A method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration

Abstract

The present invention discloses a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration, including collecting monitoring data and related data of vegetation and soil in the process of mine ecological restoration, preprocessing the monitoring data and the related data; performing time-varying screening on the monitoring data to obtain time-varying data, performing coupling coordination analysis on the time-varying data to obtain coordination data; objectively weighting the time-varying data according to the restoring force to obtain a weight coefficient, constructing a vegetation and soil coupling coordination evaluation model according to the coordination data and the weight coefficient; optimizing the vegetation and soil coupling coordination evaluation model according to the related data, inputting the data to be evaluated into the vegetation and soil coupling coordination evaluation model, and outputting the evaluation result. The method can not only improve the accuracy of the vegetation and soil coupling coordination evaluation, but also has good interpretability, and can be directly applied to the vegetation and soil coupling coordination evaluation system.

Description

Technical Field

[0001] The invention relates to the field of assessment, and in particular to a method for assessing the coupling coordination of vegetation and soil in a mine ecological restoration process. Background Art

[0002] During the mining process, large-scale excavation and mineral extraction activities often cause serious damage to the surrounding ecological environment, especially the loss of vegetation cover and the destruction of soil structure. This damage not only affects the local ecological balance, but may also lead to a series of environmental problems such as soil erosion and land degradation. Therefore, mine ecological restoration has become an important issue in today's environmental protection and sustainable development.

[0003] In order to effectively evaluate the effect of mine ecological restoration, especially the coupling coordination between vegetation and soil, a large amount of monitoring data and related data need to be collected. These data include but are not limited to vegetation types, growth conditions, coverage, and soil texture, structure, nutrient content, moisture conditions, etc. However, raw data are often complex and uncertain, and direct use for evaluation may produce misleading results.

[0004] Traditional assessment methods often focus only on a single aspect of vegetation or soil, while ignoring the interaction and coupling relationship between them. In fact, vegetation and soil are two inseparable components of the mining ecosystem, and there are complex interactions and feedback mechanisms between them. The growth of vegetation directly affects the structure and nutrient cycle of the soil, while the quality of the soil in turn restricts the growth and development of vegetation. At the same time, it is not enough to rely solely on monitoring data to build an assessment model. Other relevant data, such as climate conditions, topography, human activities and other factors, need to be considered, all of which may affect the coupling coordination of vegetation and soil.

[0005] Therefore, a new vegetation and soil coupling coordination assessment method was invented to improve the accuracy, precision and scientificity of the assessment. Summary of the invention

[0006] The purpose of the present invention is to provide a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration.

[0007] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0008] The present invention comprises the following steps:

[0009] Collecting monitoring data and related data of vegetation and soil during the process of mine ecological restoration, and preprocessing the monitoring data and related data; the monitoring data includes vegetation data and soil data; the related data includes climate factors and human interference;

[0010] Performing time-varying screening on the monitoring data to obtain time-varying data, and performing coupling coordination analysis on the time-varying data to obtain coordinated data; including:

[0011] According to the similarity of the monitoring data, random forest is used for initial classification to obtain groups, and the information degree of the monitoring data is calculated:

[0012]

[0013] The information degree of the u-th monitoring data is , the number of monitoring data for the cth group is , the u-th monitoring data of the c-th group is , the proportion of the u-th monitoring data in the c-th group is , the average value of the c-th group monitoring data is , monitoring data The probability of occurrence is ;

[0014] Calculate the degree of coupling coordination correlation of monitoring data:

[0015]

[0016] The carbon relevance of the u-th monitoring data is , the coupling value is , the coordination value is Z, and the change value of the coupling value is , the change in coordination value is , the change value of the u-th monitoring data is , the probability that the coupling value changes when the monitoring data changes is , the probability that the coordination value changes when the monitoring data changes is , the probability of changes in monitoring data is , the control factor is The reference value of the monitoring data is The reference value of the coupling value is The reference value of the coordination value is , the number of monitoring data is , the uth monitoring data is , the uth coordination value is ;

