An intelligent assessment method and system for mine tailings ecological problems

Abstract

The present application provides an intelligent assessment method and system for mine tailings ecological problems. The method debugs the tailings ecological restoration estimation results based on the independent tailings ecological restoration data debugging coefficient to obtain the independent tailings ecological restoration data results corresponding to the target mine event label. Since the tailings ecological restoration estimation results are obtained by evaluating the characteristics of the tailings ecological information itself, they reflect the quality of the tailings ecological information at the objective level. The tailings ecological restoration estimation results are debugged according to the deviation corresponding to the significant characteristics of the mine event. The independent tailings ecological restoration data results obtained by debugging are close to the evaluation results obtained based on the mine event itself. That is, the tailings ecological information is independently evaluated based on the subjective quality of the mine event and the objective quality of the tailings ecological information, so that the independent tailings ecological restoration data results meet the uniqueness of individual mine events, thereby improving the accuracy of the independent tailings ecological restoration data results.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent assessment of tailings ecological problems, and specifically to a method and system for intelligent assessment of mine tailings ecological problems. Background Art

[0002] After mining, there will be many ecological damage problems, such as metal slag pollution of soil and water sources or dust pollution and other pollution. At present, relevant technical personnel are used to conduct sampling and analysis, so as to evaluate the pollution information of samples in the area. It is impossible to conduct a comprehensive pollution information assessment. Therefore, it is necessary to intelligently identify and evaluate the ecological environment. However, how to conduct real-time evaluation of the effect of restoration is a problem that is difficult to solve at present. Summary of the Invention

[0003] In order to improve the technical problems existing in related technologies, this application provides an intelligent assessment method and system for ecological problems of mine tailings.

[0004] In a first aspect, a method for intelligently assessing ecological issues of mine tailings is provided, the method comprising:

[0005] Obtain tailings ecological information and target mine event labels that require assessment;

[0006] Extracting tailings ecological restoration element characteristics corresponding to the tailings ecological information that needs to be evaluated;

[0007] Determine the estimated results of tailings ecological restoration based on the tailings ecological information that needs to be evaluated, in combination with the characteristics of the tailings ecological restoration elements;

[0008] Determining the independent tailings ecological restoration data debugging coefficient corresponding to the target mine event label by combining the tailings ecological restoration element characteristics and the mine event significance characteristics corresponding to the target mine event label through an independent tailings ecological restoration data optimization model; the mine event significance characteristics are data reflecting the significance characteristics of the mine event;

[0009] Based on the tailings ecological restoration estimation result and the independent tailings ecological restoration data debugging coefficient, the independent tailings ecological restoration data result corresponding to the target mine event label is determined.

[0010] In this application, the method further comprises:

[0011] Acquire a plurality of reference tailings ecological information corresponding to the target mine event label, each reference tailings ecological information being annotated with an independent tailings ecological restoration data assessment value corresponding to the target mine event label;

[0012] The plurality of reference tailings ecological information are loaded into a unique mining event significant feature extraction model respectively, and the unique mining event significant features corresponding to each of the plurality of reference tailings ecological information are obtained through the unique mining event significant feature extraction model; the unique mining event significant features are significant features of the reference tailings ecological information corresponding to the unique mining event significant features;

[0013] Obtaining the maximum value and the minimum value of independent tailings ecological restoration data corresponding to each of the reference tailings ecological information and the target mine event label;

[0014] For each reference tailings ecological information, based on the independent tailings ecological restoration data evaluation value corresponding to the target mine event label, the maximum value of the independent tailings ecological restoration data, and the minimum value of the independent tailings ecological restoration data, the uniqueness of the target mine event label to the reference tailings ecological information is obtained;

[0015] Based on the uniqueness of the target mine event label to each reference tailings ecological information and the unique mine event significance characteristics corresponding to each reference tailings ecological information, the mine event significance characteristics corresponding to the target mine event label are obtained.

[0016] In this application, the extraction of tailings ecological restoration element characteristics corresponding to the tailings ecological information that needs to be evaluated includes:

[0017] Obtain a model for extracting characteristics of tailings ecological restoration elements;

[0018] The tailings ecological information that needs to be evaluated is loaded into the tailings ecological restoration factor feature extraction model, and the tailings ecological restoration factor features are output through the tailings ecological restoration factor feature extraction model.

[0019] In the present application, the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model are jointly trained; the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model share a common standard local model and respectively include corresponding output local models; the steps of jointly training the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model include:

[0020] Obtaining a first tailings ecological information example set, a second tailings ecological information example set, the tailings ecological restoration factor feature extraction model, and the unique mining matter significant feature extraction model; each first tailings ecological information example in the first tailings ecological information example set has a tailings ecological restoration factor feature configuration directory, and each second tailings ecological information example in the second tailings ecological information example set has a unique mining matter significant feature configuration directory;

[0021] Loading the first tailings ecological information example into the tailings ecological restoration factor feature extraction model, performing feature extraction on the first tailings ecological information example through a standard local model of the tailings ecological restoration factor feature extraction model, and outputting a tailings ecological restoration factor feature regression analysis result through an output local model of the tailings ecological restoration factor feature extraction model;

[0022] Loading the second tailings ecological information example into the unique mining event significant feature extraction model, performing feature extraction on the second tailings ecological information example through a standard local model of the unique mining event significant feature extraction model, and outputting a unique mining event significant feature regression analysis result through an output local model of the unique mining event significant feature extraction model;

[0023] Based on the regression analysis results of the tailings ecological restoration factor characteristics and the tailings ecological restoration factor characteristic configuration catalog, as well as the regression analysis results of the unique mining matter significant characteristics and the unique mining matter significant characteristics configuration catalog, the tailings ecological restoration factor characteristic extraction model and the unique mining matter significant characteristics extraction model are trained together.

[0024] In this application, based on the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, as well as the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory, the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model are jointly trained, including:

[0025] A first evaluation index algorithm is established based on the distinction between the regression analysis results of the tailings ecological restoration factor characteristics and the configuration catalog of the tailings ecological restoration factor characteristics, and a second evaluation index algorithm is established based on the distinction between the regression analysis results of the unique mining event significant characteristics and the configuration catalog of the unique mining event significant characteristics;

[0026] A first model pyramid coefficient is obtained by minimizing the first evaluation index algorithm, and a second model pyramid coefficient is obtained by minimizing the second evaluation index algorithm;

[0027] Combined with the first model pyramid coefficient and the second model pyramid coefficient, the tailings ecological restoration factor feature extraction model and the unique mining event significance feature extraction model are jointly trained.

[0028] In this application, the training steps of the independent tailings ecological restoration data optimization model include:

[0029] Obtaining a third tailings ecological information example corresponding to the example mine event tag and the independent tailings ecological restoration data optimization model; the third tailings ecological information example contains an independent tailings ecological restoration data debugging coefficient configuration directory corresponding to the example mine event tag;

[0030] Extracting a tailings ecological restoration element feature example corresponding to the third tailings ecological information example;

[0031] Obtaining an example of a significant feature of a mining matter corresponding to the example mining matter label;

[0032] By using the independent tailings ecological restoration data optimization model, combined with the splicing results of the tailings ecological restoration element feature examples and the mine event significant feature examples, the independent tailings ecological restoration data debugging coefficient regression analysis results corresponding to the example mine event labels are obtained;

[0033] Based on the regression analysis results of the independent tailings ecological restoration data debugging coefficients and the independent tailings ecological restoration data debugging coefficient configuration directory, the independent tailings ecological restoration data optimization model is trained.

[0034] In this application, the third tailings ecological information example corresponds to more than one example mine matter tag;

[0035] The steps for obtaining the independent tailings ecological restoration data debugging coefficient configuration directory include:

[0036] Obtaining independent tailings ecological restoration data result examples for each of the example mining event tags corresponding to the third tailings ecological information example;

[0037] Based on the independent tailings ecological restoration data result examples, an independent tailings ecological restoration data mean value example is obtained;

[0038] Based on each of the independent tailings ecological restoration data result examples and the independent tailings ecological restoration data mean examples, the independent tailings ecological restoration data debugging coefficient examples corresponding to each of the example mine event labels are obtained, and the independent tailings ecological restoration data debugging coefficient examples are determined as the independent tailings ecological restoration data debugging coefficient configuration directory corresponding to the third tailings ecological information example and each of the example mine event labels.

