Petroleum engineering enterprise staff technical training level evaluation method, device and terminal equipment

Abstract

The application relates to the technical field of petroleum engineering, and provides a petroleum engineering enterprise staff technical training level evaluation method, a device and terminal equipment. The method comprises the following steps: a multi-level evaluation system containing four first-level indexes of training content quality, training implementation effect, HSE management capability and technical support capability is constructed, at least two second-level indexes reflecting the particularity of the petroleum engineering industry are arranged under each first-level index; based on the evaluation system, a pairwise comparison judgment matrix is constructed through an analytic hierarchy process, and the standardized weight values of indexes at each level are calculated; the standardized weight values are input into a fuzzy comprehensive evaluation model, an evaluation set composed of excellent, good, medium and poor is taken as a basis, a comprehensive evaluation result is generated through product operation of a membership matrix and a weight vector, and the reliability is high through division of the first-level indexes and the second-level indexes, construction of the pairwise comparison judgment matrix according to the first-level indexes and the second-level indexes, and evaluation in combination with the weights of the indexes and a fuzzy evaluation vector.

Description

Technical Field

[0001] This application relates to the field of petroleum engineering technology, specifically to a method, device, and terminal equipment for evaluating the technical training level of employees in petroleum engineering enterprises. Background Technology

[0002] With the continuous development of petroleum engineering technology and the intensification of corporate competition, improving the technical level of employees has become one of the key factors in enhancing the core competitiveness of enterprises. However, most current evaluation methods for the technical training level of employees in petroleum engineering enterprises suffer from problems such as incomplete evaluation indicators and unscientific evaluation methods. This results in an inability to accurately and comprehensively reflect the technical training level of employees, thereby affecting the improvement of training effectiveness and the long-term development of enterprises.

[0003] Patent application CN 113256075 A discloses a method for evaluating enterprise risk levels based on analytic hierarchy process (AHP) and fuzzy comprehensive evaluation. By establishing a fuzzy comprehensive evaluation model and constructing membership functions, combined with weights obtained from AHP, it assigns the probability that each enterprise belongs to each risk level, and takes the highest probability value as the evaluation result. However, this method cannot be directly applied to evaluating the technical training level of employees in the petroleum engineering field.

[0004] Patent application CN 117852952 A discloses a comprehensive evaluation method and system for enterprise quality infrastructure. It constructs a neural network model, inputs the evaluation index weights into the neural network model for training, obtains optimized evaluation index weights, and constructs primary, secondary, and tertiary indicators for scoring to obtain corresponding evaluation indicators. However, this method cannot be directly applied to the evaluation of employee technical training levels in the petroleum engineering field.

[0005] Patent application WO2023019986A1 discloses an evaluation method that constructs primary and secondary indicators, combines subjective and objective weights using a combined weighting approach to obtain the evaluation object, and then uses grey clustering theory for evaluation. This method cannot be directly applied to the evaluation of employee technical training levels in the petroleum engineering field.

[0006] The academic paper "Practice and Exploration of Evaluation of Employee Training in Petroleum Enterprises" discloses a method for evaluating employee training in petroleum enterprises, but it does not provide clear evaluation indicators or objective evaluation methods, and it is also unable to accurately and comprehensively evaluate the technical training level of employees. Summary of the Invention

[0007] In view of this, the embodiments of this application provide a method, device and terminal equipment for evaluating the technical training level of employees in petroleum engineering enterprises. It divides the indicators into primary indicators and secondary indicators, constructs pairwise comparison judgment matrices based on the primary and secondary indicators, and performs evaluation by combining the weights of each indicator and fuzzy evaluation vectors. It has high accuracy and high reliability.

[0008] The first aspect of this application provides a method for evaluating the technical training level of employees in petroleum engineering enterprises, including:

[0009] Construct a multi-level evaluation system that includes four primary indicators: training content quality, training implementation effectiveness, HSE management capability, and technical support capability. Each primary indicator has at least two secondary indicators that reflect the special characteristics of the petroleum engineering industry.

