A pet status analysis method and system based on remote monitoring

Abstract

The present invention provides a pet status analysis method and system based on remote monitoring, relating to the technical field of pet status assessment. The method includes: collecting multi-source historical health monitoring data about the pet, determining multiple multi-source combined states of the pet; performing a physiological and behavioral joint analysis based on morphological changes on the multiple multi-source combined states of the pet, generating multiple physiological and behavioral joint patterns of the pet, and determining the implicit compensation pattern in each physiological and behavioral joint pattern; constructing a pet state association map, performing an abnormality analysis on the pet's multi-source real-time health monitoring data to determine abnormal state events; determining the target physiological and behavioral joint pattern to which the pet belongs based on the multi-source real-time health monitoring data, performing a compensation stability analysis on the pet based on the implicit compensation pattern of the target physiological and behavioral joint pattern, and generating a compensation imbalance assessment result for the pet. The present invention achieves an accurate assessment of the health status of the pet during transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of pet status assessment, and in particular to a pet status analysis method and system based on remote monitoring. Background Art

[0002] With the widespread adoption of IoT devices, pet health management has gradually shifted from traditional manual monitoring to intelligent management based on remote monitoring and data analysis. During pet transportation, the real-time collection of health data such as body temperature, heart rate, activity level, and ambient temperature and humidity, relying on wearable devices and environmental sensors, has become an important means of ensuring pet health. This real-time data enables dynamic assessment of a pet's health status and provides a basis for risk warnings and health intervention decisions during transportation.

[0003] However, some pet remote health monitoring technologies use anomaly detection methods based on fixed thresholds or single indicators, overlooking the complex environmental adaptability and behavioral compensation patterns of pets during transportation. During transportation, pets may experience short-term physiological changes, such as elevated body temperature or heart rate, due to external environmental stressors such as heat, vibration, and noise. These changes are often accompanied by behavioral changes, such as reduced activity and prolonged inactivity, as a form of self-regulation. However, these behavioral changes can mask physiological abnormalities to a certain extent, leading to misinterpretation of the pet's health status as normal, and even delaying the detection of potential health risks. In the early stages of health issues, pets use behavioral adjustments to mask physiological stress, which can easily delay or overlook abnormal signals. Developing an intelligent system that can accurately analyze the health status of pets during transportation, identify abnormal conditions, and assist in decision-making is of great practical significance for improving the health protection of pets during transportation. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a pet status analysis method and system based on remote monitoring. Based on the pet's own historical normal behavior pattern data and combined with real-time data during transportation, it dynamically evaluates changes in the pet's health status and identifies implicit compensatory behaviors. It can accurately evaluate the level of health changes of pets under short-term stress.

[0005] To achieve the above objectives, the present invention provides a first aspect of a pet status analysis method based on remote monitoring, comprising:

[0006] Collect multi-source historical health monitoring data about the pet, including the pet's historical behavioral data, historical physiological data, and historical environmental data, extract state features from the multi-source historical health monitoring data, and determine multiple multi-source combined states of the pet;

[0007] Performing a physiological and behavioral joint analysis based on morphological changes on multiple multi-source combined states of the pet to generate multiple physiological and behavioral joint patterns of the pet, extracting compensation features from multiple multi-source combined states in each physiological and behavioral joint pattern, and determining the implicit compensation pattern in each physiological and behavioral joint pattern;

[0008] A pet state association graph is constructed based on the timestamp information corresponding to each multi-source combined state. Based on the state association graph, an abnormality analysis is performed on the multi-source real-time health monitoring data of the pet during transportation to identify abnormal state events in the multi-source real-time health monitoring data.

[0009] The state feature sequence of abnormal state events is extracted from multi-source real-time health monitoring data, and physiological behavior matching is performed on the state feature sequence through multiple physiological behavior joint patterns to determine the target physiological behavior joint pattern to which the pet belongs. The pet's compensatory stability analysis is performed based on the implicit compensation pattern of the target physiological behavior joint pattern to generate the pet's compensatory imbalance assessment result.

