Green mine evaluation method and device and storage medium

Abstract

The invention discloses a green mine evaluation method and device and a storage medium, and the method comprises the steps: obtaining green mine data which comprises field survey data, questionnaire survey data and historical analysis data; preprocessing the green mine data to obtain target mine data; performing weight distribution processing on the target mine data to obtain an evaluation index weight; updating the evaluation index weight by adopting a preset weight analysis method to obtain a target index weight; calculating a comprehensive score according to the target index weight and an evaluation index in the target mine data; and performing evaluation analysis processing on the comprehensive score to obtain a green mine evaluation result. According to the method, green mine evaluation is realized, and the evaluation accuracy, the index quantification degree and the standardization degree are improved. The method can be widely applied to the technical field of mine development.

Description

Technical Field

[0001] The present invention relates to the technical field of mine development, and in particular to a green mine evaluation method, device and storage medium. Background Art

[0002] With the increasing attention paid to environmental protection and sustainable development, the problems of land destruction, resource waste, ecological damage and environmental pollution caused by traditional sand and gravel mining methods have been increasingly criticized and restricted. At present, the green construction evaluation of sand and gravel mines relies on qualitative analysis and subjective judgment. The evaluation results are difficult to compare horizontally and track vertically, and the degree of standardization is low. At the same time, the evaluation process is highly subjective, and it is difficult to accurately reflect the green construction level of the mine. The indicators are low in quantification and the evaluation accuracy is low.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The embodiments of the present invention provide a green mine evaluation method, device and storage medium, which effectively improve the evaluation accuracy, index quantification degree and standardization degree.

[0005] On the one hand, an embodiment of the present invention provides a green mine evaluation method, comprising the following steps:

[0006] Acquire green mine data, including field survey data, questionnaire survey data, and historical analysis data;

[0007] Preprocessing the green mine data to obtain target mine data;

[0008] Performing weight distribution processing on the target mine data to obtain evaluation index weights;

[0009] The evaluation index weight is updated by using a preset weight analysis method to obtain the target index weight, wherein the preset weight analysis method includes a hierarchical analysis method, a fuzzy comprehensive evaluation method, a back propagation neural network algorithm or a comprehensive index method;

[0010] Calculate a comprehensive score based on the target indicator weight and the evaluation indicators in the target mine data;

[0011] The comprehensive score is evaluated and analyzed to obtain a green mine evaluation result.

[0012] In some embodiments, the obtaining of green mine data includes:

[0013] Acquire field survey data from field survey record information of the mining area environment, wherein the field survey data includes mining area layout, mine appearance, and green coverage rate;

[0014] Acquire development status data from mining area development status record information, wherein the development status data includes natural landscape protection measures, land reclamation status, and surrounding ecological environment impact information;

[0015] Acquire development method data from the mining area resource development method record information, wherein the development method data includes development technology information, development equipment information, and development method compliance;

[0016] combining the field investigation data, the development status data and the development mode data to obtain the field investigation data;

[0017] Obtaining stakeholder feedback information from the questionnaire survey record information, wherein the stakeholder feedback information includes mining area environmental quality feedback information, resource utilization efficiency feedback information, and corporate social responsibility fulfillment feedback information;

[0018] Using statistical methods to quantitatively analyze the feedback information from stakeholders to obtain quantitative results of the feedback information;

[0019] Combining the stakeholder feedback information and the quantified results of the feedback information to obtain the questionnaire survey data;

[0020] Acquire historical production data from production record information of mining enterprises, wherein the historical production data includes output, product quality, and resource utilization rate;

[0021] Acquire energy consumption data from historical energy consumption record information, wherein the energy consumption data includes electricity consumption data, water resource consumption data, fuel consumption data, and energy consumption change trend;

[0022] Obtaining pollutant emission data from historical pollutant emission record information, wherein the pollutant emission data includes dust emission, wastewater emission, waste gas emission, and compliance with emission standards;

[0023] Performing energy conservation and emission reduction analysis on the energy consumption data and the pollutant emission data to obtain energy conservation and emission reduction effect data;

[0024] The historical production data, the energy consumption data, the pollutant emission data and the energy conservation and emission reduction effect data are combined to obtain the historical analysis data.

[0025] In some embodiments, the preprocessing of the green mine data to obtain the target mine data includes:

[0026] Performing outlier cleaning on the green mine data to obtain first mine data;

[0027] Cleaning incomplete values ​​of the first mine data to obtain second mine data;

[0028] performing data quantitative processing on the second mine data to obtain third mine data;

[0029] Performing statistical analysis on the third mine data to obtain fourth mine data;

[0030] Performing numerical standardization processing on the fourth mine data to obtain fifth mine data, wherein the numerical standardization processing includes minimum-maximum standardization and Z score standardization;

[0031] Quantitatively processing the fifth mine data to obtain sixth mine data;

[0032] The sixth mine data is verified to obtain the target mine data, wherein the verification process includes cross-validation and historical data comparison.

[0033] In some embodiments, the weight distribution process is performed on the target mine data to obtain the evaluation index weight, including:

[0034] According to the result of the indicator importance analysis, weights are assigned to the evaluation indicators in the target mine data to obtain first indicator weights, where the first indicator weights are used to characterize the contribution of the evaluation indicators to the green mine construction goal;

[0035] Performing weight verification on the weight of the first indicator by using a sensitivity analysis method to obtain a weight verification result;

[0036] According to the weight verification result, the first indicator weight is adjusted to obtain the evaluation indicator weight.

