semantic analysis, provided in an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as being processed sequentially, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. A process can be terminated when its operation is completed, but it may also have additional steps not included in the drawings. A process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0046] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0047] Existing security monitoring methods for privacy-sensitive areas mainly rely on matching preset sensitive keywords to trigger alarms. However, due to the complex environment and diverse voice scenarios in privacy-sensitive areas, and the fact that existing technologies only rely on keyword detection on a single audio channel, the false alarm rate is high, making it difficult to achieve accurate judgments and failing to meet the monitoring requirements for high reliability and low false alarms.

[0048] In this regard, such as Figure 1 As shown, this embodiment proposes an intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis, including:

[0049] S100: Through the distributed audio acquisition unit and environmental perception unit of the hardware terminal module, audio data and environmental feature data of privacy-sensitive areas are collected synchronously, and the collected data is aligned at the millisecond level based on the timestamp synchronization protocol.

[0050] S200. Perform collaborative preprocessing on the audio data and the environmental feature data. The collaborative preprocessing includes noise suppression, effective signal extraction and feature structuring, and outputs effective speech segments with timestamps and standardized environmental feature vectors.

[0051] S300: The effective speech segment is converted into structured text through an end-to-end speech-to-text model. The AI ​​semantic analysis module extracts the semantic feature vector of the text and performs multi-dimensional semantic judgment in combination with contextual dependencies, outputting quantitative scores corresponding to the safety probability, suspected request for help probability, and irrelevant probability.

[0052] S400, based on the quantitative score of the suspected request for help probability, the standardized environmental feature vector, and behavioral feedback data, performs a three-level progressive calibration through a multi-dimensional false alarm calibration module; the behavioral feedback data is the real-time collection of personnel interaction response correlation data and regional personnel flow data during the calibration process;

[0053] S500: Based on the calibration results, the alarm decision module outputs a graded alarm signal and synchronizes the graded alarm signal and associated monitoring data to the terminal management module.

[0054] In this embodiment, the intelligent monitoring method for privacy-sensitive areas first collects data through a hardware terminal module. Specifically, this hardware terminal module integrates a distributed audio acquisition unit and an environmental perception unit. The distributed audio acquisition unit can consist of multiple independent microphones, which are distributed throughout the monitoring area and each independently collects audio data. The environmental perception unit can consist of a single type of sensor, such as a decibel sensor or a vibration sensor, used to collect environmental feature data. To ensure the accuracy of subsequent analysis, the collected audio data and environmental feature data need to be synchronized. One implementation method is that each acquisition unit embeds a local timestamp in the data packet and transmits it to the central processing unit via the network, where the central processing unit performs post-alignment based on these timestamps. For example, the standard Network Time Protocol (NTP) can be used for device clock synchronization to achieve data alignment at the second or sub-second level.

[0055] Collaborative preprocessing is performed on the acquired audio data and environmental feature data. This preprocessing aims to improve data quality and extract key information. For noise suppression, traditional algorithms such as spectral subtraction or Wiener filtering can be used to reduce the impact of environmental noise on the speech signal. For effective signal extraction, an energy threshold-based speech activity detection (VAD) algorithm can be used to distinguish speech segments from non-speech segments in the audio stream. For example, when the energy of the audio signal exceeds a preset fixed threshold, it is determined to be a valid speech segment. For environmental feature data, simple linear normalization can be performed to map it to a specific numerical range, thereby achieving feature structuring. After preprocessing, the output includes valid speech segments with timestamps and standardized environmental feature vectors.

[0056] The valid speech segment is converted into structured text using an end-to-end speech-to-text model. This speech-to-text model can be a general-purpose Automatic Speech Recognition (ASR) system that directly converts speech signals into text sequences. For example, a traditional ASR architecture combining acoustic models based on Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs) with language models can be used. Next, an AI semantic analysis module extracts semantic features from the converted text. This AI semantic analysis module can convert the text into numerical vectors based on methods such as the Bag-of-Words model or TF-IDF (Term Frequency-Inverse Document Frequency). Based on this, multi-dimensional semantic analysis is performed by considering contextual dependencies. For example, keywords in the text can be identified through simple rule matching or a classifier based on shallow neural networks, and a preliminary judgment can be made by combining these keywords with a small number of words in the surrounding text, thereby outputting quantitative scores corresponding to safety probability, suspected request for help probability, and irrelevant probability.

[0057] Based on this, a three-level progressive calibration is performed using a multi-dimensional false alarm calibration module, based on the quantitative score of the suspected request for help probability, the standardized environmental feature vector, and behavioral feedback data. Behavioral feedback data can include simple human interaction information such as whether people in the area respond verbally or trigger physical buttons, as well as the number of times people enter and exit the area, counted by a simple counter. The calibration process can be set as follows: Level 1 calibration: A preliminary judgment is made based on the quantitative score of the suspected request for help probability and the environmental feature vector (e.g., whether the sound intensity exceeds a certain fixed threshold). If both meet the preset conditions, the process proceeds to Level 2 calibration; otherwise, it is directly judged as a false alarm. Level 2 calibration: Behavioral feedback data can be introduced. For example, if no one in the area responds after a suspected incident occurs, it is judged as a false alarm. If a clear judgment is still not possible, the process proceeds to Level 3 calibration, for example, by initiating manual intervention for review.

[0058] Based on the calibration results, the alarm decision module outputs tiered alarm signals and synchronizes these signals along with associated monitoring data to the terminal management module. The alarm decision module can output two levels of alarm signals based on the final judgment of the calibration module: a low-level alert, such as sending only a text message to the terminal management module; and a high-level alarm, such as sending an emergency message along with original audio clips and environmental data. Upon receiving this information, the terminal management module can display it on the monitoring interface for security personnel to view.

[0059] This embodiment of the intelligent monitoring method for privacy-sensitive areas effectively improves data quality by simultaneously collecting audio data and environmental feature data and performing collaborative preprocessing. Combining an AI semantic analysis module for deep semantic analysis of voice and text enables a more accurate understanding of the nature of events. Crucially, the introduction of a multi-dimensional false alarm calibration module to perform three-level progressive calibration and integrate behavioral feedback data significantly reduces the false alarm rate commonly found in traditional keyword matching schemes in privacy-sensitive areas. This improves the system's ability and reliability in identifying real security events, thus meeting the needs of such areas for refined, low-false-alarm monitoring.

