Method and device for evaluating influence of external environment on machine tool selection and storage medium

Abstract

The invention provides an evaluation method and device for influence of an external environment on machine tool selection and a storage medium, and the method comprises the following steps: building a machine tool selection evaluation model index system which comprises a target layer, a middle layer and a decision layer; obtaining the type and model of a target machine tool, and putting into a target layer; obtaining each influence index for evaluating the target machine tool, and putting the influence indexes into the middle layer; collecting index data of each influence index; according to the index data, determining an influence index weight by using an entropy weight method; constructing a discrimination matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool; and selecting an output target machine tool from a decision-making layer according to an evaluation result. According to the method, the weight of each influence index is determined through the entropy weight method, and the discrimination matrix is optimized, so that the influence of subjective factors is avoided, the precision of machine tool selection is improved, and the objectivity of an analysis result is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of machine tool evaluation, and in particular to a method, device and storage medium for evaluating the impact of external environment on machine tool selection. Background Art

[0002] Various environmental impact indicators have different degrees of influence on machine tool accuracy, but it is currently impossible to quantify the impact of various influencing indicators on the accuracy of the entire precision machine tool. The influencing factors of machine tool accuracy can be used to select different types of machine tools. The method of constructing a judgment matrix using the hierarchical analysis method is usually used to select machine tools. However, the discriminant matrix obtained by the hierarchical analysis method in the prior art has a certain degree of subjectivity, and the selection method of the prior art is not accurate enough. Summary of the invention

[0003] In view of the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide an evaluation method, device and storage medium for the influence of the external environment on machine tool selection, determine the weight of each influencing index by the entropy weight method, optimize its discriminant matrix, avoid the influence of subjective factors, improve the accuracy of machine tool selection, and ensure the objectivity of the analysis results.

[0004] The technical solution adopted by the present invention is to provide an evaluation method for the influence of the external environment on the selection of machine tools, comprising the following steps: Establishing a machine tool selection evaluation model indicator system, the system includes a target layer, an intermediate layer and a decision layer; Obtain the type and model of the target machine tool and put it into the target layer; Obtaining various influencing indicators for evaluating the target machine tool and placing them into the middle layer; Collecting indicator data of each of the influencing indicators; Determine the weight of the influencing indicator using the entropy weight method according to the indicator data; constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool; According to the evaluation result, the target machine tool is selected and outputted from the decision layer.

[0005] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the weights of the influencing indicators are determined by the entropy weight method, the discrimination matrix is ​​optimized, the influence of subjective factors is avoided, and the accuracy of machine tool selection can be improved.

[0006] In an optional implementation manner, after the step of collecting the indicator data of each of the influencing indicators, the step further includes: Screening the indicator data to obtain abnormal indicator data and blank indicator data; The abnormal indicator data is eliminated and smoothed.

[0007] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: after collecting the indicator data, the indicator data is screened, and the abnormal indicator data is eliminated after screening to prevent the abnormal indicator data from interfering with the evaluation results, and then the indicator data is smoothed to improve the data quality and the reliability and accuracy of the evaluation method.

[0008] In an optional implementation manner, after the step of removing and smoothing the abnormal indicator data, the method further includes: Fitting the indicator data by a fitting function to determine the position of the blank indicator data; According to the position of the blank indicator data, the position of the blank indicator data is filled by a fitting function.

[0009] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: after screening the indicator data and eliminating the abnormal indicator data, there will be blank indicator data. The position of the blank indicator data is determined by a fitting function, and then the indicator data is fitted by the fitting function to fill the position of the blank indicator data to ensure the integrity of the data. The reliability of the indicator data is improved by fitting with a fitting function.

[0010] In an optional implementation manner, the step of determining the weight of the influencing indicator using the entropy weight method according to the indicator data includes: Performing standardization processing on the indicator data; Calculate the weight of each impact indicator under each plan; Calculating the information entropy of each of the influencing indicators according to the weight of each of the influencing indicators; Calculating the weight of each of the influencing indicators according to the information entropy; According to the weights, a comprehensive score of each solution is obtained.

