A method and device for determining a mycorrhizal remediation solution, equipment and medium
By using hyperspectral data, multi-dimensional prediction models, and adaptation index calculations, mycorrhizal remediation solutions are automatically generated, solving the accuracy and efficiency problems of traditional mycorrhizal remediation solutions and improving the accuracy and efficiency of soil remediation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENHUA BAORIXILE ENERGY CO LTD
- Filing Date
- 2025-11-12
- Publication Date
- 2026-05-12
AI Technical Summary
Traditional mycorrhizal remediation solutions rely on human experience, resulting in inaccurate soil parameter acquisition, one-sided mycorrhizal compatibility matching, difficulty in predicting remediation effects, insufficient targeting of remediation solutions, and low efficiency.
By using hyperspectral data and multi-dimensional prediction models, the soil organic matter content and mechanical composition are accurately quantified. Combined with the correlation database and adaptation index calculation, targeted mycorrhizal remediation solutions are automatically generated. The mycorrhizal remediation effect prediction model is used to predict the remediation effect in advance, reducing reliance on human experience.
This has improved the precision and efficiency of soil mycorrhizal remediation, reduced the cost of trial and error in remediation, and enhanced the remediation plan's relevance and feasibility.
Smart Images
Figure CN121301891B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of ecological restoration data analysis technology, and more specifically, to a method, apparatus, equipment, and medium for determining a mycorrhizal restoration scheme. Background Technology
[0002] Mining disturbs soil structure, alters soil mechanical composition, and disrupts the original ratio of silt, clay, and sand, affecting soil physicochemical properties and hindering plant growth. Soil quality degradation has become a key issue restricting ecological restoration and sustainable land use. Traditional mycorrhizal remediation schemes often rely on manual experience to select mycorrhizae and formulate remediation parameters, which suffers from shortcomings such as inaccurate soil parameter acquisition, one-sided mycorrhizal compatibility matching, and difficulty in predicting remediation effects. This results in insufficient targeting and low remediation efficiency of mycorrhizal remediation schemes. Summary of the Invention
[0003] The purpose of this application is to provide a method, apparatus, device and medium for determining mycorrhizal remediation schemes, so as to solve the above-mentioned problems existing in the prior art, and to automatically generate targeted mycorrhizal remediation schemes for target areas.
[0004] Firstly, a method for determining a mycorrhizal remediation scheme is provided, which may include:
[0005] Acquire hyperspectral and environmental data for the target soil region;
[0006] The hyperspectral data is input into a pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area.
[0007] The hyperspectral data, the predicted organic matter content, and the predicted water content are input into a pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area.
[0008] From the associated database of different soil mechanical compositions and different mycorrhizal adaptability, match multiple candidate mycorrhizae corresponding to the predicted soil mechanical composition of the target soil region;
[0009] Based on a variety of candidate mycorrhizae, predicted soil mechanical composition, and the environmental data, the fit index of different candidate mycorrhizae is calculated.
[0010] The fitness index of different candidate mycorrhizae, the predicted soil mechanical composition, and the environmental data are input into a pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae.
[0011] Based on the compatibility index of different alternative mycorrhizae and the predicted remediation effect of corresponding remediation schemes, mycorrhizal remediation schemes for target soil areas are determined.
[0012] In an optional implementation, the method for acquiring hyperspectral data of the target soil region includes:
[0013] Acquire raw hyperspectral data of the target soil region;
[0014] The original hyperspectral data is preprocessed to obtain the first hyperspectral data;
[0015] The envelope is fitted using the envelope removal method, and the normalized second hyperspectral data is obtained by dividing the first hyperspectral data by the envelope.
[0016] From the second hyperspectral data, extract the third hyperspectral data located in the configured target band;
[0017] The third hyperspectral data is subjected to multiplicative scattering correction and first derivative transformation to obtain the fourth hyperspectral data, which is then output as the hyperspectral data of the target soil region.
[0018] In an optional implementation, the soil organic matter and water content prediction model includes:
[0019] The input layer is used to input hyperspectral data of the target soil region.
[0020] The physical attention embedding layer is used to multiply the input hyperspectral data with the configured binary attention mask to obtain mask-optimized one-dimensional spectral data;
[0021] A shared feature extraction layer is used to extract features from the masked one-dimensional spectral data to obtain a multi-dimensional shared feature vector;
[0022] The feature refinement extraction layer is used to refine the feature extraction of the multi-dimensional shared feature vector to obtain the first feature vector and the second feature vector.
[0023] A dual-target fusion prediction layer is used to generate predicted organic matter content and predicted water content based on the first feature vector and the second feature vector, respectively.
[0024] The output layer is used to output the predicted organic matter content and the predicted water content.
[0025] In an optional implementation, the soil mechanical composition prediction model includes:
[0026] The input layer is used to input hyperspectral data of the target soil region, the predicted organic matter content, and the predicted water content;
[0027] A cross-modal feature coding layer is used to encode the hyperspectral data, the predicted organic matter content, and the predicted water content respectively, to obtain the coded features corresponding to the hyperspectral data, the predicted organic matter content, and the predicted water content;
[0028] The dual attention fusion module is used to perform weighted fusion of the encoded features corresponding to the hyperspectral data, the predicted organic matter content, and the predicted water content to obtain a global feature vector.
[0029] A multi-scale particle feature extraction layer is used to extract sand, silt, and clay features separately, and then concatenates the extracted sand, silt, and clay features into a multi-scale particle feature vector.
[0030] The particle interaction calibration layer is used to calibrate the multi-scale particle feature vectors to obtain the calibrated particle feature vectors.
[0031] A multi-objective prediction head is used to obtain the predicted soil mechanical composition based on the calibrated particle feature vector; the predicted soil mechanical composition includes: predicted sand content, predicted silt content, and predicted clay content.
[0032] The output layer is used to output the predicted soil mechanical composition.
[0033] In an optional implementation, the environmental data includes soil pH, soil electrical conductivity, soil available nitrogen, phosphorus and potassium content, annual sunshine hours, annual average temperature and precipitation, soil pollution status and soil water holding capacity.
