Multi-source crop water and fertilizer data processing method based on space-ground-air integration

Abstract

The invention discloses a multi-source crop water and fertilizer data processing method based on space-ground-air integration, and the method monitors the growth data of crops through a space-ground-air integration network, and comprises the steps: obtaining the multi-source monitoring data of the crops, and carrying out the time-space alignment processing, and obtaining the multi-source monitoring data with consistent time and space; comprehensively analyzing the growth state and growth environment of the current crops according to the multi-source monitoring data, and further performing crop water and fertilizer demand analysis to obtain water and fertilizer ratio data of each water and fertilizer irrigation node; unified data prefix conversion is carried out on the multi-source monitoring data, corresponding water and fertilizer irrigation nodes are associated, and a mapping association relationship between the multi-source monitoring data and the water and fertilizer irrigation nodes is constructed; and according to the mapping association relationship and the complete growth stage of the crop, constructing a data association architecture between the multi-source monitoring data of the complete water and fertilizer irrigation of the crop and the water and fertilizer ratio data. The method has the effects of improving the centralized storage and fusion analysis capability of the multi-source heterogeneous data and solving the problem of data islands among different devices.

Description

Technical Field

[0001] This invention relates to the technical field of agricultural data management, and in particular to a multi-source crop water and fertilizer data processing method based on integrated space-air-ground data processing. Background Technology

[0002] Currently, with the development of technology, traditional agriculture is gradually transforming into smart agriculture. Data collection of farmland is carried out through various intelligent devices or sensors, which is more efficient and scientific than manual observation and collection of crop information. However, there are information barriers between crop data collected by different devices, and manual integration is still required to make decisions on crop water and fertilizer ratios. Therefore, higher requirements are placed on the integration of crop data collected by different devices that have data barriers.

[0003] Existing methods for processing crop water and fertilizer data typically rely on preset rules, which cannot adapt to changes in crop growth. Furthermore, the rules are limited by crop type and terrain variations. In particular, it is difficult to collect qualified reference data to match water and fertilizer setting rules for complex terrains. Moreover, there are barriers to data collected by different devices, and the data required for different crops and different terrain environments vary, making it difficult to effectively integrate all data for water and fertilizer ratio analysis. There is also a lack of unified water and fertilizer data analysis standards for different crop types, data types, and terrain environments. Summary of the Invention

[0004] To address the problem that existing crop water and fertilizer analysis methods are limited by crop type, data type, and terrain environment, and thus lack the ability to effectively integrate all data for analysis, this application provides a multi-source crop water and fertilizer data processing method based on an integrated air-ground-space monitoring network. This method can effectively integrate various types of crop monitoring data through an integrated air-ground-space monitoring network to perform water and fertilizer ratio analysis based on the actual needs of crops. It improves the centralized storage and fusion analysis capabilities of multi-source heterogeneous data, solves the data silo problem between different devices, and forms a unified water and fertilizer data analysis standard for different crop types, data types, and terrain environments.

[0005] Firstly, the aforementioned inventive objective of this application is achieved through the following technical solutions: A method for processing multi-source crop water and fertilizer data based on a space-air-ground integrated network, wherein the method monitors crop growth data through a space-air-ground integrated network, and the method includes: Acquire multi-source monitoring data of crops, and perform spatiotemporal alignment processing on the multi-source monitoring data to obtain spatiotemporally consistent multi-source monitoring data; Based on the comprehensive analysis of the multi-source monitoring data, the current growth status and growth environment of crops are analyzed, and the water and fertilizer requirements of crops are analyzed based on the growth status and growth environment to obtain the water and fertilizer ratio data for each irrigation node. The multi-source monitoring data is converted into a unified data prefix according to a preset data format and associated with the corresponding water and fertilizer irrigation nodes to construct a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node; Based on the mapping relationship and the complete growth stages of crops, a data association architecture is constructed between multi-source monitoring data of complete crop water and fertilizer irrigation and water and fertilizer ratio data.

[0006] In a preferred embodiment, the method may be further configured as follows: the multi-source monitoring data and the water-fertilizer ratio data are distributed and stored in the original data format, and the association between the multi-source monitoring data and the corresponding water-fertilizer ratio data of each water-fertilizer irrigation node is performed through the data association architecture.

