Intelligent monitoring system for drug rehabilitation based on multi-modal data fusion

Abstract

The application discloses a drug rehabilitation intelligent monitoring system based on multi-modal data fusion, and relates to the technical field of intelligent monitoring. The fingerprint generation module acquires the real-time geographic position of a target object, extracts specific frequency band energy features and specific pulse sequence features in an environmental voiceprint through an acoustic sensor, and generates a voiceprint fingerprint vector. The request sending module generates a behavior judgment conclusion according to a hierarchical biological verification evidence chain, and sends a verification request containing an environmental stress intensity value, a physiological behavior paradox index and the behavior judgment conclusion to a monitoring terminal through an encryption communication protocol. The application calculates the environmental stress intensity value through dynamic spatial position relationship data, realizes accurate quantification of environmental risk exposure, uses the environmental stress intensity value as a benchmark for physiological behavior analysis, provides a basis for coupling analysis of 'environmental induction-physiological response', and avoids resource loss caused by invalid biological monitoring.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system for drug rehabilitation based on multimodal data fusion. Background Technology

[0002] In recent years, the field of drug rehabilitation monitoring has gradually adopted multimodal data fusion technology to improve monitoring accuracy by integrating acoustic environment analysis and biosignal detection. Voiceprint recognition technology can effectively extract energy features in specific frequency bands and, combined with geolocation, achieve preliminary screening of high-risk locations. Meanwhile, the maturity of physiological signal analysis methods such as electromyography and skin conductance provides a technological foundation for identifying physiological abnormalities in drug users. Through a hierarchical verification mechanism, multimodal monitoring gradually filters out false alarms, forming a closed-loop monitoring chain from environmental risk perception to physiological behavior determination.

[0003] Existing technologies have shortcomings in the dynamic coupling analysis of environmental stress and physiological behavioral responses. The calculation of environmental stress intensity mostly relies on static thresholds, which cannot quantify the nonlinear interaction effects of environmental exposure duration, location type, and individual substance dependence. At the same time, when the physiological behavior paradox index is generated, the temporal alignment of electromyography and facial expression signals lacks a physiological constraint mechanism, resulting in a high rate of missed detection of short-term camouflage behavior. It is difficult to capture the dynamic game characteristics of "environmental induction-physiological concealment" in the process of drug relapse, which has become the core bottleneck to improving the accuracy of monitoring. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an intelligent monitoring system for drug rehabilitation based on multimodal data fusion to address the problem of insufficient dynamic coupling analysis of environmental stress and physiological behavioral responses in existing drug rehabilitation monitoring systems.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides an intelligent monitoring system for drug rehabilitation based on multimodal data fusion, comprising: a fingerprint generation module, which acquires the real-time geographical location of the target object and extracts specific frequency band energy features and specific pulse sequence features from the environmental acoustic fingerprint using an acoustic sensor to generate an acoustic fingerprint vector; a pressure calculation module, which matches the acoustic fingerprint vector with predefined inherent pressure benchmark data of high-risk locations to generate dynamic spatial positional relationship data between the target object and the high-risk location, calculates the environmental pressure intensity value, and activates a biosignal acquisition command when the environmental pressure intensity value exceeds a preset intensity threshold; and an electromyography (EMG) analysis module, which responds to the biosignal acquisition command, simultaneously acquires the EMG signal waveform and facial expression feature intensity, analyzes the dynamic change characteristics of the rising phase of the EMG signal waveform, and generates an EMG signal. The system comprises the following modules: Activity Intensity; Paradox Calculation Module: This module aligns the intensity of electromyographic (EMG) activity with the intensity of facial expression features in a temporal sequence, calculates the deviation between the two, and generates a physiological behavior paradox index. When the physiological behavior paradox index exceeds a preset confidence threshold, a verification mechanism is triggered. Evidence Chain Formation Module: In response to the verification mechanism, this module initiates skin conductance response detection to generate abnormal skin conductance characteristics, triggers body fluid analysis to generate abnormal body fluid marker characteristics, and simultaneously initiates salivary enzyme measurement to generate abnormal salivary enzyme activity characteristics, forming a hierarchical biological verification evidence chain. Request Sending Module: Based on the hierarchical biological verification evidence chain, this module generates a behavioral judgment conclusion and sends a verification request containing environmental pressure intensity values, the physiological behavior paradox index, and the behavioral judgment conclusion to the monitoring terminal via an encrypted communication protocol.

[0007] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for generating the voiceprint fingerprint vector are as follows: The system acquires the real-time geographical location of the target object and uses a directional microphone array to synchronously collect environmental acoustic signals, perform noise suppression, filter out interference from non-target frequency bands, and generate a clean voiceprint signal. Separate specific mid-to-high frequency bands and specific low frequency bands associated with drug environment from pure voiceprint signals, and extract energy features of specific frequency bands; A dynamic pulse detection mechanism is used to identify non-steady burst pulse clusters in pure voiceprint signals and extract specific pulse sequence features. By integrating specific frequency band energy characteristics, specific pulse sequence characteristics, and the real-time geographical location of the target object, an acoustic fingerprint vector is generated.

