CUMS automated modeling system and method

Abstract

The present application discloses a CUMS automated modeling system and method, the system includes: a stress module, a stimulation module, and a detection module, wherein: the stress module is used to determine n small animal sets corresponding to n stress cages; determine n-1 adaptation periods corresponding to n-1 experimental animal sets; the stimulation module is used to formulate stimulation schemes for n-1 experimental animal sets based on n-1 adaptation periods and m stimulation units, and obtain n-1 stimulation schemes; the small animals in the n-1 experimental animal sets are automatically stimulated according to the n-1 stimulation schemes, and at the same time, no stimulation is applied to the small animals in the control animal set; the detection module is used to determine n-1 mental test results and control mental test results; and the target modeling result is determined based on the n-1 mental test results and control mental test results. The embodiment of the present application is used to improve the standardization of the CUMS stimulation process.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a CUMS automated modeling system and method. Background Art

[0002] In experiments investigating the pathogenesis of depression, the use of stress as a stressor to establish animal models of depression has gained widespread recognition both domestically and internationally. The Chronic Unpredictable Mild Stress (CUMS) model is widely used in depression research. This modeling method closely resembles the pathogenesis of depression and is currently a commonly used depression modeling method.

[0003] At present, the standardization of the CUMS stimulation process is low, and human factors have a great impact on the experiment. Therefore, how to improve the standardization of the CUMS stimulation process has become an urgent problem to be solved. Summary of the Invention

[0004] The embodiments of the present application provide a CUMS automated modeling system and method, which improves the standardization of the CUMS stimulation process.

[0005] In a first aspect, an embodiment of the present application provides a CUMS automated modeling system, the system comprising: a stress module, a stimulation module, and a detection module, the stress module comprising n stress cages, the stimulation module comprising m stimulation units, where m and n are both integers greater than 1, wherein:

[0006] The stress module is used to determine a small animal set corresponding to each of the n stress cages to obtain n small animal sets; the n small animal sets include n-1 experimental animal sets and a control animal set; each small animal set includes at least one small animal; determine an adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods;

[0007] The stimulation module is configured to formulate a stimulation plan for the n-1 experimental animal sets based on the n-1 adaptation periods and the m stimulation units, thereby obtaining n-1 stimulation plans; automatically stimulating the small animals in the n-1 experimental animal sets according to the n-1 stimulation plans, while not applying any stimulation to the small animals in the control animal set;

[0008] The detection module is used to determine n-1 mental test results corresponding to the n-1 experimental animal sets, and control mental test results corresponding to the control animal set; determine the target modeling result based on the n-1 mental test results and the control mental test results; the target modeling result includes modeling success or modeling failure.

[0009] In a second aspect, an embodiment of the present application provides a CUMS automated modeling method, which is applied to a CUMS automated modeling system. The system includes: a stress module, a stimulation module, and a detection module. The stress module includes n stress cages, and the stimulation module includes m stimulation units, where m and n are both integers greater than 1. The method includes:

[0010] Determining a small animal set corresponding to each of the n stress cages to obtain n small animal sets; the n small animal sets include n-1 experimental animal sets and one control animal set; each small animal set includes at least one small animal; determining an adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods;

[0011] formulating a stimulation plan for the n-1 experimental animal set based on the n-1 adaptation periods and the m stimulation units to obtain n-1 stimulation plans; automatically stimulating the small animals in the n-1 experimental animal set according to the n-1 stimulation plans, while not applying any stimulation to the small animals in the control animal set;

[0012] Determine n-1 psychiatric test results corresponding to the n-1 experimental animal sets, and control psychiatric test results corresponding to the control animal set; determine a target modeling result based on the n-1 psychiatric test results and the control psychiatric test results; the target modeling result includes modeling success or modeling failure.

[0013] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the second aspect of the embodiment of the present application.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the second aspect of the embodiment of the present application.

[0015] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to execute some or all of the steps described in the second aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0016] The implementation of this application has the following beneficial effects:

[0017] It can be seen that the CUMS automated modeling system described in the present application formulates stimulation plans for n-1 sets of experimental animals through the stimulation module based on the n-1 adaptation periods and m stimulation units determined by the stress module. Since the adaptation period of each set of experimental animals is determined according to its own characteristics, the stimulation plan formulated based on this can fully take into account the tolerance and response differences of different animal sets to stimulation, making the stimulation more accurate and personalized. At the same time, a variety of stimulation methods can be combined using m stimulation units, and in the process of automatic stimulation, the preset stimulation plan can be strictly followed to ensure the consistency and repeatability of the stimulation, thereby improving the standardization of the CUMS stimulation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0019] Figure 1 This is a schematic structural diagram of a CUMS automated modeling system provided in an embodiment of the present application;

[0020] Figure 2 This is a schematic diagram of the structure of a stress module provided in an embodiment of the present application;

[0021] Figure 3 This is a flow chart of a method for determining an adaptation period provided in an embodiment of the present application;

[0022] Figure 4 This is a schematic structural diagram of a stimulation module provided in an embodiment of the present application;

[0023] Figure 5 This is a schematic diagram of the structure of a detection module provided in an embodiment of the present application;

[0024] Figure 6 This is a structural diagram of another CUMS automated modeling system provided in an embodiment of the present application;

[0025] Figure 7 This is a flow chart of a CUMS automated modeling method provided in an embodiment of the present application;

[0026] Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0029] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document indicates that the associated objects are in an "or" relationship. The "plurality" appearing in the embodiments of this application refers to two or more.

[0030] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0031] The "connection" appearing in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0032] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] The electronic device described in the embodiments of the present application may include a CUMS automated modeling system.

[0034] The following describes the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0035] First, some professional terms involved in this application are explained:

[0036] The Chronic Unpredictable Mild Stress (CUMS) model is a method for constructing animal models that simulate human psychiatric disorders such as depression. By subjecting experimental animals (such as mice and rats) to a series of long-term, unpredictable mild stress stimuli, such as noise exposure, food and water deprivation, and a reversal of day and night, the animals gradually develop behavioral, physiological, and psychological changes similar to those seen in human depression, such as weight loss, anhedonia, decreased activity, and increased anxiety-like behaviors. This provides an experimental basis for studying the pathogenesis of depression, as well as drug development and treatments.

[0037] Acclimation period: Before conducting CUMS modeling or other related experiments, experimental animals are allowed to live in a specific environment (such as a stimulation cage) for a period of time. This period is called the acclimation period. The purpose of this period is to allow the animals to become familiar with the experimental environment and equipment, eliminate stress reactions caused by factors such as environmental changes and new equipment, and stabilize the animals' physiological and psychological states. This allows subsequent experimental stimulation to more accurately simulate natural stress conditions, reduce experimental errors, and improve the reliability of experimental results.

[0038] Modeling, also known as model construction, in medical and biological research, refers to the use of specific methods and means to make experimental subjects such as animals or cells produce characteristics and manifestations similar to human diseases or specific physiological or pathological conditions, thereby establishing experimental models that can be used to study disease mechanisms, drug screening, and treatment exploration. In CUMS, a series of mild stress stimuli are used to induce depression-like states in experimental animals, thus constructing an animal model of depression.

