Mining area ecological restoration intelligent decision-making system based on multivariate data integration

Abstract

The invention provides a mining area ecological restoration intelligent decision-making system based on multivariate data integration, which comprises a mining area data acquisition module, a data fusion modeling module, an intelligent decision-making analysis module and a scheme simulation evaluation module, the data fusion modeling module is used for fusing collected multi-source data and establishing an ecological model, the intelligent decision analysis module is used for analyzing the ecological model and generating a restoration scheme, and the scheme simulation evaluation module is used for simulating and operating the restoration scheme and performing evaluation based on an operation result; according to the system, a new-generation digital solution is provided for mine ecological restoration through a technical closed loop of data intelligent fusion, scheme dynamic optimization and effect accurate pre-judgment.

Description

Technical Field

[0001] The present invention relates to the field of electronic digital data processing, and in particular to an intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration. Background Art

[0002] With the rapid development of human society, mining has caused more and more serious damage to the natural ecological environment, especially in the mining area ecosystem, where problems such as land desertification, vegetation degradation, water pollution, and soil quality decline are particularly prominent. Therefore, how to achieve ecological restoration in mining areas and restore ecological functions has become an important issue that needs to be urgently addressed in the field of mining area management and environmental protection.

[0003] Traditional ecological restoration in mining areas often relies on experience and human intervention, lacks scientificity and systematicness, and is often unable to accurately evaluate the restoration effects. It is also difficult to flexibly adjust restoration measures according to actual changes. However, the integration and analysis technology of multivariate data provides new ideas for accurately evaluating the ecological status and restoration effects of mining areas.

[0004] The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion does not acknowledge or admit that any of the material referred to is part of the common general knowledge.

[0005] Many ecological restoration systems have been developed. After a lot of searching and reference, it is found that the existing ecological restoration systems are such as the system disclosed in the publication number CN115947573B. These systems generally include parameter acquisition modules, databases, data analysis modules, restoration evaluation platforms, and promotion and display modules. This method not only directly conducts comprehensive evaluation from the perspective of heavy metal content, but also indirectly conducts comprehensive evaluation from the perspective of plant growth in the area where the heavy metal contaminated soil is located, making the evaluation results more accurate and more reasonable. However, this system only analyzes the single data of heavy metals and cannot provide a more comprehensive and reasonable restoration plan. Summary of the invention

[0006] The purpose of the present invention is to propose an intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration in response to the existing deficiencies.

[0007] The present invention adopts the following technical solution: An intelligent decision-making system for ecological restoration in mining areas based on multivariate data integration, including a mining area data acquisition module, a data fusion modeling module, an intelligent decision-making analysis module, and a solution simulation evaluation module; The mining area data acquisition module is used to collect data information of the mining area, the data fusion modeling module is used to fuse the collected multi-source data and establish an ecological model, the intelligent decision analysis module is used to analyze the ecological model and generate a restoration plan, and the plan simulation evaluation module is used to simulate the operation of the restoration plan and evaluate it based on the operation results; The mining area data acquisition module includes a remote sensing acquisition unit, an environmental monitoring unit and a meteorological monitoring unit. The remote sensing acquisition unit is used to collect ground vegetation coverage information of the mining area, the environmental monitoring unit is used to monitor water and soil quality information, and the meteorological monitoring unit is used to monitor meteorological changes in the mining area; The data fusion modeling module includes a geographic model construction unit, a data support coverage unit and a time series mapping management unit, wherein the geographic model construction unit is used to construct a basic geographic model, the data support coverage unit is used to cover the data in the basic geographic model in the spatial dimension, and the time series mapping management unit is used to map and associate the data in the time dimension; The intelligent decision analysis module includes a model feature analysis unit, a model type matching unit and a decision solution generation unit. The model feature analysis unit is used to extract features of the constructed ecological model. The model type matching unit classifies the ecological model based on the extracted feature information. The decision solution generation model is used to generate a restoration solution. The scheme simulation and evaluation module includes a simulation operation unit, an effect statistical calculation unit and a scheme evaluation and selection unit. The simulation operation unit is used to run the restoration scheme, the effect statistical calculation unit is used to calculate the differential changes of the ecological model, and the scheme evaluation and selection unit evaluates the restoration scheme based on the differential changes and selects the best scheme.

