Salt desert ecological restoration equipment and method based on saline-alkaline tolerant forage grass screening

Abstract

The invention discloses salt desert ecological restoration equipment based on saline-alkaline tolerant forage grass screening and a method thereof, and relates to the technical field of salt desert ecological restoration. Real-time and accurate acquisition of soil environment and forage grass growth data is realized through an intelligent sensor node of a data acquisition module and an LPWAN communication technology; the problems that traditional manual sampling data lags behind and is low in efficiency are solved, and a solid data support is provided for screening of saline-alkaline tolerant forage grass; through a mathematical model and an algorithm of an intelligent analysis module, a correlation coefficient, an adaptation adjustment value and a comprehensive adaptation index of a soil environment and pasture growth are calculated, scientific screening and dynamic adaptation of pasture varieties are realized, and the problems of poor suitability and poor remediation effect caused by empirical selection are avoided; the saline desert ecological restoration scheme is dynamically adjusted based on real-time data and historical data, the pertinence is high, the timeliness is high, the restoration strategy can be timely optimized according to the change of the saline desert environment, and the success rate and stability of saline desert ecological restoration are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of salt desert ecological restoration technology, specifically to salt desert ecological restoration equipment and methods based on the screening of salt-tolerant forage grasses. Background Technology

[0002] Salt desertification is one of the major ecological problems facing the world. In the process of salt desert ecological restoration, the scientific selection and appropriate application of salt-tolerant forage grasses are the core key to successful restoration. At present, salt desert ecological restoration relies heavily on manual field sampling and monitoring of soil environmental parameters, such as salinity, moisture, temperature, and pH value, and on experience to select salt-tolerant forage grass varieties. However, saline desert areas often have complex terrain and harsh environments, making manual sampling inefficient and data-lagging. This makes it impossible to capture real-time dynamic changes in the soil environment, resulting in a lack of accurate data support for forage selection, poor adaptability, and consequently affecting restoration effectiveness. Furthermore, in current restoration processes, soil environmental data and forage growth data are stored separately and lack a centralized analysis mechanism. Technicians must develop and adjust saline desert ecological restoration plans for each restoration area individually, wasting significant human and time resources. Meanwhile, traditional monitoring equipment often uses wired or short-range wireless communication technologies, which suffer from high power consumption, small coverage, and weak anti-interference capabilities, making it difficult to build a stable, comprehensive monitoring network. Data also needs to be downloaded and stored locally, easily causing equipment storage overload. In addition, the lack of intelligent decision-making models based on historical and real-time data prevents dynamic optimization of restoration strategies, resulting in insufficient targeting and timeliness in the restoration process. To address the above problems, this invention proposes a solution. Summary of the Invention

[0003] The purpose of this invention is to provide equipment and methods for ecological restoration of salt deserts based on the screening of salt-tolerant forage grasses, in order to solve the problems mentioned in the background art.

[0004] This invention provides a salt desert ecological restoration device based on the screening of salt-tolerant forage grasses, comprising: The data acquisition module is used to collect soil environmental data and pasture growth data in the salt desert restoration area in real time. The data acquisition module includes several intelligent sensor nodes, and one intelligent sensor node corresponds to one restoration sub-area. The soil environmental data includes soil salinity, soil moisture content, soil temperature, and soil pH; the forage growth data includes the plant height growth rate, vegetation coverage, biomass, and forage variety number of the candidate salt-tolerant forage grasses. The intelligent analysis module is used to perform in-depth analysis on the collected soil environment data and pasture growth data, and generate salt-tolerant pasture screening results and salt desert ecological restoration plans. The intelligent analysis module calculates the adaptability parameters between pasture and soil environment through historical data and real-time data, screens the optimal pasture varieties and formulates dynamic restoration strategies. The cloud service module is used for data transmission, storage, and template management. The cloud service module includes a data storage unit, a template management unit, and a data processing unit. The data storage unit is used to permanently store soil environmental data, pasture growth data, screening results, and remediation plans to avoid local device storage overload; the template management unit stores preset soil environmental information templates, pasture variety information templates, and remediation plan templates; the data processing unit receives the output data from the intelligent analysis module and generates a standardized salt-tolerant pasture screening recommendation table and a salt desert ecological restoration execution table based on the templates. The on-site execution module is used for data interaction and execution of repair solutions. The on-site execution module includes a display unit, an execution adjustment unit, and an anomaly feedback unit. The display unit shows the on-site staff a salt-tolerant forage grass screening recommendation table and a restoration execution table for their reference; the execution adjustment unit controls the operating parameters of irrigation, fertilization and other equipment based on the restoration execution table; the anomaly feedback unit transmits environmental data or growth data that exceeds the threshold to the cloud service module for manual intervention by technicians.

