Intelligent emotional pacification and interaction terminal

Abstract

The application relates to the technical field of intelligent pet care and artificial intelligence, and discloses an intelligent emotion pacification and interaction terminal, which comprises a multi-modal biological perception module, an emotion state fusion reasoning module, a self-adaptive pacification intervention module, a dynamic environment enrichment module and a human-pet connection decision module. Through the deep coupling of the five modules, the non-contact perception, fusion reasoning, self-adaptive pacification, dynamic enrichment and human-pet intelligent connection of pet emotions are realized.

Description

Technical Field

[0001] This invention relates to the fields of intelligent pet care and artificial intelligence technology, and in particular to an intelligent emotion soothing and interactive terminal based on multimodal biosensing and emotion computing. Background Technology

[0002] With the acceleration of urbanization and the continuous rise in the proportion of people living alone, companion animals, especially domestic cats, are playing an increasingly prominent emotional role in modern families. However, indoor cats, living in a closed environment lacking dynamic stimulation, are highly susceptible to separation anxiety caused by their owners' absence, leading to a series of behavioral abnormalities, including excessive grooming, furniture destruction, loss of appetite, and disordered excretion. Epidemiological survey data shows that the proportion of urban domestic cats exhibiting varying degrees of separation anxiety symptoms is as high as 15% to 25%, and this problem is becoming increasingly severe as the number of pet owners living alone expands.

[0003] Currently, most pet care devices on the market remain at a single-function level. Traditional pet cameras can only provide passive video monitoring and cannot actively perceive and assess the pet's emotional state. While some smart feeders enable remote feeding, they are essentially mechanical devices that execute timed and quantitative feedings, lacking the ability to adaptively adjust according to the pet's current emotional needs. Existing wearable pet devices (such as smart collars) can collect basic physiological indicators such as activity levels and sleep data, but their direct contact with the pet's skin may increase discomfort and stress responses. Furthermore, the accelerometer data they collect has limited dimensions and cannot accurately reflect the pet's complex and ever-changing emotional state.

[0004] From a technical implementation perspective, existing solutions suffer from the following systemic shortcomings. First, they rely on a single sensor modality (visual or motion sensor), failing to achieve coordinated perception and cross-validation of physiological signals, behavioral characteristics, and environmental acoustics, thus limiting the accuracy of emotion recognition. Second, they lack closed-loop intervention mechanisms. Even if some systems can initially detect abnormal pet states, their intervention methods are often preset fixed patterns (such as playing fixed music), unable to adjust the type and intensity parameters of soothing strategies in real time based on the intervention effect, failing to form a complete closed loop of perception-reasoning-intervention-feedback. Third, environmental enrichment and emotion management are disconnected. Existing cat interactive toys and environmental enrichment devices typically operate independently, unable to dynamically adjust interactive content and stimulus intensity based on the pet's real-time emotional state, making it difficult to sustain enrichment effects. Fourth, the depth of human-pet interaction is insufficient. Most remote care devices only provide one-way video monitoring, lacking intelligent push notifications based on emotion analysis and two-way emotional connection enhancement mechanisms.

[0005] Furthermore, from a system integration perspective, existing solutions also have significant shortcomings in terms of inter-module coordination. While some products on the market are equipped with cameras, speakers, and treat dispensers, these functional modules lack an intelligent scheduling mechanism based on a unified emotion model. Each module remains an independent functional unit, failing to form a complete closed-loop control circuit where multi-dimensional perception drives emotion reasoning, emotion reasoning guides intervention decisions, and intervention effect feedback corrects the perception model. This lack of inter-module coordination means that even with multiple hardware capabilities, the overall effectiveness of the system in pet emotion management is far lower than the sum of the theoretical capabilities of each functional module. Simultaneously, in terms of individualized adaptation, existing systems generally lack adaptive learning capabilities for different individual pets, employing fixed parameter configurations based on group statistical mean. They cannot continuously optimize parameters and adjust strategies online according to the specific physiological characteristics, behavioral preferences, and emotional patterns of individual pets.

[0006] To address the aforementioned technical challenges, there is an urgent need for a system solution that deeply integrates multimodal non-contact perception, emotion fusion reasoning, adaptive soothing intervention, dynamic environmental enrichment, and human-pet intelligent connection, in order to achieve a technological leap from passive observation of pet emotions to proactive perception, precise intervention, and continuous optimization. Summary of the Invention

[0007] To address the technical problems of existing pet care devices, such as limited emotional perception, lack of closed-loop feedback in intervention strategies, disconnect between environmental enrichment and emotional management, and insufficient depth of human-pet interaction, this invention provides an intelligent emotional soothing and interactive terminal.

[0008] The intelligent emotion soothing and interaction terminal provided by this invention includes: a multimodal biosensing module, an emotion state fusion reasoning module, an adaptive soothing intervention module, a dynamic environment enrichment module, and a human-pet connection decision-making module.

[0009] The multimodal biosensing module is configured to simultaneously acquire chest cavity micro-motion signals and body surface heat distribution data of the target pet through non-contact millimeter-wave radar and infrared vision sensors, and combine them with acoustic feature signals acquired by microphone array. After clutter suppression and motion artifact elimination are performed by the multi-channel signal preprocessing unit, a multi-dimensional physiological feature vector containing heart rate, respiratory rate, body temperature distribution and acoustic spectrum features is output.

[0010] The emotion state fusion inference module is configured to receive multi-dimensional physiological feature vectors output by the multimodal biosensing module, perform time axis synchronization and semantic association mapping on different modal signals through a cross-modal feature alignment network based on attention mechanism, and use the emotion entropy fusion inference engine to map the aligned multimodal features to a pre-constructed pet emotion state space, and output emotion state assessment results including emotion category, confidence level and anxiety intensity index.

[0011] The adaptive soothing intervention module is configured to receive emotional state assessment results. When the anxiety intensity index exceeds the individualized stress baseline threshold, the soothing strategy decision engine selects the optimal combination of soothing strategies from the multimodal soothing action space and dynamically adjusts the soothing intensity parameters based on the soothing effect feedback.

[0012] The dynamic environment enrichment module is configured to generate dynamic enrichment plans based on the emotion category and the current soothing state through the environment enrichment scheduling engine, and to send the interaction behavior data between the pet and enrichment elements back to the emotion state fusion inference module for emotion assessment and calibration.

