Agricultural ecology industrialization evaluation method based on GIS and digital twinborn
By combining GIS and digital twin technologies, a nutrient gradient sequence for planting subdomains is constructed and nutrient competition between adjacent planting subdomains is assessed. This solves the problem of inaccurate soil nutrient content analysis in traditional assessment methods and achieves accurate and timely assessment of agricultural ecological industrialization.
Patent Information
- Application Number
- CN202511386306.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-26
- Publication Date
- 2025-12-23
AI Technical Summary
Traditional assessment methods lack systematic analysis of soil nutrient content across multiple historical periods, fail to identify soil nutrient competition between adjacent planting subdomains, and are unable to reflect micro-level differences using GIS technology, resulting in inaccurate assessments of agricultural ecological industrialization.
Using GIS technology, soil nutrient content in planting subdomains was analyzed, a nutrient gradient sequence for planting subdomains was constructed, and digital twin technology was combined to compare and analyze adjacent planting subdomains with nutrient fluctuations, assess the nutrient content competition relationship, and quantify the digital twin assessment frequency.
It improves the accuracy and timeliness of agricultural ecological industrialization assessment, can identify soil nutrient competition relationships between adjacent planting subdomains, and provides important information for agricultural management and model correction.
Smart Images
Figure CN121189641A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of agricultural ecological industry assessment technology, specifically a method for assessing agricultural ecological industry based on GIS and digital twins. Background Technology
[0002] With the continuous growth of the global population and the increasing demand for agricultural products, the development of agricultural ecological industrialization has become an important way to ensure food security and promote sustainable agricultural development. The development of agricultural ecological industrialization emphasizes the organic integration of agricultural production and ecological environmental protection, and achieves the synergistic improvement of agricultural economic, ecological and social benefits by optimizing resource allocation and improving resource utilization efficiency.
[0003] In existing technologies, traditional assessment methods typically focus only on soil nutrient status at a specific point in time, lacking a systematic analysis and comparison of soil nutrient content across multiple historical periods, thus failing to accurately determine soil nutrient stability. In actual agricultural production, soil nutrient instability increases the uncertainty of the crop growth environment, affecting the stability of crop yield and quality. Furthermore, within agricultural planting areas, different planting sub-domains may compete for soil nutrients. When changes in soil nutrient content in adjacent planting sub-domains are correlated, they may mutually influence the absorption and utilization of nutrients by crops in the other sub-domains. Traditional assessment methods, lacking comprehensive consideration of the spatial relationships between planting sub-domains and effective analytical tools, struggle to identify such potential nutrient competition.
[0004] Furthermore, since GIS technology can only reflect the overall soil nutrient content uniformity of adjacent nutrient fluctuation planting subdomains in the macroscopic spatial dimension, it is easy to smooth out the microscopic differences between adjacent nutrient fluctuation planting subdomains, thus masking the dynamic interaction of nutrient content between adjacent nutrient fluctuation planting subdomains. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides an agricultural ecological industrialization assessment method based on GIS and digital twins, in order to solve the aforementioned technical problems in existing technologies.
[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: A method for assessing the industrialization of agricultural ecology based on GIS and digital twins, comprising: During the historical ecological assessment period, GIS technology was used to analyze the soil nutrient content of the sub-domains within the agricultural planting area to obtain the nutrient gradient sequence of the sub-domains. Nutrient content analysis was performed on the nutrient gradient sequences of planting subdomains corresponding to multiple historical ecological assessment cycles, and the stability was assessed. If unstable, they were marked as nutrient-fluctuating planting subdomains and nutrient-stable planting subdomains. The spatial location of each nutrient fluctuation planting subdomain and nutrient stable planting subdomain within the agricultural planting area is obtained, adjacent nutrient fluctuation planting subdomains are screened, and digital twin technology is used to compare and analyze the nutrient content changes of adjacent nutrient fluctuation planting subdomains to assess whether there is a nutrient content competition relationship between adjacent nutrient fluctuation planting subdomains. If there is nutrient content competition between adjacent nutrient fluctuation planting subdomains, the nutrient content changes of adjacent nutrient fluctuation planting subdomains within each group of adjacent nutrient fluctuations are obtained and quantified to obtain the digital twin evaluation frequency.
[0007] As a further aspect of the present invention, the process of constructing the seed nutrient gradient sequence is as follows: Each planting subdomain is divided into three spatial vertical dimensions to obtain three spatial analysis layers: topsoil analysis layer, subsoil analysis layer, and subsoil analysis layer. The soil nutrient content in the topsoil analysis layer, subsoil analysis layer, and subsoil analysis layer is obtained respectively, and the average value is calculated to output the soil nutrient content. The soil nutrient content of each planting sub-domain within the agricultural planting area is compared and sorted in descending order to construct a nutrient content gradient sequence for each planting sub-domain.