[0017] The time-varying importance of monitoring data is calculated based on the information degree and coupling coordination correlation degree:

[0018]

[0019] The time-varying importance of the u-th monitoring data is , the first weight is , the second weight is , the monitoring data at time s is , the monitoring data at the s+1th moment is , the coupling coordination correlation degree at the sth moment is , the coupling coordination correlation degree at the s+1th moment is , the upper limit of the observation time is ;

[0020] The monitoring data with time-varying importance greater than 0.384 are output as time-varying data;

[0021] Objectively weighting the time-varying data according to the restoring force to obtain a weight coefficient, and constructing a vegetation and soil coupling coordination assessment model according to the coordination data and the weight coefficient;

[0022] The vegetation and soil coupling coordination evaluation model is optimized according to the relevant data, the data to be evaluated is input into the vegetation and soil coupling coordination evaluation model, and the evaluation result is output.

[0023] Furthermore, the method of performing coupling coordination analysis on the time-varying data to obtain coordinated data includes:

[0024] The time-varying data were grouped according to the correlation between soil and plants to obtain soil environmental indicators and vegetation community indicators;

[0025] Calculate the coupling degree:

[0026]

[0027] The coupling degree is , the jth vegetation community index is , the number of vegetation community indicators is , the jth soil environmental index is , the number of soil environmental indicators is , the adjustment factor is , the control coefficient is , the adjustment factor of the jth soil environmental index is ;

[0028] Calculate the coordination:

[0029]

[0030] The coordination degree is , the vegetation coefficient is , the soil coefficient is , the number of the jth vegetation community index is ;

[0031] The vegetation coefficient and soil coefficient are obtained by the entropy weight method, and the vegetation-soil coupling coordination degree is calculated according to the coupling degree and coordination degree:

[0032]

[0033] The vegetation-soil coupling coordination degree is ;

[0034] The vegetation-soil coupling coordination degree is output as coordination data.

[0035] Furthermore, the method of objectively weighting the time-varying data according to the restoring force to obtain a weight coefficient includes:

[0036] Perform logarithmic processing on the time-varying data and calculate the proportion of time-varying data after logarithmic processing:

[0037]

[0038] The i-th time-varying data in the x-th dimension is , the proportion of the i-th time-varying data in the x-th dimension is , the amount of time-varying data is , the constant is ,

[0039] Calculate the restoring force at the moment:

[0040]

[0041]

[0042]

[0043] The resilience of the plant-soil system at the sth moment is , the species richness at time s is , the niche width at time s is , the species connectivity at the sth moment is , the observed ecosystem area is U, and the total number of species in the experimental area at time s is , the number of resource types is , the proportion of species' utilization of resource a at time s is , the amount of time-varying data is , the number of dimensions is , the importance of the i-th time-varying data is ;

[0044] Calculate the weight coefficient of vegetation-soil coupling coordination:

[0045]

[0046] The weight coefficient at the sth moment is , the i-th time-varying data at the s-th time is , the i-th time-varying data at the s+1th moment is The change in plant-soil system resilience caused by the change in the i-th time-varying data from the s+1th moment to the sth moment is .

[0047] Furthermore, a method for constructing a vegetation and soil coupling coordination evaluation model based on the coordination data and the weight coefficient includes:

[0048] The objective function is constructed based on the coordination data and weight coefficients, and the expression is:

[0049]

[0050] The loss function is , the weight coefficient at the sth moment is , the vegetation-soil coupling coordination degree is ;

[0051] The vegetation and soil coupling coordination assessment model includes time series analysis algorithm, impact analysis algorithm and machine learning algorithm;

[0052] The time series change analysis algorithm performs change analysis on the input data according to the time sequence to obtain the change data;

[0053] The impact analysis algorithm analyzes the change data according to the vegetation-soil coupling coordination degree to obtain the main influencing factors in different situations;

[0054] The machine learning algorithm learns the objective function evaluation model, obtains the evaluation rules of vegetation and soil coupling coordination, and obtains the predicted evaluation value of vegetation and soil coupling coordination based on the evaluation rules according to the influencing factors.

