A system and method for integrating multi-source heterogeneous data of cultivated land quality

Abstract

The present invention discloses a system and method for integrating multi-source heterogeneous data of cultivated land quality, and relates to the technical field of statistical data conversion. The present invention comprises a data analysis unit, a modeling unit and a display unit, wherein the data analysis unit is used to respectively use each corresponding alternative conversion module to perform data conversion to obtain alternative detection values ​​of various detection items at different positions in the monitoring area under the combination state of each data source and the alternative conversion module; respectively analyze and filter out the target conversion module corresponding to each data source for the alternative detection values ​​of various detection items at different positions in the monitoring area under the combination state of each data source and the alternative conversion module; integrate the detection values ​​of various detection items at different positions in the monitoring area under the combination state of each data source and the corresponding target conversion module to obtain the corrected detection values ​​of various detection items at different positions in the monitoring area. The present invention realizes the conversion and integration of cultivated land detection data.

Description

Technical Field

[0001] The present invention belongs to the field of statistical data conversion, and in particular relates to a system and method for integrating multi-source heterogeneous data of cultivated land quality. Background Art

[0002] As a basic resource for agricultural production, the quality of cultivated land is directly related to the yield and quality of crops. The quality of cultivated land not only involves physical and chemical properties such as soil fertility, structure and moisture, but also includes factors such as biodiversity and environmental pollution. With the development of social economy, cultivated land is facing the pressure of industrialization and urbanization, as well as problems such as excessive fertilizers and land degradation, which have led to a decline in the quality of cultivated land. Therefore, it is particularly important to accurately evaluate and effectively manage the quality of cultivated land.

[0003] At present, the assessment of cultivated land quality mainly relies on the collection and laboratory analysis of soil samples. In this process, the data formats of various sensors or other data collection input devices are different. In addition, due to the complexity of the cultivated land environment, many types of data need to be collected and analyzed, including remote sensing data, meteorological data, soil physical and chemical properties data, etc. These data sources are diverse and the data are different, which makes data integration difficult and prone to errors, and the information island phenomenon is serious, which directly or indirectly leads to large deviations in the collected cultivated land detection data. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for integrating multi-source heterogeneous data of cultivated land quality, which can realize the integration of cultivated land detection data by analyzing cultivated land detection data of various data at different location points of each data source.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention provides a method for integrating multi-source heterogeneous data of cultivated land quality, comprising:

[0007] Obtain the test values ​​of each test item at different locations within the monitoring area input by each data source;

[0008] Obtain a conversion module from each source data structure to a target data structure;

[0009] Performing data structure identification on the source data structure of each of the data sources to obtain a plurality of candidate conversion modules for performing data conversion on each of the data sources;

[0010] For each of the data sources, use each corresponding candidate conversion module to perform data conversion to obtain candidate detection values ​​of various detection items at different locations in the monitoring area under the combination state of each of the data sources and the candidate conversion module;

[0011] Analyze the candidate detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the candidate conversion module to select the target conversion module corresponding to each data source;

[0012] The detection values ​​of the various detection items at different positions in the monitoring area under the combination state of each data source and the corresponding target conversion module are integrated to obtain the corrected detection values ​​of the various detection items at different positions in the monitoring area.

[0013] The present invention also discloses a method for integrating multi-source heterogeneous data of cultivated land quality, comprising:

[0014] Obtaining geographic data model of the monitoring area;

[0015] Receiving corrected detection values ​​of various detection items at different locations within the monitoring area;

[0016] Marking the corrected detection values ​​of each detection item at different locations within the monitoring area in the geographic data model;

[0017] receiving data access requests;

[0018] Parsing the data access request to obtain a requested location area and a requested detection item;

[0019] According to the location points included in the requested location area and the corresponding requested detection items, the geographic data model is searched to obtain the corrected detection values ​​of the requested detection items for all the location points in the requested location area;

[0020] The corrected detection values ​​of each requested detection item are sent to all location points within the requested location area.

[0021] The present invention also discloses a method for integrating multi-source heterogeneous data of cultivated land quality, comprising:

[0022] Send a data access request;

[0023] receiving corrected detection values ​​of each requested detection item for all location points in the requested location area;

[0024] Displays the corrected detection value of each requested detection item for all location points within the requested location area.

