Data management system for confocal microscopes

Abstract

The invention relates to the technical field of data analysis and management, in particular to a data management system for a confocal microscope, which is characterized in that a data acquisition module acquires data acquired by the confocal microscope and marks an experiment number; the data analysis module analyzes the collected data, judges whether the data meets a preset standard or not, and sets a mark meeting the standard for the data if the data meets the preset standard; if not, setting a mark which does not meet the standard for the data; the marked data are sent to a data caching module; judging whether the number of the data marked as the standard in the data with the experiment number being the current experiment number stored in the data caching module is equal to a preset number or not, and if yes, sending the data to a data storage module for storage; and if not, triggering the data acquisition module to continuously acquire the data. According to the scheme, the data generated by the confocal microscope can be effectively managed, data storage is guaranteed, meanwhile, the memory demand and occupation are reduced, the quality of stored data is improved, and the data selection time of a user is saved.

Description

Technical Field

[0001] The invention relates to the technical field of data analysis management, in particular to a data management system for a confocal microscope. Background Art

[0002] Confocal microscopy is a high-resolution optical microscope that can obtain three-dimensional images of cells and tissues. This technology scans the sample point by point and uses a pinhole to eliminate light outside the focal plane to improve the contrast and clarity of the image. When conducting confocal microscopy experiments, data management is a crucial link because it involves multiple aspects such as data acquisition, processing, and storage.

[0003] The traditional method of storing data after it is collected can save the data collected each time, but it requires a huge amount of memory. When the memory is over-occupied, the user needs to clean it up. In addition, a large amount of data is not conducive to the subsequent selection and use of data by users.

[0004] Therefore, there is an urgent need for a confocal microscope data management system that can effectively manage the data generated by the confocal microscope, ensure data storage, reduce memory requirements and occupancy, improve the quality of stored data, and save users time in selecting data. Summary of the invention

[0005] The present invention aims to provide a data management system for a confocal microscope, which can effectively manage the data generated by the confocal microscope, ensure data storage, reduce memory demand and occupancy, improve the quality of stored data, and save users time in selecting data.

[0006] The present invention provides the following basic scheme: a data management system for a confocal microscope, comprising: an acquisition setting module, a data acquisition module, a data analysis module, a data cache module and a data storage module;

[0007] The collection setting module is used to set the experiment number and the amount of collected data;

[0008] A data acquisition module is used to acquire data collected by a confocal microscope and mark the data with an experiment number;

[0009] The data analysis module is used to analyze the collected data and determine whether the data meets the preset standards. If so, a mark that meets the standards is set for the data and the data is sent to the data cache module; if not, a mark that does not meet the standards is set for the data and the data is sent to the data cache module;

[0010] It is also used to determine whether the number of data marked as meeting the standard in the data with the current experiment number stored in the data cache module is equal to the preset number. If so, the data marked as meeting the standard in the data with the current experiment number is sent to the data storage module for storage; if not, the data acquisition module is triggered to continue acquiring data;

[0011] The data cache module is used to cache the data with the same experiment number;

[0012] The data storage module is used to store the data collected with the same experiment number and to back up the data using a redundant backup strategy.

[0013] Furthermore, the acquisition setting module is used to set the experimental objectives;

[0014] The data analysis module generates corresponding image quality requirements as preset standards according to the experimental objectives set in the acquisition setting module.

[0015] Furthermore, the data storage module pre-stores a relationship table corresponding to experimental target features and image quality requirements;

[0016] The data analysis module is used to extract the experimental target features according to the experimental target set in the acquisition setting module, query the relationship table, and obtain the corresponding image quality requirements as the preset standard.

[0017] Furthermore, the acquisition setting module is also used to input an experiment start signal, an experiment end signal and an experiment pause signal;

[0018] The data acquisition module is used to acquire data collected by the confocal microscope according to the experiment start signal, and mark the data with the experiment number;

[0019] The data acquisition module is also used to end the acquisition of the data of this experiment according to the experiment end signal, and trigger the data analysis module to make a judgment on whether the data collection meets the standards;

[0020] The data acquisition module is also used to pause acquiring the data of this experiment according to the experiment pause signal.