[0039] See also Figure 1 , Figure 1 : This is a schematic diagram of the structure of a CUMS automated modeling system (hereinafter referred to as the system) provided in an embodiment of the present application. It can be seen that the system includes: a stress module, a stimulation module, and a detection module. The stress module includes n stress cages, and the stimulation module includes m stimulation units, where m and n are both integers greater than 1, wherein:

[0040] The stress module is used to determine the small animal set corresponding to each of the n stress cages to obtain n small animal sets; the n small animal sets include n-1 experimental animal sets and one control animal set; each small animal set includes at least one small animal; and determine the adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods.

[0041] In the embodiment of the present application, the small animals may include at least one of the following: mice, rats, guinea pigs, hamsters, etc., which are not limited here.

[0042] In a specific embodiment, the stress module may further include a camera unit (e.g., a camera), which collects image data of n stress cages through the camera unit, performs image recognition based on the image data, and obtains n small animal sets. Alternatively, the staff may input the data of small animals placed in the n stress cages into the stress module in advance, thereby obtaining n small animal sets; then, the adaptation period corresponding to each of the n-1 experimental animal sets may be determined to obtain n-1 adaptation periods.

[0043] It should be explained that, in actual operation, the n-1 adaptation periods can be set manually, and the sizes of the n-1 adaptation periods can be equal. For example, the n-1 adaptation periods can all be 3 days.

[0044] It should be explained that the small animals in the n stress cages can be suitable small animal breeds and strains determined by the staff according to the experimental needs, such as the commonly used C57BL / 6 mice, SD rats, etc., which can be purchased from regular animal suppliers to ensure that the animals have a clear source, are in good health, and have a relatively consistent genetic background, thereby reducing the impact of individual differences on the experimental results. To ensure the objectivity and scientific nature of the CUMS experimental results, a random grouping method can be adopted. All small animals can be numbered using tools such as a random number generator, and then randomly assigned to n stress cages according to the numbers, so that the number of animals in each stress cage is roughly equal, and to ensure as much as possible that there are no significant differences in age, weight, sex and other factors among each group of animals, so as to avoid deviations in experimental results due to uneven grouping. Thus, n groups, i.e., n sets of small animals, are obtained.

[0045] In one embodiment, see Figure 2 , Figure 2 is a structural diagram of a stress module provided in an embodiment of the present application. It can be seen that the stress module includes n stress cages, specifically: a first stress cage, a second stress cage, ..., an nth stress cage; Figure 2 The ellipsis “…” in the figure indicates that there are more stress cages in the stress module, which are not fully displayed.

[0046] Taking the first stress cage as an example, the first stress cage may include the following structures:

[0047] Cage: Usually made of non-toxic, corrosion-resistant materials that are easy to clean and disinfect, such as transparent or opaque plastic. The cage should be large enough to accommodate the normal activities, rest, and diet needs of small animals. For example, a mouse cage is typically 25-30 cm long, 15-20 cm wide, and 12-15 cm high. The cage should have a retractable door to facilitate entry and exit of the animal and daily handling.

[0048] Bedding: Placed at the bottom of the cage, it provides warmth, absorbs moisture, and acts as a buffer. Common bedding materials include sawdust and corn cobs. Bedding must be replaced automatically and regularly to maintain cage cleanliness and prevent animals from contracting diseases through contact with unclean bedding. It should be noted that if water immersion is applied during the modeling process, the system will automatically replace the bedding immediately after the immersion ends.

[0049] Food and drinking water systems: These include troughs and water bottles. The trough is used to hold feed and is typically a plastic or metal container that can be fixed to the cage. Water bottles are often suspended to ensure adequate food and water supply during stressful situations. Unless under food or water deprivation, animals are free to drink and drink.

[0050] Environmental control components: may include heating components and cooling components to adjust the environmental parameters in the cage to ensure that they are within a range suitable for animal survival; there may also be ventilation devices to maintain air circulation in the cage and avoid the accumulation of harmful gases.

[0051] Stress cages are mainly used for animal placement, providing living space for small animals. Small animals are placed in different stress cages according to the experimental design to achieve group management of animals and facilitate subsequent experimental operations and observations. The system puts animals in a state of stress by controlling the environmental conditions in the cage (such as changing temperature, humidity, light cycle, etc.) or applying specific stimuli (such as sound, odor, etc.). It is the basic place for constructing experiments such as CUMS, simulating the stress conditions faced by animals in natural or diseased states.

[0052] Optional, see Figure 3 , Figure 3 This is a flow chart of a method for determining an adaptation period provided by an embodiment of the present application; in determining the adaptation period corresponding to each of the n-1 experimental animal sets to obtain the n-1 adaptation period, the stress module is specifically used to perform Figure 3 Steps shown:

[0053] A1. Acquire first basic data of a first stress cage corresponding to a first experimental animal set; the first experimental animal set is any one of the n-1 experimental animal sets;

[0054] A2. Determine the animal type of each small animal in the first experimental animal set, obtaining p animal types; p is a positive integer;

[0055] A3. Determine the first adaptation period corresponding to each of the p animal types, to obtain p first adaptation periods;

[0056] A4. Determine the adaptation period corresponding to the first set of experimental animals based on the first basic data and the p first adaptation periods.

[0057] In the embodiment of the present application, the first basic data may include at least one of the following: size data (e.g., length, width, and height), environmental data, equipment status data (e.g., the amount of food remaining in the trough of the first stress cage), etc., which are not limited here.

[0058] In a specific embodiment, first basic data of a first stress cage corresponding to the first experimental animal set is obtained. Specifically, the first basic data may be environmental data. An environmental sensor (e.g., a temperature sensor, a humidity sensor) may be provided in the first stress cage to detect the environmental data of the first stress cage through the environmental sensor, thereby obtaining the first basic data. Next, the animal type of each small animal in the first experimental animal set may be determined to obtain p animal types. Specifically, a source information document of the first experimental animal set may be obtained from a database of the system. The source information document may clearly record information such as the breed and strain of the animal. For example, assuming that the first experimental animal set consists of C57BL / 6 mice, SD rats, etc. purchased from an animal supplier, the source information provided by the animal supplier may indicate the accurate strain information of the animal. By consulting the source information document, p animal types may be obtained.

[0059] Next, the first adaptation period corresponding to each of the p animal types can be determined to obtain p first adaptation periods. Specifically, the mapping relationship between preset animal types and first adaptation periods can be pre-stored, and the p first adaptation periods corresponding to the p animal types can be determined based on the mapping relationship; then, the adaptation period corresponding to the first experimental animal set can be determined based on the first basic data and the p first adaptation periods.

[0060] By determining p animal types and corresponding p first adaptation periods, we can tailor adaptation time to each animal type. For example, C57BL / 6 mice and SD rats, due to their small size and rapid metabolism, may adapt more quickly to a new environment, while SD rats, due to their large size and relatively slow physiological regulation, may require a longer adaptation period. Setting adaptation periods based on animal type avoids the risk of under- or over-adaptation due to uniform standards. This ensures that all animals are in a relatively stable physiological and psychological state before formal experiments begin, reducing experimental errors caused by differences in adaptation and improving experimental accuracy.