[0034] In an optional implementation, the fitness index of different candidate mycorrhizae is determined based on a variety of candidate mycorrhizae, predicted soil mechanical composition, and the environmental data, including:
[0035] Based on the environmental data and the predicted soil mechanical composition, calculate the physical and functional compatibility indices of different candidate mycorrhizae.
[0036] For any candidate mycorrhizae, the product of the physical fitness index and the functional fitness index of the candidate mycorrhizae is taken as the fitness index of the candidate mycorrhizae.
[0037] The candidate mycorrhizae corresponding to the adaptation index that is higher than the configured adaptation threshold are taken as the first mycorrhizae; different first mycorrhizae are cross-combined to obtain multiple combinations of first mycorrhizae;
[0038] Calculate the cofit index for different first mycorrhizal combinations.
[0039] In one optional implementation, based on the fitness index of different alternative mycorrhizae and the predicted remediation effects of corresponding remediation schemes, a mycorrhizal remediation scheme for the target soil area is determined, including:
[0040] By dynamically optimizing the agent using pre-trained inoculation parameters, the agent deletes first mycorrhizae or first mycorrhizae combinations whose adaptation index or co-adaptation index is not greater than the configured adaptation threshold, or whose predicted repair effect is not greater than the configured repair threshold, from different first mycorrhizae and different first mycorrhizae combinations, thereby obtaining different second mycorrhizae or different second mycorrhizae combinations.
[0041] Based on the multi-dimensional comprehensive decision-making model that dynamically optimizes the configuration of the agent according to the inoculation parameters, the comprehensive decision score for different second mycorrhizae or different combinations of second mycorrhizae is calculated.
[0042] A combined optimization strategy based on configuration is adopted, with the comprehensive decision as the fitness function, and the optimal mycorrhiza is determined through multiple rounds of iteration.
[0043] Using the remediation effect as the reward function and the different inoculation parameters in the remediation scheme as the action space, an optimization algorithm is used to determine the target remediation scheme corresponding to the optimal mycorrhizae, thus obtaining the mycorrhizal remediation scheme for the target soil area.
[0044] Secondly, a device for determining a mycorrhizal remediation scheme is provided, the device may include:
[0045] The acquisition unit is used to acquire hyperspectral data and environmental data of the target soil area;
[0046] The first prediction unit is used to input the hyperspectral data into a pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area.
[0047] The second prediction unit is used to input the hyperspectral data, the predicted organic matter content, and the predicted water content into a pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area.
[0048] The matching unit is used to match multiple candidate mycorrhizae corresponding to the predicted soil mechanical composition of the target soil region from a configured association database of different soil mechanical compositions and different mycorrhizal adaptability.
[0049] The calculation unit is used to calculate the fit index of different candidate mycorrhizae based on a variety of candidate mycorrhizae, predicted soil mechanical composition and said environmental data;
[0050] The third prediction unit is used to input the adaptation index of different candidate mycorrhizae, the predicted soil mechanical composition and the environmental data into the pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae.
[0051] The determination unit is used to determine the mycorrhizal remediation scheme for the target soil area based on the adaptation index of different alternative mycorrhizae and the predicted remediation effect of the corresponding remediation scheme.
[0052] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;
[0053] Memory, used to store computer programs;
[0054] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.
[0055] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.
[0056] This application leverages hyperspectral data and multi-dimensional prediction models to accurately quantify soil organic matter content, water content, and mechanical composition, providing reliable data support for mycorrhizal screening and remediation scheme development. Through database matching and compatibility index calculation, it precisely identifies candidate mycorrhizae suitable for target soils, avoiding inefficient remediation caused by blind screening. Utilizing a mycorrhizal remediation effect prediction model, it anticipates the remediation effects of different candidate mycorrhizae in advance, and combines the compatibility index to achieve scientific decision-making for the optimal remediation scheme, improving the targeting and feasibility of the remediation plan. It constructs a data-driven, end-to-end technical system, reducing reliance on manual experience, lowering the cost of remediation trial and error, and significantly improving the efficiency and ecological benefits of soil mycorrhizal remediation. Attached Figure Description
[0057] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0058] Figure 1 An architecture diagram of a system for determining a mycorrhizal remediation scheme provided in an embodiment of this application;
[0059] Figure 2 A flowchart illustrating a method for determining a mycorrhizal remediation scheme provided in an embodiment of this application;
[0060] Figure 3 A control and inoculated soil spectral curve is provided for an embodiment of this application;
[0061] Figure 4 A schematic diagram of an original spectral curve provided for an embodiment of this application;
[0062] Figure 5 This application provides a schematic diagram of a spectral curve after continuum removal.
[0063] Figure 6 This is a schematic diagram illustrating the correlation coefficient between clay content and the spectrum after removal transformation in a continuum, as provided in an embodiment of this application.
[0064] Figure 7 This is a schematic diagram illustrating the correlation coefficient between particle content and the spectrum after removal transformation in a continuum, provided in an embodiment of this application.
[0065] Figure 8 A schematic diagram illustrating the correlation coefficient between sand particle content and the spectrum after removal transformation in a continuum, provided in an embodiment of this application;
[0066] Figure 9 A schematic diagram of a device for determining a mycorrhizal remediation scheme provided in an embodiment of this application;
[0067] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0068] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0069] The method for determining the mycorrhizal remediation scheme provided in this application embodiment can be applied to... Figure 1In the system architecture shown, such as Figure 1 As shown, the system may include a server and a terminal. The server can be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal may be a user equipment (UE) such as a mobile phone, smartphone, laptop, digital radio receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and server can be directly or indirectly connected via wired or wireless communication methods, which is not limited herein.
[0070] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.
[0071] Figure 2 This is a flowchart illustrating a method for determining a mycorrhizal remediation scheme, as provided in an embodiment of this application. Figure 2 As shown, the method may include:
[0072] Step S210: Obtain hyperspectral data and environmental data of the target soil area.
