Workshop disturbance event impact evaluation method, system, terminal and storage medium

Abstract

The present invention relates to the technical field of intelligent manufacturing and production optimization, and specifically provides a method, system, terminal and storage medium for evaluating the impact of a workshop disturbance event, including: obtaining a workshop disturbance event, an evaluation index for the impact of a workshop disturbance event and a sub-indicator for evaluating the impact of a workshop disturbance event; using the evaluation index for the impact of a workshop disturbance event as a criterion layer, using the sub-indicator for evaluating the impact of a workshop disturbance event as a sub-criterion layer, constructing an ANP network model for distinguishing the impact of a workshop disturbance, and calculating the weight of each sub-criterion in the sub-criterion layer of the ANP network model for distinguishing the impact of a workshop disturbance based on a hesitant fuzzy ANP method; using a hesitant fuzzy VIKOR method to calculate the disturbance impact of a workshop disturbance event, and sorting based on the size of the disturbance impact to obtain an evaluation of the impact of a workshop disturbance event. The present invention comprehensively evaluates workshop disturbance events through an ANP network model, thereby reducing evaluation bias caused by human subjective judgment.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing and production optimization, and specifically relates to a method, system, terminal and storage medium for evaluating the impact of a workshop disturbance event. Background Art

[0002] With the advent of the Industrial 4.0 era and the changes in manufacturing market demand, intelligent manufacturing technology has gradually become a key driving force for the transformation and upgrading of the manufacturing industry. However, various disturbance events often occur in intelligent manufacturing workshops, causing the intelligent production and manufacturing process to be affected by the real-time status of the workshop.

[0003] In the process of intelligent workshop processing, disturbance events present the characteristics of multiple disturbances occurring simultaneously and multi-level disturbances affecting each other. For the research on systematic solution strategies for multiple disturbance events at different levels of a specific workshop, it is necessary to first reasonably rank the impact of disturbance events under limited resources, so it is very important to provide an efficient and reasonable disturbance event impact evaluation method.

[0004] However, there are some shortcomings in the current research on the evaluation of the impact of workshop disturbances. First, there is a lack of systematic classification and summary of disturbance events in production workshops, and there is a lack of comprehensive regularized feature description methods for disturbance events in production workshops. Secondly, the existing related research is highly subjective, and the consideration of workshop disturbance events is not comprehensive, making it difficult to solve the problem of complex and changeable workshop disturbance event impact evaluation. Summary of the invention

[0005] In view of the problem in the prior art that confusion in the impact assessment of workshop disturbance events leads to difficulty in subsequent replacement and maintenance, the present invention provides a workshop disturbance event impact assessment method, system, terminal and storage medium to solve the above technical problems.

[0006] In a first aspect, the present invention provides a method for evaluating the impact of a workshop disturbance event, comprising:

[0007] Obtaining workshop disturbance events, workshop disturbance event impact evaluation indicators and workshop disturbance event impact evaluation sub-indicators;

[0008] Taking the workshop disturbance event impact evaluation index as the criterion layer and the workshop disturbance event impact evaluation sub-index as the sub-criterion layer, constructing an ANP network model for workshop disturbance impact discrimination, and calculating the weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination based on the hesitant fuzzy ANP method;

[0009] The hesitant fuzzy VIKOR method is used to calculate the disturbance impact of disturbance events, and the disturbance impact is sorted based on the size of the disturbance impact to obtain the impact evaluation of workshop disturbance events.

[0010] Furthermore, the workshop disturbance event includes a disturbance event name Name and disturbance event data Disturbance; the disturbance event data Disturbance includes a disturbance event type and disturbance event information ; Disturbance event information Includes disturbance event description and disturbance events ;

[0011] The evaluation indexes of the impact of workshop disturbance events include processing time , Processing quality and processing costs ;

[0012] The processing time The sub-indicators include single process processing time, production scheduling time, quality inspection and correction time; the processing quality The sub-indicators include workpiece surface roughness, dimensional accuracy and workpiece surface defects; the processing cost The sub-indicators include material cost, labor cost and equipment operation and maintenance cost.

[0013] Furthermore, the workshop disturbance event impact evaluation index is used as the criterion layer, the workshop disturbance event impact evaluation sub-index is used as the sub-criterion layer, and an ANP network model for workshop disturbance impact discrimination is constructed. The weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination is calculated based on the hesitant fuzzy ANP method, including:

[0014] The workshop disturbance event impact evaluation index is used as the criterion layer Recorded as ;

[0015] The workshop disturbance event impact evaluation sub-index is used as the sub-criteria layer, denoted as ;

[0016] Based on the pre-set hesitant fuzzy element, construct the m elements of the criterion layer The t hesitant fuzzy preference relationship matrices between ; where k=1,2,…,t; i=1,2,…,m;

[0017] Based on the pre-set hesitant fuzzy element, construct the M elements of the jth sub-criterion layer under the criterion layer Hesitant fuzzy preference relationship matrix , where j=1,2,…,M;

[0018] Hesitant fuzzy preference relationship matrix and Use the de-hesitation formula to de-hesitation and obtain the de-hesitation fuzzy relationship matrix and ; The de-hesitation formula is ;