[0008] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific frequency band energy characteristics are specifically used to identify the mid-to-high frequency band collision sound of drug preparation and consumption behavior, and the specific low frequency band is used to identify the frequency band of environmental vibration noise. The specific pulse sequence characteristics refer to the non-steady-state sudden pulse clusters caused by the physiological loss of control of drug users, which locks in physiologically abnormal pulses.

[0009] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for generating dynamic spatial location relationship data between the target object and high-risk locations are as follows: The target geocoding in the voiceprint fingerprint vector is analyzed, and the spatially associated high-risk locations and acoustic pressure benchmark features of high-risk locations are extracted from the predefined high-risk location inherent pressure benchmark data. Based on the energy characteristics of specific frequency bands and specific pulse sequence characteristics, the difference between the target object and the acoustic pressure benchmark characteristics of high-risk locations is quantified to generate dynamic spatial location relationship data between the target object and the high-risk locations.

[0010] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for activating the biosignal acquisition command are as follows: Extract the location pressure coupling intensity and target dwell time from dynamic spatial location relationship data, and extract substance dependence parameters from pre-stored drug addict files; Based on the location pressure coupling strength, target dwell time and material dependence parameters, the environmental pressure intensity value is generated through nonlinear coupling calculation. When the environmental pressure intensity value exceeds the preset intensity threshold, the biosignal acquisition command is activated; otherwise, the voiceprint monitoring state is maintained and the voiceprint fingerprint vector is regenerated.

[0011] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for generating electromyographic activity intensity are as follows: In response to the biosignal acquisition command, the system acquires the electromyographic signal waveform of the target object, extracts the time-domain segment of the rising phase of the electromyographic signal waveform, and captures the intensity of facial expression features. The time-domain segment of the rising phase of the electromyographic signal waveform is mapped to the phase space for trajectory reconstruction, and the maximum Lyapunov exponent is calculated as the chaotic response exponent. The chaotic response index is fused with the facial expression feature intensity input tensor cross-attention mechanism to output the electromyographic activity intensity.

[0012] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the trigger verification mechanism comprises the following specific steps. Based on the intensity of electromyographic activity and the intensity of facial features, a physiologically constrained dynamic time warp algorithm is used to perform temporal alignment and generate the optimal alignment path. The dynamic deviation between electromyographic activity intensity and facial expression feature intensity is calculated by entropy-weighted integral along the optimal alignment path to generate a physiological behavior paradox index. When the physiological behavior paradox index exceeds the preset credibility threshold, the verification mechanism is triggered. When the physiological behavior paradox index does not exceed the preset credibility threshold, the monitoring state is maintained and the voiceprint fingerprint vector is regenerated.

[0013] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for initiating skin conductance response detection to generate abnormal skin conductance characteristics are as follows: The response verification mechanism synchronously acquires multi-channel skin conductance signals and uses adaptive wavelet filtering to eliminate motion artifacts and generate clean conductance signals. The system calls a predefined drug type response template, performs correlation comparison between the fast sympathetic phase and the slow endocrine phase of the clean conduction signal, generates a biphase delay difference, and performs multi-channel coordinated fusion to output abnormal skin conductance characteristics.

[0014] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for forming a hierarchical biological verification evidence chain are as follows: Based on the abnormal characteristics of skin conductance, body fluid analysis is triggered and salivary enzyme measurement is initiated. The concentration of body fluid markers and salivary enzyme activity are coupled in real time to achieve antagonistic effects, and the rate of change of antagonistic factors is output. By integrating the rate of change of antagonistic factors with abnormal skin conductance characteristics, a hierarchical chain of biologically validated evidence is formed.

[0015] As a preferred embodiment of the intelligent monitoring system for drug rehabilitation based on multimodal data fusion described in this invention, the specific steps for sending a verification request containing environmental stress intensity values, physiological behavior paradox index, and behavioral judgment conclusions are as follows: Based on the abnormal characteristics of skin conductance, body fluid markers, and salivary enzyme activity in the hierarchical biological verification evidence chain, a behavioral judgment conclusion is generated through the coordinated fusion of multiple evidences. The encryption key strength is dynamically adjusted based on the risk level of the behavioral judgment conclusion. The environmental pressure intensity value, physiological behavior paradox index and behavioral judgment conclusion are spatiotemporally bound and encrypted to generate a verification request containing encrypted data packets, which is sent to the monitoring terminal through an encrypted communication protocol.

[0016] The beneficial effects of this invention are as follows: By calculating the environmental pressure intensity value through dynamic spatial location relationship data, the precise quantification of environmental risk exposure is achieved. Based on the nonlinear fusion of location pressure coupling strength, target dwell time, and substance dependence parameters, the cumulative effect of environmental pressure in different drug use scenarios is quantified. Through an intelligent activation mechanism with a preset intensity threshold, biosignal acquisition is triggered only when environmental risk exceeds limits, reducing power consumption. The output environmental pressure intensity value serves as a benchmark for physiological behavior analysis, providing a basis for the coupled analysis of "environment-induced physiological response" and avoiding resource waste caused by ineffective biomonitoring. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a module diagram of an intelligent monitoring system for drug rehabilitation based on multimodal data fusion.

[0019] Figure 2 The flowchart for generating voiceprint fingerprint vectors.

[0020] Figure 3 This is a flowchart for generating and determining environmental pressure intensity values.

