Crude oil industry chain collaborative evaluation method, device and equipment

Abstract

The invention discloses a crude oil industry chain collaborative evaluation method, device and equipment, and the method comprises the steps: obtaining original basic data corresponding to three-level indexes in a three-layer index system, and carrying out the standardization processing of the original basic data, so as to obtain standardized three-level index data; screening the standardized third-level index data based on a principal component analysis method and a correlation coefficient screening method to obtain target third-level index data; on the basis of the target three-level index data, sequentially and respectively determining index weights; and evaluating the index data of each level after assignment and weight based on a superior and inferior solution distance method so as to display collaborative evaluation results of different levels of the crude oil industry chain. According to the method, comprehensive and accurate collaborative evaluation of the crude oil industry chain can be hierarchically realized through a three-layer index system; meanwhile, by screening the three-level indexes and simplifying the evaluation indexes, the collaborative evaluation efficiency of the crude oil industry chain is improved, and the integrated collaborative degree evaluation of the crude oil industry chain is efficiently and accurately realized.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative evaluation of the crude oil industrial chain, and particularly to a method, device and equipment for collaborative evaluation of the crude oil industrial chain. Background Art

[0002] A suitable collaborative evaluation model for the crude oil industrial chain has important guiding significance for the integrated and coordinated development of the crude oil industrial chain. However, since the factors considered in the process of collaborative evaluation of the crude oil industrial chain involve various dimensions such as the evaluated enterprises and industrial chain relationships, the evaluation indicators are diverse, and the relationships between the evaluation indicators are complex, resulting in excessive time consumption in the evaluation process and inaccurate evaluation results. Summary of the Invention

[0003] In order to solve the technical problems of slow and low-precision in the current collaborative evaluation process of the crude oil industrial chain, and to enrich the evaluation technical route and increase the selection space, the embodiments of the present invention provide a method, device and equipment for collaborative evaluation of the crude oil industrial chain.

[0004] In the first aspect, the embodiments of the present invention provide a method for collaborative evaluation of the crude oil industrial chain, which may include:

[0005] Obtain the original basic data corresponding to the tertiary indicators in the three-level indicator system for collaborative evaluation of the crude oil industrial chain constructed in advance, and perform standardization processing on the original basic data to obtain standardized tertiary indicator data;

[0006] Based on the principal component analysis method and the correlation coefficient screening method, screen the standardized tertiary indicator data to obtain target tertiary indicator data;

[0007] Based on the target tertiary indicator data, determine the weights of the tertiary indicators, the weights of the secondary indicators in the three-level indicator system, and the weights of the primary indicators in the three-level indicator system in sequence;

[0008] Based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, evaluate the data of each level of indicators with assigned weights to display the collaborative evaluation results of different levels of the crude oil industrial chain.

[0009] In the second aspect, the embodiments of the present invention provide a device for collaborative evaluation of the crude oil industrial chain, which may include:

[0010] An acquisition module, configured to acquire the original basic data corresponding to the tertiary indicators in the three-level indicator system for collaborative evaluation of the crude oil industrial chain constructed in advance;

[0011] A standardization processing module, configured to perform standardization processing on the original basic data to obtain standardized tertiary indicator data;

[0012] A screening module, configured to screen the standardized three - level index data based on the principal component analysis method and the correlation coefficient screening method to obtain the target three - level index data;

[0013] A weight assignment module, configured to, based on the target three - level index data, sequentially determine the weights of the three - level indexes, the weights of the secondary indexes in the three - layer index system, and the weights of the primary indexes in the three - layer index system;

[0014] An evaluation module, configured to evaluate the data of each level of indexes with assigned weights based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to display the collaborative evaluation results of different levels of the crude oil industry chain.

[0015] In a third aspect, an embodiment of the present invention provides a computer - readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the crude oil industry chain collaborative evaluation method as described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the crude oil industry chain collaborative evaluation method as described in the first aspect.

[0017] The beneficial effects of the above - mentioned technical solutions provided by the embodiments of the present invention at least include:

[0018] Embodiments of the present invention provide a crude oil industry chain collaborative evaluation method, device, and equipment. This method can hierarchically and clearly achieve a comprehensive and accurate collaborative evaluation of the crude oil industry chain through a three - layer index system; at the same time, by screening each three - level index, the evaluation indexes are streamlined, the efficiency of the crude oil industry chain collaborative evaluation is improved, and the integrated collaborative degree of the crude oil industry chain is evaluated efficiently and accurately.

[0019] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification and the drawings.

[0020] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0021] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0022] Figure 1 is a flowchart of the crude oil industry chain collaborative evaluation method provided in the embodiment of the present invention;

[0023] Figure 2 It is a specific execution flowchart of step S13;

[0024] Figure 3 It is a comparison of the comprehensive score results of the examples provided in the embodiments of the present invention from 2017 to 2022;

[0025] Figure 4 It is a schematic diagram of the scores of the first-level indicators in 2017 in the examples provided in the embodiments of the present invention;

[0026] Figure 5 It is a schematic diagram of the comparison of the scores of the first-level indicators in the examples provided in the embodiments of the present invention from 2017 to 2022;

[0027] Figure 6 It is a schematic diagram of the structure of the crude oil industry chain collaborative evaluation device provided in the embodiments of the present invention. Detailed implementation manners