[0019] Based on the fuzzy relationship matrix after de-hesitation, the weight matrix is ​​calculated using the weight matrix formula , , the weight matrix formula is ;

[0020] Based on the weight matrix Get the weighting matrix A, the weighting matrix ;

[0021] For elements in an element group Indirect advantage comparison is performed between them to construct each element The pairwise comparison matrix of and the normalized eigenvector are calculated, and the local weighted vector matrix is ​​obtained by the eigenroot method , by the local weight vector matrix The obtained super matrix W is: ;

[0022] Based on the super matrix W and the weighting matrix A, using the formula Calculate the weighted super matrix ;

[0023] Using the formula The weighted super matrix The limit is obtained to obtain a column vector of a weighted supermatrix after the limit is obtained, wherein the column vector is the weight of each sub-criteria in the sub-criteria layer.

[0024] Furthermore, the pre-set hesitant fuzzy element is ,in, for The minimum membership degree of For elements The maximum membership degree of For elements The average membership of , the membership value interval is [0,1].

[0025] Furthermore, the method further comprises: performing a compatibility check, comprising:

[0026] Elements based on the criteria layer The t hesitant fuzzy preference relationship matrices between and the elements of the sub-criteria layer Hesitant fuzzy preference relationship matrix , respectively calculate the group hesitant fuzzy matrix and ,in, , where n is the number of all elements in the hesitant fuzzy element, The values ​​are: ;

[0027] For each element of the criteria layer The t hesitant fuzzy preference relationship matrices between , each element of the sub-criteria layer Hesitant fuzzy preference relationship matrix , Group hesitant fuzzy matrix , and group hesitant fuzzy matrix Compatibility calculation is performed. If the compatibility Cd is within the compatibility threshold, the compatibility check passes.

[0028] Furthermore, the hesitant fuzzy VIKOR method is used to calculate the disturbance impact of the workshop disturbance event, and the disturbance impact is sorted based on the size of the disturbance impact to obtain the workshop disturbance event impact evaluation, including:

[0029] Based on the benefit-based criterion, use the formula Compute positive and negative ideal solutions; where Indicates The scheme is in The value of an attribute;

[0030] Based on the cost-based criteria, use the formula Compute positive and negative ideal solutions;

[0031] Based on the weight of each sub-criteria in the sub-criteria layer, use the formula Calculate group utility value , calculated using the formula Individual hatred value ;

[0032] Based on group utility and individual hatred value , using the formula Calculate composite index ,in, , , , , represents the decision mechanism coefficient;

[0033] According to comprehensive indicators The values ​​are sorted to obtain the impact evaluation of the workshop disturbance event.

[0034] Furthermore, the method further comprises: a step of verifying the weight calculation and a step of verifying the validity of the ranking of the impact of the workshop disturbance events;

[0035] The steps of weight calculation validation include comparative analysis of HF-ANP method with traditional ANP method, hierarchical AHP method and triangular fuzzy hierarchical AHP method;

[0036] The steps for verifying the effectiveness of the ranking of the impact of workshop disturbance events include: comparing the ranking results of the HF-VIKOR method with the ranking results of the CPT-VIKOR method, the F-TOPSIS method and the F-COPRAS method.

[0037] In a second aspect, the present invention provides a system for evaluating the impact of a workshop disturbance event, comprising:

[0038] An acquisition module, used to acquire workshop disturbance events, workshop disturbance event impact evaluation indicators and workshop disturbance event impact evaluation sub-indicators;

[0039] A weight calculation module is used to use the workshop disturbance event impact evaluation index as the criterion layer, the workshop disturbance event impact evaluation sub-indicator as the sub-criterion layer, construct an ANP network model for workshop disturbance impact discrimination, and calculate the weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination based on the hesitant fuzzy ANP method;

[0040] The sorting and evaluation module is used to calculate the disturbance impact of the workshop disturbance event by using the hesitant fuzzy VIKOR method, sort the disturbance impact based on the size of the disturbance impact, and obtain the impact evaluation of the workshop disturbance event.

[0041] In a third aspect, a terminal is provided, including:

[0042] processor, memory, wherein:

[0043] The memory is used to store computer programs.

[0044] The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0045] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0046] The beneficial effects of the present invention are that the workshop disturbance event impact evaluation method, system, terminal and storage medium provided by the present invention use the ANP method to determine the weight of each evaluation index, and combine the VIKOR method to sort the disturbance events. While ensuring the accurate expression of the expert's intention, the present invention reduces the subjective bias in the expert's judgment and overcomes the problem of incomplete evaluation of complex disturbance events in traditional methods. The method can effectively solve the reasonable sorting and priority treatment of disturbance events in the workshop under the condition of limited resources, provide a reliable basis for production optimization, and significantly improve the rationality of production scheduling and the efficiency of resource allocation. Finally, the method not only improves the stability and flexibility of the production process, but also optimizes the resource utilization of the workshop, and improves production efficiency and product quality. In addition, the design principle of the present invention is reliable, the structure is simple, and it has a very wide range of application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0049] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0050] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0053] The workshop disturbance event impact assessment method provided by the embodiment of the present invention is executed by a computer device, and accordingly, the workshop disturbance event impact assessment system runs in the computer device.