[0021] Furthermore, the data analysis module is also used to determine whether data collection meets the standards, and to determine whether there is data with the current experiment number in the data storage module. If not, the difference between the number of data marked as meeting the standards in the data with the current experiment number in the data cache module and a preset number is calculated;

[0022] According to the difference and the experimental goal, the evaluation model is called up to evaluate the data marked as not meeting the standards in the data with the current experiment number in the data cache module as the data to be selected, and an evaluation value is generated. The data to be selected is sorted according to the evaluation value, and according to the difference, the difference data before the sorting result is obtained as supplementary data, and the data marked as meeting the standards in the data with the experiment number of the current experiment number is merged and sent to the data storage module for storage.

[0023] Furthermore, the data storage module is also used to store evaluation models; wherein the evaluation models include: signal-to-noise ratio evaluation model, contrast evaluation model, clarity model, resolution evaluation model, colocalization coefficient evaluation model, and Pearson correlation coefficient evaluation model.

[0024] Furthermore, the signal-to-noise ratio model is used to evaluate the clarity and reliability of the image:

[0025]

[0026] Among them, μ s is the average signal intensity in the region of interest, σ n is the standard deviation of the background area;

[0027] The contrast model, which evaluates the brightness difference between the brightest and darkest parts of an image:

[0028]

[0029] Among them, I max and I min are the maximum and minimum brightness values ​​in the image respectively;

[0030] The clarity model is used to evaluate the clarity of an image. It uses Fourier transform to convert the image from the spatial domain to the frequency domain, and then analyzes the proportion of high-frequency components in the spectrum:

[0031]

[0032] Where F(f) is the amplitude of the image at frequency f, th It is the threshold value set to distinguish low-frequency and high-frequency components;

[0033] The resolution model is used to evaluate the ability of data to distinguish subtle features in samples:

[0034]

[0035] d is the minimum distance between two point sources that can be distinguished, which is used to characterize the resolution;

[0036] The colocalization coefficient model is used to evaluate the degree of spatial overlap between two different markers:

[0037]

[0038] Among them, F 1i and F 2i are the fluorescence intensities of the two channels at pixel i, respectively, and M1 and M2 reflect the degree of colocalization of the first channel relative to the second channel and the second channel relative to the first channel, respectively;

[0039] The Pearson correlation coefficient model is used to evaluate the linear correlation between two variables. It can be used to evaluate the spatial correlation between two fluorescent markers in confocal microscopy image analysis. Therefore, the Pearson correlation coefficient model is set as follows:

[0040]

[0041] Among them, x i and i are the fluorescence intensities of the two channels at the same position, and x and y are their respective averages.

[0042] Further, the setting of the evaluation model according to the difference and the experimental target includes:

[0043] Through the acquisition setting module, input the evaluation model selection signal and its corresponding weight, obtain the corresponding evaluation model in the data storage module according to the evaluation model selection signal, and combine it with the corresponding weight to form a comprehensive evaluation model;

[0044] Alternatively, an evaluation model selection signal is input through the acquisition setting module, and a corresponding evaluation model is obtained in the data storage module according to the evaluation model selection signal, and a corresponding weight is generated, and the weight is optimized according to the data marked as meeting the standard in the data of the current experiment number, and the optimal weight is obtained and combined with the obtained evaluation model to form a comprehensive evaluation model;

[0045] Alternatively, according to the set experimental objectives, an evaluation model whose matching degree with the experimental objectives is higher than a preset matching degree threshold is obtained in the data storage module, and corresponding weights are generated. The weights are optimized according to the data marked as meeting the standards in the data of the current experimental number, and the optimal weights are obtained and combined with the obtained evaluation model to form a comprehensive evaluation model.

[0046] Furthermore, the optimization weight adopts an optimization algorithm including:

[0047] S1. Through the acquisition setting module, the data marked as meeting the standard in the data of the current experiment number are sorted to generate a pre-sorting result. The data that meets the experimental purpose of the current experiment is ranked higher;

[0048] S2. According to the obtained evaluation model, a number of initialization weight schemes are randomly generated as individual schemes to form a population;

[0049] S3, initialization iteration number n=1;

[0050] S4. For the data marked as meeting the standard in the data of the current experiment number, use each individual scheme in the population, combine it with the obtained evaluation model, calculate its evaluation value, and sort it from large to small according to the evaluation value to generate a sorting result;

[0051] S5, comparing the similarity between the sorting results and the pre-sorting results one by one;

[0052] S6, determining whether there is an individual solution with a similarity greater than a preset optimal similarity value, if so, executing S10; if not, executing S7;

[0053] S7, sorting the individual solutions in the population from large to small according to the similarity, and generating a population sorting result;

[0054] S8. According to the similarity, select, cross and mutate the individual solutions in the population, and eliminate the individual solutions that cannot be realized to form a new population;

[0055] S9, determine whether the current number of iterations n is equal to the preset number of iterations, if yes, execute S10; if not, n=n+1, and execute S4;

[0056] S10. In the population sorting result, the first k individual solutions are obtained as the optimal weighted solution, and output and displayed through the acquisition setting module.

