Pathogen culture dish for basic medicine

Abstract

The invention relates to the field of basic medicine research, in particular to a basic medicine pathogen culture dish which comprises an environment regulation and control module, a multi-dimensional monitoring module, a data analysis module, an early warning and feedback module and a data management module. Multi-parameter cooperative control is realized through a gas sensor and a temperature and humidity sensor, multi-dimensional monitoring is performed by combining optical microscopic imaging and chemical sensing technologies, and real-time data analysis and prediction are realized by using an embedded processor and a machine learning algorithm. The device can dynamically balance the gas concentration, temperature and humidity, comprehensively monitor the growth state of pathogens, improve the experiment efficiency and data reliability, and provide an efficient and accurate culture device solution for basic medical research.

Description

Technical Field

[0001] This invention belongs to the field of biomedical experimental equipment technology, specifically pathogen culture dishes for basic medicine. Background Technology

[0002] In basic medical research, pathogen culture and monitoring are crucial steps. To improve culture efficiency and the reliability of experimental data, the design of culture devices needs to meet requirements such as high-precision control, real-time monitoring, and ease of operation. However, existing pathogen culture devices still have many shortcomings in terms of gas environment control, temperature stability, and real-time monitoring capabilities, affecting the accuracy and reproducibility of experimental results.

[0003] A search revealed a patent, CN113534862B, entitled "Gas Concentration Control System and Method for a Culture Chamber," published on May 3, 2024. This patent provides a technical solution that achieves rapid equilibrium of gas concentration within the culture chamber by acquiring the target gas concentration and adjusting the gas input switch based on real-time detection results. However, this technical solution primarily focuses on gas concentration regulation and fails to adequately consider the coordinated control of other key environmental parameters such as temperature and humidity, potentially leading to insufficient overall stability of the culture environment. Furthermore, the gas detection unit and control module of this system are relatively complex, which may increase equipment costs and maintenance difficulty, hindering large-scale application.

[0004] A search revealed a patent, CN110066724B, entitled "Real-time Monitoring Device and Detection Method for Microbial Culture," published on August 2, 2024. This patent uses an imaging device to continuously monitor the growth of microorganisms in a culture dish and combines this with a storage and analysis system to record detailed information, providing high experimental traceability. However, the imaging and operating devices of this technical solution require a highly sealed culture environment, which may limit gas exchange and thus affect the normal growth of pathogens. Furthermore, the detection method of this system relies on image acquisition and statistical analysis, and its ability to monitor certain microscopic changes or non-visual features is limited, potentially failing to fully reflect the dynamic growth status of pathogens.

[0005] The aforementioned problems indicate that existing pathogen culture devices still have certain shortcomings in terms of coordinated control of multiple environmental parameters, comprehensiveness of real-time monitoring, and ease of operation. Therefore, this invention provides a novel pathogen culture dish for basic medical research, aiming to optimize the comprehensive control capability of the culture environment, improve the comprehensiveness and sensitivity of real-time monitoring, and simplify the operation process, so as to meet the needs of basic medical research for efficient and reliable pathogen culture devices. Summary of the Invention

[0006] This invention relates to the field of basic medical research, specifically a pathogen culture dish for basic medical applications. In recent years, with the deepening of basic medical research, pathogen culture devices have gradually become a core component of experimental design, serving as crucial tools for experimental data acquisition and scientific exploration. Pathogen culture devices, through multi-parameter coordinated control, real-time monitoring, and convenient operation, can significantly improve experimental efficiency and result reliability. Especially in areas such as gas environment control, temperature stability, and dynamic monitoring, optimized culture devices can provide more precise data support for basic medical research. However, existing pathogen culture devices still have certain limitations in the coordinated control of multiple environmental parameters.

[0007] For example, patent CN113534862B, entitled "Gas Concentration Control System and Method for Culture Chamber," published on May 3, 2024, proposes a technical solution for adjusting the gas input switch based on real-time detection results, achieving rapid equilibrium of gas concentration within the culture chamber. However, this technical solution primarily focuses on gas concentration control and fails to fully integrate the coordinated management of other key environmental parameters such as temperature and humidity, potentially affecting the overall stability of the culture environment. Furthermore, the design of its gas detection unit and control module is relatively complex, which may increase equipment costs and maintenance difficulty, thus limiting its large-scale application scenarios.