[0021] Figure 4 A flowchart for the formation of a hierarchical biological verification evidence chain. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4This is one embodiment of the present invention, which provides an intelligent monitoring system for drug rehabilitation based on multimodal data fusion, comprising the following steps: The fingerprint generation module obtains the real-time geographical location of the target object, extracts specific frequency band energy features and specific pulse sequence features from the environmental acoustic fingerprint using an acoustic sensor, and generates an acoustic fingerprint vector.

[0026] The system acquires the real-time geographical location of the target object and uses a directional microphone array to synchronously collect environmental acoustic signals, perform noise suppression, filter out interference from non-target frequency bands, and generate a clean voiceprint signal. Specifically, the real-time geographical location of the target object is obtained through global positioning, and the directional microphone array collects acoustic signals in the environment in an array form; Noise suppression of ambient acoustic signals is achieved through an adaptive filter. The adaptive filter dynamically adjusts the filtering parameters according to the background noise spectrum to eliminate steady-state noise interference. By filtering out non-target frequency band interference through a digital bandpass filter, the digital bandpass filter retains only the target frequency band range related to the drug environment, removes non-target frequency band signals, and generates a clean voiceprint signal.

[0027] Separate specific mid-to-high frequency bands and specific low frequency bands associated with drug environment from pure voiceprint signals, and extract energy features of specific frequency bands; Specifically, the pure voiceprint signal is converted into a frequency domain signal by using Fast Fourier Transform, separating specific mid-to-high frequency bands and specific low frequency bands; Specific mid-to-high frequency bands correspond to the frequency range of collision sounds of drug manufacturing and consumption devices, and specific low frequency bands correspond to the frequency range of environmental vibration noise. By obtaining the average energy value of each separated frequency band, the energy characteristics of specific frequency bands are extracted.

[0028] Examples: A specific mid-to-high frequency band is set to the 2kHz to 5kHz band, for example, to capture the clinking sound of a lighter or glassware; a specific low frequency band is set to the 50Hz to 150Hz band, for example, to identify low-frequency noise such as footsteps or object vibrations.

[0029] A dynamic pulse detection mechanism is used to identify non-steady burst pulse clusters in pure voiceprint signals and extract specific pulse sequence features. Specifically, based on the amplitude fluctuation characteristics of the pure voiceprint signal, a dynamic amplitude threshold is set in real time, and the amplitude of the pure voiceprint signal is scanned. When a signal segment that continuously exceeds the dynamic amplitude threshold appears, it is identified as a pulse cluster candidate. Identify non-steady-state burst pulse clusters in pure voiceprint signals. These non-steady-state burst pulse clusters are abnormal acoustic events caused by the physiological loss of control of drug users. Verify whether the pulse cluster candidates meet the non-steady-state characteristics. If a non-steady-state burst pulse cluster that meets the non-steady-state characteristics is detected, the specific pulse sequence feature is marked as present; otherwise, it is marked as non-existent.

[0030] By integrating specific frequency band energy characteristics, specific pulse sequence characteristics, and the real-time geographical location of the target object, an acoustic fingerprint vector is generated.

[0031] Specifically, by using vector concatenation, the energy value sequence of specific frequency band energy characteristics, the pulse parameter sequence of specific pulse sequence characteristics, and the latitude and longitude coordinates of the real-time geographical location of the target object are combined into a multi-dimensional vector to generate a voiceprint fingerprint vector.

[0032] It should be noted that specific frequency band energy characteristics are used to identify the collision sounds of mid-to-high frequency detection devices used for drug preparation and consumption, and specific low frequency bands are used to identify environmental vibration noise. Specific pulse sequence characteristics refer to non-steady-state sudden pulse clusters caused by the physiological loss of control of drug users, and lock in physiological abnormal pulses.

[0033] The pressure calculation module matches the voiceprint vector with the predefined inherent pressure benchmark data of high-risk locations to generate dynamic spatial location relationship data between the target object and the high-risk location. Based on the dynamic spatial location relationship data between the target object and the high-risk location, it calculates the environmental pressure intensity value. When the environmental pressure intensity value exceeds the preset intensity threshold, it activates the biosignal acquisition command.

[0034] The target geocoding in the voiceprint fingerprint vector is analyzed, and the spatially associated high-risk locations and acoustic pressure benchmark features of high-risk locations are extracted from the predefined high-risk location inherent pressure benchmark data. It should be noted that the predefined high-risk location inherent pressure reference data refers to a set of acoustic characteristic reference values ​​that are pre-stored and associated with high-risk locations for drug activities, including energy reference values ​​for specific frequency bands and pulse sequence reference states.

[0035] Specifically, latitude and longitude coordinate data are separated from the voiceprint fingerprint vector and used as the target geocode. Predefined high-risk location inherent pressure benchmark data are queried, and high-risk locations with a straight-line distance from the target geocode that is less than a preset spatial association threshold are selected. The acoustic pressure reference features of the high-risk locations are extracted from the matched high-risk location records, including specific mid-to-high frequency energy reference values, specific low frequency energy reference values, and pulse sequence reference states.

[0036] It should be noted that the preset spatial association threshold is set based on the effective range of voiceprint and fingerprint, with an example value of 200 meters.

[0037] Based on the energy characteristics of specific frequency bands and specific pulse sequence characteristics, the difference between the target object and the acoustic pressure benchmark characteristics of high-risk locations is quantified to generate dynamic spatial location relationship data between the target object and the high-risk locations.