[0010] Based on the evaluation system, a pairwise comparison judgment matrix is ​​constructed using the analytic hierarchy process (AHP) to calculate the standardized weight values ​​of the indicators at each level.

[0011] The standardized weight values ​​are input into the fuzzy comprehensive evaluation model. Based on the evaluation set consisting of excellent, good, average, and poor, a comprehensive evaluation result is generated through the product operation of the membership matrix and the weight vector.

[0012] A second aspect of this application provides a device for evaluating the technical training level of employees in petroleum engineering enterprises, comprising:

[0013] The indicator construction module is used to build a multi-level evaluation system that includes four primary indicators: training content quality, training implementation effectiveness, HSE management capability, and technical support capability. Each primary indicator has at least two secondary indicators that reflect the special characteristics of the petroleum engineering industry.

[0014] The weight calculation module is used to construct pairwise comparison judgment matrices based on the evaluation system using the analytic hierarchy process (AHP) and calculate the standardized weight values ​​of each level of indicators.

[0015] The evaluation module is used to input the standardized weight values ​​into the fuzzy comprehensive evaluation model, and generate a comprehensive evaluation result based on the evaluation set consisting of excellent, good, medium and poor, through the product operation of the membership matrix and the weight vector.

[0016] A third aspect of this application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the terminal device implements the method for evaluating the technical training level of employees in petroleum engineering enterprises as provided in the first aspect of this application.

[0017] A fourth aspect of this application provides a computer program product including a computer program that, when run, causes the method described in the first aspect of this application to be performed.

[0018] The method for evaluating the technical training level of employees in petroleum engineering enterprises, provided in the first aspect of this application, systematically integrates the unique technical elements and training management requirements of the petroleum engineering industry by constructing a multi-level evaluation system containing four core dimensions. This breaks through the limitations of traditional training assessment methods with their single-dimensionality at the evaluation framework design level. It ensures the professional adaptability of evaluation indicators and achieves refined management of evaluation elements through a tree structure of primary indicators + specialized secondary indicators. This establishes a scientific and complete cognitive framework for subsequent quantitative analysis, significantly improving the evaluation system's ability to analyze complex engineering scenarios. By using the dot product operation rule of fuzzy evaluation vectors and preset score values, while retaining the advantages of fuzzy mathematics in handling complex factors, it maps multi-dimensional evaluation results into intuitive quantitative scores through linear combination calculations. This overcomes the shortcomings of traditional fuzzy evaluation results being abstract and having low application value. Furthermore, by setting score standards, it achieves a unified dimensional conversion of cross-indicator evaluation results, making the final output both theoretically profound and application-friendly, greatly enhancing the decision-support value of the evaluation conclusions. By dividing indicators into primary and secondary indicators, constructing pairwise comparison judgment matrices based on these indicators, and combining the weights of each indicator with fuzzy evaluation vectors, the evaluation achieves high accuracy and reliability. This allows for a systematic and comprehensive evaluation of the technical training level of employees in petroleum engineering enterprises, and the development of effective improvement measures based on the evaluation results, thereby continuously enhancing the technical capabilities of employees and the overall competitiveness of the enterprise.

[0019] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for evaluating the technical training level of employees in petroleum engineering enterprises, provided in one embodiment of this application.

[0022] Figure 2 This is a flowchart illustrating a method for evaluating the technical training level of employees in petroleum engineering enterprises, provided in another embodiment of this application.

[0023] Figure 3 This is a schematic diagram of the structure of the technical training level evaluation device for employees of petroleum engineering enterprises provided in the embodiments of this application;

[0024] Figure 4 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] like Figure 1 As shown in the embodiments of this application, the method for evaluating the technical training level of employees in petroleum engineering enterprises includes the following steps S101 to S106:

[0028] Step S101: Construct a multi-level evaluation system that includes four primary indicators: training content quality, training implementation effectiveness, HSE management capability, and technical support capability. Each primary indicator shall have at least two secondary indicators that reflect the special characteristics of the petroleum engineering industry.

