CN118607923B - A digital twin scenario-based system for water and salt dynamics in irrigation areas - Google Patents
A digital twin scenario-based system for water and salt dynamics in irrigation areasInfo
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Abstract
The invention relates to a dynamic digital twin scenerization system for salinized water and salt in a irrigation area, which solves the technical problems of complex system and low accuracy, and comprises a twin data acquisition module, a twin space modeling module, a salinization analysis prediction module, a salinization treatment module and a salinization control module, wherein the twin space modeling module is used for mapping acquired physical parameters into twin entities in a virtual space to construct an irrigation area model, the project layout model comprises a dynamic water and salt distribution model and a water control system model, the salinization analysis prediction module is used for analyzing and predicting the salinization condition of the irrigation area, the salinization treatment module comprises a player strategy supply module, a strategy evaluation module and a distributed expert manual evaluation module, the player strategy supply module is used for controlling a plurality of treatment strategies of a virtual space irrigation area water control system by mass-funded players in a twin virtual space according to the salinization water and salt dynamic parameters of a certain irrigation area, and the salinization treatment module can be used for well solving the problems.
Description
Technical Field
The invention relates to the field of irrigation area salinization treatment, in particular to a dynamic digital twin scenerization system for salinized water salt in an irrigation area.
Background
In recent years, with the gradual utilization of the developable land, attention is paid to arid and semiarid regions, and in the regions, due to the lack of water resources, agricultural water saving becomes an important problem of water resource management, and due to the alternate actions of rainfall, irrigation and evaporation, salt is continuously accumulated in unsaturated zone soil to form secondary salinization. Soil salinization and secondary salinization have now become a global environmental problem commonly faced worldwide. The salinization of the soil is related to the resource problem and the ecological environment problem, directly influences the production of grains, and is a great limiting condition and obstacle factor for agricultural development and agricultural sustainable development
The invention provides a novel dynamic digital twin scenerization system for salinized water salt in a irrigation area, which is used for solving the technical problems of complex system and low accuracy in the prior art.
Disclosure of Invention
The invention aims to solve the technical problems of complex system and low accuracy in the prior art. The novel dynamic digital twin scenerization system for the salinized water salt in the irrigation area has the characteristics of simplicity, high accuracy, effective analysis, simulation of the dynamic parameters of the salinized water salt in the irrigation area and simulation and evaluation of a treatment method.
In order to solve the technical problems, the technical scheme adopted is as follows:
a system for dynamic digital twin scenery of salinized water salt in a irrigated area, the system comprising:
The twin data acquisition module is used for acquiring the engineering layout parameters of the irrigation areas, the salinized water salt dynamic parameters of the irrigation areas and the control parameters of the water system of the irrigation areas;
The twin space modeling module is used for mapping the collected physical parameters into twin entities in the virtual space and constructing a irrigated area model, and comprises an engineering layout model, a water-salt dynamic distribution model and a water control system model;
The salinization analysis prediction module is used for analyzing and predicting and analyzing the salinization condition of the irrigation area;
The salinization treatment module is used for generating a irrigation treatment strategy of an irrigation area in the virtual twin space, evaluating and screening the irrigation treatment strategy, and controlling an irrigation area water control system in the physical world according to the screened optimal irrigation treatment strategy;
the salinization treatment module comprises a player strategy supply module, a strategy evaluation module constructed according to expert knowledge and a historical database and a distributed expert manual evaluation module;
The player strategy supply module is used for crowd players to control various treatment strategies of the virtual space irrigation area water control system in the twin virtual space according to the salinized water salt dynamic parameters of a certain irrigation area;
The strategy evaluation module is used for calling a historical database to perform usability and feasibility evaluation analysis on various treatment strategies provided by the player strategy supply module by adopting an algorithm;
the distributed expert manual evaluation mode is that the distributed invitation expert carries out manual supplementary evaluation on the treatment strategy when the strategy evaluation module cannot effectively carry out automatic evaluation.
