Method for comparing and selecting reservoir basin anti-seepage scheme based on AHP fuzzy comprehensive evaluation

Abstract

The invention relates to a method for comparing and selecting a reservoir basin anti-seepage scheme based on AHP fuzzy comprehensive evaluation. The method is suitable for the technical field of decision analysis and evaluation. According to the technical scheme, the method for comparing and selecting the reservoir anti-seepage schemes based on AHP fuzzy comprehensive evaluation comprises the steps that evaluation factors of scheme comparison and selection are determined based on an evaluation target of reservoir anti-seepage scheme comparison and selection, and a hierarchical structure model is constructed; based on the importance of each factor of the same level in the hierarchical structure model relative to the factor of the previous level, determining the weight vector of each level of factor relative to the evaluation target; performing fuzzy evaluation on each reservoir basin anti-seepage scheme, and determining a membership degree matrix of each evaluation factor corresponding to each evaluation grade; and based on the weight vector of each evaluation factor and the membership matrix of each evaluation factor in each scheme, determining a fuzzy comprehensive evaluation matrix of each scheme, and based on the fuzzy comprehensive evaluation matrix, determining a comprehensive evaluation value of each reservoir basin anti-seepage scheme.

Description

Technical Field

[0001] This invention relates to a method for selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation. It is applicable to the field of decision analysis and evaluation technology. Background Technology

[0002] The choice of which seepage prevention method to adopt for a reservoir requires a systematic and scientific evaluation of different schemes in order to determine the optimal scheme. However, there are many indicators that affect the seepage prevention scheme, making it difficult to determine the degree of influence of different indicators on the seepage prevention scheme. In addition, some indicators have a certain degree of ambiguity, making it difficult to quantify them. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation, in view of the above-mentioned problems.

[0004] The technical solution adopted in this invention is: a method for evaluating and selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation, comprising: Based on the evaluation objectives of the comparison and selection of reservoir basin seepage prevention schemes, the evaluation factors for scheme comparison and selection are determined, and a hierarchical structure model is constructed. Based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model, the weight vector of each factor at each level relative to the evaluation target is determined. Fuzzy evaluation of the seepage prevention schemes for each reservoir basin was conducted to determine the membership matrix of each evaluation factor corresponding to each evaluation level. Based on the weight vectors of each evaluation factor and the membership matrix of each evaluation factor in each scheme, the fuzzy comprehensive evaluation matrix of each scheme is determined, and the comprehensive evaluation value of each reservoir seepage prevention scheme is determined based on the fuzzy comprehensive evaluation matrix.

[0005] The hierarchical model includes a target layer, a criterion layer, and a scheme layer. The target layer includes selecting the optimal reservoir basin seepage prevention scheme. The evaluation factors of the criterion layer include the performance of seepage prevention materials, the impact on the ecological environment, construction feasibility and safety, scheme economy, and monitoring and detection.

[0006] The determination of the weight vector of each factor at each level relative to the evaluation target, based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model, includes: Using the 1-9 scale method, the importance of each factor at the same level relative to the factor at the next higher level is compared pairwise to construct a judgment matrix; Perform a consistency check on the judgment matrix to determine if it has satisfactory consistency. If not, the judgment matrix needs to be adjusted until it has satisfactory consistency. The judgment matrix that has passed the consistency check is normalized to determine the weight vector of the evaluation factors at each level. Based on the weight vectors of evaluation factors at each level, the weight vectors of factors at each level relative to the evaluation target are determined.

[0007] The determination of the fuzzy comprehensive evaluation matrix based on the weight vectors of each evaluation factor and the membership matrix of each evaluation factor in each scheme includes: The membership matrix and weight vector corresponding to each scheme are combined to obtain the fuzzy comprehensive evaluation matrix of each scheme.

[0008] The determination of the comprehensive evaluation value of each reservoir basin seepage prevention scheme based on the fuzzy comprehensive evaluation matrix includes: The fuzzy comprehensive evaluation matrix is ​​normalized to obtain the comprehensive evaluation value of each scheme. Based on the magnitude of the comprehensive evaluation value, the schemes are ranked and the optimal scheme is selected.

[0009] A system for evaluating and selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation includes: The model building module is used to determine the evaluation factors for the comparison of reservoir and basin seepage prevention schemes based on the evaluation objectives, and to build a hierarchical model. The weight determination module is used to determine the weight vector of each factor at each level relative to the evaluation target based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model. The fuzzy evaluation module is used to perform fuzzy evaluations of the seepage prevention schemes for each reservoir basin and to determine the membership matrix of each evaluation factor corresponding to each evaluation level. The comprehensive evaluation module is used to determine the fuzzy comprehensive evaluation matrix of each scheme based on the weight vector of each evaluation factor and the membership matrix of each evaluation factor in each scheme, and to determine the comprehensive evaluation value of each reservoir seepage prevention scheme based on the fuzzy comprehensive evaluation matrix.