[0073] The target soil area refers to the hyperspectral data of the soil in the region where the mycorrhizal remediation scheme is to be determined. This data is collected using a calibrated near-infrared (350-2500nm) hyperspectral imager or a portable spectrometer. Data can be collected directly from the target soil area or from soil samples collected from the target soil area. The collected soil samples are then air-dried and sieved through a 1mm sieve before hyperspectral data collection. Environmental data refers to the environmental parameters of the target soil area, obtained through analysis of sensors pre-set in the target soil area and weather data. Environmental data may include soil pH, soil electrical conductivity, available nitrogen, phosphorus, and potassium content, annual sunshine hours, average annual temperature and precipitation, soil pollution status, and soil water holding capacity. Soil pollution status includes heavy metal content and organic pollutant content. In practical applications, when collecting hyperspectral data of the target soil area, it is also necessary to collect data on the light conditions, topography, and soil surface condition of the target soil area. The soil surface condition of the target soil area refers to the actual soil surface condition after removing stones, weeds, and other covering materials.
[0074] In one embodiment of this application, the method for acquiring hyperspectral data of a target soil region may include:
[0075] Acquire raw hyperspectral data of the target soil region;
[0076] The raw hyperspectral data is preprocessed to obtain the first hyperspectral data. The preprocessing may include radiometric calibration, atmospheric correction, resampling, and smoothing to convert the original gray values in the raw hyperspectral data into reflectance and eliminate water vapor and aerosol interference. Atmospheric correction may use the FLAASH algorithm; smoothing may use Savitzky-Golay filtering and outlier removal methods.
[0077] An envelope is fitted using the envelope removal method, and the normalized second hyperspectral data is obtained by dividing the first hyperspectral data by the envelope. From the second hyperspectral data, third hyperspectral data located in a designated target band is extracted; wherein the target band is the 400–2400 nm band; that is, hyperspectral data in the 400–2400 nm band is extracted from the full-band second hyperspectral data as the third hyperspectral data; preferably, the target band can be 450–700 nm, 1390–1500 nm, 1880–2100 nm, and 2170–2250 nm.
[0078] Multiplicative scattering correction and first-order derivative transformation are performed on the third hyperspectral data to obtain the fourth hyperspectral data, which is then output as the hyperspectral data of the target soil region.
[0079] In another embodiment of this application, after obtaining the fourth hyperspectral data, the method may further include:
[0080] The mean ± 3 standard deviation of the spectral values of each band in the fourth hyperspectral data is calculated. After removing outliers outside the range, normalization is performed to obtain the fifth hyperspectral data. The fifth hyperspectral data is then input into a pre-trained band correlation analysis model to obtain the hyperspectral data of the target soil region. The band correlation analysis model can use CARS or GA algorithms to screen bands from the 400-2400nm band that are strongly correlated with soil mechanical composition, organic matter, and water content. Specifically, the standardized reflectance values of each band in the fifth hyperspectral data of the target soil region are obtained and matched with the band-target attribute correlation benchmarks recorded in the pre-training stage. The comprehensive correlation coefficient between each band and sand, silt, clay, organic matter, and water content is calculated. A threshold of ≥0.7 for the absolute value of the comprehensive correlation coefficient is set to screen bands that meet the criteria. Collinear bands that still exist after screening are removed through cross-validation to avoid data redundancy. The list of screened bands and their corresponding data are output to obtain the hyperspectral data of the target soil region.
[0081] Step S220: Input the hyperspectral data into the pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area.
[0082] Among them, the soil organic matter and water content prediction models include:
[0083] The input layer is used to input hyperspectral data;
[0084] The physical attention embedding layer is used to multiply the input hyperspectral data with the configured binary attention mask to obtain the mask-optimized one-dimensional spectral data. The binary attention mask is based on the sensitive band ranges of SOM and MC preset by the soil spectral physical mechanism. The sensitive range in the binary attention mask is 1, and the non-sensitive range is 0.3.
[0085] A shared feature extraction layer employs three layers of multi-scale convolution, batch normalization, and the ReLU activation function to extract features from the masked one-dimensional spectral data, resulting in a 64-dimensional shared feature vector. The three multi-scale convolution layers include micro-scale, meso-scale, and macro-scale convolution blocks. The micro-scale convolution blocks use 1×3 kernels to capture details of absorption peaks in narrow bands; the meso-scale convolution blocks use 1×7 kernels to capture combined features of adjacent bands; and the macro-scale convolution blocks use 1×15 kernels to capture full-spectral trend features.
[0086] The feature refinement extraction layer is used to refine the features of the 64-dimensional shared feature vector to obtain the first feature vector and the second feature vector. The feature refinement extraction layer includes a SOM feature extraction module and a MC feature extraction module. The SOM feature extraction module includes two convolutional layers (1×5, 1×3) and an attention gating unit (AGU), used to dynamically adjust the weights of the SOM sensitive band features based on the 64-dimensional shared feature vector, suppressing moisture interference features unrelated to SOM, and obtaining a 32-dimensional SOM feature vector, i.e., the first feature vector. The MC feature extraction module includes two convolutional layers (1×5, 1×3) and a moisture interference calibration unit (MCU), used to correct the masking effect of SOM on MC spectral features through a pre-trained SOM-MC interaction matrix, obtaining a 32-dimensional MC feature vector, i.e., the second feature vector.
[0087] The dual-objective fusion prediction layer includes a SOM prediction head and a MC prediction head. The MC prediction head is used to generate predicted water content based on the second feature vector. The SOM prediction head is used to generate predicted organic matter content based on the first feature vector. The SOM prediction head includes a fully connected layer (64→32→1) and a Sigmoid activation. The MC prediction head includes a fully connected layer (64→32→1) and a Sigmoid activation. The dual-objective fusion prediction layer includes a dual-objective loss synergy term to strengthen the coupling relationship between the two objectives.
[0088] The output layer is used to output the prediction confidence levels for predicted organic matter content and predicted water content.
[0089] Step S230: Input the hyperspectral data, predicted organic matter content and predicted water content into the pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area.
[0090] Among them, the soil mechanical composition prediction model includes:
[0091] The input layer is used to input hyperspectral data of the target soil region, predicted organic matter content, and predicted water content. The predicted organic matter content and predicted water content are expanded into one-dimensional feature vectors, aligned with the dimensions of the hyperspectral data, to perform data verification on the input hyperspectral data of the target soil region.
