Agricultural production information navigation method and system based on big data

Abstract

The present invention relates to the field of big data processing, specifically to an agricultural production information navigation method and system based on big data. The big data platform is connected to the user terminal, and data is extracted from the big data platform according to the agricultural plot attributes to generate a global map of the agricultural plot. According to the multi-dimensional real-time monitoring data of the agricultural plot, the operation requirement information of the agricultural plot is determined to perform operation information marking processing on the global map of the agricultural plot and make refined settings for agricultural operations on the agricultural plot. According to the historical query records of the user terminal on the big data platform, the matching global map of the agricultural plot is extracted from the big data platform to realize the extraction of the full-link operation information of the agricultural plot. All the extracted global maps of the agricultural plot are integrated and processed to generate the full-link agricultural operation navigation information, and the full-link agricultural operation navigation information is regularly sent to the user terminal to provide users with comprehensive and accurate interactive navigation of agricultural production information.

Description

Technical Field

[0001] The present invention relates to the field of big data processing, and particularly 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. Especially when the crops are at different growth nodes, different operations such as fertilization, weeding, pest and disease control, pruning, and pollination are required, and the implementation of the above operations also needs to be adjusted according to the real-time weather conditions. In order to increase agricultural production and reduce agricultural production costs, technical means for intelligent predictive planning of agricultural production operations by combining climate data and crop growth data have emerged. However, the above technical means are all for large-scale agricultural production areas and cannot provide refined operation guidance for small-scale agricultural production areas such as individual farmlands and orchards. In addition, the existing agricultural production operation guidance schemes are all one-way information transmission to users, unable to form timely and efficient information interaction with users, and cannot provide users with comprehensive and accurate agricultural production information interactive navigation, reducing the accuracy and reliability of the implementation of agricultural production operations. Summary of the Invention

[0003] Aiming at the defects existing in the prior art, the present invention provides an agricultural production information navigation method and system based on big data. The big data platform is connected to the user terminal, and data is extracted from the big data platform according to the agricultural land parcel attributes and a global map of the agricultural land parcel is generated; according to the multi-dimensional real-time monitoring data of the agricultural land parcel, the operation requirement information of the agricultural land parcel is determined, so as to perform operation information marking processing on the global map of the agricultural land parcel and perform refined setting of agricultural operations on the agricultural land parcel; according to the historical query records of the user terminal on the big data platform, a matching global map of the agricultural land parcel is extracted from the big data platform to realize the extraction of the full-link operation information of the agricultural land parcel; all the extracted global maps of the agricultural land parcels are integrated and processed to generate the full-link agricultural operation navigation information of the agricultural land parcel, and the full-link agricultural operation navigation information is regularly sent to the user terminal to provide users with comprehensive and accurate agricultural production information interactive navigation 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, including the following steps:

[0005] Step S1, authenticate the user terminal and connect the big data platform to the user terminal; extract data from the big data platform according to the agricultural land parcel attributes and generate a global map of the agricultural land parcel;

[0006] Step S2, determine the operation requirement information of the agricultural land parcel according to the multi-dimensional real-time monitoring data of the agricultural land parcel; perform operation information marking processing on the global map of the agricultural land parcel according to the operation requirement information;

[0007] Step S3: According to the time-valid attribute of the global agricultural land map after the identification process is completed, upload the global agricultural land map to the big data platform; according to the historical query records of the user terminal on the big data platform, extract the matching global agricultural land map from the big data platform.

[0008] Step S4: Integrate and process all the extracted global agricultural land maps to generate the full-link agricultural operation navigation information for the agricultural land; according to the data reception status of the user terminal, regularly send the full-link agricultural operation navigation information to the user terminal.

[0009] In an embodiment disclosed in the present application, in the step S1, authenticate the user terminal and connect the big data platform to the user terminal; according to the agricultural land attributes, extract data from the big data platform and generate a global agricultural land map, including:

[0010] Authenticate the access behavior of the historical access log of the user terminal to determine the information of the data object associated with the historical abnormal access behavior of the user terminal; according to the information of the data object associated with the historical abnormal access behavior, connect a partial interval of the big data platform to the user terminal.

[0011] According to the geographical location attribute of the agricultural land, search for data on the big data platform, extract three-dimensional terrain data that matches the global scope of the agricultural land; remove the noise components from the three-dimensional terrain data and perform map processing to generate a global agricultural land map.