[0010] Preferably, a physiological behavior joint analysis based on morphological changes is performed on multiple multi-source combined states of the pet to generate multiple physiological behavior joint patterns of the pet, including:

[0011] Based on the timestamp information of the multi-source combined state, a combined state sequence of multi-source historical health monitoring data is constructed, and a sliding window analysis is performed on the combined state sequence. According to the transfer characteristics of the multiple multi-source combined states in each window, multiple local state fluctuation parameters of the combined state sequence are extracted. The transfer paths of the multiple multi-source combined states in each window are smoothed and analyzed to obtain multiple local path curvatures of the combined state sequence. A physiological and behavioral collaborative analysis is performed on the multiple multi-source combined states in each window to obtain multiple local modal synchronization parameters of the combined state sequence. Multiple local state feature vectors corresponding to the combined state sequence are constructed, and the multiple local state feature vectors are clustered to generate multiple physiological and behavioral joint patterns of the pet.

[0012] Preferably, an abnormality analysis is performed on the multi-source real-time health monitoring data of the pet during transportation based on the state association graph to determine abnormal state events in the multi-source real-time health monitoring data, including:

[0013] Determine multiple real-time combined states in multi-source real-time health monitoring data, construct multiple real-time state paths for the multi-source real-time health monitoring data, perform anomaly analysis on the multiple real-time state paths based on the state association graph, determine the abnormal transfer path, and locate the abnormal state event based on the abnormal transfer path;

[0014] The state association graph uses multiple multi-source combination states as graph nodes, calculates the transition probability between multi-source combination states based on the timestamp information corresponding to each multi-source combination state, constructs directed edges in the state association graph based on the transition probability, and determines multiple reference transfer paths in the state association graph through the path mining algorithm. If the real-time state path does not match any reference transfer path, it is marked as an abnormal transfer path.

[0015] Preferably, determining the implicit compensatory pattern in each physiological-behavioral joint pattern includes:

[0016] Multiple local state fragments are cut out from multiple local state feature vectors contained in the physiological behavior joint pattern, and the pattern frequency and pattern coverage of each local state fragment are calculated. The fragment co-occurrence parameters of each local state fragment are determined according to the pattern frequency and pattern coverage, and multiple candidate state fragments are determined. Based on the multiple candidate state fragments, multiple local state feature vectors contained in the implicit compensatory pattern in the physiological behavior joint pattern are extracted.

[0017] Preferably, the pattern coverage of a local state segment is the ratio of the total number of physiological behavior joint patterns to the number of physiological behavior joint patterns that contain the local state segment.

[0018] Preferably, the state association graph is subjected to path mining by using a Viterbi algorithm to determine a plurality of reference transfer paths in the state association graph.

[0019] A second aspect of the present invention provides a pet status analysis system based on remote monitoring, which is used to implement the above-mentioned pet status analysis method based on remote monitoring, comprising:

[0020] A state feature extraction module is used to collect multi-source historical health monitoring data about the pet, including the pet's historical behavioral data, historical physiological data, and historical environmental data, extract state features from the multi-source historical health monitoring data, and determine multiple multi-source combined states of the pet;

[0021] The implicit compensation analysis module is used to perform a physiological and behavioral joint analysis of multiple multi-source combined states of the pet based on morphological changes, generate multiple physiological and behavioral joint patterns of the pet, extract compensation features of multiple multi-source combined states in each physiological and behavioral joint pattern, and determine the implicit compensation pattern in each physiological and behavioral joint pattern;

[0022] A real-time anomaly analysis module is used to construct a pet's state association map based on the timestamp information corresponding to each multi-source combined state. Based on the state association map, an anomaly analysis is performed on the multi-source real-time health monitoring data of the pet during transportation to identify state anomaly events in the multi-source real-time health monitoring data.

[0023] The real-time status assessment module is used to extract the status feature sequence of abnormal status events from multi-source real-time health monitoring data, perform physiological and behavioral matching on the status feature sequence through multiple physiological and behavioral joint patterns, determine the target physiological and behavioral joint pattern to which the pet belongs, perform compensatory stability analysis on the pet based on the implicit compensation pattern of the target physiological and behavioral joint pattern, and generate the pet's compensatory imbalance assessment result.

