Brain tissue storage condition monitoring system for brain library

Abstract

The invention discloses a brain tissue storage condition monitoring system for a brain library, and relates to the technical field of brain tissue storage.The nondestructive tissue health monitoring module monitors the internal structure change of a brain tissue sample in real time by using advanced photoacoustic and thermoacoustic imaging technologies, so that the integrity and quality of the sample are ensured, and the monitoring accuracy is improved. Abnormal conditions can be found and processed in time on the premise that the samples are not damaged, so that the research value and the storage safety of the samples are improved; and the real-time analysis and prediction module obtains the environmental parameters of the brain library, analyzes the change trend of the environmental parameters by using a statistical analysis and prediction model, and predicts the future environmental state according to the analysis result, so that potential risks can be identified and prevented in advance, the accurate control and optimization adjustment of the environment are realized, and the storage of samples under the optimal condition is ensured. And the stability and reliability of the storage system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of brain tissue storage, and in particular, relates to a brain tissue storage condition monitoring system for a brain bank. Background Art

[0002] In modern medical research and neuroscience, the storage and management of brain tissue samples are crucial. High-quality storage of brain tissue samples is important for studying disease mechanisms, developing treatments, and conducting basic scientific research. However, brain tissue samples are easily affected by environmental factors such as temperature, humidity, light, and air quality. If these factors are not effectively monitored and controlled, they will cause the samples to deteriorate or be damaged, which will affect the accuracy and reliability of the research results.

[0003] Traditional brain tissue storage systems usually rely on manual monitoring and regular inspections, which have many shortcomings. For example, manual operation is prone to errors and cannot achieve real-time monitoring and rapid response to environmental parameters. In addition, traditional monitoring methods are mostly lossy monitoring, which will cause certain damage to the sample, affecting the integrity of the sample and the accuracy of subsequent research.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a brain tissue storage condition monitoring system for a brain bank, thereby solving the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0007] A brain tissue storage condition monitoring system for a brain bank comprises: a management unit, the management unit is used to automatically record the storage time, storage location, and storage time information of the sample, realize the rapid retrieval and tracking of the sample and process the brain shell monitoring system data;

[0008] A QR code generation unit, which obtains the data sent by the management unit and assigns a unique identifier to each sample stored in the library;

[0009] A sample status monitoring unit, used to monitor the storage status of the sample in real time and record historical data of the sample status;

[0010] Real-time analysis and prediction module: The real-time analysis and prediction module is used to obtain the environmental parameters of the brain bank and analyze the change trend of the environmental parameters, and predict the environmental status in the future based on the change trend obtained by analysis;

[0011] Non-destructive tissue health monitoring module, which is used to monitor the internal structural changes of brain tissue samples in real time using photoacoustic and thermoacoustic imaging;

[0012] Data processing and analysis unit, used to process and analyze photoacoustic and thermoacoustic imaging monitoring data and extract key information on changes in the internal structure of the sample;

[0013] Multi-parameter comprehensive control module, the multi-parameter comprehensive control module is used to automatically adjust the working state of the equipment according to the preset storage conditions and real-time monitoring data.

[0014] Optionally, it also includes a user authority management module, which is used to allocate and manage permissions for different users.

[0015] Optionally, an environmental parameter calibration unit is also included, which is used to regularly calibrate the temperature, humidity, and gas concentration parameters in the storage environment.

[0016] Optionally, an alarm and notification unit is also included. When the environmental parameters exceed the preset range, the alarm and notification unit will automatically trigger the alarm mechanism and notify relevant personnel to handle it.