[0007] In a preferred embodiment, this application may be further configured as follows: before the step of comprehensively analyzing the current crop growth status and growth environment based on the multi-source monitoring data, and performing crop water and fertilizer demand analysis based on the growth status and growth environment to obtain water and fertilizer ratio data for each irrigation node, the method further includes: For each type of crop, multiple control groups were set up with growth stage and growth environment as variables to obtain water and fertilizer application data for different growth stages under the same growth environment and for the same growth stage under different environments. Obtain the crop growth status of each control group under the corresponding water and fertilizer application data, and evaluate the water and fertilizer application effect of the corresponding control group by comparing the crop growth status before and after fertilization. The fertilization effect evaluation results are confirmed by using a pre-set expert knowledge base or manual verification, and the optimal water and fertilizer ratio for each control group is selected. By associating crop type, growth stage, growth environment, and optimal water and fertilizer ratio, a crop water and fertilizer analysis model is constructed based on crop growth status and growth environment to analyze water and fertilizer requirements.

[0008] In a preferred embodiment, this application can be further configured as follows: the step of comprehensively analyzing the current crop growth status and growth environment based on the multi-source monitoring data, and performing crop water and fertilizer demand analysis based on the growth status and growth environment to obtain water and fertilizer ratio data for each irrigation node, including: The actual crop growth images in the multi-source monitoring data are compared with standard growth images of preset crop types, and the actual growth stage and growth status parameters of the current crop are obtained based on the comparison results. The actual terrain image in the multi-source monitoring data is compared with the preset planting terrain image. Based on the comparison result, the planting terrain is obtained and combined with the planting environment parameters to obtain the actual growth environment parameters of the current crop. The actual growth stage, growth status parameters and actual growth environment parameters are input into the crop water and fertilizer analysis model for comparison of each indicator, and the crop water and fertilizer demand analysis is carried out based on the comparison results. Based on the analysis results, the water and fertilizer ratio parameters with the highest matching degree were selected as the water and fertilizer ratio data for the current irrigation node of the current crop.

[0009] In a preferred embodiment, this application can be further configured as follows: the step of comprehensively analyzing the current crop growth status and growth environment based on the multi-source monitoring data, and performing crop water and fertilizer demand analysis based on the growth status and growth environment to obtain water and fertilizer ratio data for each irrigation node, further includes: The results of the growth status assessment and the growth environment assessment are sent to the management personnel for manual confirmation. The confirmation results include confirmation of the assessment results or modification of the parameters of the assessment results. When the feedback of the manual confirmation results shows that there are modified parameters, the modified parameters are used as model training parameters to adaptively update the model parameters of the crop water and fertilizer analysis model.

[0010] In a preferred embodiment, this application can be further configured as follows: the step of performing a unified data prefix conversion on the multi-source monitoring data according to a preset data format and associating it with the corresponding water and fertilizer irrigation nodes to construct a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node includes: Based on the monitoring time and crop type, calculate the data prefix of the multi-source monitoring data within a set string length, and perform data prefix superposition processing on the multi-source monitoring data according to the data prefix calculation results; The multi-source monitoring data, after being superimposed with a unified data prefix, is associated with the corresponding water and fertilizer irrigation nodes to establish a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node.

[0011] In a preferred embodiment, this application can be further configured as follows: the acquisition of multi-source monitoring data of crops and the spatiotemporal alignment processing of the multi-source monitoring data to obtain spatiotemporally consistent multi-source monitoring data include: acquiring ground, air, and space monitoring data of crops respectively, and performing spatiotemporal alignment processing according to the monitoring time and the coordinates of the monitored crops to obtain spatiotemporally consistent integrated ground, air, and space multi-source monitoring data.