[0037] In some embodiments, the updating of the evaluation index weights by using a preset weight analysis method to obtain the target index weights includes:

[0038] Determine the indicator correlation relationship of the evaluation indicators according to the indicator characteristic information and the evaluation purpose information, wherein the correlation relationship includes a hierarchical relationship, a quantitative numerical fuzzy relationship, a nonlinear relationship, and a combinable relationship, the indicator characteristic information includes diversity information, correlation information, dynamic information, and fuzzy information, and the evaluation purpose information includes comprehensive information, objective information, guiding information, and operability information;

[0039] When the indicator correlation relationship is the hierarchical relationship, the hierarchical structure model is constructed by using the hierarchical analysis method, and the hierarchical structure model is used to perform consistency check processing on the evaluation indicator and the evaluation indicator weight to obtain the target indicator weight;

[0040] When the indicator correlation relationship is the quantitative numerical fuzzy relationship, the fuzzy comprehensive evaluation method is used to construct a fuzzy relationship matrix, and the fuzzy relationship matrix is ​​used to quantitatively process the evaluation indicators to obtain the target indicator weights;

[0041] When the indicator correlation relationship is the nonlinear relationship, the back propagation neural network algorithm is used to retrain the evaluation indicator weight to obtain the target indicator weight;

[0042] When the indicator correlation relationship is the combinable relationship, the comprehensive index method is used to perform index comprehensive processing on the weights of multiple evaluation indicators to obtain the target indicator weight.

[0043] In some embodiments, the calculating of the comprehensive score according to the target indicator weight and the evaluation indicators in the target mine data includes:

[0044] According to the target indicator weight and the indicator value of the evaluation indicator, the comprehensive score is calculated by a comprehensive score calculation formula, and the comprehensive score calculation formula is:

[0045]

[0046] In the formula, Z is the comprehensive score, N is the total number of evaluation indicators, and b i is the i-th index value, q i is the weight of the i-th target indicator.

[0047] In some embodiments, the comprehensive score is evaluated and analyzed to obtain a green mine evaluation result, including:

[0048] Comparing the comprehensive score with a preset target to obtain a comparison result;

[0049] According to the comparison result, selecting a low-scoring indicator from the evaluation indicators;

[0050] According to a preset question library, problem analysis is performed on the low-scoring indicator to obtain a problem analysis result;

[0051] According to the preset improvement measure library and the problem analysis results, the low-scoring indicators are analyzed to obtain target improvement measures, which include strengthening environmental awareness training, updating production equipment, and optimizing resource allocation;

[0052] According to the impact of green construction results, the plurality of target improvement measures are prioritized to obtain a ranking result;

[0053] According to the ranking results, an implementation plan is set for the target improvement measures to obtain implementation plan results, wherein the implementation plan includes responsibility allocation, timetable, and cost budget;

[0054] According to the preset risk library and the implementation plan results, risk analysis is performed on the target improvement measures to obtain risk response strategies;

[0055] The problem analysis results, the target improvement measures, the implementation plan results and the risk response strategies are combined to obtain the green mine evaluation results.

[0056] On the other hand, an embodiment of the present invention provides a green mine evaluation device, including:

[0057] The first module is used to obtain green mine data, which includes field survey data, questionnaire survey data, and historical analysis data;

[0058] The second module is used to pre-process the green mine data to obtain target mine data;

[0059] The third module is used to perform weight distribution processing on the target mine data to obtain the evaluation index weight;

[0060] The fourth module is used to update the evaluation index weights by using a preset weight analysis method to obtain the target index weights. The preset weight analysis method includes a hierarchical analysis method, a fuzzy comprehensive evaluation method, a back propagation neural network algorithm or a comprehensive index method;

[0061] A fifth module is used to calculate a comprehensive score according to the target indicator weight and the evaluation indicators in the target mine data;

[0062] The sixth module is used to evaluate and analyze the comprehensive score to obtain a green mine evaluation result.

[0063] In another aspect, an embodiment of the present invention provides a computer device, comprising:

[0064] at least one processor;

[0065] at least one memory for storing at least one program;

[0066] When the at least one program is executed by the at least one processor, the at least one processor implements the described method.

[0067] On the other hand, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0068] The beneficial effects of the present invention are as follows:

[0069] The present invention first acquires green mine data, preprocesses the green mine data to obtain target mine data, then performs weight distribution processing on the target mine data to obtain evaluation index weights, and uses a preset weight analysis method to update the evaluation index weights to obtain target index weights, and then calculates a comprehensive score based on the target index weights and the evaluation indexes in the target mine data, and finally evaluates and analyzes the comprehensive score to obtain a green mine evaluation result, thereby realizing green mine evaluation and further improving the evaluation accuracy, index quantification degree and standardization degree.

