CUMS automatic modeling system and method

Abstract

The invention discloses a CUMS automatic modeling system and method, the system comprises a stress module, a stimulation module and a detection module, the stress module is used for determining n small animal sets corresponding to n stress cages; (n-1) adaptation periods corresponding to the (n-1) experimental animal sets are determined; the stimulation module is used for formulating stimulation schemes for the n-1 experimental animal sets based on the n-1 adaptation periods and the m stimulation units to obtain n-1 stimulation schemes; automatically stimulating the small animals in the (n-1) experimental animal set according to the (n-1) stimulation schemes, and meanwhile, not applying any stimulation to the small animals in the control animal set; the detection module is used for determining n-1 mental detection results and contrast mental detection results; and determining a target modeling result according to the n-1 spirit detection results and the contrast spirit detection result. By adopting the embodiment of the invention, the standardization degree of the CUMS stimulation process is improved.

Description

Technical Field

[0001] This application relates to the field of medical technologies, and particularly to a CUMS automated modeling system and method. Background Art

[0002] In the research on the pathogenesis of depression, it has been widely recognized at home and abroad to establish a depressive animal model with stress as the stressor of depression. Among them, the Chronic Unpredictable Mild Stress (CUMS) model is widely used in depression research. This modeling method is relatively close to the pathogenesis process of depression and is currently a commonly used method for modeling depression.

[0003] Currently, the standardization level of the CUMS stimulation process is relatively low, and the influence of human factors on the experiment is very large. Therefore, how to improve the standardization level of the CUMS stimulation process has become an urgent problem to be solved. Summary of the Invention

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

[0005] In a first aspect, the embodiments of this application provide a CUMS automated modeling system, which 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 both m and n are integers greater than 1. Among them: The stress module is used to determine the small animal sets corresponding to each of the n stress cages, obtaining 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 the adaptation periods corresponding to each of the n - 1 experimental animal sets, obtaining n - 1 adaptation periods; The stimulation module is used to formulate stimulation plans for the n - 1 experimental animal sets based on the n - 1 adaptation periods and the m stimulation units, obtaining n - 1 stimulation plans; automatically stimulate the small animals in the n - 1 experimental animal sets according to the n - 1 stimulation plans, and at the same time, do not apply any stimulation to the small animals in the control animal set; The detection module is used to determine the n - 1 mental detection results corresponding to the n - 1 experimental animal sets, and the control mental detection result corresponding to the control animal set; determine the target modeling result according to the n - 1 mental detection results and the control mental detection result; the target modeling result includes successful modeling or failed modeling.

[0006] 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. Both m and n are integers greater than 1. The method includes: Determine the small animal sets 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 the adaptation periods corresponding to each of the n - 1 experimental animal sets to obtain n - 1 adaptation periods; Based on the n - 1 adaptation periods and the m stimulation units, formulate stimulation plans for the n - 1 experimental animal sets to obtain n - 1 stimulation plans; automatically stimulate the small animals in the n - 1 experimental animal sets according to the n - 1 stimulation plans. At the same time, do not apply any stimulation to the small animals in the control animal set; Determine the n - 1 mental detection results corresponding to the n - 1 experimental animal sets, and the control mental detection result corresponding to the control animal set; determine the target modeling result according to the n - 1 mental detection results and the control mental detection result; the target modeling result includes modeling success or modeling failure.

[0007] Third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory, a communication interface, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above processor. The above programs include instructions for executing the steps in the second aspect of the embodiment of the present application.

[0008] Fourth aspect, an embodiment of the present application provides a computer-readable storage medium. Among them, the above computer-readable storage medium stores a computer program for electronic data exchange. Among them, the above 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.

[0009] Fifth aspect, an embodiment of the present application provides a computer program product. Among them, the above computer program product includes a non-transitory computer-readable storage medium storing a computer program. The above computer program is operable to enable a computer to execute some or all of the steps described in the second aspect of the embodiment of the present application. This computer program product can be a software installation package.

[0010] Implementing the present application has the following beneficial effects: It can be seen that in the CUMS automated modeling system described in this application, the stimulation module formulates stimulation plans for n-1 sets of experimental animals according to 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 plans formulated based on this can fully consider the tolerance and response differences of different animal sets to stimulation, making the stimulation more accurate and personalized. At the same time, m stimulation units can be combined to produce diverse stimulation methods, and during the automatic execution of the stimulation process, the operation can be strictly carried out according to the preset stimulation plan, ensuring the consistency and repeatability of the stimulation. Therefore, the standardization degree of the CUMS stimulation process is improved. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this application or the background art, the following will describe the drawings required to be used in the embodiments of this application or the background art.

[0012] Figure 1 It is a schematic structural diagram of a CUMS automated modeling system provided by an embodiment of this application; Figure 2 It is a schematic structural diagram of a stress module provided by an embodiment of this application; Figure 3 It is a flowchart of a method for determining the adaptation period provided by an embodiment of this application; Figure 4 It is a schematic structural diagram of a stimulation module provided by an embodiment of this application; Figure 5 It is a schematic structural diagram of a detection module provided by an embodiment of this application; Figure 6 It is a schematic structural diagram of another CUMS automated modeling system provided by an embodiment of this application; Figure 7 It is a flowchart of a CUMS automated modeling method provided by an embodiment of this application; Figure 8 It is a schematic structural diagram of an electronic device provided by an embodiment of this application. Detailed Description of the Embodiments

[0013] In order to enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of this application.

[0014] In the description and claims of this application and the above-mentioned drawings, terms such as "first", "second", etc. are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices.

[0015] It should be understood that the term "and / or" in this article is merely an associative relationship describing associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "plurality" mentioned in the embodiments of this application refers to two or more.

[0016] The "at least one (piece)" or its similar expression in the embodiments of this application refers to any combination of these items, including any combination of single items (pieces) or plural items (pieces), and refers to one or more, and plural refers to two or more. For example, at least one (piece) of a, b, or c can represent the following seven cases: 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.

[0017] The "connection" that appears in the embodiments of this application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and this application does not make any limitations on this.

[0018] Referring to "embodiments" in this article means that the specific features, structures, or characteristics described in combination with the embodiments can be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0019] The electronic device described in the embodiments of this application may include a CUMS automated modeling system.

[0020] The following explains the relevant content, concepts, meanings, technical problems, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0021] First, some professional terms involved in this application are explained: Chronic Unpredictable Mild stress (CUMS) model: It refers to a method for constructing an animal model to simulate mental diseases such as human depression. By applying a series of long-term and unpredictable mild stress stimuli to experimental animals (such as mice and rats), such as noise, fasting, water deprivation, and circadian rhythm inversion, the animals gradually exhibit behavioral, physiological, and psychological changes similar to those of human depression, such as weight loss, anhedonia, reduced activity, and increased anxiety-like behavior, thus providing an experimental basis for studying the pathogenesis of depression, drug development, and treatment methods.

