Temperature and humidity anomaly emergency control system and its application in biological sample preservation

Abstract

The present application relates to temperature and humidity control technical field, disclose temperature and humidity abnormal emergency control system and its application in biological sample preservation, including temperature and humidity acquisition module, abnormal detection module, control module, data display module, optimization feedback module, optimization feedback module includes sample data acquisition module, best temperature and humidity analysis module and temperature and humidity threshold value optimization adjustment module, best temperature and humidity analysis module is used to utilize temperature and humidity recommendation algorithm to combine the real-time growth response data and sample image data of current biological sample and recommend the best temperature and humidity for it.The present application not only can carry out real-time monitoring and regulation to temperature and humidity, but also can recommend the best temperature and humidity for it according to the growth response data and sample image data of current biological sample, so that biological sample can grow under the best environmental conditions, effectively improve the success rate and efficiency of biological sample culture, also reduce the demand of manual intervention.

Description

Technical Field

[0001] This invention relates to the field of temperature and humidity control technology, and more specifically, to an emergency control system for abnormal temperature and humidity and its application in the preservation of biological samples. Background Technology

[0002] In the research and cultivation of biological samples, precise control of temperature and humidity is crucial to ensuring the optimal growth environment and normal life activities of the samples. Environmental temperature and humidity not only directly affect the biological properties of biological samples but can also indirectly influence experimental results, impacting the reliability and applicability of research findings. In plant growth experiments, even minute fluctuations in temperature and humidity can lead to changes in growth rate, abnormal morphological development, and even disruptions in physiological metabolic processes. For example, for plants in greenhouses, suitable temperature and humidity can simulate a natural growth environment, promoting photosynthesis and nutrient synthesis; conversely, inappropriate temperature and humidity control may cause adaptive adjustments in plant physiological mechanisms, thereby affecting growth quality and the stability of the experiment.

[0003] In animal cell culture, temperature and humidity control is equally crucial. The environmental conditions for cell culture, such as temperature and humidity, must be strictly controlled to simulate their natural living conditions, ensuring normal cell division and growth. Suitable temperature and humidity conditions support cellular metabolic balance, maintain cell membrane stability, and promote gene expression and protein synthesis. Inappropriate environmental conditions, however, can induce cellular stress, leading to slower proliferation, decreased differentiation capacity, and even triggering apoptosis, thus severely hindering the progress of cell engineering and tissue engineering research.

[0004] Currently, traditional temperature and humidity control systems mostly rely on preset, fixed parameters. While this method can maintain the stability of the experimental environment to some extent, it lacks flexibility and adaptability. These systems often cannot adjust in a timely manner to the specific needs of different biological samples or changes at different developmental stages. For example, some plants may require higher humidity and temperature during germination, while they may require lower humidity and temperature during maturity. Traditional systems struggle to achieve precise control in such cases.

[0005] With the development of technology, more intelligent and adaptive temperature and humidity control systems will become important tools for biological experimental research and sample culture. They can not only improve the accuracy and efficiency of experiments, but also help researchers better cope with environmental changes, thus playing a vital role in the field of biological science research and application. Therefore, this invention proposes an emergency control system for abnormal temperature and humidity and its application in the preservation of biological samples. Summary of the Invention

[0006] In response to the problems in related technologies, this invention proposes an emergency control system for abnormal temperature and humidity and its application in the preservation of biological samples, so as to overcome the above-mentioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, an emergency control system for abnormal temperature and humidity is provided, the emergency control system for abnormal temperature and humidity includes a temperature and humidity acquisition module, an anomaly detection module, a control module, a data display module, and an optimization feedback module;

[0009] Among them, the temperature and humidity acquisition module is used to monitor the environmental temperature and humidity of the biological sample box in real time;

[0010] The anomaly detection module is used to detect whether the ambient temperature and humidity of the biological sample box are abnormal;

[0011] The control module is used to adjust the temperature and humidity of the biosample box in real time based on the test results.

[0012] The data display module is used to display the environmental temperature and humidity data of the biological sample box and the real-time characteristic data of the biological samples in real time;

[0013] The optimization feedback module is used to optimize and adjust the temperature and humidity thresholds of biological samples in real time based on the real-time characteristic information of the biological samples.