[0012] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A multi-source crop water and fertilizer data processing system based on integrated space-ground-air-ground data processing, wherein the system is applied to the aforementioned multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground data processing, and the system includes: The multi-source data acquisition module is used to acquire multi-source monitoring data of crops and perform spatiotemporal alignment processing on the multi-source monitoring data to obtain spatiotemporally consistent multi-source monitoring data. The water and fertilizer analysis module is used to comprehensively analyze the current growth status and growth environment of crops based on the multi-source monitoring data, and to perform water and fertilizer demand analysis of crops based on the growth status and growth environment to obtain water and fertilizer ratio data for each irrigation node. The data processing module is used to perform unified data prefix conversion on the multi-source monitoring data according to a preset data format and associate it with the corresponding water and fertilizer irrigation nodes to construct a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node. The data association module is used to construct a data association architecture between multi-source monitoring data of complete crop water and fertilizer irrigation and water and fertilizer ratio data based on the mapping relationship and the complete growth stage of the crop.

[0013] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems.

[0014] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application acquires multi-source monitoring data by constructing an integrated air-ground-space network to monitor crop growth. It ensures data consistency through spatiotemporal alignment, analyzes water and fertilizer requirements based on crop growth status and environment, acquires water and fertilizer ratio data for each irrigation node based on actual crop needs, establishes mapping relationships between data by overlaying multi-source monitoring data with a unified data prefix, and constructs a data association architecture to analyze and manage water and fertilizer irrigation for different crops, different growth states, and different environments. This improves the centralized storage and fusion analysis capabilities of multi-source heterogeneous data, solves the data silo problem between different monitoring devices by using a unified data prefix, and forms a unified water and fertilizer analysis standard applicable to crop water and fertilizer management in different crop types, different monitoring data types, and complex terrain environments. 2. This application sets up control groups for each type of crop using growth stage and growth environment as variables to obtain water and fertilizer application data for different growth stages in the same growth environment and different growth environments in the same growth stage. Based on the comparison of crop growth status before and after fertilization, the water and fertilizer application effects of different control groups are judged. The fertilization effect is confirmed by using expert knowledge base or manual verification results as a reference to select the optimal water and fertilizer ratio, thereby improving the accuracy of water and fertilizer ratio allocation. A crop water and fertilizer analysis model is constructed through the correlation between crop type, growth stage, growth environment, and optimal water and fertilizer ratio to analyze water and fertilizer demand. The crop water and fertilizer analysis model fully considers crop type, crop growth changes, and planting environment to dynamically analyze the required water and fertilizer ratio, thereby improving the accuracy of water and fertilizer ratio analysis and its adaptability to actual growth conditions. The manually modified environmental parameters and corresponding water and fertilizer ratio parameters are used as new training data to update the model, and the model is intelligently updated and incrementally optimized. 3. This application calculates data prefixes for multi-source monitoring data based on monitoring time and crop type, and superimposes them onto the corresponding monitoring data to form a unified data format for heterogeneous data from different monitoring devices, facilitating searching. The multi-source heterogeneous data is stored according to the original data format, and the multi-source heterogeneous data with the same data format is associated with the corresponding water, fertilizer, irrigation and other data to establish a data mapping relationship, forming a data search index to perform association searches on multi-source heterogeneous data from air, land, and space, thereby improving the data fusion and analysis capabilities. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1This is a flowchart illustrating the implementation of the multi-source crop water and fertilizer data processing method based on the integrated space-ground-air-ground system in this embodiment.

[0018] Figure 2 This is a flowchart illustrating the implementation of step S20 of the multi-source crop water and fertilizer data processing method in this embodiment.

[0019] Figure 3 This is a flowchart illustrating the implementation of the multi-source crop water and fertilizer data processing method for model updating in this embodiment.

[0020] Figure 4 This is a flowchart illustrating the implementation of the multi-source crop water and fertilizer data processing method for constructing a crop water and fertilizer analysis model in this embodiment.

[0021] Figure 5 This is a flowchart illustrating the implementation of step S30 of the multi-source crop water and fertilizer data processing method in this embodiment.

[0022] Figure 6 This is a structural block diagram of the multi-source crop water and fertilizer data processing system based on the integrated space-ground-air-ground system in this embodiment.

[0023] Figure 7 This is a schematic diagram of the internal structure of a computer device used to implement multi-source crop water and fertilizer data processing methods. Detailed Implementation

[0024] 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, not all, of the embodiments of the present invention. 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.

