An Automatic Data Acquisition and Analysis Method and System for Laboratory Instruments

Abstract

The present invention discloses a method and system for automatic acquisition and analysis of laboratory instrument data, which relates to the technical field of acquisition and analysis of experimental instruments. It includes automatically sorting and distributing the obtained sample identification information to different analyzers, uploading the detection data to the central database in real time, and associating the sample number, detection timestamp, and instrument number; constructing a multi-dimensional feature space based on historical detection data, using the density clustering algorithm for sub-cluster division, and establishing cross-index logical constraint relationships in combination with an expert rule base, mapping the real-time detection data to the multi-dimensional feature space, determining the type of data abnormality according to the sub-cluster attribution and logical constraint relationships, triggering a secondary detection process when it is determined as abnormal, automatically scheduling the robotic arm to send the sample back to the specified instrument to perform a differential re-inspection operation, and generating a comprehensive report including the traceability of the detection process. The present invention improves the automation level of detection, reduces the labor cost, improves the detection quality and efficiency, and enhances the working efficiency of laboratory detection.

Description

Technical Field

[0001] The present invention relates to the technical field of experimental instrument acquisition and analysis, and particularly to a method and system for automatic acquisition and analysis of laboratory instrument data. Background Art

[0002] With the continuous growth of laboratory testing requirements and the rapid development of automation technology, more and more laboratories have begun to introduce automated equipment to complete the transfer and testing of samples. For example, oil chromatography analysis, moisture analysis, dielectric loss analysis, breakdown voltage analysis, etc. are usually applied to routine tests in industrial oil product analysis, power system fault diagnosis, and other energy fields. These instruments are respectively responsible for different testing items in the automated production line, and can realize the transfer and docking of samples between various instruments through robotic arms or automatic sorting systems.

[0003] However, in actual operation, the existing technology often only collects and processes data from a single testing instrument, lacking means for comprehensive correlation analysis of the test results of multiple instruments. The data of each instrument is usually stored separately in different management software or databases, and the test results are manually interpreted and compared. Once there are conflicts in the test results (for example, there are logical contradictions in the values of the same batch of samples in different testing items) or abnormal data caused by instrument failures, laboratory personnel need to manually check and re-inspect multiple times, which not only increases the workload but also easily leads to missed inspections and misjudgments. In addition, the lack of cross-instrument data correlation and automatic error correction functions makes it difficult to fully utilize the existing historical test data to continuously monitor and improve the test quality, and it is impossible to capture and correct potential errors in real time. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are as follows: By performing cross-instrument correlation analysis on various test data such as oil chromatography, moisture, dielectric loss, and breakdown voltage, and using intelligent algorithms to achieve real-time anomaly recognition and dynamic error correction of data, the accuracy and reliability of the test results are significantly improved; it can be deeply coupled with automatic transfer and sorting equipment to construct a closed-loop detection process from sample reception to abnormal re-inspection; when the test data deviates or conflicts, secondary detection can be automatically performed or the expert knowledge base can be triggered to assist in judgment without manual intervention, greatly reducing the labor input and reducing the risk of misjudgment.

[0006] To solve the above technical problems, a method for automatic acquisition and analysis of laboratory instrument data is proposed, including,

[0007] Obtain sample identification information, automatically sort and allocate it to different analyzers, upload the test data to the central database in real time, and associate the sample number, test timestamp, and instrument number;

[0008] Construct a multi-dimensional feature space based on historical detection data, use the density clustering algorithm to divide sub-clusters for the detection values of oil chromatography, moisture, dielectric loss, and breakdown voltage, and establish cross-index logical constraint relationships in combination with an expert rule library; map the real-time detection data to the multi-dimensional feature space, determine the abnormal data type according to the sub-cluster attribution and logical constraint relationships, and when it is determined to be abnormal, trigger the secondary detection process, automatically dispatch the robotic arm to send the sample back to the specified instrument for differential re-inspection operations; integrate the multi-dimensional detection data and abnormal marking information through a visualization interface, and generate a comprehensive report including the traceability of the detection process.