[0014] Preferably, the temperature and humidity acquisition module includes a temperature acquisition module and a humidity acquisition module;

[0015] Among them, the temperature acquisition module is used to acquire temperature data in the biological sample box in real time using a pre-installed temperature sensor;

[0016] The humidity acquisition module is used to acquire humidity data in the biosample box in real time using a pre-installed humidity sensor.

[0017] Preferably, the anomaly detection module includes a temperature and humidity data acquisition module, a threshold analysis and comparison module, and a detection result output module;

[0018] Among them, the temperature and humidity data acquisition module is used to acquire temperature and humidity data in the biological sample box;

[0019] The threshold analysis and comparison module is used to compare the acquired temperature and humidity data with preset temperature and humidity thresholds and determine whether any abnormal temperature and humidity phenomena have occurred.

[0020] The detection result output module is used to output the detection results and issue an alarm when the detection result is abnormal.

[0021] Preferably, the control module includes a heating module, a humidification module, a cooling module, and a dehumidification module;

[0022] The heating module is used to heat biological samples using a pre-set heating device.

[0023] The humidification module is used to humidify biological samples using a preset humidification device;

[0024] A refrigeration module is used to refrigerate biological samples using a pre-set refrigeration device.

[0025] The dehumidification module is used to dehumidify biological samples using a pre-set dehumidification device.

[0026] Preferably, the optimization feedback module includes a sample data acquisition module, an optimal temperature and humidity analysis module, and a temperature and humidity threshold optimization and adjustment module.

[0027] The sample data acquisition module is used to acquire growth response data and sample image data of biological samples under different temperature and humidity conditions in real time. The growth response data includes the growth rate, metabolic rate and health status of the organism.

[0028] The optimal temperature and humidity analysis module is used to recommend the optimal temperature and humidity for biological samples by combining the current biological sample's real-time growth response data and sample image data with a temperature and humidity recommendation algorithm.

[0029] The temperature and humidity threshold optimization and adjustment module is used to optimize and adjust the temperature and humidity thresholds in the biosample chamber based on the best temperature and humidity data.

[0030] Preferably, the optimal temperature and humidity analysis module, when recommending the optimal temperature and humidity for the biological sample by combining the real-time growth response data and sample image data of the current biological sample with the temperature and humidity recommendation algorithm, includes:

[0031] Acquire biological sample image data under different temperature and humidity conditions, and use image recognition technology to identify the species and growth stage of the biological samples;

[0032] The growth response data of biological samples under different temperature and humidity conditions were obtained, and a scoring matrix of biological samples and temperature and humidity was constructed using the preprocessed growth response data. The growth response data included historical growth response data and current growth response data.

[0033] Calculate the similarity between temperature and humidity based on biological species stage and the similarity between temperature and humidity based on score, and calculate the comprehensive similarity between temperature and humidity based on biological species stage and score.

[0034] Calculate the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity respectively, and then calculate the comprehensive prediction score based on the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity.

[0035] Temperature and humidity were sorted in descending order based on the comprehensive prediction score, and the temperature and humidity with the highest comprehensive prediction score were taken as the optimal temperature and humidity for the target biological sample.

[0036] Preferably, the similarity between temperature and humidity based on biological species stage and the similarity between temperature and humidity based on score are calculated separately, and the comprehensive similarity between temperature and humidity is calculated based on the similarity between temperature and humidity based on biological species stage and the similarity based on score, including:

[0037] Construct a matrix of temperature, humidity and biological species stage, and extract vectors of biological species stage. At the same time, calculate the similarity between temperature and humidity based on biological species stage according to the number of biological species stages with the same temperature and humidity.

[0038] Calculate the rating-based similarity between temperature and humidity using cosine similarity;

[0039] The overall similarity between temperature and humidity is calculated by combining the similarity based on biological species stage and the similarity based on score using pre-set similarity weights.

[0040] Preferably, the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity are calculated separately, and the comprehensive prediction score is calculated based on the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity, including:

[0041] Based on the ranking results of comprehensive similarity, several adjacent temperature and humidity sets of the target temperature and humidity are obtained, and a prediction score based on comprehensive similarity is obtained by calculating the prediction score of biological samples for temperature and humidity.