[0054] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a workshop disturbance event impact evaluation system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0055] To facilitate understanding of the present invention, the workshop disturbance event impact evaluation method provided by the present invention is further described below based on the principle of the workshop disturbance event impact evaluation method of the present invention and the process of managing pluggable module materials in the embodiment.

[0056] Taking the flange production workshop as the implementation background of the present invention, the present invention proposes a workshop disturbance event impact evaluation method. The production task of the case workshop is mainly to produce flanges, and the workflow is as follows: first, the composite robot (AGV + robotic arm) clamps the blank from the storage table and stores it in the transfer table; then the robotic arm clamps the blank from the transfer table to the lathe; the semi-finished product processed by the lathe is clamped by the robotic arm and stored in the transfer table; the robotic arm clamps the semi-finished product from the transfer table to the milling machine; finally, the parts processed by the milling machine are stored in the finished product area.

[0057] Specifically, Figure 1 As shown, the workshop disturbance event impact evaluation method includes:

[0058] S1. Obtain workshop disturbance events, workshop disturbance event impact evaluation indicators, and workshop disturbance event impact evaluation sub-indicators.

[0059] The workshop disturbance event includes a disturbance event name Name and a disturbance event data Disturbance; the disturbance event data Disturbance includes a disturbance event type and disturbance event information ; Disturbance event information Includes disturbance event description and disturbance events .

[0060] The evaluation indexes of the impact of workshop disturbance events include processing time , Processing quality and processing costs The processing time The sub-indicators include single process processing time, production scheduling time, quality inspection and correction time; the processing quality The sub-indicators include workpiece surface roughness, dimensional accuracy and workpiece surface defects; the processing cost The sub-indicators include material cost, labor cost and equipment operation and maintenance cost.

[0061] Specifically, it may also include constructing a disturbance feature description model based on the workshop disturbance event and the workshop disturbance event impact evaluation index.

[0062] In the selected workshop case, there is no forming processing equipment and assembly work, and the production line is small, so there is no need to consider disturbances such as the number of related equipment and personnel. All possible disturbance events in the smart manufacturing workshop are classified and summarized, including:

[0063] The disturbance events of the intelligent manufacturing workshop are classified and summarized according to the production environment layer, production task layer, and production resource layer. The disturbance events in the production process of the production environment layer mainly include temperature changes, humidity changes, and noise. The disturbance events at the production task layer are divided into two levels: order and process. The order level disturbance mainly refers to the change of the order task in a short period of time. The process level disturbance includes the increase or decrease of the process, the change of the process step, the process timeout, and the rework of defective products. The disturbance events at the production resource layer mainly include three levels: materials, personnel, and equipment. The material level disturbance mainly includes insufficient materials, material quality not meeting the requirements, and material supply delays. The equipment level disturbance covers the common disturbance of equipment and the disturbance of production line-related equipment. The personnel level disturbance involves the number of operators and operational errors. There are 27 corresponding disturbances in this workshop case. The disturbance feature description model constructed by formulas (1)-(4) is used to describe the disturbance.

[0064] First, all possible disturbance events in the intelligent manufacturing workshop are classified and summarized. Based on this, a binary case description model is used to regularize the description of disturbance cases, including:

[0065] Use the binary feature description model to describe any disturbance case Describe:

[0066] (1);

[0067] Among them, Name represents the name of the disturbance; Disturbance represents the disturbance type and information:

[0068] (2);

[0069] in, is the disturbance type, is the disturbance information; the disturbance type is numbered according to the classification and summary results of the above disturbance events, using the five-digit numbering principle; the disturbance information is a binary model including the disturbance description and the disturbance impact:

[0070] (3);

[0071] in, To describe the disturbance, that is, to explain the disturbance itself in detail; It is the disturbance effect, which is used to express whether it has an impact on the processing time, processing quality and processing cost indicators:

[0072] (4);

[0073] in, Indicates the processing time, Indicates processing quality, Indicates processing cost.

[0074] The disturbance type is numbered according to the above disturbance event classification and summary results, using the five-digit numbering principle, with the production environment, production tasks, and production resources as the first digit and corresponding to numbers 1 to 3 respectively, a specific disturbance event as the fifth digit, the levels under the three major disturbance categories as the second digit, the various equipment disturbances under the equipment level in the production resources as the third digit, and the next level of equipment classification as the fourth digit. If there is no corresponding level or classification, the number at this position is recorded as 0, and if there is no disturbance in the workshop at a certain moment, the disturbance type is recorded as 00000. The description is shown in Table 1.

[0075] Table 1 Description of disturbance characteristics of workshop case

[0076] Table 1 (continued)

[0077]

[0078] S2. Taking the workshop disturbance event impact evaluation index as the criterion layer, taking the workshop disturbance event impact evaluation sub-indicator as the sub-criterion layer, constructing an ANP network model for workshop disturbance impact discrimination, and calculating the weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination based on the hesitant fuzzy ANP method, including steps S21-S30.

