Mining area ecological restoration artificial influence weather service benefit evaluation method

Abstract

The invention relates to a mining area ecological restoration artificial influence weather service benefit evaluation method. A target mining area and a comparison mining area can be selected; obtaining loss data of vegetation of the target mining area and the comparison mining area influenced by meteorological disasters in the restoration period; constructing a meteorological reanalysis field, obtaining cloud micro physical parameters by using the meteorological reanalysis field, performing inversion to obtain a diffusion influence area, and obtaining an effectiveness evaluation result of the figure operation according to the diffusion influence area; obtaining public opinion evaluation results of each participant on the target mining area at different time nodes; and the loss data, the effectiveness evaluation result and the public opinion evaluation result are fitted on the time scale, and a benefit evaluation model is established. It can be understood that the technical scheme shown by the invention can establish a benefit evaluation model in three aspects of loss data, an effectiveness evaluation result and a public opinion evaluation result, thereby establishing a mining area ecological restoration type figure work benefit evaluation system and forming an ecological restoration type figure work service benefit evaluation index system.

Description

Technical Field

[0001] The present invention relates to the technical field of human shadow service evaluation, and in particular to a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas. Background Art

[0002] Weather modification (hereinafter referred to as "weather modification") is one of the important ways to reduce or avoid meteorological disasters by using the basic principles of cloud microphysics to implement artificial intervention on specific meteorological elements.

[0003] In the context of climate warming, the combined effects of fragile natural conditions and human activities such as mining have put great pressure on ecological protection and restoration. The risk of natural vegetation, crops, economic trees, mine restoration, etc. being affected by drought, high temperature, hail, rainstorms and floods has increased, of which drought disasters account for more than 70%. Therefore, reducing the losses caused by drought meteorological disasters and improving disaster prevention and mitigation capabilities are important ways to ensure the ecological security of the karst area in western Guangxi and adapt to climate change.

[0004] The demand for artificial rainmaking and drought relief services for ecological protection and restoration is becoming more and more urgent, and the scale of operations is also expanding year by year. With the advancement of science and technology, artificial shadow technology is also constantly improving and developing, but most of it is still mainly to meet generalized needs, resulting in the lack of pertinence, accuracy, and timeliness of artificial shadow operations, and the scientific evaluation of the effect of artificial shadow services. Blind artificial shadow operations have brought about a series of problems such as low efficiency and waste of resources, especially the evaluation of the benefits of artificial shadow services for the needs of ecological protection and restoration is still blank. The main problems and shortcomings are as follows: the response mechanism of vegetation drought damage-precipitation relief in karst areas is unclear, the application ability of numerical weather forecasts and radar data is not strong, and the accuracy of meteorological condition judgment and analysis technology for artificial shadow operations is not enough. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas, so as to solve the above-mentioned problems in the prior art to a certain extent.

[0006] According to a first aspect of an embodiment of the present invention, a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas is provided, comprising:

[0007] Obtain mining area environmental data and geographic data, and select target mining areas and comparison mining areas based on the mining area environmental data and geographic data;

[0008] Continuously monitoring the target mining area and the comparison mining area, and obtaining the vegetation loss data of the target mining area and the comparison mining area due to meteorological disasters during the restoration period based on the monitoring data;

[0009] Construct a meteorological reanalysis field and use it to obtain cloud microphysical parameters, simulate the human shadow operation based on the cloud microphysical parameters, invert the diffusion impact area, and obtain the effectiveness evaluation result of the human shadow operation based on the diffusion impact area;

[0010] Obtain the public opinion evaluation results of each participant on the target mining area at different time points;

[0011] The loss data, the effectiveness evaluation results and the public opinion evaluation results are fitted on a time scale, the overall effect correlation and benefit are calculated based on the fitting results, and a benefit evaluation model is established based on the overall effect correlation and benefit.

[0012] Preferably, the loss data of vegetation in the target mining area and the comparison mining area affected by meteorological disasters during the restoration period obtained based on the monitoring data includes:

[0013] Obtaining visible light image data according to the monitoring data, selecting an image sequence of the visible light image data, and performing image segmentation on the image sequence to obtain a green vegetation image;

[0014] According to the green vegetation image, a green vegetation visible light index is calculated and a green vegetation change trend is simulated;

[0015] Based on the green vegetation change trend, the loss data of vegetation caused by meteorological disasters during the restoration period are obtained.

