Intelligent laboratory management method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of equipment management, in particular to an intelligent laboratory management method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the data preprocessing of historical experiment data, generating an experiment feature feedback table, and carrying out the analysis of current experiment data, determining instrument setting parameters based on the instrument application parameters, respectively inputting the experimental environment parameters, the experimental material attributes and the instrument setting parameters into an experimental feature feedback table for multi-dimensional matching to obtain an experimental data set, and performing time sequence analysis on the experimental data set to obtain a time sequence analysis result; the method comprises the steps of obtaining an instrument setting relation, verifying and analyzing instrument setting parameters according to the instrument setting relation to obtain effective setting parameters, dynamically correcting the instrument setting parameters according to the effective setting parameters, and outputting the corrected instrument setting parameters. According to the invention, the accuracy and reliability of the experiment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of equipment management, and in particular to an intelligent laboratory management method, system, electronic equipment and storage medium. Background Art

[0002] In the field of scientific experiments, the precise setting and effective operation of experimental instruments play a vital role in the accuracy and reliability of experimental results.

[0003] Currently, the frequent use of experimental instruments can lead to certain accuracy issues. This is because during frequent use, the various components of the experimental instruments will inevitably wear out and age. For example, friction in mechanical parts can cause their movement accuracy to decrease, long-term operation of electronic components can cause performance drift, and the surface of optical components may be affected by dust, scratches, etc., which affect the optical performance. These physical changes can directly or indirectly affect the measurement and operation results of the experimental instrument, causing deviations between the measured data and the true value, thereby reducing the accuracy and reliability of the experiment.

[0004] In existing experimental environments, the maintenance and calibration of experimental instrument accuracy mainly rely on regular manual inspection and calibration. However, this traditional method has obvious limitations. On the one hand, the intervals for regular calibration are often set based on experience and cannot reflect in real time the changes in the accuracy of the instrument during actual use. Between two calibrations, the accuracy of the instrument may drop significantly due to overuse or unexpected factors, which is difficult for the operator to detect in time. On the other hand, the manual calibration process is easily affected by the operator's technical level, environmental factors, etc. Different operators may obtain different calibration results, making it difficult to ensure the consistency and accuracy of the calibration. Summary of the Invention

[0005] In order to solve at least one of the above technical problems, the present application provides an intelligent laboratory management method, system, electronic device and storage medium.

[0006] In the first aspect, the present application provides an intelligent laboratory management method, which adopts the following technical solutions: Collect historical experimental data of the experimental instrument within the historical experimental cycle and current experimental data of the current experimental stage, and perform data preprocessing on the historical experimental data to generate an experimental characteristic feedback table; Analyze the current experimental data to obtain experimental environment parameters, experimental material properties and instrument application parameters; determining instrument setting parameters of the experimental instrument in different experimental stages based on the instrument application parameters; Inputting the experimental environment parameters, experimental material properties and instrument setting parameters into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set; Performing time series analysis on the experimental data set to obtain instrument setting relationships of the experimental instrument at different experimental stages; Verify and analyze the instrument setting parameters according to the instrument setting relationship to obtain effective setting parameters of the experimental instrument in different experimental stages; The instrument setting parameters are dynamically corrected according to the effective setting parameters, and the corrected instrument setting parameters are output.

[0007] By employing the above technical solution, historical experimental data from the experimental instrument over the past experimental cycles and current experimental data from the current experimental phase are collected and preprocessed. Data preprocessing can remove noise, fill missing values, and standardize data formats, making the historical data more standardized and accurate. Based on the processed historical data, an experimental feature feedback table is generated. This table integrates key feature information from the historical experiments, providing a basic data framework for subsequent data matching and analysis. This ensures that subsequent steps are based on accurate and comprehensive historical data, thereby improving the reliability and accuracy of the entire technical solution. The current experimental data is analyzed to obtain experimental environment parameters, experimental material properties, and instrument application parameters. Experimental environment parameters such as temperature and humidity affect the operation of the experimental instrument and the accuracy of experimental results; experimental material properties such as material composition and purity directly affect the reaction process and results; and instrument application parameters such as frequency of use and operating mode reflect the specific application of the instrument in the current experiment. Accurately analyzing these parameters provides a comprehensive understanding of the current experimental conditions, providing a key basis for determining instrument setting parameters, helping to ensure that the instrument operates properly and obtains accurate experimental results under the current experimental environment. Instrument settings for experimental instruments during different experimental phases are determined based on instrument application parameters. Instrument application parameters reflect the specific usage requirements and characteristics of the instrument at different experimental phases. Based on these application parameters and the instrument's performance characteristics, appropriate instrument settings can be accurately determined for each experimental phase, ensuring optimal instrument operation at each stage and improving experimental efficiency and the accuracy of experimental results. Experimental environment parameters, experimental material properties, and instrument setting parameters are input into the experimental feature feedback table for multi-dimensional matching, generating an experimental data set. The experimental feature feedback table contains rich historical experimental data feature information. Through multi-dimensional matching, various parameters of the current experiment can be correlated and compared with historical data. Leveraging the experience gained from historical data provides a more comprehensive reference for the current experiment, helping to identify potential issues, optimize experimental plans, and increase experimental success rates. Time series analysis of the experimental data set reveals the relationship between instrument settings at different experimental phases. Time series analysis considers the temporal order and parameter change trends of each phase during the experiment. This reveals the underlying patterns of instrument parameter changes during the experiment, providing important evidence for subsequent parameter verification analysis and dynamic correction, helping to optimize experimental processes and improve experimental reproducibility and stability. Verify and analyze the instrument settings based on the instrument setting relationship to obtain the effective setting parameters of the experimental instrument at different experimental stages. The instrument setting relationship reflects the inherent connection between instrument parameters and experimental results. Through verification analysis, it can be determined whether the currently determined instrument setting parameters conform to this relationship.This verification analysis identifies valid parameter settings, ensuring the instrument operates with the optimal parameter combination at each experimental stage, improving the accuracy and reliability of experimental results. Based on the valid parameter settings, the instrument settings are dynamically corrected and the corrected parameters are output. During the experiment, the initial instrument parameters may not be optimal due to various factors. Dynamic correction allows for timely adjustment of instrument parameters based on the real-time experimental conditions and the valid parameter settings, ensuring the accuracy and stability of experimental results.