[0029] Step S102: Based on the evaluation system, construct pairwise comparison judgment matrices using the analytic hierarchy process (AHP) and calculate the standardized weight values ​​of the indicators at each level.

[0030] Step S103: Input the standardized weight values ​​into the fuzzy comprehensive evaluation model. Based on the evaluation set consisting of excellent, good, medium, and poor, generate the comprehensive evaluation result through the product operation of the membership matrix and the weight vector.

[0031] In application, drawing on the SERVQUAL scale model and considering the characteristics of technical training for employees in petroleum engineering enterprises, an evaluation index system was constructed, comprising four primary indicators and several secondary indicators. Each primary indicator is further subdivided into several secondary indicators. The primary indicators include the quality of training content, the effectiveness of training implementation, HSE management capabilities, and technical support capabilities.

[0032] This application embodiment constructs a multi-level evaluation system containing four core dimensions, systematically integrating the unique technical elements and training management requirements of the petroleum engineering industry. At the evaluation framework design level, it breaks through the limitations of the single dimension of traditional training evaluation methods, ensuring the professional adaptability of evaluation indicators and realizing the refined management of evaluation elements through the tree structure of primary indicators + special secondary indicators. It establishes a scientific and complete cognitive framework for subsequent quantitative analysis, significantly improving the evaluation system's ability to analyze complex engineering scenarios.

[0033] In one embodiment, the secondary indicators corresponding to the training content quality indicators include oil and gas equipment operation specifications indicators and international standards and regulations update indicators.

[0034] In one embodiment, the secondary indicators corresponding to the training implementation effectiveness indicators include the downhole accident handling success rate indicator and the energy efficiency improvement rate indicator.

[0035] In one embodiment, the secondary indicators corresponding to the HSE management capability indicators are the high-risk operation risk identification indicator and the safety culture practice indicator.

[0036] In one embodiment, the secondary indicators corresponding to the technical support capability indicator are the digital tool application level indicator and the special equipment maintenance compliance rate indicator.

[0037] In application, a technical training level evaluation index system consisting of primary and secondary indicators is shown in the table below:

[0038]

[0039]

[0040] In one embodiment, such as Figure 2 As shown, the standardized weight values ​​of each level indicator are calculated, including:

[0041] Step S201: Perform column normalization on the judgment matrix using the sum-product method, obtain the weight vector through row summation and normalization, and calculate the maximum eigenvalue λ. max ;

[0042] Step S202: Perform a consistency ratio (CR) test. When CR < 0.1, the weight validity is confirmed.

[0043] In one embodiment, the pairwise comparison judgment matrix constructed using the analytic hierarchy process includes:

[0044] The training content quality judgment matrix has elements of [1,3; 1 / 3,1], corresponding to a weight ratio of 3:1 between the oil and gas equipment operation specifications and the updated international standards.

[0045] The training implementation effectiveness judgment matrix elements are [1,5; 1 / 5,1], and the weight ratio of downhole accident handling success rate to energy efficiency improvement rate is determined to be 5:1;

[0046] The HSE management capability assessment matrix has elements of [1,7; 1 / 7,1], and the weight ratio of high-risk operation risk identification to safety culture implementation is determined to be 7:1.

[0047] The elements of the technical support capability judgment matrix are [1,3; 1 / 3,1], and the weight ratio of the application level of digital tools to the compliance rate of special equipment maintenance is determined to be 3:1.

[0048] In application, based on the evaluation index system, a pairwise comparison judgment matrix is ​​constructed, and the relative importance of each index is determined by expert scoring, thereby calculating the weight of each index.