[0060] In some implementations, due to complex environments, severe noise interference, and difficulties in synchronizing data across multiple nodes, the quality of the acquired audio data may be poor, affecting the accuracy of subsequent speech-to-text conversion and the reliability of AI semantic analysis. This poses a risk of false alarms or missed alarms when the system identifies potential security events. Therefore, the distributed audio acquisition unit adopts a master-slave architecture design, including one master control device and 1-8 array microphone sub-nodes. The master control device establishes communication links with each array microphone sub-node via an enhanced ASN bus to achieve synchronous data transmission and clock calibration. The master control device integrates a high signal-to-noise ratio differential amplifier circuit and an adaptive noise reduction chip, employing an adaptive beamforming algorithm to focus on the target speech direction and suppress sidelobe interference. The distributed audio acquisition unit supports a configurable sampling rate of 16kHz-32kHz and an adaptive bit depth of 16bit-32bit.

[0061] Specifically, the distributed audio acquisition unit adopts a master-slave architecture, where the master control device coordinates and manages multiple array microphone sub-nodes. The master control device can be an embedded processor or microcontroller, responsible for receiving, processing, and forwarding data from the sub-nodes, and performing system-level control. The array microphone sub-nodes consist of multiple microphones, typically arranged linearly or in a ring, used for sound source localization and beamforming. This architecture effectively expands the acquisition range while centrally managing data flow and control logic. The master control device establishes communication links with each array microphone sub-node via an enhanced ASN bus. The enhanced ASN bus is a communication protocol or physical interface designed for high-precision synchronous data transmission. It can employ hardware-level time synchronization mechanisms, such as those based on IEEE 1588 (PTP) or NTP, to ensure high consistency of timestamps between the master control device and all sub-nodes, achieving millisecond-level or even microsecond-level data alignment. The communication link can be a physical wired connection or a high-bandwidth wireless connection, integrating error checking and retransmission mechanisms to ensure data transmission reliability.

[0062] In addition, the main control unit integrates a high signal-to-noise ratio (SNR) differential amplifier circuit and an adaptive noise reduction chip. The high SNR differential amplifier circuit amplifies weak audio signals while suppressing common-mode noise and improving signal clarity. It typically employs a low-noise operational amplifier and a differential input design to effectively eliminate power supply noise and electromagnetic interference. The adaptive noise reduction chip can analyze environmental noise characteristics in real time and dynamically adjust the noise reduction strategy. Based on digital signal processing (DSP) technology, it can implement algorithms such as spectral subtraction, Wiener filtering, or neural network noise reduction, automatically adjusting noise reduction parameters according to changes in environmental noise to suppress noise to the maximum extent without sacrificing speech quality. Simultaneously, the system uses an adaptive beamforming algorithm to focus on the target speech direction and suppress sidelobe interference. The adaptive beamforming algorithm adjusts the weighting coefficients of each microphone in the microphone array to form a "main lobe" pointing towards the target sound source, while simultaneously creating "zeros" or "sidelobe suppression" in the direction of interference sources, thereby effectively enhancing the target speech signal and suppressing noise and reverberation from non-target directions. Implementation requires estimation of the microphone array geometry, sound velocity, and the direction of the target sound source. The distributed audio acquisition unit also supports configurable sampling rates from 16kHz to 32kHz and adaptive bit depth adjustments from 16bit to 32bit. Sampling rate and bit depth configurations are typically achieved through parameter settings of the analog-to-digital converter (ADC). Higher sampling rates capture a wider frequency range, suitable for scenarios requiring high fidelity; lower sampling rates reduce data volume, suitable for bandwidth-constrained scenarios. Adaptive bit depth adjustment can be achieved through dynamic range compression or expansion techniques, ensuring optimal audio quality under varying volume conditions and avoiding clipping or quantization noise.

[0063] Through the above embodiments, the distributed audio acquisition unit adopts a master-slave architecture design, which can effectively expand the acquisition range and centrally manage data. Simultaneously, the enhanced ASN bus ensures high-precision synchronous data transmission and clock calibration between the master control device and each array microphone sub-node, solving the problem of multi-node data alignment. The high signal-to-noise ratio differential amplifier circuit and adaptive noise reduction chip integrated into the master control device, combined with an adaptive beamforming algorithm, can significantly improve the signal-to-noise ratio of the target speech, effectively suppress environmental noise and sidelobe interference, and ensure clear, high-quality audio data acquisition even in complex noisy environments. Furthermore, it supports configurable sampling rates of 16kHz-32kHz and adaptive bit depth adjustments of 16bit-32bit, allowing the system to flexibly adjust audio acquisition parameters according to actual application scenarios, optimizing resource utilization while ensuring data accuracy. These technological improvements collectively provide cleaner and more accurate input for subsequent speech-to-text models and AI semantic analysis modules, thereby significantly improving the system's accuracy in identifying privacy-sensitive areas, effectively reducing the risk of false alarms and missed alarms, and enhancing the overall reliability of the monitoring method.

[0064] In some implementations, the environmental sensing unit employs a multi-sensor fusion architecture, integrating a triaxial accelerometer, a high-precision decibel sensor, a human infrared sensor, and an ambient light sensor. This multi-sensor fusion architecture aims to overcome the limitations of a single sensor in environmental sensing, providing more comprehensive and accurate environmental information by integrating the advantages of different types of sensors.

[0065] Specifically, the triaxial accelerometer employs a ±16g range and a 100Hz sampling rate to detect abnormal vibration signals within the area and output vibration frequency and amplitude characteristics. This sensor can capture subtle or severe vibrations generated by physical events such as falls or object impacts. By analyzing its frequency and amplitude characteristics, the intensity and type of vibration can be quantified, providing direct physical evidence for judging abnormal events. The high-precision decibel sensor has a measurement range of 30dB-130dB and an accuracy of ±0.5dB. It distinguishes between normal conversation and abnormal shouting through a preset dual-threshold mechanism. Its wide measurement range and high precision ensure accurate quantification of ambient sound, while the dual-threshold mechanism intelligently distinguishes between everyday conversations and abnormally high-decibel sounds that may represent requests for help or conflict, effectively filtering out irrelevant background noise. The human infrared sensor has a detection distance of 0.1m-10m and a response time ≤200ms. Combined with ambient light sensor data, it eliminates interference from non-personnel heat sources and confirms the status of people within the monitoring area. This sensor can detect the presence and movement of people in the area in real time, and through collaborative analysis with ambient light sensor data, it effectively avoids misjudgments caused by non-personnel heat sources (such as direct sunlight or equipment heating), thus more accurately confirming the actual stay of people.