[0028] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0029] The inventor found in actual work that the existing crude oil industry chain collaborative evaluation system has at least the following technical problems: (1) It is difficult to construct the existing index system. When constructing the evaluation index system of the crude oil industry chain, the factors considered involve various dimensions of the evaluated enterprises and the industrial chain relationship, and the internal relationship of the index system is complex; (2) It is difficult to determine the existing evaluation indexes. The evaluation indexes of the crude oil industry chain integration are diverse, and redundant evaluation indexes will consume too much time in the evaluation process, resulting in inaccurate evaluation results. At the same time, the inventor believes that a suitable crude oil industry chain collaborative evaluation model has important guiding significance for the integrated coordinated development of the crude oil industry chain. Therefore, how to accurately screen out suitable evaluation indexes, clarify the logical relationship between the indexes, and determine a complete set of evaluation index systems is a technical problem that needs to be solved urgently by those skilled in the art. In view of the above problems, the present invention is proposed to provide a crude oil industry chain collaborative evaluation method, device and equipment that overcome the above problems or at least partially solve the above problems.

[0030] In the embodiments of the present invention, a crude oil industry chain collaborative evaluation method is provided. Referring to Figure 1 as shown, the method may include the following steps:

[0031] Step S11: Obtain the original basic data corresponding to the tertiary indicators in the pre-constructed three-layer indicator system for the collaborative evaluation of the crude oil industry chain.

[0032] Step S12: Standardize the original basic data to obtain the standardized tertiary indicator data.

[0033] Step S13: Screen the standardized tertiary indicator data based on the principal component analysis method and the correlation coefficient screening method to obtain the target tertiary indicator data.

[0034] Step S14: Based on the target tertiary indicator data, determine the weights of the tertiary indicators, the weights of the secondary indicators in the three-layer indicator system, and the weights of the primary indicators in the three-layer indicator system in sequence.

[0035] Step S15: Evaluate the data of each level of indicators with assigned weights based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to display the collaborative evaluation results of different levels of the crude oil industry chain.

[0036] In the embodiment of the present invention, the above-mentioned collaborative evaluation method for the crude oil industry chain can achieve a comprehensive and accurate collaborative evaluation of the crude oil industry chain in a hierarchical manner through the three-layer indicator system; at the same time, by screening each tertiary indicator, the evaluation indicators are streamlined, the efficiency of the collaborative evaluation of the crude oil industry chain is improved, and the integrated collaborative degree of the crude oil industry chain is evaluated efficiently and accurately.

[0037] In an optional embodiment, the pre-constructed three-layer indicator system in the embodiment of the present invention has a hierarchical structure, from top to bottom, from macro to micro, gradually deepening layer by layer, forming an indivisible evaluation system to achieve a comprehensive and accurate evaluation of the crude oil industry chain. Specifically, the three-layer indicator system for the collaborative evaluation of the crude oil industry chain in the above-mentioned step S11 can be pre-constructed by the following method:

[0038] First, based on the first-level indicators included in the three-layer indicator system, the first-level indicators are divided into second-level indicators according to each link of production, transportation, refining, sales, storage, and trading in the crude oil supply chain; among them, the first-level indicators include at least one of the following: supply chain reliability, supply chain responsiveness, supply chain flexibility, supply chain cost, supply chain efficiency, and supply chain asset utilization rate; then, based on the second-level indicators, the basic data of each industrial link in the crude oil industrial chain are divided to determine the third-level indicators corresponding to each second-level indicator; among them, the second-level indicators corresponding to supply chain reliability include at least one of the following: production reliability, refining reliability, sales reliability, storage reliability, trading reliability; the second-level indicators corresponding to supply chain responsiveness include at least one of the following: production responsiveness, refining responsiveness, sales responsiveness, storage responsiveness, trading responsiveness; the second-level indicators corresponding to supply chain flexibility include at least one of the following: production flexibility, refining flexibility, sales flexibility, storage flexibility, trading flexibility; the second-level indicators corresponding to supply chain cost include at least one of the following: production cost, refining cost, sales cost, storage cost, trading cost; the second-level indicators corresponding to supply chain efficiency include at least one of the following: production efficiency, refining efficiency, sales efficiency, trading efficiency; the second-level indicators corresponding to supply chain asset utilization rate include at least one of the following: fixed asset utilization rate, operating asset utilization rate.

[0039] For the integrated collaborative evaluation of the crude oil industrial chain, a three-layer indicator system is constructed, where the first-level indicators include supply chain reliability, supply chain responsiveness, supply chain flexibility, supply chain cost, and supply chain asset utilization rate, and their respective definitions are shown in Table 1.

[0040] Table 1 First-level indicators and their definitions

[0041]

[0042] The crude oil industrial chain can be further divided into six industrial links: production (crude oil production), transportation (crude oil transportation, refined oil transportation), refining (refining and processing), sales (refined oil sales), storage (crude oil storage, refined oil storage), and trade (crude oil imports and refined oil exports). These six industrial links are used as secondary indicators under the primary indicator. That is, the secondary indicators corresponding to the supply chain reliability include at least one of the following: production reliability, refining reliability, sales reliability, storage reliability, and trade reliability; the secondary indicators corresponding to the supply chain responsiveness include at least one of the following: production responsiveness, refining responsiveness, sales responsiveness, storage responsiveness, and trade responsiveness; the secondary indicators corresponding to the supply chain flexibility include at least one of the following: production flexibility, refining flexibility, sales flexibility, storage flexibility, and trade flexibility; the secondary indicators corresponding to the supply chain cost include at least one of the following: production cost, refining cost, sales cost, storage cost, and trade cost; the secondary indicators corresponding to the supply chain efficiency include at least one of the following: production efficiency, refining efficiency, sales efficiency, and trade efficiency; the secondary indicators corresponding to the supply chain asset utilization rate include at least one of the following: fixed asset utilization rate, operating asset utilization rate. Each secondary indicator includes multiple tertiary indicators, and the tertiary indicators correspond to the basic data of the industrial link.

