Laboratory test data collection management system and method

Abstract

The present invention relates to a laboratory test data acquisition and management system and method in the field of data management technology. The specific method includes: real-time monitoring of the surface image of the reaction material in the reactor liner and the gas data in the fume hood; screening historical laboratory test data that matches the mixing ratio data of each compound in the current experiment on a historical laboratory test data storage and management platform; obtaining the first test data reliability and the second test data reliability of the current experimental test data; evaluating the data reliability level of the current experimental test data, and calibrating the data reliability level of the current experimental test data to the historical laboratory test data storage and management platform. The present invention solves the problems in the prior art of large daily laboratory data processing load, uneven test data quality, and low proportion of valid data.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and is concerned with a laboratory testing data acquisition management system and method. Background Art

[0002] In fields such as chemistry, biology, and materials science, the collection and management of laboratory test data is an important part of experimental research. Laboratory test data not only includes key parameters such as reaction conditions, reactant concentrations, temperature, pressure, and time during the experiment, but also includes microscopic information such as the surface morphology, chemical composition, and crystal structure of the reactants. These data are of great significance for analyzing experimental results, verifying experimental hypotheses, and guiding experimental design. Good data management can ensure the accuracy and reliability of data and provide a basis for scientific research and production practice. However, traditional laboratory test data management methods have problems such as backward data collection methods, irregular data storage and management, limited data analysis capabilities, and inefficient use of historical data. Faced with the huge amount of daily laboratory test data, how to improve data management efficiency and increase the proportion of valid data are important challenges that need to be overcome.

[0003] Among the existing disclosed invention technologies, the patent with application publication number CN110781725A discloses a laboratory data acquisition and management system, including: a laboratory data acquisition system, a laboratory data management system and a cloud server, wherein: the laboratory data acquisition system includes an identification module, a robot and a robot acquisition module, the identification module identifies the laboratory personnel, laboratory instruments or equipment and laboratory materials; the robot acquisition module controls the movement of the robot, collects the data of the laboratory personnel identified by the identification module during the experiment or the data of the laboratory materials, obtains the collected data, and uploads it to the cloud server for storage in real time; the laboratory data management system is used to manage the collected data.

[0004] Although the above patents are used to manage collected data, if the system is limited to data storage and basic management and lacks advanced data analysis and intelligent processing functions, it may not be possible to fully tap the potential value in the data. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the problems in the existing technology that the laboratory has a large daily data processing load, the quality of test data is uneven, and the proportion of effective data is low. A laboratory test data collection and management system and method are proposed.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the laboratory test data collection and management method of the present invention includes the following steps:

[0007] S1: Place the reactor liner of this experiment, which has been heated and pressurized, on the table of a fume hood, and monitor the surface image of the reaction material in the reactor liner and the gas data in the fume hood in real time;

[0008] S2: extracting the ratio data of each compound in the initial reaction solution of this experiment, and screening the historical laboratory test data storage and management platform for historical laboratory test data that matches the mixing ratio data of each compound in this experiment;

[0009] S3: When multiple identical historical laboratory test data are matched, the first test data reliability evaluation strategy is executed to obtain the first test data reliability of the current experimental test data;

[0010] S4: When multiple similar historical laboratory test data are matched, the second test data reliability evaluation strategy is executed to obtain the second test data reliability of the current experimental test data;

[0011] S5: Based on S3-S4, evaluate the data reliability level of the experimental test data, and calibrate the data reliability level of the experimental test data to the historical laboratory test data storage management platform.

[0012] Specifically, S1 includes the following specific steps:

[0013] S11: Place the reactor liner of this experiment, which has been heated and pressurized, evenly on the fume hood table with an interval of 10 cm;

[0014] S12: using a camera device to collect real-time images of the surface of the reaction material in the reactor liner from the top view of each reactor liner;

[0015] S13: The gas type data and concentration data of each type of gas in the fume hood are collected in real time through the gas sensor inside the fume hood.

