CN-121998413-A - Risk management and control method, system and storage medium for cigarette factory equipment control system

CN121998413ACN 121998413 ACN121998413 ACN 121998413ACN-121998413-A

Abstract

The invention relates to the field of factory equipment management, in particular to a risk management and control method, a system and a storage medium suitable for a cigarette factory equipment control system, which comprise the steps of obtaining a risk mode set; the method comprises the steps of analyzing scores of each index in a risk mode set based on an improved fuzzy analytic hierarchy process to obtain subjective weight vectors, normalizing and defuzzifying the scores of each index in the risk mode set to obtain objective weight vectors, obtaining composite weight vectors according to the subjective weight vectors and the objective weight vectors, obtaining relative closeness of each risk mode according to the composite weight vectors and sequencing to obtain risk sequencing results, and formulating preventive maintenance strategies or management measures according to the risk sequencing results. According to the invention, through the combination of the Delfei consensus, the full-flow fuzzy and the dynamic combination weighting method, the accurate grading of equipment risks is realized, and the authority, the robustness and the management and control practicability of the evaluation result are obviously improved in a complex industrial environment.

Inventors

PAN LUWEN
SONG LEI
YU ZHIPENG
WANG XIAOHUAN
BAO ANYIN
LIN YIZHEN
SHAO JIANMING
CHEN HAITAO
SUN SHUNKAI
LU YUJIE
LIN SEN
YIN YIXIAN
HU CHENGLAI

Assignees

浙江中烟工业有限责任公司

Dates

Publication Date: 20260508
Application Date: 20260107

Claims (10)