[0008] Meanwhile, the patent CN110066724B, entitled "Real-time Monitoring Device and Detection Method for Microbial Culture," published on August 2, 2024, uses an imaging device to continuously monitor the growth of microorganisms in a culture dish and records detailed information using a storage and analysis system, providing high experimental traceability. However, this technical solution requires a highly sealed culture environment, which may limit gas exchange to some extent and thus affect the normal growth of pathogens. Furthermore, its detection method relies on image acquisition and statistical analysis, and its ability to monitor certain microscopic changes or non-visual features is relatively limited, potentially failing to fully reflect the dynamic growth status of pathogens.

[0009] The aforementioned problems indicate that existing pathogen culture devices still have room for improvement in terms of coordinated control of multiple environmental parameters, comprehensiveness of real-time monitoring, and ease of operation. Therefore, this invention aims to provide a novel pathogen culture dish for basic medical research, optimizing the comprehensive control capabilities of the culture environment, improving the comprehensiveness and sensitivity of real-time monitoring, and simplifying the operation process, so as to better meet the needs of basic medical research for efficient and reliable pathogen culture devices.

[0010] The objective of this invention can be achieved through the following technical solutions:

[0011] This invention provides a basic medical pathogen culture dish, which includes the following modules:

[0012] An environmental control module is used to monitor and dynamically adjust the gas concentration, temperature, and humidity inside the petri dish in real time; the environmental control module includes a gas sensor, a temperature and humidity sensor, a micro air pump, and a heating and cooling assembly;

[0013] A multidimensional monitoring module is used to monitor the growth status of pathogens in multiple dimensions through optical microscopy and chemical sensing technology; the multidimensional monitoring module includes a high-resolution microscope lens, a fluorescently labeled probe, and an electrochemical sensor;

[0014] The data analysis module is used to process and analyze monitoring data in real time and generate dynamic growth curves; the data analysis module includes an embedded processor and machine learning algorithms.

[0015] The early warning and feedback module is used to trigger early warning signals based on the changing trends of monitoring data and adjust the culture environment parameters through an automatic adjustment mechanism.

[0016] The data management module is used to store monitoring data and analysis results and protect data security through encryption algorithms.

[0017] As a preferred embodiment of the basic medical pathogen culture dish described in this invention, the working process of the environmental control module includes the following steps:

[0018] A gas sensor is used to monitor the concentrations of oxygen, carbon dioxide, and nitrogen in the petri dish in real time, and the monitoring results are sent to a micro gas pump; the micro gas pump automatically adjusts the gas input according to a preset gas concentration range;

[0019] Temperature and humidity sensors are used to monitor the temperature and humidity inside the petri dish in real time, and the monitoring results are sent to the heating and cooling assembly. The heating and cooling assembly achieves temperature regulation and humidity compensation through thermoelectric effect according to the preset temperature and humidity range.

[0020] The PID control algorithm is used to perform closed-loop control of the gas concentration, temperature and humidity regulation process to ensure the dynamic balance of various environmental parameters.

[0021] As a preferred embodiment of the basic medical pathogen culture dish described in this invention, the operation of the multidimensional monitoring module includes the following steps:

[0022] The pathogens in the culture dish were imaged in real time using a high-resolution microscope lens, and the target area was extracted using an image segmentation algorithm.

[0023] Key biomarkers of pathogens were labeled using fluorescently labeled probes, and the labeling signals were captured using a fluorescence microscope.

[0024] Electrochemical sensors were used to detect metabolites in the culture medium in real time, and the detection results were correlated with fluorescence signals for analysis.

[0025] Imaging data, fluorescence signals, and electrochemical detection data are fused together to generate a multidimensional growth state map of the pathogen.

[0026] As a preferred embodiment of the basic medical pathogen culture dish described in this invention, the data analysis module's operation includes the following steps:

[0027] The raw data generated by the multidimensional monitoring module is preprocessed using an embedded processor, including denoising, interpolation, and normalization.

[0028] Machine learning algorithms are used to classify and cluster the preprocessed data to identify the growth patterns of pathogens.

[0029] Based on the classification and clustering results, a dynamic growth curve is constructed, and the future growth trend of the pathogen is predicted through time series analysis;

[0030] The dynamic growth curve and prediction results are sent to the early warning and feedback module to trigger subsequent operations.

