A stowing chamber operation environment multi-factor evaluation method
By constructing a hierarchical evaluation index system and a multi-factor assessment model, the problem of coordinated assessment of noise, dust and vibration in the filling chamber working environment was solved. This enabled multi-factor comprehensive assessment and risk classification of the downhole working environment, improving the scientificity and accuracy of the assessment and supporting health risk management in downhole operations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA UNIV OF MINING & TECH
- Filing Date
- 2026-05-21
- Publication Date
- 2026-07-14
Smart Images
Figure CN122390476A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of mine safety and occupational health assessment, and particularly relates to a multi-factor assessment method for the working environment of filling chambers. Background Technology
[0002] With the advancement of green and intelligent construction in mines, backfilling mining technology has been widely applied in underground mines. As a key component of backfilling operations, backfilling chambers typically house large equipment such as crushers, belt conveyors, mixers, and backfilling pumps. These devices operate simultaneously in a confined underground space, generating various occupational health hazards, including noise, dust, and mechanical vibration. Currently, evaluation methods for the working environment of backfilling chambers mostly focus on single factors, such as noise compliance assessments, dust concentration limit comparisons, or equipment vibration monitoring. Related safety regulations also primarily set limits for each factor separately.
[0003] However, in the typical scenario of multi-equipment coupled operations in filling chambers, noise, dust, and vibration often coexist and superimpose, making it difficult for independent evaluation methods of single factors to reflect the actual health risk level under the synergistic effect of multiple factors. Existing technologies lack a systematic, multi-factor comprehensive assessment model for the specific working conditions of filling chambers, failing to effectively integrate monitoring indicators of different dimensions and struggling to balance the weighting of expert experience with field data characteristics. This leads to discrepancies between the assessment results and the actual risks on site, making it difficult to meet the practical needs of comprehensive judgment and graded early warning of environmental health risks in complex underground environments. Summary of the Invention
[0004] To address the aforementioned technical problems, this invention provides a multi-factor evaluation method for the working environment of a filling chamber, comprising: Based on the operation process and equipment characteristics of the filling chamber, noise factors, dust factors and vibration factors are identified, and a hierarchical evaluation index system including target layer, evaluation layer and index layer is constructed to obtain a multi-factor evaluation system. Based on the real-time data collected by the monitoring equipment deployed in the filling chamber, a multi-factor original monitoring matrix is obtained; Based on the original multi-factor monitoring matrix, monitoring indicators with different dimensions and ranges of variation are subjected to positive transformation and dimensionless transformation to obtain a dimensionless standardized matrix. Based on the dimensionless standardized matrix, subjective weights are obtained using the analytic hierarchy process (AHP), objective weights are obtained using the entropy weight method, and the subjective and objective weights are then fused to obtain a comprehensive weight. Based on the dimensionless standardized matrix and the comprehensive weights, the TOPSIS model is used to calculate the comprehensive score of each evaluation object and obtain the multi-factor comprehensive evaluation value. Based on the score range of the multi-factor comprehensive evaluation value, a grading system is constructed to obtain the grading assessment results of the health risks of the filling chamber working environment.
[0005] Preferably, the process of obtaining the multi-factor evaluation system includes: The overall health risk level of the working environment in the filling chamber is used as the target layer, noise, dust and vibration factors are used as the evaluation layer, equivalent continuous sound level, maximum sound level and noise exposure time are used as the indicator layer for noise monitoring, respirable dust mass concentration, total dust mass concentration and dust exposure time are used as the indicator layer for dust monitoring, and daily 8-hour frequency-weighted vibration exposure, triaxial root mean square vibration acceleration and vibration action time are used as the indicator layer for vibration monitoring.
[0006] Preferably, the process of obtaining the multi-factor original monitoring matrix includes: The evaluation object is used as the row vector of the matrix, and the index of each monitoring factor is used as the column vector of the matrix to construct the original monitoring matrix. Among them, the noise monitoring adopts the integrating sound level meter, the dust monitoring adopts the continuous dust monitor or dust sampler, and the vibration monitoring adopts the triaxial accelerometer.