[0016] Preferably, performing image segmentation on the image sequence to obtain a green vegetation image includes:

[0017] Use K-means unsupervised clustering algorithm to generate training samples and screen the samples;

[0018] The screened cluster samples are used as training samples of a support vector machine vegetation-background classifier to train the classifier;

[0019] Using the trained classifier to perform image segmentation on the image sequence to obtain a background-plant segmentation image;

[0020] performing non-local mean filtering on the background-plant segmentation image;

[0021] The filtered background-plant segmentation image is subjected to RGB image segmentation using a machine learning algorithm to obtain a green vegetation image.

[0022] Preferably, after obtaining the green vegetation image, the method further includes:

[0023] Non-local mean filtering is used to filter out noise in the green vegetation image, and the edge area of ​​the foreground image contour leaves in the green vegetation image is retained.

[0024] Preferably, a composite sine function is used in the process of simulating the trend of green vegetation changes, and the calculation formula is as follows:

[0025] VU=a+bsin[2π(t day -c) / 365]

[0026] Among them, a, b, and c are all empirical coefficients; t day For the day sequence.

[0027] Preferably, the simulating of the human shadow operation according to the cloud microphysical parameters and inverting to obtain the diffusion impact area includes:

[0028] According to the cloud microphysical parameters, the three-dimensional cloud model live field re-fitting inversion technology is used to perform a three-dimensional real-time simulation of the catalyst diffusion after the human shadow operation, and the diffusion influence area is inverted.

[0029] Preferably, obtaining the effectiveness evaluation result of the human shadow operation according to the diffusion impact area includes:

[0030] According to the diffusion impact area, the impact result of the human shadow operation is obtained, and the impact result of the human shadow operation includes the coverage rate of the operation impact area, the adjustment effect of the high-impact meteorological factors in the operation area, and the cost-effectiveness ratio of the human shadow operation investment;

[0031] The effectiveness evaluation result is obtained according to the impact result of the human shadow operation.

[0032] Preferably, after obtaining the public opinion evaluation result, the method further includes:

[0033] According to the time nodes and the public opinion evaluation results, a public opinion trend sequence is sorted out, and based on the public opinion trend sequence, an overall impact evaluation system is established.

[0034] Preferably, the calculating of the overall effect correlation and benefit according to the fitting results includes:

[0035] Establishing a fuzzy comprehensive evaluation method based on the analytic hierarchy process according to the fitting results, and calculating a fuzzy evaluation matrix according to the fuzzy comprehensive evaluation method;

[0036] Using the fuzzy evaluation matrix, the vegetation restoration evaluation expectations and results of the target mining area and the comparison mining area are obtained;

[0037] Based on the expectations and results of vegetation restoration evaluation in the target and comparison mining areas, the overall effect relevance and benefits are obtained.

[0038] Preferably, the selecting of the target mining area and the comparison mining area according to the mining area environmental data and geographical data comprises:

[0039] Selecting the first mining area and the second mining area based on the mining area environmental data and geographical data;

[0040] Determine whether the first mining area and the second mining area meet preset conditions;

[0041] If so, the first mining area is used as the target mining area and the second mining area is used as the comparison mining area.

[0042] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0043] It can be understood that the present invention provides a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas, which can select target mining areas and comparison mining areas; obtain the loss data of vegetation in the target mining areas and comparison mining areas affected by meteorological disasters during the restoration period; construct a meteorological reanalysis field and use it to obtain cloud microphysical parameters, invert the diffusion impact area, and obtain the effectiveness evaluation results of human shadow operations based on the diffusion impact area; obtain the public opinion evaluation results of the target mining area by each participant at different time nodes; fit the loss data, effectiveness evaluation results and public opinion evaluation results on the time scale to establish a benefit evaluation model. It can be understood that the technical solution shown in the present invention can establish a benefit evaluation model in terms of loss data, effectiveness evaluation results and public opinion evaluation results, so as to achieve the establishment of a mining area ecological restoration type human shadow operation benefit evaluation system and form an ecological restoration type human shadow operation service benefit evaluation index system.