[0031] As a preferred embodiment of the basic medical pathogen culture dish described in this invention, the operation of the early warning and feedback module includes the following steps:

[0032] Set early warning thresholds based on the changing trends of the dynamic growth curve;

[0033] When the monitored data exceeds the warning threshold, a warning signal is triggered, and the operator is alerted through an audible and visual alarm device.

[0034] At the same time, abnormal data is sent to the environmental control module, which optimizes gas concentration, temperature and humidity through an automatic adjustment mechanism;

[0035] Record the timestamps and relevant parameters of the warning events for subsequent data analysis.

[0036] As a preferred embodiment of the basic medical pathogen culture dish described in this invention, the data management module's operation includes the following steps:

[0037] The monitoring data and analysis results are stored using an SQLite database, and each data entry is given a unique identifier and timestamp.

[0038] The stored data is encrypted using the AES encryption algorithm, and the key is changed regularly to enhance data security.

[0039] The system displays dynamic growth curves, early warning events, and optimization suggestions through a visual interface. This visual interface is developed using the Qt framework and supports data display in chart and table formats.

[0040] Data can be exported to external devices via a transmission protocol for further analysis and archiving.

[0041] This invention, by introducing multi-parameter collaborative control, multi-dimensional monitoring technology, and intelligent data analysis methods, enables a dynamic balance of gas concentration, temperature, and humidity within the culture dish, thereby improving the overall stability of the pathogen culture environment. Secondly, by combining optical microscopy and chemical sensing technologies, this invention can comprehensively reflect the dynamic growth state of pathogens, overcoming the shortcomings of traditional monitoring methods in detecting microscopic changes and non-visual characteristics. Furthermore, by integrating embedded processors and machine learning algorithms, this invention achieves real-time processing and intelligent analysis of monitoring data, providing researchers with more precise decision support. Attached Figure Description

[0042] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0043] Figure 1 This is a schematic diagram of the overall structure of the pathogen culture dish for basic medical use in this invention, showing the layout and connection relationship of each module.

[0044] Figure 2 This is a schematic diagram illustrating the working principle of the environmental control module, which details the collaborative workflow of the gas sensor, temperature and humidity sensor, micro air pump, and heating and cooling components.

[0045] Figure 3 This is a functional block diagram of the multidimensional monitoring module, demonstrating the application of high-resolution microscope lenses, fluorescently labeled probes, and electrochemical sensors in pathogen monitoring.

[0046] Figure 4 This is a flowchart of the data analysis module, which describes the process of embedded processors and machine learning algorithms analyzing monitoring data.

[0047] Figure 5 The diagram shows the operational logic of the early warning and feedback module, illustrating the implementation of the early warning signal triggering and automatic adjustment mechanism.

[0048] Figure 6 This is a schematic diagram of the storage and display interface of the data management module, showcasing the data management functions of the SQLite database and the visual interface.

[0049] The attached figures are labeled as follows:

[0050] 1. Environmental control module; 2. Multidimensional monitoring module; 3. Data analysis module; 4. Early warning and feedback module; 5. Data management module; 6. Gas sensor; 7. Temperature and humidity sensor; 8. Miniature air pump; 9. Heating and cooling components; 10. High-resolution microscope lens; 11. Fluorescent labeled probe; 12. Electrochemical sensor; 13. Embedded processor; 14. Audible and visual alarm device; 15. SQLite database; 16. Visual interface. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Specific implementation examples are given below.

[0053] This invention provides a basic medical pathogen culture dish, the overall structure of which is as follows: Figure 1 As shown, the system includes an environmental control module 1, a multi-dimensional monitoring module 2, a data analysis module 3, an early warning and feedback module 4, and a data management module 5. These modules work together through physical connections and signal transmission to perform functions such as environmental control, real-time monitoring, data analysis, and early warning feedback during pathogen culture. The specific implementation methods of each module will be described in detail below with reference to the accompanying drawings.