[0005] The method for ecological restoration of salt deserts based on the screening of salt-tolerant forage grasses includes the following steps: Step 1: Through the intelligent sensor nodes of the data acquisition module, real-time soil environmental data and pasture growth data of candidate salt-tolerant forage grasses in the salt desert restoration area are collected and transmitted to the cloud service module via LPWAN wireless communication technology. Step 2: Use the intelligent analysis module to process the collected data, and through correlation coefficient calculation, critical fit analysis, and fit index evaluation, select the salt-tolerant forage varieties with the best fit. Step 3: Based on the screening results and dynamic changes in the soil environment, a targeted ecological restoration plan is generated. The cloud service module's storage unit stores the plan and generates a standardized restoration execution table based on a preset template. Step 4: The on-site execution module's display unit shows the repair execution table. Staff members control the relevant equipment to perform repair operations based on the parameters in the table. If the parameters of the repair plan exceed the threshold, the anomaly feedback unit transmits the data to the cloud service module, where technicians optimize and reissue the execution.

[0006] Furthermore, the soil environmental data includes soil salinity, soil moisture content, soil temperature, and soil pH; the forage growth data includes the plant height growth rate, vegetation coverage, biomass, and forage variety number of the candidate salt-tolerant forage grasses.

[0007] Furthermore, in step two, the intelligent analysis module selects the salt-tolerant forage varieties with the best adaptability as follows: S11: Mark all the restoration sub-regions of the salt desert area as P1, P2, ..., Pn, n≥1, and determine the candidate salt-tolerant forage grass varieties as Q1, Q2, ..., Qm, m≥1; S12: Divide the monitoring cycle into three equal-length monitoring segments, corresponding to the seedling stage, growth stage, and stabilization stage of forage grass, respectively, and label them as T1, T2, and T3. The duration of the complete monitoring cycle is preset by the management personnel. In this application, the interval of the complete monitoring cycle is 180 days. Here, the seedling stage, growth stage, and stabilization stage of forage grass are all monitoring segments, and the interval between monitoring segments is 60 days. S13: Select the remediation sub-region P1 and the candidate salt-tolerant forage variety Q1, and obtain the soil environmental dimension X1 and forage growth dimension Y1 within a monitoring period T1: X1 = {x1, x2, x3, x4}, where x1 is soil salinity, x2 is soil moisture, x3 is soil temperature, and x4 is soil pH. Y1={y1,y2,y3}, where y1 is the plant height growth rate, y2 is the vegetation coverage rate, and y3 is the biomass; S14: Following the method in S13, obtain the soil environmental dimensions X1, X2, ..., Xt of the remediation sub-region P1 within the monitoring segment T1 of t monitoring cycles, and the growth dimensions Y1, Y2, ..., Yt of the candidate salt-tolerant forage grass variety Q1; S15: Calculate the correlation coefficients between the soil environmental dimension and the pasture growth dimension within monitoring segment T1 over t monitoring periods; S16: Calculate the forage adaptation adjustment value U1 based on the critical adaptation coefficient G1; S17: Following the steps in S16, calculate the adaptation adjustment values ​​U2, ..., Ug corresponding to the critical adaptation coefficients G2, ..., Gg; the intelligent analysis module generates the adaptation index Ik1 of the candidate salt-tolerant forage variety Q1 in monitoring segment T1 based on U1, U2, ..., Ug and the average forage growth under each critical coefficient. S18: Following the steps from S14 to S17, calculate the compatibility index Ik2 of the candidate salt-tolerant forage variety Q1 in monitoring segment T2 and the compatibility index Ik3 in monitoring segment T3, and finally generate the comprehensive compatibility index Ik=(Ik1+Ik2+Ik3) / 3 of the candidate salt-tolerant forage variety Q1. S19: Following the steps from S13 to S18, calculate the comprehensive adaptability indices Ik2, ..., Ikm of the candidate salt-tolerant forage varieties Q2, ..., Qm, select the top 3 forage varieties with the highest comprehensive adaptability indices as the optimal selection results, and generate a salt desert ecological restoration plan based on the adaptability adjustment values. S110: Repeat steps S13 to S19 to obtain the optimal forage selection results and corresponding salt desert ecological restoration schemes for all restoration sub-regions P1, P2, ..., Pn.

