Laboratory operation management intelligent system based on undergraduate scientific research ability cultivation

Abstract

The invention provides a laboratory operation management intelligent system based on undergraduate scientific research ability cultivation. The laboratory operation management intelligent system based on undergraduate scientific research ability cultivation comprises hardware modules including a laboratory instrument equipment intelligent management device, an access control system, a monitoring camera and a data transmission device; the laboratory instrument and equipment intelligent management device comprises an RFID reader-writer, a sensor and a consumable monitor, and the access control system comprises face recognition equipment, a fingerprint recognition module, an electronic door lock and a software module; equipment state monitoring, consumable use tracking, user authority management and scientific research evaluation data processing are integrated. According to the laboratory operation management intelligent system based on undergraduate scientific research ability cultivation, through the synergistic effect of the hardware module, the software module and the resource management model, the core problem in a traditional laboratory management mode is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent laboratory operation management systems, and in particular to an intelligent laboratory operation management system based on the cultivation of undergraduate scientific research capabilities. Background Art

[0002] The intelligent laboratory operation management system based on the cultivation of undergraduate scientific research ability aims to closely integrate teaching and scientific research by introducing intelligent management methods to improve undergraduate scientific research ability. The system structure includes three main modules: intelligent management module for equipment and consumables, laboratory access control and monitoring system module, and scientific research evaluation and recording module. The first module manages the use application, status monitoring and consumables allocation of laboratory instruments and equipment through digital means to reduce the error rate of manual operation; the second module realizes intelligent monitoring of laboratory operation mode to ensure the safety and efficient use of laboratory resources; the third module establishes a feedback system for the process and results of undergraduate scientific research activities through scientific research evaluation and recording functions to guide the gradual improvement of their scientific research ability. The system takes the concept of OBE (outcome-oriented education) as the core, uses the design idea of ​​"undergraduate-centered", and combines the mentoring mechanism to build a step-by-step scientific research training system.

[0003] Although the system has improved laboratory management efficiency and undergraduate scientific research capabilities to a certain extent, it still has some defects. The traditional management of laboratory instruments and consumables usually relies on manual records, which is prone to data omissions, equipment conflicts, and waste of consumables. The lack of intelligent means of laboratory access management and monitoring can easily lead to unauthorized use of laboratory resources or safety hazards. Undergraduates lack systematic evaluation records in the process of cultivating their scientific research capabilities, making it difficult for tutors to accurately grasp the changing trends of their scientific research capabilities. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention provides an intelligent laboratory operation management system based on the cultivation of undergraduate scientific research ability, which solves the problems of data omissions, equipment usage conflicts and waste of consumables that are prone to occur in traditional management methods. The lack of intelligent means for laboratory access management and monitoring can easily lead to unauthorized use of laboratory resources or safety hazards. Undergraduates lack systematic evaluation records in the process of cultivating their scientific research ability, making it difficult for tutors to accurately grasp the changing trends of their scientific research abilities.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent laboratory operation management system based on undergraduate scientific research ability training, comprising:

[0006] Hardware modules: intelligent management device for laboratory instruments and equipment, access control system, surveillance camera and data transmission device. The intelligent management device for laboratory instruments and equipment includes RFID reader / writer, sensor, consumables monitor, and the access control system includes face recognition device, fingerprint recognition module and electronic door lock;

[0007] Software module: A cloud computing-based laboratory resource management platform that integrates equipment status monitoring, consumables usage tracking, user rights management, and scientific research evaluation data processing;

[0008] Resource management model: Based on the laboratory resource allocation optimization model, the following formula is used to achieve efficient allocation of laboratory equipment and consumables:

[0009]

[0010] Among them, U is the equipment utilization efficiency, R i is the actual usage time of the device, T i is the equipment availability time, and the optimization goal is to improve U;

[0011] Research capability evaluation model: Based on the following formula, the improvement level of undergraduate research capability is quantified to form a research capability improvement formula:

[0012] ResearchCapabilityIndex is RCI, RCI = w1x1+w2x2+w3x3, where x1

[0013] is the experimental design ability, x2 is the data analysis ability, x3 is the output quality, w1, w2, w3 are the weights of each indicator, and the scientific research ability improvement formula is adjusted by the following method:

[0014]

[0015] Among them, NewAchievements is the quantitative value of new scientific research achievements;

[0016] Dynamic weight adjustment formula: adjust the weight according to the student's scientific research stage:

[0017]

[0018] Among them, P i Based on the priorities of the corresponding indicators in the scientific research stages, the weight ratios of different stages are dynamically adjusted to ensure the scientificity and accuracy of the evaluation results.

[0019] Preferably, the access control system improves laboratory security through dual verification of face and fingerprint, and stores access records in the cloud. The surveillance camera has AI image recognition function, which can automatically identify illegal operations and issue an alarm.