[0039] In this application, the step of obtaining the mining matter significant feature example corresponding to the example mining matter label includes:

[0040] Obtaining a unique mining matter significant feature example corresponding to the third tailings ecological information example;

[0041] Obtain an example of independent tailings ecological restoration data results corresponding to the example mine event label;

[0042] Based on the independent tailings ecological restoration data result example and the unique mining matter significant feature example, the mining matter significant feature example corresponding to the example mining matter label is obtained.

[0043] In this application, the steps for obtaining the tailings ecological restoration element feature example and the unique mining event significant feature example corresponding to the third tailings ecological information example include:

[0044] Obtain a model for extracting features of tailings ecological restoration elements and a model for extracting significant features of unique mining events;

[0045] Loading the third tailings ecological information example into the tailings ecological restoration factor feature extraction model, performing feature extraction on the third tailings ecological information example through a standard local model of the tailings ecological restoration factor feature extraction model, and outputting the tailings ecological restoration factor feature example through an output local model of the tailings ecological restoration factor feature extraction model;

[0046] The third tailings ecological information example is loaded into the unique mining matter significant feature extraction model, features of the third tailings ecological information example are extracted through the standard local model of the unique mining matter significant feature extraction model, and the unique mining matter significant feature example is output through the output local model of the unique mining matter significant feature extraction model.

[0047] In this application, the example mining event label corresponds to no less than two third tailings ecological information examples; the independent tailings ecological restoration data result example is an independent tailings ecological restoration data evaluation value example; the example of the mining event significance feature corresponding to the example mining event label obtained based on the independent tailings ecological restoration data result example and the unique mining event significance feature example includes:

[0048] Obtaining no less than two independent tailings ecological restoration data maximum value examples and independent tailings ecological restoration data minimum value examples of the third tailings ecological information example;

[0049] For one of the third tailings ecological information examples, based on the independent tailings ecological restoration data evaluation value example, the independent tailings ecological restoration data maximum value example, and the independent tailings ecological restoration data minimum value example corresponding to the example mine event label, the uniqueness of the example mine event label to the third tailings ecological information example is obtained;

[0050] Based on the uniqueness of the example mining matter label to each of the third tailings ecological information examples, and the unique mining matter significance feature examples corresponding to each of the third tailings ecological information examples, the mining matter significance feature examples corresponding to the example mining matter label are obtained.

[0051] In this application, the characteristic layers of the unique mining matter significant feature examples are several; the mining matter significant feature examples corresponding to the example mining matter labels are obtained based on the uniqueness and the unique mining matter significant feature examples, including:

[0052] In combination with the degree of uniqueness, the characteristic values ​​of the unique mining matter significant feature examples at each layer are adjusted to obtain the mining matter significant feature examples corresponding to the example mining matter labels.

[0053] In this application, the tailings ecological information that needs to be evaluated is a plurality of items; the independent tailings ecological restoration data result is an independent tailings ecological restoration data evaluation value; and the method further includes:

[0054] Obtaining an independent tailings ecological restoration data assessment value for each tailings ecological information that needs to be assessed, based on the target mine event tag;

[0055] According to the independent tailings ecological restoration data evaluation value, a preset number of target tailings ecological information are selected from the tailings ecological information that needs to be evaluated;

[0056] The target tailings ecological information is output to the terminal where the target mine event tag is located.

[0057] In a second aspect, an intelligent assessment system for ecological problems of mine tailings is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0058] The embodiments of the present application provide an intelligent assessment method and system for mine tailings ecological problems. The method first determines a tailings ecological restoration estimate based on the tailings ecological restoration element characteristics of the tailings ecological information to be assessed, and then determines an independent tailings ecological restoration data debugging coefficient based on the tailings ecological restoration element characteristics and the significant characteristics of the mine matter corresponding to the target mine matter label. The tailings ecological restoration estimate is then debugged based on the independent tailings ecological restoration data debugging coefficient to obtain an independent tailings ecological restoration data result corresponding to the target mine matter label. In this way, since the tailings ecological restoration estimate is obtained by evaluating the characteristics of the tailings ecological information itself, it reflects the quality of the tailings ecological information at an objective level. The tailings ecological restoration estimate is debugged based on the deviation corresponding to the significant characteristics of the mine matter, and the debugged independent tailings ecological restoration data result approaches the evaluation result obtained based on the mine matter itself. That is, the tailings ecological restoration data is independently evaluated based on the subjective quality of the mine matter and the objective quality of the tailings ecological information, so that the independent tailings ecological restoration data result meets the uniqueness of the individual mine matter, thereby improving the accuracy of the independent tailings ecological restoration data result. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 This is a flow chart of a method for intelligently assessing ecological issues in mine tailings provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0062] See also Figure 1 , shows a method for intelligent assessment of mine tailings ecological problems, which may include the technical solutions described in the following steps 202-210.

[0063] Step 202: Obtain tailings ecological information and target mine item labels that need to be evaluated.

[0064] Among them, the tailings ecological information that needs to be evaluated is the ecological information of the restoration location, such as: metal mineral information and soil information in the area, etc., which can be obtained manually or through artificial intelligence networks during actual operations.

[0065] The target mining event tag is the target mining event's mining event tag, which describes the mine location information of the mining event. By simulating the target mining event's perception of tailings ecological information, the tailings ecological information required for evaluation is evaluated, ensuring that the evaluation results are consistent with the target mining event.

[0066] Specifically, an intelligent assessment application for ecological problems of mine tailings is running on the terminal, and the target mine matter is registered with a target mine matter label based on the intelligent assessment application for ecological problems of mine tailings.

[0067] For example, in this project, the tailings ecological information and target mine matter labels that need to be evaluated are obtained through both manual and artificial intelligence, which can evaluate the accuracy of the data obtained by artificial intelligence.

[0068] Among them, the comprehensive management project of mining geological environment in coal mining subsidence areas comprehensively collected various types of data such as county meteorology, hydrology, basic geology, hydro-engineering environmental geology, geological disasters and social economy, construction planning, town and village-level district planning, satellite images, etc., and obtained a large amount of first-hand data through ground surveys, drilling, mountain engineering, remote sensing interpretation, testing and experiments, and new technologies and methods such as InSAR and drone aerial photography, laying a solid foundation for the compilation of results. The project leveraged existing data on geological hazards, engineering geology, hydrogeology, environmental geology, and geotechnical engineering surveys within and around Aba County. Incorporating digital elevation models, remote sensing imagery, and interpretation, the project systematically analyzed the key hazard-prone conditions associated with geological hazards in Aba County, including topography, geological structure, engineering geological formations, meteorology and hydrology, slope structure, prone-to-collapse and -slide formations, and human engineering activities. Based on surveys of hazard-prone geological conditions, typical geological hazards, a comprehensive review of existing geological hazards and potential hazards, an investigation of new geological hazards and potential hazards, and a review of the effectiveness of remediation projects, the project conducted a comprehensive analysis and summary of the hazard-prone geological conditions, disaster patterns, formation mechanisms, and development and distribution patterns for different types of hazards in Aba County. The project identified 28 new hazard sources and 47 high-risk or higher slopes. This method was then used to monitor the repair status after restoration, achieving an accuracy of over 90 percent.

[0069] Step 204: extract tailings ecological restoration element characteristics corresponding to the tailings ecological information that needs to be evaluated.

[0070] For example, the characteristics of tailings ecological restoration elements are understood as topography, climate characteristics, hydrological conditions, soil physical, chemical and biological characteristics, topsoil conditions, potential pollution and other factors.

[0071] In one possible embodiment, step 204 includes: obtaining a tailings ecological restoration factor feature extraction model; loading the tailings ecological information that needs to be evaluated into the tailings ecological restoration factor feature extraction model, and outputting the tailings ecological restoration factor features through the tailings ecological restoration factor feature extraction model.

[0072] For example, the feature extraction model can be understood as a convolutional neural network in the prior art.

[0073] The tailings restoration feature extraction model is a machine learning model trained to extract tailings restoration feature information. The model can be trained using tailings ecological information samples and a tailings restoration feature configuration directory.

