Agricultural production information navigation method and system based on big data

Abstract

The invention relates to the field of big data processing, in particular to an agricultural production information navigation method and system based on big data, and the method comprises the steps: connecting a big data platform to a user side, extracting data from the big data platform according to the attributes of agricultural parcels, and generating an agricultural parcel global map; according to the multi-dimensional real-time monitoring data of the agricultural plot, determining operation demand information of the agricultural plot so as to perform operation information identification processing on the global map of the agricultural plot, and performing fine setting of agricultural operation on the agricultural plot; extracting a matched agricultural plot global map from the big data platform according to a historical query record of a user side to the big data platform, and realizing full-link operation information extraction of the agricultural plot; and integrating and processing the extracted global maps of all the agricultural plots to generate full-link farming navigation information of the agricultural plots, and regularly sending the full-link farming navigation information to a user side to provide comprehensive and accurate agricultural production information interactive navigation for users.

Description

Technical Field

[0001] The present invention relates to the field of big data processing, and in particular to an agricultural production information navigation method and system based on big data. Background Art

[0002] Agricultural production activities such as crop planting are greatly affected by natural environmental factors. In particular, when crops are at different growth nodes, different operations such as fertilization, weeding, pest control, pruning, and pollination need to be carried out, and the implementation of the above operations also needs to be adjusted according to the real-time weather conditions. In order to increase agricultural output and reduce agricultural production costs, technical means have emerged to combine climate data and crop growth data for intelligent predictive planning of agricultural production operations. However, the above technical means are all aimed at large-scale agricultural production areas, and they cannot provide refined operation guidance for small-scale agricultural production areas such as individual farmland and orchards. In addition, the existing agricultural production operation guidance schemes are all one-way information transmission directly to users, which cannot form timely and efficient information interaction with users, and cannot provide users with comprehensive and accurate interactive navigation of agricultural production information, reducing the accuracy and reliability of the implementation of agricultural production operations. Summary of the invention

[0003] In view of the defects of the prior art, the present invention provides an agricultural production information navigation method and system based on big data, which connects the big data platform to the user end, extracts data from the big data platform and generates a global map of the agricultural plot according to the attributes of the agricultural plot; determines the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, performs operation information identification processing on the global map of the agricultural plot, and performs fine-grained setting of agricultural operations on the agricultural plot; extracts matching global maps of agricultural plots from the big data platform according to the historical query records of the user end on the big data platform, and realizes the extraction of full-link operation information of the agricultural plot; integrates and processes all the extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots, and sends the full-link agricultural navigation information to the user end at regular intervals, so as to provide users with comprehensive and accurate interactive navigation of agricultural production information, and improve the accuracy and reliability of the implementation of agricultural production operations.

[0004] The present invention provides an agricultural production information navigation method based on big data, comprising the following steps: Step S1, authenticating the user terminal and connecting the big data platform to the user terminal; extracting data from the big data platform and generating a global map of the agricultural plots according to the attributes of the agricultural plots; Step S2, determining the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot; and performing operation information identification processing on the global map of the agricultural plot according to the operation demand information; Step S3, uploading the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; extracting the matching global map of agricultural plots from the big data platform according to the historical query records of the user end on the big data platform; Step S4, integrating and processing all the extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots; and sending the full-link agricultural navigation information to the user terminal at regular intervals according to the data receiving status of the user terminal.

[0005] In one embodiment disclosed in the present application, in the step S1, the user terminal is authenticated, and the big data platform is connected to the user terminal; according to the attributes of the agricultural plots, data is extracted from the big data platform and a global map of the agricultural plots is generated, including: Perform access behavior authentication on the historical access logs of the user terminal to determine the historical abnormal access behavior associated data object information of the user terminal; connect part of the big data platform to the user terminal according to the historical abnormal access behavior associated data object information; According to the geographical location attributes of the agricultural plots, the big data platform is searched for data to extract three-dimensional terrain data that matches the global scope of the agricultural plots; the three-dimensional terrain data is subjected to noise component elimination and map processing to generate a global map of the agricultural plots.

[0006] In one embodiment disclosed in the present application, in the step S2, the operation demand information of the agricultural plot is determined according to the multi-dimensional real-time monitoring data of the agricultural plot; and the operation information identification processing is performed on the global map of the agricultural plot according to the operation demand information, including: According to the geographical location of the agricultural plot, some sensor devices connected to the Internet of Things are invited to obtain monitoring data; according to the monitoring data type returned by each of the sensor devices, the returned monitoring data is subjected to frame extraction verification to determine whether the returned monitoring data is returned completely; the fully returned multi-dimensional real-time monitoring data is analyzed to obtain the crop growth status information of the agricultural plot, thereby determining the operation demand information of the agricultural plot; wherein the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot; According to the type of operation and the location of the operation area to be performed on the crops of the agricultural plot as required, the map area corresponding to the operation area in the global map of the agricultural plot is processed for operation type and operation parameter identification.