[0020] Preferably, the resource management model uses a particle swarm optimization algorithm, namely PSO, to achieve dynamic device allocation and adjust the device effectiveness through the following optimization formula:

[0021] Fitness:F(x)=αU-βC

[0022] Among them, F(x) is the comprehensive benefit of equipment allocation, C is the equipment conflict cost, α and β are adjustment coefficients. The scientific research capability evaluation model combines experimental data, paper results and competition award data to generate a dynamic evaluation report.

[0023] Preferably, the scientific research capability improvement formula is adjusted by the following method:

[0024]

[0025] Among them, NewAchievements is the quantitative value of new scientific research achievements.

[0026] Preferably, the consumables monitoring module is equipped with an intelligent weighing function for detecting the inventory status of consumables and triggering replenishment reminders. The software module has a data anomaly detection function, which triggers maintenance prompts by counting the fluctuations in equipment utilization that exceed the standard deviation.

[0027] Preferably, the dynamic weight adjustment formula increases the weight of experimental design ability in the early stage of students' scientific research training, and increases the weight of output quality in the later stage. The data sources of the scientific research evaluation model include scientific research process data, results data and student feedback data.

[0028] Preferably, the resource management model generates the following allocation strategy according to different device priorities:

[0029]

[0030] Among them, Critical Level i For device importance, Usage Conflict i The number of usage conflicts.

[0031] Preferably, the scientific research ability assessment results can be intuitively displayed through a cloud platform, making it convenient for tutors and students to view the scientific research ability improvement status in real time.

[0032] The present invention provides an intelligent laboratory operation management system based on the cultivation of undergraduate scientific research ability. It has the following beneficial effects:

[0033] This intelligent laboratory operation and management system based on the cultivation of undergraduate scientific research capabilities effectively solves the core problems in traditional laboratory management methods through the synergy of hardware modules, software modules and resource management models. On the one hand, the intelligent management device for laboratory instruments and equipment combines RFID technology, sensors and consumables monitors to realize the full-process intelligent management of equipment and consumables, avoid errors in manual records, improve resource utilization and reduce consumables waste; on the other hand, the access control system uses facial recognition and fingerprint dual verification, combined with cloud access record storage functions, significantly improves the security of the laboratory and eliminates unauthorized use of resources. At the same time, the surveillance camera with AI image recognition function can detect illegal operations in real time and issue alarms, effectively ensuring the standardized use of laboratory resources. The overall design comprehensively improves the safety and management efficiency of laboratory operations.

[0034] The present invention provides scientific support for the systematic cultivation of undergraduate scientific research ability by constructing a resource management model and a scientific research ability evaluation model. The resource management model adopts the particle swarm optimization algorithm (PSO), combined with the equipment utilization formula and the priority allocation strategy, to dynamically adjust the equipment scheduling plan, maximize the utilization of laboratory equipment, and reduce resource conflicts. The scientific research ability evaluation model is based on quantitative indicators, through the scientific research ability improvement formula (RCI) and the dynamic weight adjustment formula, to comprehensively evaluate the improvement trajectory of students' scientific research ability, and to generate a dynamic evaluation report in combination with experimental data, paper results and competition award data, to help tutors grasp the changes in students' scientific research ability in real time and optimize the training strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Embodiment 1

[0038] like Figure 1As shown, the embodiment of the present invention provides an intelligent laboratory operation management system based on the cultivation of undergraduate scientific research ability, including hardware modules: an intelligent management device for laboratory instruments and equipment, an access control system, a monitoring camera and a data transmission device. The intelligent management device for laboratory instruments and equipment includes an RFID reader, a sensor, a consumables monitor, and an access control system including a face recognition device, a fingerprint recognition module and an electronic door lock. The access control system improves laboratory security through double verification of face and fingerprint, and stores access records in the cloud. The monitoring camera has an AI image recognition function, which can automatically identify illegal operations and issue an alarm. The resource management model adopts a particle swarm optimization algorithm, namely PSO, to achieve dynamic equipment allocation, and adjust equipment scheduling through the following optimization formula:

[0039] Fitness:F(x)=αU-βC

[0040] Among them, F(x) is the comprehensive benefit of equipment allocation, C is the equipment conflict cost, α, β are adjustment coefficients, and the scientific research capability evaluation model combines experimental data, paper results and competition award data to generate a dynamic evaluation report. The consumables monitoring module is equipped with an intelligent weighing function to detect the inventory status of consumables and trigger replenishment reminders. The software module has a data anomaly detection function, which triggers maintenance reminders by counting the fluctuations in equipment utilization that exceed the standard deviation. The dynamic weight adjustment formula increases the weight of experimental design ability in the early stage of student scientific research training, and increases the weight of the quality of output results in the later stage. The data sources of the scientific research evaluation model include scientific research process data, results data and student feedback data;