[0079] Specifically, the ANP network model for determining the impact of workshop disturbances consists of a target layer G, a criterion layer and sub-criteria layer constitute.

[0080] S21. Use the workshop disturbance event impact evaluation index as the criterion layer Recorded as .

[0081] Specifically, Can include processing time , Processing quality and processing costs

[0082] S22, the workshop disturbance event impact evaluation sub-index is used as the sub-criteria layer, denoted as .

[0083] Specifically, the sub-criteria layer is composed of several element groups. For example, the element group includes a first element group, a second element group and a third element group; the first element group includes a single process processing time , production scheduling time , quality inspection and correction time ; The second element group includes the workpiece surface roughness , Dimensional accuracy and workpiece surface defects ; The third element group includes material cost , Labor costs and equipment operation and maintenance costs .

[0084] Specifically, pairwise comparisons are performed on the criterion layer and the sub-criteria layer, and the hesitant fuzzy preference relationship matrix under the pairwise comparisons of each criterion and each sub-criteria is obtained according to the hesitant fuzzy element form, where For example, the criteria The pairwise comparison matrix is ​​shown in Table 2.

[0085] Table 2 Criteria The pairwise comparison matrix under

[0086]

[0087] S23, construct m elements of the criterion layer based on the pre-set hesitant fuzzy element The t hesitant fuzzy preference relationship matrices between . Wherein, k=1,2,……,t. i=1,2,……,m. The pre-set hesitant fuzzy element is ,in, for The minimum membership degree of For elements The maximum membership degree of For elements The average membership of , the membership value interval is [0,1].

[0088] S24, constructing the M elements of the j-th sub-criterion layer under the criterion layer based on the pre-set hesitant fuzzy element Hesitant fuzzy preference relationship matrix , where j=1,2,…,M.

[0089] S25. Hesitant fuzzy preference relationship matrix and Use the de-hesitation formula to de-hesitation and obtain the de-hesitation fuzzy relationship matrix and The de-hesitation formula is .

[0090] S26. Based on the fuzzy relationship matrix after de-hesitation, the weight matrix is ​​calculated using the weight matrix formula , , the weight matrix formula is .

[0091] S27, based on the weight matrix Get the weighting matrix A, the weighting matrix .

[0092] Specifically, the super matrix W and the weight matrix A can be obtained as follows:

[0093]

[0094]

[0095] S28. Elements in an element group Indirect advantage comparison is performed between them to construct each element The pairwise comparison matrix of and the normalized eigenvector are calculated, and the local weighted vector matrix is ​​obtained by the eigenroot method , by the local weight vector matrix The obtained super matrix W is: .

[0096] S29, based on the super matrix W and the weighted matrix A, using the formula Calculate the weighted super matrix .

[0097] S30, use formula The weighted super matrix The limit is obtained to obtain a column vector of a weighted supermatrix after the limit is obtained, wherein the column vector is the weight of each sub-criteria in the sub-criteria layer.

[0098] Specifically, the weights of each sub-criteria are obtained as follows: .

[0099] S3. Use the hesitant fuzzy VIKOR method to calculate the disturbance impact of the workshop disturbance event, sort them based on the size of the disturbance impact, and obtain the impact evaluation of the workshop disturbance event.

[0100] Based on the benefit-based criterion, use the formula Computes positive and negative ideal solutions. Indicates The scheme is in The value of an attribute.

[0101] Based on the cost-based criteria, use the formula Compute positive and negative ideal solutions.

[0102] For two hesitant fuzzy elements with the same number of elements and , and its Manhattan distance calculation formula is: ;

[0103] Based on the weight of each sub-criteria in the sub-criteria layer, use the formula Calculate group utility value , calculated using the formula Individual hatred value .

[0104] Based on group utility and individual hatred value , using the formula Calculate composite index ,in, , , , , represents the decision mechanism coefficient.

[0105] Specifically, the scheme layer includes 27 disturbance case events that may exist in the case workshop. The hesitant fuzzy decision matrix of each scheme under the sub-criteria is also given in the form of hesitant fuzzy elements, see Table 3.

[0106] Table 3 Hesitant fuzzy decision matrix of each scheme under sub-criteria

[0107]

[0108] Specifically, based on the group utility value and individual hatred value , calculate the comprehensive index , the calculation results are shown in Table 4.

[0109] Table 4 , value

[0110]

[0111] The influence ranking of different disturbance events on the machining process can be obtained through the calculation results in Table 4. The Q values ​​all meet condition 1. For example, in the top two and Middle: Q( )-Q( )=0.1790>1 / (27-1)=0.0385, which shows that the result is credible.

[0112] According to comprehensive indicators The values ​​are sorted to obtain the impact evaluation of the workshop disturbance event.