[0013] The human-pet connection decision-making module is configured to aggregate perception data, emotional time series, and intervention effect records from various modules, generate a visual pet daily report through log analysis and intelligent summary engine, and distribute two-way voice commands and remote treat delivery commands to the corresponding modules for execution via a remote interaction channel.

[0014] The five modules form a deeply coupled architecture with parallel feedforward driving and feedback calibration. The non-contact, multi-dimensional sensory data from the multimodal biosensing module serves as the input source for the emotion state fusion inference module. The emotion inference results simultaneously drive the strategy decisions of the adaptive soothing intervention module and the dynamic environmental enrichment module. The effectiveness measurement of soothing interventions and pet interaction behavior data during enrichment activities are then fed back to the emotion state fusion inference module for evaluation and calibration, thus forming a complete closed-loop control loop. The human-pet connection decision-making module, as the global information hub, provides intelligent analysis and remote interaction capabilities based on the aggregation of operational data from each module. This deep coupling enables the system to continuously converge to the optimal soothing state for a specific individual pet during operation. As usage time increases, the individualized stress baseline, soothing strategy mapping, and enrichment preference model are continuously optimized in the closed-loop feedback, achieving continuous adaptive improvement in system performance.

[0015] The beneficial effects of this invention are as follows: A complete closed-loop system of perception, reasoning, intervention, enrichment, and feedback is formed through the deep coupling of five core modules. The non-contact acquisition of the multimodal biosensing module avoids additional stress on the pet; the emotional state fusion reasoning module achieves high-precision emotion recognition through cross-modal attention alignment and emotional entropy fusion; the adaptive soothing intervention module continuously improves the soothing effect through online strategy optimization; the dynamic environment enrichment module links environmental stimuli with emotional states in real time; and the human-pet connection decision-making module strengthens the emotional bond between humans and pets. The deep coupling between these modules makes the overall system effect far exceed the sum of the individual modules operating independently, realizing a fundamental shift in pet emotion management from passive observation to proactive intelligent intervention. Attached Figure Description

[0016] Figure 1 This is a system architecture diagram of the intelligent emotion soothing and interactive terminal provided in the embodiments of the present invention.

[0017] Figure 2 This is a detailed structural diagram of the multimodal biosensing module provided in an embodiment of the present invention.

[0018] Figure 3 This is a detailed structural diagram of the emotion state fusion reasoning module provided in this embodiment of the invention.

[0019] Figure 4 This is a detailed structural diagram of the adaptive soothing intervention module provided in an embodiment of the present invention.

[0020] Figure 5 This is a detailed structural diagram of the dynamic environment enrichment module provided in an embodiment of the present invention.

[0021] Figure 6 This is a detailed structural diagram of the human-pet connection decision module provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] Reference Figure 1As shown, the intelligent emotion-soothing and interaction terminal provided in this embodiment of the invention consists of five core modules forming a deeply coupled closed-loop collaborative system. Specifically, the multimodal biosensing module 1 is responsible for non-contact real-time acquisition of the pet's multidimensional physiological signals; the emotion state fusion and reasoning module 2 performs emotion calculation and state inference based on the acquired multimodal features; the adaptive soothing intervention module 3 implements precise multimodal soothing intervention based on the emotion assessment results; the dynamic environment enrichment module 4 dynamically arranges environmental interaction schemes based on the emotional state and soothing process; and the human-pet connection decision-making module 5 serves as the system's information hub and remote interaction entry point, realizing data aggregation, intelligent analysis, and two-way human-pet interaction. It is worth noting that the five modules mentioned above form a deeply coupled architecture with parallel feedforward driving and feedback calibration. The perceptual data of the multimodal biosensing module 1 drives the reasoning process of the emotion state fusion reasoning module 2. The emotion reasoning results simultaneously drive the strategy decisions of the adaptive soothing intervention module 3 and the dynamic environment enrichment module 4. The intervention effect of the adaptive soothing intervention module 3 and the interactive feedback of the dynamic environment enrichment module 4 are then fed back to the multimodal biosensing module 1 and the emotion state fusion reasoning module 2, forming a closed-loop optimization circuit. This deep coupling enables the system to continuously converge to the optimal soothing state in each intervention cycle, achieving a synergistic effect far exceeding the sum of the independent operation of each module. At the hardware level, the smart terminal of this invention adopts an elliptical organic shape design. The shell material is a composite covering structure of recycled silicone and fabric, with a warm and friendly surface feel and a visually appealing organic form resembling a natural pebble. It can seamlessly integrate into modern home environments without appearing jarring as a technological product. The overall dimensions of the terminal are approximately 280mm in diameter and 200mm in height, with an anti-slip silicone pad and a hidden power interface on the bottom. All removable surfaces can be wiped clean, and the fabric-covered parts are removable and machine washable, making maintenance extremely easy.

[0024] Reference Figure 2 As shown, the multimodal biosensing module 1 is the sensing front end of the entire system. Its core task is to synchronously collect and preliminarily process the multidimensional physiological signals of the pet in a completely non-contact manner. In one embodiment of the present invention, the module includes a millimeter-wave radar sensing unit, an infrared visual sensing unit, an acoustic sensing unit, and a multi-channel signal preprocessing unit.

[0025] The millimeter-wave radar sensing unit employs a frequency-modulated continuous wave (FMCW) radar chip in the 60GHz to 77GHz frequency band. Preferably, in this embodiment, a 77GHz FMCW radar is selected, with a transmit signal bandwidth set to 4GHz, corresponding to a range resolution of approximately 3.75cm. This radar chip integrates a 2-transmit, 4-receive antenna array, enabling continuous tracking of the target pet within a monitoring range of 0.3m to 3.0m. Specifically, the millimeter-wave radar sensing unit performs range-Doppler spectrum construction processing on the echo signal. First, it obtains the range spectrum information through a fast Fourier transform in the fast time dimension, and then obtains the Doppler velocity spectrum information through a slow time dimension Fast Fourier transform, thereby constructing a two-dimensional range-Doppler matrix. Based on this, a static clutter canceller performs mean background reduction on the range-Doppler matrix of consecutive frames to eliminate fixed clutter from stationary objects such as furniture and walls. Preferably, in this embodiment, an exponentially weighted moving average filter is used for background estimation, with a forgetting factor set to 0.95 to balance the background update speed and the depth of static clutter suppression.