[0041] Software module: A cloud computing-based laboratory resource management platform that integrates equipment status monitoring, consumables usage tracking, user rights management, and scientific research evaluation data processing;

[0042] Resource management model: Based on the laboratory resource allocation optimization model, the following formula is used to achieve efficient allocation of laboratory equipment and consumables:

[0043]

[0044] Among them, U is the equipment utilization efficiency, R i is the actual usage time of the device, T i The optimization goal is to improve U for device availability time. The resource management model generates the following allocation strategies based on different device priorities:

[0045]

[0046] Among them, Critical Level i For device importance, Usage Conflict i is the number of usage conflicts;

[0047] Research capability assessment model: quantify the improvement of undergraduate research capability based on the following formula:

[0048] ResearchCapabilityIndex is RCI, RCI = w1x1+w2x2+w3x3, where x1

[0049] is the experimental design ability, x2 is the data analysis ability, x3 is the output quality, w1, w2, w3 are the weights of each indicator;

[0050] Dynamic weight adjustment formula: adjust the weight according to the student's scientific research stage:

[0051]

[0052] Among them, P i In order to prioritize the corresponding indicators in the scientific research stage, the weight ratios of different stages are dynamically adjusted to ensure the scientificity and accuracy of the evaluation results. The scientific research ability evaluation results can be intuitively displayed through the cloud platform, making it convenient for tutors and students to view the improvement of scientific research capabilities in real time.

[0053] Embodiment 2

[0054] Example: Implementation of the operation management model in the chronic disease TCM nursing intervention research laboratory of the School of Nursing, Nanjing University of Chinese Medicine

[0055] like Figure 1 As shown in the figure, the intelligent laboratory operation management system based on undergraduate scientific research ability training provided by the embodiment of the present invention has been put into practical use in the chronic disease TCM nursing intervention research laboratory of the School of Nursing of Nanjing University of Chinese Medicine. The system successfully achieves efficient utilization of laboratory resources, intelligent operation management and systematic improvement of undergraduate scientific research ability training by integrating hardware modules, software modules, resource management models and scientific research ability evaluation models.

[0056] System architecture and function implementation

[0057] Hardware Modules:

[0058] Intelligent management device for laboratory instruments and equipment:

[0059] All laboratory equipment is equipped with RFID readers to identify equipment status and usage; sensors monitor equipment operating status and performance; consumables monitors use built-in intelligent weighing functions to detect consumables inventory in real time and trigger replenishment reminders.

[0060] Access control system:

[0061] The system uses facial recognition equipment and fingerprint recognition modules, combined with electronic door locks, to achieve dual verification of face and fingerprint, thereby improving the safety of the laboratory. Access control data is uploaded to the cloud synchronously, and access records are stored in real time to ensure the traceability of resource use.

[0062] Surveillance Cameras:

[0063] The laboratory is equipped with surveillance cameras with AI image recognition capabilities to detect illegal operations in the laboratory in real time, such as unauthorized use of equipment or improper operation, and send alarm messages through the system to ensure the safety of equipment and personnel.

[0064] Software Modules:

[0065] Laboratory resource management platform:

[0066] The platform is based on cloud computing technology and integrates equipment status monitoring, consumables usage tracking, user rights management, and scientific research evaluation data processing functions. Through big data analysis technology, the system can count equipment utilization and identify abnormal fluctuations through the following formula:

[0067]

[0068] Among them, U i is the current utilization of the equipment, μ is the mean of the equipment utilization, and σ is the standard deviation of the equipment utilization. When the deviation exceeds the set threshold, the system triggers a maintenance reminder.

[0069] 3. Resource management model:

[0070] Resource allocation optimization:

[0071] The system uses the particle swarm optimization algorithm (PSO) to dynamically adjust device allocation, specifically through the following optimization formula:

[0072] Fitness:F(x)=αU-βC

[0073] Among them, F(x) is the comprehensive benefit of equipment allocation, U is the equipment utilization efficiency, C is the equipment conflict cost, and α and β are weight coefficients. By adjusting resource priorities, equipment utilization can be maximized and resource conflicts can be minimized.

[0074] Priority allocation strategy:

[0075] Calculate resource allocation priority based on device importance (CriticalLevel) and usage conflict count (UsageConflict):

[0076]

[0077] Ensure that key equipment is allocated first to meet the core needs of laboratory operations.

