Cigarette factory green workshop low-carbon benefit evaluation method and device

Abstract

The invention provides a cigarette factory green workshop low-carbon benefit evaluation method and device, and the method comprises the steps: obtaining a preset cigarette factory low-carbon benefit layering index system when a low-carbon benefit evaluation request for a plurality of to-be-evaluated cigarette factories is received; collecting actual operation data of each to-be-evaluated cigarette factory under each bottom-layer index; based on the actual operation data corresponding to each to-be-evaluated cigarette factory, performing weight adjustment on each quantitative underlying index to obtain an updated underlying index weight; based on the updated bottom-layer index weight, the corresponding actual operation data and the cigarette factory low-carbon benefit layering index system, the low-carbon benefit expression level of each upper-layer index is calculated upwards step by step, the low-carbon benefit comprehensive evaluation attribute corresponding to each to-be-evaluated cigarette factory is obtained, and the low-carbon benefit comprehensive evaluation attribute of each to-be-evaluated cigarette factory is obtained through the cigarette factory low-carbon benefit layering index system. And in combination with a data-driven weight dynamic optimization mode, the accuracy and applicability of low-carbon benefit evaluation of the green workshop of the cigarette factory are improved.

Description

Technical Field

[0001] This invention relates to the field of low-carbon analysis technology, and in particular to a method and apparatus for evaluating the low-carbon benefits of a green workshop in a cigarette factory. Background Technology

[0002] Against the backdrop of a global effort to address climate change, the tobacco industry, as a crucial component of the industrial sector, urgently needs to promote a green and low-carbon transformation. Cigarette factories, as a key link in tobacco manufacturing, are increasingly facing concerns regarding energy consumption, resource utilization, and environmental emissions during their production processes. The construction of green workshops, integrating green building materials, energy-saving technologies, and clean processes, has become an important path for cigarette factories to achieve low-carbon development. Furthermore, establishing a scientific and effective low-carbon benefit evaluation system is of significant practical importance for quantifying the effectiveness of green workshop construction and guiding the industry's green upgrading.

[0003] Currently, assessments of the green level of industrial facilities often employ static weighted index systems. These systems pre-determine the weights of each dimension based on expert experience and then calculate a comprehensive score using standardized data collection and weighted aggregation methods. This approach has been widely used in areas such as infrastructure green certification and energy management system evaluation. Its core technology relies on a fixed index structure and weight allocation, emphasizing the unified processing of qualitative standards and quantitative data.

[0004] However, the static weighting system is difficult to adapt to the differences in resource conditions, technological characteristics and management levels among different cigarette factories, resulting in a deviation between the assessment results and the actual low-carbon benefits. On the one hand, the preset weights cannot reflect the dynamic performance of each factory on specific quantitative indicators (such as energy intensity and resource recycling rate), which weakens the indicator differentiation and assessment pertinence. On the other hand, the coupling mechanism between qualitative and quantitative indicators is imperfect and lacks the ability to adaptively adjust weights based on actual operating data, which limits the application value of the assessment model in accurately identifying improvement space and supporting differentiated decision-making. Summary of the Invention

[0005] This application provides a method and apparatus for evaluating the low-carbon benefits of green workshops in cigarette factories. By using a hierarchical index system for the low-carbon benefits of cigarette factories and combining it with a data-driven dynamic optimization method for weights, the accuracy and applicability of the evaluation of the low-carbon benefits of green workshops in cigarette factories are improved.

[0006] In a first aspect, embodiments of this application provide a method for evaluating the low-carbon benefits of a green workshop in a cigarette factory, the method comprising:

[0007] When a request for low-carbon benefit evaluation of multiple cigarette factories to be evaluated is received, a preset low-carbon benefit hierarchical index system for cigarette factories is obtained; wherein, the low-carbon benefit hierarchical index system for cigarette factories includes multiple evaluation dimensions organized in a hierarchical structure, and each evaluation dimension and the underlying evaluation index under the evaluation dimension are given a corresponding initial preset weight, and the underlying evaluation index includes at least one qualitative underlying index and / or at least one quantitative underlying index.

[0008] Collect actual operating data of each of the cigarette factories to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system for cigarette factories;

[0009] Based on the actual operating data of each of the cigarette factories to be evaluated, the weights of each of the quantitative underlying indicators are adjusted to obtain the updated underlying indicator weights.

[0010] For each cigarette factory to be evaluated, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the tiered indicator system for low-carbon benefits of the cigarette factory, the low-carbon benefit performance level of each upper-level indicator is calculated step by step to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated.

[0011] Secondly, this application also provides a low-carbon benefit evaluation device for a green workshop in a cigarette factory, the device comprising:

[0012] The hierarchical indicator acquisition module is used to acquire a preset hierarchical indicator system for the low-carbon benefits of cigarette factories when receiving a request for low-carbon benefit evaluation for multiple cigarette factories to be evaluated; wherein, the hierarchical indicator system for the low-carbon benefits of cigarette factories includes multiple evaluation dimensions organized in a hierarchical structure, and each evaluation dimension and the underlying evaluation indicators under the evaluation dimension are provided with corresponding initial preset weights, and the underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator.

[0013] The data acquisition module is used to collect the actual operating data of each of the cigarette factories to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system of the cigarette factory;

[0014] The indicator weight update module is used to adjust the weights of each quantitative underlying indicator based on the actual operating data of each of the cigarette factories to be evaluated, so as to obtain the updated underlying indicator weights.

[0015] The low-carbon benefit assessment module is used to calculate the low-carbon benefit performance level of each upper-level indicator for each cigarette factory to be assessed, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the cigarette factory's low-carbon benefit hierarchical indicator system, and to obtain the comprehensive low-carbon benefit assessment attribute corresponding to each cigarette factory to be assessed.

[0016] Thirdly, embodiments of this application also provide an electronic device, which includes:

[0017] One or more processors;

[0018] Storage device for storing one or more programs.

[0019] When one or more programs are executed by one or more processors, the one or more processors implement the low-carbon benefit evaluation method for green workshops in cigarette factories as described in any of the embodiments of this application.

[0020] Fourthly, embodiments of this application also provide a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a low-carbon benefit evaluation method for a green workshop in a cigarette factory as described in any of the embodiments of this application.

[0021] This application provides a method and apparatus for evaluating the low-carbon benefits of a green workshop in a cigarette factory. The method includes: when receiving a request for low-carbon benefit evaluation of multiple cigarette factories to be evaluated, obtaining a preset hierarchical index system for low-carbon benefits of cigarette factories. The hierarchical index system includes multiple evaluation dimensions organized in a hierarchical structure. Each evaluation dimension and the underlying evaluation indicators under each evaluation dimension have corresponding initial preset weights. The underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator. Then, the actual operating data of each cigarette factory to be evaluated under each underlying indicator of the hierarchical index system for low-carbon benefits of cigarette factories is collected. Subsequently, based on the actual operating data corresponding to each cigarette factory to be evaluated, the weights of each quantitative underlying indicator are adjusted to obtain updated underlying indicator weights. Thus, for each cigarette factory to be evaluated, based on the updated underlying indicator weights, the corresponding actual operating data, and the hierarchical index system for low-carbon benefits of cigarette factories, the low-carbon benefit performance level of each upper-level indicator is calculated level by level to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated. The technical solution of this application constructs a hierarchical indicator system for the low-carbon benefits of cigarette factories, which includes qualitative and quantitative underlying indicators. It also introduces a dynamic adjustment mechanism for the weights of quantitative indicators based on actual operating data. This effectively overcomes the limitations of traditional static weight systems, which are difficult to adapt to the actual conditions of different factories. While retaining the guiding role of expert experience in qualitative indicators, it achieves objective optimization of the weights of quantitative indicators, enabling the evaluation results to accurately reflect the differences among cigarette factories in terms of basic conditions, technical equipment, and operation management, thereby improving the pertinence and practicality of the evaluation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in describing the embodiments are briefly introduced below. Obviously, the accompanying drawings described are only a portion of the embodiments to be described in this application, and not all of them. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort.

[0023] Figure 1 A flowchart illustrating a method for evaluating the low-carbon benefits of a green workshop in a cigarette factory, provided as an embodiment of this application;

[0024] Figure 2(a) is a schematic diagram of a portion of the carbon benefit stratification index system involved in the embodiments of this application;

[0025] Figure 2(b) is a schematic diagram of another part of the carbon benefit stratification index system involved in the embodiments of this application;

[0026] Figure 3 A flowchart illustrating another method for evaluating the low-carbon benefits of a green workshop in a cigarette factory, provided as an embodiment of this application;

[0027] Figure 4 A schematic diagram of a low-carbon benefit evaluation device for a green workshop in a cigarette factory, provided in an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0030] Before introducing the technical solution provided in this application, the application scenario of the solution can be explained first. This embodiment is applicable to any scenario that requires the evaluation and comparison of the low-carbon benefits of green workshops in cigarette factories. Currently, although the construction of green workshops has become an important measure for cigarette factories to achieve low-carbon development, traditional evaluation methods often use static weight systems, which are difficult to reflect the dynamic differences in actual operating data of different factories. In practical applications, due to significant differences in operation management, infrastructure, energy consumption, resource utilization, and production processes among cigarette factories, static weight allocation cannot accurately capture the actual importance of each quantitative indicator in the specific factory environment, which can easily lead to deviations between the evaluation results and the actual situation. Therefore, there is an urgent need for a method that can dynamically adjust the indicator weights according to actual operating data to improve the accuracy and pertinence of low-carbon benefit evaluation. This embodiment, by constructing a hierarchical indicator system and combining it with a dynamic weight adjustment mechanism for quantitative indicators, achieves a scientific quantitative evaluation of the low-carbon benefit level of each cigarette factory to be evaluated, thereby ensuring that the evaluation results are objective and reliable and effectively improving the scientific nature of the evaluation of the low-carbon development level of green workshops.