[0061] Optionally, in determining the adaptation period corresponding to the first set of experimental animals based on the first basic data and the p first adaptation periods, the stress module is specifically configured to:

[0062] B1. Determine the health level of each small animal in the first experimental animal set to obtain p health levels;

[0063] B2. Determine a health level greater than or equal to a preset health level, and b health levels less than the preset health level, among the p health levels; a and b are both natural numbers less than or equal to p, and a + b = p;

[0064] B3. Determine the first adaptation period corresponding to the a health levels among the p first adaptation periods, to obtain a first adaptation period;

[0065] B4. Determine the first adaptation period corresponding to the b health levels among the p first adaptation periods, to obtain b first adaptation periods;

[0066] B5. Determine a first impact factor corresponding to the first basic data;

[0067] B6. determining the second influencing factors corresponding to the m stimulation units;

[0068] B7. Adjust the b first adaptation periods according to the first influencing factor and the second influencing factor to obtain b second adaptation periods;

[0069] B8. Determine the adaptation period corresponding to the first set of experimental animals based on the a first adaptation periods and the b second adaptation periods.

[0070] In the embodiment of the present application, the health level may include one of the following: very poor, poor, average, good, excellent, etc., which is not limited here; the preset health level can be preset or defaulted in advance, for example, the preset health level can be "average".

[0071] In a specific embodiment, the health level of each small animal in the first experimental animal set can be determined to obtain p health levels. Specifically, a health check can be performed on each small animal to obtain p health levels. The health check can include at least one of the following: appearance inspection, behavioral observation, physiological indicator detection, etc., which are not limited here.

[0072] For example, suppose there are two small animals (mice) in the first experimental animal set, labeled Mouse A and Mouse B. Now they are undergoing health checks to determine their health levels. The following is the specific process and results:

[0073] Mouse A:

[0074] Appearance examination: The hair is smooth and shiny, the eyes are bright and clear, the ears are clean and have no odor, the mouth has no abnormalities, and the body shape is well-proportioned.

[0075] Behavioral observation: strong activity, agile movements, normal eating and drinking, natural sleeping and resting postures, and good interaction with companions.

[0076] Physiological indicator tests: body temperature 37.5 degrees Celsius, heart rate 400 beats / minute, respiratory rate 120 times / minute, blood routine and biochemical indicators are normal, stool is formed and no parasite eggs and occult blood are detected.

[0077] Health grade assessment: Based on the comprehensive examination results, the health of mouse A was good and was assessed as "good".

[0078] Mouse B:

[0079] Appearance examination: The hair is slightly rough, there is a small amount of secretion in the eyes, the ears are normal, and the gums in the mouth are slightly red and swollen.

[0080] Behavioral observation: slightly weaker activity ability, decreased appetite, occasional awakening during sleep, and less interaction with companions.

[0081] Physiological indicator tests: body temperature 37.8 degrees Celsius, heart rate 420 beats / minute, respiratory rate 130 times / minute, blood routine showed slightly high white blood cell count, biochemical indicators were basically normal, and stool was loose and soft.

[0082] Health grade assessment: According to the examination results, Mouse B may have a mild infection or inflammation, and its health condition is generally good, so it is assessed as "general".

[0083] Furthermore, a health level greater than or equal to the preset health level and b health levels less than the preset health level can be found out of the p health levels; then, the first adaptation period corresponding to the a health level in the p first adaptation periods can be determined to obtain a first adaptation period. Specifically, a small animal corresponding to the a health level in the first experimental animal set can be first determined, and a first adaptation period corresponding to the a small animal in the p first adaptation periods can be determined; then, the first adaptation period corresponding to the b health level in the p first adaptation periods can be determined to obtain b first adaptation periods. Specifically, the method for determining the b first adaptation periods can be the same as the method for determining the a first adaptation period.

[0084] Then, the first influencing factor corresponding to the first basic data can be determined. Specifically, the mapping relationship between the preset basic data and the influencing factor can be pre-stored, and the first influencing factor corresponding to the first basic data can be determined based on the mapping relationship; then, the second influencing factors corresponding to the m stimulation units can be determined. Specifically, the mapping relationship between the preset stimulation units and the influencing factors can be pre-stored, and the m influencing factors corresponding to the m stimulation units can be determined based on the mapping relationship. Then, m weights corresponding to the m stimulation units can be determined, each stimulation unit corresponds to a weight, and the sum of the m weights is 1. Specifically, m stimulation types corresponding to the m stimulation units can be obtained, each stimulation unit corresponds to a stimulation type, and the mapping relationship between the preset stimulation type and the weight can be pre-stored, and the m weights corresponding to the m stimulation types can be determined based on the mapping relationship. Then, a weighted operation is performed based on the m influencing factors and the m weights to obtain the second influencing factor; wherein, the value range of the first influencing factor and the second influencing factor can both be -0.3~0.3; then, the b first adaptation periods can be adjusted according to the first influencing factor and the second influencing factor, and the specific calculation formula is as follows:

[0085] Target second adaptation period = target first adaptation period × (1 + first impact factor) × (1 + second impact factor);

[0086] Among them, the target first adaptation period is any first adaptation period among the b first adaptation periods; the target second adaptation period is the second adaptation period corresponding to the target first adaptation period among the b second adaptation periods; according to the above formula, b second adaptation periods can be obtained by calculating b times; finally, the adaptation period corresponding to the first set of experimental animals can be determined based on a first adaptation period and b second adaptation periods. Specifically, the maximum value among a first adaptation periods and b second adaptation periods can be determined, and the maximum value can be used as the adaptation period corresponding to the first set of experimental animals, or the average value corresponding to a first adaptation period and b second adaptation periods can be calculated, and the average value can be used as the adaptation period corresponding to the first set of experimental animals.

[0087] In this way, by determining the health level of each small animal in the first experimental animal set, p health levels are obtained, and they are divided into a health level greater than or equal to the preset health level and b health levels less than the preset health level. This allows for a more detailed understanding of the distribution of the health status of the experimental animals, facilitating subsequent analysis of animals with different health conditions. Because animals with different health conditions may differ in their performance in the experiment and their ability to adapt to the environment, this classification helps to more accurately study the relationship between the animal adaptation period and health level; in addition, the b first adaptation periods are adjusted according to the first influencing factor and the second influencing factor to obtain b second adaptation periods. This is because animals with lower health levels may be more sensitive to various factors in the experimental environment and their adaptation period needs to be adjusted according to actual conditions; through this targeted adjustment, the adaptation needs of animals with different health conditions can be more accurately met, the interference of environmental factors on the experimental results can be reduced, and the accuracy and reliability of the experiment can be improved.

[0088] The stimulation module is used to formulate a stimulation plan for the n-1 experimental animal set based on the n-1 adaptation periods and the m stimulation units, thereby obtaining n-1 stimulation plans; and automatically stimulate the small animals in the n-1 experimental animal set according to the n-1 stimulation plans, while not applying any stimulation to the small animals in the control animal set.

[0089] In an embodiment of the present application, the stimulation module can be used to formulate a stimulation plan for n-1 sets of experimental animals based on n-1 adaptation periods and m stimulation units, thereby obtaining n-1 stimulation plans; each stimulation plan is for one set of experimental animals; then, the stimulation module can automatically control the m stimulation units to work according to the n-1 stimulation plan to stimulate the small animals in the n-1 sets of experimental animals, and at the same time, the stimulation module does not apply any stimulation to the small animals in the above-mentioned control animal set.