[0012] In an embodiment disclosed in the present application, in the step S2, determine the operation requirement information of the agricultural land according to the multi-dimensional real-time monitoring data of the agricultural land; according to the operation requirement information, perform operation information identification processing on the global agricultural land map, including:

[0013] According to the geographical location where the agricultural land is located, invite some sensing devices connected to the Internet of Things to obtain monitoring data; according to the types of monitoring data returned by the respective sensing devices, perform frame extraction verification on the returned monitoring data to determine whether the returned monitoring data is completely returned; analyze the completely returned multi-dimensional real-time monitoring data to obtain the crop growth trend information of the agricultural land, so as to determine the operation requirement information of the agricultural land; wherein, the operation requirement information includes the operation type and the operation area location that need to be implemented on the crops of the agricultural land.

[0014] According to the operation type and the operation area location that need to be implemented on the crops of the agricultural land, perform operation type and operation parameter identification processing on the map area corresponding to the operation area in the global agricultural land map.

[0015] In an embodiment disclosed in the present application, in the step S3, according to the time validity attribute of the global agricultural land map that has completed the identification process, the global agricultural land map is uploaded to the big data platform; according to the historical query records of the user terminal on the big data platform, the matching global agricultural land map is extracted from the big data platform, including:

[0016] According to the implementation time limit attribute of all the operation information marked in the global agricultural land map that has completed the identification process and the allowed access time of each interval in the big data platform, the global agricultural land map is uploaded to the corresponding interval in the big data platform;

[0017] According to the historical query records of the user terminal on the big data platform, determine the type of map navigation information expected to be queried by the user terminal during the historical query process; according to the type of map navigation information, perform a similarity comparison on the operation information marked in all the global agricultural land maps in the big data platform, so as to extract the matching global agricultural land map.

[0018] In an embodiment disclosed in the present application, in the step S4, all the extracted global agricultural land maps are integrated and processed to generate the full-link agricultural operation navigation information of the agricultural land; according to the data reception status of the user terminal, the full-link agricultural operation navigation information is sent to the user terminal regularly, including:

[0019] According to the implementation area location and real-time time corresponding to the operation information marked in all the extracted global agricultural land maps, all the extracted global agricultural land maps are integrated and processed to generate the full-link agricultural operation navigation information of the agricultural land; wherein, the full-link agricultural operation navigation information includes the spatial and time navigation information of all the agricultural operations that need to be performed during the entire plant growth stage of the agricultural land.

[0020] According to the data reception status of the user terminal, determine the allowable reception time range of the navigation information of the user terminal; according to the running frequency of the application program in the user terminal, the full-link agricultural operation navigation information is sent to the user terminal regularly.

[0021] The present invention also provides an agricultural production information navigation system based on big data, including:

[0022] A big data access module, used to authenticate the user terminal and connect the big data platform to the user terminal;

[0023] A map generation module, used to extract data from the big data platform according to the agricultural land attributes and generate a global agricultural land map;

[0024] An operation requirement information determination module, configured to determine the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot;

[0025] A map identification processing module, configured to perform operation information identification processing on the global map of the agricultural plot according to the operation requirement information;

[0026] A map uploading module, configured to upload the global map of the agricultural plot to the big data platform according to the time validity attribute of the global map of the agricultural plot after the identification processing is completed;

[0027] A map extraction module, configured to extract a matching global map of the agricultural plot from the big data platform according to the historical query record of the user terminal for the big data platform;

[0028] A navigation information generation and sending module, configured to integrate and process all the extracted global maps of the agricultural plot to generate the full-link agricultural operation navigation information of the agricultural plot; and regularly send the full-link agricultural operation navigation information to the user terminal according to the data receiving status of the user terminal.

[0029] In an 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:

[0030] Performing access behavior authentication on the historical access log of the user terminal to determine the information of the data object associated with the historical abnormal access behavior of the user terminal; and connecting a partial range of the big data platform to the user terminal according to the information of the data object associated with the historical abnormal access behavior;

[0031] The map generation module is used to extract data from the big data platform and generate a global map of the agricultural plot according to the attributes of the agricultural plot, including:

[0032] Searching for data on the big data platform according to the geographical location attributes of the agricultural plot, and extracting three-dimensional terrain data matching the global scope of the agricultural plot; and removing noise components from the three-dimensional terrain data and performing map processing to generate a global map of the agricultural plot.

[0033] In an embodiment disclosed in the present application, the operation requirement information determination module is used to determine the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, including:

[0034] Invite for obtaining monitoring data for some of the sensing devices connected to the Internet of Things according to the geographical location of the agricultural plot; verify frame extraction of the returned monitoring data according to the types of monitoring data returned by each of the said some sensing devices, and determine whether the returned monitoring data is completely returned; analyze the multi-dimensional real-time monitoring data that is completely returned to obtain the crop growth trend information of the agricultural plot, so as to determine the operation requirement information of the agricultural plot; wherein, the operation requirement information includes the type of operation to be performed on the crops in the agricultural plot and the location of the operation area.

