Petrochemical inspection and maintenance project safety supervision human resource optimal configuration method

Abstract

The invention discloses a petrochemical inspection and maintenance project safety supervision human resource optimal configuration method, and particularly relates to the technical field of safety engineering. According to the method, a petrochemical enterprise safety management area model is constructed, a petrochemical enterprise inspection and maintenance operation safety supervision comprehensive efficiency objective function is determined, and a crossover operator and a mutation operator of a genetic algorithm are optimized by adopting a self-adaptive crossover probability and a self-adaptive mutation probability based on the genetic algorithm. And calculating an optimal solution of the petrochemical enterprise inspection and maintenance operation safety supervision comprehensive efficiency objective function to obtain an optimal configuration scheme of petrochemical inspection and maintenance project safety supervision human resources. According to the method, the influence of the dynamic change of the safety risk of the inspection and maintenance operation and the difference of the safety supervision efficacy of various types of workers on the operation construction site on the human resource configuration is comprehensively considered, and the optimal configuration of the safety supervision human resources of the petrochemical inspection and maintenance project under the condition of limited safety supervision personnel is realized; and a foundation is laid for safe production of refinery enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of safety engineering, and particularly relates to a method for optimizing the allocation of human resources for safety supervision of petrochemical inspection and maintenance projects. Background Art

[0002] Inspection and maintenance of refining and chemical enterprises is the key to ensuring the long-term stable operation of the equipment and facilities of refining and chemical enterprises. Due to the complex diversity of the equipment and facilities of refining and chemical enterprises, the high risk of process media, and the intersection of multiple high-risk operations, the operation safety risks during the inspection and maintenance process are prominent, and show the characteristics of uneven regional distribution, prominent local risks, and dynamic risk changes.

[0003] The main means for petrochemical enterprises to control the operation safety risks of inspection and maintenance projects, namely, the safety supervision by Party A, the employment of a third-party safety supervision, and the self-safety supervision of contractors, still have some deficiencies. Among them, the distribution and scheduling of safety supervision personnel mainly rely on subjective experience for manual decision-making, lacking scientific optimization of the safety risk distribution in the inspection and maintenance operation area, not fully considering the difference in the supervision effectiveness between the safety supervision by Party A and the third-party safety supervision, and the self-safety supervision personnel of contractors are restricted by the territorial supervision of the device and cannot be transferred across regions, resulting in over-safety supervision in some areas, insufficient safety supervision in a few areas, and the failure to optimize and adjust the safety supervision force in a timely manner according to the change of risk distribution, etc., making the operation safety supervision effectiveness of the inspection and maintenance not reach the optimal allocation.

[0004] At present, there have been relevant studies on the problem of resource optimization allocation. Chinese invention patent CN 113268930 A discloses a method for optimizing the layout of fire pressure monitors. By opening the fire hydrant for a water discharge experiment, recording the pressure drop of the pipe network under different water discharge conditions, and constructing a fire event matrix based on the calculated pressure drop results, for a certain number of pressure monitoring points, a pressure monitoring point optimization layout model is constructed with the maximization of the number of detected fire events and the minimum detectable water discharge of fire events as the objective function, and the non-dominated sorting genetic algorithm (NSGA-II) is used to solve the model. After determining the optimized layout of a certain number of fire pressure monitors, the maximization of the number of detected fire events and the minimum detectable water consumption of fire events are used as the detection benefit indicators to explore the benefit relationship between the two.

[0005] Chinese Patent CN 110121052 A discloses an optimization method for the layout of video surveillance in a chemical industrial park. By comprehensively considering factors such as the surveillance of the entire chemical industrial park area, the surveillance of key dangerous areas, and the surveillance cost, the optimization of the overall layout of video surveillance equipment is carried out for the global monitoring of the chemical industrial park and the key monitoring of dangerous operation links, which can meet the monitoring requirements of both the entire chemical industrial park and key dangerous operation links at the same time. The layout optimization method of video surveillance in the chemical industrial park of the invention optimizes the calculation by using the random particle swarm intelligent algorithm considering the cost factor of the video surveillance layout in the chemical industrial park according to the monitoring target optimization function, and can give a layout plan with the lowest cost and the strongest monitoring ability that meets the monitoring requirements of the chemical industrial park.

[0006] However, none of the above-mentioned prior arts involve the optimal allocation problem of safety supervisors in the context of safety risk supervision of inspection and maintenance operations, do not establish a quantitative method for safety risks of operations based on operation types, levels, and quantities, do not consider the differences in safety supervision effectiveness of different types of personnel, and the relevant patent research mainly focuses on the optimal layout problem of hardware resources.

[0007] Therefore, there is an urgent need to propose an optimal allocation method for human resources for safety supervision of petrochemical inspection and maintenance projects in view of the safety risk supervision of inspection and maintenance operations in refining enterprises, so as to realize the optimal allocation of human resources for safety supervision of petrochemical inspection and maintenance projects and contribute to the safe production of refining enterprises. Summary of the Invention

[0008] In view of the problems that in the process of allocating safety supervisors for petrochemical inspection and maintenance projects at the present stage, the dynamic changes of safety risks of inspection and maintenance operations cannot be comprehensively considered, the differences in safety supervision effectiveness of various types of workers at the operation construction site, and the allocation of human resources mainly depends on subjective human experience decision-making, the present invention proposes an optimal allocation method for human resources for safety supervision of petrochemical inspection and maintenance projects. Based on the comprehensive effectiveness objective function of safety supervision of petrochemical inspection and maintenance operations, a safety management area model of petrochemical enterprises is constructed, and the genetic algorithm is used to calculate the optimal solution of the comprehensive effectiveness objective function of safety supervision of petrochemical inspection and maintenance operations. By optimizing the crossover operator and mutation operator of the genetic algorithm through the adaptive crossover probability and adaptive mutation probability, the reasonable allocation of human resources for safety supervision of petrochemical inspection and maintenance projects is realized, and the maximization of the monitoring effectiveness of safety risk supervision is effectively ensured.