[0022] Adaptation period: Before conducting CUMS modeling or other related experiments, let the experimental animals live in a specific environment (such as a stimulation cage) for a period of time, and this period is the adaptation period. Its purpose is to allow the animals to become familiar with the experimental environment and equipment, eliminate the stress response of the animals caused by factors such as environmental changes and new equipment, stabilize the physiological and psychological states of the animals, so that the subsequent experimental stimuli can more accurately simulate the stress situation under natural conditions, reduce experimental errors, and improve the reliability of experimental results.

[0023] Modeling: That is, model construction. In medical and biological research, it refers to using certain methods and means to make experimental objects such as animals or cells exhibit characteristics and manifestations similar to human diseases or specific physiological and pathological states, thereby establishing an experimental model that can be used to study the mechanism of disease occurrence and development, drug screening, and exploration of treatment methods. In CUMS, it is to make the experimental animals exhibit a depressive-like state through a series of mild stress stimuli to construct an animal model of depression.

[0024] Please refer to Figure 1 , Figure 1 FIG. The stress module is used to determine the small animal sets corresponding to each of the n stress cages, obtaining 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 the adaptation periods corresponding to each of the n - 1 experimental animal sets, obtaining n - 1 adaptation periods.

[0025] In the embodiments of the present application, the small animals may include at least one of the following: mice, rats, guinea pigs, hamsters, etc., and are not limited herein.

[0026] In a specific embodiment, the stress module may further include a camera unit (e.g., a camera). Image data of n stress cages is collected through the camera unit, and image recognition is performed on the image data to obtain n sets of small animals. Alternatively, the data of the small animals placed in the n stress cages can be input into the stress module by the staff in advance, so as to obtain n sets of small animals. Then, the adaptation period corresponding to each of the n - 1 sets of experimental animals can be determined to obtain n - 1 adaptation periods.

[0027] It should be explained that in actual operation, the n - 1 adaptation periods can all be set manually, and the sizes of the n - 1 adaptation periods can all be equal. For example, the n - 1 adaptation periods can all be 3 days.

[0028] It should be explained that the small animals in the n stress cages can be determined by the staff according to the experimental requirements, and the appropriate small animal breeds and strains can be selected, such as the commonly used C57BL / 6 mice, SD rats, etc. They can be purchased from regular animal suppliers to ensure clear animal sources, good health conditions, and relatively consistent genetic backgrounds, reducing the impact of individual differences on the experimental results. To ensure the objectivity and scientific nature of the CUMS experiment results, the random grouping method can be adopted. Using tools such as a random number generator, all small animals are numbered, and then randomly assigned to the n stress cages according to the numbers, so that the number of animals in each stress cage is roughly equal, and as much as possible to ensure that there are no significant differences in factors such as age, weight, and gender among the animals in each group, avoiding experimental result deviations caused by uneven grouping. Thus, n groups are obtained, that is, n sets of small animals.

[0029] In one embodiment, please refer to Figure 2 , Figure 2 is a schematic structural diagram of a stress module provided by an embodiment of the present application. It can be seen that the stress module includes n stress cages, specifically: the first stress cage, the second stress cage,..., the nth stress cage; Figure 2 The ellipsis "..." in

[0030] Taking the first stress cage as an example, the first stress cage may include the following structures: Cage body: Usually made of non-toxic, corrosion-resistant, and easy-to-clean and disinfect materials, such as transparent or opaque plastic materials. Its spatial size needs to meet the normal activities, rest, diet, etc. of small animals. For example, the size of a mouse cage is generally 25 - 30 cm in length, 15 - 20 cm in width, and 12 - 15 cm in height. The cage body is equipped with a switchable door, which is convenient for putting in, taking out, and daily operation of animals; Bedding: Placed at the bottom of the cage, it serves functions such as keeping warm, absorbing moisture, and buffering. Common types of bedding include wood chips, corn cobs, etc. The bedding needs to be automatically replaced regularly to maintain the cleanliness of the cage and prevent animals from getting infected by contacting unclean bedding. It should be noted that during the modeling process, if an immersion stimulus is applied, the system needs to perform an automatic bedding replacement immediately after the immersion ends.

[0031] Diet and drinking water devices: Include a food trough and a water bottle. The food trough is used to place feed, usually a plastic or metal container that can be fixed to the cage body; the water bottle is mostly of the hanging type to ensure that animals have sufficient food and water supply during the stress process. Unless in the case of water deprivation and food deprivation stimuli, animals can drink and eat freely.

[0032] Environmental control components: Can include a heating component and a cooling component, used to adjust the environmental parameters in the cage to ensure that they are within the range suitable for animal survival; there can also be a ventilation device to maintain air circulation in the cage and avoid the accumulation of harmful gases.

[0033] The stress cage is 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 grouped management of animals, facilitating subsequent experimental operations and observations. The system makes animals in a stress state by controlling the environmental conditions in the cage (such as changing temperature, humidity, light cycle, etc.) or applying specific stimuli (such as sound, smell, etc.). It is the basic place for constructing experiments such as CUMS, simulating the stress situations faced by animals in natural or disease states.

[0034] Optionally, please refer to Figure 3 , Figure 3 which is a flowchart of a method for determining the adaptation period provided by an embodiment of the present application; in the aspect of determining the adaptation period corresponding to each experimental animal set in the n - 1 experimental animal sets to obtain n - 1 adaptation periods, the stress module is specifically used to execute Figure 3 the steps shown in A1. Obtain the first basic data of the first stress cage corresponding to the first experimental animal set; the first experimental animal set is any one of the n - 1 experimental animal sets; A2. Determine the animal types of each small animal in the first experimental animal set to obtain p animal types; p is a positive integer; A3. Determine the first adaptation period corresponding to each animal type among the p animal types to obtain p first adaptation periods; A4. Determine the adaptation period corresponding to the first experimental animal set according to the first basic data and the p first adaptation periods.

[0035] In the embodiments 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, device status data (e.g., the remaining food amount in the feeding trough of the first stress cage), etc., which are not limited herein.

[0036] In a specific embodiment, the first basic data of the first stress cage corresponding to the first experimental animal set is obtained. Specifically, the first basic data may be environmental data. Environmental sensors (e.g., temperature sensors, humidity sensors) may be set in the first stress cage, and the environmental data of the first stress cage is detected through the environmental sensors, thereby obtaining the first basic data. Then, the animal type of each small animal in the first experimental animal set can be determined, and p animal types are obtained. Specifically, the source information document of the first experimental animal set can be obtained from the system database. The source information document will clearly record information such as the breed and strain of the animals. For example, assuming that the first experimental animal set is C57BL / 6 mice, SD rats, etc. purchased from a certain animal supplier, the accurate strain information of the animals will be indicated in the source information materials provided by the animal supplier. By consulting the source information document, p animal types can be obtained.