[0008] Furthermore, in S15, the correlation coefficient between the soil environmental dimension and the pasture growth dimension within monitoring period T1 over t monitoring cycles is calculated as follows: S151: Using the Pearson similarity algorithm, calculate the correlation coefficient Fi between the soil environmental dimensions Xi and Xi+1 in the i-th and (i+1)-th monitoring periods. The formula is: Where Xik is the comprehensive value of soil environment at the kth monitoring data point, Xik=0.3x1+0.25x2+0.2x3+0.25x4, with weights set based on the priority of salt desert remediation; Yik is the comprehensive value of pasture growth at the kth monitoring data point, Yik=0.4y1+0.3y2+0.3y3; S152: According to S151, calculate the correlation coefficients F1, F2, ..., Ft-1 between Xi and Xi+1 within monitoring segment T1 of t repair monitoring cycles, i=1,2,...,t-1; S153: Set a preset correlation coefficient threshold F0, and label all correlation coefficients less than F0 in F1, F2, ..., Ft-1 as critical fit coefficients, marked as G1, G2, ..., Gg, 1≤g≤t. This coefficient corresponds to the critical state of the compatibility between soil environment and pasture growth. The correlation coefficient threshold F0 is set by the management personnel according to the salt desert restoration compatibility standard.

[0009] Furthermore, in S16, the calculation of the forage adaptation adjustment value U1 based on the critical fit coefficient G1 is as follows: S161: Obtain the soil environment dimensions XG1 and XG1+1, and the pasture growth dimensions YG1 and YG1+1 for two consecutive monitoring periods corresponding to the critical fit coefficient G1. S162: Calculate the difference in soil environmental dimension variation ΔX=[x1(G1+1)-x1G1,x2(G1+1)-x2G1,x3(G1+1)-x3G1,x4(G1+1)-x4G1]; S163: Calculate the mean matrix R=[x1_avg,x2_avg,x3_avg,x4_avg] of each soil environmental parameter within monitoring segment T1 for t monitoring periods, where x1_avg is the mean soil salinity content, and the rest are calculated similarly. S164: Construct the critical fit adjustment matrix T=[k1,k2,k3,k4], where k1-k4 are the fit weights of each soil environmental parameter; S165: Utilization The formula calculates the adaptation adjustment value U1, where λ is the preset adjustment coefficient, and T T To adjust the transpose of the matrix.

[0010] Furthermore, in step three, the standardized repair execution table generated by the on-site execution module contains the following content; S21: Obtain the real-time soil environmental data Xcurrent=[x1c,x2c,x3c,x4c] of the current restoration sub-region, as well as the soil environmental data Xprev and the salt desert ecological restoration scheme parameter Sprev from the previous monitoring period; Xprev = [x1_prev, x2_prev, x3_prev, x4_prev], where x1_prev, x2_prev, x3_prev, and x4_prev are the average soil salinity, average soil moisture, average soil temperature, and average soil pH value of the previous monitoring period, respectively. Sprev=[s1_prev,s2_prev,s3_prev,...,sn_prev] s1_prev,s2_prev,s3_prev,...,sn_prev are the optimal forage seeding rate, irrigation frequency, soil conditioner dosage, ...,sn_prev are other parameters that can be expanded by the administrator as needed; S22: Based on the comprehensive adaptation index Ik and adaptation adjustment value U output by the intelligent analysis module, calculate the adjustment parameter Scurrent of the current salt desert ecological restoration plan, Scurrent=Sprev×(1+μ×Ik)+v×U×ΔXcurrent; where μ is the preset adaptation index weight, ν is the preset adjustment coefficient, and ΔXcurrent=Xcurrent-Xprev; S23: Calculate the comprehensive rationality threshold Sthreshold of the current salt desert ecological restoration plan using the formula Sthreshold=α×Ikmax, where α is the preset threshold coefficient and Ikmax is the highest fit index of all candidate forage grasses. S24: Compare Scurrent with Sthreshold: If Scurrent ≤ Sthreshold, then the current repair execution table is generated based on Scurrent and transmitted to the display unit for on-site execution; If Scurrent > Sthreshold, then the current salt desert ecological restoration plan is determined to require human intervention. The relevant data is then transmitted to the cloud service module, where technicians adjust the data and generate the final restoration execution table.

