A method and system for dynamic allocation and execution of laboratory safety inspection tasks
By dividing the inspection area in the laboratory and constructing feature vectors, the inspection tasks are dynamically allocated, which solves the problem of uneven task distribution in traditional methods and achieves balanced workload and improved accuracy of risk assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHANGSHU INSTITUTE OF TECHNOLOGY
- Filing Date
- 2026-03-03
- Publication Date
- 2026-06-09
AI Technical Summary
Traditional laboratory safety inspection task allocation methods are based on familiarity, resulting in uneven task distribution, excessive burden on some personnel, slow skill improvement, difficulty in dealing with emergencies, and neglect of changes in risk points.
By dividing the laboratory into inspection areas, acquiring historical data and operation records, constructing project-based feature vectors and operation feature vectors, and using a comprehensive objective function that minimizes personnel load variance and maximizes matching values, inspection tasks are dynamically allocated.
This achieved a balanced workload, improved team efficiency and satisfaction, and enhanced the accuracy of risk assessment and laboratory safety.
Smart Images
Figure CN122175235A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of task allocation and execution technology, specifically to a method and system for the dynamic allocation and execution of laboratory safety inspection tasks. Background Technology
[0002] In the current laboratory safety management system, safety inspections are an important part of ensuring the safety of the experimental environment and preventing accidents.
[0003] However, traditional laboratory safety inspection task allocation methods often rely on researchers' historical experimental operation records, assigning inspectors based on the principle of "most familiar with the area." While this method considers personnel's familiarity with specific experimental areas, it has significant drawbacks. It ignores the dynamic and complex nature of laboratory work, potentially leading to some experimental areas being overlooked due to the same person consistently patrolling them, thus missing newly emerging risks or changes. Furthermore, assigning tasks solely based on familiarity can overburden some experienced and skilled personnel, while others may experience slow skill development or even burnout due to insufficient practice opportunities. In addition, this static task allocation model is ill-suited for rapid response demands in emergency situations, impacting overall safety management levels. Summary of the Invention
[0004] The purpose of this invention is to provide a method and system for the dynamic allocation and execution of laboratory safety inspection tasks, and to solve the following technical problems.
[0005] The objective of this invention can be achieved through the following technical solutions: A method for dynamically allocating and executing laboratory safety inspection tasks includes the following steps: Step S1: Divide the laboratory into several inspection areas, obtain all experimental items, obtain the laboratory's historical usage data, and obtain the item usage feature vector of each inspection area based on the historical usage data; and obtain the historical experimental operation records of each experimental personnel, and obtain the item operation feature vector of each experimental personnel based on the historical experimental operation records. The feature vectors of the items in the inspection area are matched with the feature vectors of the items operated by each experimenter to obtain the matching values between the feature vectors of the items and the feature vectors of each item's operation. Step S2: Obtain historical inspection data for all experimental personnel, including the time spent by experimental personnel in inspecting each inspection area in the past; for any inspection area, obtain the inspection load value of the inspection area based on the historical inspection data. Step S3: When inspecting the laboratory, construct a comprehensive objective function with the goal of minimizing the variance of the total inspection load of each laboratory personnel and maximizing the matching value of each inspection area. Solve the comprehensive objective function to obtain the optimal personnel to perform each inspection area.
[0006] As a further aspect of the present invention: the historical usage data includes all experimental items operated in each inspection area, and the historical experimental operation records include all experimental items operated by the experimental personnel.
[0007] As a further aspect of the present invention: the process of obtaining the project usage feature vector of each inspection area based on the historical usage data includes: All experimental projects operated in the laboratory are numbered, with each project corresponding to a unique number. All experimental projects operated within the inspection area are recorded as historical experimental projects. These historical projects are sorted by number, and the total number of times each historical project has been operated within the inspection area is obtained. A coordinate system is established with the project number as the x-axis and the number of operations as the y-axis. The project number and the total number of operations for each historical experimental project are converted into coordinate points in the coordinate system. Linear regression fitting is performed on each coordinate point using the least squares method to obtain a regression line. Taking the coordinate point with the smallest number on the regression line as the starting point and the coordinate point with the largest number as the ending point, a vector is obtained with the regression line as the modulus and the direction from the starting point to the ending point as the direction. This vector is denoted as the project use feature vector of the inspection area.