[0044] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] Figure 1 It is a schematic diagram of the steps of a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas according to an exemplary embodiment. DETAILED DESCRIPTION

[0047] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0048] At present, the demand for artificial rainmaking and drought relief services for ecological protection and restoration is becoming more and more urgent, and the scale of operations is also expanding year by year. With the advancement of science and technology, artificial shadow technology is also constantly improving and developing, but most of it is still mainly to meet generalized needs, resulting in the lack of pertinence, accuracy, and timeliness of artificial shadow operations, and the scientific evaluation of the effect of artificial shadow services. Blind artificial shadow operations have brought about a series of problems such as low efficiency and waste of resources, especially the evaluation of the benefits of artificial shadow services for the needs of ecological protection and restoration. There is still a blank, mainly the following problems and deficiencies: the response mechanism of vegetation drought damage-precipitation relief in karst areas is unclear, the application ability of numerical weather forecasts and radar data is not strong, the accuracy of meteorological condition judgment and analysis technology for artificial shadow operations is not enough, and there is a lack of a set of refined artificial shadow operation benefit evaluation technology system suitable for ecological protection and restoration, especially for the reclamation and restoration work in mining areas with clear goals, there is a lack of a set of methods and tools for evaluating the benefits of artificial shadows.

[0049] Therefore, the present invention is aimed at the urgent needs of continuous protection, restoration and governance of the ecological environment in mining areas, focusing on the key technology of human shadow for precise drought rainfall enhancement, using multi-source fusion data such as satellite remote sensing, drone imaging, meteorological observation, numerical weather forecast model, and human shadow operation parameters, combining meteorological principles, cloud precipitation physics and human shadow business, and applying a variety of mathematical and statistical methods to calculate the service benefits of human shadow operations in the restoration mining area over a period of time from multiple dimensions, studying the impact of human shadow operation technical methods on the governance of rocky desertification in mining areas, establishing an ecological restoration human shadow operation benefit evaluation system for mining areas, and forming a set of ecological restoration human shadow operation service benefit evaluation index systems and technical solutions, as follows:

[0050] Embodiment 1

[0051] Figure 1 is a schematic diagram of the steps of a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas according to an exemplary embodiment, see Figure 1 , providing a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas, including:

[0052] Step S11, obtaining mining area environmental data and geographic data, and selecting a target mining area and a comparison mining area according to the mining area environmental data and geographic data;

[0053] Step S12, continuously monitoring the target mining area and the comparison mining area, and obtaining vegetation loss data of the target mining area and the comparison mining area due to meteorological disasters during the restoration period according to the monitoring data;

[0054] Step S13, constructing a meteorological reanalysis field and using it to obtain cloud microphysical parameters, simulating the human shadow operation according to the cloud microphysical parameters, inverting to obtain a diffusion influence area, and obtaining an effectiveness evaluation result of the human shadow operation according to the diffusion influence area;

[0055] Step S14, obtaining the public opinion evaluation results of each participant on the target mining area at different time points;

[0056] Step S15: Fit the loss data, the effectiveness evaluation results and the public opinion evaluation results on a time scale, calculate the overall effect correlation and benefit based on the fitting results, and establish a benefit evaluation model based on the overall effect correlation and benefit.

[0057] It can be understood that this embodiment provides a method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas, which can select target mining areas and comparison mining areas; obtain the loss data of vegetation in the target mining areas and comparison mining areas affected by meteorological disasters during the restoration period; construct a meteorological reanalysis field and use it to obtain cloud microphysical parameters, invert the diffusion impact area, and obtain the effectiveness evaluation results of human shadow operations based on the diffusion impact area; obtain the public opinion evaluation results of the target mining area by each participant at different time nodes; fit the loss data, effectiveness evaluation results and public opinion evaluation results on the time scale to establish a benefit evaluation model. It can be understood that the technical solution shown in this embodiment can establish a benefit evaluation model in terms of loss data, effectiveness evaluation results and public opinion evaluation results, so as to achieve the establishment of a benefit evaluation system for ecological restoration human shadow operations in mining areas and form an ecological restoration human shadow operation service benefit evaluation index system.

[0058] It should be noted that, in step S11, the target mining area and the comparison mining area are selected according to the mining area environmental data and geographical data, including:

[0059] Selecting the first mining area and the second mining area based on the mining area environmental data and geographical data;

[0060] Determine whether the first mining area and the second mining area meet preset conditions;

[0061] If so, the first mining area is used as the target mining area and the second mining area is used as the comparison mining area.

[0062] In specific practice, the technical difficulties and key scientific issues that this embodiment focuses on solving are: the selection of target (affected) mining areas and comparison mining areas. The difference between the target (affected) mining areas and comparison mining areas directly determines the credibility of the comparison results. It is planned to compare the similarities of weather systems in various regions through historical meteorological data, compare the similarities of regional geological landforms and vegetation through remote sensing and field surveys, and complete the final delineation of the human shadow service impact area and comparison area through the relationship between the distribution of human shadow operation sites and the weather system.

