A Smart Laboratory Integrated Management Method and System Integrating Big Data Analytics

Abstract

This invention belongs to the field of smart laboratory management and involves data analysis technology. It addresses the problem that existing technologies cannot dynamically set parameters based on static equipment data. Specifically, it is a comprehensive smart laboratory management method and system integrating big data analysis. The system includes a comprehensive 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 performs static data analysis on the equipment in the smart laboratory: marking the laboratory equipment in use as analysis objects, and marking the analysis objects as regular objects or indented objects by maintaining proximity values ​​and consumable inventory values. This invention can perform static data analysis on the equipment in the smart laboratory, predict the probability of equipment failure during experiments based on the equipment's maintenance stage and consumable inventory quantity, and provide data support for the dynamic adjustment process.

Description

Technical Field

[0001] This invention belongs to the field of smart laboratory management and involves data analysis technology. Specifically, it is a smart laboratory integrated management method and system that integrates big data analysis. Background Technology

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

[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, which facilitates the corresponding modification and upgrade of the laboratory management system. However, this management system cannot set dynamic parameters based on static data such as equipment maintenance data and consumable 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 guarantee the continuity and safety of the experimental process.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a smart laboratory integrated management method and system that integrates big data analysis, in order to solve the problem that existing technologies cannot set dynamic parameters based on static equipment data;

[0006] The technical problem to be solved by this invention is: how to provide a smart laboratory integrated management method and system that integrates big data analysis and allows for dynamic parameter setting based on static equipment data.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A smart laboratory integrated management system that integrates big data analytics 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.

[0009] The static analysis module is used to perform static data analysis on the equipment in the smart laboratory: the laboratory equipment in use is marked as the analysis object, and before the analysis object performs the experiment, the maintenance proximity value and consumable inventory value of the analysis object are obtained, and the analysis object is marked as a regular object or an indented object by 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 either a conventional adjustment mode or a recessed adjustment mode.

[0011] The fault monitoring module is used to monitor and analyze the equipment in the smart laboratory: during the experiment, it monitors the operating status of the analyzed object in real time and determines whether the analyzed object has a fault: if so, the analyzed object is marked as a faulty 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 of the last maintenance of the analysis object 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 quantity of consumable materials in the warehouse for the analysis object to perform the experimental process.

[0014] Furthermore, the specific process of marking the analysis object as a regular object or an indented object includes: obtaining the maintenance proximity threshold and consumable inventory threshold from the database; comparing the maintenance proximity value and consumable inventory value of the analysis object with the maintenance proximity threshold and consumable inventory threshold respectively; if the maintenance proximity value is greater than the maintenance proximity threshold and the consumable inventory value is greater than the consumable inventory threshold, then the static analysis state 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 state of the analysis object is determined to 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: it obtains the range of all operating parameters and environmental parameters of the conventional object, randomly selects a value from the range of operating parameters as the adjustment value of the corresponding operating parameter of the conventional object, randomly selects a value from the range of environmental parameters as the adjustment value of the corresponding environmental parameter of the conventional object, and sets 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 uses the indentation adjustment mode to adjust the parameters of the indented object: it obtains the range of all operating parameters and environmental parameters of the indented object, performs indentation processing on the operating parameter range and environmental parameter range to obtain the operating indentation range and the environmental indentation range; it randomly selects a value from the operating indentation range as the adjustment value of the corresponding operating parameter of the indented object, and randomly selects a value from the environmental indentation range as the adjustment value of the corresponding environmental parameter of the indented object, and sets all operating parameters and environmental parameters of the indented 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 using the operating high value and the operating low value to form 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 using the environmental high value and the environmental low value to form the environmental indentation range of the corresponding environmental parameter. t1 and t2 are both proportionality 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: it generates a management cycle, marks the number of times operation verification signals and maintenance processing signals are received within the management cycle as operation verification values ​​and maintenance processing values, respectively, obtains operation verification thresholds and maintenance processing thresholds from the database, compares the operation verification values ​​and maintenance processing values ​​with the operation verification thresholds and maintenance processing thresholds, and determines whether the management cycle has the necessity for operation optimization and maintenance optimization based on the comparison results.

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

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

[0021] Step 2: Perform dynamic adjustment and analysis on the operating parameters and environmental parameters of the equipment in the smart laboratory using either the conventional adjustment mode or the indentation adjustment mode;

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

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

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

[0025] 1. The static analysis module can perform static data analysis on the equipment in the smart laboratory. Based on the maintenance stage of the equipment and the inventory of consumables, the probability of equipment failure during the experiment can be predicted, thereby differentiating the analysis objects and providing data support for the dynamic adjustment process.

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

[0027] 3. The fault monitoring module can perform fault monitoring and analysis on the equipment in the smart laboratory. When different types of faults occur in the equipment, timely feedback and alarms can be provided. Combined with the marking results of the analysis objects, processing decisions can be generated quickly to improve the efficiency of fault handling. From dynamic fault monitoring results to static maintenance timing selection, the health status of the equipment throughout its life cycle can be improved.