[0008] Furthermore, the data support coverage unit includes a basic coverage processor, an attribute parsing processor and an extended coverage processor. The basic coverage processor is used to save the collected water and soil data to a corresponding storage space, the attribute parsing processor is used to perform attribute parsing on a unit space that does not directly store data, and the extended coverage processor calculates the water and soil data based on the attribute parsing results and saves them to the corresponding storage space.

[0009] Furthermore, the attribute analysis processor calculates the attribute weight coefficient b of the target unit space according to the following formula: ; in, is the type cardinality, d1 is the distance between the target unit space and the nearest boundary, and d2 is the distance between the target collection point and the nearest boundary; The target collection point is the collection point closest to the target unit space; The extended coverage processor calculates the soil moisture content w of the target unit space according to the following formula: ; Among them, w2 is the soil moisture content at the target collection point.

[0010] Furthermore, the decision solution generation unit includes a case analysis processor, a solution reconstruction processor and a value reset processor. The case analysis processor is used to perform similarity analysis on actual repair cases. The solution reconstruction processor reorganizes measures in actual cases to form a repair solution framework. The value reset processor is used to reset specific values ​​in the repair solution framework to obtain a repair solution. The case analysis processor calculates the similarity P between the current ecological model and the case ecological model according to the following formula: ; Among them, m is the number of defective items in the case ecological model, n is the number of defective items in the current ecological model, V0(i) is the defect value of the i-th defective item in the current ecological model, and V1(i) is the defect value of the i-th defective item in the case ecological model.

[0011] Further, the scheme evaluation and selection unit includes a parameter input processor, an evaluation calculation processor and a scheme display processor, wherein the parameter input processor is used to input parameter information for evaluating the scheme, the evaluation calculation processor is used to perform score calculation on each scheme, and the scheme display processor is used to display specific information of the optimal scheme; The evaluation calculation processor calculates the score value Q of the solution according to the following formula: ; in, is the time base, T0 is the repair time limit, T is the simulated repair time, k i is the repair coefficient of the i-th defect item, is the change value of the i-th defect item.

[0012] The beneficial effects achieved by the present invention are: This system solves the three major industry pain points of "data fragmentation, empirical decision-making, and uncontrollable effects" in mining ecological restoration through a technical closed loop of intelligent data fusion, dynamic optimization of solutions, and accurate prediction of effects, providing a new generation of digital solutions for mine ecological restoration.

[0013] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the overall structural framework of the present invention; Figure 2 This is a schematic diagram of the mining area data acquisition module of the present invention; Figure 3 This is a schematic diagram of the data fusion modeling module of the present invention; Figure 4 This is a schematic diagram of the intelligent decision-making analysis module of the present invention; Figure 5 This is a schematic diagram of the simulation evaluation module of the present invention. DETAILED DESCRIPTION

[0015] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0016] Embodiment 1: This embodiment provides an intelligent decision-making system for ecological restoration in mining areas based on multivariate data integration, including a mining area data acquisition module, a data fusion modeling module, an intelligent decision-making analysis module, and a solution simulation evaluation module; The mining area data acquisition module is used to collect data information of the mining area, the data fusion modeling module is used to fuse the collected multi-source data and establish an ecological model, the intelligent decision analysis module is used to analyze the ecological model and generate a restoration plan, and the plan simulation evaluation module is used to simulate the operation of the restoration plan and evaluate it based on the operation results; The mining area data acquisition module includes a remote sensing acquisition unit, an environmental monitoring unit and a meteorological monitoring unit. The remote sensing acquisition unit is used to collect ground vegetation coverage information of the mining area, the environmental monitoring unit is used to monitor water and soil quality information, and the meteorological monitoring unit is used to monitor meteorological changes in the mining area; The data fusion modeling module includes a geographic model construction unit, a data support coverage unit and a time series mapping management unit, wherein the geographic model construction unit is used to construct a basic geographic model, the data support coverage unit is used to cover the data in the basic geographic model in the spatial dimension, and the time series mapping management unit is used to map and associate the data in the time dimension; The intelligent decision analysis module includes a model feature analysis unit, a model type matching unit and a decision solution generation unit. The model feature analysis unit is used to extract features of the constructed ecological model. The model type matching unit classifies the ecological model based on the extracted feature information. The decision solution generation model is used to generate a restoration solution. The scheme simulation and evaluation module includes a simulation operation unit, an effect statistical calculation unit and a scheme evaluation and selection unit. The simulation operation unit is used to run the restoration scheme, the effect statistical calculation unit is used to calculate the differential changes of the ecological model, and the scheme evaluation and selection unit evaluates the restoration scheme based on the differential changes and selects the best scheme.