[0009] As a preferred solution of the automatic data acquisition and analysis method for laboratory instruments described in the present invention, among them: the different analyzers include an oil chromatography analyzer, a moisture analyzer, a dielectric loss analyzer, and a breakdown voltage analyzer;

[0010] The real-time uploaded detection data includes that the data interface adopts a dynamic protocol adaptation mechanism, uniformly parses the heterogeneous data frames of the protocol through a protocol conversion gateway, and generates a unique check code based on the hash algorithm. During the sorting process, the mapping relationship with the task queue is verified through secondary matching. When the verification fails, a redundant communication link is triggered for retransmission.

[0011] As a preferred solution of the automatic data acquisition and analysis method for laboratory instruments described in the present invention, among them: the density clustering algorithm includes dynamically calculating the neighborhood radius according to the mean and standard deviation of the historical data sample spacing, combined with a scaling factor, and determining the minimum number of samples based on the total amount of historical data and the preset number of sub-clusters, and initializing the sub-cluster center using an improved K-Means++ algorithm;

[0012] The formula for calculating the neighborhood radius is:

[0013]

[0014] Where, is the neighborhood radius, is the mean of the historical data sample spacing, is the standard deviation of the historical data sample spacing, is the scaling factor;

[0015] The formula for determining the minimum number of samples is:

[0016]

[0017] Where, is the total amount of historical data, k is the preset number of sub-clusters, and m is the minimum number of samples;

[0018] The formula for initializing the sub-cluster center is expressed as:

[0019]

[0020] Among them, is the centroid of the sub-cluster, is the sub-cluster, x is the sample point, j is the variable index, and k is the preset number of sub-clusters.

[0021] As a preferred solution of the automatic data acquisition and analysis method for laboratory instruments according to the present invention, wherein: the sub-cluster division includes determining a reasonable fluctuation range of the detection value based on the sub-cluster standard deviation and the confidence coefficient, storing the multi-variable logical constraint relationship. When the dielectric loss value exceeds the first threshold, the breakdown voltage must be lower than the second threshold, and the abnormal scenario is dynamically matched through the fuzzy inference engine to trigger the error correction process;

[0022] The establishment of the cross-index logical constraint relationship includes training a negative correlation model between the dielectric loss value and the breakdown voltage based on historical data to obtain the regression coefficient and the prediction interval. If the real-time detection value exceeds the prediction interval and exceeds the tolerance threshold, it is marked as a logical conflict and the cross-instrument calibration is triggered;

[0023] The multi-variable logical constraint relationship is expressed as:

[0024]

[0025] Among them, and are the linkage thresholds, D is the dielectric loss value, and B is the breakdown voltage;

[0026] The negative correlation model is expressed as:

[0027]

[0028] Among them, D is the dielectric loss value, B is the breakdown voltage, and b are the regression coefficients, and g is the residual term.

[0029] As a preferred solution of the automatic data acquisition and analysis method for laboratory instruments according to the present invention, wherein: the execution of the differential re-inspection operation includes dynamically adjusting the instrument cleaning time according to the ratio of the abnormal score to the preset maximum abnormal value, and selecting the cleaning, calibration, and environmental control parameter adjustment schemes from the preset strategy library for different abnormal types;

[0030] The formula for dynamically adjusting the instrument cleaning time is expressed as:

[0031]

[0032] Among them, is the reference cleaning time, is the maximum adjustment amount, E is the current abnormal score, is the system preset maximum abnormal value, is the adjusted cleaning time.

[0033] As a preferred solution of a method for automatically collecting and analyzing laboratory instrument data according to the present invention, wherein: the preset policy library includes calculating the posterior probability based on the historical abnormal scenario type and the success rate of the re-inspection action, selecting the optimal re-inspection strategy, and when the new abnormal scenario does not match the existing strategy, calling the expert knowledge base to supplement the rules and updating the policy library;

[0034] The preset policy library uses a Bayesian network to generate re-inspection instructions, and the conditional probability is expressed as:

[0035]

[0036] where A is the re-inspection action and S is the abnormal scenario type. is the probability that the re-inspection action A occurs under the given abnormal scenario type S. is the probability that the abnormal scenario type S is observed under the given re-inspection action A. is the prior probability that the re-inspection action A occurs, and is the probability that the abnormal scenario type S occurs.

