Influence degree calculation method and device of influence factors, equipment and storage medium

Abstract

The invention belongs to the technical field of ground system process analysis and evaluation, and provides an influence degree calculation method and device of influence factors, equipment and a storage medium, and the method comprises the steps: obtaining first data; based on the first data, constructing a judgment matrix by using an analytic hierarchy process, and determining subjective weight factors corresponding to the influence factors according to the judgment matrix; generating a decision matrix based on the historical data corresponding to each influence factor, and determining an objective weight factor corresponding to each influence factor according to the decision matrix; and calculating a comprehensive weight factor corresponding to each influence factor based on the subjective weight factor and the objective weight factor, and determining the influence degree of each influence factor according to the comprehensive weight factor. According to the method, the sequence of the influence factors influencing the index level is determined according to the weight relation of the influence factors, the problems of large evaluation workload, large subjective difference of evaluation results and the like in the prior art are solved, and a basis is provided for making ground system operation management and process technical measures in a targeted manner.

Description

Technical Field

[0001] The present invention relates to the technical field of ground system process analysis and evaluation, and in particular to a method, device, equipment and storage medium for calculating the degree of influence of influencing factors. Background Art

[0002] The evaluation and analysis of ground process indicators is an important means to objectively and effectively evaluate the production and management level of ground systems. However, how to further and more accurately determine the next step of optimization and improvement based on the benchmarking results requires analysis and evaluation of the main factors affecting the indicators, so as to set clear goals for the formulation of the next countermeasures. At present, the evaluation of the main factors affecting the indicators mainly relies on subjective evaluation and judgment methods, which overly rely on the experience of evaluators and lack objectivity. Especially for evaluations of larger time spans or larger system units, the analysis workload is huge, the difficulty is high, and the accuracy is poor. It is necessary to establish an efficient and easy-to-operate evaluation method for the influencing factors of ground technical and economic indicators to provide standardized evaluation and analysis methods to improve the reliability and scientificity of the evaluation results. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, equipment and storage medium for calculating the influence degree of influencing factors, so as to improve the above-mentioned problems.

[0004] In order to achieve the above objectives, the present application provides the following technical solutions:

[0005] On the one hand, an embodiment of the present application provides a method for calculating the influence degree of an influencing factor, the method comprising:

[0006] Acquire first data, wherein the first data includes each influencing factor to be evaluated and an indicator affected by each of the influencing factors;

[0007] Based on the first data, a judgment matrix is ​​constructed using a hierarchical analysis method, and a subjective weight factor corresponding to each influencing factor is determined according to the judgment matrix;

[0008] Generate a decision matrix based on the historical data corresponding to each of the influencing factors, and determine the objective weight factor corresponding to each of the influencing factors according to the decision matrix;

[0009] Based on the subjective weight factor and the objective weight factor, a comprehensive weight factor corresponding to each of the influencing factors is calculated, and the influence degree of each of the influencing factors is determined according to the comprehensive weight factor.

[0010] Optionally, based on the first data, a judgment matrix is ​​constructed using a hierarchical analysis method, and a subjective weight factor corresponding to each influencing factor is determined according to the judgment matrix, including:

[0011] According to the influence degree of each influencing factor on the indicator, a pairwise comparison is performed to construct a judgment matrix; the maximum eigenvalue of the judgment matrix and the normalized eigenvector corresponding to the maximum eigenvalue are calculated, and the eigenvector is the importance ranking of each influencing factor;

[0012] A consistency check is performed on the judgment matrix, wherein a consistency ratio corresponding to the judgment matrix is ​​calculated, and the consistency ratio is compared with a preset value. When the consistency ratio is less than the preset value, each value in the feature vector is determined as a subjective weight factor of each influencing factor, otherwise the judgment matrix is ​​adjusted until it passes the consistency check.