[0049] Specifically, after establishing a hierarchical comprehensive evaluation system for service quality, a pairwise comparison judgment matrix is ​​established based on the hierarchical relationship between the upper and lower levels. This can be done by using a specific element included in a primary indicator as a criterion, and then comparing the relative weights of each secondary element with its corresponding primary element based on the hierarchical relationship between the selected element and its secondary elements. This relative weight is represented by a comparative scale, using numbers 1 to 9 as the comparison scale: number 1 represents "equally important," number 3 represents "slightly important," number 5 represents "relatively important," number 7 represents "very important," and number 9 represents "very important." Numbers 2, 4, 6, and 8 are four sub-levels representing the degree of importance between two adjacent scales. The AHP method is used to assign weights to the service quality evaluation indicator system, and experts collectively discuss each of the five primary indicators to avoid disagreements among experts. The resulting pairwise comparison judgment matrix is ​​as follows:

[0050] Training Content Quality Judgment Matrix:

[0051] Operating Procedures for Oil and Gas Equipment (C1) Updates to International Standards and Regulations (C2) Operating Procedures for Oil and Gas Equipment (C1) 1 3 Updates to International Standards and Regulations (C2) 1 / 3 1

[0052] Training Implementation Effectiveness Evaluation Matrix:

[0053] Downhole accident handling success rate (C3) Energy efficiency improvement rate (C4) Downhole accident handling success rate (C3) 1 5 Energy efficiency improvement rate (C4) 1 / 5 1

[0054] HSE Management Capability Assessment Matrix:

[0055] Risk identification for high-risk operations (C5) Safety culture implementation level (C6) Risk identification for high-risk operations (C5) 1 7 Safety culture implementation level (C6) 1 / 7 1

[0056] Technical support capability assessment matrix:

[0057] Digital tool application level (C7) Special equipment maintenance compliance rate (C8) Digital tool application level (C7) 1 3 Special equipment maintenance compliance rate (C8) 1 / 3 1

[0058] This application's embodiments precisely define the numerical relationships and weight ratios of the judgment matrices for each dimension, transforming the consensus of expert experience in the petroleum engineering field into quantifiable mathematical priority parameters. For example, the importance of equipment operation specifications is quantified as three times the weight of updated international standards. This not only preserves the core value of industry knowledge but also ensures mathematical logical consistency through the reciprocity constraints of matrix elements. This allows the evaluation criteria to maintain professionalism while possessing a rigorous mathematical derivation foundation, thereby enhancing the reliability of the evaluation results.

[0059] In one embodiment, the judgment matrix is ​​normalized column-wise using the sum-product method, and the weight vector is obtained through row summation and normalization. The maximum eigenvalue λ is then calculated. max ,include:

[0060] The judgment matrix is ​​normalized column-wise to obtain the normalized matrix.

[0061] Calculate the row summation vector of the normalized matrix

[0062] Through the standardized formula w i =w′ i / n generates a weight vector;

[0063] Determine the largest eigenvalue of the matrix based on the weight vector.

[0064] Where i, j = 1, 2, ..., n, b′ ij For the normalized matrix, b ij To determine the value of the element in the i-th row and j-th column of a matrix, where n is the matrix order, w′ i For the row summation vector, w i Let λ be the i-th weight vector. max To determine the largest eigenvalue of the matrix, let A be the original judgment matrix and w be the weight vector, (Aw) i These are scalar elements of the matrix product.

[0065] In application, solving the judgment matrix involves processing the judgment matrix obtained above to obtain the weight values ​​for the importance ranking of each secondary service quality element corresponding to the primary service quality element. The fundamental goal of AHP is to find the largest eigenvalue of the judgment matrix and its corresponding eigenvector. Based on the obtained mutually inverse judgment matrix, the non-zero eigenvalues ​​of the mutually inverse matrix are set using λ. maxThe eigenvector corresponding to the judgment matrix is ​​denoted by w. After standardization of the eigenvectors, the corresponding weight values ​​can be obtained. The primary purpose of using the AHP method to find the largest eigenvalue and its corresponding eigenvector in the judgment matrix is ​​to determine the largest eigenvalue and its corresponding eigenvector. During the calculation, approximation algorithms such as the sum-product method, least squares method, and root method are employed. Because the judgment matrix problem has shifted from qualitative to quantitative, higher precision is not required; therefore, the sum-product method is used.