[0008] As a further aspect of this invention, nutrient content analysis of the same seed domain is performed on the nutrient gradient sequences of seed crops corresponding to multiple historical ecological assessment cycles, as follows: Within multiple historical ecological assessment periods, following the rules of time series, the planting nutrient gradient sequences corresponding to each historical ecological assessment period are matrix-combined to obtain a sequence alignment matrix. Using the same planting subdomain as the spatial selection benchmark, the first-ranked planting subdomain within the planting nutrient gradient sequence corresponding to the first-ranked historical ecological assessment period in the time series is extracted as the target content analysis subdomain. Soil nutrient content in the target content analysis subdomain was obtained for each historical ecological assessment period, and the standard deviation was calculated to output the standard deviation of the target nutrient content. The target content analysis subdomain is ranked in each seed nutrient gradient sequence, and the standard deviation is calculated to output the target ranking standard deviation. The target nutrient standard deviation and the target ordination standard deviation are summed to output the single-domain nutrient analysis value.
[0009] As a further aspect of the present invention, the labeling process for the nutrient fluctuation planting subdomain and the nutrient stable planting subdomain is as follows: Based on the sorting method within the nutrient gradient sequence corresponding to the first historical ecological assessment cycle, the planting subdomains are selected as target content analysis subdomains in turn, and the average value of the single domain nutrient analysis value corresponding to each target content analysis subdomain is calculated to output the nutrient stability analysis value. If the nutrient stability analysis value is less than or equal to the nutrient stability analysis threshold, it is displayed as a nutrient stability signal in the same domain and marked as a nutrient stable planting subdomain. If the nutrient stability analysis value is greater than the nutrient stability analysis threshold, it is displayed as a nutrient fluctuation signal in the same domain and marked as a nutrient fluctuation planting subdomain.
[0010] As a further aspect of the present invention, the screening process for adjacent nutrient fluctuation planting subdomains is as follows: Digital twin technology is used to transform agricultural planting areas into two-dimensional space to obtain a two-dimensional grid space model. Within the two-dimensional grid space model, the position of each nutrient fluctuation planting sub-domain is extracted. The center of each nutrient fluctuation planting sub-domain is taken as the nutrient fluctuation coordinate point, and the nutrient fluctuation planting sub-domains corresponding to adjacent nutrient fluctuation coordinate points are taken as adjacent nutrient fluctuation planting sub-domains.
[0011] As a further aspect of this invention, digital twin technology is used to compare and analyze the changes in nutrient content in adjacent nutrient fluctuation planting subdomains. The process is as follows: Adjacent nutrient fluctuation planting subdomains are combined to obtain adjacent nutrient fluctuation stability groups. Soil nutrient content of each nutrient fluctuation coordinate point in each adjacent nutrient fluctuation stability group corresponding to the nutrient fluctuation planting subdomain in each historical ecological assessment period is extracted. All are sorted according to the actual sequence to obtain the previous nutrient fluctuation content sequence and the subsequent nutrient fluctuation content sequence. Soil nutrient content of each nutrient fluctuation coordinate point in the adjacent nutrient fluctuation stability group in the same historical ecological assessment period is obtained and marked as the previous and subsequent nutrient content, respectively. Within the first historical ecological assessment period of the time series ranking, the nutrient amounts corresponding to the previous and subsequent nutrient fluctuation coordinate points in adjacent nutrient fluctuation groups are summed to obtain the baseline nutrient amount. Based on the method of obtaining the baseline nutrient levels, multiple historical ecological assessment periods were selected, excluding the first historical ecological assessment period in the time series ranking. Within each historical ecological assessment period, the nutrient levels corresponding to the previous and subsequent nutrient fluctuation coordinate points in adjacent nutrient fluctuation groups were summed to obtain the comparative nutrient levels.
[0012] As a further aspect of the present invention, the process of evaluating whether there is a nutrient content competition relationship between adjacent nutrient fluctuation planting subdomains is as follows: The comparison of the same-period nutrient quantity is sequentially input into the Euclidean distance formula according to the time series of the corresponding historical ecological assessment cycle, and the adjacent nutrient wave competition value is output. If the adjacent nutrient wave competition value is greater than the adjacent nutrient wave competition threshold, it will be displayed as a strong competition signal between adjacent nutrients. If the adjacent nutrient wave competition value is less than or equal to the adjacent nutrient wave competition threshold, it is displayed as a weak competition signal between adjacent nutrients.