[0011] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: by using the entropy weight method to determine the degree of change of each influencing indicator, and then determining the weight of each influencing indicator, the objectivity of the result is guaranteed, and the weight is determined based on the discrete degree of the data itself, avoiding the influence of subjective factors, making the evaluation method more objective.

[0012] In an optional implementation, the step of calculating the weight of each of the influencing indicators according to the information entropy further includes: Calculating the redundancy of the information entropy according to the information entropy; The weight of each of the influencing indicators is calculated according to the redundancy.

[0013] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the weight of each influencing index is calculated according to the redundancy, so that the calculation efficiency can be improved.

[0014] In an optional implementation manner, the step of constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool includes: Assume the initial matrix B = (b ij ) n×n , b ij >0,b ji = 1 / b ij ; The initial matrix B is normalized and scaled, and the discriminant matrix C is ij =10(b ij -min(b ij )) / max(b ij )-min(b ij ), obtaining the scores of each influencing indicator on the target machine tool, wherein i represents the order of the influencing indicators, j represents the weight of each influencing indicator, and n represents the serial number of the target machine tool; The quantification result of the machine tool is: D n =w1×C 1n +w2×C 2n +...+w i ×C in , select the final machine tool as Max(D n ).

[0015] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the discriminant matrix is ​​normalized and scaled to obtain the score of each influencing index, thereby avoiding the influence of subjective factors and improving the accuracy of the evaluation result.

[0016] In an optional implementation manner, after the steps of constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool, the method further includes: Get the consistency ratio CR; Judging whether the discriminant matrix passes the one-time test by the size of CR; If CR<0.1, the discriminant matrix passes the one-time test; If CR≥0.1, the indicator data is collected again and the weight of each influencing indicator in the middle layer is calculated using the entropy weight method, and the discriminant matrix is ​​reconstructed.

[0017] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: judging whether the discriminant matrix is ​​reasonable through consistency testing can help identify and eliminate unreasonable judgments and ensure the overall reliability of the judgment matrix.

[0018] In an optional implementation, the index data includes at least one of temperature, humidity, dust and noise data.

[0019] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the index data includes at least one of temperature, humidity, dust and noise, and the index is an evaluation index for selecting machine tools, which basically covers the influencing index of the machine tools affected by the environment, thereby improving the accuracy of selecting machine tools.

[0020] The present invention further provides an evaluation device for the influence of the external environment on the selection of a machine tool, wherein the evaluation device implements the evaluation method as described in any one of the above embodiments, and the evaluation device comprises: An acquisition module, the acquisition module is used to acquire the type and model of the target machine tool and various influencing indicators of the target machine tool; A collection module, the collection module is used to collect indicator data of each of the influencing indicators; A weight calculation module, the weight calculation module is used to determine the weight of the influencing indicator by using an entropy weight method; A data evaluation module, the data evaluation module is used to construct a discriminant matrix, quantify the score of the target machine tool, and obtain an evaluation result of the target machine tool; An execution module is used to select and output the target machine tool from the decision layer.

[0021] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: an evaluation device for the influence of external environment on machine tool selection in an embodiment of the present invention is used to implement an evaluation method as in any technical solution of the present invention, and thus has all the beneficial effects of the evaluation method as in any embodiment of the present invention, which will not be repeated here.

[0022] The present invention also provides a readable storage medium, wherein the readable storage medium stores a program, and the program is executed by a processor to perform the evaluation method as described in any one of the above embodiments.

[0023] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: a readable storage medium in an embodiment of the present invention is used to implement an evaluation method such as any technical solution of the present invention, and thus has all the beneficial effects of an evaluation method such as any embodiment of the present invention, which will not be repeated here.

[0024] The beneficial effects of the embodiments of the present invention include: the present invention determines the degree of change of each influencing indicator by using the entropy weight method, and then determines the weight of each influencing indicator, thereby ensuring the objectivity of the result, determining the weight based on the discrete degree of the data itself, optimizing its discriminant matrix, avoiding the influence of subjective factors, improving the accuracy of machine tool selection, and ensuring the objectivity of the analysis result. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic flow chart of a method for evaluating the influence of external environment on machine tool selection provided by an embodiment of the present invention is shown; Figure 2 The embodiment of the present invention is shown Figure 1 Schematic diagram of the process of step S500; Figure 3 A schematic diagram of an evaluation device for the influence of external environment on machine tool selection according to an embodiment of the present invention; Figure 4 It is a schematic diagram of an evaluation device for the influence of external environment on the selection of machine tools according to an embodiment of the present invention.