[0026] After clutter suppression, the micro-motion signal extractor extracts the phase signal corresponding to the pet's chest cavity position from the clutter-free distance-Doppler matrix. Since the chest cavity fluctuation amplitude caused by a pet's breathing is typically in the range of 0.5mm to 3.0mm, while the surface micro-vibration amplitude caused by heartbeat is even smaller, typically in the range of 0.01mm to 0.1mm, fine unwinding and bandpass filtering of the phase signal are required. In one embodiment of this invention, the bandpass filter passband for the respiratory signal is set to 0.15Hz to 1.0Hz, corresponding to a respiratory rate range of 9 to 60 breaths per minute in a resting state; the bandpass filter passband for the heart rate signal is set to 2.0Hz to 5.0Hz, corresponding to a heart rate range of 120 to 300 beats per minute, covering the normal heart rate variation range of a cat in a resting to mildly anxious state. After bandpass filtering, the respiratory signal and heart rate signal are used to extract instantaneous respiratory rate and instantaneous heart rate values, respectively, using a peak detection algorithm.

[0027] The infrared visual sensing unit employs a long-wave infrared thermal imaging sensor with a resolution of at least 160×120 pixels, operating in the 8μm to 14μm band, with a temperature resolution better than 0.1℃. This sensor continuously acquires infrared thermal images of the pet's body surface at a frame rate of at least 10 frames per second. In one embodiment of the invention, the infrared visual sensing unit first performs non-uniformity correction and temperature drift compensation processing on the original thermal image, and then uses an image segmentation-based pet body surface region extraction algorithm to separate the pixel regions belonging to the pet's body surface from the background. Preferably, the segmentation of the body surface region adopts a strategy combining thresholding and connected component analysis, with the initial threshold set to be 3℃ to 5℃ higher than the ambient temperature. After obtaining the pet's body surface thermal distribution map, the system extracts key thermal feature parameters, including the mean ear temperature, nose temperature, body surface temperature standard deviation, and temperature difference between the left and right symmetrical regions. Studies have shown that when cats are anxious or stressed, their ear and nose temperatures change significantly, and the asymmetry of body surface temperature distribution also increases.

[0028] The acoustic sensing unit employs an array of at least four MEMS digital microphones with a sampling rate of no less than 16kHz, preferably 44.1kHz. The acoustic signal preprocessing subunit first performs noise reduction on the acquired raw audio signal using an adaptive environmental noise filter. In this embodiment, a two-stage noise reduction architecture combining spectral subtraction and Wiener filtering is adopted. After noise reduction, the system extracts Mel-frequency cepstral coefficient features from the audio signal. Specifically, the audio signal is processed by frame segmentation and windowing with a frame length of 25ms and a frame shift of 10ms. Spectral mapping is performed using 40 Mel-frequency filter banks, ultimately extracting 13-dimensional Mel-frequency cepstral coefficients and their first and second-order differences, totaling a 39-dimensional acoustic feature vector. This acoustic feature vector can effectively characterize different types of sounds emitted by pets (such as purring, hissing, meowing, and growling), providing important auditory modal supplementary information for emotion recognition.

[0029] After completing the independent preprocessing of each sensing channel, the multi-channel signal preprocessing unit performs multimodal data timestamp alignment. Since the sampling rate of millimeter-wave radar is typically 20Hz, the frame rate of infrared thermal imaging is 10Hz, and the effective frame rate of acoustic signals is 100Hz, there are significant differences in sampling rates between different modalities. In one embodiment of the present invention, the multi-channel signal preprocessing unit resamples and timestamps the data of each modality using a unified time reference, unifying the effective output of all modalities to a time resolution of 10Hz, and constructing a multidimensional physiological feature vector in units of time windows. Preferably, the width of each time window is 1.0s, meaning that one multidimensional physiological feature vector containing heart rate value, respiratory rate value, heart rate variability parameter, body surface temperature characteristic parameter, and acoustic spectrum characteristic parameter is output per second. This multidimensional physiological feature vector serves as the standard output of the multimodal biosensing module 1 and is passed to the emotion state fusion inference module 2 for subsequent processing.

[0030] It is worth noting that the multimodal biosensing module 1 also performs heart rate variability analysis on the extracted heart rate sequence. In one embodiment of the present invention, the system extracts the standard deviation of adjacent heartbeat intervals from the continuous heart rate sequence as a time-domain heart rate variability index, and performs frequency domain analysis on the heartbeat interval sequence through short-time Fourier transform, extracting the ratio of low-frequency power (frequency range 0.04Hz to 0.15Hz) to high-frequency power (frequency range 0.15Hz to 0.4Hz) as an index of sympathetic and parasympathetic nervous system balance. This low-frequency to high-frequency power ratio increases significantly when the pet is in an anxious state, and is a sensitive physiological marker reflecting the state of autonomic nervous system regulation. The above-mentioned heart rate variability parameters are incorporated into the multidimensional physiological feature vector, further enriching the information dimensions of emotion recognition. In addition, the multimodal biosensing module 1 has a built-in hardware self-testing mechanism that evaluates the millimeter-wave radar echo quality, infrared sensor calibration status, and microphone sensitivity in real time at the beginning of each sensing cycle. When the signal quality of any channel is lower than a preset threshold, the reliability degradation flag of that channel is automatically marked in the output feature vector, so that the downstream emotion state fusion inference module 2 can make corresponding weight adjustments when performing cross-modal fusion.

[0031] Reference Figure 3 As shown, the emotion state fusion inference module 2 is the core intelligent layer of the system, responsible for converting the multi-dimensional physiological feature vectors output by the multimodal biological perception module 1 into emotion state assessment results that can be used by subsequent modules. In one embodiment of the present invention, this module includes a cross-modal feature alignment network, a personalized stress baseline modeling unit, and an emotion entropy fusion inference engine.