[0013] As a further aspect of the present invention, the changes in nutrient content of adjacent nutrient fluctuation planting subdomains within each group of adjacent nutrient fluctuation groups are analyzed, as follows: If the signal shows strong competition for nutrients between adjacent cycles, obtain the interval length corresponding to each adjacent cycle interval and the proportion of the historical ecological assessment cycle length to obtain the interval ratio of adjacent cycle units. The standard deviation of the time interval ratio between adjacent periodic cells is calculated, and the standard deviation of the cell time interval is output.
[0014] As a further aspect of the present invention, the quantization process is as follows: The standard deviation of the time interval ratio between adjacent periodic cells is calculated, and the standard deviation of the cell time interval is output.
[0015] As a further aspect of the present invention, the process for obtaining the digital twin evaluation frequency is as follows: If the standard deviation of the unit interval is greater than the standard deviation threshold of the unit interval, then the unit interval ratios of all adjacent periods are compared, the smallest unit interval ratio of adjacent periods is selected and processed by reciprocal, and the digital twin evaluation frequency is output. If the standard deviation of the unit interval is less than or equal to the threshold of the standard deviation of the unit interval, then the average value of the unit interval ratio of all adjacent periods is calculated, and the digital twin evaluation frequency is output.
[0016] The beneficial effects of this invention are as follows: 1. This invention utilizes GIS technology to analyze soil nutrient content in sub-regions of agricultural planting areas within a historical ecological assessment period. Based on the soil nutrient content of each sub-region, a nutrient gradient sequence is obtained, which helps to reflect the relative levels of soil nutrients in different sub-regions. This provides an objective basis for assessing the soil health, productivity, and ecological sustainability of agricultural planting areas. Furthermore, it analyzes whether the nutrient content of the same sub-region is stable within different historical ecological assessment periods. If it is unstable, it is marked as a nutrient-fluctuating sub-region. This not only allows us to understand the spatial changes in soil nutrients within agricultural planting areas and promptly investigate and resolve the causes of nutrient content fluctuations in fluctuating sub-regions, but also provides important information for subsequent analysis of soil nutrient content changes within agricultural planting areas using digital twin technology. 2. This invention obtains the spatial location of each nutrient-fluctuating and nutrient-stable planting subdomain within the agricultural planting area, filters out adjacent nutrient-fluctuating planting subdomains, and uses digital twin technology to compare and analyze the nutrient content changes of adjacent nutrient-fluctuating planting subdomains. If there is nutrient content competition between adjacent nutrient-fluctuating planting subdomains, the frequency of nutrient content changes of adjacent nutrient-fluctuating planting subdomains within each group of adjacent nutrient fluctuations is obtained and quantified to obtain the digital twin assessment frequency. This solves the problem that GIS technology can only reflect the overall soil nutrient content uniformity of adjacent nutrient-fluctuating planting subdomains in the macroscopic spatial dimension, which can easily smooth out microscopic differences between adjacent nutrient-fluctuating planting subdomains and mask the dynamic interaction of nutrient content between adjacent nutrient-fluctuating planting subdomains. It can assist GIS technology in analyzing the dynamic interaction of nutrient content between adjacent nutrient-fluctuating planting subdomains, improve the accuracy and timeliness of agricultural ecological industrialization assessment. Attached Figure Description
[0017] Figure 1 This is a flowchart of the steps of the agricultural ecological industrialization assessment method based on GIS and digital twins of the present invention; Figure 2 This is a flowchart of the judgment process in the agricultural ecological industrialization assessment method based on GIS and digital twins of this invention. Detailed Implementation
[0018] The technical solution of the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.