[0008] In a possible implementation, the performing data preprocessing on the historical experimental data to generate an experimental feature feedback table includes: Marking abnormal operation nodes in the historical experimental data to generate experimental abnormality marking points; Based on the abnormal marking points, the experimental data segment is intercepted to obtain a valid experimental data set; Parameter classification is performed on each set of valid experimental data in all valid experimental data sets, and experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data and actual instrument data are extracted from the valid experimental data; An initial experimental feature table is constructed, and the experimental time series data, the environmental monitoring data, the preset instrument parameters, the experimental material data and the actual instrument data are input into the initial experimental feature table in groups to obtain an experimental feature feedback table.

[0009] In one possible implementation, the experimental environment parameters, experimental material properties, and instrument setting parameters are respectively input into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set, including: Using the experimental material attributes as the first screening dimension, matching and screening the experimental material data in the experimental feature feedback table to obtain a primary data group; Using the instrument setting parameters as a second screening dimension, performing a secondary matching screening on the preset instrument parameters in the primary data group to obtain a secondary data group; Using the experimental environment parameter as the third screening dimension, the environmental monitoring data in the secondary data group are subjected to three matching screenings to obtain a tertiary data group; Performing experimental time sequence overlap filtering on the three-level data group to obtain time sequence conflict data; It is determined whether the timing conflict data has any inconsistency. If the timing conflict data does not have any inconsistency, the first timing conflict data is retained and redundant data is filtered to obtain an experimental data set.

[0010] In one possible implementation, performing a time series analysis on the experimental data set to obtain an instrument setting relationship of the experimental instrument at different experimental stages includes: Sorting the experimental data set according to time series to obtain an experimental data sequence; Extracting the preset instrument parameters and the actual instrument data from the experimental data sequence, and comparing the preset instrument parameters with the actual instrument data according to a time sequence correspondence to obtain a data comparison result; Determine whether there is a consistency problem in the data comparison result. If so, input the preset instrument parameters with the consistency problem and the actual instrument data into the preset relationship algorithm for calculation, and bind the obtained relationship constant term and relationship coefficient to the experimental data sequence respectively to obtain the instrument setting relationship of the experimental instrument in different experimental stages.

[0011] In one possible implementation, the verifying and analyzing the instrument setting parameters according to the instrument setting relationship to obtain the effective setting parameters of the experimental instrument in different experimental stages includes: Determining, based on the instrument setting relationship, a change in the error relationship of the experimental instrument when performing a single experiment on different experimental materials; Deducing the error relationship change periodically according to the time sequence to obtain the current relationship change corresponding to the current experimental period; determining a change in a current relationship constant term and a change in a current relationship coefficient according to the current relationship change; The instrument setting parameters are input into the preset relationship algorithm, and the current relationship constant term change and the current relationship coefficient change are input into the preset relationship algorithm for calculation according to the time node to obtain the effective setting parameters of the experimental instrument in different experimental stages.

[0012] In a possible implementation, determining whether the timing conflict data is inconsistent includes: If there is inconsistency in the timing conflict data, determine the data location of the experimental data set in the experimental feature feedback table where the timing conflict data is located and the data set time sequence of the experimental data set, and determine whether the experimental data set corresponding to the timing conflict data is a data set generated when the experimental instrument is first used according to the data set time sequence. If so, use the timing conflict data as optimized data, and filter out the timing conflict data other than the optimized data to obtain the experimental data set; If the experimental data set corresponding to the timing conflict data is not the data group generated when the experimental instrument is first used, then check whether the data positioning corresponding to the timing conflict data is consistent. If the data positioning corresponding to the timing conflict data is inconsistent, then the timing conflict data with a high sequence priority is used as the optimized data according to the data positioning, and the timing conflict data other than the optimized data is filtered out from the three-level data group to obtain the experimental data set.

[0013] In a possible implementation, the verifying whether the data locations corresponding to the timing conflicting data are consistent further includes: If the data locations corresponding to the timing conflict data are consistent, the data volumes of the experimental data sets corresponding to each timing conflict data are compared, and the target experimental data set with the highest data volume is determined. The target timing conflict data corresponding to the target experimental data set is determined based on the corresponding relationship between the timing conflict data and the experimental data set. The target timing conflict data is used as optimization data, and timing conflict data other than the optimization data is filtered out from the three-level data group to obtain an experimental data set.

[0014] In a second aspect, the present application provides an intelligent laboratory management system, which adopts the following technical solutions: An intelligent laboratory management system, comprising: The data acquisition module is used to collect historical experimental data of the experimental instrument in the historical experimental cycle and current experimental data of the current experimental stage, and perform data preprocessing on the historical experimental data to generate an experimental feature feedback table; A data analysis module is used to analyze the current experimental data to obtain experimental environment parameters, experimental material properties and instrument application parameters; A parameter determination module, configured to determine instrument setting parameters of the experimental instrument in different experimental stages based on the instrument application parameters; A multidimensional matching module is used to input the experimental environment parameters, experimental material properties and instrument setting parameters into the experimental feature feedback table for multidimensional matching to obtain an experimental data set; A time series analysis module, configured to perform time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages; A calibration and analysis module, configured to perform calibration and analysis on the instrument setting parameters according to the instrument setting relationship, and obtain effective setting parameters of the experimental instrument in different experimental stages; The dynamic correction module is used to dynamically correct the instrument setting parameters according to the effective setting parameters and output the corrected instrument setting parameters.

