Cigarette factory equipment control system risk management and control method and system and storage medium

Abstract

The invention relates to the field of factory equipment management, in particular to a risk management and control method and system suitable for a cigarette factory equipment control system and a storage medium. Analyzing the score of each index in the risk mode set based on an improved fuzzy analytic hierarchy process to obtain a subjective weight vector; performing standardization and defuzzification processing on the score of each index in the risk mode set to obtain an objective weight vector; obtaining a composite weight vector according to the subjective weight vector and the objective weight vector; obtaining the relative close degree of each risk mode according to the composite weight vector, and carrying out the sorting, so as to obtain a risk sorting result; and formulating preventive maintenance strategies or management measures according to a risk sorting result. According to the method, through combination of Delphi consensus, full-process fuzzy and dynamic combination weighting methods, accurate grading of equipment risks is realized, and authority, robustness and management and control practicability of an evaluation result are remarkably improved in a complex industrial environment.

Description

Technical Field

[0001] This invention relates to the field of factory equipment management, and more specifically to a risk control method, system, and storage medium applicable to the control system of key equipment in a cigarette factory. Background Technology

[0002] In the cigarette manufacturing industry, the reliability of control systems for key equipment such as tobacco processing lines, cigarette making machines, and packaging machines directly determines production efficiency and product quality. To achieve proactive risk management, the industry commonly employs a methodology based on Failure Mode and Effects Analysis (FMEA). However, traditional FMEA relies on Risk Priority Numbers (RPNs) for ranking, which has significant limitations in handling the ambiguity of expert subjective judgment and the weighting of risk factors. For example, RPN values ​​may have different risk implications, and the assumption that the weights of the three major factors are equal is illogical. To overcome these limitations, researchers have proposed an improved scheme that combines fuzzy theory with multi-criteria decision-making methods.

[0003] Patent document CN112257933B, entitled "A Method for Assessing Equipment Health Based on Improved Delphi Method and Fuzzy Comprehensive Evaluation," discloses a method that uses a multi-round anonymous Delphi method to gather expert opinions and then processes them using fuzzy comprehensive evaluation. This technique emphasizes the importance of reaching expert consensus in the assessment of complex systems. Patent document CN110210677A, entitled "A Risk Assessment Method for Industrial Equipment Based on Fuzzy AHP and TOPSIS," discloses a technical solution: First, potential failure modes of equipment are identified through FMEA; second, the weights of various risk factors (such as severity, occurrence, and detectability) are calculated using the Fuzzy Hierarchical Analysis (IFAHP); finally, the Top-Approximation Ideal Solution Ranking Method (TOPSIS) is used to rank the identified failure modes by risk.

[0004] Although this existing technology represents an improvement over traditional FMEA, it still has the following significant shortcomings when applied to the specific scenario of critical equipment control systems in cigarette factories: The expert opinion integration mechanism is inadequate: No structured expert group decision-making mechanism has been embedded in the application of IFAHP. Although patent CN112257933B points out the importance of the Delphi method, it does not integrate this method with the IFAHP-TOPSIS framework. The equipment control system of a cigarette factory is a complex system integrating mechanics, electronics, software, and control, and its risk assessment requires the integration of multidisciplinary experts' wisdom. The lack of an iterative, anonymous, and feedback consensus-building mechanism like the Delphi method results in the initial judgment matrix of IFAHP being built on scattered and unconverged expert opinions, affecting the fundamental authority and stability of the weight calculation.

[0005] The scheme fails to handle the approximation of ideal solutions in fuzzy environments: In the TOPSIS ranking stage, the scheme uses precise numerical calculations. However, the weights generated in the IFAHP stage and the expert scores for failure modes are themselves fuzzy numbers. The scheme defuzzifies these fuzzy numbers before entering the TOPSIS process, resulting in the loss of original fuzzy information. In a scenario like a cigarette factory, which heavily relies on experience-based judgment, this "precision-first, ranking-later" approach fails to incorporate fuzzy uncertainty throughout the decision-making process. This means the final ranking result does not fully reflect the fuzzy nature of the risk state, reducing the robustness of risk assessment and its inclusiveness of fuzzy information.

[0006] In view of the shortcomings of existing technologies in the integration of expert consensus and the continuity of fuzzy information processing, there is an urgent need to build a risk assessment scheme that integrates multi-source expert consensus and full-process fuzzy decision-making. This scheme can improve the ability to identify differences in risk connotations, adapt to complex industrial site environments, and tolerate fuzzy uncertainties, thus providing reliable technical support for the advanced and precise control of risks in the control systems of key equipment in cigarette factories. Summary of the Invention

[0007] The purpose of this invention is to provide a risk management method, system, and storage medium applicable to the control system of key equipment in a cigarette factory, in order to solve the technical problems of insufficient risk assessment authority, poor result stability, and weak decision robustness in the face of the specific scenario of the integrated mechanical, electrical, software, and control control system of a cigarette factory, due to the lack of a systematic expert consensus mechanism and incomplete processing of fuzzy information.