[0038] Specifically, extract specific mid-to-high frequency band energy feature values ​​from the energy features of a specific frequency band, and use the ratio of the specific mid-to-high frequency band energy feature value to the corresponding mid-to-high frequency band reference value of the acoustic pressure reference feature as the mid-to-high frequency band energy difference value; Extract specific low-frequency band energy feature values ​​from the energy features of a specific frequency band, and use the ratio of the specific low-frequency band energy feature value to the corresponding low-frequency band reference value as the low-frequency band energy difference value; Extract the specific pulse sequence state from the specific pulse sequence features, compare the specific pulse sequence state with the pulse sequence reference state of the acoustic pressure reference features. If both are present or both are absent, output a pulse sequence difference value of 0; otherwise, output 1. The energy difference values ​​of the mid-to-high frequency band, the energy difference values ​​of the low frequency band, and the pulse sequence difference values ​​are combined into a three-dimensional vector to generate dynamic spatial location relationship data between the target object and the high-risk location.

[0039] Extract the location pressure coupling intensity and target dwell time from dynamic spatial location relationship data, and extract substance dependence parameters from pre-stored drug addict files; It should be noted that the pre-stored drug addict file refers to the pre-stored qualitative parameter values ​​that store the drug rehabilitation information of the target individual.

[0040] Specifically, the site pressure coupling strength is extracted from the energy difference values ​​in the mid-to-high frequency bands; The target dwell time is obtained by accumulating the continuous generation time of the voiceprint fingerprint vector, with the starting point being the moment when the voiceprint fingerprint vector associated with the current high-risk location is first detected.

[0041] By querying the pre-stored drug addict file database, the pre-stored qualitative parameter values ​​bound to the target object are obtained, and the substance dependence parameter is obtained.

[0042] Specifically, based on the location pressure coupling strength, target dwell time, and material dependence parameters, an environmental pressure intensity value is generated through nonlinear coupling calculation, expressed as:

[0043] ; In the formula, Indicates the environmental pressure intensity value. Indicates the location pressure coupling strength. Represents the natural logarithm function. Indicates the duration of the stay at the target. This represents the substance dependence coefficient.

[0044] It should be noted that, Dimensionless The dimension is s. It is dimensionless, and the final output is... Since it is dimensionless, we maintain dimensional consistency. The substance dependence coefficient is determined based on the type of drug dependence recorded in the pre-stored drug addict file. An example value is: (No drug dependence) (Cannabis dependence) (Heroin or methamphetamine dependence).

[0045] When the environmental pressure intensity value exceeds the preset intensity threshold, the biosignal acquisition command is activated; otherwise, the voiceprint monitoring state is maintained and the voiceprint fingerprint vector is regenerated.

[0046] It should be noted that the preset intensity threshold is set based on the nonlinear coupling calculation result of the environmental pressure intensity value, and the example value is 5.0.

[0047] Specifically, the environmental pressure intensity value is directly compared with a preset intensity threshold: If the environmental pressure intensity value is greater than the preset intensity threshold, the biosignal acquisition command is activated; If the environmental pressure intensity value is less than or equal to the preset intensity threshold, the voiceprint monitoring state is maintained, and the voiceprint fingerprint vector is regenerated, including obtaining the real-time geographical location of the target object, extracting specific frequency band energy features, extracting specific pulse sequence features, and fusing them to generate the voiceprint fingerprint vector.

[0048] The electromyography (EMG) analysis module responds to biosignal acquisition commands, synchronously acquires EMG signal waveforms and facial expression feature intensity, analyzes the dynamic changes in the rising phase of the EMG signal waveform, and generates EMG activity intensity. In response to the biosignal acquisition command, the system acquires the electromyographic signal waveform of the target object, extracts the time-domain segment of the rising phase of the electromyographic signal waveform, and captures the intensity of facial expression features. Specifically, when the biosignal acquisition command is activated, the electromyography (EMG) waveform of the target object is synchronously acquired through the EMG sensor; The starting point of the rising edge of the electromyographic signal waveform is detected, and a time-domain signal segment with a fixed time window centered on the starting point is taken as the time-domain segment of the rising phase of the electromyographic signal waveform. Simultaneously, the facial video stream of the target object is captured by a facial image sensing device, and the root mean square value of the predefined muscle group movement intensity is extracted as the expression feature intensity.

[0049] It should be noted that the root mean square value of the predefined muscle group movement intensity refers to the statistical measure of the fluctuation amplitude of the movement intensity of the orbicularis oculi and orbicularis oris muscles of the target object calculated by the facial image sensing device.

[0050] Specifically, the time-domain segment of the rising phase of the electromyographic signal waveform is mapped to the phase space for trajectory reconstruction, and the maximum Lyapunov exponent is calculated as the chaotic response exponent. The expression is as follows:

[0051] ;

[0052] In the formula, This represents the maximum Lyapunov index. This indicates the duration of the rising phase of the electromyographic signal waveform. This represents the reciprocal of the rise phase duration of the electromyographic signal waveform. Indicates to arrive Perform definite integrals within the range. This represents the trajectory vector of the electromyographic signal waveform in phase space. Represents a time variable. Indicates the rate of change of the trajectory. Indicates the magnitude of the rate of change of the trajectory. Represents the time derivative. Indicates the intensity of facial features. The L2 norm represents the intensity of facial expression features. This represents the electromyographic amplitude normalization factor.