[0019] Example 1 like Figures 1-2 As shown in the embodiment of the present invention, an agricultural ecological industrialization assessment method based on GIS and digital twins includes the following steps: Step 1: During the historical ecological assessment period, use GIS technology to analyze the soil nutrient content of the sub-regions within the agricultural planting area, and obtain the nutrient content gradient sequence of each sub-region based on the soil nutrient content. It should be noted that the historical ecological assessment cycle refers to the cycle of ecological assessment of agricultural planting areas before crop seeds were sown in the past. The planting sub-domains are the agricultural planting areas that are equally divided into grids using GIS technology, and each planting sub-domain has an equal area. In some embodiments, each planting subdomain is divided into three spatial vertical dimensions to obtain three spatial analysis layers, specifically the topsoil analysis layer, the subsoil analysis layer, and the subsoil analysis layer, wherein the volumes of the topsoil analysis layer, the subsoil analysis layer, and the subsoil analysis layer are all equal. For example, the soil nutrient content in the topsoil analysis layer, subsoil analysis layer and bottom soil analysis layer is obtained respectively, and the average value is calculated to output the soil nutrient content; The soil nutrient content of each planting sub-domain within the agricultural planting area is compared and sorted in descending order to construct a nutrient content gradient sequence for each planting sub-domain. It should be noted that the significance of obtaining the nutrient gradient sequence of the planting area is that it helps agricultural managers to clearly understand the differences in the distribution of soil nutrients throughout the planting area, providing a scientific basis for subsequent precise fertilization, irrigation and other agricultural management measures, avoiding resource waste and environmental pollution caused by blind fertilization, and improving agricultural production efficiency and agricultural product quality. Since soil nutrient content is one of the key factors affecting crop growth and the function of agricultural ecosystems, the nutrient gradient sequence of planting subdomains can reflect the spatial distribution characteristics of soil nutrients in different planting subdomains within an agricultural planting area. This provides basic data support for the subsequent integration of digital twins to create virtual models that are highly similar to real agricultural planting areas, enabling real-time monitoring, simulation, and prediction of agricultural ecosystems. By constructing a nutrient gradient sequence for different planting subdomains, the relative levels of soil nutrients in different planting subdomains can be reflected, providing an objective basis for assessing the soil health, productivity, and ecological sustainability of agricultural planting areas. Step 2: Based on the nutrient gradient sequence of planting subdomains corresponding to multiple historical ecological assessment cycles, analyze whether the nutrient content of the same planting subdomain is stable within different historical ecological assessment cycles. If it is unstable, mark it as a planting subdomain with nutrient fluctuation and a planting subdomain with stable nutrient. It should be noted that the multiple historical ecological assessment periods are not consecutive in time series, and the time intervals between adjacent historical ecological assessment periods are not equal. However, the duration of each historical ecological assessment period is equal. For example, the multiple historical ecological assessment periods are A, B, C, D, and E, where A and B are adjacent historical ecological assessment periods, B and C are adjacent historical ecological assessment periods, C and D are adjacent historical ecological assessment periods, and D and E are adjacent historical ecological assessment periods. Therefore, the time interval between historical ecological assessment periods A and B is not equal to the time interval between historical ecological assessment periods B and C, the time interval between historical ecological assessment periods B and C is not equal to the time interval between historical ecological assessment periods C and D, and the time interval between historical ecological assessment periods C and D is not equal to the time interval between historical ecological assessment periods D and E. Furthermore, it can be understood that the time interval between the historical ecological assessment cycles of A and B can be the length of time corresponding to the growth cycle of crops sown in the agricultural planting area. In some embodiments, within multiple historical ecological assessment periods, the seed nutrient gradient sequences corresponding to each historical ecological assessment period are matrix-combined according to the rules of time series to obtain a sequence alignment matrix. Specifically, the sequence alignment matrix is as follows: ,in, This represents the total soil nutrient content of all planting subdomains within each planting sub-nutrient gradient sequence, and the total soil nutrient content of all planting subdomains within the planting sub-nutrient gradient sequence corresponding to each historical ecological assessment period is equal. This represents the soil nutrient content of the nth planting subdomain within the planting sub-nutrient gradient sequence corresponding to the first historical ecological assessment period, ordered according to time series rules across multiple historical ecological assessment periods. It represents the soil nutrient content of the nth planting subdomain within the planting sub-nutrient gradient sequence corresponding to the last historical ecological assessment period, sorted according to time series rules within multiple historical ecological assessment periods. Within the sequence alignment matrix, using the same planting subdomain as the spatial selection benchmark, the first-ranked planting subdomain in the historical ecological assessment cycle corresponding to the first-ranked planting subdomain in the time series is extracted as the target content analysis subdomain. Soil nutrient content in the target content analysis subdomain was obtained for each historical ecological assessment period, and the standard deviation was calculated to output the standard deviation of the target nutrient content. The target content analysis subdomain is ranked in each seed nutrient gradient sequence, and the