[0043] Among them, the tertiary indicators of supply chain reliability include: 1) Production reliability includes: crude oil production capacity, newly discovered proven reserves, newly technically recoverable reserves, newly economically recoverable volume, and crude oil commercial rate, etc.; 2) Refining reliability includes: crude oil processing capacity, chemical production capacity, refined oil yield, ethylene yield, comprehensive commodity yield, processing load, and diesel-gasoline ratio matching rate, etc.; 3) The tertiary indicators of sales reliability include: the number of operating gas stations, domestic sales market share, domestic retail market occupancy rate, refined oil retail ratio, refined oil sales volume, and refined oil retail volume, etc.; 4) Storage reliability (or called warehousing reliability) includes: turnover plan execution rate, oil depot storage capacity, oil depot maintenance rate, oil depot equipment failure rate, and comprehensive oil product loss rate, etc.; 5) Trade reliability includes: import dependence, import concentration, import risk degree, etc.

[0044] The tertiary indicators of supply chain responsiveness include: 1) Production responsiveness includes: crude oil production plan execution rate, crude oil delivery plan execution rate, crude oil delivery quality qualification rate, etc.; 2) Refining responsiveness includes: crude oil processing plan execution rate, refined oil delivery plan execution rate, refined oil delivery quality qualification rate, chemical product delivery plan execution rate, chemical product order delivery quality qualification rate, etc.; 3) Sales responsiveness includes: customer order delivery satisfaction rate, customer order delivery quality qualification rate, refined oil sales plan execution rate, etc.; 4) Storage responsiveness (or called warehousing responsiveness) includes: tons of oil operation time, oil depot turnover time, etc.; 5) Trade responsiveness includes: crude oil import volume plan execution rate, refined oil export volume plan execution rate, imported crude oil unloading cycle, etc.

[0045] The third-level indicators of supply chain flexibility include: 1) production flexibility, including production capacity flexibility, minimum production volume flexibility, etc.; 2) refining flexibility, including processing capacity flexibility, minimum processing flexibility, oil control and chemical production increase ratio, etc.; 3) sales flexibility, including sales forecast accuracy, gas station storage capacity flexibility, gas station storage capacity flexibility, etc.; 4) warehousing flexibility, including crude oil storage capacity flexibility, refined oil storage capacity flexibility, crude oil safety inventory flexibility, refined oil safety inventory flexibility, etc.; 5) trading flexibility, including oil price forecast accuracy, import price fluctuation index, import diversification flexibility, import interruption flexibility, etc.

[0046] The third-level indicators of supply chain cost include: 1) production cost, including unit production cost of crude oil; 2) refining cost, including complete refining processing fee, complete chemical processing fee, refining and sales management fee, etc.; 3) sales cost, including marketing cost per ton of oil, sales expense per ton of oil, etc.; 4) storage cost, including crude oil storage cost, refined oil storage cost, etc.; 5) trading cost, including unit import cost of crude oil, unit export expense of refined oil, etc.

[0047] The third-level indicators of supply chain efficiency include: production efficiency, refining efficiency, sales efficiency, trading efficiency.

[0048] The third-level indicators of supply chain asset utilization rate include: 1) fixed asset utilization rate, including fixed asset output value rate, fixed asset profit rate, fixed asset turnover rate, etc.; 2) operating asset utilization rate, including accounts receivable turnover rate, inventory turnover rate, etc.

[0049] It can be seen that the three-layer indicator system adopted in the embodiments of the present invention has good hierarchy, and from top to bottom, it deepens layer by layer from macro to micro, forming an inseparable evaluation system, which can realize a comprehensive and accurate evaluation of the crude oil industry chain.

[0050] In an alternative embodiment, the above step S12 performs standardization processing on the original basic data. In specific implementation, the embodiments of the present invention usually conduct collaborative evaluation of the crude oil industry chain for a certain year, and can obtain the original basic data corresponding to the third-level indicators for that year, and perform standardization processing on the original basic data. Preferably, the standardization processing is the Z-score standardization method, that is, first calculate the mean and standard deviation of the corresponding third-level indicators, then subtract the mean from the original basic data, and then divide by the standard deviation to obtain the standardized third-level indicator data.

[0051] In another optional embodiment, in step S13 above, the standardized three-level index data is screened based on the principal component analysis method and the correlation coefficient screening method to obtain the target three-level index data. That is, the three-level indexes under each secondary index can be used as a category for screening. The principal component analysis method and the correlation coefficient screening method can be used to eliminate the three-level indexes with less influence on the collaborative evaluation result under each secondary index, and the remaining three-level indexes constitute the target three-level indexes. Refer to Figure 2 as shown, the specific steps may include the following:

[0052] Step S131: Determine the covariance matrix or the first correlation coefficient matrix corresponding to the standardized three-level index data to determine the eigenvalues and eigenvectors corresponding to the covariance matrix or the first correlation coefficient matrix.

[0053] Step S132: Based on the eigenvalues and eigenvectors, determine the principal component analysis scores of each standardized three-level index data by the principal component analysis method.