[0066] In application, the eigenvectors of the four primary service quality evaluation indicators and their corresponding maximum eigenvalues ​​λ are obtained by following the steps described above. max 1. Determine the eigenvectors of the total risk matrix pairwise and their corresponding largest eigenvalues ​​λ. max CI and CR values ​​are detailed in the tables for the results of solving the secondary indicator judgment matrix and the primary indicator judgment matrix. As shown in the table below, all primary service quality evaluation indicators are consistent.

[0067] Table of results for solving the secondary indicator judgment matrix:

[0068]

[0069] Table of results for solving the first-level indicator judgment matrix:

[0070]

[0071]

[0072] In the application, the top five secondary indicators by weight are: the pass rate of students' grades (weight 21.64%), ensuring the employment rate and satisfaction of students (weight 21.48%), ensuring the high level of teaching by teachers (weight 10.74%), the execution of service commitments (weight 8.23%), and the timeliness of employees in solving students' problems (weight 7.26%).

[0073] In this application, fuzzy comprehensive evaluation is used to assess the service quality of technical training projects. A factor set and an evaluation set are established. The factor set comprises the primary service quality evaluation indicators, secondary service quality evaluation indicators, and overall evaluation for the UAV application technology training project. An evaluation set is set up, including four categories of service quality indicators: "Excellent," "Good," "Average," and "Poor," to effectively define each secondary service quality evaluation indicator.

[0074] This application's embodiments introduce a dual verification mechanism of sum-product method and consistency test. In the weight calculation process, a mathematical standardization process and statistical verification procedure are set up. The scale difference of subjective judgment is eliminated by column normalization processing. The CR value verification is used to ensure the synergistic optimization of expert experience and mathematical logic. This makes the weight allocation retain the professional value of industry cognition and conform to the objective laws of mathematical statistics. It effectively solves the pain points of strong subjective weighting arbitrariness and low credibility in traditional evaluation methods.

[0075] In one embodiment, a comprehensive evaluation result is generated by multiplying the membership matrix and the weight vector, including:

[0076] The fuzzy evaluation vector [B1,B2,B3,B4] is multiplied by the preset score vector [V1,V2,V3,V4] to obtain the comprehensive evaluation result.

[0077] Among them, V1=100, V2=75, V3=50, and V4=25 correspond to excellent, good, average, and poor scores, respectively.

[0078] In application, during the single-factor evaluation phase, each primary service quality indicator is evaluated using single-factor methods based on the evaluation criteria for each indicator. A calculation formula is established using the fuzzy evaluation method:

[0079] Bi = Wi × Ri;

[0080] Where Bi represents the membership vector of the i-th primary service quality indicator in the fuzzy evaluation, Wi represents the weight vector of each secondary indicator under the primary indicator, and Ri represents the membership matrix of each secondary indicator evaluation.

[0081] In the comprehensive evaluation phase, a secondary fuzzy comprehensive evaluation is conducted by combining the single-factor evaluation vectors and their weights for each primary service quality indicator to obtain the fuzzy evaluation results for the secondary service quality indicators. The results of the primary fuzzy comprehensive evaluation are set as the membership matrix, calculated according to the formula B = W × R.

[0082] To display the magnitudes of various indicators more intuitively and clearly, the fuzzy vectors are processed into single values. The score standards for high service quality, good service quality, medium service quality, and poor service quality are set to 100, 75, 50, and 25 respectively. All primary and secondary indicators in the project are then processed into single values ​​using fuzzy vectors.

[0083] Multiply each element of the fuzzy vector by its corresponding score standard, and then sum them to obtain the single-valued result.

[0084] For example, for the comprehensive membership vector B = [0.25, 0.475, 0.275, 0]:

[0085] The single-valued result = 0.25×100 + 0.475×75 + 0.275×50 + 0×25

[0086] =25 + 35.625 + 13.75 + 0

[0087] =74.375

[0088] This indicates that the overall service quality is between "good" and "average", leaning more towards "good".

[0089] Through the above steps, a systematic fuzzy comprehensive evaluation of the technical training level of employees in petroleum engineering enterprises was completed.