[0054] The environmental control module 1 is one of the core components of the entire petri dish, and its main function is to maintain the dynamic balance of gas concentration, temperature, and humidity within the petri dish. For example... Figure 2As shown, this module includes a gas sensor 6, a temperature and humidity sensor 7, a miniature air pump 8, and a heating and cooling assembly 9. The gas sensor 6 is installed inside the petri dish near the top to detect changes in the concentrations of oxygen, carbon dioxide, and nitrogen, transmitting the results as electrical signals to the miniature air pump 8. The miniature air pump 8 automatically adjusts the gas input according to a preset gas concentration range, ensuring that the gas composition in the culture environment remains within a suitable range. The temperature and humidity sensor 7 is located at the bottom of the petri dish near the culture medium, monitoring temperature and humidity changes in real time and sending the results to the heating and cooling assembly 9. The heating and cooling assembly 9 uses thermoelectric effect technology, achieving heating or cooling functions through changes in current direction, while simultaneously adjusting the humidity level inside the petri dish using a humidity compensation mechanism. To ensure the stability and accuracy of environmental parameters, the environmental control module 1 employs a PID control algorithm, achieving dynamic balance through closed-loop control of gas concentration, temperature, and humidity. Specifically, the PID controller receives real-time data from the gas sensor 6 and the temperature and humidity sensor 7, performs calculations, and outputs control signals to the miniature air pump 8 and the heating and cooling assembly 9, thereby achieving precise control of the culture environment.

[0055] The multidimensional monitoring module 2 is responsible for monitoring the growth status of pathogens from multiple dimensions. Its functional block diagram is as follows: Figure 3 As shown, this module includes a high-resolution microscope lens 10, a fluorescently labeled probe 11, and an electrochemical sensor 12. The high-resolution microscope lens 10 is fixed on a support above the culture dish and performs real-time imaging of pathogens within the dish using an optical system. The imaging data is processed by an image segmentation algorithm to extract the target region for subsequent analysis. The fluorescently labeled probe 11 is a specific chemical reagent that binds to key biomarkers of pathogens and emits fluorescent signals. These fluorescent signals are captured by a fluorescence microscope to form a visual image, reflecting the metabolic activity of pathogens or the expression of specific genes. The electrochemical sensor 12 is embedded in the bottom of the culture dish and in direct contact with the culture medium to detect the concentration of metabolites in the culture medium, such as glucose and lactic acid. The electrochemical sensor 12 converts the concentration of metabolites into an electrical signal through electrode reactions and transmits the signal to the data analysis module 3. The multidimensional monitoring module 2 generates a multidimensional growth state map of the pathogen by fusing optical imaging, fluorescence signals, and electrochemical detection data, thereby comprehensively reflecting the dynamic growth characteristics of the pathogen.

[0056] The function of data analysis module 3 is to process and analyze the data generated by multidimensional monitoring module 2 in real time. Its processing flow is as follows: Figure 4As shown in the diagram, this module uses an embedded processor 13 as its core, supplemented by machine learning algorithms to complete data classification, clustering, and prediction tasks. First, the embedded processor 13 preprocesses the received raw data, including denoising, interpolation, and normalization, to eliminate noise interference and improve data quality. Next, the machine learning algorithm performs classification and clustering analysis on the preprocessed data to identify different growth patterns of the pathogen. For example, the support vector machine algorithm can distinguish between normal and abnormal growth states, while the K-means clustering algorithm can discover the characteristic differences between different growth stages. Based on the classification and clustering results, the data analysis module 3 constructs a dynamic growth curve and predicts the future growth trend of the pathogen through time series analysis. Finally, the dynamic growth curve and prediction results are sent to the early warning and feedback module 4 to trigger subsequent operations.

[0057] The function of the early warning and feedback module 4 is to trigger early warning signals based on the changing trends of monitoring data and optimize the cultivation environment parameters through an automatic adjustment mechanism. Its operating logic is as follows: Figure 5 As shown in the diagram, this module includes an audible and visual alarm device 14 to alert operators to abnormal situations. When the dynamic growth curve shows that the monitoring data exceeds the preset warning threshold, the warning and feedback module 4 will immediately trigger a warning signal and issue an alarm through the audible and visual alarm device 14. Simultaneously, this module sends the abnormal data to the environmental control module 1, activating an automatic adjustment mechanism to optimize gas concentration, temperature, and humidity. For example, if the monitoring data shows that the carbon dioxide concentration in the petri dish is too high, the micro air pump 8 will increase the oxygen input to balance the gas composition; if the temperature deviates from the set range, the heating and cooling components 9 will quickly adjust the temperature to a suitable level. Furthermore, the warning and feedback module 4 will record the timestamp and relevant parameters of each warning event for subsequent data analysis and problem tracing.