[0025] The present invention also discloses a system for integrating multi-source heterogeneous data of cultivated land quality, comprising:

[0026] A data analysis unit, used to obtain the detection values ​​of various detection items at different locations within the monitoring area input by each data source;

[0027] Obtain a conversion module from each source data structure to a target data structure;

[0028] Performing data structure identification on the source data structure of each of the data sources to obtain a plurality of candidate conversion modules for performing data conversion on each of the data sources;

[0029] For each of the data sources, use each corresponding candidate conversion module to perform data conversion to obtain candidate detection values ​​of various detection items at different locations in the monitoring area under the combination state of each of the data sources and the candidate conversion module;

[0030] Analyze the candidate detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the candidate conversion module to select the target conversion module corresponding to each data source;

[0031] Integrate the detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the corresponding target conversion module to obtain the corrected detection values ​​of each detection item at different locations in the monitoring area;

[0032] A modeling unit, used for receiving and acquiring a geographic data model of a monitoring area;

[0033] Receiving corrected detection values ​​of various detection items at different locations within the monitoring area;

[0034] Marking the corrected detection values ​​of each detection item at different locations within the monitoring area in the geographic data model;

[0035] A presentation unit, used for sending a data access request;

[0036] The modeling unit is also used to receive data access requests;

[0037] Parsing the data access request to obtain a requested location area and a requested detection item;

[0038] According to the location points included in the requested location area and the corresponding requested detection items, the geographic data model is searched to obtain the corrected detection values ​​of the requested detection items for all the location points in the requested location area;

[0039] Send the corrected detection value of each requested detection item for all location points in the requested location area;

[0040] The display unit is further used to receive the corrected detection values ​​of each requested detection item of all location points within the requested location area;

[0041] Displays the corrected detection value of each requested detection item for all location points within the requested location area.

[0042] The present invention uses a data analysis unit to preliminarily identify data sources with different source data structures to select candidate conversion modules, and analyzes the farmland quality grade and farmland quality detection abnormality index of the candidate detection values ​​converted under the combined state of each candidate conversion module, thereby determining the target conversion module corresponding to each data source. This achieves the correct conversion and data aggregation of different data structures of different data sources. Afterwards, the converted farmland quality detection data is organized by the modeling unit, and the farmland quality detection data can also be intuitively and interactively displayed by the display unit.

[0043] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 A schematic diagram of functional units and information flows of a system for integrating multi-source heterogeneous data of cultivated land quality according to the present invention in one embodiment;

[0046] Figure 2 A schematic diagram of a step flow of a data analysis unit according to an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a step flow chart of a modeling unit according to an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of a step flow chart of a display unit according to an embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the step flow of step S5 in one embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of a step flow chart of step S51 in an embodiment of the present invention;

[0051] Figure 7 A schematic diagram of a step flow chart of step S52 in an embodiment of the present invention;

[0052] Figure 8 This is a schematic diagram of a step flow chart of step S523 in an embodiment of the present invention;

[0053] Fig. 9 This is a schematic diagram of a step flow chart of step S5233 in an embodiment of the present invention;

[0054] Fig.10 FIG. 4 is a schematic diagram of a step flow chart of step S53 in an embodiment of the present invention.

[0055] In the accompanying drawings, the components represented by the reference numerals are listed as follows:

[0056] 1-Data Analysis Unit, 2-Modeling Unit, 3-Presentation Unit. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0058] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0059] Cultivated land quality testing is a multidisciplinary process that aims to assess the land's production capacity, environmental conditions, and suitability for agricultural production. Cultivated land quality can be assessed based on multiple aspects, including soil physical and chemical properties, biological activity, structural stability, and historical land use. In order to achieve digital modeling and information services for cultivated land quality, it is necessary to convert and integrate the test value data structures of each source data format for different test items from different data sources.

[0060] However, the detection values ​​uploaded by different data sources are obtained by different sensors or data interfaces, and have different data format specifications, which leads to confusion in the cultivated land quality data in the detection area obtained by aggregation and cannot be used normally. In view of this, the present invention provides the following solution.