[0025] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0026] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] In one embodiment, such as Figure 1 As shown, this application discloses a multi-source crop water and fertilizer data processing method based on a space-air-ground integrated network. This method monitors crop growth data through a space-air-ground integrated network. In this embodiment, a space-air-ground integrated monitoring network is constructed by integrating multi-source data such as meteorological stations, satellite remote sensing, UAV spectral data, and soil sensors. The specific steps include the following: S10: Acquire multi-source monitoring data of crops, perform spatiotemporal alignment processing on the multi-source monitoring data, and obtain spatiotemporally consistent multi-source monitoring data.

[0029] Specifically, the monitoring data of crops from the ground, air, and space are acquired separately, and spatiotemporal alignment is performed based on the monitoring time and crop coordinates to obtain spatiotemporally consistent multi-source monitoring data integrating the ground, air, and space.

[0030] During crop cultivation, high-precision sensor devices such as satellites, drones, and soil sensors are used to collect data in real time, including soil moisture, nutrient content, satellite remote sensing data, drone spectra, and images. Meteorological data is also obtained from weather stations to obtain aerial and ground monitoring data for crops. The aerial and ground monitoring data are aligned according to the dual dimensions of monitoring time and crop coordinates to obtain spatiotemporally consistent multi-source monitoring data.

[0031] S20: Based on the comprehensive analysis of multi-source monitoring data, analyze the current growth status and environment of crops, and conduct water and fertilizer demand analysis of crops based on the growth status and environment to obtain water and fertilizer ratio data for each irrigation node.

[0032] Specifically, such as Figure 2 As shown, step S20 includes: S201: Compare the actual crop growth images in the multi-source monitoring data with the standard growth images of the preset crop type, and obtain the actual growth stage and growth status parameters of the current crop based on the comparison results.

[0033] Specifically, by using pre-stored standard growth images of the entire growth cycle of different crop types, including comparison indicators such as spectral features, morphological indicators, and physiological states, the actual growth images of crops in the real-time multi-source monitoring data are compared with each indicator in the standard growth images of the corresponding crop types, and the actual growth stage and growth status of the current crops are comprehensively evaluated based on the comparison results.

[0034] S202: Compare the actual terrain image in the multi-source monitoring data with the preset planting terrain image, obtain the planting terrain based on the comparison results, and combine the planting environment parameters to obtain the actual growth environment parameters of the current crop.

[0035] Specifically, corresponding planting scenarios are set according to crop types. The terrain environment of the possible planting scenarios for each crop type is associated and stored with the crop type, such as plains, farmland, and hillsides. The actual terrain image is compared with the preset planting image. Based on the comparison results, the planting terrain of the current crop is obtained. This can be applied to planting scenarios with different planting terrains and environments. Combined with planting environment parameters such as soil nutrients and weather, the actual growth environment parameters of the current crop are obtained, such as planting environments with complex karst landforms.

[0036] S203: Input the actual growth stage, growth status parameters and actual growth environment parameters into the crop water and fertilizer analysis model for item-by-item comparison, and conduct crop water and fertilizer demand analysis based on the comparison results.

[0037] Specifically, the actual growth stage, growth status parameters, and actual growth environment parameters of the currently monitored crops are input into the crop water and fertilizer analysis model and compared with various indicators of the same type of crops. Based on the comparison results, the water and fertilizer ratio requirements of the same crop type, growth status, and similar growth environment can be selected.

[0038] S204: Based on the analysis results, select the water and fertilizer ratio parameter with the highest matching degree as the water and fertilizer ratio data for the current irrigation node of the current crop.

[0039] Specifically, based on the analysis results, the water and fertilizer ratio parameter with the highest matching degree or the highest similarity of each indicator is selected from all the matching indicators as the water and fertilizer ratio data of the current irrigation node for the current crop.

[0040] like Figure 3 As shown, this embodiment also includes: S205: Send the growth status judgment result and growth environment judgment result to the management personnel for manual confirmation. The confirmation result includes confirmation of the judgment result or modification of the judgment result parameters.

[0041] Specifically, the results of the growth status assessment and the growth environment assessment are sent to the management personnel. The assessment results are then manually verified to see if they match the actual crop conditions. If they match, the assessment results are confirmed. If they do not match, the parameters with errors are manually modified, and the correct growth status or growth environment assessment results and the corresponding water and fertilizer ratio parameters are entered. The manual confirmation results are then fed back to the crop water and fertilizer analysis model.