[0092] The cross-modal feature encoding layer includes a hyperspectral feature encoding module, a physicochemical feature encoding module, and a cross-modal alignment module. These modules are used to encode the hyperspectral data, predicted organic matter content, and predicted water content, respectively, to obtain the corresponding encoded features. Specifically, the hyperspectral feature encoding module encodes the hyperspectral data into a [1,128]-dimensional spectral feature vector using 1×3 convolutional kernels (128 kernels), BN, and GELU activation to capture the correlation between the spectrum and particle composition. The physicochemical feature encoding module encodes the predicted organic matter content and predicted water content into [1,64]-dimensional physicochemical feature vectors using two fully connected layers (1→32→64), BN, and GELU activation to enhance the regulatory features of predicted organic matter content and predicted water content on particle composition. The cross-modal alignment module unifies the dimensions of the three types of feature vectors to [1,64] through feature dimension mapping.
[0093] The dual-attention fusion module is used to perform weighted fusion of the coding features corresponding to hyperspectral data, predicted organic matter content, and predicted water content to obtain a global feature vector. Specifically, a self-attention mechanism is applied to the coding features corresponding to hyperspectral data, predicted organic matter content, and predicted water content respectively, and the internal weights of the features are calculated to obtain three types of weighted features. Based on the pre-constructed modal correlation matrix, the interaction weights of the three types of weighted features are calculated, and the three types of weighted features are fused element by element according to the interaction weights. The three types of coding features are added to the fused features, and basic information is retained to obtain a global feature vector that integrates multi-source information.
[0094] A multi-scale particle feature extraction layer includes: a sand feature extraction block, a powder feature extraction block, and a clay feature extraction block; these are used to extract sand, powder, and clay features respectively, and then concatenate the extracted sand, powder, and clay features into a [1, 96]-dimensional multi-scale particle feature vector; wherein, the sand feature extraction block is used to extract sand features through 1×11 convolution kernels (64), BN, GELU, and pooling to retain the wide range of features corresponding to coarse particles; the powder feature extraction block is used to extract powder features through 1×7 convolution kernels (64), BN, GELU, and pooling to capture the medium-scale features corresponding to medium particles; the clay feature extraction block is used to extract clay features through 1×3 convolution kernels (64), BN, GELU, and pooling to focus on the fine features corresponding to fine particles;
[0095] The particle interaction calibration layer is used to calibrate the multi-scale particle feature vectors to obtain calibrated particle feature vectors. Specifically, based on the trained sand-silt-clay particle ratio matrix, constraint factors are constructed. The mutual inhibition or promotion relationship of the three types of particles is learned through a fully connected layer (96→128→96) (such as the masking calibration of sand spectral features by high clay content) to calibrate the multi-scale particle feature vectors. Swish activation is used to enhance the nonlinear fitting ability of the multi-scale particle feature vectors to obtain calibrated particle feature vectors.
[0096] A multi-objective prediction head is used to predict the predicted soil mechanical composition of a target soil area based on the calibrated particle feature vector. The multi-objective prediction head includes a sand prediction head, a silt prediction head, and a clay prediction head, which are used to generate predicted values for sand content, silt content, and clay content, respectively, based on the calibrated particle feature vector.
[0097] The output layer is used to normalize the predicted values of sand content, silt content, and clay content to ensure that the sum of the three predicted values is 100%. Based on the matching degree between the predicted values of sand content, silt content, and clay content and the pre-trained soil texture database, the confidence score is output.
[0098] Step S240: From the configured association database of different soil mechanical compositions and different mycorrhizal adaptability, match multiple alternative mycorrhizae corresponding to the predicted soil mechanical composition of the target soil area.
[0099] In one embodiment of this application, the correlation database of different soil mechanical compositions and different mycorrhizal adaptability is obtained by integrating collected experimental measurement data and acquired public database and literature data; wherein, the method for obtaining experimental measurement data includes: air-drying the soil collected in the field and passing it through a 1 mm sieve, measuring the soil mechanical composition of soil with a diameter of 0-1 mm, referring to the common soil particle size classification standards in the industry, clay particles are 0~1µm, silt particles are 2~20µm, and sand particles are 50~1000µm; the number of soil mechanical composition ratio designs is not less than 10 groups, and the ratio is based on the mass ratio of raw materials; the mass ratio of clay to sand can be: 10∶1, 9∶1, 8∶1, 7∶1, 6∶1, 5∶1, 9∶1, 8∶1, 7∶1, 6∶1, 5∶1, 9∶1, 8∶1, 8∶1, 7∶1, 6∶1, 9∶1, 8 ... Different mycorrhizal fungi were inoculated into soils with varying mechanical compositions, including 1:1, 4:1, 3:1, 2:1, 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 3:5, 3:2, 5:3, and 2:3. After the incubation period, the mycorrhizal infection rate and spore count were measured to assess the compatibility of the soil. Simultaneously, the proportions of sand, silt, and clay particles in the soil were precisely determined using instruments such as a laser particle size analyzer. Experimental conditions (temperature, humidity, etc.) were recorded as experimental data. The publicly available database was obtained by extracting basic data on the symbiotic relationships between different plants and mycorrhizae from authoritative databases and then matching this data with soil mechanical composition data for the corresponding regions in the World Soil Database. By statistically analyzing the collected experimental data and the data from publicly available databases and literature, the correlation between soil mechanical composition and mycorrhizal compatibility was determined. For example, correlation analysis was used to determine the positive or negative correlation coefficient between clay content and arbuscular mycorrhizal infection rate, and regression models were used to quantify the influence of sand content on mycorrhizal spore production. The correlation between soil mechanical composition and mycorrhizal compatibility was used as the core logic of the database, forming a correspondence between soil mechanical composition, mycorrhizal type and compatibility index, resulting in a correlation database of different soil mechanical compositions and different mycorrhizal compatibility.
[0100] Step S250: Determine the compatibility index of different candidate mycorrhizae based on multiple candidate mycorrhizae, predicted soil mechanical composition and environmental data.