[0009] The present invention specifically adopts the following technical solutions:

[0010] An optimal allocation method for human resources for safety supervision of petrochemical inspection and maintenance projects specifically includes the following steps:

[0011] Step 1, construct a safety management area model of petrochemical enterprises and determine the comprehensive effectiveness objective function of safety supervision of petrochemical inspection and maintenance operations;

[0012] Step 2: Calculate the optimal solution of the comprehensive effectiveness objective function for the safety supervision of inspection, repair and maintenance operations in petrochemical enterprises based on the genetic algorithm;

[0013] Step 3: Output the optimal solution of the comprehensive effectiveness objective function for the safety supervision of inspection, repair and maintenance operations in petrochemical enterprises to obtain the optimal allocation plan of human resources for safety supervision of petrochemical inspection and repair projects.

[0014] Preferably, in Step 1, the following steps are specifically included:

[0015] Step 1.1: Obtain the distribution of devices in the safety management area of the petrochemical enterprise, determine the locations of each device, and divide the safety management area of the petrochemical enterprise into multiple safety supervision and management areas;

[0016] Step 1.2: According to the operation application information of each safety supervision and management area, determine the special operations and general operations in each safety supervision and management area, and calculate the operation safety risk value of each safety supervision and management area;

[0017] Step 1.3: Based on the operation safety risk values of each safety supervision and management area, determine the comprehensive effectiveness objective function for the safety supervision of inspection, repair and maintenance operations in petrochemical enterprises, and construct a safety management area model for petrochemical enterprises;

[0018] Step 1.4: Set the constraint conditions of the safety management area model for petrochemical enterprises according to the number of Party A's safety supervision personnel and the number of third-party supervision personnel.

[0019] Preferably, the operation application information of the safety supervision and management area includes the operation type, operation level, and cross operations involved.

[0020] Preferably, the operation safety risk value of the safety supervision and management area is as shown in formula (1):

[0021]

[0022] In the formula, i is the serial number of the safety supervision and management area, 1 ≤ i ≤ m, where m is the total number of safety supervision and management areas; R i is the operation safety risk value of the i-th safety supervision and management area; j is the serial number of the special operation in the safety supervision and management area, 1 ≤ j ≤ n, where n is the number of special operations in the safety supervision and management area; S ij is the safety risk base number of the j-th special operation in the i-th safety supervision and management area, L ij is the level coefficient of the j-th special operation in the i-th safety supervision and management area, C ijis the special operation risk coefficient for cross operations involved in the j-th special operation within the i-th safety supervision and management area; k is the serial number of general operations within the safety supervision and management area, 1 ≤ k ≤ w, where w is the number of special operations within the safety supervision and management area; T ik is the safety risk base number for the k-th general operation within the i-th safety supervision and management area, P ik is the risk coefficient for the k-th general operation within the i-th safety supervision and management area.

[0023] Preferably, determine the level of special operations within the safety supervision and management area according to the "Safety Code for Special Operations in Hazardous Chemical Enterprises" (GB30871-2022).

[0024] Preferably, the safety supervision comprehensive effectiveness objective function for petrochemical enterprise inspection and maintenance operations is:

[0025]

[0026] In the formula, G is the safety supervision comprehensive effectiveness objective function for petrochemical enterprise inspection and maintenance operations; min(·) is the minimum value function; X i is the number of Party A's safety supervision personnel within the i-th safety supervision and management area, and a is the safety supervision effectiveness coefficient of Party A; Y i is the number of third-party supervision personnel within the i-th safety supervision and management area, and b is the safety supervision effectiveness coefficient of the third party; Z i is the number of the contractor's self-safety management personnel within the i-th safety supervision and management area, and c is the self-safety supervision effectiveness coefficient of the contractor, X i 、Y i 、Z i are all positive integers.

[0027] Preferably, the constraint conditions for the petrochemical enterprise safety management area model are:

[0028]

[0029]

[0030] In the formula, E is the total number of Party A's safety supervision personnel, and F is the total number of third-party supervision personnel.

[0031] Preferably, in step 2, it specifically includes the following steps:

[0032] Step 2.1, chromosome coding and population initialization;

[0033] Use binary for chromosome coding, set the population size and the maximum number of iterations, and complete chromosome coding and population initialization;

[0034] Step 2.2: Establish a fitness function for evaluating the size of the survival selection opportunity of the population, and calculate the fitness function values of each individual in the population;

[0035] Step 2.3: Perform a selection operator operation based on the roulette wheel method, determine the selection probability according to the mapping relationship between each chromosome and the fitness function, and calculate the probability that each individual is selected and inherited into the next generation population;

[0036] Step 2.4: Perform a crossover operation;

[0037] Randomly select a pair of parental chromosome individuals in the population for pairing, randomly set two crossover points for the paired chromosome individuals, and perform gene exchange between the crossover points according to the adaptive crossover probability to generate new chromosome individuals;

[0038] Step 2.5: Perform a mutation operation;

[0039] Randomly select a gene position on the chromosome of the parental individual, and perform gene mutation according to the adaptive mutation probability to obtain the offspring individual;

[0040] Step 2.6: Determine whether the number of calculations of the genetic algorithm has reached the preset maximum number of iterations;

[0041] If the number of calculations of the genetic algorithm has not reached the preset maximum number of iterations, return to Step 2.2 to continue the iterative calculation;

[0042] If the number of calculations of the genetic algorithm has reached the preset maximum number of iterations, stop the iterative calculation and enter Step 3.