[0024] The present invention has the following beneficial effects:

[0025] The present invention performs time-series discrete coding and morphological analysis on multi-source health monitoring data of pets, constructs a joint feature vector based on fluctuation intensity, path curvature and modal synchronization, and quantifies the dynamic change relationship of pet physiological behavior patterns; through cross-window fragment co-occurrence parameter extraction and path transfer probability modeling, it captures the characteristic change law of pet self-regulation behavior under environmental pressure, mines abnormal state change phenomena of pets through state association graphs, and combines real-time transfer path anomaly detection to evaluate pet state anomalies, realizes accurate identification of complex compensatory failures during pet transportation, effectively distinguishes healthy adaptive behaviors from potential health risks, improves the reliability and warning timeliness of pet health status assessment under stress state, provides decision-making basis for remote pet status monitoring, and can provide good health protection for pet transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The figure is a flowchart of an exemplary pet status analysis method based on remote monitoring in an embodiment of the present invention.

[0027] Figure 2 This is a structural diagram of an exemplary pet status analysis system based on remote monitoring in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] See Figure 1 , an embodiment of the present invention provides a pet status analysis method based on remote monitoring, comprising the following steps:

[0030] Step S1: Collect multi-source historical health monitoring data about the pet, including the pet's historical behavior data, historical physiological data, and historical environmental data, extract state features from the multi-source historical health monitoring data, and determine multiple multi-source combined states of the pet.

[0031] It's worth noting that during pet transportation, various smart sensors, such as temperature and humidity sensors, microphones, and light sensors, can be used to remotely collect a variety of health data from pets in transport devices such as pet crates, enabling remote monitoring of the pet's condition. Compared to the environment pets inhabit in their daily lives, during transportation, pets may experience short-term physiological changes due to external environmental stressors such as high temperature, vibration, and noise. Pets also undergo relevant self-regulation, which can mask some of their abnormalities. Therefore, in-depth analysis of multi-source data from pets during transportation is necessary to promptly detect potential abnormal signals and improve their health during transportation.

[0032] The multi-source historical health monitoring data for pets, specifically historical normal data corresponding to the individual's daily behavior, includes the pet's normal self-regulation to changes in the external environment under environmental disturbances and can be used to model the pet's daily health status. Multi-source historical health monitoring data includes, but is not limited to, historical behavioral data, historical physiological data, and historical environmental data. Historical behavioral data characterizes the pet's activity, such as acceleration data and activity patterns collected by an accelerometer. Historical physiological data includes information related to vital signs, such as the pet's body temperature, heart rate, and respiratory rate. Historical environmental data includes external stress factors, such as the temperature, humidity, and noise of the pet's environment. By dynamically discretizing and encoding the multi-source historical health monitoring data, for example, dividing heart rate, body temperature, and environmental noise into multiple discrete coding symbols, and combining the discrete coding symbols corresponding to the various pet health-related data within a certain time period, for example, the pet's activity level within a certain window is distributed in the M3 interval, the heart rate is in the A1 interval, the body temperature is in the T2 interval, and the ambient temperature is in the H4 interval, the corresponding multi-source combined state of the pet in different time periods is obtained.

[0033] Step S2: Perform a physiological behavior joint analysis based on morphological changes on the pet's multiple multi-source combination states to generate multiple physiological behavior joint patterns of the pet, extract compensatory features from the multiple multi-source combination states in each physiological behavior joint pattern, and determine the implicit compensation pattern in each physiological behavior joint pattern.

[0034] Among them, in the process of mining the joint physiological and behavioral patterns, we focus on the evolutionary characteristics of the pet's comprehensive morphology, integrate the overall physiological and behavioral performance of the pet, capture the dynamic process characteristics of the pet's state changes to explore the uniqueness of individual physiological patterns.

[0035] In this process, the combined state sequence of multi-source historical health monitoring data is first constructed based on the timestamp information of the multi-source combined state, and the combined state sequence is subjected to sliding window analysis to extract multiple deep features in different windows.

