Crop management method and device based on knowledge graph

Abstract

The present invention discloses a crop management method and device based on a knowledge graph. The method includes: collecting crop growth data; inputting the growth data into a knowledge graph model, obtaining management measures output by the knowledge graph model, wherein the knowledge graph model is a model that uses the knowledge graph to analyze the growth data and outputs management measures; managing crops based on the management measures, wherein the management measures are measures for managing the growth status of crops during the growth of crops. The present invention solves the technical problem of low farm management efficiency caused by the prior art of managing farms based on artificial experience.

Description

Technical Field

[0001] The present invention relates to the field of farm management, and in particular to a crop management method and device based on a knowledge graph. Background Art

[0002] Agricultural production management refers to the management of a series of agricultural production activities, such as farmland infrastructure construction, crop cultivation, pest control, etc. Among them, agricultural production management includes farm management.

[0003] At present, in terms of farm management, management decisions for farm crops are usually determined based on the manager's own knowledge system, and then traced on the management system. Growers with rich management experience can make accurate decisions using the current farm management method, but due to differences in the types, varieties, regions, climates, etc. of crops planted on farms, it is difficult for most people who lack management experience to make accurate decisions suitable for farm crops, thereby reducing the efficiency of farm management and may even reduce the yield of farm crops, causing significant economic losses to the farm.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a crop management method and device based on a knowledge graph, so as to at least solve the technical problem of low farm management efficiency caused by the prior art of managing farms based on manual experience.

[0006] According to one aspect of an embodiment of the present invention, a crop management method based on a knowledge graph is provided, comprising: collecting growth data of crops; inputting the growth data into a knowledge graph model, and obtaining management measures output by the knowledge graph model, wherein the knowledge graph model is a model that uses a knowledge graph to analyze the growth data and outputs management measures; managing crops based on the management measures, wherein the management measures are measures for managing the growth status of crops during the growth process of the crops.

[0007] Optionally, the growth data includes at least one of the following: climate data, regional data, crop type data, soil state data, time stage data, variety data and plant phenotype data.

[0008] Optionally, the elements of the knowledge graph include at least: entities, attributes and attribute values, wherein the crop management method based on the knowledge graph further includes: before inputting the growth data into the knowledge graph model, obtaining at least one preset entity, wherein different entities correspond to different crops; obtaining current growth data collected by the sensor for the current crop, wherein the current growth data includes at least data attributes and data attribute values, and different sensors collect data corresponding to different data attributes; and constructing a knowledge graph based on the entities, data attributes and data attribute values.

[0009] Optionally, the elements of the knowledge graph also include: multiple entities and the relationships between the multiple entities. The crop management method based on the knowledge graph also includes: obtaining management measures corresponding to different crops; determining the relationship between entities corresponding to different crops based on the management measures corresponding to different crops; and constructing a knowledge graph based on entities, data attributes, data attribute values, and the relationships between multiple entities.

[0010] Optionally, the crop management method based on the knowledge graph also includes: detecting the attribute values ​​corresponding to the crops in the knowledge graph; and obtaining the management time and corresponding management measures for managing the crops output by the knowledge graph model according to the attribute values.

[0011] Optionally, the crop management method based on the knowledge graph also includes: after managing the crops based on the management measures, obtaining management results of the growth status of the crops based on the management measures; displaying the management results, wherein the management results include at least: a first management result and a second management result, the first management result characterizing that the management measures have a positive effect on the growth of the crops, and the second management result characterizing that the management measures have a negative effect on the growth of the crops.

[0012] Optionally, when the management result is the second management result, the processing result after optimizing the management measure is obtained, and the knowledge graph is updated based on the processing result.

[0013] Optionally, the crop management method based on the knowledge graph also includes: displaying the knowledge graph and receiving adjustment instructions for adjusting the knowledge graph; updating the knowledge graph based on the adjustment instructions; and displaying the updated knowledge graph.

[0014] Optionally, the crop management method based on the knowledge graph also includes: in the case of not receiving an adjustment instruction to adjust the knowledge graph, detecting whether there are entities corresponding to the crops in the knowledge graph to obtain a detection result; determining an update measure corresponding to the detection result to update the knowledge graph.

[0015] Optionally, the crop management method based on the knowledge graph also includes: when the detection result indicates that there are entities corresponding to the crops in the knowledge graph, obtaining multiple management measures for the crops; performing big data analysis based on the growth status corresponding to each management measure to determine the optimal management measure; and updating the attributes and / or attribute values ​​corresponding to the crops in the knowledge graph based on the optimal management measure.

[0016] Optionally, the crop management method based on the knowledge graph also includes: when the detection result indicates that there is no entity corresponding to the crop in the knowledge graph, obtaining multiple management measures for the crop; performing big data analysis based on the growth status corresponding to each management measure to determine the optimal management measure; creating an entity corresponding to the crop in the knowledge graph, and updating the structure of the knowledge graph based on the optimal management measure.

[0017] According to another aspect of an embodiment of the present invention, a crop management method based on a knowledge graph is also provided, including: collecting growth data of crops; analyzing the growth data based on the knowledge graph to obtain management measures for managing the growth status of crops; obtaining management results after managing the growth status of crops based on the management measures; and updating the knowledge graph based on the management results.