[0011] Compared with existing technologies, it has the following advantages: This invention constructs a wide-coverage, low-power, and highly interference-resistant full-domain monitoring network through the intelligent sensor nodes of the data acquisition module and LPWAN communication technology. It realizes real-time and accurate collection of soil environment and pasture growth data, solves the problems of data lag and low efficiency of traditional manual sampling, and provides solid data support for the screening of salt-tolerant pasture. This invention uses the mathematical model and algorithm of the intelligent analysis module to calculate the correlation coefficient, adaptation adjustment value and comprehensive adaptation index between soil environment and forage growth, thereby realizing the scientific screening and dynamic adaptation of forage varieties and avoiding the problems of poor adaptability and poor restoration effect caused by empirical selection. This invention centrally stores data and solution templates through a cloud service module, allowing users to view the repair execution table online without the need for local download and storage, thus avoiding device storage overload. At the same time, manual intervention is only required for abnormal solutions that exceed the threshold, significantly reducing the workload of technical personnel and saving human and time resources. The salt desert ecological restoration scheme in this invention is dynamically adjusted based on real-time and historical data, making it highly targeted and timely. It can optimize the restoration strategy in a timely manner according to changes in the salt desert environment, effectively improving the success rate and stability of salt desert ecological restoration. Attached Figure Description