[0058] Data management module 5 is responsible for storing and displaying monitoring data and analysis results. Its interface design is as follows: Figure 6 As shown, this module is based on the SQLite database 15 and protects data security through the AES encryption algorithm. The SQLite database 15 adds a unique identifier and timestamp to each data entry to ensure traceability and integrity. The AES encryption algorithm uses a 256-bit key to encrypt the stored data, and the key is changed periodically to enhance security. The data management module 5 also features a visualization interface 16 developed based on the Qt framework, used to display dynamic growth curves, early warning events, and optimization suggestions. The visualization interface 16 supports data display in chart and table formats, allowing users to intuitively view the growth status of pathogens and the changing trends of environmental parameters. Furthermore, the data management module 5 supports exporting data to external devices via transmission protocols for further analysis and archiving.

[0059] The modules described above are physically connected via signal and power lines. Environmental control module 1 is connected to multi-dimensional monitoring module 2 via a signal line to acquire real-time monitoring data and adjust environmental parameters. Multi-dimensional monitoring module 2 is connected to data analysis module 3 via a signal line to transmit raw monitoring data. Data analysis module 3 is connected to early warning and feedback module 4 via a signal line to send dynamic growth curves and prediction results. Early warning and feedback module 4 is connected to environmental control module 1 via a signal line to trigger automatic adjustment mechanisms. Data management module 5 is connected to all other modules via a signal line to receive and store various types of data. Data interaction between modules is achieved through standardized interfaces, ensuring system compatibility and scalability.

[0060] In practical applications, the basic medical pathogen culture dishes of this invention can be used for the research of various pathogens, such as bacteria, viruses, and fungi. Taking bacterial culture as an example, the operator first inoculates the bacterial sample to be cultured into the culture dish and sets the target gas concentration, temperature, and humidity range through the visual interface 16. Subsequently, the environmental control module 1 starts working, monitoring the culture environment parameters in real time through the gas sensor 6 and the temperature and humidity sensor 7, and dynamically adjusting them through the micro air pump 8 and the heating and cooling components 9. At the same time, the multidimensional monitoring module 2 monitors the growth status of the bacteria in multiple dimensions, generating a multidimensional growth status map that includes optical imaging, fluorescence signals, and electrochemical detection data. The data analysis module 3 processes and analyzes the monitoring data in real time, constructs a dynamic growth curve, and predicts future growth trends. If the monitoring data exceeds the warning threshold, the warning and feedback module 4 will trigger a warning signal and remind the operator through the audible and visual alarm device 14, while simultaneously activating the automatic adjustment mechanism to optimize the culture environment parameters. All monitoring data and analysis results are stored in the SQLite database 15 and displayed to the user through the visual interface 16.

[0061] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0062] In practical applications of bacterial culture, operators first inoculate the bacterial sample to be cultured into a petri dish and set the target gas concentration, temperature, and humidity range through a visual interface 16. For example, the oxygen concentration is set to 20%, the carbon dioxide concentration to 5%, the temperature to 37°C, and the humidity to 90%. Subsequently, the gas sensor 6 in the environmental control module 1 detects changes in gas concentration within the petri dish in real time and transmits the detection results to the micro air pump 8 in the form of electrical signals. The micro air pump 8 dynamically adjusts the input of oxygen, carbon dioxide, and nitrogen according to the preset gas concentration range to ensure that the gas composition in the culture environment is always maintained within a suitable range. At the same time, the temperature and humidity sensor 7 monitors changes in temperature and humidity within the petri dish in real time and sends the monitoring data to the heating and cooling assembly 9. The heating and cooling assembly 9 uses thermoelectric effect technology to heat or cool the petri dish by changing the direction of current, while simultaneously adjusting the humidity level within the petri dish in conjunction with a humidity compensation mechanism. The PID control algorithm achieves dynamic balance of various environmental parameters through closed-loop control of gas concentration, temperature, and humidity. For example, when the temperature and humidity sensor 7 detects a temperature deviation of 37°C, the PID controller calculates the deviation value and outputs a control signal to the heating and cooling component 9, causing it to quickly adjust the temperature to the target value.