[0031] Example 1

[0032] Figure 1 This is a flowchart illustrating a method for evaluating the low-carbon benefits of a green workshop in a cigarette factory, as provided in this application embodiment. This embodiment is applicable to any situation where it is necessary to evaluate and compare the low-carbon benefits of a green workshop in a cigarette factory. This method can be executed by a device for evaluating the low-carbon benefits of a green workshop in a cigarette factory. This device can be implemented in the form of software and / or hardware. The hardware can be a controller, such as a mobile terminal, a PC, or a server.

[0033] like Figure 1 As shown, the method includes:

[0034] S110. When receiving a request for low-carbon benefit evaluation for multiple cigarette factories to be evaluated, obtain the preset low-carbon benefit stratification index system for cigarette factories.

[0035] The cigarette factories to be evaluated refer to specific cigarette production plant entities that require assessment and comparison of their low-carbon benefits as green workshops. These factories are the target objects of this evaluation activity. A low-carbon benefit evaluation request is a formal instruction or demand trigger signal aimed at initiating a green workshop low-carbon benefit evaluation process for a specific object (i.e., multiple cigarette factories to be evaluated).

[0036] The "Hierarchical Index System for Low-Carbon Benefits of Cigarette Factories" refers to a hierarchical evaluation framework pre-defined for systematically assessing the low-carbon benefits of green workshops in cigarette factories. This system comprises multiple evaluation dimensions organized hierarchically. Each evaluation dimension and its underlying evaluation indicators have corresponding initial preset weights. The underlying evaluation indicators include at least one qualitative and / or at least one quantitative underlying indicator. In essence, the system consists of multiple evaluation levels arranged from macro to micro and from top to bottom. Each evaluation dimension and its underlying evaluation indicators have corresponding initial preset weights, meaning that each level of evaluation element is assigned a benchmark value representing its relative importance within the system. The inclusion of at least one qualitative and / or at least one quantitative underlying indicator in the underlying evaluation indicators indicates that the most fundamental assessment data in the system is composed of qualitative descriptive indicators requiring subjective judgment and objectively measurable quantitative data indicators, either together or separately.

[0037] Specifically, when it is necessary to conduct low-carbon benefit assessments on multiple cigarette factories, a pre-built and stored hierarchical index system for low-carbon benefits of cigarette factories can be invoked. This system is organized in a hierarchical structure, containing multiple evaluation dimensions and their subordinate lower-level evaluation indicators. Each evaluation dimension and lower-level evaluation indicator is associated with a corresponding initial preset weight. The lower-level evaluation indicators cover at least one qualitative lower-level indicator and / or at least one quantitative lower-level indicator. By obtaining this system, a unified evaluation framework and benchmark can be provided for subsequent data collection, weight adjustment, and calculation of the low-carbon benefit performance level of each cigarette factory to be evaluated.

[0038] In this embodiment, the optional structural diagrams of the carbon benefit stratification index system are shown in Figures 2(a) and 2(b). The evaluation dimensions included in the tobacco factory's low-carbon benefit stratification index system are: green operation and management, high-end infrastructure, low-carbon energy consumption, circular resource utilization, and clean production processes. Specifically, the green operation and management dimension refers to the degree to which an enterprise implements green and low-carbon concepts in its organization, systems, and daily management; the high-end infrastructure dimension refers to the energy conservation, environmental protection, and technological advancement reflected in the planning, material selection, and construction of factory buildings and fixed equipment; the low-carbon energy consumption dimension refers to the level and ability of an enterprise to reduce carbon emissions in terms of energy usage types, structure, and efficiency; the circular resource utilization dimension refers to the efficiency and effectiveness of an enterprise in recycling and reducing the use of resources such as water and materials; and the clean production process dimension refers to the performance of the cigarette manufacturing process in reducing pollutant emissions and improving environmental friendliness. More specifically, each evaluation dimension of the tobacco factory's low-carbon benefit stratification index system has multiple secondary evaluation dimensions, meaning that the above five main aspects are further decomposed into more specific and targeted intermediate-level evaluation categories. Each secondary evaluation dimension has multiple underlying evaluation indicators, indicating that these intermediate evaluation categories are ultimately composed of a series of fundamental, atomic-level evaluation items that can be directly observed or measured, thus forming a hierarchical and comprehensive systematic evaluation framework that ranges from macro-level to micro-level indicators.

[0039] In this embodiment, the method for determining the initial preset weights corresponding to each evaluation index includes the following steps:

[0040] S1. Receive weighted scoring data submitted by multiple review experts for each evaluation indicator.

[0041] In this context, "review experts" refers to professionals invited to participate in the iterative weighted scoring process. They are responsible for submitting weighted scoring data for each evaluation indicator. Weighted scoring data refers to the numerical values ​​submitted by review experts for each level of evaluation dimensions and underlying evaluation indicators within the system, used to quantify their relative importance.

[0042] Specifically, multiple review experts with relevant professional backgrounds can be invited and gathered to subjectively score the importance of each evaluation dimension and its subordinate bottom-level evaluation indicators in the tobacco factory's low-carbon efficiency stratification indicator system based on their experience and judgment, thus forming their respective weighted score data.

[0043] S2. Perform statistical analysis on the collected weighted scoring data, and calculate the median, quartiles and weighted arithmetic mean of the scores for each evaluation indicator.

[0044] The median is the value in the middle after sorting the weighted scores of multiple experts on the same indicator from smallest to largest. The quartiles are the values ​​at the three dividing points after sorting the weighted scores of multiple experts on the same indicator from smallest to largest and dividing the data into four equal parts: the lower quartile (Q1), the median (Q2), and the upper quartile (Q3). The weighted arithmetic mean can be determined using the following formula:

[0045] ;

[0046] in, Let i be the weight of the i-th expert. Let be the prediction value of the i-th expert.

[0047] Specifically, the weighted scores from multiple review experts can be summarized for each evaluation indicator. First, the scores are sorted to obtain the median in the middle position to show the central tendency. Then, the quartiles are obtained from the segment points of the sorted data to characterize the distribution span. At the same time, each score value is weighted according to the preset or real-time determined weight coefficients, and the sum is calculated and divided by the total weight to obtain the weighted arithmetic mean. This comprehensively reflects the importance of the evaluation indicator in the eyes of the experts and the concentration and dispersion characteristics of the scores, providing a quantitative basis for feeding back the statistical results to the review experts and continuing the iteration.

[0048] S3. The statistical results, including individual scores from the reviewers and the median, quartiles, and weighted arithmetic mean, will be fed back to each reviewer.

[0049] Specifically, after completing the statistical processing of the weighted scoring data, the individual scores given by each review expert for each evaluation indicator, along with the median, quartiles determined by sorting all scores, and the weighted arithmetic mean obtained by weighting according to preset weight coefficients, are compiled into a clear statistical summary. This summary is then returned to each review expert who participated in the scoring, allowing them to intuitively see their scoring position and overall distribution characteristics within the group. This enables them to adjust their judgments in the next round of scoring by referring to these statistics, thus promoting the gradual convergence of expert opinions.

[0050] S4, the steps of repeatedly receiving weighted scoring data and statistical results and feeding them back to the review experts.

[0051] Specifically, after completing the first round of weighted scoring data collection and statistical result feedback, the closed-loop operation of "receiving the weighted scoring data again from each review expert based on the statistical information seen in the previous round" and "feeding back the updated statistical results, which include individual scores from multiple review experts as well as the median, quartiles, and weighted arithmetic mean, to each review expert" continues to be executed. This allows review experts to continuously revise their judgments by referring to the statistical characteristics of the group in multiple iterations until the difference between the previous and subsequent rounds drops below the preset threshold, ensuring that the experts' opinions tend to be consistent.

[0052] S5. Based on the difference in statistical results between the previous and subsequent rounds, determine the consistency level of the review experts' opinions, and stop iterating when the difference is lower than the preset threshold.

[0053] In this embodiment, after completing the collection and statistics of weighted scoring data for each round, the median, quartiles, and weighted arithmetic mean obtained in this round are compared with the corresponding statistics in the previous round. The difference between the two is calculated as the degree of difference, which is used to quantify the change in the overall opinions of the review experts. If the degree of difference is still higher than the preset threshold, it indicates that the expert group has not yet reached a consensus. Then, the next round of scoring and feedback is started, allowing the review experts to resubmit weighted scoring data with reference to the latest statistical information. Once the degree of difference drops below the preset threshold, it indicates that the opinions of the review experts have become consistent. Then, the iteration is terminated, and the scoring data of the final round is locked to determine the initial preset weights, thereby ensuring that the weight results fully reflect the stable consensus of the expert group.

[0054] S6. Based on the scoring data of the final round, the initial preset weight of each evaluation indicator is determined by the average calculation method, and the weight is associated with the corresponding evaluation indicator and stored.

[0055] Specifically, after multiple iterations, the arithmetic mean of the weight scores submitted by each review expert for each evaluation indicator in the final round is calculated. This average is used as the initial preset weight for that evaluation indicator. Then, a one-to-one mapping relationship is established between this weight value and its corresponding evaluation indicator and written into the database or configuration file to ensure that it can be called up in time and will not change in the subsequent low-carbon benefit evaluation process, providing a stable benchmark for step-by-step calculation.

[0056] S120. Collect actual operating data of each cigarette factory to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system for cigarette factories.

[0057] Among them, actual operating data refers to the quantitative measurement values ​​and qualitative description information of each cigarette factory to be evaluated, obtained through actual monitoring, recording and statistics under each bottom-level indicator of the cigarette factory's low-carbon efficiency stratification indicator system, reflecting its true status in dimensions such as operation management, infrastructure, energy consumption, resource utilization and production process.