[0061] See also Figures 1 to 4 As shown, the present invention provides a system for integrating multi-source heterogeneous data of cultivated land quality, which includes a data analysis unit 1, a modeling unit 2 and a display unit 3 in terms of functional modules. The data analysis unit 1 can realize the conversion of the detection values ​​of cultivated land quality of various source data structures into the corrected detection values ​​of the target data structure, and the modeling unit 2 can digitally model the corrected detection values ​​of various detection items at different locations in the monitoring area, so as to facilitate the display unit 3 to call data. The display unit 3 can realize convenient human-computer interaction with the user.

[0062] In the specific application process, the data analysis unit 1 executes step S1 through the network or storage medium to obtain the detection values ​​of various detection items at different locations in the monitoring area input by each data source. The data source can be a sensor, such as an online temperature and humidity sensor, and the detection values ​​of the two detection items of temperature and humidity are uploaded to the data analysis unit 1. Next, step S2 can be executed to obtain a conversion module from each source data structure to a target data structure, which is usually a part of software or code module preset for realizing data structure conversion, including conversion rules, algorithms and logic, so as to map the source data structure to the target data structure.

[0063] The functions of the conversion module include data parsing, data reorganization, data type conversion, and data mapping. Data parsing is responsible for parsing the source data structure through the conversion module and decomposing it into individual data elements or fields. It involves the use of data parsers to handle different data formats and encodings. During the data reorganization process, the data of the source data structure needs to be reorganized and reorganized according to the requirements of the target data structure, involving operations such as extraction, merging, splitting, filtering or sorting of data fields. The conversion module in the data type conversion function needs to perform data type conversion to convert the data type in the source data structure to the data type required by the target data structure, for example, converting a string to integer or date format data. The conversion module in the data mapping function is responsible for defining the mapping rules between the source data structure and the target data structure. This includes field-level mapping, mapping the fields in the source data structure to the corresponding fields in the target data structure. The conversion module in the data conversion rule application process performs appropriate conversion and processing on the source data according to the predefined data conversion rules. It includes operations such as data cleaning, data normalization, data calculation or data supplementation. The transformation module can be implemented using programming languages ​​(such as Python, Java, C#, etc.), or configured and executed using ETL (Extract, Transform, Load) tools or data integration platforms. The specific implementation method depends on the complexity of the data transformation and the characteristics of the tools used.

[0064] Since the source data structure of the data source may have some obvious characteristics, it can be preliminarily matched by software automatic identification or manual verification identification, that is, step S3 can be executed next to identify the data structure of the source data structure of each data source to obtain several alternative conversion modules for data conversion of each data source. Of course, this step is only a preliminary screening, and the conversion modules that may meet the requirements are all used as alternative conversion modules. For each data source, step S4 can be executed next to respectively use each corresponding alternative conversion module to perform data conversion to obtain the alternative detection value of each detection item at different positions in the monitoring area under the combination state of each data source and the alternative conversion module. Next, step S5 can be executed to analyze and select the target conversion module corresponding to each data source respectively under the combination state of each data source and the alternative conversion module, that is, find the target conversion module corresponding to each data source by comparing and checking one by one. Finally, step S6 can be executed to integrate the detection values ​​of each detection item at different positions in the monitoring area under the combination state of each data source and the corresponding target conversion module to obtain the revised detection value of each detection item at different positions in the monitoring area, that is, to integrate and summarize the cultivated land quality detection data that has been converted.

[0065] See also Figure 1 and 3 As shown, in order to facilitate the call of the corrected detection values ​​of each detection item at different locations in the monitoring area, the modeling unit 2 can first execute step S021 to receive and obtain the geographic data model of the monitoring area. Next, step S022 can be executed to receive the corrected detection values ​​of each detection item at different locations in the monitoring area. Next, step S023 can be executed to mark the corrected detection values ​​of each detection item at different locations in the monitoring area in the geographic data model, that is, to complete the digital modeling of the geographic space of cultivated land quality.