[0113] In one possible implementation, the method further includes the step of performing a compatibility check, including:

[0114] Elements based on the criteria layer The t hesitant fuzzy preference relationship matrices between and the elements of the sub-criteria layer Hesitant fuzzy preference relationship matrix , respectively calculate the group hesitant fuzzy matrix and ,in, , where n is the number of all elements in the hesitant fuzzy element, The values ​​are: ;

[0115] For each element of the criteria layer The t hesitant fuzzy preference relationship matrices between , each element of the sub-criteria layer Hesitant fuzzy preference relationship matrix , Group hesitant fuzzy matrix , and group hesitant fuzzy matrix Compatibility calculation is performed. If the compatibility Cd is within the compatibility threshold, the compatibility check passes.

[0116] Specifically, the hesitant fuzzy preference relationship matrix under the pairwise comparison is de-hesitated to obtain the corresponding fuzzy preference matrix. The hesitant fuzzy preference matrices under each criterion are aggregated to obtain the group hesitant fuzzy preference matrix, as shown in Table 5.

[0117] Table 5 Group hesitant fuzzy preference matrix

[0118]

[0119] Among them, G in Table 5 is the target layer of the ANP network model for workshop disturbance impact determination.

[0120] In order to enhance the acceptability of decision making and improve the quality of decision making, the compatibility of the obtained hesitant fuzzy preference matrix and the group hesitant fuzzy matrix is ​​calculated. After calculation, the compatibility Cd of the constructed matrix and the group hesitant fuzzy matrix belongs to the threshold The compatibility is acceptable.

[0121] In one possible implementation, the method further includes the step of verifying the weight calculation and the step of verifying the validity of the ranking of the impact of the workshop disturbance events.

[0122] The verification steps of weight calculation include comparing and analyzing the HF-ANP method with the traditional ANP method, hierarchical AHP method and triangular fuzzy hierarchical AHP method.

[0123] The steps for verifying the effectiveness of the ranking of the impact of workshop disturbance events include: comparing the ranking results of the HF-VIKOR method with the ranking results of the CPT-VIKOR method, the F-TOPSIS method and the F-COPRAS method.

[0124] Specifically, the effectiveness of HF-ANP weight calculation is verified. The present invention introduces the hesitant fuzzy theory on the traditional ANP method, reflects the expert membership in the form of hesitant fuzzy elements, and then obtains the weights of 9 indicators. In order to verify the effectiveness of the method, the traditional ANP method is used to calculate the indicator weights for comparison. In order to prove the superiority of the method, it is considered to compare with the AHP method, but the indicator dimension of the workshop disturbance impact evaluation is complex and large in scale. Therefore, the hierarchical AHP (L-AHP) method and the triangular fuzzy hierarchical AHP (FL-AHP) method are used for comparative verification. The indicator weight values ​​corresponding to different methods are shown in Table 6.

[0125] Table 6 Comparison of weights calculated by four methods

[0126]

[0127] It can be seen that the ANP method has a wider weight range than the AHP method, which shows that the ANP method can highlight the expert's scoring preference and give more weight to multi-dimensional complex indicators. Secondly, the maximum weight indicator of the ANP method is the production scheduling time. , the minimum weight indicators are quality inspection and correction time , while when using the two AHP methods, the maximum and minimum weight indicators become the workpiece surface defects and single process processing time This is because the ANP method takes into account the relationship between indicators and has a higher degree of fit. For example, for the indicator single process processing time , has the smallest weight in the AHP related methods, but in actual production and processing, the index It will affect the production scheduling time and cost related indicators. The ANP method takes this situation into consideration, so the weight of this indicator is greater. Finally, the HF-ANP method proposed in this invention reduces the weight interval to a certain extent compared with the traditional ANP method, and the result is more consistent and robust.

[0128] The validity verification of HF-VIKOR impact ranking. The present invention also introduces the hesitant fuzzy theory to improve the VIKOR method, and obtains the index weight of the workshop disturbance event based on the index weight. In order to verify the validity of the HF-VIKOR method, the CPT-VIKOR method, F-TOPSIS method and F-COPRAS method are used to rank the schemes at the same scale. The ranking results of the four methods are shown in Table 7.

[0129] Table 7 Comparison of sorting results of different methods

[0130]

[0131] In some embodiments, the workshop disturbance event impact assessment system may include multiple functional modules composed of computer program segments. The computer program of each program segment in the workshop disturbance event impact assessment system may be stored in the memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) The function of evaluating the impact of disturbance events in the workshop.

[0132] In this embodiment, the workshop disturbance event impact assessment system can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules of the system 200 may include: an acquisition module 210, a disturbance feature model construction module 220, a weight calculation module 230 and a ranking evaluation module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0133] An acquisition module, used to acquire workshop disturbance events, workshop disturbance event impact evaluation indicators and workshop disturbance event impact evaluation sub-indicators;

[0134] A weight calculation module is used to use the workshop disturbance event impact evaluation index as the criterion layer, the workshop disturbance event impact evaluation sub-indicator as the sub-criterion layer, construct an ANP network model for workshop disturbance impact discrimination, and calculate the weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination based on the hesitant fuzzy ANP method;

[0135] The sorting and evaluation module is used to calculate the disturbance impact of the workshop disturbance event by using the hesitant fuzzy VIKOR method, sort the disturbance impact based on the size of the disturbance impact, and obtain the impact evaluation of the workshop disturbance event.