[0074] It can be understood that the tailings ecological restoration factor characteristics extracted by the general machine learning model with the ability to extract tailings ecological restoration factor characteristics meet the requirements of the embodiment of this application for tailings ecological restoration factor characteristics. The general machine learning model with the ability to extract tailings ecological restoration factor characteristics can be used as the tailings ecological restoration factor characteristic extraction model of the embodiment of this application.

[0075] Specifically, the computer equipment obtains the tailings ecological information that needs to be evaluated and the tailings ecological restoration factor feature extraction model, and the computer equipment loads the tailings ecological information that needs to be evaluated into the tailings ecological restoration factor feature extraction model to obtain the tailings ecological restoration factor features output by the tailings ecological restoration factor feature extraction model.

[0076] For example, in this application, the characteristics of the tailings ecological restoration elements are to carry out comprehensive management and ecological restoration for the occupation and damage of land and vegetation resources by mine slag, mine geological safety disasters and other ecological problems.

[0077] Step 206: Determine the tailings ecological restoration estimation result of the tailings ecological information that needs to be evaluated based on the tailings ecological restoration element characteristics.

[0078] For example, the preliminary assessment in the estimated results of tailings ecological restoration can be understood as an assessment of all the characteristics of the tailings ecological restoration elements. The assessment here is not based on the impact weights, and only theoretical influencing factors are evaluated in this step.

[0079] The following technical methods can usually be used to obtain the estimated results of tailings ecological restoration: "Analytic Hierarchy Process": This method is used to determine the weight of each signal, and decompose the relevant elements of the decision-making problem into levels such as goals, criteria, and plans through a hierarchical model, and conduct qualitative and quantitative analysis on this basis.

[0080] ‌Long Short-Term Memory Network‌: This is a technology that trains predictive models based on historical data, optimizes model parameters through cross-validation, and predicts the future state of the device.

[0081] Cross-validation: This is a model evaluation technique that ensures the stability of model performance by splitting the dataset into multiple subsets for multiple training and testing. Common cross-validation methods include k-fold cross-validation and leave-one-out cross-validation.

[0082] Data Mining: Through data cleaning, integration, and preprocessing, we ensure data accuracy and consistency, providing a reliable data foundation for strategic assessment. Data mining techniques also include confusion matrices, which are used to evaluate the performance of classification models.

[0083] According to the characteristics of tailings ecological restoration elements and the significant characteristics of mining matters corresponding to the target mining matter labels, the independent tailings ecological restoration data debugging coefficient corresponding to the target mining matter labels is determined.

[0084] Among them, the significant characteristics of mining events are data that reflect the significant characteristics of mining events, and the significant characteristics of mining events are correlated with the effects of mining events on tailings ecological information.

[0085] In this application, considering that different mining events have different evaluation standards for tailings ecological information, and the significance of mining events is an important factor affecting the uniqueness of mining events, the coefficient of the significance characteristic of mining events is introduced. The significance characteristic of mining events is a coefficient associated with the effect of mining events on tailings ecological information.

[0086] In one possible implementation, the mining event significance features may include several feature layers, each corresponding to a significance characteristic. The feature value of each feature layer can be used to represent the score of the mining event in the corresponding significance characteristic. This score is related not only to the significance characteristic of the mining event itself, but also to the effect of the mining event on tailings ecological information.

[0087] Among them, the independent tailings ecological restoration data debugging coefficient is used to debug the tailings ecological restoration estimation results, so that the independent tailings ecological restoration data results obtained by debugging match the restoration effect of the target mining matter.

[0088] In one possible implementation embodiment, the significant features of the mining matter corresponding to the target mining matter label can be predetermined, and the computer device can obtain the significant features of the mining matter corresponding to the loaded target mining matter label, or extract the significant features of the mining matter corresponding to the target mining matter label from the pre-stored significant features of the mining matter.

[0089] In one possible implementation embodiment, step 208 includes: obtaining an independent tailings ecological restoration data optimization model; and obtaining an independent tailings ecological restoration data debugging coefficient based on the characteristics of tailings ecological restoration elements and the significant characteristics of mining matters through the independent tailings ecological restoration data optimization model.

[0090] The independent tailings restoration data optimization model is a machine learning model trained to calculate independent tailings restoration data tuning coefficients. The model can be trained using examples of tailings restoration element characteristics, examples of significant mining event characteristics, and a configuration directory for independent tailings restoration data tuning coefficients.

[0091] In one possible embodiment, the computer device obtains the independent tailings ecological restoration data debugging coefficient through the independent tailings ecological restoration data optimization model based on the splicing results of the tailings ecological restoration element characteristics and the significant characteristics of mining matters.

[0092] Specifically, the computer device obtains the tailings ecological restoration factor characteristics, the mine event significant characteristics, and the independent tailings ecological restoration data optimization model. The computer device first determines the splicing result of the tailings ecological restoration factor characteristics and the mine event significant characteristics, and then loads the splicing result into the independent tailings ecological restoration data optimization model to obtain the independent tailings ecological restoration data debugging coefficient output by the independent tailings ecological restoration data optimization model. Alternatively, the computer device loads the tailings ecological restoration factor characteristics and the mine event significant characteristics into the independent tailings ecological restoration data optimization model. The independent tailings ecological restoration data optimization model first determines the splicing result of the tailings ecological restoration factor characteristics and the mine event significant characteristics, and then outputs the independent tailings ecological restoration data debugging coefficient based on the splicing result.

[0093] Step 210 , based on the tailings ecological restoration estimation result and the independent tailings ecological restoration data debugging coefficient, determine the independent tailings ecological restoration data result corresponding to the target mine event label.

[0094] During the specific implementation process, the quality of the repair can be monitored to ensure the construction quality of the project and the effect of the subsequent repair.

[0095] Among them, the independent tailings ecological restoration data results are the implementation assessment results of the tailings ecological information that needs to be evaluated by simulating the target mining matters. This step is aimed at the current tailings restoration situation (for example: xxx town, xxx city, xxx province).

[0096] Specifically, the computer equipment adjusts the tailings ecological restoration estimation results according to the independent tailings ecological restoration data debugging coefficient to obtain the independent tailings ecological restoration data results corresponding to the target mine event label.

[0097] In one possible embodiment, an independent tailings ecological restoration data model is provided in one embodiment. It can be seen that the independent tailings ecological restoration data model includes a tailings ecological restoration element feature extraction model, a restoration effect evaluation model, and an independent tailings ecological restoration data optimization model. First, the tailings ecological information that needs to be evaluated is loaded into the tailings ecological restoration element feature extraction model, and the tailings ecological restoration element features are output through the tailings ecological restoration element feature extraction model; then, the tailings ecological restoration element features are loaded into the restoration effect evaluation model, and the tailings ecological restoration estimation results are output through the restoration effect evaluation model; then, through the independent tailings ecological restoration data optimization model, the independent tailings ecological restoration data debugging coefficient is obtained according to the tailings ecological restoration element features and the mine event significance characteristics of the target mine event label; finally, based on the tailings ecological restoration estimation results and the independent tailings ecological restoration data debugging coefficient, the independent tailings ecological restoration data results corresponding to the target mine event label are determined.

[0098] In the above-mentioned intelligent assessment method for mine tailings ecological problems, the tailings ecological restoration estimation result is first determined based on the tailings ecological restoration element characteristics of the tailings ecological information to be assessed, and the independent tailings ecological restoration data debugging coefficient is determined based on the tailings ecological restoration element characteristics and the significant characteristics of the mine matter corresponding to the target mine matter label. Then, the tailings ecological restoration estimation result is debugged based on the independent tailings ecological restoration data debugging coefficient to obtain the independent tailings ecological restoration data result corresponding to the target mine matter label. In this way, since the tailings ecological restoration estimation result is the result obtained by evaluating the characteristics of the tailings ecological information itself, it reflects the restoration effect of the tailings ecological information at the objective level. The tailings ecological restoration estimation result is debugged according to the significant characteristics of the mine matter, and the independent tailings ecological restoration data result obtained by debugging is close to the evaluation result obtained based on the mine matter itself. That is, the tailings ecological restoration data is independently evaluated based on the subjective evaluation of the mine matter and the objective evaluation quality of the tailings ecological information, so that the independent tailings ecological restoration data result meets the single characteristics of the individual mine matter, thereby improving the accuracy of the independent tailings ecological restoration data result.