[0007] Preferably, the process of obtaining the dimensionless normalized matrix includes: For contrarian indicators where larger values indicate higher risks, the reciprocal is used for positive transformation to obtain a positive matrix. The normalized matrix is then subjected to dimensionless processing using the geometric mean method to obtain a dimensionless normalized matrix.
[0008] Preferably, the process of obtaining the subjective weight includes: A judgment matrix is constructed based on the relative importance of noise, dust, and vibration factors; The judgment matrix is calculated using the arithmetic mean method to obtain the subjective weight vector, and the judgment matrix is then subjected to a consistency check.
[0009] Preferably, the process of obtaining the objective weights includes: Based on the dimensionless standardized matrix, the weight of each indicator under each evaluation object is calculated; Calculate the information entropy of each indicator based on the stated proportion; Calculate the entropy redundancy of each indicator based on the information entropy; The objective weight of each indicator is calculated based on the entropy redundancy to obtain the objective weight vector.
[0010] Preferably, the process of obtaining the comprehensive weight includes: A linear weighting method is adopted, and the subjective weight and the objective weight are integrated by adjusting the subjective and objective weights to obtain a comprehensive weight.
[0011] Preferably, the process of obtaining the multi-factor comprehensive evaluation value includes: Based on the dimensionless standardized matrix and the comprehensive weights, a weighted standardized matrix is constructed; Determine the positive and negative ideal solutions of the weighted normalization matrix; Calculate the Euclidean distance from each evaluation object to the positive ideal solution and the negative ideal solution; The proximity coefficient of each evaluation object is calculated based on the Euclidean distance, and the proximity coefficient is used as the multi-factor comprehensive evaluation value.
[0012] Preferably, the process of obtaining the grading assessment results includes: Calculate the average of the multi-factor comprehensive evaluation values for all evaluation objects; When the average value is greater than or equal to the first threshold, it is determined to be at the safe level; when the average value is less than the first threshold but greater than or equal to the second threshold, it is determined to be at the attention level; when the average value is less than the second threshold but greater than or equal to the third threshold, it is determined to be at the warning level; and when the average value is less than the third threshold, it is determined to be at the high-risk level.
[0013] Compared with the prior art, the present invention has the following advantages and technical effects: This invention effectively solves the problem that existing single-factor evaluation methods cannot reflect the superimposed effects of multiple factors by constructing a multi-factor collaborative assessment system for noise, dust, and vibration under specific working conditions of filling chambers, significantly improving the comprehensiveness and accuracy of risk identification. Simultaneously, by integrating the subjective weights of the analytic hierarchy process (AHP) and the objective weights of the entropy weight method, it avoids the bias of a single weighting method, ensuring that the comprehensive weights incorporate both expert experience and the variability of field data, thus enhancing the scientific rigor and robustness of the assessment results. Furthermore, this invention establishes a standardized data processing mechanism and quantitative risk classification results based on the TOPSIS model, enabling the classification of health risks in the working environment of filling chambers into four levels: safe, concern, alert, and high risk. This provides a clear and operable decision-making basis for on-site risk control, equipment optimization, and worker health protection. Attached Figure Description
[0014] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention; Figure 2 This is a diagram of the hierarchical evaluation index system according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating the specific steps of an embodiment of the present invention. Detailed Implementation
[0015] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0016] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0017] like Figure 1 As shown, this embodiment provides a multi-factor evaluation method for the working environment of a filling chamber, including: Based on the operation process and equipment characteristics of the filling chamber, noise factors, dust factors and vibration factors are identified, and a hierarchical evaluation index system including target layer, evaluation layer and index layer is constructed to obtain a multi-factor evaluation system. Based on the real-time data collected by the monitoring equipment deployed in the filling chamber, a multi-factor original monitoring matrix is obtained; Based on the original multi-factor monitoring matrix, monitoring indicators with different dimensions and ranges of variation are subjected to positive transformation and dimensionless transformation to obtain a dimensionless standardized matrix. Based on the dimensionless standardized matrix, subjective weights are obtained using the analytic hierarchy process (AHP), objective weights are obtained using the entropy weight method, and the subjective and objective weights are then integrated to obtain the comprehensive weight. Based on the dimensionless standardized matrix and comprehensive weights, the TOPSIS model is used to calculate the comprehensive score of each evaluation object and obtain the multi-factor comprehensive evaluation value. Based on the score range of the multi-factor comprehensive evaluation value, a graded system is constructed to obtain the graded assessment results of the health risks of the filling chamber working environment.