[0090] In addition, in order to avoid affecting the animals in the control animal set when stimulating the animals in the n-1 experimental animal set, the stress cage of the control animal set can be placed away from the stress cage of the experimental animal set. Alternatively, the experimental area (i.e., the area where the animals in the n-1 experimental animal set are located) and the control area (i.e., the area where the animals in the control animal set are located) can be wrapped with sound insulation materials to avoid the noise and vibration during the experiment from affecting the animals in the control animal set.

[0091] It should be explained that, in the embodiment of the present application, only one control animal set is set. In actual operation, one or more control animal sets can be set according to experimental requirements.

[0092] In one embodiment, see Figure 4 , Figure 4: is a schematic diagram of the structure of a stimulation module provided in an embodiment of the present application. It can be seen that the stimulation module may include: a crowding stimulation unit, a vertical stimulation unit, ..., a spray stimulation unit, etc., which are not limited here; Figure 4 The ellipsis “…” in the figure indicates that there are more stimulus units in the stimulus module, which are not fully displayed.

[0093] In one embodiment, m may be equal to 9, and the stimulation module may include: a crowding stimulation unit, a vertical stimulation unit, a wire stimulation unit, a food (drink) deprivation unit, a light stimulation unit, a noise stimulation unit, a humidity stimulation unit, a spray stimulation unit, an air blowing stimulation unit, and the like, which are not limited here. Through these 9 stimulation units, 20 contactless modeling methods can be achieved, including crowded space, tilted cage position (left), tilted cage position (right), bumps, wire mesh floor, sleep deprivation (a mixture of bumps, electric shocks, and air), water deprivation, food deprivation, food and water deprivation, wet bedding, 5-minute spraying, short-term strong noise, continuous white noise, night illumination, strong white light strobe, foot electric shock, cold air (4 degrees Celsius), hot air (40 degrees Celsius), air blowing (room temperature), and immersion in water. The details are as follows:

[0094] Crowding stimulation unit: It can be composed of a stepper motor and a baffle, and is used to simulate a crowded space environment. The baffle is pushed by the stepper motor to move, thereby adjusting the size of the animal's activity space in the stress cage. The activity space can be artificially reduced by 0%~90%.

[0095] Vertical stimulation unit: This can be two push rods set at the bottom of the stress cage; by moving the push rods, the stress cage can be tilted to the left or right, corresponding to the "tilted cage position (left)" and "tilted cage position (right)" modeling methods.

[0096] Wire stimulation unit: A structure with wires that can achieve stimulation such as wire mesh flooring and foot electric shock.

[0097] Drinking (food) withholding unit: can be an electrically controlled valve; by controlling the switch of the electrically controlled valve, the animal's diet can be controlled to achieve water withholding, food withholding, food withholding + water withholding and other states.

[0098] Light stimulation unit: used to achieve light-related stimulation such as night lighting, strong white light strobe, etc.

[0099] Noise stimulation unit: generates white noise, noise of a specific intensity (such as 100 decibels), etc.

[0100] Humidity stimulation unit: creates a moist litter or waterlogged environment.

[0101] Spray stimulation unit: perform spray operation, such as spraying for 5 to 30 minutes.

[0102] Air blowing stimulation unit: blows out airflow of different temperatures (such as 4 degrees Celsius cold air, 40 degrees Celsius hot air, room temperature air).

[0103] Optionally, the system further includes an automatic bedding changing module. In terms of formulating a stimulation plan for the n-1 set of experimental animals based on the n-1 adaptation periods and the m stimulation units to obtain the n-1 stimulation plan, the stimulation module is specifically configured to:

[0104] C1. Obtain a second set of experimental animals and its corresponding third adaptation period; the second set of experimental animals is any one of the n-1 experimental animal sets;

[0105] C2. Determining a first stimulation cycle based on the third adaptation period; the first stimulation cycle is greater than the third adaptation period; the first stimulation cycle includes c days; c is a positive integer;

[0106] C3. Determine the working time points of the automatic bedding material changing module in the first stimulation cycle based on a preset time interval, and obtain d working time points; d is a positive integer;

[0107] C4. For each day of the first stimulation cycle, randomly select e stimulation units from the m stimulation units to obtain c stimulation unit sets; e is an integer greater than 1 and less than or equal to m;

[0108] C5. determining a control parameter set corresponding to each of the c stimulation unit sets to obtain c control parameter sets;

[0109] C6. Determine a stimulation scheme corresponding to the second set of experimental animals based on the first stimulation cycle, the d working time points, the c stimulation unit sets, and the c control parameter sets.

[0110] In the embodiment of the present application, the preset time interval can be preset in advance or defaulted.

[0111] In a specific embodiment, the second set of experimental animals and their corresponding third adaptation period can be obtained first; then, the first stimulation cycle can be determined based on the third adaptation period; then, the working time points of the automatic bedding changing module in the first stimulation cycle can be determined based on the preset time interval, and d working time points can be obtained. Specifically, the first day in the first stimulation cycle can be used as the first working time point, and the first day can be used as the starting point to calculate backward in sequence, thereby obtaining d working time points. For example, if the preset time interval is 3 days, then the second working time point is the 4th day of the first stimulation cycle, and the third working time point is the 7th day, and so on, until d working time points are determined; this method is simple and direct, and is suitable for situations where bedding consumption is relatively stable and experimental conditions do not change much. It can ensure that the bedding is replaced at relatively regular time intervals to maintain the stability of the experimental environment.

[0112] It should be explained that the start time of the first stimulation cycle is later than the end time of the third adaptation period.

[0113] Then, for each day in the first stimulation cycle, e stimulation units can be randomly selected from the m stimulation units to obtain c stimulation unit sets; further, the control parameter set corresponding to each stimulation unit set in the c stimulation unit sets can be determined to obtain c control parameter sets; then, the stimulation scheme corresponding to the second set of experimental animals is determined based on the first stimulation cycle, d working time points, c stimulation unit sets and c control parameter sets. Specifically, the first stimulation cycle is divided into c intervals, one interval represents one day, and a mapping relationship between the c intervals and the c stimulation unit sets is established to obtain a first mapping relationship. Then, a mapping relationship between the c stimulation unit sets and the c control parameter sets can be obtained to obtain a second mapping relationship. The stimulation scheme corresponding to the second set of experimental animals is determined based on the first mapping relationship, the second mapping relationship and the d working time points. For example, assuming that c is equal to 2 and d is equal to 1, the first stimulation cycle can be divided into a first interval (first day) and a second interval (second day). The first interval corresponds to the noise stimulation unit set and the second interval corresponds to the fasting stimulation unit set. The working time point is 8 am on the first day. The stimulation scheme is as follows:

[0114] Day 1: At 8 a.m., the automatic bedding changing module is controlled to work and replace the bedding in the stress cage. Then, the noise stimulation unit set for the first day can be determined according to the first mapping relationship, and the noise control parameter set corresponding to the noise stimulation unit set can be determined according to the second mapping relationship. The noise stimulation unit set is controlled to work with the noise control parameter set to stimulate the small animals in the stress cage.

[0115] The second day: the fasting stimulation unit set for the second day can be determined according to the first mapping relationship, the fasting control parameter set corresponding to the fasting stimulation unit set can be determined according to the second mapping relationship, and the fasting stimulation unit set can be controlled to work according to the fasting control parameter set to stimulate the small animals in the stress cage.