[0007] In one embodiment disclosed in the present application, in the step S3, the global map of agricultural plots is uploaded to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; and the matching global map of agricultural plots is extracted from the big data platform according to the historical query record of the user terminal on the big data platform, including: According to the comparison of the implementation time limit attribute of all the operation information marked in the global map of the agricultural plots that have completed the marking process with the respective allowed access time of all the intervals in the big data platform, the global map of the agricultural plots is uploaded to the corresponding intervals in the big data platform; Based on the historical query records of the user terminal on the big data platform, the type of map navigation information that the user terminal expects to query during the historical query process is determined; based on the type of map navigation information, the operation information of all agricultural plot global map markers in the big data platform is compared for similarity, so as to extract the matching agricultural plot global map.

[0008] In one embodiment disclosed in the present application, in the step S4, all extracted global maps of agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; and according to the data receiving state of the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular interval, including: According to the implementation area location and real-time time corresponding to the operation information identified in the global map of all the extracted agricultural plots, the global maps of all the extracted agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; wherein the full-link agricultural navigation information includes the spatial and temporal navigation information of all agricultural activities that need to be performed in all stages of plant growth of the agricultural plots; According to the data receiving status of the user terminal, the allowed receiving time range of the navigation information of the user terminal is determined; according to the running frequency of the application in the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular interval.

[0009] The present invention also provides an agricultural production information navigation system based on big data, including: A big data access module, used to authenticate the user terminal and connect the big data platform to the user terminal; A map generation module, used to extract data from the big data platform and generate a global map of agricultural plots according to the attributes of agricultural plots; An operation demand information determination module, used to determine the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot; A map identification processing module, used for performing operation information identification processing on the global map of the agricultural plot according to the operation demand information; A map uploading module, used for uploading the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; A map extraction module, used to extract a matching global map of agricultural plots from the big data platform according to the historical query records of the user terminal on the big data platform; The navigation information generation and sending module is used to integrate and process the extracted global maps of all agricultural plots to generate full-link agricultural navigation information of the agricultural plots; and send the full-link agricultural navigation information to the user terminal at regular intervals according to the data receiving status of the user terminal.

[0010] In one embodiment disclosed in the present application, the big data access module is used to authenticate the user terminal and connect the big data platform to the user terminal, including: Perform access behavior authentication on the historical access logs of the user terminal to determine the historical abnormal access behavior associated data object information of the user terminal; connect part of the big data platform to the user terminal according to the historical abnormal access behavior associated data object information; The map generation module is used to extract data from the big data platform and generate a global map of agricultural plots according to the attributes of the agricultural plots, including: According to the geographical location attributes of the agricultural plots, the big data platform is searched for data to extract three-dimensional terrain data that matches the global scope of the agricultural plots; the three-dimensional terrain data is subjected to noise component elimination and map processing to generate a global map of the agricultural plots.

[0011] In one embodiment disclosed in the present application, the operation demand information determination module is used to determine the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, including: According to the geographical location of the agricultural plot, some sensor devices connected to the Internet of Things are invited to obtain monitoring data; according to the monitoring data type returned by each of the sensor devices, the returned monitoring data is subjected to frame extraction verification to determine whether the returned monitoring data is returned completely; the fully returned multi-dimensional real-time monitoring data is analyzed to obtain the crop growth status information of the agricultural plot, thereby determining the operation demand information of the agricultural plot; wherein the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot; The map identification processing module is used to perform operation information identification processing on the global map of the agricultural plot according to the operation demand information, including: According to the type of operation and the location of the operation area to be performed on the crops of the agricultural plot as required, the map area corresponding to the operation area in the global map of the agricultural plot is processed for operation type and operation parameter identification.

[0012] In one embodiment disclosed in the present application, the map uploading module is used to upload the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing, including: According to the comparison of the implementation time limit attribute of all the operation information marked in the global map of the agricultural plots that have completed the marking process with the respective allowed access time of all the intervals in the big data platform, the global map of the agricultural plots is uploaded to the corresponding intervals in the big data platform; The map extraction module is used to extract a matching global map of agricultural plots from the big data platform according to the historical query records of the user terminal on the big data platform, including: Based on the historical query records of the user terminal on the big data platform, the type of map navigation information that the user terminal expects to query during the historical query process is determined; based on the type of map navigation information, the operation information of all agricultural plot global map markers in the big data platform is compared for similarity, so as to extract the matching agricultural plot global map.

[0013] In one embodiment disclosed in the present application, the navigation information generation and sending module is used to integrate and process all extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots; according to the data receiving status of the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular interval, including: According to the implementation area location and real-time time corresponding to the operation information identified in the global map of all the extracted agricultural plots, the global maps of all the extracted agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; wherein the full-link agricultural navigation information includes the spatial and temporal navigation information of all agricultural activities that need to be performed in all stages of plant growth of the agricultural plots; According to the data receiving status of the user terminal, the allowed receiving time range of the navigation information of the user terminal is determined; according to the running frequency of the application in the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular interval.