[0012] Figure 1 This is a block diagram of the device of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figure 1 , Figure 2 This application provides a salt desert ecological restoration device and method based on the screening of salt-tolerant forage grasses, including a data acquisition module, an intelligent analysis module, a cloud service module and an on-site execution module; The data acquisition module is used to collect real-time soil environmental data and pasture growth data in the saline desert restoration area. The module includes several intelligent sensor nodes, each corresponding to a specific restoration sub-area. These intelligent sensor nodes are deployed on the saline desert surface and at different soil depths, transmitting data via a low-power wide-area network (LPWAN). They are characterized by high temperature resistance, wind and sand resistance, low power consumption, and long battery life. Specifically, the intelligent sensor nodes are deployed in the saline desert area to be restored at a 50m x 50m spacing, with sensor depths of 0-20cm, 20-50cm, and 50-80cm. The collected data is transmitted to a local gateway via LoRa communication technology, and then uploaded to the cloud service module. The data collection frequency is once every 2 hours, with soil salinity measurement accuracy ≤0.1‰, soil moisture measurement accuracy ≤1%, and pH value measurement range 4.0-10.0 with an accuracy ≤0.1. The soil environmental data includes soil salinity, soil moisture content, soil temperature, and soil pH value. In this application, the units for soil salinity are %, the units for soil moisture content are %, and the units for soil temperature are ℃. The forage growth data includes the plant height growth rate (unit: cm / day), vegetation coverage (unit: %), biomass (unit: kg / m²), and forage variety number of the candidate salt-tolerant forage. In this application, the unit for plant height growth rate is cm / day, the unit for vegetation coverage is %, and the unit for biomass is kg / m². Forage variety number is a code used to uniquely identify forage varieties. It is usually issued by the provincial or national forage variety approval committee. The format varies from region to region, but generally includes elements such as the approval year, regional abbreviation, and serial number. The intelligent analysis module is used to perform in-depth analysis of the collected soil environmental data and pasture growth data to generate salt-tolerant pasture screening results and salt desert ecological restoration plans. The intelligent analysis module uses a preset algorithm model, combined with historical and real-time data, to calculate the compatibility parameters between forage grass and soil environment, screen the optimal forage grass varieties, and formulate dynamic restoration strategies. The analysis steps of the intelligent analysis module are as follows: S11: Mark all the restoration sub-regions of the salt desert area as P1, P2, ..., Pn, n≥1, and determine the candidate salt-tolerant forage grass varieties as Q1, Q2, ..., Qm, m≥1; S12: Divide the monitoring cycle into three equal-length monitoring segments, corresponding to the seedling stage, growth stage, and stabilization stage of forage grass, respectively, and label them as T1, T2, and T3. The duration of the complete monitoring cycle is preset by the management personnel. In this application, the interval of the complete monitoring cycle is 180 days. Here, the seedling stage, growth stage, and stabilization stage of forage grass are all monitoring segments, and the interval between monitoring segments is 60 days. S13: Select the remediation sub-region P1 and the candidate salt-tolerant forage variety Q1, and obtain the soil environmental dimension X1 and forage growth dimension Y1 within a monitoring period T1: X1 = {x1, x2, x3, x4}, where x1 is soil salinity, x2 is soil moisture, x3 is soil temperature, and x4 is soil pH. Y1={y1,y2,y3}, where y1 is the plant height growth rate, y2 is the vegetation coverage rate, and y3 is the biomass; S14: Following the method in S13, obtain the soil environmental dimensions X1, X2, ..., Xt of the remediation sub-region P1 within the monitoring segment T1 of t monitoring cycles, and the growth dimensions Y1, Y2, ..., Yt of the candidate salt-tolerant forage grass variety Q1; S15: Calculate the correlation coefficient between the soil environmental dimension and the pasture growth dimension within monitoring period T1 over t monitoring cycles. The specific steps are as follows: S151: Using the Pearson similarity algorithm, calculate the correlation coefficient Fi between the soil environmental dimensions Xi and Xi+1 in the i-th and (i+1)-th monitoring periods. The formula is: Where Xik is the comprehensive value of soil environment at the kth monitoring data point, Xik=0.3x1+0.25x2+0.2x3+0.25x4, with weights set based on the priority of salt desert remediation; Yik is the comprehensive value of pasture growth at the kth monitoring data point, Yik=0.4y1+0.3y2+0.3y3; S152: According to S151, calculate the correlation coefficients F1, F2, ..., Ft-1 between Xi and Xi+1 within monitoring segment T1 of t repair monitoring cycles, i=1,2,...,t-1; S153: Set a preset correlation coefficient threshold F0, and label all correlation coefficients less than F0 in F1, F2, ..., Ft-1 as critical fit coefficients, marked as G1, G2, ..., Gg, 1≤g≤t. This coefficient corresponds to the critical state of the compatibility between soil environment and pasture growth. The correlation coefficient threshold F0 is set by the management personnel according to the salt desert restoration compatibility standard. Preferably, F0=0.65. S16: Calculate the forage fitness adjustment value U1 based on the critical fitness coefficient G1. The specific steps are as follows: S161: Obtain the soil environment dimensions XG1 and XG1+1, and the pasture growth dimensions YG1 and YG1+1 for two consecutive monitoring periods corresponding to the critical fit coefficient G1. S162: Calculate the difference in soil environmental dimension variation ΔX=[x1(G1+1)-x1G1,x2(G1+1)-x2G1,x3(G1+1)-x3G1,x4(G1+1)-x4G1]; S163: Calculate the mean matrix R=[x1_avg,x2_avg,x3_avg,x4_avg] of each soil environmental parameter within monitoring segment T1 for t monitoring periods, where x1_avg is the mean soil salinity content, and the rest are calculated similarly. S164: Construct the critical adaptation adjustment matrix T=[k1,k2,k3,k4], where k1-k4 are the adaptation weights of each soil environmental parameter, which are set based on the growth requirements of salt-tolerant forage grass. Preferably, k1=0.4, k2=0.3, k3=0.15, k4=0.15. S165: Utilization The formula calculates the adaptation adjustment value U1, where λ is a preset adjustment coefficient, ranging from 0.8 to 1.2, and T... T To adjust the transpose of the matrix; S17: Following the steps in S16, calculate the adaptation adjustment values ​​U2, ..., Ug corresponding to the critical adaptation coefficients G2, ..., Gg; the intelligent analysis module generates the adaptation index Ik1 of the candidate salt-tolerant forage variety Q1 in monitoring segment T1 based on U1, U2, ..., Ug and the average forage growth under each critical coefficient. S18: Following the steps from S14 to S17, calculate the compatibility index Ik2 of the candidate salt-tolerant forage variety Q1 in monitoring segment T2 and the compatibility index Ik3 in monitoring segment T3, and finally generate the comprehensive compatibility index Ik=(Ik1+Ik2+Ik3) / 3 of the candidate salt-tolerant forage variety Q1. S19: Following the steps from S13 to S18, calculate the comprehensive adaptability indices Ik2, ..., Ikm of the candidate salt-tolerant forage varieties Q2, ..., Qm, select the top 3 forage varieties with the highest comprehensive adaptability indices as the optimal selection results, and generate a salt desert ecological restoration plan based on the adaptability adjustment values, including parameters such as seeding rate, irrigation frequency, and soil conditioner dosage. S110: Repeat steps S13 to S19 to obtain the optimal forage selection results and corresponding salt desert ecological restoration schemes for all restoration sub-regions P1, P2, ..., Pn; The cloud service module is used for data transmission, storage, and template management. The cloud service module includes a data storage unit, a template management unit, and a data processing unit. The data storage unit is used to permanently store soil environmental data, pasture growth data, screening results, and salt desert ecological restoration plans, avoiding local device storage overload. The template management unit stores preset templates for soil environmental information, forage variety information, and salt desert ecological restoration plans. The data processing unit receives the output data from the intelligent analysis module and generates a standardized salt-tolerant forage grass screening recommendation table and a salt desert ecological restoration implementation table based on the template.