[0008] As a further aspect of the present invention: the process by which the experimenter obtains the feature vector of the experimental project's operation includes: All experimental items performed by the experimenter are collected and recorded as historical operation items. The number of each historical operation item is obtained, and the total number of times each historical operation item was performed by the experimenter is obtained. The number of each historical operation item and its total number of times are converted into new coordinate points in the coordinate system. Linear regression fitting is performed on each new coordinate point based on the least squares method to obtain a new regression line. The new coordinate point with the smallest number on the new regression line is taken as the new starting point, and the new coordinate point with the largest number is taken as the new ending point. Then, a vector is obtained with the new regression line as the modulus and the direction from the new starting point to the new ending point as the direction. This vector is called the operation feature vector.
[0009] As a further aspect of the present invention, the process of obtaining the matching value between the project usage feature vector and the project operation feature vector includes obtaining the cosine value of the angle between the project usage feature vector and the project operation feature vector, and recording the cosine value of the angle as the matching value between the project usage feature vector and the project operation feature vector.
[0010] As a further aspect of the present invention: the process of obtaining the inspection load value of the inspection area based on the historical inspection data includes: For any inspection area, obtain the percentage of time spent by the experimenter inspecting that area. , where t k The time consumed by the experimenter in inspecting the k-th inspection area is represented by T, where T represents the time consumed by the experimenter in inspecting the inspection area, and k is the index. The percentage of time spent by all experimenters in inspecting the inspection area is obtained, and the average of the percentages of all time spent is obtained, which is recorded as the inspection load value of the inspection area.
[0011] As a further aspect of the present invention, the process of constructing the objective optimization function includes: Step S3.1: Define decision variable x ij When the j-th inspection area is assigned to the i-th experimenter, the decision variable is denoted as x. ij =1, otherwise, denote the decision variable as x. ij =0; Step S3.2: Obtain the total inspection load of the i-th experimenter. L j This represents the inspection load value of the j-th inspection area; Step S3.3: Taking minimizing the variance of the total inspection load of each experimenter as the first objective, the first objective function of the first objective is: Where M is the total number of experimental personnel and N is the total number of inspection areas; Step S3.4: With maximizing the matching value of each inspection area as the second objective, the second objective function of the second objective is: M ij This represents the matching value between the feature vector of the project operation of the i-th experimenter and the feature vector of the project use of the j-th inspection area; Step S3.5: Construct the comprehensive objective function based on the linear weighting method , where α and β are preset weighting coefficients, and α+β=1.
[0012] This application also provides a dynamic allocation and execution system for laboratory safety inspection tasks, including: Historical Project Analysis Module: The laboratory is divided into several inspection areas, and all experimental projects are acquired. Historical usage data of the laboratory is obtained, and project usage feature vectors for each inspection area are obtained based on the historical usage data. Historical experimental operation records of each experimental personnel are also acquired, and project operation feature vectors for each experimental personnel are obtained based on the historical experimental operation records. The feature vectors of the items in the inspection area are matched with the feature vectors of the items operated by each experimenter to obtain the matching values between the feature vectors of the items and the feature vectors of each item's operation. Load analysis module: acquires historical inspection data of all experimental personnel, including the time consumed by experimental personnel in inspecting each inspection area in the past; for any inspection area, obtains the inspection load value of the inspection area based on the historical inspection data; Allocation Module: When inspecting the laboratory, the goal is to minimize the variance of the total inspection load of each laboratory personnel and maximize the matching value of each inspection area. A comprehensive objective function is constructed, and the optimal personnel to perform each inspection area are obtained by solving the comprehensive objective function.
[0013] The beneficial effects of this invention are: Traditional methods can easily lead to uneven task allocation. This invention ensures a balanced workload mathematically by minimizing the variance of the total inspection load of personnel, effectively preventing excessive burden on individual staff and improving the overall efficiency and satisfaction of the team. By quantifying regional characteristics and personnel experience, and aiming to maximize the matching value, this invention can automatically assign inspection tasks for specific areas to the personnel with the most suitable experience. This allocation based on professional suitability makes hazard identification more accurate and risk assessment more in-depth, thereby directly enhancing the intrinsic safety level of the laboratory. Attached Figure Description
[0014] The invention will now be further described with reference to the accompanying drawings.
[0015] Figure 1 This is a schematic diagram illustrating the steps of a method for dynamically allocating and executing laboratory safety inspection tasks according to the present invention.