[0008] To achieve the above objectives, the present invention provides a risk management method applicable to the control system of key equipment in a cigarette factory, the method comprising: Obtain a risk pattern set; The scores of each indicator in the risk pattern set are analyzed based on the improved fuzzy hierarchical analysis method to obtain the subjective weight vector. The scores of each indicator in the risk pattern set are normalized and defuzzified to obtain an objective weight vector. A composite weight vector is obtained based on the subjective weight vector and the objective weight vector. The relative proximity of each risk pattern is obtained based on the composite weight vector and sorted to obtain the risk ranking result; Based on the risk ranking results, develop preventative maintenance strategies or management measures.

[0009] Optionally, the scores of each indicator in the risk pattern set are analyzed based on an improved fuzzy hierarchical analysis method to obtain a subjective weight vector, including: The organization assigned experts to score each indicator in each criterion layer of the risk pattern set; Based on the score of each indicator in each criterion layer, obtain the hierarchical single-sorting weight vector of the corresponding criterion layer; The subjective weight vector is obtained based on the hierarchical single-order weight vector of each criterion layer.

[0010] Optionally, based on the score of each indicator in each criterion layer, a hierarchical single-ranking weight vector for the corresponding criterion layer is obtained, including: The fuzzy consistency judgment matrix is ​​obtained according to formulas (1) to (4): (1) (2) (3) (4) in, The priority judgment matrix of the current criterion layer is the first... The sum of the elements in the row. The priority judgment matrix of the current criterion layer is the first... The sum of the column elements, To satisfy the mathematical consistency condition, the first Compared to the first evaluation indicator, the second evaluation indicator is... The importance of each evaluation indicator This is a fuzzy consistency judgment matrix; Obtain the reciprocal matrix according to formulas (5) to (6): (5) (6) in, For the first Compared to the first evaluation indicator, the second evaluation indicator is... The ultimate importance of each evaluation indicator It is a reciprocal matrix.

[0011] Optionally, based on the score of each indicator in each criterion layer, a hierarchical single-ranking weight vector for the corresponding criterion layer is obtained, including: According to formulas (7) to (9), obtain the hierarchical single ranking weight of each evaluation indicator under the corresponding criterion layer: (7) (8) (9) in, It is the product of the matrix rows corresponding to the i-th evaluation index under the corresponding criterion layer. This represents the geometric mean of the i-th evaluation index under the corresponding criterion layer. This refers to the hierarchical single-ranking weights of each evaluation indicator under the corresponding criteria layer.

[0012] Optionally, a subjective weight vector is obtained based on the hierarchical single-order weight vector of each criterion layer, including: Obtain the subjective weights according to formula (10): (10) in, For the first criterion in L2 The subjective weight of each indicator relative to the overall goal For the first criterion in L2 The index is relative to its L1 layer. Hierarchical single-ranking weights of each evaluation indicator For the first criterion in L1 The single ranking weight of each evaluation indicator relative to the overall goal.

[0013] Optionally, the scores of each indicator in the risk pattern set are normalized and defuzzified to obtain an objective weight vector, including: The organization's experts scored the aforementioned risk rating indicators and obtained the comprehensive fuzzy evaluation value according to formulas (11) to (12): (11) (12) in, For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation index. For the first The risk pattern in the first The most likely value of the comprehensive fuzzy evaluation value given for each evaluation indicator. For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation index. For the first The expert commented on the first The risk pattern in the first The lower bound of the triangular fuzzy number evaluation given for each evaluation index. For the first The expert commented on the first The risk pattern in the first The most likely value of the triangular fuzzy number evaluation given for each evaluation index. For the first The expert commented on the first The risk pattern in the first The upper bound of the triangular fuzzy number evaluation given for each evaluation index. To comprehensively evaluate the fuzzy evaluation value, The weight of the k-th expert; Obtain the normalized fuzzy decision matrix according to formulas (13) to (15): (13) (14) (15) in, For the first A benchmark value calculated from each evaluation indicator. To standardize the fuzzy decision matrix, For elements in the fuzzy decision matrix The corresponding value after normalization; Obtain the standardized decision matrix according to formulas (16) and (17): (16) (17) in, This is the precise value of the triangular fuzzy number. For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For standardized decision matrices; The objective weight of each evaluation indicator is obtained according to formulas (18) to (19): (18) (19) in, For the first The entropy value of each evaluation indicator The coefficient is a constant for entropy. For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For the first Objective weights of each evaluation indicator.

[0014] Optionally, obtaining a composite weight vector based on the subjective weight vector and the objective weight vector includes: Obtain the composite weights according to formula (20): (20) in, For the first A composite weight, This is the dynamic preference coefficient. For the first A subjective weight, For the first An objective weight, Let Kronecker function be used.

[0015] Optionally, the relative proximity of each risk pattern is obtained based on the composite weight vector and sorted to obtain a risk ranking result, including: The relative similarity of each risk model is obtained according to formulas (21) to (23): ,(twenty one) ,(twenty two) ,(twenty three) in, For the first The distance from a risk model to the ideal solution. For the first The distance from a risk model to a negative ideal solution. For the first A composite weight, For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For the first The positive ideal solution score of each evaluation indicator. For the first The negative ideal solution score of each evaluation indicator For the first The relative progress of each risk model.

