Distributed farm data management method based on real scene virtualization platform

Abstract

The invention relates to a distributed farm data management method based on a real scene virtualization platform, and belongs to the technical field of platform data processing, and the method comprises the following steps: 1), a user sends a demand signal, and a platform receives the demand signal of the user in real time; step 2), the platform processes the demand signal of the user through an AI algorithm; 3) after the platform processes the demand signal of the user, the distributed farm system receives and starts to execute the demand signal of the user processed by the platform; step 4), the distributed farm system uploads the data in the execution process to a platform, and the platform receives the data in the execution process of the distributed farm system in real time to realize real-time data acquisition and analysis; 5) the platform provides the data in the execution process of the distributed farm for the user in a visual mode; the beneficial effects of the invention are that the platform employs a data optimization distribution method to process the demand signal of the user, and achieves the reasonable processing of farm data.

Description

Technical Field

[0001] The present invention belongs to the technical field of platform data processing, and in particular relates to a distributed farm data management method based on a real-scene virtualization platform. Background Art

[0002] Platform digital management refers to the use of computer, communication, network and other technologies, through statistical techniques, to quantitatively manage objects and management behaviors, and to achieve management activities and methods for functions such as R&D, production, coordination, sales, service and innovation. It can provide real-time feedback on the latest progress of production activities, allowing users to understand the current work status in a timely manner, so as to better carry out the next work plan.

[0003] Farm data management faces various decisions about agricultural field management, including: what crops to plant, what type of crop seeds to plant, when to harvest crops, whether to till, irrigate, apply pesticides, fungicides and fertilizers. However, farm data management is mainly about making reasonable decisions on farm production activities, such as: reasonably evaluating the utilization efficiency of various resources, such as: labor, agricultural materials, equipment, etc., and reasonably adjusting resource allocation to reduce production costs. Summary of the invention

[0004] The main technical problem that the present invention solves is how to reasonably process farm data. The present invention provides a distributed farm data management method based on a real-scene virtualization platform. The platform uses a data optimization allocation method to process user demand signals. The distributed farm system then uploads the data in the execution process to the platform. The platform realizes real-time data collection and analysis. The platform uses a data allocation comparison method to analyze whether the user's demand signal is the same as the data in the distributed farm system execution process, thereby realizing reasonable processing of farm data.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] The distributed farm data management method based on the real-scene virtualization platform includes the following steps:

[0007] Step 1): The user sends a demand signal, and the platform receives the user's demand signal in real time;

[0008] Step 2): The platform processes the user's demand signal through an AI algorithm; wherein the platform uses a data optimization allocation method to process the user's demand signal;

[0009] Step 3): After the platform processes the user's demand signal, the distributed farm system receives and starts to execute the user's demand signal processed by the platform;

[0010] Step 4): The distributed farm system then uploads the data during the execution process to the platform, and the platform receives the data during the execution process of the distributed farm system in real time to realize real-time data collection and analysis; wherein, the platform uses the data distribution comparison method to analyze whether the user's demand signal is the same as the data during the execution process of the distributed farm system;

[0011] Step 5): The platform provides the data of the distributed farm execution process to users in a visual way.

[0012] Optionally, in step 1), the platform uses a time data receiving method to receive the user's demand signal. The time data receiving method is the following formula (1):

[0013] (1);

[0014] in, For the time, To set the platform Receive user demand signals at all times. For the platform The user demand signals received at all times, To set the platform Receive user demand signals at all times;

[0015] For the platform Receive user demand signals at all times , For the platform Receive user demand signals corresponding to the time ;

[0016] For the corresponding execution operation, used to compare the platform in the Whether the time corresponds to the reception of user demand signals .

[0017] Optionally, in step 2), when the platform processes the user's demand signal, the platform allocates the specific demand category to the distributed farm system according to the user's specific demand category, and the distributed farm system performs production.

[0018] Furthermore, the data optimization allocation method is as follows:

[0019] (2);

[0020] in, , and They are task data of different categories. is a collection of task data of different categories. is the marking number, To select the mark number, Select the mark number for execution. =1 or 2 or 3, For selection , and One, two or three of the following tasks;

[0021] For different types of task data , and In the , and One, two or three of the following tasks;

[0022] or To assign and execute the selected tasks.

[0023] Optionally, in step 3), the distributed farm system executes the assigned tasks, where the assigned tasks are one, two or three.

[0024] Furthermore, in step 3), the distributed farm system receives the assigned tasks in real time.

[0025] Optionally, in step 4), the data distribution comparison method adopts the following formula (3):

[0026] (3);

[0027] in, The user's demand signal, For the The user's demand signal, is the total number of items in the user's demand signal, To list the Item user demand signal;

[0028] It is the data during the execution of the distributed farm system. For the The data during the execution of the distributed farm system has a total of item, To list the Data during the execution of the distributed farm system;

[0029] For having Select the first item from the user's demand signal The user's demand signal, in the Select the first The data from the distributed farm system execution process is compared with the The demand signal of the first user Whether the data in the distributed farm system execution process is the same; For the comparison result.