[0058] In this embodiment, according to all the underlying evaluation indicators determined by the obtained low-carbon efficiency stratification indicator system for cigarette factories, for each cigarette factory to be evaluated, the actual and verifiable current operating values ​​or status descriptions of the qualitative and quantitative underlying indicators can be obtained through on-site monitoring, ledger reading, system docking or questionnaire filling, etc. These data contents are the actual operating data.

[0059] S130. Based on the actual operating data of each cigarette factory to be evaluated, the weights of each quantitative underlying indicator are adjusted to obtain the updated weights of the underlying indicators.

[0060] Among them, the weight of the bottom-level indicator refers to the quantitative coefficient of importance of the lowest-level bottom-level evaluation indicator relative to other bottom-level evaluation indicators of the same level in the tiered indicator system for low-carbon benefits of tobacco factories. This coefficient includes both the initial preset weight determined by the review experts through multiple rounds of iteration and the updated value obtained by dynamically adjusting the quantitative bottom-level indicators based on the actual operating data of each tobacco factory to be evaluated.

[0061] Specifically, after the initial preset weights are set, the original initial preset weights are dynamically corrected based on the actual operating data collected by each cigarette factory to be evaluated on each quantitative bottom-level indicator of the cigarette factory's low-carbon efficiency stratification indicator system, through preset weight adjustment rules or algorithms. This allows the adjusted weights to reflect the actual performance differences of the factory on the corresponding indicators, thereby forming updated bottom-level indicator weights specific to the factory.

[0062] S140. For each cigarette factory to be evaluated, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the tiered indicator system for low-carbon benefits of the cigarette factory, the low-carbon benefit performance level of each upper-level indicator is calculated step by step to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated.

[0063] Among them, the comprehensive assessment attribute of low-carbon benefits refers to the comprehensive result that can fully and quantitatively characterize the overall low-carbon benefit level of each cigarette factory to be assessed in dimensions such as green operation and management, high-end infrastructure, low-carbon energy consumption, circular resource utilization, and clean production process.

[0064] In this embodiment, the evaluation process for each cigarette factory to be evaluated is consistent. To clearly illustrate this technical solution, we will use one of the cigarette factories to be evaluated as an example. Based on the actual operational data of the cigarette factory to be evaluated across all underlying evaluation indicators, the data is weighted and synthesized using the corresponding updated weights of the underlying indicators. This first yields the low-carbon benefit performance level of the secondary evaluation dimension. Then, according to the weights of the secondary evaluation dimension itself, the data is aggregated upwards, progressively advancing to the primary evaluation dimension. Finally, it is summarized into a single quantitative result that can comprehensively reflect the low-carbon effectiveness of the factory's green workshop, i.e., the comprehensive low-carbon benefit evaluation attribute. This achieves a complete quantitative chain from underlying on-site data to top-level comprehensive evaluation.

[0065] For example, for a cigarette factory to be evaluated, the following six quantitative underlying indicators from its actual operating data are used as a basis: boiler flue gas emissions, boiler exhaust gas dust emission concentration, boiler exhaust gas sulfur dioxide emission concentration, boiler exhaust gas nitrogen oxide emission concentration, volatile organic compound emission concentration, and tobacco dust emission concentration. Each of these underlying indicators is then multiplied by its corresponding data or grade score using its updated weights to obtain the low-carbon benefit performance level of the secondary evaluation dimension "exhaust gas emissions" under the clean production process dimension. The low-carbon benefit performance level of the secondary evaluation dimension "wastewater discharge" under the same dimension is then calculated in the same way. Finally, the low-carbon benefit performance level of the primary "clean production process dimension" is synthesized according to the weights of each secondary dimension. This process continues, with the green operation management dimension, high-end infrastructure dimension, circular resource utilization dimension, and clean production process dimension calculated upwards level by level. Finally, the low-carbon benefit performance levels of all primary evaluation dimensions are weighted and merged to form the factory's unique comprehensive low-carbon benefit assessment attribute, achieving complete quantification from bottom-level on-site data to top-level overall evaluation.

[0066] This application provides a method for evaluating the low-carbon benefits of a green workshop in a cigarette factory. When a request for low-carbon benefit evaluation of multiple cigarette factories to be evaluated is received, a preset hierarchical index system for low-carbon benefits of cigarette factories is obtained. The hierarchical index system includes multiple evaluation dimensions organized in a hierarchical structure. Each evaluation dimension and the underlying evaluation indicators under each evaluation dimension have corresponding initial preset weights. The underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator. Then, the actual operating data of each cigarette factory to be evaluated under each underlying indicator of the hierarchical index system for low-carbon benefits of cigarette factories is collected. Subsequently, based on the actual operating data corresponding to each cigarette factory to be evaluated, the weights of each quantitative underlying indicator are adjusted to obtain updated underlying indicator weights. Thus, for each cigarette factory to be evaluated, based on the updated underlying indicator weights, the corresponding actual operating data, and the hierarchical index system for low-carbon benefits of cigarette factories, the low-carbon benefit performance level of each upper-level indicator is calculated level by level to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated. The technical solution of this application constructs a hierarchical indicator system for the low-carbon benefits of cigarette factories, which includes qualitative and quantitative underlying indicators. It also introduces a dynamic adjustment mechanism for the weights of quantitative indicators based on actual operating data. This effectively overcomes the limitations of traditional static weight systems, which are difficult to adapt to the actual conditions of different factories. While retaining the guiding role of expert experience in qualitative indicators, it achieves objective optimization of the weights of quantitative indicators, enabling the evaluation results to accurately reflect the differences among cigarette factories in terms of basic conditions, technical equipment, and operation management, thereby improving the pertinence and practicality of the evaluation.

[0067] In the above embodiments, a complete evaluation system covering indicator system establishment, data collection, dynamic weight adjustment, and comprehensive evaluation attribute calculation is constructed. Based on this, in order to extend closed-loop management from current status assessment to precise improvement, the low-carbon benefit evaluation method for green workshops in cigarette factories provided in this embodiment also includes the following steps:

[0068] S150: Obtain a low-carbon improvement strategy library containing multiple low-carbon improvement strategies.

[0069] The low-carbon improvement strategy library refers to a centralized knowledge base or database that pre-builds and stores multiple low-carbon improvement strategies. A low-carbon improvement strategy is a specific improvement measure or technical solution pre-stored in the library and correlated with specific underlying evaluation indicators in the tobacco factory's low-carbon benefit hierarchical indicator system. It aims to provide actionable optimization directions for identified underlying evaluation indicators to be improved, thereby helping to enhance the low-carbon benefit performance of relevant indicators. Each low-carbon improvement strategy is correlated with an underlying evaluation indicator in the tobacco factory's low-carbon benefit hierarchical indicator system. In the low-carbon improvement strategy library, each specific low-carbon improvement strategy forms a one-to-one or one-to-many directional connection with a specific underlying evaluation indicator in the tobacco factory's low-carbon benefit hierarchical indicator system through preset correspondence rules.

[0070] In this embodiment, all available low-carbon improvement strategies can be read from a pre-built and persistently stored low-carbon improvement strategy library. Each low-carbon improvement strategy in the library has a clear correlation with the underlying evaluation indicators in the tobacco factory's low-carbon benefit hierarchical indicator system, thereby ensuring that the corresponding strategy information can be called in real time when the underlying evaluation indicators to be improved are identified in the future, providing a data basis for accurate matching and pushing of improvement measures.

[0071] S160. Automatically analyze the collected actual operation data to identify underlying evaluation indicators with missing data and underlying evaluation indicators whose low-carbon benefit comprehensive assessment attributes have not reached the optimal level and need to be improved.

[0072] Among them, the underlying evaluation indicators that need improvement refer to those that are automatically identified as missing data in the collected actual operation data, or whose performance is insufficient, resulting in the low-carbon benefit comprehensive assessment attributes failing to reach the optimal level. These underlying evaluation indicators that need improvement are considered key weak links affecting the overall low-carbon benefits, and their operation needs to be optimized through subsequent matching and implementation of corresponding low-carbon improvement strategies, thereby improving the overall performance of the entire cigarette factory in the cigarette factory's low-carbon benefit hierarchical indicator system.

[0073] In this embodiment, based on the complete indicator list of the low-carbon benefit stratification indicator system for cigarette factories, the actual operating data of each cigarette factory to be evaluated can be scanned for null values, outliers, and boundary values. Any missing or unresolved underlying evaluation indicators are marked as missing data. At the same time, the calculated low-carbon benefit comprehensive evaluation attributes are compared with the preset optimal range to locate the underlying evaluation indicators that cause the overall score to deviate from the optimal range. Both are judged as underlying evaluation indicators to be improved, providing precise targets for subsequent strategy matching.

[0074] S170. Based on the correlation, automatically match the corresponding low-carbon improvement strategy for each identified underlying evaluation index to be improved.

[0075] In this embodiment, based on a pre-established "Low-Carbon Improvement Strategy - Underlying Evaluation Indicator" correlation table, upon identifying an underlying evaluation indicator to be improved, all strategy entries in the low-carbon improvement strategy library that are correlated with this indicator are immediately retrieved. Then, one or more of the most suitable low-carbon improvement strategies are automatically selected according to preset priority or matching rules and pushed to the corresponding cigarette factory. This solution constructs a low-carbon improvement strategy library associated with the indicator system and automatically identifies underlying evaluation indicators with missing data or unmet benefits based on actual operational data. Ultimately, it achieves precise matching of improvement strategies, forming a closed-loop management system from assessment and diagnosis to improvement and optimization, effectively enhancing the accuracy, relevance, and operability of low-carbon development in cigarette factory green workshops.

[0076] Example 2

[0077] Figure 3 This is a schematic diagram illustrating a low-carbon benefit evaluation method for a green workshop in a cigarette factory, provided as an embodiment of this application. Based on the aforementioned embodiments, this embodiment provides a more detailed explanation of steps S120 and S140. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0078] like Figure 3 As shown, the method specifically includes the following steps:

[0079] S210. When receiving a request for low-carbon benefit evaluation for multiple cigarette factories to be evaluated, obtain the preset low-carbon benefit stratification index system for cigarette factories.