[0043] Preferably, in Step 2.2, the fitness function is the reciprocal of the comprehensive effectiveness objective function for the safety supervision of inspection and maintenance operations in petrochemical enterprises, as shown in formula (5):

[0044]

[0045] In the formula, f(·) is the fitness function for evaluating the size of the survival selection opportunity of the population, x is the independent variable in the fitness function, and G is the comprehensive effectiveness objective function for the safety supervision of inspection and maintenance operations in petrochemical enterprises.

[0046] Preferably, in Step 2.3, the probability that each individual is selected and inherited into the next generation population is as shown in formula (6):

[0047]

[0048] In the formula, x α is an individual in the population, f(x α ) is the fitness function value of each chromosome individual, and P(x α ) is the individual α in the population.

[0049] Preferably, in step 2.4, the calculation formula of the adaptive crossover probability is as follows:

[0050]

[0051] In the formula, P c is the adaptive crossover probability, f max is the maximum fitness function value of the individuals in the population, f min is the minimum fitness function value of the individuals in the population, f ∧ is the larger fitness function value of the two individuals that produce crossover, K1 is a constant, and the value range of K1 is 0 < K1 < 1.

[0052] Preferably, in step 2.5, the calculation formula of the adaptive mutation probability is as follows:

[0053]

[0054] In the formula, P m is the adaptive mutation probability, f max is the maximum fitness function value of the individuals in the population, f min is the minimum fitness function value of the individuals in the population, f is the fitness function value of the chromosome individual, K2 is a constant, and the value range of K2 is 0 < K2 < 1.

[0055] The beneficial effects of the present invention are as follows:

[0056] This patent provides a method for optimizing the allocation of human resources for safety supervision in petrochemical inspection and maintenance projects. This method solves the problem of the allocation of safety supervision personnel under the background of safety risk supervision of inspection and maintenance operations. By dividing the safety supervision and management areas according to the positions of each device in the safety management area of petrochemical enterprises, and cooperating with the comprehensive efficiency objective function of safety supervision for inspection and maintenance operations in petrochemical enterprises, the genetic algorithm is used to calculate the optimal solution of the comprehensive efficiency objective function of safety supervision for inspection and maintenance operations in petrochemical enterprises, providing a basis for formulating an accurate and highly efficient optimal personnel allocation plan under the condition of limited safety supervision personnel, realizing the optimization of the allocation of human resources for safety supervision in petrochemical inspection and maintenance projects, and being beneficial to the safe production of refining enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flowchart for calculating the optimal solution of the comprehensive efficiency objective function of safety supervision for inspection and maintenance operations in petrochemical enterprises based on the genetic algorithm.

[0058] Figure 2 is a schematic diagram of the crossover operation.

[0059] Figure 3 is a schematic diagram of the mutation operation. DETAILED DESCRIPTION OF THE INVENTION

[0060] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0061] Embodiment 1

[0062] This embodiment proposes a method for optimizing the allocation of human resources for safety supervision of petrochemical inspection and maintenance projects, which specifically includes the following steps:

[0063] Step 1: Construct a safety management area model for petrochemical enterprises, and determine the comprehensive efficiency objective function for safety supervision of inspection and maintenance operations in petrochemical enterprises, which specifically includes the following steps:

[0064] Step 1.1: Obtain the distribution of devices in the safety management area of the petrochemical enterprise, determine the positions of each device, and divide the safety management area of the petrochemical enterprise into multiple safety supervision and management areas.

[0065] Step 1.2: According to the operation application information of each safety supervision and management area, including operation type, operation level, and cross operations involved, determine the special operations and general operations in each safety supervision and management area, and calculate the operation safety risk value of each safety supervision and management area, as shown in formula (1):

[0066]

[0067] In the formula, i is the serial number of the safety supervision and management area, 1 ≤ i ≤ m, where m is the total number of safety supervision and management areas; R i is the operation safety risk value of the i-th safety supervision and management area; j is the serial number of the special operation in the safety supervision and management area, 1 ≤ j ≤ n, where n is the number of special operations in the safety supervision and management area; S ij is the safety risk base number of the j-th special operation in the i-th safety supervision and management area, L ij is the level coefficient of the j-th special operation in the i-th safety supervision and management area, C ij is the special operation risk coefficient of the cross operation involved in the j-th special operation in the i-th safety supervision and management area; k is the serial number of the general operation in the safety supervision and management area, 1 ≤ k ≤ w, where w is the number of special operations in the safety supervision and management area; T ik is the safety risk base number of the k-th general operation in the i-th safety supervision and management area, P ik is the risk coefficient of the k-th general operation in the i-th safety supervision and management area.

[0068] Step 1.3: Based on the operation safety risk values of each safety supervision and management area, determine the comprehensive efficiency objective function for safety supervision of inspection and maintenance operations in petrochemical enterprises, and construct a safety management area model for petrochemical enterprises.