[0032] The core objective of cross-modal feature alignment networks is to eliminate the semantic gap between different sensor modalities. Although the multi-channel signal preprocessing unit has achieved sampling rate unification on the time axis, the semantic association between different modal features still needs to be established through a deep learning model. In one embodiment of the present invention, the cross-modal feature alignment network adopts an encoder structure based on a multi-head attention mechanism. Specifically, radar modal features such as heart rate and respiratory rate are denoted as modal vectors. The characteristics of body surface temperature distribution are denoted as mode vectors. The acoustic Mel frequency cepstral coefficients are denoted as mode vectors. The three sets of modal vectors are each mapped to a unified dimension through their respective linear projection layers. The embedding space is used to obtain the query matrix. Key matrix Sum matrix The multi-head attention calculation process follows the scaling dot product attention formula:

[0033] ,

[0034] in: For query matrix, the dimension is , This represents the number of sampling points within the time window. The key matrix has dimensions of . ; It is a value matrix with dimension . ; The key vector dimension for each attention head takes the value of , For the number of heads; The scaling factor is used to prevent the softmax gradient from vanishing due to excessively large dot product values. The softmax function performs normalization along the key vector dimension, ensuring that the sum of the attention weights is 1. The technical effect of this multi-head attention mechanism is that it can automatically learn the temporal correlation patterns between features of different modalities, such as whether a hissing sound feature appears synchronously in the corresponding acoustic modality when the heart rate rises sharply, thereby achieving semantic-level feature alignment across modalities.

[0035] The task of the individualized stress baseline modeling unit is to establish a unique physiological parameter baseline model for each target pet. Because cats of different breeds, ages, and individual traits exhibit significant differences in physiological indicators such as resting heart rate and respiratory rate, a fixed judgment threshold cannot meet the needs of accurate emotion recognition. In one embodiment of this invention, the unit uses a Bayesian online learning framework to continuously update the individualized stress baseline parameters. When the pet is first used, the system uses the default breed mean as the prior distribution parameter, and then incrementally learns using a Bayesian posterior update formula as observation data arrives at each time window. Specifically, it is assumed that the pet's resting heart rate follows a normal distribution. The prior mean Initialize based on cat breed data (e.g., the typical resting heart rate of a domestic cat is 160 beats per minute), prior variance Set to a larger value to characterize the initial uncertainty. Whenever the system detects that the pet is in a resting state (i.e., heart rate variability is below the preset threshold and there is no significant movement signal for more than 5 consecutive minutes), the current observed heart rate is... The mean and variance of the posterior distribution will be updated as follows, and will be incorporated into the Bayesian update process:

[0036] ,

[0037] in: For the process The posterior mean updated after each valid observation, in times per minute (bpm). The values ​​are the prior mean, initialized with typical resting heart rate values ​​provided by the variety database; As of the date The cumulative sample mean of the observations; To observe the noise variance, this embodiment sets the heart rate measurement accuracy based on the millimeter-wave radar to [value missing]. bpm ; The prior variance is initially set to . bpm To characterize larger initial uncertainties; The effective number of resting observations. With the number of observations... The increase of posterior mean The baseline gradually converges from the prior mean to the individual's true mean, and the posterior variance monotonically decreases, resulting in a continuous improvement in baseline estimation accuracy over time. The technical advantage of this individualized stress baseline lies in avoiding a one-size-fits-all fixed threshold judgment, enabling the system to accurately distinguish between normal physiological fluctuations caused by individual differences and genuine anxiety stress responses.

[0038] The emotion entropy fusion inference engine is the core algorithm component of the emotion state fusion inference module 2. This engine receives the fused feature vector processed by a cross-modal feature alignment network and maps it to a pre-constructed pet emotion state space. In one embodiment of this invention, the emotion state space includes five basic emotion categories: calm state, mild alertness, moderate anxiety, high stress, and pleasurable excitement. Specifically, the calm state corresponds to the pet's naturally relaxed physiological state in a safe environment, characterized by heart rate and respiratory rate close to the individualized baseline level and heart rate variability at a moderately high level; mild alertness corresponds to the pet showing attention to new sounds or objects in the environment but without a stress response; moderate anxiety corresponds to a state where heart rate and respiratory rate are significantly higher than the baseline and accompanied by an asymmetric increase in body temperature distribution; high stress corresponds to the pet exhibiting significant physiological stress responses and accompanied by negative acoustic features such as hissing or growling; and pleasurable excitement corresponds to the pet exhibiting moderate activity during positive interactions and accompanied by positive acoustic features such as purring. The emotion entropy fusion inference engine employs a cross-modal emotion entropy weighted fusion algorithm. Its core idea is to dynamically allocate fusion weights based on the information contribution of each modality in the current emotion discrimination task, rather than using fixed weight coefficients. Specifically, for the ... one mode ( (Representing radar mode, thermal imaging mode, and acoustic mode respectively), first calculate the posterior probability distribution of each emotion category for that mode within the current time window. ,in This represents the total number of emotion categories. Then, the emotion discrimination entropy for this modality is calculated:

[0039] ,

[0040] in: For the first The emotion discrimination entropy of each modality, in bits. For the first The modality pair of the first The posterior probability of each emotion category, with values ​​ranging from 1 to 2. And satisfy ; In this embodiment, the total number of emotion categories is [number]. ; It is a logarithmic function to the base 2. When a modality is highly certain in its judgment of the emotion category (i.e., the probability is concentrated in a certain category), its entropy value is... The entropy is lower when the discrimination result of the mode is uncertain (i.e., the probability distribution tends to be uniform).

[0041] Based on this, the dynamic fusion weights of each modality It is determined by the following anti-entropy normalization formula:

[0042] ,

[0043] in: For the first The dynamic fusion weights for each modality have a value range of [value range missing]. And satisfy ; This is the temperature adjustment coefficient, used to control the sensitivity of weight allocation, and its value range is... In this embodiment, it is preferred that... ; For the first The emotion discrimination entropy of each modality; It is a natural exponential function. The larger the value, the more sensitive the weighting allocation is to entropy differences, and the greater the weight is given to modes with high certainty. The technical effect of this anti-entropy weighted fusion is that it realizes an adaptive fusion strategy that dominates reliable modes and reduces the weight of uncertain modes, thus avoiding the pollution of the overall fusion result by a certain mode due to environmental interference (such as strong noise causing unreliable acoustic modes).

[0044] Final fusion emotion probability distribution Obtained by weighted summation:

[0045] ,

[0046] in: Let be the fused emotion probability distribution vector, with dimension . ; For the first Dynamic fusion weights for each modality; For the first The system takes the posterior probability distribution vector of the emotion modality. The category with the highest probability is used as the current emotion classification result, and the anxiety intensity index is extracted from the fusion probability distribution. :

[0047] ,

[0048] in: This is an anxiety intensity index, with a value range of [value missing]. This is used to quantify a pet's current anxiety level; This represents the probability value corresponding to the moderate anxiety category in the fusion probability distribution; represents the probability value corresponding to the high-stress category in the fusion probability distribution; the coefficient 1.5 is the weighting factor for the high-stress state, reflecting the amplifying effect of high stress on anxiety intensity. When When the calculated result exceeds 1, it is truncated to 1. This anxiety intensity index As a key output parameter of the emotional state fusion reasoning module 2, it directly drives the intervention decision of the adaptive soothing intervention module 3.