[0078] 4. Scientific research capability assessment model:

[0079] Dynamic evaluation and weight adjustment:

[0080] The system records the improvement trajectory of undergraduates' scientific research capabilities from entering the laboratory to graduation. The scientific research capabilities are quantified by the following formula:

[0081] Research Capability Index (RCI)=w1x1+w2x2+w3x3

[0082] Among them, x1, x2, x3 represent the experimental design ability, data analysis ability and output quality respectively, and w1, w2, w3 are weights. The weight values ​​are dynamically adjusted according to the scientific research stage:

[0083]

[0084] Among them, P i It is the priority of the scientific research stage, with an initial emphasis on experimental design capabilities and a later emphasis on the quality of output results.

[0085] Implementation process

[0086] In the Chronic Disease Traditional Chinese Medicine Nursing Intervention Research Laboratory of the School of Nursing of Nanjing University of Chinese Medicine, the existing laboratory equipment was first transformed into an intelligent one, and RFID readers, sensors and consumables monitors were installed on all equipment.

[0087] An access control system is deployed at the laboratory entrance, and the biometric information of all instructors and undergraduates is entered into the system for face and fingerprint verification.

[0088] The system monitors equipment status and consumables inventory in real time through the laboratory resource management platform and generates resource allocation strategies.

[0089] When undergraduates enter the laboratory, they fill out the initial scientific research ability questionnaire through the platform. The system records their initial scientific research ability, mainly experimental design ability. During the scientific research stage, the tutor can view the improvement trajectory of students' scientific research ability and dynamic evaluation results through the system, and adjust the direction of scientific research training according to the specific situation of the students.

[0090] Scientific research data, experimental records, paper publications, and competition awards are uploaded through the platform and combined with the scientific research capability assessment model to generate a dynamic evaluation report. The results are displayed in the form of charts on the cloud platform for tutors and students to view at any time.

[0091] The system regularly analyzes laboratory operating efficiency, conducts comprehensive evaluations on equipment utilization, consumables consumption rate, and the effects of improving scientific research capabilities, and continuously optimizes the laboratory operation and management model.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent laboratory operation and management system based on the cultivation of undergraduate scientific research capabilities, characterized by: include: Hardware modules: intelligent management device for laboratory instruments and equipment, access control system, surveillance camera and data transmission device. The intelligent management device for laboratory instruments and equipment includes RFID reader / writer, sensor, consumables monitor, and the access control system includes face recognition device, fingerprint recognition module and electronic door lock; Software module: A cloud computing-based laboratory resource management platform that integrates equipment status monitoring, consumables usage tracking, user rights management, and scientific research evaluation data processing; Resource management model: Based on the laboratory resource allocation optimization model, the following formula is used to achieve efficient allocation of laboratory equipment and consumables: Among them, U is the equipment utilization efficiency, R i is the actual usage time of the device, T i is the equipment availability time, and the optimization goal is to improve U; Research capability evaluation model: quantify the improvement level of undergraduate research capability based on the following formula: ResearchCapabilityIndex is RCI, RCI = w1x1+w2x2+w3x3, where x1 is the experimental design ability, x2 is the data analysis ability, x3 is the output quality, w1, w2, w3 are the weights of each indicator; Dynamic weight adjustment formula: adjust the weight according to the student's scientific research stage: Among them, P i Based on the priorities of the corresponding indicators in the scientific research stages, the weight ratios of different stages are dynamically adjusted to ensure the scientificity and accuracy of the evaluation results.

2. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The access control system improves laboratory security through dual verification of face and fingerprint, and stores access records in the cloud. The surveillance camera has AI image recognition function, which can automatically identify illegal operations and issue alarms.

3. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The resource management model uses the particle swarm optimization algorithm, namely PSO, to achieve dynamic device allocation and adjust device scheduling through the following optimization formula: Fitness:F(x)=αU-βC Among them, F(x) is the comprehensive benefit of equipment allocation, C is the equipment conflict cost, α and β are adjustment coefficients. The scientific research capability evaluation model combines experimental data, paper results and competition award data to generate a dynamic evaluation report.

4. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The scientific research capability improvement formula is adjusted by the following method: Among them, NewAchievements is the quantitative value of new scientific research achievements.

5. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The consumables monitoring module is equipped with an intelligent weighing function for detecting the inventory status of consumables and triggering replenishment reminders. The software module has a data anomaly detection function, which triggers maintenance reminders by counting the fluctuations in equipment utilization that exceed the standard deviation.

6. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The dynamic weight adjustment formula increases the weight of experimental design ability in the early stage of students' scientific research training, and increases the weight of the quality of output results in the later stage. The data sources of the scientific research evaluation model include scientific research process data, results data and student feedback data.

7. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The resource management model generates the following allocation strategies based on different device priorities: Among them, CriticalLevel i For device importance, Usage Conflict i The number of usage conflicts.

8. The intelligent laboratory operation management system based on undergraduate scientific research ability training according to claim 1 is characterized by: The scientific research ability assessment results can be intuitively displayed through the cloud platform, making it convenient for tutors and students to view the improvement of scientific research ability in real time.

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