[0080] The low-carbon efficiency stratified indicator system for tobacco factories includes multiple evaluation dimensions organized in a hierarchical structure. Each evaluation dimension and the underlying evaluation indicators under the evaluation dimension have corresponding initial preset weights. The underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator.

[0081] S220: Distribute data collection tasks for underlying evaluation indicators to each cigarette factory to be evaluated.

[0082] Among them, the data collection task refers to the directive work arrangement distributed to each cigarette factory to be evaluated, which explicitly requires them to provide specific qualitative and quantitative indicator values.

[0083] In this embodiment, based on the definitions and data requirements of all the underlying evaluation indicators in the low-carbon efficiency stratified indicator system for cigarette factories, a data collection task instruction containing a list of compliance certification materials required for qualitative underlying indicators and a monitoring data interface specification required for quantitative underlying indicators can be automatically generated. This instruction is then distributed to each cigarette factory to be evaluated through an information platform, instructing them to submit the actual operating data of the corresponding indicators according to unified standards and time nodes. This ensures that the original data required for subsequent weight adjustment, weighted calculation, and hierarchical aggregation is complete, comparable, and traceable.

[0084] S230. For qualitative underlying indicators, receive compliance verification materials submitted through standardized forms to determine the qualitative indicator values; for quantitative underlying indicators, receive monitoring values ​​automatically collected through data interfaces to determine the quantitative indicator values.

[0085] The compliance documentation refers to the normative documents and records used to prove that the cigarette factory under evaluation meets the green and low-carbon requirements in a specific evaluation dimension. Its content includes at least objective evidence such as management system certification status, system establishment status, and implementation record documents. Management system certification status proves that the factory has obtained and effectively maintained the relevant green or energy management system certification. System establishment status demonstrates the formally issued rules and regulations formulated by the factory around energy conservation and emission reduction goals. Implementation record documents provide process traces and result records of the continuous implementation of the above systems in actual production activities. These three together constitute the basis for determining whether the qualitative underlying indicator has been met. The qualitative indicator value refers to the assessment value used to quantitatively characterize the degree to which the factory meets the green and low-carbon requirements for that qualitative underlying indicator.

[0086] Among them, monitoring values ​​refer to the raw metering data automatically collected from the monitoring equipment or system of the cigarette factory being evaluated through a data interface, reflecting its actual operating status. This data includes at least directly quantifiable physical quantities or statistical values ​​such as energy consumption metering data, resource utilization efficiency data, and pollutant emission concentration data. Energy consumption metering data reflects the real-time total consumption and distribution of various energy sources during the cigarette factory's operation; resource utilization efficiency data reflects the input-output ratio and recycling level of key resources such as raw materials, water, and steam; and pollutant emission concentration data records the concentration levels of major pollutants at discharge points such as waste gas, wastewater, and solid waste. These three together constitute the basic monitoring information supporting the calculation of quantitative underlying indicator values ​​and subsequent weight adjustments and hierarchical aggregation calculations. Quantitative indicator values ​​refer to the specific numerical values ​​used to quantitatively characterize the factory's actual operating performance on that quantitative underlying indicator.

[0087] In this embodiment, the actual operating data includes qualitative and quantitative indicator values. This can be understood as follows: each cigarette factory to be evaluated needs to submit two types of data at the underlying evaluation indicator level: one type is the qualitative indicator value uploaded through a standardized form, the content of which is reflected in the form of compliance certification materials, used to reflect the status of systems, management or certification; the other type is the quantitative indicator value automatically collected through a data interface, the content of which is reflected in the form of monitoring values, used to reflect measurable information such as energy consumption, efficiency or emission concentration. Together, the two constitute the complete actual operating data that supports subsequent weight adjustment, weighted calculation and hierarchical aggregation.

[0088] Specifically, for qualitative underlying indicators, a standardized form with a unified format is sent to the cigarette factories to be evaluated, requiring them to upload compliance proof materials, including management system certification status, system construction status, and implementation record documents. After verification, these materials are converted into corresponding qualitative indicator values. For quantitative underlying indicators, monitoring values ​​such as energy consumption metering data, resource utilization efficiency data, and pollutant emission concentration data are directly captured through a preset data interface. After cleaning and verification, quantitative indicator values ​​are formed, thereby ensuring that the actual operating data is complete, accurate, and corresponds one-to-one with the underlying evaluation indicators.

[0089] S240. Establish a correlation between each qualitative and quantitative indicator value and its corresponding underlying evaluation indicator, and store them in different data partitions of the database.

[0090] In this embodiment, qualitative indicator values ​​from standardized forms and quantitative indicator values ​​from data interfaces can be bound one-to-one according to the underlying evaluation indicator codes of the tobacco factory's low-carbon efficiency hierarchical indicator system, forming a clear indicator-data mapping record. Subsequently, the two types of data are physically isolated and written into different storage areas specially allocated in the same database, so that subsequent weight adjustment, weighted calculation and hierarchical aggregation can be efficiently called in a clear classification environment, while ensuring data security and traceability.

[0091] S250. Based on the actual operating data of each cigarette factory to be evaluated, the weights of each quantitative underlying indicator are adjusted to obtain the updated weights of the underlying indicators.

[0092] S260. For each cigarette factory to be evaluated, for qualitative underlying indicators, the initial preset weights and corresponding actual operating data are used to perform weighted calculations to obtain the weighted evaluation attributes of the qualitative indicators; for quantitative underlying indicators, the updated weights and corresponding actual operating data are used to perform weighted calculations to obtain the weighted evaluation attributes of the quantitative indicators.

[0093] In this embodiment, the evaluation process for each cigarette factory to be evaluated is consistent. To clearly illustrate this technical solution, we will use one of the cigarette factories to be evaluated as an example. For any cigarette factory to be evaluated, the qualitative index value can be kept unchanged and multiplied by the initial preset weight to obtain the contribution of the qualitative part. Then, the quantitative index value can be multiplied by the updated weight of the underlying index after field data correction to obtain the contribution of the quantitative part. This generates the weighted evaluation attributes of the two types of underlying evaluation indicators, providing a quantitative basis for the subsequent summation to form the comprehensive evaluation attribute of the underlying indicators, which distinguishes data types and has dynamically consistent weights.

[0094] S270. Summing the weighted evaluation attributes of qualitative indicators and the weighted evaluation attributes of quantitative indicators yields the comprehensive evaluation attributes of the underlying indicators.

[0095] Among them, the comprehensive evaluation attribute refers to the single quantitative result generated for each underlying evaluation indicator.

[0096] In this embodiment, within the same underlying evaluation index, the qualitative results after initial preset weighting are directly added to the quantitative results after updated underlying index weighting to form a merged single quantitative value. This value is the comprehensive evaluation attribute of this underlying evaluation index, which is used for subsequent aggregation upwards according to the hierarchical structure.

[0097] S280. Based on the hierarchical structure of the low-carbon benefit stratified index system of the tobacco factory, the comprehensive evaluation attributes of the lower-level indicators are aggregated and calculated step by step according to the corresponding weights of the upper-level indicators until the comprehensive evaluation attributes of the highest-level indicators are obtained, which are used as the low-carbon benefit attribute values ​​of the current evaluation dimension.

[0098] The current evaluation dimension refers to the evaluation dimension that is currently being processed.

[0099] Specifically, starting from the underlying indicators, we can first multiply the comprehensive evaluation attribute of all underlying indicators belonging to the same secondary evaluation dimension by their respective updated or initial weights and sum them up to obtain the comprehensive evaluation attribute of the secondary evaluation dimension. Then, we multiply these comprehensive evaluation attributes of the secondary evaluation dimensions by the corresponding weights of the primary evaluation dimensions and continue to sum them up. Finally, we converge into the unique comprehensive evaluation attribute of the primary evaluation dimension, i.e., the current evaluation dimension, which is called its low-carbon benefit attribute value. This completes the bottom-up unidirectional weighted aggregation, ensuring that the contribution of each level is accurately transmitted with fixed weights and centrally reflected in the overall result of the current evaluation dimension.

[0100] S290. Calculate the low-carbon benefit attribute value for each evaluation dimension, and perform a weighted summation of the attribute values ​​for each evaluation dimension to generate the comprehensive low-carbon benefit evaluation attribute for the current cigarette factory to be evaluated.

[0101] Specifically, after completing the bottom-up aggregation of all primary evaluation dimensions, the low-carbon benefit attribute values ​​of the green operation and management dimension, the high-end infrastructure dimension, the low-carbon energy consumption dimension, the circular resource utilization dimension, and the clean production process dimension are obtained. Then, these attribute values ​​are multiplied by their corresponding evaluation dimension weights and added together to form an overall value. This overall value is the comprehensive low-carbon benefit evaluation attribute of the cigarette factory to be evaluated, which is used to compare the overall low-carbon effectiveness of its green workshops among factories in the same batch.

[0102] The technical solution of this application embodiment, when collecting actual operating data of each cigarette factory to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification indicator system, specifically includes the following implementation methods: distributing data collection tasks for the bottom-level evaluation indicators to each cigarette factory to be evaluated; then, for qualitative bottom-level indicators, receiving compliance certification materials submitted through standardized forms to determine the qualitative indicator values, wherein the compliance certification materials at least include management system certification status, system construction status, and implementation record documents; for quantitative bottom-level indicators, receiving monitoring values ​​automatically collected through data interfaces to determine the quantitative indicator values, wherein the monitoring data at least includes energy consumption metering data, resource utilization efficiency data, and pollutant emission concentration data; finally, establishing association relationships between each qualitative and quantitative indicator value and the corresponding bottom-level evaluation indicator, and storing them in different data partitions of the database. The technical solution of this application achieves standardized data collection through a task distribution mechanism, uses compliant supporting materials to assign standardized values ​​to qualitative indicators, automatically obtains monitoring values ​​for quantitative indicators through data interfaces, and establishes a data-indicator association storage system. This effectively ensures the reliability of data sources, the standardization of the collection process, and the systematic nature of data management, providing high-quality data support for subsequent accurate assessments.