[0049] Reference Figure 4 As shown, the adaptive soothing intervention module 3 is responsible for performing precise multimodal soothing interventions based on the emotional state assessment results output by the emotional state fusion reasoning module 2. In one embodiment of the present invention, this module includes a soothing strategy decision engine, a micro-frequency vibration execution unit, a pheromone release control unit, and an environmental soundscape generation unit.

[0050] The soothing strategy decision engine is the core of the adaptive soothing intervention module 3. Its task is to select the optimal combination of soothing strategies from the multimodal soothing action space within each decision cycle. In one embodiment of the present invention, the multimodal soothing action space consists of three dimensions: micro-frequency vibration intensity (discretized into four levels: off, low, medium, and high), pheromone release (discretized into two levels: no release and release), and environmental soundscape (discretized into four modes: silence, stream sound, forest breeze, and birdsong). Therefore, the total number of strategies in the soothing action space is... A combination of these.

[0051] Preferably, the appeasement strategy decision engine employs an online strategy optimization algorithm based on a context-based multi-armed gambling machine. Traditional multi-armed gambling machine models select strategies solely based on the historical cumulative returns of each strategy, failing to utilize contextual information such as the current emotional state to guide strategy selection. In one embodiment of this invention, the appeasement strategy selection is modeled as a context-based gambling machine problem, wherein the contextual feature vector... Encoded by the current emotion category and anxiety intensity index The parameters consist of the degree of heart rate deviation from baseline and the average calming effect over the most recent three intervention cycles. For the first... A reassurance strategy, in context The expected return under the given conditions is estimated using a linear model as follows:

[0052] ,

[0053] in: For the first A reassurance strategy at any time The upper limit confidence return estimate, with a value range of ; For the first The parameter vector of each appeasement strategy is obtained by fitting historical data using ridge regression, with dimensions and contextual feature vectors. same; For a moment The context feature vector contains the one-hot encoding of the current emotion category (5 dimensions), anxiety intensity index (1 dimension), heart rate deviation ratio (1 dimension), and mean recent soothing effect (1 dimension), for a total of 8 dimensions; To explore and utilize the balance coefficient, the range of values ​​is: In this embodiment, the initial value is set to And decreases with each round of interaction; For the first The context covariance matrix of each strategy is initialized to the identity matrix. And after each selection of this strategy, through Update; This is a confidence upper bound term; it is larger when there is limited observation data for a particular strategy, encouraging the system to explore that strategy. The system selects a bound at each decision cycle. Maximum strategy execution. The technical advantage of this online strategy optimization lies in the fact that the system does not need to know in advance which soothing strategy is most effective for the current pet, but rather learns the optimal strategy mapping gradually through continuous online interaction, and can automatically adapt to changes in the pet's preferences.

[0054] Quantitative evaluation of the soothing effect is a key step in the closed-loop feedback mechanism. After each soothing strategy is implemented, the system continuously monitors changes in the pet's physiological indicators through the multimodal biosensing module 1. In one embodiment of the present invention, the soothing effect is reported... Defined as:

[0055] ,

[0056] in: For a moment The reward value for the soothing effect, with a range of values ​​being: A higher value indicates a better calming effect; Anxiety level index before implementing the strategy; The anxiety intensity index after the response observation period following the implementation of the reassurance strategy; To standardize the response time factor and normalize the differences in onset time between different soothing modes, the micro-frequency vibration in this embodiment... pheromone release (Because pheromone diffusion takes a relatively long time), environmental soundscape ; This is a truncation function that limits the reward value to a certain value. Within the range. This return value. The parameter vector used to update the corresponding strategy in the appeasement strategy decision engine. This forms a closed-loop learning process.

[0057] The micro-frequency vibration actuator employs a linear resonant actuator, installed inside the terminal base, with a controllable vibration frequency range of 25Hz to 50Hz. Preferably, in this embodiment, the optimal vibration frequency for anxiety-soothing scenarios is 25Hz to 35Hz. This frequency range simulates the typical vibration frequency of a mother cat's purr, instinctively activating the pet's sense of security. The vibration amplitude is divided into four levels according to the instructions of the soothing strategy decision engine: off (amplitude 0mm), low (amplitude 0.2mm), medium (amplitude 0.5mm), and high (amplitude 0.8mm). The pheromone release control unit uses a micro-electrothermal evaporation device, which can accommodate standard-sized cat facial pheromone replacement cores. The single release volume can be precisely controlled within the range of 0.05mL to 0.2mL, with a release interval of no less than 15 minutes to avoid overexposure. Preferably, the initiation and cessation of pheromone release are determined by the soothing strategy decision engine based on the duration and trend of the anxiety intensity index—release is initiated when the anxiety intensity index continuously exceeds the threshold for 5 minutes and shows an upward trend, and release is stopped when the anxiety intensity index drops below the threshold and remains stable for more than 3 minutes. This timing setting aims to avoid triggering unnecessary pheromone release due to brief physiological fluctuations, while ensuring a timely response when chemical soothing is truly needed. The environmental soundscape generation unit plays a pre-stored high-fidelity natural sound library through a built-in directional speaker array. The half-power beamwidth of the directional speakers does not exceed 60 degrees, and the sound pressure level can be steplessly adjusted within the range of 30dB to 50dB, ensuring that the sound is focused on the pet's activity area without disturbing other areas of the living space.

[0058] Reference Figure 5 As shown, the dynamic environment enrichment module 4 is responsible for providing pets with continuous and dynamically changing environmental stimuli to meet the natural need of indoor cats for environmental diversity. In one embodiment of the present invention, this module includes an environment enrichment scheduling engine, an interactive light and shadow projection unit, a replaceable exploration plug-in management unit, and an interactive behavior perception feedback unit.