[0016] Specifically, in S3, the first detection data reliability assessment strategy includes:

[0017] S31: importing the surface image of the reaction material into image processing software, obtaining pixel value information of each pixel point in the surface image of the reaction material, and simultaneously using an image segmentation algorithm to identify the crystallized area in the reaction material;

[0018] S32: According to S1, calculate and obtain the crystallization image verification value h1. The specific calculation strategy is:

[0019]

[0020] Where t is the subscript, indicating the time point of data collection, T is the total time the reactor liner is placed in the fume hood for ventilation; nt is a subscript, indicating that the nth t crystalline regions, N t It represents the total number of crystallized areas in the surface image of the reaction material at the data acquisition time point t;

[0021] is the nth image of the surface of the reaction material at the data acquisition time point t. t The pixel sum of the used pixels in each crystallization area; is the number of N in the surface image of the reaction material at the data acquisition time point t. t The average value of the pixel sum of the crystalline regions.

[0022] Specifically, in S3, the first detection data reliability assessment strategy further includes:

[0023] S33: Extract the gas data in the fume hood and calculate the reaction gas verification value h2. The specific calculation strategy is:

[0024]

[0025] Among them, m t is a subscript, indicating that at the data collection time point t, the mth t Gas, M t It represents the total number of gas types contained in the gas in the fume hood at the data collection time point t;

[0026] is the mth value of the gas in the fume hood at the data collection time point t. t Gas concentration of the gas; is the M in the gas in the fume hood at the data collection time point t t The mean gas concentration of the gas;

[0027] S34: Based on S31-S33, the reliability of the first detection data is evaluated by integration, specifically:

[0028] kd1=exph1+exph2;

[0029] Wherein, kd1 is the reliability of the first detection data, and exp is the exponential operation.

[0030] Specifically, in S4, the second detection data reliability assessment strategy includes:

[0031] S41: extracting pixel value information of each pixel point in the surface image of the reaction substance, and calculating the pixel difference between the maximum pixel value point and the minimum pixel value point;

[0032] S42: According to S41, calculate the crystal composition verification value g1. The specific calculation strategy is:

[0033]

[0034] Among them, cz t It represents the pixel difference in the surface image of the reaction material at the data acquisition time point t;

[0035] cz max ,cz min They are the maximum pixel difference and the minimum pixel difference in the surface image of the reaction material at the data collection time point t in multiple similar historical laboratory test data.

[0036] Specifically, in S4, the second detection data reliability assessment strategy further includes:

[0037] S43: Prioritize extracting gas type data in the fume hood, and compare the gas type data of this experiment with the target gas type data of this experimental reaction theory. If a new gas type appears in the gas type data of this experiment, directly execute step S5 to determine that the data reliability level of the test data of this experiment is a particularly abnormal level; otherwise, execute step S44;

[0038] S44: Extract the gas release rate collected by the gas flow meter built into the fume hood and calculate the gas release rate verification value g2, specifically:

[0039]

[0040] Among them, sl t represents the gas release rate at the data collection time point t;

[0041] sl max ,sl min are the maximum and minimum gas release rates at the data collection time point t in multiple similar historical laboratory test data;

[0042] S45: Based on S41-S44, the reliability of the second detection data is evaluated by integration, specifically:

[0043] kd2=expg1+expg2;

[0044] Wherein, kd2 is the reliability of the second detection data.

[0045] Specifically, S5 includes the following specific steps:

[0046] S51: extracting the reliability of the first detection data and the reliability of the second detection data, and calculating the comprehensive reliability of the detection data, wherein the calculation strategy of the comprehensive reliability of the detection data is specifically as follows:

[0047] KD=α1×kd1+α2×kd2;

[0048] Among them, α1 and α2 are the comprehensive contribution factors of the reliability of the first detection data and the reliability of the second detection data respectively;

[0049] S52: Evaluating the data reliability level of the test data of this experiment based on the comprehensive reliability of the test data, wherein the evaluating the data reliability level of the test data of this experiment based on the comprehensive reliability of the test data specifically includes: presetting different level evaluation thresholds Q1, Q2, and Q3 for the data reliability level of the test data of the experiment;

[0050] When 0≤KD≤Q1, the data reliability level of the experimental test data is judged to be particularly reliable;

[0051] When Q1<KD≤Q2, the data reliability level of this experimental test data is judged to be a general reliability level;

[0052] When Q2<KD≤Q3, the data reliability level of the experimental test data is judged to be particularly abnormal level;

[0053] S53: The data reliability level of the experimental test data of this time is calibrated to the historical laboratory test data storage management platform, and the experimental test data with a particularly abnormal reliability level is simultaneously sent to the laboratory manager, and the laboratory manager confirms to save or delete the experimental test data of this time on the historical laboratory test data storage management platform.