The invention is based on the working principle that the invention aims at the problems that the operation condition of the existing irrigation area salinization treatment irrigation and drainage engineering is not clear, so that the water-saving and salt-controlling and drainage effects cannot be mastered in time, and the like, and constructs the irrigation and drainage engineering layout comprising regional channels, motor-pumped wells, drainage channels, concealed pipe facilities and the like, salinization distribution, crop planting structures, crop growth vigor and output, groundwater burial depth and water quality, irrigation drainage water quantity and water quality, meteorological information, soil moisture content, saline-alkali degree and other irrigation area water salt dynamic bottom information base plate databases, so that bidirectional real-time mapping and interaction between the physical irrigation area saline-alkali soil and groundwater burial depth distribution and a digital twin water salt migration sceneries are realized, and the real-time property and accuracy of data are ensured. The method comprises the steps of constructing a cloud computing water-salt dynamic change analysis model, fully utilizing the water-salt dynamic physical model, running history drainage amount, water quality, salinization distribution, underground water burial depth change and other data, integrating water conservancy, agriculture, geographic information, mathematics and computer multidisciplinary, constructing a digital twin system for the salinization water-salt migration in a irrigation area to simulate and analyze the water-salt migration rules under different irrigation and drainage scenes, carrying out simulation deduction on the water-salt migration state of the saline-alkali soil in the irrigation area, predicting the future development trend, and carrying out monitoring and early warning on the water-salt migration state of the irrigation area through data analysis and technical treatment.
The invention creatively adds a player strategy supply module, which is a plurality of treatment strategies for controlling a virtual space irrigation area water control system, wherein the treatment strategies are obtained by crowd-funding according to the dynamic parameters of salinized water salt of a certain irrigation area in a twin virtual space. And the public treatment strategies are evaluated automatically or by an expert, so that the effective treatment strategy matched with the current salinization degree is selected, and the defects that the expert design strategy is incomplete and the history data is needed as support for the automatic generation strategy are overcome.
In the above scheme, for optimization, further, a salinization analysis prediction module is built with a water salt dynamic parameter analysis prediction algorithm, which includes:
Step 1, collecting 1 satellite remote sensing image data as global data, collecting s m unmanned aerial vehicle remote sensing image data as local data in real time, correcting and correlating coordinates of s m unmanned aerial vehicle remote sensing image data with coordinates of the satellite remote sensing image data, and constructing a two-layer data image layer, wherein s m is a positive integer not less than 1;
Step 2, presetting a water salt dynamic analysis algorithm library, wherein s n algorithm models are built in the algorithm library, and inputting the global data in the step 1 into the h i algorithm models to obtain a global output result;
Step 3, inputting the s m local data into a i、ji+1,...,ji +m-1 algorithm model correspondingly to obtain s m local output results, wherein j i is a positive integer not less than 1;
and 4, respectively carrying out calculation error evaluation on the insides of the s m local output results, determining positive local output results with h being less than or equal to s m error values smaller than a predefined threshold value, carrying out matching settlement on the h positive local output results and the global output result, calculating the number of positive local output results with the error value smaller than the predefined threshold value, if the number is smaller than the predefined threshold value, defining an h i algorithm model as a global fit algorithm basic model for water salt dynamic parameter analysis and prediction, otherwise defining h i＝hi +1, and returning to the step 2.
According to the invention, satellite remote sensing data and local unmanned aerial vehicle data are synthesized and used as the basis for analyzing and predicting a salinization algorithm, and the matching degree of the cost and the precision is comprehensively considered by controlling the number and the layout of unmanned aerial vehicles.
Further, the method also comprises the step 5:
grid tagging is carried out on the global, and h positive local output results are marked in grid tags to serve as positive local output nodes;
h r(hr = 1,2,., h) positive local output nodes are selected as starting points, a diffusion radius R is defined, a grid label node is selected as a diffusion node, and the algorithm model which is the same as the starting points is adopted for the definition of the diffusion node;
Traversing h positive local output nodes, defining diffusion nodes corresponding to the h positive local output nodes, and determining an algorithm model of each diffusion node;
On the basis of a global fitting algorithm basic model, replacing an algorithm model of a positive local output node and an algorithm model of a diffusion node, and optimizing to obtain a global fitting algorithm model combination which is used for analyzing and predicting water-salt dynamic parameters;
The probability that the node h r selects the node h s as the diffusion node to access is P k (r, s), and the diffusion node may be accessed by a plurality of positive local output nodes, and the positive local output node corresponding to the maximum probability is determined to be the right;
Wherein L k (r) represents the set of all the next nodes to be accessed of node h r where ant k is located, node h v∈Lk (r);
H k (r) represents a set of nodes that are not accessed by the node H r where ant k is located, node H u∈Zk (r);
Delta (r, s) is the degree of selectivity between node h r and node h s;
η (r, s) =1/d (r, s), d (r, s) is expressed as the distance between node h r and node h s;
Importance coefficients for a predefined degree of selectivity;
Beta is a relatively significant coefficient of predefined visibility;
e(s) represents the remaining energy to access the next neighbor node h s;
representing a sum of the remaining energy of the next neighbor node that is accessible;
r is the diffusion radius.