[0018] Furthermore, the process of obtaining a multi-factor evaluation system includes: The overall health risk level of the working environment in the filling chamber is used as the target layer, noise, dust and vibration factors are used as the evaluation layer, equivalent continuous sound level, maximum sound level and noise exposure time are used as the indicator layer for noise monitoring, respirable dust mass concentration, total dust mass concentration and dust exposure time are used as the indicator layer for dust monitoring, and daily 8-hour frequency-weighted vibration exposure, triaxial root mean square vibration acceleration and vibration action time are used as the indicator layer for vibration monitoring.
[0019] Furthermore, Part Five of the "Coal Mine Safety Regulations," on the prevention and control of occupational hazards, points out that the main physical factors threatening human health in coal mine workplaces include dust, heat, noise, and harmful gases. In specific locations within filling chambers, the intense vibrations generated by numerous mechanical equipment, such as crushers and mixers, can also damage the health of workers. Among these influencing factors, heat and harmful gases are usually within safe limits under normal ventilation conditions, while noise, dust, and vibration are the factors that dynamically change drastically with equipment start-up and shutdown and material flow, and pose the highest risk of exceeding standards. Based on the working process and equipment characteristics of filling chambers, noise factor sets, dust factor sets, and vibration factor sets are identified, resulting in a hierarchical evaluation index system consisting of a target layer, an evaluation layer, and an indicator layer. It should be noted beforehand that this embodiment prioritizes monitoring noise, dust, and vibration in the filling chamber. Based on the aforementioned relevant national standards or industry specifications, these three factors are the main physical factors leading to typical occupational diseases in mines, such as silicosis, noise-induced hearing loss, and hand-arm vibration syndrome. In the filling chamber, the variation range of other environmental factors (such as temperature and humidity) is generally within a safe range, while noise, dust, and vibration are factors that dynamically change drastically with equipment start-up and shutdown and material flow, and have the highest risk of exceeding standards. Therefore, the selection of indicators in this embodiment is not arbitrary.
[0020] Specifically, such as Figure 2 As shown, the hierarchical evaluation index system includes: The target layer is the overall level of health risks in the working environment of the filling chamber; The evaluation layer includes noise, dust, and vibration factors; In the indicator layer, noise monitoring indicators include the equivalent continuous sound level L. Aeq Maximum sound level L max and noise exposure time T v Indicators such as respirable dust concentration (C) are included in dust monitoring. R Total dust concentration C T and dust exposure time T d Vibration monitoring indicators include daily 8-hour frequency-weighted vibration exposure A(8) and triaxial root mean square vibration acceleration a. r.m.s and vibration duration T g Indicators such as...
[0021] Furthermore, the process of obtaining the multi-factor raw monitoring matrix includes: The evaluation object is used as the row vector of the matrix, and the index of each monitoring factor is used as the column vector of the matrix to construct the original monitoring matrix. Among them, the noise monitoring adopts the integrating sound level meter, the dust monitoring adopts the continuous dust monitor or dust sampler, and the vibration monitoring adopts the triaxial accelerometer.