[0013] Optionally, generating a decision matrix based on historical data corresponding to each of the influencing factors, and determining an objective weight factor corresponding to each of the influencing factors according to the decision matrix includes:

[0014] Acquire historical data corresponding to each evaluation object, wherein the historical data corresponding to the evaluation object includes historical data corresponding to each of the influencing factors and historical data corresponding to the indicators;

[0015] A basic matrix is ​​constructed according to the historical data corresponding to each evaluation object, the basic matrix is ​​dimensionlessly processed to establish a standard matrix, a decision matrix is ​​established according to the standard matrix, and the objective weight factors corresponding to each influencing factor are calculated according to the decision matrix and the entropy weight method.

[0016] Optionally, based on the subjective weight factor and the objective weight factor, calculating a comprehensive weight factor corresponding to each of the influencing factors includes:

[0017] Obtain subjective coefficient values ​​and objective coefficient values;

[0018] The subjective coefficient value is multiplied by the subjective weight factor to obtain a first calculation result, the objective coefficient value is multiplied by the objective weight factor to obtain a second calculation result, and the first calculation result and the second calculation result are summed to obtain a comprehensive weight factor corresponding to each of the influencing factors.

[0019] In a second aspect, an embodiment of the present application provides a device for calculating the degree of influence of an influencing factor, the device comprising an acquisition module, a first calculation module, a second calculation module and a third calculation module.

[0020] An acquisition module, used for acquiring first data, wherein the first data includes various influencing factors to be evaluated and indicators affected by the various influencing factors;

[0021] A first calculation module is used to construct a judgment matrix based on the first data using a hierarchical analysis method, and determine a subjective weight factor corresponding to each influencing factor according to the judgment matrix;

[0022] A second calculation module is used to generate a decision matrix based on the historical data corresponding to each of the influencing factors, and determine the objective weight factor corresponding to each of the influencing factors according to the decision matrix;

[0023] The third calculation module is used to calculate the comprehensive weight factor corresponding to each of the influencing factors based on the subjective weight factor and the objective weight factor, and determine the influence degree of each of the influencing factors according to the comprehensive weight factor.

[0024] Optionally, the first computing module includes:

[0025] The first calculation unit is used to compare the influence of each influencing factor on the indicator in pairs and construct a judgment matrix; calculate the maximum eigenvalue of the judgment matrix and the normalized eigenvector corresponding to the maximum eigenvalue, and the eigenvector is the importance ranking of each influencing factor;

[0026] A verification unit is used to perform a consistency check on the judgment matrix, wherein a consistency ratio corresponding to the judgment matrix is ​​calculated, and the consistency ratio is compared with a preset value. When the consistency ratio is less than the preset value, each value in the feature vector is determined as a subjective weight factor of each influencing factor, otherwise the judgment matrix is ​​adjusted until it passes the consistency check.

[0027] Optionally, the second computing module includes:

[0028] A first acquisition unit is used to acquire historical data corresponding to each evaluation object, wherein the historical data corresponding to the evaluation object includes historical data corresponding to each of the influencing factors and historical data corresponding to the indicators;

[0029] The second calculation unit is used to construct a basic matrix according to the historical data corresponding to each evaluation object, perform dimensionless processing on the basic matrix, establish a standard matrix, establish a decision matrix according to the standard matrix, and calculate the objective weight factor corresponding to each influencing factor according to the decision matrix and the entropy weight method.

[0030] Optionally, the third computing module includes:

[0031] A second acquisition unit, used to acquire a subjective coefficient value and an objective coefficient value;

[0032] The third calculation unit is used to multiply the subjective coefficient value by the subjective weight factor to obtain a first calculation result, multiply the objective coefficient value by the objective weight factor to obtain a second calculation result, and sum the first calculation result with the second calculation result to obtain a comprehensive weight factor corresponding to each of the influencing factors.

[0033] In a third aspect, an embodiment of the present application provides an influence degree calculation device for an influence factor, the device comprising a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the influence degree calculation method of the influence factor when executing the computer program.

[0034] In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for calculating the degree of influence of influencing factors are implemented.

[0035] The beneficial effects of the present invention are:

[0036] 1. The present invention determines the ranking of various influencing factors affecting the index level through the weight relationship of various influencing factors, solves the problems of large existing evaluation workload and large subjective differences in evaluation results, and provides a basis for the targeted formulation of ground system operation management and process technology measures.