[0017] Optionally, the real-time analysis and prediction module is used to obtain the environmental parameters of the brain bank and analyze the change trend of the environmental parameters. The steps of predicting the environmental state in the future period of time according to the change trend obtained by analysis are as follows:

[0018] Acquire monitoring temperature, humidity, light, air quality and transmit the data collected by the sensors to a central processing unit or cloud server;

[0019] Statistical analysis methods are used to analyze real-time data streams, identify the changing trends of environmental parameters, set thresholds, monitor whether environmental parameters exceed the normal range, detect abnormal situations in time and issue alarms to analyze the correlation between different environmental parameters. The statistical analysis expression is:

[0020] in, represents the average value of the environmental parameter, N represents the number of data points, E(t i ) represents the set of environmental parameters collected at the i-th time point, and the correlation analysis expression is: Among them, ρ X，Y represents the correlation coefficient between parameters X and Y, X i and Y i Respectively represent the parameter values ​​at the i-th time point, and Represent the mean values ​​of parameters X and Y respectively;

[0021] According to the characteristics of environmental parameters and forecasting requirements, the ARIMA model is used to train the forecasting model using historical data, and the forecasting accuracy and stability of the model are evaluated using methods such as cross-validation. The expression is: Among them, MSE represents the mean square error, which is used to evaluate the prediction accuracy of the model, y i Indicates the actual value, represents the predicted value;

[0022] The latest environmental parameter data is input into the trained prediction model. The model calculates the predicted value of the environmental status in the future based on the input data and evaluates the prediction results to adjust and optimize the model according to the actual situation.

[0023] Optionally, the steps for processing and analyzing the photoacoustic and thermoacoustic imaging monitoring data and extracting key information of the internal structural changes of the sample are:

[0024] Derive the time series signal from the photoacoustic or thermoacoustic imaging device, analyze the waveform, amplitude, phase and other time domain characteristics of the time series signal, and identify the key events or changes in the signal. The expression is: Where, Δs i (t) represents the signal s i (t) rate of change, ds i (t) is the overall shape of the signal waveform. By analyzing Δs i The peak or mutation point of (t) can be used to identify key events or change points.

[0025] Calculate the statistical parameters of the signal and perform Fourier transform on the signal to analyze the spectral components and frequency distribution characteristics of the signal;

[0026] Wavelet transform is used to perform multi-scale analysis on the signal to extract characteristic information at different time scales and frequency scales;

[0027] Using the physical principles of photoacoustic or thermoacoustic imaging, appropriate back-projection algorithms are used to convert the signal into a two-dimensional or three-dimensional image;

[0028] Measure the size, shape, position and other parameters of key areas in the image. Then, perform quantitative analysis and compare the measurement results at different time points or under different conditions to evaluate the changes in the internal structure of the sample. Finally, analyze the causes and mechanisms of the changes and adjust the storage environment.

[0029] Optionally, perform the following steps when the sample is stored:

[0030] Assign a unique QR code to each sample as an identification mark and affix a barcode or QR code label to the sample container;

[0031] When entering the warehouse, use a barcode scanner to scan the QR code of the sample and select the sample information;

[0032] Record the current time as the sample's storage time and associate it with the sample's unique identifier;

[0033] Automatically assign storage locations based on sample type and collection time. Record the assigned storage location information and associate it with the sample's unique identifier.

[0034] When a sample enters the warehouse, the system automatically records the entry time and associates it with the sample's unique identifier; the system automatically allocates and records the storage location based on the preset storage rules;

[0035] The following steps are performed when the sample is shipped out of the library:

[0036] Submit a sample shipment application through the system, specifying the samples and quantity to be shipped;

[0037] Verify whether the sample has been put into storage and whether the quantity is sufficient. If it meets the standards, use a barcode scanner to scan the QR code of the sample to trigger the outbound process;

[0038] When the sample is shipped out, the current time is automatically recorded as the sample's shipping time, and the sample's inventory status is updated;

[0039] Check whether the detailed information of the outbound samples is consistent with the system records. After completing the outbound operation, confirm the outbound delivery in the system.