[0016] Figure 2 This is a flowchart illustrating a dynamic allocation and execution system for laboratory safety inspection tasks according to the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.
[0018] Please see Figure 1 As shown, this invention provides a method for the dynamic allocation and execution of laboratory safety inspection tasks, comprising the following steps: Step S1: Divide the laboratory into several inspection areas, obtain all experimental items, obtain the laboratory's historical usage data, and obtain the item usage feature vector of each inspection area based on the historical usage data; and obtain the historical experimental operation records of each experimental personnel, and obtain the item operation feature vector of each experimental personnel based on the historical experimental operation records. The feature vectors of the items in the inspection area are matched with the feature vectors of the items operated by each experimenter to obtain the matching values between the feature vectors of the items and the feature vectors of each item's operation.
[0019] In a preferred embodiment of the present invention, the historical usage data includes all experimental items operated in each inspection area, and the historical experimental operation record includes all experimental items operated by the experimental personnel.
[0020] In a preferred embodiment of the present invention, the process of obtaining the project usage feature vector of each inspection area based on the historical usage data includes: All experimental projects operated in the laboratory are numbered, with each project corresponding to a unique number. All experimental projects operated within the inspection area are recorded as historical experimental projects. These historical projects are sorted by number, and the total number of times each historical project has been operated within the inspection area is obtained. A coordinate system is established with the project number as the x-axis and the number of operations as the y-axis. The project number and the total number of operations for each historical experimental project are converted into coordinate points in the coordinate system. Linear regression fitting is performed on each coordinate point using the least squares method to obtain a regression line. Taking the coordinate point with the smallest number on the regression line as the starting point and the coordinate point with the largest number as the ending point, a vector is obtained with the regression line as the modulus and the direction from the starting point to the ending point as the direction. This vector is denoted as the project use feature vector of the inspection area.
[0021] The direction of the feature vector used in the project reflects the overall trend of the frequency of project operations within the region as the project number changes.
[0022] In a preferred embodiment of the present invention, the process by which the experimenter obtains the feature vector of the experimental project's operation includes: All experimental items performed by the experimenter are collected and recorded as historical operation items. The number of each historical operation item is obtained, and the total number of times each historical operation item was performed by the experimenter is obtained. The number of each historical operation item and its total number of times are converted into new coordinate points in the coordinate system. Linear regression fitting is performed on each new coordinate point based on the least squares method to obtain a new regression line. The new coordinate point with the smallest number on the new regression line is taken as the new starting point, and the new coordinate point with the largest number is taken as the new ending point. Then, a vector is obtained with the new regression line as the modulus and the direction from the new starting point to the new ending point as the direction. This vector is called the operation feature vector.
[0023] In a preferred embodiment of the present invention, the process of obtaining the matching value between the project usage feature vector and the project operation feature vector includes obtaining the cosine value of the angle between the project usage feature vector and the project operation feature vector, and recording the cosine value of the angle as the matching value between the project usage feature vector and the project operation feature vector.
[0024] It should be noted that the matching value is between [0, 1]. The closer it is to 1, the more similar the operator's operating experience pattern is to the usage pattern of the area, that is, the higher the degree of fit between the operator's knowledge background and the inspection needs of the area.
[0025] Step S2: Obtain historical inspection data for all experimental personnel, including the time spent by experimental personnel in inspecting each inspection area in the past; for any inspection area, obtain the inspection load value of the inspection area based on the historical inspection data.
[0026] In a preferred embodiment of the present invention, the process of obtaining the inspection load value of the inspection area based on the historical inspection data includes: For any inspection area, obtain the percentage of time spent by the experimenter inspecting that area. , where t k The time consumed by the experimenter in inspecting the k-th inspection area is represented by T, where T represents the time consumed by the experimenter in inspecting the inspection area, and k is the index. The percentage of time spent by all experimenters in inspecting the inspection area is obtained, and the average of the percentages of all time spent is obtained, which is recorded as the inspection load value of the inspection area.
[0027] It is worth noting that in the historical inspection data, if there is a situation where the inspection area has been inspected by the experimenter several times, the average time consumed by each inspection of the inspection area is taken and substituted into the calculation of the time consumption ratio; that is, if the same person has multiple inspection records of the same area, the average time is calculated to ensure the stability of the data.