[0036] Specifically, multiple local state fluctuation parameters F of the combined state sequence are extracted based on the transfer characteristics of multiple multi-source combined states in each window. In this process, F = c a ·log2(1+c u ), c u Indicates the number of state types, that is, the number of types of multi-source combination states involved in the window, c a It represents the number of state transitions, that is, the number of state transitions involving multiple sources within the window. The local state fluctuation parameter is used to quantify the severity of state changes per unit time, to avoid frequent switching between a few states being mistaken for high fluctuations.

[0037] A smoothing analysis was performed on the transfer paths of multiple multi-source combination states in each window, and multiple local path curvatures of the combination state sequence were calculated, including the angle between the directed edges corresponding to any two adjacent multi-source combination states in the window. The average of the multiple angles in the window was taken as the local path curvature of the multiple multi-source combination states in the window to characterize the smoothness or mutation of the physiological state changes, that is, considering that the pet may mask physiological abnormalities by behavioral stillness, but the path turning will expose the internal disorder.

[0038] A physiological and behavioral synergy analysis is performed on multiple multi-source combination states within each window, and multiple local modal synchronization parameters of the combination state sequence are calculated, including determining the maximum number of changes in the pet's behavior and physiology within the window, and then analyzing the number of synergistic changes between the pet's behavior and physiology within a specific period of time. The ratio of the number of synergistic changes between behavior and physiology to the maximum number of changes in the pet's behavior and physiology is used as the local modal synchronization parameter within the window, which is used to describe the synergistic efficiency between behavioral regulation and physiological response. A higher ratio indicates that the pet's compensatory behavior is healthy compensation, such as reduced activity accompanied by a decrease in heart rate. A lower ratio indicates compensation failure, such as activity cessation but a continuous increase in heart rate.

[0039] Finally, the local state feature vectors corresponding to the combined state sequence in different windows are obtained through the above-mentioned feature construction, which are used to describe the overall morphological characteristics of the pet's physiological-behavioral patterns in different windows. Then, multiple local state feature vectors are clustered, for example, through the DBSCAN algorithm to generate multiple physiological-behavioral joint patterns of the pet. Different physiological-behavioral joint patterns represent the behavioral-physiological pattern categories of the pet under different macroscopic perspectives in a normal state, such as the overall performance of the pet in a quiet state, a sleeping state, an exercise state, etc.

[0040] On the basis of multiple physiological and behavioral joint patterns, we further consider physiological compensatory behaviors caused by some minor external pressures in real life, such as a temporary increase in heart rate and a decrease in activity, that is, the actual performance of pets within the normal adaptation range of healthy individuals. The above physiological and behavioral joint patterns represent the overall performance of pets in specific modes, but they also include some data related to the normal compensatory behaviors of pets. For example, in quiet mode, some data may be affected by certain external environments, such as excessive noise, causing the pet to temporarily change to a quiet state, and at the same time, due to minor influences, causing the heart rate to increase to a certain extent compared to the normal state. For these implicit compensatory behaviors that are stress changes but are still within the normal range because they have not reached pathological levels, we further explore the implicit compensatory patterns.

[0041] The extraction of implicit compensatory patterns contained in different physiological behavior joint patterns includes cutting out multiple local state fragments from multiple local state feature vectors contained in the physiological behavior joint pattern, that is, determining multiple micro-combinations, such as the combination of fluctuation characteristics and modal synchronization characteristics, the combination of path transfer and modal synchronization, etc., and calculating the pattern frequency and pattern coverage of each local state fragment, wherein the pattern frequency is specifically the frequency of occurrence of the local state fragment in the physiological behavior joint pattern to which it belongs, and the pattern coverage is the ratio of the total number of physiological behavior joint patterns to the corresponding number of multiple physiological behavior joint patterns containing the local state fragment, that is, the state fragments that appear frequently in a specific pattern but are relatively rare in other patterns are representative compensatory behavior patterns, and finally the pattern frequency of the local state fragments is compared. The pattern coverage of local state fragments is weightedly corrected to obtain the fragment co-occurrence parameters of each local state fragment, and representative local state fragments are screened out. For example, local state fragments with fragment co-occurrence parameters greater than the preset co-occurrence threshold are marked as candidate state fragments, and according to the total number of candidate state fragments involved in the local state feature vector, representative local state feature vectors are selected to characterize the implicit compensation pattern in the physiological and behavioral joint pattern, thereby realizing the mining of the implicit compensation pattern in the physiological and behavioral joint pattern. On the basis of discarding the conventional features with broad consensus in the physiological and behavioral joint pattern, the features with consensus in the pattern are used as the implicit metabolic pattern exhibited by the pet in a normal state, which are used to characterize the normal stress state of the pet in response to external environmental stimuli under normal performance.