[0014] Compared with the existing technology, the agricultural production information navigation method and system based on big data connects the big data platform to the user end, extracts data from the big data platform and generates a global map of the agricultural plot according to the attributes of the agricultural plot; determines the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, performs operation information identification processing on the global map of the agricultural plot, and performs fine-grained settings for agricultural operations on the agricultural plot; extracts matching global maps of agricultural plots from the big data platform according to the historical query records of the user end on the big data platform, and realizes the extraction of full-link operation information of the agricultural plot; integrates and processes all extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plot, and sends the full-link agricultural navigation information to the user end at regular intervals, providing users with comprehensive and accurate interactive navigation of agricultural production information, and improving the accuracy and reliability of the implementation of agricultural production operations.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0016] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic flow chart of the agricultural production information navigation method based on big data provided by the present invention.

[0019] Figure 2 A schematic diagram of the framework of the agricultural production information navigation system based on big data provided by the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1, is a flow chart of an agricultural production information navigation method based on big data provided by an embodiment of the present invention. The agricultural production information navigation method based on big data includes: Step S1, authenticating the user terminal and connecting the big data platform to the user terminal; extracting data from the big data platform and generating a global map of the agricultural plots according to the attributes of the agricultural plots; Step S2, determining the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot; and performing operation information identification processing on the global map of the agricultural plot according to the operation demand information; Step S3, uploading the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; extracting the matching global map of agricultural plots from the big data platform according to the historical query records of the user end on the big data platform; Step S4, integrating and processing all the extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots; and sending the full-link agricultural navigation information to the user end at regular intervals according to the data reception status of the user end.

[0022] The agricultural production information navigation method based on big data connects the big data platform to the user end, extracts data from the big data platform and generates a global map of the agricultural plot according to the attributes of the agricultural plot; determines the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, performs operation information identification processing on the global map of the agricultural plot, and performs fine-scale setting of agricultural operations on the agricultural plot; extracts the matching global map of the agricultural plot from the big data platform according to the historical query records of the user end on the big data platform, and realizes the extraction of full-link operation information of the agricultural plot; integrates and processes all the extracted global maps of the agricultural plot to generate full-link agricultural navigation information of the agricultural plot, and sends the full-link agricultural navigation information to the user end at regular intervals, so as to provide users with comprehensive and accurate interactive navigation of agricultural production information and improve the accuracy and reliability of the implementation of agricultural production operations.

[0023] Preferably, in step S1, the user terminal is authenticated and the big data platform is connected to the user terminal; according to the attributes of the agricultural plots, data is extracted from the big data platform and a global map of the agricultural plots is generated, including: Perform access behavior authentication on the historical access logs of the user end to determine the data object information associated with the historical abnormal access behavior of the user end; connect part of the big data platform to the user end based on the data object information associated with the historical abnormal access behavior; According to the geographical location attributes of the agricultural plots, data search is performed on the big data platform to extract three-dimensional terrain data that matches the global range of the agricultural plots. The three-dimensional terrain data is subjected to noise removal and map processing to generate a global map of the agricultural plots.

[0024] The big data platform stores planting and cultivation data of different agricultural plots. These planting and cultivation data are sensitive data and are not open to all users. In order to ensure the data security of the big data platform, it is necessary to authenticate the user first, that is, to authenticate the access behavior of the user's own historical access log, and determine the data object information corresponding to the abnormal access behavior initiated by the user during the historical access to the big data platform; wherein the above abnormal access behavior may be but not limited to the illegal copying or illegal tampering of the big data platform; the above data object information may be but not limited to the storage interval location of the data corresponding to the abnormal access behavior initiated by the user. Then, according to the above historical abnormal access behavior associated data object information, some storage intervals in the big data platform are connected to the user, wherein the part of the storage intervals allowed to be accessed by the user refers to other storage intervals in the big data platform except for the storage intervals where the data corresponding to the abnormal access behavior initiated by the user is located, so as to avoid the user repeating the above abnormal access behavior, ensure that the user obtains partial access rights to the big data platform and ensure the data security of the big data platform. In addition, based on the geographical location attributes of the agricultural plots, such as longitude, latitude and altitude, data search is performed on the big data platform to extract three-dimensional terrain data that matches the global range of the agricultural plots. The noise components of the above three-dimensional terrain data are eliminated and mapped to generate a global map of the agricultural plots. The global map of the agricultural plots can fully represent the terrain conditions and plant planting conditions of the agricultural plots.

[0025] Preferably, in step S2, the operation demand information of the agricultural plot is determined according to the multi-dimensional real-time monitoring data of the agricultural plot; and the operation information identification processing is performed on the global map of the agricultural plot according to the operation demand information, including: According to the geographical location of the agricultural plot, some sensor devices connected to the Internet of Things are invited to obtain monitoring data; according to the monitoring data type returned by some sensor devices, frame extraction and verification are performed on the returned monitoring data to determine whether the returned monitoring data is complete; the fully returned multi-dimensional real-time monitoring data is analyzed to obtain the crop growth status information of the agricultural plot, thereby determining the operation demand information of the agricultural plot; wherein the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot; According to the type of operation and the location of the operation area to be performed on the crops in the agricultural plot as required, the map area corresponding to the operation area in the global map of the agricultural plot is processed with the operation type and operation parameter identification.