[0069] In this embodiment, the comprehensive effectiveness objective function for the safety supervision of the inspection, repair and maintenance operations in petrochemical enterprises is as follows:

[0070]

[0071] In the formula, G is the comprehensive effectiveness objective function for the safety supervision of the inspection, repair and maintenance operations in petrochemical enterprises; min(·) is the minimum value function; X i is the number of Party A's safety supervision personnel in the i-th safety supervision and management area, and a is the safety supervision effectiveness coefficient of Party A; Y i is the number of third-party supervision personnel in the i-th safety supervision and management area, and b is the safety supervision effectiveness coefficient of the third party; Z i is the number of the contractor's self-safety management personnel in the i-th safety supervision and management area, and c is the contractor's self-safety supervision effectiveness coefficient, X i 、Y i 、Z i are all positive integers.

[0072] Step 1.4, according to the number of Party A's safety supervision personnel and the number of third-party supervision personnel, set the constraint conditions of the safety management area model of petrochemical enterprises as follows:

[0073]

[0074]

[0075] In the formula, E is the total number of Party A's safety supervision personnel, and F is the total number of third-party supervision personnel.

[0076] Step 2, calculate the optimal solution of the comprehensive effectiveness objective function for the safety supervision of the inspection, repair and maintenance operations in petrochemical enterprises based on the genetic algorithm. As Figure 1 shown, it specifically includes the following steps:

[0077] Step 2.1, chromosome coding and population initialization;

[0078] Use binary for chromosome coding, set the population size and the maximum number of iterations, and complete chromosome coding and population initialization.

[0079] Step 2.2, establish a fitness function for evaluating the survival and selection opportunity size of the population. The fitness function is the reciprocal of the comprehensive effectiveness objective function for the safety supervision of the inspection, repair and maintenance operations in petrochemical enterprises, and calculate the fitness function values of each individual in the population;

[0080] In this embodiment, the fitness function for evaluating the survival and selection opportunity size of the population is as shown in formula (5):

[0081]

[0082] In the formula, f(·) is the fitness function used to evaluate the survival selection opportunity of the population, x is the independent variable in the fitness function, and G is the comprehensive effectiveness objective function for the safety supervision of inspection and maintenance operations in petrochemical enterprises;

[0083] Step 2.3, perform the selection operator operation based on the roulette wheel method, determine the selection probability according to the mapping relationship between each chromosome and the fitness function, and calculate the probability that each individual is selected and inherited to the next generation population.

[0084] In the said Step 2.3, the probability that each individual is selected and inherited to the next generation population is shown in formula (6):

[0085]

[0086] In the formula, x α is an individual in the population, f(x α ) is the fitness function value of each chromosome individual, and P(x α ) is the individual α in the population.

[0087] Step 2.4, perform the crossover operation;

[0088] Randomly select a pair of parental chromosome individuals in the population for pairing, randomly set two crossover points for the paired chromosome individuals, and perform gene exchange between the crossover points according to the adaptive crossover probability to generate new chromosome individuals, as Figure 2 shown.

[0089] In the said Step 2.4, the calculation formula for the adaptive crossover probability is:

[0090]

[0091] In the formula, P c is the adaptive crossover probability, f max is the maximum fitness function value of the individuals in the population, f min is the minimum fitness function value of the individuals in the population, f ∧ is the larger fitness function value of the two individuals that generate crossover, and K1 is a constant, and the value range of K1 is 0 < K1 < 1.

[0092] Step 2.5, perform the mutation operation;

[0093] Randomly select a gene position on the chromosome of the parental individual, perform gene mutation according to the adaptive mutation probability, and obtain the offspring individual, as Figure 3 shown.

[0094] In the said Step 2.5, the calculation formula for the adaptive mutation probability is:

[0095]

[0096] Wherein, P m is the adaptive mutation probability, f max is the maximum fitness function value of individuals in the population, f min is the minimum fitness function value of individuals in the population, f is the fitness function value of chromosome individuals, K2 is a constant, and the value range of K2 is 0 < K2 < 1.

[0097] Step 2.6, determine whether the number of calculations of the genetic algorithm reaches the preset maximum number of iterations;

[0098] If the number of calculations of the genetic algorithm does not reach the preset maximum number of iterations, return to Step 2.2 to continue iterative calculation;

[0099] If the number of calculations of the genetic algorithm has reached the preset maximum number of iterations, stop iterative calculation and enter Step 3.

[0100] Step 3, output the optimal solution of the objective function of the comprehensive efficiency of safety supervision for inspection and maintenance operations in petrochemical enterprises, and obtain the optimal allocation plan of safety supervision human resources for petrochemical inspection and maintenance projects.

[0101] It can be seen that in this embodiment, the genetic algorithm is used to calculate the optimal solution of the objective function of the comprehensive efficiency of safety supervision for inspection and maintenance operations in petrochemical enterprises. By optimizing the crossover operator and mutation operator of the genetic algorithm through the adaptive crossover probability and adaptive mutation probability, the reasonable allocation of safety supervision human resources for petrochemical inspection and maintenance projects is realized, and the maximization of the supervision and monitoring efficiency of operation safety risks is effectively ensured.

[0102] Embodiment 2

[0103] This embodiment proposes a method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects, which specifically includes the following steps:

[0104] Step 1, construct a safety management area model for petrochemical enterprises, and determine the objective function of the comprehensive efficiency of safety supervision for inspection and maintenance operations in petrochemical enterprises, which specifically includes the following steps:

[0105] Step 1.1, obtain the distribution of devices in the safety management area of petrochemical enterprises, determine the positions of each device, and divide the safety management area of petrochemical enterprises into multiple safety supervision and management areas.