[0099] In one possible embodiment, there are several tailings ecological information that need to be evaluated; the independent tailings ecological restoration data result is an independent tailings ecological restoration data evaluation value; the method also includes: obtaining the independent tailings ecological restoration data evaluation value of the target mine matter label for each tailings ecological information that needs to be evaluated; according to the independent tailings ecological restoration data evaluation value, selecting a preset number of target tailings ecological information from each tailings ecological information that needs to be evaluated; and outputting the target tailings ecological information to the terminal where the target mine matter label is located.

[0100] Specifically, the computer equipment obtains several tailings ecological information that need to be evaluated corresponding to the tailings ecological information evaluation task, obtains the independent tailings ecological restoration data evaluation value of the target mine matter label for each tailings ecological information that needs to be evaluated, selects a preset number of target tailings ecological information from high to low according to the independent tailings ecological restoration data evaluation value, and outputs the target tailings ecological information to the terminal where the target mine matter label is located. In this way, in each tailings ecological information evaluation task, the computer equipment gives priority to displaying the tailings ecological information that meets the target mine matter to the target mine matter.

[0101] This application involves significant features of mining events, which can be predetermined or determined in the application scenario based on reference tailings ecological information. A method for obtaining significant features of mining events corresponding to a target mining event label is provided below.

[0102] In one possible implementation, a process for obtaining significant features of mining matters in one embodiment can be seen as follows: obtaining significant features of mining matters corresponding to target mining matter labels includes:

[0103] Step 502, obtain more than one reference tailings ecological information corresponding to the target mine event label, each reference tailings ecological information is annotated with an independent tailings ecological restoration data result corresponding to the target mine event label; the independent tailings ecological restoration data result is an independent tailings ecological restoration data evaluation value.

[0104] In one possible implementation, the computer device outputs more than one piece of reference tailings ecological information to the target mine event tag, and receives the more than one piece of reference tailings ecological information returned by the target mine event tag, annotated with the independent tailings ecological restoration data results. Alternatively, the computer device directly receives the more than one piece of reference tailings ecological information corresponding to the target mine event tag, each piece of reference tailings ecological information annotated with the independent tailings ecological restoration data results corresponding to the target mine event tag.

[0105] In one possible implementation, the reference tailings ecological information may only be annotated with the independent tailings ecological restoration data evaluation value corresponding to the target mine matter label, or the independent tailings ecological restoration data evaluation value corresponding to the target mine matter label and the independent tailings ecological restoration data evaluation values ​​corresponding to other mine matter labels may be annotated at the same time.

[0106] Step 504: obtain a unique mining event significant feature extraction model, load more than one reference tailings ecological information into the unique mining event significant feature extraction model respectively, and obtain the unique mining event significant features corresponding to more than one reference tailings ecological information through the unique mining event significant feature extraction model.

[0107] Exemplarily, unique mining matters in this application are understood to be mining bodies that require monitoring.

[0108] For a piece of reference tailings ecological information, the unique mining event significant feature is the significant feature unique to that reference tailings ecological information. The unique mining event significant feature may include several feature layers, each corresponding to a significant characteristic. The feature value of each feature layer is used to represent the score of the mining event in the corresponding significant characteristic. This score is only related to the significant characteristic of the mining event itself.

[0109] It can be understood that the characteristic value of the significant feature of a unique mining event is only related to the significant characteristics of the mining event itself. The characteristic value of the significant feature of the aforementioned mining event is not only related to the significant characteristics of the mining event itself, but also related to the effect of the mining event on the tailings ecological information.

[0110] The unique mining event salient feature extraction model is a machine learning model trained to extract the salient features of unique mining events. The model can be trained using tailings ecological information examples and a configuration directory of salient features of unique mining events.

[0111] Specifically, the computer equipment obtains reference tailings ecological information and a unique mining event significant feature extraction model, and the computer equipment loads more than one reference tailings ecological information into the unique mining event significant feature extraction model respectively, and obtains the unique mining event significant features corresponding to the more than one reference tailings ecological information output by the unique mining event significant feature extraction model.

[0112] Step 506: Obtain the maximum value and the minimum value of the independent tailings ecological restoration data corresponding to each reference tailings ecological information and the target mine event label.

[0113] Specifically, the computer device selects the maximum value of the independent tailings ecological restoration data and the minimum value of the independent tailings ecological restoration data from the independent tailings ecological restoration data evaluation values ​​corresponding to each reference tailings ecological information and the target mine matter label.

[0114] For example, five reference tailings ecological information corresponding to the target mine issue label are obtained: reference tailings ecological information A, reference tailings ecological information B, reference tailings ecological information C, reference tailings ecological information D, and reference tailings ecological information E. The independent tailings ecological restoration data evaluation value corresponding to the reference tailings ecological information A and the target mine issue label is 1, the independent tailings ecological restoration data evaluation value corresponding to the reference tailings ecological information B and the target mine issue label is 2, the independent tailings ecological restoration data evaluation value corresponding to the reference tailings ecological information C and the target mine issue label is 3, the independent tailings ecological restoration data evaluation value corresponding to the reference tailings ecological information D and the target mine issue label is 4, and the independent tailings ecological restoration data evaluation value corresponding to the reference tailings ecological information E and the target mine issue label is 5. Therefore, the maximum value of the independent tailings ecological restoration data is 5, and the minimum value of the independent tailings ecological restoration data is 1.

[0115] Step 508, for one of the reference tailings ecological information, based on the independent tailings ecological restoration data evaluation value, the independent tailings ecological restoration data maximum value and the independent tailings ecological restoration data minimum value corresponding to the target mine event label, obtain the uniqueness of the target mine event label to the reference tailings ecological information.

[0116] In one possible implementation, a computer device obtains a first difference between the maximum value of the independent tailings ecological restoration data and the minimum value of the independent tailings ecological restoration data, obtains a second difference between the independent tailings ecological restoration data evaluation value corresponding to the target mine matter label on the reference tailings ecological information and the minimum value of the independent tailings ecological restoration data, and determines the ratio between the second difference and the first difference as the degree of uniqueness of the target mine matter label to the reference tailings ecological information.

[0117] Step 510 , based on the uniqueness of the target mine event label to each reference tailings ecological information and the unique mine event significance features corresponding to each reference tailings ecological information, obtain the mine event significance features corresponding to the target mine event label.

[0118] In this application, the significance characteristics of mining matters are adjusted by the uniqueness of mining matters on tailings ecological information, so that the characteristic value of the significance characteristics of mining matters is related to the effect of mining matters on tailings ecological information.

[0119] In one possible implementation, for one of the reference tailings ecological information, the computer device adjusts the characteristic values ​​of each layer of the unique mining event significance feature corresponding to the reference tailings ecological information based on the uniqueness of the target mining event label with respect to the reference tailings ecological information, thereby obtaining the mining event significance feature of the target mining event label relative to the reference tailings ecological information. The computer device obtains the average value or weighted average value of each layer of the mining event significance feature of the target mining event label relative to each reference tailings ecological information to obtain the mining event significance feature corresponding to the target mining event label.

[0120] In this embodiment, the mining matter significance characteristics of the mining matter are obtained based on the uniqueness of the mining matter to the reference tailings ecological information and the unique mining matter significance characteristics of the reference tailings ecological information. The obtained mining matter significance characteristics are related to the effect of the mining matter on the tailings ecological information. Subsequently, the evaluation results are determined based on the mining matter significance characteristics and the tailings ecological restoration element characteristics, which can improve the accuracy of the evaluation results.

[0121] In one possible implementation embodiment, it can be seen that the independent tailings ecological restoration data model includes a tailings ecological restoration element feature extraction model, a unique mining event significance feature extraction model, a restoration effect evaluation model and an independent tailings ecological restoration data optimization model.

[0122] Specifically, first, the computer device obtains more than one reference tailings ecological information corresponding to the target mine issue label, each reference tailings ecological information being annotated with an independent tailings ecological restoration data assessment value corresponding to the target mine issue label. The computer device then loads each of the more than one reference tailings ecological information into a unique mine issue salience feature extraction model, and uses the unique mine issue salience feature extraction model to obtain the unique mine issue salience features corresponding to each of the more than one reference tailings ecological information. The computer device then obtains the mine issue salience features corresponding to the target mine issue label based on the independent tailings ecological restoration data assessment values ​​annotated on each reference tailings ecological information corresponding to the target mine issue label, as well as the unique mine issue salience features corresponding to each reference tailings ecological information.