[0054] Step S133: Select the standardized three-level index data with the principal component analysis score greater than the first preset threshold as the candidate three-level index data.

[0055] Step S134: Obtain the second correlation coefficient matrix corresponding to the candidate three-level index data to determine each pair of candidate three-level index data with the second correlation coefficient greater than the second preset threshold.

[0056] Step S135: Select any one candidate three-level index data from each pair of candidate three-level index data by the correlation coefficient screening method, and form the target three-level index data with other candidate three-level index data.

[0057] In the embodiment of the present invention, the three-level indexes with less influence on the evaluation result are eliminated by the principal component analysis method in steps S131 to S133. That is, the calculated eigenvalues and eigenvectors can be combined to perform classification analysis on the actual problem, so as to assign different evaluation indexes, calculate the comprehensive evaluation value, and then according to the calculation result of the comprehensive evaluation value, the comprehensive evaluation value with a smaller value is eliminated. When the comprehensive evaluation value is closer to 1 or -1, the index is more relevant to the actual problem and should be retained for subsequent step calculations. Through the correlation coefficient screening method in steps S134 and S135, that is, the correlation coefficient matrix of the candidate three-level index data is determined. Each element of the correlation coefficient matrix is a correlation coefficient, and each element of the correlation coefficient matrix is between [-1, 1]. The closer the two indexes are to 1 or -1, the stronger the correlation. At this time, any one of the two indexes can be arbitrarily eliminated to avoid index redundancy.

[0058] In the embodiment of the present invention, the principal component analysis method and the correlation coefficient screening are used to screen each three-level index data, reduce useless indexes and redundant indexes, and improve the subsequent evaluation efficiency.

[0059] In step S14 of the embodiment of the present invention, based on the target three-level index data, the weights of the three-level indexes, the weights of the secondary indexes in the three-layer index system, and the weights of the primary indexes in the three-layer index system are determined in sequence. Specifically in implementation, it may specifically include:

[0060] First, based on the target three-level index data, the subjective weight of the three-level index is determined based on the analytic hierarchy process included in the subjective assignment method; the objective weight of the three-level index is determined based on the entropy weight method included in the objective assignment method; based on the subjective weight and the objective weight of the three-level index, the combined weight of the three-level index is determined; then, based on the combined weight of the three-level index, the weight of the secondary index is determined based on the subjective assignment method; finally, based on the weight of the secondary index, the weight of the primary index is determined based on the subjective assignment method.

[0061] In the above steps, based on the target three-level index data, the subjective weight of the three-level index is determined based on the analytic hierarchy process included in the subjective assignment method. Specifically, it may include:

[0062] The first step is to construct a judgment matrix based on the pairwise index data comparison and scoring results of the target three-level index data according to the analytic hierarchy process; the second step is to determine the maximum eigenvalue of each judgment matrix and its corresponding eigenvector; finally, the eigenvector is normalized to determine the subjective weight of the three-level index.

[0063] In the embodiment of the present invention, the subjective weighting method adopts the analytic hierarchy process. The key of this analytic hierarchy process is the construction of the judgment matrix, that is, the matrix formed by pairwise index comparison and scoring. First, define the judgment (pairwise comparison) matrix, and the element z i,j represents the comparison result of the i-th factor relative to the j-th factor. In this embodiment, the values are given according to Santy's 1-9 scale, and the specific content is shown in Table 2.

[0064] Table 2 Values Given by Santy's 1-9 Scale

[0065]

[0066]

[0067] Secondly, find the maximum eigenvalue of each judgment (pairwise comparison) matrix and its corresponding eigenvector, and normalize the eigenvector to obtain the subjective weight.

[0068] Finally, define the consistency test. The so-called consistency test refers to determining the allowable range of inconsistency for the pairwise comparison matrix. In this step, based on the ratio of the consistency index and the random consistency index, it is judged whether the degree of inconsistency of the constructed judgment matrix is within the preset range; if so, the subjective weight of the third-level index is determined based on the judgment matrix; otherwise, the judgment matrix is reconstructed.

[0069] First, calculate the consistency index CI using formula (1):

[0070]

[0071] where λ is the largest eigenvalue and n is the eigenvalue. If CI = 0, it means complete consistency; if CI is close to 0, there is satisfactory consistency; if CI is larger, the degree of inconsistency is more serious.

[0072] To measure the size of CI, the random consistency index RI is introduced, as shown in Table 3. When the consistency ratio is satisfied, it is considered that the degree of inconsistency of the judgment matrix is within the allowable range, with satisfactory consistency, passing the consistency test, and its normalized eigenvector can be used as the weight vector; otherwise, the discriminant matrix needs to be reconstructed and adjusted for z i,j .

[0073] Table 3 Random Consistency Index

[0074] n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.52 1.54 1.56 1.58 1.59

[0075] In the embodiments of the present invention, the objective weight of the third-level index is determined based on the entropy weight method included in the objective assignment method, specifically based on the proportion, information entropy, and information entropy redundancy of each third-level index in the third-level index data, to determine the objective weight of the third-level index.

[0076] In specific implementation, the objective weight assignment method is the entropy weight method, and its main steps are as follows: Calculate the proportion of each index value using formula (2), calculate the information entropy of each index using formula (3), calculate the information entropy redundancy using formula (4), and calculate the objective weight using formula (5).

[0077]

[0078] where k = 1 / ln(n) (3)

[0079] d j = 1 - e j (4)

[0080]

[0081] Then, the combined weight is obtained by combining the subjective weighting method (AHP) and the objective weighting method (entropy weight method), and the combined weights of each index are calculated as shown in formula (6):

[0082]

[0083] Among them, α j is the subjective weight, and β j is the objective weight.