Further, the characteristic parameters of the salinized water salt dynamic parameters comprise vegetation parameters and salinity parameters.
Further, collecting sub-parameters of the characteristic parameters of all salinized water salt dynamic parameters, performing relevance evaluation sequencing on the sub-parameters, and fusing the sub-parameters into the characteristic parameters, wherein the method comprises the following steps:
Step A1, carrying out relevance evaluation sequencing on all the feature parameter fusion values by taking feature sub-parameters as units, wherein the step A1 comprises the following steps:
a1.1, constructing an ant colony classification model by training random samples, and defining deviation coefficients as follows:
wherein c x is the category of sub-parameter classification in characteristic parameters, tx is the algorithm step number of the ant colony separation model, p x is the relative probability of cx, c, cj are the pixel values of coordinates (x, y), (x+Deltax, y+Deltay) on the image gray characteristic matrix; V ci,cj is a matrix element, and N is the number of the matrix elements;
A1.2 for the sub-parameter X, calculating the deviation coefficient of the off-diameter data of each worker ant, and recording as
A1.3 for the sub-parameter X, adding the existing Gaussian noise interference to the extra-diameter data sample to reconstruct to obtain a new extra-diameter data sample, and estimating the deviation coefficient of the new extra-diameter data sample
A1.4 calculation of the correlation coefficient value of the sub-parameter XThe correlation evaluation is used for representing the subparameter X, and NS is the number of workers in the ant classification model;
A2, eliminating sub-parameters with the correlation larger than a predefined threshold according to the sorting result of the A1, and defining weight weighting fusion to calculate the characteristic parameters.
Further, step A2 includes:
a2.1, sorting NN subparameter features in the feature data subparameter set according to the magnitude of the correlation coefficient value NR, the result of the ordering is { z1, z2, zi., zNN };
A2.2 fusing j characteristic parameter subparameters with the correlation coefficient value NR smaller than a predefined threshold value into correlation characteristics;
wherein the features (b 1, b2,..bj) belong to { z1, z2,.. zi.., zNR, xfc (·) is the covariance function and fc (·) is the bias function;
a2.3, fusing j characteristic parameter subparameters with the correlation coefficient value NR larger than a predefined threshold value into correlation characteristics;
measurement variance for the ni-th characteristic parameter subparameter
Y ni＝H·xni+e;xni is the estimated value of the sub-parameter of the ni-th characteristic parameter;
H= [1,..1 ] T, H is an NN-j dimensional vector;
A2.4, defining the final fusion characteristics in the steps A2.2 and A2.3 as fusion characteristics A and fusion characteristics B respectively, calculating Euclidean distances from a certain point in the fusion characteristics A to all points in the fusion characteristics B, taking the minimum value, traversing all points in the fusion characteristics A, and taking the average value of the minimum value as min a1;
calculating Euclidean distances from a certain point in the fusion feature B to all points in the fusion feature A, taking a minimum value, and traversing all points in the fusion feature B to take an average value of the minimum value as min B1;
and A2.5, predefining fusion weight of the fusion feature A to alpha a, fusing fusion weight of the fusion feature B to beta b, calculating T AB = max (min a1, min B1) as a fusion feature threshold value, and calling a predefining feature fusion algorithm to finally finish fusion of the fusion feature A and the fusion feature B.