[0040] Optionally, a control module is further included, and the control module is used to receive the signal generated by the multi-parameter comprehensive control module. The multi-parameter comprehensive control module sends the signal to the control module and needs to perform the following steps:

[0041] Enter the preset storage condition parameters and set the ideal working state parameters of the equipment under different storage conditions;

[0042] Compare and analyze the acquired real-time data with the preset storage conditions to determine whether the current storage environment meets the requirements;

[0043] Based on the results of the comparative analysis, the control module makes a logical judgment to determine whether the working state of the equipment needs to be adjusted. If adjustment is required, the control module makes a specific adjustment decision based on the preset equipment working parameters and current real-time data;

[0044] According to the adjustment decision, the control module outputs the corresponding control signal to the actuator, and after receiving the control signal, the actuator automatically performs the corresponding adjustment operation.

[0045] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0046] The non-destructive tissue health monitoring module of the present invention utilizes advanced photoacoustic and thermoacoustic imaging technologies to monitor the internal structural changes of brain tissue samples in real time, which not only ensures the integrity and quality of the samples, but also can promptly detect and handle abnormal situations without destroying the samples, thereby improving the research value and storage security of the samples; while the real-time analysis and prediction module obtains the environmental parameters of the brain bank, uses statistical analysis and prediction models to analyze the changing trends of the environmental parameters, and predicts the future environmental status based on the analysis results. It can identify and prevent potential risks in advance, achieve precise control and optimization of the environment, ensure that the samples are stored under optimal conditions, and improve the stability and reliability of the storage system.

[0047] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0049] In the figure:

[0050] Figure 1 Block diagram of the brain tissue storage condition monitoring system of the brain bank.

[0051] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0052] The present invention will now be described in further detail with reference to the accompanying drawings.

[0053] See also Figure 1 As shown, in this embodiment, a brain tissue storage condition monitoring system for a brain bank is provided, including a management unit, which is used to automatically record the storage time, storage location, and storage time information of the sample, realize rapid retrieval and tracking of the sample, and process the brain shell monitoring system data;

[0054] The QR code generation unit obtains the data sent by the management unit and assigns a unique identifier to each sample stored in the warehouse. The present invention first generates a unique QR code for each sample stored in the warehouse to ensure the uniqueness and traceability of the sample identity. Through the use of QR codes, the automated management of samples is achieved, manual errors are reduced, and work efficiency is improved.

[0055] The sample status monitoring unit is used to monitor the storage status of the sample in real time and record the historical data of the sample status. It can monitor the storage status of the sample in real time to ensure that the sample is stored under the best conditions. Recording the historical status data of the sample provides a reliable basis for subsequent analysis and management.

[0056] Real-time analysis and prediction module: The real-time analysis and prediction module is used to obtain the environmental parameters of the brain bank and analyze the changing trends of the environmental parameters. Based on the changing trends obtained by analysis, the environmental status in the future period is predicted; the environmental parameters of the brain bank are obtained in real time to fully grasp the changes in the storage environment. The changing trends of environmental parameters are analyzed, the future environmental status is predicted in advance, and preventive adjustments are made to ensure the safety of samples.

[0057] Non-destructive tissue health monitoring module: The non-destructive tissue health monitoring module is used to monitor the internal structural changes of brain tissue samples in real time using photoacoustic and thermoacoustic imaging; photoacoustic and thermoacoustic imaging technology is used to monitor the internal structural changes of samples in real time without destroying the integrity of the samples. Accurately detecting the internal structural changes of brain tissue samples can timely discover and handle abnormal situations, ensuring the quality and research value of the samples.

[0058] The data processing and analysis unit is used to process and analyze the photoacoustic and thermoacoustic imaging monitoring data, and extract key information about the changes in the internal structure of the sample; it processes and analyzes the monitoring data, and extracts key information about the changes in the internal structure of the sample. It supports scientific research and sample management by extracting valuable information from massive data.