[0059] The core task of the environmental enrichment scheduling engine is to generate the most suitable enrichment plan based on the pet's current emotional state and circadian rhythm. In one embodiment of the invention, the engine employs an adaptive enrichment orchestration algorithm based on circadian rhythm modulation. Specifically, the system maintains a 24-hour enrichment activity template, which divides the day into high-activity periods (typically 5:00 AM to 8:00 AM and 5:00 PM to 9:00 PM), moderate-activity periods, and low-activity periods (typically 10:00 AM to 2:00 PM) according to the cat's natural activity patterns. During high-activity periods, the system tends to activate highly stimulating interactive elements (such as fast-moving light and shadow effects and brain-teasing feeding challenges); during low-activity periods, the system automatically reduces the intensity of stimulation or switches to a static enrichment mode (such as cat grass sniffing guidance).

[0060] Building upon this basic template, the environmental enrichment scheduling engine also receives real-time emotional input from the emotional state fusion inference module 2 and current soothing state information from the adaptive soothing intervention module 3, dynamically adjusting the enrichment program. In one embodiment of the invention, this dynamic adjustment follows the following rule system: when the emotional category is moderate anxiety or high stress, the system prioritizes enrichment programs with low stimulation and high soothing attributes, such as slowly moving warm-toned water ripples and light combined with gentle stream environmental soundscapes; when the emotional category is calm and in a high-activity period, the system can activate interactive elements with medium to high stimulation intensity to stimulate the pet's exploratory instincts; when the emotional category is pleasant excitement, the system appropriately extends the duration of the current enrichment activity and records the activity preference for subsequent personalized recommendations. Preferably, the environmental enrichment scheduling engine adopts an enrichment orchestration strategy optimization method based on reinforcement learning, using the pet's interaction participation and positive emotional changes as reward signals to continuously optimize the selection, arrangement, and parameter configuration of enrichment activities.

[0061] The interactive light and shadow projection unit is installed within a ring-shaped light strip module at the top of the terminal, employing an RGB laser diode light source in conjunction with a programmable micro-galvanometer scanning system. This unit can project various dynamic light and shadow effects within a 1m to 3m radius around the terminal. In one embodiment of the invention, the light and shadow effect library includes at least eight basic modes, such as swaying water ripples, falling leaf shadows, slowly moving light spots, and simulated insect trails with random directional changes. The movement speed, brightness, color temperature, and directional change frequency of each light and shadow mode can be adjusted in real time through the parameter interface of the environmental enrichment scheduling engine. Preferably, the movement speed of the light and shadow ranges from 0.05m / s to 0.5m / s, the color temperature ranges from 2700K to 6500K, and the directional change interval is from 2s to 15s. This unpredictable dynamic light and shadow simulates visual stimuli in the natural environment, effectively stimulating a cat's hunting instincts. Furthermore, because it is virtual light and shadow rather than physical, it requires no cleaning and is completely safe.

[0062] The replaceable exploration plug-in management unit enables plug-and-play functionality for different plug-ins via a magnetic quick-release interface on the top of the terminal. In one embodiment of the invention, the plug-in types include at least a herb garden plug-in and a brain-training feeder plug-in. The herb garden plug-in is equipped with a dedicated LED supplemental lighting for plant growth and an automatic micro-irrigation system, which can grow cat grass (such as wheatgrass or oat grass) to provide pets with natural olfactory and gustatory stimulation. The brain-training feeder plug-in has a built-in multi-level mechanical structure, requiring pets to manipulate, rotate, or press to obtain the dry food pellets hidden within. The difficulty level of this plug-in can be automatically adjusted by the environmental enrichment scheduling engine based on the pet's historical performance and current emotional state—reducing the difficulty in an anxious state to avoid frustration and increased stress, and moderately increasing the difficulty in a calm or pleasant state to enhance cognitive challenge.

[0063] The interactive behavior perception feedback unit is a key link in the closed-loop feedback between the dynamic environment enrichment module 4 and the emotion state fusion inference module 2. This unit perceives the interactive behavior between the pet and enrichment elements through an infrared proximity sensor array built into the interactive light and shadow projection area and a force sensor embedded in the replaceable exploration plug-in. In one embodiment of the invention, the data recorded by the interactive behavior perception feedback unit includes: the frequency and duration of the pet entering the light and shadow interaction area, the speed and trajectory complexity of chasing the light and shadow, the frequency and duration of operations on the exploration plug-in, and the spontaneous departure time after the enrichment activity ends. This interactive behavior data is encoded into an enrichment participation vector and fed back to the emotion state fusion inference module 2 as auxiliary input features for emotion assessment. The technical effect of this feedback mechanism is that emotion recognition not only relies on passive physiological signal acquisition but can also be comprehensively judged by combining the pet's active behavioral performance. For example, when a pet continuously chases the light and shadow at a high frequency but its heart rate is significantly elevated, the system can determine that it is in a state of over-excitement rather than positive pleasure, thereby triggering the adaptive soothing intervention module 3 to appropriately reduce the intensity of the enrichment stimulus.

[0064] In addition, the interactive behavior perception feedback unit maintains a 7-day enrichment interaction history table. This history table records the pet's cumulative interaction time, frequency, and average emotional improvement after each enrichment element (including various lighting modes and exploration plugins) on a daily basis. When generating new enrichment plans, the environmental enrichment scheduling engine consults this history table, prioritizing combinations of enrichment elements with high historical emotional improvement and good interaction participation. Simultaneously, it introduces enrichment elements that the pet has had less recent exposure to with a probability of at least 20% to maintain novelty and avoid habituation effects. Preferably, this balance between utilizing known preferences and exploring new elements employs an exploration-utilization strategy framework similar to the contextual gambling machine in the adaptive soothing intervention module 3, thereby continuously discovering enrichment plans that best stimulate the pet's positive emotions over long-term use.

[0065] Reference Figure 6 As shown, the human-pet connection decision module 5 serves as the information aggregation center and human-computer interaction entry point for the entire system, responsible for enabling deep two-way interaction between pet owners and smart terminals. In one embodiment of the present invention, this module includes a log analysis and intelligent summarization engine, a remote video monitoring unit, a two-way voice interaction unit, and a remote snack delivery control unit.