Further, the salinization control module performs the following steps:
step s1, visually presenting the result of the salinization analysis prediction module to a plurality of mass players, wherein the mass players generate player strategies in the virtual twin space for managing salinization risks in the virtual twin space;
step s2, virtually controlling a water control system model in a virtual twin space according to a player strategy, and generating virtual irrigation parameters;
step s3, grid tagging is carried out on the overall situation of the irrigation area, virtual irrigation parameters are divided into a plurality of local virtual irrigation parameters, historical data consistent with the local virtual irrigation data are searched in a historical database, if the historical data exist, the corresponding local virtual irrigation parameters are defined as positive local virtual irrigation parameters, otherwise, the positive local virtual irrigation parameters are defined as negative local virtual irrigation parameters, grids of the positive local virtual irrigation parameters can be combined to form the overall situation of the irrigation area, step s4 is executed, otherwise, a distributed expert manual evaluation module is called, a plurality of experts which are distributed are required to carry out manual evaluation, and step s5 is executed;
Step s4, calling a salinization analysis prediction module, and according to the corresponding salinization water salt dynamic parameter characteristic parameters in the global irrigation virtual twin data, calling historical data, analyzing the salinization condition;
And step s5, evaluating the treatment effect of the salinization risk condition, if the treatment effect reaches a predefined threshold value, defining the player strategy expert strategy, storing the player strategy expert strategy into an expert strategy library, controlling a irrigation area water control system in the physical world according to the expert strategy, and treating the salinization risk of the irrigation area.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a dynamic digital twin scenerization system for salinized water salts in irrigation areas in example 1.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a dynamic digital twin scenery system for salinized water salt in a irrigation area, as shown in fig. 1, comprising:
The twin data acquisition module is used for acquiring the engineering layout parameters of the irrigation areas, the salinized water salt dynamic parameters of the irrigation areas and the control parameters of the water system of the irrigation areas;
The twin space modeling module is used for mapping the collected physical parameters into twin entities in the virtual space and constructing a irrigated area model, and comprises an engineering layout model, a water-salt dynamic distribution model and a water control system model;
The salinization analysis prediction module is used for analyzing and predicting and analyzing the salinization condition of the irrigation area;
The salinization treatment module is used for generating a irrigation treatment strategy of an irrigation area in the virtual twin space, evaluating and screening the irrigation treatment strategy, and controlling an irrigation area water control system in the physical world according to the screened optimal irrigation treatment strategy;
the salinization treatment module comprises a player strategy supply module, a strategy evaluation module constructed according to expert knowledge and a historical database and a distributed expert manual evaluation module;
The player strategy supply module is used for crowd players to control various treatment strategies of the virtual space irrigation area water control system in the twin virtual space according to the salinized water salt dynamic parameters of a certain irrigation area;
The strategy evaluation module is used for calling a historical database to perform usability and feasibility evaluation analysis on various treatment strategies provided by the player strategy supply module by adopting an algorithm;
the distributed expert manual evaluation mode is that the distributed invitation expert carries out manual supplementary evaluation on the treatment strategy when the strategy evaluation module cannot effectively carry out automatic evaluation.
Aiming at the problems that the operation condition of the existing irrigation area salinization treatment irrigation and drainage engineering is not clear, the water saving and salt control and drainage effects cannot be mastered in time and the like, the embodiment is constructed and comprises irrigation and drainage engineering layouts such as regional channels, motor-pumped wells, drainage channels, hidden pipe facilities and the like, salinization distribution, crop planting structures, crop growth vigor and yield, underground water burial depth and water quality, irrigation drainage water quantity and water quality, meteorological information, soil moisture content, saline-alkali degree and other irrigation area water salt dynamic bottom information bottom plate databases, and bidirectional real-time mapping and interaction between physical irrigation area saline-alkali soil and underground water burial depth distribution and digital twin water salt migration sceneries are realized, so that the real-time property and accuracy of data are ensured. The method comprises the steps of constructing a cloud computing water-salt dynamic change analysis model, fully utilizing the water-salt dynamic physical model, running history drainage amount, water quality, salinization distribution, underground water burial depth change and other data, integrating water conservancy, agriculture, geographic information, mathematics and computer multidisciplinary, constructing a digital twin system for the salinization water-salt migration in a irrigation area to simulate and analyze the water-salt migration rules under different irrigation and drainage scenes, carrying out simulation deduction on the water-salt migration state of the saline-alkali soil in the irrigation area, predicting the future development trend, and carrying out monitoring and early warning on the water-salt migration state of the irrigation area through data analysis and technical treatment.