[0090] This application embodiment, through the dot product operation rule of fuzzy evaluation vector and preset score value, retains the advantages of fuzzy mathematics in handling complex factors, and uses linear combination calculation to reduce the dimensionality of multidimensional evaluation results into intuitive quantitative scores. This not only overcomes the shortcomings of traditional fuzzy evaluation results being abstract in expression and having low application value, but also achieves unified dimensional conversion of cross-index evaluation results through score standard setting, making the final output both theoretically profound and application-friendly, and greatly improving the decision support value of the evaluation conclusions.

[0091] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0092] This application also provides a device for evaluating the technical training level of employees in petroleum engineering enterprises, used to execute the steps in the above-described embodiments of the method for evaluating the technical training level of employees in petroleum engineering enterprises. The device for evaluating the technical training level of employees in petroleum engineering enterprises can be a virtual appliance in a terminal device, run by the processor of the terminal device, or it can be the terminal device itself.

[0093] like Figure 3 As shown in the embodiment of this application, the petroleum engineering enterprise employee technical training level evaluation device 100 includes:

[0094] The indicator construction module 101 is used to construct a multi-level evaluation system that includes four primary indicators: training content quality, training implementation effectiveness, HSE management capability, and technical support capability. Each primary indicator has at least two secondary indicators that reflect the special characteristics of the petroleum engineering industry.

[0095] The weight calculation module 102 is used to construct pairwise comparison judgment matrices based on the evaluation system using the analytic hierarchy process and calculate the standardized weight values ​​of indicators at each level.

[0096] Evaluation module 103 is used to input standardized weight values ​​into the fuzzy comprehensive evaluation model. Based on the evaluation set consisting of excellent, good, medium and poor, it generates a comprehensive evaluation result through the product operation of the membership matrix and the weight vector.

[0097] In one embodiment, the evaluation module is also used for:

[0098] The fuzzy evaluation vector [B1,B2,B3,B4] is multiplied by the preset score vector [V1,V2,V3,V4] to obtain the comprehensive evaluation result.

[0099] Among them, V1=100, V2=75, V3=50, and V4=25 correspond to excellent, good, average, and poor scores, respectively.

[0100] In applications, the modules in the evaluation device for the technical training level of employees in petroleum engineering enterprises can be software program modules, or they can be implemented through different logic circuits integrated in the processor, or they can be implemented through multiple distributed processors.

[0101] like Figure 4 As shown, this application embodiment also provides a terminal device 200, including: at least one processor 201 ( Figure 4 The diagram shows only one processor, memory 202, and computer program 203 stored in memory 202 and executable on at least one processor 201. When processor 201 executes computer program 203, it implements the steps in the various method embodiments described above.

[0102] In applications, terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that... Figure 4 This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the illustration, or a combination of certain components, or different components.

[0103] In applications, the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0104] In applications, the memory may be an internal storage unit of the terminal device in some embodiments, such as the hard drive or RAM of the terminal device. In other embodiments, the memory may be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the terminal device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of a computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0105] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0106] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0107] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps described in the various method embodiments above.

[0108] This application provides a computer program product, including a computer program, which, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0110] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0111] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0112] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0113] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for evaluating the technical training level of employees in petroleum engineering enterprises, characterized in that, include: Construct a multi-level evaluation system that includes four primary indicators: training content quality, training implementation effectiveness, HSE management capability, and technical support capability. Each primary indicator has at least two secondary indicators that reflect the special characteristics of the petroleum engineering industry. Based on the evaluation system, a pairwise comparison judgment matrix is ​​constructed using the analytic hierarchy process (AHP) to calculate the standardized weight values ​​of the indicators at each level. The standardized weight values ​​are input into the fuzzy comprehensive evaluation model. Based on the evaluation set consisting of excellent, good, medium, and poor, the comprehensive evaluation result is generated by multiplying the membership matrix and the weight vector.

2. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The calculation of the standardized weight values ​​of each level of indicators includes: The judgment matrix is ​​normalized column-wise using the sum-product method. The weight vector is obtained through row summation and normalization. The largest eigenvalue λ is then calculated. max ; Perform a consistency ratio (CR) test; if CR < 0.1, the weights are considered valid.

3. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 2, characterized in that, The judgment matrix is ​​normalized using a sum-product method, and the weight vector is obtained through row summation and normalization. The maximum eigenvalue λ is then calculated. max ,include: The judgment matrix is ​​normalized column-wise to obtain the normalized matrix. Calculate the row summation vector of the normalized matrix Through the standardized formula w i =w′ i / n generates a weight vector; Determine the largest eigenvalue of the matrix based on the weight vector. Where i, j = 1, 2, ..., n, b′ ij For the normalized matrix, b ij To determine the value of the element in the i-th row and j-th column of a matrix, where n is the matrix order, w′ i For the row summation vector, w i Let λ be the i-th weight vector. max To determine the largest eigenvalue of the matrix, let A be the original judgment matrix and w be the weight vector, (Aw) i These are scalar elements of the matrix product.

4. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The pairwise comparison judgment matrix constructed by the analytic hierarchy process includes: The training content quality judgment matrix has elements of [1,3; 1 / 3,1], corresponding to a weight ratio of 3:1 between the oil and gas equipment operation specifications and the updated international standards. The training implementation effectiveness judgment matrix elements are [1,5; 1 / 5,1], and the weight ratio of downhole accident handling success rate to energy efficiency improvement rate is determined to be 5:1; The HSE management capability assessment matrix has elements of [1,7; 1 / 7,1], and the weight ratio of high-risk operation risk identification to safety culture implementation is determined to be 7:

1. The elements of the technical support capability judgment matrix are [1,3; 1 / 3,1], and the weight ratio of the application level of digital tools to the compliance rate of special equipment maintenance is determined to be 3:

1.

5. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The process of generating a comprehensive evaluation result through the product of the membership matrix and the weight vector includes: The fuzzy evaluation vector [B1,B2,B3,B4] is multiplied by the preset score vector [V1,V2,V3,V4] to obtain the comprehensive evaluation result. Among them, V1=100, V2=75, V3=50, and V4=25 correspond to excellent, good, average, and poor scores, respectively.

6. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The secondary indicators corresponding to the quality indicators of the training content include indicators for oil and gas equipment operation procedures and indicators for updating international standards and regulations.

7. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The secondary indicators corresponding to the training implementation effectiveness indicators include the downhole accident handling success rate indicator and the energy efficiency improvement rate indicator.

8. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The secondary indicators corresponding to the HSE management capability indicators are the high-risk operation risk identification indicator and the safety culture implementation indicator.

9. The method for evaluating the technical training level of employees in petroleum engineering enterprises as described in claim 1, characterized in that, The secondary indicators corresponding to the technical support capability indicators are the digital tool application level indicator and the special equipment maintenance compliance rate indicator.

10. A device for evaluating the technical training level of employees in petroleum engineering enterprises, characterized in that, include: The indicator construction module is used to build a multi-level evaluation system that includes four primary indicators: training content quality, training implementation effectiveness, HSE management capability, and technical support capability. Each primary indicator has at least two secondary indicators that reflect the special characteristics of the petroleum engineering industry. The weight calculation module is used to construct pairwise comparison judgment matrices based on the evaluation system using the analytic hierarchy process (AHP) and calculate the standardized weight values ​​of each level of indicators. The evaluation module is used to input the standardized weight values ​​into the fuzzy comprehensive evaluation model, and generate a comprehensive evaluation result based on the evaluation set consisting of excellent, good, medium and poor, through the product operation of the membership matrix and the weight vector.

11. The evaluation device for the technical training level of employees in petroleum engineering enterprises as described in claim 10, characterized in that, The evaluation module is also used for: The fuzzy evaluation vector [B1,B2,B3,B4] is multiplied by the preset score vector [V1,V2,V3,V4] to obtain the comprehensive evaluation result. Among them, V1=100, V2=75, V3=50, and V4=25 correspond to excellent, good, average, and poor scores, respectively.

12. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it causes the terminal device to implement the method as described in any one of claims 1-9.

Images