[0016] On the other hand, the present invention also provides a risk management system applicable to the control system of key equipment in a cigarette factory, the management system comprising: The risk identification module is used to identify failure modes and form a risk mode set; The indicator evaluation system construction module is used to build a risk management hierarchy model; The indicator weight determination module is used to calculate indicator weights; The risk ranking module is used to rank risks. The risk response strategy generation module is used to form a long-term risk management mechanism; A processor is used to connect a risk identification module, an indicator evaluation system construction module, an indicator weight determination module, a risk ranking module, and a risk response strategy generation module, wherein the processor is configured to execute any of the methods described above.

[0017] In another aspect, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement any of the methods described above.

[0018] The beneficial effects of this invention are: This invention, by integrating multi-source expert consensus with a full-process fuzzy decision-making framework, achieves efficient, accurate, and adaptive risk assessment of the control systems of key equipment in cigarette factories. Specifically, it includes: This invention possesses the ability to simultaneously integrate expert group decision-making processes with data-driven objective mechanisms, and can also achieve adaptive fusion of subjective and objective information based on dynamic combination and weighting. By combining the consensus stability of the Delphi method with the robustness of full-process fuzzy decision-making, it realizes the function of deeply utilizing expert wisdom and fully processing uncertain information in risk assessment.

[0019] This invention enhances the model's authority based on the Delphi consensus enhancement mechanism, improving evaluation stability in complex industrial scenarios. Furthermore, it outperforms traditional FMEA and existing improved schemes in terms of evaluation accuracy and adaptability. Traditional methods are insensitive to weight differences in risk factors and fuzzy information, leading to significant performance deviations when facing complex systems with highly coupled mechanical, electrical, software, and control systems. This invention, through fuzzy consistency transformation and dynamic combination weighting, enables the model to maintain stable decision-making capabilities under different risk patterns and uncertainties, greatly enhancing its practical application value.

[0020] Because the present invention has achieved a deep integration of consensus building, fuzzy computing and dynamic weighting in its framework design, the Delphi method, IFAHP, IFTOPSIS and entropy weight method have clear process logic in practical applications and do not need to rely on temporary subjective adjustments. At the same time, they support the continuous inclusion and evaluation of new risk models and expert knowledge, and can continuously adapt to the development and changes of equipment systems and risk characteristics, and can gradually improve the accuracy, stability and foresight of risk management systems. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating a risk management method applicable to a critical equipment control system in a cigarette factory according to an embodiment of the present invention; Figure 2 This is a hierarchical structure diagram of a factory PLC risk management model according to an embodiment of the present invention; Figure 3A flowchart of a subjective weight acquisition method according to an embodiment of the present invention; Figure 4 This is a flowchart of an objective weight acquisition method according to an embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0023] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0024] This embodiment takes the PLC (Programmable Logic Controller) control system of key equipment in a cigarette factory as an example to further illustrate the present invention.

[0025] like Figure 1 The diagram shows a flowchart of a risk management method applicable to a critical equipment control system in a cigarette factory, according to the present invention. Figure 1 The detection method may include the following steps: In step S10, a risk pattern set is obtained; In step S11, the scores of each indicator in the risk pattern set are analyzed based on the improved fuzzy hierarchical analysis method to obtain the subjective weight vector. In step S12, the scores of each indicator in the risk pattern set are normalized and defuzzified to obtain an objective weight vector. In step S13, a composite weight vector is obtained based on the subjective weight vector and the objective weight vector; In step S14, the relative proximity of each risk pattern is obtained based on the composite weight vector and sorted to obtain the risk ranking result; In step S15, preventive maintenance strategies or management measures are developed based on the risk ranking results.

[0026] In such Figure 1In the method shown, step S10 is used to obtain a risk mode set. Specifically, in this embodiment, the specific method for obtaining the risk mode set may be as follows: First, for the target PLC control system, Failure Mode and Effects Analysis (FMEA) is used to identify five typical risk modes existing in the system by comprehensively considering the operating status of field equipment and historical fault records. These include: hardware failure FM1, software failure FM2, communication failure FM3, external module failure FM4, and maintenance spare parts problem FM5, thereby forming a risk mode set. Second, a hierarchical structure model is established based on the Analytic Hierarchy Process (AHP). The specific model is as follows: Figure 2 As shown in the diagram. The target layer is PLC risk management, the criteria layer consists of three primary evaluation indicators derived from FMEA analysis: fault severity (S), fault occurrence frequency (O), and fault detectability (D), and the solution layer comprises the five risk patterns identified above.

[0027] In this embodiment, to refine the evaluation, the primary evaluation indicators of the criteria layer are decomposed. Fault severity S is further decomposed into three secondary evaluation indicators: impact on overall equipment operation C1, impact on the control system where the PLC resides C2, and impact on the PLC itself C3. Fault occurrence frequency O corresponds to a secondary evaluation indicator, fault occurrence ratio C4. Fault detectability D is decomposed into two secondary evaluation indicators: difficulty of online fault diagnosis C5 and difficulty of fault diagnosis during maintenance C6. This complete hierarchical structure forms the basic framework for subsequent quantitative risk assessment and ranking.