standard deviation is calculated to output the target ranking standard deviation. The target nutrient standard deviation and the target ordination standard deviation are summed to output the single-domain nutrient analysis value; Based on the sorting method within the nutrient gradient sequence corresponding to the first historical ecological assessment cycle, the planting subdomains are selected as target content analysis subdomains in turn, and the average value of the single domain nutrient analysis value corresponding to each target content analysis subdomain is calculated to output the nutrient stability analysis value. It is understandable that the meaning of the nutrient stability analysis value is: it is obtained by averaging the single-domain nutrient analysis values and is used to measure the stability of soil nutrient content in various planting subdomains within a specific agricultural planting area over different historical ecological assessment periods. However, the single-domain nutrient analysis value is calculated by summing the target nutrient standard deviation and the target ordination standard deviation. Therefore, on the one hand, the target nutrient standard deviation reflects the fluctuation of soil nutrient content in the planting subdomain over time, and on the other hand, the target ordination standard deviation reflects the stability of the soil nutrient content ranking of the planting subdomain within the overall agricultural planting area. Specifically, the nutrient stability analysis value can quantify the stability of soil nutrients. A stable soil nutrient environment is conducive to the stable growth of crops, improves the quality and yield of agricultural products, and reduces agricultural production risks, thereby having a positive impact on the economic and ecological benefits of the agricultural ecological industry. It provides important reference data for assessing the ecological quality, productivity level and sustainable development capacity of agricultural planting areas. Nutrient stability analysis values can reveal the spatial variations of soil nutrients within an agricultural planting area. For areas with low nutrient stability analysis values and relatively stable soil nutrients, the existing agricultural management model can be maintained. However, for areas with high nutrient stability analysis values and large fluctuations in soil nutrients, it is necessary to conduct in-depth analysis of the causes of the fluctuations. Therefore, this helps agricultural managers to develop targeted agricultural management measures for each planting sub-region within the agricultural planting area. Nutrient stability analysis values can provide important information for subsequent analysis of soil nutrient content changes in agricultural planting areas using digital twin technology. Furthermore, by comparing and analyzing nutrient stability analysis values with simulation results from digital twin models, it is possible to further identify shortcomings in the models constructed using digital twin technology, and make timely corrections and updates to improve the accuracy of the models in reflecting the actual situation in agricultural planting areas. If the nutrient stability analysis value is less than or equal to the nutrient stability analysis threshold, it indicates that the soil nutrients in the entire agricultural planting area are relatively stable in different historical ecological assessment cycles, which is displayed as a nutrient stability signal in the same area. The planting sub-domain that displays the nutrient fluctuation signal in the same area is marked as a nutrient stable planting sub-domain. If the nutrient stability analysis value is greater than the nutrient stability analysis threshold, it indicates that the soil nutrients in the entire agricultural planting area are less stable in different historical ecological assessment cycles, which is displayed as a nutrient fluctuation signal in the same area. The planting sub-domain that is displayed as a nutrient fluctuation signal in the same area is marked as a nutrient fluctuation planting sub-domain. The specific scheme of this embodiment is as follows: During the historical ecological assessment period, GIS technology is used to analyze the soil nutrient content of the sub-domains divided within the agricultural planting area. Based on the soil nutrient content of each sub-domain, a nutrient gradient sequence is obtained, which helps to reflect the relative levels of soil nutrients in different sub-domains. This provides an objective basis for assessing the soil health, productivity level, and ecological sustainability of the agricultural planting area. Furthermore, it analyzes whether the nutrient content of the same sub-domain is stable within different historical ecological assessment periods. If it is unstable, it is marked as a nutrient fluctuation sub-domain. This not only allows us to understand the spatial changes of soil nutrients within the agricultural planting area and promptly investigate and resolve the reasons for the fluctuations in nutrient content in the fluctuation sub-domains, but also provides important information for subsequent analysis of soil nutrient content changes within the agricultural planting area using digital twin technology.
[0020] Example 2 like Figures 1-2 As shown in the embodiment of the present invention, an agricultural ecological industrialization assessment method based on GIS and digital twins further includes the following steps: Step 3: Obtain the spatial location of each nutrient fluctuation planting subdomain and nutrient stable planting subdomain within the agricultural planting area, screen out adjacent nutrient fluctuation planting subdomains, and combine them as adjacent nutrient fluctuation groups. Then, use digital twin technology to compare and analyze the nutrient content changes of adjacent nutrient fluctuation planting subdomains within each adjacent nutrient fluctuation group to assess whether there is a nutrient content competition relationship between adjacent nutrient fluctuation planting subdomains. In some embodiments, digital twin technology is used to transform agricultural planting areas into two-dimensional spaces to obtain a two-dimensional grid space model; Within the two-dimensional grid space model, the position of each nutrient fluctuation planting subdomain is extracted, and the center of each nutrient fluctuation planting subdomain is used as the nutrient fluctuation coordinate point. Similarly, the position of each nutrient-stable planting subdomain is extracted, and the center of each nutrient-stable planting