[0026] In order to obtain the real-time weather conditions, plant growth trends, and soil characteristics of the agricultural plots, the agricultural plots are locally distributed with multiple different types of sensor devices. These sensor devices may be, but are not limited to, rainfall sensors, temperature and humidity sensors, wind speed / wind direction sensors, camera sensors, soil moisture sensors, and soil element sensors. All sensors are connected to the Internet of Things, and the real-time weather status data, plant growth trend data, and soil characteristic data of the agricultural plots detected in real time are uploaded to the platform through the Internet of Things, thereby comprehensively and accurately monitoring the agricultural plots and enriching the agricultural production-related data of the agricultural plots. Specifically, the geographical location of the agricultural plot is compared with the geographical locations of all the sensor devices connected to the Internet of Things, the sensor devices installed in the range of the agricultural plot are determined, and an invitation to obtain monitoring data is issued to the above-mentioned sensor devices. After receiving and parsing the monitoring data acquisition invitation, the above-mentioned sensor device will return the monitoring data generated by itself in the corresponding time period. At this time, according to the type of monitoring data returned by the sensor device, each returned monitoring data is subjected to frame extraction verification to determine whether the monitoring data is complete or not. Only when the returned monitoring data is complete, the fully returned multi-dimensional real-time monitoring data (i.e., the fully returned monitoring data from different types of sensor devices) is parsed to obtain the crop growth status information of the agricultural plot; wherein, the above-mentioned crop growth status information may be, but is not limited to, the growth status of the stems, leaves, flowers, fruits and other parts of the plant crops on the agricultural plot. Considering that the requirements of plant crops for water, fertilizer and nutrients at different growth stages are different, and the pest and disease conditions at different growth stages are also different, the requirements of plant crops on agricultural plots for irrigation, fertilization and spraying of pesticides or growth hormones at different growth stages are also different. Based on the crop growth status information of the agricultural plots, the types of operations and the locations of the operation areas that need to be implemented on the crops in the agricultural plots are determined, and the map areas corresponding to the operation areas in the global map of the agricultural plots are marked with operation types and operation parameters. The types of operations such as irrigation, fertilization, and spraying of pesticides or growth hormones and the locations of the operation implementation areas required for the actual growth stage of the crops on the corresponding agricultural plots are accurately determined, providing a reliable basis for agricultural production management of crops.

[0027] Preferably, in step S3, the global map of agricultural plots is uploaded to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; and the matching global map of agricultural plots is extracted from the big data platform according to the historical query records of the user end on the big data platform, including: According to the comparison of the implementation time limit attribute of all operation information marked in the global map of the agricultural plots that have completed the marking process with the respective allowed access time of all intervals in the big data platform, the global map of the agricultural plots is uploaded to the corresponding interval in the big data platform; Based on the historical query records of the user end on the big data platform, the type of map navigation information that the user end expects to query during the historical query process is determined; based on the type of map navigation information, the operation information of all agricultural plot global map markers in the big data platform is compared for similarity, so as to extract the matching agricultural plot global map.

[0028] There are time limits for irrigation, fertilization, and spraying of pesticides or growth hormones on agricultural plots, that is, the above operations need to be completed within the specified time range. Once the specified time range is exceeded and the corresponding operations are not implemented, the normal growth of the crops will be seriously affected, thereby affecting the final yield of the crops. In order to ensure that the user end can obtain notification messages about the implementation of corresponding operations on agricultural plots from the big data platform within the specified time range, the implementation time limit attributes of all operation information marked in the global map of the agricultural plots that have completed the identification processing are compared with the respective allowed access time of all intervals in the big data platform, and the global map of the agricultural plots is uploaded to the corresponding intervals in the big data platform, ensuring that the user end can timely obtain information about the implementation of corresponding operations on the crops in the agricultural plots from the corresponding access in the big data platform, avoiding delays in the implementation of operations, and providing reliable support for the growth of crops in the agricultural plots. In addition, based on the historical query records of the user terminal on the big data platform, the type of map navigation information that the user terminal expects to query during the historical query process is determined. The above-mentioned map navigation information type may include but is not limited to text-type or picture-type navigation information. Then, based on the type of map navigation information, the operation information of the global map marks of all agricultural plots in the big data platform is compared for similarity, and the global maps of agricultural plots with similarity greater than or equal to a preset similarity threshold are extracted and processed, thereby meeting the user terminal's information navigation needs for the agricultural plots.

[0029] Preferably, in step S4, all extracted global maps of agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; and the full-link agricultural navigation information is sent to the user terminal at regular intervals according to the data receiving status of the user terminal, including: According to the implementation area location and real-time time corresponding to the operation information identified in the global map of all agricultural plots extracted, the global maps of all agricultural plots extracted are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; wherein the full-link agricultural navigation information includes the spatial and temporal navigation information of all agricultural activities that need to be performed during the entire plant growth stage of the agricultural plots; According to the data reception status of the user end, the allowed time range for receiving navigation information of the user end is determined; according to the running frequency of the application in the user end, the full-link agricultural navigation information is sent to the user end at a regular interval.