The player strategy supply module is adopted in the embodiment, and is a plurality of treatment strategies which are crowd-funded to control the irrigation area water control system of the virtual space according to the dynamic parameters of salinized water salt of a certain irrigation area in the twin virtual space. The public treatment strategies are evaluated automatically or by an expert, so that the effective treatment strategy matched with the current salinization degree is selected, and the defects that the expert design strategy is incomplete and the automatic generation strategy needs historical data as support are overcome
Preferably, a salinization analysis prediction module is internally provided with a water salt dynamic parameter analysis prediction algorithm, which comprises the following steps:
Step 1, collecting 1 satellite remote sensing image data as global data, collecting s m unmanned aerial vehicle remote sensing image data as local data in real time, correcting and correlating coordinates of s m unmanned aerial vehicle remote sensing image data with coordinates of the satellite remote sensing image data, and constructing a two-layer data image layer, wherein s m is a positive integer not less than 1;
Step 2, presetting a water salt dynamic analysis algorithm library, wherein s n algorithm models are built in the algorithm library, and inputting the global data in the step 1 into the h i algorithm models to obtain a global output result;
Step 3, inputting the s m local data into a i、ji+1,...,ji +m-1 algorithm model correspondingly to obtain s m local output results, wherein j i is a positive integer not less than 1;
and 4, respectively carrying out calculation error evaluation on the insides of the s m local output results, determining positive local output results with h being less than or equal to s m error values smaller than a predefined threshold value, carrying out matching settlement on the h positive local output results and the global output result, calculating the number of positive local output results with the error value smaller than the predefined threshold value, if the number is smaller than the predefined threshold value, defining an h i algorithm model as a global fit algorithm basic model for water salt dynamic parameter analysis and prediction, otherwise defining h i＝hi +1, and returning to the step 2.
According to the embodiment of the invention, the satellite remote sensing data and the local unmanned aerial vehicle data are integrated to serve as the basis for analyzing and predicting the salinization algorithm, and the matching degree of the cost and the precision is comprehensively considered by controlling the number and the layout of the unmanned aerial vehicles.
Specifically, the method further comprises the step 5:
grid tagging is carried out on the global, and h positive local output results are marked in grid tags to serve as positive local output nodes;
h r(hr = 1,2,., h) positive local output nodes are selected as starting points, a diffusion radius R is defined, a grid label node is selected as a diffusion node, and the algorithm model which is the same as the starting points is adopted for the definition of the diffusion node;
Traversing h positive local output nodes, defining diffusion nodes corresponding to the h positive local output nodes, and determining an algorithm model of each diffusion node;
On the basis of a global fitting algorithm basic model, replacing an algorithm model of a positive local output node and an algorithm model of a diffusion node, and optimizing to obtain a global fitting algorithm model combination which is used for analyzing and predicting water-salt dynamic parameters;
The probability that the node h r selects the node h s as the diffusion node to access is P k (r, s), and the diffusion node may be accessed by a plurality of positive local output nodes, and the positive local output node corresponding to the maximum probability is determined to be the right;
Wherein L k (r) represents the set of all the next nodes to be accessed of node h r where ant k is located, node h v∈Lk (r);
H k (r) represents a set of nodes that are not accessed by the node H r where ant k is located, node H u∈Zk (r);
Delta (r, s) is the degree of selectivity between node h r and node h s;
η (r, s) =1/d (r, s), d (r, s) is expressed as the distance between node h r and node h s;
Importance coefficients for a predefined degree of selectivity;
Beta is a relatively significant coefficient of predefined visibility;
e(s) represents the remaining energy to access the next neighbor node h s;
representing a sum of the remaining energy of the next neighbor node that is accessible;
r is the diffusion radius.
Specifically, the characteristic parameters of the salinized water salt dynamic parameters comprise vegetation parameters and salinity parameters.
Preferably, collecting sub-parameters of the characteristic parameters of all salinized water salt dynamic parameters, performing relevance evaluation sequencing on the sub-parameters, and fusing the sub-parameters into the characteristic parameters, wherein the method comprises the following steps:
Step A1, carrying out relevance evaluation sequencing on all the feature parameter fusion values by taking feature sub-parameters as units, wherein the step A1 comprises the following steps:
a1.1, constructing an ant colony classification model by training random samples, and defining deviation coefficients as follows:
wherein c x is the category of sub-parameter classification in characteristic parameters, tx is the algorithm step number of the ant colony separation model, p x is the relative probability of cx, c, cj are the pixel values of coordinates (x, y), (x+Deltax, y+Deltay) on the image gray characteristic matrix; V ci,cj is a matrix element, and N is the number of the matrix elements;
A1.2 for the sub-parameter X, calculating the deviation coefficient of the off-diameter data of each worker ant, and recording as
A1.3 for the sub-parameter X, adding the existing Gaussian noise interference to the extra-diameter data sample to reconstruct to obtain a new extra-diameter data sample, and estimating the deviation coefficient of the new extra-diameter data sample
A1.4 calculation of the correlation coefficient value of the sub-parameter XThe correlation evaluation is used for representing the subparameter X, and NS is the number of workers in the ant classification model;
A2, eliminating sub-parameters with the correlation larger than a predefined threshold according to the sorting result of the A1, and defining weight weighting fusion to calculate the characteristic parameters.