[0042] S206: When there are modified parameters in the feedback of manual confirmation results, the modified parameters will be used as model training parameters to adaptively update the crop water and fertilizer analysis model parameters.

[0043] Specifically, the manual feedback results are compared with the judgment results item by item. If the comparison results are consistent, the model judgment result is considered correct. If the comparison results are inconsistent, the manual feedback result is considered to have been modified. When there are modified parameters in the manual confirmation feedback results, including modified items of growth status or growth environment judgment results and corresponding water and fertilizer ratio parameter modifications, the modified parameters are used as model training parameters to adaptively update the crop water and fertilizer analysis model parameters.

[0044] In one embodiment, such as Figure 4 As shown, before step S20, the procedure further includes: S207: For each type of crop, multiple control groups are set up with growth stage and growth environment as variables to obtain water and fertilizer application data for different growth stages under the same growth environment and for the same growth stage under different environments.

[0045] Specifically, experiments were conducted according to crop type. For each type of crop, multiple control groups were set up with growth stage and growth environment as variables. Water and fertilizer application data were obtained during the planting of control groups at different growth stages under the same growth environment and at different growth stages under different growth environments.

[0046] S208: Obtain the crop growth status of each control group under the corresponding water and fertilizer application data, and evaluate the water and fertilizer application effect of the corresponding control group based on the comparison of crop growth status before and after fertilization.

[0047] Specifically, multi-source monitoring data for each control group is acquired through a pre-defined integrated air-ground-space network to analyze the crop growth status under corresponding water and fertilizer application data. The effect of water and fertilizer application is evaluated by comparing the crop growth status before and after fertilization. If the growth status after fertilization is better than before fertilization, such as positive changes in crop plant height, stem diameter, ear length, ear grains and other population characteristics, increased crop leaf area index, and increased chlorophyll content through spectral comparison, different fertilization effect evaluation indicators and evaluation standards are set for different crop types.

[0048] S209: Confirm the fertilization effect evaluation results through a pre-set expert knowledge base or manual verification, and select the optimal water and fertilizer ratio for each control group.

[0049] Specifically, the fertilization effect evaluation results are confirmed through a pre-set expert knowledge base or manual verification. The confirmation results include two types: correct evaluation and incorrect evaluation. When the evaluation is incorrect, the incorrect parameter items are manually corrected. When the evaluation is correct, the evaluation results are marked as correct, and the best water and fertilizer ratio is selected from multiple control group data as the optimal water and fertilizer ratio data.

[0050] S210: By associating crop type, growth stage, growth environment, and optimal water and fertilizer ratio, a crop water and fertilizer analysis model is constructed to analyze water and fertilizer requirements based on crop growth status and growth environment.

[0051] Specifically, crop types, multiple growth stages, multiple growth environments, and corresponding optimal water and fertilizer ratios are correlated, and a crop water and fertilizer analysis model is constructed based on the correlation between multiple parameters to analyze water and fertilizer requirements based on crop growth status and growth environment.

[0052] S30: Perform unified data prefix conversion on the multi-source monitoring data according to the preset data format and associate it with the corresponding water and fertilizer irrigation nodes to build a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node.

[0053] Specifically, such as Figure 5 As shown, step S30 includes: S301: Calculate the data prefix of multi-source monitoring data within a set string length based on the monitoring time and crop type, and perform data prefix superposition processing on the multi-source monitoring data according to the data prefix calculation results.

[0054] Specifically, the monitoring time and crop type of the data collected by each monitoring device are converted into binary according to a preset data format. A unified data prefix is ​​generated within a set string length, and then the data prefix is ​​superimposed on the corresponding multi-source monitoring data to obtain multi-source monitoring data with a unified data prefix that is easy to query.

[0055] S302: Associate the multi-source monitoring data after superimposing a unified data prefix with the corresponding water and fertilizer irrigation nodes, and establish a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node.

[0056] Specifically, the multi-source monitoring data after being overlaid with a unified data prefix is ​​associated with the corresponding water and fertilizer irrigation nodes to establish a data mapping relationship between each water and fertilizer irrigation node and the corresponding multi-source monitoring data.