[0022] Furthermore, this embodiment uses monitoring equipment deployed in the crusher, belt conveyor, mixer, filling pump and personnel operation area to collect real-time data on noise, dust and vibration, and obtain a multi-factor original monitoring matrix; Specifically, the multi-factor original monitoring matrix is denoted as: Where m is the number of evaluation objects and n is the number of indicators, both of which are 3; This represents the original monitoring value of the i-th evaluation object under the j-th index; noise monitoring uses a standard-compliant integrating sound level meter with a sampling interval of 1–60 s; dust monitoring uses a continuous dust monitor or dust sampler obtained through mass conversion; vibration monitoring uses a triaxial accelerometer with a sampling frequency selected in the range of 10–1000 Hz based on the equipment rotation speed.
[0023] Furthermore, the process of obtaining the dimensionless normalized matrix includes: For contrarian indicators where larger values indicate higher risks, the reciprocal is used for positive transformation to obtain a positive matrix. The normalized matrix is then subjected to dimensionless processing using the geometric mean method to obtain a dimensionless standardized matrix.
[0024] Furthermore, in this embodiment, based on monitoring indicators with different dimensions and different ranges of variation, a minimization method is used for positive processing, and then a dimensionless standardized matrix is obtained based on the geometric mean method. Specifically, for contrarian indicators that fall under the category of "the larger the value, the higher the risk," a method of minimizing the reciprocal and converting them to positive values is adopted. The calculation formula is as follows: Matrix after forward transformation The standardized matrix is then obtained using the geometric mean method.
[0025] The geometric mean method uses the following formula: in, is a dimensionless standardized matrix element, where m is the number of evaluation objects, used to eliminate dimensional differences between different indicators and maintain relative differences.
[0026] Furthermore, the process of obtaining subjective weights includes: A judgment matrix is constructed based on the relative importance of noise, dust, and vibration factors; The judgment matrix is calculated using the arithmetic mean method to obtain the subjective weight vector, and the consistency of the judgment matrix is then checked.
[0027] Furthermore, this embodiment constructs a decision matrix based on the Analytic Hierarchy Process (AHP). ,satisfy: in, This represents the weight of the k-th evaluation indicator relative to other indicators in the same layer. This represents the weight value of the l-th evaluation indicator.
[0028] The subjective weight vector is obtained using the arithmetic mean method. Then, find the largest eigenvalue of the judgment matrix A. And perform a consistency check on the judgment matrix and calculate the consistency index: Among them, CI and RI are consistency test indicators. RI changes with the order n of the judgment matrix, which can be found in Table 1.
[0029] Table 1 Furthermore, the process of obtaining objective weights includes: Based on the dimensionless standardized matrix, the weight of each indicator under each evaluation object is calculated; Calculate the information entropy of each indicator based on its proportion; Calculate the entropy redundancy of each indicator based on information entropy; The objective weight of each indicator is calculated based on the entropy redundancy, thus obtaining the objective weight vector.
[0030] Furthermore, the process of obtaining objective weights using the Entropy Weight Method (EWM) in this embodiment includes: The weight of the j-th indicator under the i-th evaluation object is calculated based on the standardized matrix: Calculate the information entropy of the j-th indicator: Calculate the entropy redundancy of the j-th index: Calculate objective weights: Obtain the objective weight vector .
[0031] Furthermore, the process of obtaining the overall weight includes: A linear weighting method is adopted, and subjective and objective weights are integrated by adjusting the subjective and objective weights with subjective and objective weight adjustment coefficients to obtain a comprehensive weight.
[0032] Furthermore, this embodiment constructs a judgment matrix based on the Analytic Hierarchy Process (AHP). Since the crusher is the absolute main source of dust and noise, and the harm of dust to the human body is cumulative and irreversible, dust is given a higher priority in terms of weight, followed by noise and vibration. Subjective weights are calculated through arithmetic mean method, consistency test, etc., while objective weights are obtained by combining entropy weight method (EWM). Finally, the comprehensive weight is obtained based on the linear weighted weight fusion model. Specifically, the weighted fusion model uses a linear weighting method, and the formula for calculating the comprehensive weight is as follows: in, Let j be the final comprehensive weight of the j-th indicator. Subjective weighting, λ represents the objective weight, and λ is the adjustment coefficient for the objective and subjective weights, with a value range of 0 to 1, preferably 0.4 to 0.6.