[0015] In a possible implementation, when the data acquisition module performs data preprocessing on the historical experimental data to generate the experimental feature feedback table, it is specifically configured to: Marking abnormal operation nodes in the historical experimental data to generate experimental abnormality marking points; Based on the abnormal marking points, the experimental data segment is intercepted to obtain a valid experimental data set; Parameter classification is performed on each set of valid experimental data in all valid experimental data sets, and experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data and actual instrument data are extracted from the valid experimental data; An initial experimental feature table is constructed, and the experimental time series data, the environmental monitoring data, the preset instrument parameters, the experimental material data and the actual instrument data are input into the initial experimental feature table in groups to obtain an experimental feature feedback table.

[0016] In another possible implementation, when the multidimensional matching module inputs the experimental environment parameters, experimental material properties, and instrument setting parameters into the experimental feature feedback table for multidimensional matching to obtain the experimental data set, it is specifically used to: Using the experimental material attributes as the first screening dimension, matching and screening the experimental material data in the experimental feature feedback table to obtain a primary data group; Using the instrument setting parameters as a second screening dimension, performing a secondary matching screening on the preset instrument parameters in the primary data group to obtain a secondary data group; Using the experimental environment parameter as the third screening dimension, the environmental monitoring data in the secondary data group are subjected to three matching screenings to obtain a tertiary data group; Performing experimental time sequence overlap filtering on the three-level data group to obtain time sequence conflict data; It is determined whether the timing conflict data has any inconsistency. If the timing conflict data does not have any inconsistency, the first timing conflict data is retained and redundant data is filtered to obtain an experimental data set.

[0017] In another possible implementation, when the time series analysis module performs time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages, it is specifically configured to: Sorting the experimental data set according to time series to obtain an experimental data sequence; Extracting the preset instrument parameters and the actual instrument data from the experimental data sequence, and comparing the preset instrument parameters with the actual instrument data according to a time sequence correspondence to obtain a data comparison result; Determine whether there is a consistency problem in the data comparison result. If so, input the preset instrument parameters with the consistency problem and the actual instrument data into the preset relationship algorithm for calculation, and bind the obtained relationship constant term and relationship coefficient to the experimental data sequence respectively to obtain the instrument setting relationship of the experimental instrument in different experimental stages.

[0018] In another possible implementation, when the verification and analysis module performs verification and analysis on the instrument setting parameters according to the instrument setting relationship to obtain the effective setting parameters of the experimental instrument in different experimental stages, it is specifically configured to: Determining, based on the instrument setting relationship, a change in the error relationship of the experimental instrument when performing a single experiment on different experimental materials; Deducing the error relationship change periodically according to the time sequence to obtain the current relationship change corresponding to the current experimental period; determining a change in a current relationship constant term and a change in a current relationship coefficient according to the current relationship change; The instrument setting parameters are input into the preset relationship algorithm, and the current relationship constant term change and the current relationship coefficient change are input into the preset relationship algorithm for calculation according to the time node to obtain the effective setting parameters of the experimental instrument in different experimental stages.

[0019] In another possible implementation, when determining whether the timing conflict data is inconsistent, the timing analysis module is specifically configured to: If there is inconsistency in the timing conflict data, determine the data location of the experimental data set in the experimental feature feedback table where the timing conflict data is located and the data set time sequence of the experimental data set, and determine whether the experimental data set corresponding to the timing conflict data is a data set generated when the experimental instrument is first used according to the data set time sequence. If so, use the timing conflict data as optimized data, and filter out the timing conflict data other than the optimized data to obtain the experimental data set; If the experimental data set corresponding to the timing conflict data is not the data group generated when the experimental instrument is first used, then check whether the data positioning corresponding to the timing conflict data is consistent. If the data positioning corresponding to the timing conflict data is inconsistent, then the timing conflict data with a high sequence priority is used as the optimized data according to the data positioning, and the timing conflict data other than the optimized data is filtered out from the three-level data group to obtain the experimental data set.

[0020] In another possible implementation, the system further includes: a data determination module and a data filtering module, wherein: The data determination module is configured to compare the data volume of the experimental data sets corresponding to each timing conflict data when the data locations corresponding to the timing conflict data are consistent, and determine the target experimental data set with the highest data volume, and determine the target timing conflict data corresponding to the target experimental data set based on the corresponding relationship between the timing conflict data and the experimental data sets; The data filtering module is configured to use the target timing conflict data as optimization data, and filter out timing conflict data other than the optimization data from the three-level data group to obtain an experimental data set.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an intelligent laboratory management method as described in any one of the first aspects.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program thereon, which, when executed in a computer, causes the computer to execute an intelligent laboratory management method as described in any one of the first aspects.

