FAHP-DEMATELTRIZ-based cultivation equipment construction method

Abstract

The invention discloses a cultivation equipment construction method based on FAHP-DEMATELTRIZ, and belongs to the technical field of cultivation equipment, and the method specifically comprises the following steps: S1, obtaining and analyzing user demands; s2, constructing a hierarchical model; s3, confirming an index weight and carrying out weight sorting on user demands of the farming equipment; s4, confirming the user demand mutual influence relationship of the farming equipment; s5, calculating the FAHP-DEMATELFENI, and calculating the weighted centrality of the FAHP-DEMATELFENI; and S6, performing conflict analysis and solution based on TRIZ. According to the cultivation equipment construction method based on the FAHP-DEMATELTRIZ, the diversified requirements of the user in different cultivated land reclamation and management scenes are analyzed, and the corresponding cultivation equipment is designed, so that the equipment can adapt to various working environments and functional requirements and has the characteristic of light weight.

Description

Technical Field

[0001] The present invention relates to the technical field of farming equipment, and in particular to a farming equipment construction method based on FAHP-DEMATELTRIZ. Background Art

[0002] To ensure food security and the sustainable use of arable land resources, rationally optimizing arable land management and agricultural production methods is crucial. By 2020, my country's total agricultural machinery power reached 1.07 billion kilowatts, and the proportion of comprehensive mechanization for major crop cultivation and harvesting, including cultivation and harvesting, exceeded 70%. my country is currently at a critical stage in the transition from agricultural mechanization to agricultural intelligence, and intelligent farming equipment is a core component of this transformation. Due to factors such as fierce competition in the domestic and international agricultural machinery industries, lagging research in intelligent farming equipment in my country, and significant differences in farming environments, the level of intelligent farming equipment in China still lags behind that of foreign countries. Summary of the Invention

[0003] The purpose of the present invention is to provide a farming equipment construction method based on FAHP-DEMATELTRIZ. By analyzing the diverse needs of users in different farmland reclamation and management scenarios, corresponding farming equipment is designed so that the equipment can adapt to various working environments and functional requirements and has lightweight characteristics.

[0004] To achieve the above objectives, the present invention provides a method for constructing farming equipment based on FAHP-DEMATELTRIZ, which specifically includes the following steps:

[0005] S1. User demand acquisition and analysis;

[0006] S2, hierarchical model construction;

[0007] S3. Confirm the indicator weights and rank the weights of the farming equipment user needs;

[0008] S4. Confirm the mutual influence relationship between the needs of users of farming equipment;

[0009] S5. Calculate the weighted centrality using FAHP-DEMATELFENI.

[0010] S6. Conduct conflict analysis and solution based on TRIZ.

[0011] Preferably, after collecting keyword requirements through the SETC method in S2, user requirements are divided into the following five aspects through questionnaire surveys and interviews, combined with survey data: appearance, function, ease of operation, economy and structure requirements; the requirements of the indicator layer in five aspects are confirmed, and a hierarchical model is constructed. The top layer is the target layer, that is, the intelligent farming equipment design U; the middle layer is the criterion layer, which includes five items: appearance requirement F, function requirement G, operation requirement C, economy requirement D, and structure requirement E; the bottom layer is the indicator layer from F1 to E3, which correspond to the subdivided demand elements of the previous level.

[0012] Preferably, in S3, the 0.1-0.9 scaling method is used to perform pairwise comparison and judgment on the five partial demand factors of the criterion layer to obtain the scale r ij Construct the fuzzy judgment matrix A=(a ij ) n×n , calculate the weight index of each criterion layer respectively, which specifically includes the following steps:

[0013] S3.1. Calculation and verification of hierarchical weights;

[0014] S3.2. Consistency check.

[0015] Preferably, the specific process of S3.1 is as follows:

[0016] The analytic hierarchy process has a multi-level structure in its analytic hierarchy model, and it is necessary to calculate and verify the weights of each level to obtain the single factor weight vector W. i , the calculation formula is:

[0017]

[0018] Where W i is the initial weight, is the sum of the demand factor scores in the i-th row, and n is the total number of demand factors in the i-th row.