[0116] Thus, for each day of the first stimulation cycle, e stimulation units were randomly selected from the m stimulation units, resulting in c stimulation unit sets. This random selection method increases the diversity and complexity of the stimulation factors in the experiment, allowing for a more comprehensive study of the effects of different stimulation combinations on experimental animals, avoiding the one-sided results caused by a single fixed stimulation pattern, and helping to discover some potential and complex biological phenomena and laws.

[0117] Optionally, in determining the first stimulation cycle according to the third adaptation period, the stimulation module is specifically configured to:

[0118] D1. Obtain the preset modeling target;

[0119] D2. Determine the target onset period corresponding to the preset modeling target;

[0120] D3. determining a second stimulation cycle according to the third adaptation period and the target onset period;

[0121] D4. determining a stimulation level corresponding to each of the m stimulation units to obtain m stimulation levels;

[0122] D5. determining an average stimulation level corresponding to the m stimulation levels;

[0123] D6. when the average stimulation level is greater than or equal to a preset stimulation level, determining the first stimulation cycle according to the second stimulation cycle;

[0124] D7. When the average stimulation level is lower than the preset stimulation level, determine a target optimization factor corresponding to the average stimulation level; and optimize the second stimulation cycle according to the target optimization factor to obtain the first stimulation cycle.

[0125] In the embodiment of the present application, the preset modeling target can be preset or defaulted in advance; the stimulation level can be represented by a numerical value of 1 to 3, for example, "low stimulation level" corresponds to 1, "medium stimulation level" corresponds to 2, and "high stimulation level" corresponds to 3.

[0126] In a specific embodiment, a preset modeling target can be obtained first; then, a target onset period corresponding to the preset modeling target can be determined. Specifically, scientific literature, research reports and professional books related to the preset modeling target can be consulted to understand the pathogenesis of the preset modeling target in previous studies, the common onset time range and factors affecting the onset period, and other information, thereby obtaining the target onset period. For example, if a certain depression animal model is to be established, information such as the onset time and duration of the depressive symptoms in the animal must be understood, and based on this information, the onset period of depression in the animal can be determined.

[0127] Then, the second stimulation cycle can be determined based on the third adaptation period and the target onset period. Specifically, the third adaptation period and the target onset period can be added together to obtain the second stimulation cycle; then, the stimulation level corresponding to each stimulation unit in the m stimulation units can be determined to obtain m stimulation levels. Specifically, the mapping relationship between preset stimulation units and stimulation levels can be pre-stored, and the m stimulation levels corresponding to the m stimulation units can be determined based on the mapping relationship; then, the average value of the m stimulation levels, that is, the average stimulation level, can be calculated; when the average stimulation level is greater than or equal to the preset stimulation level, the second stimulation cycle can be directly used as the first stimulation cycle.

[0128] When the average stimulation level is less than the preset stimulation level, a target optimization factor corresponding to the average stimulation level can be determined. Specifically, a mapping relationship between preset stimulation units and optimization factors can be pre-stored, and the target optimization factor corresponding to the average stimulation level can be determined based on the mapping relationship, wherein the value range of the target optimization factor can be 0-0.5; then, the second stimulation cycle can be optimized according to the target optimization factor. The specific calculation formula is as follows:

[0129] First stimulation cycle = second stimulation cycle × (1 + target optimization factor);

[0130] According to the above calculation formula, the first stimulation cycle can be obtained.

[0131] By determining the second stimulation cycle based on the third adaptation period and the target onset period, the experimental animal's adaptation time and the critical stage of disease development are comprehensively considered. This second stimulation cycle allows for targeted stimulation during the time period when the disease is likely to develop, after the animal has adapted to the environment. This improves the success rate and stability of modeling and reduces experimental errors and individual differences caused by inappropriate stimulation timing.

[0132] Optionally, in determining the control parameter set corresponding to each of the c stimulation unit sets to obtain the c control parameter sets, the stimulation module is specifically configured to:

[0133] E1. Obtain a first stimulation unit set and its corresponding first stimulation date; the first stimulation unit set is any stimulation unit set among the c stimulation unit sets;

[0134] E2. Determine e first control parameters corresponding to e first stimulation units in the first stimulation unit set; one first control parameter corresponding to each first stimulation unit;

[0135] E3. Determine a first fine-tuning parameter corresponding to the first stimulation date;

[0136] E4. Fine-tune the e first control parameters according to the first fine-tuning parameter to obtain e second control parameters;

[0137] E5. Obtain the animal type of each small animal in the second experimental animal set, obtaining f animal types; f is a positive integer;

[0138] E6. Determine the target control parameter ranges corresponding to the f animal types;

[0139] E7. If the e second control parameters are all within the target control parameter range, determine a control parameter set corresponding to the first stimulation unit set according to the e second control parameters.

[0140] In an embodiment of the present application, the first stimulation unit set and the first stimulation date corresponding to the first stimulation unit set in the first stimulation cycle can be obtained first. For example, assuming that the first stimulation unit set corresponds to the third day in the first stimulation cycle, the first stimulation date can be recorded as 3; then, the e first control parameters corresponding to the e first stimulation units in the first stimulation unit set can be determined. Specifically, the e initial control parameters corresponding to the e first stimulation units, that is, the e first control parameters, can be obtained from the system data.

[0141] Next, the first fine-tuning parameter corresponding to the first stimulation date can be determined. For example, a mapping relationship between preset stimulation dates and fine-tuning parameters can be pre-stored, and the first fine-tuning parameter corresponding to the first stimulation date can be determined based on the mapping relationship. The value range of the first fine-tuning parameter can be -30% to 30%. Then, the e first control parameters can be fine-tuned according to the first fine-tuning parameter. The specific calculation formula is as follows:

[0142] Target second control parameter = target first control parameter × (1 + first fine-tuning parameter);

[0143] Among them, the target first control parameter is any first control parameter among the e first control parameters; the target second control parameter is the second control parameter corresponding to the target first control parameter among the e second control parameters; according to the above formula, e second control parameters can be obtained by calculating e times; then, the animal type of each small animal in the second experimental animal set can be obtained to obtain f animal types. Specifically, the method for obtaining f animal types can be the same as the above method for obtaining p animal types.

[0144] Next, the target control parameter range corresponding to the f animal types can be determined. Specifically, the mapping relationship between the preset animal types and the control parameter range can be pre-stored, and the f control parameter ranges corresponding to the f animal types can be determined based on the mapping relationship. The maximum control parameter and the minimum control parameter corresponding to the f control parameter ranges can be determined. Then, the maximum control parameter can be used as the upper limit and the minimum control parameter can be used as the lower limit to obtain the target control parameter range; if the e second control parameters are all within the target control parameter range, the control parameter set corresponding to the first stimulation unit set can be composed of the e second control parameters.

[0145] In this way, by determining the first fine-tuning parameter corresponding to the first stimulation date and fine-tuning the first control parameter accordingly to obtain the second control parameter, the flexibility of the modeling experiment is increased. Based on the specific circumstances of the modeling experiment or preliminary results, the control parameters can be appropriately adjusted to better meet the modeling needs and improve the accuracy and reliability of the modeling results.