[0084] In the embodiment of the present invention, the weights of the first-level indicators and the third-level indicators are determined by using the above-mentioned subjective weighting method, the subjective weights of the third-level indicators are determined by using the above-mentioned subjective weighting method, the objective weights of the third-level indicators are determined according to the above-mentioned objective weighting method, and the subjective and objective weights are combined to obtain the combined weights of the third-level indicators.

[0085] In an alternative embodiment, the above step S15 evaluates the data of each level of indicators with assigned weights based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to display the collaborative evaluation results of different levels of the crude oil industrial chain.

[0086] Specifically, based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the score results of the first-level indicators, second-level indicators, and third-level indicators of the collaborative evaluation of the crude oil industrial chain for each year are calculated to display the collaborative evaluation results of the crude oil industrial chain at each level of the first-level indicators, second-level indicators, and third-level indicators for each year.

[0087] Among them, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) includes the following steps: for the data of each level of indicators with corresponding weights, its positive ideal solution and negative ideal solution are determined. Among them, the positive ideal solution is composed of the optimal values of all indicators, and the negative ideal solution is composed of the worst values of all indicators. By calculating the Euclidean distance between the indicator data of each year and the ideal solution, the collaborative evaluation results of each year are determined, and each year is ranked according to the collaborative evaluation results.

[0088] In this embodiment, displaying the collaborative evaluation results of different levels of the crude oil industrial chain includes displaying at least one of the following:

[0089] Displaying the comparison of the comprehensive score results of multiple collaborative evaluation samples of the crude oil industrial chain to be measured;

[0090] Displaying the comparison of the first-level indicator score results of multiple collaborative evaluation samples of the crude oil industrial chain to be measured;

[0091] Displaying the first-level indicator score results of each collaborative evaluation sample of the crude oil industrial chain to be measured;

[0092] Displaying the second-level indicator score results of each collaborative evaluation sample of the crude oil industrial chain to be measured;

[0093] And, show the three-level indicator score results of each crude oil industry chain collaborative evaluation sample to be tested.

[0094] In a specific example, the degree of synergy of the crude oil industry chain in the five years from 2017 to 2021 is evaluated. The original basic data of these five years are evaluated through the embodiment of the present invention. The comprehensive score results of the synergy evaluation of these five years can be displayed by using a bar chart according to the annual time, which can intuitively and conveniently understand the synergy development trend of the crude oil industry chain, and further analyze the years with low comprehensive score results, such as displaying the first-level indicator score results of the year, determining the first-level indicator with a low score, and then selecting the second-level indicator score results under the first-level indicator with a low score, determining the second-level indicator with a low score, and then displaying the third-level indicator score results under the second-level indicator, determining the third-level indicator with a low score, so that the staff can improve and optimize the basic data in the corresponding industrial links. In addition, the hexagonal dimension diagram can also be used to simultaneously display the score results of each first-level indicator in the five years from 2017 to 2022, so that the change trend of each first-level indicator can be intuitively understood.

[0095] The embodiment of the present invention can process data by constructing crude oil industry chain collaborative evaluation system software on a computer device.

[0096] First, the user logs in to the crude oil industry chain collaborative evaluation system using an account and password and enters the data management interface; creates a new project on the data management interface, enters the name of the new project and a brief introduction to the evaluation project, and enters the data import interface; on the data import interface, click the corresponding first-level indicator module->second-level indicator module-third-level indicator module in sequence to import the original basic data of the corresponding third-level indicators for these five years, and standardizes the original basic data through the background to obtain and display standardized third-level indicator data.

[0097] Then the background performed principal component analysis on the standardized three-level indicator data, selected 0.08 as the threshold, and eliminated indicators with little impact on the evaluation results. The results are shown in Tables 3, 4, 5, and 6 below.

[0098] Table 3 Results of principal component analysis of production reliability

[0099]

[0100] It can be seen from Table 3 that the scores of the three-level indicators under production reliability are all greater than 0.08 and do not need to be eliminated.

[0101] Table 4 Results of principal component analysis of refining reliability

[0102]

[0103] As can be seen from Table 4, the scores of the third-level indicators under refining reliability are all greater than 0.08, and there is no need to exclude them.

[0104] Table 5 Results of Principal Component Analysis for Sales Reliability

[0105]

[0106] As can be seen from Table 5, the score of the refined oil retail volume among the third-level indicators under sales reliability is less than 0.08, and the indicator of refined oil retail volume needs to be excluded.

[0107] Table 6 Results of Principal Component Analysis for Trade Reliability

[0108] Index Import Dependency Import Concentration Import Risk Degree Score 0.681 1.081 0.049

[0109] As can be seen from Table 6, the third-level indicator that needs to be excluded under trade reliability is the import risk degree indicator.

[0110] Then, the correlation coefficient screening method is used to further screen the remaining third-level indicators. According to the experience of those skilled in the art, ±0.95 is set as the threshold of the correlation coefficient. If it exceeds this threshold, one of the two needs to be excluded to remove the indicators reflecting information redundancy, and the correlation coefficient matrices are shown in Tables 7, 8, 9, 10, and 11 as follows.

[0111] Table 7 Results of Correlation Coefficient Matrix for Production Reliability

[0112]

[0113] As can be seen from Table 7, one of the new technically recoverable reserves and new economically recoverable reserves needs to be selected among the third-level indicators of production reliability in this step. At this time, either one can be arbitrarily selected or through expert mandatory selection, and it is determined to exclude the indicator of new technically recoverable reserves.