[0057] Furthermore, the acquisition setting module is also used to obtain an optimal weight scheme selection signal;

[0058] The data analysis module is also used to select signals according to the optimal weight scheme, obtain the corresponding weight scheme as the optimal weight, and combine it with the obtained evaluation model to form a comprehensive evaluation model.

[0059] The beneficial effects of this solution are as follows: the system is provided with five modules, wherein the acquisition setting module is used to set the experiment number and the amount of collected data; the data acquisition module is used to acquire the data collected by the confocal microscope and mark the data with the experiment number, so as to accurately distinguish the experiment to which the data belongs; the collected data is analyzed by the data analysis module to determine whether the data meets the preset standards, and the data that meets the standards and does not meet the standards are marked accordingly, and all are sent to the data cache module for caching, and the data with the same experiment number in the data cache module are cached accordingly, so as to facilitate the subsequent retrieval of all the data of the experiment according to the experiment number; the data analysis module is also used to determine whether the data meets the preset standards, and the data that meets the standards and does not meet the standards are marked accordingly, and all are sent to the data cache module for caching. Whether the number of data marked as meeting the standards among the data with the current experiment number stored in the data cache module is equal to the preset number, if so, this batch of data meeting the preset standard is transmitted to the data storage module for long-term storage, and the data storage module performs corresponding storage for the data with the same experiment number, so as to facilitate the subsequent retrieval of all data of the current experiment according to the experiment number, and at the same time, a redundant backup strategy is adopted to perform data backup, improve disaster resistance, and ensure the security and stability of data storage; if not, the data acquisition module is triggered to continue to acquire data, so as to ensure that data that meets the quantity requirements and meets the preset standards can be finally acquired for subsequent analysis.

[0060] This solution divides storage into a data cache module and a data storage module. Each data collected in an experiment will be analyzed to see if it meets the standards and marked. When the number of data that meets the standards reaches the standard, it will be placed in the data storage module for storage. This eliminates the need to store data that does not meet the preset standards, reduces the use of storage resources, and ensures the quality of stored data. Subsequent users do not need to exclude data that does not meet the standards, saving users time in selecting data. The data cache module will regularly clean up data that does not meet the preset standards, thereby reducing memory usage.

[0061] In summary, this solution can effectively manage the data generated by the confocal microscope, ensuring data storage while reducing memory requirements and occupancy, improving the quality of stored data, and saving users time in selecting data. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a logic block diagram of an embodiment of a data management system for a confocal microscope. DETAILED DESCRIPTION

[0063] The following is further described in detail through specific implementation methods:

[0064] Embodiment 1

[0065] This embodiment is basically as shown in the attached Figure 1As shown: A data management system for a confocal microscope, comprising: an acquisition setting module, a data acquisition module, a data analysis module, a data cache module and a data storage module;

[0066] The collection setting module is used to set the experiment number, experiment target and the type and quantity of the corresponding collected data;

[0067] The acquisition setting module is also used to input the experiment start signal, the experiment end signal and the experiment pause signal; thereby controlling the data acquisition module to acquire data, end data acquisition, and pause data acquisition;

[0068] The data acquisition module is used to acquire the data collected by the confocal microscope according to the experiment start signal, and mark the data with the experiment number; specifically, the data acquisition module is connected to the confocal microscope, which uses laser as the light source. The light beam is focused and falls on a tiny point at different depths of the sample, and performs mobile scanning. Through the color imaging of electrical signals, the image formed by the reflected light at any point in the sample can be accurately received and a signal can be generated. The data acquisition module acquires the image data collected by the confocal microscope;

[0069] The data acquisition module is also used to end the acquisition of the data of this experiment according to the experiment end signal, and trigger the data analysis module to make a judgment on whether the data collection meets the standards;