[0123] Secondly, after the computer equipment obtains the significant characteristics of the mining matter corresponding to the target mining matter label, the tailings ecological information that needs to be evaluated is loaded into the tailings ecological restoration factor feature extraction model, and the tailings ecological restoration factor features are output through the tailings ecological restoration factor feature extraction model. The tailings ecological restoration factor features are then loaded into the restoration effect evaluation model, and the tailings ecological restoration estimation results are output through the restoration effect evaluation model. Then, through the independent tailings ecological restoration data optimization model, the independent tailings ecological restoration data debugging coefficient is obtained based on the tailings ecological restoration factor features and the significant characteristics of the mining matter of the target mining matter label. Finally, based on the tailings ecological restoration estimation results and the independent tailings ecological restoration data debugging coefficient, the independent tailings ecological restoration data results corresponding to the target mining matter label are determined.

[0124] In this embodiment, an independent tailings ecological restoration data model is constructed using both objective factors of tailings ecological information and subjective factors of mining matters. That is, the distinction between personalized and popular tailings ecological information is modeled using the significant characteristics of different mining matters, so that the independent tailings ecological restoration data results obtained through the independent tailings ecological restoration data model can better meet the uniqueness of individual mining matters.

[0125] This application relates to a training method for an independent tailings ecological restoration data model, which is specifically illustrated through the following embodiments.

[0126] First, the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model can be trained separately. That is, the tailings ecological restoration factor feature extraction model is trained using tailings ecological information examples and a tailings ecological restoration factor feature configuration catalog, and the unique mining event significant feature extraction model is trained using tailings ecological information examples and a unique mining event significant feature configuration catalog. The tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model can also be trained together. The following provides a joint training method.

[0127] In a possible embodiment, the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model are jointly trained; the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model share a common standard local model, and respectively include corresponding output local models; the steps of jointly training the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model include: obtaining a first tailings ecological information example set, a second tailings ecological information example set, a tailings ecological restoration factor feature extraction model and a unique mining matter significant feature extraction model; each first tailings ecological information example in the first tailings ecological information example set has a tailings ecological restoration factor feature configuration directory, and each second tailings ecological information example in the second tailings ecological information example set has a unique mining matter significant feature configuration directory; the first tailings ecological information example is loaded into the tailings ecological restoration factor feature extraction model. Extraction model, extract features of the first tailings ecological information example through the standard local model of the tailings ecological restoration factor feature extraction model, and output the tailings ecological restoration factor feature regression analysis results through the output local model of the tailings ecological restoration factor feature extraction model; load the second tailings ecological information example into the unique mining matter significant feature extraction model, extract features of the second tailings ecological information example through the standard local model of the unique mining matter significant feature extraction model, and output the unique mining matter significant feature regression analysis results through the output local model of the unique mining matter significant feature extraction model; based on the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, as well as the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory, jointly train the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model.

[0128] Among them, the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model share a common standard local model, and each includes a corresponding output local model. The number of the standard local models can be one or two. When the number of the standard local models is one, the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model share a standard local model, and the standard local model is connected to the two output local models; when the number of the standard local models is two, the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model each correspond to a standard local model, and the two standard local models are each connected to an output local model, and the model structures of the two standard local models are the same.

[0129] Specifically, using two standard local models as an example, the computer device loads the first tailings ecological information example into the tailings ecological restoration factor feature extraction model. The computer device then extracts features from the first tailings ecological information example using the standard local model of the tailings ecological restoration factor feature extraction model. The computer device then outputs the tailings ecological restoration factor feature regression analysis results using the output local model of the tailings ecological restoration factor feature extraction model. Next, the computer device loads the second tailings ecological information example into the unique mining event significant feature extraction model. The computer device then extracts features from the second tailings ecological information example using the standard local model of the unique mining event significant feature extraction model. The computer device then jointly trains the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model based on the distinction between the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, as well as the distinction between the unique mining event significant feature regression analysis results and the unique mining event significant feature configuration directory.

[0130] In a possible embodiment, based on the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, as well as the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory, the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model are jointly trained, including: building a first evaluation index algorithm based on the distinction between the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, and building a second evaluation index algorithm based on the distinction between the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory; obtaining a first model pyramid coefficient by minimizing the first evaluation index algorithm, and obtaining a second model pyramid coefficient by minimizing the second evaluation index algorithm; combining the first model pyramid coefficient and the second model pyramid coefficient to jointly train the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model.

[0131] The evaluation index algorithm in this application can be understood as a loss function. The first evaluation index algorithm is set in advance, and the second evaluation index algorithm is debugged according to the actual situation.

[0132] Among them, the specific calculation method of the first evaluation indicator algorithm is as follows:

[0133] For a set of predicted values ​​\hat{y}_i and true values ​​y_i, where i = 1,2,\cdots,n, the mean square error loss function is calculated as: MSE=\frac{1}{n}\sum_{i = 1}^{n}(\hat{y}_i - y_i)^2

[0134] Calculation steps

[0135] 1. Calculate the difference between the predicted value and the true value: For each data point, calculate \hat{y}_i - y_i.

[0136] 2. Square the difference: Square the difference obtained in the previous step, i.e. (\hat{y}_i - y_i)^2. This can prevent positive and negative errors from canceling each other out, while emphasizing the impact of larger errors.

[0137] 3. Sum: Add the squared differences of all data points to get \sum_{i = 1}^{n}(\hat{y}_i -y_i)^2.

[0138] 4. Find the average: Divide the sum by the number of data points n to get the mean square error (MSE).

[0139] The mean square error loss function is often used in regression problems to measure the average difference between the model's predicted value and the true value. The smaller its value, the better the model's prediction effect. Specifically, the computer device builds a first evaluation index algorithm based on the distinction between the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, such as by building a first evaluation index algorithm through the Euclidean distance between the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory; and builds a second evaluation index algorithm based on the distinction between the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory, such as by building a second evaluation index algorithm through the Euclidean distance between the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory. Then, the computer device obtains the first model pyramid coefficient by minimizing the first evaluation index algorithm, and obtains the second model pyramid coefficient by minimizing the second evaluation index algorithm. Then, the computer equipment combines the first model pyramid coefficient and the second model pyramid coefficient to jointly train the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model, such as obtaining the sum of the first model pyramid coefficient and the second model pyramid coefficient, and using the sum of the first model pyramid coefficient and the second model pyramid coefficient to backpropagate and update the model coefficients of the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model.

[0140] In this embodiment, since the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model share a common standard local model, the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model can be trained together, thereby saving computer resources and improving the model training speed; and, obtaining the objective factors of tailings ecological information and the subjective factors of mining events in the same model has practical value for the study of independent tailings ecological restoration data.

[0141] In a possible embodiment, the steps of extracting a tailings ecological restoration factor feature example corresponding to the fourth tailings ecological information example include: obtaining a tailings ecological restoration factor feature extraction model; loading the fourth tailings ecological information example into the tailings ecological restoration factor feature extraction model, and performing feature extraction on the fourth tailings ecological information example through the tailings ecological restoration factor feature extraction model to obtain a tailings ecological restoration factor feature example.

[0142] Specifically, the computer device loads the fourth tailings ecological information example into the tailings ecological restoration factor feature extraction model, and extracts the tailings ecological restoration factor feature example corresponding to the fourth tailings ecological information example through the tailings ecological restoration factor feature extraction model.

[0143] It can be understood that the estimated results of tailings ecological restoration can be obtained by taking the weighted sum of the evaluation value probability distribution regression analysis results and the corresponding evaluation values.

[0144] In a possible embodiment, the training steps of the independent tailings ecological restoration data optimization model include: obtaining a third tailings ecological information example and an independent tailings ecological restoration data optimization model corresponding to the example mine event label; the third tailings ecological information example has an independent tailings ecological restoration data debugging coefficient configuration directory corresponding to the example mine event label; extracting a tailings ecological restoration element feature example corresponding to the third tailings ecological information example; obtaining a mine event significant feature example corresponding to the example mine event label; through the independent tailings ecological restoration data optimization model, according to the splicing results of the tailings ecological restoration element feature example and the mine event significant feature example, obtain the independent tailings ecological restoration data debugging coefficient regression analysis result corresponding to the example mine event label; based on the independent tailings ecological restoration data debugging coefficient regression analysis result and the independent tailings ecological restoration data debugging coefficient configuration directory, train the independent tailings ecological restoration data optimization model.

