Intelligent laboratory comprehensive management method and system fused with big data analysis

Abstract

The invention belongs to the field of smart laboratory management, relates to a data analysis technology, and aims to solve the problem that dynamic parameter setting cannot be performed on the basis of static data of equipment in the prior art, in particular to a smart laboratory comprehensive management method and system fused with big data analysis. The comprehensive management platform is in communication connection with a static analysis module, a dynamic adjustment module, a fault monitoring module, a management optimization module and a database; the static analysis module is used for performing static data analysis on the equipment of the intelligent laboratory; the laboratory equipment put into use is marked as an analysis object, and the analysis object is marked as a conventional object or an indented object through a maintenance proximity value and a consumable stock value; static data analysis can be carried out on the equipment in the smart laboratory, the fault probability of the equipment in the experiment process is predicted according to the maintenance stage of the equipment and the consumable stock number, and data support is provided for the dynamic adjustment process.

Description

Technical Field

[0001] The present invention belongs to the field of smart laboratory management and relates to data analysis technology, specifically a smart laboratory comprehensive management method and system integrating big data analysis. Background Art

[0002] The comprehensive management method of smart laboratories is to achieve intelligent management of laboratory resources, processes, security and data through technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), thereby improving scientific research efficiency, compliance, and safety.

[0003] The invention patent with publication number CN107730125B discloses a laboratory management system. When the functional requirements of the laboratory or the number of laboratories changes or expands, it can flexibly configure independent applications for running the changed or expanded business function logic, thereby facilitating the corresponding transformation and upgrade of the laboratory management system. However, the management system cannot perform dynamic parameter settings based on static data such as equipment maintenance data and consumables inventory data. That is, the dynamic parameter settings of the equipment cannot be effectively combined with the failure probability prediction results, resulting in the inability to effectively curb the risk of equipment failure and the inability to ensure the continuity and safety of the experimental process.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a comprehensive management method and system for smart laboratories that integrates big data analysis, which is used to solve the problem that the existing technology cannot perform dynamic parameter setting based on static equipment data;

[0006] The technical problem to be solved by the present invention is: how to provide a smart laboratory comprehensive management method and system that can perform integrated big data analysis based on dynamic parameter settings of equipment static data.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A smart laboratory integrated management system integrating big data analysis includes an integrated management platform, wherein the integrated management platform is communicatively connected to a static analysis module, a dynamic adjustment module, a fault monitoring module, a management optimization module, and a database;

[0009] The static analysis module is used to perform static data analysis on the equipment in the smart laboratory: marking the laboratory equipment in use as the analysis object, obtaining the maintenance proximity value and consumable inventory value of the analysis object before the analysis object performs the experimental process, and marking the analysis object as a regular object or an indented object based on the maintenance proximity value and consumable inventory value;

[0010] The dynamic adjustment module is used to dynamically adjust and analyze the operating parameters and environmental parameters of the equipment in the smart laboratory using a conventional adjustment mode or an indentation adjustment mode;

[0011] The fault monitoring module is used to perform fault monitoring and analysis on the equipment in the smart laboratory: during the experiment, the operating status of the analysis object is monitored in real time, and whether the analysis object has a fault is determined: if so, the analysis object is marked as a fault object; if not, no marking or processing is performed;

[0012] The management optimization module is used to perform management optimization analysis on the equipment in the smart laboratory.

[0013] Furthermore, the process of obtaining the maintenance proximity value includes: marking the time difference between the system time and the time when the analysis object was last maintained as the maintenance value, and marking the difference between the duration of the fixed maintenance cycle of the analysis object and the maintenance value as the maintenance proximity value; the consumables inventory value is the inventory quantity of the consumable materials used by the analysis object to perform the experimental process in the warehouse.