[0019] Preferably, the specific process of S3.2 is as follows:

[0020] Calculate the feature matrix W * , let W=(W1,W2,…,W N ) T is the weight vector of the fuzzy judgment matrix A, where make:

[0021]

[0022] Where W ij W is the value of the factor row in the feature matrix divided by the sum of the values of the row and column. j is the value of the column where the factor is located in the feature matrix;

[0023] Get the n-order characteristic matrix: W * =(W ij ) n×n ;

[0024] Let A=(a ij ) n×n and J=(b ij ) n×n Both are fuzzy judgment matrices. The calculation formula of the compatibility index of fuzzy judgment matrices A and J is:

[0025]

[0026] The fuzzy judgment matrix A and the feature matrix W * Calculate the compatibility index I(A,W * ), if I(A,W * )≤α, where α is the consistency ratio, it proves that the verified consistency test is qualified.

[0027] Preferably, in S4, the direct influence matrix Z of the DEMATEL method is constructed and the matrix is normalized using the maximum method, which specifically includes the following steps:

[0028] S4.1. Sum each row of the direct influence matrix Z, select the maximum value among all the summations, and then divide all elements in the matrix Z by the maximum value to obtain the standardized influence matrix B. The calculation formula is as follows:

[0029]

[0030] Where x ij is the value that directly affects the corresponding row i and column j in the matrix Z;

[0031] The comprehensive influence matrix T is constructed to fully reflect the comprehensive influence between the elements in the system. Its calculation formula is:

[0032]

[0033] Where I is the identity matrix, k is the total number of elements in the influence matrix;

[0034] S4.2. Calculate the various elements based on the results of the DEMATEL calculation process;

[0035] Impact D i It refers to the sum of the values of the rows corresponding to the elements in the comprehensive influence matrix T, which represents the overall impact of each row of elements on all other elements in the entire system. The calculation formula is:

[0036]

[0037] Influence C i It is the sum of the values corresponding to the elements in each column of the matrix T, reflecting the overall degree of influence of each column element on the entire impact system by other elements. The calculation formula is:

[0038]

[0039] Centrality M i =D i +C i Reflects the relative position of factors in the evaluation system and the size of their role; causal degree R = D i -C i It is the difference between the influence degree and the influence degree of a single factor. Finally, the centrality data corresponds to the x-axis and the cause degree data corresponds to the y-axis to draw a causal relationship diagram.

[0040] Preferably, the initial weight W obtained in S5 based on the FAHP method is i The user demand centrality M obtained by DEMATEL method i Perform weighted processing to obtain the FAHP-DEMATEL weighted centrality W i ×M i Finally, the comprehensive weight of the user's design requirements is sorted from high to low.

[0041] Therefore, the present invention adopts the above-mentioned FAHP-DEMATELTRIZ-based farming equipment construction method, analyzes the diverse needs of users in different farmland reclamation and management scenarios, and designs corresponding farming equipment so that the equipment can adapt to various working environments and functional requirements and has lightweight characteristics.

[0042] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for constructing farming equipment based on FAHP-DEMATELTRIZ of the present invention;

[0044] Figure 2 It is a hierarchical model construction diagram of a method for constructing farming equipment based on FAHP-DEMATELTRIZ of the present invention;

[0045] Figure 3 This is a diagram showing the functional modules of the farming equipment according to the FAHP-DEMATELTRIZ-based farming equipment construction method of the present invention, wherein: Figure 3 (a) is a diagram showing the mining module. Figure 3(b) is a diagram showing the soil improvement and farming module. Figure 3 (c) is a diagram showing the transport module. Figure 3 (d) is a diagram showing the morphology of the rotary tillage module;

[0046] Figure 4 This invention is an intelligent system control solution for a farming equipment construction method based on FAHP-DEMATELTRIZ;

[0047] Figure 5 It is a cause-effect relationship diagram of a method for constructing farming equipment based on FAHP-DEMATELTRIZ of the present invention. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0049] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0050] Example 1

[0051] like Figure 1 As shown, the present invention provides a method for constructing farming equipment based on FAHP-DEMATELTRIZ, which specifically includes the following steps:

[0052] S1. User demand acquisition and analysis;

[0053] The SETC method uses a comprehensive analysis of social, economic, technological, and cultural factors to identify multidimensional needs and design opportunities. This method helps identify product trends, align existing technologies with user economic needs, and ultimately design products that meet user expectations.