[0057] S40: Based on the mapping relationship and the complete growth stages of crops, construct a data association architecture between multi-source monitoring data of complete water and fertilizer irrigation of crops and water and fertilizer ratio data.

[0058] Specifically, based on the complete growth stages of crops, water and fertilizer irrigation nodes at different growth stages are sequentially associated. Combined with the mapping and association relationship of multi-source monitoring data for each water and fertilizer irrigation node, a data association architecture is constructed with water and fertilizer irrigation nodes as the vertical association and multi-source monitoring data mapping and association relationship and water and fertilizer ratio data as the horizontal association, so as to effectively integrate multi-source heterogeneous data from different monitoring devices.

[0059] This embodiment also includes: multi-source monitoring data and water-fertilizer ratio data are distributed and stored in the original data format, and the association between multi-source monitoring data and corresponding water-fertilizer ratio data of each water-fertilizer irrigation node is performed through a data association architecture.

[0060] Specifically, in this embodiment, the multi-source monitoring data and water-fertilizer ratio data are distributed and stored separately according to their original data formats. When water and fertilizer demand analysis is required, the multi-source monitoring data and corresponding water-fertilizer ratio data of each water and fertilizer irrigation node are associated and searched through the data association architecture, and the multi-source heterogeneous data are called, so as to achieve effective fusion of multi-source data without changing the original data format.

[0061] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0062] In one embodiment, a multi-source crop water and fertilizer data processing system based on integrated space-ground-air-ground technology is provided. This system corresponds one-to-one with the multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground technology described in the above embodiments. Figure 6 As shown, this multi-source crop water and fertilizer data processing system based on integrated space-ground-ground data processing includes a multi-source data acquisition module, a water and fertilizer analysis module, a data processing module, and a data association module. Detailed descriptions of each functional module are as follows: The multi-source data acquisition module is used to acquire multi-source monitoring data of crops, perform spatiotemporal alignment processing on the multi-source monitoring data, and obtain spatiotemporally consistent multi-source monitoring data.

[0063] The water and fertilizer analysis module is used to comprehensively analyze the current growth status and growth environment of crops based on multi-source monitoring data, and to analyze the water and fertilizer requirements of crops based on the growth status and growth environment, so as to obtain the water and fertilizer ratio data for each irrigation node.

[0064] The data processing module is used to perform unified data prefix conversion on multi-source monitoring data according to a preset data format and associate it with the corresponding water and fertilizer irrigation nodes, thereby constructing a mapping relationship between multi-source monitoring data and each water and fertilizer irrigation node. The data association module is used to construct a data association architecture between multi-source monitoring data of complete crop water and fertilizer irrigation and water and fertilizer ratio data based on mapping relationships and complete crop growth stages.

[0065] Preferably, this embodiment also includes: The data association storage module is used to distribute and store multi-source monitoring data and water-fertilizer ratio data in the original data format. It uses a data association architecture to perform association lookup between multi-source monitoring data and corresponding water-fertilizer ratio data for each water-fertilizer irrigation node.

[0066] Preferably, the water and fertilizer analysis module also includes: The control experiment data acquisition submodule is used to set up multiple control groups for each type of crop with growth stage and growth environment as variables, and to obtain water and fertilizer application data for different growth stages under the same growth environment and for the same growth stage under different environments.

[0067] The fertilization effect evaluation submodule is used to obtain the crop growth status of each control group under the corresponding water and fertilizer fertilization data, and evaluate the water and fertilizer fertilization effect of the corresponding control group by comparing the crop growth status before and after fertilization.

[0068] The optimal fertilization ratio selection submodule is used to confirm the fertilization effect evaluation results through a preset expert knowledge base or manual verification, and to select the optimal water and fertilizer ratio for each control group.

[0069] The model building submodule is used to associate crop type, growth stage, growth environment, and optimal water and fertilizer ratio to build a crop water and fertilizer analysis model based on crop growth status and growth environment to analyze water and fertilizer requirements.

[0070] Preferably, the water and fertilizer analysis module includes: The growth parameter acquisition submodule is used to compare the actual crop growth images in the multi-source monitoring data with the standard growth images of the preset crop type, and obtain the actual growth stage and growth status parameters of the current crop based on the comparison results.