[0035] The map marking processing module is used to perform operation information marking processing on the global map of the agricultural plot according to the operation requirement information, including:

[0036] Perform operation type and operation parameter marking processing on the map area corresponding to the operation area in the global map of the agricultural plot according to the type of operation to be performed on the crops in the agricultural plot and the location of the operation area.

[0037] In an embodiment disclosed in the present application, the map uploading module is used to upload the global map of the agricultural plot to the big data platform according to the time validity attribute of the global map of the agricultural plot after the marking processing, including:

[0038] Compare the implementation time limit attribute of all the operation information marked in the global map of the agricultural plot after the marking processing with the allowed access time of each interval in the big data platform, and upload the global map of the agricultural plot to the corresponding interval in the big data platform;

[0039] The map extraction module is used to extract the matching global map of the agricultural plot from the big data platform according to the historical query record of the user terminal for the big data platform, including:

[0040] Determine the type of map navigation information expected to be queried by the user terminal during the historical query according to the historical query record of the user terminal for the big data platform; compare the similarity of the operation information marked in all the global maps of the agricultural plots in the big data platform according to the type of map navigation information, so as to extract the matching global map of the agricultural plot.

[0041] In an embodiment disclosed in the present application, the navigation information generation and sending module is used to integrate and process all the extracted global maps of the agricultural plots to generate the full-link agricultural operation navigation information of the agricultural plot; regularly send the full-link agricultural operation navigation information to the user terminal according to the data reception status of the user terminal, including:

[0042] Integrate and process all the extracted global maps of agricultural plots according to the implementation area location and real-time time corresponding to the operation information marked on the global maps of all agricultural plots, and generate the full-link agricultural operation navigation information for the agricultural plots; wherein, the full-link agricultural operation navigation information includes the spatial and time navigation information of all agricultural operations that need to be performed in all stages of plant growth of the agricultural plots.

[0043] Determine the time range allowed for receiving the navigation information of the client according to the data receiving status of the client; regularly send the full-link agricultural operation navigation information to the client according to the running frequency of the application program in the client.

[0044] Compared with the prior art, this navigation method and system for agricultural production information based on big data connect the big data platform to the client, extract data from the big data platform according to the attributes of agricultural plots and generate global maps of agricultural plots; determine the operation requirement information of agricultural plots according to the multi-dimensional real-time monitoring data of agricultural plots, so as to perform operation information marking processing on the global maps of agricultural plots and make refined settings for agricultural operations on agricultural plots; extract the matching global maps of agricultural plots from the big data platform according to the historical query records of the client on the big data platform, and realize the extraction of full-link operation information of agricultural plots; integrate and process all the extracted global maps of agricultural plots to generate the full-link agricultural operation navigation information for agricultural plots, and regularly send the full-link agricultural operation navigation information to the client, providing comprehensive and accurate interactive navigation of agricultural production information for users and improving the implementation accuracy and reliability of agricultural production operations.

[0045] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0046] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the accompanying drawings required for describing the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a schematic flow chart of the navigation method for agricultural production information based on big data provided by the present invention.

[0049] Figure 2 It is a schematic framework diagram of an agricultural production information navigation system based on big data provided by the present invention. Specific implementation manners

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Refer to Figure 1 , which is a schematic flow diagram 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:

[0052] Step S1: Authenticate the user terminal and connect the big data platform to the user terminal; extract data from the big data platform according to the agricultural plot attributes and generate a global map of the agricultural plot.

[0053] Step S2: Determine the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot; perform operation information marking processing on the global map of the agricultural plot according to the operation requirement information.

[0054] Step S3: Upload the global map of the agricultural plot to the big data platform according to the time validity attribute of the global map of the agricultural plot after the marking processing; extract the matching global map of the agricultural plot from the big data platform according to the historical query record of the user terminal on the big data platform.

[0055] Step S4: Integrate and process all the extracted global maps of the agricultural plot to generate the full-link agricultural operation navigation information of the agricultural plot; send the full-link agricultural operation navigation information to the user terminal regularly according to the data reception status of the user terminal.

[0056] The big data-based agricultural production information navigation method connects the big data platform to the user side, extracts data from the big data platform according to the agricultural plot attributes, and generates a global map of the agricultural plot; determines the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, so as to perform operation information marking processing on the global map of the agricultural plot and make a refined 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 side on the big data platform, and realizes the extraction of the full-link operation information of the agricultural plot; integrates and processes all the extracted global maps of the agricultural plot, generates the full-link agricultural operation navigation information of the agricultural plot, and regularly sends the full-link agricultural operation navigation information to the user side to provide the user with a comprehensive and accurate interactive navigation of agricultural production information, and improve the implementation accuracy and reliability of agricultural production operations.