[0037] Next, the first adaptation period corresponding to each animal type among the p animal types can be determined, and p first adaptation periods are obtained. Specifically, the mapping relationship between the preset animal type and the first adaptation period can be stored in advance, and the p first adaptation periods corresponding to the p animal types are determined based on this mapping relationship. Then, the adaptation period corresponding to the first experimental animal set can be determined according to the first basic data and the p first adaptation periods.

[0038] In this way, by determining p animal types and the corresponding p first adaptation periods, the adaptation time can be arranged according to the characteristics of each animal type. For example, for C57BL / 6 mice and SD rats, the former is small in size and fast in metabolism, and may adapt to the new environment faster; the latter is large in size and relatively slow in physiological regulation, and may require a longer adaptation period. Setting the adaptation period according to the animal type can avoid the situation that a unified standard may lead to insufficient or excessive adaptation of some animals, enabling the experimental animals to be in a relatively stable physiological and psychological state before entering the formal experiment, reducing experimental errors caused by adaptation differences, and improving the experimental accuracy.

[0039] Optionally, in terms of determining the adaptation period corresponding to the first experimental animal set according to the first basic data and the p first adaptation periods, the stress module is specifically configured to: B1. Determine the health level of each small animal in the first experimental animal set, and obtain p health levels; B2. Determine a health levels greater than or equal to the preset health level and b health levels less than the preset health level among the p health levels; both a and b are natural numbers less than or equal to p, and a + b = p; B3. Determine the first adaptation periods corresponding to the a health levels among the p first adaptation periods, obtaining a first adaptation periods; B4. Determine the first adaptation periods corresponding to the b health levels among the p first adaptation periods, obtaining b first adaptation periods; B5. Determine the first influencing factor corresponding to the first basic data; B6. Determine the second influencing factor corresponding to the m stimulation units; B7. Adjust the b first adaptation periods according to the first influencing factor and the second influencing factor, obtaining b second adaptation periods; B8. Determine the adaptation period corresponding to the first experimental animal set according to the a first adaptation periods and the b second adaptation periods.

[0040] In the embodiments of the present application, the health level may include one of the following: very poor, poor, average, good, excellent, etc., which is not limited herein; the preset health level may be preset in advance or by default. For example, the preset health level may be "average".

[0041] In a specific embodiment, the health level of each small animal in the first experimental animal set may be determined, obtaining p health levels. Specifically, for each small animal, health detection may be performed on it, so as to obtain p health levels. The health detection may include at least one of the following: appearance inspection, behavior observation, physiological index detection, etc., which is not limited herein.

[0042] For example, assume that there are 2 small animals (mice) in the first experimental animal set, labeled as mouse A and mouse B respectively. Now, health checks are performed on them to determine the health levels. The following are the specific processes and results: Mouse A: Appearance inspection: The hair is smooth and shiny, the eyes are bright and clear, the ears are clean and odorless, the mouth is normal, and the body shape is symmetrical.

[0043] Behavior observation: Strong activity ability, agile movement, normal eating and drinking, natural sleep and rest postures, and good interaction with companions.

[0044] Physiological index detection: Body temperature is 37.5 degrees Celsius, heart rate is 400 beats per minute, respiratory rate is 120 breaths per minute, blood routine and biochemical indexes are normal, feces are formed and no parasite eggs and occult blood are detected.

[0045] Health level assessment: Based on the comprehensive inspection results, the health condition of mouse A is good and is rated as the "good" level.

[0046] Mouse B: Appearance inspection: The hair is slightly rough, there is a small amount of secretion in the eyes, the ears are normal, and the oral gums are slightly red and swollen.

[0047] Behavior observation: The activity ability is slightly weak, the appetite has decreased, there are occasional waking-up phenomena during sleep, and the interaction with companions has decreased.

[0048] Physiological index detection: Body temperature is 37.8 degrees Celsius, heart rate is 420 beats per minute, respiratory rate is 130 breaths per minute. Blood routine shows that the white blood cell count is slightly high, the biochemical indexes are basically normal, and the feces are soft.

[0049] Health level assessment: According to the inspection results, mouse B may have a mild infection or inflammation, and its general health condition is average, so it is rated as the "average" level.

[0050] Furthermore, a healthy levels greater than or equal to the preset health level and b healthy levels less than the preset health level can be found among the p healthy levels; then, the first adaptation periods corresponding to the a healthy levels among the p first adaptation periods can be determined, obtaining a first adaptation periods. Specifically, a small animals corresponding to the a healthy levels in the first experimental animal set can be determined first, and then the a first adaptation periods corresponding to the a small animals among the p first adaptation periods can be determined; then, the first adaptation periods corresponding to the b healthy levels among the p first adaptation periods can be determined, obtaining 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 periods.

[0051] Next, the first influence factor corresponding to the first basic data can be determined. Specifically, the mapping relationship between the preset basic data and the influence factor can be stored in advance, and the first influence factor corresponding to the first basic data can be determined based on this mapping relationship; then, the second influence factors corresponding to the m stimulus units can be determined. Specifically, the mapping relationship between the preset stimulus units and the influence factor can be stored in advance, and the m influence factors corresponding to the m stimulus units can be determined based on this mapping relationship. Then, the m weights corresponding to the m stimulus units can be determined. Each stimulus unit corresponds to a weight, and the sum of the m weights is 1. Specifically, the m stimulus types corresponding to the m stimulus units can be obtained. Each stimulus unit corresponds to a stimulus type. The mapping relationship between the preset stimulus type and the weight can be stored in advance, and the m weights corresponding to the m stimulus types can be determined based on this mapping relationship. Then, weighted operation is performed based on the m influence factors and the m weights to obtain the second influence factor; where the value ranges of the first influence factor and the second influence factor can both be -0.3 to 0.3; then, the b first adaptation periods can be adjusted according to the first influence factor and the second influence factor. The specific calculation formula is as follows: Target second adaptation period = Target first adaptation period × (1 + First influence factor) × (1 + Second influence factor); Among them, the target first adaptation period is any one of 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; by calculating according to the above formula b times, b second adaptation periods can be obtained; finally, the adaptation period corresponding to the first experimental animal set can be determined based on the a first adaptation periods and the b second adaptation periods. Specifically, the maximum value among the a first adaptation periods and the b second adaptation periods can be determined and used as the adaptation period corresponding to the first experimental animal set. Alternatively, the average value corresponding to the a first adaptation periods and the b second adaptation periods can also be calculated and used as the adaptation period corresponding to the first experimental animal set.