[0066] Through the above embodiments, the environmental perception unit can acquire rich and complementary environmental feature data from multiple dimensions. This multi-source, heterogeneous environmental feature data is fused and denoised using a Kalman filter algorithm, effectively improving the accuracy and stability of the data and outputting a standardized environmental feature vector. The Kalman filter algorithm can effectively handle noise and uncertainty in sensor data, optimizing and integrating information from different sensors to generate a more reliable and consistent environmental state estimate. This allows the subsequent collaborative preprocessing module to receive a high-quality, low-noise standardized environmental feature vector, providing more accurate contextual information for the AI ​​semantic analysis module. This significantly improves the system's accuracy and response speed in identifying potential security events in privacy-sensitive areas, effectively reducing the false alarm rate and ensuring the reliability and effectiveness of monitoring.

[0067] In some implementations, the environmental noise in privacy-sensitive areas is complex and variable, which may lead to inaccurate extraction of effective speech segments. Simultaneously, accurate alignment of audio and environmental data, as well as effective representation of environmental features, also present challenges. Therefore, the collaborative preprocessing steps include: using an LSTM-based AI noise reduction model to eliminate environmental noise, the AI ​​noise reduction model being trained on typical noise samples from privacy areas (such as air conditioning noise, footsteps, and equipment operating noise); using a dual-threshold endpoint detection algorithm to segment the audio data, distinguishing between speech segments and non-speech segments, where the starting threshold for speech segments is set to -35dB to -25dB, and the ending threshold is set to -45dB to -35dB; aligning the audio data with environmental feature data based on UTC timestamps, with a time deviation ≤10ms; performing min-max normalization on the environmental feature data to generate a normalized feature vector in the 0-1 interval, and labeling it with risk levels according to preset rules.

[0068] Specifically, the LSTM-based AI noise reduction model is a deep learning model that effectively learns the time-series dependencies in audio signals through the structure of a Long Short-Term Memory (LSTM) network. During the training phase, the model utilizes a large amount of sample data containing typical noises from privacy regions (such as low-frequency humming from air conditioners, footsteps, and mechanical noise from various equipment). In this way, the model can accurately identify and suppress these specific types of environmental noise, thereby significantly improving the signal-to-noise ratio of audio data while preserving the integrity of the speech signal.

[0069] The dual-threshold endpoint detection algorithm is used to accurately identify the start and end positions of speech segments in audio data. This algorithm avoids misjudgments caused by noise fluctuations by setting two different energy thresholds—a higher start threshold (-35dB to -25dB) and a lower end threshold (-45dB to -35dB). When the audio energy first exceeds the start threshold, the system determines that speech has started; when the audio energy remains below the end threshold, the system determines that speech has ended. This dual-threshold mechanism effectively enhances the robustness of speech activity detection and ensures the accurate extraction of valid speech segments.

[0070] The alignment of audio data and environmental feature data based on UTC timestamps refers to the system using a unified Coordinated Universal Time (UTC) as a time base to accurately timestamp and synchronize the distributed audio data and environmental feature data. By ensuring that the time deviation is less than or equal to 10ms, this application achieves millisecond-level multimodal data alignment. This high-precision time synchronization is crucial for subsequent contextual analysis; for example, it can accurately determine whether an abnormal environmental event (such as severe vibration) occurs simultaneously with specific voice content (such as a cry for help).

[0071] The min-max normalization process applied to environmental feature data scales environmental feature data of different dimensions (such as decibel values, vibration amplitude, and infrared sensor intensity) to a uniform range of 0-1. This normalization eliminates dimensional differences between features, ensuring that all features have equal importance in the subsequent machine learning model and preventing certain features with large numerical ranges from unfairly dominating the model training and inference results. Simultaneously, the normalized feature vectors are labeled with risk levels according to preset rules; for example, vibration amplitudes within a specific range are labeled as "high-risk vibrations," providing an intuitive and quantitative basis for subsequent false alarm calibration and alarm decisions.

[0072] Through the above embodiments, the LSTM-based AI noise reduction model can effectively filter out complex and variable environmental noise in privacy-sensitive areas, ensuring higher purity of the audio signal input to the subsequent speech-to-text model, thereby significantly improving the accuracy of effective speech segment extraction. The introduction of a dual-threshold endpoint detection algorithm further accurately identifies the start and end points of speech, avoiding interference from non-speech parts and ensuring the effectiveness of semantic analysis. Furthermore, the millisecond-level UTC timestamp alignment mechanism ensures high temporal consistency between audio events and environmental events, providing a solid foundation for the fusion analysis of multimodal data. Performing min-max normalization and risk level labeling on environmental feature data allows for the unified and effective use of different types of environmental information, greatly enhancing the decision-making accuracy and robustness of the multi-dimensional false alarm calibration module. Overall, these collaborative preprocessing optimizations provide high-quality, high-precision input data for subsequent AI semantic analysis and three-level progressive calibration, thereby significantly improving the overall performance of the intelligent monitoring method for privacy-sensitive area security, effectively reducing the false alarm rate, and improving the efficiency of identifying real security events.

[0073] In some implementations, the AI ​​semantic analysis module adopts the BERT model architecture. This BERT model architecture includes INT8 quantization and structured pruning optimization, with the pruning rate controlled between 30% and 50%. A domain-adaptive pre-training mechanism is introduced, and the model is fine-tuned based on privacy-preserving security monitoring scenario corpora (including help requests, conflict statements, and everyday conversational statements) to optimize the semantic feature extraction layer. During contextual association analysis, a bidirectional attention mechanism is used to strengthen the feature weights of key semantics (such as "help," "ask for help," and "danger"), and semantic similarity calculation is used to match with the scene semantic database, outputting quantitative scores (0-1 range) for safety probability, suspected help request probability, and irrelevant probability.

[0074] Specifically, the AI ​​semantic analysis module adopts the BERT model architecture, a Transformer-based bidirectional encoder representation model that captures contextual information of words in text through deep learning, thereby generating high-quality semantic representations. In intelligent monitoring scenarios with privacy-sensitive areas, the BERT model architecture provides powerful semantic understanding capabilities for subsequent semantic analysis, laying the foundation for identifying potential security risks. To improve the model's operating efficiency, it undergoes INT8 quantization and structured pruning optimization. INT8 quantization converts model parameters from floating-point numbers to 8-bit integers, effectively reducing model size and computational load. Structured pruning further reduces model size and computational complexity by removing unimportant neurons, layers, or connections, with the pruning rate controlled between 30% and 50%. This aims to significantly reduce the computational resource consumption and inference latency of the AI ​​semantic analysis module, enabling it to run efficiently on resource-constrained hardware terminals and meet the needs of real-time monitoring.