[0057] Preferably, in step S1, authenticate the user side, connect the big data platform to the user side; extract data from the big data platform according to the agricultural plot attributes and generate a global map of the agricultural plot, including:

[0058] Authenticate the access behavior of the historical access log of the user side to determine the information of the data object associated with the historical abnormal access behavior of the user side; connect a partial interval of the big data platform to the user side according to the information of the data object associated with the historical abnormal access behavior;

[0059] Search for data on the big data platform according to the geographical location attributes of the agricultural plot, extract the three-dimensional terrain data matching the global scope of the agricultural plot; remove the noise components from the three-dimensional terrain data and perform map processing to generate a global map of the agricultural plot.

[0060] The big data platform stores the planting and cultivation related data of different agricultural plots. These planting and cultivation related data are sensitive data and are not open to all client terminals for access. To ensure the data security of the big data platform, it is necessary to authenticate the client terminals first, that is, to perform access behavior authentication on the historical access logs of the client terminals themselves, and determine the data object information corresponding to the accessed data when the client terminals initiate abnormal access behaviors during the historical access process to the big data platform. Among them, the above abnormal access behaviors can be but are not limited to the behaviors of illegally copying or illegally tampering with the big data platform; the above data object information can be but is not limited to the storage interval position where the data accessed by the client terminals initiating abnormal access behaviors is located. 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 client terminals. The part of the storage interval allowed to be connected to the client terminals refers to other storage intervals in the big data platform except for the storage interval where the data accessed by the client terminals initiating abnormal access behaviors is located, so as to avoid the client terminals repeating the above abnormal access behaviors, ensure that the client terminals obtain partial access rights to the big data platform, and ensure the data security of the big data platform. Also, according to the geographical location attributes such as the geographical longitude, latitude, and altitude of the agricultural plot, data search is performed on the big data platform, three-dimensional terrain data matching the global scope of the agricultural plot is extracted, and noise components are removed and map processing is performed on the above three-dimensional terrain data to generate a global map of the agricultural plot, so that the global map of the agricultural plot can completely represent the terrain situation and plant planting situation of the agricultural plot.

[0061] Preferably, in step S2, according to the multi-dimensional live monitoring data of the agricultural plot, the operation demand information of the agricultural plot is determined; according to the operation demand information, operation information marking processing is performed on the global map of the agricultural plot, including:

[0062] An invitation for obtaining monitoring data is sent to some of the sensing devices connected to the Internet of Things according to the geographical location of the agricultural plot; according to the types of monitoring data returned by some of the sensing devices respectively, frame extraction verification is performed on the returned monitoring data to judge whether the returned monitoring data is completely returned; the multi-dimensional live monitoring data that is completely returned is analyzed to obtain the crop growth trend information of the agricultural plot, so as to determine the operation demand information of the agricultural plot; among them, the operation demand information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot;

[0063] According to the operation type and operation area location that need to be implemented on the crops of the agricultural plot, operation type and operation parameter marking processing are performed on the map area corresponding to the operation area in the global map of the agricultural plot.

[0064] In order to obtain the real-time weather conditions, plant growth trends, and soil characteristics of a local agricultural plot, multiple different types of sensing devices are distributed locally in the agricultural plot. These sensing devices can be, but are not limited to, rain sensors, temperature and humidity sensors, wind speed / direction sensors, camera sensors, soil moisture sensors, and soil element sensors. All sensors are connected to the Internet of Things (IoT), and the real-time weather condition data, plant growth trend data, and soil characteristic data of the local agricultural plot detected in real time are uploaded to the platform through the IoT, so as to comprehensively and accurately monitor the agricultural plot and enrich the agricultural production-related data of the agricultural plot. Specifically, the geographical location of the agricultural plot is compared with the geographical locations of all the sensing devices connected to the IoT to determine the sensing devices installed within the scope of the agricultural plot, and an invitation to obtain monitoring data is sent to the above-mentioned sensing devices. After receiving and parsing the invitation to obtain monitoring data, the above-mentioned sensing devices will return the monitoring data generated during the corresponding time period. At this time, according to the type of the monitoring data returned by the sensing devices, frame extraction verification is performed on each returned monitoring data to determine whether the monitoring data is complete. Only when the returned monitoring data is complete, the multi-dimensional live monitoring data returned completely (i.e., the monitoring data from different types of sensing devices returned completely) is parsed to obtain the crop growth trend information of the agricultural plot; among them, the above-mentioned crop growth trend information can be, but is not limited to, the growth trends of parts such as the stems, leaves, flowers, and fruits of the plant crops on the agricultural plot. Considering that plant crops have different requirements for water, fertilizer, and nutrients at different growth stages, and the pest and disease situations are also different at different growth stages, the requirements for irrigation, fertilization, spraying pesticides or growth hormones for the plant crops on the agricultural plot are also different at different growth stages. According to the crop growth trend information of the agricultural plot, the types of operations and the locations of the operation areas to be implemented on the crops of the agricultural plot are determined, and the map areas corresponding to the operation areas in the global map of the agricultural plot are marked with the types of operations and operation parameters, accurately determining the types of operations such as irrigation, fertilization, spraying pesticides or growth hormones and the locations of the operation implementation areas actually required at the actual growth stage of the crops on the corresponding agricultural plot, providing a reliable basis for the agricultural production management of the crops.