[0054] To identify breakthroughs in intelligent farming equipment that meet the needs of agricultural practitioners, we distributed 89 questionnaires using a seven-step Likert scale on an agricultural exchange platform and collected 13 interview materials through field research. We obtained 85 valid questionnaires and 13 interview materials. We categorized user needs using the SETC factor and identified the top five most-focused needs. The results are shown in Table 2.

[0055] Table 2 SETC method has the most attention

[0056] society economy technology culture Emphasis on skills education Reduced cost of use AI technology development National culture Pay attention to agricultural safety Personalized consumption Motor technology breakthrough Rural revitalization Improved quality of life Low maintenance costs Remote control development Cultural identity Diversified development Residents' income increased Sensor technology development Policy support Sharing model promotion Improved consumer awareness Simple operation cultural industries

[0057] As shown in Table 2, from a social perspective, the design of intelligent farming equipment should not only focus on its basic functions but also consider the importance society places on agricultural skills training and safe operation. Improving users' operational skills and safety awareness can reduce accidents and promote the sustainable development of modern agriculture. From an economic perspective, the design of intelligent farming equipment should focus on reducing operating and maintenance costs. Through personalized and diversified designs, it can meet different operational needs, reduce overall costs, improve resource utilization, and ultimately enhance user economic benefits. From a technical perspective, the introduction of modern technologies such as artificial intelligence, remote control, and sensors can significantly enhance the intelligence level and operational ease of intelligent farming equipment. From a cultural perspective, the design of intelligent farming equipment should integrate regional culture and policy support to enhance localization and cultural identity. Incorporating local cultural elements and policy guidance to design equipment that adapts to regional needs will not only promote agricultural production but also broaden market acceptance and realize cultural value.

[0058] S2, hierarchical model construction;

[0059] After collecting keyword requirements through the SETC method, through questionnaire surveys and interviews, combined with research data, user requirements are divided into the following five aspects: appearance, function, ease of operation, economy and structural requirements; the requirements of the indicator layer in these five aspects are confirmed, and a hierarchical structure model is constructed as follows: Figure 2 As shown in the figure, the top layer is the target layer, namely the intelligent farming equipment design (U); the middle layer is the criterion layer, which includes five items: appearance requirements (F), functional requirements (G), operation requirements (C), economic requirements (D), and structural requirements (E); the bottom layer is the indicator layer, from F1 to E3, which correspond to the subdivided demand elements of the previous layer.

[0060] S3. Confirm the indicator weights and rank the weights of the farming equipment user needs;

[0061] An evaluation team consisting of 15 design experts and 5 agricultural practitioners participated in the discussion on the necessity of user needs. The 0.1-0.9 scale method was used to conduct pairwise comparisons on the five demand factors of the criterion layer to obtain the scale r ij Construct the fuzzy judgment matrix A=(a ij ) n×n , calculate the weight index of each criterion layer separately, perform weighted averaging on the obtained comparison data, construct the fuzzy complementary judgment matrix, and calculate the weight index of each criterion layer separately. The same method is used to construct the complementary matrix of the indicator layer. The criterion layer fuzzy complementary judgment matrix U is shown in Table 3.

[0062] Table 3 Criterion layer fuzzy complementary matrix data

[0063] U F G C D E F 0.5 0.2 0.4 0.4 0.4 G 0.8 0.5 0.4 0.6 0.5 C 0.6 0.6 0.5 0.9 0.7 D 0.6 0.4 0.1 0.5 0.4 E 0.6 0.5 0.3 0.6 0.5

[0064] The specific steps include:

[0065] S3.1. Calculation and verification of hierarchical weights;

[0066] The specific process is as follows:

[0067] The analytic hierarchy process has a multi-level structure in its analytic hierarchy model, and it is necessary to calculate and verify the weights of each level to obtain the single factor weight vector W. i , the calculation formula is:

[0068]

[0069] Where W i is the initial weight, is the sum of the demand factor scores in the i-th row, and n is the total number of demand factors in the i-th row.

[0070] According to formula (1), the weights of appearance requirements (F), functional requirements (G), operational requirements (C), economic requirements (D), and structural requirements (E) at the criterion level can be calculated as follows: W F =0.17, W G =0.215, W C =0.24, W D =0.175, W E =0.20.