[0014] Furthermore, the specific process of marking the analysis object as a regular object or an indented object includes: obtaining a maintenance proximity threshold and a consumables inventory threshold through a database, and comparing the maintenance proximity value and the consumables inventory value of the analysis object with the maintenance proximity threshold and the consumables inventory threshold respectively: if the maintenance proximity value is greater than the maintenance proximity threshold and the consumables inventory value is greater than the consumables inventory threshold, then it is determined that the static analysis state of the analysis object meets the requirements, the corresponding analysis object is marked as a regular object, a regular adjustment signal is generated and the regular adjustment signal is sent to the dynamic adjustment module; otherwise, it is determined that the static analysis state of the analysis object does not meet the requirements, the corresponding analysis object is marked as an indented object, an indentation adjustment signal is generated and the indentation adjustment signal is sent to the dynamic adjustment module.

[0015] Furthermore, when the dynamic adjustment module receives a conventional adjustment signal, it uses the conventional adjustment mode to adjust the parameters of the conventional object: obtain all operating parameter ranges and environmental parameter ranges of the conventional object, randomly select a value from the operating parameter range as the adjustment value of the corresponding operating parameter of the conventional object, randomly select a value from the environmental parameter range as the adjustment value of the corresponding environmental parameter of the conventional object, and set all operating parameters and environmental parameters of the conventional object according to the adjustment value.

[0016] Furthermore, when the dynamic adjustment module receives the indentation adjustment signal, it adopts the indentation adjustment mode to adjust the parameters of the indentation object: obtain all operating parameter ranges and environmental parameter ranges of the indentation object, and indent the operating parameter range and the environmental parameter range to obtain the operating indentation range and the environmental indentation range; randomly select a numerical value from the operating indentation range as the adjustment value of the operating parameter corresponding to the indentation object, and randomly select a numerical value from the environmental indentation range as the adjustment value of the environmental parameter corresponding to the indentation object, and set all operating parameters and environmental parameters of the indentation object according to the adjustment value.

[0017] Furthermore, the process of obtaining the operating indentation range and the environmental indentation range includes: taking the product of the maximum boundary value of the operating parameter range and t1 as the operating high value, taking the product of the minimum boundary value of the operating parameter range and t2 as the operating low value, and the operating high value and the operating low value constitute the operating indentation range of the corresponding operating parameter, taking the product of the maximum boundary value of the environmental parameter range and t1 as the environmental high value, taking the product of the minimum boundary value of the environmental parameter range and t2 as the environmental low value, and the environmental high value and the environmental low value constitute the environmental indentation range of the corresponding environmental parameter, t1 and t2 are both proportional coefficients, and 0.75≤t1≤0.85, 1.15≤t2≤1.25.

[0018] Furthermore, the management optimization module is used to perform management optimization analysis on the equipment in the smart laboratory: generate a management cycle, mark the number of times the operation verification signal and the maintenance processing signal are received within the management cycle as the operation verification value and the maintenance processing value respectively, obtain the operation verification threshold and the maintenance processing threshold through the database, compare the operation verification value and the maintenance processing value with the operation verification threshold and the maintenance processing threshold respectively, and judge whether the management cycle has the necessity of operation optimization and maintenance optimization based on the comparison results.

[0019] A comprehensive management method for smart laboratories integrating big data analysis includes the following steps:

[0020] Step 1: Perform static data analysis on the equipment in the smart laboratory;

[0021] Step 2: Use the conventional adjustment mode or the indentation adjustment mode to dynamically adjust and analyze the operating parameters and environmental parameters of the equipment in the smart laboratory;

[0022] Step 3: Conduct fault monitoring and analysis on the equipment in the smart laboratory;

[0023] Step 4: Conduct management optimization analysis on the equipment in the smart laboratory.

[0024] The present invention has the following beneficial effects:

[0025] 1. The static analysis module can be used to perform static data analysis on the equipment in the smart laboratory. The failure probability of the equipment during the experiment can be predicted based on the equipment's maintenance stage and the inventory of consumables. This allows for differentiated labeling of the analysis objects and provides data support for the dynamic adjustment process.