[0033] Furthermore, the process of obtaining the multi-factor comprehensive evaluation value includes: Based on the dimensionless standardized matrix and the comprehensive weights, a weighted standardized matrix is constructed; Determine the positive and negative ideal solutions of the weighted normalization matrix; Calculate the Euclidean distance from each evaluation object to the positive and negative ideal solutions; The proximity coefficient of each evaluation object is calculated based on Euclidean distance, and the proximity coefficient is used as the multi-factor comprehensive evaluation value.
[0034] Furthermore, this embodiment uses a multi-attribute decision-making method (TOPSIS model) based on a standardized matrix and comprehensive weights to calculate the comprehensive score of each evaluation object, thereby obtaining a multi-factor comprehensive evaluation value of the filling chamber working environment; Specifically, the comprehensive score for each evaluation object calculated based on the TOPSIS model includes: Construct a weighted normalization matrix: Determine the positive and negative ideal solutions: Calculate the Euclidean distance from the i-th evaluation object to the positive and negative ideal solutions: Calculate the proximity coefficient: in As the multi-factor comprehensive evaluation value of the i-th evaluation object, the larger the value, the lower the risk of the working environment.
[0035] Furthermore, the process of obtaining the tiered assessment results includes: Calculate the average of the multi-factor comprehensive evaluation values for all evaluation objects; When the average value is greater than or equal to the first threshold, it is classified as a safe level; when the average value is less than the first threshold but greater than or equal to the second threshold, it is classified as a concern level; when the average value is less than the second threshold but greater than or equal to the third threshold, it is classified as a warning level; and when the average value is less than the third threshold, it is classified as a high-risk level.
[0036] Furthermore, this embodiment constructs a four-level classification system (safety-concern-alert-high risk) based on multi-factor comprehensive evaluation values and comprehensive score ranges to achieve quantitative classification of health risks in the working environment of filling chambers.
[0037] Specifically, this embodiment is based on average The scope is constructed into a four-level hierarchical system, when A value ≥ 0.8 is considered safe; 0.6 ≤ <0.8 indicates a level of concern; 0.4≤ A value less than 0.6 is considered a warning level. A value <0.4 indicates a high-risk level. The threshold was determined by considering multiple technical factors, including national occupational health standards, equipment safety operation thresholds, and accident probability inflection points derived from historical data regression analysis. Typical equipment such as crushers, mixers, and filling pumps are classified as high-risk under different conditions. The operating conditions within the range are shown in Table 2.
[0038] Table 2 The classification system for the health risk level of the working environment in the filling chamber is shown in Table 3.
[0039] Table 3 This embodiment constructs a multi-factor evaluation system for specific working conditions in filling chambers, enabling coordinated assessment of noise, dust, and vibration. Compared to traditional single-factor evaluation methods, this invention can reflect the cumulative effects of multiple factors in the working environment, significantly improving the comprehensiveness and accuracy of risk identification.
[0040] This embodiment integrates subjective and objective weighting to enhance the scientific rigor and robustness of the evaluation results. By combining the AHP method with the EWM method, the bias of a single weighting method is effectively avoided, ensuring that the comprehensive weights incorporate both expert experience and the variability of environmental data.
[0041] This embodiment establishes a standardized data processing mechanism to improve the comparability of multi-source monitoring data. By using a unified positive transformation and standardization method to perform dimensionless processing on indicators with different dimensions, the evaluation results become more uniform and standardized.
[0042] This embodiment can generate quantitative risk classification results, which have good interpretability and applicability.
[0043] This embodiment features clear structure, convenient implementation, and wide applicability, and can be widely applied to environmental health risk assessment in various downhole operation scenarios.