[0023] In summary, this application includes at least one of the following beneficial technical effects: By employing the above technical solution, historical experimental data from the experimental instrument over the past experimental cycles and current experimental data from the current experimental phase are collected and preprocessed. Data preprocessing can remove noise, fill missing values, and standardize data formats, making the historical data more standardized and accurate. Based on the processed historical data, an experimental feature feedback table is generated. This table integrates key feature information from the historical experiments, providing a basic data framework for subsequent data matching and analysis. This ensures that subsequent steps are based on accurate and comprehensive historical data, thereby improving the reliability and accuracy of the entire technical solution. The current experimental data is analyzed to obtain experimental environment parameters, experimental material properties, and instrument application parameters. Experimental environment parameters such as temperature and humidity affect the operation of the experimental instrument and the accuracy of experimental results; experimental material properties such as material composition and purity directly affect the reaction process and results; and instrument application parameters such as frequency of use and operating mode reflect the specific application of the instrument in the current experiment. Accurately analyzing these parameters provides a comprehensive understanding of the current experimental conditions, providing a key basis for determining instrument setting parameters, helping to ensure that the instrument operates properly and obtains accurate experimental results under the current experimental environment. Instrument settings for experimental instruments during different experimental phases are determined based on instrument application parameters. Instrument application parameters reflect the specific usage requirements and characteristics of the instrument at different experimental phases. Based on these application parameters and the instrument's performance characteristics, appropriate instrument settings can be accurately determined for each experimental phase, ensuring optimal instrument operation at each stage and improving experimental efficiency and the accuracy of experimental results. Experimental environment parameters, experimental material properties, and instrument setting parameters are input into the experimental feature feedback table for multi-dimensional matching, generating an experimental data set. The experimental feature feedback table contains rich historical experimental data feature information. Through multi-dimensional matching, various parameters of the current experiment can be correlated and compared with historical data. Leveraging the experience gained from historical data provides a more comprehensive reference for the current experiment, helping to identify potential issues, optimize experimental plans, and increase experimental success rates. Time series analysis of the experimental data set reveals the relationship between instrument settings at different experimental phases. Time series analysis considers the temporal order and parameter change trends of each phase during the experiment. This reveals the underlying patterns of instrument parameter changes during the experiment, providing important evidence for subsequent parameter verification analysis and dynamic correction, helping to optimize experimental processes and improve experimental reproducibility and stability. Verify and analyze the instrument settings based on the instrument setting relationship to obtain the effective setting parameters of the experimental instrument at different experimental stages. The instrument setting relationship reflects the inherent connection between instrument parameters and experimental results. Through verification analysis, it can be determined whether the currently determined instrument setting parameters conform to this relationship.This verification analysis identifies valid parameter settings, ensuring the instrument operates with the optimal parameter combination at each experimental stage, improving the accuracy and reliability of experimental results. Based on the valid parameter settings, the instrument settings are dynamically corrected and the corrected parameters are output. During the experiment, the initial instrument parameters may not be optimal due to various factors. Dynamic correction allows for timely adjustment of instrument parameters based on the real-time experimental conditions and the valid parameter settings, ensuring the accuracy and stability of experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of an intelligent laboratory management method provided in an embodiment of the present application.

[0025] Figure 2 A schematic diagram of the structure of an intelligent laboratory management system provided in an embodiment of the present application.

[0026] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following is combined with Figure 1-3 This application is described in further detail.

[0028] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the present application, they are protected by patent law.

[0029] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0031] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0032] The embodiment of the present application provides a method for intelligent laboratory management, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: collecting historical experimental data of the experimental instrument in the historical experimental cycle and current experimental data of the current experimental stage, and performing data preprocessing on the historical experimental data to generate an experimental characteristic feedback table.

[0033] Specifically, experimental instruments refer to various equipment used for scientific experiments, research, etc., such as pressure gauges, spectrometers, etc., which can generate various types of data related to the experiment. Historical experimental data refers to various data records generated by experimental instruments during the historical experimental cycle. These data may include temperature values, pressure changes, material composition ratios, etc.; current experimental data refers to data generated by experimental instruments in real time or periodically during the current experimental stage, such as the hardness test data of the material at the current moment; data preprocessing refers to a series of operations on the collected raw data to make it more suitable for subsequent analysis and use, such as removing noise, filling missing values, data normalization, etc.; experimental feature feedback table refers to a table containing key feature information of experimental data generated after data preprocessing, which is used to feedback the characteristics of experimental data for subsequent analysis and decision-making.

[0034] Specifically, abnormal operation nodes in historical experimental data are marked to generate experimental abnormality markers. Experimental data segments are then intercepted based on these markers to obtain a valid experimental data set. Parameters are classified for each valid experimental data set, and experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data, and actual instrument data are extracted from the valid experimental data. An initial experimental feature table is constructed, and the experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data, and actual instrument data are entered into the initial experimental feature table in groups to obtain an experimental feature feedback table.

[0035] In this embodiment of the present application, the intercepted valid experimental data set is a data set consisting of abnormal data segments between adjacent experimental abnormality marking points that are not caused by human operation abnormalities, rather than a data set consisting of abnormal data segments between adjacent experimental abnormality marking points that are caused by human operation abnormalities. This step is intended to filter out experimental equipment abnormalities caused by abnormal human operation, rather than abnormalities caused by abnormalities in the experimental instrument itself.

[0036] Step S11: Analyze the current experimental data to obtain experimental environment parameters, experimental material properties and instrument application parameters.

[0037] In the embodiments of the present application, experimental environment parameters refer to the quantitative representation of various environmental factors that affect the experimental results during the experiment, such as the temperature, humidity, air pressure, etc. of the experimental site. These parameters reflect the external conditions of the experiment; experimental material properties refer to the characteristic indicators of various materials used in the experiment, such as the density, hardness, conductivity, chemical composition ratio, etc. of the material, which determine the performance and reaction of the material in the experiment; instrument application parameters refer to the various parameters set and used by the experimental instrument in the current experimental stage, such as the measurement range, sampling frequency, working mode, etc. of the instrument. These parameters affect the way the instrument collects and records experimental data.