[0063] In actual applications, when determining the target mining area and the comparison mining area, the main preset conditions are: the two mining areas have similar historical weather conditions before the restoration period, comparable landforms and vegetation coverage, and similar restoration investment per unit area; the interval between the two areas cannot be too large, and they should not be in the upstream and downstream relationship of the weather system, nor in the rainwater confluence transit area; the area of ​​the two areas should not be too large, otherwise the impact of human shadow operations may be weakened; during the restoration experiment period, no human shadow operations will be carried out near the comparison mining area, and the impact of human shadow operations on the comparison area will be avoided as much as possible; human shadow operations will be carried out normally in the affected area, and artificial rainmaking, artificial hail suppression and prevention, and artificial rain reduction (karst desertification areas are very sensitive to surface runoff, and concentrated and excessive precipitation will lead to soil erosion) operations will be carried out through ground rockets, aircraft, etc.

[0064] It should be noted that in step S12, the loss data of vegetation in the target mining area and the comparison mining area affected by meteorological disasters during the restoration period obtained according to the monitoring data includes:

[0065] Obtaining visible light image data according to the monitoring data, selecting an image sequence of the visible light image data, and performing image segmentation on the image sequence to obtain a green vegetation image;

[0066] According to the green vegetation image, a green vegetation visible light index is calculated and a green vegetation change trend is simulated;

[0067] Based on the green vegetation change trend, the loss data of vegetation caused by meteorological disasters during the restoration period are obtained.

[0068] In specific practice, the technical difficulties and key scientific issues that this embodiment focuses on solving are: objective quantitative estimation of the loss of vegetation in the restored mining area caused by meteorological disasters. The planting and growth of vegetation in the reclamation area are affected by multiple factors. Light, temperature, water, heat, natural disasters, and agricultural disasters may have adverse effects. It is very difficult to distinguish which specific factor or factors cause agricultural losses. It is planned to adopt the method of contrast subtraction to treat the factors that cannot be effectively intervened by human shadow operations as background factors. Affected by the growth period of plants, the requirements for the external environment at different stages are also different. The ecological disaster assessment and loss assessment work is planned to be carried out in a timely manner according to the plant growth cycle.

[0069] In practical applications, the loss of vegetation due to meteorological disasters during the growth period is determined by using the vegetation growth disaster verification method of ground-based visible light images. The main approach is to obtain the visible light image data of the vegetation canopy, select the image sequence based on the visible light image data, perform image segmentation on the image sequence to obtain the green vegetation image, calculate the green vegetation visible light index and simulate the trend of green vegetation changes, and monitor the vegetation growth disaster.

[0070] It should be noted that the step of performing image segmentation on the image sequence to obtain a green vegetation image includes:

[0071] Use K-means unsupervised clustering algorithm to generate training samples and screen the samples;

[0072] The screened cluster samples are used as training samples of a support vector machine (SVM) vegetation-background classifier to train the classifier;

[0073] Using the trained classifier to perform image segmentation on the image sequence to obtain a background-plant segmentation image;

[0074] Performing non-local mean filtering on the background-plant segmentation image to remove edge misclassified pixels;

[0075] The filtered background-plant segmentation image is subjected to RGB image segmentation using a machine learning algorithm to obtain a green vegetation image: preferably, a mixed machine learning algorithm is used to segment the RGB image of the near-ground canopy layer of the karst bare rock and vegetation mixed landforms in the selected mining area.

[0076] It should be noted that after obtaining the green vegetation image, it also includes:

[0077] The non-local mean filter is used to filter out the noise in the green vegetation image, and the edge area of ​​the foreground image contour leaves in the green vegetation image is retained to improve the accuracy.

[0078] Preferably, the impact of meteorological disasters on vegetation in the affected area and the comparison area during the restoration period is obtained, and the specific amount of damage is calculated in combination with the agricultural disaster situation.

[0079] It should be noted that a composite sine function is used in the process of simulating the trend of green vegetation changes. The calculation formula is as follows:

[0080] VI=a+bsin[2π(t day -c) / 365]

[0081] Among them, a, b, and c are all empirical coefficients; t day It is a daily sequence; it can better simulate the daily dynamic change characteristics of GLA, NDYI, NGRDI and VARI.