[0055] Furthermore, the method for optimizing the vegetation and soil coupling coordination evaluation model according to the relevant data includes:

[0056] Introducing particle populations, taking relevant data as background, and correcting the assessment results of the vegetation and soil coupling coordination assessment model to obtain a corrected assessment value;

[0057] The variance of the corrected evaluation value and the actual evaluation value is used as the fitness function, the corrected evaluation values ​​at different times are used as particles, the position of the particle with the smallest fitness is used as the optimal position, and the position of the particle is calculated:

[0058]

[0059] The position of the wth particle in the t+1th iteration is , the best position of the tth iteration is , the current number of iterations is t, and the maximum number of iterations is , the random numbers from 0 to 1 are , , the initial position of the wth particle is , the current number of iterations is t;

[0060] Calculate the fitness variance of the particle population:

[0061]

[0062]

[0063] The fitness variance is , the calibration factor is , the fitness of the wth particle is The average fitness is , the number of particles is ;

[0064] When the fitness variance is greater than 0.029, the Levy flight strategy is introduced to increase the diversity of the particle population, and the particle position is updated using the step information to obtain the step position:

[0065]

[0066]

[0067] The step length information is , the normal distribution of the direction vector v is , the normal distribution of the direction vector y is , the gamma function is , the step position of the wth particle in the t+1th iteration is , the position of the wth particle in the tth iteration is , the step length is ;

[0068] Introduce the nonlinear cosine convergence factor, use the nonlinear cosine convergence factor to update the particle position, and obtain the converged position. The expression is:

[0069]

[0070]

[0071] The nonlinear cosine convergence factor is , the constant coefficient is , a random number from 0 to 1 is , the initial value of the nonlinear cosine convergence factor is , the convergence position of the wth particle in the t+1th iteration is , the step position of the wth particle in the tth iteration is ;

[0072] Continue to iterate until the maximum number of iterations is reached, otherwise update the nonlinear cosine convergence factor until the fitness reaches the minimum.

[0073] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0074] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0075] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0076] The beneficial effects of the present invention are:

[0077] The present invention is a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration. Compared with the prior art, the present invention has the following technical effects:

[0078] The present invention provides a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration. Through the steps of data preprocessing, time-varying screening, coupling coordination analysis, resilience weighting and model optimization, the interaction between vegetation and soil and the influence of related factors can be comprehensively considered, the evaluation accuracy and interpretability can be improved, and it can be directly applied to actual systems, with good practicality and operability. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a flow chart of the steps of a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration of the present invention;

[0080] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0081] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0082] The present invention provides a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration, comprising the following steps:

[0083] like Figure 1 As shown, in this embodiment, the following steps are included:

[0084] Collecting monitoring data and related data of vegetation and soil during the process of mine ecological restoration, and preprocessing the monitoring data and related data; the monitoring data includes vegetation data and soil data; the related data includes climate factors and human interference;

[0085] In the actual assessment, the A mine restoration area was taken as the research object, and the monitoring data and related data from 2019 to 2023 were obtained. The time node C was taken as an example to obtain the vegetation and soil coupling coordination assessment results at the time node P;

[0086] Vegetation data include vegetation coverage, vegetation species diversity, dominant populations and dominance, vegetation growth, vegetation type and quantity, and vegetation root development; soil data include soil bulk density, soil moisture content, pH value, organic matter content, enzyme activity and enzyme types, microbial community quantity and types, nutrient element content, pH, texture, electrical conductivity, organic carbon content, total nitrogen content, total phosphorus content, and total potassium content;

[0087] Obtain species richness and niche breadth based on monitoring data;