[0042] Calculate a prediction score based on the similarity between biological samples;

[0043] A comprehensive prediction score is calculated by combining a pre-set scoring weight with a prediction score based on overall similarity and a prediction score based on biological sample similarity.

[0044] Preferably, the formula for calculating the similarity based on biological species stage is as follows:

[0045]

[0046] The formula for calculating the overall similarity is:

[0047] S z (T m ,Tn )=(1-α)·S s (T m ,T n )+α·S p (T m ,T n )

[0048]

[0049] The formula for calculating the comprehensive prediction score is as follows:

[0050] P Z =β·P L +(1-β)·P U

[0051] In the formula, S s (T m ,T n () indicates temperature and humidity T m With temperature and humidity T n The similarity between species is based on the biological stage, where N represents the number of 1s in the result vector, and C... m Temperature and humidity T m Vector of the species stage in the middle, C n Temperature and humidity T n Vectors representing the stages of biological species, & denote sum, S z (T m ,T n () indicates temperature and humidity T m With temperature and humidity T n The overall similarity between them, S p (T m ,T n () indicates temperature and humidity T m With temperature and humidity T n The similarity between them is based on the rating, where α represents the similarity weight, and U m Indicates biological sample U m U n Indicates biological sample U n P Z This represents the overall predicted score, where β represents the score weight, and P... L P represents the predicted score based on comprehensive similarity. U This represents a predicted score based on the similarity of biological samples.

[0052] According to another aspect of the present invention, the application of the above-mentioned emergency control system for abnormal temperature and humidity in the preservation of biological samples is also provided.

[0053] Compared with the prior art, the present invention provides an emergency control system for abnormal temperature and humidity and its application in the preservation of biological samples, which has the following beneficial effects:

[0054] 1) This invention can not only monitor and control the temperature and humidity of the biological sample chamber in real time, but also use a temperature and humidity recommendation algorithm to recommend the optimal temperature and humidity for the biological sample based on the current growth response data and sample image data, so that the biological sample can grow under the best environmental conditions, effectively improving the success rate and efficiency of biological sample culture, while also reducing the need for manual intervention.

[0055] 2) This invention calculates the similarity between temperature and humidity based on biological species stage and score, and controls the weight of the two similarities by changing the similarity weighting factor to calculate the comprehensive similarity between temperature and humidity. At the same time, it uses the score weighting factor to combine the comprehensive prediction score and the prediction score based on biological sample similarity to improve the recommendation quality. Finally, it recommends the optimal temperature and humidity for the target biological sample based on the ranking result of the comprehensive prediction score, thereby effectively improving the sparsity problem of data and improving the recommendation quality of optimal temperature and humidity. Attached Figure Description

[0056] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0057] Figure 1 This is a structural block diagram of an emergency control system for abnormal temperature and humidity according to an embodiment of the present invention.

[0058] In the picture:

[0059] 1. Temperature and humidity acquisition module; 11. Temperature acquisition module; 12. Humidity acquisition module; 2. Anomaly detection module; 21. Temperature and humidity data acquisition module; 22. Threshold analysis and comparison module; 23. Detection result output module; 3. Control module; 31. Heating module; 32. Humidification module; 33. Cooling module; 34. Dehumidification module; 4. Data display module; 5. Optimization feedback module; 51. Sample data acquisition module; 52. Optimal temperature and humidity analysis module; 53. Temperature and humidity threshold optimization and adjustment module. Detailed Implementation

[0060] 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.

[0061] According to embodiments of the present invention, an emergency control system for abnormal temperature and humidity and its application in the preservation of biological samples are provided.

[0062] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, according to an embodiment of the present invention, an emergency control system for abnormal temperature and humidity is provided. The emergency control system for abnormal temperature and humidity includes a temperature and humidity acquisition module 1, an abnormality detection module 2, a control module 3, a data display module 4, and an optimization feedback module 5.

[0063] Among them, temperature and humidity acquisition module 1 is used to monitor the environmental temperature and humidity of the biological sample box in real time.

