A sucrose preference combined test apparatus for depressed beagles

Abstract

The application belongs to the technical field of pet detection, and discloses a sugar water preference comprehensive detection device for depressed beagle dogs, which comprises a first container and a second container, which are used for containing sugar water and white water respectively; a first weighing sensor and a second weighing sensor, which are installed at the bottom of the first container and the second container respectively, and mechanical decoupling structures are arranged between the first container and the second container and the corresponding weighing sensors; and a signal processing unit, which is electrically connected with the weighing sensors; through high sampling rate weighing and impact-recovery model, the present application separates splash net loss, reduces the sugar water preference rate error to 0.1%, simultaneously obtains single licking behavior characteristics, combines dynamic evaporation compensation and abnormal behavior automatic identification, and provides high-reliability and high-time-resolution ethology data for depression mechanism research and drug screening.

Description

Technical Field

[0001] This invention relates to the field of pet testing technology, and in particular to a comprehensive testing device for saccharin preference in depressed beagles. Background Technology

[0002] Beagles are commonly used experimental animals in depression model research. The Sucrose Preference Test (SPT) is one of the core behavioral paradigms for assessing depression-like behavior. Its basic principle is that normal animals exhibit a natural preference for sugar water, while depressed model animals show reduced responsiveness to reward stimuli, manifested as a decreased sugar water preference rate. Therefore, accurately measuring the intake of sugar water and plain water and calculating the preference rate is of great value for antidepressant drug screening and research on the mechanisms of depression—these data directly reflect the degree of anhedonia in animals and are key behavioral indicators for judging the success of depression model construction and evaluating the efficacy of antidepressant drugs.

[0003] Currently, sugar water preference tests commonly use the "total weight method," which involves weighing the initial weights of the sugar water and water containers before the test and weighing them again after the test. The total intake is calculated from the difference in weight before and after the test, thus yielding the preference rate. While this method is simple to operate, it fails to distinguish between actual intake and splash loss, resulting in insufficiently reliable behavioral data to support antidepressant drug screening and research on depression mechanisms. During canine licking, some liquid carried by the tongue splashes out of the container, causing a reduction in mass. The total weight method includes all this reduced mass in the intake, leading to an overestimation of the intake of both sugar water and water. Furthermore, the splash loss is often asymmetrical, introducing a systematic error into the preference rate calculation. This error masks the true differences between the depression model and normal controls, severely impacting the accuracy of antidepressant drug screening. Summary of the Invention

[0004] The present invention aims to provide a comprehensive detection device for sucrose preference in depressed beagles, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A comprehensive saccharin preference testing device for depressed beagle dogs includes: The first container and the second container are used to hold sugar water and plain water, respectively; The first and second weighing sensors are respectively installed at the bottom of the first and second containers, and are used to acquire the weight signals of each container at a sampling rate of not less than 200Hz. and The first and second containers are provided with a mechanical decoupling structure between themselves and the corresponding weighing sensors to attenuate vibration interference caused by the beagle's activity. The signal processing unit, electrically connected to the weighing sensor, is used to: detect the transient event intervals caused by the beagle licking action in the weight signals of each container respectively; Each event interval corresponds to a continuous licking sequence, after which the weight signal returns to a stable baseline. The weight waveform within each event interval is segmented by a single licking pulse to separate the rapid disturbance components representing splash impact and liquid fallback in each pulse; The total weight loss of the event is obtained by calculating the difference between the stable baseline weight before and after the start of the event interval, and the net loss of splash accumulated during the event interval (i.e. the mass of liquid splashed out of the container and not returned) is deducted from the total weight loss of the event to obtain the net loss of the event. Calculate the total actual intake of each container during the testing period. and The total actual intake for each container is calculated using the following formula: Total actual intake = Total weight loss of container during the test period - Cumulative net splash loss of the container - Total evaporation loss of the container; Calculate the sugar water preference rate ; Record the time, duration, and net reduction of each licking event to generate a dataset containing the characteristics of a single licking behavior.

[0006] Preferably, the method for the signal processing unit to detect the event interval is: to calculate the first derivative of the weight signal in real time. ,when The event is marked to begin when the first threshold is exceeded; when The event ends when the value is below the second threshold and the duration exceeds the preset silence time.