[0145] In one possible implementation embodiment, the computer device first builds an independent tailings ecological restoration data optimization model composed of a fully connected model, and then uses the third tailings ecological information example set and the independent tailings ecological restoration data debugging coefficient configuration directory to train the independent tailings ecological restoration data optimization model.

[0146] In one possible embodiment, the third tailings ecological information example corresponds to more than one example mine matter label; the step of obtaining the independent tailings ecological restoration data debugging coefficient configuration directory includes: obtaining the independent tailings ecological restoration data result example of each example mine matter label corresponding to the third tailings ecological information example; obtaining the independent tailings ecological restoration data mean example based on each independent tailings ecological restoration data result example; obtaining the independent tailings ecological restoration data debugging coefficient example corresponding to each example mine matter label based on each independent tailings ecological restoration data result example and the independent tailings ecological restoration data mean example, and determining each independent tailings ecological restoration data debugging coefficient example as the independent tailings ecological restoration data debugging coefficient configuration directory corresponding to the third tailings ecological information example and each example mine matter label.

[0147] Specifically, since the third tailings ecological information example has an evaluation value corresponding to an example mine event label, for one of the third tailings ecological information examples, the average evaluation value corresponding to the third tailings ecological information example is obtained. For one of the example mine event labels, the difference between its corresponding evaluation value and the average evaluation value is the independent tailings ecological restoration data debugging coefficient of the example mine event label.

[0148] Specifically, the computer device loads the third tailings ecological information example into the tailings ecological restoration factor feature extraction model, and extracts features from the third tailings ecological information example through the tailings ecological restoration factor feature extraction model to obtain a tailings ecological restoration factor feature example. Next, the computer device obtains a mine matter significance feature example corresponding to the example mine matter label. Next, the computer device uses the independent tailings ecological restoration data optimization model to obtain an independent tailings ecological restoration data debugging coefficient regression analysis result corresponding to the example mine matter label based on the splicing result of the tailings ecological restoration factor feature example and the mine matter significance feature example. Next, the computer device builds an evaluation index algorithm based on the distinction between the independent tailings ecological restoration data debugging coefficient regression analysis result and the independent tailings ecological restoration data debugging coefficient configuration directory, and trains the independent tailings ecological restoration data optimization model in the direction of minimizing the evaluation index algorithm.

[0149] This application relates to examples of significant features of mining matters. The following provides a method for obtaining examples of significant features of mining matters corresponding to example mining matter labels.

[0150] In one possible embodiment, obtaining an example of significant features of mining matters corresponding to an example mining matter label includes: obtaining an example of significant features of unique mining matters corresponding to a third tailings ecological information example; obtaining an example of independent tailings ecological restoration data results corresponding to the example mining matter label; and obtaining an example of significant features of mining matters corresponding to the example mining matter label based on the independent tailings ecological restoration data result example and the unique example of significant features of mining matters.

[0151] In one possible embodiment, the example mine matter label corresponds to no less than two third tailings ecological information examples; the independent tailings ecological restoration data result example is an independent tailings ecological restoration data evaluation value example; based on the independent tailings ecological restoration data result example and the unique mine matter significance feature example, the mine matter significance feature example corresponding to the example mine matter label is obtained, including: obtaining no less than two independent tailings ecological restoration data maximum value examples and independent tailings ecological restoration data minimum value examples of the third tailings ecological information examples; for one of the third tailings ecological information examples, based on the independent tailings ecological restoration data evaluation value example, the independent tailings ecological restoration data maximum value example and the independent tailings ecological restoration data minimum value example corresponding to the example mine matter label, the uniqueness of the example mine matter label to the third tailings ecological information example is obtained; based on the uniqueness of the example mine matter label to each third tailings ecological information example, and the unique mine matter significance feature example corresponding to each third tailings ecological information example, the mine matter significance feature example corresponding to the example mine matter label is obtained.

[0152] In one possible embodiment, there are several feature layers of unique mining matter significant feature examples; based on the degree of uniqueness and the unique mining matter significant feature examples, the mining matter significant feature examples corresponding to the example mining matter labels are obtained, including: according to the degree of uniqueness, adjusting the feature values ​​of the unique mining matter significant feature examples in each layer to obtain the mining matter significant feature examples corresponding to the example mining matter labels.

[0153] The process of obtaining the example of significant features of mining matters corresponding to the example mining matter label may specifically refer to the aforementioned method of obtaining significant features of mining matters corresponding to the target mining matter label, which will not be repeated here.

[0154] This application also provides an application scenario, which applies the above-mentioned intelligent assessment method for ecological problems of mine tailings. Specifically, the application of the intelligent assessment method for ecological problems of mine tailings in this application scenario is as follows:

[0155] Step 802: Obtain tailings ecological information and target mine item labels that need to be evaluated.

[0156] Step 804: obtain a tailings ecological restoration element feature extraction model, load the tailings ecological information that needs to be evaluated into the tailings ecological restoration element feature extraction model, and output the tailings ecological restoration element features through the tailings ecological restoration element feature extraction model.

[0157] Step 806: Obtain a restoration effect evaluation model, load the tailings ecological restoration element characteristics into the restoration effect evaluation model, and output the tailings ecological restoration estimation result through the restoration effect evaluation model.

[0158] Step 808, obtain an independent tailings ecological restoration data optimization model, and through the independent tailings ecological restoration data optimization model, obtain the independent tailings ecological restoration data debugging coefficient based on the splicing results of the tailings ecological restoration element characteristics and the significant characteristics of mining matters.

[0159] Among them, the method for obtaining the significant features of mining matters is as follows: the computer equipment obtains more than one reference tailings ecological information corresponding to the target mining matter label, and each reference tailings ecological information is annotated with an independent tailings ecological restoration data evaluation value corresponding to the target mining matter label; then, the computer equipment obtains a unique mining matter significant feature extraction model, and loads more than one reference tailings ecological information into the unique mining matter significant feature extraction model respectively, and obtains the unique mining matter significant features corresponding to more than one reference tailings ecological information through the unique mining matter significant feature extraction model; then, the computer equipment obtains the reference tailings ecological information and the target mining matter. The maximum value of the independent tailings ecological restoration data and the minimum value of the independent tailings ecological restoration data corresponding to the item label are obtained; then, for one of the reference tailings ecological information, the computer equipment obtains the uniqueness of the target mine matter label to the reference tailings ecological information based on the independent tailings ecological restoration data evaluation value, the maximum value of the independent tailings ecological restoration data and the minimum value of the independent tailings ecological restoration data corresponding to the target mine matter label; then, the computer equipment obtains the mine matter significance characteristics corresponding to the target mine matter label based on the uniqueness of the target mine matter label to each reference tailings ecological information and the unique mine matter significance characteristics corresponding to each reference tailings ecological information.

[0160] Step 810: Based on the tailings ecological restoration estimation result and the independent tailings ecological restoration data debugging coefficient, determine the independent tailings ecological restoration data result corresponding to the target mine event label.

[0161] In one possible embodiment, in one embodiment, a tailings ecological restoration factor feature extraction model is trained. For the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model, the first tailings ecological information example is loaded into the tailings ecological restoration factor feature extraction model, and the tailings ecological restoration factor feature regression analysis result is output through the tailings ecological restoration factor feature extraction model. The second tailings ecological information example is loaded into the unique mining matter significant feature extraction model, and the unique mining matter significant feature regression analysis result is output through the unique mining matter significant feature extraction model. Based on the distinction between the tailings ecological restoration factor feature regression analysis result and the tailings ecological restoration factor feature configuration directory, a first evaluation index algorithm is established, and based on the distinction between the unique mining matter significant feature regression analysis result and the unique mining matter significant feature configuration directory, a second evaluation index algorithm is established. The first model pyramid coefficient is obtained by minimizing the first evaluation index algorithm, and the second model pyramid coefficient is obtained by minimizing the second evaluation index algorithm. The first model pyramid coefficient and the second model pyramid coefficient are combined to jointly train the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model.