[0070] The data acquisition module is also used to pause the acquisition of data of this experiment according to the experiment pause signal;

[0071] The data analysis module is used to analyze the collected data and determine whether the data meets the preset standards. If so, a mark that meets the standards is set for the data and the data is sent to the data cache module; if not, a mark that does not meet the standards is set for the data and the data is sent to the data cache module;

[0072] The collected data is analyzed to determine whether the data meets the preset standard, including: generating corresponding image quality requirements as preset standards according to the experimental objectives set in the collection setting module, and determining whether the data meets the preset standards; the image quality requirements in this embodiment include: clarity requirements and resolution requirements;

[0073] Specifically, a relationship table corresponding to experimental target features and image quality requirements is pre-stored in the data storage module. According to the experimental target set in the acquisition setting module, the experimental target features are extracted, and then the relationship table is queried to obtain the corresponding image quality requirements, including: clarity requirements and resolution requirements, as preset standards; wherein the clarity requirement is a minimum clarity threshold, and the resolution requirement is a minimum resolution threshold; the clarity and resolution of the collected data are analyzed, and if the clarity and resolution are correspondingly greater than or equal to the minimum clarity threshold and the minimum resolution threshold, it is determined that the data meets the preset standard, a standard compliance mark is set for the data, and it is sent to the data cache module; if there is a clarity and / or resolution correspondingly less than the minimum clarity threshold and / or the minimum resolution threshold, it is determined that the data does not meet the preset standard, a standard compliance mark is set for the data, and it is sent to the data cache module;

[0074] In other embodiments, the collected data is analyzed to determine whether the data meets the preset standards, and further includes: performing an artifact check on the collected data to check whether there are any abnormal or non-biological features in the data (image), such as dust, bubbles, blurred areas caused by focal plane errors, scanning stripes or other types of optical artifacts. If so, the data is determined to be non-compliant with the standards. If not, and there is a clarity and / or resolution corresponding to a minimum clarity threshold and / or a minimum resolution threshold, the data is determined to be compliant with the standards. In addition, the image quality requirements can also be set according to actual needs during the specific implementation process;

[0075] The data analysis module is also used to determine whether the number of data marked as meeting the standards in the data with the current experiment number stored in the data cache module is equal to the preset number. If so, the data marked as meeting the standards in the data with the current experiment number is sent to the data storage module for storage; if not, the data acquisition module is triggered to continue acquiring data; the data storage module performs long-term storage;

[0076] The data analysis module is also used to determine whether the data collection meets the standards, and to determine whether there is data with the current experiment number in the data storage module. If not, the difference between the number of data marked as meeting the standards in the data cache module and the preset number is calculated;

[0077] According to the difference and the experimental goal, the evaluation model is called to set up, and the data marked as not meeting the standards in the data cache module with the current experimental number as the selected data is evaluated to generate an evaluation value, and the selected data is sorted according to the evaluation value. According to the difference, the difference data before the sorting result is obtained as supplementary data, and the data marked as meeting the standards in the data with the experimental number of the current experimental number is merged and sent to the data storage module for storage;

[0078] When the experiment end signal is input, the data acquisition module will end the data acquisition according to the experiment end signal, but the number of collected data for this experiment is initially set. When the number of data does not meet the requirement, this solution obtains part of the data marked as non-compliant in the data of the current experiment number in the data cache module as supplementary data to ensure that the data meets the quantity requirement. However, when acquiring it, it is necessary to consider the data that does not meet the standard and which ones are more suitable for this experiment. Therefore, an evaluation model is set to evaluate the data marked as non-compliant, and then select after generating the evaluation value; the evaluation model is not a single-factor evaluation, but a multi-dimensional comprehensive evaluation model that considers multiple factors, which is composed of multiple evaluation models combined with weights, so that the data can be evaluated and selected more accurately;

[0079] The above evaluation model is called and set according to the difference and experimental objectives, including:

[0080] Through the acquisition setting module, the evaluation model selection signal and its corresponding weight are input, and the corresponding evaluation model is obtained in the data storage module according to the evaluation model selection signal, and combined with the corresponding weight to form a comprehensive evaluation model; thus, the comprehensive evaluation model is manually set according to actual needs to evaluate the data that does not meet the standards;