[0070] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0072] Figure 1 This is a flow chart of a green mine evaluation method according to an embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram of an overall process of evaluating a green mine according to an embodiment of the present invention;

[0074] Figure 3 This is a schematic diagram of the structure of a green mine evaluation device according to an embodiment of the present invention;

[0075] Figure 4 The figure is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0077] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0078] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0080] In the relevant technologies, with the increasing global attention to environmental protection and sustainable development, traditional sand and gravel mining methods have been increasingly criticized and restricted due to their negative impact on the environment. These traditional methods often lead to problems such as land destruction, resource waste, impaired ecological balance, and environmental pollution. In order to meet these challenges and promote the green transformation of the mining industry, relevant specifications provide standardized operating guidelines for the construction of green mines in the sand and gravel industry, which cover the basic requirements of mining area environment, resource development methods, comprehensive resource utilization, energy conservation and emission reduction, scientific and technological innovation and digital mines, enterprise management and corporate image, and set clear goals and directions for green mine construction. However, although construction standards are provided, they do not include a systematic and quantitative evaluation method to measure and verify the green construction results of sand and gravel mines. At present, the green construction evaluation of sand and gravel mines mostly relies on qualitative analysis and subjective judgment, and lacks objective and quantitative evaluation standards and tools. There are deficiencies in existing evaluation methods, including the lack of unified evaluation standards, which makes it difficult to compare evaluation results horizontally and track them vertically, and the degree of standardization is low; the lack of quantitative indicators makes the evaluation process highly subjective and difficult to accurately reflect the green construction level of the mine, the indicator quantification degree is low, and the evaluation accuracy is low; the evaluation results are mainly used for qualitative descriptions, and there is a lack of specific guidance for mine improvement and enhancement.

[0081] In view of this, the embodiments of the present invention provide a green mine evaluation method, device and storage medium to objectively measure the green construction effect of the mine and provide a clear direction and basis for the continuous improvement of the mine. Through quantitative evaluation, the green construction level of the sand and gravel mine is comprehensively measured to provide a scientific basis and guidance for the improvement of the mine. By evaluating the mining area environment, the functional zoning layout, mine appearance, and mining area greening of the mining area are evaluated to ensure that the overall environment of the mining area is clean and beautiful. By evaluating the resource development method, the coordination between resource development and environmental protection, resource protection and rural construction, and the resource conservation and environmental friendliness of the development method are evaluated. By evaluating the comprehensive utilization of resources, the utilization efficiency and method of processing by-products such as stone powder, mud powder, topsoil and slag are evaluated. By evaluating energy conservation and emission reduction, an energy consumption accounting system is established to evaluate the implementation effect of energy conservation and consumption reduction measures, as well as the control of dust and sewage emissions. By evaluating scientific and technological innovation and digital mines, the establishment of scientific and technological research and development teams, the transformation of scientific and technological achievements, the intensity of technological transformation, and the progress of digital mine construction are evaluated. By evaluating the enterprise management and corporate image, the establishment of the enterprise management system, the green mine management system, and the corporate culture, corporate integrity and harmonious development between the enterprise and the land are evaluated. In the evaluation method, the evaluation method of the embodiment of the present invention adopts a quantitative scoring system, and each evaluation indicator is equipped with a corresponding scoring standard and weight. By collecting relevant data, quantitative scoring is performed according to the scoring standard, and the weighted sum is obtained to obtain the final comprehensive score of green mine construction. In data processing, the data collected during the evaluation process is analyzed by specific data processing software to ensure the accuracy and objectivity of the evaluation results. The data processing software can automatically calculate the score, generate an evaluation report, and put forward improvement suggestions. In terms of the evaluation report, the evaluation report records the evaluation process, scoring results and improvement suggestions in detail, providing mining enterprises with clear green construction results feedback and future development directions. In terms of implementation effect, sand and gravel mining enterprises can clearly understand their own advantages and disadvantages in green mine construction, so as to make targeted improvements and promote enterprises to develop in a more environmentally friendly, efficient and sustainable direction.

[0082] A green mine evaluation method provided in an embodiment of the present application relates to the field of mine development technology. A green mine evaluation method provided in an embodiment of the present application can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a green mine evaluation method, etc., but is not limited to the above forms.

[0083] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0084] The following is a detailed explanation of the embodiments of the present application in conjunction with the accompanying drawings:

[0085] Figure 1 is an optional flow chart of a green mine evaluation method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0086] Step S101, obtaining green mine data, where the green mine data includes field survey data, questionnaire survey data, and historical analysis data;

[0087] Step S102, preprocessing the green mine data to obtain target mine data;

[0088] Step S103, performing weight distribution processing on the target mine data to obtain evaluation index weights;

[0089] Step S104: using a preset weight analysis method to update the evaluation index weight to obtain the target index weight, the preset weight analysis method includes a hierarchical analysis method, a fuzzy comprehensive evaluation method, a back propagation neural network algorithm or a comprehensive index method;

[0090] Step S105, calculating a comprehensive score according to the target indicator weight and the evaluation indicators in the target mine data;

[0091] Step S106: Evaluate and analyze the comprehensive score to obtain a green mine evaluation result.

[0092] Steps S101 to S106 shown in the embodiment of the present application realize green mine evaluation, thereby improving the evaluation accuracy, indicator quantification degree and standardization degree.

[0093] In some embodiments, in step S101, obtaining green mine data may include but is not limited to the following steps:

[0094] Obtain field survey data from the field survey record information of the mining area environment, including mining area layout, mine appearance, and green coverage rate;

[0095] Obtaining development status data from mining area development status record information, including natural landscape protection measures, land reclamation status, and surrounding ecological environment impact information;

[0096] Acquire development method data from the mining area resource development method record information, the development method data includes development technology information, development equipment information, and development method compliance;

[0097] The field investigation data, development status data and development mode data are combined to obtain the field investigation data;

[0098] Obtaining stakeholder feedback information from questionnaire records, including feedback information on mining area environmental quality, resource utilization efficiency, and corporate social responsibility performance;

[0099] Use statistical methods to conduct quantitative analysis on feedback information from stakeholders and obtain quantitative results of feedback information;

[0100] Combine the feedback information from stakeholders and the quantitative results of the feedback information to obtain the questionnaire survey data;

[0101] Obtain historical production data from the production record information of mining enterprises, including output, product quality, and resource utilization;

[0102] Obtain energy consumption data from historical energy consumption record information, including electricity consumption data, water resource consumption data, fuel consumption data, and energy consumption change trends;

[0103] Obtain pollutant emission data from historical pollutant emission record information, including dust emission, wastewater emission, waste gas emission, and compliance with emission standards;

[0104] Conduct energy-saving and emission-reduction analysis on energy consumption data and pollutant emission data to obtain energy-saving and emission-reduction effect data;

[0105] Combine historical production data, energy consumption data, pollutant emission data and energy conservation and emission reduction effect data to obtain historical analysis data.