[0064] Specifically, the temperature and humidity acquisition module 1 includes a temperature acquisition module 11 and a humidity acquisition module 12;

[0065] Among them, the temperature acquisition module 11 is used to acquire temperature data in the biological sample box in real time using a pre-installed temperature sensor;

[0066] The humidity acquisition module 12 is used to acquire humidity data in the biological sample box in real time using a pre-installed humidity sensor.

[0067] Anomaly detection module 2 is used to detect whether the ambient temperature and humidity of the biological sample box are abnormal;

[0068] Specifically, the anomaly detection module 2 includes a temperature and humidity data acquisition module 21, a threshold analysis and comparison module 22, and a detection result output module 23;

[0069] Among them, the temperature and humidity data acquisition module 21 is used to acquire temperature and humidity data in the biological sample box;

[0070] The threshold analysis and comparison module 22 is used to compare the acquired temperature and humidity data with the preset temperature and humidity thresholds and determine whether there is an abnormal temperature and humidity phenomenon.

[0071] Specifically, the preset temperature and humidity thresholds are obtained through expert experience: ideal temperature and humidity conditions are collected for different biological samples at different growth stages, and this data often comes from long-term experimental research and expert experience. The temperature and humidity ranges for various biological samples are determined through expert consultation, and these ranges can be used as initial thresholds.

[0072] The detection result output module 23 is used to output the detection result and to issue an alarm when the detection result is abnormal. At the same time, it sends an abnormal signal to the control module, so that the control module can adjust the temperature and humidity accordingly based on the abnormal signal.

[0073] Control module 3 is used to adjust the temperature and humidity of the biological sample box in real time according to the detection results;

[0074] Specifically, the control module 3 includes a heating module 31, a humidification module 32, a cooling module 33, and a dehumidification module 34;

[0075] The heating module 31 is used to heat the biological sample using a preset heating device.

[0076] Humidification module 32 is used to humidify biological samples using a preset humidification device;

[0077] The refrigeration module 33 is used to refrigerate biological samples using a preset refrigeration device;

[0078] The dehumidification module 34 is used to dehumidify biological samples using a preset dehumidification device.

[0079] Data display module 4 is used to display the environmental temperature and humidity data of the biological sample box and the real-time characteristic data of the biological samples in real time;

[0080] The optimization feedback module 5 is used to optimize and adjust the temperature and humidity thresholds of biological samples in real time based on the real-time characteristic information of the biological samples.

[0081] The reason for optimizing and adjusting the temperature and humidity thresholds in real time based on the real-time characteristic information of biological samples in this embodiment is as follows:

[0082] 1) Adaptability to Environmental Changes: Biological samples are highly sensitive to changes in environmental conditions, especially temperature and humidity. Even minor environmental changes can significantly impact sample growth. Real-time threshold adjustments ensure that biological samples are always in the optimal growth environment.

[0083] 2) Dynamic changes in sample status: The growth status and requirements of biological samples change over time; for example, different growth stages may have different requirements for temperature and humidity. Real-time monitoring and adjustment of thresholds can better meet the specific needs of samples at each stage.

[0084] 3) Improve the accuracy and efficiency of the recommendation system: Real-time optimization ensures that the recommendation system makes decisions based on the latest data, thereby improving the accuracy and efficiency of its recommendations.

[0085] 4) Prevention and reduction of risks: Through real-time monitoring and adjustment, environmental changes that may be detrimental to sample growth can be detected and addressed in a timely manner, thereby reducing losses or risks caused by unsuitable environments.

[0086] Therefore, real-time optimization and adjustment of temperature and humidity thresholds based on the real-time characteristic information of biological samples can help improve the growth efficiency and quality of biological samples, while enhancing the adaptability and flexibility of the system.

[0087] Specifically, the optimization feedback module 5 includes a sample data acquisition module 51, an optimal temperature and humidity analysis module 52, and a temperature and humidity threshold optimization and adjustment module 53;

[0088] Among them, the sample data acquisition module 51 is used to acquire in real time the growth response data and sample image data of biological samples under different temperature and humidity conditions. The growth response data includes the growth rate, metabolic rate and health status of the organism.