[0103] The technical solution of this application embodiment, when determining the comprehensive evaluation attribute of low-carbon benefits for each cigarette factory to be evaluated, specifically includes: for each cigarette factory to be evaluated, for qualitative bottom-level indicators, weighted calculation is performed using initial preset weights and corresponding actual operating data to obtain the weighted evaluation attribute of the qualitative indicators; for quantitative bottom-level indicators, weighted calculation is performed using updated weights and corresponding actual operating data to obtain the weighted evaluation attribute of the quantitative indicators; then, the weighted evaluation attributes of the qualitative indicators and the weighted evaluation attributes of the quantitative indicators are summed to obtain the comprehensive evaluation attribute of the bottom-level indicators; according to the hierarchical structure of the cigarette factory's low-carbon benefit stratified indicator body, the comprehensive evaluation attributes of the lower-level indicators are aggregated and calculated step by step according to the corresponding upper-level indicator weights until the comprehensive evaluation attribute of the highest-level indicator is obtained, which is used as the low-carbon benefit attribute value of the current evaluation dimension; then, the low-carbon benefit attribute value of each evaluation dimension is calculated separately, and the attribute values ​​of each evaluation dimension are weighted and summed to generate the comprehensive evaluation attribute of the low-carbon benefits of the current cigarette factory to be evaluated. The technical solution of this application calculates the weighted evaluation attributes of qualitative and quantitative indicators separately and sums them to obtain the comprehensive evaluation attributes of the underlying indicators. Then, it obtains the attribute values ​​of each evaluation dimension through hierarchical aggregation calculation and finally generates the comprehensive evaluation attributes by weighting. This achieves the organic unity of institutional constraints on qualitative indicators and dynamic adaptive adjustment of quantitative indicators, and establishes a complete quantitative evaluation chain from micro indicators to macro dimensions, which significantly improves the scientificity and credibility of low-carbon benefit assessment.

[0104] Example 3

[0105] This application provides a method for evaluating the low-carbon benefits of a green workshop in a cigarette factory. Based on the aforementioned embodiments, this embodiment provides a more detailed explanation of step S130. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. The method specifically includes the following steps:

[0106] S310. When receiving a request for low-carbon benefit evaluation for multiple cigarette factories to be evaluated, obtain the preset low-carbon benefit stratification index system for cigarette factories.

[0107] The low-carbon efficiency stratified indicator system for tobacco factories includes multiple evaluation dimensions organized in a hierarchical structure. Each evaluation dimension and the underlying evaluation indicators under the evaluation dimension have corresponding initial preset weights. The underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator.

[0108] S320. Collect actual operating data of each cigarette factory to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system for cigarette factories.

[0109] S330. Based on the actual operating data of each cigarette factory to be evaluated, the weights of each quantitative underlying indicator are adjusted to obtain the updated underlying indicator weights.

[0110] In this embodiment, the specific implementation of weight adjustment for each quantitative underlying index can include at least two methods: one is a weight update method based on the coefficient of variation, and the other is a weight update method based on the sorting of the approximation of the ideal solution.

[0111] For the first weight update method, the specific steps for adjusting the weights of each quantitative underlying indicator to obtain the updated underlying indicator weights may include:

[0112] S1. For each cigarette factory to be evaluated, extract the actual operating data corresponding to all quantitative underlying indicators.

[0113] In this embodiment, the measured monitoring values ​​corresponding to all quantitative bottom-level indicators in the low-carbon efficiency stratification index system of each cigarette factory to be evaluated can be completely read from the collected database, forming a set that only includes quantifiable fields such as energy consumption measurement, resource utilization efficiency, and pollutant emission concentration.

[0114] S2. Based on actual operational data, calculate the average data level of each quantitative underlying indicator in all cigarette factories to be evaluated, as well as the data dispersion measure corresponding to each quantitative underlying indicator.

[0115] The average data level refers to a representative value used to characterize the overall data center location or general level of this indicator among all the cigarette factories being evaluated. The data dispersion measure is a value used to quantify the magnitude of variation or fluctuation in the distribution of this indicator among all the cigarette factories being evaluated.

[0116] In this embodiment, the measured values ​​of the quantitative underlying index from all cigarette factories to be evaluated can be summarized. First, the average data level that can represent the overall central tendency can be obtained. Then, the data dispersion measure that can reflect the deviation of each factory's value from the average data level can be obtained, thereby providing a quantitative basis for subsequently determining the relative dispersion coefficient and the updated weight of the underlying index.

[0117] S3. Based on the average data level and the data dispersion measure, determine the relative dispersion coefficient of each quantitative underlying indicator.

[0118] The relative coefficient of variation is a statistical measure used to dimensionlessly characterize the relative fluctuation or distinguishing ability of the index value among all the cigarette factories to be evaluated.

[0119] In this embodiment, a dimensionless ratio can be obtained by dividing the data dispersion measure of a specific quantitative underlying indicator in all cigarette factories to be evaluated by the average data level. This ratio is the relative dispersion coefficient, which is used to measure the relative magnitude of data fluctuations among different indicators, and thus determine its sensitivity or importance trend in weight adjustment.

[0120] S4. Determine the updated weight of each quantitative underlying indicator based on the relative dispersion coefficient of each quantitative underlying indicator.

[0121] In this embodiment, the relative dispersion coefficient obtained by comparing the average data level with the data dispersion measure can be used as the basis for weight allocation. The quantitative underlying indicators with larger coefficient values ​​are assigned higher updated weights, and the indicators with smaller coefficient values ​​are assigned lower weights. After normalization, a new set of weights is formed to replace the original initial preset weights and participate in subsequent hierarchical aggregation calculations.

[0122] For example, the algorithm implementation steps of the weight update method based on the coefficient of variation may include:

[0123] (1) Obtain the quantitative indicator values ​​of all cigarette factories to be evaluated for a certain quantitative underlying indicator (which can be called indicator reporting data);

[0124] (2) Calculate the average of the reported data for the indicator, which is used to characterize the average data level of this quantitative underlying indicator in all cigarette factories to be evaluated:

[0125] ;

[0126] in, This represents the average of the reported data for the i-th quantitative underlying indicator. This indicates the number of cigarette factories to be evaluated. This represents the quantitative index value of the i-th quantitative underlying index corresponding to the j-th cigarette factory to be evaluated.

[0127] (3) Calculate the standard deviation, which is used to characterize the data dispersion of the underlying quantitative indicator:

[0128] ;

[0129] in, This represents the data dispersion measure corresponding to the i-th quantitative underlying indicator.

[0130] (4) Calculate the coefficient of variation (CV) for each indicator, which characterizes the relative dispersion coefficient of the underlying quantitative indicator. The CV reflects the degree of dispersion of the data relative to its mean, eliminating the influence of dimensions. coefficient of variation of each indicator The calculation formula is:

[0131] ;

[0132] The larger the coefficient of variation, the greater the dispersion of the indicator, and the more important it may be in the comprehensive evaluation.

[0133] (5) Calculate the weights of each quantitative underlying indicator. Determine the weights of each quantitative underlying indicator based on its coefficient of variation. The sum of the weights of each indicator is 1. The formula for calculating the weight of each indicator is as follows:

[0134] ;

[0135] The formula divides the coefficient of variation of each quantitative underlying indicator by the sum of the coefficients of variation of all quantitative indicators to obtain the weight of that quantitative underlying indicator in the comprehensive evaluation.

[0136] For the second weight update method, the specific steps for adjusting the weights of each quantitative underlying indicator to obtain the updated underlying indicator weights may include:

[0137] S1. Construct a data evaluation matrix based on the actual operating data of each cigarette factory to be evaluated on the quantitative underlying indicators.

[0138] In this embodiment, the data evaluation matrix refers to a numerical table consisting of all cigarette factories to be evaluated as rows, all quantitative underlying indicators as columns, and the actual operating data of the corresponding indicators of each factory as elements. After the table is converted from cost-type indicators to benefit-type indicators and standardized, it is used to identify positive and negative ideal solutions in the approximation ideal solution ranking method, and then calculate the relative closeness and complete the updated weight allocation of the underlying indicators.

[0139] In this embodiment, optionally, the specific steps for constructing a data evaluation matrix based on the actual operational data of each cigarette factory to be evaluated on quantitative underlying indicators may include:

[0140] (1) Based on the actual operating data of each cigarette factory to be evaluated on the quantitative underlying indicators, an initial evaluation matrix is ​​constructed.

[0141] Specifically, all cigarette factories to be evaluated can be used as rows and all quantitative underlying indicators as columns. The actual measured monitoring values ​​of each factory for each indicator can be directly filled into the corresponding cells to form a raw data table without any transformation or standardization. This table is the initial evaluation matrix, which is used for subsequent indicator type identification, cost-to-benefit conversion and standardization processing to generate a standardized data evaluation matrix.

[0142] (2) Identify the index types of the initial evaluation matrix, and convert the identified cost-type index values ​​into benefit-type index values ​​through mathematical transformation methods to obtain the first evaluation matrix.

[0143] In this embodiment, it can be determined whether each quantitative underlying indicator belongs to the cost type ("the smaller the better") or the benefit type ("the larger the better"). For cost-type indicator values, mathematical transformation methods such as taking the reciprocal or subtracting the upper limit are used to transform them into values ​​in the same direction as benefit-type indicators, so that all elements of the entire matrix show the "the larger the better" direction. The output result after this consistency processing is the first evaluation matrix.

[0144] (3) Standardize the first evaluation matrix to generate a standardized data evaluation matrix.