[0038] For the embodiments of the present application, a data analysis platform is built. The platform integrates a variety of data analysis tools and interfaces, which can automatically identify experimental data in different formats. Then, using machine learning algorithms, the experimental data are feature extracted and classified, and the training model is used to accurately identify experimental environment parameters, experimental material properties, and instrument application parameters. For example, a decision tree algorithm is used to determine the parameter type based on the data characteristics and extract the corresponding value.

[0039] Step S12: determining the instrument setting parameters of the experimental instrument in different experimental stages based on the instrument application parameters.

[0040] Specifically, the instrument setting parameters of an experimental instrument refer to the specific parameter values ​​that need to be set for the experimental instrument in order to meet the needs of different experimental stages. These parameter values ​​will determine the operation mode and performance of the instrument in the experiment. For example, different measurement ranges and sampling frequencies may need to be set in different experimental stages.

[0041] After obtaining the instrument application parameters, first divide the experiment into phases, clarifying the specific tasks and objectives for each phase. For example, a material properties testing experiment might be divided into the initial state measurement phase, the stress loading phase, and the steady-state monitoring phase. Next, analyze the instrument performance and functionality requirements for each phase. For example, the initial state measurement phase requires a high-precision measurement range and a low sampling frequency, while the stress loading phase requires a higher sampling frequency to capture material changes. Then, based on the instrument application parameters, determine the instrument settings according to the requirements of each experimental phase. For example, if the instrument application parameters indicate a maximum measurement range of 1000N, but a particular experimental phase requires measuring forces within 500N, the instrument's measurement range for that phase can be set to 500N-600N (allowing for some margin). Furthermore, adjust the sampling frequency based on the real-time data acquisition requirements for that phase. Finally, input the determined instrument settings into the experimental instrument to ensure the experiment runs according to the specified parameters.

[0042] Step S13: Input the experimental environment parameters, experimental material properties and instrument setting parameters into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set.

[0043] Specifically, the experimental material properties are used as the first screening dimension, and the experimental material data in the experimental feature feedback table are matched and screened to obtain a first-level data group. The instrument setting parameters are used as the second screening dimension, and the preset instrument parameters in the first-level data group are matched and screened twice to obtain a second-level data group. The experimental environment parameters are used as the third screening dimension, and the environmental monitoring data in the second-level data group are matched and screened three times to obtain third-level data. The third-level data group is subjected to experimental timing overlap filtering to obtain timing conflict data. It is determined whether there is any inconsistency in the timing conflict data. If there is no inconsistency in the timing conflict data, the first timing conflict data is retained and the redundant data is filtered to obtain the experimental data set.

[0044] In addition to the aforementioned cases where there is no inconsistency in the timing conflict data, there are several other cases and the data processing methods for each case: When there is inconsistency in the timing conflict data, determine the data location of the experimental data set in the experimental feature feedback table and the data set timing of the experimental data set, and determine whether the experimental data set corresponding to the timing conflict data is the data set generated when the experimental instrument is first used based on the data set timing. If so, use the timing conflict data as optimized data, and filter out the timing conflict data other than the optimized data to obtain the experimental data set.

[0045] When the experimental data set corresponding to the timing conflict data is not the data set generated when the experimental instrument is first used, check whether the data positioning corresponding to the timing conflict data is consistent. If the data positioning corresponding to the timing conflict data is inconsistent, the timing conflict data with a high sequence priority is used as the optimized data according to the data positioning, and the timing conflict data other than the optimized data is filtered out from the three-level data group to obtain the experimental data set.

[0046] When the data locations corresponding to the timing conflict data are consistent, the data volume of the experimental data sets corresponding to each timing conflict data is compared, and the target experimental data set with the highest data volume is determined. Based on the correspondence between the timing conflict data and the experimental data set, the target timing conflict data corresponding to the target experimental data set is determined. The target timing conflict data is used as the optimized data, and the timing conflict data outside the optimized data is filtered out from the three-level data set to obtain the experimental data set.

[0047] In an embodiment of the present application, data positioning refers to the unit time positioning of the data generated by the experimental instrument to complete a complete experiment within a historical time period. For example, there are experimental data set A and experimental data set B, where the time used for experimental data set A is from 3:22 pm on April 16, 2023 to 5:43 pm on April 16, 2023, and the time used for experimental data set B is from 11:12 am on April 16, 2023 to 2:12 pm on April 16, 2023. Since both experimental data set A and experimental data set B were generated on the same day, April 16, 2023, the two data are combined into time-series conflict data. On the premise that the data corresponding to experimental data set A and experimental data set B are inconsistent and both data sets are not generated by the first application of the experimental equipment, the data positioning of experimental data set B is 11:12 am on April 16, 2023, and the data positioning of experimental data set A is 3:22 pm on April 16, 2023. Compared with the two, the data positioning of experimental data set B is higher than that of experimental data set A. Therefore, experimental data set B is selected as the optimized data.

[0048] Step S14: Perform time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages.

[0049] Specifically, the experimental data set is sorted according to the time series to obtain the experimental data sequence, the preset instrument parameters and the actual instrument data in the experimental data sequence are extracted, and the preset instrument parameters are compared with the actual instrument data according to the time series correspondence to obtain the data comparison result, and it is judged whether there is a consistency problem in the data comparison result. If so, the preset instrument parameters and the actual instrument data with the consistency problem are input into the preset relationship algorithm for calculation, and the obtained relationship constant term and relationship coefficient are respectively bound to the experimental data sequence to obtain the instrument setting relationship of the experimental instrument in different experimental stages.

[0050] In the embodiment of the present application, the preset relationship algorithm is: y=ax+b, where a is the relationship coefficient, b is the relationship constant term, y is the preset instrument parameter, and x is the actual instrument data.