[0146] Optionally, the system is further specifically used for:

[0147] F1. If the e second control parameters are not all within the target control parameter range, determining g second control parameters within the target control parameter range and h second control parameters not within the target control parameter range among the e second control parameters; g and h are both natural numbers less than or equal to e, and g + h = e;

[0148] F2. Determine the intermediate value corresponding to the target control parameter range;

[0149] F3. Determine the deviations between the h second control parameters and the intermediate value to obtain h deviations;

[0150] F4. Adjust the h second control parameters according to the h deviations to obtain h third control parameters;

[0151] F5. Determine a control parameter set corresponding to the first stimulation unit set based on the g second control parameters and the h third control parameters.

[0152] In the embodiment of the present application, if the e second control parameters are not all within the target control parameter range, then g second control parameters within the target control parameter range and h second control parameters not within the target control parameter range can be determined among the e second control parameters; then, the middle value corresponding to the target control parameter range can be determined. For example, assuming that the target control parameter range is 0-60%, the middle value is 30%; then, the degree of deviation between the h second control parameters and the middle value can be determined. The specific calculation formula is as follows:

[0153] Target deviation = (target second control parameter - middle value) / middle value × 100%;

[0154] Among them, the target second control parameter is any second control parameter among the h second control parameters; the target deviation is the deviation corresponding to the target second control parameter among the h deviations; according to the above formula, h deviations can be calculated h times to obtain h deviations; then, the h second control parameters can be adjusted according to the h deviations to obtain h third control parameters. Specifically, a mapping relationship between preset deviations and adjustment factors can be pre-stored, and h adjustment factors corresponding to the h deviations can be determined based on the mapping relationship; the corresponding second control parameters among the h second control parameters are adjusted according to the h adjustment factors. The specific calculation formula is as follows:

[0155] Target third control parameter = target second control parameter × (1 + target adjustment factor);

[0156] Among them, the target second control parameter is any second control parameter among the h second control parameters; the target adjustment factor is the adjustment factor corresponding to the target second control parameter among the h adjustment factors; the target third control parameter is the third control parameter corresponding to the target second control parameter among the h third control parameters; according to the above formula, h times of calculation can obtain h third control parameters,

[0157] Finally, the control parameter set corresponding to the first stimulation unit set may be constituted by g second control parameters and h third control parameters.

[0158] Thus, when e second control parameters are not all within the target control parameter range, they are divided into g parameters within the range and h parameters outside the range. This classification method helps to deal with parameters in different situations in a targeted manner, reflecting the flexibility of problem solving. Parameters within the range can be used directly, while parameters outside the range are further adjusted, avoiding the tedious process of re-adjusting all parameters due to some parameters not meeting the requirements.

[0159] The detection module is used to determine n-1 mental test results corresponding to the n-1 experimental animal sets, and control mental test results corresponding to the control animal set; determine the target modeling result based on the n-1 mental test results and the control mental test results; the target modeling result includes modeling success or modeling failure.

[0160] In an embodiment of the present application, the detection module is used to perform mental tests on small animals in the n-1 experimental animal set after the implementation of n-1 stimulation schemes, and obtain n-1 mental test results. In addition, the detection module can also detect the mental state of small animals in the control animal set to obtain control mental test results; finally, the target modeling result can be determined based on the n-1 mental test results and the control mental test results.

[0161] In one embodiment, see Figure 5 , Figure 5 This is a schematic diagram of the structure of a detection module provided in an embodiment of the present application. It can be seen that the detection module may include: a weight detection unit, a video detection unit, a mental detection unit, etc., which are not limited here; wherein:

[0162] Weight monitoring unit: Animals' weight is regularly measured using weighing equipment (such as high-precision electronic scales) to generate weight change data. Weight changes can, to a certain extent, reflect the animal's health and physiological state. For example, a depressed animal may experience weight loss due to factors such as decreased appetite. In a normal state, weight is typically relatively stable or increases regularly, providing additional information for assessing its mental state.

[0163] Video monitoring unit: Utilizing cameras and other equipment, video recordings of experimental animals are generated to generate video data. This allows observation of behavioral indicators such as activity level, social behavior, and feeding behavior. For example, depressed animals may exhibit decreased activity and reduced interaction with their surroundings, while healthy animals may be more active and exhibit normal social behavior. This can be used to assist in assessing their mental state.

[0164] Mental state detection unit: The mental state of the animal can be evaluated based on weight change data and video data to obtain mental state detection results. Alternatively, the mental state detection unit can also perform professional behavioral tests (such as forced swimming test, tail suspension test, etc.) or specific mental state assessment scales, combined with the animal's behavioral responses (such as struggling time, stillness time, etc.), to comprehensively judge whether the animal is in a normal or depressed mental state.

[0165] Optionally, each mental test result includes the mental state of all corresponding small animals; the mental state includes one of the following: normal, depressed; in determining the target modeling result based on the n-1 mental test results and the control mental test results, the detection module is specifically used to:

[0166] G1. determining a first depression rate corresponding to the control mental test result;

[0167] G2. When the first depression rate is greater than a first preset depression rate, determining that the target modeling result is the modeling failure;

[0168] G3. When the first depression rate is not greater than the first preset depression rate, determining the depression rate corresponding to each of the n-1 mental health test results to obtain n-1 depression rates;

[0169] G4. Determine a qualified depression rate greater than a second preset depression rate among the n-1 depression rates, obtaining i qualified depression rates; the second preset depression rate is greater than the first preset depression rate; i is a natural number less than or equal to n-1;

[0170] G5. Determine a target qualified ratio based on the i qualified depression rates and the n-1 depression rates;

[0171] G6. When the target qualified ratio is greater than a preset ratio, determining the target modeling result as the modeling success;

[0172] G7. When the target qualified ratio is not greater than the preset ratio, determine that the target modeling result is the modeling failure.

[0173] In the embodiment of the present application, the first preset depression rate, the second preset depression rate and the preset ratio can all be preset or defaulted in advance.

[0174] In a specific embodiment, the first depression rate corresponding to the control mental test result can be determined first. Specifically, the number of animals with depressed mental state in the control animal set can be determined based on the control mental test result to obtain the first depression number. The first depression number can be divided by the total number of the control animal set to obtain the first depression rate. For example, assuming that the control animal set contains 4 small animals, the control mental test result includes the mental states of these 4 small animals. Assuming that the mental states of these 4 small animals are specifically: depressed, normal, normal, and normal, the first depression number is 1, and the total number is 4, then the first depression rate is 25%; when the first depression rate is greater than the first preset depression rate, it indicates that the main cause of animal depression may be environmental factors, rather than stimulus factors. At this time, it can be determined that the target modeling result is modeling failure.

[0175] It should be explained that the mental state in the embodiment of the present application includes not only normal and depression but also anxiety. That is to say, the stimulation module can not only make animals depressed but also make them anxious, so as to conduct research on anxiety disorders.