[0065] Preferably, in step S3, according to the time validity attribute of the global map of the agricultural plot after the marking process is completed, the global map of the agricultural plot is uploaded to the big data platform; according to the historical query records of the user terminal on the big data platform, the matching global map of the agricultural plot is extracted from the big data platform, including:

[0066] By comparing the implementation time limit attribute of all the operation information marked in the global map of the agricultural plot after the marking process is completed with the allowed access time of each interval in the big data platform, the global map of the agricultural plot is uploaded to the corresponding interval in the big data platform;

[0067] Based on the historical query records of the client for the big data platform, determine the type of map navigation information that the client expects to query during the historical query process; according to the type of map navigation information, compare the similarity of the operation information marked on all global agricultural land parcel maps in the big data platform, and extract the matching global agricultural land parcel maps accordingly.

[0068] There are time limits for operations such as irrigation, fertilization, spraying pesticides or growth hormones on the plants and crops in the agricultural land parcels, that is, the above operations need to be completed within the specified time range. Once the corresponding operations are not carried out beyond the specified time range, it will seriously affect the normal growth of the plants and crops, thus affecting the final yield of the plants and crops. In order to ensure that the client can obtain the notification message for performing the corresponding operations on the agricultural land parcels from the big data platform within the specified time range, compare the implementation time limit attributes of all the operation information marked in the global agricultural land parcel maps processed according to the completion marks with the allowed access times of all intervals in the big data platform, and upload the global agricultural land parcel maps to the corresponding intervals in the big data platform, ensuring that the client can timely obtain the information about performing the corresponding operations on the plants and crops in the agricultural land parcels from the corresponding access in the big data platform, avoiding the situation of delayed operation implementation, and providing reliable support for the growth of the plants and crops in the agricultural land parcels. Also, based on the historical query records of the client for the big data platform, determine the type of map navigation information that the client expects to query during the historical query process. The above map navigation information type may include but is not limited to text-based or picture-based navigation information. Then, according to the type of map navigation information, compare the similarity of the operation information marked on all global agricultural land parcel maps in the big data platform, and extract and process the global agricultural land parcel maps with a similarity greater than or equal to the preset similarity threshold, so as to meet the information navigation requirements of the client for the agricultural land parcels.

[0069] Preferably, in step S4, integrate and process all the extracted global agricultural land parcel maps to generate the full-link agricultural operation navigation information for the agricultural land parcels; according to the data reception status of the client, regularly send the full-link agricultural operation navigation information to the client, including:

[0070] Integrate and process all the extracted global agricultural land parcel maps according to the implementation area locations and real-time times corresponding to the operation information marked on all the extracted global agricultural land parcel maps to generate the full-link agricultural operation navigation information for the agricultural land parcels; among them, the full-link agricultural operation navigation information includes the spatial and time navigation information of all the agricultural operations that need to be performed during the entire growth stage of the plants in the agricultural land parcels.

[0071] Determine the allowed reception time range of the navigation information of the client according to the data reception status of the client; regularly send the full-link agricultural operation navigation information to the client according to the running frequency of the application program in the client.

[0072] As can be seen from the above, the operation information extracted for each global map identifier of the agricultural plot corresponds to the operation information that needs to be implemented in a certain area within the agricultural plot during a certain time interval. In order for the client to obtain the complete operation information that needs to be implemented at different time stages and different plot areas during the entire plant growth process of the agricultural plot, according to the implementation area location and real-time time corresponding to the operation information of all global map identifiers of the agricultural plot, all the extracted global maps of the agricultural plot are integrated and processed to generate the full-link agricultural operation navigation information of the agricultural plot, so as to completely represent the spatial and time navigation information of all agricultural operations that need to be performed during the entire plant growth stage of the agricultural plot, providing accurate reference for farmers to implement agricultural operations. Also, according to the data reception status of the client, determine the time range allowed for receiving the navigation information of the client, and according to the running frequency of the application program in the client, regularly send the full-link agricultural operation navigation information to the client to provide comprehensive and accurate interactive navigation of agricultural production information for users, improving the implementation accuracy and reliability of agricultural production operations.