[0053] It should be noted that, The dimension of s -1 , The dimension of is mV·s. The dimension is s. The dimension of is mV. The dimension is s. Dimensionless The dimension is mV. Through definite integral and elimination using the electromyographic amplitude normalization factor, the final... Since it is dimensionless, we maintain dimensional consistency. The electromyography amplitude normalization factor is derived from the calibration of the maximum physiological amplitude range of the electromyography signal of the target object, with an example value of 500.

[0054] The chaotic response index is fused with the facial expression feature intensity input tensor cross-attention mechanism to output the electromyographic activity intensity.

[0055] Specifically, the chaotic response index and facial expression feature intensity are converted into three-dimensional tensors to generate scalar forms of the chaotic response index and facial expression feature intensity. The scalar form of the chaotic response exponent is copied and expanded into a square matrix, and channel dimension identifiers are added to form a three-dimensional tensor structure. Similarly, the scalar form of the facial expression feature intensity is copied and expanded into a square matrix, and channel dimension identifiers are added to form a three-dimensional tensor structure. The interaction weight matrix between the chaotic response exponent tensor and the facial feature intensity tensor is calculated through the tensor cross-attention mechanism. The attention score matrix is ​​generated by dot product and the weight distribution is obtained by normalization. The facial expression feature intensity tensor is weighted and fused according to the weight distribution, and the fused feature vector is output. The fused feature vector is then mapped to electromyographic activity intensity through a linear projection layer.

[0056] The paradox calculation module aligns the intensity of electromyographic activity with the intensity of facial expression features in a temporal sequence, calculates the deviation between the intensity of electromyographic activity and the intensity of facial expression features to generate a physiological behavior paradox index, and triggers a verification mechanism when the physiological behavior paradox index exceeds a preset credibility threshold.

[0057] Based on the intensity of electromyographic activity and the intensity of facial features, a physiologically constrained dynamic time warp algorithm is used to perform temporal alignment and generate the optimal alignment path. Specifically, the temporal signals of electromyographic activity intensity and facial expression feature intensity are extracted and input into the physiological constraint dynamic time warp algorithm, and physiological delay time difference constraint conditions are added under the standard framework of dynamic time warp. A cumulative distance matrix is ​​constructed using physiological delay time difference constraints, and Euclidean distance is used to measure the instantaneous difference between electromyographic activity intensity and facial feature intensity. The physiological delay time difference constraint is manifested as the maximum allowable time difference limit between the temporal signal of electromyographic activity intensity and the temporal signal of facial expression feature intensity on the time axis, which is a forced alignment path. The minimum cost path of the backtracking cumulative distance matrix is ​​used to generate the optimal alignment path.

[0058] It should be noted that the physiological delay time difference constraint refers to the upper limit of the maximum allowable lag time between the temporal signal of electromyographic activity intensity and the temporal signal of facial expression feature intensity.

[0059] Specifically, the dynamic deviation between electromyographic activity intensity and facial expression feature intensity is calculated by entropy-weighted integral along the optimal alignment path to generate a physiological behavior paradox index, expressed as: ; ; In the formula, Indicators of the physiological-behavioral paradox index This represents the index of the optimal alignment path point. This represents the total number of points in the optimal alignment path. Indicates from arrive Perform a traversal and summation. Indicates the optimal alignment path point The degree of deviation, Represents the natural logarithm function. Indicates the optimal alignment path point The probability of deviation, This indicates the electromyographic activity intensity at the optimal alignment path point. This represents the facial feature strength of the optimal alignment path point. Indicates the absolute difference. Indicates taking and The maximum value.

[0060] It should be noted that, Dimensionless Dimensionless Dimensionless, ultimately It is dimensionless, but we maintain dimensional consistency.

[0061] When the physiological behavior paradox index exceeds the preset credibility threshold, the verification mechanism is triggered. When the physiological behavior paradox index does not exceed the preset credibility threshold, the monitoring state is maintained and the voiceprint fingerprint vector is regenerated.

[0062] It should be noted that the preset credibility threshold is set based on the entropy-weighted integral result of the physiological behavior paradox index, with an example value of 0.8.

[0063] Specifically, the physiological behavior paradox index is directly compared numerically with a preset credibility threshold: If the physiological behavior paradox index is greater than the preset credibility threshold, the verification mechanism is triggered; If the physiological behavior paradox index is less than or equal to the preset confidence threshold, the voiceprint monitoring status is maintained, and the real-time geographical location of the target object is reacquired, specific frequency band energy features are extracted, specific pulse sequence features are extracted, and voiceprint fingerprint vectors are generated by fusion.

[0064] A superior approach utilizes a physiologically constrained dynamic time warp algorithm to achieve temporal alignment between electromyographic (EMG) activity intensity and facial expression feature intensity. An entropy-weighted integral is then used to calculate a physiological behavior paradox index based on the dynamic deviation along the optimal alignment path. When the index exceeds a preset confidence threshold, a verification mechanism is triggered, improving the accuracy of identifying short-term deception behavior in drug users. Compared to conventional technologies (which use a standard dynamic time warp algorithm for EMG and facial expression signal temporal alignment without physiological constraints), the physiological delay time difference constraint (such as the maximum allowable time difference between EMG and facial expression signals) prevents excessive distortion of the alignment path. Simultaneously, the entropy-weighted integral calculation of the dynamic deviation more accurately quantifies physiological inconsistencies, effectively capturing the dynamic game-theoretic characteristics of "environmental triggering - physiological concealment" during drug relapse.