[0026] Reference numerals: 100, evaluation device; 110, acquisition module; 120, collection module; 130, weight calculation module; 140, data evaluation module; 150, execution module. DETAILED DESCRIPTION

[0027] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention 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 accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention. It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. See also Figures 1 to 3 As shown, the present invention provides an evaluation method for the influence of the external environment on the selection of machine tools, comprising the following steps: S100, establish a machine tool selection evaluation model index system, which includes a target layer, an intermediate layer and a decision layer; S200, obtaining the type and model of the target machine tool and placing it in the target layer; S300, obtaining various influencing indicators for evaluating the target machine tool and placing them in the middle layer; S400, collecting indicator data of various influencing indicators; S500, according to the indicator data, using the entropy weight method to determine the weight of the influencing indicator; S600, constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool; S700. According to the evaluation result, select the output target machine tool from the decision layer.

[0028] Specifically, the target layer refers to the purpose to be solved, which is the selection of different types of machine tools; the middle layer puts influencing indicators, and the decision-making layer puts in the machine tools that need to be compared and analyzed.

[0029] The weights of the influencing indicators are determined by the entropy weight method, and the discrimination matrix is ​​optimized, thereby avoiding the influence of subjective factors and improving the accuracy of machine tool selection.

[0030] In an optional implementation manner, after the step of collecting the indicator data of each influencing indicator, the method further includes: Screen the indicator data to obtain abnormal indicator data and blank indicator data; Abnormal indicator data are eliminated and smoothed.

[0031] Specifically, abnormal data indicators refer to the measurement data that may be unrealistic or lost due to environmental interference or human factors during its collection and transmission. After collecting the indicator data, the indicator data is screened and the abnormal indicator data is removed to prevent the abnormal indicator data from interfering with the evaluation results and improve the reliability and accuracy of the evaluation method. Smoothing is to remove noise interference from the indicator data and improve the quality of the indicator data.

[0032] Specifically, the Lagrangian method can be used to remove and smooth abnormal data.

[0033] In an optional implementation manner, after the step of removing and smoothing the abnormal indicator data, the method further includes: Fit the indicator data through the fitting function to determine the position of the blank indicator data; According to the position of the blank indicator data, the position of the blank indicator data is filled by the fitting function.

[0034] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: after screening the indicator data and eliminating the abnormal indicator data, there will be blank indicator data. The position of the blank indicator data is determined by a fitting function, and then the indicator data is fitted by the fitting function to fill the position of the blank indicator data to ensure the integrity of the data. The reliability of the indicator data is improved by fitting with a fitting function.

[0035] In an optional implementation manner, the step of determining the weight of the influencing indicator using the entropy weight method according to the indicator data includes: S510, standardizing the indicator data; S520, calculating the proportion of each influencing indicator under each scheme; S530, calculating the information entropy of each influencing indicator according to the weight of each influencing indicator; S540, calculating the weight of each influencing indicator according to information entropy; S550. Obtain a comprehensive score for each solution based on the weight.

[0036] Specifically, in step S510, the index data is standardized by the range standardization method. The calculation process is as follows: first, the maximum value max(X i ) and the minimum value min(X i ); then calculate the difference R between the maximum and minimum values ​​of the indicator data; then calculate each observation value (X ij ) minus the minimum value min(X i ) after the value R1; finally, divide R1 by the range R.

[0037] Specifically, in step S520, the ratio of each indicator under each scheme is calculated, that is, the variation of the indicator is calculated.

[0038] By using the entropy weight method to determine the degree of change of each influencing indicator and then determine the weight of each influencing indicator, the objectivity of the results is guaranteed. The weight is determined based on the discrete degree of the data itself, avoiding the influence of subjective factors and making the evaluation method more objective.