[0106] In some embodiments, in terms of field investigation, field investigation data can be first obtained from the field investigation record information of the mining area environment, wherein the field investigation data can include the layout of the mining area, the mine capacity and appearance, and the green coverage rate. The field investigation record information of the mining area environment can be obtained after a detailed field investigation of the mining area environment. Then, the development status data is obtained from the mining area development status record information, wherein the development status data can include natural landscape protection measures, land reclamation status, and surrounding ecological environment impact information. Then, the development mode data is obtained from the mining area resource development mode record information, wherein the development mode data can include development technology information, development equipment information, and development mode compliance. The mining area resource development mode record information can be obtained after investigating and evaluating the resource development process. Finally, the field investigation data, development status data, and development mode data are combined to obtain the field investigation data. In terms of questionnaire survey, the stakeholder feedback information can be first obtained from the questionnaire survey record information, wherein the stakeholder feedback information can include mining area environmental quality feedback information, resource utilization efficiency feedback information, and corporate social responsibility fulfillment feedback information. Questionnaire survey record information can be obtained after investigating the opinions, feelings and suggestions of stakeholders such as employees of mining enterprises, residents of surrounding communities, and industry experts on the green construction of mines. Then, statistical methods are used to quantitatively analyze the feedback information of stakeholders to obtain the quantitative results of feedback information to quantify the feedback of stakeholders. Finally, the feedback information of stakeholders and the quantitative results of feedback information are combined to obtain questionnaire survey data. In terms of historical analysis data, historical production data can be obtained from the production record information of mining enterprises, where the historical production data can include output, product quality, and resource utilization. Then, energy consumption data can be obtained from historical energy consumption record information, where the energy consumption data can include electricity consumption data, water resource consumption data, fuel consumption data, and energy consumption change trends. Energy consumption change trends are used to characterize the trend of energy consumption over time. Then, pollutant emission data are obtained from historical pollutant emission record information, where the pollutant emission data can include dust emissions, wastewater emissions, waste gas emissions, and compliance with emission standards. Then, energy consumption data and pollutant emission data are analyzed for energy conservation and emission reduction, the effects of energy conservation and emission reduction measures in mines are evaluated, and energy conservation and emission reduction effect data are obtained. Finally, historical production data, energy consumption data, pollutant emission data and energy conservation and emission reduction effect data are combined to obtain historical analysis data. Moreover, historical analysis data can be used to evaluate the progress of scientific and technological innovation and digital mine construction.

[0107] In some embodiments, in step S102, preprocessing the green mine data to obtain target mine data may include but is not limited to the following steps:

[0108] Clean the green mine data for outliers and obtain the first mine data;

[0109] Cleaning the incomplete values ​​of the first mine data to obtain the second mine data;

[0110] Performing data quantitative processing on the second mine data to obtain third mine data;

[0111] Performing statistical analysis on the third mine data to obtain the fourth mine data;

[0112] Performing numerical standardization processing on the fourth mine data to obtain the fifth mine data, wherein the numerical standardization processing includes minimum-maximum standardization and Z score standardization;

[0113] The fifth mine data is quantified to obtain the sixth mine data;

[0114] The sixth mine data is verified and processed to obtain the target mine data. The verification process includes cross-validation and historical data comparison.

[0115] In some embodiments, the green mine data may be first cleaned of outliers to obtain the first mine data, and the first mine data may be cleaned of incomplete values ​​to obtain the second mine data to exclude outliers and incomplete records. Then the second mine data may be subjected to data quantitative processing to obtain the third mine data, wherein the data quantitative processing may be used to convert qualitative data into quantitative data, such as converting the questionnaire survey results into satisfaction scores. Then the third mine data may be subjected to statistical analysis processing to obtain the fourth mine data, wherein the statistical analysis processing may be used to perform necessary statistical analysis on the data, such as calculating the mean, median, standard deviation, etc., to support subsequent evaluation and analysis. Then the fourth mine data may be subjected to numerical standardization processing, and the data may be converted into a standardized numerical form according to the requirements of the evaluation index to obtain the fifth mine data, so as to facilitate use in the evaluation model, and eliminate the influence of different dimensions, so that the data of different indicators can be compared at the same scale. Among them, the numerical standardization processing may include minimum-maximum standardization and Z score standardization. Then the fifth mine data may be subjected to quantitative processing, and clear quantitative standards may be set for each evaluation index to obtain the sixth mine data to ensure the comparability and consistency of the data. Exemplarily, the green coverage rate of the mining area can be expressed as a percentage, calculated by dividing the green area of ​​the mining area by the total greenable area and then multiplying it by 100%. The dust emission can be expressed as the emission per ton of mineral production, calculated by dividing the total amount of dust emitted by the total output of the mineral production in the same period. The qualitative results in the questionnaire survey can be converted into a quantitative satisfaction score, for example, using a 5-point or 10-point system, where 5 or 10 points represent the highest satisfaction. When designing the questionnaire, include quantifiable questions such as "Are you satisfied with the greening of the mining area?" and provide quantitative satisfaction options. And use statistical methods to process the questionnaire data, such as calculating the arithmetic mean of each question to obtain an overall satisfaction score. It is understandable that the quantitative processing method is consistent and repeatable, which can improve the reliability of the evaluation results. Finally, the data of the sixth mine is verified and processed to obtain the target mine data to ensure the accuracy and reliability of the data, wherein the verification process may include cross-validation, historical data comparison, and expert review. Furthermore, after obtaining the target mine data, the target mine data can be entered into the evaluation model for subsequent weight distribution and comprehensive score calculation. Professional data analysis software or programming language can be used for data processing to improve efficiency and accuracy.