[0052] In this way, by determining the health levels of each small animal in the first experimental animal set, p health levels are obtained and divided into a health levels greater than or equal to the preset health level and b health levels less than the preset health level. This can more precisely understand the distribution of the health status of experimental animals and facilitate subsequent analysis of animals with different health statuses. Since animals with different health statuses may have different performances in experiments and different adaptation abilities to the environment, this classification helps to more accurately study the relationship between the animal adaptation period and the health level. Additionally, by adjusting the b first adaptation periods according to the first influencing factor and the second influencing factor, b second adaptation periods are obtained. This is because animals with lower health levels may be more sensitive to various factors in the experimental environment, and their adaptation periods need to be adjusted according to the actual situation. Through this targeted adjustment, the adaptation needs of animals with different health statuses can be more accurately met, the interference of environmental factors on experimental results can be reduced, and the accuracy and reliability of the experiment can be improved.

[0053] The stimulation module is used to formulate stimulation plans for the n - 1 experimental animal sets based on the n - 1 adaptation periods and the m stimulation units, obtaining n - 1 stimulation plans; and automatically stimulating the small animals in the n - 1 experimental animal sets according to the n - 1 stimulation plans. At the same time, no stimulation is applied to the small animals in the control animal set.

[0054] In the embodiments of the present application, the stimulation module can be used to formulate stimulation plans for the n - 1 experimental animal sets based on the n - 1 adaptation periods and the m stimulation units, obtaining n - 1 stimulation plans; each stimulation plan is for one experimental animal set; then, the stimulation module can automatically control the m stimulation units to work according to the n - 1 stimulation plans to stimulate the small animals in the n - 1 experimental animal sets. At the same time, the stimulation module does not apply any stimulation to the small animals in the above control animal set.

[0055] In addition, in order not to affect the animals in the control animal set when stimulating the animals in the n - 1 experimental animal sets, the stress cages of the control animal set can be placed far away from the stress cages of the experimental animal sets. Alternatively, sound - insulating materials can also be used to wrap the experimental area (i.e., the area where the animals in the n - 1 experimental animal sets are located) and the control area (i.e., the area where the animals in the control animal set are located) to avoid the influence of noise and vibration during the experiment on the animals in the control animal set.

[0056] It should be explained that only one control animal set is set in the embodiments of the present application. In actual operation, one or more control animal sets can be set according to experimental requirements.

[0057] In one embodiment, please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a stimulation module provided by the embodiments of the present application. It can be seen that the stimulation module can include: a crowding stimulation unit, a vertical stimulation unit,..., a spray stimulation unit, etc., which are not limited herein; Figure 4 The ellipsis "..." in represents that there are more stimulation units in the stimulation module, which are not fully shown.

[0058] In one embodiment, m can be equal to 9. The stimulation module can include: a crowding stimulation unit, a vertical stimulation unit, a wire stimulation unit, a food / water deprivation unit, a light stimulation unit, a noise stimulation unit, a humidity stimulation unit, a spray stimulation unit, a blowing stimulation unit, etc., which are not limited herein; Through these 9 stimulation units, 20 non - contact modeling methods such as crowded space, tilted cage position (left), tilted cage position (right), jolting, wire mesh floor, sleep deprivation (a mixture of jolting, electric shock, air, etc.), water deprivation, food deprivation, food and water deprivation, wet bedding, spraying for 5 minutes, short - term strong noise, continuous white noise, night - time light, strong white light strobing, foot shock, cold air (4 °C), hot air (40 °C), blowing (room temperature), immersion in water, etc. can be realized; specifically as follows: Crowding stimulation unit: It can be composed of a stepper motor and a baffle, and is used to simulate a crowded space environment. By pushing the baffle to move through the stepper motor, the activity space size of the animals in the stress cage can be adjusted, and the reduction of the activity space can be artificially set from 0% to 90%.

[0059] Vertical stimulation unit: It can be two push rods arranged at the bottom of the stress cage; by moving the position of the push rods, the stress cage can be tilted to the left or right, corresponding to the modeling methods of "tilted cage position (left)" and "tilted cage position (right)".

[0060] Wire stimulation unit: A structure with wires can realize stimulations such as wire mesh floor and foot shock.

[0061] Food and water restriction unit: It can be an electronically controlled valve; by controlling the opening and closing of the electronically controlled valve, the feeding situation of animals can be controlled to achieve states such as water restriction, food restriction, and both food and water restriction.

[0062] Light stimulation unit: Used to achieve light-related stimulations such as night lighting and strong white light strobing.

[0063] Noise stimulation unit: Generates white noise, noise at a specific intensity (such as 100 decibels), etc.

[0064] Humidity stimulation unit: Creates a humid bedding or immersion environment.

[0065] Spray stimulation unit: Performs spray operations, such as spraying for 5 to 30 minutes.

[0066] Air blowing stimulation unit: Blows out airflows at different temperatures (such as cold air at 4 degrees Celsius, hot air at 40 degrees Celsius, and room temperature air).

[0067] Optionally, the system further includes an automatic bedding changing module. In terms of formulating a stimulation plan for the n - 1 experimental animal sets based on the n - 1 adaptation periods and the m stimulation units, the stimulation module is specifically configured to: C1. Obtain 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; C2. Determine 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; C3. Based on a preset time interval, determine the working time points of the automatic bedding changing module in the first stimulation cycle, obtaining d working time points; d is a positive integer; C4. For each day in the first stimulation cycle, randomly select e stimulation units from the m stimulation units, obtaining c sets of stimulation units; e is an integer greater than 1 and less than or equal to m; C5. Determine the control parameter set corresponding to each set of stimulation units in the c sets of stimulation units, obtaining c sets of control parameters; C6. Determine the stimulation plan corresponding to the second experimental animal set according to the first stimulation cycle, the d working time points, the c sets of stimulation units, and the c sets of control parameters.

[0068] In the embodiments of the present application, the preset time interval can be preset in advance or default.

[0069] In a specific embodiment, the second experimental animal set and its corresponding third adaptation period can be obtained first; then, the first stimulation period can be determined according to the third adaptation period; next, based on a preset time interval, the working time points of the automatic bedding-changing module in the first stimulation period can be determined, obtaining d working time points. Specifically, the first day in the first stimulation period can be used as the first working time point, and starting from the first day, it can be calculated sequentially backward, so as to obtain 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 period, 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, applicable to the situation where the bedding consumption is relatively stable and the experimental conditions change little, and can ensure that the bedding is replaced at relatively regular time intervals to maintain the stability of the experimental environment.

[0070] It should be noted that the starting moment of the first stimulation period is later than the ending moment of the third adaptation period.