[0026] 2. The dynamic adjustment module can be used to dynamically adjust and analyze the equipment in the smart laboratory. By analyzing the differentiated marking results of the analysis object, different adjustment methods are used to set the operating parameters and environmental parameters of the analysis object. This allows equipment with a higher probability of failure to obtain parameter settings closer to the standard value before the experiment, reducing the probability of equipment failure during the experiment and the difficulty of handling failures.

[0027] 3. The fault monitoring module can be used to monitor and analyze faults in smart laboratory equipment. It can provide timely feedback and alarms when different types of equipment faults occur. It can also quickly generate processing decisions based on the marked results of the analysis objects, thereby improving fault handling efficiency. From dynamic fault monitoring results to static maintenance opportunity selection, it can improve the health of equipment throughout its life cycle.

[0028] 4. The management optimization module can be used to perform management optimization analysis on the equipment in the smart laboratory, and the optimization direction of the management cycle can be determined through the operation verification value and maintenance processing value, thereby reducing the probability of equipment failure during the experiment in the subsequent management cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 work.

[0030] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0031] Figure 2 This is a flow chart of the method of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] Example 1: Figure 1As shown, a smart laboratory comprehensive management system integrating big data analysis includes a comprehensive management platform, which is connected to a static analysis module, a dynamic adjustment module, a fault monitoring module, a management optimization module and a database.

[0034] The static analysis module is used to perform static data analysis on the equipment in the smart laboratory: the laboratory equipment put into use is marked as the analysis object, and before the analysis object performs the experimental process, the maintenance proximity value and consumable inventory value of the analysis object are obtained. The process of obtaining the maintenance proximity value includes: marking the time difference between the system time and the time when the analysis object was last maintained as the maintenance value, and marking the difference between the duration of the fixed maintenance cycle of the analysis object and the maintenance value as the maintenance proximity value; the consumable inventory value is the inventory quantity of the loss materials in the warehouse when the analysis object performs the experimental process; the maintenance proximity threshold and consumable inventory threshold are obtained through the database, and the maintenance proximity value and consumable inventory value of the analysis object are compared with the maintenance proximity threshold and consumable inventory threshold respectively. Row comparison: If the maintenance proximity value is greater than the maintenance proximity threshold and the consumables inventory value is greater than the consumables inventory threshold, the static analysis status of the analysis object is determined to meet the requirements, the corresponding analysis object is marked as a regular object, a regular adjustment signal is generated and the regular adjustment signal is sent to the dynamic adjustment module; otherwise, the static analysis status of the analysis object is determined to not meet the requirements, the corresponding analysis object is marked as an indentation object, an indentation adjustment signal is generated and the indentation adjustment signal is sent to the dynamic adjustment module; static data analysis is performed on the equipment in the smart laboratory, and the failure probability of the equipment during the experiment is predicted based on the maintenance stage of the equipment and the consumables inventory quantity, so as to differentiate the analysis objects and provide data support for the dynamic adjustment process.