[0106] Step 1.2, according to the operation application information of each safety supervision and management area, including operation type, operation level, and cross-operation involved, determine the special operations and general operations in each safety supervision and management area, and calculate the operation safety risk value of each safety supervision and management area, as shown in formula (1):

[0107]

[0108] where \(i\) is the serial number of the safety supervision and management area, \(1\leq i\leq m\), and \(m\) is the total number of safety supervision and management areas; \(R\) i is the operation safety risk value of the \(i\)-th safety supervision and management area; \(j\) is the serial number of the special operation in the safety supervision and management area, \(1\leq j\leq n\), and \(n\) is the number of special operations in the safety supervision and management area; \(S\) ij is the safety risk base number of the \(j\)-th special operation in the \(i\)-th safety supervision and management area, \(L\) ij is the level coefficient of the \(j\)-th special operation in the \(i\)-th safety supervision and management area, \(C\) ij is the special operation risk coefficient of the cross operation involved in the \(j\)-th special operation in the \(i\)-th safety supervision and management area; \(k\) is the serial number of the general operation in the safety supervision and management area, \(1\leq k\leq w\), and \(w\) is the number of special operations in the safety supervision and management area; \(T\) ik is the safety risk base number of the \(k\)-th general operation in the \(i\)-th safety supervision and management area, \(P\) ik is the risk coefficient of the \(k\)-th general operation in the \(i\)-th safety supervision and management area.

[0109] Step 1.3: Based on the operation safety risk values of each safety supervision and management area, determine the comprehensive efficiency objective function for the safety supervision of the petrochemical enterprise's inspection and maintenance operations, and construct the safety management area model of the petrochemical enterprise.

[0110] In this embodiment, the comprehensive efficiency objective function for the safety supervision of the petrochemical enterprise's inspection and maintenance operations is:

[0111]

[0112] where \(G\) is the comprehensive efficiency objective function for the safety supervision of the petrochemical enterprise's inspection and maintenance operations; \(\min(\cdot)\) is the minimum value function; \(X\) i is the number of Party A's safety supervision personnel in the \(i\)-th safety supervision and management area, and \(a\) is the safety supervision effectiveness coefficient of Party A; \(Y\) i is the number of third-party supervision personnel in the \(i\)-th safety supervision and management area, and \(b\) is the safety supervision effectiveness coefficient of the third party; \(Z\) i is the number of the contractor's self-safety management personnel in the \(i\)-th safety supervision and management area, and \(c\) is the self-safety supervision effectiveness coefficient of the contractor, \(X\) i 、\(Y\) i 、\(Z\) i are all positive integers.

[0113] Step 1.4: According to the number of Party A's safety supervision personnel and the number of third-party supervision personnel, set the constraint conditions of the petrochemical enterprise's safety management area model as:

[0114]

[0115]

[0116] In the formula, E is the total number of Party A's safety supervision personnel, and F is the total number of third-party supervision personnel.

[0117] Step 2: Calculate the optimal solution of the comprehensive effectiveness objective function for the safety supervision of the inspection and maintenance operations of petrochemical enterprises based on the genetic algorithm, which specifically includes the following steps:

[0118] Step 2.1: Chromosome coding and population initialization;

[0119] Use binary for chromosome coding, set the population size and termination conditions, and complete chromosome coding and population initialization.

[0120] Step 2.2: Establish a fitness function for evaluating the survival selection opportunity of the population. The fitness function is the reciprocal of the comprehensive effectiveness objective function for the safety supervision of the inspection and maintenance operations of petrochemical enterprises, and calculate the fitness function values of each individual in the population.

[0121] In this embodiment, the fitness function for evaluating the survival selection opportunity of the population is as shown in formula (5):

[0122]

[0123] In the formula, f(·) is the fitness function for evaluating the survival selection opportunity of the population, x is the independent variable in the fitness function, and G is the comprehensive effectiveness objective function for the safety supervision of the inspection and maintenance operations of petrochemical enterprises.

[0124] Step 2.3: Perform selection operator operations based on the roulette wheel method, determine the selection probability according to the mapping relationship between each chromosome and the fitness function, and calculate the probability that each individual is selected and inherited to the next generation population.

[0125] In step 2.3, the probability that each individual is selected and inherited to the next generation population is as shown in formula (6):

[0126]

[0127] In the formula, x α is an individual in the population, f(x α ) is the fitness function value of each chromosome individual, and P(x α ) is the individual α in the population.

[0128] Step 2.4: Perform crossover operations;

[0129] Randomly select a pair of parental chromosome individuals in the population for pairing, randomly set two crossover points for the paired chromosome individuals, and perform gene exchange between the crossover points according to the adaptive crossover probability to generate new chromosome individuals.

[0130] In step 2.4, the calculation formula for the adaptive crossover probability is as follows:

[0131]

[0132] In the formula, P c is the adaptive crossover probability, f max is the maximum fitness function value of individuals in the population, f min is the minimum fitness function value of individuals in the population, f ∧ is the larger fitness function value among the two individuals that produce crossover, and K1 is a constant, where the value range of K1 is 0 < K1 < 1.

[0133] Step 2.5, perform mutation operation;

[0134] Randomly select a gene position on the chromosome of the parental individual, and perform gene mutation according to the adaptive mutation probability to obtain the offspring individual.