[0071] Next, for each day in the first stimulation period, e stimulation units can be randomly selected from m stimulation units to obtain c stimulation unit sets; further, the control parameter sets corresponding to each stimulation unit set in the c stimulation unit sets can be determined, obtaining c control parameter sets; then, according to the first stimulation period, d working time points, c stimulation unit sets, and c control parameter sets, the stimulation plan corresponding to the second experimental animal set can be determined. Specifically, the first stimulation period is divided into c intervals, with one interval representing one day, and a mapping relationship between the c intervals and the c stimulation unit sets is established to obtain the first mapping relationship. Then, a mapping relationship between the c stimulation unit sets and the c control parameter sets can also be obtained to obtain the second mapping relationship. According to the first mapping relationship, the second mapping relationship, and the d working time points, the stimulation plan corresponding to the second experimental animal set can be determined. For example, assuming c is equal to 2 and d is equal to 1, the first stimulation period can be divided into the first interval (the first day) and the second interval (the 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, and the stimulation plan is as follows: On the first day: Control the automatic bedding-changing module to work at 8 am to change the bedding in the stress cage. Then, according to the first mapping relationship, the noise stimulation unit set for the first day can be determined, and according to the second mapping relationship, the noise control parameter set corresponding to the noise stimulation unit set can be determined, and control the noise stimulation unit set to work with the noise control parameter set to stimulate the small animals in the stress cage; On the second day: According to the first mapping relationship, the fasting stimulation unit set for the second day can be determined, and according to the second mapping relationship, the fasting control parameter set corresponding to the fasting stimulation unit set can be determined, and control the fasting stimulation unit set to work with the fasting control parameter set to stimulate the small animals in the stress cage.

[0072] Thus, for each day in the first stimulation period, e stimulation units are randomly selected from the m stimulation units, resulting in c sets of stimulation units. This random selection method increases the diversity and complexity of the stimulation factors in the experiment, enabling a more comprehensive study of the effects of different stimulation combinations on experimental animals, avoiding the one-sidedness of results caused by a single fixed stimulation pattern, and helping to discover some potential and complex biological phenomena and laws.

[0073] Optionally, in determining the first stimulation period according to the third adaptation period, the stimulation module is specifically configured to: D1. Obtain a preset modeling target; D2. Determine the target onset period corresponding to the preset modeling target; D3. Determine the second stimulation period according to the third adaptation period and the target onset period; D4. Determine the stimulation level corresponding to each of the m stimulation units to obtain m stimulation levels; D5. Determine the average stimulation level corresponding to the m stimulation levels; D6. When the average stimulation level is greater than or equal to a preset stimulation level, determine the first stimulation period according to the second stimulation period; D7. When the average stimulation level is less than the preset stimulation level, determine the target optimization factor corresponding to the average stimulation level; optimize the second stimulation period according to the target optimization factor to obtain the first stimulation period.

[0074] In the embodiments of the present application, the preset modeling target can be preset in advance or by default; the stimulation level can be represented by a value from 1 to 3. For example, "low stimulation level" corresponds to 1, "medium stimulation level" corresponds to 2, and "high stimulation level" corresponds to 3.

[0075] In a specific embodiment, the preset modeling target can be obtained first; then, the target onset period corresponding to the preset modeling target can be determined. Specifically, relevant scientific literature, research reports, and professional books related to the preset modeling target can be consulted to understand information such as the pathogenesis, common onset time range, and factors affecting the onset period in previous studies, so as to obtain the target onset period. For example, if an animal model of a certain depression is to be established, information such as the appearance time and duration of the depressive symptoms in the animal needs to be understood, and based on this information, the onset period of animal depression can be determined.

[0076] Next, the second stimulation period can be determined according to 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 period. Next, the stimulation level corresponding to each of the m stimulation units can be determined to obtain m stimulation levels. Specifically, the mapping relationship between the preset stimulation units and the stimulation levels can be pre-stored, and based on this mapping relationship, the m stimulation levels corresponding to the m stimulation units can be determined. Next, 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 period can be directly used as the first stimulation period.

[0077] When the average stimulation level is less than the preset stimulation level, the target optimization factor corresponding to the average stimulation level can be determined. Specifically, the mapping relationship between the preset stimulation units and the optimization factors can be pre-stored, and based on this mapping relationship, the target optimization factor corresponding to the average stimulation level can be determined. Among them, the value range of the target optimization factor can be 0 to 0.5. Next, the second stimulation period can be optimized according to the target optimization factor. The specific calculation formula is as follows: The first stimulation period = the second stimulation period × (1 + the target optimization factor); According to the above calculation formula, the first stimulation period can be obtained.

[0078] In this way, by determining the second stimulation period according to the third adaptation period and the target onset period, the adaptation time of the experimental animals to the environment and the critical stage of disease development are comprehensively considered. The second stimulation period set in this way can apply stimulation during the time period when the disease may occur and develop after the animals adapt to the environment, improving the success rate and stability of model establishment and reducing experimental errors and individual differences caused by inappropriate stimulation time.

[0079] Optionally, in terms of 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 used for: E1. Obtain the first stimulation unit set and its corresponding first stimulation date; the first stimulation unit set is any one of the c stimulation unit sets; E2. Determine the e first control parameters corresponding to the e first stimulation units in the first stimulation unit set; one first control parameter corresponds to each first stimulation unit; E3. Determine the first fine-tuning parameter corresponding to the first stimulation date; E4. Fine-tune the e first control parameters according to the first fine-tuning parameter to obtain e second control parameters; E5. Obtain the animal types of each small animal in the second experimental animal set to obtain f animal types; f is a positive integer; E6. Determine the target control parameter range corresponding to the f animal types; E7. If the e second control parameters are all within the target control parameter range, determine the control parameter set corresponding to the first stimulation unit set according to the e second control parameters.

[0080] In the embodiments 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 3rd 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 can be obtained from the data of the system, that is, the e first control parameters.

[0081] Then, the first fine-tuning parameter corresponding to the first stimulation date can be determined. For example, the mapping relationship between the preset stimulation date and the fine-tuning parameter can be stored in advance, and the first fine-tuning parameter corresponding to the first stimulation date can be determined based on this 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: Target second control parameter = Target first control parameter × (1 + First fine-tuning parameter); Wherein, the target first control parameter is any one of 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; calculating e times according to the above formula can obtain the e second control parameters. Then, the animal type of each small animal in the second experimental animal set can be obtained to get f animal types. Specifically, the method for obtaining the f animal types can be the same as the method for obtaining the p animal types above.

[0082] Then, the target control parameter range corresponding to the f animal types can be determined. Specifically, the mapping relationship between the preset animal type and the control parameter range can be stored in advance, and the f control parameter ranges corresponding to the f animal types can be determined based on this mapping relationship. The maximum control parameter and the minimum control parameter corresponding to these 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.

[0083] 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. The control parameter can be appropriately adjusted according to the specific situation or previous results of the modeling experiment to better meet the modeling requirements and improve the accuracy and reliability of the modeling results.