[0089] Specifically, growth rate is an important indicator of how quickly a biological sample grows. Growth rate can be calculated by measuring the size, height, or other growth parameters of the sample using size measurement sensors (such as laser rangefinders or image analysis systems). Changes in growth rate may reflect whether environmental conditions, such as temperature and humidity, are suitable.

[0090] Metabolic rate refers to the rate at which an organism carries out chemical reactions that support its growth and maintain its life activities. Changes in metabolic rate can be indirectly obtained by measuring changes in the concentration of specific compounds in a medium (such as oxygen, carbon dioxide, certain nutrients, or waste products), typically requiring chemical or biosensors.

[0091] Health status can be assessed by observing an animal's appearance, behavior, and response to environmental changes. Health status assessment also includes pathogen detection and evaluation of immune system function. For animal samples, physiological states can be directly measured using devices such as heart rate sensors and thermometers; this data can serve as indicators of health. For plant samples, health status can be indirectly obtained using color sensors (for detecting changes in leaf color) or spectral sensors (for measuring chlorophyll fluorescence).

[0092] By monitoring this data, we can gain a more precise understanding of how biological samples respond to specific temperature and humidity conditions, thereby enabling more accurate temperature and humidity optimization and adjustment in recommendation systems. This not only helps improve the growth efficiency and quality of biological samples but also ensures stable growth under different environmental conditions.

[0093] The sample data acquisition module 51 can also be used to preprocess the acquired growth response data and sample image data, specifically including:

[0094] Data cleaning is performed on the growth response data to handle missing and outlier values, ensuring data quality. Features (including growth stage of biological samples, sample type, etc.) are extracted, and non-numerical features are converted into numerical forms, such as using one-hot encoding to represent sample type.

[0095] The sample image data undergoes the following processes: image cropping (removing irrelevant parts of the image as needed, retaining only the region containing the biological sample. This reduces the complexity of data processing and improves the accuracy of subsequent analysis), grayscale conversion (converting the image to grayscale mode if color information is not necessary, thus reducing data volume and accelerating subsequent processing), image scaling (scaling the image to ensure all sample images have the same size, facilitating subsequent data processing and analysis), image enhancement (improving image quality by adjusting parameters such as contrast, brightness, and sharpness, making the sample features more prominent), noise filtering (removing noise from the image, such as noise generated by JPEG compression or uneven lighting when the camera captures the image), standardization (standardizing the pixel values ​​of the image to distribute them within a fixed range, such as 0 to 1 or -1 to 1, which helps improve the stability and efficiency of subsequent model training), and feature extraction (extracting useful information from the image through image processing techniques such as edge detection and texture analysis, or machine learning methods such as deep learning convolutional neural networks).

[0096] The optimal temperature and humidity analysis module 52 is used to recommend the optimal temperature and humidity for the current biological sample by combining the real-time growth response data and sample image data of the current biological sample with the temperature and humidity recommendation algorithm.

[0097] The temperature and humidity recommendation algorithm in this embodiment is a hybrid recommendation algorithm. Specifically, the optimal temperature and humidity analysis module 52 recommends the optimal temperature and humidity by combining the temperature and humidity recommendation algorithm with the real-time growth response data and sample image data of the current biological sample.

[0098] Acquire image data of biological samples under different temperature and humidity conditions, and use image recognition technology to identify the species and growth stage of the biological samples, specifically including:

[0099] Image data collection: Capture images of biological samples periodically or continuously under varying temperature and humidity conditions. Ensure good image quality, covering key features of the samples such as shape, size, and color.

[0100] Image preprocessing: Captured images are preprocessed, including cropping, resolution adjustment, and contrast enhancement, to improve the accuracy of image analysis. Image standardization is also performed to ensure consistency in image analysis.

[0101] Feature extraction: Using image processing techniques to extract key features of samples, such as contours, textures, and color distribution. Applying deep learning or other machine learning techniques for feature learning to capture complex image features.

[0102] Image recognition model training: A large number of labeled sample images are collected to train the image recognition model. Deep learning models such as Convolutional Neural Networks (CNNs) are used for training, and the model is optimized to improve recognition accuracy.