[0066] In order to facilitate human-computer interaction with the user, and also to more intuitively display the quality of cultivated land in the monitoring area, first, the display unit 3 can execute step S031 to send a data access request. After that, the modeling unit 2 executes step S024 to receive the data access request. Next, step S025 can be executed to parse the data access request to obtain the requested location area and the requested detection item. Next, step S026 can be executed to retrieve the corrected detection values ​​of each requested detection item of all the location points in the requested location area in the geographic data model according to the location points contained in the requested location area and the corresponding requested detection items. Next, step S027 can be executed to send the corrected detection values ​​of each requested detection item of all the location points in the requested location area. Finally, the display unit 3 executes steps S032 and S033 to receive the corrected detection values ​​of each requested detection item of all the location points in the requested location area and display them.

[0067] See also Figure 5 As shown, if the conversion module selected for each data source is correct, the quality grade of cultivated land at different locations in the monitoring area after conversion is in accordance with the normal distribution. In order to measure the normal distribution of the quality grade of cultivated land at different locations in the monitoring area and determine whether it is normal, firstly, step S51 can be executed according to the alternative detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the alternative conversion module to obtain the quality grade of cultivated land at different locations in the monitoring area under the combination state of each data source and the alternative conversion module. The quality grade of cultivated land is a system for evaluating and classifying the quality of agricultural cultivated land. Different countries and regions may adopt different cultivated land quality grade systems. The calculation method of cultivated land quality grade in the national standard GB / T 33469-2016 will be specifically introduced later, and will not be repeated here.

[0068] In order to quantitatively evaluate the abnormality of cultivated land quality detection in the monitoring area under the combination of different data sources and alternative conversion modules, step S52 can be performed next to obtain the abnormality index of cultivated land quality detection in the monitoring area under the combination of each data source and alternative conversion module according to the quality level of cultivated land at different locations in the monitoring area under the combination of each data source and alternative conversion module. Finally, step S53 can be performed to screen the target conversion module corresponding to each data source according to the abnormality index of cultivated land quality detection in the monitoring area under the combination of each data source and alternative conversion module, which can be the combination of the data source and the alternative conversion module corresponding to the minimum value of the abnormality index of cultivated land quality detection as the corresponding target conversion module, or other methods can be used.

[0069] See also Figure 6As shown, the evaluation of arable land quality grade can be obtained by superimposing various artificially preset factors. In this scheme, the national standard GB / T 33469-2016 arable land quality grade is adopted. This standard stipulates specific specifications such as arable land quality area division, indicator determination, and arable land quality grade division process, which can be used as the arable land quality grade judgment standard in step S511.

[0070] Since each data source corresponds to a number of alternative conversion modules, and all data sources constitute the detection values ​​of various detection items used to determine the quality level of cultivated land, for each of the data sources, firstly, step S512 can be executed to obtain the alternative detection values ​​of various detection items at different positions in the monitoring area generated by the transformation of all data sources in each group of alternative conversion module combination states according to the total combination states of the several alternative conversion modules corresponding to each data source, that is, the several alternative conversion modules corresponding to each data source are extracted and combined with replacement to obtain multiple groups of alternative conversion module combination states.

[0071] Afterwards, the alternative detection values ​​of each detection item of the data source in each group of alternative conversion module combination states are respectively calculated for the arable land quality grade, that is, step S513 is executed to perform a comprehensive arable land quality evaluation on the alternative detection values ​​of each detection item at different locations in the monitoring area generated by the transformation of all data sources in each group of alternative conversion module combination states according to the arable land quality grade judgment standard to obtain the arable land quality grades of different locations in the monitoring area generated by the transformation of all data sources in each group of alternative conversion module combination states as the arable land quality grades of different locations in the monitoring area in the combination state of each data source and the alternative conversion module.

