Data processing system for mouse experiment

Abstract

The invention provides a data processing system for a mouse experiment, and relates to the field of mouse experiment systems. The data processing system for the mouse experiment comprises an activity monitoring module, the activity monitoring module is connected with a feeding monitoring module, the feeding monitoring module is connected with an environment monitoring module, and the environment monitoring module is connected with a data storage module. The data storage module is connected with a data analysis module, a data mining module and a behavior analysis module, and the data analysis module, the data mining module and the behavior analysis module are connected with an authority management module. By establishing a systematized experiment plan and process, the consistency and repeatability of the experiment can be ensured, and researchers can perform experiment operation and data acquisition according to predetermined steps, so that the operation error and data deviation are reduced, and meanwhile, the behaviors of the mouse can be automatically identified and analyzed by utilizing image identification and a deep learning algorithm, and the accuracy of the mouse behavior analysis is improved. The subjectivity is eliminated; and the accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of mouse experiment systems, in particular to a data processing system for mouse experiments. Background Art

[0002] Mouse experiments are a common animal testing method used in scientific research. Experiments on mice are used to study human diseases, drug efficacy, and aspects of gene function. Mouse experiments can provide valuable information on biology, physiology, pathology, and pharmacology. Because mice have genetic backgrounds and physiological characteristics similar to humans, they can serve as models for human disease treatment and drug development. Mouse experiments typically involve injecting drugs, observing mouse behavior, and collecting biological samples. Detailed control of mouse experiments requires a data processing system.

[0003] Existing technologies usually rely on the subjective judgment of researchers when conducting behavioral observations, which is easily affected by individual differences and subjective biases, resulting in inaccurate results. In addition, traditional behavioral analysis requires manual recording and processing of large amounts of data, which is time-consuming and prone to errors. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a data processing system for mouse experiments, which solves the problems of relying on the subjective judgment of researchers, which easily leads to inaccurate results, and manually recording and processing large amounts of data, which is time-consuming and prone to errors.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data processing system for mouse experiments, comprising an activity monitoring module, the activity monitoring module connected to a feeding monitoring module, the feeding monitoring module connected to an environment monitoring module, the environment monitoring module connected to a data storage module, the data storage module connected to a data analysis module, a data mining module, and a behavior analysis module, the data analysis module, the data mining module, and the behavior analysis module connected to a rights management module, and the rights management module connected to an export sharing module;

[0008] The activity monitoring module, feeding monitoring module, environment monitoring module and data storage module are connected to a plan reminder module, and the plan reminder module is connected to the activity monitoring module, feeding monitoring module, environment monitoring module and data storage module.

[0009] Preferably, the data mining module includes a data collection unit, the data collection unit is connected to a data exploration unit, the data exploration unit is connected to a preprocessing unit, the preprocessing unit is connected to a model selection unit, the model selection unit is connected to a model training unit, and the model training unit is connected to a model application unit.

[0010] Preferably, the permission management module includes a user registration unit, the user registration unit is connected to an identity authentication unit, the identity authentication unit is connected to a role allocation unit, the role allocation unit is connected to a permission setting unit, the permission setting unit is connected to a permission approval unit, the permission approval unit is connected to a permission management unit, and the permission management unit is connected to a log monitoring unit.

[0011] Preferably, the behavior analysis module includes a data acquisition unit, the data acquisition unit is connected to a video processing unit, the video processing unit is connected to a behavior recognition unit, the behavior recognition unit is connected to a behavior analysis unit, the behavior analysis unit is connected to a data management unit, the data management unit is connected to a data visualization unit, the data visualization unit is connected to a report generation unit, the report generation unit is connected to a real-time monitoring unit, the real-time monitoring unit is connected to a data prediction unit, and the data prediction unit is connected to a remote monitoring unit.

[0012] Preferably, the data visualization unit is connected to a behavior trajectory diagram, a heat map and a time series diagram.

[0013] Preferably, the plan reminder module includes an experiment design unit, the experiment design unit is connected to the experiment preparation unit, the experiment preparation unit is connected to the mouse processing unit, the mouse processing unit is connected to the operation and collection unit, and the operation and collection unit is connected to the analysis and processing unit.

[0014] Working principle: When using this system, the system first monitors the mouse's activities in real time through sensors installed in the mouse cage, including exercise, sleep, and eating behaviors. Then, it records the mouse's feeding time and food intake, and generates a feeding curve so that researchers can analyze the mouse's eating behavior. At the same time, it monitors the temperature, humidity, and light parameters of the mouse experimental environment and associates them with the mouse activity data to analyze the impact of the environment on the mouse's behavior. At this time, the system will automatically collect various data during the mouse experiment and store them in the database so that researchers can access and analyze them at any time. According to the needs of the researchers, the experimental data will be statistically analyzed and corresponding reports and charts will be generated to better understand and interpret the experimental results. At the same time, the system supports multi-user login and provides different permission management to ensure the security and confidentiality of the data.