[0162] The training of the feature extraction model is understood as the training of the convolutional neural network. The convolutional neural network (CNN) mainly consists of convolutional layers, pooling layers, and fully connected layers. The following is the calculation method of each layer:

[0163] Convolutional layer

[0164] Convolution operation: Convolution is performed by sliding the convolution kernel over the input data. Assume that the input data is a three-dimensional tensor X with a shape of (H_{in}, W_{in}, C_{in}), which represent the height, width, and number of channels respectively. The shape of the convolution kernel K is (h, k, C_{in}, C_{out}), where h and k are the height and width of the convolution kernel, and C_{out} is the number of output channels. For each position (i, j) in the input data, the output Y of the convolution layer at the corresponding position (i', j') is calculated as:

[0165] Y(i',j',c)=\sum_{c' = 0}^{C_{in}-1}\sum_{m = 0}^{h - 1}\sum_{n = 0}^{k - 1}X(i'+m,j'+n,c')\times K(m,n,c',c)

[0166] The value range of i' and j' is determined by the size, step size and filling method of the convolution kernel.

[0167] Bias addition: A bias term b is usually added to the result obtained by the convolution operation, with a shape of (1,1,C_{out}), and the final output of the convolution layer is Z = Y + b.

[0168] Pooling layer

[0169] Max pooling: Divide the input data into several non-overlapping subregions and take the maximum value in each subregion as the output of that region. For example, for a 2\times2 max pooling kernel with a stride of 2, the shape of the input data X is (H_{in}, W_{in}, C_{in}), then the shape of the output data Y is (\frac{H_{in}}{2},\frac{W_{in}}{2}, C_{in}), where Y(i,j,c)=\max_{m = 0}^{1}\max_{n = 0}^{1}X(2i + m,2j + n,c).

[0170] Average pooling: Similar to maximum pooling, but the average value is taken in each sub-region as the output.

[0171] Fully connected layer

[0172] The fully connected layer expands the output of the previous layer into a one-dimensional vector and then performs calculations through matrix multiplication and bias addition. Assuming that the length of the vector output of the previous layer is n, the shape of the weight matrix W of the fully connected layer is (n, m), and the shape of the bias vector b is (1, m), then the output of the fully connected layer is Y = XW + b, where X is the one-dimensional vector after expansion of the previous layer and Y is a one-dimensional vector of length m.

[0173] In practical applications, convolutional neural networks usually contain multiple convolutional layers, pooling layers, and fully connected layers, which are combined to achieve feature extraction and classification or regression of input data.

[0174] In one possible embodiment, the training of the restoration effect evaluation model in one embodiment. The fourth tailings ecological information example is loaded into the tailings ecological restoration element feature extraction model, and the tailings ecological restoration element feature example is output through the tailings ecological restoration element feature extraction model. Then, the tailings ecological restoration element feature example is loaded into the restoration effect evaluation model, and the evaluation value probability distribution regression analysis result is obtained through the restoration effect evaluation model. Then, based on the distinction between the evaluation value probability distribution regression analysis result and the evaluation value probability distribution configuration directory, an evaluation index algorithm is established, and the restoration effect evaluation model is trained in the direction of minimizing the evaluation index algorithm.

[0175] In one possible embodiment, an independent tailings ecological restoration data optimization model is trained in one embodiment. The third tailings ecological information example is loaded into the tailings ecological restoration element feature extraction model, and the tailings ecological restoration element feature example is output through the tailings ecological restoration element feature extraction model. Then, the third tailings ecological information example is loaded into the unique mine matter significant feature extraction model, and the unique mine matter significant feature example is output through the unique mine matter significant feature extraction model. Based on the unique mine matter significant feature example, the mine matter significant feature example corresponding to the example mine matter label is obtained. Then, through the independent tailings ecological restoration data optimization model, according to the splicing results of the tailings ecological restoration element feature example and the mine matter significant feature example, the independent tailings ecological restoration data debugging coefficient regression analysis result corresponding to the example mine matter label is obtained. Then, based on the distinction between the independent tailings ecological restoration data debugging coefficient regression analysis result and the independent tailings ecological restoration data debugging coefficient configuration directory, an evaluation index algorithm is established, and the independent tailings ecological restoration data optimization model is trained in the direction of minimizing the evaluation index algorithm.

[0176] Based on the above, a device for intelligent assessment of ecological problems of mine tailings is provided, which includes:

[0177] A knowledge fragment extraction module is used to extract knowledge fragments from the example collapse hidden danger data to obtain minor knowledge fragments and important knowledge fragments of the example collapse hidden danger data;

[0178] a label building module, configured to build secondary description labels based on secondary knowledge fragments corresponding to a plurality of said example collapse hazard data, and to build important description labels based on important knowledge fragments corresponding to a plurality of said example collapse hazard data;

[0179] A knowledge fragment obtaining module is used to extract knowledge fragments from the to-be-processed collapse hidden danger data to obtain minor knowledge fragments and important knowledge fragments of the to-be-processed collapse hidden danger data;

[0180] a tag analysis module, configured to perform similarity analysis on the secondary description tags based on the secondary knowledge fragments of the to-be-processed collapse hazard data to obtain similar secondary description tags, and perform similarity analysis on the important description tags based on the important knowledge fragments of the to-be-processed collapse hazard data to obtain similar important description tags;

[0181] The information module is used to determine the example collapse hazard data corresponding to the similar secondary description tags and at least part of the example collapse hazard data corresponding to the similar important description tags as the collapse hazard warning information of the collapse hazard data to be processed.

[0182] Based on the above, an intelligent assessment system for ecological problems of mine tailings is shown, which includes a processor and a memory that communicate with each other. The processor is used to read a computer program from the memory and execute it to implement the above method.

[0183] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0184] In summary, based on the above scheme, the tailings ecological restoration estimation result is first determined according to the tailings ecological restoration element characteristics of the tailings ecological information that needs to be evaluated, and the independent tailings ecological restoration data debugging coefficient is determined according to the tailings ecological restoration element characteristics and the significant characteristics of the mine matter corresponding to the target mine matter label. Then, the tailings ecological restoration estimation result is debugged based on the independent tailings ecological restoration data debugging coefficient to obtain the independent tailings ecological restoration data result corresponding to the target mine matter label. In this way, since the tailings ecological restoration estimation result is the result obtained by evaluating the characteristics of the tailings ecological information itself, it reflects the quality of the tailings ecological information at the objective level, and the tailings ecological restoration estimation result is debugged according to the deviation corresponding to the significant characteristics of the mine matter, the independent tailings ecological restoration data result obtained by debugging is close to the evaluation result obtained based on the mine matter itself, that is, the tailings ecological information is independently evaluated based on the subjective quality of the mine matter and the objective quality of the tailings ecological information, so that the independent tailings ecological restoration data result meets the uniqueness of individual mine matters, thereby improving the accuracy of the independent tailings ecological restoration data result.

[0185] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of the present application. Not only can hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented using software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0186] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