[0065] The evidence chain formation module and response verification mechanism initiate skin conductance response detection to generate abnormal skin conductance characteristics. Based on the abnormal skin conductance characteristics, body fluid analysis is triggered to generate abnormal body fluid marker characteristics. Simultaneously, salivary enzyme measurement is initiated to generate abnormal salivary enzyme activity characteristics, thus forming a hierarchical biological verification evidence chain.

[0066] The response verification mechanism synchronously acquires multi-channel skin conductance signals and uses adaptive wavelet filtering to eliminate motion artifacts and generate clean conductance signals. Specifically, when the verification mechanism is triggered, multi-channel skin conductance signals of the target object are simultaneously acquired through a multi-channel skin conductance sensor; Multi-scale wavelet decomposition is performed on the multi-channel skin conductance signal. High-frequency interference components are separated based on the characteristics of "motion artifacts". The high-frequency interference components are set to zero and the signal is reconstructed to generate a clean conductance signal.

[0067] It should be noted that motion artifacts refer to non-physiological conductivity fluctuations caused by the movement of the target limb during the acquisition of skin conductivity signals.

[0068] The system calls a predefined drug type response template, performs correlation comparison between the fast sympathetic phase and the slow endocrine phase of the clean conduction signal, generates a biphase delay difference, and performs multi-channel coordinated fusion to output abnormal skin conductance characteristics.

[0069] It should be noted that the predefined drug type response template refers to a standard reference dataset that stores the skin conductance response characteristics corresponding to different drug types, including fast sympathetic phase reference waveforms and slow endocrine phase reference waveforms.

[0070] Specifically, it invokes a predefined drug type response template that matches the drug rehabilitation type of the target object; Based on the clean conductance signal, the actual waveforms of the fast sympathetic phase and the slow endocrine phase are extracted respectively, and the peak time difference between the actual waveform of the fast sympathetic phase and the reference waveform of the fast sympathetic phase is used as the fast delay difference. The slow delay difference is defined as the peak time difference between the actual waveform of the slow endocrine phase and the reference waveform of the slow endocrine phase. The two-phase delay difference is generated based on the difference between the fast delay difference and the slow delay difference; The operation of generating biphase delay difference is repeated for each clean conductance signal to obtain channel-level biphase delay difference. All channel-level biphase delay differences are integrated by weighted averaging to output abnormal skin conductance characteristics.

[0071] Based on the abnormal characteristics of skin conductance, body fluid analysis is triggered and salivary enzyme measurement is initiated. The concentration of body fluid markers and salivary enzyme activity are coupled in real time to achieve antagonistic effects, and the rate of change of antagonistic factors is output. Specifically, when abnormal skin conductance characteristics exceed a preset abnormal threshold, the body fluid collection device is activated to collect sweat samples from the target object, detect the absolute value of the concentration of drug metabolite markers in the sweat samples, and generate body fluid marker concentrations. When the abnormal skin conductance characteristics do not exceed the preset abnormal threshold, the body fluid collection device remains in standby mode and does not collect sweat samples. The salivary enzyme measurement device also remains in standby mode and does not collect saliva samples. The current abnormal skin conductance characteristics are used as the only input to generate a hierarchical biological verification evidence chain. The salivary enzyme measuring device is started simultaneously to detect the absolute value of amylase activity in the saliva sample and generate salivary enzyme activity. Input the concentration of bodily fluid markers and salivary enzyme activity into a logic AND gate: If the concentration of the body fluid marker exceeds the preset concentration threshold and the salivary enzyme activity is lower than the preset activity threshold, the output antagonist factor change rate = 1; otherwise, the output antagonist factor change rate = 0.

[0072] It should be noted that the rate of change of the antagonistic factor is essentially a binary state marker (0 / 1), which characterizes whether the antagonistic effect exists; The preset abnormal threshold is set based on the statistical boundary of historical response data of abnormal skin conductance characteristics; the example value is 0.7. The preset concentration threshold is set based on the clinical standards for drug metabolite detection corresponding to the type of drug rehabilitation of the target subject. Example value: heroin metabolite concentration is 5 ng / ml. The preset activity threshold is set based on the lower limit of the normal range of salivary enzyme activity in healthy individuals. Example value: salivary amylase activity is 80 U / L.

[0073] By integrating the rate of change of antagonistic factors with abnormal skin conductance characteristics, a hierarchical chain of biologically validated evidence is formed.

[0074] Specifically, abnormal skin conductance characteristics serve as first-level evidence, and the rate of change in antagonist factors serves as second-level evidence. The first-level and second-level evidence are integrated into a two-dimensional vector structure through data combination operations; The first dimension stores numerical values ​​of abnormal skin conductance characteristics, and the second dimension stores the status values ​​of the rate of change of antagonistic factors, generating a hierarchical biological verification evidence chain.