[0010] A storage medium storing a computer program executable by a processor, wherein the computer program, when executed, implements the steps of the method for selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation.

[0011] A device for comparing and selecting reservoir seepage prevention schemes includes a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the method for comparing and selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation.

[0012] The beneficial effects of this invention are as follows: The AHP fuzzy comprehensive evaluation method applied in the comparison and selection of reservoir seepage prevention schemes combines qualitative and quantitative analysis, enabling a scientific and objective evaluation of the merits of each scheme. This invention considers multiple influencing factors, comprehensively reflecting the characteristics of each scheme. This invention is applicable to complex decision-making problems involving multiple criteria and objectives, is simple to operate, easy to promote and apply, and has broad application prospects. Attached Figure Description

[0013] Figure 1 The flowchart is for an example.

[0014] Figure 2 This is a schematic diagram of the hierarchical structure model for an embodiment. Detailed Implementation

[0015] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0016] In the description of this invention, "multiple" means two or more. The use of "first" and "second" is for distinguishing technical features only and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0017] Example 1: As Figure 1 As shown in the figure, this embodiment is a method for selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation. The specific steps are as follows: S100. Based on the evaluation objectives of the reservoir basin seepage prevention scheme comparison, determine the evaluation factors for scheme comparison and construct a hierarchical structure model.

[0018] In this example, the evaluation objective is to select the optimal reservoir basin seepage prevention scheme. The influencing factors are analyzed from aspects such as seepage prevention material performance, impact on the ecological environment, construction feasibility and safety, scheme economy, monitoring and detection, etc., and the main evaluation factors affecting the comparison of reservoir basin seepage prevention schemes are screened out.

[0019] In this embodiment, a hierarchical model is constructed based on the Analytic Hierarchy Process (AHP). The hierarchical model includes an objective layer, a criterion layer, and a scheme layer. The objective layer represents the evaluation objectives. The evaluation factors of the criterion layer include the performance of the seepage-proof material, its impact on the ecological environment, construction feasibility and safety, scheme economics, and monitoring and detection. The evaluation factors of the scheme layer include seepage-proof performance and quality, seepage-proof effect, environmental impact, soil impact, hydrogeological impact, construction technical difficulty, total construction period, construction technical reliability, construction safety, project construction cost, maintenance and operation cost, long-term effectiveness assessment, long-term monitoring and maintenance, sustainability of seepage-proof measures, and operational reliability (see...). Figure 2 ).

[0020] S200. Based on the importance of each factor at the same level in the hierarchical structure model relative to the factor at the next higher level, determine the weight vector of each factor at each level relative to the evaluation target.

[0021] S210. Using the 1-9 scale method, the importance of each factor at the same level relative to the factor at the next higher level is compared pairwise to construct a judgment matrix, as shown in the table below.

[0022] S220. Perform a consistency check on the judgment matrix to determine if it has satisfactory consistency. If not, the judgment matrix needs to be adjusted until it has satisfactory consistency.

[0023] In this embodiment, the maximum eigenvalue λmax of the judgment matrix and the consistency index CI are calculated. The random consistency index RI is queried (see the table below), and the consistency ratio CR is calculated. If CR < 0.1, the judgment matrix is ​​considered to have satisfactory consistency; otherwise, the judgment matrix needs to be adjusted.

[0024] S230. Normalize the judgment matrix after consistency verification to determine the weight vector of evaluation factors between each level.

[0025] S240. Based on the weight vectors of evaluation factors at each level, determine the weight vectors of factors at each level relative to the evaluation target.

[0026] S300. Conduct fuzzy evaluation of the seepage prevention schemes for each reservoir basin and determine the membership matrix of each evaluation factor corresponding to each evaluation level.

[0027] The evaluation indicators of each scheme are used as evaluation factors, and a set of comments is determined. The evaluation results are divided into several levels, such as "excellent", "good", "average", and "poor". Expert scoring or fuzzy statistical methods are used to count the proportion of each indicator rated at each level, determine the membership degree of each scheme to each evaluation factor, and determine the membership degree matrix R.

[0028] S400. Based on the weight vectors of each evaluation factor and the membership matrix of each evaluation factor in each scheme, determine the fuzzy comprehensive evaluation matrix of each scheme, and determine the comprehensive evaluation value of each reservoir seepage prevention scheme based on the fuzzy comprehensive evaluation matrix.

[0029] S410. Combine the membership matrix and weight vector corresponding to each scheme to obtain the fuzzy comprehensive evaluation matrix of each scheme.

[0030] S420. Normalize the fuzzy comprehensive evaluation matrix to obtain the comprehensive evaluation value of each scheme. Sort the schemes according to the magnitude of the comprehensive evaluation value and select the optimal scheme.