[0037] 2. The present invention can meet the needs of systematic evaluation of multiple process indicators and realize the formation of weight factors in an autonomous, scientific and quantitative manner, thus providing support for benchmarking analysis, clarifying the direction of indicator improvement, and carrying out targeted production management and operation analysis.

[0038] 3. Compared with the existing patents, the advantages of this invention are:

[0039] (1) Invention patent, a method and system for evaluating the service level of power transmission and transformation facilities based on comprehensive evaluation indicators, CN201911190545.2, determines multi-level evaluation indicators, determines the influence relationship between multiple secondary evaluation indicators under multiple primary evaluation indicators, and determines the weight coefficient of the super matrix according to the influence degree between each indicator of the super matrix. The main body is still the hierarchical analysis method, which lacks objective evaluation compared with this application.

[0040] (2) Invention patent, Method, device and storage medium for determining factors affecting energy efficiency of oilfield water injection system, ZL201711341139.2, based on evaluating the target water injection pump system efficiency, the average efficiency of the water injection pump unit and the water injection pump network efficiency index level, the maximum target group process index is determined, and then the factors affecting energy efficiency of oilfield water injection system are determined. It is limited to the analysis of factors affecting water injection system, and the scope of application is limited. This application is applicable to various process units of ground system, and has a wider scope of application.

[0041] (3) Invention patent, method, device and storage medium for determining optimization information of water injection system, ZL201711282846.9, based on multiple sets of water injection data, the working parameters of the water injection system are determined. The working parameters include multiple water injection efficiencies and multiple unit consumptions, so as to determine the parts of the water injection system that need to be upgraded and optimized. Multiple sets of indicators and clear and complete calculation parameter values ​​are required, the historical data demand is large, and the completeness requirement is high. This application is applicable to various process units of the ground system and has a wider range of applications.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 is a flow chart of a method for calculating the degree of influence of an influencing factor described in an embodiment of the present invention;

[0045] Figure 2 is a schematic diagram of the structure of a device for calculating the degree of influence of influence factors described in an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of the structure of an influence degree calculation device of an influence factor described in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] It should be noted that similar reference numerals or letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0049] Example 1

[0050] like Figure 1 As shown, this embodiment provides a method for calculating the influence degree of an influencing factor, which includes step S1, step S2, step S3 and step S4.

[0051] Step S1, obtaining first data, wherein the first data includes various influencing factors to be evaluated and indicators affected by the influencing factors;

[0052] In this step, taking the analysis of factors affecting the efficiency of a water injection system in an oil field block as an example, the factors to be evaluated may be the total amount of water injection, the total amount of electricity consumption, the average wellhead oil pressure of the water injection well, the average outlet pressure of the water injection pump, and the average inlet pressure of the water injection pump; the indicator is the efficiency of the water injection system;

[0053] Step S2: Based on the first data, a judgment matrix is ​​constructed using the hierarchical analysis method, and the subjective weight factor corresponding to each influencing factor is determined according to the judgment matrix;

[0054] The specific implementation steps of this step include step S21 and step S22;

[0055] Step S21: Compare the influence of each influencing factor on the indicator in pairs and construct a judgment matrix; calculate the maximum eigenvalue of the judgment matrix and the normalized eigenvector corresponding to the maximum eigenvalue, wherein the eigenvector is the importance ranking of each influencing factor;

[0056] In this step, pairwise comparisons are performed according to Table 1 to construct a judgment matrix;

[0057] Table 1 Comparison scale value table

[0058]

[0059]

[0060] The judgment matrix S can be, for example:

[0061]

[0062] At the same time, in this step, the eigenvector can be understood as the subjective weight factor of each influencing factor;

[0063] Step S22: performing a consistency check on the judgment matrix, wherein a consistency ratio corresponding to the judgment matrix is ​​calculated, and the consistency ratio is compared with a preset value. When the consistency ratio is less than the preset value, each value in the feature vector is determined as a subjective weight factor of each influencing factor. Otherwise, the judgment matrix is ​​adjusted until it passes the consistency check.