[0103] Species and growth stage identification: The trained image recognition model is applied to analyze new sample images. Based on image features, the model can identify the species and current growth stage of the sample.

[0104] The growth response data of biological samples under different temperature and humidity conditions were obtained, and a scoring matrix of biological samples and temperature and humidity was constructed using the preprocessed growth response data. The growth response data included historical growth response data and current growth response data.

[0105] The similarity between temperature and humidity based on biological species stage and the similarity between temperature and humidity based on score are calculated separately. Finally, a comprehensive similarity between temperature and humidity is calculated based on both the biological species stage similarity and the score-based similarity. Specifically, this includes:

[0106] Construct a matrix relating temperature, humidity, and biological species stage, and extract the vector C representing the biological species stage from it. i Simultaneously, the similarity between temperature and humidity levels based on biological species stages is calculated according to the number of biological species stages at two different temperature and humidity levels, i.e., the vector C of biological species stages at two different temperature and humidity levels. m &C n The number of 1s in the result and C m C n The result is represented by the ratio of the number of 1s in the result;

[0107] The formula for calculating the similarity based on biological species stage is:

[0108]

[0109] In the formula, S s (T m ,T n () indicates temperature and humidity T m With temperature and humidity T n The similarity between species is based on the biological stage, where N represents the number of 1s in the result vector, and C... m Temperature and humidity T m Vector of the species stage in the middle, C n Temperature and humidity T n Vectors representing biological species stages, where & denotes sum;

[0110] Calculate the rating-based similarity between temperature and humidity using cosine similarity;

[0111] The formula for calculating similarity based on ratings is:

[0112]

[0113] In the formula, S p (T m ,T n () indicates temperature and humidity T m With temperature and humidity T n Based on the similarity of ratings, R mt Indicates the biological sample's response to temperature and humidity T m Rating nt Indicates the biological sample's response to temperature and humidity T n The rating, This represents the average score of the biological sample Um. Let represent the average score of biological sample Un, T represent several adjacent temperature and humidity sets of the target temperature and humidity, and u represent biological sample u;

[0114] The overall similarity between temperature and humidity is calculated by combining the similarity based on biological species stage and the similarity based on score using pre-set similarity weights.

[0115] The formula for calculating the overall similarity is:

[0116] S z (T m ,T n )=(1-α)·S s (T m ,T n )+α·S p (T m ,T n )

[0117]

[0118] In the formula, S z (T m ,T n () indicates temperature and humidity T m With temperature and humidity T n The overall similarity between them, where α represents the similarity weight, U m Indicates biological sample U m U n Indicates biological sample U n .

[0119] Calculate the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity separately, and then calculate the comprehensive prediction score based on the comprehensive similarity prediction score and the prediction score based on biological sample similarity. Specifically, this includes:

[0120] Based on the ranking results of comprehensive similarity, several adjacent temperature and humidity sets of the target temperature and humidity are obtained, and a prediction score based on comprehensive similarity is obtained by calculating the prediction score of biological samples for temperature and humidity.

[0121] Calculate a prediction score based on the similarity between biological samples;

[0122] A comprehensive prediction score is calculated by combining a pre-set scoring weight with a prediction score based on overall similarity and a prediction score based on biological sample similarity.

[0123] Specifically, after obtaining the predicted score based on comprehensive similarity and the predicted score based on biological sample similarity, the two scores are linearly combined using a weighting factor β. The specific value of the weighting factor is determined through experiments, and finally, a comprehensive predicted score is obtained for extrapolation.

[0124] The formula for calculating the comprehensive prediction score is as follows:

[0125] P Z =β·P L +(1-β)·P U

[0126] In the formula, P Z This represents the overall predicted score, where β represents the score weight, and P... L P represents the predicted score based on comprehensive similarity. U This represents a predicted score based on biological sample similarity, and P... L and P U The predicted rating is calculated using the formula found in traditional collaborative filtering recommendation algorithms, which will not be discussed in detail here.

[0127] Temperature and humidity were sorted in descending order based on the comprehensive prediction score, and the temperature and humidity with the highest comprehensive prediction score were taken as the optimal temperature and humidity for the target biological sample.