[0017] The data support coverage unit includes a basic coverage processor, an attribute parsing processor and an extended coverage processor. The basic coverage processor is used to save the collected water and soil data to a corresponding storage space. The attribute parsing processor is used to perform attribute parsing on a unit space that does not directly store data. The extended coverage processor calculates the water and soil data based on the attribute parsing results and saves them to a corresponding storage space.

[0018] The attribute analysis processor calculates the attribute weight coefficient b of the target unit space according to the following formula: ; in, is the type cardinality, d1 is the distance between the target unit space and the nearest boundary, and d2 is the distance between the target collection point and the nearest boundary; The target collection point is the collection point closest to the target unit space; The extended coverage processor calculates the soil moisture content w of the target unit space according to the following formula: ; Among them, w2 is the soil moisture content at the target collection point.

[0019] The decision solution generation unit includes a case analysis processor, a solution reconstruction processor and a value reset processor. The case analysis processor is used to perform similarity analysis on actual repair cases. The solution reconstruction processor reorganizes the measures in the actual cases to form a repair solution framework. The value reset processor is used to reset specific values ​​in the repair solution framework to obtain a repair solution. The case analysis processor calculates the similarity P between the current ecological model and the case ecological model according to the following formula: ; Among them, m is the number of defective items in the case ecological model, n is the number of defective items in the current ecological model, V0(i) is the defect value of the i-th defective item in the current ecological model, and V1(i) is the defect value of the i-th defective item in the case ecological model.

[0020] The scheme evaluation and selection unit includes a parameter input processor, an evaluation calculation processor and a scheme display processor, wherein the parameter input processor is used to input parameter information for evaluating the scheme, the evaluation calculation processor is used to perform score calculation on each scheme, and the scheme display processor is used to display specific information of the optimal scheme; The evaluation calculation processor calculates the score value Q of the solution according to the following formula: ; in, is the time base, T0 is the repair time limit, T is the simulated repair time, k i is the repair coefficient of the i-th defect item, is the change value of the i-th defect item.