[0030] Optionally, the user's demand signal and the data during the execution of the distributed farm system are analyzed; if the user's demand signal is the same as the data during the execution of the distributed farm system, the distributed farm system allocates execution according to the user's demand signal; if the user's demand signal is different from the data during the execution of the distributed farm system, the distributed farm system does not allocate execution according to the user's demand signal.

[0031] Optionally, in step 5), the user performs comprehensive real-time monitoring to achieve standardized production on the farm.

[0032] Beneficial effects of the present invention:

[0033] The present invention is that the platform adopts the data optimization allocation method to process the user's demand signal. After the platform processes the user's demand signal, the distributed farm system receives and starts to execute the user's demand signal processed by the platform. The distributed farm system then uploads the data in the execution process to the platform. The platform receives the data in the execution process of the distributed farm system in real time, realizes real-time data collection and analysis, and the platform adopts the data allocation comparison method to analyze whether the user's demand signal is the same as the data in the execution process of the distributed farm system, thereby realizing reasonable processing of farm data. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] 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.

[0035] Figure 1 It is a schematic diagram of the system framework structure of the present invention;

[0036] Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0037] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0038] Example 1

[0039] like Figure 1 As shown, the user places an order through the platform. After the platform receives the user's demand, the platform assigns the task to the distributed farm system for production through AI algorithm. After the distributed farm system confirms the production task, it uploads the relevant information to the platform. The platform displays information through real-time collection and analysis of big data. Among them, the platform displays information to users through information visualization and traceability, and users can visualize and trace the production process in real time.

[0040] Example 2

[0041] Based on Example 1, Figure 2 As shown, this embodiment provides a distributed farm data management method based on a real-scene virtualization platform, comprising the following steps:

[0042] Step 1): The user sends a demand signal, and the platform receives the user's demand signal in real time;

[0043] Step 2): The platform processes the user's demand signal through an AI algorithm; wherein the platform uses a data optimization allocation method to process the user's demand signal;

[0044] Step 3): After the platform processes the user's demand signal, the distributed farm system receives and starts to execute the user's demand signal processed by the platform;

[0045] Step 4): The distributed farm system then uploads the data during the execution process to the platform, and the platform receives the data during the execution process of the distributed farm system in real time to realize real-time data collection and analysis; wherein, the platform uses the data distribution comparison method to analyze whether the user's demand signal is the same as the data during the execution process of the distributed farm system;

[0046] Step 5): The platform provides the data of the distributed farm execution process to users in a visual way.

[0047] Specifically, in step 1), the platform uses a time data receiving method to receive the user's demand signal. The time data receiving method is the following formula (1):

[0048] (1);

[0049] in, For the time, To set the platform Receive user demand signals at all times. For the platform The user demand signals received at all times, To set the platform Receive user demand signals at all times;

[0050] For the platform Receive user demand signals at all times , For the platform Receive user demand signals corresponding to the time ;

[0051] For the corresponding execution operation, used to compare the platform in the Whether the time corresponds to the reception of user demand signals .

[0052] In formula (1), the platform The moment is to receive the user's demand signal of, The platform can record the corresponding demand signals received from users at each moment.

[0053] In step 2), when the platform processes the user's demand signal, the platform allocates the specific demand category to the distributed farm system according to the user's specific demand category, and the distributed farm system performs production.

[0054] The data optimization allocation method is as follows:

[0055] (2);

[0056] in, , and They are task data of different categories. is a collection of task data of different categories. is the marking number, To select the mark number, Select the mark number for execution. =1 or 2 or 3, For selection , and One, two or three of the following tasks;

[0057] For different types of task data , and In the , and One, two or three of the following tasks;

[0058] or To assign and execute the selected tasks.

[0059] In formula (2), according to the marking number , execute the selection mark number ,because =1 or 2 or 3, execute the selection mark number Select , and As for the specific task category, it needs to be automatically identified by the platform. This method allocates the selected tasks.

[0060] In step 3), the distributed farm system executes the assigned tasks, which may be one, two or three tasks.

[0061] In step 3), the distributed farm system receives the assigned tasks in real time.

[0062] In step 4), the data distribution comparison method uses the following formula (3):

[0063] (3);

[0064] in, The user's demand signal, For the The user's demand signal, is the total number of items in the user's demand signal, To list the Item user demand signal;

[0065] It is the data during the execution of the distributed farm system. For the The data during the execution of the distributed farm system has a total of item, To list the Data during the execution of the distributed farm system;

[0066] For having Select the first item from the user's demand signal The user's demand signal, in the Select the first The data from the distributed farm system execution process is compared with the The demand signal of the first user Whether the data in the distributed farm system execution process is the same; For the comparison result.