[0114] Table 8 Results of Correlation Coefficient Matrix for Refining Reliability

[0115]

[0116] As can be seen from Table 8, the third-level indicators of refining reliability do not need to be excluded in this step.

[0117] Table 9 Results of Correlation Coefficient Matrix for Sales Reliability

[0118]

[0119] As can be seen from Table 9, one of the domestic sales market share and domestic retail market share needs to be selected among the third-level indicators of sales reliability in this step. After expert comparison and selection, finally, the indicator of domestic retail market share is excluded.

[0120] Table 10 Results of the Correlation Coefficient Matrix for Trade Reliability

[0121] Index Import Dependency Import Concentration Import Dependency 1 -0.634 Import Concentration -0.634 1

[0122] As can be seen from Table 10, the third-level indicators of trade reliability do not need to be excluded in this step.

[0123] Similarly, the same method is applied to all third-level indicators. For those with only two or one third-level indicators under some second-level indicators, no index screening will be carried out, and the intermediate process will not be elaborated. The final index system selected is shown in Table 11.

[0124] Table 11 Final Results of the Integrated Synergy Evaluation Index System for the Crude Oil Industry Chain

[0125]

[0126]

[0127] Then, for the number of target third-level indicators in Table 11, the subjective weights of each third-level indicator are determined by the analytic hierarchy process as shown in Table 12, the objective weights of each third-level indicator are determined by the entropy weight method as shown in Table 13, and the combined weights of each target third-level indicator are determined according to the subjective weights and objective weights as shown in Table 14.

[0128] Table 12 Results of the Subjective Weights of the Integrated Synergy Evaluation Indicators for the Crude Oil Industry Chain

[0129]

[0130]

[0131] Table 13 Results of the Objective Weights of the Integrated Synergy Evaluation Indicators for the Crude Oil Industry Chain

[0132]

[0133]

[0134] Table 14 Results of the Combined Weights of the Integrated Synergy Evaluation Indicators for the Crude Oil Industry Chain

[0135]

[0136]

[0137] The subjective weights of the second-level indicators are determined by the analytic hierarchy process as shown in Table 15 below, and the subjective weights of the first-level indicators are determined by the analytic hierarchy process as shown in Table 16 below.

[0138] Table 15 Subjective Weights of the Second-Level Indicators for the Integrated Synergy Evaluation of the Crude Oil Industry Chain

[0139]

[0140]

[0141] Table 16 Weights of the First-level Indicators for the Integrated Synergy Evaluation of the Crude Oil Industry Chain

[0142] First-Level Index Weight Supply Chain Reliability 0.330 Supply Chain Responsiveness 0.105 Supply Chain Flexibility 0.079 Total Supply Chain Cost 0.156 Supply Chain Benefit 0.330

[0143] In summary, the corresponding weights of each level of indicators are shown in Table 17 as follows:

[0144] Table 17 Results of the Weights of the Integrated Synergy Evaluation Indicators of the Crude Oil Industry Chain

[0145]

[0146]

[0147] The integrated synergy evaluation of the crude oil industry chain was carried out on the data from 2017 to 2021. The TOPSIS method was used for evaluation, and the score range was (0, 1). As Figure 3 This is a schematic diagram showing the comparison of the comprehensive score results for the years 2017 - 2022 provided by the embodiments of the present invention. As Figure 3 shown, the degree of integrated synergy in 2021 was the best, with a score reaching 0.578, while the degree of integrated synergy in 2017 was relatively poor, with a score of only 0.468, and the fluctuations in other years were relatively gentle. Looking vertically, when the degree of integrated synergy was gradually increasing, there was a first decrease in the degree of synergy in 2020, but the synergy level recovered again in 2021 and even increased compared to 2019.

[0148] To further obtain detailed information, the scores for each year can be visually adjusted (score range: (60, 100)) and analyzed. Taking 2017 as an example, the scores of the third-level indicators for 2017 are shown in Table 18, and the scores of the second-level indicators are shown in Table 19.

[0149] Table 18 Scores of the Third-level Indicators for the Integrated Synergy Evaluation of the Crude Oil Industry Chain in 2017

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[0151]

[0152] Table 19 Scores of the Second-level Indicators for the Integrated Synergy Evaluation of the Crude Oil Industry Chain in 2017

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[0154]

[0155]

[0156] Figure 4 This is a schematic diagram showing the scores of the first-level indicators in 2017 provided by an embodiment of the present invention. As Figure 4 shown, the best-performing link in 2017 was the supply chain efficiency link, with an evaluation score reaching 85 points. It can be seen from Table 19 that the highest score for sales efficiency was 99 points. The relatively poor-performing link in 2017 was the supply chain responsiveness link, with a score of only 74 points. It can be seen from Table 19 that the score for sales responsiveness was only 62 points.

[0157] Figure 5 This is a schematic diagram showing the comparison of the scores of the first-level indicators from 2017 to 2022 provided by an embodiment of the present invention. From Figure 5 it can be more intuitively seen the comparison of the scores of each link in a certain year with those of other years, which points out the direction for the collaborative work of the subsequent supply chain.