[0021] Embodiment 2: This embodiment includes all the contents of Embodiment 1, and provides an intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration, including a mining area data acquisition module, a data fusion modeling module, an intelligent decision-making analysis module, and a solution simulation evaluation module; The mining area data acquisition module is used to collect data information of the mining area, the data fusion modeling module is used to fuse the collected multi-source data and establish an ecological model, the intelligent decision analysis module is used to analyze the ecological model and generate a restoration plan, and the plan simulation evaluation module is used to simulate the operation of the restoration plan and evaluate it based on the operation results; Combination Figure 2 The mining area data acquisition module includes a remote sensing acquisition unit, an environmental monitoring unit and a meteorological monitoring unit. The remote sensing acquisition unit is used to collect ground vegetation coverage information of the mining area, the environmental monitoring unit is used to monitor water and soil quality information, and the meteorological monitoring unit is used to monitor meteorological changes in the mining area; Combination Figure 3 The data fusion modeling module includes a geographic model building unit, a data support covering unit and a time series mapping management unit. The geographic model building unit is used to build a basic geographic model, the data support covering unit is used to cover the data in the basic geographic model in the spatial dimension, and the time series mapping management unit is used to map and associate the data in the time dimension; Combination Figure 4The intelligent decision analysis module includes a model feature parsing unit, a model type matching unit and a decision solution generation unit. The model feature parsing unit is used to extract features of the constructed ecological model. The model type matching unit classifies the ecological model based on the extracted feature information. The decision solution generation model is used to generate a restoration solution. Combination Figure 5 The scheme simulation evaluation module includes a simulation operation simulation unit, an effect statistical calculation unit and a scheme evaluation selection unit. The simulation operation simulation unit is used to run the restoration scheme, the effect statistical calculation unit is used to calculate the difference change of the ecological model, and the scheme evaluation selection unit evaluates the restoration scheme based on the difference change and selects the best scheme; The remote sensing acquisition unit includes an image acquisition processor, an image recognition processor and a partition setting processor, wherein the image acquisition processor is used to acquire image information of the mining area, the image recognition processor is used to identify type information in the image, and the partition setting processor sets partition data based on the identified type information; The environmental monitoring unit includes a soil sensor processor, a transmission network processor and a positioning deployment processor, wherein the soil sensor processor is used to collect soil moisture information, the transmission network processor is used to transmit and summarize the collected information, and the positioning deployment processor is used to perform fixed-point binding processing on the collected information; The meteorological monitoring unit includes a rain monitoring processor, a light monitoring processor and an air monitoring processor, wherein the rain monitoring processor is used to monitor rain information, the light monitoring processor is used to monitor light intensity information, and the air monitoring processor is used to monitor air humidity information; The geographic model building unit includes a terrain modeling processor, a region division processor and a mapping storage processor, wherein the terrain modeling processor is used to build a three-dimensional terrain model, the region division processor is used to divide the model into a mining area, a vegetation covered area and a non-vegetation covered area, and the mapping storage processor is used to create a storage space that is mapped one-to-one with the unit space; The unit space is the smallest unit that constitutes the three-dimensional terrain model. Each unit space belongs to one of the three regions. The region type to which the unit space belongs is called the attribute of the unit space. The data support coverage unit includes a basic coverage processor, an attribute parsing processor and an extended coverage processor. The basic coverage processor is used to save the collected water and soil data to a corresponding storage space. The attribute parsing processor is used to perform attribute parsing on a unit space that does not directly store data. The extended coverage processor calculates the water and soil data based on the attribute parsing result and saves it to a corresponding storage space. The attribute analysis processor calculates the attribute weight coefficient b of the target unit space according to the following formula: ; in, is the type cardinality, d1 is the distance between the target unit space and the nearest boundary, and d2 is the distance between the target collection point and the nearest boundary; The target collection point is the collection point closest to the target unit space; The extended coverage processor calculates the soil moisture content w of the target unit space according to the following formula: ; Among them, w2 is the soil moisture content at the target collection point; The time series mapping management unit includes a node division processor, a meteorological information register and a mapping association processor, wherein the node division processor is used to divide and obtain time nodes, the meteorological information register is used to store meteorological data between two adjacent time nodes, and the mapping association processor is used to associate and map the time series segment with the associated water and soil data; The time period between two adjacent time nodes is called a time series segment; The time segment obtained by the node division processor satisfies the condition that: the standard deviation of the same meteorological data in the time segment is less than the set threshold; The node division processor homogenizes the air data in the time series segment and then saves it to the meteorological information register; The model feature analysis unit includes a feature data information library, a feature calculation processor and a feature screening processor, wherein the feature data information library is used to store data information of each model feature item, the feature calculation processor processes the model data based on the data information to obtain a feature value of each feature item, and the feature screening processor is used to screen out abnormal feature items; The model type matching unit includes an ecological type database, a matching calculation processor and a defect calculation processor, wherein the ecological type database is used to store information about ecological types, the matching calculation processor matches the corresponding ecological type based on the screened feature items, and the defect calculation processor calculates defect information existing in the current ecological model based on the ecological type; The decision solution generation unit includes a case analysis processor, a solution reconstruction processor and a value reset processor. The case analysis processor is used to perform similarity analysis on actual repair cases. The solution reconstruction processor reorganizes the measures in the actual cases to form a repair solution framework. The value reset processor is used to reset specific values ​​in the repair solution framework to obtain a repair solution. The case analysis processor calculates the similarity P between the current ecological model and the case ecological model according to the following formula: ; Where m is the number of defective items in the case ecological model, n is the number of defective items in the current ecological model, V0(i) is the defect value of the i-th defective item in the current ecological model, and V1(i) is the defect value of the i-th defective item in the case ecological model; The solution reconstruction processor uses measures existing in all cases with a similarity greater than a first threshold as basic measures, and uses measures existing in cases with a similarity greater than a second threshold as extended measures. The solution reconstruction processor generates multiple repair solutions by recombining different extended measures with basic measures. The simulation operation unit includes a condition configuration processor, an accelerated operation simulator and a result output processor, wherein the condition configuration processor is used to configure the condition information when executing the repair plan, the accelerated operation simulator is used for the high-frequency operation environment change process, and the result output processor is used to output the ecological data after the execution of the plan; The effect statistics calculation unit includes a difference calculation processor, a weight adjustment processor and a defect comparison processor, wherein the difference calculation processor is used to calculate the data difference value of the same unit space, the weight adjustment processor performs weighted adjustment on the difference value, and the defect comparison processor is used to calculate the change value of the original defect information; The scheme evaluation and selection unit includes a parameter input processor, an evaluation calculation processor and a scheme display processor, wherein the parameter input processor is used to input parameter information for evaluating the scheme, the evaluation calculation processor is used to perform score calculation on each scheme, and the scheme display processor is used to display specific information of the optimal scheme; The evaluation calculation processor calculates the score value Q of the solution according to the following formula: ; in, is the time base, T0 is the repair time limit, T is the simulated repair time, k i is the repair coefficient of the i-th defect item, is the change value of the i-th defect item; The i appearing in the above text is an ordinal number used to indicate a sequence number and has no actual meaning.