[0067] In formula (3), The demand signal of a user in the item is different from the demand signal of a user in the The data of a certain item in the distributed farm system execution process are compared with the data of a certain item in the distributed farm system execution process.

[0068] The user's demand signal and the data during the execution of the distributed farm system are analyzed; if the user's demand signal is the same as the data during the execution of the distributed farm system, the distributed farm system allocates and executes according to the user's demand signal; if the user's demand signal is different from the data during the execution of the distributed farm system, the distributed farm system does not allocate and execute according to the user's demand signal.

[0069] In step 5), users conduct comprehensive real-time monitoring to achieve standardized production on the farm.

[0070] Example 3

[0071] Based on all the above embodiments, the present invention is that the user sends a demand signal to the platform, and the platform distributes and processes the user's demand signal. After the platform processes the user's demand signal, the distributed farm system starts to execute the user's demand signal processed by the platform, and the platform then receives and compares the data during the execution of the distributed farm system and the user's demand signal processed by the platform. The user can comprehensively monitor the data during the execution of the distributed farm in real time or visually monitor the data during the execution of the distributed farm.

[0072] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope of the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed farm data management method based on a real-world scenario virtualization platform, characterized in that: The steps include: Step 1): The user sends a demand signal, and the platform receives the user's demand signal in real time; Step 2): The platform processes the user's demand signal through the AI ​​algorithm; wherein, the platform uses the data optimization allocation method to process the user's demand signal; Step 3): After the platform processes the user's demand signal, the distributed farm system receives and starts to execute the user's demand signal processed by the platform; Step 4): The distributed farm system then uploads the data during the execution process to the platform, and the platform receives the data during the execution process of the distributed farm system in real time to realize real-time data collection and analysis; wherein, the platform uses the data distribution comparison method to analyze whether the user's demand signal is the same as the data during the execution process of the distributed farm system; Step 5): The platform provides the data of the distributed farm execution process to users in a visual way.

2. The distributed farm data management method based on the real scene virtualization platform according to claim 1 is characterized in that: In the step 1), the platform uses a time data receiving method to receive the user's demand signal. The time data receiving method is the following formula (1): (1)； in, For the time, To set the platform Receive user demand signals at all times. For the platform The user demand signals received at all times, To set the platform Receive user demand signals at all times; For the platform Receive user demand signals at all times , For the platform Receive user demand signals corresponding to the time ; For the corresponding execution operation, used to compare the platform in the Whether the time corresponds to the reception of user demand signals .

3. The distributed farm data management method based on the real scene virtualization platform according to claim 1 is characterized in that: In the step 2), when the platform processes the user's demand signal, the platform allocates the specific demand category to the distributed farm system according to the user's specific demand category, and the distributed farm system performs production.

4. The distributed farm data management method based on the real scene virtualization platform according to claim 3 is characterized in that: The data optimization allocation method is as follows: (2)； in, , and They are task data of different categories. is a collection of task data of different categories. is the marking number, To select the mark number, Select the mark number for execution. =1 or 2 or 3, For selection , and One, two or three of the following tasks; For different types of task data , and In the , and One, two or three of the following tasks; or To assign and execute the selected tasks.

5. The distributed farm data management method based on the real scene virtualization platform according to claim 1 is characterized in that: In the step 3), the distributed farm system executes the assigned tasks, where the assigned tasks are one, two or three.

6. The distributed farm data management method based on the real scene virtualization platform according to claim 5 is characterized in that: In the step 3), the distributed farm system receives the assigned tasks in real time.

7. The distributed farm data management method based on the real scene virtualization platform according to claim 1 is characterized in that: In step 4), the data distribution comparison method adopts the following formula (3): (3)； in, The user's demand signal, For the The user's demand signal, is the total number of items in the user's demand signal, To list the Item user demand signal; It is the data during the execution of the distributed farm system. For the The data during the execution of the distributed farm system has a total of item, To list the Data during the execution of the distributed farm system; For having Select the first item from the user's demand signal The user's demand signal, in the Select the first The data from the distributed farm system execution process is compared with the The demand signal of the user Whether the data in the distributed farm system execution process is the same; For the comparison result.

8. The distributed farm data management method based on the real scene virtualization platform according to claim 7 is characterized in that: Analyze the user's demand signal and the data during the execution of the distributed farm system; if the user's demand signal is the same as the data during the execution of the distributed farm system, the distributed farm system allocates and executes according to the user's demand signal; If the user's demand signal is different from the data in the distributed farm system execution process, the distributed farm system does not allocate and execute according to the user's demand signal.

9. The distributed farm data management method based on the real scene virtualization platform according to claim 1 is characterized in that: In step 5), the user performs comprehensive real-time monitoring to achieve standardized production on the farm.

Images