[0031] Example 2: This example is a system for evaluating and selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation, specifically including: The model building module is used to determine the evaluation factors for the comparison of reservoir and basin seepage prevention schemes based on the evaluation objectives, and to build a hierarchical model. The weight determination module is used to determine the weight vector of each factor at each level relative to the evaluation target based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model. The fuzzy evaluation module is used to perform fuzzy evaluations of the seepage prevention schemes for each reservoir basin and to determine the membership matrix of each evaluation factor corresponding to each evaluation level. The comprehensive evaluation module is used to determine the fuzzy comprehensive evaluation matrix based on the weight vector of each evaluation factor and the membership matrix of each evaluation factor in each scheme, and to determine the comprehensive evaluation value of each reservoir seepage prevention scheme based on the fuzzy comprehensive evaluation matrix.

[0032] Example 3: This example is a storage medium that stores a computer program that can be executed by a processor. When the computer program is executed, it implements the steps of the method for selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation in Example 1.

[0033] Example 4: This example is a device for comparing and selecting reservoir seepage prevention schemes. It has a memory and a processor. The memory stores a computer program that can be executed by the processor. When the computer program is executed, it implements the steps of the method for comparing and selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation described in Example 1.

[0034] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0035] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0036] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0037] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0038] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0039] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0040] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0041] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for evaluating and selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation, characterized in that, include: Based on the evaluation objectives of the comparison and selection of reservoir basin seepage prevention schemes, the evaluation factors for scheme comparison and selection are determined, and a hierarchical structure model is constructed. Based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model, the weight vector of each factor at each level relative to the evaluation target is determined. Fuzzy evaluation of the seepage prevention schemes for each reservoir basin was conducted to determine the membership matrix of each evaluation factor corresponding to each evaluation level. Based on the weight vectors of each evaluation factor and the membership matrix of each evaluation factor in each scheme, the fuzzy comprehensive evaluation matrix of each scheme is determined, and the comprehensive evaluation value of each reservoir basin seepage prevention scheme is determined based on the fuzzy comprehensive evaluation matrix.

2. The method for selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation according to claim 1, characterized in that, The hierarchical model includes a target layer, a criterion layer, and a scheme layer. The target layer includes selecting the optimal reservoir basin seepage prevention scheme. The evaluation factors of the criterion layer include the performance of seepage prevention materials, the impact on the ecological environment, construction feasibility and safety, scheme economy, and monitoring and detection.

3. The method for selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation according to claim 1, characterized in that, The determination of the weight vector of each factor at each level relative to the evaluation target, based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model, includes: Using the 1-9 scale method, the importance of each factor at the same level relative to the factor at the next higher level is compared pairwise to construct a judgment matrix; Perform a consistency check on the judgment matrix to determine if it has satisfactory consistency. If not, the judgment matrix needs to be adjusted until it has satisfactory consistency. The judgment matrix that has passed the consistency check is normalized to determine the weight vector of the evaluation factors at each level. Based on the weight vectors of evaluation factors at each level, the weight vectors of factors at each level relative to the evaluation target are determined.

4. The method for selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation according to claim 1, characterized in that, The determination of the fuzzy comprehensive evaluation matrix based on the weight vectors of each evaluation factor and the membership matrix of each evaluation factor in each scheme includes: The membership matrix and weight vector corresponding to each scheme are combined to obtain the fuzzy comprehensive evaluation matrix of each scheme.

5. The method for evaluating and selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation according to claim 1, characterized in that, The determination of the comprehensive evaluation value of each reservoir basin seepage prevention scheme based on the fuzzy comprehensive evaluation matrix includes: The fuzzy comprehensive evaluation matrix is ​​normalized to obtain the comprehensive evaluation value of each scheme. Based on the magnitude of the comprehensive evaluation value, the schemes are ranked and the optimal scheme is selected.

6. A system for evaluating and selecting reservoir-basin seepage prevention schemes based on AHP fuzzy comprehensive evaluation, characterized in that, include: The model building module is used to determine the evaluation factors for the comparison of reservoir and basin seepage prevention schemes based on the evaluation objectives, and to build a hierarchical model. The weight determination module is used to determine the weight vector of each factor at each level relative to the evaluation target based on the importance of each factor at the same level relative to the factor at the next higher level in the hierarchical structure model. The fuzzy evaluation module is used to perform fuzzy evaluations of the seepage prevention schemes for each reservoir basin and to determine the membership matrix of each evaluation factor corresponding to each evaluation level. The comprehensive evaluation module is used to determine the fuzzy comprehensive evaluation matrix based on the weight vector of each evaluation factor and the membership matrix of each evaluation factor in each scheme, and to determine the comprehensive evaluation value of each reservoir seepage prevention scheme based on the fuzzy comprehensive evaluation matrix.

7. A storage medium having a computer program stored thereon that can be executed by a processor, characterized in that, When the computer program is executed, it implements the steps of the method for selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation according to any one of claims 1 to 5.

8. A device for comparing seepage prevention schemes in reservoir basins, comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, characterized in that, When the computer program is executed, it implements the steps of the method for selecting reservoir seepage prevention schemes based on AHP fuzzy comprehensive evaluation according to any one of claims 1 to 5.

Images