[0075] To ensure the model's accuracy in specific application scenarios, this embodiment introduces a domain-adaptive pre-training mechanism. This mechanism, based on a general pre-trained model, utilizes a large amount of unlabeled text data from the privacy region security monitoring domain for further pre-training, enabling the model to better understand the language patterns and semantics of this domain. Subsequently, the model is fine-tuned using labeled privacy region security monitoring scenario corpora (including help requests, conflict statements, and everyday conversational statements), with a particular focus on optimizing the model's semantic feature extraction layer. This allows it to more accurately capture subtle semantic features related to security events, such as distinguishing between genuine help requests and complaints in everyday conversations, thereby improving the accuracy and robustness of semantic judgment. During contextual association analysis, a bidirectional attention mechanism is employed, allowing the model to simultaneously consider the contextual information surrounding the current word while processing text, and dynamically adjust the weights of different words based on the importance of the context. Here, it is used to strengthen the feature weights of key semantics such as "help," "ask for help," and "danger," ensuring these core words receive sufficient attention in semantic analysis. Semantic similarity calculation quantifies the degree of similarity between the extracted semantic feature vectors and the standard semantic vectors in a pre-built scenario semantic library. The scene semantic library contains typical semantic patterns of various security incidents, requests for help, conflict situations, and normal communication. Through these mechanisms, the AI ​​semantic analysis module can more accurately identify potential risk signals in text and ultimately output quantitative scores in the 0-1 range for the probability of safety, the probability of suspected requests for help, and the probability of irrelevant information, providing a refined basis for subsequent false alarm calibration and alarm decision-making.

[0076] Through the above embodiments, the AI ​​semantic analysis module adopts the BERT model architecture and combines INT8 quantization, structured pruning optimization, and domain-adaptive pre-training and fine-tuning mechanisms. This application can significantly improve the model's semantic understanding ability and inference efficiency in intelligent monitoring scenarios for privacy-sensitive areas. Specifically, after optimization, the model's computational resource consumption and inference latency are significantly reduced, making real-time and efficient semantic analysis possible on the hardware terminal module. Simultaneously, through domain-adaptive pre-training and scene-based corpus-based fine-tuning, the AI ​​semantic analysis module can more accurately capture security-related semantic features unique to privacy areas, effectively distinguishing between genuine requests for help and false alarms. Furthermore, the introduction of a bidirectional attention mechanism in context association analysis strengthens the weight of key semantics such as "help," "request for help," and "danger." Combined with semantic similarity calculation and scene semantic library matching, the system can more accurately and precisely output quantitative scores for safety probability, suspected request for help probability, and irrelevant probability, thus providing more reliable input for the subsequent multi-dimensional false alarm calibration module. Ultimately, this improves the accuracy and response speed of the entire monitoring method, effectively reducing false alarm and false negative rates.

[0077] In some implementations, relying solely on preliminary semantic analysis results and limited calibration mechanisms may lead to inaccurate event assessments in complex or ambiguous scenarios, resulting in a high risk of false alarms or missed alarms. This is particularly problematic in privacy-sensitive areas, where excessive alarms can cause unnecessary interference, while missed alarms can have serious consequences. Therefore, a three-level progressive calibration mechanism aims to improve the accuracy and reliability of the system's assessment of potential security events through a layered and progressively refined verification mechanism. This calibration mechanism includes first-level calibration, second-level calibration, and third-level calibration.

[0078] The first-level calibration, serving as an initial screening stage, primarily relies on the quantitative score of the suspected request for help output by the AI ​​semantic analysis module, combined with preliminary environmental characteristic data for rapid judgment. Specifically, the system sets the first threshold range for the quantitative score of the suspected request for help to [0.3, 0.5), the second threshold range to [0.5, 0.8), and the third threshold range to [0.8, 1.0]. These threshold ranges are used to initially classify the suspected request for help probability output by the AI ​​semantic analysis module, reflecting different levels of urgency or credibility of the event. For example, [0.3, 0.5) represents a low level of suspicion, [0.5, 0.8) represents a medium level of suspicion, and [0.8, 1.0] represents a high level of suspicion. If the quantitative score of the suspected request for help falls within the first threshold range, and the decibel sensor detects a sound intensity <60dB and the accelerometer shows no abnormal vibration, it is judged as a false alarm and marked. 60dB is generally considered the upper limit of normal conversation; below this value and without abnormal vibration, it indicates a relatively calm environment, reducing the likelihood of the event occurring. Accelerometers are used to detect abnormal vibration signals within the area, such as falls or impacts. The absence of abnormal vibrations further supports the judgment of false alarms. If the quantification score of the suspected distress call probability is within the second threshold range, and the decibel sensor detects a sound intensity ≥85dB, or the accelerometer detects abnormal vibration, then the system enters the second-level calibration. 85dB is generally considered the lower limit of loud shouts or noise; combined with abnormal vibration, it strongly indicates a possible emergency. These conditions trigger the system to enter a deeper level of second-level calibration for more detailed analysis. If the quantification score of the suspected distress call probability is within the third threshold range, the system directly enters the third-level calibration. When the quantification score of the suspected distress call probability reaches the highest threshold range, it indicates that the AI ​​semantic analysis has a high degree of confidence in the existence of an emergency. At this point, the second-level calibration is skipped, and the system directly enters the highest level of third-level calibration to ensure the fastest possible response.

[0079] Level 2 calibration is a stage that conducts in-depth semantic analysis of events that could not be clearly identified in Level 1 calibration. Its aim is to confirm the authenticity of events through more refined text understanding. In this stage, the system calls a scenario-fine-tuned BERT model to perform semantic parsing of the speech text, extracting sentiment features and intent keywords, and performing intent matching in conjunction with a dynamically updated scene semantic database. The BERT model is a pre-trained language model based on the Transformer architecture, possessing powerful contextual understanding capabilities. Here, it has been fine-tuned for a specific scenario (such as security monitoring in privacy-sensitive areas) to better understand the specific context and expressions within that domain. Sentiment features refer to the emotions expressed in the text, such as anxiety, fear, anger, or calmness. Intent keywords are words that directly indicate the speaker's purpose or behavior, such as "help," "help," or "uncomfortable." The scene semantic database is a knowledge base containing vocabulary, phrases, sentence structures, and their corresponding intents related to security events in privacy-sensitive areas. Dynamic updates mean that this database can continuously learn and optimize based on new event data or expert feedback. Intent matching involves comparing the intent extracted from the speech text with known intents in the semantic database to determine their relevance. If the matching degree is ≥80%, it is judged as a suspected real event; otherwise, it is judged as a false alarm. The matching degree is an indicator that measures the similarity between the extracted intent and the intent in the scene semantic library. The 80% threshold is an empirical value that represents a high degree of matching, thereby improving the accuracy of the judgment.