[0081] Alternatively, an evaluation model selection signal is input through the acquisition setting module, and a corresponding evaluation model is obtained in the data storage module according to the evaluation model selection signal, and a corresponding weight is generated, and the weight is optimized according to the data marked as meeting the standard in the data of the current experiment number, and the optimal weight is obtained and combined with the obtained evaluation model to form a comprehensive evaluation model;

[0082] Alternatively, according to the set experimental goal, an evaluation model with a matching degree with the experimental goal higher than a preset matching degree threshold is obtained in the data storage module, and a corresponding weight is generated, and the weight is optimized according to the data marked as meeting the standard in the data of the current experimental number, and the optimal weight is obtained and combined with the obtained evaluation model to form a comprehensive evaluation model;

[0083] The evaluation model and weights can be directly set and selected by the user, or they can be matched and generated. For the weights, this solution will also optimize the weights according to the data marked as meeting the standards in the data of the current experiment number to ensure the rationality of the weights, so that the comprehensive evaluation model can accurately evaluate and better meet the needs of the current experiment. The specific optimization process is as follows:

[0084] Optimize weights and use optimization algorithms. The specific process is as follows:

[0085] S1. Through the acquisition setting module, the data marked as meeting the standard in the data of the current experiment number are sorted to generate a pre-sorting result. The more the data meets the experimental purpose of the current experiment, the higher the ranking. In this embodiment, the sorting is performed manually. During the sorting process, researchers can conduct preliminary monitoring and judgment on the data, and sort the data according to their actual needs to best meet the purpose of their current experiment.

[0086] S2. According to the obtained evaluation model, a number of initialization weight schemes are randomly generated as individual schemes to form a population;

[0087] S3, initialization iteration number n=1;

[0088] S4. For the data marked as meeting the standard in the data of the current experiment number, use each individual scheme in the population, combine it with the obtained evaluation model, calculate its evaluation value, and sort it from large to small according to the evaluation value to generate a sorting result;

[0089] S5, comparing the similarity between the sorting results and the pre-sorting results one by one;

[0090] S6, determining whether there is an individual solution with a similarity greater than a preset optimal similarity value, if so, executing S10; if not, executing S7;

[0091] S7, sorting the individual solutions in the population from large to small according to the similarity, and generating a population sorting result;

[0092] S8. According to the similarity, select, cross and mutate the individual solutions in the population, and eliminate the individual solutions that cannot be realized to form a new population;

[0093] S9, determine whether the current number of iterations n is equal to the preset number of iterations, if yes, execute S10; if not, n=n+1, and execute S4;

[0094] S10. In the population sorting result, the first k individual solutions are obtained as the optimal weighted solution, and output and displayed through the acquisition setting module.

[0095] The scheme optimizes the weights by combining the data that meet the standards and the sorting of the data that meet the standards by the user, wherein the sorting of the data that meet the standards by the user indicates that the user has selected the data that meet the standards and evaluated the data according to their own needs to obtain the pre-sorting result; with this as a comparison, the weight scheme is substituted into the data that meet the standards to obtain the sorting result, and the pre-sorting result and the sorting result are compared to evaluate whether the weight scheme meets the user's needs and expectations. The higher the similarity of the two results, the more the current weight scheme meets the user's needs and expectations. Therefore, the individual schemes in the population are sorted from large to small according to the similarity, and a population sorting result is generated; and the population is iteratively updated, and the optimization is gradually performed to obtain the optimal weight scheme; during the iterative optimization process, if a population scheme with a similarity greater than the preset optimal similarity value appears, the iteration can be exited in advance, because the weight scheme at this time has met the user's needs and expectations. In this embodiment, the preset optimal similarity value is 100%; the weight scheme is optimized to better meet the user's needs, and the data that does not meet the standards is reasonably evaluated to supplement the auxiliary data. At the same time, an early exit mechanism is also set to improve the optimization efficiency and reduce the consumption of computing resources.

[0096] The acquisition setting module is also used to obtain the optimal weight scheme selection signal;

[0097] The data analysis module is also used to select signals according to the optimal weighting scheme, obtain the corresponding weighting scheme as the optimal weight, and combine it with the obtained evaluation model to form a comprehensive evaluation model;

[0098] In addition, after each evaluation model evaluates each data, the evaluation results will be normalized and then combined with the weights to calculate its evaluation value to ensure the consistency of the evaluation value.