[0037] As a preferred solution of a method for automatically collecting and analyzing laboratory instrument data according to the present invention, wherein: the generation of a comprehensive report including the traceability of the detection process includes synchronously displaying the oil chromatogram, moisture curve and breakdown voltage trend through a parallel coordinate system, supporting multi-dimensional data comparison, and grading the abnormal risk level with colors according to the real-time abnormal density distribution, and the threshold is dynamically adjusted based on the upper quartile and interquartile range of the real-time data;

[0038] The generation of a comprehensive report including the traceability of the detection process further includes pushing structured alarm information to the laboratory terminal when the abnormal score exceeds the preset threshold after the secondary detection, and the alarm priority is dynamically adjusted according to the logarithmic relationship between the abnormal score and the number of re-inspections;

[0039] The abnormal score formula is expressed as:

[0040]

[0041] where is the weight coefficient of the detection index. is the abnormal membership value of the i-th detection index, i is the variable index, and F is the abnormal score; when F is greater than or equal to 0.8, it is determined that an alarm needs to be pushed; when F is less than 0.8, it is determined to be normal and no alarm needs to be pushed.

[0042] As a preferred solution of an automatic laboratory instrument data acquisition and analysis system according to the present invention, it is characterized by including a data acquisition and sorting module, a data processing and analysis module, a differential re-inspection operation module, and a report generation and visualization module.

[0043] The data acquisition and sorting module is used to automatically obtain sample identification information, sort and allocate samples to different analyzers, upload detection data to the central database in real time, and generate a unique verification code through a dynamic protocol adaptation mechanism and a hash algorithm.

[0044] The data processing and analysis module includes a density clustering algorithm unit, a logical constraint relationship establishment unit, and a secondary detection process trigger unit. It is used to construct a multi-dimensional feature space based on historical detection data, use the density clustering algorithm to divide the detection values into sub-clusters, establish cross-index logical constraint relationships in combination with an expert rule library, map real-time detection data to the multi-dimensional feature space, determine the type of data anomaly according to the sub-cluster attribution and logical constraint relationships, and trigger a secondary detection process when it is determined to be abnormal.

[0045] The differential re-inspection operation module includes a differential re-inspection operation execution unit, an instrument cleaning time dynamic adjustment unit, and a parameter adjustment scheme selection unit. It is used to automatically dispatch the robotic arm to send the sample back to the specified instrument to perform differential re-inspection operations after determining data anomalies, dynamically adjust the instrument cleaning time according to the ratio of the anomaly score to the preset maximum anomaly value, and select a suitable cleaning, calibration, and environmental control parameter adjustment scheme from the preset strategy library.

[0046] The report generation and visualization module is used to integrate multi-dimensional detection data and anomaly marking information through a visualization interface, generate a comprehensive report including the traceability of the detection process, and provide intuitive and comprehensive detection results and analysis for users.

[0047] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a method for automatic laboratory instrument data acquisition and analysis.

[0048] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of a method for automatic laboratory instrument data acquisition and analysis.

[0049] Advantages of the present invention: By automatically acquiring sample identification information and sorting it into different analyzers, and uploading and associating the detection data in real time, the present invention realizes the efficient acquisition and management of data, ensuring the integrity and traceability of the data; constructs a multi-dimensional feature space based on historical data and uses a density clustering algorithm for sub-cluster division, and establishes cross-index logical constraint relationships in combination with an expert rule base, effectively improving the determination accuracy of data anomaly types and reducing false positives and missed detections; after mapping the real-time detection data to the multi-dimensional feature space, triggers a secondary detection process according to the sub-cluster attribution and logical constraint relationships, and executes differential re-inspection operations by automatically scheduling the robotic arm, significantly improving the pertinence and efficiency of re-inspection; integrates multi-dimensional detection data and anomaly marking information through a visualization interface to generate a comprehensive report including the traceability of the detection process, providing users with intuitive and comprehensive analysis results and enhancing the convenience of data interpretation; in addition, the application of technologies such as dynamic protocol adaptation mechanism, hash algorithm verification, and redundant communication link retransmission greatly enhances the stability and security of data transmission; dynamically adjusts the instrument cleaning time and selects parameter adjustment schemes based on anomaly scores, optimizing the instrument maintenance process and extending the service life of the instrument; generates re-inspection instructions by combining a preset policy library with a Bayesian network to ensure the optimization of re-inspection strategies, and at the same time, the supplementary rule update of the expert knowledge base enhances the adaptive ability and flexibility of the system; the present invention improves the automation level of detection, reduces labor costs, improves the detection quality and efficiency, and brings significant beneficial effects to laboratory detection work. Brief Description of the Drawings

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is the overall flowchart of a method for automatically collecting and analyzing laboratory instrument data provided by an embodiment of the present invention.