[0059] Multi-parameter comprehensive control module: The multi-parameter comprehensive control module is used to automatically adjust the working state of the equipment according to the preset storage conditions and real-time monitoring data. According to the preset storage conditions and real-time monitoring data, the working state of the equipment is automatically adjusted to ensure that the samples are stored in the best environment. Through automated control, the efficiency of equipment operation is improved, energy is saved, and operating costs are reduced.

[0060] The present embodiment also includes a user rights management module, which is used to allocate and manage rights for different users. Through the fine division of user rights, the access rights of different users to system functions and data can be restricted, unauthorized operations and data leakage can be reduced, and the security level of the system can be improved. In addition, the module can also record the user's operation log, which is convenient for tracing and auditing, and increases the transparency of system operations and the ability to track responsibilities. This management method can not only protect sensitive data, but also ensure the efficient operation and maintenance of the system in a multi-user environment, and reduce system risks caused by human errors or malicious operations.

[0061] This embodiment also includes an environmental parameter calibration unit, which is used to regularly calibrate the temperature, humidity, and gas concentration parameters in the storage environment. Through regular calibration, the drift and error caused by long-term use of the sensor can be eliminated, ensuring the accuracy of the environmental monitoring system. Accurate environmental data is crucial to maintaining the storage conditions of brain tissue samples, and can effectively reduce sample damage or deterioration caused by environmental parameter deviations. The introduction of the calibration unit not only improves the credibility and stability of the overall system, but also provides a reliable data basis for subsequent analysis and prediction, ensuring that the storage environment is always in the best condition.

[0062] The present embodiment also includes an alarm and notification unit. When the environmental parameters exceed the preset range, the alarm and notification unit will automatically trigger the alarm mechanism and notify relevant personnel to handle it. Automatically triggering the alarm mechanism and notifying relevant personnel to handle it greatly improves the security and response efficiency of the system. When the storage environment changes abnormally, timely alarms and notifications can quickly attract the attention of staff, quickly take corrective measures, and reduce the damage to samples due to adverse environmental conditions. This module can not only monitor the environmental status in real time, but also ensure that relevant personnel obtain information at the first time through text messages, emails, and system notification personnel. In this way, the sample loss caused by environmental factors can be significantly reduced, and the long-term safe storage and research value of the samples can be guaranteed.

[0063] In this embodiment, the real-time analysis and prediction module is used to obtain the environmental parameters of the brain bank and analyze the change trend of the environmental parameters. The steps of predicting the environmental state in the future period of time according to the change trend obtained by analysis are as follows:

[0064] Acquire monitoring temperature, humidity, light, air quality and transmit the data collected by the sensors to a central processing unit or cloud server;

[0065] Statistical analysis methods are used to analyze real-time data streams, identify the changing trends of environmental parameters, set thresholds, monitor whether environmental parameters exceed the normal range, detect abnormal situations in time and issue alarms to analyze the correlation between different environmental parameters. The statistical analysis expression is:

[0066] in, represents the average value of the environmental parameter, N represents the number of data points, E(t i ) represents the set of environmental parameters collected at the i-th time point, and the correlation analysis expression is: Among them, ρ X，Y represents the correlation coefficient between parameters X and Y, X i and Y i Respectively represent the parameter values ​​at the i-th time point, and Represent the mean values ​​of parameters X and Y respectively;

[0067] According to the characteristics of environmental parameters and forecasting requirements, the ARIMA model is used to train the forecasting model using historical data, and the forecasting accuracy and stability of the model are evaluated using methods such as cross-validation. The expression is: Among them, MSE represents the mean square error, which is used to evaluate the prediction accuracy of the model, y i Indicates the actual value, represents the predicted value;

[0068] The latest environmental parameter data is input into the trained prediction model. The model calculates the predicted value of the environmental state in the future based on the input data and evaluates the prediction results. The model is adjusted and optimized according to the actual situation. By acquiring temperature, humidity, light, air quality and other data in real time and transmitting them to the central processing unit or cloud server, abnormal conditions in the environment can be identified in time and alarms can be issued to ensure that environmental parameters are always within the preset range. At the same time, the ARIMA model is used for prediction. Through historical data training and cross-validation evaluation, the accuracy and stability of the prediction are ensured, so that managers can predict future environmental changes in advance, take preventive measures, optimize storage conditions, and ensure the safety and stability of brain tissue samples.