[0054] In addition, the laboratory test data acquisition and management system of the present invention includes the following modules:

[0055] Data acquisition module, historical data matching module, first detection data reliability calculation module, second detection data reliability calculation module and data reliability level assessment module;

[0056] The data acquisition module places the reactor liner of this experiment, which has been heated and pressurized, on the table of the fume hood, and monitors the surface image of the reaction material in the reactor liner and the gas data in the fume hood in real time;

[0057] The historical data matching module is used to extract the ratio data of each compound in the initial reaction solution of this experiment, and screen the historical laboratory test data storage and management platform for the historical laboratory test data that matches the mixing ratio data of each compound in this experiment;

[0058] The first detection data reliability calculation module is used to execute the first detection data reliability evaluation strategy to obtain the first detection data reliability of the current experimental detection data;

[0059] The second detection data reliability calculation module is used to execute the second detection data reliability evaluation strategy to obtain the second detection data reliability of the current experimental detection data;

[0060] The data reliability level evaluation module is used to evaluate the data reliability level of the experimental test data and calibrate the data reliability level of the experimental test data to the historical laboratory test data storage management platform.

[0061] A storage medium stores instructions, and when a computer reads the instructions, the computer executes the laboratory test data collection and management method.

[0062] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned laboratory test data acquisition and management method is implemented.

[0063] Compared with the prior art, the technical effects of the present invention are as follows:

[0064] 1. The present invention significantly improves the accuracy and reliability of experimental test data by real-time monitoring of multiple key data during the reaction process, such as the surface image of the reaction material and the gas data in the fume hood, and combining it with historical laboratory test data for comparative analysis. Through the first and second test data reliability evaluation strategies, the credibility of the experimental data can be more comprehensively evaluated, thereby guiding subsequent experiments or production activities.

[0065] 2. The present invention realizes intelligent data management of laboratory data. Faced with a large amount of experimental data, traditional processing methods are inefficient and prone to errors. The present invention introduces intelligent data management means, which significantly improves the efficiency and accuracy of data processing. This not only improves the efficiency of data processing, but also reduces the possibility of human operation errors, providing scientific researchers with more convenient and efficient data support.

[0066] 3. This invention calibrates the reliability level of experimental test data to the historical laboratory test data storage and management platform. This technical solution provides strong support for optimizing experimental processes. Researchers can adjust experimental plans or make more scientific decisions based on the data reliability level, thereby improving experimental efficiency and success rate.

[0067] 4. The implementation of this invention not only significantly improves the quality of laboratory test data collection, but also ensures the reliability and accuracy of experimental results through intelligent data analysis and comprehensive data reliability assessment. Furthermore, by comparing and analyzing historical data, scientific research efficiency can be improved, duplication of experiments can be avoided, and scientific research resources can be conserved. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0069] in:

[0070] Figure 1 Schematic diagram of the process of the laboratory test data collection and management method of the present invention;

[0071] Figure 2 It is a structural diagram of the laboratory test data acquisition and management system of the present invention. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0073] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0074] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0075] Example 1:

[0076] like Figure 1 As shown, the laboratory test data collection and management method of the embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0077] S1: Place the reactor liner of this experiment, which has been heated and pressurized, on the table of a fume hood, and monitor the surface image of the reaction material in the reactor liner and the gas data in the fume hood in real time;

[0078] S1 includes the following specific steps:

[0079] S11: Place the reactor liner of this experiment, which has been heated and pressurized, evenly on the fume hood table with an interval of 10 cm;

[0080] S12: using a camera device to collect real-time images of the surface of the reaction material in the reactor liner from the top view of each reactor liner;

[0081] S13: The gas type data and concentration data of each type of gas in the fume hood are collected in real time through the gas sensor inside the fume hood.