[0072] See also Figure 7As shown, in the natural state, the cultivated land quality levels of adjacent cultivated land areas are also the same or close. In other words, if the cultivated land quality levels of different positions in the monitoring area are too different, it means that there are large differences in the alternative detection values ​​obtained by the corresponding transformation. In view of this, in order to compare and calculate the abnormal state of the cultivated land quality level in the monitoring area under the combination state of each data source and the alternative conversion module, for each group of alternative conversion module combination states, step S521 can be first executed to divide the area where the cultivated land quality level is the same and adjacent to the position point into the same equal quality sub-area according to the cultivated land quality level of different position points in the monitoring area, so that there is no need to calculate each position point one by one. Next, step S522 can be executed to obtain the boundary between adjacent equal quality sub-areas, because the difference in cultivated land quality level only exists on the boundary. Next, step S523 can be executed to obtain the adjacent difference index of each equal quality sub-area according to the difference between the cultivated land quality levels of the position points on both sides of the boundary between adjacent equal quality sub-areas, that is, the abnormal degree of each equal quality sub-area. Next, step S524 can be executed to accumulate the adjacent difference indexes of all equal-quality sub-regions in the detection area to obtain the abnormal index of farmland quality detection in the monitoring area, that is, to calculate the abnormal degree of the monitoring area. Finally, step S525 can be executed to summarize and obtain the abnormal index of farmland quality detection in the monitoring area under the combination state of each data source and the alternative conversion module.

[0073] See also Figure 8 As shown, since each equal-quality sub-region may be adjacent to more than one other equal-quality sub-region, separate calculations are required in the specific calculation process. Specifically, for each equal-quality sub-region, step S5231 can be first executed to obtain the boundary line segment that divides the equal-quality sub-region and each adjacent equal-quality sub-region according to the boundary between adjacent equal-quality sub-regions, and each boundary line segment has and only has one equal-quality sub-region on both sides. For each boundary line segment, step S5232 can be executed to calculate and obtain the cumulative sum of the differences in the quality grades of cultivated land of several pairs of adjacent position points belonging to both sides of the boundary line segment as the local adjacent difference index of the equal-quality sub-region and the adjacent equal-quality sub-region on the other side of the boundary line segment, that is, the degree of difference between two adjacent equal-quality sub-regions.

[0074] After that, step S5233 can be executed to calculate and obtain all local adjacent difference indexes to obtain adjacent difference indexes of equal quality sub-regions. Since the number of equal quality sub-regions under different groups of alternative conversion module combinations is different, in order to facilitate mutual comparison, step S52331 can be executed next to obtain the area of ​​each adjacent equal quality sub-region. Then, step S52332 can be executed to use the area of ​​each adjacent equal quality sub-region as the corresponding weight coefficient to calculate and obtain the weighted cumulative value of all local adjacent difference indexes as the adjacent difference index of the equal quality sub-region. The above method balances the interference caused by the different numbers of equal quality sub-regions under different groups of alternative conversion module combinations on the subsequent comparison of abnormal indexes of different cultivated land quality detection by area weighting. Finally, step S5234 is executed to summarize the adjacent difference index of each equal quality sub-region, that is, to summarize the data.

[0075] In order to supplement the implementation process of the above steps S5231 to S5234, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0076]

[0077]

[0078]

[0079]

[0080] In this code, three classes are defined to represent the concepts in the problem: Point represents the location of cultivated land, Boundary represents the boundary line, and SubRegion represents the sub-region of equal quality. Each class has a corresponding member function to calculate the relevant information. For example, the Boundary class has a function to calculate the local difference index, and the SubRegion class has a function to calculate the adjacent difference index.

[0081] Each equal-quality subregion is associated with an adjacent subregion through the addAdjacentRegion method, and the boundary line is recorded. The adjacent difference index is obtained by multiplying the local difference index of each adjacent subregion by its area (as a weight coefficient) and then summing them. The main function demonstrates the creation of several subregions and boundaries, and sets the adjacent relationship between them. Then the adjacent difference index of each subregion is calculated and the result is output.

[0082] In short, this code can process a series of equal-quality sub-regions, calculate and summarize the neighboring difference index between each sub-region and its neighboring sub-regions based on the difference in farmland quality level. This calculation process takes into account the area of ​​the neighboring sub-region as a weight coefficient, ensuring that the calculation of the difference index reflects the actual importance of the neighboring area.