[0042] Step S3: construct a state association map of the pet based on the timestamp information corresponding to each multi-source combination state, perform an abnormality analysis on the multi-source real-time health monitoring data of the pet during transportation based on the state association map, and determine the state abnormal events in the multi-source real-time health monitoring data.

[0043] Among them, the state association graph is used to describe the state transition characteristics of the pet. For the construction of the state association graph, multiple multi-source combination states are used as graph nodes of the state association graph. Each multi-source combination state is unique in the graph, and the transition probability between the multi-source combination states is calculated in combination with the timestamp information corresponding to the multi-source combination state. For example, the total number of times the current multi-source combination state is transformed into the remaining multi-source combination states in history is counted, and the transition probability between any two multi-source combination states is calculated according to the number of transitions corresponding to each multi-source combination state, including the corresponding transition probabilities in bidirectional transfers. Finally, the directed edges in the state association graph are constructed in combination with the directionality of the transition probability, and multiple reference transfer paths in the state association graph are determined by the path mining algorithm. For example, the reference transfer path is determined by path mining through the Viterbi algorithm to reflect the most likely evolution law of physiological behavior.

[0044] For the multi-source real-time health monitoring data collected in real time during the transportation of pets, first determine the multiple real-time combination states in the multi-source real-time health monitoring data, associate any two adjacent real-time combination states, and construct multiple real-time state paths of the multi-source real-time health monitoring data. Perform an abnormality analysis on the multiple real-time state paths based on the state association map. If a real-time state path does not match any reference transfer path in the state association map, it will be marked as an abnormal transfer path. In this way, some abnormal performances of pets during transportation are excavated, and in this way, the abnormal transfer path of the pet is determined and the abnormal state event is located based on the abnormal transfer path, which is used to characterize the abnormal phenomena exhibited by the pet during transportation compared with normal performance.

[0045] Step S4: extract the state feature sequence of abnormal state events from multi-source real-time health monitoring data, perform physiological behavior matching on the state feature sequence through multiple physiological behavior joint patterns, determine the target physiological behavior joint pattern to which the pet belongs, perform compensatory stability analysis on the pet according to the implicit compensation pattern of the target physiological behavior joint pattern, and generate the pet's compensatory imbalance assessment result.

[0046] Among them, for the detected abnormal state event, the relevant state feature sequence is extracted from the real-time health monitoring data. Specifically, the target data of this event can be extracted from the real-time health monitoring data based on the abnormal transfer path involved in the abnormal state event and a certain time range before and after the time information of the abnormal transfer path. The selection of target data can be reasonably set according to the actual performance of the pet, and is not specifically limited in this embodiment. Thus, multiple state feature sequences of the pet regarding the abnormal state event are generated based on the target data, that is, the state feature sequences corresponding to the target data. With reference to the construction of the aforementioned combined state sequence, it is used to characterize the deep state characteristics of the pet in the abnormal state event. Then, the state feature sequences are respectively matched with the physiological behavior joint pattern to determine the target physiological behavior joint pattern to which the pet belongs. That is, considering that the pet exhibits certain compensatory behaviors in its current state, it may be difficult to accurately identify the current state pattern of the pet based on, for example, behavioral thresholds. After determining the target physiological-behavioral joint pattern to which the pet belongs, a compensatory stability analysis is performed on the pet based on the implicit compensation pattern of the target physiological-behavioral joint pattern, including determining the offset characteristics of the pet's current implicit compensation pattern from the target physiological-behavioral joint pattern, which can specifically be the distance from the cluster center of the implicit compensation pattern. The cluster center of the implicit compensation pattern can be determined based on the average level corresponding to the multiple local state feature vectors contained therein. At the same time, based on the offset direction between the target physiological-behavioral joint pattern and the implicit compensation pattern of the target physiological-behavioral joint pattern, a state evaluation of the pet's current offset characteristics is performed.