[0030] From the above content, it can be seen that the operation information extracted from the global map of each agricultural plot corresponds to the operation information that needs to be implemented in a certain area within the agricultural plot in a certain time interval. In order to enable the user end to obtain the complete operation information that needs to be implemented in different time stages and different plot areas of the agricultural plot during the entire plant crop growth process, according to the implementation area location and real-time time corresponding to the operation information extracted from the global map of all agricultural plots, the global maps of all agricultural plots extracted are integrated and processed to generate the full-link agricultural navigation information of the agricultural plot, thereby fully representing the spatial and temporal navigation information of all agricultural activities that need to be performed during the entire plant growth stage of the agricultural plot, and providing accurate reference for farmers to implement agricultural activities. In addition, according to the data reception status of the user end, the allowed reception time range of the navigation information of the user end is determined, and according to the running frequency of the application in the user end, the full-link agricultural navigation information is sent to the user end at a regular interval, providing users with comprehensive and accurate interactive navigation of agricultural production information, and improving the accuracy and reliability of the implementation of agricultural production operations.

[0031] See also Figure 2 , is a schematic diagram of the framework of an agricultural production information navigation system based on big data provided by an embodiment of the present invention. The agricultural production information navigation system based on big data includes: The big data access module is used to authenticate the user end and connect the big data platform to the user end; A map generation module is used to extract data from the big data platform and generate a global map of agricultural plots according to the attributes of agricultural plots; An operation demand information determination module is used to determine the operation demand information of the agricultural plot based on the multi-dimensional real-time monitoring data of the agricultural plot; A map identification processing module is used to perform operation information identification processing on the global map of agricultural plots according to operation demand information; A map uploading module, used to upload the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; A map extraction module is used to extract a matching global map of agricultural plots from the big data platform based on the historical query records of the user end on the big data platform; The navigation information generation and sending module is used to integrate and process the extracted global maps of all agricultural plots to generate full-link agricultural navigation information for the agricultural plots; and send the full-link agricultural navigation information to the user end at a regular interval according to the data reception status of the user end.

[0032] The agricultural production information navigation system based on big data connects the big data platform to the user end, extracts data from the big data platform and generates a global map of the agricultural plots according to the attributes of the agricultural plots; determines the operation demand information of the agricultural plots according to the multi-dimensional real-time monitoring data of the agricultural plots, performs operation information identification processing on the global map of the agricultural plots, and performs fine-scale settings for agricultural operations on the agricultural plots; extracts matching global maps of agricultural plots from the big data platform according to the historical query records of the big data platform by the user end, and realizes the extraction of full-link operation information of the agricultural plots; integrates and processes all extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots, and sends the full-link agricultural navigation information to the user end at regular intervals, providing users with comprehensive and accurate interactive navigation of agricultural production information, and improving the accuracy and reliability of the implementation of agricultural production operations.

[0033] Preferably, the big data access module is used to authenticate the user terminal and connect the big data platform to the user terminal, including: Perform access behavior authentication on the historical access logs of the user end to determine the data object information associated with the historical abnormal access behavior of the user end; connect part of the big data platform to the user end based on the data object information associated with the historical abnormal access behavior; The map generation module is used to extract data from the big data platform and generate a global map of agricultural plots based on the attributes of agricultural plots, including: According to the geographical location attributes of the agricultural plots, data search is performed on the big data platform to extract three-dimensional terrain data that matches the global range of the agricultural plots. The three-dimensional terrain data is subjected to noise removal and map processing to generate a global map of the agricultural plots.

[0034] The big data platform stores planting and cultivation data of different agricultural plots. These planting and cultivation data are sensitive data and are not open to all users. In order to ensure the data security of the big data platform, it is necessary to authenticate the user first, that is, to authenticate the access behavior of the user's own historical access log, and determine the data object information corresponding to the abnormal access behavior initiated by the user during the historical access to the big data platform; wherein the above abnormal access behavior may be but not limited to the illegal copying or illegal tampering of the big data platform; the above data object information may be but not limited to the storage interval location of the data corresponding to the abnormal access behavior initiated by the user. Then, according to the above historical abnormal access behavior associated data object information, some storage intervals in the big data platform are connected to the user, wherein the part of the storage intervals allowed to be accessed by the user refers to other storage intervals in the big data platform except for the storage intervals where the data corresponding to the abnormal access behavior initiated by the user is located, so as to avoid the user repeating the above abnormal access behavior, ensure that the user obtains partial access rights to the big data platform and ensure the data security of the big data platform. In addition, based on the geographical location attributes of the agricultural plots, such as longitude, latitude and altitude, data search is performed on the big data platform to extract three-dimensional terrain data that matches the global range of the agricultural plots. The noise components of the above three-dimensional terrain data are eliminated and mapped to generate a global map of the agricultural plots. The global map of the agricultural plots can fully represent the terrain conditions and plant planting conditions of the agricultural plots.