[0071] The environmental parameter acquisition submodule is used to compare the actual terrain image in the multi-source monitoring data with the preset planting terrain image, obtain the planting terrain based on the comparison results, and combine it with the planting environmental parameters to obtain the actual growth environment parameters of the current crop.

[0072] The water and fertilizer demand analysis submodule is used to input the actual growth stage, growth status parameters and actual growth environment parameters into the crop water and fertilizer analysis model for comparison of each indicator, and to conduct crop water and fertilizer demand analysis based on the comparison results.

[0073] The optimal water-fertilizer ratio submodule is used to select the water-fertilizer ratio parameters with the highest matching degree as the water-fertilizer ratio data for the current irrigation node of the current crop based on the analysis results.

[0074] Preferably, the water and fertilizer analysis module also includes: The manual confirmation submodule is used to send the growth status judgment result and the growth environment judgment result to the management personnel for manual confirmation. The confirmation result includes confirmation of the judgment result or modification of the judgment result parameters.

[0075] The model update submodule is used to adaptively update the crop water and fertilizer analysis model parameters when there are modified parameters in the feedback of manual confirmation results.

[0076] Preferably, the data processing module includes: The data conversion submodule is used to calculate the data prefix of multi-source monitoring data within a set string length based on the monitoring time and crop type, and to perform data prefix superposition processing on the multi-source monitoring data according to the data prefix calculation results.

[0077] The data association submodule is used to associate multi-source monitoring data with corresponding water and fertilizer irrigation nodes after superimposing a unified data prefix, and to establish a mapping relationship between multi-source monitoring data and each water and fertilizer irrigation node.

[0078] Specific limitations regarding the integrated space-ground-air-ground multi-source crop water and fertilizer data processing system can be found in the limitations of the integrated space-ground-air-ground multi-source crop water and fertilizer data processing method described above, and will not be repeated here. Each module in the aforementioned integrated space-ground-air-ground multi-source crop water and fertilizer data processing system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0079] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores multi-source crop water and fertilizer data. The network interface communicates with external terminals via a network. When the computer program is executed by the processor, it implements a multi-source crop water and fertilizer data processing method based on a space-ground integrated system.

[0080] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of a multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems.

[0081] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0082] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0083] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0084] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A multi-source crop water and fertilizer data processing method based on integrated space-ground-ground data processing, characterized in that... The method monitors crop growth data through an integrated space-air-ground network, and the method includes: Acquire multi-source monitoring data of crops, and perform spatiotemporal alignment processing on the multi-source monitoring data to obtain spatiotemporally consistent multi-source monitoring data; Based on the comprehensive analysis of the multi-source monitoring data, the current growth status and growth environment of crops are analyzed, and the water and fertilizer requirements of crops are analyzed based on the growth status and growth environment to obtain the water and fertilizer ratio data for each irrigation node. The multi-source monitoring data is converted into a unified data prefix according to a preset data format and associated with the corresponding water and fertilizer irrigation nodes to construct a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node; Based on the mapping relationship and the complete growth stages of crops, a data association architecture is constructed between multi-source monitoring data of complete crop water and fertilizer irrigation and water and fertilizer ratio data.

2. The multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems according to claim 1, characterized in that, The method further includes: The multi-source monitoring data and the water-fertilizer ratio data are distributed and stored in the original data format. The association architecture is used to search for the association between the multi-source monitoring data and the corresponding water-fertilizer ratio data of each water-fertilizer irrigation node.

3. The multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems according to claim 1, characterized in that, Before the step of comprehensively analyzing the current crop growth status and environment based on the multi-source monitoring data, and performing crop water and fertilizer demand analysis based on the growth status and environment to obtain water and fertilizer ratio data for each irrigation node, the method further includes: For each type of crop, multiple control groups were set up with growth stage and growth environment as variables to obtain water and fertilizer application data for different growth stages under the same growth environment and for the same growth stage under different environments. Obtain the crop growth status of each control group under the corresponding water and fertilizer application data, and evaluate the water and fertilizer application effect of the corresponding control group by comparing the crop growth status before and after fertilization. The fertilization effect evaluation results are confirmed by using a pre-set expert knowledge base or manual verification, and the optimal water and fertilizer ratio for each control group is selected. By associating crop type, growth stage, growth environment, and optimal water and fertilizer ratio, a crop water and fertilizer analysis model is constructed based on crop growth status and growth environment to analyze water and fertilizer requirements.