[0015] (3) Beneficial effects

[0016] The present invention provides a data processing system for mouse experiments. This system has the following beneficial effects: By establishing a systematic experimental planning and execution process, it ensures experimental consistency and reproducibility. Researchers can perform experimental operations and collect data according to predetermined steps, reducing human operational errors and data bias. Furthermore, it utilizes image recognition and deep learning algorithms to automatically identify and analyze mouse behavior, eliminating subjectivity and improving accuracy. Ultimately, this system can help researchers more effectively organize and manage mouse activity monitoring and feeding monitoring experiments, improving experimental accuracy and reliability, and ultimately leading to a better understanding of mouse behavioral characteristics and physiological states. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the overall system flow of the present invention;

[0018] Figure 2 This is a schematic diagram of the system flow of the data mining module of the present invention;

[0019] Figure 3 This is a schematic diagram of the system flow of the rights management module of the present invention;

[0020] Figure 4 This is a system flow diagram of the behavior analysis module of the present invention;

[0021] Figure 5 is a schematic diagram of a data visualization unit of the present invention;

[0022] Figure 6 This is a system flow diagram of the planning reminder module of the present invention.

[0023] Among them, 1. Activity monitoring module; 2. Feeding monitoring module; 3. Environmental monitoring module; 4. Data storage module; 5. Data analysis module; 6. Data mining module; 7. Permission management module; 8. Export sharing module; 9. Behavior analysis module; 10. Plan reminder module; 601. Data collection unit; 602. Data exploration unit; 603. Preprocessing unit; 604. Model selection unit; 605. Model training unit; 606. Model application unit; 701. User registration unit; 702. Identity authentication unit; 703. Role assignment unit; 704. Permission setting unit; 705. Permission approval unit; 706. Permission management unit; 707, log monitoring unit; 901, data acquisition unit; 902, video processing unit; 903, behavior recognition unit; 904, behavior analysis unit; 905, data management unit; 906, data visualization unit; 907, report generation unit; 908, real-time monitoring unit; 909, data prediction unit; 910, remote monitoring unit; 911, behavior trajectory diagram; 912, heat map; 913, time series diagram; 1001, experimental design unit; 1002, experimental preparation unit; 1003, mouse handling unit; 1004, operation and acquisition unit; 1005, analysis and processing unit. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] Example:

[0026] like Figure 1-6As shown, an embodiment of the present invention provides a data processing system for mouse experiments, including an activity monitoring module, which first monitors the activity of mice in real time, including exercise, sleep, and eating behaviors. The activity monitoring module is connected to a feeding monitoring module, which then records the feeding time and food intake of the mice and generates a feeding curve. The feeding monitoring module is connected to an environment monitoring module, which then monitors the temperature, humidity, and light parameters of the mouse experimental environment. The environment monitoring module is connected to a data storage module and automatically collects various data during the mouse experiment. The data storage module is connected to a data analysis module, a data mining module, and a behavior analysis module. The data analysis module, the data mining module, and the behavior analysis module are connected to a rights management module. The rights management module is connected to an export and sharing module, which can export experimental data into a common data format for sharing and collaboration with other researchers. The activity monitoring module, the feeding monitoring module, the environment monitoring module, and the data storage module are connected to a plan reminder module. Specifically, the experimental plan and reminder system process should be adjusted and optimized according to the specific experimental objectives and research questions. Before conducting an experiment, the requirements of the experimental operation and data collection should be fully understood, and the normal operation of the experimental equipment and tools should be ensured. During the experiment, the situation of experimental operation and data collection is recorded in time, and data processing and analysis are carried out according to the experimental plan. Finally, according to experimental result and discussion, corresponding conclusions and suggestions are proposed, and further research and experiment are carried out, the planned reminder module is connected to activity monitoring module, feeding monitoring module, environmental monitoring module and data storage module, the planned reminder module includes experimental design unit, first determines the purpose and research problem of the experiment, such as studying the activity pattern of mice and the response of dietary behavior to specific factors. Then the time range, experimental group and control group of the design experiment, and other experimental conditions, the experimental design unit is connected with the experimental preparation unit, then according to the experimental design, prepare required activity monitor, some weighing devices of feeder, some experimental equipment and tools, ensure the normal operation and accuracy of these equipment and tools, the experimental preparation unit is connected with the mouse processing unit, selects the mouse strain and individual suitable for experiment, and carries out necessary processing, such as marking and acclimatization. Ensure that mice possess relatively stable physiological state before experiment, the mouse processing unit is connected with operation and acquisition unit, then according to the experimental design, activity monitor is installed in the cage or laboratory of mice, and feeder and weighing device are connected with feeding monitoring system. According to the experimental plan, the activity and feeding behavior of mice are regularly monitored and recorded, including some indicators such as the number of movements, movement distance, food intake and frequency of eating. The operation and collection unit are connected to the analysis and processing unit, and finally the collected data are sorted and processed, such as calculating the average number of activities, total movement distance and average food intake in each time period.According to the experimental purpose and research questions, appropriate statistical methods and data analysis tools are selected to perform statistical analysis and graphical presentation of the data, which ultimately helps researchers obtain experimental data in a timely manner and conduct experimental management, and facilitates subsequent data processing and analysis.