[0083] See also Fig.10 As shown, since the distribution of cultivated land quality in geographic space is continuous, it is reflected in the actual situation that the cultivated land quality levels of adjacent locations are the same or similar. Therefore, the cultivated land quality detection anomaly index in the monitoring area under the combination of the correct data source and the alternative conversion module should be much smaller than the cultivated land quality detection anomaly index in the monitoring area under the combination of other data sources and alternative conversion modules. In view of this, in order to screen the target conversion module corresponding to each data source, step S531 can be first executed to obtain the cultivated land quality detection anomaly index with the smallest value. Next, step S532 can be executed to obtain the number of cultivated land quality detection anomaly indexes other than the cultivated land quality detection anomaly index with the smallest value and the difference between the maximum and minimum values, thereby completing the data preparation work.

[0084] Next, the judgment procedure in step S533 and step S534 can be executed. First, the ratio of the difference between the maximum and minimum values ​​of the other cultivated land quality detection anomaly indexes other than the cultivated land quality detection anomaly index with the smallest value and the number of other cultivated land quality detection anomaly indexes is calculated as the expansion amplitude value, and it is judged whether the cultivated land quality detection anomaly index with the smallest value is less than the difference between the minimum value of the cultivated land quality detection anomaly index other than the cultivated land quality detection anomaly index with the smallest value minus the expansion amplitude value. This is to judge whether it is far less than the cultivated land quality detection anomaly index in the monitoring area under the combination state of other data sources and alternative conversion modules. If so, it means that the combination state of the correct data source and the alternative conversion module is obtained, so next, step S535 can be executed to use the alternative conversion module combination state corresponding to the cultivated land quality detection anomaly index with the smallest value as the target conversion module corresponding to each data source. If not, it means that the preset conversion module cannot complete the data structure conversion character, so next, step S536 can be executed to determine that the existing conversion module is not enough to complete the data integration and report an error.

[0085] It should be noted that an error does not mean that this solution cannot solve the technical problem, but requires the staff to provide other conversion modules at the same time, or extract other conversion modules from other functional units, and make judgments and selections.

[0086] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0087] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0088] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0089] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for integrating multi-source heterogeneous data of cultivated land quality, characterized in that: include, Obtain the test values ​​of each test item at different locations within the monitoring area input by each data source; Obtain a conversion module from each source data structure to a target data structure; Performing data structure identification on the source data structure of each of the data sources to obtain a plurality of candidate conversion modules for performing data conversion on each of the data sources; For each of the data sources, use each corresponding candidate conversion module to perform data conversion to obtain candidate detection values ​​of various detection items at different locations in the monitoring area under the combination state of each of the data sources and the candidate conversion module; Analyze the candidate detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the candidate conversion module to select the target conversion module corresponding to each data source; Integrate the detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the corresponding target conversion module to obtain the corrected detection values ​​of each detection item at different locations in the monitoring area; The step of analyzing the candidate detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the candidate conversion module to select the target conversion module corresponding to each data source includes: Obtaining the farmland quality level of different positions in the monitoring area under each combination state of the data source and the alternative conversion module according to the alternative detection values ​​of each detection item at different positions in the monitoring area under each combination state of the data source and the alternative conversion module; Obtaining the abnormality index of cultivated land quality detection in the monitoring area under each combination state of the data source and the alternative conversion module according to the quality levels of cultivated land at different locations in the monitoring area under each combination state of the data source and the alternative conversion module; According to the abnormal index of cultivated land quality detection in the monitoring area under the combination state of each data source and the candidate conversion module, the target conversion module corresponding to each data source is obtained by screening; The step of obtaining the abnormality index of cultivated land quality detection in the monitoring area under each combination state of the data source and the alternative conversion module according to the quality level of cultivated land at different locations in the monitoring area under each combination state of the data source and the alternative conversion module comprises: For each set of candidate conversion module combination states, According to the farmland quality levels of different locations within the monitoring area, the areas with adjacent locations with the same farmland quality level are divided into the same equal-quality sub-areas. Obtaining the boundaries between adjacent equal-quality sub-regions, The adjacent difference index of each equal-quality sub-region is obtained according to the difference between the cultivated land quality levels of the position points on both sides of the boundary between the adjacent equal-quality sub-regions. Accumulating the adjacent difference indexes of all the equal-quality sub-regions in the monitoring area to obtain the abnormal index of cultivated land quality detection in the monitoring area; The abnormal index of cultivated land quality detection in the monitoring area under the combination state of each data source and the alternative conversion module is summarized and obtained.