[0128] The temperature and humidity threshold optimization and adjustment module 53 is used to optimize and adjust the temperature and humidity thresholds in the biosample box based on the best temperature and humidity data.

[0129] Specifically, the pre-set temperature and humidity thresholds in the biosample chamber are optimized and adjusted based on the best temperature and humidity data, including raising or lowering the thresholds of specific parameters.

[0130] According to another embodiment of the present invention, an application of the above-mentioned abnormal temperature and humidity emergency control system in the preservation of biological samples is provided. The main function of the system is to quickly implement emergency control when abnormal temperature and humidity conditions occur during the preservation of biological samples, thereby protecting the biological samples from damage.

[0131] In summary, by utilizing the above-mentioned technical solution of the present invention, the present invention can not only monitor and control the temperature and humidity of the biological sample chamber in real time, but also use a temperature and humidity recommendation algorithm to recommend the optimal temperature and humidity for the biological sample based on the current growth response data and sample image data, thereby enabling the biological sample to grow under optimal environmental conditions, effectively improving the success rate and efficiency of biological sample culture, while also reducing the need for manual intervention.

[0132] Furthermore, this invention calculates the similarity between temperature and humidity based on biological species stage and score, and controls the weight of the two similarities by changing the similarity weighting factor to obtain the comprehensive similarity between temperature and humidity. At the same time, it uses the score weighting factor to combine the comprehensive prediction score and the prediction score based on biological sample similarity to improve the recommendation quality. Finally, it recommends the optimal temperature and humidity for the target biological sample based on the ranking result of the comprehensive prediction score, thereby effectively improving the sparsity problem of data and improving the recommendation quality of optimal temperature and humidity.

[0133] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it includes the steps described in the above methods. The storage medium may be, for example, ROM / RAM, magnetic disk, optical disk, etc.

[0134] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. An emergency control system for abnormal temperature and humidity, characterized in that, The temperature and humidity abnormality emergency control system includes a temperature and humidity acquisition module (1), an abnormality detection module (2), a control module (3), a data display module (4), and an optimization feedback module (5). The temperature and humidity acquisition module (1) is used to monitor the environmental temperature and humidity of the biological sample box in real time. The anomaly detection module (2) is used to detect whether the ambient temperature and humidity of the biological sample box are abnormal; The control module (3) is used to adjust the temperature and humidity of the biological sample box in real time according to the detection results. The data display module (4) is used to display the environmental temperature and humidity data of the biological sample box and the real-time characteristic data of the biological sample in real time. The optimization feedback module (5) is used to optimize and adjust the temperature and humidity thresholds of biological samples in real time based on the real-time characteristic information of the biological samples. The optimization feedback module (5) includes a sample data acquisition module (51), an optimal temperature and humidity analysis module (52), and a temperature and humidity threshold optimization and adjustment module (53). The sample data acquisition module (51) is used to acquire in real time the growth response data and sample image data of biological samples under different temperature and humidity conditions. The growth response data includes the growth rate, metabolic rate and health status of the organism. The optimal temperature and humidity analysis module (52) is used to recommend the optimal temperature and humidity for the current biological sample by combining the real-time growth response data and sample image data of the current biological sample with the temperature and humidity recommendation algorithm. The optimal temperature and humidity analysis module (52) recommends the optimal temperature and humidity for the biological sample by combining the real-time growth response data and sample image data of the current biological sample with the temperature and humidity recommendation algorithm, including: Acquire biological sample image data under different temperature and humidity conditions, and use image recognition technology to identify the species and growth stage of the biological samples; The growth response data of biological samples under different temperature and humidity conditions were obtained, and a scoring matrix of biological samples and temperature and humidity was constructed using the preprocessed growth response data. The growth response data included historical growth response data and current growth response data. Calculate the similarity between temperature and humidity based on biological species stage and the similarity between temperature and humidity based on score, and calculate the comprehensive similarity between temperature and humidity based on biological species stage and score. Calculate the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity respectively, and then calculate the comprehensive prediction score based on the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity. Temperature and humidity were sorted in descending order based on the comprehensive prediction score, and the temperature and humidity with the highest comprehensive prediction score were taken as the optimal temperature and humidity for the target biological sample. The temperature and humidity threshold optimization and adjustment module (53) is used to optimize and adjust the temperature and humidity thresholds in the biological sample box according to the best temperature and humidity data. The formula for calculating the similarity based on biological species stage is: ； The formula for calculating the overall similarity is: ； The formula for calculating the comprehensive prediction score is as follows: ； In the formula, S s (T) m ,T n (T represents temperature and humidity) m With temperature and humidity T n The similarity between species is based on the biological stage, where N represents the number of 1s in the result vector, and C... m Temperature and humidity T m Vector of the species stage in the middle, C n Temperature and humidity T n Vectors representing the stages of biological species, & denote sum, S z (T) m ,T n (T represents temperature and humidity) m With temperature and humidity T n The overall similarity between them, S p (T) m ,T n (T represents temperature and humidity) m With temperature and humidity T n Based on the similarity of ratings, U represents the similarity weight. m Indicates biological sample U m U n Indicates biological sample U n P Z This represents the overall prediction score. P represents the rating weight. L P represents the predicted score based on comprehensive similarity. U This represents a predicted score based on the similarity of biological samples.