[0063] The multidimensional monitoring module 2 begins operation simultaneously with the environmental control module 1. A high-resolution microscope lens 10, fixed to a support above the culture dish, performs real-time imaging of the bacteria within the dish using an optical system. The imaging data is processed by an image segmentation algorithm to extract the target region for subsequent analysis. Fluorescently labeled probes 11 bind to key bacterial biomarkers, emitting fluorescent signals. These signals are captured by a fluorescence microscope to form a visual image reflecting bacterial metabolic activity or specific gene expression. An electrochemical sensor 12 is embedded in the bottom of the culture dish and in direct contact with the culture medium. Through electrode reactions, it converts the concentrations of glucose and lactic acid in the culture medium into electrical signals, which are then transmitted to the data analysis module 3. The multidimensional monitoring module 2 generates a multidimensional growth state map of the bacteria by fusing optical imaging, fluorescence signals, and electrochemical detection data, thus comprehensively reflecting the dynamic growth characteristics of the bacteria.

[0064] Data analysis module 3 receives raw data from multidimensional monitoring module 2 and preprocesses it using embedded processor 13. Preprocessing steps include denoising, interpolation, and normalization to eliminate noise interference and improve data quality. Subsequently, machine learning algorithms perform classification and clustering analysis on the received preprocessed data. For example, support vector machine algorithms distinguish between normal and abnormal growth states, while K-means clustering algorithms identify characteristic differences between different growth stages. Based on the classification and clustering results, data analysis module 3 constructs a dynamic growth curve and predicts the future growth trend of bacteria through time series analysis. If the prediction shows that the concentration of bacterial metabolites will significantly increase within the next two hours, the dynamic growth curve and prediction results are sent to early warning and feedback module 4 to trigger subsequent operations.

[0065] The early warning and feedback module 4 determines whether the preset early warning threshold has been exceeded based on the changing trend of the dynamic growth curve. For example, if the monitoring data shows that the carbon dioxide concentration in the petri dish exceeds 5.5%, the early warning and feedback module 4 immediately triggers an early warning signal and issues an alarm through the audible and visual alarm device 14 to remind the operator. Simultaneously, this module sends the abnormal data to the environmental control module 1, activating an automatic adjustment mechanism to optimize gas concentration, temperature, and humidity. For example, the micro air pump 8 increases the oxygen input to balance the gas composition, and the heating and cooling components 9 quickly adjust the temperature to 37°C. Furthermore, the early warning and feedback module 4 records the timestamp and relevant parameters of each early warning event for subsequent data analysis and problem tracing.

[0066] Data management module 5 receives and stores all monitoring data and analysis results. The SQLite database 15 adds a unique identifier and timestamp to each data entry to ensure traceability and integrity. The AES encryption algorithm uses a 256-bit key to encrypt the stored data, and the key is changed periodically to enhance security. Data management module 5 also displays dynamic growth curves, early warning events, and optimization suggestions through a visualization interface 16 developed based on the Qt framework. Users can intuitively view the growth status of bacteria and the changing trends of environmental parameters through the visualization interface 16. Furthermore, data management module 5 supports exporting data to external devices via transmission protocols for further analysis and archiving.

[0067] The above process demonstrates the specific operating principle of this invention in practical applications. Through the dynamic balancing capability of the environmental control module 1, the comprehensive monitoring methods of the multi-dimensional monitoring module 2, the intelligent analysis methods of the data analysis module 3, and the rapid response mechanism of the early warning and feedback module 4, this invention can significantly improve the stability of the pathogen culture environment and the reliability of monitoring data, providing efficient and accurate technical support for basic medical research.

[0068] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A pathogen culture dish for basic medical applications, characterized in that: include An environmental control module (1) is used to monitor and dynamically adjust the gas concentration, temperature and humidity in the petri dish in real time. The environmental control module (1) includes a gas sensor (6), a temperature and humidity sensor (7), a micro air pump (8) and a heating and cooling assembly (9). The multidimensional monitoring module (2) is used to monitor the growth status of pathogens in multiple dimensions through optical microscopy and chemical sensing technology. The multidimensional monitoring module (2) includes a high-resolution microscope lens (10), a fluorescently labeled probe (11), and an electrochemical sensor (12). The data analysis module (3) is used to process and analyze the monitoring data in real time and generate dynamic growth curves. The data analysis module (3) includes an embedded processor (13) and a machine learning algorithm. The early warning and feedback module (4) is used to trigger an early warning signal based on the changing trend of the monitoring data and adjust the culture environment parameters through an automatic adjustment mechanism; The data management module (5) is used to store monitoring data and analysis results and protect data security through encryption algorithms.