[0069] In this embodiment, the steps for processing and analyzing the photoacoustic and thermoacoustic imaging monitoring data and extracting key information of the internal structure changes of the sample are:

[0070] Derive the time series signal from the photoacoustic or thermoacoustic imaging device, analyze the waveform, amplitude, phase and other time domain characteristics of the time series signal, and identify the key events or changes in the signal. The expression is: Where, Δs i (t) represents the signal s i (t) rate of change, ds i (t) is the overall shape of the signal waveform. By analyzing Δs i The peak or mutation point of (t) can be used to identify key events or change points.

[0071] Calculate the statistical parameters of the signal and perform Fourier transform on the signal to analyze the spectral components and frequency distribution characteristics of the signal;

[0072] Wavelet transform is used to perform multi-scale analysis on the signal to extract characteristic information at different time scales and frequency scales;

[0073] Using the physical principles of photoacoustic or thermoacoustic imaging, appropriate back-projection algorithms are used to convert the signal into a two-dimensional or three-dimensional image;

[0074] Measure the size, shape, position and other parameters of key areas in the image, then conduct quantitative analysis and compare the measurement results at different time points or under different conditions to evaluate the changes in the internal structure of the sample. Finally, analyze the causes and mechanisms of the changes and adjust the storage environment. The non-destructive tissue health monitoring module uses photoacoustic and thermoacoustic imaging technology to monitor the internal structural changes of brain tissue samples in real time without destroying the samples. This non-destructive testing method ensures the integrity and quality of the samples, and at the same time provides high-precision data support by deriving time series signals, analyzing time domain features such as waveforms, amplitudes and phases, identifying key events and change points. Combining Fourier transform and wavelet transform for frequency domain and multi-scale analysis can extract rich feature information, reconstruct two-dimensional or three-dimensional images using back-projection algorithms, accurately measure the size, shape and position of samples, evaluate the internal changes of samples, provide reliable data support for scientific research, and improve the accuracy and efficiency of sample management.

[0075] In this embodiment, the following steps are performed when a sample is stored:

[0076] Assign a unique QR code to each sample as an identification mark and affix a barcode or QR code label to the sample container;

[0077] When entering the warehouse, use a barcode scanner to scan the QR code of the sample and select the sample information;

[0078] Record the current time as the sample's storage time and associate it with the sample's unique identifier;

[0079] Automatically assign storage locations based on sample type and collection time. Record the assigned storage location information and associate it with the sample's unique identifier.

[0080] When a sample enters the warehouse, the system automatically records the entry time and associates it with the sample's unique identifier; the system automatically allocates and records the storage location based on the preset storage rules;

[0081] The following steps are performed when the sample is shipped out of the library:

[0082] Submit a sample shipment application through the system, specifying the samples and quantity to be shipped;

[0083] Verify whether the sample has been put into storage and whether the quantity is sufficient. If it meets the standards, use a barcode scanner to scan the QR code of the sample to trigger the outbound process;

[0084] When the sample is shipped out, the current time is automatically recorded as the sample's shipping time, and the sample's inventory status is updated;

[0085] Check whether the detailed information of the sample leaving the warehouse is consistent with the system records. After completing the outbound operation, confirm the outbound operation in the system. By assigning a unique QR code to each sample and recording the detailed entry time and storage location information when the sample enters the warehouse, the full life cycle management and accurate tracking of the sample are achieved. The automated code scanning process not only reduces human errors, but also improves work efficiency and data accuracy. The system automatically records the operation time and updates the inventory status when the sample enters and leaves the warehouse, ensuring real-time updating and accurate tracking of sample information. When leaving the warehouse, the inventory status and detailed information of the sample are verified by the system to ensure the accuracy of the outbound operation. This automated and precise management method greatly improves the efficiency and reliability of sample management and ensures the safety and traceability of samples during storage and use.