[0073] See Figure 2 , which is a schematic framework diagram of the big data-based agricultural production information navigation system provided by the embodiment of the present invention. The big data-based agricultural production information navigation system includes:

[0074] A big data access module for authenticating the client and connecting the big data platform to the client;

[0075] A map generation module for extracting data from the big data platform and generating a global map of the agricultural plot according to the agricultural plot attributes;

[0076] An operation requirement information determination module for determining the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot;

[0077] A map identification processing module for performing operation information identification processing on the global map of the agricultural plot according to the operation requirement information;

[0078] A map upload module for uploading the global map of the agricultural plot to the big data platform according to the time validity attribute of the completed global map of the agricultural plot;

[0079] A map extraction module for extracting a matching global map of the agricultural plot from the big data platform according to the historical query record of the client on the big data platform;

[0080] A navigation information generation and sending module for integrating and processing all the extracted global maps of the agricultural plot to generate the full-link agricultural operation navigation information of the agricultural plot; regularly sending the full-link agricultural operation navigation information to the client according to the data reception status of the client.

[0081] The agricultural production information navigation system based on big data connects the big data platform to the user terminal, extracts data from the big data platform according to the agricultural plot attributes, and generates a global map of the agricultural plot; determines the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, so as to perform operation information marking processing on the global map of the agricultural plot and make a refined 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 terminal on the big data platform, and realizes the extraction of the full-link operation information of the agricultural plot; integrates and processes all the extracted global maps of the agricultural plot, generates the full-link agricultural operation navigation information, and regularly sends the full-link agricultural operation navigation information to the user terminal, providing comprehensive and accurate interactive navigation of agricultural production information for the user, and improving the implementation accuracy and reliability of agricultural production operations.

[0082] Preferably, the big data access module is used to authenticate the user terminal and connect the big data platform to the user terminal, including:

[0083] Authenticate the access behavior of the historical access log of the user terminal to determine the associated data object information of the historical abnormal access behavior of the user terminal; according to the associated data object information of the historical abnormal access behavior, connect a partial interval of the big data platform to the user terminal;

[0084] The map generation module is used to extract data from the big data platform according to the agricultural plot attributes and generate a global map of the agricultural plot, including:

[0085] Search for data on the big data platform according to the geographical location attributes of the agricultural plot, and extract the three-dimensional terrain data matching the global scope of the agricultural plot; remove the noise components from the three-dimensional terrain data and perform map processing to generate the global map of the agricultural plot.

[0086] The big data platform stores planting and cultivation related data of different agricultural plots. These planting and cultivation related data are sensitive data and are not open to all client terminals for access. To ensure the data security of the big data platform, it is necessary to authenticate the client terminals first, that is, to perform access behavior authentication on the historical access logs of the client terminals themselves, and determine the data object information corresponding to the accessed data when the client terminals initiate abnormal access behaviors during the historical access process to the big data platform. Among them, the above abnormal access behaviors can be but are not limited to behaviors such as illegally copying or illegally tampering with the big data platform; the above data object information can be but is not limited to the storage interval position where the data accessed by the client terminal initiating the abnormal access behavior is located. Then, based on the above historical abnormal access behavior associated data object information, some storage intervals in the big data platform are connected to the client terminal. The part of the storage interval that the client terminal is allowed to access refers to other storage intervals in the big data platform except for the storage interval where the data accessed by the client terminal initiating the abnormal access behavior is located, so as to avoid the client terminal repeating the above abnormal access behavior, ensure that the client terminal obtains partial access rights to the big data platform, and ensure the data security of the big data platform. Also, according to geographical location attributes such as the geographical longitude, latitude, and altitude of the agricultural plot, data search is performed on the big data platform, three-dimensional terrain data matching the global scope of the agricultural plot is extracted, and noise components are removed and map processing is performed on the above three-dimensional terrain data to generate a global map of the agricultural plot, so that the global map of the agricultural plot can completely represent the terrain situation and plant planting situation of the agricultural plot.