[0028] Step S11 is used to analyze the scores of each indicator in the risk pattern set based on the improved fuzzy hierarchical analysis method to obtain the subjective weight vector. In this embodiment, the specific method for obtaining the subjective weight vector in step S11 can be of various forms known to those skilled in the art. In one embodiment of the present invention, step S11 may include, for example... Figure 3 The steps shown are described in this. Figure 3 In this context, step S11 may include: In step S20, experts are organized to score each indicator in each criterion layer of the risk pattern set; In step S21, based on the score of each indicator in each criterion layer, the hierarchical single-ranking weight vector of the corresponding criterion layer is obtained; In step S22, the subjective weight vector is obtained based on the hierarchical single-order weight vector of each criterion layer.

[0029] In such Figure 3 In the methods shown, firstly, the scoring method can be the 0.1-0.9 scale method. This method uses the 0.1-0.9 scale instead of the 1-9 scale in the traditional method. The 0.1-0.9 scale method and its definition are shown in Table 1: Table 10.1-0.9 Scaling Methods and Their Definitions

[0030] After determining to use the 0.1-0.9 scaling method, step S20 involves organizing experts to score each indicator in each criterion layer of the risk model set. Specifically, in this implementation, the scoring method for each indicator can be to have five experts (TM1 to TM5) from different fields conduct multiple rounds of Delphi scoring until a consensus is reached on the relative importance of the evaluation indicators, as shown in Tables 2 to 5: Table 2 Priority Judgment Matrix of Primary Evaluation Indicators

[0031] Table 2 is a priority judgment matrix among the primary evaluation indicators of fault severity S, fault occurrence frequency O, and fault detectability D. For example, the value 0.6 in the second row and third column indicates that the fault severity S is considered "slightly more important" than the fault occurrence frequency O by the experts.

[0032] Table 3 Priority Judgment Matrix of Secondary Indicators under Primary Evaluation Indicator S

[0033] Table 3 is a priority judgment matrix among the sub-indicators of the primary evaluation index S (impact on overall equipment operation C1, impact on the control system where the PLC is located C2, and impact on the PLC itself C3). For example, the value 0.6 in the second row and third column indicates that the impact on overall equipment operation C1 is considered "slightly more important" than the impact on the control system where the PLC is located C2 by experts.

[0034] Table 4 Priority Judgment Matrix of Secondary Indicators under Primary Evaluation Indicator D

[0035] Table 3 is a priority judgment matrix among the sub-indicators of primary evaluation index D (online fault diagnosis difficulty C5 and fault diagnosis difficulty during maintenance C6). For example, the value 0.6 in the second row and third column indicates that the online fault diagnosis difficulty C5 is considered "slightly more important" by experts than the fault diagnosis difficulty during maintenance C6.

[0036] Table 5 Expert Priority Judgment Matrix

[0037] Table 4 is a matrix showing the priority of authority evaluation among the five experts (TM1 to TM5). For example, the value 0.6 in the second row and third column indicates that expert TM1 is considered "slightly more important" than expert TM2 by the other experts.

[0038] Step S21 is used to obtain the hierarchical single-ranking weight vector of the corresponding criterion layer based on the score of each indicator in each criterion layer. Specifically, in this embodiment, obtaining the hierarchical single-ranking weight vector of the corresponding criterion layer can be based on the consensus result, and the fuzzy complementary judgment matrix can be obtained according to formula (1): (1) in, For fuzzy complementary judgment matrix, The number of evaluation indicators in the criteria layer. To make a preliminary judgment under the following conditions Compared to the first evaluation indicator, the second evaluation indicator is... The importance of each evaluation indicator. Simultaneously, the fuzzy complementary judgment matrix. Must meet ,in, To make a preliminary judgment under the following conditions Compared to the first evaluation indicator, the second evaluation indicator is... The importance of each evaluation indicator.

[0039] Obtain the fuzzy consistency judgment matrix according to formulas (2) to (5): (2) (3) (4) (5) in, The priority judgment matrix of the current criterion layer is the first... The sum of the elements in the row. The priority judgment matrix of the current criterion layer is the first... The sum of the column elements, To satisfy the mathematical consistency condition, the first Compared to the first evaluation indicator, the second evaluation indicator is... The importance of each evaluation indicator This is a fuzzy consistency judgment matrix; Obtain the reciprocal matrix according to formulas (6) to (7): (6) (7) in, For the first Compared to the first evaluation indicator, the second evaluation indicator is... The ultimate importance of each evaluation indicator It is a reciprocal matrix.

[0040] Obtain the hierarchical single sorting weights according to formulas (8) to (10): (8) (9) (10) in, For the corresponding criterion level, the first The product of the rows of the matrix corresponding to each evaluation index For the corresponding criterion level, the first The geometric mean of the evaluation indicators, This refers to the hierarchical single-ranking weights of each evaluation indicator under the corresponding criteria layer.