[0158] Based on the same inventive concept, an embodiment of the present invention also provides a collaborative evaluation device for the crude oil industrial chain. Referring to Figure 6 shown, the device may include: an acquisition module 61, a standardization processing module 62, a screening module 63, a weight assignment module 64, and an evaluation module 65. Its working principle is as follows:

[0159] The acquisition module 61 is used to acquire the original basic data corresponding to the third-level indicators in the three-level indicator system for the collaborative evaluation of the crude oil industrial chain pre-constructed;

[0160] The standardization processing module 62 is used to perform standardization processing on the original basic data to obtain standardized third-level indicator data;

[0161] The screening module 63 is used to screen the standardized third-level indicator data based on the principal component analysis method and the correlation coefficient screening method to obtain target third-level indicator data;

[0162] The weight assignment module 64 is used to sequentially determine the weights of the third-level indicators, the weights of the second-level indicators in the three-level indicator system, and the weights of the first-level indicators in the three-level indicator system based on the target third-level indicator data;

[0163] The evaluation module 65 is used to evaluate the data of each level of indicators with assigned weights based on the technique for order preference by similarity to an ideal solution (TOPSIS) to display the collaborative evaluation results at different levels of the crude oil industrial chain.

[0164] In an alternative embodiment, the weight assignment module 64 is specifically used for:

[0165] Based on the target three - level index data, determine the subjective weight of the three - level index based on the analytic hierarchy process included in the subjective assignment method; determine the objective weight of the three - level index based on the entropy weight method included in the objective assignment method; determine the combined weight of the three - level index based on the subjective weight and objective weight of the three - level index.

[0166] Based on the combined weight of the three - level index, determine the weight of the secondary index based on the subjective assignment method.

[0167] Based on the weight of the secondary index, determine the weight of the primary index based on the subjective assignment method.

[0168] In another alternative embodiment, the weight assignment module 64 is specifically configured to: based on the pairwise comparison and scoring results of the target three - level index data, construct a judgment matrix based on the analytic hierarchy process.

[0169] Determine the maximum eigenvalue of each judgment matrix and its corresponding eigenvector.

[0170] Perform normalization processing on the eigenvector to determine the subjective weight of the three - level index.

[0171] In another alternative embodiment, the weight assignment module 64 is specifically configured to: based on the ratio of the consistency index to the random consistency index, determine whether the inconsistency degree of the constructed judgment matrix is within a preset range; if so, determine the subjective weight of the three - level index based on the judgment matrix; otherwise, reconstruct the judgment matrix.

[0172] In another alternative embodiment, the weight assignment module 64 is specifically configured to: based on the proportion, information entropy, and information entropy redundancy of each three - level index in the three - level index data, determine the objective weight of the three - level index.

[0173] In another alternative embodiment, the screening module 63 is specifically configured to:

[0174] Determine the covariance matrix or the first correlation coefficient matrix corresponding to the standardized three - level index data to determine the eigenvalues and eigenvectors corresponding to the covariance matrix or the first correlation coefficient matrix.

[0175] Based on the eigenvalues and the eigenvectors, determine the principal component analysis score of each standardized three - level index data by the principal component analysis method.

[0176] Select the standardized three - level index data with the principal component analysis score greater than the first preset threshold as the candidate three - level index data.

[0177] Obtain the second correlation coefficient matrix corresponding to the candidate tertiary index data, and determine each pair of candidate tertiary index data with a second correlation coefficient greater than the second preset threshold;

[0178] Select one candidate tertiary index data from each pair of candidate tertiary index data by using the correlation coefficient screening method, and form the target tertiary index data with other candidate tertiary index data.

[0179] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned collaborative evaluation method for the crude oil industry chain is implemented.

[0180] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned collaborative evaluation method for the crude oil industry chain is implemented.

[0181] The principle of solving the problems by the above-mentioned device, medium, and related equipment in the embodiments of the present invention is similar to that of the foregoing method. Therefore, the implementation thereof can refer to the implementation of the foregoing method, and the repeated parts will not be described again.

[0182] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program codes.

[0183] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0184] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means embodying the function specified in the flowchart Figure 1 a flow or flows and / or blocks Figure 1 specified in one or more of the blocks.

[0185] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in the flowchart Figure 1 a flow or flows and / or blocks Figure 1 specified in one or more of the blocks.

[0186] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A collaborative evaluation method for the crude oil industrial chain, characterized in that, Including: Obtaining the original basic data corresponding to the tertiary indicators in the pre-constructed three-layer indicator system for collaborative evaluation of the crude oil industry chain, and performing standardization processing on the original basic data to obtain standardized tertiary indicator data; Screening the standardized tertiary indicator data based on the principal component analysis method and the correlation coefficient screening method to obtain target tertiary indicator data; Based on the target tertiary indicator data, successively determining the weights of the tertiary indicators, the weights of the secondary indicators in the three-layer indicator system, and the weights of the primary indicators in the three-layer indicator system; Evaluating the data of each level of indicators with assigned weights based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to display the collaborative evaluation results of different levels of the crude oil industry chain.

2. The method according to claim 1, wherein The step of, based on the target tertiary indicator data, successively determining the weights of the tertiary indicators, the weights of the secondary indicators in the three-layer indicator system, and the weights of the primary indicators in the three-layer indicator system, includes: Based on the target tertiary indicator data, determining the subjective weights of the tertiary indicators based on the Analytic Hierarchy Process (AHP) included in the subjective assignment method; determining the objective weights of the tertiary indicators based on the entropy weight method included in the objective assignment method; and determining the combined weights of the tertiary indicators based on the subjective weights and objective weights of the tertiary indicators; Based on the combined weights of the tertiary indicators, determining the weights of the secondary indicators based on the subjective assignment method; Based on the weights of the secondary indicators, determining the weights of the primary indicators based on the subjective assignment method.