[0145] Specifically, based on the transformation from cost-based to benefit-based evaluation in the first evaluation matrix, the values ​​of each quantitative underlying indicator are compressed into a dimensionless range with a unified range through range transformation or vector normalization, eliminating differences in dimensions and orders of magnitude, making different indicators comparable, and thus outputting a standardized data evaluation matrix for subsequent identification of positive and negative ideal solutions and calculation of relative distances.

[0146] S2. Based on the data evaluation matrix, identify the best value of each quantitative underlying index in all cigarette factories to be evaluated as the positive ideal solution, and the worst value as the negative ideal solution.

[0147] Among them, the positive ideal solution is the optimal value selected from the index column after the data evaluation matrix is ​​transformed and standardized for each quantitative underlying index corresponding to all cigarette factories to be evaluated. It is the maximum value for benefit-type indicators and the minimum value for cost-type indicators. It represents the ideal optimal level of the quantitative underlying index in the evaluation group and is used as the benchmark reference point for calculating the first relative distance between each factory and the ideal state.

[0148] Among them, the negative ideal solution is the worst value that each quantitative underlying indicator can take in all the standardized data evaluation matrices corresponding to all the cigarette factories to be evaluated. For benefit-type indicators, it is the minimum value, and for cost-type indicators, it corresponds to the maximum value. It represents the most unfavorable level of the indicator in the evaluation group and is used as a benchmark reference point to calculate the second relative distance between each factory and the worst state, so as to work with the positive ideal solution to determine the relative closeness.

[0149] In this embodiment, the standardized data evaluation matrix can be scanned column by column. The positive ideal solution is formed by taking the maximum value of the benefit-type indicators and the minimum value of the cost-type indicators, and the negative ideal solution is formed by taking the minimum value of the benefit-type indicators and the maximum value of the cost-type indicators. This provides a benchmark reference point for subsequent calculation of the distance between each factory and the ideal state.

[0150] S3. For each quantitative underlying index, calculate the first relative distance between each cigarette factory to be evaluated and the positive ideal solution, and the second relative distance between each factory and the negative ideal solution under the initial preset weighted state.

[0151] The first relative distance represents the deviation of each quantitative underlying index value of the factory from the positive ideal solution under the initial preset weighted state, while the second relative distance represents the deviation of the same weighted vector from the negative ideal solution.

[0152] In this embodiment, based on a standardized data evaluation matrix, the index values ​​of each factory are weighted with corresponding initial preset weights to form a weighted vector. Then, the deviation of this vector from the positive ideal solution is measured for each factory to obtain the first relative distance, and the deviation from the negative ideal solution is measured to obtain the second relative distance. This characterizes the positional differences of each factory in the weighted space between the optimal and worst benchmarks, providing a basis for the subsequent calculation of relative proximity.

[0153] S4. Based on the first relative distance and the second relative distance, calculate the relative proximity between each cigarette factory to be evaluated and the ideal solution.

[0154] Among them, relative proximity is a dimensionless ratio value calculated for each cigarette factory to be evaluated. It is used to characterize the comprehensive result of how close the factory's data vector is to the positive ideal solution relative to the distance from the negative ideal solution under the initial preset weighted state.

[0155] In this embodiment, the second relative distance obtained by each factory under the initial preset weighted state can be used as the numerator, and the sum of the first relative distance and the second relative distance can be used as the denominator. A dimensionless relative proximity can be constructed by the ratio of the two. The larger the value, the closer the factory data vector is to the positive ideal solution and the farther away it is from the negative ideal solution, thereby quantifying its superior and inferior position on the quantitative underlying indicators and providing a unified scale for subsequent sorting and determination of the updated underlying indicator weights.

[0156] S5. Sort each quantitative underlying indicator according to its relative proximity, and determine the updated underlying indicator weights of each quantitative underlying indicator based on the sorting results.

[0157] Specifically, quantitative underlying indicators can be ranked according to the relative closeness calculated by each factory. Indicators with higher closeness and stronger distinguishing ability are given greater weights in the updated underlying indicators, while indicators with lower closeness and weaker distinguishing ability are given smaller weights. After normalization, a new weight allocation scheme is formed to replace the original initial preset weights and is used for subsequent hierarchical aggregation calculations.

[0158] For example, the algorithm implementation steps for a weight update method based on approximating the ideal solution may include:

[0159] (1) Constructing the data evaluation matrix:

[0160] The data matrix can be formed by treating all cigarette factories to be evaluated as rows and all quantitative underlying indicators as columns, and then directly filling the corresponding cells with the measured monitoring values ​​of each indicator for each factory, thus creating an n-row, m-column data matrix. ,in, Indicates the first The first cigarette factory to be evaluated The index value corresponding to each quantitative underlying indicator .

[0161] (2) Forwarding the original matrix: Using the reciprocal method 1 / x, the cost-type indicators (the smaller the better) in the original data matrix are transformed into benefit-type indicators (the larger the better), so that all indicators are unified as benefit-type. This step is to eliminate the influence of different dimensions between indicators, which facilitates subsequent calculations and comparisons.

[0162] (3) Data standardization: In order to eliminate the influence of dimensions and orders of magnitude, the original data is standardized to obtain a standardized matrix. Commonly used standardization methods include vector normalization, with the following formula:

[0163] ;

[0164] (4) Calculate the positive ideal solution: For benefit-type indicators (indicators where the larger the value, the better; step (3) has already converted the cost-type indicator values ​​to benefit-type, so only benefit-type is considered here), the positive ideal solution is the maximum value of the indicator; let the positive ideal solution be ,but .

[0165] (5) Calculate the negative ideal solution: In contrast to the positive ideal solution, for benefit-type indicators, the negative ideal solution is the minimum value of the indicator; let the negative ideal solution be... ,but: .

[0166] (6) Calculate the distance between the evaluation object and the positive ideal solution: Calculate the distance between the cigarette factory to be evaluated and the positive ideal solution. The formula is:

[0167] ;

[0168] (7) Calculate the distance between the evaluation object and the negative ideal solution. Similarly, calculate the distance between the evaluation object and the negative ideal solution. Distance between each evaluation object and the negative ideal solution The formula is:

[0169] ;

[0170] (8) Calculate the proximity of each cigarette factory to be evaluated to the distance: Calculate the first The comprehensive score for each cigarette factory to be evaluated ranges from 0 to 1. The closer the score is to 1, the closer the evaluation object is to the positive ideal solution, and the better the overall performance. The calculation formula is as follows:

[0171] ;

[0172] (9) Assign greater weight to the updated underlying indicators for indicators with higher proximity and stronger distinguishing ability, and assign less weight to indicators with lower proximity and weaker distinguishing ability.

[0173] S340. For each cigarette factory to be evaluated, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the tiered indicator system for low-carbon benefits of the cigarette factory, the low-carbon benefit performance level of each upper-level indicator is calculated step by step to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated.

[0174] The technical solution of this application embodiment, when adjusting the weights of each quantitative underlying indicator, includes the following specific implementation method: for each cigarette factory to be evaluated, extract the actual operating data corresponding to all quantitative underlying indicators; based on the actual operating data, calculate the average data level of each quantitative underlying indicator in all cigarette factories to be evaluated, and the data dispersion measure value corresponding to each quantitative underlying indicator; based on the average data level and the data dispersion measure value, determine the relative dispersion coefficient of each quantitative underlying indicator; and based on the relative dispersion coefficient of each quantitative underlying indicator, determine the updated underlying indicator weight of each quantitative underlying indicator. Another specific implementation method may include: constructing a data evaluation matrix based on the actual operating data of each cigarette factory to be evaluated on the quantitative underlying indicators; based on the data evaluation matrix, identifying the best value of each quantitative underlying indicator among all cigarette factories to be evaluated as the positive ideal solution, and the worst value as the negative ideal solution; for each quantitative underlying indicator, calculating the first relative distance between each cigarette factory to be evaluated and the positive ideal solution, and the second relative distance between each cigarette factory to be evaluated and the negative ideal solution under the initial preset weighted state; calculating the relative closeness between each cigarette factory to be evaluated and the ideal solution based on the first and second relative distances; ranking each quantitative underlying indicator according to the relative closeness, and determining the updated underlying indicator weights of each quantitative underlying indicator based on the ranking results. The technical solution of this application achieves dynamic optimization of the weights of quantitative underlying indicators by providing two weight adjustment methods: one based on the coefficient of variation and the other based on approximating the ideal solution. The former calculates the relative dispersion coefficient of the indicators to make the weight allocation more in line with the actual distribution characteristics of the data, thereby enhancing the responsiveness to the indicator discrimination. The latter constructs an evaluation matrix and calculates the relative closeness to ensure that the weight adjustment takes into account both the importance of the indicators and their actual performance. This ensures that the evaluation results reflect both expert experience and objective data patterns, thereby significantly improving the scientific nature of the weight determination and the adaptability of the evaluation system.

[0175] Example 4

[0176] Figure 4 A schematic diagram of a low-carbon benefit evaluation device for a green workshop in a cigarette factory, provided as an embodiment of this application, is shown. The device includes:

[0177] The hierarchical indicator acquisition module 410 is used to acquire a preset hierarchical indicator system for the low-carbon benefits of cigarette factories when receiving a request for low-carbon benefit evaluation for multiple cigarette factories to be evaluated; wherein, the hierarchical indicator system for the low-carbon benefits of cigarette factories includes multiple evaluation dimensions organized in a hierarchical structure, and each evaluation dimension and the underlying evaluation indicators under the evaluation dimension are provided with corresponding initial preset weights, and the underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator.

[0178] The data acquisition module 420 is used to collect the actual operating data of each of the cigarette factories to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system of the cigarette factory.

[0179] The indicator weight update module 430 is used to adjust the weights of each quantitative underlying indicator based on the actual operating data corresponding to each of the cigarette factories to be evaluated, so as to obtain the updated underlying indicator weights.