[0041] Step S21 constructs a fuzzy complementary judgment matrix based on expert scores, and transforms it into a fuzzy consistent judgment matrix and a reciprocal matrix through mathematical transformation. Finally, the relative weights of each evaluation index within the criterion layer are calculated, namely the hierarchical single ranking weights, to provide a scientific and consistent quantitative basis for subsequent comprehensive evaluation.

[0042] Step S22 is used to obtain the subjective weight vector based on the hierarchical single-order weight vector of each criterion layer. Specifically, in this embodiment, the specific method for obtaining the subjective weight vector can be to set the criterion layer in the hierarchical structure model as follows: Includes m evaluation indicators its next criterion layer Includes n evaluation indicators The subjective weight vector is obtained according to formula (11): (11) in, For the first criterion in L2 The subjective weight vector of each indicator relative to the overall goal. For the first criterion in L1 The single ranking weight of each evaluation indicator relative to the overall goal, that is, the single ranking weight relative to the overall goal obtained from the data in Table 1 using formulas (1) to (10). For the first criterion in L2 The index is relative to its L1 layer. The hierarchical single ranking weight of each evaluation indicator is obtained from the data in Tables 2 and 3 using formulas (1) to (10) and its corresponding L1 level. The hierarchical single ranking weights corresponding to each evaluation indicator are as follows: Since evaluation indicator O only contains one secondary indicator C4, it does not need to be obtained through formulas (1) to (10). The hierarchical single ranking weights of the indicators in its criterion layer L2 relative to the evaluation indicators of its L1 layer are fixed at 1.

[0043] The subjective weights obtained above are integrated into a subjective weight vector, and a subjective weight table for the secondary evaluation indicators is generated, as shown in Table 6. This is the improved fuzzy analytic hierarchy process weights. Table 6 Subjective Weights of Secondary Evaluation Indicators

[0044] Step S12 is used to normalize and defuzzify the scores of each indicator in the risk pattern set to obtain an objective weight vector. Specifically, in this embodiment, the specific method for obtaining the objective weight vector can be of various forms known to those skilled in the art. In one embodiment of the present invention, step S12 may include, for example: Figure 4 The steps are shown. In this Figure 4 In this context, step S12 may include: In step S30, the comprehensive fuzzy evaluation value of the evaluation index for each risk mode is quantified; In step S31, a complete fuzzy decision matrix is ​​constructed based on the comprehensive fuzzy evaluation value; In step S32, the normalized fuzzy evaluation value is obtained based on the complete fuzzy decision matrix; In step S33, the precise value of the triangular fuzzy number is obtained based on the normalized fuzzy evaluation value; In step S34, a standardized decision matrix is ​​obtained based on the precise value of the triangular fuzzy number; In step S35, the objective weight vector is obtained based on the standardized decision matrix.

[0045] In such Figure 4 In the method shown, firstly, five experts (TM1 to TM5) need to be organized to conduct fuzzy evaluations of the five risk models under six secondary evaluation indicators. Based on formula (12), a comprehensive fuzzy evaluation indicator model for each risk model is constructed: (12) in, For the first The risk pattern in the first The comprehensive fuzzy evaluation value on each evaluation indicator It is the first A fuzzy evaluation index for the secondary evaluation indicators of risk models by renowned experts. For the first The expert weights, obtained from Table 4 using formulas (1) to (10), are shown in Table 7. , : Table 7. Weights of the Hierarchical Analysis of Experts in Scoring

[0046] After constructing the comprehensive fuzzy evaluation index model for each risk mode, the comprehensive fuzzy evaluation value of each risk mode is quantified in step S30. Specifically, in this embodiment, the quantification process can be performed by using the commonly used triangular fuzzy number membership function to quantify the comprehensive fuzzy evaluation value of each risk mode according to formulas (13) to (14): (13) (14) in, For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation indicator, i.e., the minimum score. For the first The risk pattern in the first The most likely value of the comprehensive fuzzy evaluation value given for each evaluation indicator. For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation indicator, i.e., the highest score. For the first The expert commented on the first The risk pattern in the first For each evaluation indicator, the lower bound of the triangular fuzzy number evaluation is given, and the lowest score considered by expert k is also considered. For the first The expert commented on the first The risk pattern in the first For each evaluation indicator, the most likely value of the given triangular fuzzy number evaluation, i.e., the most likely score as considered by expert k, is... For the first The expert commented on the first The risk pattern in the first For each evaluation indicator, the upper bound of the triangular fuzzy number evaluation is the lowest possible score considered by expert k. For the first Expert weighting This is a comprehensive fuzzy evaluation value.

[0047] Step S31 is used to construct a complete fuzzy decision matrix based on the comprehensive fuzzy evaluation values. Specifically, in this embodiment, the specific method for constructing the complete fuzzy decision matrix can be to arrange all the corresponding comprehensive fuzzy evaluation values ​​according to the risk mode and evaluation index, and construct the complete fuzzy decision matrix according to formula (15): (15) in, Let be the comprehensive triangular fuzzy number evaluation value of the m-th risk model on the n-th evaluation index, and satisfy . , .