[0015] The on-site execution module is used for data interaction and execution of salt desert ecological restoration plans. The on-site execution module includes a display unit, an execution adjustment unit, and an anomaly feedback unit. S21: Obtain the real-time soil environmental data Xcurrent=[x1c,x2c,x3c,x4c] of the current restoration sub-region, as well as the soil environmental data Xprev and the salt desert ecological restoration scheme parameter Sprev from the previous monitoring period; Xprev = [x1_prev, x2_prev, x3_prev, x4_prev], where x1_prev is the average soil salinity of the previous monitoring period (in %), x2_prev is the average soil moisture of the previous monitoring period (in %), x3_prev is the average soil temperature of the previous monitoring period (in °C), and x4_prev is the average soil pH of the previous monitoring period. Sprev=[s1_prev,s2_prev,s3_prev,...,sn_prev], where s1_prev is the optimal forage seeding rate for the previous monitoring period, in kg / mu; s2_prev is the irrigation frequency for the previous monitoring period, in times / week and the irrigation amount per irrigation, in m³ / mu; s3_prev is the soil conditioner dosage for the previous monitoring period, in kg / mu; ..., sn_prev are other parameters, such as fertilizer application rate, replanting density, etc., which can be expanded by managers as needed; S22: Based on the comprehensive adaptation index Ik and adaptation adjustment value U output by the intelligent analysis module, calculate the adjustment parameter Scurrent of the current salt desert ecological restoration scheme, Scurrent=Sprev×(1+μ×Ik)+v×U×ΔXcurrent; Where μ is the preset adaptation index weight, with a preferred value of 0.3, ν is the preset adjustment coefficient, with a preferred value of 0.2, and ΔXcurrent = Xcurrent - Xprev; S23: Calculate the comprehensive rationality threshold Sthreshold of the current salt desert ecological restoration plan using the formula Sthreshold=α×Ikmax, where α is the preset threshold coefficient, the preferred value is 1.1, and Ikmax is the highest fit index of all candidate forage grasses. S24: Compare Scurrent with Sthreshold: If Scurrent ≤ Sthreshold, then the current repair execution table is generated based on Scurrent and transmitted to the display unit for on-site execution; If Scurrent > Sthreshold, it is determined that the current salt desert ecological restoration plan requires human intervention. The relevant data is then transmitted to the cloud service module, where technicians adjust it and generate the final restoration execution table. The display unit shows the on-site staff a salt-tolerant forage grass screening recommendation table and a remediation implementation table for their reference. The execution adjustment unit controls the operating parameters of irrigation, fertilization, and other equipment based on the repair execution table; The anomaly feedback unit transmits environmental or growth data that exceeds the threshold to the cloud service module for manual intervention by technical personnel. Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0016] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A salt desert ecological restoration device based on the screening of salt-tolerant forage grasses, characterized in that, include: The data acquisition module is used to collect soil environmental data and pasture growth data in the salt desert restoration area in real time. The data acquisition module includes several intelligent sensor nodes, and one intelligent sensor node corresponds to one restoration sub-area. The soil environmental data includes soil salinity, soil moisture content, soil temperature, and soil pH; the forage growth data includes the plant height growth rate, vegetation coverage, biomass, and forage variety number of the candidate salt-tolerant forage grasses. The intelligent analysis module is used to perform in-depth analysis on the collected soil environment data and pasture growth data, and generate salt-tolerant pasture screening results and salt desert ecological restoration plans. The intelligent analysis module calculates the adaptability parameters between pasture and soil environment through historical data and real-time data, screens the optimal pasture varieties and formulates dynamic restoration strategies. The cloud service module is used for data transmission, storage, and template management. The cloud service module includes a data storage unit, a template management unit, and a data processing unit. The data storage unit is used to permanently store soil environmental data, pasture growth data, screening results, and remediation plans to avoid local device storage overload; the template management unit stores preset soil environmental information templates, pasture variety information templates, and remediation plan templates; the data processing unit receives the output data from the intelligent analysis module and generates a standardized salt-tolerant pasture screening recommendation table and a salt desert ecological restoration execution table based on the templates. The on-site execution module is used for data interaction and execution of repair solutions. The on-site execution module includes a display unit, an execution adjustment unit, and an anomaly feedback unit. The display unit shows the on-site staff a salt-tolerant forage grass screening recommendation table and a restoration execution table for their reference; the execution adjustment unit controls the operating parameters of irrigation, fertilization and other equipment based on the restoration execution table; the anomaly feedback unit transmits environmental data or growth data that exceeds the threshold to the cloud service module for manual intervention by technicians.