[0116] In some embodiments, in step S103, weight distribution processing is performed on the target mine data to obtain evaluation index weights, which may include but is not limited to the following steps:

[0117] According to the results of the indicator importance analysis, weights are assigned to the evaluation indicators in the target mine data to obtain the first indicator weight, which is used to characterize the contribution of the evaluation indicators to the green mine construction goals;

[0118] The weight of the first indicator is verified by sensitivity analysis to obtain the weight verification result;

[0119] According to the weight verification result, the weight of the first indicator is adjusted to obtain the evaluation indicator weight.

[0120] In some embodiments, according to the results of the indicator importance analysis, the evaluation indicators in the target mine data can be assigned weights, and corresponding weights can be assigned to each evaluation indicator to obtain a first indicator weight, wherein the first indicator weight is used to characterize the contribution of the evaluation indicator to the green mine construction goal. For example, the energy conservation and emission reduction indicator may obtain a higher weight due to its direct impact on environmental protection, while the comprehensive resource utilization indicator may obtain a higher weight due to its contribution to resource efficiency and circular economy. It is understandable that an interdisciplinary expert team composed of mining experts, environmental experts, economists, sociologists, etc. can be formed, and the actual impact and importance of each evaluation indicator on the green mine construction can be analyzed and discussed through expert team analysis or through expert meetings, Delphi method or other suitable methods to obtain the indicator importance analysis results to ensure the comprehensiveness and professionalism of the evaluation. Then, the weight of the first indicator is verified by the sensitivity analysis method to verify the rationality of the weight distribution, analyze the impact of the weight change on the evaluation result, and obtain the weight verification result to ensure the stability and reliability of the weight distribution. Finally, according to the weight verification result, the weight of the first indicator is adjusted to obtain the evaluation indicator weight to ensure the fairness and accuracy of the evaluation model. Furthermore, in the process of obtaining the weights of evaluation indicators, a weight record document can be generated by recording in detail the process and results of weight allocation, including the composition of experts, the method of indicator importance analysis, the determination and verification process of weights, etc. The document is used to save records so that the determined weights can be subsequently applied to the comprehensive score calculation to calculate the comprehensive green construction score of the sand and gravel mine.

[0121] In some embodiments, in step S104, the evaluation index weight is updated using a preset weight analysis method to obtain the target index weight, which may include but is not limited to the following steps:

[0122] According to the indicator characteristic information and evaluation purpose information, determine the indicator correlation relationship of the evaluation indicators. The correlation relationship includes hierarchical relationship, quantitative numerical fuzzy relationship, nonlinear relationship, and combinable relationship. The indicator characteristic information includes diversity information, correlation information, dynamic information, and fuzzy information. The evaluation purpose information includes comprehensive information, objective information, guiding information, and operability information.

[0123] When the indicator correlation is a hierarchical relationship, the hierarchical analysis method is used to build a hierarchical structure model, and the hierarchical structure model is used to perform consistency test on the evaluation indicators and the evaluation indicator weights to obtain the target indicator weights;

[0124] When the correlation between indicators is a quantitative numerical fuzzy relationship, the fuzzy comprehensive evaluation method is used to construct a fuzzy relationship matrix, and the fuzzy relationship matrix is ​​used to quantitatively process the evaluation indicators to obtain the target indicator weights;

[0125] When the indicator correlation is nonlinear, the back propagation neural network algorithm is used to retrain the evaluation indicator weights to obtain the target indicator weights;

[0126] When the correlation between indicators is a combinable relationship, the comprehensive index method is used to perform index comprehensive processing on the weights of multiple evaluation indicators to obtain the target indicator weight.