[0048] Step S32 is used to obtain the normalized fuzzy evaluation value based on the complete fuzzy decision matrix. Specifically, in this embodiment, the method for obtaining the normalized fuzzy evaluation value can be based on formulas (16) to (17): (16) (17) in, For the first A baseline value calculated from a cost-based evaluation index serves as the upper bound of the comprehensive fuzzy evaluation value for all risk models under that index. The minimum value in, This is the first The risk pattern in the first For each evaluation indicator, the standardized fuzzy evaluation value after standardization processing is... For the first The risk pattern in the first For each evaluation indicator, the lower bound of the comprehensive fuzzy evaluation value is given. For the first The risk pattern in the first For each evaluation indicator, the most likely value of the given comprehensive fuzzy evaluation value is... For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation index. For the fuzzy decision matrix... Each element in Perform normalization calculations to obtain the corresponding normalized elements. The aim is to eliminate the dimensional differences between different evaluation indicators.

[0049] Step S33 is used to obtain the precise value of the triangular fuzzy number based on the normalized fuzzy evaluation value. Specifically, in this embodiment, the method for obtaining the precise value of the triangular fuzzy number can be to use all calculated normalized elements... According to its original matrix Arrange the rows and columns of the data, and obtain the normalized fuzzy matrix according to formula (18): (18) in, To standardize the fuzzy decision matrix, Decision matrix medium elements The corresponding value after normalization. This matrix serves as the basis for subsequent defuzzification and accurate calculation; all its elements have been converted into normalized triangular fuzzy numbers with the same comparison benchmark.

[0050] After obtaining the normalized fuzzy matrix Then, the centroid method was used to analyze the matrix. The core of the centroid method for defuzzification is to calculate the fuzz number of each triangle. The geometric centroid is used to represent the most likely precise value of the fuzzy evaluation. Based on this, the precise value of the triangular fuzzy number can be obtained according to formula (19): (19) in, This is the precise value of the triangular fuzzy number. for The membership function.

[0051] Step S34 is used to obtain the standardized decision matrix based on the precise value of the triangular fuzzy number. Specifically, in this embodiment, to eliminate the differences in the data scale of each indicator, column-wise standardization can be performed so that the sum of all values ​​under each indicator is 1, thereby achieving comparability. Based on this, standardization can be performed according to formula (20): (20) in, For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator This is the precise value of the triangular fuzzy number.

[0052] Obtain the standardized decision matrix according to formula (21): ,(twenty one) in, This is a standardized decision matrix. The elements of this matrix are dimensionless scalars, which not only preserve the relative relationships of the original evaluation data, but also provide a unified and reliable input basis for the subsequent entropy weight method to obtain objective weights and improve the fuzzy TOPSIS ranking.

[0053] Step S35 is used to obtain the objective weight vector based on the standardized decision matrix. Specifically, in this embodiment, the specific method for obtaining the objective weight vector can be based on the matrix... Determine the positive and negative ideal solutions and obtain the objective weight vector according to formulas (22) to (23): ,(twenty two) ,(twenty three) in, For the first The entropy value of each evaluation indicator The coefficient is a constant for entropy. For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For the first Objective weights of each evaluation indicator.

[0054] The objective weights obtained above are integrated into an objective weight vector, and an objective weight table for secondary evaluation indicators is generated, as shown in Table 8: Table 8 Objective Weights of Secondary Evaluation Indicators

[0055] Step S13 is used to obtain a composite weight vector based on the subjective weight vector and the objective weight vector. Specifically, in this embodiment, the specific method for obtaining the composite weight vector can be to obtain the composite weight according to formula (24): ,(twenty four) in, It is the dynamic preference coefficient, and , The larger the value, the more it leans towards the subjective weight vector. For the first A subjective weight, For the first An objective weight, Let Kronecker function be used, and The value is 1 if and only if the two indices are equal, otherwise it is 0. For the first A composite weight.

[0056] The composite weights obtained above are integrated into a composite weight vector, and a composite weight table of secondary evaluation indicators is generated, as shown in Table 9: Table 9 Composite Weights of Secondary Evaluation Indicators

[0057] Step S14 is used to obtain the relative proximity of each risk pattern according to the composite weight vector and sort them to obtain the risk ranking result. Specifically, in this embodiment, the specific method for obtaining the risk ranking result can be to obtain the relative proximity of each risk pattern according to formulas (25) to (27): (25) (26) (27) in, For the first The distance from a risk model to the ideal solution. For the first The distance from a risk model to a negative ideal solution. For the first A composite weight, For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For the first The positive ideal solution score of each evaluation index, i.e., the standardized decision matrix. The maximum value in each column. For the first The negative ideal solution score of each evaluation index, i.e., the standardized decision matrix. The minimum value in each column. For the first The relative progress of each risk model is shown in Table 10: Table 10. Ranking of PLC risk modes based on the IFAHP-IFTOPSIS method.

[0058] Table 10 directly outputs the priority conclusions of the risk assessment, providing a clear decision-making basis for the subsequent development of targeted risk response measures.