[0035] The dynamic adjustment module is used to perform dynamic adjustment analysis on the equipment of the smart laboratory: when the dynamic adjustment module receives a conventional adjustment signal, it adopts the conventional adjustment mode to adjust the parameters of the conventional object: obtain all the operating parameter ranges and environmental parameter ranges of the conventional object, randomly select a value from the operating parameter range as the adjustment value of the corresponding operating parameter of the conventional object, randomly select a value from the environmental parameter range as the adjustment value of the corresponding environmental parameter of the conventional object, and set all the operating parameters and environmental parameters of the conventional object according to the adjustment value; when the dynamic adjustment module receives an indentation adjustment signal, it adopts the indentation adjustment mode to adjust the parameters of the indentation object: obtain all the operating parameter ranges and environmental parameter ranges of the conventional object, and indent the operating parameter range and the environmental parameter range: take the product of the maximum boundary value of the operating parameter range and t1 as the operating high value, take the product of the minimum boundary value of the operating parameter range and t2 as the operating low value, and the operating high value and the operating low value constitute the operating value of the corresponding operating parameter. Indentation range, the product of the maximum boundary value of the environmental parameter range and t1 is taken as the environmental high value, and the product of the minimum boundary value of the environmental parameter range and t2 is taken as the environmental low value. The environmental high value and the environmental low value constitute the environmental indentation range of the corresponding environmental parameter, t1 and t2 are both proportional coefficients, and 0.75≤t1≤0.85, 1.15≤t2≤1.25; randomly select a value from the operating indentation range as the adjustment value of the operating parameter corresponding to the indentation object, and randomly select a value from the environmental indentation range as the adjustment value of the environmental parameter corresponding to the indentation object, and set all operating parameters and environmental parameters of the indentation object according to the adjustment value; perform dynamic adjustment analysis on the equipment of the smart laboratory, and use different adjustment methods to set the operating parameters and environmental parameters of the analysis object through the differentiated marking results of the analysis object, so that the equipment with a higher failure probability can obtain parameter settings closer to the standard value before the experiment, reducing the failure probability of the equipment during the experiment and the difficulty of handling the failure.

[0036] The fault monitoring module is used to perform fault monitoring and analysis on the equipment in the smart laboratory: during the experiment, the operating status of the analysis object is monitored in real time, and whether the analysis object has a fault: if so, the analysis object is marked as a faulty object. If the faulty object is marked as a regular object, an operation verification signal is generated and sent to the management optimization module and the administrator's mobile terminal; if the faulty object is marked as an indented object, a maintenance processing signal is generated and sent to the management optimization module and the administrator's mobile terminal; if not, no marking and processing are performed; the monitored fault types include sudden faults, degradation faults, temporary faults, permanent faults, etc.; fault monitoring and analysis of the equipment in the smart laboratory can provide timely feedback and alarms when different types of faults occur in the equipment, and quickly generate processing decisions based on the marking results of the analysis object, thereby improving fault handling efficiency, feedback from dynamic fault monitoring results to static maintenance opportunity selection, and improving the health status of the equipment during its life cycle; equipment fault monitoring can be achieved through equipment operation status monitoring. For example, fault monitoring of the spectrometer in the laboratory can enable the equipment self-diagnosis program to check the status of core components such as detectors, circuit boards, and communication modules.

[0037] The management optimization module is used to perform management optimization analysis on the equipment in the smart laboratory: generate a management cycle, mark the number of times the operation verification signal and the maintenance processing signal are received within the management cycle as the operation verification value and the maintenance processing value respectively, obtain the operation verification threshold and the maintenance processing threshold through the database, and compare the operation verification value and the maintenance processing value with the operation verification threshold and the maintenance processing threshold respectively: if the operation verification value is less than the maintenance processing value, it is determined that the management cycle does not need to be optimized; if the operation verification value is greater than or equal to the operation verification threshold and the maintenance processing value is less than the maintenance processing threshold, it is determined that the management cycle needs to be optimized, generate a compliance operation training signal and send the compliance operation training signal to the mobile terminal of the manager; if the operation verification value is less than If the operation verification value is greater than or equal to the operation verification threshold and the maintenance processing value is greater than or equal to the maintenance processing threshold, it is determined that the management cycle has the necessity of maintenance optimization, a maintenance training signal is generated, and the maintenance training signal is sent to the mobile terminal of the manager; if the operation verification value is greater than or equal to the operation verification threshold and the maintenance processing value is greater than or equal to the maintenance processing threshold, it is determined that the management cycle has the necessity of both operation optimization and maintenance optimization, a compliance operation training signal and a maintenance training signal are generated, and the compliance operation training signal and the maintenance training signal are sent to the mobile terminal of the manager; management optimization analysis is performed on the equipment in the smart laboratory, and the optimization direction of the management cycle is determined by the operation verification value and the maintenance processing value, so as to reduce the probability of equipment failure during the experiment in the subsequent management cycle.