[0080] Level 3 calibration is the highest level of calibration, primarily using proactive human-computer interaction and multimodal perception to ultimately confirm the authenticity of an event. It is particularly suitable for situations where the first two levels of calibration cannot reach a clear conclusion. If the first two levels of calibration do not yield a clear result, the system sends a low-power alert tone via a hardware terminal module. A low-power alert tone is a sound that does not interfere with the normal environment but is sufficient to attract attention, such as a gentle voice prompt or a specific alert tone. Directional transmission means that the sound can be accurately transmitted to the area where the suspected event occurred. Within a preset time of 3-10 seconds, if a person's voice response or a one-button alarm trigger signal is detected, it is determined to be a real event. A voice response is a person's direct response to the alert tone, such as "I'm fine" or "Please help me." A one-button alarm trigger signal refers to the pressing of a physical or virtual alarm button set up in the area. If no response is detected, and the human infrared sensor detects that the person has left the area, it is determined to be a false alarm. If the person has already left the area, it can be reasonably inferred that the previous suspected event was a false alarm. If no response is detected but the person remains, a second audio acquisition and semantic analysis are initiated. This situation indicates that the person may be unable to respond or unwilling to respond. The system will then restart the audio acquisition and semantic analysis process for longer or more detailed listening and analysis.

[0081] Through the above embodiments, the three-level progressive calibration mechanism introduced in this application effectively solves the problem of false alarms or missed alarms that may be caused by a single semantic analysis result. The first-level calibration uses preliminary semantic probability and environmental features for rapid screening, quickly eliminating most low-confidence false alarms and avoiding unnecessary resource consumption. For suspected events with medium to high confidence, the second-level calibration uses a scenario-fine-tuned BERT model for deep semantic analysis, combining sentiment and intent keywords, and matching them with a dynamically updated scenario semantic library, greatly improving the accuracy of identifying the true intent of the event and effectively distinguishing between genuine requests for help and false triggers in daily communication. When the event still has uncertainty, the third-level calibration proactively sends a low-power alert tone and combines it with personnel interaction responses and multimodal perception data to achieve final confirmation of the event. Especially when personnel cannot proactively seek help or the situation is complex, it can ensure that no real emergency is missed through secondary collection and analysis. This layered, multimodal, and proactive interactive calibration strategy significantly improves the accuracy, reliability, and robustness of the intelligent monitoring system for privacy-sensitive areas, reduces the false alarm rate, and ensures timely response to real emergency events, thereby improving overall monitoring efficiency and security.

[0082] In some implementations, the AI ​​semantic analysis module employs a multi-classifier ensemble learning architecture to output quantitative scores corresponding to safety probability, suspected request for help probability, and irrelevant probability. Specifically, this AI semantic analysis module integrates five base classifiers: logistic regression, random forest, support vector machine, lightweight neural network, and XGBoost. These base classifiers each possess different learning mechanisms and advantages. For example, logistic regression excels at handling linearly separable problems and provides probabilistic interpretations; random forest effectively reduces overfitting risk and improves generalization ability by ensembled multiple decision trees; support vector machine performs classification in high-dimensional space by finding the optimal hyperplane; lightweight neural network can capture complex nonlinear relationships while maintaining computational efficiency; and XGBoost is an efficient and flexible gradient boosting algorithm that performs exceptionally well in processing structured data. Each base classifier performs parallel inference based on the same semantic feature vector, meaning they simultaneously receive semantic features extracted from structured text and independently generate their respective classification prediction results.

[0083] To fully leverage the strengths of each base classifier while mitigating their limitations, this application employs an improved AdaBoost algorithm to weightedly fuse the inference results of each base classifier. This improved AdaBoost algorithm dynamically allocates fusion weights based on the performance of each base classifier during training and its ability to recognize different samples. These fusion weights are dynamically optimized through cross-validation, ensuring the effectiveness and robustness of the fusion strategy. This allows the final quantitative score to comprehensively consider the judgments of multiple models, thereby improving overall accuracy.

[0084] Furthermore, to ensure the AI ​​semantic analysis module can continuously adapt to the ever-changing language patterns and semantic contexts within privacy-sensitive areas, this application designs a periodic model update mechanism. Specifically, every 24 hours, the model is incrementally fine-tuned using 500-1000 newly collected labeled text data. The incremental fine-tuning process employs a gradient accumulation strategy, which allows for simulating larger batch sizes with limited computational resources, thereby obtaining more stable gradient estimates and better model convergence performance. Simultaneously, the learning rate is dynamically adjusted to [5×10^6]. −6 1×10 −5 To adapt to the model state during the fine-tuning phase and avoid overfitting or training stagnation, the batch size is set to 16 to balance computational efficiency and the frequency of model updates.

[0085] By employing a multi-classifier ensemble learning architecture, this application effectively overcomes the limitations of single AI semantic analysis models in complex privacy-sensitive scenarios, which may suffer from insufficient generalization ability and poor robustness. It integrates five base classifiers—logistic regression, random forest, support vector machine, lightweight neural network, and XGBoost—and performs parallel inference based on the same semantic feature vectors. This enables the capture of semantic information from different dimensions, improving the depth and breadth of understanding text semantic features. Furthermore, an improved AdaBoost algorithm is used to weight and fuse the inference results of each base classifier, and cross-validation is used to dynamically optimize the fusion weights, ensuring the accuracy and reliability of the final quantitative score and effectively reducing the false positive and false negative rates. In addition, the model is incrementally fine-tuned every 24 hours. Combined with gradient accumulation and dynamic learning rates, the AI ​​semantic analysis module can continuously learn and adapt to the constantly changing language patterns and semantic context within privacy-sensitive areas, maintaining the real-time performance and advanced nature of the model. This significantly improves the accuracy and stability of the quantitative scores for safety probability, suspected assistance probability, and irrelevant probability, providing a more solid data foundation for subsequent false positive calibration and alarm decision-making.

[0086] In some implementations, the graded alarm signal is divided into three levels.

[0087] Specifically, tiered alarm signals refer to classifying alarm information into different levels based on the urgency and potential risk of an event, enabling the recipient to quickly identify the priority of the event and take appropriate action. This tiered mechanism avoids a "one-size-fits-all" approach to alarm handling, improving response efficiency and the rationality of resource allocation. For example, different notification methods, notification recipients, or subsequent processing procedures can be triggered based on different risk levels.

[0088] In this case, a Level 1 alarm signal corresponds to a suspected assistance probability score within the range of [0.5, 0.7), and calibration confirms that the current event poses no urgent risk. The system then only sends a notification to the terminal management module. This notification is typically presented in a non-intrusive manner, such as through log entries, low-priority notifications, or status updates, aiming to alert administrators to potential anomalies without requiring immediate emergency action, thus avoiding excessive intervention in non-urgent events.