[0071] S3.2, consistency check;

[0072] The specific process is as follows:

[0073] Calculate the feature matrix W * , let W=(W1,W2,…,W N ) T is the weight vector of the fuzzy judgment matrix A, where make:

[0074]

[0075] Where W ij W is the value of the factor row in the feature matrix divided by the sum of the values of the row and column. j is the value of the column where the factor is located in the feature matrix;

[0076] Get the n-order characteristic matrix: W * =(W ij ) n×n ;

[0077] Let A=(a ij ) n×n and J=(b ij )n×n Both are fuzzy judgment matrices. The calculation formula of the compatibility index of fuzzy judgment matrices A and J is:

[0078]

[0079] The fuzzy judgment matrix A and the feature matrix W * Calculate the compatibility index I(A,W * ), if I(A,W * )≤α, where α is the consistency ratio, then it is proved that the consistency test has passed. When the value of α is small, it means that the decision maker has a high requirement for the consistency of the fuzzy judgment matrix. Generally, α=0.1 can be taken. According to formula (2) and formula (3), it can be calculated that I(U,W * )=0.0905<0.1; consistency check passed.

[0080] The same calculation method is used to calculate the indicator layer weight. The weighted average weight of the indicator layer can be obtained by weighting the indicator layer weight with the criterion layer weight. The comprehensive weight data and ranking are shown in Table 4.

[0081] Table 4 FAHP comprehensive weight data and ranking

[0082]

[0083] Table 4 FAHP comprehensive weight data and ranking

[0084]

[0085] S4. Confirm the mutual influence relationship between the needs of users of farming equipment;

[0086] Construct the direct influence matrix Z of the DEMATEL method and normalize the matrix using the maximum method, which includes the following steps:

[0087] S4.1. Sum each row of the direct influence matrix Z, select the maximum value among all the summations, and then divide all elements in the matrix Z by the maximum value to obtain the standardized influence matrix B. The calculation formula is as follows:

[0088]

[0089] Where x ij is the value that directly affects the corresponding row i and column j in the matrix Z;

[0090] The comprehensive influence matrix T is constructed to fully reflect the comprehensive influence between the elements in the system. Its calculation formula is:

[0091]

[0092] Where I is the identity matrix, k is the total number of elements in the influence matrix;

[0093] S4.2. Calculate the various elements based on the results of the DEMATEL calculation process;

[0094] Impact D i It refers to the sum of the values of the rows corresponding to the elements in the comprehensive influence matrix T, which represents the overall impact of each row of elements on all other elements in the entire system. The calculation formula is:

[0095]

[0096] Influence C i It is the sum of the values corresponding to the elements in each column of the matrix T, reflecting the overall degree of influence of each column element on the entire impact system by other elements. The calculation formula is:

[0097]

[0098] Centrality M i =D i +C i Reflects the relative position of factors in the evaluation system and the size of their role; causal degree R = D i -C i is the difference between the influence degree and the influence degree of a single factor. Finally, the centrality data corresponds to the x-axis and the cause degree data corresponds to the y-axis to draw a causal relationship diagram, such as Figure 5 shown.

[0099] After obtaining the normalized influence matrix according to formula (4), the comprehensive influence matrix T is calculated according to formula (5). The influence degree D of each factor is obtained according to formula (6) and formula (7): i , influence degree C i , centrality M i , Cause R i , the calculation results are shown in Table 5.

[0100] Table 5 Factor influence, influence, centrality, cause and ranking

[0101] elements Influence Influence Centrality Cause degree Sorting <![CDATA[F1]]> 1.849 2.500 4.349 -0.650 16 <![CDATA[F2]]> 2.567 2.923 5.490 -0.357 15 <![CDATA[F3]]> 2.814 4.383 7.197 -1.569 9 <![CDATA[G1]]> 3.981 3.855 7.836 0.125 5 <![CDATA[G2]]> 3.911 3.739 7.650 0.172 7 <![CDATA[G3]]> 4.291 3.834 8.125 0.457 3 <![CDATA[C1]]> 2.442 3.286 5.728 -0.844 14 <![CDATA[C2]]> 3.802 3.375 7.177 0.426 10 <![CDATA[C3]]> 4.605 3.638 8.242 0.967 2 <![CDATA[C4]]> 4.555 3.839 8.394 0.716 1