2. A method for ecological restoration of salt deserts based on the screening of salt-tolerant forage grasses, characterized in that, Includes the following steps: Step 1: Through the intelligent sensor nodes of the data acquisition module, real-time soil environmental data and pasture growth data of candidate salt-tolerant forage grasses in the salt desert restoration area are collected and transmitted to the cloud service module via LPWAN wireless communication technology. Step 2: Use the intelligent analysis module to process the collected data, and through correlation coefficient calculation, critical fit analysis, and fit index evaluation, select the salt-tolerant forage varieties with the best fit. Step 3: Based on the screening results and dynamic changes in the soil environment, a targeted ecological restoration plan is generated. The cloud service module's storage unit stores the plan and generates a standardized restoration execution table based on a preset template. Step 4: The on-site execution module's display unit shows the repair execution table. Staff members control the relevant equipment to perform repair operations based on the parameters in the table. If the parameters of the repair plan exceed the threshold, the anomaly feedback unit transmits the data to the cloud service module, where technicians optimize and reissue the execution.

3. The method for salt desert ecological restoration based on the screening of salt-tolerant forage grasses according to claim 2, characterized in that, The soil environmental data includes soil salinity, soil moisture content, soil temperature, and soil pH; the forage growth data includes the plant height growth rate, vegetation coverage, biomass, and forage variety number of the candidate salt-tolerant forage grasses.

4. The method for salt desert ecological restoration based on the screening of salt-tolerant forage grasses according to claim 2, characterized in that, In step two, the intelligent analysis module selects the salt-tolerant forage varieties with the best adaptability as follows: S11: Mark all the restoration sub-regions of the salt desert area as P1, P2, ..., Pn, n≥1, and determine the candidate salt-tolerant forage grass varieties as Q1, Q2, ..., Qm, m≥1; S12: Divide the monitoring cycle into three equal-length monitoring segments, corresponding to the seedling stage, growth stage, and stabilization stage of forage grass, respectively, and label them as T1, T2, and T3. The duration of the complete monitoring cycle is preset by the management personnel. In this application, the interval of the complete monitoring cycle is 180 days. Here, the seedling stage, growth stage, and stabilization stage of forage grass are all monitoring segments, and the interval between monitoring segments is 60 days. S13: Select the remediation sub-region P1 and the candidate salt-tolerant forage variety Q1, and obtain the soil environmental dimension X1 and forage growth dimension Y1 within a monitoring period T1: X1 = {x1, x2, x3, x4}, where x1 is soil salinity, x2 is soil moisture, x3 is soil temperature, and x4 is soil pH. Y1={y1,y2,y3}, where y1 is the plant height growth rate, y2 is the vegetation coverage rate, and y3 is the biomass; S14: Following the method in S13, obtain the soil environmental dimensions X1, X2, ..., Xt of the remediation sub-region P1 within the monitoring segment T1 of t monitoring cycles, and the growth dimensions Y1, Y2, ..., Yt of the candidate salt-tolerant forage grass variety Q1; S15: Calculate the correlation coefficients between the soil environmental dimension and the pasture growth dimension within monitoring segment T1 over t monitoring periods; S16: Calculate the forage adaptation adjustment value U1 based on the critical adaptation coefficient G1; S17: Following the steps in S16, calculate the adaptation adjustment values ​​U2, ..., Ug corresponding to the critical adaptation coefficients G2, ..., Gg; the intelligent analysis module generates the adaptation index Ik1 of the candidate salt-tolerant forage variety Q1 in monitoring segment T1 based on U1, U2, ..., Ug and the average forage growth under each critical coefficient. S18: Following the steps from S14 to S17, calculate the compatibility index Ik2 of the candidate salt-tolerant forage variety Q1 in monitoring segment T2 and the compatibility index Ik3 in monitoring segment T3, and finally generate the comprehensive compatibility index Ik=(Ik1+Ik2+Ik3) / 3 of the candidate salt-tolerant forage variety Q1. S19: Following the steps from S13 to S18, calculate the comprehensive adaptability indices Ik2, ..., Ikm of the candidate salt-tolerant forage varieties Q2, ..., Qm, select the top 3 forage varieties with the highest comprehensive adaptability indices as the optimal selection results, and generate a salt desert ecological restoration plan based on the adaptability adjustment values. S110: Repeat steps S13 to S19 to obtain the optimal forage selection results and corresponding salt desert ecological restoration schemes for all restoration sub-regions P1, P2, ..., Pn.