[0135] In step 2.5, the calculation formula for the adaptive mutation probability is as follows:

[0136]

[0137] In the formula, P m is the adaptive mutation probability, f max is the maximum fitness function value of individuals in the population, f min is the minimum fitness function value of individuals in the population, f is the fitness function value of the chromosome individual, and K2 is a constant, where the value range of K2 is 0 < K2 < 1.

[0138] Step 2.6, determine whether the number of calculation times of the genetic algorithm reaches the preset termination condition;

[0139] If the number of calculation times of the genetic algorithm does not reach the preset termination condition, return to step 2.2 to continue iterative calculation;

[0140] If the number of calculation times of the genetic algorithm has reached the preset termination condition, stop iterative calculation and enter step 3.

[0141] Step 3, output the optimal solution of the comprehensive efficiency objective function for the safety supervision of petrochemical enterprise inspection and maintenance operations, and obtain the optimal allocation plan for the safety supervision human resources of petrochemical inspection and maintenance projects.

[0142] It can be seen that in this embodiment, the genetic algorithm is used to calculate the optimal solution of the comprehensive efficiency objective function for the safety supervision of petrochemical enterprise inspection and maintenance operations, and the crossover operator and mutation operator of the genetic algorithm are optimized through the adaptive crossover probability and the adaptive mutation probability, realizing the reasonable allocation of the safety supervision human resources of petrochemical inspection and maintenance projects and effectively ensuring the maximization of the operation safety risk supervision and monitoring efficiency.

[0143] Example 3

[0144] This embodiment proposes a method for optimizing the allocation of human resources for safety supervision of petrochemical inspection and maintenance projects, which specifically includes the following steps:

[0145] Step 1: Construct a safety management area model for petrochemical enterprises, and determine the comprehensive effectiveness objective function for safety supervision of inspection and maintenance operations in petrochemical enterprises, which specifically includes the following steps:

[0146] Step 1.1: Obtain the distribution of devices in the safety management area of the petrochemical enterprise, determine the locations of each device, and divide the safety management area of the petrochemical enterprise into multiple safety supervision and management areas.

[0147] Step 1.2: According to the operation application information of each safety supervision and management area, including operation type, operation level, and cross-operations involved, determine the special operations and general operations in each safety supervision and management area, and calculate the operation safety risk value of each safety supervision and management area, as shown in formula (1):

[0148]

[0149] In the formula, i is the serial number of the safety supervision and management area, 1 ≤ i ≤ m, where m is the total number of safety supervision and management areas; R i is the operation safety risk value of the i-th safety supervision and management area; j is the serial number of the special operation in the safety supervision and management area, 1 ≤ j ≤ n, where n is the number of special operations in the safety supervision and management area; S ij is the safety risk base number of the j-th special operation in the i-th safety supervision and management area, L ij is the level coefficient of the j-th special operation in the i-th safety supervision and management area, C ij is the special operation risk coefficient of the cross-operation involved in the j-th special operation in the i-th safety supervision and management area; k is the serial number of the general operation in the safety supervision and management area, 1 ≤ k ≤ w, where w is the number of special operations in the safety supervision and management area; T ik is the safety risk base number of the k-th general operation in the i-th safety supervision and management area, P ik is the risk coefficient of the k-th general operation in the i-th safety supervision and management area.

[0150] Step 1.3: Based on the operation safety risk values of each safety supervision and management area, determine the comprehensive effectiveness objective function for safety supervision of inspection and maintenance operations in petrochemical enterprises, and construct a safety management area model for petrochemical enterprises.

[0151] In this embodiment, the comprehensive effectiveness objective function for safety supervision of inspection and maintenance operations in petrochemical enterprises is:

[0152]

[0153] In the formula, G is the comprehensive effectiveness objective function of safety supervision for inspection, repair and maintenance operations in petrochemical enterprises; min(·) is the minimum value function; X i is the number of Party A's safety supervision personnel in the i-th safety supervision and management area, and a is the safety supervision effectiveness coefficient of Party A; Y i is the number of third-party supervision personnel in the i-th safety supervision and management area, and b is the safety supervision effectiveness coefficient of the third party; Z i is the number of self-safety management personnel of the contractor in the i-th safety supervision and management area, and c is the self-safety supervision effectiveness coefficient of the contractor, X i 、Y i 、Z i are all positive integers.

[0154] Step 1.4, according to the number of Party A's safety supervision personnel and the number of third-party supervision personnel, set the constraint conditions of the safety management area model of petrochemical enterprises as:

[0155]

[0156]

[0157] In the formula, E is the total number of Party A's safety supervision personnel, and F is the total number of third-party supervision personnel.

[0158] Step 2, calculate the optimal solution of the comprehensive effectiveness objective function of safety supervision for inspection, repair and maintenance operations in petrochemical enterprises based on the genetic algorithm, which specifically includes the following steps:

[0159] Step 2.1, chromosome coding and population initialization;

[0160] Adopt binary for chromosome coding, set the population size and termination conditions, and complete chromosome coding and population initialization.