[0084] Optionally, the system is further specifically configured to: F1. If the e second control parameters are not all within the target control parameter range, determine g second control parameters among the e second control parameters that are within the target control parameter range, and h second control parameters that are not within the target control parameter range; both g and h are natural numbers less than or equal to e, and g + h = e; F2. Determine the intermediate value corresponding to the target control parameter range; F3. Determine the deviation degrees between the h second control parameters and the intermediate value to obtain h deviation degrees; F4. Adjust the h second control parameters according to the h deviation degrees to obtain h third control parameters; F5. Determine the control parameter set corresponding to the first stimulation unit set according to the g second control parameters and the h third control parameters.

[0085] 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 among the e second control parameters that are within the target control parameter range and h second control parameters that are not within the target control parameter range can be determined; then, the intermediate value corresponding to the target control parameter range can be determined. For example, assuming the target control parameter range is 0 - 60%, then the intermediate value is 30%; then, the deviation degrees between the h second control parameters and the intermediate value can be determined. The specific calculation formula is as follows: Target deviation degree = (Target second control parameter - Intermediate value) / Intermediate value × 100%; Wherein, the target second control parameter is any one of the h second control parameters; the target deviation degree is the deviation degree corresponding to the target second control parameter among the h deviation degrees; by calculating according to the above formula h times, h deviation degrees can be obtained; then, the h second control parameters can be adjusted according to the h deviation degrees to obtain h third control parameters. Specifically, the mapping relationship between the preset deviation degree and the adjustment factor can be pre-stored, and based on this mapping relationship, the h adjustment factors corresponding to the h deviation degrees are determined; 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: Target third control parameter = Target second control parameter × (1 + Target adjustment factor); Among them, the target second control parameter is any one of 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; by calculating according to the above formula h times, h third control parameters can be obtained. Finally, the control parameter set corresponding to the first stimulation unit set can be composed of g second control parameters and h third control parameters.

[0086] In this way, when the 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 not within the range. This classification method helps to deal with parameters in different situations specifically, reflecting the flexibility in handling problems. For the parameters within the range, they can be directly used, while for the parameters not within the range, they are further adjusted, avoiding the cumbersome process of readjusting all parameters due to some parameters not meeting the requirements.

[0087] The detection module is used to determine the n - 1 mental detection results corresponding to the n - 1 experimental animal sets, and the control mental detection result corresponding to the control animal set; determine the target modeling result according to the n - 1 mental detection results and the control mental detection result; the target modeling result includes modeling success or modeling failure.

[0088] In the embodiment of the present application, the detection module is used to perform mental detection on the small animals in the n - 1 experimental animal sets after the implementation of the n - 1 stimulation schemes to obtain n - 1 mental detection results. Moreover, the detection module can also detect the mental state of the small animals in the control animal set to obtain the control mental detection result; finally, the target modeling result can be determined according to the n - 1 mental detection results and the control mental detection result.

[0089] In a certain embodiment, please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a detection module provided by an embodiment of the present application. It can be seen that the detection module may include: a body weight detection unit, a video detection unit, a mental detection unit, etc., which are not limited herein; among them: Body weight detection unit: Regularly measure the body weight of animals through a weighing device (such as a high-precision electronic scale) to obtain body weight change data. The body weight change can, to a certain extent, reflect the health status and physiological state of animals. For example, when an animal is in a depressive state, its body weight may decrease due to reasons such as reduced appetite; while under normal conditions, the body weight is usually relatively stable or shows regular growth, providing an auxiliary reference for judging the mental state.

[0090] Video detection unit: Use devices such as cameras to record videos of experimental animals to obtain video data. The behavioral manifestations of animals can be observed, such as activity level, social behavior, feeding behavior, etc. For example, animals in a depressive state may have a reduced activity level and less interaction with the surrounding environment; normal animals are more active and have normal social behavior, which can be used to assist in judging the mental state.

[0091] Mental detection unit: It can evaluate the mental state of animals based on weight change data and video data to obtain mental detection results. Or, the mental detection unit can also perform professional behavioral tests (such as forced swimming test, tail suspension test, etc.) or specific mental state assessment scales, and combine the behavioral responses of animals (such as struggling time, immobile time, etc.) to comprehensively judge whether the animal is in a normal or depressive mental state.

[0092] Optionally, each mental detection result includes the mental states of all corresponding small animals; the mental state includes one of the following: normal, depressive; in terms of determining the target modeling result based on the n - 1 mental detection results and the control mental detection result, the detection module is specifically used for: G1. Determine the first depression rate corresponding to the control mental detection result; G2. When the first depression rate is greater than the first preset depression rate, determine that the target modeling result is modeling failure; G3. When the first depression rate is not greater than the first preset depression rate, determine the depression rate corresponding to each mental detection result among the n - 1 mental detection results to obtain n - 1 depression rates; G4. Determine the qualified depression rates greater than the 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; G5. Determine the target qualified ratio based on the i qualified depression rates and the n - 1 depression rates; G6. When the target qualified ratio is greater than the preset ratio, determine that the target modeling result is modeling success; G7. When the target qualified ratio is not greater than the preset ratio, determine that the target modeling result is modeling failure.

[0093] In the embodiments of the present application, the first preset depression rate, the second preset depression rate, and the preset ratio can all be preset in advance or default.

[0094] In a specific embodiment, the first depression rate corresponding to the control mental test result may be determined first. Specifically, the number of animals with a depressive mental state in the control animal set can be determined according to the control mental test result to obtain the first depression number. Dividing the first depression number by the total number of the control animal set can 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: depressive, normal, normal, 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 stimulation factors. At this time, the target modeling result can be determined as modeling failure.

[0095] It should be explained that in addition to normal and depressive, the mental state in the embodiments of the present application may also include anxiety. That is to say, in addition to making animals depressive, the stimulation module can also make animals anxious for the study of anxiety disorders.

[0096] When the first depression rate is not greater than the first preset depression rate, the depression rate corresponding to each mental test result in the n - 1 mental test 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 rates 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 according to the i qualified depression rates and the n - 1 depression rates. The specific calculation formula is as follows: Target qualified ratio = i / (n - 1) × 100%; 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 as modeling success.

[0097] When the target qualified ratio is not greater than the preset ratio, the target modeling result is determined as modeling failure.

[0098] In summary, in the CUMS automated modeling system described in the present application, the stimulation module formulates a stimulation plan for n - 1 experimental animal sets based on the n - 1 adaptation periods and m stimulation units determined by the stress module. Since the adaptation period of each experimental animal set is determined according to its own characteristics, the stimulation plan formulated based on this can fully consider the tolerance and response differences of different animal sets to stimulation, making the stimulation more accurate and personalized. At the same time, m stimulation units can be combined to produce diverse stimulation methods, and during the automatic execution of the stimulation process, the operation can be strictly carried out according to the preset stimulation plan, ensuring the consistency and repeatability of the stimulation. Thus, the standardization degree of the CUMS stimulation process is improved.