[0082] It should be noted that, in step S13, the simulation of the human shadow operation according to the cloud microphysical parameters and the inversion to obtain the diffusion impact area include:

[0083] According to the cloud microphysical parameters, the three-dimensional cloud model live field re-fitting inversion technology is used to perform a three-dimensional real-time simulation of the catalyst diffusion after the human shadow operation, and the diffusion influence area is inverted.

[0084] In practical applications, a meteorological reanalysis field is constructed to determine the impact area of ​​the operation effect: a three-dimensional stereoscopic section analysis of the meteorological conditions of the rain-making operation is carried out by superimposing the meteorological field and the catalytic particle diffusion concentration field. Based on the fusion of numerical model results, radar stereo observations, and real-time positioning information of rain-making aircraft and shadow rockets, cloud microphysical parameters obtained from the meteorological reanalysis field are used, and the three-dimensional cloud model live field re-fitting inversion technology is used. Through a three-dimensional real-time simulation of the catalyst diffusion after the shadow operation, the inverted diffusion impact area is obtained, and then the effectiveness of the operation is evaluated.

[0085] It should be noted that the effectiveness evaluation result of the human shadow operation obtained according to the diffusion impact area includes:

[0086] According to the diffusion impact area, the impact result of the human shadow operation is obtained, and the impact result of the human shadow operation includes the coverage rate of the operation impact area, the adjustment effect of the high-impact meteorological factors in the operation area, and the cost-effectiveness ratio of the human shadow operation investment;

[0087] The effectiveness evaluation result is obtained according to the impact result of the human shadow operation.

[0088] It should be noted that in step S14, a social benefit evaluation survey is conducted. During the entire project implementation period, objective data monitoring and overall evaluation of the target impact mining area are carried out on the participants according to different time nodes. The participants include: enterprises, reclamation and planting units, meteorological and human resources departments, and government environment and natural resources supervision departments.

[0089] It should be noted that after obtaining the public opinion evaluation results, it also includes:

[0090] According to the time nodes and the public opinion evaluation results, a public opinion trend sequence is sorted out, and based on the public opinion trend sequence, an overall impact evaluation system is established.

[0091] It should be noted that, in step S15, the calculation of the overall effect correlation and benefit according to the fitting result includes:

[0092] Establishing a fuzzy comprehensive evaluation method based on the analytic hierarchy process according to the fitting results, and calculating a fuzzy evaluation matrix according to the fuzzy comprehensive evaluation method;

[0093] Using the fuzzy evaluation matrix, the vegetation restoration evaluation expectations and results of the target mining area and the comparison mining area are obtained;

[0094] Based on the expectations and results of vegetation restoration evaluation in the target and comparison mining areas, the overall effect relevance and benefits are obtained.

[0095] In practical applications, a time series evaluation factor database is established, the loss data, the effectiveness evaluation results and the public opinion evaluation results are time-fitted, a fuzzy comprehensive evaluation method based on the analytic hierarchy process (AHP-PCE) is established, the fuzzy evaluation matrix is ​​calculated, the vegetation restoration evaluation expectations and results of the comparison area and the impact area are obtained, and the overall effect correlation and benefits are calculated. A benefit evaluation model is established.

[0096] In terms of the design of the human shadow service benefit evaluation plan, there is currently no mature and in-use human shadow service benefit evaluation system for multiple dimensions, and there are not many mature plans and cases for reference. How to establish a scientific and standardized human shadow service benefit evaluation plan is the focus of this embodiment. The plan should be highly targeted, and the selected key factors and processing methods should be practical; the plan should be highly operational and be able to complete the entire evaluation plan through appropriate work under existing conditions; the plan should have a certain degree of credibility and be able to provide an objective basis for cost-effectiveness evaluation.

[0097] The key technical difficulties and scientific issues that this embodiment focuses on solving are also: the selection and execution of the human shadow operation plan. The grasp of the timing of human shadow operation has a decisive influence on the effect and benefit of human shadow operation. It is difficult to achieve full control with existing technical means and operating conditions, which is consistent with objective reality. However, human shadow operations with inconsistent standards and practices will not only make it difficult to determine the effect of the operation, but may also make the entire experiment not referenceable. Therefore, it is necessary to formulate human shadow operation plans with unified standards, and decide on the timing of human shadow operations in an objective and quantitative manner as much as possible. It is planned to adopt the human shadow operation indicator system currently used in Guangxi business to determine the operation method and timing, so as to achieve the same human shadow operation service standards.