subdomain is used as the nutrient-stable coordinate point; Within the two-dimensional grid space model, adjacent nutrient fluctuation coordinate points and adjacent nutrient stability coordinate points are obtained respectively, thus obtaining adjacent nutrient fluctuation groups and adjacent nutrient fluctuation stability groups. Among them, the adjacent nutrient fluctuation group contains the coordinate points of adjacent nutrient fluctuations, the nutrient fluctuation coordinate points, and the nutrient stability coordinate points adjacent to the nutrient fluctuation coordinate points. Specifically, taking adjacent nutrient fluctuation groups as an example, the soil nutrient content of each nutrient fluctuation coordinate point in each adjacent nutrient fluctuation group corresponding to the nutrient fluctuation planting subdomain in each historical ecological assessment cycle is extracted, and sorted according to the actual sequence to obtain the front nutrient wave content sequence and the back nutrient wave content sequence. Based on the same historical ecological assessment period, the soil nutrient content of each nutrient fluctuation coordinate point in adjacent nutrient wave stability groups within the same historical ecological assessment period was obtained and marked as the nutrient content of the previous and subsequent periods, respectively. Within the first historical ecological assessment period of the time series ranking, the nutrient amounts corresponding to the previous and subsequent nutrient fluctuation coordinate points in adjacent nutrient fluctuation groups are summed to obtain the baseline nutrient amount. Based on the method of obtaining the baseline nutrient content, multiple historical ecological assessment periods were selected, excluding the first historical ecological assessment period in the time series ranking. Within each historical ecological assessment period, the nutrient content of the previous and subsequent periods corresponding to the nutrient fluctuation coordinate points in adjacent nutrient fluctuation groups were summed to obtain the comparative nutrient content. The comparison of the same-period nutrient quantity is sequentially input into the Euclidean distance formula according to the time series of the corresponding historical ecological assessment cycle, and the adjacent nutrient wave competition value is output. It is understandable that the meaning of the adjacent nutrient wave competition value is: to reflect the changes in soil nutrient content between adjacent nutrient wave planting subdomains. Specifically, if the adjacent nutrient wave competition value is larger, it indicates that there is strong nutrient content competition between adjacent nutrient wave planting subdomains; if the adjacent nutrient wave competition value is smaller, it indicates that there is weak nutrient content competition between adjacent nutrient wave planting subdomains. Furthermore, the nutrient wave competition value provides a specific indicator for quantitatively assessing the ecological interactions between adjacent nutrient fluctuation planting subdomains. In the assessment of agricultural ecological industrialization, since ecological interaction is an important consideration, it can not only affect the yield and quality of crops, but also relate to the stability and sustainability of the entire agricultural ecosystem. Therefore, by using the nutrient wave competition value, we can determine the degree of mutual influence between different adjacent planting subdomains, thereby providing a reference for formulating ecological protection and restoration measures. Because GIS technology has powerful spatial analysis and visualization functions, combined with the two-dimensional grid spatial model constructed by digital twin, it is possible to locate the position of each nutrient fluctuation planting subdomain and nutrient stable planting subdomain. By calculating the nutrient wave competition value, it is possible not only to reflect whether the overall soil nutrient content of adjacent nutrient fluctuation planting subdomains is stable in the macro spatial dimension through GIS technology, but also to reflect the competition for soil nutrient content between adjacent nutrient fluctuation planting subdomains in the micro spatial dimension through digital twin technology. The comparison between adjacent wavelet race values and adjacent wavelet race thresholds is performed as follows: If the adjacent nutrient wave competition value is greater than the adjacent nutrient wave competition threshold, it indicates that there is strong nutrient content competition between adjacent nutrient fluctuation planting subdomains, which is shown as a strong nutrient content competition signal between adjacent subdomains. If the adjacent nutrient wave competition value is less than or equal to the adjacent nutrient wave competition threshold, it indicates that there is weak nutrient content competition between adjacent nutrient fluctuation planting subdomains, which is shown as a weak competition signal between adjacent nutrients. Step 4: If there is nutrient content competition between adjacent nutrient fluctuation planting subdomains, obtain the nutrient content changes of adjacent nutrient fluctuation planting subdomains within each group of adjacent nutrient fluctuation groups, and quantify them to obtain the digital twin assessment frequency, thus completing the assessment of the agricultural ecological industry. In some embodiments, if the signal is a strong competition signal for adjacent nutrients, since multiple historical ecological assessment cycles are not continuous in time series and the time interval between adjacent historical ecological assessment cycles is not equal, the time interval between adjacent historical ecological assessment cycles is taken as the adjacent cycle interval period. Obtain the interval duration corresponding to each adjacent cycle interval and its proportion of the historical ecological assessment cycle duration to obtain the interval ratio of adjacent cycle units; The standard deviation of the time interval ratio between adjacent periodic cells is calculated, and the standard deviation of the cell time interval is output. If the standard deviation of the unit time interval is greater than the threshold of the standard deviation of the unit time interval, it indicates that the time interval between adjacent historical ecological assessment cycles is unevenly distributed. The unit time interval ratios of all adjacent cycles are compared, and the smallest unit time interval ratio of adjacent cycles is selected and processed