[0127] In some embodiments, the indicator correlation relationship of the evaluation indicator can be determined based on the indicator characteristic information and the evaluation purpose information. Among them, the correlation relationship may include a hierarchical relationship, a quantitative numerical fuzzy relationship, a nonlinear relationship, and a combinable relationship. The indicator characteristic information may include diversity information, correlation information, dynamic information, and fuzzy information. The evaluation purpose information may include comprehensive information, objective information, guiding information, and operability information. Diversity information is used to characterize that the evaluation indicators may include quantitative indicators (such as energy consumption, output) and qualitative indicators (such as corporate culture and social responsibility). Correlation information is used to characterize that there may be a certain correlation between indicators, and the interaction between them needs to be considered. Dynamic information is used to characterize that the weight and importance of indicators may change over time, and an evaluation method that can adapt to changes is required. Fuzzy information is used to characterize that some indicators may be difficult to quantify accurately, and there is ambiguity or uncertainty. Comprehensive information is used to characterize that the evaluation method needs to be able to fully consider all relevant indicators to ensure the comprehensiveness of the evaluation results. Objective information is used to characterize that the evaluation results should minimize the influence of subjective judgment and improve the objectivity of the evaluation. Guiding information is used to characterize that the evaluation results should be able to propose specific improvement directions and measures for mining enterprises. The operability information used to characterize the evaluation method should be easy to implement and convenient for enterprises to operate and understand. When the indicator correlation is a hierarchical relationship, that is, there is a hierarchical relationship between the evaluation indicators, the hierarchical analysis method can be used to construct a hierarchical model, and the hierarchical model can be used to perform pairwise comparison and consistency test on the evaluation indicators and the evaluation indicator weights to obtain the target indicator weights, thereby determining the weights of each indicator relative to the target. When the indicator correlation is a quantitative numerical fuzzy relationship, that is, the quantitative numerical value of the evaluation indicator is imprecise or there is ambiguity, the fuzzy comprehensive evaluation method can be used to construct a fuzzy relationship matrix, and the fuzzy relationship matrix can be used to quantitatively process the evaluation indicators, convert the qualitative evaluation into quantitative analysis, and obtain the target indicator weights. When the indicator correlation is a nonlinear relationship, the back propagation neural network algorithm can be used to retrain the evaluation indicator weights to obtain the target indicator weights. For example, when evaluating scientific and technological innovation and digital mines, multiple technical factors and innovative achievements may be involved, and there may be complex nonlinear relationships between these factors. The back propagation neural network algorithm based on artificial intelligence can handle this situation better. When the indicator correlation is a combinable relationship, the comprehensive index method can be used to perform index comprehensive processing on the weights of multiple evaluation indicators. By combining multiple indicators into one indicator, the target indicator weight is obtained, thereby simplifying the evaluation process. For example, if the evaluation purpose is to measure the overall green construction level of the mine, multiple indicators such as energy conservation and emission reduction, comprehensive resource utilization, etc. can be combined into a "green index" for easy comparison and tracking. It can be understood that in actual applications, the choice of which evaluation method depends on the specific application scenario and needs.For example, if the evaluation index has an obvious hierarchical structure, the analytic hierarchy process (AHP) may be a good choice; if the evaluation index data is uncertain, the fuzzy comprehensive evaluation method may be more suitable. Each method has its own unique advantages and applicable conditions. Therefore, when designing an evaluation model, it is necessary to flexibly select or combine multiple evaluation methods according to the specific situation.

[0128] In some embodiments, in step S105, the comprehensive score is calculated according to the target indicator weight and the evaluation indicators in the target mine data, which may include but is not limited to the following steps:

[0129] According to the target indicator weight and the indicator value of the evaluation indicator, the comprehensive score is calculated by the comprehensive score calculation formula. The comprehensive score calculation formula is:

[0130]

[0131] In the formula, Z is the comprehensive score, N is the total number of evaluation indicators, and b i is the i-th index value, q i is the weight of the i-th target indicator.

[0132] In some embodiments, after calculating the target indicator weight of each evaluation indicator according to the selected evaluation method, the comprehensive score can be calculated by the comprehensive score calculation formula according to the target indicator weight and the indicator value of the evaluation indicator. The comprehensive score calculation formula is: In the formula, Z is the comprehensive score, N is the total number of evaluation indicators, and b i is the i-th index value, q i is the weight of the i-th target indicator. Moreover, after obtaining the comprehensive score, the comprehensive score results can be analyzed to determine the green construction level of the mine and compared with the preset score standard or historical score. Based on the comprehensive score results, an evaluation report can be compiled to record the scoring process, results and improvement suggestions in detail.

[0133] In some embodiments, in step S106, the comprehensive score is evaluated and analyzed to obtain a green mine evaluation result, which may include but is not limited to the following steps:

[0134] Compare the comprehensive score with the preset target to obtain a comparison result;

[0135] According to the comparison results, select low-scoring indicators from the evaluation indicators;

[0136] According to the preset question library, analyze the low-scoring indicators and obtain the analysis results;

[0137] According to the preset improvement measures library and problem analysis results, measures are analyzed for low-scoring indicators to obtain target improvement measures, which include strengthening environmental awareness training, updating production equipment, and optimizing resource allocation;

[0138] According to the impact of green construction results, multiple target improvement measures are prioritized to obtain the ranking results;

[0139] According to the sorting results, set up an implementation plan for the target improvement measures and obtain the implementation plan results. The implementation plan includes responsibility allocation, timetable, and cost budget;

[0140] According to the preset risk library and implementation plan results, conduct risk analysis on the target improvement measures and obtain risk response strategies;

[0141] The problem analysis results, target improvement measures, implementation plan results and risk response strategies are combined to obtain the green mine evaluation results.