[0027] The data mining module includes a data collection unit. First, it is necessary to collect relevant time series data. These data can be numerical values ​​arranged in chronological order, and usually a long period of historical data is collected to better analyze and predict future values. The data collection unit is connected to a data exploration unit. Before performing time series prediction, the data needs to be explored and visualized. This includes drawing a time series graph and observing the overall trend, periodicity and seasonality of the data. The data exploration unit is connected to a preprocessing unit. Before performing time series prediction, the data usually needs to be preprocessed, which includes processing missing values, outliers and noise, smoothing data to reduce random fluctuations, and performing data transformation to meet the assumptions of the prediction model. The preprocessing unit is connected to a model selection unit, and a model based on a recurrent neural network RNN ​​can be selected. The model selection unit is connected to a model training unit, which uses historical data to train the selected model and uses a portion of data to verify and evaluate the model. The model training unit is connected to a model application unit. After the model passes the evaluation, it can be applied to actual time series prediction. Based on the new input data, the model can predict future values ​​and give corresponding confidence intervals, which can help experimenters make corresponding decisions and plans.

[0028] The rights management module includes a user registration unit. Users must provide necessary personal information and complete the registration process, including a username, password, and email address. Upon successful registration, the system generates a unique user identifier for subsequent authentication and rights management. The user registration unit is connected to an identity verification unit. When a user logs into the system, the system requires the user to enter their username and password for authentication. The system verifies the information provided by the user to ensure they are legitimate. The identity verification unit is connected to a role assignment unit. System administrators assign users to roles based on their responsibilities and needs. Different roles have different permissions and access scopes. For example, system administrators have the highest permissions and can configure and manage the system; researchers can view and analyze data but cannot modify system settings; and operators can perform experimental operations but cannot access or modify sensitive data. The role assignment unit is connected to a permissions setting unit. System administrators can set permissions for different functional modules and data based on user roles and needs. For example, administrators can restrict access and operation of certain functions to users with specific roles and limit certain users to accessing specific data sets. The permissions setting unit is connected to a permissions approval unit. The system may require permission approval for sensitive operations and data access. For example, if a researcher needs to access a specific dataset, an administrator will review and approve the application, granting or restricting access as needed. The permission approval unit is connected to the permission management unit, and system administrators need to regularly manage and maintain user permissions. For example, when a user's role changes or a user leaves the company, administrators need to promptly adjust their permissions to ensure they are appropriate and secure. The permission management unit is connected to a log monitoring unit, which records user operations, including logins and operation records. Administrators can use this log monitoring to track user behavior and identify anomalies and risks.