5. The method for salt desert ecological restoration based on the screening of salt-tolerant forage grasses according to claim 4, characterized in that, S15, the correlation coefficient between the soil environmental dimension and the pasture growth dimension within monitoring period T1 over t monitoring cycles is as follows: S151: Using the Pearson similarity algorithm, calculate the correlation coefficient Fi between the soil environmental dimensions Xi and Xi+1 in the i-th and (i+1)-th monitoring periods. The formula is: ， Wherein, Xik is the comprehensive value of soil environment at the kth monitoring data point, Xik=0.3x1+0.25x2+0.2x3+0.25x4, with weights set based on the priority of salt desert remediation; Yik is the comprehensive value of pasture growth at the kth monitoring data point, Yik=0.4y1+0.3y2+0.3y3; S152: According to S151, calculate the correlation coefficients F1, F2, ..., Ft-1 between Xi and Xi+1 within monitoring segment T1 of t repair monitoring cycles, i=1,2,...,t-1; S153: Set a preset correlation coefficient threshold F0, and label all correlation coefficients less than F0 in F1, F2, ..., Ft-1 as critical fit coefficients, marked as G1, G2, ..., Gg, 1≤g≤t. This coefficient corresponds to the critical state of the compatibility between soil environment and pasture growth. The correlation coefficient threshold F0 is set by the management personnel according to the salt desert restoration compatibility standard.

6. The method for salt desert ecological restoration based on the screening of salt-tolerant forage grasses according to claim 4, characterized in that, S16, the calculation of the forage adaptation adjustment value U1 based on the critical fit coefficient G1 is as follows: S161: Obtain the soil environment dimensions XG1 and XG1+1, and the pasture growth dimensions YG1 and YG1+1 for two consecutive monitoring periods corresponding to the critical fit coefficient G1. S162: Calculate the difference in soil environmental dimension variation ΔX=[x1(G1+1)-x1G1,x2(G1+1)-x2G1,x3(G1+1)-x3G1,x4(G1+1)-x4G1]; S163: Calculate the mean matrix R=[x1_avg,x2_avg,x3_avg,x4_avg] of each soil environmental parameter within monitoring segment T1 for t monitoring periods, where x1_avg is the mean soil salinity content, and the rest are calculated similarly. S164: Construct the critical fit adjustment matrix T=[k1,k2,k3,k4], where k1-k4 are the fit weights of each soil environmental parameter; S165: Utilization The formula calculates the adaptation adjustment value U1, where λ is the preset adjustment coefficient, and T T To adjust the transpose of the matrix.

7. The method for salt desert ecological restoration based on the screening of salt-tolerant forage grasses according to claim 4, characterized in that, In step three, the standardized repair execution table generated by the on-site execution module contains the following content; S21: Obtain the real-time soil environmental data Xcurrent=[x1c,x2c,x3c,x4c] of the current restoration sub-region, as well as the soil environmental data Xprev and the salt desert ecological restoration scheme parameter Sprev from the previous monitoring period; Xprev = [x1_prev, x2_prev, x3_prev, x4_prev], where x1_prev, x2_prev, x3_prev, and x4_prev are the average soil salinity, average soil moisture, average soil temperature, and average soil pH value of the previous monitoring period, respectively. Sprev=[s1_prev,s2_prev,s3_prev,...,sn_prev] s1_prev,s2_prev,s3_prev,...,sn_prev are the optimal forage seeding rate, irrigation frequency, soil conditioner dosage, ...,sn_prev are other parameters that can be expanded by the administrator as needed; S22: Based on the comprehensive adaptation index Ik and adaptation adjustment value U output by the intelligent analysis module, calculate the adjustment parameter Scurrent of the current salt desert ecological restoration plan, Scurrent=Sprev×(1+μ×Ik)+v×U×ΔXcurrent; where μ is the preset adaptation index weight, ν is the preset adjustment coefficient, and ΔXcurrent=Xcurrent-Xprev; S23: Calculate the comprehensive rationality threshold Sthreshold of the current salt desert ecological restoration plan using the formula Sthreshold=α×Ikmax, where α is the preset threshold coefficient and Ikmax is the highest fit index of all candidate forage grasses. S24: Compare Scurrent with Sthreshold: If Scurrent ≤ Sthreshold, then the current repair execution table is generated based on Scurrent and transmitted to the display unit for on-site execution; If Scurrent > Sthreshold, then the current salt desert ecological restoration plan is determined to require human intervention. The relevant data is then transmitted to the cloud service module, where technicians adjust the data and generate the final restoration execution table.

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