[0051] Step S15: Verify and analyze the instrument setting parameters according to the instrument setting relationship to obtain effective setting parameters of the experimental instrument in different experimental stages.

[0052] Specifically, the error relationship changes of the experimental instrument when conducting a single experiment on different experimental materials are determined based on the instrument setting relationship. The error relationship changes are periodically deduced in chronological order to obtain the current relationship changes corresponding to the current experimental period. The current relationship constant term changes and the current relationship coefficient changes are determined based on the current relationship changes. The instrument setting parameters are input into the preset relationship algorithm, and the current relationship constant term changes and the current relationship coefficient changes are input into the preset relationship algorithm according to the time node for calculation to obtain the effective setting parameters of the experimental instrument in different experimental stages.

[0053] In the embodiment of the present application, a bidirectional LSTM model is used to periodically deduce and predict the error relationship changes to obtain the current relationship changes corresponding to the current experimental time period. Of course, in addition to the bidirectional LSTM model, other periodic deduction models can also be used to deduce and predict the error relationship changes. This application only uses this model as an example.

[0054] Step S16: Dynamically correct the instrument setting parameters according to the effective setting parameters, and output the corrected instrument setting parameters.

[0055] The present application embodiment provides an intelligent laboratory management method that collects historical experimental data of experimental instruments within the historical experimental cycle and current experimental data of the current experimental stage, and performs data preprocessing on the historical experimental data. Data preprocessing can remove noise, fill missing values, unify data formats, etc., making historical data more standardized and accurate. Based on the processed historical data, an experimental feature feedback table is generated. This table integrates various key feature information in the historical experiments, provides a basic data framework for subsequent data matching and analysis, ensures that subsequent steps can be operated based on accurate and comprehensive historical data, and improves the reliability and accuracy of the entire technical solution. The current experimental data is parsed to obtain experimental environment parameters, experimental material properties, and instrument application parameters. Experimental environment parameters such as temperature and humidity will affect the operation of the experimental instrument and the accuracy of the experimental results; experimental material properties such as material composition and purity will directly affect the reaction process and results of the experiment; instrument application parameters such as frequency of use and operating mode reflect the specific application of the instrument in the current experiment. Accurately parsing these parameters can fully understand the various conditions of the current experiment, provide a key basis for subsequently determining the instrument setting parameters, and help ensure that the instrument can operate normally under the current experimental environment and obtain accurate experimental results. Instrument settings for experimental instruments during different experimental phases are determined based on instrument application parameters. Instrument application parameters reflect the specific usage requirements and characteristics of the instrument at different experimental phases. Based on these application parameters and the instrument's performance characteristics, appropriate instrument settings can be accurately determined for each experimental phase, ensuring optimal instrument operation at each stage and improving experimental efficiency and the accuracy of experimental results. Experimental environment parameters, experimental material properties, and instrument setting parameters are input into the experimental feature feedback table for multi-dimensional matching, generating an experimental data set. The experimental feature feedback table contains rich historical experimental data feature information. Through multi-dimensional matching, various parameters of the current experiment can be correlated and compared with historical data. Leveraging the experience gained from historical data provides a more comprehensive reference for the current experiment, helping to identify potential issues, optimize experimental plans, and increase experimental success rates. Time series analysis of the experimental data set reveals the relationship between instrument settings at different experimental phases. Time series analysis considers the temporal order and parameter change trends of each phase during the experiment. This reveals the underlying patterns of instrument parameter changes during the experiment, providing important evidence for subsequent parameter verification analysis and dynamic correction, helping to optimize experimental processes and improve experimental reproducibility and stability. Verify and analyze the instrument settings based on the instrument setting relationship to obtain the effective setting parameters of the experimental instrument at different experimental stages. The instrument setting relationship reflects the inherent connection between instrument parameters and experimental results. Through verification analysis, it can be determined whether the currently determined instrument setting parameters conform to this relationship.This verification analysis identifies valid parameter settings, ensuring the instrument operates with the optimal parameter combination at each experimental stage, improving the accuracy and reliability of experimental results. Based on the valid parameter settings, the instrument settings are dynamically corrected and the corrected parameters are output. During the experiment, the initial instrument parameters may not be optimal due to various factors. Dynamic correction allows for timely adjustment of instrument parameters based on the real-time experimental conditions and the valid parameter settings, ensuring the accuracy and stability of experimental results.

[0056] The following is an introduction to an intelligent laboratory management system provided by an embodiment of the present application. The intelligent laboratory management system described below and the intelligent laboratory management method described above can be referenced to each other. Please refer to Figure 2 , Figure 2 : is a schematic diagram of the structure of an intelligent laboratory management system 20 provided in an embodiment of the present application, including: The data acquisition module 21 is used to collect historical experimental data of the experimental instrument in the historical experimental cycle and current experimental data of the current experimental stage, and perform data preprocessing on the historical experimental data to generate an experimental feature feedback table; The data analysis module 22 is used to analyze the current experimental data to obtain experimental environment parameters, experimental material properties and instrument application parameters; A parameter determination module 23 is used to determine the instrument setting parameters of the experimental instrument in different experimental stages based on the instrument application parameters; The multi-dimensional matching module 24 is used to input the experimental environment parameters, experimental material properties and instrument setting parameters into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set; The time series analysis module 25 is used to perform time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages; The calibration and analysis module 26 is used to perform calibration and analysis on the instrument setting parameters according to the instrument setting relationship to obtain the effective setting parameters of the experimental instrument in different experimental stages; The dynamic correction module 27 is used to dynamically correct the instrument setting parameters according to the effective setting parameters and output the corrected instrument setting parameters.