[0086] The present embodiment also includes a control module, which is used to receive the signal generated by the multi-parameter comprehensive control module. The multi-parameter comprehensive control module needs to perform the following steps to send the signal to the control module:

[0087] Enter the preset storage condition parameters and set the ideal working state parameters of the equipment under different storage conditions;

[0088] Compare and analyze the acquired real-time data with the preset storage conditions to determine whether the current storage environment meets the requirements;

[0089] Based on the results of the comparative analysis, the control module makes a logical judgment to determine whether the working state of the equipment needs to be adjusted. If adjustment is required, the control module makes a specific adjustment decision based on the preset equipment working parameters and current real-time data;

[0090] According to the adjustment decision, the control module outputs the corresponding control signal to the actuator. After receiving the control signal, the actuator automatically performs the corresponding adjustment operation. By receiving the signal sent by the multi-parameter comprehensive control module, intelligent control and automatic adjustment of the storage device are realized. According to the comparative analysis of the preset storage condition parameters and real-time data, the control module can quickly determine whether the storage environment meets the requirements and automatically adjust the working state of the equipment when necessary. Through logical judgment and specific adjustment decisions, the control module can output the corresponding control signal to the actuator to ensure that the equipment operates in the best state. This intelligent control method not only improves the operating efficiency and stability of the equipment, but also can respond to environmental changes in a timely manner, reduce the impact of poor storage conditions on samples, and ensure the safety and quality of samples.

[0091] Furthermore, the non-destructive tissue health monitoring module uses advanced photoacoustic and thermoacoustic imaging technologies to monitor the internal structural changes of brain tissue samples in real time without destroying the samples. This not only ensures the integrity and quality of the samples, but also detects and handles abnormal situations in a timely manner, thereby improving the research value and storage security of the samples. At the same time, the real-time analysis and prediction module obtains the environmental parameters of the brain bank, uses statistical analysis and prediction models to analyze the changing trends of the environmental parameters, and predicts the future environmental status based on the analysis results. In this way, the system can identify and prevent potential risks in advance, achieve precise control and optimization of the environment, ensure that the samples are stored under optimal conditions, and improve the stability and reliability of the storage system. By combining the two major modules of non-destructive tissue health monitoring and real-time analysis and prediction, the present invention not only makes breakthroughs in real-time monitoring and environmental control of samples, but also achieves innovation in the automation, precision and efficiency of sample management.

[0092] Example 1: On a typical workday, a new batch of brain tissue samples arrives at the research institute. These samples are immediately transferred to the brain bank for storage after being obtained from surgery.

[0093] First, the staff assigns a unique QR code to each newly arrived brain tissue sample as an identification mark and affixes the QR code label to the sample container. When entering the warehouse, the staff uses a barcode scanner to scan the QR code of each sample. The system automatically records the sample's entry time and automatically allocates a suitable storage location based on the sample type and collection time.

[0094] Once the sample is stored in the freezer, the real-time analysis and prediction module immediately begins to acquire and monitor various parameters of the storage environment, such as temperature, humidity, light, and air quality. Sensor data is continuously transmitted to the central processing unit, and the system uses statistical analysis methods to analyze the changing trends of these data in real time. For example, if the system detects that the temperature in the freezer has an upward trend in a short period of time, it will predict that the temperature may rise further in the future through ARIMA model analysis. Based on the prediction results, the system automatically adjusts the working status of the refrigeration equipment, increases the cooling effect, and ensures that the temperature remains within the optimal range for sample preservation.