1. The utility model provides a be applicable to cigarette mill key equipment control system risk management and control method which characterized in that, the management and control method includes: acquiring a risk mode set; Analyzing the scores of each index in the risk pattern set based on an improved fuzzy analytic hierarchy process to obtain a subjective weight vector; normalizing and defuzzifying the scores of each index in the risk mode set to obtain an objective weight vector; Acquiring a composite weight vector according to the subjective weight vector and the objective weight vector; acquiring the relative closeness of each risk mode according to the composite weight vector and sequencing the relative closeness to acquire a risk sequencing result; And formulating preventive maintenance strategies or management measures according to the risk ranking result.
2. The method of claim 1, wherein analyzing the score of each index in the set of risk patterns based on improved fuzzy analytic hierarchy process to obtain a subjective weight vector comprises: The organization expert scores each index in each criterion layer in the risk mode set; Obtaining a hierarchical single-order weight vector of a corresponding criterion layer according to the score of each index in each criterion layer; and obtaining the subjective weight vector according to the hierarchical single-order weight vector of each criterion layer.
3. The method according to claim 2, wherein obtaining the hierarchical single-ranking weight vector of the corresponding criterion layer according to the score of each index in each criterion layer comprises: obtaining a fuzzy consistency judgment matrix according to the formulas (1) to (4): ,(1) ,(2) ,(3) ,(4) Wherein, the Priority judgment matrix for current criterion layer The sum of the row elements, Priority judgment matrix for current criterion layer The sum of the column elements, To meet the condition of mathematical consistency The evaluation index is compared with the first The importance level of each evaluation index is determined, Is a fuzzy consistency judgment matrix; obtaining a reciprocal matrix according to the formulas (5) to (6): ,(5) ,(6) Wherein, the Is the first The evaluation index is compared with the first The final importance level of each evaluation index, Is a reciprocal matrix.
4. The method according to claim 2, wherein obtaining the hierarchical single-ranking weight vector of the corresponding criterion layer according to the score of each index in each criterion layer comprises: obtaining the hierarchical single sequencing weight of each evaluation index under the corresponding criterion layer according to the formulas (7) to (9): ,(7) ,(8) ,(9) Wherein, the For the product of the matrix row corresponding to the ith evaluation index under the corresponding criterion layer, For the geometric mean of the ith evaluation index under the corresponding criterion layer, And (5) the hierarchical single sequencing weight of each evaluation index under the corresponding criterion layer.
5. The method of claim 2, wherein obtaining the subjective weight vector based on the hierarchical single-ranking weight vector for each criterion layer comprises: Obtaining subjective weights according to formula (10): ,(10) Wherein, the Is the first in criterion layer L 2 Subjective weight of the individual indicators relative to the total target, Is the first in criterion layer L 2 The index is relative to the L 1 layer to which it belongs The hierarchical single ordering weight of the individual evaluation indicators, Is the first in criterion layer L 1 Single ranking weights of individual evaluation indicators relative to total target.
6. The method of claim 1, wherein normalizing and defuzzifying the scores of each indicator in the set of risk patterns to obtain an objective weight vector comprises: The organization expert scores the risk rating evaluation index and obtains a comprehensive fuzzy evaluation value according to the formulas (11) to (12): ,(11) ,(12) Wherein, the Is the first The risk pattern is at the first The lower bound of the comprehensive fuzzy evaluation value given on the evaluation index, Is the first The risk pattern is at the first The most probable value of the comprehensive fuzzy evaluation value given on the individual evaluation indexes, Is the first The risk pattern is at the first The lower bound of the comprehensive fuzzy evaluation value given on the evaluation index, Is the first Expert pair of bits The risk pattern is at the first The lower bound of the triangular fuzzy number evaluation given on the evaluation index, Is the first Expert pair of bits The risk pattern is at the first The most probable value of the triangular fuzzy number evaluation given on the evaluation index, Is the first Expert pair of bits The risk pattern is at the first The upper bound of the triangular fuzzy number evaluation given on the evaluation index, In order to synthesize the fuzzy evaluation value, The expert weight is the kth expert weight; obtaining a normalized fuzzy decision matrix according to formulas (13) to (15): ,(13) ,(14) ,(15) Wherein, the Is the first A reference value calculated by the evaluation index, In order to normalize the fuzzy decision matrix, For elements in fuzzy decision matrices Normalized corresponding values; obtaining a standardized decision matrix according to formulas (16) to (17): ,(16) ,(17) Wherein, the Is an accurate value of the triangular blur number, Is the first The risk pattern is at the first A standardized evaluation value on each evaluation index, Is a standardized decision matrix; Obtaining objective weights of each evaluation index according to formulas (18) to (19): ,(18) ,(19) Wherein, the Is the first The entropy value of the individual evaluation index, For the entropy value to be a constant coefficient, Is the first The risk pattern is at the first A standardized evaluation value on each evaluation index, Is the first Objective weight of each evaluation index.
7. The method of claim 1, wherein obtaining a composite weight vector from the subjective weight vector and the objective weight vector comprises: Obtaining composite weights according to formula (20): ,(20) Wherein, the Is the first The number of composite weights is set to be equal, In order for the coefficient of dynamic preference to be a factor, Is the first The number of subjective weights is set to be, Is the first The number of objective weights to be used in the system, Is a kronecker function.
8. The method of claim 1, wherein obtaining the relative closeness of each risk pattern and ordering according to the composite weight vector to obtain a risk ordering result comprises: Acquiring the relative closeness of each risk mode according to the formulas (21) to (23): ,(21) ,(22) ,(23) Wherein, the Is the first The distance of the individual risk patterns to the ideal solution, Is the first The distance of the individual risk patterns to the negative ideal solution, Is the first The number of composite weights is set to be equal, Is the first The risk pattern is at the first A standardized evaluation value on each evaluation index, Is the first Positive ideal solution scores for the individual evaluation indicators, Is the first Negative ideal solution values for the individual evaluation indicators, Is the first Relative paste progress of individual risk patterns.
9. The utility model provides a be applicable to cigarette mill key equipment control system risk management and control system which characterized in that, management and control system includes: the risk identification module is used for identifying fault modes and forming a risk mode set; the index evaluation system construction module is used for constructing a risk management and control hierarchical model; the index weight determining module is used for calculating index weights; The risk ranking module is used for ranking risks; The risk coping strategy generation module is used for forming a long-acting risk management and control mechanism; A processor for interfacing with a risk identification module, an index rating system construction module, an index weight determination module, a risk ranking module, and a risk coping strategy generation module, the processor being configured to perform the method of any of claims 1 to 8.
10. A computer readable storage medium having instructions stored thereon which, when executed by a processor, implement the method of any of claims 1 to 8.