[0064] In this step, the calculation formula of the consistency ratio CR is as follows:

[0065] CR=CI / RI (1)

[0066] CI=(λ max-n ) / ( n-1 ) (2)

[0067] In formula (1) and formula (2), CI is the consistency index, RI is the random consistency index, λmax is the maximum eigenvalue, and n is the number of influencing factors;

[0068] Meanwhile, in this step, the preset value can be customized according to user needs. In this embodiment, the preset value is 0.1;

[0069] Meanwhile, in this step, otherwise the judgment matrix is ​​adjusted until the consistency check is passed, which can be understood as: adjusting the value of each element in the judgment matrix, and then reallocating the subjective weight factor of each influencing factor;

[0070] Step S3, generating a decision matrix based on the historical data corresponding to each of the influencing factors, and determining the objective weight factor corresponding to each of the influencing factors according to the decision matrix;

[0071] The specific implementation steps of this step include step S31 and step S32;

[0072] Step S31, acquiring historical data corresponding to each evaluation object, wherein the historical data corresponding to the evaluation object includes historical data corresponding to each of the influencing factors and historical data corresponding to the indicators;

[0073] In this step, the analysis of factors affecting the efficiency of the water injection system in a certain oil field block is taken as an example. There are three water injection stations in this block, which are responsible for different water injection systems. The operation of the block in the past five years is analyzed and evaluated, and the influencing factors are analyzed. The efficiency indicators of the water injection system are shown in Table 2.

[0074] Table 2 Efficiency indicators of three water injection systems in a block in the past five years

[0075]

[0076] In this step, the historical data corresponding to each evaluation object is the data in Table 2, and each evaluation object can be understood as water injection station 1, water injection station 2 and water injection station 3; in addition, the evaluation object can also be set to other objects, which can be customized according to user needs; for example: the evaluation object can be divided according to time, space or process flow. For example, when evaluating the influencing factors of energy consumption per ton of oil, the values ​​of each influencing factor can be listed according to different years, or the values ​​of each influencing factor can be listed according to each subordinate block, or the values ​​of each influencing factor related to each process unit can be listed according to the well site, transfer station, etc.;

[0077] Step S32: construct a basic matrix based on the historical data corresponding to each evaluation object, perform dimensionless processing on the basic matrix, establish a standard matrix, establish a decision matrix based on the standard matrix, and calculate the objective weight factors corresponding to each influencing factor based on the decision matrix and the entropy weight method.

[0078] In this step, according to the data in Table 2, the basic matrix Y=(y ij ) m×n , m is the number of evaluation objects, n is the number of influencing factors, and the basic matrix Y is:

[0079]

[0080] In this step, the basic matrix is ​​dimensionless processed, and the specific steps of establishing the standard matrix Y' are:

[0081] i=1,2,…,m; j=1,2,…,n

[0082] Among them, y′ jj are the elements in the standard matrix;

[0083] In this step, the specific steps of establishing a decision matrix according to the standard matrix and calculating the objective weight factors corresponding to each influencing factor according to the decision matrix and the entropy weight method are as follows:

[0084] Establish a decision matrix P, matrix element P ij The calculation formula is:

[0085]

[0086] Calculate the objective weight factor W corresponding to each influencing factor j , the calculation formula is as follows:

[0087]

[0088] D j =1-E j (5)

[0089]

[0090] In formula (6), constant K = 1 / ln(m);

[0091] According to the basic matrix Y, according to the above formula, we can calculate:

[0092] E j =(0.6761,0.5941,0.5409,0.5755,0.0384)

[0093] D j =(0.3239,0.4059,0.4591,0.4245,0.9616)

[0094] W j =(0.1258,0.1577,0.1783,0.0.1649,0.3734)

[0095] Step S4: based on the subjective weight factor and the objective weight factor, calculate the comprehensive weight factor corresponding to each of the influencing factors, and determine the influence degree of each of the influencing factors according to the comprehensive weight factor.