[0044] As a preferred implementation method, such as Figure 3 As shown, taking a certain mine as an example, the specific implementation steps are as follows: S1. Based on common monitoring indicators of noise, dust, and vibration factors in the mine filling chamber environment, a hierarchical evaluation index system consisting of a target layer, an evaluation layer, and an index layer is constructed, such as... Figure 1 As shown.
[0045] S2. Based on the on-site survey, the noise monitoring data includes: equivalent continuous sound level L Aeq 83.5 dB(A), maximum sound level L max The noise exposure time T is 96.0 dB(A) and the noise exposure time T v The monitoring time was 3.0 h; the dust monitoring data included: respirable dust mass concentration C. R 2.4 mg / m 3 Total dust concentration C T 8.0 mg / m 3 The dust exposure time Td was 2.5 h; the vibration monitoring data included: the daily 8-hour frequency-weighted vibration exposure A(8) was 0.78 m / s. 2 The triaxial root mean square vibration acceleration a r.m.s The vibration speed is 0.62 m / s² and the vibration duration is T. g It is 2.0h. Considering the equivalent continuous sound level L Aeq and maximum sound level L max The values for L are very large, and the values for total dust mass concentration (CT) are also large. To make the calculation results more reasonable, L... Aeq and L max Take the common logarithm for C T Take the second root. Using the monitored objects as row vectors and the indicators of each monitored factor as column vectors, the original multi-factor monitoring matrix X is obtained as follows: S3. Based on the original monitoring matrix X, a forward transformation is performed using the reciprocal minimization method to calculate the matrix. For the matrix The dimensionless standardized matrix is obtained using the geometric mean method. .
[0046] S4. Based on the degree of impact of on-site noise, dust, and vibration on the health of workers, the order is: dust > noise > vibration. The judgment matrix A is constructed using the Analytic Hierarchy Process (AHP) as follows: Normalization of each column: Sum of each row: The subjective weight vector is obtained by further normalization. : calculate: Maximum eigenvalue : Consistency check: RI is the random consistency index. From Table 1, the RI for a third-order matrix is 0.58. Calculate... The consistency test is passed for matrix A.
[0047] Based on the standardized matrix obtained in step S2 The process of obtaining objective weights using the Entropy Weight Method (EWM) is as follows: (a) Calculate the weight of the j-th indicator under the i-th evaluation object. That is, normalizing matrix Z by columns yields matrix P: (b) Calculate the information entropy of the j-th index. (Formula (9)) yields: (c) Calculate the entropy redundancy of the j-th index. (Formula (10)) yields: (d) Calculate the objective weights (Formula (11)) yields: Subjective weights obtained based on the analytic hierarchy process (AHP) Objective weights obtained by the entropy weight method (EWM) The final comprehensive weight W of noise, dust, and vibration was obtained through calculation, with the subjective and objective weight adjustment coefficient λ set to 0.5. The results are as follows: S5. The steps for calculating the comprehensive score of each evaluation object based on the TOPSIS model include: (a) Constructing a weighted normalization matrix yields: Right now: (b) Determine the positive ideal solution and the negative ideal solution, and obtain: (c) Calculate the Euclidean distance from the i-th evaluation object to the positive and negative ideal solutions, and obtain: (d) Calculate the proximity coefficient (Formula (16)) to obtain: S6, based on the steps The numerical range is used to construct a four-level classification system for... Taking the average value yields: turn out This indicates that the health risk level of the working environment in the filling chamber of the mine is high.