[0102] Table 5 Factor influence, influence, centrality, cause and ranking

[0103] <![CDATA[D1]]> 4.174 3.483 7.657 0.691 6 <![CDATA[D2]]> 2.634 3.545 6.180 -0.911 12 <![CDATA[D3]]> 2.702 3.469 6.172 -0.767 13 <![CDATA[E1]]> 4.417 3.470 7.887 0.946 4 <![CDATA[E2]]> 4.409 3.155 7.564 1.254 8 <![CDATA[E3]]> 2.817 3.473 6.290 -0.656 11

[0104] Based on the data analysis in Table 6, the top ten factors in terms of centrality are: high scalability C4, easy assembly and disassembly C3, flexible and efficient G3, lightweight E1, intelligent work G1, energy saving D1, diverse functions G2, safe and stable structure E2, local characteristics F3, and data visualization C2. These are the core requirements that determine the design of the entire intelligent farming equipment. Therefore, it is necessary to combine them with the weight data of FAHP to confirm the final key demand factors for design.

[0105] S5. Calculate the weighted centrality using FAHP-DEMATELFENI.

[0106] The initial weight W obtained based on the FAHP method i The user demand centrality M obtained by DEMATEL method i Perform weighted processing to obtain the FAHP-DEMATEL weighted centrality W i ×M i Finally, the user design requirements were ranked from high to low by their comprehensive weights, resulting in the following weights: E1 = 0.6310, G1 = 0.6175, D1 = 0.5582, E2 = 0.5544, G2 = 0.5485, G3 = 0.5241, C3 = 0.5110, C4 = 0.4869, F3 = 0.4692, C2 = 4593, D2 = 0.3424, C1 = 0.3208, F2 = 0.3113, E3 = 0.2937, D3 = 0.2882, and F1 = 0.2096. Based on the actual application of intelligent agricultural machinery identified in the survey, a comparative analysis of the key design elements of the top ten requirements ranked by comprehensive impact was conducted. This research revealed conflicts and contradictions between the design solutions and parameters corresponding to these key requirements, necessitating the introduction of TRIZ for conflict analysis and design resolution.

[0107] S6. Conduct conflict analysis and solution based on TRIZ.

[0108] Based on TRIZ's 48 universal engineering parameters, requirements are mapped to these universal engineering parameters, and then the parameters are mapped to conflict combinations within TRIZ. Conflicts with differing engineering parameters are technical conflicts, which are resolved using the Altshuller conflict matrix to obtain the corresponding invention principles. Consistent engineering parameters are physical conflicts, which are resolved using TRIZ's four separation principles. The three conflict groups in user requirements are classified as: functional diversity versus lightweight structure; structural safety and stability versus ease of assembly and disassembly; and structural safety and stability versus flexibility and efficiency. These conflicts are then converted into a TRIZ key conflict model; see Table 6 for details.

[0109] Table 6 TRIZ key conflict problem model

[0110]

[0111] After analyzing these conflicts and converting them into corresponding engineering parameters, we used the Altshuller Conflict Matrix and TRIZ invention principles to analyze the three pairs of conflicts in the requirements and resolve the resulting technical and physical contradictions. The specific solutions are shown in Table 7.

[0112] Table 7 TRIZ conflict resolution solutions

[0113]

[0114] (a) Farmland reclamation and maintenance require numerous functions, including site clearing, land reclamation, soil quality testing and improvement, and crop planting. At the same time, the machine itself must be lightweight for easy storage and energy conservation. After analysis by the expert team, they determined that the invention principle: 1. The segmentation principle should be employed to resolve this conflict.

[0115] (b) Confirming the product's multifunctional modules, the stability of the connection between the modules and the main body became a pressing issue. When equipment managers maintain and assemble the modules, the safety and stability of the product's structure and ease of assembly and disassembly create a technical conflict. On the one hand, ease of assembly and disassembly reduces maintenance costs for the equipment manager; on the other hand, the stability of the connection during operation determines the product's service life, safety, and maintenance costs. After analysis, the panelists chose option 17: Change to a new dimension to resolve this conflict.

[0116] (c) Intelligent farming equipment requires a robust structure to withstand the immense stresses of tilling, yet also requires a degree of flexibility to adapt to varying terrain. The author believes that, depending on the usage scenario, locations with complex and rugged terrain require a more flexible structure to ensure the equipment can adapt to the working environment; however, flatter terrain requires a more stable structural design to ensure efficient operation. Problems within the spatial domain can be solved using the principle of spatial separation.