4. The multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems according to claim 3, characterized in that, The process involves comprehensively analyzing the current crop growth status and environment based on the multi-source monitoring data, and conducting a crop water and fertilizer requirement analysis based on the growth status and environment to obtain water and fertilizer ratio data for each irrigation node, including: The actual crop growth images in the multi-source monitoring data are compared with standard growth images of preset crop types, and the actual growth stage and growth status parameters of the current crop are obtained based on the comparison results. The actual terrain image in the multi-source monitoring data is compared with the preset planting terrain image. Based on the comparison result, the planting terrain is obtained and combined with the planting environment parameters to obtain the actual growth environment parameters of the current crop. The actual growth stage, growth status parameters and actual growth environment parameters are input into the crop water and fertilizer analysis model for comparison of each indicator, and the crop water and fertilizer demand analysis is carried out based on the comparison results. Based on the analysis results, the water and fertilizer ratio parameters with the highest matching degree were selected as the water and fertilizer ratio data for the current irrigation node of the current crop.

5. The multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems according to claim 4, characterized in that, The step of comprehensively analyzing the current crop growth status and environment based on the multi-source monitoring data, and conducting crop water and fertilizer demand analysis based on the growth status and environment to obtain water and fertilizer ratio data for each irrigation node, also includes: The growth status assessment results and growth environment assessment results are sent to the management personnel for manual confirmation. The confirmation results include confirmation of the assessment results or modification of the assessment parameters. When the feedback of the manual confirmation results shows that there are modified parameters, the modified parameters are used as model training parameters to adaptively update the model parameters of the crop water and fertilizer analysis model.

6. The multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems according to claim 1, characterized in that, The step of performing a unified data prefix conversion on the multi-source monitoring data according to a preset data format and associating it with the corresponding water and fertilizer irrigation nodes to construct a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node includes: Based on the monitoring time and crop type, the data prefix of the multi-source monitoring data is calculated within a set string length, and the data prefix is ​​superimposed on the multi-source monitoring data according to the data prefix calculation result. The multi-source monitoring data, after being superimposed with a unified data prefix, is associated with the corresponding water and fertilizer irrigation nodes to establish a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node.

7. The multi-source crop water and fertilizer data processing method based on integrated space-ground-air-ground systems according to claim 1, characterized in that, The process of acquiring multi-source monitoring data of crops and performing spatiotemporal alignment processing on the multi-source monitoring data to obtain spatiotemporally consistent multi-source monitoring data includes: Data from the air, ground, and space of crops are acquired separately. Based on the monitoring time and crop coordinates, spatiotemporal alignment is performed to obtain spatiotemporally consistent multi-source monitoring data integrating air, ground, and space.

8. A multi-source crop water and fertilizer data processing system based on integrated space-ground-ground data processing, characterized in that: The system is applied to the multi-source crop water and fertilizer data processing method based on the integrated space-ground-air-ground system as described in any one of claims 1-7. The system includes: The multi-source data acquisition module is used to acquire multi-source monitoring data of crops and perform spatiotemporal alignment processing on the multi-source monitoring data to obtain spatiotemporally consistent multi-source monitoring data. The water and fertilizer analysis module is used to comprehensively analyze the current growth status and growth environment of crops based on the multi-source monitoring data, and to perform water and fertilizer demand analysis of crops based on the growth status and growth environment to obtain water and fertilizer ratio data for each irrigation node. The data processing module is used to perform unified data prefix conversion on the multi-source monitoring data according to a preset data format and associate it with the corresponding water and fertilizer irrigation nodes to construct a mapping relationship between the multi-source monitoring data and each water and fertilizer irrigation node. The data association module is used to construct a data association architecture between multi-source monitoring data of complete crop water and fertilizer irrigation and water and fertilizer ratio data based on the mapping relationship and the complete growth stage of the crop.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-source crop water and fertilizer data processing method based on the integration of space, air, and ground as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-source crop water and fertilizer data processing method based on the integration of space, air, and ground as described in any one of claims 1 to 7.

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