[0057] In one possible implementation of the embodiment of the present application, the data acquisition module 21 performs data preprocessing on historical experimental data to generate an experimental feature feedback table, specifically for: Mark abnormal operation nodes in historical experimental data and generate experimental abnormality marking points; Based on the abnormal marking points, the experimental data segments are intercepted to obtain the effective experimental data set; Parameter classification is performed on each set of valid experimental data in all valid experimental data sets, and experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data and actual instrument data are extracted from the valid experimental data; An initial experimental feature table is constructed, and the experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data and actual instrument data are input into the initial experimental feature table in groups to obtain an experimental feature feedback table.

[0058] In another possible implementation of the embodiment of the present application, the multi-dimensional matching module 24 inputs the experimental environment parameters, experimental material properties, and instrument setting parameters into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set, specifically for: Taking the experimental material properties as the first screening dimension, the experimental material data in the experimental feature feedback table are matched and screened to obtain the first-level data group; Using the instrument setting parameters as the second screening dimension, the preset instrument parameters in the primary data group are screened for secondary matching to obtain the secondary data group; Taking the experimental environment parameters as the third screening dimension, the environmental monitoring data in the secondary data group were matched and screened three times to obtain the tertiary data group; Perform experimental time series overlap filtering on the three-level data group to obtain time series conflict data; It is determined whether there is any inconsistency in the timing conflict data. If there is no inconsistency in the timing conflict data, the first timing conflict data is retained and redundant data is filtered to obtain an experimental data set.

[0059] In another possible implementation of the embodiment of the present application, when the time series analysis module 25 performs time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages, it is specifically configured to: Sort the experimental data set according to the time series to obtain the experimental data sequence; Extract the preset instrument parameters and actual instrument data in the experimental data sequence, and compare the preset instrument parameters with the actual instrument data according to the time sequence correspondence to obtain the data comparison results; Determine whether there is a consistency problem in the data comparison results. If so, input the preset instrument parameters with consistency problems and the actual instrument data into the preset relationship algorithm for calculation, and bind the obtained relationship constant term and relationship coefficient to the experimental data sequence respectively to obtain the instrument setting relationship of the experimental instrument at different experimental stages.

[0060] In another possible implementation of the embodiment of the present application, the verification and analysis module 26 performs verification and analysis on the instrument setting parameters according to the instrument setting relationship to obtain the effective setting parameters of the experimental instrument in different experimental stages, specifically for: Determine the error relationship changes of the experimental instrument when conducting a single experiment on different experimental materials based on the instrument setting relationship; The error relationship change is deduced periodically according to the time sequence to obtain the current relationship change corresponding to the current experimental period; Determining a change in a current relationship constant term and a change in a current relationship coefficient based on a change in the current relationship; The instrument setting parameters are input into the preset relationship algorithm, and the current relationship constant term change and the current relationship coefficient change are input into the preset relationship algorithm according to the time node for calculation to obtain the effective setting parameters of the experimental instrument in different experimental stages.

[0061] In another possible implementation of the embodiment of the present application, the timing analysis module 25 is specifically configured to: If there is inconsistency in the timing conflict data, determine the data location of the experimental data set in the experimental feature feedback table and the data set time sequence of the experimental data set, and determine whether the experimental data set corresponding to the timing conflict data is the data set generated when the experimental instrument is first used according to the data set time sequence. If so, use the timing conflict data as optimized data, and filter out the timing conflict data other than the optimized data to obtain the experimental data set; If the experimental data set corresponding to the timing conflict data is not the data set generated when the experimental instrument is first used, then check whether the data positioning corresponding to the timing conflict data is consistent. If the data positioning corresponding to the timing conflict data is inconsistent, the timing conflict data with a high sequence priority is used as the optimized data according to the data positioning, and the timing conflict data other than the optimized data is filtered out from the three-level data group to obtain the experimental data set.

[0062] In another possible implementation of the embodiment of the present application, the system 20 further includes: a data determination module and a data filtering module, wherein: The data determination module is used to compare the data volume of the experimental data set corresponding to each timing conflict data when the data locations corresponding to the timing conflict data are consistent, and determine the target experimental data set with the highest data volume, and determine the target timing conflict data corresponding to the target experimental data set based on the correspondence between the timing conflict data and the experimental data set; The data filtering module is used to use the target timing conflict data as the optimization data, and filter out the timing conflict data other than the optimization data from the three-level data group to obtain the experimental data set.

[0063] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0064] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein. Processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0065] The bus 302 may include a path for transmitting information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0066] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0067] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0068] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0069] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0070] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned intelligent laboratory management method are implemented.

[0071] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0072] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0073] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent laboratory management method, characterized in that: include: Collect historical experimental data of the experimental instrument within the historical experimental cycle and current experimental data of the current experimental stage, and perform data preprocessing on the historical experimental data to generate an experimental characteristic feedback table; Analyze the current experimental data to obtain experimental environment parameters, experimental material properties and instrument application parameters; determining instrument setting parameters of the experimental instrument in different experimental stages based on the instrument application parameters; Inputting the experimental environment parameters, experimental material properties and instrument setting parameters into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set; Performing time series analysis on the experimental data set to obtain instrument setting relationships of the experimental instrument at different experimental stages; Verify and analyze the instrument setting parameters according to the instrument setting relationship to obtain effective setting parameters of the experimental instrument in different experimental stages; The instrument setting parameters are dynamically corrected according to the effective setting parameters, and the corrected instrument setting parameters are output.