[0117] Solutions based on conflicting requirements

[0118] 1. Resolution of Conflict A

[0119] According to the process of farmland reclamation and maintenance, the functional modules required for the whole process are designed, including bulldozer module, rotary tiller module, soil improvement and cultivation module, waste soil transporter module and other modules. When using the product, users can choose the required modules according to their own needs to reduce the volume of the product during operation and achieve the lightweight demand of the product. Figure 3 shown.

[0120] 2. Solution to Conflict B

[0121] In order to solve the problem of possible instability in the module joints of modular structures, the inventive principle of dimensional change is adopted to replace the two-dimensional surface contact at the module joints with a three-dimensional structure that can be coupled. The mechanism of the joints relying on the main body can move axially to couple the linked modules in multiple dimensions, and the internal electromagnetic coil is powered to generate suction to reinforce the mechanism and power the modules, thereby improving the safety and stability of the module joints while ensuring the convenience of disassembly.

[0122] 3. Resolution of Conflict C

[0123] The product has physical contradictions in different working scenarios. The principle of spatial separation is adopted to design different chassis modules. A multi-angle and height-adjustable wheel base is installed in working scenarios that require flexible adaptability to rugged locations, and a crawler chassis is installed in other situations to ensure work efficiency.

[0124] Conflict-free demand resolution

[0125] 1. Intelligent system design

[0126] Intelligent farming equipment collects relevant data information of the task area, conducts comprehensive decision analysis, and automatically controls the operation of the equipment according to the intelligent system parameters; Intelligent farming equipment collects relevant data information of the task area, conducts comprehensive decision analysis, and automatically controls the operation of the equipment according to the intelligent system parameters; The intelligent system design includes three parts: data layer, functional layer and application layer. The data layer collects and analyzes data and formulates decision strategies through technologies such as the Internet of Things, cloud computing and machine learning; the functional layer integrates multiple functions such as data collection, intelligent control, autonomous work, information visualization and terminal operations to realize the intelligent operation of the system; the application layer is responsible for module selection, function setting, structure execution and work processing, and ultimately completes specific tasks. This design idea ensures intelligent management of the entire process from data acquisition to decision execution, and realizes efficient and automated system operation. The logic is as follows: Figure 4 shown.

[0127] 2. Information visualization interactive UI design analysis:

[0128] The design of an interactive UI for information visualization should fully consider user-friendliness. This interactive UI adopts an integrated and visual design approach, achieving comprehensive equipment management through real-time monitoring of the operating status of farming equipment, multi-dimensional analysis, and intuitive data display. The system displays the operating status of farming equipment in real time through images and parameters, and uses color and indicators for alarm prompts to ensure that abnormal situations are discovered and handled promptly. In addition, the system includes equipment lists and status information, performance analysis charts, and geographic location data to help users fully understand the operating status of multiple devices. The entire interface design is simple and clear, and the effective use of different colors and graphic elements makes important information more prominent.

[0129] Based on the above research and analysis, the intelligent farming equipment finally adopts a modular design. It has four functional modules: excavation module, transportation module, soil improvement and sowing module, and rotary tillage module; two morphological modules enable it to meet different environmental requirements; the appearance is different from traditional agricultural machinery, adopting a more technological appearance and design language, and the overall color scheme uses a conspicuous yellow and white design, making it a unique local design; the application of clean energy is more in line with the principles of environmentally friendly design.

[0130] Therefore, the present invention adopts the above-mentioned FAHP-DEMATELTRIZ-based farming equipment construction method, analyzes the diverse needs of users in different farmland reclamation and management scenarios, and designs corresponding farming equipment so that the equipment can adapt to various working environments and functional requirements and has lightweight characteristics.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing farming equipment based on FAHP-DEMATELTRIZ, characterized by: The specific steps include: S1. User demand acquisition and analysis; S2, hierarchical model construction; S3. Confirm the indicator weights and rank the weights of the farming equipment user needs; S4. Confirm the mutual influence relationship between the needs of users of farming equipment; S5. Calculate the weighted centrality using FAHP-DEMATELFENI. S6. Conduct conflict analysis and solution based on TRIZ.