[0136] Optionally, as an embodiment of the present invention, the disturbance characteristic model building module includes:

[0137] An acquisition unit, used for acquiring an evaluation index of the impact of a workshop disturbance event;

[0138] The first expression constructing unit is used to construct a disturbance event impact expression based on the workshop disturbance event impact evaluation index, and the disturbance event impact expression is ;in, is the processing time, For processing quality, is the processing cost. If the disturbance event has an impact on a certain indicator, it is recorded as 1, and if it has no impact, it is recorded as 0;

[0139] The second expression constructing unit is used to construct a disturbance event information expression based on the disturbance event impact expression and the disturbance event description, wherein the disturbance event information includes a binary model of the disturbance event description and the disturbance event impact, and the disturbance event information expression is: ;in, Description of the disturbance event; To determine whether the indicators are affected by disturbance events, the indicators include processing time , Processing quality and processing costs ;

[0140] The third expression constructing unit is used to construct a disturbance event data expression based on the disturbance information expression and the disturbance event type, wherein the expression of the disturbance event data is: ;in, is the disturbance event type, is the disturbance event information;

[0141] The expression model building unit is used to build a disturbance feature expression model based on the disturbance event data expression and the name of the disturbance event. ; The disturbance feature representation model is a binary feature representation model, the perturbation feature representation model The expression is: ; Name is the name of the disturbance event; Disturbance is the disturbance event data.

[0142] Optionally, as an embodiment of the present invention, the weight calculation module includes:

[0143] The criterion layer acquisition unit uses the workshop disturbance event impact evaluation index as the criterion layer Recorded as ;

[0144] The sub-criteria layer acquisition unit is used to take the workshop disturbance event impact evaluation sub-index as the sub-criteria layer, which is recorded as ;

[0145] The first calculation unit is used to construct m elements of the criterion layer based on the pre-set hesitant fuzzy element The t hesitant fuzzy preference relationship matrices between ; where k=1,2,…,t; i=1,2,…,m;

[0146] The second calculation unit is used to construct the M elements of the j-th sub-criterion layer under the criterion layer based on the pre-set hesitant fuzzy element Hesitant fuzzy preference relationship matrix , where j=1,2,…,M;

[0147] De-hesitation unit, used to hesitant fuzzy preference relationship matrix and Use the de-hesitation formula to de-hesitation and obtain the de-hesitation fuzzy relationship matrix and ; The de-hesitation formula is ;

[0148] The weight matrix generation unit is used to calculate the weight matrix based on the fuzzy relationship matrix after de-hesitation using the weight matrix formula , , the weight matrix formula is ;

[0149] A weight matrix generating unit is used to generate a weight matrix based on the weight matrix Get the weighting matrix A, the weighting matrix ;

[0150] Supermatrix generation unit, used to generate the elements in the element group Indirect advantage comparison is performed between them to construct each element The pairwise comparison matrix of and the normalized eigenvector are calculated, and the local weighted vector matrix is ​​obtained by the eigenroot method , by the local weight vector matrix The obtained super matrix W is:

[0151] ;

[0152] A weighted super-matrix generating unit is used to generate a weighted super-matrix based on the super-matrix W and the weighted matrix A using the formula Calculate the weighted super matrix ;

[0153] Sub-criteria weight generation unit, used to use the formula The weighted super matrix The limit is obtained to obtain a column vector of a weighted supermatrix after the limit is obtained, wherein the column vector is the weight of each sub-criteria in the sub-criteria layer.

[0154] Optionally, as an embodiment of the present invention, the method further comprises: a hesitant fuzzy element presetting module, comprising:

[0155] The hesitant fuzzy element presetting unit is used for the presetting hesitant fuzzy element to be ,in, for The minimum membership degree of For elements The maximum membership degree of For elements The average membership of , the membership value interval is [0,1].

[0156] Optionally, as an embodiment of the present invention, it further includes: a compatibility checking module, including:

[0157] Elements based on the criteria layer The t hesitant fuzzy preference relationship matrices between and the elements of the sub-criteria layer Hesitant fuzzy preference relationship matrix , respectively calculate the group hesitant fuzzy matrix and ,in, , where n is the number of all elements in the hesitant fuzzy element, The values ​​are: ;

[0158] For each element of the criteria layer The t hesitant fuzzy preference relationship matrices between , each element of the sub-criteria layer Hesitant fuzzy preference relationship matrix , Group hesitant fuzzy matrix , and group hesitant fuzzy matrix Compatibility calculation is performed. If the compatibility Cd is within the compatibility threshold, the compatibility check passes.

[0159] Optionally, as an embodiment of the present invention, the ranking evaluation module includes:

[0160] The first calculation unit is used based on the benefit-based criterion, using the formula Compute positive and negative ideal solutions; where Indicates The scheme is in The value of an attribute;

[0161] The second calculation unit is used based on the cost-based criterion, using the formula Compute positive and negative ideal solutions;

[0162] The third calculation unit is used to calculate the weight of each sub-criteria in the sub-criteria layer using the formula Calculate group utility value , calculated using the formula Individual hatred value ;

[0163] Comprehensive indicator calculation unit for group utility value and individual hatred value , using the formula Calculate composite index ,in, , , , , represents the decision mechanism coefficient;

[0164] Sorting unit, used to sort by comprehensive indicators The values ​​are sorted to obtain the impact evaluation of the workshop disturbance event.