[0088] Performing time-varying screening on the monitoring data to obtain time-varying data, and performing coupling coordination analysis on the time-varying data to obtain coordinated data; including:

[0089] According to the similarity of the monitoring data, random forest is used for initial classification to obtain groups, and the information degree of the monitoring data is calculated:

[0090]

[0091] The information degree of the u-th monitoring data is , the number of monitoring data for the cth group is , the u-th monitoring data of the c-th group is , the proportion of the u-th monitoring data in the c-th group is , the average value of the c-th group monitoring data is , monitoring data The probability of occurrence is ;

[0092] Calculate the degree of coupling coordination correlation of monitoring data:

[0093]

[0094] The carbon relevance of the u-th monitoring data is , the coupling value is , the coordination value is Z, and the change value of the coupling value is , the change in coordination value is , the change value of the u-th monitoring data is , the probability that the coupling value changes when the monitoring data changes is , the probability that the coordination value changes when the monitoring data changes is , the probability of changes in monitoring data is , the control factor is The reference value of the monitoring data is The reference value of the coupling value is The reference value of the coordination value is , the number of monitoring data is , the uth monitoring data is , the uth coordination value is ;

[0095] The time-varying importance of monitoring data is calculated based on the information degree and coupling coordination correlation degree:

[0096]

[0097] The time-varying importance of the u-th monitoring data is , the first weight is , the second weight is , the monitoring data at time s is , the monitoring data at the s+1th moment is , the coupling coordination correlation degree at the sth moment is , the coupling coordination correlation degree at the s+1th moment is , the upper limit of the observation time is ;

[0098] The monitoring data with time-varying importance greater than 0.384 are output as time-varying data;

[0099] In the actual assessment, time-varying data include vegetation coverage, vegetation species diversity, dominant populations and dominance, vegetation growth, vegetation type and quantity, vegetation root development, soil bulk density, soil moisture content, pH value, organic matter content, enzyme activity and enzyme types, microbial community quantity and types, nutrient element content, pH, texture, electrical conductivity, organic carbon content, total nitrogen content, total phosphorus content, and total potassium content;

[0100] The coordination data of time node C is 0.36;

[0101] Objectively weighting the time-varying data according to the restoring force to obtain a weight coefficient, and constructing a vegetation and soil coupling coordination assessment model according to the coordination data and the weight coefficient;

[0102] In the actual evaluation, the weight coefficient of time node C is 0.402;

[0103] Optimizing the vegetation and soil coupling coordination evaluation model according to the relevant data, inputting the data to be evaluated into the vegetation and soil coupling coordination evaluation model, and outputting the evaluation result;

[0104] In the actual evaluation, the coordination data of the optimized time node C is 0.397, and the evaluation value of the time node P is 0.408.

[0105] In this embodiment, the method of performing coupling coordination analysis on the time-varying data to obtain coordinated data includes:

[0106] The time-varying data were grouped according to the correlation between soil and plants to obtain soil environmental indicators and vegetation community indicators;

[0107] Calculate the coupling degree:

[0108]

[0109] The coupling degree is , the jth vegetation community index is , the number of vegetation community indicators is , the jth soil environmental index is , the number of soil environmental indicators is , the adjustment factor is , the control coefficient is , the adjustment factor of the jth soil environmental index is ;

[0110] Calculate the coordination:

[0111]

[0112] The coordination degree is , the vegetation coefficient is , the soil coefficient is , the number of the jth vegetation community index is ;

[0113] The vegetation coefficient and soil coefficient are obtained by the entropy weight method, and the vegetation-soil coupling coordination degree is calculated according to the coupling degree and coordination degree:

[0114]

[0115] The vegetation-soil coupling coordination degree is ;

[0116] The vegetation-soil coupling coordination degree is output as coordination data.