[0101] In practice, based on soil water holding capacity and predicted soil mechanical composition, the soil structure damping coefficient for different candidate mycorrhizae is calculated; specifically, the formula for calculating the soil structure damping coefficient is as follows: D = α Sand particles and sticky particles + β (1 (Powder content); among which, D Indicates the soil structure damping coefficient; α The weights of the candidate mycorrhizae are either obtained through custom configuration or determined by the fitness of the candidate mycorrhizae.β For 1- α The soil structure damping coefficient was corrected by soil water holding capacity. The soil structure damping coefficients of different candidate mycorrhizae were mapped to the 0-1 interval to obtain the physical fitness index of different candidate mycorrhizae.
[0102] Based on environmental data, the functional fitness index of different candidate mycorrhizae is calculated. Specifically, the calculation method of the functional fitness index includes: for any candidate mycorrhizae, based on the functional response curves of different configured candidate mycorrhizae, calculating the functional decay ratio corresponding to different environmental data; the formula for calculating the functional decay ratio is: F =1 Maximum functional value / Actual functional value; where the actual functional value is obtained by substituting each environmental data into the functional response curves of different candidate mycorrhizae, and the maximum functional value is 100%; random forest regression is used to learn the functional decay ratio corresponding to different environmental data to generate the functional fitness index corresponding to the candidate mycorrhizae.
[0103] For any candidate mycorrhizae, the product of the physical fitness index and the functional fitness index of the candidate mycorrhizae is taken as the fitness index of the candidate mycorrhizae; candidate mycorrhizae with fitness indices higher than the configured fitness threshold are taken as first mycorrhizae; different first mycorrhizae are cross-combined to obtain multiple first mycorrhizae combinations; the co-fitness index of different first mycorrhizae combinations is calculated; wherein, the formula for calculating the co-fitness index is as follows: ; where SCAI represents the Collaborative Comprehensive Adaptation Index; and Indicates the weighting coefficient; Indicates the total number of mycorrhizal species; This represents the fitness index of the i-th first mycorrhiza; This represents the fitness index of the j-th first mycorrhiza; This represents the synergistic effect value between the i-th and j-th first mycorrhizae.
[0104] Step S260: Input the adaptation index of different candidate mycorrhizae, the predicted soil mechanical composition and environmental data into the pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae.
[0105] The mycorrhizal remediation effect prediction model was trained using historical data. This historical data includes different soil mechanical compositions from different soil samples and the mycorrhizal remediation effects determined after inoculating different mycorrhizae onto these soil samples. The mycorrhizal remediation effect prediction model includes:
[0106] The input layer is used to input the fitness index of different candidate mycorrhizae, predict soil mechanical composition and environmental data;
[0107] The remediation scheme generation layer is used to determine remediation schemes corresponding to different candidate mycorrhizae based on the input fit index, predicted soil mechanical composition, and environmental data. Specifically, standard remediation schemes corresponding to different mycorrhizae, along with corresponding standard fit indices, standard soil mechanical composition, and standard environmental data, are pre-generated for each standard mycorrhizae. The standard remediation schemes are adjusted using the differences between the fit index, predicted soil mechanical composition, and environmental data and the corresponding standard fit index, standard soil mechanical composition, and standard environmental data to obtain the remediation schemes corresponding to the respective candidate mycorrhizae.
[0108] The repair effect prediction layer is used to predict the repair effect of different alternative mycorrhizae based on the repair schemes corresponding to different alternative mycorrhizae.
[0109] The output layer is used to output the predicted repair effects of different alternative mycorrhizal restoration schemes.
[0110] Step S270: Based on the compatibility index of different alternative mycorrhizae and the predicted remediation effect of the corresponding remediation schemes, determine the mycorrhizal remediation scheme for the target soil area.
[0111] The mycorrhizal repair program includes three inoculation parameters: inoculation amount, inoculation timing, and application method.
[0112] In practice, based on the compatibility index of different candidate mycorrhizae and the predicted remediation effects of corresponding remediation schemes, mycorrhizal remediation schemes for the target soil area are determined, including:
[0113] Using a pre-trained inoculation parameter dynamic optimization agent, the agent deletes first mycorrhizae or first mycorrhizae combinations whose fitness index or cofitting index is not greater than the configured fitness threshold or whose predicted repair effect is not greater than the configured repair threshold from different first mycorrhizae and different first mycorrhizae combinations, thereby obtaining different second mycorrhizae or different second mycorrhizae combinations; wherein, the constraint quantification indicators of the inoculation parameter dynamic optimization agent include cost budget limit, project deadline, repair effect threshold and operation complexity limit;
[0114] Based on the multi-dimensional comprehensive decision-making model that dynamically optimizes the configuration of the agent according to the inoculation parameters, the comprehensive decision score of different second mycorrhizae or different combinations of second mycorrhizae is calculated.
[0115] A combined optimization strategy of genetic algorithm and Bayesian optimization was adopted, with the fitness function based on the comprehensive decision score, to determine the optimal mycorrhizae through multiple iterations. In the first iteration, three sets of initial parameters were simulated for different second mycorrhizae or combinations of second mycorrhizae. Combined with soil-environment data, the remediation effect and cost were predicted, and the fitness value was calculated to initially identify the top two candidates (denoted as M1 and M2, which may be single or combinations of any form). In the second iteration, a dedicated parameter search space was constructed for M1 and M2. Bayesian optimization was used to sample 12 parameter combinations and predict the optimal mycorrhizae for each combination. The fitness value is updated based on the repair effect, cost, and constraint satisfaction. In the third iteration, the top three parameter combinations of M1 and M2 are checked for constraint satisfaction. If conflicts exist, such as high effectiveness but cost overruns or insufficient combination stability, dynamic adjustments are made: when costs exceed the limit, the cost of non-core parameters for single mycorrhizae is reduced (e.g., replacing low-cost amendments), and the ratio of combined mycorrhizae is optimized (increasing the proportion of low-cost mycorrhizae to ensure no decrease in effectiveness); when stability is insufficient, the inoculation order of the combination is adjusted (inoculating mycorrhizae with strong stress resistance first to establish a growth foundation), and the fitness value is recalculated after conflict resolution. In the fourth iteration, the final fitness values of M1 and M2 are compared, and candidates with higher scores and more comprehensive constraint satisfaction are selected. Their morphology (single / combination) and optimal parameter combination are determined, which is the optimal mycorrhizae.