[0029] The behavior analysis module includes a data acquisition unit. The system collects the mouse's behavior video in real time by connecting to a camera in the mouse cage. The data acquisition unit is connected to a video processing unit. The system preprocesses the collected video, including denoising and image enhancement, to improve the accuracy of subsequent behavior analysis. The video processing unit is connected to a behavior recognition unit. The system uses image recognition and deep learning algorithms to perform behavior recognition on the preprocessed video. Through the training model, the system can automatically identify different types of mouse behavior, such as activity, sleep, and eating. The behavior recognition unit is connected to a behavior analysis unit. The system analyzes the identified behavior, including changes in behavior frequency, duration, distance, and speed. The behavior analysis unit is connected to a data management unit. The system stores the analyzed behavior data in a database and provides data management functions. The data management unit is connected to a data visualization unit. Specifically, the data visualization unit is connected to a behavior trajectory map, a heat map, and a time series map. The behavior trajectory map can draw the mouse's movement trajectory in the form of lines or points on a plan view of the experimental environment. Different color or shape markers can be used to distinguish different behavior types, such as activity, rest, and exploration. Behavior trajectory maps can visually display the movement paths and behavior distribution of mice, helping researchers observe and analyze behavioral patterns and trends. Heat maps can visualize the intensity of mice's activity in the experimental environment using color. Color depth is typically used to represent activity intensity, with darker colors indicating stronger activity and lighter colors indicating weaker activity. Heat maps can help researchers observe and analyze the activity preferences and distribution of mice in different areas. Time series graphs can visualize changes in mouse behavioral data over time. Line graphs or bar graphs can be used to show changes in the frequency or duration of different behavior types over time. Time series graphs can help researchers observe and analyze the temporal characteristics and cyclical changes of behavior. The data visualization unit is connected to a report generation unit. The system generates reports and charts based on researchers' needs, allowing them to interpret and share experimental results. The report generation unit is also connected to a real-time monitoring unit, which monitors the behavioral status of mice in real time and sets alarms for abnormalities.When mice exhibit abnormal behavior, such as escape and abnormal activity, the system will promptly send an alarm message to researchers so that appropriate measures can be taken. The real-time monitoring unit is connected to a data prediction unit. The system can apply machine learning and data mining algorithms to analyze and mine experimental data, discover potential associations and patterns, and predict trends and results of mouse behavior. The data prediction unit is connected to a remote monitoring unit. The system can support remote monitoring and control of the mouse experimental process. Researchers can remotely view experimental data and videos through mobile phones or computers, adjust experimental parameters and environmental conditions, and ultimately, the above-mentioned behavioral analysis system process can help researchers more effectively organize and manage mouse activity monitoring and feeding monitoring experiments, improve the accuracy and reliability of the experiment, and thus better understand the behavioral characteristics and physiological status of mice.

[0030] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A data processing system for mouse experiments, comprising an activity monitoring module (1), characterized in that: The activity monitoring module (1) is connected to a feeding monitoring module (2), the feeding monitoring module (2) is connected to an environment monitoring module (3), the environment monitoring module (3) is connected to a data storage module (4), the data storage module (4) is connected to a data analysis module (5), a data mining module (6) and a behavior analysis module (9), the data analysis module (5), the data mining module (6) and the behavior analysis module (9) are connected to a rights management module (7), and the rights management module (7) is connected to an export sharing module (8); The activity monitoring module (1), the feeding monitoring module (2), the environment monitoring module (3) and the data storage module (4) are connected to a plan reminder module (10), and the plan reminder module (10) is connected to the activity monitoring module (1), the feeding monitoring module (2), the environment monitoring module (3) and the data storage module (4).

2. A data processing system for mouse experiments according to claim 1, characterized in that: The data mining module (6) includes a data collection unit (601), the data collection unit (601) is connected to a data exploration unit (602), the data exploration unit (602) is connected to a preprocessing unit (603), the preprocessing unit (603) is connected to a model selection unit (604), the model selection unit (604) is connected to a model training unit (605), and the model training unit (605) is connected to a model application unit (606).

3. The data processing system for mouse experiments according to claim 1, characterized in that: The authority management module (7) includes a user registration unit (701), the user registration unit (701) is connected to an identity authentication unit (702), the identity authentication unit (702) is connected to a role assignment unit (703), the role assignment unit (703) is connected to a authority setting unit (704), the authority setting unit (704) is connected to a authority approval unit (705), the authority approval unit (705) is connected to a authority management unit (706), and the authority management unit (706) is connected to a log monitoring unit (707).

4. The data processing system for mouse experiments according to claim 1, characterized in that: The behavior analysis module (9) includes a data acquisition unit (901), the data acquisition unit (901) is connected to a video processing unit (902), the video processing unit (902) is connected to a behavior recognition unit (903), the behavior recognition unit (903) is connected to a behavior analysis unit (904), the behavior analysis unit (904) is connected to a data management unit (905), the data management unit (905) is connected to a data visualization unit (906), the data visualization unit (906) is connected to a report generation unit (907), the report generation unit (907) is connected to a real-time monitoring unit (908), the real-time monitoring unit (908) is connected to a data prediction unit (909), and the data prediction unit (909) is connected to a remote monitoring unit (910).

5. The data processing system for mouse experiments according to claim 4, characterized in that: The data visualization unit (906) is connected to a behavior trajectory diagram (911), a heat map (912) and a time series diagram (913).

6. The data processing system for mouse experiments according to claim 1, characterized in that: The plan reminder module (10) includes an experiment design unit (1001), the experiment design unit (1001) is connected to an experiment preparation unit (1002), the experiment preparation unit (1002) is connected to a mouse processing unit (1003), the mouse processing unit (1003) is connected to an operation and collection unit (1004), and the operation and collection unit (1004) is connected to an analysis and processing unit (1005).

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