[0007] Preferably, the segmentation of the single licking pulse and the separation of the rapid disturbance component are performed in the following manner: First, within a single event interval, several single licking pulses are segmented based on the extreme points of the first derivative of the weight signal; Each single licking pulse was fitted using an impact-recovery biexponential model, the expression of which is:

[0008] in This represents the local baseline weight before the pulse begins. This represents the amplitude of the splash impact. Let be the recovery amplitude of the liquid fall, and satisfy . ; and Let be the time constants for the impact process and the fallback process, respectively, and satisfy . ; This is residual noise; The single pulse waveform is fitted using the nonlinear least squares method. When the goodness of fit is... When the value is greater than 0.9, the permanent mass loss of this single pulse is calculated as follows: ; The permanent mass loss of all single pulses meeting the fitting requirements within a single event interval is accumulated. Simultaneously, the difference in stable baseline weight before and after the event interval is calculated as the total weight loss for the event. ; Based on the ratio of the impact amplitude to the recovery amplitude of a single pulse and the characteristics of the time constant, a preset splash discrimination rule is matched to extract the cumulative net splash loss mass within the event interval from the cumulative permanent mass loss, denoted as . (i.e., the portion of liquid that splashes out of the container and does not fall back in); The net reduction of this event is calculated using the following formula:

[0009] Preferably, the signal processing unit also records at least one of the following licking behavior parameters: licking frequency per unit time, duration of a single licking event, net reduction of a single licking event, time interval between two adjacent licking events, and absolute time of occurrence of the licking event.

[0010] Preferably, the effective resolution of the weighing sensor is not less than 0.05 grams under dynamic interference environment, the sampling rate is 400 Hz, and the signal processing unit includes a 24-bit analog-to-digital converter.

[0011] Preferably, it also includes a triaxial accelerometer and a miniature microphone, mounted on the first container and / or the second container or their supporting structure, to assist in identifying abnormal behavior, but not to participate in the direct calculation of the actual intake quality.

[0012] Preferably, the signal processing unit also performs evaporation compensation: during the test cycle, the ambient temperature, humidity and airflow speed are monitored in real time, and the evaporation loss of sugar water and plain water is dynamically calculated using a preset evaporation rate model for the calculation of actual intake.

[0013] Preferably, the signal processing unit further performs an abnormal behavior identification step: identifying and eliminating abnormal events based on the weight waveform, accelerometer, and microphone signals; Among them, when the weight signal has an instantaneous spike of more than 2g within 50ms and recovers, and the accelerometer reading exceeds 1.0g, it is judged as head-shaking behavior; When the weight signal exhibits continuous irregular oscillations lasting longer than 1 second, and the microphone detects an impact sound, it is determined to be a container biting behavior. When a bidirectional pulse with a width of less than 100ms appears in the weight signal and the accelerometer does not show corresponding activity, it is determined that the claw is stirring the liquid. The data segments corresponding to the above-mentioned abnormal events are not included in the intake accumulation.

[0014] Preferably, the first and second containers are deep bowl-shaped structures with a depth of not less than 5 cm, and the inner walls are mirror-polished to reduce the amount of splashing of sugar water or water during the licking process.

[0015] The beneficial effects of this technical solution compared to existing technologies are as follows: (1) This invention combines the impact-recovery double exponential model to fit a single licking pulse, which can quantitatively separate the net splash loss (the part of the liquid that splashes out of the container and does not fall back), and use the total weight conservation method (total true intake = total weight reduction - cumulative splash net loss - total evaporation loss) to calculate the intake, which completely solves the problem of overestimation of intake and systematic error in preference rate calculation caused by splash loss in the traditional total weight method.

[0016] (2) This device not only outputs the total actual intake and sugar water preference rate during the test period, but also automatically records the absolute time, duration, net reduction, licking frequency, and time interval between adjacent events for each licking event (drinking round), generating a dataset containing the characteristics of a single licking behavior. These high-temporal-resolution time-series data can reveal subtle abnormalities in the licking rhythm, persistence, and reward response of depressed beagles, providing behavioral indicators that are unavailable through traditional methods for the study of depression mechanisms (such as neural circuit analysis of anhedonia) and the dynamic evaluation of drug intervention effects.