2. The intelligent laboratory management method according to claim 1, characterized in that: The performing data preprocessing on the historical experimental data to generate an experimental feature feedback table includes: Marking abnormal operation nodes in the historical experimental data to generate experimental abnormality marking points; Based on the abnormal marking points, the experimental data segment is intercepted to obtain a valid experimental data set; Parameter classification is performed on each set of valid experimental data in all valid experimental data sets, and experimental time series data, environmental monitoring data, preset instrument parameters, experimental material data and actual instrument data are extracted from the valid experimental data; An initial experimental feature table is constructed, and the experimental time series data, the environmental monitoring data, the preset instrument parameters, the experimental material data and the actual instrument data are input into the initial experimental feature table in groups to obtain an experimental feature feedback table.

3. The intelligent laboratory management method according to claim 1, characterized in that: The experimental environment parameters, experimental material properties and instrument setting parameters are respectively input into the experimental feature feedback table for multi-dimensional matching to obtain an experimental data set, including: Using the experimental material attributes as the first screening dimension, matching and screening the experimental material data in the experimental feature feedback table to obtain a primary data group; Using the instrument setting parameters as a second screening dimension, performing a secondary matching screening on the preset instrument parameters in the primary data group to obtain a secondary data group; Using the experimental environment parameter as the third screening dimension, the environmental monitoring data in the secondary data group are subjected to three matching screenings to obtain a tertiary data group; Performing experimental time sequence overlap filtering on the three-level data group to obtain time sequence conflict data; It is determined whether the timing conflict data has any inconsistency. If the timing conflict data does not have any inconsistency, the first timing conflict data is retained and redundant data is filtered to obtain an experimental data set.

4. The intelligent laboratory management method according to claim 3, characterized in that: The performing of time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages includes: Sorting the experimental data set according to time series to obtain an experimental data sequence; Extracting the preset instrument parameters and the actual instrument data from the experimental data sequence, and comparing the preset instrument parameters with the actual instrument data according to a time sequence correspondence to obtain a data comparison result; Determine whether there is a consistency problem in the data comparison result. If so, input the preset instrument parameters with the consistency problem and the actual instrument data into the preset relationship algorithm for calculation, and bind the obtained relationship constant term and relationship coefficient to the experimental data sequence respectively to obtain the instrument setting relationship of the experimental instrument in different experimental stages.

5. The intelligent laboratory management method according to claim 4, characterized in that: The calibration and analysis of the instrument setting parameters according to the instrument setting relationship to obtain the effective setting parameters of the experimental instrument in different experimental stages includes: Determining, based on the instrument setting relationship, a change in the error relationship of the experimental instrument when performing a single experiment on different experimental materials; Deducing the error relationship change periodically according to the time sequence to obtain the current relationship change corresponding to the current experimental period; determining a change in a current relationship constant term and a change in a current relationship coefficient according to the current relationship change; The instrument setting parameters are input into the preset relationship algorithm, and the current relationship constant term change and the current relationship coefficient change are input into the preset relationship algorithm for calculation according to the time node to obtain the effective setting parameters of the experimental instrument in different experimental stages.

6. The intelligent laboratory management method according to claim 3, characterized in that: The determining whether the timing conflict data is inconsistent includes: If there is inconsistency in the timing conflict data, determine the data location of the experimental data set in the experimental feature feedback table where the timing conflict data is located and the data set time sequence of the experimental data set, and determine whether the experimental data set corresponding to the timing conflict data is a data set generated when the experimental instrument is first used according to the data set time sequence. If so, use the timing conflict data as optimized data, and filter out the timing conflict data other than the optimized data to obtain the experimental data set; If the experimental data set corresponding to the timing conflict data is not the data group generated when the experimental instrument is first used, then check whether the data positioning corresponding to the timing conflict data is consistent. If the data positioning corresponding to the timing conflict data is inconsistent, then the timing conflict data with a high sequence priority is used as the optimized data according to the data positioning, and the timing conflict data other than the optimized data is filtered out from the three-level data group to obtain the experimental data set.

7. The intelligent laboratory management method according to claim 6, characterized in that: The checking whether the data positions corresponding to the timing conflict data are consistent also includes: If the data locations corresponding to the timing conflict data are consistent, the data volumes of the experimental data sets corresponding to each timing conflict data are compared, and the target experimental data set with the highest data volume is determined. The target timing conflict data corresponding to the target experimental data set is determined based on the corresponding relationship between the timing conflict data and the experimental data set. The target timing conflict data is used as optimization data, and timing conflict data other than the optimization data is filtered out from the three-level data group to obtain an experimental data set.

8. An intelligent laboratory management system, characterized in that: include: The data acquisition module is used to collect historical experimental data of the experimental instrument in the historical experimental cycle and current experimental data of the current experimental stage, and perform data preprocessing on the historical experimental data to generate an experimental feature feedback table; A data analysis module is used to analyze the current experimental data to obtain experimental environment parameters, experimental material properties and instrument application parameters; A parameter determination module, configured to determine instrument setting parameters of the experimental instrument in different experimental stages based on the instrument application parameters; A multidimensional matching module is used to input the experimental environment parameters, experimental material properties and instrument setting parameters into the experimental feature feedback table for multidimensional matching to obtain an experimental data set; A time series analysis module, configured to perform time series analysis on the experimental data set to obtain the instrument setting relationship of the experimental instrument at different experimental stages; A calibration and analysis module, configured to perform calibration and analysis on the instrument setting parameters according to the instrument setting relationship, and obtain effective setting parameters of the experimental instrument in different experimental stages; The dynamic correction module is used to dynamically correct the instrument setting parameters according to the effective setting parameters and output the corrected instrument setting parameters.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute an intelligent laboratory management method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that include: The device stores a computer program that can be loaded by a processor and executes an intelligent laboratory management method according to any one of claims 1 to 7.

Images