[0089] A Level 2 alarm signal corresponds to a suspected distress call probability score within the range of [0.7, 0.9), and calibration confirms that the current event poses a potential risk. At this point, the system sends a more specific alarm message to the terminal management module and automatically initiates area video linkage acquisition. By linking video surveillance equipment within the area, real-time visual information can be obtained, providing managers with an intuitive view of the scene. This assists them in judging the authenticity and urgency of the event, thus providing a more comprehensive basis for subsequent decision-making.

[0090] A Level 3 alarm signal corresponds to a suspected emergency call probability score of 0.9 or higher, or, after verification by the calibration module, a confirmed emergency has occurred. At this highest risk level, the system simultaneously sends an alarm signal to the terminal management module and immediately activates the audible and visual alarm devices to alert on-site personnel or attract external attention. Simultaneously, the system pushes real-time monitoring data (such as audio and video streams) to relevant emergency response personnel to ensure the fastest and most comprehensive response and handling of the emergency.

[0091] Through the above embodiments, this application can finely classify alarm signals into three levels based on the probability score of suspected assistance and calibration results. This hierarchical alarm mechanism enables the system to adopt differentiated response strategies for events of different risk levels. Level 1 alarms only send a notification message, avoiding excessive intervention in non-emergency events; Level 2 alarms simultaneously send alarm information and initiate area video linkage acquisition, providing managers with more intuitive on-site information, which helps in rapid judgment and decision-making; Level 3 alarms trigger audible and visual alarm devices and push real-time monitoring data, ensuring that emergency events receive the fastest and most comprehensive response. This significantly improves the response efficiency and accuracy of the intelligent monitoring system for privacy-sensitive areas, effectively avoiding the negative impacts of false alarms or missed alarms, and protecting personnel safety and area order.

[0092] It should be noted that the method of this disclosure embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this disclosure embodiment, and the multiple devices will interact with each other to complete the method described.

[0093] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] like Figure 2 As shown, based on the same inventive concept and corresponding to any of the above embodiments, this embodiment proposes an intelligent monitoring system for privacy-sensitive areas based on AI semantic analysis. This system is applied to the above-mentioned intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis, and includes a data acquisition module 100, a preprocessing module 200, an analysis module 300, a calibration module 400, and an alarm module 500.

[0095] The acquisition module is responsible for the system's data input. Through the distributed audio acquisition unit and environmental sensing unit of the hardware terminal module, it synchronously collects audio data and environmental feature data from privacy-sensitive areas. The core function of this module is to ensure millisecond-level alignment of the acquired data, typically achieved through a timestamp synchronization protocol. Specifically, the acquisition module can coordinate different types of sensors, such as array microphones and various environmental sensors, to obtain comprehensive and accurate raw data. Its implementation may include integrating multiple communication interfaces to support data access from different sensors, and possessing data caching and preliminary verification functions to ensure data quality and transmission stability.

[0096] The preprocessing module receives the raw data output from the acquisition module and performs collaborative preprocessing on it. This module aims to improve the quality and usability of the data, laying the foundation for subsequent intelligent analysis. Collaborative preprocessing typically includes key steps such as noise suppression, effective signal extraction, and feature structuring. For example, advanced signal processing algorithms, such as deep learning-based noise reduction models, can be used to effectively remove environmental noise; effective speech segments can be accurately identified through voice activity detection (VAD) algorithms; and environmental feature data can be standardized to convert it into feature vectors of a unified format. This module ultimately outputs timestamped effective speech segments and standardized environmental feature vectors, ensuring data consistency in time and format.

[0097] The analysis module is the intelligent core of the system, responsible for deep semantic understanding of the preprocessed data. This module first converts valid speech segments into structured text using an end-to-end speech-to-text model. Subsequently, the AI ​​semantic analysis module extracts semantic features from the text and performs multi-dimensional semantic analysis based on contextual dependencies. This can be achieved by deploying high-performance natural language processing models, such as those based on the Transformer architecture, to perform deep semantic analysis and identify potential requests for help, conflicts, or unusual information. Finally, this module outputs quantitative scores corresponding to the probability of safety, the probability of suspected requests for help, and the probability of irrelevance, providing a quantitative basis for subsequent risk assessment.

[0098] The calibration module aims to improve system accuracy and reduce false alarm rates. Based on a quantitative score of the probability of suspected assistance output by the analysis module, standardized environmental feature vectors, and real-time collected behavioral feedback data, this module performs a three-level progressive calibration. Behavioral feedback data can include correlation data on personnel interaction responses and regional personnel flow data, providing crucial external information for the calibration process. The calibration module uses multi-dimensional data fusion and logical judgment to progressively verify or eliminate potential risk events. Its implementation may involve designing multi-level decision logic, combining different thresholds and rules to refine and correct the initial risk assessment, ensuring that only fully verified events trigger the final alarm.

[0099] The alarm module is the executor of the system's response mechanism. Based on the calibration results from the calibration module, this module outputs tiered alarm signals through the alarm decision module. Simultaneously, it synchronizes these tiered alarm signals and associated monitoring data to the terminal management module. The alarm module can be implemented by configuring multiple alarm levels, such as alert messages, level two alarms, and level three alarms, and triggering corresponding response actions based on different alarm levels, such as sending notifications, activating video linkage, and activating audible and visual alarm devices. This module ensures that when a real risk is detected, it can promptly and accurately issue alerts to relevant parties and provide necessary on-site information.

[0100] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, in implementing this disclosure, the functions of each module can be implemented in one or more software and / or hardware.

[0101] The system described in the above embodiments is used to implement the corresponding intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0102] Based on the same inventive concept, corresponding to any of the above embodiments, this embodiment also discloses a computer-readable storage device that tangibly stores computer program instructions, which, when executed by one or more processors, cause the processors to perform the above-described intelligent monitoring method for privacy-sensitive area security based on AI semantic analysis.

[0103] Specifically, a computer-readable storage device (CHDD) refers to a non-transitory medium capable of storing computer program instructions, data, or other information. Its function is to provide persistent storage space for computer programs, ensuring that program instructions are retained even after power is lost and can be read and executed by the processor when needed. Common CHDDs include, but are not limited to, hard disk drives (HDDs), solid-state drives (SSDs), random access memory (RAM), read-only memory (ROM), flash memory, optical discs, and various forms of non-volatile memory. Choosing the appropriate storage type requires comprehensive consideration of factors such as storage capacity, read / write speed, cost, reliability, and environmental adaptability.