Further, step A2 includes:
a2.1, sorting NN subparameter features in the feature data subparameter set according to the magnitude of the correlation coefficient value NR, the result of the ordering is { z1, z2, zi., zNN };
A2.2 fusing j characteristic parameter subparameters with the correlation coefficient value NR smaller than a predefined threshold value into correlation characteristics;
wherein the features (b 1, b2,..bj) belong to { z1, z2,.. zi.., zNR, xfc (·) is the covariance function and fc (·) is the bias function;
a2.3, fusing j characteristic parameter subparameters with the correlation coefficient value NR larger than a predefined threshold value into correlation characteristics;
measurement variance for the ni-th characteristic parameter subparameter
Y ni＝H·xni+e;xni is the estimated value of the sub-parameter of the ni-th characteristic parameter;
H= [1,..1 ] T, H is an NN-j dimensional vector;
A2.4, defining the final fusion characteristics in the steps A2.2 and A2.3 as fusion characteristics A and fusion characteristics B respectively, calculating Euclidean distances from a certain point in the fusion characteristics A to all points in the fusion characteristics B, taking the minimum value, traversing all points in the fusion characteristics A, and taking the average value of the minimum value as min a1;
calculating Euclidean distances from a certain point in the fusion feature B to all points in the fusion feature A, taking a minimum value, and traversing all points in the fusion feature B to take an average value of the minimum value as min B1;
and A2.5, predefining fusion weight of the fusion feature A to alpha a, fusing fusion weight of the fusion feature B to beta b, calculating T AB = max (min a1, min B1) as a fusion feature threshold value, and calling a predefining feature fusion algorithm to finally finish fusion of the fusion feature A and the fusion feature B.
Preferably, the salinization control module performs the following steps:
step s1, visually presenting the result of the salinization analysis prediction module to a plurality of mass players, wherein the mass players generate player strategies in the virtual twin space for managing salinization risks in the virtual twin space;
step s2, virtually controlling a water control system model in a virtual twin space according to a player strategy, and generating virtual irrigation parameters;
step s3, grid tagging is carried out on the overall situation of the irrigation area, virtual irrigation parameters are divided into a plurality of local virtual irrigation parameters, historical data consistent with the local virtual irrigation data are searched in a historical database, if the historical data exist, the corresponding local virtual irrigation parameters are defined as positive local virtual irrigation parameters, otherwise, the positive local virtual irrigation parameters are defined as negative local virtual irrigation parameters, grids of the positive local virtual irrigation parameters can be combined to form the overall situation of the irrigation area, step s4 is executed, otherwise, a distributed expert manual evaluation module is called, a plurality of experts which are distributed are required to carry out manual evaluation, and step s5 is executed;
Step s4, calling a salinization analysis prediction module, and according to the corresponding salinization water salt dynamic parameter characteristic parameters in the global irrigation virtual twin data, calling historical data, analyzing the salinization condition;
And step s5, evaluating the treatment effect of the salinization risk condition, if the treatment effect reaches a predefined threshold value, defining the player strategy expert strategy, storing the player strategy expert strategy into an expert strategy library, controlling a irrigation area water control system in the physical world according to the expert strategy, and treating the salinization risk of the irrigation area.
The content not described in this embodiment may adopt a technology and a method in the prior art, and this embodiment is not described in detail.
While the foregoing describes the illustrative embodiments of the present invention so that those skilled in the art may understand the present invention, the present invention is not limited to the specific embodiments, and all inventive innovations utilizing the inventive concepts are herein within the scope of the present invention as defined and defined by the appended claims, as long as the various changes are within the spirit and scope of the present invention.