[0087] Preferably, the operation requirement information determination module is used to determine the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, including:

[0088] Send an invitation to obtain monitoring data for some sensing devices connected to the Internet of Things according to the geographical location of the agricultural plot; according to the types of monitoring data returned by each of the some sensing devices, perform frame extraction verification on the returned monitoring data to determine whether the returned monitoring data is completely returned; analyze the completely returned multi-dimensional real-time monitoring data to obtain the crop growth trend information of the agricultural plot, so as to determine the operation requirement information of the agricultural plot; among them, the operation requirement information includes the operation type and operation area location that need to be implemented on the crops of the agricultural plot.

[0089] 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 requirement information, including:

[0090] Perform operation type and operation parameter identification processing on the map area corresponding to the operation area in the global map of the agricultural plot according to the operation type and operation area location that need to be implemented on the crops of the agricultural plot.

[0091] In order to obtain the real-time weather conditions, plant growth trends, and soil characteristics of a local agricultural plot, multiple different types of sensing devices are distributed locally in the agricultural plot. These sensing devices can be, but are not limited to, rain sensors, temperature and humidity sensors, wind speed / 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 condition data, plant growth trend data, and soil characteristic data of the local agricultural plot detected in real time are uploaded to the platform through the Internet of Things, so as to comprehensively and accurately monitor the agricultural plot and enrich the agricultural production-related data of the agricultural plot. Specifically, the geographical location of the agricultural plot is compared with the geographical locations of all the sensing devices connected to the Internet of Things, and the sensing devices installed within the scope of the agricultural plot are determined, and an invitation to obtain monitoring data is sent to the above-mentioned sensing devices. After receiving and parsing the invitation to obtain monitoring data, the above-mentioned sensing devices will return the monitoring data generated during the corresponding time period. At this time, according to the type of the monitoring data returned by the sensing device, frame extraction verification is performed on each returned monitoring data to determine whether the monitoring data is complete. Only when the returned monitoring data is complete, the multi-dimensional live monitoring data returned completely (that is, the monitoring data from different types of sensing devices returned completely) is parsed to obtain the crop growth trend information of the agricultural plot; among them, the above-mentioned crop growth trend information can be, but is not limited to, the growth trends of the stem, leaf, flower, fruit and other parts of the plant crops on the agricultural plot. Considering that the water, fertilizer, and nutrient requirements of plant crops are different at different growth stages, and the pest and disease situations are also different at different growth stages, the irrigation, fertilization, and spraying of pesticides or growth hormones required by the plant crops on the agricultural plot are also different at different growth stages. According to the crop growth trend information of the agricultural plot, the operation types and operation area locations to be implemented on the crops of the agricultural plot are determined, and the map areas corresponding to the operation areas in the global map of the agricultural plot are marked with operation types and operation parameters, so as to accurately determine the operation types such as irrigation, fertilization, and spraying of pesticides or growth hormones and the operation implementation area locations actually required at the actual growth stage of the crops on the corresponding agricultural plot, providing a reliable basis for the agricultural production management of the crops.

[0092] Preferably, the map uploading module is used to upload the global map of the agricultural plot to the big data platform according to the time validity attribute of the global map of the agricultural plot after the marking process, including:

[0093] Compare the implementation time limit attribute of all the operation information marked in the global map of the agricultural plot after the marking process with the allowed access time of each interval in the big data platform, and upload the global map of the agricultural plot to the corresponding interval in the big data platform;

[0094] The map extraction module is used to extract a matching global agricultural plot map from the big data platform according to the historical query records of the user terminal on the big data platform, including:

[0095] According to the historical query records of the user terminal on the big data platform, determine the type of map navigation information expected by the user terminal during the historical query process; according to the type of map navigation information, compare the similarity of the operation information marked on all global agricultural plot maps in the big data platform, so as to extract the matching global agricultural plot map.

[0096] There are time limits for operations such as irrigation, fertilization, pesticide or auxin spraying on the plants and crops in the agricultural plot, that is, the above operations need to be completed within the specified time range. Once the corresponding operations are not carried out beyond the specified time range, it will seriously affect the normal growth of the plants and crops, thus affecting the final yield of the plants and crops. In order to ensure that the user terminal can obtain the notification message for performing corresponding operations on the agricultural plot from the big data platform within the specified time range, compare the implementation time limit attributes of all operation information marked in the global agricultural plot map after completion of the identification process with the allowed access time of each interval in the big data platform, and upload the global agricultural plot map to the corresponding interval in the big data platform, so as to ensure that the user terminal can timely obtain the information about performing corresponding operations on the plants and crops in the agricultural plot from the corresponding access in the big data platform, avoid the situation of delayed operation implementation, and provide reliable support for the growth of the plants and crops in the agricultural plot. Also, according to the historical query records of the user terminal on the big data platform, determine the type of map navigation information expected by the user terminal during the historical query process. The above map navigation information type may include but is not limited to text-based or picture-based navigation information. Then, according to the type of map navigation information, compare the similarity of the operation information marked on all global agricultural plot maps in the big data platform, and extract and process the global agricultural plot maps with a similarity greater than or equal to the preset similarity threshold, so as to meet the information navigation needs of the user terminal for the agricultural plot.