by reciprocal to output the digital twin assessment frequency. If the standard deviation of the unit time interval is less than or equal to the threshold of the standard deviation of the unit time interval, it indicates that the time interval between adjacent historical ecological assessment cycles is evenly distributed. The average value of the unit time interval ratio of all adjacent cycles is calculated, and the digital twin assessment frequency is output. It is helpful to understand that the significance of obtaining the digital twin assessment frequency lies in reflecting the frequency of nutrient content changes in adjacent nutrient fluctuation planting subdomains. Since GIS technology can only reflect whether the overall soil nutrient content uniformity of adjacent nutrient fluctuation planting subdomains is stable in the macro-spatial dimension, it is easy to smooth out the micro-differences between adjacent nutrient fluctuation planting subdomains, thus masking the dynamic interaction process of nutrient content between adjacent nutrient fluctuation planting subdomains. Therefore, introducing digital twin technology and combining it with GIS technology can assist GIS technology in analyzing the dynamic interaction of nutrient content between adjacent nutrient fluctuation planting subdomains, improving the accuracy and timeliness of agricultural ecological industrialization assessment. The specific solution of this embodiment is as follows: The spatial location of each nutrient-fluctuating and nutrient-stable planting sub-domain within the agricultural planting area is obtained. Adjacent nutrient-fluctuating planting sub-domains are screened, and digital twin technology is used to compare and analyze the nutrient content changes of adjacent nutrient-fluctuating planting sub-domains. If there is nutrient content competition between adjacent nutrient-fluctuating planting sub-domains, the frequency of nutrient content changes in adjacent nutrient-fluctuating planting sub-domains within each group of adjacent nutrient fluctuations is obtained and quantified to obtain the digital twin assessment frequency. This solves the problem that GIS technology can only reflect the overall uniformity of soil nutrient content in adjacent nutrient-fluctuating planting sub-domains in a macroscopic spatial dimension, which can easily smooth out microscopic differences between adjacent nutrient-fluctuating planting sub-domains and mask the dynamic interaction of nutrient content between them. This solution can assist GIS technology in analyzing the dynamic interaction of nutrient content between adjacent nutrient-fluctuating planting sub-domains, improving the accuracy and timeliness of agricultural ecological industrialization assessment.
[0021] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and various changes and modifications can be made without departing from the spirit and scope of the invention; all such changes and modifications will fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for assessing the industrialization of agricultural ecology based on GIS and digital twins, characterized in that, include: During the historical ecological assessment period, GIS technology was used to analyze the soil nutrient content of the sub-domains within the agricultural planting area to obtain the nutrient gradient sequence of the sub-domains. Nutrient content analysis was performed on the nutrient gradient sequences of planting subdomains corresponding to multiple historical ecological assessment cycles, and the stability was assessed. If the stability was not achieved, the planting subdomains were marked as nutrient fluctuation subdomains. If stable, it is marked as a nutrient-stable planting subdomain; The spatial location of each nutrient fluctuation planting subdomain and nutrient stable planting subdomain within the agricultural planting area is obtained, adjacent nutrient fluctuation planting subdomains are screened, and digital twin technology is used to compare and analyze the nutrient content changes of adjacent nutrient fluctuation planting subdomains to assess whether there is a nutrient content competition relationship between adjacent nutrient fluctuation planting subdomains. If there is nutrient content competition between adjacent nutrient fluctuation planting subdomains, the nutrient content changes of adjacent nutrient fluctuation planting subdomains within each group of adjacent nutrient fluctuations are obtained and quantified to obtain the digital twin evaluation frequency.
2. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that, The process of constructing the seed nutrient gradient sequence is as follows: Each planting subdomain is divided according to the spatial vertical dimension to obtain three spatial analysis layers: topsoil analysis layer, subsoil analysis layer, and subsoil analysis layer. The soil nutrient content in the topsoil analysis layer, subsoil analysis layer, and subsoil analysis layer is obtained respectively, and the average value is calculated to output the soil nutrient content. The soil nutrient content of each planting sub-domain within the agricultural planting area is compared and sorted in descending order to construct a nutrient content gradient sequence for each planting sub-domain.
3. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that: Nutrient content analysis of the same seed domain was performed on the nutrient gradient sequences of planting seed domains corresponding to multiple historical ecological assessment periods, as follows: Within multiple historical ecological assessment periods, following the rules of time series, the planting nutrient gradient sequences corresponding to each historical ecological assessment period are matrix-combined to obtain a sequence alignment matrix. Using the same planting subdomain as the spatial selection benchmark, the first-ranked planting subdomain within the planting nutrient gradient sequence corresponding to the first-ranked historical ecological assessment period in the time series is extracted as the target content analysis subdomain. Soil nutrient content in the target content analysis subdomain was obtained for each historical ecological assessment period, and the standard deviation was calculated to output the standard deviation of the target nutrient content. The target content analysis subdomain is ranked in each seed nutrient gradient sequence, and the standard deviation is calculated to output the target ranking standard deviation. The target nutrient standard deviation and the target ordination standard deviation are summed to output the single-domain nutrient analysis value.
4. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that, The labeling process for nutrient fluctuation planting subdomains and nutrient stable planting subdomains is as follows: Based on the sorting method within the nutrient gradient sequence corresponding to the first historical ecological assessment cycle, the planting subdomains are selected as target content analysis subdomains in turn, and the average value of the single domain nutrient analysis value corresponding to each target content analysis subdomain is calculated to output the nutrient stability analysis value. If the nutrient stability analysis value is less than or equal to the nutrient stability analysis threshold, it is displayed as a nutrient stability signal in the same domain and marked as a nutrient stable planting subdomain. If the nutrient stability analysis value is greater than the nutrient stability analysis threshold, it is displayed as a nutrient fluctuation signal in the same domain and marked as a nutrient fluctuation planting subdomain.
5. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that, The screening process for adjacent nutrient fluctuation planting subdomains is as follows: Digital twin technology is used to transform agricultural planting areas into two-dimensional space to obtain a two-dimensional grid space model. Within the two-dimensional grid space model, the position of each nutrient fluctuation planting sub-domain is extracted. The center of each nutrient fluctuation planting sub-domain is taken as the nutrient fluctuation coordinate point, and the nutrient fluctuation planting sub-domains corresponding to adjacent nutrient fluctuation coordinate points are taken as adjacent nutrient fluctuation planting sub-domains.
6. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that: The changes in nutrient content in adjacent nutrient fluctuation planting subdomains were compared and analyzed using digital twin technology, as follows: Adjacent nutrient fluctuation planting subdomains are combined to obtain adjacent nutrient fluctuation groups. Soil nutrient content of each nutrient fluctuation coordinate point in each adjacent nutrient fluctuation group corresponding to the nutrient fluctuation planting subdomain in each historical ecological assessment period is extracted. All are sorted according to time series to obtain the previous nutrient wave content sequence and the subsequent nutrient wave content sequence. Soil nutrient content of each nutrient fluctuation coordinate point in the adjacent nutrient wave stability group in the same historical ecological assessment period is obtained and marked as the previous and subsequent nutrient content, respectively. Within the first historical ecological assessment period of the time series ranking, the nutrient amounts corresponding to the previous and subsequent nutrient fluctuation coordinate points in adjacent nutrient fluctuation groups are summed to obtain the baseline nutrient amount. Based on the method of obtaining the baseline nutrient levels, multiple historical ecological assessment periods were selected, excluding the first historical ecological assessment period in the time series ranking. Within each historical ecological assessment period, the nutrient levels corresponding to the previous and subsequent nutrient fluctuation coordinate points in adjacent nutrient fluctuation groups were summed to obtain the comparative nutrient levels.
7. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 6, characterized in that: The process for assessing whether there is nutrient content competition between adjacent nutrient fluctuation planting subdomains is as follows: The comparison of the same-period nutrient quantity is sequentially input into the Euclidean distance formula according to the time series of the corresponding historical ecological assessment cycle, and the adjacent nutrient wave competition value is output. If the adjacent nutrient wave competition value is greater than the adjacent nutrient wave competition threshold, it will be displayed as a strong competition signal between adjacent nutrients. If the adjacent nutrient wave competition value is less than or equal to the adjacent nutrient wave competition threshold, it is displayed as a weak competition signal between adjacent nutrients.
8. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that: The changes in nutrient content in adjacent nutrient fluctuation subdomains within each group of adjacent nutrient fluctuation groups were analyzed, as follows: If the signal shows strong competition for nutrients between adjacent cycles, obtain the interval length corresponding to each adjacent cycle interval and the proportion of the historical ecological assessment cycle length to obtain the interval ratio of adjacent cycle units. The standard deviation of the time interval ratio between adjacent periodic cells is calculated, and the standard deviation of the cell time interval is output.
9. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 8, characterized in that, The quantification process is as follows: The standard deviation of the time interval ratio between adjacent periodic cells is calculated, and the standard deviation of the cell time interval is output.
10. The agricultural ecological industrialization assessment method based on GIS and digital twins according to claim 1, characterized in that, The process of obtaining the digital twin evaluation frequency is as follows: If the standard deviation of the unit interval is greater than the standard deviation threshold of the unit interval, then the unit interval ratios of all adjacent periods are compared, the smallest unit interval ratio of adjacent periods is selected and processed by reciprocal, and the digital twin evaluation frequency is output. If the standard deviation of the unit interval is less than or equal to the threshold of the standard deviation of the unit interval, then the average value of the unit interval ratio of all adjacent periods is calculated, and the digital twin evaluation frequency is output.