3. The method according to claim 2, wherein The step of, based on the target tertiary indicator data, determining the subjective weights of the tertiary indicators based on the Analytic Hierarchy Process (AHP) included in the subjective assignment method, includes: Based on the pairwise comparison and scoring results of the target tertiary indicator data, constructing a judgment matrix based on the Analytic Hierarchy Process (AHP); Determining the maximum eigenvalue of each judgment matrix and its corresponding eigenvector; Performing normalization processing on the eigenvector to determine the subjective weights of the tertiary indicators.

4. The method according to claim 3, characterized in that, Also including: Based on the ratio of the consistency index and the random consistency index, determining whether the degree of inconsistency of the constructed judgment matrix is within a preset range; If so, determining the subjective weights of the tertiary indicators based on the judgment matrix; Otherwise, reconstructing the judgment matrix.

5. The method according to claim 2, wherein The step of determining the objective weights of the tertiary indicators based on the entropy weight method included in the objective assignment method includes: Based on the proportion, information entropy, and information entropy redundancy of each tertiary indicator in the tertiary indicator data, determining the objective weights of the tertiary indicators.

6. The method according to claim 1, characterized in that The step of screening the standardized tertiary indicator data based on the principal component analysis method and the correlation coefficient screening method to obtain target tertiary indicator data includes: Determining the covariance matrix or the first correlation coefficient matrix corresponding to the standardized tertiary indicator data to determine the eigenvalues and eigenvectors corresponding to the covariance matrix or the first correlation coefficient matrix; Based on the eigenvalues and the eigenvectors, determining the principal component analysis scores of each standardized tertiary indicator data by the principal component analysis method; Select the standardized three - level index data with the score of the principal component analysis method greater than the first preset threshold as the candidate three - level index data; Obtain the second correlation coefficient matrix corresponding to the candidate three - level index data to determine each pair of candidate three - level index data with the second correlation coefficient greater than the second preset threshold; Select one candidate three - level index data from each pair of candidate three - level index data by the correlation coefficient screening method, and form the target three - level index data with other candidate three - level index data.

7. The method according to any one of claims 1 to 6, characterized in that The three - level index system is pre - constructed according to the following method: Based on the first - level indicators included in the three - level index system, divide the first - level indicators into second - level indicators based on each link of production, transportation, refining, sales, storage, and trade in the crude oil supply chain; among them, the first - level indicators include at least one of the following: supply chain reliability, supply chain responsiveness, supply chain flexibility, supply chain cost, supply chain efficiency, and supply chain asset utilization rate; Based on the second - level indicators, divide the basic data of each industrial link in the crude oil industrial chain to determine the third - level indicators corresponding to each second - level indicator; Among them, the second - level indicators corresponding to the supply chain reliability include at least one of the following: production reliability, refining reliability, sales reliability, storage reliability, trade reliability; the second - level indicators corresponding to the supply chain responsiveness include at least one of the following: production responsiveness, refining responsiveness, sales responsiveness, storage responsiveness, trade responsiveness; the second - level indicators corresponding to the supply chain flexibility include at least one of the following: production flexibility, refining flexibility, sales flexibility, storage flexibility, trade flexibility; the second - level indicators corresponding to the supply chain cost include at least one of the following: production cost, refining cost, sales cost, storage cost, trade cost; the second - level indicators corresponding to the supply chain efficiency include at least one of the following: production efficiency, refining efficiency, sales efficiency, trade efficiency; the second - level indicators corresponding to the supply chain asset utilization rate include at least one of the following: fixed - asset utilization rate, operating - asset utilization rate.

8. The method according to any one of claims 1 to 6, characterized in that Displaying the collaborative evaluation results at different levels of the crude oil industrial chain includes displaying at least one of the following: Display the comparison of the comprehensive score results of multiple samples to be evaluated for the collaborative evaluation of the crude oil industrial chain; Display the comparison of the first - level indicator score results of multiple samples to be evaluated for the collaborative evaluation of the crude oil industrial chain; Display the first - level indicator score results of each sample to be evaluated for the collaborative evaluation of the crude oil industrial chain; Display the second - level indicator score results of each sample to be evaluated for the collaborative evaluation of the crude oil industrial chain; And display the third - level indicator score results of each sample to be evaluated for the collaborative evaluation of the crude oil industrial chain.

9. An apparatus for collaborative evaluation of the crude oil industrial chain, characterized in that, It includes: An acquisition module for acquiring the original basic data corresponding to the third - level indicators in the pre - constructed three - level index system for the collaborative evaluation of the crude oil industrial chain; A standardization processing module for performing standardization processing on the original basic data to obtain standardized three - level index data; A screening module for screening the standardized three - level index data based on the principal component analysis method and the correlation coefficient screening method to obtain the target three - level index data; A weight assignment module, which is used to sequentially determine the weights of the third-level indicators, the weights of the second-level indicators in the three-layer indicator system, and the weights of the first-level indicators in the three-layer indicator system based on the target third-level indicator data; An evaluation module, which is used to evaluate the indicator data at each level with assigned weights based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, so as to display the collaborative evaluation results at different levels of the crude oil industry chain.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the crude oil industry chain collaborative evaluation method according to any one of claims 1 to 8.

11. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the crude oil industry chain collaborative evaluation method according to any one of claims 1 to 8.

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