[0104] "Tangibly storing computer program instructions" emphasizes that program instructions exist in physical form on a storage medium, rather than merely as a transient data stream or abstract concept. These instructions are organized according to a specific encoding format (such as binary code) and can be directly recognized and interpreted by the processor. Tangible storage ensures that the intelligent monitoring method for securing privacy-sensitive areas based on AI semantic analysis can be solidified, distributed, and deployed across different hardware platforms, achieving standardization and reproducibility.

[0105] The phrase "execution by one or more processors" refers to the processor acting as the core computing unit of the computer system, responsible for interpreting and executing computer program instructions. The program instructions stored in the memory can be executed by a single central processing unit, graphics processing unit, digital signal processor, or application-specific integrated circuit (ASIC), or a combination of these units. The processor drives the entire monitoring method by reading instructions from memory and performing corresponding arithmetic and logical operations, data transmission, and control flow operations based on the instruction content. Multiprocessor systems can process tasks in parallel, improving computational efficiency and response speed, and are particularly suitable for computationally intensive tasks such as AI semantic analysis.

[0106] By tangibly storing the aforementioned intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis in the form of computer program instructions in a computer-readable storage device and executing it through one or more processors, this application provides a concrete, deployable, and efficient implementation method. This implementation method solidifies the abstract method flow into an executable software entity, ensuring the stability and consistent operation of the monitoring method across different hardware platforms. The processor's execution of the program instructions can efficiently complete complex computational tasks such as data acquisition, collaborative preprocessing, AI semantic analysis, multi-dimensional false alarm calibration, and tiered alarm decision-making, thereby ensuring that the monitoring system can continuously and accurately perform intelligent monitoring of privacy-sensitive areas. Furthermore, this storage and execution mechanism greatly improves the system's maintainability, upgradeability, and deployment flexibility, facilitating iterative optimization and functional expansion of the monitoring method. It effectively solves the problems of continuity, maintainability, and deployment efficiency limitations that the method may face in practical applications, ensuring the long-term stable operation and high performance of the monitoring system.

[0107] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application. The scope of this application is determined by the scope of the claims.

Claims

1. An intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis, characterized in that, include: The distributed audio acquisition unit and environmental perception unit of the hardware terminal module synchronously collect audio data and environmental feature data of privacy-sensitive areas, achieving millisecond-level alignment of the collected data based on a timestamp synchronization protocol. The distributed audio acquisition unit adopts a master-slave architecture design, including one master control device and 1-8 array microphone sub-nodes. The master control device establishes a communication link with each array microphone sub-node through an enhanced ASN bus to achieve synchronous data transmission and clock calibration. The master control device integrates a high signal-to-noise ratio differential amplifier circuit and an adaptive noise reduction chip, and uses an adaptive beamforming algorithm to focus on the target speech direction and suppress sidelobe interference. The distributed audio acquisition unit supports a configurable sampling rate of 16kHz-32kHz and an adaptive bit depth of 16bit-32bit. The audio data and the environmental feature data are subjected to collaborative preprocessing, which includes noise suppression, effective signal extraction and feature structuring, and outputs effective speech segments with timestamps and standardized environmental feature vectors. The effective speech segments are converted into structured text using an end-to-end speech-to-text model. The AI ​​semantic analysis module extracts the semantic feature vectors of the text and performs multi-dimensional semantic analysis based on contextual dependencies, outputting quantitative scores corresponding to safety probability, suspected request for help probability, and irrelevant probability. The AI ​​semantic analysis module adopts the BERT model architecture, including: model optimization through INT8 quantization and structured pruning, with the pruning rate controlled between 30% and 50%; introduction of a domain-adaptive pre-training mechanism, fine-tuning based on privacy-region security monitoring scenario corpus, and optimization of the semantic feature extraction layer; wherein the scenario corpus includes request for help statements, conflict statements, and daily communication statements; during contextual association analysis, a bidirectional attention mechanism is used to strengthen the feature weights of key semantics, and semantic similarity calculation is used to match with the scenario semantic library, outputting quantitative scores for safety probability, suspected request for help probability, and irrelevant probability. Based on the quantitative score of the suspected request for help probability, the standardized environmental feature vector, and behavioral feedback data, a three-level progressive calibration is performed through a multi-dimensional false alarm calibration module; the behavioral feedback data consists of real-time personnel interaction response correlation data and regional personnel flow data collected during the calibration process; the three-level progressive calibration includes: Level 1 Calibration: Set the first threshold range for the quantification score of suspected help request probability to [0.3, 0.5], the second threshold range to [0.5, 0.8], and the third threshold range to [0.8, 1.0]. If the quantification score of suspected help request probability is within the first threshold range, and the decibel sensor detects a sound intensity < 60dB and the accelerometer does not detect abnormal vibration, it is judged as a false alarm and marked. If the quantification score of suspected help request probability is within the second threshold range, and the decibel sensor detects a sound intensity ≥ 85dB, or the accelerometer detects abnormal vibration, it proceeds to Level 2 Calibration. If the quantification score of suspected help request probability is within the third threshold range, it directly proceeds to Level 3 Calibration. Secondary calibration involves calling a scenario-tuned BERT model to perform semantic parsing on the structured text, extracting the text's sentiment characteristics and intent keywords, and performing intent matching in conjunction with a dynamically updated scenario semantic library. If the matching degree is ≥80%, it is judged as a suspected real event; otherwise, it is judged as a false alarm. The three-level calibration process involves sending a low-power alert tone via a hardware terminal module if the first two levels of calibration do not yield clear results. If a voice response or a one-click alarm trigger signal is detected within a preset time of 3-10 seconds, it is determined to be a real event. If no response is detected and the human infrared sensor detects that the person has left the area, it is determined to be a false alarm. If no response is detected but the person remains, a second audio acquisition and semantic analysis process is initiated. Based on the calibration results, the alarm decision module outputs a graded alarm signal and synchronizes the graded alarm signal and associated monitoring data to the terminal management module.

2. The intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis according to claim 1, characterized in that, The environmental perception unit adopts a multi-sensor fusion architecture, integrating a triaxial accelerometer, a high-precision decibel sensor, a human infrared sensor, and an ambient light sensor. The triaxial accelerometer is used to detect abnormal vibration signals within the area and output vibration frequency and amplitude characteristics; The high-precision decibel sensor distinguishes the sound intensity of normal conversation from abnormal shouting through a preset dual-threshold mechanism; the human infrared sensor, combined with ambient light sensor data, eliminates interference from non-personnel heat sources and confirms the status of personnel in the monitoring area; the environmental feature data collected by the environmental perception unit is fused and denoised using a Kalman filter algorithm to output a standardized environmental feature vector.