[0082] S2: extracting the ratio data of each compound in the initial reaction solution of this experiment, and screening the historical laboratory test data storage and management platform for historical laboratory test data that matches the mixing ratio data of each compound in this experiment;

[0083] For example, in this embodiment, a specific implementation strategy for matching historical laboratory test data through the historical laboratory test data storage and management platform in S2 is provided, specifically: the historical laboratory test data that matches the mixing ratio data of each compound in this experiment is screened on the historical laboratory test data storage and management platform, and the specific matching strategy is: extracting the proportion data of the main reaction compounds in the initial reaction liquid of this experiment, and arranging them in descending order according to the proportion data of each main reaction compound, using the molar amount of the top three reaction compounds in the arrangement sequence of the proportion content of the main reaction compounds in the initial reaction liquid as the matching standard, screening the historical laboratory test data with the same molar amount of the top three reaction compounds in the arrangement sequence of this experiment, and matching to obtain the same historical laboratory test data; conversely, if there is one or two reaction compound molar amounts in the historical laboratory test data obtained by screening that are the same as the molar amounts of the top three reaction compounds in the arrangement sequence of this experiment but the types of reaction compounds are the same, matching to obtain similar historical laboratory test data.

[0084] S3: When multiple identical historical laboratory test data are matched, the first test data reliability evaluation strategy is executed to obtain the first test data reliability of the current experimental test data;

[0085] In S3, the first detection data reliability assessment strategy includes:

[0086] S31: importing the surface image of the reaction material into image processing software, obtaining pixel value information of each pixel point in the surface image of the reaction material, and simultaneously using an image segmentation algorithm to identify the crystallized area in the reaction material;

[0087] S32: According to S1, calculate and obtain the crystallization image verification value h1. The specific calculation strategy is:

[0088]

[0089] Where t is the subscript, indicating the time point of data collection, T is the total time the reactor liner is placed in the fume hood for ventilation; n t is a subscript, indicating that the nth t crystalline regions, N t It represents the total number of crystallized areas in the surface image of the reaction material at the data acquisition time point t;

[0090] is the nth image of the surface of the reaction material at the data acquisition time point t. t The pixel sum of the used pixels in each crystallization area; is the number of N in the surface image of the reaction material at the data acquisition time point t. t The average value of the pixel sum of the crystalline regions.

[0091] In S3, the first detection data reliability assessment strategy further includes:

[0092] S33: Extract the gas data in the fume hood and calculate the reaction gas verification value h2. The specific calculation strategy is:

[0093]

[0094] Among them, m t is a subscript, indicating that at the data collection time point t, the mth t Gas, M t It represents the total number of gas types contained in the gas in the fume hood at the data collection time point t;

[0095] is the mth value of the gas in the fume hood at the data collection time point t. t Gas concentration of the gas; is the M in the gas in the fume hood at the data collection time point t t The mean gas concentration of the gas;

[0096] S34: Based on S31-S33, the reliability of the first detection data is evaluated by integration, specifically:

[0097] kd1=exph1+exph2;

[0098] Wherein, kd1 is the reliability of the first detection data, and exp is the exponential operation.

[0099] S4: When multiple similar historical laboratory test data are matched, the second test data reliability evaluation strategy is executed to obtain the second test data reliability of the current experimental test data;

[0100] In S4, the second detection data reliability assessment strategy includes:

[0101] S41: extracting pixel value information of each pixel point in the surface image of the reaction substance, and calculating the pixel difference between the maximum pixel value point and the minimum pixel value point;

[0102] S42: According to S41, calculate the crystal composition verification value g1. The specific calculation strategy is:

[0103]

[0104] Among them, cz t It represents the pixel difference in the surface image of the reaction material at the data acquisition time point t;

[0105] cz max ,cz min They are the maximum pixel difference and the minimum pixel difference in the surface image of the reaction material at the data collection time point t in multiple similar historical laboratory test data.