[0099] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of another CUMS automated modeling system provided by an embodiment of the present application; it can be seen that in addition to including a stress module, a stimulation module, and a detection module, the system may further include: a control module; wherein: Stress module: mainly used to apply various stress factors to experimental animals to simulate stress scenarios that humans may encounter in life. For example, by setting a crowded environment, reversing day and night, fasting and water deprivation, etc., the experimental animals can have psychological and physiological stress responses, thereby constructing an animal model that meets the research needs, and is commonly used in the study of stress-related diseases, such as depression, anxiety, etc.

[0100] Stimulation module: provides diverse stimulation methods, including physical stimulation (such as electric shock, vibration), environmental stimulation (such as temperature change, light change), chemical stimulation (such as odor stimulation), etc. Accurately select and combine stimulation types according to the experimental purpose, and adjust control parameters such as stimulation intensity, frequency, and duration to induce specific physiological and behavioral changes in animals, helping to simulate the occurrence and development process of diseases.

[0101] Detection module: responsible for detecting various indicators of experimental animals. The detection module may include: a body weight detection unit, a video detection unit, a mental detection unit, etc., which are not limited here. For example, the body weight detection unit can measure the body weight change of animals to reflect their nutritional and health status; the video detection unit records the behavior of animals and analyzes the activity level, social behavior, etc.; the mental detection unit evaluates the mental state of animals to determine whether there are abnormalities such as depression and anxiety, thereby providing researchers with comprehensive information of animals during the modeling process, facilitating the evaluation of the modeling effect and the health status of animals.

[0102] Control module (for example, programmable logic controller, single-chip microcomputer, microcontroller unit, etc.): is the control core of the entire system, and conducts overall management and coordination of other modules. On the one hand, it can receive instructions from a remote operating system and control the operation of the stress module and the stimulation module according to a preset program, accurately setting and adjusting the control parameters of the stimulation module; on the other hand, it collects data from the detection module, performs preliminary processing and storage to ensure the stable and orderly operation of the entire system. The control module can also communicate with Figure 6 the communication module shown in

[0103] Communication module (for example, communication devices such as Bluetooth, 5G, WiFi, etc.): used to Figure 6The control instructions issued by the remote operating system shown are accurately transmitted to the control module. At the same time, data collected by the detection module in the system, information such as the operating status of the system, etc. can also be fed back to the remote operating system to ensure the smooth progress of remote operations.

[0104] Please refer to Figure 7 , Figure 7 which is a flowchart of a CUMS automated modeling method provided by an embodiment of the present application; this method is applied to a CUMS automated modeling system, and the system includes: a stress module, a stimulation module, a detection module. The stress module includes n stress cages, and the stimulation module includes m stimulation units. Both m and n are integers greater than 1. The method may include the following steps: S1. Determine the small animal set corresponding to each stress cage among 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 the adaptation period corresponding to each experimental animal set among the n - 1 experimental animal sets to obtain n - 1 adaptation periods; S2. Based on the n - 1 adaptation periods and the m stimulation units, formulate stimulation plans for the n - 1 experimental animal sets to obtain n - 1 stimulation plans; automatically stimulate the small animals in the n - 1 experimental animal sets according to the n - 1 stimulation plans. At the same time, do not apply any stimulation to the small animals in the control animal set; S3. Determine the n - 1 mental detection results corresponding to the n - 1 experimental animal sets, and the control mental detection result corresponding to the control animal set; determine the target modeling result according to the n - 1 mental detection results and the control mental detection result; the target modeling result includes modeling success or modeling failure.

[0105] In specific implementation, the CUMS automated modeling method described in the embodiments of the present invention may also include other implementation manners described in the CUMS automated modeling system provided by the embodiments of the present invention, which will not be elaborated here.

[0106] Please refer to Figure 8 , Figure 8 which is a schematic structural diagram of an electronic device provided by 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, the memory, and the communication interface may be interconnected through a bus; the above one or more programs are stored in the above memory and are configured to be executed by the above processor; in the embodiments of the present application, the above programs include parts or all of the steps for executing the above CUMS automated modeling method.

[0107] The embodiments of the present application further provide 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 some or all of the steps of any of the methods described in the foregoing method embodiments, and the above computer includes an electronic device.

[0108] The embodiments of the present application further provide a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps of any of the methods described in the foregoing method embodiments. The computer program product may be a software installation package, and the above computer includes an electronic device.

[0109] It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, some steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0110] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0111] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection of the device or unit may be in an electrical or other form.

[0112] Those of ordinary skill in the art can understand the entire or part of the process of implementing the above method embodiments. This process can be completed by a computer program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: ROM or random access memory RAM, magnetic disk, or optical disk and other media that can store program codes.

[0113] The steps of the methods or algorithms described in the embodiments of the present application can be implemented in a hardware manner or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), register, hard disk, removable hard disk, CD-ROM, or any other form of storage medium well-known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. Additionally, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist as discrete components in the terminal device or the management device.

[0114] Those skilled in the art should be able to realize 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 by software, hardware, firmware, or any combination thereof. When implemented using software, it 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.

[0115] The above computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media.

[0116] Among them, the available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0117] Each device and product described in the above embodiments, and each module / unit included therein, can be a software module / unit, a hardware module / unit, or can be partially a software module / unit and partially a hardware module / unit. For example, for each device and product applied to or integrated into a chip, each module / unit included therein can be implemented in a hardware manner such as a circuit, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip, and the remaining (if any) part of the modules / units can be implemented in a hardware manner such as a circuit; for each device and product applied to or integrated into a chip module, each module / unit included therein can be implemented in a hardware manner such as a circuit, and different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components of the chip module, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) part of the modules / units can be implemented in a hardware manner such as a circuit; for each device and product applied to or integrated into a terminal device, each module / unit included therein can be implemented in a hardware manner such as a circuit, and different modules / units can be located in the same component (such as a chip, a circuit module, etc.) or different components inside the terminal device, or at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal device, and the remaining (if any) part of the modules / units can be implemented in a hardware manner such as a circuit.

[0118] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific embodiments of the embodiments of the present application and is not used to limit the protection scope 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 shall be included in the protection scope of the embodiments of the present application.

Claims

1. An automated CUMS 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. Both m and n are integers greater than 1. Among them: The stress module is used to determine the small animal sets corresponding to each of the n stress cages, obtaining 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 the adaptation periods corresponding to each of the n - 1 experimental animal sets, obtaining n - 1 adaptation periods; The stimulation module is used to formulate stimulation plans for the n - 1 experimental animal sets based on the n - 1 adaptation periods and the m stimulation units, obtaining n - 1 stimulation plans; automatically stimulate the small animals in the n - 1 experimental animal sets according to the n - 1 stimulation plans. At the same time, no stimulation is applied to the small animals in the control animal set; The detection module is used to determine the n - 1 mental detection results corresponding to the n - 1 experimental animal sets, and the control mental detection result corresponding to the control animal set; determine the target modeling result according to the n - 1 mental detection results and the control mental detection result; the target modeling result includes successful modeling or failed modeling.