[0028] 4. The management optimization module can perform management optimization analysis on the equipment in the smart laboratory. By using operation verification values ​​and maintenance processing values, the optimization direction of the management cycle can be determined, reducing the probability of equipment failure during experiments in subsequent management cycles. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0031] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0032] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: As Figure 1As shown, a smart laboratory integrated management system that integrates big data analysis 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.

[0034] The static analysis module is used to perform static data analysis on the equipment in the smart laboratory. It marks the laboratory equipment in use as the analysis object. Before the analysis object performs an experiment, it obtains the maintenance proximity value and consumable inventory value. The process of obtaining the maintenance proximity value includes: marking the time difference between the system time and the time of the last maintenance of the analysis object 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 quantity of consumable materials in the warehouse for the analysis object during the experiment. It also obtains the maintenance proximity threshold and consumable inventory threshold from the database, and compares the maintenance proximity value and consumable inventory value of the analysis object 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 consumable inventory value is greater than the consumable inventory threshold, then the static analysis state of the analyzed object is determined to meet the requirements. The corresponding analyzed 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 state of the analyzed object is determined to not meet the requirements. The corresponding analyzed 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. Static data analysis is performed on the equipment in the smart laboratory. Based on the equipment's maintenance stage and consumable inventory quantity, the failure probability of the equipment during the experiment is predicted, thereby differentiating the analyzed objects and providing data support for the dynamic adjustment process.

[0035] The dynamic adjustment module is used to perform dynamic adjustment analysis on the equipment in the smart laboratory. When the dynamic adjustment module receives a regular adjustment signal, it uses the regular adjustment mode to adjust the parameters of the regular object: it acquires all operating parameter ranges and environmental parameter ranges of the regular object, randomly selects a value from the operating parameter range as the adjustment value for the corresponding operating parameter of the regular object, and randomly selects a value from the environmental parameter range as the adjustment value for the corresponding environmental parameter of the regular object, and sets all operating parameters and environmental parameters of the regular object according to the adjustment value. When the dynamic adjustment module receives an indentation adjustment signal, it uses the indentation adjustment mode to adjust the parameters of the indented object: it acquires all operating parameter ranges and environmental parameter ranges of the regular object, and performs indentation processing on the operating parameter ranges and environmental parameter ranges: the product of the maximum boundary value of the operating parameter range and t1 is taken as the operating high value, and the product of the minimum boundary value of the operating parameter range and t2 is taken as the operating low value. The operating high value and the operating low value constitute the operating parameters of the corresponding operating parameters. The indentation range is defined as follows: 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 environmental low value constitute the environmental indentation range for the corresponding environmental parameter. t1 and t2 are both proportional coefficients, and 0.75≤t1≤0.85, 1.15≤t2≤1.25. A value is randomly selected from the operational indentation range as the adjustment value for the corresponding operational parameter of the indented object, and a value is randomly selected from the environmental indentation range as the adjustment value for the corresponding environmental parameter of the indented object. All operational and environmental parameters of the indented object are set according to the adjustment value. Dynamic adjustment analysis is performed on the equipment in the smart laboratory. By analyzing the differentiated labeling results of the analyzed objects, different adjustment methods are used to set the operational and environmental parameters of the analyzed objects. This allows equipment with a high probability of failure to obtain parameter settings closer to the standard values ​​before the experiment, reducing the probability of failure and the difficulty of handling failures during the experiment.

[0036] The fault monitoring module is used to monitor and analyze the equipment in the smart laboratory. During experiments, it monitors the operational status of the analyzed objects in real time and determines if any faults exist. If a faulty object is identified, it is marked as such. 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. Otherwise, no marking or processing is performed. Monitored fault types include sudden faults, deterioration faults, temporary faults, and permanent faults. Fault monitoring and analysis of the smart laboratory equipment allows for timely feedback and alarms when different types of faults occur. Combined with the marking results of the analyzed objects, it quickly generates processing decisions, improving fault handling efficiency. Dynamic fault monitoring results are fed back to static maintenance timing selection, improving the health status of the equipment throughout its lifecycle. Equipment fault monitoring can be achieved through equipment operational status monitoring. For example, fault monitoring of a spectrometer in the laboratory can enable the equipment's self-diagnostic 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: It generates a management cycle, marking the number of operation verification signals and maintenance processing signals received within the cycle as operation verification values ​​and maintenance processing values, respectively. It retrieves operation verification thresholds and maintenance processing thresholds from the database and compares these values ​​with the respective thresholds: if the operation verification value is less than the maintenance processing value, the management cycle is deemed not necessary for optimization; 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, the management cycle is deemed necessary for operation optimization, a compliance operation training signal is generated, and the signal is sent to the administrator's mobile terminal; if the operation verification value is less than the maintenance processing threshold... If both the operation verification value and the maintenance processing value are greater than or equal to the maintenance processing value, the management cycle is deemed necessary for maintenance optimization. A maintenance training signal is generated and sent to the administrator's mobile terminal. If both the operation verification value and the maintenance processing value are greater than or equal to the operation verification threshold, the management cycle is deemed necessary for both operation optimization and maintenance optimization. A compliant operation training signal and a maintenance training signal are generated and sent to the administrator's mobile terminal. The system performs management optimization analysis on the equipment in the smart laboratory, using the operation verification value and maintenance processing value to determine the optimization direction of the management cycle, thereby reducing the probability of equipment failure during experiments in subsequent management cycles.