[0117] In this embodiment, the method of objectively weighting the time-varying data according to the restoring force to obtain the weight coefficient includes:

[0118] Perform logarithmic processing on the time-varying data and calculate the proportion of time-varying data after logarithmic processing:

[0119]

[0120] The i-th time-varying data in the x-th dimension is , the proportion of the i-th time-varying data in the x-th dimension is , the amount of time-varying data is , the constant is ,

[0121] Calculate the restoring force at the moment:

[0122]

[0123]

[0124]

[0125] The resilience of the plant-soil system at the sth moment is , the species richness at time s is , the niche width at time s is , the species connectivity at the sth moment is , the observed ecosystem area is U, and the total number of species in the experimental area at time s is , the number of resource types is , the proportion of species' utilization of resource a at time s is , the amount of time-varying data is , the number of dimensions is , the importance of the i-th time-varying data is ;

[0126] Calculate the weight coefficient of vegetation-soil coupling coordination:

[0127]

[0128] The weight coefficient at the sth moment is , the i-th time-varying data at the s-th time is , the i-th time-varying data at the s+1th moment is The change in plant-soil system resilience caused by the change in the i-th time-varying data from the s+1th moment to the sth moment is .

[0129] In this embodiment, the method for constructing a vegetation and soil coupling coordination evaluation model according to the coordination data and the weight coefficient includes:

[0130] The objective function is constructed based on the coordination data and weight coefficients, and the expression is:

[0131]

[0132] The loss function is , the weight coefficient at the sth moment is , the vegetation-soil coupling coordination degree is ;

[0133] The vegetation and soil coupling coordination assessment model includes time series analysis algorithm, impact analysis algorithm and machine learning algorithm;

[0134] The time series change analysis algorithm performs change analysis on the input data according to the time sequence to obtain the change data;

[0135] The impact analysis algorithm analyzes the change data according to the vegetation-soil coupling coordination degree to obtain the main influencing factors in different situations;

[0136] The machine learning algorithm learns the objective function evaluation model, obtains the evaluation rules of vegetation and soil coupling coordination, and obtains the predicted evaluation value of vegetation and soil coupling coordination based on the evaluation rules according to the influencing factors.

[0137] In this embodiment, the method for optimizing the vegetation and soil coupling coordination evaluation model according to the relevant data includes:

[0138] Introducing particle populations, taking relevant data as background, and correcting the assessment results of the vegetation and soil coupling coordination assessment model to obtain a corrected assessment value;

[0139] The variance of the corrected evaluation value and the actual evaluation value is used as the fitness function, the corrected evaluation values ​​at different times are used as particles, the position of the particle with the smallest fitness is used as the optimal position, and the position of the particle is calculated:

[0140]

[0141] The position of the wth particle in the t+1th iteration is , the best position of the tth iteration is , the current number of iterations is t, and the maximum number of iterations is , the random numbers from 0 to 1 are , , the initial position of the wth particle is , the current number of iterations is t;

[0142] Calculate the fitness variance of the particle population:

[0143]

[0144]

[0145] The fitness variance is , the calibration factor is , the fitness of the wth particle is The average fitness is , the number of particles is ;

[0146] When the fitness variance is greater than 0.029, the Levy flight strategy is introduced to increase the diversity of the particle population, and the particle position is updated using the step information to obtain the step position:

[0147]

[0148]

[0149] The step length information is , the normal distribution of the direction vector v is , the normal distribution of the direction vector y is , the gamma function is , the step position of the wth particle in the t+1th iteration is , the position of the wth particle in the tth iteration is , the step length is ;

[0150] Introduce the nonlinear cosine convergence factor, use the nonlinear cosine convergence factor to update the particle position, and obtain the converged position. The expression is:

[0151]

[0152]

[0153] The nonlinear cosine convergence factor is , the constant coefficient is , a random number from 0 to 1 is , the initial value of the nonlinear cosine convergence factor is , the convergence position of the wth particle in the t+1th iteration is , the step position of the wth particle in the tth iteration is ;

[0154] Continue to iterate until the maximum number of iterations is reached, otherwise update the nonlinear cosine convergence factor until the fitness reaches the minimum.