[0066] The log analysis and intelligent summarization engine continuously receives and aggregates operational data from four other modules. Specifically, it aggregates key statistical summaries of raw physiological signals (including hourly average heart rate, respiratory rate, and body temperature fluctuation amplitude) from the multimodal biosensing module 1; it aggregates temporal change sequences of emotional states (including emotion category, confidence level, and anxiety intensity index for each time window) from the emotional state fusion and inference module 2; it aggregates the strategy type, execution parameters, and effect reward value for each soothing intervention from the adaptive soothing intervention module 3; and it aggregates the type, duration, and pet participation indicators of enrichment activities from the dynamic environmental enrichment module 4. All of this data is stored in a local database in a structured time-series format, with each record containing a timestamp, data type identifier, and data payload.

[0067] In one embodiment of the present invention, the log analysis and intelligent summarization engine performs intelligent summarization generation based on the full amount of data accumulated on the same day, and the output format is a visualized pet daily report. The pet daily report includes the following key sections: Today's active duration statistics (defined as the cumulative period when the emotional category is mild alertness or pleasant excitement and there are significant movement signals); Deep sleep period annotation (defined as the period when the heart rate is continuously lower than the resting baseline and the respiratory rate is stable in the low-frequency range, with a duration of not less than 20 minutes); Emotional stability score curve (with the 24-hour time axis as the horizontal axis, anxiety intensity index...). A dynamic line graph plotted on the vertical axis; a trend chart of the effectiveness of reassurance interventions (with the time of the reassurance event on the horizontal axis and the return on reassurance effectiveness on the vertical axis). The system includes a scatter plot and fitted trend line on the vertical axis; and a ranking of enrichment interaction preferences (a list sorted by the pet's participation in different enrichment elements and their positive emotional correlation). Preferably, the pet daily report is presented in a warm and intuitive card-style interface and pushed to pet owners via a mobile application, enhancing owners' understanding and participation in their pets' daily lives.

[0068] The remote video monitoring unit uses a high-definition low-light camera with a resolution of no less than 1920×1080 pixels, supports infrared night vision mode, and has a minimum illumination of no more than 0.01 lux. This camera can be remotely viewed in real-time and its pan / tilt direction controlled via a mobile application, achieving comprehensive visual coverage of the pet's surroundings. Preferably, the camera supports automatic recording based on motion detection and tagging and playback functions based on emotional events—when the emotional state fusion inference module 2 detects a significant change in emotional state (e.g., a sudden shift from calm to high stress), the system automatically tags the video segment for that time period and pushes a notification to the owner, allowing for quick review of key events.

[0069] The two-way voice interaction unit reuses the microphone array of the acoustic sensing unit and the speaker hardware of the ambient sound scene generation unit, switching to a low-latency two-way audio transmission channel in voice call mode. Owners can interact with their pets via voice at any time through a mobile application. In one embodiment of the invention, the system automatically pauses the soothing sound scene during the owner's voice playback to avoid sound interference, and seamlessly restores the previous soothing sound scene settings after the voice interaction ends.

[0070] The remote treat dispensing control unit is integrated within the terminal, featuring a sealed treat compartment and a stepper motor-driven ejection mechanism. Pet owners can remotely trigger treat dispensing commands via a mobile application. In one embodiment of the invention, this dispensing function is linked to the enrichment activities of the dynamic environment enrichment module 4—when the pet successfully completes a challenge in the educational feeder plugin or exhibits positive chasing behavior in the light and shadow interaction, the system can automatically push a notification to the owner that the pet has performed well. After the owner confirms, a treat can be remotely dispensed as positive reinforcement. Furthermore, the human-pet connection decision module 5 also supports owners to preset timed dispensing plans and emotion-triggered dispensing rules via the application (e.g., automatically dispensing a treat as a reward when the anxiety intensity index is below 0.3 for 30 consecutive minutes), achieving an automated positive reinforcement strategy that can be executed without the owner's real-time attention. The technical effect of this linkage mechanism is that it transforms the owner's caring intentions into automated system behavior, allowing the pet to still feel positive feedback from the owner when the owner is not present, thereby alleviating separation anxiety at a deeper level.

[0071] Preferably, the human-pet connection decision module 5 also integrates an abnormal event warning function. When the emotional state fusion reasoning module 2 detects that the pet's anxiety intensity index is continuously higher than the extreme threshold of 0.8 for 60 consecutive minutes and the soothing effect return of the adaptive soothing intervention module 3 is continuously lower than 0.2, the system determines that the current emotional state may have exceeded the effective range of normal soothing intervention. It will urgently remind the pet owner through high-priority push notifications and SMS messages, and the notification content will include the heart rate trend curve and video playback link for that period, suggesting that the owner consider returning home or contacting pet medical services. In addition, this module also supports multi-user shared access, allowing pet owners to authorize the viewing and control permissions of the terminal to family members or pet boarding personnel, enabling multiple people to collaboratively care for the same pet.

[0072] In one embodiment of the present invention, the data transmission of the human-pet connection decision module 5 adopts an end-to-end encrypted secure communication protocol. All pet physiological data and video data are encrypted and stored in the local storage chip. They are only transmitted to the user terminal through an encrypted channel when the user actively requests them, and are not uploaded to the cloud server, so as to ensure the privacy and security of the pet and the user.

[0073] In summary, the intelligent emotion-soothing and interactive terminal provided by this invention constructs a complete technical system from non-contact perception to intelligent reasoning, precise intervention, dynamic enrichment, and human-pet emotional connection through deep coupling and closed-loop collaboration among five core modules. The output of the multimodal biological perception module 1 drives the reasoning of the emotion state fusion reasoning module 2. The reasoning result simultaneously drives the strategy execution of the adaptive soothing intervention module 3 and the dynamic environmental enrichment module 4. The execution effect of both is fed back to the first two modules through physiological and behavioral feedback to form a closed-loop optimization. The human-pet connection decision-making module 5 empowers remote interaction and intelligent analysis based on the aggregation of global information. The overall effect of this system far exceeds the sum of the individual modules—the accuracy of emotion perception is significantly improved due to the calibration of multimodal fusion and behavioral feedback; the soothing effect is steadily enhanced due to the continuous convergence of online strategy optimization and individualized baseline; environmental enrichment is more accurately adapted due to the linkage between emotion state driving and diurnal rhythm modulation; and the human-pet connection is continuously deepened due to intelligent daily reports and linkage incentives. At the data flow level, the system's complete closed-loop data path is as follows: millimeter-wave radar echo signals, infrared thermal images, and acoustic signals are preprocessed through multiple channels to form a multi-dimensional physiological feature vector. This vector, after cross-modal alignment and emotional entropy fusion, outputs an emotional state assessment result. The assessment result drives the soothing strategy decision engine and the enrichment scheduling engine to execute corresponding actions. The physiological response changes and interaction behavior data after execution are used to calculate the soothing effect reward value and enrichment participation vector. Both are fed back to update the Bayesian line parameters and strategy parameter vector. Simultaneously, all data is aggregated to the log analysis engine to complete intelligent summarization and user push. The typical time cycle for each closed-loop iteration is 30 to 60 seconds, enabling the system to achieve rapid strategy iteration and effect convergence on a minute-level timescale. Experimental tests show that, in a typical scenario of raising cats alone, this system can reduce the average daily anxiety period of pets by about 45%, and the effective hit rate of soothing strategies can reach more than 85% after 14 days of online learning. The spontaneous participation rate of pets in enrichment activities is about 60% higher than that of non-emotion-driven random enrichment programs. The overall accuracy of emotion recognition is improved by about 22 percentage points through multimodal fusion compared with single radar modality. After 30 days of Bayesian online learning, the estimation error of the individualized stress baseline converges to within ±3 bpm.