[0035] Preferably, the operation demand information determination module is used to determine the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, including: According to the geographical location of the agricultural plot, some sensor devices connected to the Internet of Things are invited to obtain monitoring data; according to the monitoring data type returned by some sensor devices, frame extraction and verification are performed on the returned monitoring data to determine whether the returned monitoring data is complete; the fully returned multi-dimensional real-time monitoring data is analyzed to obtain the crop growth status information of the agricultural plot, thereby determining the operation demand information of the agricultural plot; wherein the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot; The map identification processing module is used to process the operation information identification of the global map of agricultural plots according to the operation demand information, including: According to the type of operation and the location of the operation area to be performed on the crops in the agricultural plot as required, the map area corresponding to the operation area in the global map of the agricultural plot is processed with the operation type and operation parameter identification.

[0036] In order to obtain the real-time weather conditions, plant growth trends, and soil characteristics of the agricultural plots, the agricultural plots are locally distributed with multiple different types of sensor devices. These sensor devices may be, but are not limited to, rainfall sensors, temperature and humidity sensors, wind speed / wind direction sensors, camera sensors, soil moisture sensors, and soil element sensors. All sensors are connected to the Internet of Things, and the real-time weather status data, plant growth trend data, and soil characteristic data of the agricultural plots detected in real time are uploaded to the platform through the Internet of Things, thereby comprehensively and accurately monitoring the agricultural plots and enriching the agricultural production-related data of the agricultural plots. Specifically, the geographical location of the agricultural plot is compared with the geographical locations of all the sensor devices connected to the Internet of Things, the sensor devices installed in the range of the agricultural plot are determined, and an invitation to obtain monitoring data is issued to the above-mentioned sensor devices. After receiving and parsing the monitoring data acquisition invitation, the above-mentioned sensor device will return the monitoring data generated by itself in the corresponding time period. At this time, according to the type of monitoring data returned by the sensor device, each returned monitoring data is subjected to frame extraction verification to determine whether the monitoring data is complete or not. Only when the returned monitoring data is complete, the fully returned multi-dimensional real-time monitoring data (i.e., the fully returned monitoring data from different types of sensor devices) is parsed to obtain the crop growth status information of the agricultural plot; wherein, the above-mentioned crop growth status information may be, but is not limited to, the growth status of the stems, leaves, flowers, fruits and other parts of the plant crops on the agricultural plot. Considering that the requirements of plant crops for water, fertilizer and nutrients at different growth stages are different, and the pest and disease conditions at different growth stages are also different, the requirements of plant crops on agricultural plots for irrigation, fertilization and spraying of pesticides or growth hormones at different growth stages are also different. Based on the crop growth status information of the agricultural plots, the types of operations and the locations of the operation areas that need to be implemented on the crops in the agricultural plots are determined, and the map areas corresponding to the operation areas in the global map of the agricultural plots are marked with operation types and operation parameters. The types of operations such as irrigation, fertilization, and spraying of pesticides or growth hormones and the locations of the operation implementation areas required for the actual growth stage of the crops on the corresponding agricultural plots are accurately determined, providing a reliable basis for agricultural production management of crops.

[0037] Preferably, the map uploading module is used to upload the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing, including: According to the comparison of the implementation time limit attribute of all operation information marked in the global map of the agricultural plots that have completed the marking process with the respective allowed access time of all intervals in the big data platform, the global map of the agricultural plots is uploaded to the corresponding interval in the big data platform; The map extraction module is used to extract the matching global map of agricultural plots from the big data platform based on the historical query records of the user end on the big data platform, including: Based on the historical query records of the user end on the big data platform, the type of map navigation information that the user end expects to query during the historical query process is determined; based on the type of map navigation information, the operation information of all agricultural plot global map markers in the big data platform is compared for similarity, so as to extract the matching agricultural plot global map.

[0038] There are time limits for irrigation, fertilization, and spraying of pesticides or growth hormones on agricultural plots, that is, the above operations need to be completed within the specified time range. Once the specified time range is exceeded and the corresponding operations are not implemented, the normal growth of the crops will be seriously affected, thereby affecting the final yield of the crops. In order to ensure that the user end can obtain notification messages about the implementation of corresponding operations on agricultural plots from the big data platform within the specified time range, the implementation time limit attributes of all operation information marked in the global map of the agricultural plots that have completed the identification processing are compared with the respective allowed access time of all intervals in the big data platform, and the global map of the agricultural plots is uploaded to the corresponding intervals in the big data platform, ensuring that the user end can timely obtain information about the implementation of corresponding operations on the crops in the agricultural plots from the corresponding access in the big data platform, avoiding delays in the implementation of operations, and providing reliable support for the growth of crops in the agricultural plots. In addition, based on the historical query records of the user terminal on the big data platform, the type of map navigation information that the user terminal expects to query during the historical query process is determined. The above-mentioned map navigation information type may include but is not limited to text-type or picture-type navigation information. Then, based on the type of map navigation information, the operation information of the global map marks of all agricultural plots in the big data platform is compared for similarity, and the global maps of agricultural plots with similarity greater than or equal to a preset similarity threshold are extracted and processed, thereby meeting the user terminal's information navigation needs for the agricultural plots.