[0052] Figure 2 It is the system scheme flowchart of a system for automatically collecting and analyzing laboratory instrument data provided by an embodiment of the present invention. Detailed Embodiments

[0053] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the accompanying drawings of the specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0055] Example 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for automatic acquisition and analysis of laboratory instrument data, including:

[0056] S1: Automatically sort and assign the sample identification information to different analyzers, and upload the detection data to the central database in real time, and associate the sample number, detection timestamp, and instrument number.

[0057] The different analyzers include an oil chromatograph analyzer, a moisture analyzer, a dielectric loss analyzer, and a breakdown voltage analyzer;

[0058] The real-time upload of detection data includes that the data interface adopts a dynamic protocol adaptation mechanism, uniformly parses the heterogeneous data frames of the protocol through a protocol conversion gateway, and generates a unique check code based on the hash algorithm. During the sorting process, the mapping relationship with the task queue is verified through secondary matching. When the verification fails, a redundant communication link is triggered for retransmission.

[0059] S2: Construct a multi-dimensional feature space based on historical detection data, use the density clustering algorithm to divide sub-clusters for the detection values of oil chromatography, moisture, dielectric loss, and breakdown voltage, and establish cross-index logical constraint relationships in combination with an expert rule library.

[0060] Further, the density clustering algorithm includes dynamically calculating the neighborhood radius according to the mean and standard deviation of the historical data sample spacing, combining a scaling factor, and determining the minimum number of samples based on the total amount of historical data and the preset number of sub-clusters, and initializing the sub-cluster centers using an improved K-Means++ algorithm;

[0061] The formula for calculating the neighborhood radius is:

[0062]

[0063] Where, is the neighborhood radius, is the mean of the historical data sample spacing, is the standard deviation of the historical data sample spacing, is the scaling factor;

[0064] The formula for determining the minimum number of samples is:

[0065]

[0066] where, is the total amount of historical data, k is the preset number of sub-clusters, and m is the minimum number of samples;

[0067] The formula for initializing the sub-cluster center is expressed as:

[0068]

[0069] where, is the centroid of the sub-cluster, is the sub-cluster, x is the sample point, j is the variable index, and k is the preset number of sub-clusters.

[0070] It should be noted that the dynamic parameters reduce the interference of outliers and avoid overfitting caused by fixed thresholds. K-Means++ optimizes the centroid initialization, accelerates the clustering convergence, and improves the accuracy.

[0071] Furthermore, the sub-cluster division includes determining a reasonable fluctuation range of the detection value based on the sub-cluster standard deviation and the confidence coefficient, storing the multivariate logical constraint relationship. When the dielectric loss value exceeds the first threshold, the breakdown voltage must be lower than the second threshold, and the abnormal scenario is dynamically matched through the fuzzy inference engine to trigger the error correction process;

[0072] The establishment of the cross-index logical constraint relationship includes training a negative correlation model between the dielectric loss value and the breakdown voltage based on historical data to obtain the regression coefficient and the prediction interval. If the real-time detection value exceeds the prediction interval and the tolerance threshold, it is marked as a logical conflict and the cross-instrument calibration is triggered;

[0073] The multivariate logical constraint relationship is expressed as:

[0074]

[0075] where, and are the linkage thresholds, D is the dielectric loss value, and B is the breakdown voltage;

[0076] The negative correlation model is expressed as:

[0077]

[0078] where D is the dielectric loss value, B is the breakdown voltage, and b are the regression coefficients, and g is the residual term.