[0180] The low-carbon benefit assessment module 440 is used to calculate the low-carbon benefit performance level of each upper-level indicator for each cigarette factory to be assessed, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the cigarette factory's low-carbon benefit hierarchical indicator system, and to obtain the comprehensive low-carbon benefit assessment attribute corresponding to each cigarette factory to be assessed.

[0181] This application provides a device for evaluating the low-carbon benefits of a green workshop in a cigarette factory. When the device receives a request to evaluate the low-carbon benefits of multiple cigarette factories to be evaluated, it acquires a preset hierarchical index system for low-carbon benefits of the cigarette factory. This hierarchical index system includes multiple evaluation dimensions organized hierarchically. Each evaluation dimension and its underlying evaluation indicators have corresponding initial preset weights. The underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator. Then, the device collects actual operating data for each cigarette factory to be evaluated under each underlying indicator of the hierarchical index system. Subsequently, based on the actual operating data of each cigarette factory to be evaluated, the weights of each quantitative underlying indicator are adjusted to obtain updated underlying indicator weights. Thus, for each cigarette factory to be evaluated, based on the updated underlying indicator weights, the corresponding actual operating data, and the hierarchical index system for low-carbon benefits of the cigarette factory, the device calculates the low-carbon benefit performance level of each upper-level indicator level upwards, obtaining the comprehensive low-carbon benefit evaluation attribute for each cigarette factory to be evaluated. The technical solution of this application constructs a hierarchical indicator system for the low-carbon benefits of cigarette factories, which includes qualitative and quantitative underlying indicators. It also introduces a dynamic adjustment mechanism for the weights of quantitative indicators based on actual operating data. This effectively overcomes the limitations of traditional static weight systems, which are difficult to adapt to the actual conditions of different factories. While retaining the guiding role of expert experience in qualitative indicators, it achieves objective optimization of the weights of quantitative indicators, enabling the evaluation results to accurately reflect the differences among cigarette factories in terms of basic conditions, technical equipment, and operation management, thereby improving the pertinence and practicality of the evaluation.

[0182] Based on the above-mentioned device, optionally, the low-carbon benefit evaluation device for green workshops in cigarette factories further includes: a preset weight determination module, used to receive weighted scoring data submitted by multiple review experts for each of the evaluation indicators; perform statistical analysis on the collected weighted scoring data to calculate the mean, median, quartiles, and standard deviation of the scores for each evaluation indicator; feed back the statistical results, including the individual scores of the review experts and the mean, median, quartiles, and standard deviation, to each review expert; repeat the steps of receiving weighted scoring data and feeding back the statistical results to each review expert; determine the consistency level of the review experts' opinions based on the difference between the statistical results of the previous and subsequent rounds, until the difference is lower than a preset threshold and the iteration stops; based on the scoring data of the final round, determine the initial preset weight of each evaluation indicator using the mean calculation method, and associate and store the weight with the corresponding evaluation indicator.

[0183] Based on the above-mentioned device, optionally, the evaluation dimensions included in the low-carbon benefit hierarchical index system of the tobacco factory include: green operation and management dimension, high-end infrastructure dimension, low-carbon energy consumption dimension, circular resource utilization dimension, and clean production process dimension. Each of the evaluation dimensions has multiple secondary evaluation dimensions, and each of the secondary evaluation dimensions has multiple underlying evaluation indicators. The underlying evaluation indicators include at least one qualitative indicator and / or at least one quantitative indicator.

[0184] Based on the aforementioned device, optionally, the actual operating data includes qualitative and quantitative indicator values. The operating data acquisition module 420 is specifically used to distribute data acquisition tasks for underlying evaluation indicators to each of the cigarette factories to be evaluated. For qualitative underlying indicators, it receives compliance documentation submitted through standardized forms to determine the qualitative indicator values. The compliance documentation includes at least management system certification status, system construction status, and implementation record documents. For quantitative underlying indicators, it receives monitoring values ​​automatically collected through a data interface to determine the quantitative indicator values. The monitoring data includes at least energy consumption metering data, resource utilization efficiency data, and pollutant emission concentration data. Each qualitative and quantitative indicator value is associated with its corresponding underlying evaluation indicator and stored in different data partitions of the database.

[0185] Based on the above-mentioned device, optionally, the indicator weight update module 430 is specifically used to extract the actual operating data corresponding to all quantitative underlying indicators for each of the cigarette factories to be evaluated; based on the actual operating data, calculate the average data level of each quantitative underlying indicator in all the cigarette factories to be evaluated, and the data dispersion measure value corresponding to each quantitative underlying indicator; based on the average data level and the data dispersion measure value, determine the relative dispersion coefficient of each quantitative underlying indicator; and determine the updated underlying indicator weight of each quantitative underlying indicator according to the relative dispersion coefficient of each quantitative underlying indicator.

[0186] Based on the above-mentioned device, optionally, the indicator weight update module 430 is further used to construct a data evaluation matrix based on the actual operating data of each cigarette factory to be evaluated on the quantitative underlying indicators; based on the data evaluation matrix, identify the best value of each quantitative underlying indicator among all cigarette factories to be evaluated as the positive ideal solution and the worst value as the negative ideal solution; for each quantitative underlying indicator, calculate the first relative distance between each cigarette factory to be evaluated and the positive ideal solution and the second relative distance between each cigarette factory to be evaluated and the negative ideal solution under the initial preset weighted state; based on the first relative distance and the second relative distance, calculate the relative closeness between each cigarette factory to be evaluated and the ideal solution; sort each quantitative underlying indicator according to the relative closeness, and determine the updated underlying indicator weight of each quantitative underlying indicator based on the sorting result.

[0187] Based on the above-mentioned device, optionally, the indicator weight update module 430 is further used to construct an initial evaluation matrix based on the actual operating data of each cigarette factory to be evaluated on the quantitative underlying indicators; to identify the indicator type of the initial evaluation matrix, and to uniformly convert the identified cost-type indicator values ​​into benefit-type indicator values ​​through mathematical conversion methods to obtain a first evaluation matrix; and to standardize the first evaluation matrix to generate a standardized data evaluation matrix.

[0188] Based on the aforementioned device, optionally, a low-carbon benefit assessment module 440 is used to: for each of the cigarette factories to be assessed, perform weighted calculations on qualitative bottom-level indicators using initial preset weights and corresponding actual operating data to obtain weighted assessment attributes for qualitative indicators; perform weighted calculations on quantitative bottom-level indicators using updated weights and corresponding actual operating data to obtain weighted assessment attributes for quantitative indicators; sum the weighted assessment attributes of the qualitative indicators and the weighted assessment attributes of the quantitative indicators to obtain comprehensive assessment attributes for the bottom-level indicators; according to the hierarchical structure of the cigarette factory's low-carbon benefit tiered indicator body, aggregate and calculate the comprehensive assessment attributes of the lower-level indicators according to the corresponding upper-level indicator weights until the comprehensive assessment attribute of the highest-level indicator is obtained, which serves as the low-carbon benefit attribute value for the current evaluation dimension; calculate the low-carbon benefit attribute value for each of the evaluation dimensions respectively, and perform weighted summation of the attribute values ​​for each evaluation dimension to generate the comprehensive low-carbon benefit assessment attribute for the current cigarette factory to be assessed.

[0189] Based on the aforementioned device, the optional low-carbon benefit evaluation device for a cigarette factory's green workshop further includes: a strategy recommendation module, used to acquire a low-carbon improvement strategy library storing multiple low-carbon improvement strategies; wherein each low-carbon improvement strategy establishes a correlation with the underlying evaluation indicators in the cigarette factory's low-carbon benefit hierarchical indicator system; automatically analyzes the collected actual operating data to identify underlying evaluation indicators with missing data and underlying evaluation indicators whose low-carbon benefit comprehensive evaluation attributes have not reached the optimal level; and automatically matches a corresponding low-carbon improvement strategy for each identified underlying evaluation indicator to be improved based on the correlation.

[0190] The low-carbon benefit evaluation device for green workshops in cigarette factories provided in this application can execute the low-carbon benefit evaluation method for green workshops in cigarette factories provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method.

[0191] It is worth noting that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this application.

[0192] Example 5

[0193] Figure 5 This is a schematic diagram of the structure of a controller provided in an embodiment of this application. Figure 5 A block diagram is shown of an exemplary controller 50 suitable for implementing embodiments of the present application. Figure 5 The controller 50 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0194] like Figure 5 As shown, the controller 50 is presented in the form of a general-purpose computing device. The components of the controller 50 may include, but are not limited to: one or more processors or processing units 501, system memory 502, and bus 503 connecting different system components (including system memory 502 and processing unit 501).

[0195] Bus 503 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0196] The controller 50 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the controller 50, including volatile and non-volatile media, and removable and non-removable media.

[0197] System memory 502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 504 and / or cache memory 505. Controller 50 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 506 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 Not shown; usually referred to as a "hard drive"). Although Figure 5 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 503 via one or more data media interfaces. Memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0198] A program / utility 508 having a set (at least one) of program modules 507 may be stored, for example, in memory 502. Such program modules 507 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 507 typically perform the functions and / or methods described in the embodiments of this application.

[0199] The controller 50 can also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), and with one or more devices that enable a user to interact with the controller 50, and / or with any device that enables the controller 50 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 511. Furthermore, the controller 50 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 512. As shown, network adapter 512 communicates with other modules of the controller 50 via bus 503. It should be understood that, although... Figure 5 As not shown, other hardware and / or software modules can be used in conjunction with controller 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0200] The processing unit 501 executes various functional applications and page processing by running programs stored in the system memory 502, such as implementing the low-carbon benefit evaluation method for green workshops in cigarette factories provided in the embodiments of this application.