Claims

1. An intelligent assessment method for ecological problems of mine tailings, characterized in that: The method comprises: Obtain tailings ecological information and target mine event labels that require assessment; Extracting tailings ecological restoration element characteristics corresponding to the tailings ecological information that needs to be evaluated; Determine the estimated results of tailings ecological restoration based on the tailings ecological information that needs to be evaluated, in combination with the characteristics of the tailings ecological restoration elements; Determining the independent tailings ecological restoration data debugging coefficient corresponding to the target mine event label by combining the tailings ecological restoration element characteristics and the mine event significance characteristics corresponding to the target mine event label through an independent tailings ecological restoration data optimization model; the mine event significance characteristics are data reflecting the significance characteristics of the mine event; Determining an independent tailings ecological restoration data result corresponding to the target mine event label based on the tailings ecological restoration estimation result and the independent tailings ecological restoration data debugging coefficient; The method further comprises: Acquire a plurality of reference tailings ecological information corresponding to the target mine event label, each reference tailings ecological information being annotated with an independent tailings ecological restoration data assessment value corresponding to the target mine event label; The plurality of reference tailings ecological information are loaded into a unique mining event significant feature extraction model respectively, and the unique mining event significant features corresponding to each of the plurality of reference tailings ecological information are obtained through the unique mining event significant feature extraction model; the unique mining event significant features are significant features of the reference tailings ecological information corresponding to the unique mining event significant features; Obtaining the maximum value and the minimum value of independent tailings ecological restoration data corresponding to each of the reference tailings ecological information and the target mine event label; For each reference tailings ecological information, based on the independent tailings ecological restoration data evaluation value corresponding to the target mine event label, the maximum value of the independent tailings ecological restoration data, and the minimum value of the independent tailings ecological restoration data, the uniqueness of the target mine event label to the reference tailings ecological information is obtained; Based on the uniqueness of the target mine event label to each reference tailings ecological information and the unique mine event significance characteristics corresponding to each reference tailings ecological information, the mine event significance characteristics corresponding to the target mine event label are obtained; The extraction of tailings ecological restoration element characteristics corresponding to the tailings ecological information that needs to be evaluated includes: Obtain a model for extracting characteristics of tailings ecological restoration elements; Loading the tailings ecological information that needs to be evaluated into the tailings ecological restoration factor feature extraction model, and outputting the tailings ecological restoration factor features through the tailings ecological restoration factor feature extraction model; The tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model are jointly trained; the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model share a common standard local model and respectively include corresponding output local models; the steps of jointly training the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model include: Obtaining a first tailings ecological information example set, a second tailings ecological information example set, the tailings ecological restoration factor feature extraction model, and the unique mining matter significant feature extraction model; each first tailings ecological information example in the first tailings ecological information example set has a tailings ecological restoration factor feature configuration directory, and each second tailings ecological information example in the second tailings ecological information example set has a unique mining matter significant feature configuration directory; Loading the first tailings ecological information example into the tailings ecological restoration factor feature extraction model, performing feature extraction on the first tailings ecological information example through a standard local model of the tailings ecological restoration factor feature extraction model, and outputting a tailings ecological restoration factor feature regression analysis result through an output local model of the tailings ecological restoration factor feature extraction model; Loading the second tailings ecological information example into the unique mining event significant feature extraction model, performing feature extraction on the second tailings ecological information example through a standard local model of the unique mining event significant feature extraction model, and outputting a unique mining event significant feature regression analysis result through an output local model of the unique mining event significant feature extraction model; Based on the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, as well as the unique mining event significant feature regression analysis results and the unique mining event significant feature configuration directory, jointly training the tailings ecological restoration factor feature extraction model and the unique mining event significant feature extraction model; The method of jointly training the tailings ecological restoration factor feature extraction model and the unique mining matter significant feature extraction model based on the tailings ecological restoration factor feature regression analysis results and the tailings ecological restoration factor feature configuration directory, as well as the unique mining matter significant feature regression analysis results and the unique mining matter significant feature configuration directory, includes: A first evaluation index algorithm is established based on the distinction between the regression analysis results of the tailings ecological restoration factor characteristics and the configuration catalog of the tailings ecological restoration factor characteristics, and a second evaluation index algorithm is established based on the distinction between the regression analysis results of the unique mining event significant characteristics and the configuration catalog of the unique mining event significant characteristics; A first model pyramid coefficient is obtained by minimizing the first evaluation index algorithm, and a second model pyramid coefficient is obtained by minimizing the second evaluation index algorithm; Combined with the first model pyramid coefficient and the second model pyramid coefficient, the tailings ecological restoration factor feature extraction model and the unique mining event significance feature extraction model are jointly trained.

2. The method according to claim 1, characterized in that The training steps of the independent tailings ecological restoration data optimization model include: Obtaining a third tailings ecological information example corresponding to the example mine event tag and the independent tailings ecological restoration data optimization model; the third tailings ecological information example contains an independent tailings ecological restoration data debugging coefficient configuration directory corresponding to the example mine event tag; Extracting a tailings ecological restoration element feature example corresponding to the third tailings ecological information example; Obtaining an example of a significant feature of a mining matter corresponding to the example mining matter label; By using the independent tailings ecological restoration data optimization model, combined with the splicing results of the tailings ecological restoration element feature examples and the mine event significant feature examples, the independent tailings ecological restoration data debugging coefficient regression analysis results corresponding to the example mine event labels are obtained; Training the independent tailings ecological restoration data optimization model based on the independent tailings ecological restoration data debugging coefficient regression analysis results and the independent tailings ecological restoration data debugging coefficient configuration directory; Wherein, the third tailings ecological information example corresponds to more than one example mining matter tag; The steps for obtaining the independent tailings ecological restoration data debugging coefficient configuration directory include: Obtaining independent tailings ecological restoration data result examples for each of the example mining event tags corresponding to the third tailings ecological information example; Based on the independent tailings ecological restoration data result examples, an independent tailings ecological restoration data mean value example is obtained; Based on each of the independent tailings ecological restoration data result examples and the independent tailings ecological restoration data mean examples, the independent tailings ecological restoration data debugging coefficient examples corresponding to each of the example mine event labels are obtained, and the independent tailings ecological restoration data debugging coefficient examples are determined as the independent tailings ecological restoration data debugging coefficient configuration directory corresponding to the third tailings ecological information example and each of the example mine event labels.

3. The method according to claim 2, characterized in that The obtaining of an example of a significant feature of a mining matter corresponding to the example mining matter label includes: Obtaining a unique mining matter significant feature example corresponding to the third tailings ecological information example; Obtain an example of independent tailings ecological restoration data results corresponding to the example mine event label; Based on the independent tailings ecological restoration data result example and the unique mining matter significant feature example, obtain the mining matter significant feature example corresponding to the example mining matter label; The steps for obtaining the tailings ecological restoration element characteristic example and the unique mining event significant characteristic example corresponding to the third tailings ecological information example include: Obtain a model for extracting features of tailings ecological restoration elements and a model for extracting significant features of unique mining events; Loading the third tailings ecological information example into the tailings ecological restoration factor feature extraction model, performing feature extraction on the third tailings ecological information example through a standard local model of the tailings ecological restoration factor feature extraction model, and outputting the tailings ecological restoration factor feature example through an output local model of the tailings ecological restoration factor feature extraction model; The third tailings ecological information example is loaded into the unique mining matter significant feature extraction model, features of the third tailings ecological information example are extracted through the standard local model of the unique mining matter significant feature extraction model, and the unique mining matter significant feature example is output through the output local model of the unique mining matter significant feature extraction model.

4. The method according to claim 3, characterized in that The example mining event label corresponds to at least two third tailings ecological information examples; the independent tailings ecological restoration data result example is an independent tailings ecological restoration data evaluation value example; the example mining event significant feature corresponding to the example mining event label is obtained based on the independent tailings ecological restoration data result example and the unique mining event significant feature example, including: Obtaining no less than two independent tailings ecological restoration data maximum value examples and independent tailings ecological restoration data minimum value examples of the third tailings ecological information example; For one of the third tailings ecological information examples, based on the independent tailings ecological restoration data evaluation value example, the independent tailings ecological restoration data maximum value example, and the independent tailings ecological restoration data minimum value example corresponding to the example mine event label, the uniqueness of the example mine event label to the third tailings ecological information example is obtained; Based on the uniqueness of the example mining matter label to each of the third tailings ecological information examples, and the unique mining matter significant feature examples corresponding to each of the third tailings ecological information examples, obtaining the mining matter significant feature examples corresponding to the example mining matter label; The example of the unique mining matter significant feature has multiple feature layers; the example of the mining matter significant feature corresponding to the example mining matter label is obtained based on the uniqueness and the example of the unique mining matter significant feature, including: In combination with the degree of uniqueness, the characteristic values ​​of the unique mining matter significant feature examples at each layer are adjusted to obtain the mining matter significant feature examples corresponding to the example mining matter labels.

5. The method according to claim 1, wherein The tailings ecological information that needs to be evaluated is a plurality of items; the independent tailings ecological restoration data result is an independent tailings ecological restoration data evaluation value; the method further includes: Obtaining an independent tailings ecological restoration data assessment value for each tailings ecological information that needs to be assessed, based on the target mine event tag; According to the independent tailings ecological restoration data evaluation value, a preset number of target tailings ecological information are selected from the tailings ecological information that needs to be evaluated; The target tailings ecological information is output to the terminal where the target mine event tag is located.

6. An intelligent assessment system for mine tailings ecological problems, characterized in that: The method comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 5.

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