[0165] Figure 3 The present invention is a schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the workshop disturbance event impact assessment method provided in an embodiment of the present invention.

[0166] The terminal 300 may include: a processor 310, a memory 320 and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0167] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0168] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0169] The communication unit 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0170] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0171] Therefore, the present invention uses the ANP method to determine the weight of each evaluation index, and combines the VIKOR method to sort the disturbance events. While ensuring the accurate expression of the expert's intention, the present invention reduces the subjective bias in the expert's judgment and overcomes the problem of incomplete evaluation of complex disturbance events in traditional methods. This method can effectively solve the reasonable sorting and priority treatment of disturbance events in the workshop under the condition of limited resources, provides a reliable basis for production optimization, and significantly improves the rationality of production scheduling and the efficiency of resource allocation. Finally, this method not only improves the stability and flexibility of the production process, but also optimizes the resource utilization of the workshop, improves production efficiency and product quality. The technical effects that can be achieved by this embodiment can be found in the description above, which will not be repeated here.

[0172] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0173] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0174] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0175] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0177] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, and all of these shall be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for evaluating the impact of a workshop disturbance event, characterized in that: include: Obtain workshop disturbance events, workshop disturbance event impact evaluation indicators and workshop disturbance event impact evaluation sub-indicators, wherein the workshop disturbance event includes the disturbance event name Name and the disturbance event data Disturbance; the disturbance event data Disturbance includes the disturbance event type and disturbance event information ; Disturbance event information Includes disturbance event description and disturbance events ; The evaluation indexes of the impact of workshop disturbance events include processing time , Processing quality and processing costs ; The processing time The sub-indicators include single process processing time, production scheduling time, quality inspection and correction time; the processing quality The sub-indicators include workpiece surface roughness, dimensional accuracy and workpiece surface defects; the processing cost The sub-indicators include material cost, labor cost and equipment operation and maintenance cost; Taking the workshop disturbance event impact evaluation index as the criterion layer, taking the workshop disturbance event impact evaluation sub-index as the sub-criterion layer, constructing an ANP network model for workshop disturbance impact discrimination, and calculating the weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination based on the hesitant fuzzy ANP method, including: The workshop disturbance event impact evaluation index is used as the criterion layer, denoted as ; The workshop disturbance event impact evaluation sub-index is used as the sub-criteria layer, denoted as ; Based on the pre-set hesitant fuzzy element, construct the m elements of the criterion layer The t hesitant fuzzy preference relationship matrices between ; where k=1,2,…,t; i=1,2,…,m; Based on the pre-set hesitant fuzzy element, construct the M elements of the jth sub-criterion layer under the criterion layer Hesitant fuzzy preference relationship matrix , where j=1,2,…,M; Hesitant fuzzy preference relationship matrix and Use the de-hesitation formula to de-hesitation and obtain the de-hesitation fuzzy relationship matrix and ; The de-hesitation formula is ; Based on the fuzzy relationship matrix after de-hesitation, the weight matrix is ​​calculated using the weight matrix formula , , the weight matrix formula is ; Based on the weight matrix Get the weighting matrix A, the weighting matrix ; For elements in an element group Indirect advantage comparison is performed between them to construct each element The pairwise comparison matrix of and the normalized eigenvector are calculated, and the local weighted vector matrix is ​​obtained by the eigenroot method , by the local weight vector matrix The obtained super matrix W is: ; Based on the super matrix W and the weighting matrix A, using the formula Calculate the weighted super matrix ; Using the formula The weighted super matrix Finding the limit, obtaining a column vector of a weighted supermatrix after finding the limit, wherein the column vector is the weight of each sub-criteria in the sub-criteria layer; The hesitant fuzzy VIKOR method is used to calculate the disturbance impact of the workshop disturbance event, and the disturbance impact is sorted based on the size of the disturbance impact to obtain the workshop disturbance event impact evaluation, including: Based on the benefit-based criterion, use the formula Compute positive and negative ideal solutions; where Indicates The scheme is in The value of an attribute; Based on the cost-based criteria, use the formula Compute positive and negative ideal solutions; Based on the weight of each sub-criteria in the sub-criteria layer, use the formula Calculate group utility value , calculated using the formula Individual hatred value ; Based on group utility and individual hatred value , using the formula Calculate composite index ,in, , , , , represents the decision mechanism coefficient; According to comprehensive indicators The values ​​are sorted to obtain the impact evaluation of the workshop disturbance event.

2. The method according to claim 1, characterized in that The pre-set hesitant fuzzy element is ,in, for The minimum membership degree of For elements The maximum membership degree of For elements The average membership of , the membership value interval is [0,1].