[0075] A superior approach involves triggering body fluid analysis through abnormal skin conductance characteristics and simultaneously initiating salivary enzyme measurement. This couples the abnormal characteristics of body fluid markers with the abnormal characteristics of salivary enzyme activity in real time, forming a hierarchical bioverification evidence chain. This enhances the specificity and reliability of drug relapse detection. Compared to single biosignal detection methods that rely solely on independent judgment based on skin conductance response, constructing a hierarchical bioverification system with cross-validation to form a closed-loop evidence chain reduces the false alarm rate. In particular, it effectively distinguishes between false positives caused by environmental stress and genuine drug relapse behavior, solving the problem of misjudgment caused by the susceptibility of biosignals to interference.

[0076] The request sending module generates a behavioral judgment conclusion based on the hierarchical biological verification evidence chain, and sends a verification request containing environmental stress intensity value, physiological behavior paradox index and behavioral judgment conclusion to the monitoring terminal through an encrypted communication protocol.

[0077] Based on the abnormal characteristics of skin conductance, body fluid markers, and salivary enzyme activity in the hierarchical biological verification evidence chain, a behavioral judgment conclusion is generated through the coordinated fusion of multiple evidences. Specifically, abnormal characteristics of skin conductance, abnormal characteristics of body fluid markers, and abnormal characteristics of salivary enzyme activity are extracted from the hierarchical biological verification evidence chain. Execution logic AND gate determination: When the abnormal characteristics of skin conductance exceed the preset abnormal threshold, the abnormal characteristics of body fluid markers exceed the preset concentration threshold, and the abnormal characteristics of salivary enzyme activity are lower than the preset activity threshold, the output behavior judgment conclusion is "high-risk relapse behavior". Otherwise, if only two conditions are met, the output behavior judgment conclusion is "medium-risk suspicious behavior"; When one or zero conditions are met, the output behavior judgment conclusion is "low-risk normal behavior".

[0078] The encryption key strength is dynamically adjusted based on the risk level of the behavioral judgment conclusion. The environmental pressure intensity value, physiological behavior paradox index and behavioral judgment conclusion are spatiotemporally bound and encrypted to generate a verification request containing encrypted data packets, which is sent to the monitoring terminal through an encrypted communication protocol.

[0079] Specifically, the encryption key strength is selected based on the risk level of the behavior judgment conclusion: high-risk uses high-strength key, medium-risk uses medium-strength key, and low-risk uses low-strength key. The environmental stress intensity value, physiological behavior paradox index, behavior judgment conclusion, real-time geographic location coordinates of the target object and current timestamp are integrated into a spatiotemporal bound data package; The data packet is encrypted with AES using the selected encryption key strength, and an authentication request containing the encrypted data packet is generated. The verification request is sent to the monitoring terminal via the SSL / TLS encrypted communication protocol.

[0080] In summary, this invention achieves precise quantification of environmental risk exposure by calculating environmental pressure intensity values ​​from dynamic spatial location data. Based on the nonlinear fusion of location pressure coupling strength, target dwell time, and substance dependence parameters, the cumulative effect of environmental pressure in different drug use scenarios is quantified. Through an intelligent activation mechanism with a preset intensity threshold, biosignal acquisition is triggered only when environmental risk exceeds limits, reducing power consumption. The output environmental pressure intensity value serves as a benchmark for physiological behavior analysis, providing a basis for the coupled analysis of "environment-induced physiological response" and avoiding resource waste caused by ineffective biomonitoring.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart monitoring system for drug rehabilitation based on multimodal data fusion, characterized in that: include, The fingerprint generation module obtains the real-time geographical location of the target object, extracts specific frequency band energy features and specific pulse sequence features from the environmental acoustic fingerprint through an acoustic sensor, and generates an acoustic fingerprint vector. The pressure calculation module matches the voiceprint vector with the predefined inherent pressure benchmark data of high-risk locations to generate dynamic spatial location relationship data between the target object and the high-risk location and calculates the environmental pressure intensity value. When the environmental pressure intensity value exceeds the preset intensity threshold, the biosignal acquisition command is activated. The electromyography (EMG) analysis module responds to biosignal acquisition commands, synchronously acquires EMG signal waveforms and facial expression feature intensity, analyzes the dynamic changes in the rising phase of the EMG signal waveform, and generates EMG activity intensity. The paradox calculation module aligns the intensity of electromyographic activity with the intensity of facial expression features in a temporal sequence, calculates the deviation between the intensity of electromyographic activity and the intensity of facial expression features to generate a physiological behavior paradox index, and triggers a verification mechanism when the physiological behavior paradox index exceeds a preset confidence threshold. The evidence chain formation module responds to the verification mechanism, initiates skin conductance response detection to generate abnormal skin conductance characteristics, triggers body fluid analysis to generate abnormal body fluid marker characteristics, and simultaneously initiates salivary enzyme measurement to generate abnormal salivary enzyme activity characteristics, thus forming a hierarchical biological verification evidence chain. The request sending module generates a behavioral judgment conclusion based on the hierarchical biological verification evidence chain, and sends a verification request containing environmental stress intensity value, physiological behavior paradox index and behavioral judgment conclusion to the monitoring terminal through an encrypted communication protocol.

2. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 1, characterized in that: The specific steps for generating the voiceprint fingerprint vector are as follows: The system acquires the real-time geographical location of the target object and uses a directional microphone array to synchronously collect environmental acoustic signals, perform noise suppression, filter out interference from non-target frequency bands, and generate a clean voiceprint signal. Separate specific mid-to-high frequency bands and specific low frequency bands associated with drug environment from pure voiceprint signals, and extract energy features of specific frequency bands; A dynamic pulse detection mechanism is used to identify non-steady burst pulse clusters in pure voiceprint signals and extract specific pulse sequence features. By integrating specific frequency band energy characteristics, specific pulse sequence characteristics, and the real-time geographical location of the target object, an acoustic fingerprint vector is generated.

3. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 2, characterized in that: The specific frequency band energy characteristics are used to identify the collision sound of mid-to-high frequency capture devices for drug preparation and consumption behavior, and the specific low frequency band is used to identify the frequency band of environmental vibration noise. The specific pulse sequence characteristics refer to the non-steady-state sudden pulse clusters caused by the physiological loss of control of drug users, which locks in physiologically abnormal pulses.

4. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 1, characterized in that: The specific steps for generating dynamic spatial location relationship data between the target object and high-risk locations are as follows: The target geocoding in the voiceprint fingerprint vector is analyzed, and the spatially associated high-risk locations and acoustic pressure benchmark features of high-risk locations are extracted from the predefined high-risk location inherent pressure benchmark data. Based on the energy characteristics of specific frequency bands and specific pulse sequence characteristics, the difference between the target object and the acoustic pressure benchmark characteristics of high-risk locations is quantified to generate dynamic spatial location relationship data between the target object and the high-risk locations.

5. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 1, characterized in that: The specific steps for activating the biosignal acquisition command are as follows: Extract the location pressure coupling intensity and target dwell time from dynamic spatial location relationship data, and extract substance dependence parameters from pre-stored drug addict files; Based on the location pressure coupling strength, target dwell time and material dependence parameters, the environmental pressure intensity value is generated through nonlinear coupling calculation. When the environmental pressure intensity value exceeds the preset intensity threshold, the biosignal acquisition command is activated; otherwise, the voiceprint monitoring state is maintained and the voiceprint fingerprint vector is regenerated.

6. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 5, characterized in that: The specific steps for generating electromyographic activity intensity are as follows: In response to the biosignal acquisition command, the system acquires the electromyographic signal waveform of the target object, extracts the time-domain segment of the rising phase of the electromyographic signal waveform, and captures the intensity of facial expression features. The time-domain segment of the rising phase of the electromyographic signal waveform is mapped to the phase space for trajectory reconstruction, and the maximum Lyapunov exponent is calculated as the chaotic response exponent. The chaotic response index is fused with the facial expression feature intensity input tensor cross-attention mechanism to output the electromyographic activity intensity.

7. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 6, characterized in that: The specific steps of the trigger verification mechanism are as follows: Based on the intensity of electromyographic activity and the intensity of facial features, a physiologically constrained dynamic time warp algorithm is used to perform temporal alignment and generate the optimal alignment path. The dynamic deviation between electromyographic activity intensity and facial expression feature intensity is calculated by entropy-weighted integral along the optimal alignment path to generate a physiological behavior paradox index. When the physiological behavior paradox index exceeds the preset credibility threshold, the verification mechanism is triggered. When the physiological behavior paradox index does not exceed the preset credibility threshold, the monitoring state is maintained and the voiceprint fingerprint vector is regenerated.

8. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 7, characterized in that: The specific steps for initiating skin conductivity response detection to generate abnormal skin electrical characteristics are as follows: The response verification mechanism synchronously acquires multi-channel skin conductance signals and uses adaptive wavelet filtering to eliminate motion artifacts and generate clean conductance signals. The system calls a predefined drug type response template, performs correlation comparison between the fast sympathetic phase and the slow endocrine phase of the clean conduction signal, generates a biphase delay difference, and performs multi-channel coordinated fusion to output abnormal skin conductance characteristics.

9. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 1, characterized in that: The specific steps for forming a hierarchical chain of biological verification evidence are as follows. Based on the abnormal characteristics of skin conductance, body fluid analysis is triggered and salivary enzyme measurement is initiated. The concentration of body fluid markers and salivary enzyme activity are coupled in real time to achieve antagonistic effects, and the rate of change of antagonistic factors is output. By integrating the rate of change of antagonistic factors with abnormal skin conductance characteristics, a hierarchical chain of biologically validated evidence is formed.

10. The intelligent monitoring system for drug rehabilitation based on multimodal data fusion as described in claim 9, characterized in that: The specific steps for sending the verification request, which includes environmental stress intensity values, physiological behavior paradox index, and behavioral judgment conclusions, are as follows: Based on the abnormal characteristics of skin conductance, body fluid markers, and salivary enzyme activity in the hierarchical biological verification evidence chain, a behavioral judgment conclusion is generated through the coordinated fusion of multiple evidences. The encryption key strength is dynamically adjusted based on the risk level of the behavioral judgment conclusion. The environmental pressure intensity value, physiological behavior paradox index and behavioral judgment conclusion are spatiotemporally bound and encrypted to generate a verification request containing encrypted data packets, which is sent to the monitoring terminal through an encrypted communication protocol.

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