[0099] The data cache module is used to cache the data with the same experiment number and regularly clear the data with a predicted time difference from the current time. In addition, metadata will be added to the data during caching, including but not limited to: date, time, instrument settings, and sample information.

[0100] The data storage module is used to store the data collected under the same experiment number, and adopts a redundant backup strategy to back up the data; the data storage module stores the data under the same experiment number, so that all the data of the experiment can be retrieved according to the experiment number. At the same time, a redundant backup strategy is adopted to back up the data, improve the disaster resistance, and ensure the security and stability of data storage;

[0101] Also used to store evaluation models;

[0102] The evaluation models include, but are not limited to: signal-to-noise ratio evaluation model, contrast evaluation model, clarity model, resolution evaluation model, colocalization coefficient evaluation model, and Pearson correlation coefficient evaluation model;

[0103] Signal-to-noise ratio model,Signal-to-noise ratio (SNR) is an important parameter to measure the ratio of signal intensity to background noise in an image. It can be used to indicate the clarity and reliability of an image. A high SNR means that the signal intensity is significantly greater than the noise. The SNR can be optimized by adjusting parameters such as laser power, detection sensitivity (gain), and integration time. Therefore, the signal-to-noise ratio model is set as follows:

[0104]

[0105] Among them, μ s is the average signal intensity in the region of interest (ROI), and σ n is the standard deviation of the background area;

[0106] Contrast model. Contrast refers to the brightness difference between the brightest and darkest parts of an image. An image should have good contrast so that the region of interest can be easily distinguished from the background. If fluorescent dyes are used, the fluorescent signal should be significantly higher than the background fluorescence. Therefore, set the contrast model:

[0107]

[0108] Among them, I max and I min are the maximum and minimum brightness values ​​in the image respectively;

[0109] Clarity model. In confocal microscope image analysis, the method of calculating the sharpness of an image usually involves edge detection and frequency domain analysis. In this embodiment, frequency domain analysis is used to evaluate the sharpness of an image. The sharpness of an image is evaluated by converting it to the frequency domain. The more high-frequency components there are, the clearer the image is. First, Fourier transform is used to convert the image from the spatial domain to the frequency domain. Then, the proportion of high-frequency components in the spectrum diagram is analyzed. The more high-frequency components there are, the clearer the image is. Therefore, a clarity model is set. In addition, when the data analysis module determines the clarity requirement, the clarity model can also be directly called to calculate the image clarity:

[0110]

[0111] Where F(f) is the amplitude of the image at frequency f, th It is the threshold value set to distinguish low-frequency and high-frequency components;

[0112] Resolution model. The theoretical resolution limit can be described by the Rayleigh criterion. For optical microscopes, it is determined by the numerical aperture (NA) and wavelength (λ). To ensure that the resolution of the image is sufficient to distinguish the subtle features in the sample, a resolution model is set. In addition, when the data analysis module determines the resolution requirement, it can also directly call the resolution model to calculate the image resolution:

[0113]

[0114] d is the minimum distance between two point sources that can be distinguished, which is used to characterize the resolution;

[0115] Colocalization coefficient model: When using multi-channel fluorescence imaging, the colocalization coefficient is used to measure the degree of spatial overlap between two different markers. The commonly used indicator is Mander's colocalization coefficient, so set the colocalization coefficient model:

[0116]

[0117] Among them, F 1i and F 2i are the fluorescence intensities of the two channels at pixel i, respectively, and M1 and M2 reflect the degree of colocalization of the first channel relative to the second channel and the second channel relative to the first channel, respectively;

[0118] Pearson correlation coefficient model, Pearson correlation coefficient is a statistical method used to measure the linear correlation between two variables. It can be used to evaluate the spatial correlation between two fluorescent markers in confocal microscopy image analysis. Therefore, the Pearson correlation coefficient model is set:

[0119]

[0120] Among them, x i and i are the fluorescence intensities of the two channels at the same position, and are their respective average values.