[0039] In an optional implementation, the step of calculating the weight of each influencing indicator according to the information entropy further includes: According to the information entropy, the redundancy of the information entropy is calculated; According to the redundancy, the weight of each influencing indicator is calculated.

[0040] Calculating the weight of each influencing indicator according to redundancy can improve calculation efficiency.

[0041] In an optional implementation, the steps of constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool include: Assume the initial matrix B = (b ij ) n×n , b ij >0,b ij = 1 / b ij ; The initial matrix B is normalized and scaled, and the discriminant matrix C is ij =10(b ij -min(b ij )) / max(b ij )-min(b ij ), and obtain the scores of each influencing indicator on the target machine tool, wherein i represents the order of the influencing indicators, j represents the weight of each influencing indicator, and n represents the serial number of the target machine tool.

[0042] The quantification result of the machine tool is: D n =w1×C 1n +w2×C 2n +...+w i ×C in , select the final machine tool as Max(D n ).

[0043] For example, if n=1, that is, the target machine number is 1, its score is: D1=w1×C 11 +w2×C 21 +...+w4×C 41 .

[0044] Specifically, the discriminant matrix is ​​normalized and scaled to obtain the scores of various influencing indicators, thereby avoiding the influence of subjective factors and improving the accuracy of the evaluation results.

[0045] In an optional implementation, after the steps of constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool, the method further includes: Get the consistency ratio CR; Judging whether the discriminant matrix passes the one-time test by the size of CR; If CR < 0.1, the discriminant matrix passes the one-time test; If CR ≥ 0.1, the indicator data is collected again and the entropy weight method is used to calculate the weights of each influencing indicator in the middle layer, and the discriminant matrix is ​​reconstructed.

[0046] Specifically, the calculation formula of the consistency ratio is: CR=CI / RI, where RI is obtained by looking up a table.

[0047] Define the one-time indicator CI as: ,in max is the maximum eigenvalue of the discriminant matrix.

[0048] If CR < 0.1, the discriminant matrix passes the one-time test; If CR ≥ 0.1, the indicator data is collected again and the entropy weight method is used to calculate the weights of each influencing indicator in the middle layer, and the discriminant matrix is ​​reconstructed.

[0049] Judging whether the discriminant matrix is ​​reasonable through consistency testing can help identify and eliminate unreasonable judgments and ensure the overall reliability of the judgment matrix.

[0050] In an optional embodiment, the indicator data includes at least one of temperature, humidity, dust and noise data.

[0051] See also Figure 4 As shown, the present invention further provides an evaluation device for the influence of the external environment on the selection of a machine tool. The evaluation device 100 implements the evaluation method of any one of the above embodiments. The evaluation device 100 includes: An acquisition module 110, which is used to acquire the type and model of a target machine tool and various influencing indicators of the target machine tool; A collection module 120, the collection module 120 is used to collect indicator data of each of the influencing indicators; A weight calculation module 130, the weight calculation module 130 is used to determine the weight of the influencing indicator by using an entropy weight method; A data evaluation module 140, which is used to construct a discriminant matrix, quantify the score of the target machine tool, and obtain an evaluation result of the target machine tool; The execution module 150 is used to select and output the target machine tool from the decision layer.

[0052] An evaluation device for the impact of external environment on machine tool selection according to an embodiment of the present invention is used to implement an evaluation method according to any technical solution of the present invention, and thus has all the beneficial effects of the evaluation method according to any embodiment of the present invention, which will not be described in detail herein.

[0053] The present invention also provides a readable storage medium, wherein the readable storage medium stores a program, and the program is executed by a processor to perform the evaluation method as described in any one of the above embodiments.

[0054] A readable storage medium in an embodiment of the present invention is used to implement an evaluation method as in any technical solution of the present invention, and thus has all the beneficial effects of the evaluation method as in any embodiment of the present invention, which will not be described in detail herein.

[0055] The above description is intended to be illustrative rather than restrictive, and those skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present disclosure. Moreover, the above examples (or one or more of them) may be used in combination with each other, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations.