[0201] Example 6

[0202] This application embodiment also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform a method for evaluating the low-carbon benefits of a green workshop in a cigarette factory. The method includes:

[0203] When a request for low-carbon benefit evaluation of multiple cigarette factories to be evaluated is received, a preset low-carbon benefit hierarchical index system for cigarette factories is obtained; wherein, the low-carbon benefit hierarchical index system for cigarette factories includes multiple evaluation dimensions organized in a hierarchical structure, and each evaluation dimension and the underlying evaluation index under the evaluation dimension are given a corresponding initial preset weight, and the underlying evaluation index includes at least one qualitative underlying index and / or at least one quantitative underlying index.

[0204] Collect actual operating data of each of the cigarette factories to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system for cigarette factories;

[0205] Based on the actual operating data of each of the cigarette factories to be evaluated, the weights of each of the quantitative underlying indicators are adjusted to obtain the updated underlying indicator weights.

[0206] For each cigarette factory to be evaluated, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the tiered indicator system for low-carbon benefits of the cigarette factory, the low-carbon benefit performance level of each upper-level indicator is calculated step by step to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated.

[0207] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0208] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0209] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0210] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0211] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A method for evaluating the low-carbon benefits of a green workshop in a cigarette factory, characterized in that, The method includes: When a request for low-carbon benefit evaluation of multiple cigarette factories to be evaluated is received, a preset low-carbon benefit hierarchical index system for cigarette factories is obtained; wherein, the low-carbon benefit hierarchical index system for cigarette factories includes multiple evaluation dimensions organized in a hierarchical structure, and each evaluation dimension and the underlying evaluation index under the evaluation dimension are given a corresponding initial preset weight, and the underlying evaluation index includes at least one qualitative underlying index and / or at least one quantitative underlying index. Collect actual operating data of each of the cigarette factories to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system for cigarette factories; Based on the actual operating data of each of the cigarette factories to be evaluated, the weights of each of the quantitative underlying indicators are adjusted to obtain the updated underlying indicator weights. For each cigarette factory to be evaluated, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the tiered indicator system for low-carbon benefits of the cigarette factory, the low-carbon benefit performance level of each upper-level indicator is calculated step by step to obtain the comprehensive low-carbon benefit evaluation attribute corresponding to each cigarette factory to be evaluated.

2. The method according to claim 1, characterized in that, The methods for determining the initial preset weights corresponding to each of the aforementioned evaluation indicators include: Receive weighted scoring data submitted by multiple review experts for each of the aforementioned evaluation indicators; Statistical analysis was performed on the collected weighted scoring data to calculate the mean, median, quartiles, and standard deviation of the scores for each evaluation indicator; The statistical results, including individual scores from the reviewers and the mean, median, quartiles, and standard deviation, will be fed back to each reviewer. The process of repeatedly receiving weighted scoring data and statistical results and feeding them back to the review experts; The consistency level of the review experts' opinions is judged based on the difference between the statistical results of the previous and subsequent rounds, and the iteration stops when the difference is lower than a preset threshold. Based on the scoring data of the final round, the initial preset weights of each evaluation indicator are determined by the average calculation method, and the weights are associated with and stored with the corresponding evaluation indicators.

3. The method according to claim 1, characterized in that, The evaluation dimensions included in the low-carbon efficiency stratified indicator system for tobacco factories are: green operation and management, high-end infrastructure, low-carbon energy consumption, circular resource utilization, and clean production process. Each of these evaluation dimensions has multiple secondary evaluation dimensions, and each of these secondary evaluation dimensions has multiple underlying evaluation indicators.

4. The method according to claim 1, characterized in that, The actual operating data includes qualitative and quantitative indicator values. The collection of actual operating data for each of the cigarette factories to be evaluated under each bottom-level indicator of the cigarette factory low-carbon efficiency stratification indicator system includes: Data collection tasks targeting underlying evaluation indicators were distributed to each of the cigarette factories to be evaluated. For qualitative underlying indicators, compliance documentation submitted through standardized forms is received to determine the qualitative indicator values; wherein, the compliance documentation includes at least the management system certification status, system establishment status, and implementation record documents; For quantitative underlying indicators, monitoring values ​​are automatically collected through a data interface to determine the quantitative indicator values; wherein, the monitoring data includes at least energy consumption metering data, resource utilization efficiency data, and pollutant emission concentration data; Each qualitative and quantitative indicator value is associated with its corresponding underlying evaluation indicator and stored in different data partitions of the database.

5. The method according to claim 1, characterized in that, The step involves adjusting the weights of each quantitative underlying indicator based on the actual operating data corresponding to each of the cigarette factories to be evaluated, resulting in updated underlying indicator weights, including: For each of the cigarette factories to be evaluated, extract the actual operating data corresponding to all quantitative underlying indicators; Based on the actual operating data, the average data level of each quantitative underlying indicator in all the cigarette factories to be evaluated is calculated, as well as the data dispersion measure corresponding to each quantitative underlying indicator. Based on the average data level and the data dispersion measure, the relative dispersion coefficient of each quantitative underlying indicator is determined; The updated weight of each quantitative underlying indicator is determined based on the relative dispersion coefficient of each quantitative underlying indicator.

6. The method according to claim 1, characterized in that, The step involves adjusting the weights of each quantitative underlying indicator based on the actual operating data corresponding to each of the cigarette factories to be evaluated, resulting in updated underlying indicator weights, including: Based on the actual operational data of each cigarette factory to be evaluated on the quantitative underlying indicators, a data evaluation matrix is ​​constructed. Based on the data evaluation matrix, the best value of each quantitative underlying index in all cigarette factories to be evaluated is identified as the positive ideal solution, and the worst value is identified as the negative ideal solution. For each of the quantitative underlying indicators, calculate the first relative distance between each cigarette factory to be evaluated and the positive ideal solution, and the second relative distance between each factory and the negative ideal solution under the initial preset weighted state; Based on the first relative distance and the second relative distance, the relative closeness between each cigarette factory to be evaluated and the ideal solution is calculated; The quantitative underlying indicators are sorted according to the relative proximity, and the updated weights of the underlying indicators are determined based on the sorting results.

7. The method according to claim 6, characterized in that, The data evaluation matrix is ​​constructed based on the actual operating data of each cigarette factory to be evaluated on quantitative underlying indicators, including: Based on the actual operational data of each cigarette factory to be evaluated on the quantitative underlying indicators, an initial evaluation matrix is ​​constructed. The initial evaluation matrix is ​​subjected to indicator type identification, and the identified cost-type indicator values ​​are uniformly converted into benefit-type indicator values ​​through mathematical transformation methods to obtain the first evaluation matrix. The first evaluation matrix is ​​standardized to generate a standardized data evaluation matrix.

8. The method according to claim 1, characterized in that, Based on the updated weights of the underlying indicators, the corresponding actual operating data, and the hierarchical indicator system for low-carbon benefits of tobacco factories, the low-carbon benefit performance level of each upper-level indicator is calculated progressively upwards to obtain the comprehensive low-carbon benefit assessment attribute for each tobacco factory to be evaluated, including: For each of the cigarette factories to be evaluated, the qualitative underlying indicators are weighted by the initial preset weights and the corresponding actual operating data to obtain the weighted evaluation attributes of the qualitative indicators. For quantitative underlying indicators, the updated weights are used to perform weighted calculations with the corresponding actual operating data to obtain the weighted evaluation attributes of the quantitative indicators. The weighted evaluation attributes of the qualitative indicators and the weighted evaluation attributes of the quantitative indicators are summed to obtain the comprehensive evaluation attributes of the underlying indicators. Based on the hierarchical structure of the tobacco factory's low-carbon benefit stratified index, the comprehensive evaluation attributes of the lower-level indicators are aggregated and calculated step by step according to the corresponding weights of the upper-level indicators until the comprehensive evaluation attributes of the highest-level indicators are obtained, which serve as the low-carbon benefit attribute value of the current evaluation dimension. Calculate the low-carbon benefit attribute value for each of the evaluation dimensions, and perform a weighted summation of the attribute values ​​for each evaluation dimension to generate the comprehensive low-carbon benefit evaluation attribute for the current cigarette factory to be evaluated.

9. The method according to claim 1, characterized in that, The method further includes: Obtain a low-carbon improvement strategy library containing multiple low-carbon improvement strategies; wherein, each of the low-carbon improvement strategies is associated with the underlying evaluation indicators in the tobacco factory's low-carbon benefit hierarchical indicator system. The system automatically analyzes the collected actual operation data to identify underlying evaluation indicators with missing data and underlying evaluation indicators that do not meet the optimal level for the comprehensive evaluation of low-carbon benefits. Based on the aforementioned correlation, a corresponding low-carbon improvement strategy is automatically matched for each identified underlying evaluation index to be improved.

10. A low-carbon benefit evaluation device for a green workshop in a cigarette factory, characterized in that, The device includes: The hierarchical indicator acquisition module is used to acquire a preset hierarchical indicator system for the low-carbon benefits of cigarette factories when receiving a request for low-carbon benefit evaluation for multiple cigarette factories to be evaluated; wherein, the hierarchical indicator system for the low-carbon benefits of cigarette factories includes multiple evaluation dimensions organized in a hierarchical structure, and each evaluation dimension and the underlying evaluation indicators under the evaluation dimension are provided with corresponding initial preset weights, and the underlying evaluation indicators include at least one qualitative underlying indicator and / or at least one quantitative underlying indicator. The data acquisition module is used to collect the actual operating data of each of the cigarette factories to be evaluated under each bottom-level indicator of the low-carbon efficiency stratification index system of the cigarette factories; The indicator weight update module is used to adjust the weights of each quantitative underlying indicator based on the actual operating data of each of the cigarette factories to be evaluated, so as to obtain the updated underlying indicator weights. The low-carbon benefit assessment module is used to calculate the low-carbon benefit performance level of each upper-level indicator for each cigarette factory to be assessed, based on the updated weights of the underlying indicators, the corresponding actual operating data, and the cigarette factory's low-carbon benefit hierarchical indicator system, and to obtain the comprehensive low-carbon benefit assessment attribute corresponding to each cigarette factory to be assessed.

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