2. The method according to claim 1, characterized in that The step of obtaining the farmland quality level of different positions in the monitoring area under each combination state of the data source and the alternative conversion module according to the alternative detection values ​​of each detection item at different positions in the monitoring area under each combination state of the data source and the alternative conversion module, include, Obtain the criteria for determining the quality level of cultivated land; For each of the data sources, according to the total combination states of the candidate conversion modules corresponding to each of the data sources, the candidate detection values ​​of the detection items at different positions in the monitoring area generated by the conversion of all the data sources under each group of the candidate conversion module combination states are obtained respectively; According to the arable land quality grade judgment standard, a comprehensive arable land quality assessment is performed on the alternative detection values ​​of each detection item at different locations within the monitoring area generated by the transformation of all the data sources in each group of alternative conversion module combination states to obtain the arable land quality grades of different locations within the monitoring area generated by the transformation of all the data sources in each group of alternative conversion module combination states as the arable land quality grades of different locations within the monitoring area in the combination states of each data source and alternative conversion module.

3. The method according to claim 1, characterized in that The step of obtaining the adjacent difference index of each equal-quality sub-region according to the difference between the farmland quality levels of the position points on both sides of the boundary between the adjacent equal-quality sub-regions, include, For each of the equal-mass sub-regions, According to the boundaries between the adjacent equal-mass sub-regions, a boundary line segment for dividing the equal-mass sub-region and each adjacent equal-mass sub-region is obtained, For each of the dividing line segments, the cumulative sum of the differences in the quality grades of the cultivated land of a plurality of pairs of adjacent position points belonging to both sides of the dividing line segment is calculated as the local adjacent difference index of the equal-quality sub-region and the equal-quality sub-region adjacent to the other side of the dividing line segment; Calculating and acquiring all the local adjacent difference indexes to obtain adjacent difference indexes of the equal-quality sub-regions; The adjacent difference index of each of the equal-quality sub-regions is obtained by summing up.

4. The method according to claim 3, characterized in that The step of calculating and acquiring all the local adjacent difference indices to obtain adjacent difference indices of the equal-quality sub-regions includes: Get the area of ​​each adjacent sub-region of equal quality, The area of ​​each adjacent equal-quality sub-region is used as a corresponding weight coefficient, and a weighted cumulative value of all the local adjacent difference indexes is calculated to obtain the adjacent difference index of the equal-quality sub-region.

5. The method according to claim 1 or 2, characterized in that: The step of selecting and obtaining the target conversion module corresponding to each data source according to the abnormal index of farmland quality detection in the monitoring area under the combination state of each data source and the candidate conversion module, include, Obtaining the farmland quality detection abnormality index with the smallest value; Obtaining the number of the farmland quality detection anomaly indexes other than the farmland quality detection anomaly index with the smallest value and the difference between the maximum value and the minimum value; Calculate the ratio of the difference between the maximum value and the minimum value of the other farmland quality detection anomaly indexes other than the farmland quality detection anomaly index with the smallest value to the number of the other farmland quality detection anomaly indexes as the expansion amplitude value; Determine whether the farmland quality detection anomaly index with the smallest value is less than the difference between the minimum value of the other farmland quality detection anomaly indexes other than the farmland quality detection anomaly index with the smallest value and the expansion amplitude value; If yes, then the candidate conversion module combination state corresponding to the farmland quality detection abnormality index with the smallest value is used as the target conversion module corresponding to each of the data sources; If not, it is determined that the existing conversion module is insufficient to complete the data integration and an error is reported.