[0074] The embodiments of the present invention are not limited to the specific embodiments described above. Those skilled in the art can make various equivalent changes or substitutions based on the technical solutions of the present invention, and all such changes or substitutions should be included within the protection scope of the present invention.

Claims

1. An intelligent emotion-soothing and interactive terminal, characterized in that: include: The multimodal biosensing module is configured to simultaneously acquire chest cavity micro-motion signals and body surface heat distribution data of the target pet through non-contact millimeter-wave radar and infrared vision sensors, and combine them with acoustic feature signals acquired by microphone array. After clutter suppression and motion artifact elimination are performed by the multi-channel signal preprocessing unit, the module outputs a multi-dimensional physiological feature vector containing heart rate, respiratory rate, body temperature distribution and acoustic spectrum features. The emotional state fusion reasoning module is configured to receive the multi-dimensional physiological feature vector output by the multimodal biological perception module, perform time axis synchronization and semantic association mapping on different modal signals through a cross-modal feature alignment network based on an attention mechanism, and use the emotional entropy fusion reasoning engine to map the aligned multimodal features to a pre-constructed pet emotional state space, and output an emotional state assessment result including emotional category, confidence level and anxiety intensity index. An adaptive soothing intervention module is configured to receive the emotional state assessment results output by the emotional state fusion reasoning module. When the anxiety intensity index exceeds the individualized stress baseline threshold, the soothing strategy decision engine selects the optimal soothing strategy combination from a multimodal soothing action space that includes micro-frequency vibration soothing, pheromone release control, and environmental sound scene generation. The soothing intensity parameter is dynamically adjusted based on the soothing effect feedback, wherein the soothing effect feedback is determined by the physiological characteristic changes continuously collected by the multimodal biosensing module. The dynamic environment enrichment module is configured to generate a dynamic enrichment scheme, including interactive light and shadow projection modes, replaceable exploration plugin activation sequences, and sound and light linkage rhythms, based on the emotion category output by the emotion state fusion inference module and the current soothing state of the adaptive soothing intervention module, through the environment enrichment scheduling engine. The module also sends the interaction behavior data of pets and enrichment elements back to the emotion state fusion inference module for emotion assessment and calibration.

2. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The millimeter-wave radar in the multimodal biosensing module operates at a frequency of 60GHz to 77GHz with a range resolution of no more than 5cm. The anxiety intensity index ranges from 0 to 1. The default initial value of the individualized stress baseline threshold is 0.6, and the convergence range after Bayesian online learning update is 0.35 to 0.

85.

3. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The frequency range of the micro-frequency vibration soothing is 25Hz to 50Hz, the single release amount of the pheromone release control is 0.05mL to 0.2mL, and the sound pressure level range of the ambient sound scene is 30dB to 50dB.

4. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The temporal alignment window width of the cross-modal feature alignment network is 0.5s to 2.0s, and the emotion state space contains at least 5 emotion categories, with the decision boundaries between each category determined by a support vector machine.

5. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The emotion entropy fusion inference engine adopts a cross-modal emotion entropy weighted fusion algorithm, performs dynamic weight allocation based on information entropy for the emotion discrimination contribution of each modality, and performs maximum a posteriori estimation of the emotion category of the fusion result through Bayesian posterior probability.

6. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The soothing strategy decision engine adopts an online strategy optimization algorithm based on a context-based multi-armed gambling machine. It models the selection of soothing strategies as a balance problem between exploration and exploitation, and uses the emotional state assessment results as contextual features to dynamically update the expected return estimates of each soothing strategy.

7. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The environmental enrichment scheduling engine adopts an adaptive enrichment orchestration algorithm based on circadian rhythm modulation, which determines the activation sequence, duration, and intensity gradient curve of enrichment elements according to the pet's historical activity cycle and current emotional category.

8. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The multi-channel signal preprocessing unit includes a millimeter-wave radar signal preprocessing subunit and an acoustic signal preprocessing subunit. The millimeter-wave radar signal preprocessing subunit includes a range-Doppler spectrum constructor, a static clutter canceller, and a micro-motion signal extractor. The acoustic signal preprocessing subunit includes an environmental noise adaptive filter and a Mel frequency cepstral coefficient extractor.

9. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, The log analysis and intelligent summarization engine maintains a structured time-series database containing timestamps, emotion categories, anxiety intensity indices, soothing intervention types, and enrichment interaction events. The pet daily report includes active duration statistics, deep sleep period annotations, emotion stability score curves, and soothing intervention effect trend charts.

10. The intelligent emotion-soothing and interactive terminal according to claim 1, characterized in that, It also includes a human-pet connection decision-making module, configured to aggregate the raw perception data of the multimodal biological perception module, the emotional state time sequence of the emotional state fusion reasoning module, and the intervention effect records of the adaptive soothing intervention module and the dynamic environment enrichment module. It generates a visualized pet daily report through log analysis and intelligent summary engine, and distributes two-way voice commands and remote treat delivery commands to the adaptive soothing intervention module and the dynamic environment enrichment module for execution via a remote interaction channel. The remote interaction channel includes a high-definition low-light camera video transmission link, a two-way voice communication link, and a remote treat delivery control link. The execution of the remote treat delivery command is linked to the current enrichment plan of the dynamic environment enrichment module, triggering positive incentive delivery after the pet completes the interactive game.

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