[0116] Using the remediation effect as the reward function and the inoculation parameters as the action space, a Bayesian optimization algorithm is used to determine the target remediation scheme corresponding to the optimal mycorrhizae, thus obtaining the mycorrhizal remediation scheme for the target soil area.
[0117] In one embodiment of this application, the target band can be determined through the following experiment:
[0118] Step 1: Black clay and sandy soil were selected as test samples. A total of 25 different mass ratios of sandy soil and black clay were designed. The clay-to-sand ratios were: 10:1, 9:1, 8:1, 7:1, 6:1, 5:1, 4:1, 3:1, 2:1, 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 3:5, 3:2, 5:3, 2:3, and clay-to-sand ratios, with three replicates for each ratio. Soil samples collected in the field were placed in a well-ventilated and dry place for natural air drying. A portion of the air-dried soil samples and the prepared soil samples were then sieved through a 1mm sieve for hyperspectral data acquisition. The remaining soil samples were used to determine the soil mechanical composition. The soil mechanical composition was determined using a Malvern laser particle size analyzer. The soil particle size distribution was based on common domestic and international soil particle size classification standards in soil science. Clay particles range from 0 to 1 μm, powder particles from 2 to 20 μm, and sand particles from 50 to 1000 μm.
[0119] Step 2: Perform hyperspectral data analysis on the soil samples in a darkroom. Place the sieved dry soil sample in a black light-absorbing rubber container measuring 10cm long, 15cm wide, and 5mm high, and smooth the surface. Before measurement, perform whiteboard calibration to reduce the influence of instrument dark current and lamp source on spectral quality. Collect 5 spectral curves for each soil sample, and use the arithmetic mean of these 5 sample lines as the final spectral curve for that soil sample.
[0120] Step 3: Resampling and envelope removal methods were used to process the measured soil spectral data. The 400nm~2400nm band was selected as the spectral data of the tested soil samples for processing and analysis. The measured spectral data was resampled in the 400~2400nm range with a resampling interval of 1nm. Continuum removal was performed on the measured spectral data. Correlation analysis software was used to calculate the correlation coefficient between the soil mechanical composition and the spectra after envelope removal.
[0121] Step 4: Average the spectral reflectance of all soil samples from the control area and the inoculation area, respectively, to obtain the following results: Figure 3 The spectral curves of the control and inoculated soils are shown below. (From...) Figure 3 It can be seen that the soil spectral reflectance increased after inoculation compared to before inoculation. Apart from the magnitude of the spectral reflectance, the trends and positions of the characteristic absorption bands in the spectral curves were the same between the control and inoculated treatments. This indicates that while inoculation increases soil sand content and decreases soil clay content, the soil spectral reflectance increases accordingly. This means that as clay content decreases and sand content increases, soil spectral reflectance increases. Soil reflectance spectral characteristics are a comprehensive reflection of factors such as soil texture, organic matter, soil type, and soil moisture. Figure 4 The image shows the spectral curves of all air-dried soil samples that have passed through a 1mm sieve in the field. Figure 5 The image shows the soil spectral curves after continuum removal. The curves share the following common characteristics:
[0122] First, the original spectral curves and the spectral curves after continuum removal of all soil samples are basically similar in morphology, and the spectral reflectance of soil samples with different contents of clay, silt and sand is also different.
[0123] Second, as the wavelength increases, the spectral reflectance of the original soil increases rapidly in the range of 400~1350nm, and the change is relatively gradual after 1350nm. The spectral reflectance is slightly concave in the range of 400~650nm and slightly convex in the range of 650~1350nm.
[0124] Third, the original spectral curves show an upward trend in the wavelength ranges of 400~1350nm, 1400~1850nm, 1900~2100nm, and 2200~2250nm, and a downward trend in the wavelength ranges of 1350~1400nm, 1850~1900nm, 2100~2200nm, and 2250~2240nm.
[0125] Fourth, the original spectral curve shows obvious absorption characteristics around 1400nm, 1900nm, and 2200nm, and the reflectance shows varying degrees of increase and decrease around these three wavelengths;
[0126] Fifth, after the continuum removal transformation, the absorption features at 1400nm, 1900nm, and 2200nm in the original spectral curve were significantly amplified, the absorption valleys were deepened, and a relatively obvious absorption feature also appeared at 500nm.
[0127] Step 5: Perform correlation analysis between the content of soil clay, silt, and sand and the transformed spectrum to determine the target band; Figure 6 , Figure 7 and Figure 8 These are the correlation coefficients between the content of clay particles, silt particles, and sand particles and the spectra after removal transformation in the continuum.
[0128] Depend on Figure 6 It can be seen that the correlation between clay content and spectral reflectance is good in the wavelength ranges of 450~700nm, 1380~1530nm, 1880~2100nm, and 2170~2250nm, and all show a negative correlation, with absolute values of correlation coefficients between 0.80 and 0.95. Although there are two correlated reflectance peaks at 1080~1200nm and 2290~2340nm, the absolute values of the correlation coefficients are around 0.50, indicating that the correlation between clay content and reflectance is weaker in these two wavelength bands, and its impact on clay content inversion is smaller. After correlation analysis, five sensitive wavelength bands were selected from the spectral data after continuum removal according to the magnitude of correlation: 598nm, 646nm, 1496nm, 1889nm, and 2244nm. The reflectance values at these five sensitive wavelength bands are used as input factors for clay model inversion.
[0129] Depend on Figure 7It can be seen that the correlation between particle content and spectral reflectance is good in the wavelength ranges of 420~730nm, 1390~1500nm, 1880~2100nm, and 2170~2250nm, and all show a negative correlation, with absolute values of correlation coefficients between 0.85 and 0.97. Although there are two correlated reflectance peaks at 1020~1250nm and 2290~2330nm, the absolute values of their correlation coefficients are smaller than those of the spectral reflectance in the above four wavelength ranges, indicating that the correlation between particle content and reflectance is weaker in these two wavelength ranges, and its impact on particle content inversion is smaller. After correlation analysis, five sensitive wavelength ranges were selected from the spectral data after continuum removal according to their correlation magnitude: 548nm, 647nm, 1478nm, 1896nm, and 2228nm. The reflectance values at these five sensitive wavelength ranges were used as input factors for particle model inversion.