[0106] In S4, the second detection data reliability assessment strategy further includes:

[0107] S43: Prioritize extracting gas type data in the fume hood, and compare the gas type data of this experiment with the target gas type data of this experimental reaction theory. If a new gas type appears in the gas type data of this experiment, directly execute step S5 to determine that the data reliability level of the test data of this experiment is a particularly abnormal level; otherwise, execute step S44;

[0108] S44: Extract the gas release rate collected by the gas flow meter built into the fume hood and calculate the gas release rate verification value g2, specifically:

[0109]

[0110] Among them, sl t represents the gas release rate at the data collection time point t;

[0111] sl max ,sl min are the maximum and minimum gas release rates at the data collection time point t in multiple similar historical laboratory test data;

[0112] S45: Based on S41-S44, the reliability of the second detection data is evaluated by integration, specifically:

[0113] kd2=expg1+expg2;

[0114] Wherein, kd2 is the reliability of the second detection data.

[0115] S5: Based on S3-S4, evaluate the data reliability level of the experimental test data, and calibrate the data reliability level of the experimental test data to the historical laboratory test data storage management platform.

[0116] S5 includes the following specific steps:

[0117] S51: extracting the reliability of the first detection data and the reliability of the second detection data, and calculating the comprehensive reliability of the detection data, wherein the calculation strategy of the comprehensive reliability of the detection data is specifically as follows:

[0118] KD=α1×kd1+α2×kd2;

[0119] Among them, α1 and α2 are the comprehensive contribution factors of the reliability of the first detection data and the reliability of the second detection data respectively;

[0120] S52: Evaluating the data reliability level of the test data of this experiment based on the comprehensive reliability of the test data, wherein the evaluating the data reliability level of the test data of this experiment based on the comprehensive reliability of the test data specifically includes: presetting different level evaluation thresholds Q1, Q2, and Q3 for the data reliability level of the test data of the experiment;

[0121] When 0≤KD≤Q1, the data reliability level of the experimental test data is judged to be particularly reliable;

[0122] When Q1<KD≤Q2, the data reliability level of this experimental test data is judged to be a general reliability level;

[0123] When Q2<KD≤Q3, the data reliability level of the experimental test data is judged to be particularly abnormal level;

[0124] S53: The data reliability level of the experimental test data of this time is calibrated to the historical laboratory test data storage management platform, and the experimental test data with a particularly abnormal reliability level is simultaneously sent to the laboratory manager, and the laboratory manager confirms to save or delete the experimental test data of this time on the historical laboratory test data storage management platform.

[0125] Example 2:

[0126] like Figure 2 As shown, the laboratory test data collection and management system of the embodiment of the present invention is as follows Figure 2 As shown, it includes the following modules:

[0127] Data acquisition module, historical data matching module, first detection data reliability calculation module, second detection data reliability calculation module and data reliability level assessment module;

[0128] The data acquisition module places the reactor liner of this experiment, which has been heated and pressurized, on the table of the fume hood, and monitors the surface image of the reaction material in the reactor liner and the gas data in the fume hood in real time;

[0129] The historical data matching module is used to extract the ratio data of each compound in the initial reaction solution of this experiment, and screen the historical laboratory test data storage and management platform for the historical laboratory test data that matches the mixing ratio data of each compound in this experiment;

[0130] The first detection data reliability calculation module is used to execute the first detection data reliability evaluation strategy to obtain the first detection data reliability of the current experimental detection data;

[0131] The second detection data reliability calculation module is used to execute the second detection data reliability evaluation strategy to obtain the second detection data reliability of the current experimental detection data;

[0132] The data reliability level evaluation module is used to evaluate the data reliability level of the experimental test data and calibrate the data reliability level of the experimental test data to the historical laboratory test data storage management platform.

[0133] Example 3:

[0134] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0135] The processor executes the above-mentioned laboratory test data collection and management method by calling the computer program stored in the memory.

[0136] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the laboratory test data acquisition management method provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface to input and output data. This embodiment will not be described in detail here.

[0137] Example 4:

[0138] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0139] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned laboratory test data collection and management method.

[0140] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0141] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0142] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0143] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0144] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0145] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0146] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0149] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0150] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0151] 1. The present invention significantly improves the accuracy and reliability of experimental test data by real-time monitoring of multiple key data during the reaction process, such as the surface image of the reaction material and the gas data in the fume hood, and combining it with historical laboratory test data for comparative analysis. Through the first and second test data reliability evaluation strategies, the credibility of the experimental data can be more comprehensively evaluated, thereby guiding subsequent experiments or production activities.