[0038] Example 2: Figure 2As shown, a comprehensive management method for smart laboratories integrating big data analysis includes the following steps:

[0039] Step 1: Perform static data analysis on the equipment in the smart laboratory: Mark the laboratory equipment in use as analysis objects. Before the analysis objects perform the experimental process, mark the analysis objects as regular objects or indented objects by maintaining the proximity value and consumable inventory value;

[0040] Step 2: Use the conventional adjustment mode or the indentation adjustment mode to dynamically adjust and analyze the operating parameters and environmental parameters of the equipment in the smart laboratory;

[0041] Step 3: Perform fault monitoring and analysis on the equipment in the smart laboratory: Monitor the operating status of the analysis object in real time during the experiment and determine whether there is any fault in the analysis object;

[0042] Step 4: Conduct management optimization analysis on the equipment in the smart laboratory: Generate a management cycle, mark the number of times the operation verification signal and maintenance processing signal are received within the management cycle as the operation verification value and maintenance processing value respectively, and use the operation verification value and maintenance processing value to determine whether the management cycle has the necessity of operation optimization and maintenance optimization.

[0043] A comprehensive management method and system for smart laboratories integrating big data analysis. During operation, laboratory equipment in use is marked as an analysis object. Before the analysis object performs an experiment, the analysis object is marked as a regular object or an indented object by maintaining a proximity value and a consumables inventory value. The operating parameters and environmental parameters of the equipment in the smart laboratory are dynamically adjusted and analyzed using a regular adjustment mode or an indented adjustment mode. The operating status of the analysis object is monitored in real time during the experiment, and it is determined whether the analysis object has a fault. A management cycle is generated, and the number of times an operation verification signal and a maintenance processing signal are received within the management cycle are marked as an operation verification value and a maintenance processing value, respectively. The operation verification value and the maintenance processing value are used to determine whether the management cycle has the necessity of operation optimization and the necessity of maintenance optimization.

[0044] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0045] 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.

[0046] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart laboratory integrated management system integrating big data analysis, characterized by: It includes an integrated management platform, which is communicatively connected to a static analysis module, a dynamic adjustment module, a fault monitoring module, a management optimization module, and a database; The static analysis module is used to perform static data analysis on the equipment in the smart laboratory: marking the laboratory equipment in use as the analysis object, obtaining the maintenance proximity value and consumable inventory value of the analysis object before the analysis object performs the experimental process, and marking the analysis object as a regular object or an indented object based on the maintenance proximity value and consumable inventory value; The dynamic adjustment module is used to dynamically adjust and analyze the operating parameters and environmental parameters of the equipment in the smart laboratory using a conventional adjustment mode or an indentation adjustment mode; The fault monitoring module is used to perform fault monitoring and analysis on the equipment in the smart laboratory: during the experiment, the operating status of the analysis object is monitored in real time, and whether the analysis object has a fault is determined: if so, the analysis object is marked as a fault object; if not, no marking or processing is performed; The management optimization module is used to perform management optimization analysis on the equipment in the smart laboratory.

2. The intelligent laboratory integrated management system integrating big data analysis according to claim 1 is characterized in that: The process of obtaining the maintenance proximity value includes: marking the time difference between the system time and the time when the analysis object was last maintained as the maintenance value, and marking the difference between the duration of the fixed maintenance cycle of the analysis object and the maintenance value as the maintenance proximity value; the consumables inventory value is the inventory quantity of the consumable materials used by the analysis object to perform the experimental process in the warehouse.