[0130] Depend on Figure 8 It can be seen that the correlation between sand grain content and spectral reflectance is good in the wavelength ranges of 440~740nm, 1380~1540nm, 1880~2100nm, and 2170~2250nm, and all show a positive correlation, with absolute values of correlation coefficients between 0.85 and 0.97. Although there are two correlation absorption valleys at 1020~1235nm and 2290~2330nm, the absolute values of the correlation coefficients are smaller than those of the spectral reflectance in the above four bands, indicating that the correlation between sand grain content and reflectance is weaker in these two bands, and its influence on sand grain content inversion is smaller. After correlation analysis, five sensitive bands were selected from the spectral data after the continuum was removed, based on the magnitude of the correlation: 548nm, 646nm, 1496nm, 1890nm, and 2244nm. The reflectance values at these five sensitive bands were used as input factors for sand grain model inversion.
[0131] In summary, the target bands with good correlation to soil mechanical composition are 450~700nm, 1390~1500nm, 1880~2100nm, and 2170~2250nm. The sensitive bands selected for clay, silt, and sand inversion are also within the above four band ranges.
[0132] Corresponding to the above method, embodiments of this application also provide a device for determining a mycorrhizal remediation scheme, such as... Figure 9 As shown, the device includes:
[0133] Acquisition unit 910 is used to acquire hyperspectral data and environmental data of the target soil area;
[0134] The first prediction unit 920 is used to input hyperspectral data into a pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area.
[0135] The second prediction unit 930 is used to input hyperspectral data, predicted organic matter content and predicted water content into a pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area.
[0136] Matching unit 940 is used to match multiple candidate mycorrhizae corresponding to the predicted soil mechanical composition of the target soil area from a configured association database of different soil mechanical compositions and different mycorrhizal adaptability.
[0137] The computing unit 950 is used to calculate the fit index of different candidate mycorrhizae based on a variety of candidate mycorrhizae, predicted soil mechanical composition and environmental data.
[0138] The third prediction unit 960 is used to input the adaptation index of different candidate mycorrhizae, the predicted soil mechanical composition and environmental data into the pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae.
[0139] Unit 970 is used to determine the mycorrhizal remediation scheme for the target soil area based on the compatibility index of different alternative mycorrhizae and the prediction of the remediation effect of the corresponding remediation scheme.
[0140] The functions of each functional unit in the mycorrhizal remediation scheme determination device provided in the above embodiments of this application can be implemented through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the mycorrhizal remediation scheme determination device provided in the embodiments of this application will not be repeated here.
[0141] This application also provides an electronic device, such as... Figure 10 As shown, it includes a processor 1010, a communication interface 1020, a memory 1030, and a communication bus 1040, wherein the processor 1010, the communication interface 1020, and the memory 1030 communicate with each other through the communication bus 1040.
[0142] Memory 1030 is used to store computer programs;
[0143] When the processor 1010 executes the program stored in the memory 1030, it performs the following steps:
[0144] Acquire hyperspectral and environmental data for the target soil region;
[0145] Hyperspectral data is input into a pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area.
[0146] Hyperspectral data, predicted organic matter content, and predicted water content are input into a pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area.
[0147] From the association database of different soil mechanical compositions and different mycorrhizal adaptability, match multiple candidate mycorrhizae corresponding to the predicted soil mechanical composition of the target soil area;
[0148] Based on multiple candidate mycorrhizae, predicted soil mechanical composition, and environmental data, the compatibility index of different candidate mycorrhizae was calculated.
[0149] The compatibility index of different candidate mycorrhizae, the predicted soil mechanical composition and environmental data are input into a pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae.
[0150] Based on the compatibility index of different alternative mycorrhizae and the predicted remediation effect of corresponding remediation schemes, mycorrhizal remediation schemes for target soil areas are determined.
[0151] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.
[0152] The communication interface is used for communication between the aforementioned electronic devices and other devices.
[0153] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.
[0154] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0155] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.
[0156] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the method for determining the mycorrhizal repair scheme described in any of the above embodiments.
[0157] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for determining the mycorrhizal repair scheme described in any of the above embodiments.
[0158] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can 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 code.
[0159] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0162] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.
[0163] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of this application and its equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.
Claims
1. A method for determining a mycorrhizal remediation scheme, characterized in that, The method includes: Acquire hyperspectral and environmental data for the target soil region; The hyperspectral data is input into a pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area. The hyperspectral data, the predicted organic matter content, and the predicted water content are input into a pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area. From the associated database of different soil mechanical compositions and different mycorrhizal adaptability, match multiple candidate mycorrhizae corresponding to the predicted soil mechanical composition of the target soil region; Based on multiple candidate mycorrhizae, predicted soil mechanical composition, and the environmental data, the fitness index of different candidate mycorrhizae is calculated, including: calculating the physical fitness index and functional fitness index of different candidate mycorrhizae based on the environmental data and the predicted soil mechanical composition; for any candidate mycorrhizae, the product of the physical fitness index and the functional fitness index of the candidate mycorrhizae is used as the fitness index of the candidate mycorrhizae; candidate mycorrhizae with fitness indices higher than a configured fitness threshold are designated as first mycorrhizae; different first mycorrhizae are cross-combined to obtain multiple first mycorrhizae combinations; and the synergistic fitness index of different first mycorrhizae combinations is calculated. The fitness index of different candidate mycorrhizae, the predicted soil mechanical composition, and the environmental data are input into a pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae. Based on the fitness index of different candidate mycorrhizae and the predicted remediation effect of corresponding remediation schemes, a mycorrhizal remediation scheme for the target soil area is determined, including: using a pre-trained inoculation parameter dynamic optimization agent to delete first mycorrhizae or first mycorrhizal combinations whose fitness index or co-fitness index is not greater than a configured fitness threshold, or whose predicted remediation effect is not greater than a configured remediation threshold, from different first mycorrhizae and different first mycorrhizal combinations, to obtain different second mycorrhizae or different second mycorrhizal combinations; calculating the comprehensive decision score of different second mycorrhizae or different second mycorrhizal combinations according to the multi-dimensional comprehensive decision model configured by the inoculation parameter dynamic optimization agent; using a configured combination optimization strategy, with the comprehensive decision score as the fitness function, to determine the optimal mycorrhizae through multiple iterations; using the remediation effect as the reward function and the different inoculation parameters in the remediation scheme as the action space, using an optimization algorithm to determine the target remediation scheme corresponding to the optimal mycorrhizae, thus obtaining the mycorrhizal remediation scheme for the target soil area.