[0176] When the first depression rate is not greater than the first preset depression rate, the depression rate corresponding to each of the n-1 mental examination results can be determined to obtain n-1 depression rates. Specifically, the method for obtaining the n-1 depression rates can be the same as the method for obtaining the first depression rate described above. Then, the qualified depression rate greater than the second preset depression rate among the n-1 depression rates can be determined to obtain i qualified depression rates. Then, the target qualified ratio can be determined based on the i qualified depression rates and the n-1 depression rates. The specific calculation formula is as follows:

[0177] Target qualified ratio = i / (n-1) × 100%;

[0178] By calculating according to the above formula, the target qualified ratio can be obtained; when the target qualified ratio is greater than the preset ratio, the target modeling result is determined to be a successful modeling.

[0179] When the target qualified ratio is not greater than the preset ratio, the target modeling result is determined to be a modeling failure.

[0180] In summary, the CUMS automated modeling system described in the present application formulates stimulation plans for n-1 sets of experimental animals through the stimulation module based on the n-1 adaptation periods and m stimulation units determined by the stress module. Since the adaptation period of each set of experimental animals is determined according to its own characteristics, the stimulation plan formulated based on this can fully take into account the tolerance and response differences of different animal sets to stimulation, making the stimulation more accurate and personalized. At the same time, a variety of stimulation methods can be combined using m stimulation units, and in the process of automatic stimulation, the preset stimulation plan can be strictly followed to ensure the consistency and repeatability of the stimulation, thereby improving the standardization of the CUMS stimulation process.

[0181] See also Figure 6 , Figure 6 This is a schematic diagram of the structure of another CUMS automated modeling system provided in an embodiment of the present application; it can be seen that in addition to the stress module, the stimulation module, and the detection module, the system may also include: a control module; wherein:

[0182] Stress Module: This module is primarily used to apply various stressors to experimental animals, simulating stressful situations that humans may encounter in real life. For example, by creating crowded environments, reversing day and night schedules, and depriving animals of food and water, psychological and physiological stress responses can be induced in experimental animals. This allows the construction of animal models that meet research needs. This module is often used to study stress-related disorders such as depression and anxiety.

[0183] Stimulation Module: Provides a variety of stimulation methods, including physical stimulation (such as electric shock and vibration), environmental stimulation (such as temperature and light changes), and chemical stimulation (such as odor stimulation). Based on the experimental objectives, precise selection and combination of stimulation types are made, and control parameters such as stimulation intensity, frequency, and duration are adjusted to induce specific physiological and behavioral changes in animals, helping to simulate the development of diseases.

[0184] Detection module: responsible for testing various indicators of experimental animals. The detection module may include: weight detection unit, video detection unit, mental detection unit, etc., which are not limited here. For example, the weight detection unit can be used to measure the weight changes of animals to reflect their nutrition and health status; the video detection unit records animal behavior, analyzes activity level, social behavior, etc.; the mental detection unit evaluates the mental state of animals and determines whether depression, anxiety and other abnormalities occur, thereby providing researchers with comprehensive information about animals during the modeling process, facilitating the evaluation of modeling effects and animal health.

[0185] Control module (e.g., programmable logic controller, single chip microcomputer, micro control unit, etc.): It is the control core of the entire system and manages and coordinates other modules. On the one hand, it can receive instructions from the remote operating system, control the operation of the stress module and stimulation module according to the preset program, and accurately set and adjust the control parameters of the stimulation module; on the other hand, it collects data from the detection module, performs preliminary processing and storage, and ensures the stable and orderly operation of the entire system. The control module can also work with Figure 6 The communication modules shown communicate to realize information interaction between the two.

[0186] Communication module (for example, Bluetooth, 5G, WiFi and other communication devices): used to Figure 6 The control instructions issued by the remote operating system shown are accurately transmitted to the control module. At the same time, the data collected by the detection module in the system, the system operation status and other information can be fed back to the remote operating system to ensure the smooth progress of remote operation.

[0187] See also Figure 7 , Figure 7 This is a flow chart of a CUMS automated modeling method provided in an embodiment of the present application; the method is applied to a CUMS automated modeling system, the system comprising: a stress module, a stimulation module, and a detection module, the stress module comprising n stress cages, the stimulation module comprising m stimulation units, where m and n are both integers greater than 1, and the method may comprise the following steps:

[0188] S1. Determine a small animal set corresponding to each of the n stress cages to obtain n small animal sets; the n small animal sets include n-1 experimental animal sets and one control animal set; each small animal set includes at least one small animal; determine an adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods;

[0189] S2. formulating a stimulation plan for the n-1 experimental animal set based on the n-1 adaptation periods and the m stimulation units to obtain n-1 stimulation plans; automatically stimulating the small animals in the n-1 experimental animal set according to the n-1 stimulation plans, while not applying any stimulation to the small animals in the control animal set;

[0190] S3. Determine n-1 mental test results corresponding to the n-1 experimental animal sets, and control mental test results corresponding to the control animal set; determine the target modeling result based on the n-1 mental test results and the control mental test results; the target modeling result includes modeling success or modeling failure.

[0191] In a specific implementation, the CUMS automated modeling method described in the embodiment of the present invention may also include other implementations described in the CUMS automated modeling system provided in the above embodiment of the present invention, which will not be repeated here.

[0192] See also Figure 8 , Figure 8 It is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface and one or more programs. The processor, memory and communication interface may be interconnected through a bus; the one or more programs are stored in the memory and configured to be executed by the processor; in the embodiment of the present application, the program includes part or all of the steps for executing the CUMS automated modeling method.

[0193] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0194] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0195] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0196] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0198] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0199] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also exist as discrete components in the terminal device or the management device.

[0200] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0201] The aforementioned computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media.

[0202] The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0203] The modules / units included in the various devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for various devices and products applied to or integrated into a chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated into a chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0204] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A CUMS automated modeling system, characterized in that: The system includes: a stress module, a stimulation module, and a detection module. The stress module includes n stress cages, and the stimulation module includes m stimulation units, where m and n are both integers greater than 1, wherein: The stress module is used to determine a small animal set corresponding to each of the n stress cages to obtain n small animal sets; the n small animal sets include n-1 experimental animal sets and a control animal set; each small animal set includes at least one small animal; determine an adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods; The stimulation module is configured to formulate a stimulation plan for the n-1 experimental animal sets based on the n-1 adaptation periods and the m stimulation units, thereby obtaining n-1 stimulation plans; automatically stimulating the small animals in the n-1 experimental animal sets according to the n-1 stimulation plans, while not applying any stimulation to the small animals in the control animal set; The detection module is used to determine n-1 mental test results corresponding to the n-1 experimental animal sets and control mental test results corresponding to the control animal sets; determine a target modeling result based on the n-1 mental test results and the control mental test results; the target modeling result includes modeling success or modeling failure; The system further includes an automatic bedding replacement module. In terms of formulating a stimulation plan for the n-1 set of experimental animals based on the n-1 adaptation periods and the m stimulation units to obtain the n-1 stimulation plan, the stimulation module is specifically configured to: Obtaining a second experimental animal set and its corresponding third adaptation period; the second experimental animal set is any one of the n-1 experimental animal sets; Determining a first stimulation cycle according to the third adaptation period; the first stimulation cycle is greater than the third adaptation period; the first stimulation cycle includes c days; c is a positive integer; Determine the working time points of the automatic bedding material changing module in the first stimulation cycle based on a preset time interval, and obtain d working time points; d is a positive integer; For each day of the first stimulation cycle, randomly selecting e stimulation units from the m stimulation units to obtain c stimulation unit sets; e is an integer greater than 1 and less than or equal to m; determining a control parameter set corresponding to each of the c stimulation unit sets to obtain c control parameter sets; A stimulation scheme corresponding to the second set of experimental animals is determined according to the first stimulation cycle, the d working time points, the c stimulation unit sets and the c control parameter sets.