[0038] Example 2: Figure 2As shown, a comprehensive management method for smart laboratories integrating big data analytics 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 the analysis object. Before the analysis object performs the experiment, mark the analysis object as a regular object or an indented object by maintaining the proximity value and consumable inventory value.

[0040] Step 2: Perform dynamic adjustment and analysis on the operating parameters and environmental parameters of the equipment in the smart laboratory using either the conventional adjustment mode or the indentation adjustment mode;

[0041] Step 3: Perform fault monitoring and analysis on the equipment in the smart laboratory: Monitor the operating status of the analyzed objects in real time during the experiment and determine whether the analyzed objects have faults;

[0042] Step 4: Perform management optimization analysis on the equipment in the smart laboratory: Generate a management cycle, and mark the number of times operation verification signals and maintenance processing signals are received within the management cycle as operation verification values ​​and maintenance processing values, respectively. Use the operation verification values ​​and maintenance processing values ​​to determine whether the management cycle has the necessity for operation optimization and maintenance optimization.

[0043] A comprehensive management method and system for smart laboratories integrating big data analytics is disclosed. During operation, laboratory equipment in use is marked as analysis objects. Before an analysis object performs an experiment, it is marked as a regular object or an indented object by maintaining proximity values ​​and consumable inventory values. The system dynamically adjusts and analyzes the operating parameters and environmental parameters of the smart laboratory equipment using either a regular adjustment mode or an indented adjustment mode. During the experiment, the system monitors the operating status of the analysis objects in real time and determines whether any faults exist. A management cycle is generated, and the number of operation verification signals and maintenance processing signals received within the management cycle are marked as operation verification values ​​and maintenance processing values, respectively. These operation verification values ​​and maintenance processing values ​​are used to determine whether the management cycle requires operational and maintenance optimization.

[0044] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0045] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above 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 one or more embodiments or examples.

[0046] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A smart laboratory integrated management system that integrates big data analytics, characterized in that, 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: the laboratory equipment in use is marked as the analysis object, and before the analysis object performs the experiment, the maintenance proximity value and consumable inventory value of the analysis object are obtained, and the analysis object is marked as a regular object or an indented object by 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 either a conventional adjustment mode or a recessed adjustment mode. The fault monitoring module is used to monitor and analyze the equipment in the smart laboratory: during the experiment, it monitors the operating status of the analyzed object in real time and determines whether the analyzed object has a fault: if so, the analyzed object is marked as a faulty object; if not, no marking or processing is performed. The management optimization module is used for management optimization analysis of the equipment in the smart laboratory; When the dynamic adjustment module receives a regular adjustment signal, it uses the regular adjustment mode to adjust the parameters of the regular object: it obtains the range of all operating parameters and environmental parameters of the regular object, randomly selects a value from the range of operating parameters as the adjustment value of the corresponding operating parameter of the regular object, randomly selects a value from the range of environmental parameters as the adjustment value of the corresponding environmental parameter of the regular object, and sets all operating parameters and environmental parameters of the regular object according to the adjustment value. When the dynamic adjustment module receives an indentation adjustment signal, it uses the indentation adjustment mode to adjust the parameters of the indented object: it obtains the range of all operating parameters and environmental parameters of the indented object, performs indentation processing on the operating parameter range and environmental parameter range to obtain the operating indentation range and the environmental indentation range; it randomly selects a value from the operating indentation range as the adjustment value of the corresponding operating parameter of the indented object, and randomly selects a value from the environmental indentation range as the adjustment value of the corresponding environmental parameter of the indented object, and sets all operating parameters and environmental parameters of the indented object according to the adjustment value; 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 using the operating high value and the operating low value to form 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 using the environmental high value and the environmental low value to form 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; The management optimization module is used to perform management optimization analysis on the equipment in the smart laboratory: it generates a management cycle, marks the number of times operation verification signals and maintenance processing signals are received within the management cycle as operation verification values ​​and maintenance processing values, respectively, obtains operation verification thresholds and maintenance processing thresholds from the database, compares the operation verification values ​​and maintenance processing values ​​with the operation verification thresholds and maintenance processing thresholds, and determines whether the management cycle is necessary for operation optimization and maintenance optimization based on the comparison results.

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

3. The intelligent laboratory integrated management system integrating big data analysis according to claim 2, 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 consumable inventory threshold from the database; comparing the maintenance proximity value and consumable inventory value of the analysis object with the maintenance proximity threshold and consumable inventory threshold respectively; if the maintenance proximity value is greater than the maintenance proximity threshold and the consumable inventory value is greater than the consumable inventory threshold, then the static analysis state 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 sent to the dynamic adjustment module; otherwise, the static analysis state of the analysis object is determined to not meet the requirements, the corresponding analysis object is marked as an indented object, an indentation adjustment signal is generated and sent to the dynamic adjustment module.

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