3. The intelligent laboratory integrated management system integrating big data analysis according to claim 2 is characterized in that: The specific process of marking the analysis object as a regular object or an indented object includes: obtaining the maintenance proximity threshold and the consumables inventory threshold through the database, and comparing the maintenance proximity value and the consumables inventory value of the analysis object with the maintenance proximity threshold and the consumables inventory threshold respectively: if the maintenance proximity value is greater than the maintenance proximity threshold and the consumables inventory value is greater than the consumables inventory threshold, then it is determined that the static analysis state of the analysis object meets the requirements, the corresponding analysis object is marked as a regular object, a regular adjustment signal is generated and the regular adjustment signal is sent to the dynamic adjustment module; otherwise, it is determined that the static analysis state of the analysis object does not meet the requirements, the corresponding analysis object is marked as an indented object, an indentation adjustment signal is generated and the indentation adjustment signal is sent to the dynamic adjustment module.

4. The intelligent laboratory integrated management system integrating big data analysis according to claim 3 is characterized in that: When the dynamic adjustment module receives a conventional adjustment signal, it uses the conventional adjustment mode to adjust the parameters of the conventional object: obtain all operating parameter ranges and environmental parameter ranges of the conventional object, randomly select a value from the operating parameter range as the adjustment value of the corresponding operating parameter of the conventional object, randomly select a value from the environmental parameter range as the adjustment value of the corresponding environmental parameter of the conventional object, and set all operating parameters and environmental parameters of the conventional object according to the adjustment value.

5. The intelligent laboratory integrated management system integrating big data analysis according to claim 4 is characterized in that: When the dynamic adjustment module receives the indentation adjustment signal, it uses the indentation adjustment mode to adjust the parameters of the indentation object: obtain all the operating parameter ranges and environmental parameter ranges of the indentation object, and indent the operating parameter ranges and environmental parameter ranges to obtain the operating indentation range and the environmental indentation range; randomly select a value from the operating indentation range as the adjustment value of the operating parameter corresponding to the indentation object, and randomly select a value from the environmental indentation range as the adjustment value of the environmental parameter corresponding to the indentation object, and set all the operating parameters and environmental parameters of the indentation object according to the adjustment value.

6. The intelligent laboratory integrated management system integrating big data analysis according to claim 5 is characterized in that: The process of obtaining the operating indentation range and the environmental indentation range includes: taking the product of the maximum boundary value of the operating parameter range and t1 as the operating high value, taking the product of the minimum boundary value of the operating parameter range and t2 as the operating low value, and the operating high value and the operating low value constitute the operating indentation range of the corresponding operating parameter, taking the product of the maximum boundary value of the environmental parameter range and t1 as the environmental high value, taking the product of the minimum boundary value of the environmental parameter range and t2 as the environmental low value, and the environmental high value and the environmental low value constitute the environmental indentation range of the corresponding environmental parameter, t1 and t2 are both proportional coefficients, and 0.75≤t1≤0.85, 1.15≤t2≤1.

25.

7. The intelligent laboratory integrated management system integrating big data analysis according to claim 6 is characterized in that: The management optimization module is used to perform management optimization analysis on the equipment in the smart laboratory: generate a management cycle, mark the number of operation verification signals and maintenance processing signals received within the management cycle as operation verification values and maintenance processing values respectively, obtain the operation verification threshold and maintenance processing threshold through the database, compare the operation verification value and maintenance processing value with the operation verification threshold and maintenance processing threshold respectively, and judge whether the management cycle has the necessity of operation optimization and maintenance optimization based on the comparison results.

8. A comprehensive management method for smart laboratories integrating big data analysis, characterized in that: The following steps are involved: Step 1: Perform static data analysis on the equipment in the smart laboratory; Step 2: Use the conventional adjustment mode or the indentation adjustment mode to dynamically adjust and analyze the operating parameters and environmental parameters of the equipment in the smart laboratory; Step 3: Conduct fault monitoring and analysis on the equipment in the smart laboratory; Step 4: Conduct management optimization analysis on the equipment in the smart laboratory.

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