2. The method for constructing farming equipment based on FAHP-DEMATELTRIZ according to claim 1, characterized in that: In S2, after collecting keyword requirements through the SETC method, questionnaires and interviews were conducted, and combined with research data, user requirements were divided into the following five aspects: appearance, function, ease of operation, economy, and structure. The requirements of the indicator layer in five aspects were confirmed, and a hierarchical model was constructed. The top layer is the target layer, namely the intelligent farming equipment design U; the middle layer is the criterion layer, which includes five items: appearance requirement F, function requirement G, operation requirement C, economy requirement D, and structure requirement E; the bottom layer is the indicator layer, from F1 to E3, which correspond to the subdivided demand elements of the previous level.

3. The method for constructing farming equipment based on FAHP-DEMATELTRIZ according to claim 1, characterized in that: In S3, the 0.1-0.9 scaling method is used to make pairwise comparisons and judgments on the five demand factors of the criterion layer, and the scale r is obtained. ij Construct the fuzzy judgment matrix A=(a ij ) n×n , calculate the weight index of each criterion layer respectively, which specifically includes the following steps: S3.

1. Calculation and verification of hierarchical weights; S3.

2. Consistency check.

4. The method for constructing farming equipment based on FAHP-DEMATELTRIZ according to claim 3, characterized in that: The specific process of S3.1 is as follows: The analytic hierarchy process has a multi-level structure in its analytic hierarchy model, and it is necessary to calculate and verify the weights of each level to obtain the single factor weight vector W. i , the calculation formula is: Where W i is the initial weight, is the sum of the demand factor scores in the i-th row, and n is the total number of demand factors in the i-th row.

5. The method for constructing farming equipment based on FAHP-DEMATELTRIZ according to claim 4, characterized in that: The specific process of S3.2 is as follows: Calculate the feature matrix W * , let W=(W1,W2,…,W N ) T is the weight vector of the fuzzy judgment matrix A, where W i ≥0(i=1,2,…,n), let: Where W ij W is the value of the factor row in the feature matrix divided by the sum of the values of the row and column. j is the value of the column where the factor is located in the feature matrix; Get the n-order characteristic matrix: W * =(W ij ) n×n ; Let A=(a ij ) n×n and J=(b ij ) n×n Both are fuzzy judgment matrices. The calculation formula of the compatibility index of fuzzy judgment matrices A and J is: The fuzzy judgment matrix A and the feature matrix W * Calculate the compatibility index I(A,W * ), if I(H,W * )≤α, where α is the consistency ratio, it proves that the verified consistency test is qualified.

6. The method for constructing farming equipment based on FAHP-DEMATELTRIZ according to claim 5, characterized in that: In S4, the direct influence matrix Z of the DEMATEL method is constructed and the maximum value method is used to normalize the matrix, which specifically includes the following steps: S4.

1. Sum each row of the direct influence matrix Z, select the maximum value among all the summations, and then divide all elements in the direct influence matrix Z by the maximum value to obtain the standardized influence matrix B. The calculation formula is as follows: Where x ij is the value that directly affects the corresponding row i and column j in the matrix Z; The comprehensive influence matrix T is constructed to fully reflect the comprehensive influence between the elements in the system. Its calculation formula is: Where I is the identity matrix, k is the total number of elements in the influence matrix; S4.

2. Calculate the various elements based on the results of the DEMATEL calculation process; Impact D i It refers to the sum of the values of the rows corresponding to the elements in the comprehensive influence matrix T, which represents the overall impact of each row of elements on all other elements in the entire system. The calculation formula is: Influence C i It is the sum of the values corresponding to the elements in each column of the matrix T, reflecting the overall degree of influence of each column element on the entire impact system by other elements. The calculation formula is: Centrality M i =D i +C i Reflects the relative position of factors in the evaluation system and the size of their role; causal degree R = D i -C i It is the difference between the influence degree and the influence degree of a single factor. Finally, the centrality data corresponds to the x-axis and the cause degree data corresponds to the y-axis to draw a causal relationship diagram.

7. The method for constructing farming equipment based on FAHP-DEMATELTRIZ according to claim 6, characterized in that: The initial weight W obtained based on the FAHP method in S5 i The user demand centrality M obtained by DEMATEL method i Perform weighted processing to obtain the FAHP-DEMATEL weighted centrality W i ×M i Finally, the comprehensive weight of the user's design requirements is sorted from high to low.

Images