[0022] Some code information of this system is as follows: def collect_data(self): # Data Collection vegetation_info = self.remote_sensing_unit.collect_vegetation_info() soil_quality_info = self.environment_monitoring_unit.monitor_soil_quality() weather_info = self.weather_monitoring_unit.monitor_weather_changes() return { 'vegetation_info': vegetation_info, 'soil_quality_info': soil_quality_info, 'weather_info': weather_info } class RemoteSensingUnit: def collect_vegetation_info(self): # 实现遥感数据采集 return {"vegetation_cover": "some data"} class EnvironmentMonitoringUnit: def monitor_soil_quality(self): # 实现环境监测数据采集 return {"soil_quality": "some data"} class WeatherMonitoringUnit: def monitor_weather_changes(self): # 实现气象变化监测 return {"weather_data": "some data"} class DataFusionModelingModule: def __init__(self): self.geographic_model_builder = GeographicModelBuilder() self.data_support_overlay_unit = DataSupportOverlayUnit() self.temporal_mapping_unit = TemporalMappingUnit() def fuse_data(self, data): Data Fusion geo_model = self.geographic_model_builder.build_geographic_model() overlayed_data = self.data_support_overlay_unit.overlay_data_on_geographic_model(data, geo_model) temporal_mapped_data = self.temporal_mapping_unit.map_data_over_time(overlayed_data) return temporal_mapped_data def build_ecological_model(self, data): # Build an ecological model return {"ecological_model": "some ecological model based on data"} class GeographicModelBuilder: def build_geographic_model(self): # Build basic geographic model return {"geo_model": "some base geographic model"} class DataSupportOverlayUnit: def overlay_data_on_geographic_model(self, data, geo_model): # Overlay data onto the base geographic model return {"overlayed_data": "data overlaid on geo_model"} class TemporalMappingUnit: def map_data_over_time(self, data): # 在时间维度上进行映射关联 return {"temporal_data": "time-mapped data"} class IntelligentDecisionAnalysisModule: def __init__(self): self.model_feature_extractor = ModelFeatureExtractor() self.model_type_matcher = ModelTypeMatcher() self.decision_scheme_generator = DecisionSchemeGenerator() def generate_decision_scheme(self, ecological_model): # 生成决策方案 model_features = self.model_feature_extractor.extract_features(ecological_model) model_type = self.model_type_matcher.match_model_type(model_features) decision_scheme = self.decision_scheme_generator.generate_scheme(model_type) return decision_scheme class ModelFeatureExtractor: def extract_features(self, ecological_model): # Extract features from the ecological model return {"model_features": "some features from ecological model"} class ModelTypeMatcher: def match_model_type(self, features): # Match model type based on features return {"model_type": "matched model type based on features"} class DecisionSchemeGenerator: def generate_scheme(self, model_type): # Generate repair plan according to model type return {"decision_scheme": "generated restoration scheme"} class SchemeSimulationEvaluationModule: def __init__(self): self.simulation_runner = SimulationRunner() self.effect_statistic_calculator = EffectStatisticCalculator() self.scheme_evaluator = SchemeEvaluator() def simulate_scheme(self, scheme): # Run the repair solution simulation return self.simulation_runner.run_simulation(scheme) def evaluate_scheme(self, simulation_results): # Evaluate repair options and choose the best one effect_stats = self.effect_statistic_calculator.calculate_statistics(simulation_results) best_scheme = self.scheme_evaluator.select_best_scheme(effect_stats) return best_scheme class SimulationRunner: def run_simulation(self, scheme): # Run the simulation of the repair solution return {"simulation_results": "simulated results of the restoration scheme"} class EffectStatisticCalculator: def calculate_statistics(self, simulation_results): # Calculate performance statistics return {"effect_stats": "calculated effect statistics"} class SchemeEvaluator: def select_best_scheme(self, effect_stats): # Select the best solution based on performance statistics return {"best_scheme": "selected best restoration scheme"}.