3. The method according to claim 1, characterized in that Also includes: The steps for compatibility testing include: Elements based on the criteria layer The t hesitant fuzzy preference relationship matrices between and the elements of the sub-criteria layer Hesitant fuzzy preference relationship matrix , respectively calculate the group hesitant fuzzy matrix and ,in, , where n is the number of all elements in the hesitant fuzzy element, The values ​​are: ; For each element of the criteria layer The t hesitant fuzzy preference relationship matrices between , each element of the sub-criteria layer Hesitant fuzzy preference relationship matrix , Group hesitant fuzzy matrix , and group hesitant fuzzy matrix Compatibility calculation is performed. If the compatibility Cd is within the compatibility threshold, the compatibility check passes.

4. The method according to claim 1, characterized in that: Also includes: Steps for verifying the weight calculation and the validity of the ranking of the impact of workshop disturbance events; The steps of weight calculation validation include comparative analysis of HF-ANP method with traditional ANP method, hierarchical AHP method and triangular fuzzy hierarchical AHP method; The steps for verifying the effectiveness of the ranking of the impact of workshop disturbance events include: comparing the ranking results of the HF-VIKOR method with the ranking results of the CPT-VIKOR method, the F-TOPSIS method and the F-COPRAS method.

5. A workshop disturbance event impact evaluation system, characterized in that: include: The acquisition module is used to obtain the workshop disturbance event, the workshop disturbance event impact evaluation index and the workshop disturbance event impact evaluation sub-indicator, wherein the workshop disturbance event includes the disturbance event name Name and the disturbance event data Disturbance; the disturbance event data Disturbance includes the disturbance event type and disturbance event information ; Disturbance event information Includes disturbance event description and disturbance events ; The evaluation indexes of the impact of workshop disturbance events include processing time , Processing quality and processing costs ; The processing time The sub-indicators include single process processing time, production scheduling time, quality inspection and correction time; the processing quality The sub-indicators include workpiece surface roughness, dimensional accuracy and workpiece surface defects; the processing cost The sub-indicators include material cost, labor cost and equipment operation and maintenance cost; The weight calculation module is used to use the workshop disturbance event impact evaluation index as the criterion layer, the workshop disturbance event impact evaluation sub-indicator as the sub-criterion layer, construct an ANP network model for workshop disturbance impact discrimination, and calculate the weight of each sub-criterion in the sub-criterion layer of the ANP network model for workshop disturbance impact discrimination based on the hesitant fuzzy ANP method, including: The workshop disturbance event impact evaluation index is used as the criterion layer, denoted as ; The workshop disturbance event impact evaluation sub-index is used as the sub-criteria layer, denoted as ; Based on the pre-set hesitant fuzzy element, construct the m elements of the criterion layer The t hesitant fuzzy preference relationship matrices between ; where k=1,2,…,t; i=1,2,…,m; Based on the pre-set hesitant fuzzy element, construct the M elements of the jth sub-criterion layer under the criterion layer Hesitant fuzzy preference relationship matrix , where j=1,2,…,M; Hesitant fuzzy preference relationship matrix and Use the de-hesitation formula to de-hesitation and obtain the de-hesitation fuzzy relationship matrix and ; The de-hesitation formula is ; Based on the fuzzy relationship matrix after de-hesitation, the weight matrix is ​​calculated using the weight matrix formula , , the weight matrix formula is ; Based on the weight matrix Get the weighting matrix A, the weighting matrix ; For elements in an element group Indirect advantage comparison is performed between them to construct each element The pairwise comparison matrix of and the normalized eigenvector are calculated, and the local weighted vector matrix is ​​obtained by the eigenroot method , by the local weight vector matrix The obtained super matrix W is: ; Based on the super matrix W and the weighting matrix A, using the formula Calculate the weighted super matrix ; Using the formula The weighted super matrix Finding the limit to obtain a column vector of a weighted supermatrix after finding the limit, wherein the column vector is the weight of each sub-criteria in the sub-criteria layer; The sorting evaluation module is used to calculate the disturbance impact of the workshop disturbance event using the hesitant fuzzy VIKOR method, sort the disturbance impact based on the size of the disturbance impact, and obtain the workshop disturbance event impact evaluation, including: Based on the benefit-based criterion, use the formula Compute positive and negative ideal solutions; where Indicates The scheme is in The value of an attribute; Based on the cost-based criteria, use the formula Compute positive and negative ideal solutions; Based on the weight of each sub-criteria in the sub-criteria layer, use the formula Calculate group utility value , calculated using the formula Individual hatred value ; Based on group utility and individual hatred value , using the formula Calculate composite index ,in, , , , , represents the decision mechanism coefficient; According to comprehensive indicators The values ​​are sorted to obtain the impact evaluation of the workshop disturbance event.

6. A terminal, characterized in that: include: A memory, used for storing a program for evaluating the impact of disturbance events in a workshop; A processor is used to implement the steps of the workshop disturbance event impact assessment method as described in any one of claims 1 to 4 when executing the workshop disturbance event impact assessment program.

7. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a workshop disturbance event impact assessment program, and when the workshop disturbance event impact assessment program is executed by the processor, the steps of the workshop disturbance event impact assessment method according to any one of claims 1 to 4 are implemented.

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