[0096] The specific implementation steps of this step include step S41 and step S42;

[0097] Step S41, obtaining subjective coefficient values ​​and objective coefficient values;

[0098] In this step, the subjective coefficient can be taken as 0.2, and the objective coefficient can be taken as 0.8;

[0099] At the same time, the subjective coefficient value and the objective coefficient value are data uploaded by the staff. The staff can refer to Table 3 to obtain the subjective coefficient value and the objective coefficient value. At the same time, the degree of perfection in Table 3 can be defined by the staff;

[0100] Table 3 Table of subjective and objective coefficient values

[0101]

[0102] Step S42: multiply the subjective coefficient value by the subjective weight factor to obtain a first calculation result, multiply the objective coefficient value by the objective weight factor to obtain a second calculation result, and sum the first calculation result and the second calculation result to obtain a comprehensive weight factor corresponding to each of the influencing factors.

[0103] In this step, according to the values ​​obtained by the above calculation, the comprehensive weight factors corresponding to the total amount of water injection, the total amount of electricity consumption, the average wellhead oil pressure of the injection well, the average outlet pressure of the injection pump, and the average inlet pressure of the injection pump can be calculated as (0.22, 0.17, 0.16, 0.14, 0.31).

[0104] After the comprehensive weight factor is calculated, the influence degree of each influencing factor can be determined. The larger the value, the greater the influence degree. A method for determining the influencing factors of ground process indicators is established through the method in this embodiment. The weight relationship of each influencing factor can be quickly and quantitatively calculated, and then the ranking of the influencing factors of the influencing indicator can be determined, which can provide a basis for the targeted formulation of ground system operation management and process technology measures.

[0105] Example 2

[0106] like Figure 2 As shown, this embodiment provides an apparatus for calculating the influence degree of an influencing factor, and the apparatus includes an acquisition module 701 , a first calculation module 702 , a second calculation module 703 and a third calculation module 704 .

[0107] An acquisition module 701 is used to acquire first data, where the first data includes various influencing factors to be evaluated and indicators affected by the various influencing factors;

[0108] A first calculation module 702 is used to construct a judgment matrix based on the first data using a hierarchical analysis method, and determine a subjective weight factor corresponding to each influencing factor according to the judgment matrix;

[0109] The second calculation module 703 is used to generate a decision matrix based on the historical data corresponding to each of the influencing factors, and determine the objective weight factor corresponding to each of the influencing factors according to the decision matrix;

[0110] The third calculation module 704 is used to calculate the comprehensive weight factor corresponding to each of the influencing factors based on the subjective weight factor and the objective weight factor, and determine the influence degree of each of the influencing factors according to the comprehensive weight factor.

[0111] In a specific implementation of the present disclosure, the first calculation module 702 further includes a first calculation unit 7021 and a verification unit 7022 .

[0112] The first calculation unit 7021 is used to compare the influence of each influencing factor on the indicator in pairs and construct a judgment matrix; calculate the maximum eigenvalue of the judgment matrix and the normalized eigenvector corresponding to the maximum eigenvalue, and the eigenvector is the importance ranking of each influencing factor;

[0113] The verification unit 7022 is used to perform a consistency check on the judgment matrix, wherein the consistency ratio corresponding to the judgment matrix is ​​calculated, and the consistency ratio is compared with a preset value. When the consistency ratio is less than the preset value, each value in the feature vector is determined as a subjective weight factor of each influencing factor, otherwise the judgment matrix is ​​adjusted until it passes the consistency check.

[0114] In a specific implementation of the present disclosure, the second calculation module 703 further includes a first acquisition unit 7031 and a second calculation unit 7032 .

[0115] The first acquisition unit 7031 is used to acquire the historical data corresponding to each evaluation object, wherein the historical data corresponding to the evaluation object includes the historical data corresponding to each of the influencing factors and the historical data corresponding to the indicators;

[0116] The second calculation unit 7032 is used to construct a basic matrix according to the historical data corresponding to each evaluation object, perform dimensionless processing on the basic matrix, establish a standard matrix, establish a decision matrix according to the standard matrix, and calculate the objective weight factor corresponding to each influencing factor according to the decision matrix and the entropy weight method.