[0121] The system is provided with five modules, among which the acquisition setting module is used to set the experiment number and the number of collected data; the data acquisition module is used to acquire the data acquired by the confocal microscope and mark the data with the experiment number, so as to accurately distinguish the experiment to which the data belongs; the collected data is analyzed by the data analysis module to determine whether the data meets the preset standard, and the data that meets the standard and does not meet the standard are marked accordingly, and are all sent to the data cache module for caching, and the data cache module caches the data with the same experiment number accordingly, so as to facilitate the subsequent retrieval of all data of the experiment according to the experiment number; the data analysis module is also used to determine whether the number of data marked as meeting the standard in the data with the experiment number of the current experiment number stored in the data cache module is equal to the preset number, if so, the batch of data meeting the preset standard is transmitted to the data storage module for long-term storage, and the data storage module stores the data with the same experiment number accordingly, so as to facilitate the subsequent retrieval of all data of the experiment according to the experiment number, and at the same time adopts a redundant backup strategy to perform data backup, improve disaster resistance, and ensure the security and stability of data storage; if not, the data acquisition module is triggered to continue to acquire data, so as to ensure that the data that meets the quantity requirements and meets the preset standards can be finally acquired for subsequent analysis.

[0122] This solution divides storage into a data cache module and a data storage module. Each data collected in an experiment will be analyzed to see if it meets the standards and marked. When the number of data that meets the standards reaches the standard, it will be placed in the data storage module for storage. This eliminates the need to store data that does not meet the preset standards, reduces the use of storage resources, and ensures the quality of stored data. Subsequent users do not need to exclude data that does not meet the standards, saving users time in selecting data. The data cache module will regularly clean up data that does not meet the preset standards, thereby reducing memory usage.

[0123] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A data management system for a confocal microscope, characterized in that: include: Collection setting module, data acquisition module, data analysis module, data cache module and data storage module; The collection setting module is used to set the experiment number and the amount of collected data; A data acquisition module is used to acquire data collected by a confocal microscope and mark the data with an experiment number; The data analysis module is used to analyze the collected data and determine whether the data meets the preset standards. If so, a mark that meets the standards is set for the data and the data is sent to the data cache module; if not, a mark that does not meet the standards is set for the data and the data is sent to the data cache module; It is also used to determine whether the number of data marked as meeting the standard in the data with the current experiment number stored in the data cache module is equal to the preset number. If so, the data marked as meeting the standard in the data with the current experiment number is sent to the data storage module for storage; if not, the data acquisition module is triggered to continue acquiring data; The data cache module is used to cache the data with the same experiment number; The data storage module is used to store the data collected with the same experiment number and to back up the data using a redundant backup strategy.

2. The data management system for confocal microscopy according to claim 1, characterized in that: The acquisition setting module is used to set the experimental target; The data analysis module generates corresponding image quality requirements as preset standards according to the experimental objectives set in the acquisition setting module.

3. The data management system for confocal microscopy according to claim 2, characterized in that: The data storage module pre-stores a relationship table corresponding to experimental target features and image quality requirements; The data analysis module is used to extract the experimental target features according to the experimental target set in the acquisition setting module, query the relationship table, and obtain the corresponding image quality requirements as the preset standard.

4. The data management system for confocal microscopy according to claim 3, characterized in that: The acquisition setting module is also used to input an experiment start signal, an experiment end signal and an experiment pause signal; The data acquisition module is used to acquire data collected by the confocal microscope according to the experiment start signal, and mark the data with the experiment number; The data acquisition module is also used to end the acquisition of the data of this experiment according to the experiment end signal, and trigger the data analysis module to make a judgment on whether the data collection meets the standards; The data acquisition module is also used to pause the acquisition of data for this experiment according to an experiment pause signal.

5. The data management system for confocal microscopy according to claim 4, characterized in that: The data analysis module is also used to determine whether data collection meets the standards, and to determine whether there is data of the current experiment number in the data storage module. If not, the difference between the number of data marked as meeting the standards in the data of the current experiment number in the data cache module and the preset number is calculated; According to the difference and the experimental goal, the evaluation model is called up to evaluate the data marked as not meeting the standards in the data with the current experiment number in the data cache module as the data to be selected, and an evaluation value is generated. The data to be selected is sorted according to the evaluation value, and according to the difference, the difference data before the sorting result is obtained as supplementary data, and the data marked as meeting the standards in the data with the experiment number of the current experiment number is merged and sent to the data storage module for storage.

6. The data management system for confocal microscopy according to claim 5, characterized in that: The data storage module is also used to store evaluation models; wherein the evaluation models include: signal-to-noise ratio evaluation model, contrast evaluation model, clarity model, resolution evaluation model, colocalization coefficient evaluation model, and Pearson correlation coefficient evaluation model.