[0097] Preferably, the navigation information generation and sending module is used to integrate and process all the extracted global agricultural plot maps to generate the full-link agricultural operation navigation information of the agricultural plot; according to the data reception status of the user terminal, regularly send the full-link agricultural operation navigation information to the user terminal, including:

[0098] Integrate and process all the extracted global agricultural plot maps according to the implementation area location and real-time time corresponding to the operation information marked on all the extracted global agricultural plot maps to generate the full-link agricultural operation navigation information of the agricultural plot; among them, the full-link agricultural operation navigation information includes the spatial and time navigation information of all agricultural operations that need to be performed during the entire growth stage of the plants in the agricultural plot.

[0099] Determine the time range during which the navigation information of the client is allowed to be received according to the data reception status of the client; regularly send the full-link agricultural operation navigation information to the client according to the running frequency of the application program in the client.

[0100] As can be seen from the above content, the operation information extracted for each global map identifier of the agricultural plot is the operation information that needs to be implemented in a certain area within the agricultural plot during a certain time interval. In order for the client 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 growth process of the plant crop, according to the implementation area location and real-time time corresponding to the operation information of all the global map identifiers of the agricultural plot extracted, all the global maps of the agricultural plot extracted are integrated and processed to generate the full-link agricultural operation navigation information of the agricultural plot, so as to completely represent the spatial and time navigation information of all the agricultural operations that need to be performed during the entire growth stage of the plants in the agricultural plot, and provide an accurate reference for farmers to carry out agricultural operations. Also, according to the data reception status of the client, determine the time range during which the navigation information of the client is allowed to be received, and regularly send the full-link agricultural operation navigation information to the client according to the running frequency of the application program in the client, so as to provide users with a comprehensive and accurate interactive navigation of agricultural production information and improve the implementation accuracy and reliability of agricultural production operations.

[0101] As can be seen from the content of the above embodiments, the agricultural production information navigation method and system based on big data connect the big data platform to the client, extract data from the big data platform according to the agricultural plot attributes and generate the global map of the agricultural plot; determine the operation requirement information of the agricultural plot according to the multi-dimensional real-time monitoring data of the agricultural plot, so as to perform operation information marking processing on the global map of the agricultural plot and carry out refined setting of agricultural operations on the agricultural plot; extract the matching global map of the agricultural plot from the big data platform according to the historical query records of the client on the big data platform, and realize the extraction of the full-link operation information of the agricultural plot; integrate and process all the extracted global maps of the agricultural plot to generate the full-link agricultural operation navigation information of the agricultural plot, and regularly send the full-link agricultural operation navigation information to the client, so as to provide users with a comprehensive and accurate interactive navigation of agricultural production information and improve the implementation accuracy and reliability of agricultural production operations.

[0102] 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 equivalent technologies, the present invention also intends to include these changes and modifications.

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, specifically: 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 type of monitoring data returned by each of the some sensor devices, frame extraction and verification are performed on the returned monitoring data to determine whether the returned monitoring data is returned completely; Parsing the fully returned multi-dimensional real-time monitoring data to obtain 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 type of operation to be performed on the crops of the agricultural plot and the location of the operation area; According to the operation type and operation area location to be performed on the crops of the agricultural plot as needed, the map area corresponding to the operation area in the global map of the agricultural plot is processed to identify the operation type and operation parameters; 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, specifically: 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; Extracting 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; 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 the step S3, according to the historical query records of the user terminal on the big data platform, a matching global map of agricultural plots is extracted from 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.

4. 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.

5. 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; 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, specifically: 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 type of monitoring data returned by each of the some sensor devices, frame extraction and verification are performed on the returned monitoring data to determine whether the returned monitoring data is returned completely; Parsing the fully returned multi-dimensional real-time monitoring data to obtain 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 type of operation to be performed on the crops of the agricultural plot and the location of the operation area; 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, specifically: According to the operation type and operation area location to be performed on the crops of the agricultural plot as needed, the map area corresponding to the operation area in the global map of the agricultural plot is processed to identify the operation type and operation parameters; 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 after the identification processing, specifically: 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; 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.

6. The agricultural production information navigation system based on big data as claimed in claim 5, 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.

7. The agricultural production information navigation system based on big data as claimed in claim 5, characterized in that: 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.

8. The agricultural production information navigation system based on big data as claimed in claim 5, 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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