[0059] Step S15 is used to formulate preventive maintenance strategies or management measures based on the risk ranking results. Specifically, in this embodiment, the formulated preventive maintenance strategies or management measures can be compiled into a risk response table as shown in Table 11: Table 11 Countermeasures for PLC Risks

[0060] Based on the results obtained in step S14, the highest risk in this stage is "communication failure" (FM3). Based on this risk warning, specific countermeasures were developed, including network redundancy design, adding network monitoring programs, and regularly checking connectors, which effectively guided the factory's maintenance work.

[0061] On the other hand, this invention also provides a risk management system applicable to the control system of key equipment in a cigarette factory. Specifically, the system may include a risk identification module, an indicator evaluation system construction module, an indicator weight determination module, a risk ranking module, and a risk response strategy generation module. The risk identification module identifies all potential failure modes of the target key equipment control system, forming a risk mode set. The indicator evaluation system construction module establishes a risk management hierarchical structure model with failure severity, frequency of occurrence, and detectability as primary indicators, further refined secondary indicators as the criterion layer, and the aforementioned risk mode set as the solution layer. The indicator weight determination module integrates the Delphi method and the improved fuzzy hierarchical analysis method, organizes experts to conduct multiple rounds of anonymous feedback scoring, constructs a fuzzy complementary judgment matrix, and obtains the subjective weight vector of each level of indicator through mathematical transformation and calculation. The risk ranking module integrates the entropy weight method, the dynamic combination weighting method, and the improved fuzzy approximation ideal solution ranking method. Based on the fuzzy evaluation of risk modes by experts, it calculates objective weights, combines subjective weights through dynamic combination weighting to obtain composite weights, and calculates the relative proximity of each risk mode, thereby ranking the risks. The risk response strategy generation module generates and outputs corresponding preventative maintenance strategies or management measures for high-risk patterns based on risk ranking results, forming a long-term control mechanism. The processor connects the risk identification module, indicator evaluation system construction module, indicator weight determination module, risk ranking module, and risk response strategy generation module. The processor is configured to execute any of the risk management methods applicable to the control system of critical equipment in a cigarette factory.

[0062] In another aspect, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement any of the risk management methods applicable to a control system for critical equipment in a cigarette factory.

[0063] The beneficial effects of this invention are: This invention, by integrating multi-source expert consensus with a full-process fuzzy decision-making framework, achieves efficient, accurate, and adaptive risk assessment of the control systems of key equipment in cigarette factories. Specifically, it includes: This invention possesses the ability to simultaneously integrate expert group decision-making processes with data-driven objective mechanisms, and can also achieve adaptive fusion of subjective and objective information based on dynamic combination and weighting. By combining the consensus stability of the Delphi method with the robustness of full-process fuzzy decision-making, it realizes the function of deeply utilizing expert wisdom and fully processing uncertain information in risk assessment.

[0064] This invention enhances the model's authority based on the Delphi consensus enhancement mechanism, improving evaluation stability in complex industrial scenarios. Furthermore, the method of this invention outperforms traditional FMEA and its existing improvements in both evaluation accuracy and adaptability. Traditional methods are insensitive to weight differences in risk factors and fuzzy information, leading to significant performance deviations when facing complex systems with highly coupled mechanical, electrical, software, and control systems. This invention, through fuzzy consistency transformation and dynamic combination weighting, enables the model to maintain stable decision-making capabilities under different types of risk modes and uncertainties, greatly enhancing its practical application value.

[0065] Because the present invention has achieved a deep integration of consensus building, fuzzy computing and dynamic weighting in its framework design, the Delphi method, IFAHP, IFTOPSIS and entropy weight method have clear process logic in practical applications and do not need to rely on temporary subjective adjustments. At the same time, they support the continuous inclusion and evaluation of new risk models and expert knowledge, and can continuously adapt to the development and changes of equipment systems and risk characteristics, and can gradually improve the accuracy, stability and foresight of risk management systems.

[0066] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0067] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0068] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0069] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0070] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0071] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash memory (ilash RAM). Memory is an example of computer-readable media.

[0072] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0073] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0074] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A risk management method applicable to the control system of key equipment in a cigarette factory, characterized in that, The control methods include: Obtain a set of risk patterns; The scores of each indicator in the risk pattern set are analyzed based on the improved fuzzy hierarchical analysis method to obtain the subjective weight vector. The scores of each indicator in the risk pattern set are normalized and defuzzified to obtain an objective weight vector. A composite weight vector is obtained based on the subjective weight vector and the objective weight vector. The relative proximity of each risk pattern is obtained based on the composite weight vector and sorted to obtain the risk ranking result; Based on the risk ranking results, develop preventative maintenance strategies or management measures.

2. The control method according to claim 1, characterized in that, The scores of each indicator in the risk pattern set are analyzed based on the improved fuzzy hierarchical analysis method to obtain a subjective weight vector, including: The organization assigned experts to score each indicator in each criterion layer of the risk pattern set; Based on the score of each indicator in each criterion layer, obtain the hierarchical single-sorting weight vector of the corresponding criterion layer; The subjective weight vector is obtained based on the hierarchical single-order weight vector of each criterion layer.