[0161] Step 2.2, establish a fitness function for evaluating the survival selection opportunity size of the population. The fitness function is the reciprocal of the comprehensive effectiveness objective function of safety supervision for inspection, repair and maintenance operations in petrochemical enterprises, and calculate the fitness function values of each individual in the population;

[0162] In this embodiment, the fitness function for evaluating the survival selection opportunity size of the population is shown in formula (5):

[0163]

[0164] In the formula, f(·) is the fitness function for evaluating the survival selection opportunity size of the population, x is the independent variable in the fitness function, and G is the comprehensive effectiveness objective function of safety supervision for inspection, repair and maintenance operations in petrochemical enterprises;

[0165] Step 2.3, perform the selection operator operation based on the roulette wheel method, determine the selection probability according to the mapping relationship between each chromosome and the fitness function, and calculate the probability of each individual being selected and inherited into the next-generation population.

[0166] In the said Step 2.3, the probability of each individual being selected and inherited into the next-generation population is shown in Formula (6):

[0167]

[0168] In the formula, x α is an individual in the population, f(x α ) is the fitness function value of each chromosome individual, and P(x α ) is individual α in the population.

[0169] Step 2.4, perform the crossover operation;

[0170] Randomly select a pair of parental chromosome individuals in the population for pairing, randomly set two crossover points for the paired chromosome individuals, and perform gene exchange between the crossover points according to the adaptive crossover probability to generate new chromosome individuals.

[0171] In the said Step 2.4, the calculation formula of the adaptive crossover probability is:

[0172]

[0173] In the formula, P c is the adaptive crossover probability, f max is the maximum fitness function value of individuals in the population, f min is the minimum fitness function value of individuals in the population, f ∧ is the larger fitness function value of the two individuals that generate crossover, and K1 is a constant. In this embodiment, the value range of K1 is 0.5.

[0174] Step 2.5, perform the mutation operation;

[0175] Randomly select a gene position on the chromosome of the parental individual, and perform gene mutation according to the adaptive mutation probability to obtain the offspring individual.

[0176] In the said Step 2.5, the calculation formula of the adaptive mutation probability is:

[0177]

[0178] In the formula, P m is the adaptive mutation probability, f max is the maximum fitness function value of individuals in the population, f min$f_{min}$ is the minimum fitness function value of individuals in the population, $f$ is the fitness function value of chromosome individuals, $K_2$ is a constant, and in this embodiment, the value of $K_2$ is 0.5.

[0179] Step 2.6, determine whether the number of calculations of the genetic algorithm reaches a preset termination condition;

[0180] If the number of calculations of the genetic algorithm does not reach the preset termination condition, return to Step 2.2 to continue iterative calculations;

[0181] If the number of calculations of the genetic algorithm has reached the preset termination condition, stop iterative calculations and enter Step 3.

[0182] Step 3, output the optimal solution of the comprehensive effectiveness objective function for the safety supervision of petrochemical enterprise inspection and maintenance operations, and obtain the optimal allocation plan for the safety supervision human resources of petrochemical inspection and maintenance projects.

[0183] Thus, this embodiment uses the genetic algorithm to calculate the optimal solution of the comprehensive effectiveness objective function for the safety supervision of petrochemical enterprise inspection and maintenance operations, optimizes the crossover operator and mutation operator of the genetic algorithm through the adaptive crossover probability and adaptive mutation probability, realizes the reasonable allocation of the safety supervision human resources of petrochemical inspection and maintenance projects, and effectively ensures the maximization of the operation safety risk supervision and monitoring effectiveness.

[0184] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms used here are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used here, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0185] In the present invention, terms such as "upper", "lower", "bottom", "top", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only relational terms determined for facilitating the description of the structural relationship of each component or element of the present invention, and do not specifically refer to any component or element of the present invention, and should not be construed as a limitation of the present invention.

[0186] In the present invention, terms such as "connected" and "coupled" should be understood in a broad sense, which may mean a fixed connection, an integral connection or a detachable connection; it may be directly connected or indirectly connected through an intermediate medium. For those related scientific research or technical personnel in the field, the specific meanings of the above terms in the present invention can be determined according to specific circumstances, and should not be construed as a limitation of the present invention.

[0187] Certainly, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by those skilled in the art within the scope of the essence of the present invention shall also fall within the protection scope of the present invention.

Claims

1. A method for optimizing the allocation of safety supervision human resources in petrochemical inspection, repair and maintenance projects, characterized in that, Specifically, it includes the following steps: Step 1: Construct a safety management regional model for petrochemical enterprises, and determine the objective function of the comprehensive effectiveness of safety supervision for inspection and maintenance operations in petrochemical enterprises; Step 2: Calculate the optimal solution of the objective function of the comprehensive effectiveness of safety supervision for inspection and maintenance operations in petrochemical enterprises based on the genetic algorithm; Step 3: Output the optimal solution of the objective function of the comprehensive effectiveness of safety supervision for inspection and maintenance operations in petrochemical enterprises, and obtain the optimal allocation plan of safety supervision human resources for petrochemical inspection and maintenance projects.

2. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection, repair and maintenance projects according to claim 1, wherein In Step 1, it specifically includes the following steps: Step 1.1: Obtain the distribution of devices in the safety management area of petrochemical enterprises, determine the locations of each device, and divide the safety management area of petrochemical enterprises into multiple safety supervision and management areas; Step 1.2: According to the operation application information of each safety supervision and management area, determine the special operations and general operations in each safety supervision and management area, and calculate the operation safety risk value of each safety supervision and management area; Step 1.3: Based on the operation safety risk values of each safety supervision and management area, determine the objective function of the comprehensive effectiveness of safety supervision for inspection and maintenance operations in petrochemical enterprises, and construct a safety management regional model for petrochemical enterprises; Step 1.4: Set the constraint conditions of the safety management regional model for petrochemical enterprises according to the number of Party A's safety supervision personnel and the number of third-party supervision personnel.

3. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects according to claim 2, wherein, The operation application information of the safety supervision and management area includes operation type, operation level, and cross operations involved.

4. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects according to claim 2, wherein The operation safety risk value of the safety supervision and management area is shown in Formula (1): where \(i\) is the serial number of the safety supervision and management area, \(1\leq i\leq m\), and \(m\) is the total number of safety supervision and management areas; \(R\) i is the operation safety risk value of the \(i\)-th safety supervision and management area; j is the serial number of special operations within the safety supervision and management area, 1 ≤ j ≤ n, where n is the number of special operations within the safety supervision and management area; S ij is the safety risk base number of the j-th special operation in the i-th safety supervision and management area, L ij is the level coefficient of the j-th special operation in the i-th safety supervision and management area, C ij is the special operation risk coefficient of cross operations involved in the j-th special operation in the i-th safety supervision and management area; k is the serial number of general operations within the safety supervision and management area, 1 ≤ k ≤ w, where w is the number of special operations within the safety supervision and management area; T ik is the safety risk base number of the k-th general operation in the i-th safety supervision and management area, P ik is the risk coefficient of the k-th general operation in the i-th safety supervision and management area.

5. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection, repair and maintenance projects according to claim 4, characterized in that, Determine the level of special operations in the safety supervision and management area according to the "Safety Code for Special Operations of Hazardous Chemical Enterprises" (GB30871-2022).

6. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects according to claim 4, wherein, The objective function of the comprehensive effectiveness of safety supervision for inspection and maintenance operations in petrochemical enterprises is: Wherein, G is the comprehensive effectiveness objective function of safety supervision for inspection, repair and maintenance operations in petrochemical enterprises; min(·) is the minimum value function; X i is the number of Party A's safety supervision personnel in the i-th safety supervision and management area, and a is the safety supervision effectiveness coefficient of Party A; Y i is the number of third-party supervision personnel in the i-th safety supervision and management area, and b is the safety supervision effectiveness coefficient of the third party; Z i is the number of the contractor's self-safety management personnel in the i-th safety supervision and management area, and c is the self-safety supervision effectiveness coefficient of the contractor. X i 、Y i 、Z i are all positive integers.

7. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects according to claim 6, characterized in that, The constraint conditions of the safety management regional model for petrochemical enterprises are: In the formula, E is the total number of Party A's safety supervision personnel, and F is the total number of third-party supervision personnel.

8. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects according to claim 2, characterized in that, In Step 2, it specifically includes the following steps: Step 2.1: Chromosome coding and population initialization; Use binary for chromosome coding, set the population size and the maximum number of iterations, and complete chromosome coding and population initialization; Step 2.2: Establish a fitness function for evaluating the survival selection opportunity size of the population, and calculate the fitness function values of each individual in the population; Step 2.3: Perform selection operator operations based on the roulette wheel method, determine the selection probability according to the mapping relationship between each chromosome and the fitness function, and calculate the probability of each individual being selected and inherited to the next generation population; Step 2.4: Perform crossover operations; Randomly select a pair of parental chromosome individuals in the population for pairing, randomly set two crossover points for the paired chromosome individuals, and exchange genes between the crossover points according to the adaptive crossover probability to generate new chromosome individuals; Step 2.5: Perform mutation operations; Randomly select a gene position on the chromosome of the parental individual, and perform gene mutation according to the adaptive mutation probability to obtain the offspring individual; Step 2.6: Determine whether the number of calculations of the genetic algorithm has reached the preset maximum number of iterations; If the number of calculations of the genetic algorithm has not reached the preset maximum number of iterations, return to step 2.2 to continue iterative calculation; If the number of calculations of the genetic algorithm has reached the preset maximum number of iterations, stop the iterative calculation and enter step 3.

9. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection, repair and maintenance projects according to claim 8, characterized in that, In step 2.2, the fitness function is the reciprocal of the comprehensive effectiveness objective function for the safety supervision of inspection, repair and maintenance operations in petrochemical enterprises, as shown in formula (5): In the formula, f(·) is the fitness function used to evaluate the survival and selection opportunity size of the population, x is the independent variable in the fitness function, and G is the comprehensive effectiveness objective function for the safety supervision of inspection, repair and maintenance operations in petrochemical enterprises.

10. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection, repair and maintenance projects according to claim 8, characterized in that In step 2.3, the probability that each individual is selected and inherited to the next generation population is as shown in formula (6): where x α is an individual in the population, f(x α ) is the fitness function value of each chromosome individual, and P(x α ) is individual α in the population.

11. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection and maintenance projects according to claim 8, characterized in that, In step 2.4, the calculation formula for the adaptive crossover probability is: Where P c is the adaptive crossover probability, f max is the maximum fitness function value of individuals in the population, f min is the minimum fitness function value of individuals in the population, f ∧ is the larger fitness function value of the two individuals that produce crossover, K1 is a constant, and the value range of K1 is 0 < K1 < 1.

12. The method for optimizing the allocation of safety supervision human resources for petrochemical inspection, repair and maintenance projects according to claim 8, characterized in that, In step 2.5, the calculation formula for the adaptive mutation probability is: Where P m is the adaptive mutation probability, f max is the maximum fitness function value of individuals in the population, f min is the minimum fitness function value of individuals in the population, f is the fitness function value of chromosome individuals, K2 is a constant, and the value range of K2 is 0 < K2 < 1.

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