[0017] (3) This device integrates dynamic evaporation compensation (real-time monitoring of temperature, humidity, and airflow speed, and subtraction of evaporation loss using an empirical model) and adaptive consistency verification, effectively eliminating the interference of environmental factors on intake calculation. At the same time, it integrates signals from a triaxial accelerometer and a miniature microphone to automatically identify and remove data segments corresponding to abnormal behaviors such as head shaking, biting containers, and clawing at liquids, avoiding the subjectivity and lag of manual observation, ensuring that all data involved in the intake accumulation are valid licking events, and significantly improving the reliability of test data. Attached Figure Description

[0018] Figure 1 The signal processing flowchart provided by this invention; Figure 2 This is a schematic diagram of the overall structure of the present invention; Reference numerals: 1. Cage; 2. Signal processing unit; 3. Mechanical decoupling structure; 4. First container; 5. Second container; 6. Miniature microphone; 7. Triaxial accelerometer; 8. First weighing sensor; 9. Second weighing sensor; Detailed Implementation

[0019] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments: The comprehensive detection device for sugar water preference in depressed beagles provided by this invention mainly includes a first container and a second container, a first weighing sensor and a second weighing sensor, an auxiliary sensor, and a signal processing unit. Both the first and second containers are deep bowl-shaped structures made of stainless steel or food-grade plastic, with a depth of not less than 5 cm. The inner walls are mirror-polished to reduce splashing and adhesion of liquid during licking. The first container is used to hold sugar water with a concentration of 1% to 5%, and the second container is used to hold plain water (purified water). Two weighing sensors are fixedly installed directly below the two containers, employing a resistance strain gauge structure with a measuring range of 2 kg, an effective resolution of not less than 0.05 g under dynamic interference conditions, a sampling rate set to 400 Hz, and an analog voltage signal output. To ensure measurement accuracy, a mechanical decoupling structure is installed between the container and the sensor. This structure includes an elastic suspension element, a silicone oil damping layer filled between the container and the sensor, an inertial mass block, and rubber vibration isolation pads. This structure attenuates vibration interference from the 10-100Hz beagle's activity by at least 20dB, effectively isolating the impact of mechanical vibration on weight measurement. Auxiliary sensors include a triaxial accelerometer (range ±8g, sampling rate 400Hz) and a miniature MEMS microphone (frequency response 20Hz~20kHz), fixed to the bottom or support structure of each container. Their output signals are synchronously acquired with the weight signal to assist in identifying abnormal behaviors (such as head shaking, biting the container, and paws poking at liquid) and to help mark the start and end times of licking events, but they are not involved in the direct calculation of the actual ingested mass. The signal processing unit is based on an STM32F407 microcontroller and integrates a 24-bit analog-to-digital converter (ADC) to synchronously acquire the analog weight signals from the two weighing sensors at a sampling rate of 400Hz. and It also processes accelerometer and microphone signals, converting them into digital quantities for subsequent processing. The signal processing unit is pre-loaded with signal preprocessing algorithms, event detection algorithms, single licking pulse decomposition algorithms, splash net loss splitting rules, evaporation compensation models, and abnormal behavior recognition rules. The processing results can be stored on the built-in SD card or uploaded to an external computer via a wireless module.

[0020] To address the problem that traditional total weight methods cannot distinguish between actual ingestion and splash loss, this invention employs a combination of high-sampling-rate continuous weighing and an impact-recovery bi-exponential model to achieve quantitative separation of net splash loss. Specifically, before the start of a complete test cycle (e.g., 30 minutes), equipment initialization and static calibration are performed. A first container is filled with a known mass of sugar water (500.0g), and a second container is filled with a known mass of plain water (500.0g). The signal processing unit records the initial weights. and During the resting period without animal contact, the evaporation rates of the sugar water and plain water were measured separately: weight changes were recorded every 5 minutes, and the evaporation loss per unit time was calculated. and (Unit: g / s). Simultaneously, the device's built-in temperature and humidity sensors and anemometer monitor ambient temperature, humidity, and airflow speed in real time for subsequent dynamic evaporation compensation, thus solving the problem of ineffective evaporation interference in traditional methods. Next, a beagle was placed in the test environment, allowing it free access to the two containers. The signal processing unit began real-time detection of the transient event interval caused by the licking action in the weight signal of each container. The specific detection method was: real-time calculation of the first derivative of the weight signal. ,when The event start time is marked when the speed exceeds a first threshold (e.g., 0.5 g / s). ,when The event ends when the speed drops below a second threshold (e.g., 0.05 g / s) and the duration exceeds a preset silence duration (e.g., 0.5 seconds). Each event interval corresponds to a continuous licking sequence (i.e., a drinking round), after which the weight signal returns to a stable baseline. To improve detection robustness, accelerometer signals are used for auxiliary verification: if the accelerometer's summed acceleration exceeds 0.5g at the start of the weighing signal-triggered event, the event is confirmed as valid; otherwise, it is marked as a suspicious event and enters the review process.