[0028] Step S3: When inspecting the laboratory, construct a comprehensive objective function with the goal of minimizing the variance of the total inspection load of each laboratory personnel and maximizing the matching value of each inspection area. Solve the comprehensive objective function to obtain the optimal personnel to perform each inspection area.
[0029] In a preferred embodiment of the present invention, the process of constructing the objective optimization function includes: Step S3.1: Define decision variable x ij When the j-th inspection area is assigned to the i-th experimenter, the decision variable is denoted as x. ij =1, otherwise, denote the decision variable as x. ij =0; Step S3.2: Obtain the total inspection load of the i-th experimenter. L j This represents the inspection load value of the j-th inspection area; Step S3.3: Taking minimizing the variance of the total inspection load of each experimenter as the first objective, the first objective function of the first objective is: Where M is the total number of experimental personnel and N is the total number of inspection areas; Step S3.4: With maximizing the matching value of each inspection area as the second objective, the second objective function of the second objective is: M ij This represents the matching value between the feature vector of the project operation of the i-th experimenter and the feature vector of the project use of the j-th inspection area; Step S3.5: Construct the comprehensive objective function based on the linear weighting method , where α and β are preset weighting coefficients, and α+β=1.
[0030] In a preferred embodiment of the present invention, the process of constructing the objective optimization function further includes setting several constraints, including: Each inspection area must be assigned to one and only one laboratory personnel, i.e. The decision variable is a 0-1 variable, i.e. .
[0031] In a preferred embodiment of the present invention, after solving the comprehensive objective function, each inspection area is assigned an experimental personnel, and this experimental personnel is recorded as the optimal personnel for the inspection area.
[0032] A dynamic allocation and execution system for laboratory safety inspection tasks, comprising: Historical Project Analysis Module: The laboratory is divided into several inspection areas, and all experimental projects are acquired. Historical usage data of the laboratory is obtained, and project usage feature vectors for each inspection area are obtained based on the historical usage data. Historical experimental operation records of each experimental personnel are also acquired, and project operation feature vectors for each experimental personnel are obtained based on the historical experimental operation records. The feature vectors of the items in the inspection area are matched with the feature vectors of the items operated by each experimenter to obtain the matching values between the feature vectors of the items and the feature vectors of each item's operation. Load analysis module: acquires historical inspection data of all experimental personnel, including the time consumed by experimental personnel in inspecting each inspection area in the past; for any inspection area, obtains the inspection load value of the inspection area based on the historical inspection data; Allocation Module: When inspecting the laboratory, the goal is to minimize the variance of the total inspection load of each laboratory personnel and maximize the matching value of each inspection area. A comprehensive objective function is constructed, and the optimal personnel to perform each inspection area are obtained by solving the comprehensive objective function.
[0033] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.
Claims
1. A method for dynamically allocating and executing laboratory safety inspection tasks, characterized in that, Includes the following steps: Step S1: Divide the laboratory into several inspection areas, obtain all experimental items, obtain the laboratory's historical usage data, and obtain the item usage feature vector of each inspection area based on the historical usage data; and obtain the historical experimental operation records of each experimental personnel, and obtain the item operation feature vector of each experimental personnel based on the historical experimental operation records. The feature vectors of the items in the inspection area are matched with the feature vectors of the items operated by each experimenter to obtain the matching values between the feature vectors of the items and the feature vectors of each item's operation. Step S2: Obtain historical inspection data for all experimental personnel, including the time spent by experimental personnel in inspecting each inspection area in the past; for any inspection area, obtain the inspection load value of the inspection area based on the historical inspection data. Step S3: When inspecting the laboratory, construct a comprehensive objective function with the goal of minimizing the variance of the total inspection load of each laboratory personnel and maximizing the matching value of each inspection area. Solve the comprehensive objective function to obtain the optimal personnel to perform each inspection area.
2. The method for dynamic allocation and execution of laboratory safety inspection tasks according to claim 1, characterized in that, The historical usage data includes all experimental items operated in each inspection area, and the historical experimental operation records include all experimental items operated by the experimental personnel.