[0098] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0099] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0100] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0101] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0102] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0103] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0104] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0105] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0106] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the benefits of artificial weather modification services for ecological restoration in mining areas, characterized in that: include: Obtain mining area environmental data and geographic data, and select target mining areas and comparison mining areas based on the mining area environmental data and geographic data; Continuously monitoring the target mining area and the comparison mining area, and obtaining the vegetation loss data of the target mining area and the comparison mining area due to meteorological disasters during the restoration period based on the monitoring data; Construct a meteorological reanalysis field and use it to obtain cloud microphysical parameters, simulate the human shadow operation based on the cloud microphysical parameters, invert the diffusion influence area, and obtain the effectiveness evaluation result of the human shadow operation based on the diffusion influence area; Obtain the public opinion evaluation results of each participant on the target mining area at different time points; The loss data, the effectiveness evaluation results and the public opinion evaluation results are fitted on a time scale, the overall effect correlation and benefit are calculated based on the fitting results, and a benefit evaluation model is established based on the overall effect correlation and benefit.

2. The method according to claim 1, characterized in that The loss data of vegetation in the target mining area and the comparison mining area affected by meteorological disasters during the restoration period obtained based on the monitoring data include: Obtaining visible light image data according to the monitoring data, selecting an image sequence of the visible light image data, and performing image segmentation on the image sequence to obtain a green vegetation image; According to the green vegetation image, a green vegetation visible light index is calculated and a green vegetation change trend is simulated; Based on the green vegetation change trend, the loss data of vegetation caused by meteorological disasters during the restoration period are obtained.

3. The method according to claim 2, characterized in that The step of performing image segmentation on the image sequence to obtain a green vegetation image comprises: Use K-means unsupervised clustering algorithm to generate training samples and screen the samples; The screened cluster samples are used as training samples of a support vector machine vegetation-background classifier to train the classifier; Using the trained classifier to perform image segmentation on the image sequence to obtain a background-plant segmentation image; performing non-local mean filtering on the background-plant segmentation image; The filtered background-plant segmentation image is subjected to RGB image segmentation using a machine learning algorithm to obtain a green vegetation image.

4. The method according to claim 3, characterized in that: After obtaining the green vegetation image, it also includes: Non-local mean filtering is used to filter out noise in the green vegetation image, and the edge area of ​​the foreground image contour leaves in the green vegetation image is retained.

5. The method according to claim 2, characterized in that: In the process of simulating the trend of green vegetation change, a composite sine function is used, and the calculation formula is as follows: VI=a+b sin[2π(t day -c) / 365] Among them, a, b, and c are all empirical coefficients; t day For the day sequence.

6. The method according to claim 1, characterized in that The human shadow operation is simulated according to cloud microphysical parameters, and the diffusion impact area is obtained by inversion, including: According to the cloud microphysical parameters, the three-dimensional cloud model live field re-fitting inversion technology is used to perform a three-dimensional real-time simulation of the catalyst diffusion after the human shadow operation, and the diffusion influence area is inverted.

7. The method according to claim 6, characterized in that The effectiveness evaluation result of the human shadow operation obtained according to the diffusion impact area includes: According to the diffusion impact area, the impact result of the human shadow operation is obtained, and the impact result of the human shadow operation includes the coverage rate of the operation impact area, the adjustment effect of the high-impact meteorological factors in the operation area, and the cost-effectiveness ratio of the human shadow operation investment; The effectiveness evaluation result is obtained according to the impact result of the human shadow operation.

8. The method according to claim 1, characterized in that: After obtaining the public opinion evaluation results, it also includes: According to the time nodes and the public opinion evaluation results, a public opinion trend sequence is sorted out, and based on the public opinion trend sequence, an overall impact evaluation system is established.

9. The method according to claim 1, characterized in that: The calculation of the overall effect correlation and benefit according to the fitting results includes: Establishing a fuzzy comprehensive evaluation method based on the analytic hierarchy process according to the fitting results, and calculating a fuzzy evaluation matrix according to the fuzzy comprehensive evaluation method; Using the fuzzy evaluation matrix, the vegetation restoration evaluation expectations and results of the target mining area and the comparison mining area are obtained; Based on the expectations and results of vegetation restoration evaluation in the target and comparison mining areas, the overall effect relevance and benefits are obtained.

10. The method according to claim 1, characterized in that The target mining area and the comparison mining area are selected according to the mining area environmental data and geographical data, including: Selecting the first mining area and the second mining area based on the mining area environmental data and geographical data; Determine whether the first mining area and the second mining area meet preset conditions; If so, the first mining area is used as the target mining area and the second mining area is used as the comparison mining area.

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