Claims (5)
1. A dynamic digital twin scenerising system for salinized water salt in a irrigated area is characterized by comprising the following components:
The twin data acquisition module is used for acquiring the engineering layout parameters of the irrigation areas, the salinized water salt dynamic parameters of the irrigation areas and the control parameters of the water system of the irrigation areas;
The twin space modeling module is used for mapping the collected physical parameters into twin entities in the virtual space and constructing a irrigated area model, and comprises an engineering layout model, a water-salt dynamic distribution model and a water control system model;
The salinization analysis prediction module is used for analyzing and predicting and analyzing the salinization condition of the irrigation area;
The salinization treatment module is used for generating a irrigation treatment strategy of an irrigation area in the virtual twin space, evaluating and screening the irrigation treatment strategy, and controlling an irrigation area water control system in the physical world according to the screened optimal irrigation treatment strategy;
the salinization treatment module comprises a player strategy supply module, a strategy evaluation module constructed according to expert knowledge and a historical database and a distributed expert manual evaluation module;
The player strategy supply module is used for crowd players to control various treatment strategies of the virtual space irrigation area water control system in the twin virtual space according to the salinized water salt dynamic parameters of a certain irrigation area;
The strategy evaluation module is used for calling a historical database to perform usability and feasibility evaluation analysis on various treatment strategies provided by the player strategy supply module by adopting an algorithm;
The distributed expert manual evaluation mode is that when the strategy evaluation module cannot effectively perform automatic evaluation, the distributed invitation expert performs manual supplementary evaluation on the treatment strategy;
the salinization analysis prediction module is internally provided with a water salt dynamic parameter analysis prediction algorithm, which comprises the following steps:
Step 1, collecting 1 satellite remote sensing image data as global data, collecting s m unmanned aerial vehicle remote sensing image data as local data in real time, correcting and correlating coordinates of s m unmanned aerial vehicle remote sensing image data with coordinates of the satellite remote sensing image data, and constructing a two-layer data image layer, wherein s m is a positive integer not less than 1;
Step 2, presetting a water salt dynamic analysis algorithm library, wherein s n algorithm models are built in the algorithm library, and inputting the global data in the step 1 into the h i algorithm models to obtain a global output result;
Step 3, inputting the s m local data into a i、ji+1,...,ji +m-1 algorithm model correspondingly to obtain s m local output results, wherein j i is a positive integer not less than 1;
step 4, respectively carrying out calculation error evaluation on the insides of the s m local output results, determining positive local output results with h being less than or equal to s m error values smaller than a predefined threshold value, carrying out matching settlement on the h positive local output results and the global output result, calculating the number of positive local output results with the error value smaller than the predefined threshold value, if the number is smaller than the predefined threshold value, defining an h i algorithm model as a global fit algorithm basic model for water salt dynamic parameter analysis and prediction, otherwise defining h i＝hi +1, and returning to the step 2;
Further comprising the step 5:
grid tagging is carried out on the global, and h positive local output results are marked in grid tags to serve as positive local output nodes;
h r(hr = 1,2,., h) positive local output nodes are selected as starting points, a diffusion radius R is defined, a grid label node is selected as a diffusion node, and the algorithm model which is the same as the starting points is adopted for the definition of the diffusion node;
Traversing h positive local output nodes, defining diffusion nodes corresponding to the h positive local output nodes, and determining an algorithm model of each diffusion node;
On the basis of a global fitting algorithm basic model, replacing an algorithm model of a positive local output node and an algorithm model of a diffusion node, and optimizing to obtain a global fitting algorithm model combination which is used for analyzing and predicting water-salt dynamic parameters;
The probability that the node h r selects the node h s as the diffusion node to access is P k (r, s), and the diffusion node may be accessed by a plurality of positive local output nodes, and the positive local output node corresponding to the maximum probability is determined to be the right;
Wherein L k (r) represents the set of all the next nodes to be accessed of node h r where ant k is located, node h v∈Lk (r);
H k (r) represents a set of nodes that are not accessed by the node H r where ant k is located, node H u∈Zk (r);
Delta (r, s) is the degree of selectivity between node h r and node h s;
η (r, s) =1/d (r, s), d (r, s) is expressed as the distance between node h r and node h s;
Importance coefficients for a predefined degree of selectivity;
Beta is a relatively significant coefficient of predefined visibility;
e(s) represents the remaining energy to access the next neighbor node h s;
representing a sum of the remaining energy of the next neighbor node that is accessible;
r is the diffusion radius.
2. The salinized water salt dynamic digital twin scenery system of a irrigated area of claim 1 wherein the characteristic parameters of the salinized water salt dynamic parameters comprise vegetation parameters and salinity parameters.