3. The control method according to claim 2, characterized in that, Based on the score of each indicator in each criterion layer, obtain the hierarchical single-ranking weight vector for the corresponding criterion layer, including: The fuzzy consistency judgment matrix is ​​obtained according to formulas (1) to (4): ，（1） ，（2） ，（3） ，（4） in, The priority judgment matrix of the current criterion layer is the first... The sum of the elements in the row. The priority judgment matrix of the current criterion layer is the first... The sum of the column elements, To satisfy the mathematical consistency condition, the first Compared to the first evaluation indicator, the second evaluation indicator is... The importance of each evaluation indicator This is a fuzzy consistency judgment matrix; Obtain the reciprocal matrix according to formulas (5) to (6): ，（5） ，（6） in, For the first Compared to the first evaluation indicator, the second evaluation indicator is... The ultimate importance of each evaluation indicator It is a reciprocal matrix.

4. The control method according to claim 2, characterized in that, Based on the score of each indicator in each criterion layer, obtain the hierarchical single-ranking weight vector for the corresponding criterion layer, including: According to formulas (7) to (9), obtain the hierarchical single ranking weight of each evaluation indicator under the corresponding criterion layer: ，（7） ，（8） ，（9） in, It is the product of the matrix rows corresponding to the i-th evaluation index under the corresponding criterion layer. This represents the geometric mean of the i-th evaluation index under the corresponding criterion layer. This refers to the hierarchical single-ranking weights of each evaluation indicator under the corresponding criteria layer.

5. The control method according to claim 2, characterized in that, Based on the hierarchical single-order weight vector of each criterion layer, obtain the subjective weight vector, including: Obtain the subjective weights according to formula (10): ，（10） in, For the first criterion in L2 The subjective weight of each indicator relative to the overall goal For the first criterion in L2 The index is relative to its L1 layer. Hierarchical single-ranking weights of each evaluation indicator For the first criterion in L1 The single ranking weight of each evaluation indicator relative to the overall goal.

6. The control method according to claim 1, characterized in that, The scores for each indicator in the risk pattern set are normalized and defuzzified to obtain an objective weight vector, including: The organization's experts scored the aforementioned risk rating indicators and obtained the comprehensive fuzzy evaluation value according to formulas (11) to (12): ，（11） ，（12） in, For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation index. For the first The risk pattern in the first The most likely value of the comprehensive fuzzy evaluation value given for each evaluation indicator. For the first The risk pattern in the first The lower bound of the comprehensive fuzzy evaluation value given for each evaluation index. For the first The expert commented on the first The risk pattern in the first The lower bound of the triangular fuzzy number evaluation given for each evaluation index. For the first The expert commented on the first The risk pattern in the first The most likely value of the triangular fuzzy number evaluation given for each evaluation index. For the first The expert commented on the first The risk pattern in the first The upper bound of the triangular fuzzy number evaluation given for each evaluation index. To comprehensively evaluate the fuzzy evaluation value, The weight of the k-th expert; Obtain the normalized fuzzy decision matrix according to formulas (13) to (15): ，（13） ，（14） ，（15） in, For the first A benchmark value calculated from each evaluation indicator. To standardize the fuzzy decision matrix, For elements in the fuzzy decision matrix The corresponding value after normalization; Obtain the standardized decision matrix according to formulas (16) and (17): ，（16） ，（17） in, This is the precise value of the triangular fuzzy number. For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For standardized decision matrices; The objective weight of each evaluation indicator is obtained according to formulas (18) to (19): ，（18） ，（19） in, For the first The entropy value of each evaluation indicator The coefficient is a constant for entropy. For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For the first Objective weights of each evaluation indicator.

7. The control method according to claim 1, characterized in that, Obtaining a composite weight vector based on the subjective weight vector and the objective weight vector includes: Obtain the composite weights according to formula (20): ，（20） in, For the first A composite weight, This is the dynamic preference coefficient. For the first A subjective weight, For the first An objective weight, Let Kronecker function be used.

8. The control method according to claim 1, characterized in that, The relative proximity of each risk pattern is obtained based on the composite weight vector and then sorted to obtain the risk ranking result, including: The relative similarity of each risk model is obtained according to formulas (21) to (23): ，（21） ，（22） ,（23） in, For the first The distance from a risk model to the ideal solution. For the first The distance from a risk model to a negative ideal solution. For the first A composite weight, For the first The risk pattern in the first Standardized evaluation values ​​for each evaluation indicator For the first The positive ideal solution score of each evaluation index For the first The negative ideal solution score of each evaluation indicator For the first The relative progress of each risk model.

9. A risk management system applicable to the control system of key equipment in a cigarette factory, characterized in that, The control system includes: The risk identification module is used to identify failure modes and form a risk mode set; The indicator evaluation system construction module is used to build a risk management hierarchy model; The indicator weight determination module is used to calculate indicator weights; The risk ranking module is used to rank risks. The risk response strategy generation module is used to form a long-term risk management mechanism; A processor is used to connect a risk identification module, an indicator evaluation system construction module, an indicator weight determination module, a risk ranking module, and a risk response strategy generation module, wherein the processor is configured to execute the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 8.

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