[0023] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. An intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration, characterized in that: It includes mining area data acquisition module, data fusion modeling module, intelligent decision analysis module and solution simulation evaluation module; The mining area data acquisition module is used to collect data information of the mining area, the data fusion modeling module is used to fuse the collected multi-source data and establish an ecological model, the intelligent decision analysis module is used to analyze the ecological model and generate a restoration plan, and the plan simulation evaluation module is used to simulate the operation of the restoration plan and evaluate it based on the operation results; The mining area data acquisition module includes a remote sensing acquisition unit, an environmental monitoring unit and a meteorological monitoring unit. The remote sensing acquisition unit is used to collect ground vegetation coverage information of the mining area, the environmental monitoring unit is used to monitor water and soil quality information, and the meteorological monitoring unit is used to monitor meteorological changes in the mining area; The data fusion modeling module includes a geographic model construction unit, a data support coverage unit and a time series mapping management unit, wherein the geographic model construction unit is used to construct a basic geographic model, the data support coverage unit is used to cover the data in the basic geographic model in the spatial dimension, and the time series mapping management unit is used to map and associate the data in the time dimension; The intelligent decision analysis module includes a model feature analysis unit, a model type matching unit and a decision solution generation unit. The model feature analysis unit is used to extract features of the constructed ecological model. The model type matching unit classifies the ecological model based on the extracted feature information. The decision solution generation model is used to generate a restoration solution. The scheme simulation and evaluation module includes a simulation operation unit, an effect statistical calculation unit and a scheme evaluation and selection unit. The simulation operation unit is used to run the restoration scheme, the effect statistical calculation unit is used to calculate the differential changes of the ecological model, and the scheme evaluation and selection unit evaluates the restoration scheme based on the differential changes and selects the best scheme.

2. The intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration as claimed in claim 1, characterized in that: The data support coverage unit includes a basic coverage processor, an attribute parsing processor and an extended coverage processor. The basic coverage processor is used to save the collected water and soil data to a corresponding storage space. The attribute parsing processor is used to perform attribute parsing on a unit space that does not directly store data. The extended coverage processor calculates the water and soil data based on the attribute parsing results and saves them to a corresponding storage space.

3. The intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration as claimed in claim 2, characterized in that: The attribute analysis processor calculates the attribute weight coefficient b of the target unit space according to the following formula: ； in, is the type cardinality, d1 is the distance between the target unit space and the nearest boundary, and d2 is the distance between the target collection point and the nearest boundary; The target collection point is the collection point closest to the target unit space; The extended coverage processor calculates the soil moisture content w of the target unit space according to the following formula: ； Among them, w2 is the soil moisture content at the target collection point.

4. The intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration as claimed in claim 3, characterized in that: The decision solution generation unit includes a case analysis processor, a solution reconstruction processor and a value reset processor. The case analysis processor is used to perform similarity analysis on actual repair cases. The solution reconstruction processor reorganizes the measures in the actual cases to form a repair solution framework. The value reset processor is used to reset specific values ​​in the repair solution framework to obtain a repair solution. The case analysis processor calculates the similarity P between the current ecological model and the case ecological model according to the following formula: ； Among them, m is the number of defective items in the case ecological model, n is the number of defective items in the current ecological model, V0(i) is the defect value of the i-th defective item in the current ecological model, and V1(i) is the defect value of the i-th defective item in the case ecological model.

5. The intelligent decision-making system for ecological restoration of mining areas based on multivariate data integration as claimed in claim 4, characterized in that: The scheme evaluation and selection unit includes a parameter input processor, an evaluation calculation processor and a scheme display processor, wherein the parameter input processor is used to input parameter information for evaluating the scheme, the evaluation calculation processor is used to perform score calculation on each scheme, and the scheme display processor is used to display specific information of the optimal scheme; The evaluation calculation processor calculates the score value Q of the solution according to the following formula: ； in, is the time base, T0 is the repair time limit, T is the simulated repair time, k i is the repair coefficient of the i-th defect item, is the change value of the i-th defect item.

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