[0117] In a specific implementation of the present disclosure, the third calculation module 704 further includes a second acquisition unit 7041 and a third calculation unit 7042 .

[0118] The second acquisition unit 7041 is used to acquire the subjective coefficient value and the objective coefficient value;

[0119] The third calculation unit 7042 is used to multiply the subjective coefficient value by the subjective weight factor to obtain a first calculation result, multiply the objective coefficient value by the objective weight factor to obtain a second calculation result, and sum the first calculation result with the second calculation result to obtain a comprehensive weight factor corresponding to each of the influencing factors.

[0120] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0121] Example 3

[0122] Corresponding to the above method embodiment, the embodiment of the present disclosure further provides an influence degree calculation device for influence factors. The influence degree calculation device for influence factors described below and the influence degree calculation method for influence factors described above can refer to each other.

[0123] Figure 3FIG. 8 is a block diagram of an apparatus 800 for calculating the degree of influence of an influence factor according to an exemplary embodiment. Figure 3 As shown, the influence degree calculation device 800 of the influence factor may include: a processor 801 and a memory 802. The influence degree calculation device 800 of the influence factor may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0124] The processor 801 is used to control the overall operation of the influence degree calculation device 800 of the influence factor to complete all or part of the steps in the above-mentioned influence degree calculation method of the influence factor. The memory 802 is used to store various types of data to support the operation of the influence degree calculation device 800 of the influence factor, and these data may include, for example, instructions for any application or method used to operate on the influence degree calculation device 800 of the influence factor, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the influence degree calculation device 800 of the influencing factor and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0125] In an exemplary embodiment, the influence degree calculation device 800 of the influencing factor can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components, and are used to execute the above-mentioned influence degree calculation method of the influencing factors.

[0126] In another exemplary embodiment, a computer storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for calculating the degree of influence of the influencing factors are implemented. For example, the computer storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the device 800 for calculating the degree of influence of the influencing factors to complete the above-mentioned method for calculating the degree of influence of the influencing factors.

[0127] Example 4

[0128] Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a storage medium. The storage medium described below and the method for calculating the degree of influence of the influencing factors described above can refer to each other.

[0129] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for calculating the degree of influence of the influence factors of the above method embodiment.

[0130] The storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other storage medium that can store program codes.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating the degree of influence of influencing factors, characterized in that: include: Acquire first data, wherein the first data includes each influencing factor to be evaluated and an indicator affected by each of the influencing factors; Based on the first data, a judgment matrix is ​​constructed using a hierarchical analysis method, and a subjective weight factor corresponding to each influencing factor is determined according to the judgment matrix; Generate a decision matrix based on the historical data corresponding to each of the influencing factors, and determine the objective weight factor corresponding to each of the influencing factors according to the decision matrix; Based on the subjective weight factor and the objective weight factor, a comprehensive weight factor corresponding to each of the influencing factors is calculated, and the influence degree of each of the influencing factors is determined according to the comprehensive weight factor.

2. The method for calculating the influence degree of influencing factors according to claim 1, characterized in that: Based on the first data, a judgment matrix is ​​constructed using the hierarchical analysis method, and the subjective weight factor corresponding to each influencing factor is determined according to the judgment matrix, including: According to the influence degree of each influencing factor on the indicator, a pairwise comparison is performed to construct a judgment matrix; the maximum eigenvalue of the judgment matrix and the normalized eigenvector corresponding to the maximum eigenvalue are calculated, and the eigenvector is the importance ranking of each influencing factor; A consistency check is performed on the judgment matrix, wherein a consistency ratio corresponding to the judgment matrix is ​​calculated, and the consistency ratio is compared with a preset value. When the consistency ratio is less than the preset value, each value in the feature vector is determined as a subjective weight factor of each influencing factor, otherwise the judgment matrix is ​​adjusted until it passes the consistency check.