3. The intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis according to claim 1, characterized in that, The collaborative preprocessing includes: An LSTM-based AI noise reduction model is used to eliminate environmental noise. The AI ​​noise reduction model is trained on typical noise samples in a privacy region. Typical noises include air conditioning noise, footsteps, and equipment operating noise. A dual-threshold endpoint detection algorithm is used to segment audio data to distinguish between speech segments and non-speech segments. The starting threshold for speech segments is set to -35dB to -25dB, and the ending threshold is set to -45dB to -35dB. Alignment between audio data and environmental feature data is achieved based on UTC timestamps, with a time deviation of ≤10ms; min-max standardization is performed on the environmental feature data to generate a standardized feature vector in the 0-1 range, and risk level is marked according to preset rules.

4. The intelligent monitoring method for privacy-sensitive area security based on AI semantic analysis according to claim 1, characterized in that, The AI ​​semantic analysis module employs a multi-classifier ensemble learning architecture to output quantitative scores corresponding to safety probability, suspected request for help probability, and irrelevant probability, including: The AI ​​semantic analysis module integrates five base classifiers: logistic regression, random forest, support vector machine, lightweight neural network, and XGBoost. Each base classifier performs parallel inference based on the same semantic feature vector. An improved AdaBoost algorithm is used to weight and fuse the inference results of each base classifier, and the fusion weights are dynamically optimized through cross-validation. Every 24 hours, the model is incrementally fine-tuned using 500-1000 newly collected labeled text data points. The fine-tuning process employs a gradient accumulation strategy, and the learning rate is dynamically adjusted to [5×10]. −6 1×10 −5 Set batchsize to 16.

5. The intelligent monitoring method for privacy-sensitive area security based on AI semantic analysis according to claim 1, characterized in that, The graded alarm signal is divided into three levels: A Level 1 alarm signal corresponds to a suspected request for help probability score of [0.5, 0.7) and calibration confirms there is no emergency risk; only a prompt message is sent to the terminal management module. A level 2 alarm signal corresponds to a suspected request for help probability score of [0.7, 0.9) and calibration confirms the existence of potential risks. An alarm message is sent to the terminal management module and regional video linkage acquisition is initiated. A Level 3 alarm signal corresponds to a suspected assistance probability score ≥ 0.9 or a confirmed emergency event. Simultaneously, an alarm signal is sent to the terminal management module, activating the audible and visual alarm device and pushing real-time monitoring data from the site.

6. An intelligent monitoring system for privacy-sensitive areas based on AI semantic analysis, applied to the intelligent monitoring method for privacy-sensitive areas based on AI semantic analysis as described in any one of claims 1 to 5, characterized in that, include: The acquisition module is used to synchronously acquire audio data and environmental feature data of privacy-sensitive areas through the distributed audio acquisition unit and environmental perception unit of the hardware terminal module, and to achieve millisecond-level alignment of the acquired data based on a timestamp synchronization protocol. The distributed audio acquisition unit adopts a master-slave architecture design, including one master control device and 1-8 array microphone sub-nodes. The master control device establishes a communication link with each array microphone sub-node through an enhanced ASN bus to achieve synchronous data transmission and clock calibration. The master control device integrates a high signal-to-noise ratio differential amplifier circuit and an adaptive noise reduction chip, and uses an adaptive beamforming algorithm to focus on the target speech direction and suppress sidelobe interference. The distributed audio acquisition unit supports a configurable sampling rate of 16kHz-32kHz and an adaptive bit depth of 16bit-32bit. The preprocessing module is used to perform collaborative preprocessing on the audio data and the environmental feature data. The collaborative preprocessing includes noise suppression, effective signal extraction and feature structuring, and outputs effective speech segments with timestamps and standardized environmental feature vectors. The analysis module converts the effective speech segments into structured text using an end-to-end speech-to-text model. The AI ​​semantic analysis module extracts the semantic feature vectors from the text and performs multi-dimensional semantic analysis based on contextual dependencies, outputting quantitative scores corresponding to safety probability, suspected request for help probability, and irrelevant probability. The AI ​​semantic analysis module adopts a BERT model architecture, including: model optimization through INT8 quantization and structured pruning, with the pruning rate controlled between 30% and 50%; introduction of a domain-adaptive pre-training mechanism, fine-tuning based on privacy-region security monitoring scenario corpus, and optimization of the semantic feature extraction layer; wherein the scenario corpus includes request for help statements, conflict statements, and daily communication statements; during contextual association analysis, a bidirectional attention mechanism is used to strengthen the feature weights of key semantics, and semantic similarity calculation is used to match with the scenario semantic library, outputting quantitative scores for safety probability, suspected request for help probability, and irrelevant probability. The calibration module is used to perform a three-level progressive calibration based on the quantitative score of the suspected request for help probability, the standardized environmental feature vector, and real-time behavioral feedback data, through a multi-dimensional false alarm calibration module; the behavioral feedback data consists of real-time personnel interaction response correlation data and regional personnel flow data collected during the calibration process; the three-level progressive calibration includes: Level 1 Calibration: Set the first threshold range for the quantification score of suspected help request probability to [0.3, 0.5], the second threshold range to [0.5, 0.8], and the third threshold range to [0.8, 1.0]. If the quantification score of suspected help request probability is within the first threshold range, and the decibel sensor detects a sound intensity < 60dB and the accelerometer does not detect abnormal vibration, it is judged as a false alarm and marked. If the quantification score of suspected help request probability is within the second threshold range, and the decibel sensor detects a sound intensity ≥ 85dB, or the accelerometer detects abnormal vibration, it proceeds to Level 2 Calibration. If the quantification score of suspected help request probability is within the third threshold range, it directly proceeds to Level 3 Calibration. Secondary calibration involves calling a scenario-tuned BERT model to perform semantic parsing on the structured text, extracting the text's sentiment characteristics and intent keywords, and performing intent matching in conjunction with a dynamically updated scenario semantic library. If the matching degree is ≥80%, it is judged as a suspected real event; otherwise, it is judged as a false alarm. The three-level calibration process involves sending a low-power alert tone via a hardware terminal module if the first two levels of calibration do not yield clear results. If a voice response or a one-click alarm trigger signal is detected within a preset time of 3-10 seconds, it is determined to be a real event. If no response is detected and the human infrared sensor detects that the person has left the area, it is determined to be a false alarm. If no response is detected but the person remains, a second audio acquisition and semantic analysis process is initiated. The alarm module is used to output graded alarm signals through the alarm decision module based on the calibration results, and to synchronize the graded alarm signals and associated monitoring data to the terminal management module.

7. A computer-readable storage device that tangibly stores computer program instructions, which, when executed by one or more processors, cause the processors to perform any one of the intelligent monitoring methods for privacy-sensitive areas based on AI semantic analysis as described in any one of claims 1 to 5.

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