[0142] In some embodiments, the comprehensive score can be interpreted in detail first to clarify the level of green construction it represents, and the comprehensive score can be compared with the preset target or industry standard to obtain a comparison result. Then, according to the comparison result, the score of each evaluation indicator is analyzed, the indicators with higher and lower scores are identified, and the low-scoring indicators are selected from the evaluation indicators. Then, according to the preset problem library, the low-scoring indicators are deeply analyzed, the possible causes are diagnosed, and the problem analysis results are obtained, wherein the possible causes may include management defects, insufficient technology, and improper resource allocation. Then, according to the preset improvement measures library and the problem analysis results, the low-scoring indicators are analyzed, and targeted improvement measures are formulated to obtain target improvement measures, wherein the target improvement measures may include strengthening environmental awareness training, updating production equipment, and optimizing resource allocation. Then, according to the impact of green construction results, multiple target improvement measures are prioritized to obtain a ranking result, so as to give priority to solving the problems that have the greatest impact on the effectiveness of green construction. Then, according to the ranking results, the implementation plan is set for the target improvement measures, and a detailed implementation plan is formulated for each improvement measure to obtain the implementation plan results. Among them, the implementation plan may include responsibility allocation, timetable, and cost budget. Then, according to the preset risk library and the results of the implementation plan, the target improvement measures are risk analyzed, the effects of the improvement measures are predicted, the possible risks are evaluated, and corresponding response strategies are formulated to obtain risk response strategies. Finally, the problem analysis results, target improvement measures, implementation plan results and risk response strategies are combined to obtain the green mine evaluation results. Moreover, after obtaining the green mine evaluation results, a result analysis report can be compiled to record the analysis process, problem diagnosis, improvement suggestions and implementation plans in detail. A continuous tracking and evaluation mechanism is established to regularly check the implementation effects of improvement measures and make adjustments as needed.

[0143] In some embodiments, the overall process of conducting green mine assessment is as follows: Figure 2 As shown, we can first conduct on-site investigations, questionnaire surveys, and historical data analysis, and then quantify the indicators, convert the collected data into quantitative values, and assign indicator weights according to the importance of each indicator. Then, based on the indicator values ​​and indicator weights, use the preset evaluation criteria and algorithms to calculate the comprehensive score. Finally, analyze the comprehensive score to obtain analysis results and improvement suggestions.

[0144] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of the present invention first obtain green mine data, pre-process the green mine data, obtain target mine data, then perform weight distribution processing on the target mine data to obtain evaluation index weights, and use a preset weight analysis method to update the evaluation index weights to obtain target index weights, and then calculate a comprehensive score based on the target index weights and the evaluation indexes in the target mine data, and finally evaluate and analyze the comprehensive score to obtain a green mine evaluation result, thereby realizing green mine evaluation, and further improving the evaluation accuracy, index quantification degree and standardization degree. This embodiment objectively measures the green construction effectiveness of mines and provides a clear direction and basis for the continuous improvement of mines. And through quantitative evaluation, it comprehensively measures the green construction level of sand and gravel mines, provides a scientific basis and guidance for the improvement of mines, and helps guide enterprises to continuously improve in green mine construction and promote the sustainable development of the sand and gravel industry.

[0145] like Figure 3 As shown, the embodiment of the present invention further provides a green mine evaluation device, including:

[0146] The first module 801 is used to obtain green mine data, which includes field survey data, questionnaire survey data, and historical analysis data;

[0147] The second module 802 is used to pre-process the green mine data to obtain target mine data;

[0148] The third module 803 is used to perform weight distribution processing on the target mine data to obtain the evaluation index weight;

[0149] The fourth module 804 is used to update the evaluation index weights by using a preset weight analysis method to obtain the target index weights. The preset weight analysis method includes a hierarchical analysis method, a fuzzy comprehensive evaluation method, a back propagation neural network algorithm or a comprehensive index method.

[0150] The fifth module 805 is used to calculate the comprehensive score according to the target indicator weight and the evaluation indicators in the target mine data;

[0151] The sixth module 806 is used to evaluate and analyze the comprehensive score to obtain the green mine evaluation result.

[0152] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0153] like Figure 4 As shown, an embodiment of the present invention further provides a computer device, including:

[0154] at least one processor 901;

[0155] At least one memory 902, used to store at least one program;

[0156] When at least one program is executed by at least one processor, the at least one processor implements Figure 1 The method shown.

[0157] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0158] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, Figure 1 The method shown.

[0159] The contents of the above method embodiments are all applicable to the present storage medium embodiments. The functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0160] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A green mine evaluation method, characterized in that: The following steps are involved: Acquire green mine data, including field survey data, questionnaire survey data, and historical analysis data; Preprocessing the green mine data to obtain target mine data; Performing weight distribution processing on the target mine data to obtain evaluation index weights; The evaluation index weights are updated by using a preset weight analysis method to obtain the target index weights, wherein the preset weight analysis method includes a hierarchical analysis method, a fuzzy comprehensive evaluation method, a back propagation neural network algorithm or a comprehensive index method; Calculate a comprehensive score based on the target indicator weight and the evaluation indicators in the target mine data; The comprehensive score is evaluated and analyzed to obtain a green mine evaluation result.

2. The method according to claim 1, characterized in that: The obtaining of green mine data includes: Acquire field survey data from field survey record information of the mining area environment, wherein the field survey data includes mining area layout, mine appearance, and green coverage rate; Acquire development status data from mining area development status record information, wherein the development status data includes natural landscape protection measures, land reclamation status, and surrounding ecological environment impact information; Acquire development method data from the mining area resource development method record information, wherein the development method data includes development technology information, development equipment information, and development method compliance; combining the field investigation data, the development status data and the development mode data to obtain the field investigation data; Obtaining stakeholder feedback information from the questionnaire survey record information, wherein the stakeholder feedback information includes mining area environmental quality feedback information, resource utilization efficiency feedback information, and corporate social responsibility fulfillment feedback information; Using statistical methods to quantitatively analyze the feedback information from stakeholders to obtain quantitative results of the feedback information; Combining the stakeholder feedback information and the quantified results of the feedback information to obtain the questionnaire survey data; Acquire historical production data from production record information of mining enterprises, wherein the historical production data includes output, product quality, and resource utilization rate; Acquire energy consumption data from historical energy consumption record information, wherein the energy consumption data includes electricity consumption data, water resource consumption data, fuel consumption data, and energy consumption change trend; Obtaining pollutant emission data from historical pollutant emission record information, wherein the pollutant emission data includes dust emission, wastewater emission, waste gas emission, and compliance with emission standards; Performing energy conservation and emission reduction analysis on the energy consumption data and the pollutant emission data to obtain energy conservation and emission reduction effect data; The historical production data, the energy consumption data, the pollutant emission data and the energy conservation and emission reduction effect data are combined to obtain the historical analysis data.