[0047] For example, a pet will show a stress response of reduced activity after being stimulated by external stimuli, and its heart rate will be slightly higher than the normal level. By analyzing the changes in different health monitoring items between the target physiological and behavioral joint pattern and the implicit compensation pattern of the target physiological and behavioral joint pattern, such as determining the reference levels of body temperature, heart rate, and activity level corresponding to the target physiological and behavioral joint pattern and the corresponding implicit compensation pattern, the pet's current offset for different health monitoring items is determined, and the pet's current compensation imbalance score is calculated according to the scoring weights corresponding to different health monitoring items determined in advance based on expert experience, thereby generating the pet's compensation imbalance assessment result. Some pets are timid in nature, and their heart rates will increase significantly after being stimulated by external sounds. In the process of calculating the compensatory imbalance score, the score weight corresponding to the heart rate offset can be increased, and the focus can be on the pet's current heart rate changes. In this way, the health status of the pet during transportation can be accurately analyzed and evaluated, and abnormal performance of the pet compared to the normal state can be identified. By analyzing the changes in the pet's compensatory imbalance score in real time, it can be assisted in evaluating whether the pet can restore its normal health state through self-adjustment. The changing trend of the pet's health level under external pressure can be analyzed, and health intervention decision support can be provided for the changes in the pet's status during transportation, thereby providing health protection for the pet during transportation.

[0048] Based on the same concept as the above-mentioned pet status analysis method based on remote monitoring, an embodiment of the present invention further provides a pet status analysis system based on remote monitoring.

[0049] See Figure 2 , an embodiment of the present invention provides a pet status analysis system based on remote monitoring, comprising:

[0050] The state feature extraction module 01 is used to collect multi-source historical health monitoring data about the pet, including the pet's historical behavioral data, historical physiological data, and historical environmental data, extract state features from the multi-source historical health monitoring data, and determine multiple multi-source combined states of the pet;

[0051] Implicit compensation analysis module 02 is used to perform a physiological behavior joint analysis based on morphological changes on multiple multi-source combination states of the pet, generate multiple physiological behavior joint patterns of the pet, extract compensation features from multiple multi-source combination states in each physiological behavior joint pattern, and determine the implicit compensation pattern in each physiological behavior joint pattern;

[0052] Real-time anomaly analysis module 03 is used to construct a pet's state association map based on the timestamp information corresponding to each multi-source combination state, perform an anomaly analysis on the multi-source real-time health monitoring data of the pet during transportation based on the state association map, and determine state anomaly events in the multi-source real-time health monitoring data;

[0053] The real-time status assessment module 04 is used to extract the status feature sequence of abnormal status events from multi-source real-time health monitoring data, perform physiological behavior matching on the status feature sequence through multiple physiological behavior joint patterns, determine the target physiological behavior joint pattern to which the pet belongs, perform compensation stability analysis on the pet according to the implicit compensation pattern of the target physiological behavior joint pattern, and generate the pet's compensation imbalance assessment result.

[0054] The foregoing description is merely a detailed description of the present invention, which is intended to enable those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art. Portions not described in detail in this specification are well known to those skilled in the art.