2. The method as described in claim 1, characterized in that, The method for acquiring hyperspectral data of the target soil region includes: Acquire raw hyperspectral data of the target soil region; The original hyperspectral data is preprocessed to obtain the first hyperspectral data; The envelope is fitted using the envelope removal method, and the normalized second hyperspectral data is obtained by dividing the first hyperspectral data by the envelope. From the second hyperspectral data, extract the third hyperspectral data located in the configured target band; The third hyperspectral data is subjected to multiplicative scattering correction and first derivative transformation to obtain the fourth hyperspectral data, which is then output as the hyperspectral data of the target soil region.
3. The method as described in claim 1, characterized in that, The soil organic matter and water content prediction model includes: The input layer is used to input hyperspectral data of the target soil region. The physical attention embedding layer is used to multiply the input hyperspectral data with the configured binary attention mask to obtain mask-optimized one-dimensional spectral data; A shared feature extraction layer is used to extract features from the masked one-dimensional spectral data to obtain a multi-dimensional shared feature vector; The feature refinement extraction layer is used to refine the feature extraction of the multi-dimensional shared feature vector to obtain the first feature vector and the second feature vector. A dual-target fusion prediction layer is used to generate predicted organic matter content and predicted water content based on the first feature vector and the second feature vector, respectively. The output layer is used to output the predicted organic matter content and the predicted water content.
4. The method as described in claim 1, characterized in that, The soil mechanical composition prediction model includes: The input layer is used to input hyperspectral data of the target soil region, the predicted organic matter content, and the predicted water content; A cross-modal feature coding layer is used to encode the hyperspectral data, the predicted organic matter content, and the predicted water content respectively, to obtain the coded features corresponding to the hyperspectral data, the predicted organic matter content, and the predicted water content; The dual attention fusion module is used to perform weighted fusion of the encoded features corresponding to the hyperspectral data, the predicted organic matter content, and the predicted water content to obtain a global feature vector. A multi-scale particle feature extraction layer is used to extract sand, silt, and clay features separately, and then concatenates the extracted sand, silt, and clay features into a multi-scale particle feature vector. The particle interaction calibration layer is used to calibrate the multi-scale particle feature vectors to obtain the calibrated particle feature vectors. A multi-objective prediction head is used to obtain the predicted soil mechanical composition based on the calibrated particle feature vector; the predicted soil mechanical composition includes: predicted sand content, predicted silt content, and predicted clay content. The output layer is used to output the predicted soil mechanical composition.
5. The method as described in claim 4, characterized in that, The environmental data include soil pH, soil electrical conductivity, soil available nitrogen, phosphorus and potassium content, annual sunshine hours, annual average temperature and precipitation, soil pollution status and soil water holding capacity.
6. A device for determining a mycorrhizal remediation scheme, characterized in that, The device includes: The acquisition unit is used to acquire hyperspectral data and environmental data of the target soil area; The first prediction unit is used to input the hyperspectral data into a pre-trained soil organic matter and water content prediction model to obtain the predicted organic matter content and predicted water content of the target soil area. The second prediction unit is used to input the hyperspectral data, the predicted organic matter content, and the predicted water content into a pre-trained soil mechanical composition prediction model to obtain the predicted soil mechanical composition of the target soil area. The matching unit is used to match multiple candidate mycorrhizae corresponding to the predicted soil mechanical composition of the target soil region from a configured association database of different soil mechanical compositions and different mycorrhizal adaptability. The calculation unit is configured to calculate the fitness index of different candidate mycorrhizae based on multiple candidate mycorrhizae, predicted soil mechanical composition, and the environmental data, including: calculating the physical fitness index and functional fitness index of different candidate mycorrhizae based on the environmental data and the predicted soil mechanical composition; for any candidate mycorrhizae, using the product of the physical fitness index and the functional fitness index as the fitness index of the candidate mycorrhizae; designating candidate mycorrhizae with fitness indices higher than a configured fitness threshold as first mycorrhizae; cross-combining different first mycorrhizae to obtain multiple first mycorrhizae combinations; and calculating the synergistic fitness index of different first mycorrhizae combinations. The third prediction unit is used to input the adaptation index of different candidate mycorrhizae, the predicted soil mechanical composition and the environmental data into the pre-trained mycorrhizal remediation effect prediction model to obtain the remediation scheme and predicted remediation effect corresponding to different candidate mycorrhizae. The determination unit is used to determine the mycorrhizal remediation scheme for the target soil area based on the fitness index of different candidate mycorrhizae and the predicted remediation effect of corresponding remediation schemes. This includes: using a pre-trained inoculation parameter dynamic optimization agent to delete first mycorrhizae or first mycorrhizal combinations whose fitness index or co-fitting index is not greater than a configured fitness threshold, or whose predicted remediation effect is not greater than a configured remediation threshold, from different first mycorrhizae and different first mycorrhizal combinations, thus obtaining different second mycorrhizae or different second mycorrhizal combinations; calculating the comprehensive decision score of different second mycorrhizae or different second mycorrhizal combinations based on the multi-dimensional comprehensive decision model configured for the inoculation parameter dynamic optimization agent; using a configured combinatorial optimization strategy, with the comprehensive decision score as the fitness function, iterating through multiple rounds to determine the optimal mycorrhizae; and using the remediation effect as the reward function and the different inoculation parameters in the remediation scheme as the action space, using an optimization algorithm to determine the target remediation scheme corresponding to the optimal mycorrhizae, thus obtaining the mycorrhizal remediation scheme for the target soil area.
7. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-5.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-5.