2. The basic medical pathogen culture dish as described in claim 1, characterized in that: The working process of the environmental control module (1) includes the following steps: The concentrations of oxygen, carbon dioxide and nitrogen in the petri dish are monitored in real time using a gas sensor (6), and the monitoring results are sent to a micro gas pump (8). The micro gas pump (8) automatically adjusts the gas input according to a preset gas concentration range. The temperature and humidity inside the petri dish are monitored in real time using a temperature and humidity sensor (7), and the monitoring results are sent to the heating and cooling assembly (9). The heating and cooling assembly (9) achieves temperature regulation and humidity compensation through thermoelectric effect according to the preset temperature and humidity range. The PID control algorithm is used to perform closed-loop control of the gas concentration, temperature and humidity regulation process to ensure the dynamic balance of various environmental parameters.

3. The basic medical pathogen culture dish as described in claim 1, characterized in that: The working process of the multidimensional monitoring module (2) includes the following steps: The pathogens in the culture dish were imaged in real time using a high-resolution microscope lens (10) and the target area was extracted using an image segmentation algorithm. Key biomarkers of pathogens were labeled using fluorescently labeled probes (11) and the labeling signals were captured by a fluorescence microscope; The metabolites in the culture medium were detected in real time using an electrochemical sensor (12), and the detection results were correlated with the fluorescence signal for analysis. Imaging data, fluorescence signals, and electrochemical detection data are fused together to generate a multidimensional growth profile of the pathogen.

4. The basic medical pathogen culture dish as described in claim 1, characterized in that: The working process of the data analysis module (3) includes the following steps: The raw data generated by the multidimensional monitoring module (2) is preprocessed using an embedded processor (13), including denoising, interpolation and normalization; Machine learning algorithms are used to classify and cluster the preprocessed data to identify pathogen growth patterns. Dynamic growth curves are constructed based on classification and clustering results, and the future growth trend of pathogens is predicted through time series analysis. The dynamic growth curve and prediction results are sent to the early warning and feedback module (4) to trigger subsequent operations.

5. The basic medical pathogen culture dish as described in claim 1, characterized in that: The working process of the early warning and feedback module (4) includes the following steps: Set early warning thresholds based on the changing trends of the dynamic growth curve; When the monitored data exceeds the warning threshold, a warning signal is triggered and the operator is alerted via an audible and visual alarm device (14); At the same time, abnormal data is sent to the environmental control module (1) to optimize gas concentration, temperature and humidity through an automatic adjustment mechanism; Record the timestamps and relevant parameters of the warning events for subsequent data analysis.

6. The basic medical pathogen culture dish as described in claim 1, characterized in that: The working process of the data management module (5) includes the following steps: using the SQLite database (15) to store monitoring data and analysis results and adding a unique identifier and timestamp to each piece of data; The stored data is encrypted using the AES encryption algorithm, and the key is changed regularly to enhance data security. The visualization interface (16) displays dynamic growth curves, early warning events and optimization suggestions. The visualization interface (16) is developed using the Qt framework and supports data display in the form of charts and tables. Data is exported to external devices via a transmission protocol for further analysis and archiving.

7. The basic medical pathogen culture dish as described in claim 1, characterized in that: The environmental control module (1) is connected to the multidimensional monitoring module (2) via a signal line to obtain real-time monitoring data and adjust environmental parameters.

8. The basic medical pathogen culture dish as described in claim 1, characterized in that: The multidimensional monitoring module (2) is connected to the data analysis module (3) via a signal line for transmitting raw monitoring data.

9. The basic medical pathogen culture dish as described in claim 1, characterized in that: The data analysis module (3) is connected to the early warning and feedback module (4) via a signal line to send dynamic growth curves and prediction results.

10. The basic medical pathogen culture dish as described in claim 1, characterized in that: The data management module (5) is connected to all other modules via signal lines to receive and store various types of data.

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