2. The system according to claim 1, characterized in that, In terms of determining the adaptation periods corresponding to each of the n - 1 experimental animal sets, obtaining n - 1 adaptation periods, the stress module specifically is used for: Obtain the first basic data of the first stress cage corresponding to the first experimental animal set; the first experimental animal set is any one of the n - 1 experimental animal sets; Determine the animal types of each small animal in the first experimental animal set, obtaining p animal types; p is a positive integer; Determine the first adaptation periods corresponding to each of the p animal types, obtaining p first adaptation periods; Determine the adaptation period corresponding to the first experimental animal set 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 experimental animal set according to the first basic data and the p first adaptation periods, the stress module specifically is used for: Determine the health levels of each small animal in the first experimental animal set, obtaining p health levels; Determine a health levels greater than or equal to the preset health level and b health levels less than the preset health level among the p health levels; Both a and b are natural numbers less than or equal to p, and a + b = p; Determine the first adaptation periods corresponding to the a health levels among the p first adaptation periods, obtaining a first adaptation periods; Determine the first adaptation periods corresponding to the b health levels among the p first adaptation periods, obtaining b first adaptation periods; Determine the first influencing factor corresponding to the first basic data; Determine the second influencing factors corresponding to the m stimulation units; Adjust the b first adaptation periods according to the first influencing factor and the second influencing factors, obtaining b second adaptation periods; Determine the adaptation period corresponding to the first set of experimental animals according to the a first adaptation periods and the b second adaptation periods.

4. The system according to any one of claims 1 to 3, characterized in that, The system further includes an automatic bedding-changing module. In terms of formulating a stimulation plan for the n-1 sets of experimental animals based on the n-1 adaptation periods and the m stimulation units to obtain n-1 stimulation plans, the stimulation module is specifically configured to: 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 sets of experimental animals; Determine a first stimulation period according to the third adaptation period; the first stimulation period is greater than the third adaptation period; the first stimulation period includes c days; c is a positive integer; Based on a preset time interval, determine the working time points of the automatic bedding-changing module in the first stimulation period to obtain d working time points; d is a positive integer; For each day in the first stimulation period, randomly select e stimulation units from the m stimulation units to obtain c sets of stimulation units; e is an integer greater than 1 and less than or equal to m; Determine the control parameter set corresponding to each set of stimulation units in the c sets of stimulation units to obtain c control parameter sets; Determine the stimulation plan corresponding to the second set of experimental animals according to the first stimulation period, the d working time points, the c sets of stimulation units, and the c control parameter sets.

5. The system according to claim 4, characterized in that, In terms of determining the first stimulation period according to the third adaptation period, the stimulation module is specifically configured to: Obtain a preset modeling target; Determine the target onset period corresponding to the preset modeling target; Determine a second stimulation period according to the third adaptation period and the target onset period; Determine the stimulation level corresponding to each of the m stimulation units to obtain m stimulation levels; Determine the average stimulation level corresponding to the m stimulation levels; When the average stimulation level is greater than or equal to a preset stimulation level, determine the first stimulation period according to the second stimulation period; When the average stimulation level is less than the preset stimulation level, determine the target optimization factor corresponding to the average stimulation level; optimize the second stimulation period according to the target optimization factor to obtain the first stimulation period.

6. The system according to claim 4, wherein In terms of determining the control parameter set corresponding to each set of stimulation units in the c sets of stimulation units to obtain c control parameter sets, the stimulation module is specifically configured to: Obtain a first set of stimulation units and its corresponding first stimulation date; the first set of stimulation units is any one of the c sets of stimulation units; Determine the e first control parameters corresponding to the e first stimulation units in the first set of stimulation units; one first control parameter corresponds to each first stimulation unit; Determine the 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 set of experimental animals to obtain f animal types; f is a positive integer; Determine the target control parameter range corresponding to the f animal types; If all of the e second control parameters are within the target control parameter range, determine a control parameter set corresponding to the first set of stimulation units according to the e second control parameters.

7. The system according to claim 6, wherein The system is further specifically configured to: If all of the e second control parameters are not within the target control parameter range, determine g second control parameters that are within the target control parameter range and h second control parameters that are not within the target control parameter range among the e second control parameters; both g and h are natural numbers less than or equal to e, and g + h = e; Determine an intermediate value corresponding to the target control parameter range; Determine a deviation degree between each of the h second control parameters and the intermediate value, obtaining h deviation degrees; Adjust the h second control parameters according to the h deviation degrees, obtaining h third control parameters; Determine a control parameter set corresponding to the first set of stimulation units according to the g second control parameters and the h third control parameters.

8. The system according to any one of claims 1 to 3, characterized in that, Each mental detection result includes the mental states of all small animals corresponding thereto; the mental states include one of the following: normal, depressed; in terms of determining a target modeling result according to the n - 1 mental detection results and the control mental detection result, the detection module is specifically configured to: Determine a first depression rate corresponding to the control mental detection result; When the first depression rate is greater than a first preset depression rate, determine that the target modeling result is modeling failure; When the first depression rate is not greater than the first preset depression rate, determine a depression rate corresponding to each mental detection result among the n - 1 mental detection results, obtaining n - 1 depression rates; Determine qualified depression rates 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; Determine a target qualified ratio according to the i qualified depression rates and the n - 1 depression rates; When the target qualified ratio is greater than a preset ratio, determine that the target modeling result is modeling success; When the target qualified ratio is not greater than the preset ratio, determine that the target modeling result is modeling failure.

9. An automated CUMS modeling method, characterized in that, 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. Both m and n are integers greater than 1. The method includes: Determine a set of small animals corresponding to each of the n stress cages, obtaining n sets of small animals; the n sets of small animals include n - 1 sets of experimental animals and one set of control animals; each set of small animals includes at least one small animal; determine an adaptation period corresponding to each set of experimental animals among the n - 1 sets of experimental animals, obtaining n - 1 adaptation periods; Based on the n - 1 adaptation periods and the m stimulation units, formulate stimulation schemes for the n - 1 sets of experimental animals to obtain n - 1 stimulation schemes; automatically stimulate the small animals in the n - 1 sets of experimental animals according to the n - 1 stimulation schemes, and at the same time, do not apply any stimulation to the small animals in the control animal set; Determine the n - 1 mental test results corresponding to the n - 1 sets of experimental animals, and the control mental test results corresponding to the control animal set; determine the target modeling result according to the n - 1 mental test results and the control mental test results; the target modeling result includes modeling success or modeling failure.

10. A computer-readable storage medium, characterized in that, Store a computer program for electronic data interchange, wherein the computer program causes a computer to execute the method according to claim 9.

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