[0079] It should be noted that through the dual-path complementarity of data-driven (clustering) and experience-driven (rules), misjudgment of a single method is reduced, and sub-cluster division is updated with historical data to adapt to the detection characteristics of different batches of samples.

[0080] S3: Map the real-time detection data to a multi-dimensional feature space, determine the type of data anomaly according to the sub-cluster attribution and logical constraint relationship. When it is determined as an anomaly, trigger the secondary detection process, and automatically dispatch the robotic arm to send the sample back to the specified instrument to perform a differential re-inspection operation.

[0081] Furthermore, the execution of the differential re-inspection operation includes dynamically adjusting the instrument cleaning time according to the ratio of the anomaly score to the preset maximum anomaly value, and selecting cleaning, calibration, and environmental control parameter adjustment schemes from the preset strategy library for different types of anomalies;

[0082] The formula for dynamically adjusting the instrument cleaning time is expressed as:

[0083]

[0084] where, is the reference cleaning time, is the maximum adjustment amount, E is the current anomaly score, is the system preset maximum anomaly value, is the adjusted cleaning time.

[0085] It should be noted that the differential re-inspection strategy is optimized for different types of anomalies to avoid wasting resources in non-discriminatory re-inspection; the secondary detection excludes accidental errors and improves the credibility of the results.

[0086] Even further, the preset strategy library includes calculating the posterior probability based on the historical anomaly scenario types and the success rate of re-inspection actions, selecting the optimal re-inspection strategy. When a new anomaly scenario does not match the existing strategy, call the expert knowledge base to supplement the rules and update the strategy library;

[0087] The preset strategy library uses a Bayesian network to generate re-inspection instructions, and the conditional probability is expressed as:

[0088]

[0089] where, A is the re-inspection action, S is the anomaly scenario type, is the probability of the re-inspection action A occurring given the anomaly scenario type S, is the probability of observing the anomaly scenario type S given the re-inspection action A, is the prior probability of the re-inspection action A occurring, and is the probability of the anomaly scenario type S occurring.

[0090] S4: Integrate multi-dimensional detection data and anomaly marking information through a visualization interface, and generate a comprehensive report including the traceability of the detection process.

[0091] The generation of the comprehensive report including the traceability of the detection process includes synchronously displaying the oil chromatogram, moisture curve, and breakdown voltage trend through a parallel coordinate system, supporting multi-dimensional data comparison, marking the anomaly risk level with color grading according to the real-time anomaly density distribution, and dynamically adjusting the threshold based on the upper quartile and interquartile range of the real-time data;

[0092] The generation of the comprehensive report including the traceability of the detection process also includes pushing structured alarm information to the laboratory terminal when the anomaly score exceeds the preset threshold after secondary detection, and dynamically adjusting the alarm priority according to the logarithmic relationship between the anomaly score and the number of retest times;

[0093] The anomaly score formula is expressed as:

[0094]

[0095] Where, is the weight coefficient of the detection index, is the anomaly membership value of the i-th detection index, i is the variable index, F is the anomaly score; when F is greater than or equal to 0.8, it is determined that an alarm needs to be pushed; when F is less than 0.8, it is determined to be normal and no alarm needs to be pushed.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0097] Example 2, referring to Figure 2 , is the second embodiment of the present invention. This embodiment provides an automatic data acquisition and analysis system for laboratory instruments, including a data acquisition and sorting module, a data processing and analysis module, a differential retest operation module, and a report generation and visualization module.

[0098] The data acquisition and sorting module is used to automatically obtain sample identification information, sort and allocate samples to different analyzers, upload detection data to the central database in real time, and generate a unique verification code through a dynamic protocol adaptation mechanism and a hash algorithm.

[0099] The data processing and analysis module includes a density clustering algorithm unit, a logical constraint relationship establishment unit, and a secondary detection process triggering unit, which are used to construct a multi-dimensional feature space based on historical detection data, divide the detection values into sub-clusters using the density clustering algorithm, establish cross-index logical constraint relationships in combination with an expert rule base, map real-time detection data to the multi-dimensional feature space, determine the type of data anomaly according to the sub-cluster attribution and logical constraint relationships, and trigger a secondary detection process when an anomaly is determined.