[0039] Preferably, the navigation information generation and sending module is used to integrate and process all extracted global maps of agricultural plots to generate full-link agricultural navigation information of agricultural plots; according to the data receiving status of the user terminal, the full-link agricultural navigation information is sent to the user terminal at regular intervals, including: According to the implementation area location and real-time time corresponding to the operation information identified in the global map of all agricultural plots extracted, the global maps of all agricultural plots extracted are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; wherein the full-link agricultural navigation information includes the spatial and temporal navigation information of all agricultural activities that need to be performed during the entire plant growth stage of the agricultural plots; According to the data reception status of the user end, the allowed time range for receiving navigation information of the user end is determined; according to the running frequency of the application in the user end, the full-link agricultural navigation information is sent to the user end at a regular interval.

[0040] From the above content, it can be seen that the operation information extracted from the global map of each agricultural plot corresponds to the operation information that needs to be implemented in a certain area within the agricultural plot in a certain time interval. In order to enable the user end to obtain the complete operation information that needs to be implemented in different time stages and different plot areas of the agricultural plot during the entire plant crop growth process, according to the implementation area location and real-time time corresponding to the operation information extracted from the global map of all agricultural plots, the global maps of all agricultural plots extracted are integrated and processed to generate the full-link agricultural navigation information of the agricultural plot, thereby fully representing the spatial and temporal navigation information of all agricultural activities that need to be performed during the entire plant growth stage of the agricultural plot, and providing accurate reference for farmers to implement agricultural activities. In addition, according to the data reception status of the user end, the allowed reception time range of the navigation information of the user end is determined, and according to the running frequency of the application in the user end, the full-link agricultural navigation information is sent to the user end at a regular interval, providing users with comprehensive and accurate interactive navigation of agricultural production information, and improving the accuracy and reliability of the implementation of agricultural production operations.

[0041] From the contents of the above embodiments, it can be seen that the agricultural production information navigation method and system based on big data connects the big data platform to the user end, extracts data from the big data platform and generates a global map of the agricultural plot according to the attributes of the agricultural plot; determines the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, performs operation information identification processing on the global map of the agricultural plot, and performs fine-grained settings for agricultural operations on the agricultural plot; extracts matching global maps of agricultural plots from the big data platform according to the historical query records of the big data platform by the user end, and realizes the extraction of full-link operation information of the agricultural plot; integrates and processes all extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plot, and sends the full-link agricultural navigation information to the user end at regular intervals, so as to provide users with comprehensive and accurate interactive navigation of agricultural production information and improve the accuracy and reliability of the implementation of agricultural production operations.

[0042] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The agricultural production information navigation method based on big data is characterized by: It includes the following steps: Step S1, authenticating the user terminal and connecting the big data platform to the user terminal; extracting data from the big data platform and generating a global map of the agricultural plots according to the attributes of the agricultural plots; Step S2, determining the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot; and performing operation information identification processing on the global map of the agricultural plot according to the operation demand information; Step S3, uploading the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; extracting the matching global map of agricultural plots from the big data platform according to the historical query records of the user end on the big data platform; Step S4, integrating and processing all the extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots; and sending the full-link agricultural navigation information to the user terminal at regular intervals according to the data receiving status of the user terminal.

2. The agricultural production information navigation method based on big data as claimed in claim 1, characterized in that: In the step S1, the user terminal is authenticated and the big data platform is connected to the user terminal; according to the attributes of the agricultural plots, data is extracted from the big data platform and a global map of the agricultural plots is generated, including: Perform access behavior authentication on the historical access logs of the user terminal to determine the historical abnormal access behavior associated data object information of the user terminal; connect part of the big data platform to the user terminal according to the historical abnormal access behavior associated data object information; According to the geographical location attributes of the agricultural plots, the big data platform is searched for data to extract three-dimensional terrain data that matches the global scope of the agricultural plots; the three-dimensional terrain data is subjected to noise component elimination and map processing to generate a global map of the agricultural plots.

3. The agricultural production information navigation method based on big data as claimed in claim 1, characterized in that: In step S2, the operation demand information of the agricultural plot is determined according to the multi-dimensional real-time monitoring data of the agricultural plot; and the operation information identification processing is performed on the global map of the agricultural plot according to the operation demand information, including: According to the geographical location of the agricultural plot, some sensor devices connected to the Internet of Things are invited to obtain monitoring data; according to the monitoring data type returned by each of the sensor devices, the returned monitoring data is subjected to frame extraction verification to determine whether the returned monitoring data is returned completely; the fully returned multi-dimensional real-time monitoring data is analyzed to obtain the crop growth status information of the agricultural plot, thereby determining the operation demand information of the agricultural plot; wherein the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot; According to the type of operation and the location of the operation area to be performed on the crops of the agricultural plot as required, the map area corresponding to the operation area in the global map of the agricultural plot is processed for operation type and operation parameter identification.

4. The agricultural production information navigation method based on big data as claimed in claim 1, characterized in that: In the step S3, the global map of agricultural plots is uploaded to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; and the matching global map of agricultural plots is extracted from the big data platform according to the historical query record of the user end on the big data platform, including: According to the comparison of the implementation time limit attribute of all the operation information marked in the global map of the agricultural plots that have completed the marking process with the respective allowed access time of all the intervals in the big data platform, the global map of the agricultural plots is uploaded to the corresponding intervals in the big data platform; Based on the historical query records of the user terminal on the big data platform, the type of map navigation information that the user terminal expects to query during the historical query process is determined; based on the type of map navigation information, the operation information of all agricultural plot global map markers in the big data platform is compared for similarity, so as to extract the matching agricultural plot global map.