[0152] 2. The present invention realizes intelligent data management of laboratory data. Faced with a large amount of experimental data, traditional processing methods are inefficient and prone to errors. The present invention introduces intelligent data management means, which significantly improves the efficiency and accuracy of data processing. This not only improves the efficiency of data processing, but also reduces the possibility of human operation errors, providing scientific researchers with more convenient and efficient data support.

[0153] 3. This invention calibrates the reliability level of experimental test data to the historical laboratory test data storage and management platform. This technical solution provides strong support for optimizing experimental processes. Researchers can adjust experimental plans or make more scientific decisions based on the data reliability level, thereby improving experimental efficiency and success rate.

[0154] 4. The implementation of this invention not only significantly improves the quality of laboratory test data collection, but also ensures the reliability and accuracy of experimental results through intelligent data analysis and comprehensive data reliability assessment. Furthermore, by comparing and analyzing historical data, scientific research efficiency can be improved, duplication of experiments can be avoided, and scientific research resources can be conserved.

[0155] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A laboratory test data collection and management method, characterized by: The method comprises the following specific steps: S1: Place the reactor liner of this experiment, which has been heated and pressurized, on the table of a fume hood, and monitor the surface image of the reaction material in the reactor liner and the gas data in the fume hood in real time; S2: extracting the ratio data of each compound in the initial reaction solution of this experiment, and screening the historical laboratory test data storage and management platform for historical laboratory test data that matches the mixing ratio data of each compound in this experiment; S3: When multiple identical historical laboratory test data are matched, the first test data reliability evaluation strategy is executed to obtain the first test data reliability of the current experimental test data; The first detection data reliability assessment strategy includes: S31: importing the surface image of the reaction material into image processing software, obtaining pixel value information of each pixel point in the surface image of the reaction material, and simultaneously using an image segmentation algorithm to identify the crystallized area in the reaction material; S32: According to S1, calculate and obtain the crystallization image verification value h1. The specific calculation strategy is: Where t is the subscript, indicating the time point of data collection, T is the total time the reactor liner is placed in the fume hood for ventilation; n t is a subscript, indicating that the nth t crystalline regions, N t It represents the total number of crystallized areas in the surface image of the reaction material at the data acquisition time point t; is the nth image of the surface of the reaction material at the data acquisition time point t. t The pixel sum of the used pixels in each crystallization area; is the number of N in the surface image of the reaction material at the data acquisition time point t. t The mean value of the sum of pixels in the crystallized area; The first detection data reliability assessment strategy also includes: S33: Extract the gas data in the fume hood and calculate the reaction gas verification value h2. The specific calculation strategy is: Among them, m t is a subscript, indicating that at the data collection time point t, the mth t Gas, M t It represents the total number of gas types contained in the gas in the fume hood at the data collection time point t; is the mth value of the gas in the fume hood at the data collection time point t. t Gas concentration of the gas; is the M in the gas in the fume hood at the data collection time point t t The mean gas concentration of the gas; S34: Based on S31-S33, the reliability of the first detection data is evaluated by integration, specifically: kd1=exph1+exph2; Wherein, kd1 is the reliability of the first detection data, and exp is the exponential operation; S4: When multiple similar historical laboratory test data are matched, the second test data reliability evaluation strategy is executed to obtain the second test data reliability of the current experimental test data; The second detection data reliability assessment strategy includes: S41: extracting pixel value information of each pixel point in the surface image of the reaction substance, and calculating the pixel difference between the maximum pixel value point and the minimum pixel value point; S42: According to S41, calculate the crystal composition verification value g1. The specific calculation strategy is: Among them, cz t It represents the pixel difference in the surface image of the reaction material at the data acquisition time point t; cz max ,cz min are the maximum pixel difference and the minimum pixel difference in the surface image of the reaction substance at the data collection time point t in multiple similar historical laboratory test data; The second detection data reliability assessment strategy also includes: S43: Prioritize extracting gas type data in the fume hood, and compare the gas type data of this experiment with the target gas type data of this experimental reaction theory. If a new gas type appears in the gas type data of this experiment, directly execute step S5 to determine that the data reliability level of the test data of this experiment is a particularly abnormal level; otherwise, execute step S44; S44: Extract the gas release rate collected by the gas flow meter built into the fume hood and calculate the gas release rate verification value g2, specifically: Among them, sl t represents the gas release rate at the data collection time point t; sl max ,sl min are the maximum and minimum gas release rates at the data collection time point t in multiple similar historical laboratory test data; S45: Based on S41-S44, the reliability of the second detection data is evaluated by integration, specifically: kd2=expg1+expg2; Wherein, kd2 is the reliability of the second detection data; S5: Based on S3-S4, evaluate the data reliability level of the experimental test data, and calibrate the data reliability level of the experimental test data to the historical laboratory test data storage management platform.