[0100] The differential re-inspection operation module includes a differential re-inspection operation execution unit, an instrument cleaning time dynamic adjustment unit, and a parameter adjustment plan selection unit, which are used to automatically dispatch the robotic arm to send the sample back to the specified instrument to perform differential re-inspection operations after determining data anomalies, dynamically adjust the instrument cleaning time according to the ratio of the anomaly score to the preset maximum anomaly value, and select a suitable cleaning, calibration, and environmental control parameter adjustment plan from the preset policy library.

[0101] The report generation and visualization module is used to integrate multi-dimensional detection data and anomaly marking information through a visualization interface, generate a comprehensive report including detection process traceability, and provide users with intuitive and comprehensive detection results and analysis.

[0102] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0103] Embodiment 3, the third embodiment of the present invention, which is different from the previous two embodiments:

[0104] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definitional sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0106] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0107] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

Claims

1. An automatic data acquisition and analysis method for laboratory instruments, characterized in that: Including, Automatically sorting and allocating the sample identification information to different analyzers, uploading the detection data to the central database in real time, and associating the sample number, detection timestamp, and instrument number; Constructing a multi-dimensional feature space based on historical detection data, using the density clustering algorithm to divide sub-clusters for the detection values of oil chromatography, moisture, dielectric loss, and breakdown voltage, and establishing cross-index logical constraint relationships in combination with the expert rule base; Mapping the real-time detection data to the multi-dimensional feature space, determining the data anomaly type according to the sub-cluster attribution and logical constraint relationships. When it is determined as an anomaly, trigger the secondary detection process, and automatically schedule the robotic arm to send the sample back to the designated instrument to perform differential re-inspection operations; Integrating multi-dimensional detection data and anomaly marking information through a visualization interface, and generating a comprehensive report including the traceability of the detection process; The sub-cluster division includes, based on the sub-cluster standard deviation and confidence coefficient, determining the reasonable fluctuation range of the detection value, storing the multi-variable logical constraint relationship. When the dielectric loss value exceeds the first threshold, the breakdown voltage must be lower than the second threshold, and dynamically match the anomaly scenario through a fuzzy inference engine to trigger the error correction process; The establishment of cross-index logical constraint relationships includes training a negative correlation model between the dielectric loss value and the breakdown voltage based on historical data to obtain the regression coefficient and prediction interval. If the real-time detection value exceeds the prediction interval and exceeds the tolerance threshold, it is marked as a logical conflict and triggers cross-instrument verification; The multi-variable logical constraint relationship is expressed as: , where and are linkage thresholds, D is the dielectric loss value, and B is the breakdown voltage; The negative correlation model is expressed as: , where D is the dielectric loss value, B is the breakdown voltage, a and b are regression coefficients, and g is the residual term; The execution of differential re-inspection operations includes dynamically adjusting the instrument cleaning time according to the ratio of the anomaly score to the preset maximum anomaly value, and selecting cleaning, calibration, and environmental control parameter adjustment schemes from the preset strategy library for different anomaly types; The formula for dynamically adjusting the instrument cleaning time is expressed as: , where t0 is the reference cleaning time, is the maximum adjustment amount, E is the current anomaly score, E max is the maximum anomaly value preset by the system, t clean is the adjusted cleaning time; The preset strategy library includes calculating the posterior probability based on the success rate of historical anomaly scenario types and re-inspection actions, selecting the optimal re-inspection strategy. When the new anomaly scenario does not match the existing strategy, call the expert knowledge base to supplement the rules and update the strategy library.

2. The automatic data acquisition and analysis method of a laboratory instrument according to claim 1, characterized in that: The different analyzers include an oil chromatography analyzer, a moisture analyzer, a dielectric loss analyzer, and a breakdown voltage analyzer; The real-time uploading of detection data includes that the data interface adopts a dynamic protocol adaptation mechanism, uniformly parsing the heterogeneous data frames of the protocol through a protocol conversion gateway, and generating a unique check code based on the hash algorithm. During the sorting process, by re-matching and verifying the mapping relationship with the task queue, when the verification fails, trigger the redundant communication link to retransmit.