2. The system according to claim 1, wherein In determining the adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods, the stress module is specifically configured to: Acquiring first basic data of a first stress cage corresponding to a first experimental animal set; the first experimental animal set is any one of the n-1 experimental animal sets; Determine the animal type of each small animal in the first experimental animal set, obtaining p animal types; p is a positive integer; Determining a first adaptation period corresponding to each of the p animal types to obtain p first adaptation periods; The adaptation period corresponding to the first set of experimental animals is determined according to the first basic data and the p first adaptation periods.

3. The system according to claim 2, wherein: In terms of determining the adaptation period corresponding to the first set of experimental animals based on the first basic data and the p first adaptation periods, the stress module is specifically configured to: Determine the health level of each small animal in the first experimental animal set to obtain p health levels; Determining a health level greater than or equal to a preset health level, and b health levels less than the preset health level, among the p health levels; a and b are both natural numbers less than or equal to p, and a+b=p; Determine the first adaptation period corresponding to the a health levels in the p first adaptation periods, to obtain a first adaptation period; Determine the first adaptation periods corresponding to the b health levels in the p first adaptation periods, to obtain b first adaptation periods; Determining a first impact factor corresponding to the first basic data; Determining second influencing factors corresponding to the m stimulation units; Adjusting the b first adaptation periods according to the first influencing factor and the second influencing factor to obtain b second adaptation periods; The adaptation period corresponding to the first set of experimental animals is determined based on the a first adaptation periods and the b second adaptation periods.

4. The system according to claim 1, wherein: In determining the first stimulation cycle according to the third adaptation period, the stimulation module is specifically configured to: Obtaining preset modeling targets; Determining the target onset period corresponding to the preset modeling target; determining a second stimulation period according to the third adaptation period and the target onset period; determining a stimulation level corresponding to each stimulation unit in the m stimulation units to obtain m stimulation levels; Determining an average stimulation level corresponding to the m stimulation levels; When the average stimulation level is greater than or equal to a preset stimulation level, determining the first stimulation cycle according to the second stimulation cycle; When the average stimulation level is less than the preset stimulation level, a target optimization factor corresponding to the average stimulation level is determined; and the second stimulation cycle is optimized according to the target optimization factor to obtain the first stimulation cycle.

5. The system according to claim 1, wherein: In determining the control parameter set corresponding to each of the c stimulation unit sets to obtain c control parameter sets, the stimulation module is specifically configured to: Obtaining a first stimulation unit set and its corresponding first stimulation date; the first stimulation unit set is any stimulation unit set among the c stimulation unit sets; Determining e first control parameters corresponding to e first stimulation units in the first stimulation unit set; one first control parameter corresponding to each first stimulation unit; determining a first fine-tuning parameter corresponding to the first stimulation date; Fine-tune the e first control parameters according to the first fine-tuning parameter to obtain e second control parameters; Obtain the animal type of each small animal in the second experimental animal set, obtaining f animal types; f is a positive integer; Determining target control parameter ranges corresponding to the f animal types; If the e second control parameters are all within the target control parameter range, then a control parameter set corresponding to the first stimulation unit set is determined according to the e second control parameters.

6. The system according to claim 5, wherein: The system is also specifically used for: If the e second control parameters are not all within the target control parameter range, determining g second control parameters within the target control parameter range and h second control parameters not within the target control parameter range among the e second control parameters; g and h are both natural numbers less than or equal to e, and g+h=e; Determining an intermediate value corresponding to the target control parameter range; Determining the deviations between the h second control parameters and the intermediate value to obtain h deviations; Adjusting the h second control parameters according to the h deviations to obtain h third control parameters; A control parameter set corresponding to the first stimulation unit set is determined according to the g second control parameters and the h third control parameters.

7. The system according to any one of claims 1 to 3, wherein: Each mental test result includes the mental state of all corresponding small animals; the mental state includes one of the following: normal, depressed; in determining the target modeling result based on the n-1 mental test results and the control mental test results, the detection module is specifically used to: determining a first depression rate corresponding to the control mental test result; When the first depression rate is greater than a first preset depression rate, determining that the target modeling result is the modeling failure; When the first depression rate is not greater than the first preset depression rate, determining the depression rate corresponding to each of the n-1 mental examination results to obtain n-1 depression rates; Determine a qualified depression rate greater than a second preset depression rate among the n-1 depression rates, to obtain i qualified depression rates; the second preset depression rate is greater than the first preset depression rate; i is a natural number less than or equal to n-1; Determining a target qualified ratio based on the i qualified depression rates and the n-1 depression rates; When the target qualified ratio is greater than a preset ratio, determining the target modeling result as the modeling success; When the target qualified ratio is not greater than the preset ratio, the target modeling result is determined to be the modeling failure.

8. A CUMS automated modeling method, characterized in that: Applied to the CUMS automated modeling system, the system includes: a stress module, a stimulation module, and a detection module. The stress module includes n stress cages, and the stimulation module includes m stimulation units, where m and n are both integers greater than 1. The method includes: Determining a small animal set corresponding to each of the n stress cages to obtain n small animal sets; the n small animal sets include n-1 experimental animal sets and one control animal set; each small animal set includes at least one small animal; determining an adaptation period corresponding to each of the n-1 experimental animal sets to obtain n-1 adaptation periods; formulating a stimulation plan for the n-1 experimental animal set based on the n-1 adaptation periods and the m stimulation units to obtain n-1 stimulation plans; automatically stimulating the small animals in the n-1 experimental animal set according to the n-1 stimulation plans, while not applying any stimulation to the small animals in the control animal set; Determining n-1 psychiatric test results corresponding to the n-1 experimental animal sets and control psychiatric test results corresponding to the control animal sets; determining a target modeling result based on the n-1 psychiatric test results and the control psychiatric test results; the target modeling result includes modeling success or modeling failure; The system further includes an automatic bedding changing module, wherein a stimulation plan is formulated for the n-1 experimental animal sets based on the n-1 adaptation periods and the m stimulation units, and the obtained n-1 stimulation plans include: Obtaining a second experimental animal set and its corresponding third adaptation period; the second experimental animal set is any one of the n-1 experimental animal sets; Determining a first stimulation cycle according to the third adaptation period; the first stimulation cycle is greater than the third adaptation period; the first stimulation cycle includes c days; c is a positive integer; Determine the working time points of the automatic bedding material changing module in the first stimulation cycle based on a preset time interval, and obtain d working time points; d is a positive integer; For each day of the first stimulation cycle, randomly selecting e stimulation units from the m stimulation units to obtain c stimulation unit sets; e is an integer greater than 1 and less than or equal to m; determining a control parameter set corresponding to each of the c stimulation unit sets to obtain c control parameter sets; A stimulation scheme corresponding to the second set of experimental animals is determined according to the first stimulation cycle, the d working time points, the c stimulation unit sets and the c control parameter sets.

9. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program causes a computer to execute the method according to claim 8.

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