2. The laboratory test data collection and management method according to claim 1, characterized in that: S5 includes the following specific steps: S51: extracting the reliability of the first detection data and the reliability of the second detection data, and calculating the comprehensive reliability of the detection data, wherein the calculation strategy of the comprehensive reliability of the detection data is specifically as follows: KD=α1×kd1+α2×kd2; Among them, α1 and α2 are the comprehensive contribution factors of the reliability of the first detection data and the reliability of the second detection data respectively; S52: Evaluating the data reliability level of the test data of this experiment based on the comprehensive reliability of the test data, wherein the evaluating the data reliability level of the test data of this experiment based on the comprehensive reliability of the test data specifically includes: presetting different level evaluation thresholds Q1, Q2, and Q3 for the data reliability level of the test data of the experiment; When 0≤KD≤Q1, the data reliability level of the experimental test data is judged to be particularly reliable; When Q1<KD≤Q2, the data reliability level of this experimental test data is judged to be a general reliability level; When Q2<KD≤Q3, the data reliability level of the experimental test data is judged to be particularly abnormal level; S53: The data reliability level of the experimental test data of this time is calibrated to the historical laboratory test data storage management platform, and the experimental test data with a particularly abnormal reliability level is simultaneously sent to the laboratory manager, and the laboratory manager confirms to save or delete the experimental test data of this time on the historical laboratory test data storage management platform.

3. The laboratory test data collection and management method according to claim 2, characterized in that: S1 includes the following specific steps: S11: Place the reactor liner of this experiment, which has been heated and pressurized, evenly on the fume hood table with an interval of 10 cm; S12: using a camera device to collect real-time images of the surface of the reaction material in the reactor liner from the top view of each reactor liner; S13: The gas type data and concentration data of each type of gas in the fume hood are collected in real time through the gas sensor inside the fume hood.

4. A laboratory test data collection and management system, which is implemented based on the laboratory test data collection and management method according to any one of claims 1 to 3, characterized in that: The system includes the following modules: Data acquisition module, historical data matching module, first detection data reliability calculation module, second detection data reliability calculation module and data reliability level assessment module; The data acquisition module places the reactor liner of this experiment, which has been heated and pressurized, on the table of the fume hood, and monitors the surface image of the reaction material in the reactor liner and the gas data in the fume hood in real time; The historical data matching module is used to extract the ratio data of each compound in the initial reaction solution of this experiment, and screen the historical laboratory test data storage and management platform for the historical laboratory test data that matches the mixing ratio data of each compound in this experiment; The first detection data reliability calculation module is used to execute the first detection data reliability evaluation strategy to obtain the first detection data reliability of the current experimental detection data; The second detection data reliability calculation module is used to execute the second detection data reliability evaluation strategy to obtain the second detection data reliability of the current experimental detection data; The data reliability level evaluation module is used to evaluate the data reliability level of the experimental test data and calibrate the data reliability level of the experimental test data to the historical laboratory test data storage management platform.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the laboratory test data collection and management method as described in any one of claims 1 to 3 is implemented.

6. An electronic device, characterized in that: include: a memory for storing instructions; The processor is used to execute the instructions so that the device performs the operations of the laboratory test data collection and management method according to any one of claims 1 to 3.

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