7. The data management system for confocal microscopy according to claim 6, characterized in that: Furthermore, the signal-to-noise ratio model is used to evaluate the clarity and reliability of the image: Among them, μ s is the average signal intensity in the region of interest, σ n is the standard deviation of the background area; The contrast model, which evaluates the brightness difference between the brightest and darkest parts of an image: Among them, I max and I min are the maximum and minimum brightness values ​​in the image respectively; The clarity model is used to evaluate the clarity of an image. It uses Fourier transform to convert the image from the spatial domain to the frequency domain, and then analyzes the proportion of high-frequency components in the spectrum: Where F(f) is the amplitude of the image at frequency f, th It is the threshold value set to distinguish low-frequency and high-frequency components; The resolution model is used to evaluate the ability of data to distinguish subtle features in samples: d is the minimum distance between two point sources that can be distinguished, which is used to characterize the resolution; The colocalization coefficient model is used to evaluate the degree of spatial overlap between two different markers: Among them, F 1i and F 2i are the fluorescence intensities of the two channels at pixel i, respectively, and M1 and M2 reflect the degree of colocalization of the first channel relative to the second channel and the second channel relative to the first channel, respectively; The Pearson correlation coefficient model is used to evaluate the linear correlation between two variables. It can be used to evaluate the spatial correlation between two fluorescent markers in confocal microscopy image analysis. Therefore, the Pearson correlation coefficient model is set as follows: Among them, x i and i are the fluorescence intensities of the two channels at the same position, and are their respective average values.

8. The data management system for confocal microscopy according to claim 7, characterized in that: The step of calling and setting the evaluation model according to the difference and the experimental target includes: Through the acquisition setting module, input the evaluation model selection signal and its corresponding weight, obtain the corresponding evaluation model in the data storage module according to the evaluation model selection signal, and combine it with the corresponding weight to form a comprehensive evaluation model; Alternatively, an evaluation model selection signal is input through the acquisition setting module, and a corresponding evaluation model is obtained in the data storage module according to the evaluation model selection signal, and a corresponding weight is generated, and the weight is optimized according to the data marked as meeting the standard in the data of the current experiment number, and the optimal weight is obtained and combined with the obtained evaluation model to form a comprehensive evaluation model; Alternatively, according to the set experimental objectives, an evaluation model whose matching degree with the experimental objectives is higher than a preset matching degree threshold is obtained in the data storage module, and corresponding weights are generated. The weights are optimized according to the data marked as meeting the standards in the data of the current experimental number, and the optimal weights are obtained and combined with the obtained evaluation model to form a comprehensive evaluation model.

9. The data management system for confocal microscopy according to claim 8, characterized in that: The optimization weight adopts an optimization algorithm, including: S1. Through the acquisition setting module, the data marked as meeting the standard in the data of the current experiment number are sorted to generate a pre-sorting result. The data that meets the experimental purpose of the current experiment is ranked higher; S2. According to the obtained evaluation model, a number of initialization weight schemes are randomly generated as individual schemes to form a population; S3, initialization iteration number n=1; S4. For the data marked as meeting the standard in the data of the current experiment number, use each individual scheme in the population, combine it with the obtained evaluation model, calculate its evaluation value, and sort it from large to small according to the evaluation value to generate a sorting result; S5, comparing the similarity between the sorting results and the pre-sorting results one by one; S6, determining whether there is an individual solution with a similarity greater than a preset optimal similarity value, if so, executing S10; if not, executing S7; S7, sorting the individual solutions in the population from large to small according to the similarity, and generating a population sorting result; S8. According to the similarity, select, cross and mutate the individual solutions in the population, and eliminate the individual solutions that cannot be realized to form a new population; S9, determine whether the current number of iterations n is equal to the preset number of iterations, if yes, execute S10; if not, n=n+1, and execute S4; S10. In the population sorting result, the first k individual solutions are obtained as the optimal weighted solution, and output and displayed through the acquisition setting module.

10. The data management system for confocal microscopy according to claim 9, characterized in that: The acquisition setting module is also used to obtain an optimal weight scheme selection signal; The data analysis module is also used to select signals according to the optimal weight scheme, obtain the corresponding weight scheme as the optimal weight, and combine it with the obtained evaluation model to form a comprehensive evaluation model.

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