3. The system for dynamic digital twin scenery of salinized water and salt in irrigation areas according to claim 2, wherein the system for dynamic digital twin scenery of salinized water and salt in irrigation areas is characterized by collecting sub-parameters of the characteristic parameters of all the salinized water and salt dynamic parameters, performing relevance evaluation sequencing on the sub-parameters, and fusing the sub-parameters into the characteristic parameters, and comprises the following steps:
Step A1, carrying out relevance evaluation sequencing on all the feature parameter fusion values by taking feature sub-parameters as units, wherein the step A1 comprises the following steps:
a1.1, constructing an ant colony classification model by training random samples, and defining deviation coefficients as follows:
wherein c x is the category of sub-parameter classification in characteristic parameters, tx is the algorithm step number of the ant colony separation model, p x is the relative probability of cx, c, cj are the pixel values of coordinates (x, y), (x+Deltax, y+Deltay) on the image gray characteristic matrix; V ci,cj is a matrix element, and N is the number of the matrix elements;
A1.2 for the sub-parameter X, calculating the deviation coefficient of the off-diameter data of each worker ant, and recording as
A1.3 for the sub-parameter X, adding the existing Gaussian noise interference to the extra-diameter data sample to reconstruct to obtain a new extra-diameter data sample, and estimating the deviation coefficient of the new extra-diameter data sample
A1.4 calculation of the correlation coefficient value of the sub-parameter XThe correlation evaluation is used for representing the subparameter X, and NS is the number of workers in the ant classification model;
A2, eliminating sub-parameters with the correlation larger than a predefined threshold according to the sorting result of the A1, and defining weight weighting fusion to calculate the characteristic parameters.
4. The system for dynamic digital twinning of salinized water salts in irrigation areas according to claim 3, wherein the step A2 comprises:
a2.1, sorting NN subparameter features in the feature data subparameter set according to the magnitude of the correlation coefficient value NR, the result of the ordering is { z1, z2, zi., zNN };
A2.2 fusing j characteristic parameter subparameters with the correlation coefficient value NR smaller than a predefined threshold value into correlation characteristics;
wherein the features (b 1, b2,..bj) belong to { z1, z2,.. zi.., zNR, xfc (·) is the covariance function and fc (·) is the bias function;
a2.3, fusing j characteristic parameter subparameters with the correlation coefficient value NR larger than a predefined threshold value into correlation characteristics;
measurement variance for the ni-th characteristic parameter subparameter
Y ni＝H·xni+e;xni is the estimated value of the sub-parameter of the ni-th characteristic parameter;
H= [1,..1 ] T, H is an NN-j dimensional vector;
A2.4, defining the final fusion characteristics in the steps A2.2 and A2.3 as fusion characteristics A and fusion characteristics B respectively, calculating Euclidean distances from a certain point in the fusion characteristics A to all points in the fusion characteristics B, taking the minimum value, traversing all points in the fusion characteristics A, and taking the average value of the minimum value as min a1;
calculating Euclidean distances from a certain point in the fusion feature B to all points in the fusion feature A, taking a minimum value, and traversing all points in the fusion feature B to take an average value of the minimum value as min B1;
and A2.5, predefining fusion weight of the fusion feature A to alpha a, fusing fusion weight of the fusion feature B to beta b, calculating T AB = max (min a1, min B1) as a fusion feature threshold value, and calling a predefining feature fusion algorithm to finally finish fusion of the fusion feature A and the fusion feature B.
5. The salinized water salt dynamic digital twin scenery system of a irrigated area of claim 1 wherein said salinization control module performs the steps of:
step s1, visually presenting the result of the salinization analysis prediction module to a plurality of mass players, wherein the mass players generate player strategies in the virtual twin space for managing salinization risks in the virtual twin space;
step s2, virtually controlling a water control system model in a virtual twin space according to a player strategy, and generating virtual irrigation parameters;
step s3, grid tagging is carried out on the overall situation of the irrigation area, virtual irrigation parameters are divided into a plurality of local virtual irrigation parameters, historical data consistent with the local virtual irrigation data are searched in a historical database, if the historical data exist, the corresponding local virtual irrigation parameters are defined as positive local virtual irrigation parameters, otherwise, the positive local virtual irrigation parameters are defined as negative local virtual irrigation parameters, grids of the positive local virtual irrigation parameters can be combined to form the overall situation of the irrigation area, step s4 is executed, otherwise, a distributed expert manual evaluation module is called, a plurality of experts which are distributed are required to carry out manual evaluation, and step s5 is executed;
Step s4, calling a salinization analysis prediction module, and according to the corresponding salinization water salt dynamic parameter characteristic parameters in the global irrigation virtual twin data, calling historical data, analyzing the salinization condition;
And step s5, evaluating the treatment effect of the salinization risk condition, if the treatment effect reaches a predefined threshold value, defining the player strategy expert strategy, storing the player strategy expert strategy into an expert strategy library, controlling a irrigation area water control system in the physical world according to the expert strategy, and treating the salinization risk of the irrigation area.
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