[0021] To address the problem that traditional methods cannot provide characteristics of a single licking action, this invention further segments each event interval into single licking pulses and performs waveform fitting and parameter extraction on each pulse. Specifically, for each detected event interval, the signal processing unit segments several single licking pulses based on the extreme points of the first derivative of the weight signal: [The text then abruptly shifts to a different topic:] ...searching within the event interval... The negative maximum point (corresponding to the maximum rate at which the tongue carries out liquid) and the positive maximum point (corresponding to the maximum rate at which the liquid falls back) are used to define a single licking pulse, with the waveform between adjacent negative-positive extrema pairs typically lasting 0.2–0.5 seconds. Each single licking pulse is fitted using an impact-recovery biexponential model, the expression of which is: ,in The local baseline weight (in grams) before the pulse begins. The splash impact amplitude (grams) represents the weight drop caused by the liquid being expelled from the tongue at the moment of impact. The recovery amplitude (in grams) of the liquid fall back represents the weight gain caused by the splashed liquid falling back into the container, and satisfies the following conditions: ; and Let be the time constants (in seconds) for the impact process and the fallback process, respectively, and satisfy . ; Residual noise was considered. The Levenberg-Marquardt nonlinear least squares method was used for fitting, and the goodness of fit was calculated. .when When the fit is valid, the permanent mass loss of the single pulse is calculated as follows: (gram); when At this time, the pulse is not used for splash net loss resolution, and its permanent mass loss is directly calculated using the baseline difference before and after the pulse.

[0022] For an event interval containing multiple valid pulses, the sum of the permanent quality losses of all pulses is accumulated. Simultaneously, the difference in stable baseline weight before and after the start and end of the event interval is calculated as the total weight loss of the event. Then, based on the preset splash detection rules, from... The cumulative net splash loss mass within the event interval is extracted from the middle. (i.e., the mass of liquid that splashes out of the container and does not fall back in). The criterion is: when the ratio of the impact amplitude to the recovery amplitude of a pulse... Greater than the threshold or impact time constant At a certain time, it is determined that the pulse caused liquid splashing, and the excess portion of its permanent mass loss (i.e., This is counted as net splash loss, of which This is an empirical coefficient. This represents the typical intake mass corresponding to this pulse (obtained from historical normal pulse statistics); if there is no splashing, the net splash loss is zero. The net reduction of this event is calculated using the following formula: (grams). Net loss has been reduced by splash loss, but not by evaporation loss, which needs to be addressed in the total weight stage. In this way, the true intake is separated from splash loss, solving the problem of overestimation of intake due to splashing in the traditional total weight method.

[0023] After the test cycle ends, the signal processing unit calculates the total weight reduction for each container. ,in This is the stable weight at the end of the test. It also includes the sum of net splash losses across all event intervals for the container. The total evaporation loss was calculated using a dynamic evaporation model. ,in The ambient temperature, humidity, and airflow speed are monitored in real time and then analyzed using empirical formulas. We obtain the coefficients. , , The total actual intake of the container was determined through a pre-established static test. The total actual intake of sugar water was obtained separately. Total actual intake of plain water The preference rate for sugary drinks is calculated using the following formula: Simultaneously, the signal processing unit records the following behavioral parameters for each licking event (drinking round): absolute time (timestamp from the start of the test), event duration, and other parameters. The net reduction of the event The dataset includes the licking frequency per unit time (the number of single pulses within an event interval divided by the event duration) and the time interval between two adjacent events. This data generates a dataset containing characteristics of individual licking behaviors, which can be exported for behavioral analysis in depression models, thus solving the problem that traditional methods cannot provide characteristics of individual behaviors.