[0048] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A multi-factor evaluation method for the working environment of a filling chamber, characterized in that, include: Based on the operation process and equipment characteristics of the filling chamber, noise factors, dust factors and vibration factors are identified, and a hierarchical evaluation index system including target layer, evaluation layer and index layer is constructed to obtain a multi-factor evaluation system. Based on the real-time data collected by the monitoring equipment deployed in the filling chamber, a multi-factor original monitoring matrix is obtained; Based on the original multi-factor monitoring matrix, monitoring indicators with different dimensions and ranges of variation are subjected to positive transformation and dimensionless transformation to obtain a dimensionless standardized matrix. Based on the dimensionless standardized matrix, subjective weights are obtained using the analytic hierarchy process (AHP), objective weights are obtained using the entropy weight method, and the subjective and objective weights are then fused to obtain a comprehensive weight. Based on the dimensionless standardized matrix and the comprehensive weights, the TOPSIS model is used to calculate the comprehensive score of each evaluation object and obtain the multi-factor comprehensive evaluation value. Based on the score range of the multi-factor comprehensive evaluation value, a grading system is constructed to obtain the grading assessment results of the health risks of the filling chamber working environment.
2. The method according to claim 1, characterized in that, The process of obtaining the multi-factor evaluation system includes: The overall health risk level of the working environment in the filling chamber is used as the target layer, noise, dust and vibration factors are used as the evaluation layer, equivalent continuous sound level, maximum sound level and noise exposure time are used as the indicator layer for noise monitoring, respirable dust mass concentration, total dust mass concentration and dust exposure time are used as the indicator layer for dust monitoring, and daily 8-hour frequency-weighted vibration exposure, triaxial root mean square vibration acceleration and vibration action time are used as the indicator layer for vibration monitoring.
3. The method according to claim 1, characterized in that, The process of obtaining the original multi-factor monitoring matrix includes: The evaluation object is used as the row vector of the matrix, and the index of each monitoring factor is used as the column vector of the matrix to construct the original monitoring matrix. Among them, the noise monitoring adopts the integrating sound level meter, the dust monitoring adopts the continuous dust monitor or dust sampler, and the vibration monitoring adopts the triaxial accelerometer.
4. The method according to claim 1, characterized in that, The process of obtaining the dimensionless normalized matrix includes: For contrarian indicators where larger values indicate higher risks, the reciprocal is used for positive transformation to obtain a positive matrix. The normalized matrix is then subjected to dimensionless processing using the geometric mean method to obtain a dimensionless normalized matrix.
5. The method according to claim 1, characterized in that, The process of obtaining the subjective weights includes: A judgment matrix is constructed based on the relative importance of noise, dust, and vibration factors; The judgment matrix is calculated using the arithmetic mean method to obtain the subjective weight vector, and the judgment matrix is then subjected to a consistency check.
6. The method according to claim 1, characterized in that, The process of obtaining the objective weights includes: Based on the dimensionless standardized matrix, the weight of each indicator under each evaluation object is calculated; Calculate the information entropy of each indicator based on the stated proportion; Calculate the entropy redundancy of each indicator based on the information entropy; The objective weight of each indicator is calculated based on the entropy redundancy to obtain the objective weight vector.
7. The method according to claim 1, characterized in that, The process of obtaining the comprehensive weights includes: A linear weighting method is adopted, and the subjective weight and the objective weight are integrated by adjusting the subjective and objective weights to obtain a comprehensive weight.
8. The method according to claim 1, characterized in that, The process of obtaining the multi-factor comprehensive evaluation value includes: Based on the dimensionless standardized matrix and the comprehensive weights, a weighted standardized matrix is constructed; Determine the positive and negative ideal solutions of the weighted normalization matrix; Calculate the Euclidean distance from each evaluation object to the positive ideal solution and the negative ideal solution; The proximity coefficient of each evaluation object is calculated based on the Euclidean distance, and the proximity coefficient is used as the multi-factor comprehensive evaluation value.
9. The method according to claim 1, characterized in that, The process of obtaining the grading assessment results includes: Calculate the average of the multi-factor comprehensive evaluation values for all evaluation objects; When the average value is greater than or equal to the first threshold, it is determined to be at the safe level; when the average value is less than the first threshold but greater than or equal to the second threshold, it is determined to be at the attention level; when the average value is less than the second threshold but greater than or equal to the third threshold, it is determined to be at the warning level; and when the average value is less than the third threshold, it is determined to be at the high-risk level.