3. The automatic data acquisition and analysis method for laboratory instruments according to claim 2, characterized in that: The density clustering algorithm includes dynamically calculating the neighborhood radius according to the mean and standard deviation of the historical data sample spacing, combined with a scaling factor, and determining the minimum number of samples based on the total amount of historical data and the preset number of sub-clusters, and initializing the sub-cluster center using an improved K-Means++ algorithm; The formula for calculating the neighborhood radius is: , where ∈ is the neighborhood radius, μ d is the mean of the historical data sample spacing, σ d is the standard deviation of the historical data sample spacing, and α is the scaling factor; The formula for determining the minimum number of samples is: , where is the total amount of historical data, k is the preset number of sub-clusters, and m is the minimum number of samples; The formula for initializing the sub-cluster center is expressed as: , where μ j is the centroid of the sub-cluster, C j is the sub-cluster, x is the sample point, and j is the variable index.

4. A method for automatically collecting and analyzing laboratory instrument data according to claim 3, characterized in that: The generation of a comprehensive report including traceability of the detection process includes synchronously displaying the oil chromatogram, moisture curve, and breakdown voltage trend through a parallel coordinate system, supporting multi-dimensional data comparison, grading the abnormal risk level with color based on the real-time abnormal density distribution, and dynamically adjusting the threshold based on the upper quartile and interquartile range of the real-time data; The generation of a comprehensive report including traceability of the detection process further includes pushing structured alarm information to the laboratory terminal when the abnormal score exceeds the preset threshold after secondary detection, and dynamically adjusting the alarm priority according to the logarithmic relationship between the abnormal score and the number of retest times; The abnormal score formula is expressed as: , where W i is the weight coefficient of the detection index, is the abnormal membership degree value of the i-th detection index, i is the variable index, and F is the abnormal score; when F is greater than or equal to 0.8, it is determined that an alarm needs to be pushed; when F is less than 0.8, it is determined to be normal and no alarm needs to be pushed.

5. The automatic data acquisition and analysis method for laboratory instruments according to claim 4, characterized in that: The preset policy library uses a Bayesian network to generate a retest instruction, and the conditional probability is expressed as: , where A is the re-inspection action, S is the type of abnormal scenario, is the probability of the re-inspection action A occurring under the given abnormal scenario type S, is the probability of observing the abnormal scenario type S under the given re-inspection action A, P(A) is the prior probability of the re-inspection action A occurring, and P(S) is the probability of the abnormal scenario type S occurring.

6. A system adopting an automatic data acquisition and analysis method for a laboratory instrument as described in any one of claims 1 to 5, characterized in that: It includes a data collection and sorting module, a data processing and analysis module, a differential retest operation module, and a report generation and visualization module; The data collection and sorting module is used to automatically obtain sample identification information, sort and allocate samples to different analyzers, upload detection data to the central database in real time, and generate a unique verification code through a dynamic protocol adaptation mechanism and a hash algorithm; The data processing and analysis module includes a density clustering algorithm unit, a logical constraint relationship establishment unit, and a secondary detection process trigger unit, and is used to construct a multi-dimensional feature space based on historical detection data, use the density clustering algorithm to divide the detection values into sub-clusters, establish cross-index logical constraint relationships in combination with the expert rule library, map the real-time detection data to the multi-dimensional feature space, determine the abnormal data type according to the sub-cluster attribution and logical constraint relationships, and trigger the secondary detection process when it is determined to be abnormal; The differential retest operation module includes a differential retest operation execution unit, an instrument cleaning time dynamic adjustment unit, and a parameter adjustment scheme selection unit, and is used to automatically dispatch the robotic arm to send the sample back to the designated instrument to perform differential retest operations after determining data abnormalities, dynamically adjust the instrument cleaning time according to the ratio of the abnormal score to the preset maximum abnormal value, and select a suitable cleaning, calibration, and environmental control parameter adjustment scheme from the preset policy library; The report generation and visualization module is used to integrate multi-dimensional detection data and abnormal marking information through a visual interface, generate a comprehensive report including traceability of the detection process, and provide users with intuitive and comprehensive detection results and analysis.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of an automatic data collection and analysis method for laboratory instruments according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of an automatic data collection and analysis method for laboratory instruments according to any one of claims 1 to 5.

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