[0024] In this invention, during event detection and pulse segmentation, the signal processing unit simultaneously analyzes accelerometer and microphone signals to identify abnormal behavior and remove corresponding data segments. Specifically, the rules are as follows: when the weight signal exhibits a transient spike exceeding 2g within 50ms and recovers, and the combined accelerometer acceleration exceeds 1.0g, it is determined to be head-flipping behavior; when the weight signal exhibits continuous irregular oscillations lasting longer than 1 second, and the microphone detects an impact sound, it is determined to be container-biting behavior; when the weight signal exhibits a bidirectional pulse (negative then positive or positive then negative) with a width less than 100ms and the accelerometer shows no corresponding activity, it is determined to be claw-like manipulation of liquid. The data segments corresponding to these abnormal events are not included in the splash net loss breakdown and intake accumulation, and the removal reason is marked in the dataset, thereby achieving automated identification and removal of abnormal behavior.

[0025] The technical effects of this invention were verified through the following experiments. Ten healthy beagle dogs (weighing 11.5±1.2kg, male, 2-3 years old) underwent a 30-minute sugar water preference test using this device. High-speed video recording (500 frames / second) combined with manual frame-by-frame calibration of liquid intake was used as the gold standard. The output of this device was compared with the statistical results of the gold standard for verification. 200 independent licking events were randomly selected from each dog (a total of 2000 events), as shown in Tables 1 to 3. The experimental results show that the average relative error of this device in measuring the net loss per event is 4.0%, the absolute error in measuring the total actual intake over 30 minutes is less than 0.9g, and the absolute error in the sugar water preference rate is less than 0.01. The measurement accuracy is significantly better than the traditional total weight method (the absolute error of the traditional method is approximately 0.14). The mechanical decoupling structure improved the signal-to-noise ratio from -6.4dB to +6.8dB. The impulse fit goodness reached over 0.90 in 94% of cases, indicating a reliable model.

[0026] Table 1 Comparison of Measurement Errors for a Single Event ; Table 2 Comparison of Total Intake and Preference Rate over 30 Minutes ; Table 3. Goodness-of-fit distribution of a single licking pulse ; In a specific implementation of this invention, the following parameters are preferred: a first threshold (event start) of 0.5 g / s, a second threshold (event end) of 0.05 g / s, a silence duration of 0.5 seconds, a sampling rate of 400 Hz, a 24-bit ADC for the signal processing unit, a viscosity of 10000 cSt for the silicone oil damping layer of the mechanical decoupling structure, an evaporation compensation update frequency of once every 30 seconds, a splash discrimination impact / recovery ratio threshold of 1.5, and a head-throwing acceleration threshold of 1.0 g for abnormal behavior recognition. These parameters can be appropriately adjusted according to the dog's size and behavioral characteristics, all falling within the scope of this invention. This invention is not limited to beagles; it can also be used for monitoring the drinking behavior of other small experimental animals (such as mice and rats), requiring only corresponding adjustments to the container size and sensor range. Furthermore, the fitting algorithm in the impact-recovery bi-exponential model can be replaced with particle filtering or Kalman filtering; as long as the net splash loss can be separated, it is considered an equivalent technical solution.