6. A method for integrating multi-source heterogeneous data of cultivated land quality, characterized in that: include, Obtaining geographic data model of the monitoring area; Receiving the corrected detection values ​​of each detection item at different locations within the monitoring area in the method for integrating multi-source heterogeneous data of cultivated land quality according to any one of claims 1 to 5; Marking the corrected detection values ​​of each detection item at different locations within the monitoring area in the geographic data model; receiving data access requests; Parsing the data access request to obtain a requested location area and a requested detection item; According to the location points included in the requested location area and the corresponding requested detection items, the geographic data model is searched to obtain the corrected detection values ​​of the requested detection items for all the location points in the requested location area; The corrected detection values ​​of each requested detection item are sent to all location points within the requested location area.

7. A method for integrating multi-source heterogeneous data of cultivated land quality, characterized in that: include, Send a data access request; Receiving the corrected detection values ​​of each requested detection item of all location points in the requested location area in the method for integrating multi-source heterogeneous data of cultivated land quality as described in claim 6; Displays the corrected detection value of each requested detection item for all location points within the requested location area.

8. A system for integrating multi-source heterogeneous data of cultivated land quality, characterized in that: include, A data analysis unit, used to obtain the detection values ​​of various detection items at different locations within the monitoring area input by each data source; Obtain a conversion module from each source data structure to a target data structure; Performing data structure identification on the source data structure of each of the data sources to obtain a plurality of candidate conversion modules for performing data conversion on each of the data sources; For each of the data sources, use each corresponding candidate conversion module to perform data conversion to obtain candidate detection values ​​of various detection items at different locations in the monitoring area under the combination state of each of the data sources and the candidate conversion module; Analyze the candidate detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the candidate conversion module to select the target conversion module corresponding to each data source; Integrate the detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the corresponding target conversion module to obtain the corrected detection values ​​of each detection item at different locations in the monitoring area; A modeling unit, used for receiving and acquiring a geographic data model of a monitoring area; Receiving corrected detection values ​​of various detection items at different locations within the monitoring area; Marking the corrected detection values ​​of each detection item at different locations within the monitoring area in the geographic data model; A presentation unit, used for sending a data access request; The modeling unit is also used to receive data access requests; Parsing the data access request to obtain a requested location area and a requested detection item; According to the location points included in the requested location area and the corresponding requested detection items, the geographic data model is searched to obtain the corrected detection values ​​of the requested detection items for all the location points in the requested location area; Send the corrected detection value of each requested detection item for all location points in the requested location area; The display unit is further used to receive the corrected detection values ​​of each requested detection item of all location points within the requested location area; Display the corrected detection values ​​of each requested detection item for all location points within the requested location area; The step of analyzing the candidate detection values ​​of each detection item at different locations in the monitoring area under the combination state of each data source and the candidate conversion module to select the target conversion module corresponding to each data source includes: Obtaining the farmland quality level of different positions in the monitoring area under each combination state of the data source and the alternative conversion module according to the alternative detection values ​​of each detection item at different positions in the monitoring area under each combination state of the data source and the alternative conversion module; Obtaining the abnormality index of cultivated land quality detection in the monitoring area under each combination state of the data source and the alternative conversion module according to the quality levels of cultivated land at different locations in the monitoring area under each combination state of the data source and the alternative conversion module; According to the abnormal index of cultivated land quality detection in the monitoring area under the combination state of each data source and the candidate conversion module, the target conversion module corresponding to each data source is obtained by screening; The step of obtaining the abnormality index of cultivated land quality detection in the monitoring area under each combination state of the data source and the alternative conversion module according to the quality level of cultivated land at different locations in the monitoring area under each combination state of the data source and the alternative conversion module comprises: For each set of candidate conversion module combination states, According to the farmland quality levels of different locations within the monitoring area, the areas with adjacent locations with the same farmland quality level are divided into the same equal-quality sub-areas. Obtaining the boundaries between adjacent equal-quality sub-regions, The adjacent difference index of each equal-quality sub-region is obtained according to the difference between the cultivated land quality levels of the position points on both sides of the boundary between the adjacent equal-quality sub-regions. Accumulating the adjacent difference indexes of all the equal-quality sub-regions in the monitoring area to obtain the abnormal index of cultivated land quality detection in the monitoring area; The abnormal index of cultivated land quality detection in the monitoring area under the combination state of each data source and the alternative conversion module is summarized and obtained.

Images