[0155] Figure 2This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0156] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0157] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0158] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a vegetation and soil coupling coordination assessment device in the process of mine ecological restoration at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned vegetation and soil coupling coordination assessment methods in the process of mine ecological restoration.

[0159] The above application Figure 1The embodiment disclosed in the present invention discloses a method for evaluating the coupling coordination of vegetation and soil in the process of ecological restoration of a mine, which can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0160] The electronic device may also perform Figure 1 A method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration is proposed. Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0161] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple applications, any one of the aforementioned methods for evaluating the coordination of vegetation and soil coupling in the process of mine ecological restoration is executed.

[0162] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0163] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0167] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0168] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0169] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0170] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration, characterized in that: The following steps are involved: Collecting monitoring data and related data of vegetation and soil during the process of mine ecological restoration, and preprocessing the monitoring data and related data; the monitoring data includes vegetation data and soil data; the related data includes climate factors and human interference; Performing time-varying screening on the monitoring data to obtain time-varying data, and performing coupling coordination analysis on the time-varying data to obtain coordinated data; including: According to the similarity of the monitoring data, random forest is used for initial classification to obtain groups, and the information degree of the monitoring data is calculated: The information degree of the u-th monitoring data is , the number of monitoring data for the cth group is , the u-th monitoring data of the c-th group is , the proportion of the u-th monitoring data in the c-th group is , the average value of the c-th group monitoring data is , monitoring data The probability of occurrence is ; Calculate the degree of coupling coordination correlation of monitoring data: The carbon relevance of the u-th monitoring data is , the coupling value is , the coordination value is Z, and the change value of the coupling value is , the change in coordination value is , the change value of the u-th monitoring data is , the probability that the coupling value changes when the monitoring data changes is , the probability that the coordination value changes when the monitoring data changes is , the probability of changes in monitoring data is , the control factor is The reference value of the monitoring data is The reference value of the coupling value is The reference value of the coordination value is , the number of monitoring data is , the uth monitoring data is , the uth coordination value is ; The time-varying importance of monitoring data is calculated based on the information degree and coupling coordination correlation degree: The time-varying importance of the u-th monitoring data is , the first weight is , the second weight is , the monitoring data at time s is , the monitoring data at the s+1th moment is , the coupling coordination correlation degree at the sth moment is , the coupling coordination correlation degree at the s+1th moment is , the upper limit of the observation time is ; The monitoring data with time-varying importance greater than 0.384 are output as time-varying data; Objectively weighting the time-varying data according to the restoring force to obtain a weight coefficient, and constructing a vegetation and soil coupling coordination assessment model according to the coordination data and the weight coefficient; The vegetation and soil coupling coordination evaluation model is optimized according to the relevant data, the data to be evaluated is input into the vegetation and soil coupling coordination evaluation model, and the evaluation result is output.

2. According to the method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration according to claim 1, it is characterized in that: The method of performing coupling coordination analysis on the time-varying data to obtain coordinated data includes: The time-varying data were grouped according to the correlation between soil and plants to obtain soil environmental indicators and vegetation community indicators; Calculate the coupling degree: The coupling degree is , the jth vegetation community index is , the number of vegetation community indicators is , the jth soil environmental index is , the number of soil environmental indicators is , the adjustment factor is , the control coefficient is , the adjustment factor of the jth soil environmental index is ; Calculate the coordination: The coordination degree is , the vegetation coefficient is , the soil coefficient is , the number of the jth vegetation community index is ; The vegetation coefficient and soil coefficient are obtained by the entropy weight method, and the vegetation-soil coupling coordination degree is calculated according to the coupling degree and coordination degree: The vegetation-soil coupling coordination degree is ; The vegetation-soil coupling coordination degree is output as coordination data.