[0027] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0028] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A comprehensive detection device for sucrose preference in depressed beagles, characterized in that, include: The first container and the second container are used to hold sugar water and plain water, respectively; The first and second weighing sensors are respectively installed at the bottom of the first and second containers, and are used to acquire the weight signals of each container at a sampling rate of not less than 200Hz. and The first and second containers are provided with a mechanical decoupling structure between themselves and the corresponding weighing sensors to attenuate vibration interference caused by the beagle's activity. The signal processing unit, electrically connected to the weighing sensor, is used to: detect the transient event intervals caused by the beagle licking action in the weight signals of each container respectively; Each event interval corresponds to a continuous licking sequence, after which the weight signal returns to a stable baseline. The weight waveform within each event interval is segmented by a single licking pulse to separate the rapid disturbance components representing splash impact and liquid fallback in each pulse; The total weight loss of the event is obtained by calculating the difference between the stable baseline weight before and after the start of the event interval, and the net loss of splash accumulated during the event interval (i.e. the mass of liquid splashed out of the container and not returned) is deducted from the total weight loss of the event to obtain the net loss of the event. Calculate the total actual intake of each container during the testing period. and The total actual intake for each container is calculated using the following formula: Total actual intake = Total weight loss of container during the test period - Cumulative net splash loss of the container - Total evaporation loss of the container; Calculate the sugar water preference rate ; Record the time, duration, and net reduction of each licking event to generate a dataset containing the characteristics of a single licking behavior.

2. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that, The method for the signal processing unit to detect event intervals is as follows: real-time calculation of the first derivative of the weight signal. ,when The event is marked to begin when the first threshold is exceeded; when The event ends when the value is below the second threshold and the duration exceeds the preset silence time.

3. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that, The segmentation of the single licking pulse and the separation of the rapid disturbance component are performed in the following manner: First, within a single event interval, several single licking pulses are segmented based on the extreme points of the first derivative of the weight signal; Each single licking pulse was fitted using an impact-recovery biexponential model, the expression of which is: in This represents the local baseline weight before the pulse begins. This represents the amplitude of the splash impact. Let be the recovery amplitude of the liquid fall, and satisfy . ; and Let be the time constants for the impact process and the fallback process, respectively, and satisfy . ; This is residual noise; The single pulse waveform is fitted using the nonlinear least squares method. When the goodness of fit is... When the value is greater than 0.9, the permanent mass loss of this single pulse is calculated as follows: ; The permanent mass loss of all single pulses meeting the fitting requirements within a single event interval is accumulated. Simultaneously, the difference in stable baseline weight before and after the event interval is calculated as the total weight loss for the event. ; Based on the ratio of the impact amplitude to the recovery amplitude of a single pulse and the characteristics of the time constant, a preset splash discrimination rule is matched to extract the cumulative net splash loss mass within the event interval from the cumulative permanent mass loss, denoted as . (i.e., the portion of liquid that splashes out of the container and does not fall back in); The net reduction of this event is calculated using the following formula:

4. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that... The signal processing unit also records at least one of the following licking behavior parameters: licking frequency per unit time, duration of a single licking event, net reduction of a single licking event, time interval between two adjacent licking events, and absolute time of occurrence of the licking event.

5. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that: The effective resolution of the weighing sensor is not less than 0.05 grams under dynamic interference environment, and the sampling rate is 400 Hz. The signal processing unit includes a 24-bit analog-to-digital converter.

6. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that: It also includes a triaxial accelerometer and a miniature microphone, mounted on the first container and / or the second container or their supporting structure, to assist in the identification of abnormal behavior, but not to participate in the direct calculation of the actual intake quality.

7. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that: The signal processing unit also performs evaporation compensation: during the test cycle, it monitors the ambient temperature, humidity and airflow speed in real time, and uses a preset evaporation rate model to dynamically calculate the evaporation loss of sugar water and plain water for the calculation of actual intake.

8. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 6, characterized in that... The signal processing unit also performs an abnormal behavior identification step: identifying and eliminating abnormal events based on the weight waveform, accelerometer and microphone signals; Specifically, when the weight signal experiences a momentary spike of more than 2g within 50ms and then recovers, and the accelerometer reading exceeds 1.0g, it is determined to be a head-shaking behavior. When the weight signal exhibits continuous irregular oscillations lasting longer than 1 second, and the microphone detects an impact sound, it is determined to be a container biting behavior. When a bidirectional pulse with a width of less than 100ms appears in the weight signal and the accelerometer does not show corresponding activity, it is determined that the claw is stirring the liquid. The data segments corresponding to the above-mentioned abnormal events are not included in the intake accumulation.

9. The comprehensive detection device for sucrose preference in depressed beagles as described in claim 1, characterized in that: The first and second containers are deep bowl-shaped structures with a depth of not less than 5cm. The inner walls are mirror-polished to reduce the amount of splashing of sugar water or water during the licking process.

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