Claims

1. A pet status analysis system based on remote monitoring, characterized in that: include: A state feature extraction module is used to collect multi-source historical health monitoring data about the pet, including the pet's historical behavioral data, historical physiological data, and historical environmental data, extract state features from the multi-source historical health monitoring data, and determine multiple multi-source combined states of the pet; An implicit compensation analysis module is used to perform a physiological and behavioral joint analysis of multiple multi-source combination states of a pet based on morphological changes, and generate multiple physiological and behavioral joint patterns of the pet, including constructing a combined state sequence of multi-source historical health monitoring data based on the timestamp information of the multi-source combination state, performing a sliding window analysis on the combined state sequence, extracting multiple local state fluctuation parameters of the combined state sequence based on the transfer characteristics of the multiple multi-source combination states in each window, performing a smoothing analysis on the transfer paths of the multiple multi-source combination states in each window, and calculating multiple local path curvatures of the combined state sequence, including calculating the angle between the directed edges corresponding to any two adjacent multi-source combination states in the window, taking the average of the multiple angles in the window as the local path curvature of the multiple multi-source combination states in the window, performing a physiological and behavioral collaborative analysis on the multiple multi-source combination states in each window, calculating multiple local modal synchronization parameters of the combined state sequence, constructing multiple local state feature vectors corresponding to the combined state sequence, and clustering the multiple local state feature vectors to generate multiple physiological and behavioral joint patterns of the pet; Extracting compensation features from multiple multi-source combination states in each physiological behavior joint pattern, and determining the implicit compensation pattern in each physiological behavior joint pattern, including cutting out multiple local state fragments from multiple local state feature vectors contained in the physiological behavior joint pattern, wherein the local state fragment is composed of any two parameters of the local state fluctuation parameter, the local path curvature and the local modal synchronization parameter, and calculating the pattern frequency and pattern coverage of each local state fragment, the pattern frequency being the frequency of occurrence of the local state fragment in the physiological behavior joint pattern to which it belongs, and the pattern coverage being the ratio of the total number of physiological behavior joint patterns to the number of physiological behavior joint patterns containing the local state fragment, determining the fragment co-occurrence parameter of each local state fragment according to the pattern frequency and the pattern coverage, and determining multiple candidate state fragments, and extracting multiple local state feature vectors contained in the implicit compensation pattern in the physiological behavior joint pattern based on the multiple candidate state fragments; A real-time anomaly analysis module is used to construct a pet's state association map based on the timestamp information corresponding to each multi-source combined state. Based on the state association map, an anomaly analysis is performed on the multi-source real-time health monitoring data of the pet during transportation to identify state anomaly events in the multi-source real-time health monitoring data. The real-time status assessment module is used to extract the status feature sequence of abnormal status events from multi-source real-time health monitoring data, perform physiological behavior matching on the status feature sequence through multiple physiological behavior joint patterns, determine the target physiological behavior joint pattern to which the pet belongs, perform compensation stability analysis on the pet based on the implicit compensation pattern of the target physiological behavior joint pattern, determine the offset characteristics of the pet's current implicit compensation pattern with the target physiological behavior joint pattern, including the pet's current offset with respect to different health monitoring items, calculate the pet's current compensation imbalance score based on the scoring weights corresponding to different health monitoring items, and generate the pet's compensation imbalance assessment result.

2. A pet status analysis system based on remote monitoring according to claim 1, characterized in that: Based on the state association graph, anomaly analysis is performed on the multi-source real-time health monitoring data of pets during transportation to identify abnormal state events in the multi-source real-time health monitoring data, including: Determine multiple real-time combined states in multi-source real-time health monitoring data, construct multiple real-time state paths for the multi-source real-time health monitoring data, perform anomaly analysis on the multiple real-time state paths based on the state association graph, determine the abnormal transfer path, and locate the abnormal state event based on the abnormal transfer path; The state association graph uses multiple multi-source combination states as graph nodes, calculates the transition probability between multi-source combination states based on the timestamp information corresponding to each multi-source combination state, constructs directed edges in the state association graph based on the transition probability, and determines multiple reference transfer paths in the state association graph through the path mining algorithm. If the real-time state path does not match any reference transfer path, it is marked as an abnormal transfer path.

3. A pet status analysis system based on remote monitoring according to claim 2, characterized in that: The Viterbi algorithm is used to mine the path of the state association graph and determine multiple reference transfer paths in the state association graph.

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