3. The method for calculating the influence degree of influencing factors according to claim 1, characterized in that: Generating a decision matrix based on the historical data corresponding to each of the influencing factors, and determining the objective weight factor corresponding to each of the influencing factors according to the decision matrix, including: Acquire historical data corresponding to each evaluation object, wherein the historical data corresponding to the evaluation object includes historical data corresponding to each of the influencing factors and historical data corresponding to the indicators; A basic matrix is ​​constructed according to the historical data corresponding to each evaluation object, the basic matrix is ​​dimensionlessly processed to establish a standard matrix, a decision matrix is ​​established according to the standard matrix, and the objective weight factors corresponding to each influencing factor are calculated according to the decision matrix and the entropy weight method.

4. The method for calculating the influence degree of influencing factors according to claim 1, characterized in that: Based on the subjective weight factor and the objective weight factor, calculating the comprehensive weight factor corresponding to each of the influencing factors includes: Obtain subjective coefficient values ​​and objective coefficient values; The subjective coefficient value is multiplied by the subjective weight factor to obtain a first calculation result, the objective coefficient value is multiplied by the objective weight factor to obtain a second calculation result, and the first calculation result and the second calculation result are summed to obtain a comprehensive weight factor corresponding to each of the influencing factors.

5. A device for calculating the degree of influence of an influencing factor, characterized in that: include: An acquisition module, used for acquiring first data, wherein the first data includes various influencing factors to be evaluated and indicators affected by the various influencing factors; A first calculation module is used to construct a judgment matrix based on the first data using a hierarchical analysis method, and determine a subjective weight factor corresponding to each influencing factor according to the judgment matrix; A second calculation module is used to generate a decision matrix based on the historical data corresponding to each of the influencing factors, and determine the objective weight factor corresponding to each of the influencing factors according to the decision matrix; The third calculation module is used to calculate the comprehensive weight factor corresponding to each of the influencing factors based on the subjective weight factor and the objective weight factor, and determine the influence degree of each of the influencing factors according to the comprehensive weight factor.

6. The device for calculating the degree of influence of influencing factors according to claim 5, characterized in that: The first computing module includes: The first calculation unit is used to compare the influence of each influencing factor on the indicator in pairs and construct a judgment matrix; calculate the maximum eigenvalue of the judgment matrix and the normalized eigenvector corresponding to the maximum eigenvalue, and the eigenvector is the importance ranking of each influencing factor; A verification unit is used to perform a consistency check on the judgment matrix, wherein a consistency ratio corresponding to the judgment matrix is ​​calculated, and the consistency ratio is compared with a preset value. When the consistency ratio is less than the preset value, each value in the feature vector is determined as a subjective weight factor of each influencing factor, otherwise the judgment matrix is ​​adjusted until it passes the consistency check.

7. The device for calculating the degree of influence of influencing factors according to claim 5, characterized in that: The second computing module includes: A first acquisition unit is used to acquire historical data corresponding to each evaluation object, wherein the historical data corresponding to the evaluation object includes historical data corresponding to each of the influencing factors and historical data corresponding to the indicators; The second calculation unit is used to construct a basic matrix according to the historical data corresponding to each evaluation object, perform dimensionless processing on the basic matrix, establish a standard matrix, establish a decision matrix according to the standard matrix, and calculate the objective weight factor corresponding to each influencing factor according to the decision matrix and the entropy weight method.

8. The device for calculating the degree of influence of influencing factors according to claim 5, characterized in that: The third computing module includes: A second acquisition unit, used to acquire a subjective coefficient value and an objective coefficient value; The third calculation unit is used to multiply the subjective coefficient value by the subjective weight factor to obtain a first calculation result, multiply the objective coefficient value by the objective weight factor to obtain a second calculation result, and sum the first calculation result with the second calculation result to obtain a comprehensive weight factor corresponding to each of the influencing factors.

9. A device for calculating the degree of influence of an influencing factor, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the method for calculating the degree of influence of an influencing factor as described in any one of claims 1 to 4 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for calculating the degree of influence of an influencing factor as claimed in any one of claims 1 to 4.

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