3. The method according to claim 1, characterized in that The preprocessing of the green mine data to obtain target mine data includes: Performing outlier cleaning on the green mine data to obtain first mine data; Cleaning incomplete values ​​of the first mine data to obtain second mine data; performing data quantitative processing on the second mine data to obtain third mine data; Performing statistical analysis on the third mine data to obtain fourth mine data; Performing numerical standardization processing on the fourth mine data to obtain fifth mine data, wherein the numerical standardization processing includes minimum-maximum standardization and Z score standardization; Quantitatively processing the fifth mine data to obtain sixth mine data; The sixth mine data is verified to obtain the target mine data, wherein the verification process includes cross-validation and historical data comparison.

4. The method according to claim 1, characterized in that: The step of performing weight distribution processing on the target mine data to obtain evaluation index weights includes: According to the result of the indicator importance analysis, weights are assigned to the evaluation indicators in the target mine data to obtain first indicator weights, where the first indicator weights are used to characterize the contribution of the evaluation indicators to the green mine construction goal; Performing weight verification on the weight of the first indicator by using a sensitivity analysis method to obtain a weight verification result; According to the weight verification result, the first indicator weight is adjusted to obtain the evaluation indicator weight.

5. The method according to claim 1, characterized in that The method of updating the evaluation index weights by using a preset weight analysis method to obtain the target index weights includes: Determine the indicator correlation relationship of the evaluation indicators according to the indicator characteristic information and the evaluation purpose information, wherein the correlation relationship includes a hierarchical relationship, a quantitative numerical fuzzy relationship, a nonlinear relationship, and a combinable relationship, the indicator characteristic information includes diversity information, correlation information, dynamic information, and fuzzy information, and the evaluation purpose information includes comprehensive information, objective information, guiding information, and operability information; When the indicator correlation relationship is the hierarchical relationship, the hierarchical structure model is constructed by using the hierarchical analysis method, and the hierarchical structure model is used to perform consistency check processing on the evaluation indicator and the evaluation indicator weight to obtain the target indicator weight; When the indicator correlation relationship is the quantitative numerical fuzzy relationship, the fuzzy comprehensive evaluation method is used to construct a fuzzy relationship matrix, and the fuzzy relationship matrix is ​​used to quantitatively process the evaluation indicators to obtain the target indicator weights; When the indicator correlation relationship is the nonlinear relationship, the back propagation neural network algorithm is used to retrain the evaluation indicator weight to obtain the target indicator weight; When the indicator correlation relationship is the combinable relationship, the comprehensive index method is used to perform index comprehensive processing on the weights of multiple evaluation indicators to obtain the target indicator weight.

6. The method according to claim 1, characterized in that The calculating of the comprehensive score according to the target indicator weight and the evaluation indicators in the target mine data includes: According to the target indicator weight and the indicator value of the evaluation indicator, the comprehensive score is calculated by a comprehensive score calculation formula, and the comprehensive score calculation formula is: In the formula, Z is the comprehensive score, N is the total number of evaluation indicators, and b i is the i-th index value, q i is the weight of the i-th target indicator.

7. The method according to claim 1, characterized in that The comprehensive score is evaluated and analyzed to obtain a green mine evaluation result, including: Comparing the comprehensive score with a preset target to obtain a comparison result; According to the comparison result, selecting a low-scoring indicator from the evaluation indicators; According to a preset question library, problem analysis is performed on the low-scoring indicator to obtain a problem analysis result; According to the preset improvement measure library and the problem analysis results, the low-scoring indicators are analyzed to obtain target improvement measures, which include strengthening environmental awareness training, updating production equipment, and optimizing resource allocation; According to the impact of green construction results, the plurality of target improvement measures are prioritized to obtain a ranking result; According to the ranking results, an implementation plan is set for the target improvement measures to obtain implementation plan results, wherein the implementation plan includes responsibility allocation, timetable, and cost budget; According to the preset risk library and the implementation plan results, risk analysis is performed on the target improvement measures to obtain risk response strategies; The problem analysis results, the target improvement measures, the implementation plan results and the risk response strategies are combined to obtain the green mine evaluation results.

8. A green mine evaluation device, characterized in that: include: The first module is used to obtain green mine data, which includes field survey data, questionnaire survey data, and historical analysis data; The second module is used to pre-process the green mine data to obtain target mine data; The third module is used to perform weight distribution processing on the target mine data to obtain the evaluation index weight; The fourth module is used to update the evaluation index weights by using a preset weight analysis method to obtain the target index weights. The preset weight analysis method includes a hierarchical analysis method, a fuzzy comprehensive evaluation method, a back propagation neural network algorithm or a comprehensive index method; A fifth module is used to calculate a comprehensive score according to the target indicator weight and the evaluation indicators in the target mine data; The sixth module is used to evaluate and analyze the comprehensive score to obtain a green mine evaluation result.

9. A computer device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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