[0095] During long-term storage of samples, the non-destructive tissue health monitoring module regularly scans the samples using photoacoustic and thermoacoustic imaging techniques to generate real-time images of the internal structure of the samples. The system analyzes these images to identify any minor changes or abnormalities inside the samples. For example, during a routine scan, if the system detects an abnormal change in the internal structure of a sample, the monitoring module will immediately issue an alarm to notify staff to check. Subsequently, the staff discovered that the sample was affected by a brief temperature fluctuation in the freezer. To prevent further damage, the sample was immediately transferred to a backup freezer and the temperature control strategy of the original freezer was adjusted.

[0096] In addition, to ensure the accuracy of monitoring data, the environmental parameter calibration unit regularly calibrates all sensors. During one calibration process, the system found that the reading of a humidity sensor deviated from the normal value and automatically issued a warning. After receiving the notification, the staff calibrated and tested the sensor to restore its normal working state to ensure the reliability of the data.

[0097] In emergencies, the system's emergency mechanism can also play a key role. One day, there was a sudden power outage in the area where the brain bank was located, and the backup power system immediately started automatically to ensure the continuous operation of the refrigeration equipment. At the same time, the temperature monitoring system detected a rise in the temperature of the freezer, and the system automatically notified the administrator. After receiving the notification, the administrator quickly rushed to the site to check the operating status of all equipment to ensure the normal operation of the backup power supply and the safety of the samples.

[0098] A few months later, a research project requires the use of specific brain tissue samples. After the researchers submit a request for outbound delivery in the system, the system automatically verifies the inventory status and quantity of the samples. After confirmation, the staff uses a barcode scanner to scan the QR code of the sample and record the outbound delivery time. Finally, the staff checks whether the sample information is consistent with the system record. After completing the outbound delivery operation, the system automatically updates the inventory status of the sample. This seamless sample management process ensures the safe and efficient use of samples throughout the entire storage cycle.

[0099] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A brain tissue storage condition monitoring system for a brain bank, characterized in that: include: A management unit, which is used to automatically record the sample's storage time, storage location, and storage time information and process the skull monitoring system data; A QR code generation unit, which obtains the data sent by the management unit and assigns a unique identifier to each sample stored in the library; A sample status monitoring unit, used to monitor the storage status of the sample in real time and record historical data of the sample status; Real-time analysis and prediction module: The real-time analysis and prediction module is used to obtain the environmental parameters of the brain bank and analyze the change trend of the environmental parameters, and predict the environmental status in the future based on the change trend obtained by analysis; Non-destructive tissue health monitoring module, which is used to monitor the internal structural changes of brain tissue samples in real time using photoacoustic and thermoacoustic imaging; Data processing and analysis unit, used to process and analyze photoacoustic and thermoacoustic imaging monitoring data and extract key information on changes in the internal structure of the sample; Multi-parameter comprehensive control module, the multi-parameter comprehensive control module is used to automatically adjust the working state of the equipment according to the preset storage conditions and real-time monitoring data.

2. A brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: It also includes a user authority management module, which is used to allocate and manage permissions for different users.

3. The brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: It also includes an environmental parameter calibration unit, which is used to regularly calibrate the temperature, humidity, and gas concentration parameters in the storage environment.

4. The brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: It also includes an alarm and notification unit. When the environmental parameters exceed the preset range, the alarm and notification unit will automatically trigger the alarm mechanism and notify relevant personnel to handle it.