3. The method for dynamic allocation and execution of laboratory safety inspection tasks according to claim 1, characterized in that, The process of obtaining the project usage feature vector for each inspection area based on the historical usage data includes: All experimental projects operated in the laboratory are numbered, with each project corresponding to a unique number. All experimental projects operated within the inspection area are recorded as historical experimental projects. These historical projects are sorted by number, and the total number of times each historical project has been operated within the inspection area is obtained. A coordinate system is established with the project number as the x-axis and the number of operations as the y-axis. The project number and the total number of operations for each historical experimental project are converted into coordinate points in the coordinate system. Linear regression fitting is performed on each coordinate point using the least squares method to obtain a regression line. Taking the coordinate point with the smallest number on the regression line as the starting point and the coordinate point with the largest number as the ending point, a vector is obtained with the regression line as the modulus and the direction from the starting point to the ending point as the direction. This vector is denoted as the project use feature vector of the inspection area.
4. The method for dynamic allocation and execution of laboratory safety inspection tasks according to claim 3, characterized in that, The process by which experimenters obtain the feature vectors of the experimental project's operations includes: All experimental items performed by the experimenter are collected and recorded as historical operation items. The number of each historical operation item is obtained, and the total number of times each historical operation item was performed by the experimenter is obtained. The number of each historical operation item and its total number of times are converted into new coordinate points in the coordinate system. Linear regression fitting is performed on each new coordinate point based on the least squares method to obtain a new regression line. The new coordinate point with the smallest number on the new regression line is taken as the new starting point, and the new coordinate point with the largest number is taken as the new ending point. Then, a vector is obtained with the new regression line as the modulus and the direction from the new starting point to the new ending point as the direction. This vector is called the operation feature vector.
5. The method for dynamic allocation and execution of laboratory safety inspection tasks according to claim 1, characterized in that, The process of obtaining the matching value between the project usage feature vector and the project operation feature vector includes obtaining the cosine value of the angle between the project usage feature vector and the project operation feature vector, and recording the cosine value of the angle as the matching value between the project usage feature vector and the project operation feature vector.
6. The method for dynamic allocation and execution of laboratory safety inspection tasks according to claim 1, characterized in that, The process of obtaining the inspection load value of the inspection area based on the historical inspection data includes: For any inspection area, obtain the percentage of time spent by the experimenter inspecting that area. , where t k The time consumed by the experimenter in inspecting the k-th inspection area is represented by T, where T represents the time consumed by the experimenter in inspecting the inspection area, and k is the index. The percentage of time spent by all experimenters in inspecting the inspection area is obtained, and the average of the percentages of all time spent is obtained, which is recorded as the inspection load value of the inspection area.
7. The method for dynamic allocation and execution of laboratory safety inspection tasks according to claim 1, characterized in that, The process of constructing the objective optimization function includes: Step S3.1: Define decision variable x ij When the jth inspection area is assigned to the ith inspector, the decision variable is denoted as x ij = 1, otherwise, the decision variable is denoted as x ij = 0. Step S3.2: Obtain the total inspection load of the i-th experimenter. L j This represents the inspection load value of the j-th inspection area; Step S3.3: Taking minimizing the variance of the total inspection load of each experimenter as the first objective, the first objective function of the first objective is: Where M is the total number of experimental personnel and N is the total number of inspection areas; Step S3.4: With maximizing the matching value of each inspection area as the second objective, the second objective function of the second objective is: M ij This represents the matching value between the feature vector of the project operation of the i-th experimenter and the feature vector of the project use of the j-th inspection area; Step S3.5: Construct the comprehensive objective function based on the linear weighting method , where α and β are preset weighting coefficients, and α+β=1.
8. A dynamic allocation and execution system for laboratory safety inspection tasks, characterized in that, include: Historical Project Analysis Module: The laboratory is divided into several inspection areas, and all experimental projects are acquired. Historical usage data of the laboratory is obtained, and project usage feature vectors for each inspection area are obtained based on the historical usage data. Historical experimental operation records of each experimental personnel are also acquired, and project operation feature vectors for each experimental personnel are obtained based on the historical experimental operation records. The feature vectors of the items in the inspection area are matched with the feature vectors of the items operated by each experimenter to obtain the matching values between the feature vectors of the items and the feature vectors of each item's operation. Load analysis module: acquires historical inspection data of all experimental personnel, including the time consumed by experimental personnel in inspecting each inspection area in the past; for any inspection area, obtains the inspection load value of the inspection area based on the historical inspection data; Allocation Module: When inspecting the laboratory, the goal is to minimize the variance of the total inspection load of each laboratory personnel and maximize the matching value of each inspection area. A comprehensive objective function is constructed, and the optimal personnel to perform each inspection area are obtained by solving the comprehensive objective function.