Description

Risk management and control method, system and storage medium for cigarette factory equipment control system Technical Field The invention relates to the field of factory equipment management, in particular to a risk management and control method, a risk management and control system and a storage medium suitable for a key equipment control system of a cigarette factory. Background In the cigarette manufacturing industry, the reliability of key equipment control systems such as thread making, a cigarette making machine, a packaging machine and the like directly determines the production efficiency and the product quality. To achieve advanced management of risk, methodologies based on failure mode and impact analysis (FMEA) are commonly employed in the industry. However, conventional FMEA relies on Risk Priority (RPN) ordering, which has significant limitations in dealing with ambiguity of expert subjective judgment and risk factor weights, such as the same RPN value but different risk connotations, three factors weights being considered equal and not logical, etc. To overcome these limitations, related researchers have proposed improvements that combine fuzzy theory with multi-criteria decision methods. Patent document CN112257933B, "an equipment health evaluation method based on improved delphies and fuzzy comprehensive judgment", discloses that multiple rounds of anonymous delphies are adopted to collect expert opinions and the fuzzy comprehensive judgment is used for processing. This technique underscores the importance of achieving expert consensus in complex system assessment. Patent document CN110210677a entitled "a fuzzy AHP and TOPSIS based industrial equipment risk assessment method". The document discloses a technical scheme that firstly, potential failure modes of equipment are identified through FMEA, secondly, weights of risk factors (such as severity, occurrence degree and detection degree) are calculated through a fuzzy analytic hierarchy process (IFAHP), and finally, risk ranking is carried out on the identified failure modes through a near ideal solution ranking process (TOPSIS). Although this prior art is an improvement over the conventional FMEA, it still suffers from the following significant drawbacks when actually applied in the specific context of a cigarette factory critical equipment control system: The expert opinion integration mechanism is imperfect, and a structured expert group decision mechanism is not embedded when IFAHP is applied. Although patent CN112257933B points out the importance of the delphire method, it does not fuse this method to the IFAHP-TOPSIS framework. The control system of the cigarette factory equipment is a complex system integrating mechanical, electrical, soft and control, and risk assessment needs to integrate the intelligence of multidisciplinary specialists. The lack of iterative, anonymous, feedback consensus mechanisms such as delta-film results in the initial decision matrix of IFAHP being built on top of the scattered, unconverged expert opinion, affecting the underlying authority and stability of the weight calculation. The ideal solution approximation in the fuzzy environment cannot be processed, and in the TOPSIS sequencing stage, accurate numerical calculation is used in the scheme. However, the weights generated at stage IFAHP and the expert's score for failure mode are themselves fuzzy numbers. The scheme deblurs these ambiguities before entering the TOPSIS flow, which results in the loss of the original ambiguous information. In the scene of highly dependent experience judgment of a cigarette factory, the method of 'firstly accurate and then sequencing' fails to penetrate the fuzzy uncertainty in the whole decision making process, so that the final sequencing result fails to completely reflect the fuzzy nature of the risk state, and the robustness of risk assessment and the inclusion of fuzzy information are reduced. Aiming at the defects of the prior art in the aspects of expert consensus integration and fuzzy information processing continuity, a risk assessment scheme integrating multi-source expert consensus and full-flow fuzzy decision is needed to be constructed so as to improve the identification force of risk connotation differences, the adaptability of complex industrial field environments and the tolerance capability of fuzzy uncertainty, and provide reliable technical support for realizing advanced and accurate management and control of the risks of key equipment control systems of cigarette factories. Disclosure of Invention The embodiment of the invention aims to provide a risk management and control method, a risk management and control system and a storage medium suitable for a key equipment control system of a cigarette factory, so as to solve the technical problems of insufficient risk assessment authority, poor result stability and weak decision robustness caused by lack of a systematic expert consensus mechanism and incomp