5. The brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: The real-time analysis and prediction module is used to obtain the environmental parameters of the brain bank and analyze the changing trends of the environmental parameters. The steps for predicting the environmental status in the future based on the changing trends obtained by analysis are as follows: Acquires monitoring temperature, humidity, light, air quality and transmits the data collected by the sensors to the central processing unit; Statistical analysis methods are used to analyze real-time data streams, identify the changing trends of environmental parameters, set thresholds, monitor whether environmental parameters exceed the normal range, detect abnormal situations in time and issue alarms to analyze the correlation between different environmental parameters. The statistical analysis expression is: in, represents the average value of the environmental parameter, N represents the number of data points, E(t i ) represents the set of environmental parameters collected at the i-th time point, and the correlation analysis expression is: Among them, ρ X，Y represents the correlation coefficient between parameters X and Y, X i and Y i Respectively represent the parameter values ​​at the i-th time point, and Represent the mean values ​​of parameters X and Y respectively; According to the characteristics of environmental parameters and forecasting requirements, the ARIMA model is used to train the forecasting model using historical data, and the forecasting accuracy and stability of the model are evaluated using methods such as cross-validation. The expression is: Among them, MSE represents the mean square error, which is used to evaluate the prediction accuracy of the model, y i Indicates the actual value, represents the predicted value; The latest environmental parameter data is input into the trained prediction model. The model calculates the predicted value of the environmental status in the future based on the input data and evaluates the prediction results to adjust and optimize the model according to the actual situation.

6. The brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: The steps for processing and analyzing the photoacoustic and thermoacoustic imaging monitoring data and extracting the key information of the internal structure changes of the sample are as follows: Derive the time series signal from the photoacoustic or thermoacoustic imaging device, analyze the waveform, amplitude, phase and other time domain characteristics of the time series signal, and identify the key events or changes in the signal. The expression is: Among them, △s i (t) represents the signal s i (t) rate of change, ds i (t) is the overall shape of the signal waveform. By analyzing △s i (t) peak or mutation point, identifying key events or change points; Calculate the statistical parameters of the signal and perform Fourier transform on the signal to analyze the spectral components and frequency distribution characteristics of the signal; Wavelet transform is used to perform multi-scale analysis on the signal to extract characteristic information at different time scales and frequency scales; Using the physical principles of photoacoustic or thermoacoustic imaging, the back-projection algorithm is used to convert the signal into a two-dimensional or three-dimensional image; Measure the size, shape, and position of key areas in the image. Then, perform quantitative analysis and compare the measurement results at different time points or under different conditions to evaluate the changes in the internal structure of the sample. Finally, analyze the cause and mechanism of the change and adjust the storage environment.

7. The brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: The following steps are performed when the sample is stored: Assign a unique QR code to each sample as an identification mark and affix a barcode or QR code label to the sample container; When entering the warehouse, use a barcode scanner to scan the QR code of the sample and select the sample information; Record the current time as the sample's storage time and associate it with the sample's unique identifier; Automatically assign storage locations based on sample type and collection time. Record assigned storage location information and associate it with the sample's unique identifier. When a sample enters the warehouse, the system automatically records the entry time and associates it with the sample's unique identifier; the system automatically allocates and records the storage location based on the preset storage rules; The following steps are performed when the sample is shipped out of the library: Submit a sample shipment application through the system, specifying the samples and quantity to be shipped; Verify whether the sample has been put into storage and whether the quantity is sufficient. If it meets the standards, use a barcode scanner to scan the QR code of the sample to trigger the outbound process; When the sample is shipped out, the current time is automatically recorded as the sample's shipping time, and the sample's inventory status is updated; Check whether the detailed information of the outbound samples is consistent with the system records. After completing the outbound operation, confirm the outbound delivery in the system.

8. The brain tissue storage condition monitoring system for a brain bank according to claim 1, characterized in that: The multi-parameter integrated control module is used to receive the signal generated by the multi-parameter integrated control module. The multi-parameter integrated control module sends the signal to the control module and needs to perform the following steps: Enter the preset storage condition parameters and set the working state parameters of the equipment under different storage conditions; Compare and analyze the acquired real-time data with the preset storage conditions to determine whether the current storage environment meets the requirements; Based on the results of the comparative analysis, the control module makes a logical judgment to determine whether the working state of the equipment needs to be adjusted. If adjustment is required, the control module makes a specific adjustment decision based on the preset equipment working parameters and current real-time data; According to the adjustment decision, the control module outputs the corresponding control signal to the actuator, and after receiving the control signal, the actuator automatically performs the corresponding adjustment operation.

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