2. The emergency control system for abnormal temperature and humidity according to claim 1, characterized in that, The temperature and humidity acquisition module (1) includes a temperature acquisition module (11) and a humidity acquisition module (12). The temperature acquisition module (11) is used to acquire temperature data in the biological sample box in real time using a pre-installed temperature sensor. The humidity acquisition module (12) is used to acquire humidity data in the biological sample box in real time using a pre-installed humidity sensor.

3. The emergency control system for abnormal temperature and humidity according to claim 2, characterized in that, The anomaly detection module (2) includes a temperature and humidity data acquisition module (21), a threshold analysis and comparison module (22), and a detection result output module (23). The temperature and humidity data acquisition module (21) is used to acquire temperature and humidity data in the biological sample box. The threshold analysis and comparison module (22) is used to compare the acquired temperature and humidity data with the preset temperature and humidity thresholds and determine whether there is an abnormal temperature and humidity phenomenon. The detection result output module (23) is used to output the detection result and to issue an alarm when the detection result is abnormal.

4. The emergency control system for abnormal temperature and humidity according to claim 3, characterized in that, The control module (3) includes a heating module (31), a humidification module (32), a cooling module (33), and a dehumidification module (34). The heating module (31) is used to heat the biological sample using a preset heating device; The humidification module (32) is used to humidify biological samples using a preset humidification device; The refrigeration module (33) is used to refrigerate biological samples using a preset refrigeration device; The dehumidification module (34) is used to dehumidify biological samples using a preset dehumidification device.

5. The emergency control system for abnormal temperature and humidity according to claim 4, characterized in that, The calculation of similarity between temperature and humidity based on biological species stage, similarity between temperature and humidity based on score, and calculation of comprehensive similarity between temperature and humidity based on biological species stage and score include: Construct a matrix of temperature, humidity and biological species stage, and extract vectors of biological species stage. At the same time, calculate the similarity between temperature and humidity based on biological species stage according to the number of biological species stages with the same temperature and humidity. Calculate the rating-based similarity between temperature and humidity using cosine similarity; The overall similarity between temperature and humidity is calculated by combining the similarity based on biological species stage and the similarity based on score using pre-set similarity weights.

6. The emergency control system for abnormal temperature and humidity according to claim 5, characterized in that, The process of calculating the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity, and then calculating the comprehensive prediction score based on the prediction score based on comprehensive similarity and the prediction score based on biological sample similarity, includes: Based on the ranking results of comprehensive similarity, several adjacent temperature and humidity sets of the target temperature and humidity are obtained, and a prediction score based on comprehensive similarity is obtained by calculating the prediction score of biological samples for temperature and humidity. Calculate a prediction score based on the similarity between biological samples; A comprehensive prediction score is calculated by combining a pre-set scoring weight with a prediction score based on overall similarity and a prediction score based on biological sample similarity.

7. The application of the temperature and humidity anomaly emergency control system according to claim 6 in the preservation of biological samples.

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