Claims

1. A method for evaluating the influence of external environment on machine tool selection, characterized in that: The following steps are involved: Establishing a machine tool selection evaluation model indicator system, the system includes a target layer, an intermediate layer and a decision layer; Obtain the type and model of the target machine tool and put it into the target layer; Obtaining various influencing indicators for evaluating the target machine tool and placing them into the middle layer; Collecting indicator data of each of the influencing indicators; Determine the weight of the influencing indicator using the entropy weight method according to the indicator data; constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool; According to the evaluation result, the target machine tool is selected and outputted from the decision layer.

2. The evaluation method for the influence of external environment on machine tool selection according to claim 1 is characterized in that: After the step of collecting the indicator data of each of the influencing indicators, the method further includes: Screening the indicator data to obtain abnormal indicator data and blank indicator data; The abnormal indicator data is eliminated and smoothed.

3. The evaluation method of the external environment affecting the selection of machine tools according to claim 2, characterized in that: After the step of removing and smoothing the abnormal indicator data, the method further includes: Fitting the indicator data by a fitting function to determine the position of the blank indicator data; According to the position of the blank indicator data, the position of the blank indicator data is filled by a fitting function.

4. The evaluation method for the influence of external environment on machine tool selection according to claim 1 is characterized in that: The step of determining the weight of the influencing indicator by using the entropy weight method according to the indicator data comprises: Performing standardization processing on the indicator data; Calculate the weight of each impact indicator under each plan; Calculating the information entropy of each of the influencing indicators according to the weight of each of the influencing indicators; Calculating the weight of each of the influencing indicators according to the information entropy; According to the weights, a comprehensive score of each solution is obtained.

5. The evaluation method of the external environment affecting the selection of machine tools according to claim 4, characterized in that: The step of calculating the weight of each of the influencing indicators according to the information entropy also includes: Calculating the redundancy of the information entropy according to the information entropy; The weight of each of the influencing indicators is calculated according to the redundancy.

6. The evaluation method of the external environment affecting the selection of machine tools according to claim 1, characterized in that: The step of constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool includes: Assume the initial matrix B = (b ij ) n×n , b ij >0,b ji = 1 / b ij ; The initial matrix B is normalized and scaled, and the discriminant matrix C is in =10(b ij -min(b ij )) / max(b ij )-min(b ij ), obtaining the scores of each influencing indicator on the target machine tool, wherein i represents the order of the influencing indicators, j represents the weight of each influencing indicator, and n represents the serial number of the target machine tool; The quantification result of the machine tool is: D n =w1×C 1n +w2×C 2n +...+w i ×C in , select the final machine tool as Max(D n ), where w i Indicates the weight of each influencing indicator, C in Indicates the influence weight of each influencing indicator.

7. The method for evaluating the influence of external environment on machine tool selection according to claim 1, characterized in that: After the steps of constructing a discriminant matrix, quantifying the score of the target machine tool, and obtaining an evaluation result of the target machine tool, the method further includes: Get the consistency ratio CR; Judging whether the discriminant matrix passes the one-time test by the size of CR; If CR<0.1, the discriminant matrix passes the one-time test; If CR≥0.1, the indicator data is collected again and the weight of each influencing indicator in the middle layer is calculated using the entropy weight method, and the discriminant matrix is ​​reconstructed.

8. The method for evaluating the influence of external environment on machine tool selection according to claim 1, characterized in that: The index data includes at least one of temperature, humidity, dust and noise data.

9. An evaluation device for the influence of external environment on machine tool selection, characterized in that: The evaluation device implements the evaluation method according to any one of claims 1 to 8, and the evaluation device comprises: An acquisition module, the acquisition module is used to acquire the type and model of the target machine tool and various influencing indicators of the target machine tool; A collection module, the collection module is used to collect indicator data of each of the influencing indicators; A weight calculation module, the weight calculation module is used to determine the weight of the influencing indicator by using an entropy weight method; A data evaluation module, the data evaluation module is used to construct a discriminant matrix, quantify the score of the target machine tool, and obtain an evaluation result of the target machine tool; An execution module is used to select and output the target machine tool from the decision layer.

10. A readable storage medium storing a program, characterized in that: The program is executed by a processor to execute the evaluation method according to any one of claims 1 to 8.

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