5. The agricultural production information navigation method based on big data as claimed in claim 1, characterized in that: In the step S4, all extracted global maps of agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; According to the data receiving state of the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular time, including: According to the implementation area location and real-time time corresponding to the operation information identified in the global map of all the extracted agricultural plots, the global maps of all the extracted agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; wherein the full-link agricultural navigation information includes the spatial and temporal navigation information of all agricultural activities that need to be performed in all stages of plant growth of the agricultural plots; According to the data receiving status of the user terminal, the allowed receiving time range of the navigation information of the user terminal is determined; according to the running frequency of the application in the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular interval.

6. The agricultural production information navigation system based on big data is characterized by: include: A big data access module, used to authenticate the user terminal and connect the big data platform to the user terminal; A map generation module, used to extract data from the big data platform and generate a global map of agricultural plots according to the attributes of agricultural plots; An operation demand information determination module, used to determine the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot; A map identification processing module, used for performing operation information identification processing on the global map of the agricultural plot according to the operation demand information; A map uploading module, used for uploading the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing; A map extraction module, used to extract a matching global map of agricultural plots from the big data platform according to the historical query records of the user terminal on the big data platform; The navigation information generation and sending module is used to integrate and process the extracted global maps of all agricultural plots to generate full-link agricultural navigation information of the agricultural plots; and send the full-link agricultural navigation information to the user terminal at regular intervals according to the data receiving status of the user terminal.

7. The agricultural production information navigation system based on big data as claimed in claim 6, characterized in that: The big data access module is used to authenticate the user terminal and connect the big data platform to the user terminal, including: Perform access behavior authentication on the historical access logs of the user terminal to determine the historical abnormal access behavior associated data object information of the user terminal; connect part of the big data platform to the user terminal according to the historical abnormal access behavior associated data object information; The map generation module is used to extract data from the big data platform and generate a global map of agricultural plots according to the attributes of the agricultural plots, including: According to the geographical location attributes of the agricultural plots, the big data platform is searched for data to extract three-dimensional terrain data that matches the global scope of the agricultural plots; the three-dimensional terrain data is subjected to noise component elimination and map processing to generate a global map of the agricultural plots.

8. The agricultural production information navigation system based on big data as claimed in claim 6, characterized in that: The operation demand information determination module is used to determine the operation demand information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, including: According to the geographical location of the agricultural plot, some sensor devices connected to the Internet of Things are invited to obtain monitoring data; according to the monitoring data type returned by each of the sensor devices, the returned monitoring data is subjected to frame extraction verification to determine whether the returned monitoring data is returned completely; the fully returned multi-dimensional real-time monitoring data is analyzed to obtain the crop growth status information of the agricultural plot, thereby determining the operation demand information of the agricultural plot; wherein the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot; The map identification processing module is used to perform operation information identification processing on the global map of the agricultural plot according to the operation demand information, including: According to the type of operation and the location of the operation area to be performed on the crops of the agricultural plot as required, the map area corresponding to the operation area in the global map of the agricultural plot is processed for operation type and operation parameter identification.

9. The agricultural production information navigation system based on big data as claimed in claim 6, characterized in that: The map uploading module is used to upload the global map of agricultural plots to the big data platform according to the time validity attribute of the global map of agricultural plots that has completed the identification processing, and includes: According to the comparison of the implementation time limit attribute of all the operation information marked in the global map of the agricultural plots that have completed the marking process with the respective allowed access time of all the intervals in the big data platform, the global map of the agricultural plots is uploaded to the corresponding intervals in the big data platform; The map extraction module is used to extract a matching global map of agricultural plots from the big data platform according to the historical query records of the user terminal on the big data platform, including: Based on the historical query records of the user terminal on the big data platform, the type of map navigation information that the user terminal expects to query during the historical query process is determined; based on the type of map navigation information, the operation information of all agricultural plot global map markers in the big data platform is compared for similarity, so as to extract the matching agricultural plot global map.

10. The agricultural production information navigation system based on big data according to claim 6, characterized in that: The navigation information generation and sending module is used to integrate and process all extracted global maps of agricultural plots to generate full-link agricultural navigation information of the agricultural plots; According to the data receiving state of the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular time, including: According to the implementation area location and real-time time corresponding to the operation information identified in the global map of all extracted agricultural plots, the global maps of all extracted agricultural plots are integrated and processed to generate full-link agricultural navigation information of the agricultural plots; wherein the full-link agricultural navigation information includes the spatial and temporal navigation information of all agricultural activities that need to be performed in the whole stage of plant growth of the agricultural plots; according to the data reception status of the user terminal, the allowed reception time range of the navigation information of the user terminal is determined; according to the running frequency of the application in the user terminal, the full-link agricultural navigation information is sent to the user terminal at a regular interval.

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