3. According to the method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration in claim 1, it is characterized in that: The method of objectively weighting the time-varying data according to the restoring force to obtain a weight coefficient includes: Perform logarithmic processing on the time-varying data and calculate the proportion of time-varying data after logarithmic processing: The i-th time-varying data in the x-th dimension is , the proportion of the i-th time-varying data in the x-th dimension is , the amount of time-varying data is , the constant is , Calculate the restoring force at the moment: The resilience of the plant-soil system at the sth moment is , the species richness at time s is , the niche width at time s is , the species connectivity at the sth moment is , the observed ecosystem area is U, and the total number of species in the experimental area at time s is , the number of resource types is , the proportion of species' utilization of resource a at time s is , the amount of time-varying data is , the number of dimensions is , the importance of the i-th time-varying data is ; Calculate the weight coefficient of vegetation-soil coupling coordination: The weight coefficient at the sth moment is , the i-th time-varying data at the s-th time is , the i-th time-varying data at the s+1th moment is The change in plant-soil system resilience caused by the change in the i-th time-varying data from the s+1th moment to the sth moment is .

4. According to claim 1, a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration, characterized in that: The method for constructing a vegetation and soil coupling coordination evaluation model according to the coordination data and the weight coefficient comprises: The objective function is constructed based on the coordination data and weight coefficients, and the expression is: The loss function is , the weight coefficient at the sth moment is , the vegetation-soil coupling coordination degree is ; The vegetation and soil coupling coordination assessment model includes time series analysis algorithm, impact analysis algorithm and machine learning algorithm; The time series change analysis algorithm performs change analysis on the input data according to the time sequence to obtain the change data; The impact analysis algorithm analyzes the change data according to the vegetation-soil coupling coordination degree to obtain the main influencing factors in different situations; The machine learning algorithm learns the objective function evaluation model, obtains the evaluation rules of vegetation and soil coupling coordination, and obtains the predicted evaluation value of vegetation and soil coupling coordination based on the evaluation rules according to the influencing factors.

5. According to claim 1, a method for evaluating the coupling coordination of vegetation and soil in the process of mine ecological restoration, characterized in that: The method for optimizing the vegetation and soil coupling coordination evaluation model according to the relevant data comprises: Introducing particle populations, taking relevant data as background, and correcting the assessment results of the vegetation and soil coupling coordination assessment model to obtain a corrected assessment value; The variance of the corrected evaluation value and the actual evaluation value is used as the fitness function, the corrected evaluation values ​​at different times are used as particles, the position of the particle with the smallest fitness is used as the optimal position, and the position of the particle is calculated: The position of the wth particle in the t+1th iteration is , the best position of the tth iteration is , the current number of iterations is t, and the maximum number of iterations is , the random numbers from 0 to 1 are , , the initial position of the wth particle is , the current number of iterations is t; Calculate the fitness variance of the particle population: The fitness variance is , the calibration factor is , the fitness of the wth particle is The average fitness value is , the number of particles is ; When the fitness variance is greater than 0.029, the Levy flight strategy is introduced to increase the diversity of the particle population, and the particle position is updated using the step information to obtain the step position: The step length information is , the normal distribution of the direction vector v is , the normal distribution of the direction vector y is , the gamma function is , the step position of the w-th particle in the t+1th iteration is, and the position of the w-th particle in the tth iteration is , the step length is ; Introduce the nonlinear cosine convergence factor, use the nonlinear cosine convergence factor to update the particle position, and obtain the converged position. The expression is: The nonlinear cosine convergence factor is , the constant coefficient is , a random number from 0 to 1 is , the initial value of the nonlinear cosine convergence factor is , the convergence position of the wth particle in the t+1th iteration is , the step position of the wth particle in the tth iteration is ; Continue to iterate until the maximum number of iterations is reached, otherwise update the nonlinear cosine convergence factor until the fitness reaches the minimum.

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