[0018] According to another aspect of an embodiment of the present invention, a crop management device based on a knowledge graph is also provided, including: a collection module for collecting crop growth data; an acquisition module for inputting the growth data into a knowledge graph model, and acquiring management measures output by the knowledge graph model, wherein the knowledge graph model is a model that uses a knowledge graph to analyze the growth data and outputs management measures; a management module for managing crops based on management measures, wherein the management measures are measures for managing the growth status of crops during the growth process of the crops.

[0019] According to another aspect of an embodiment of the present invention, a storage medium is further provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned crop management method based on knowledge graph.

[0020] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein the above-mentioned crop management method based on knowledge graph is executed when the program is run.

[0021] In an embodiment of the present invention, a farm management knowledge system is constructed based on a knowledge graph. After the growth data of the crops is obtained, the growth data is input into a knowledge graph model, and management measures output by the knowledge graph model are obtained. Finally, the crops are managed based on the management measures, wherein the knowledge graph model is a model that uses a knowledge graph to analyze the growth data and outputs management measures.

[0022] From the above content, it can be seen that this application uses knowledge graphs to construct a management system (i.e., management measures) for crop management, and manages crops based on this management system. Since knowledge graphs have good transferability of knowledge, it is easy to copy management knowledge, provide reference decisions for managers with insufficient farm management experience, and provide more management measures for managers with farm management experience, thereby improving farm management efficiency.

[0023] It can be seen that the solution provided in this application achieves the purpose of automatically managing the farm, thereby realizing the technical effect of improving the management efficiency of the farm, and further solving the technical problem of low farm management efficiency caused by the existing technology of managing the farm based on manual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 is a flow chart of a crop management method based on a knowledge graph according to an embodiment of the present invention;

[0026] Figure 2 is a schematic diagram of the structure of an optional knowledge graph according to an embodiment of the present invention;

[0027] Figure 3 is a framework diagram of an optional knowledge graph-based crop management method according to an embodiment of the present invention;

[0028] Figure 4 is a flow chart of a crop management method based on a knowledge graph according to an embodiment of the present invention; and

[0029] Figure 5 is a schematic diagram of a crop management device based on a knowledge graph according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Example 1

[0033] According to an embodiment of the present invention, an embodiment of a crop management method based on a knowledge graph is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0034] Figure 1 is a flow chart of a crop management method based on a knowledge graph according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0035] Step S102, collecting crop growth data.

[0036] It should be noted that, in this embodiment, the farm management system can serve as the execution subject of this embodiment, wherein the farm management system at least includes: data acquisition equipment, data processing equipment, wherein the data acquisition equipment can be a sensor, and different data acquisition equipment is used to collect different growth data of crops. Optionally, the growth data includes at least one of the following: climate data, regional data, crop type data, soil state data, time stage data, variety data, and plant phenotypic data. For example, soil temperature sensors, soil moisture sensors, soil nutrient detectors, soil PH testers, etc. are used to collect soil state data, and air temperature sensors, air humidity sensors, light sensors, etc. are used to collect climate data.

[0037] It should be noted that by setting up data collection equipment to automatically collect crop growth data, not only can the time of manual data collection be saved, but also the problem of inaccurate data collection during manual data collection can be avoided, thereby improving the efficiency and accuracy of data collection.

[0038] Step S104: input the growth data into the knowledge graph model to obtain the management measures output by the knowledge graph model, wherein the knowledge graph model is a model that uses the knowledge graph to analyze the growth data and outputs management measures.

[0039] In step S104, the knowledge graph is a theory that combines the theories and methods of applied mathematics, graphics, information visualization technology, information science and other disciplines with methods such as metrology citation analysis and co-occurrence analysis, and uses a visualized graph to vividly display the core structure, development history, frontier fields and overall knowledge architecture of the discipline to achieve the purpose of multidisciplinary integration. In this application, the knowledge graph can achieve the integration of multiple disciplines such as agriculture and mathematics.

[0040] In an optional embodiment, the data processing device of the farm management system can obtain the growth data collected by the data collection device, and use the knowledge graph to analyze the growth data to determine the measures for managing the crops. Optionally, the data processing device determines the type and variety of the crop based on the crop type data and variety data, and then determines the identifier corresponding to the crop in the knowledge graph model. For example, the identifier corresponding to the crop of type rice and variety Wuyou Rice No. 2 is 0001. After the data processing device inputs the crop type data and variety data into the knowledge graph model, the knowledge graph model can determine that the identifier corresponding to the crop is 0001, and then obtains the attributes and attribute values ​​of the entity corresponding to 0001 in the knowledge graph, and then determines the management measures for the crop identified as 0001, and outputs the management measures.

[0041] Step S106, managing the crops based on the management measures, wherein the management measures are measures for managing the growth status of the crops during the growth of the crops.

[0042] In step S106, the management measures for managing crops may include but are not limited to spraying time, spraying dosage, fertilizing time, fertilizing dosage, watering time, watering cycle, watering amount, etc.

[0043] It should be noted that the management measures obtained through step S104 are usually measures that can promote crop growth and increase crop yields. Therefore, in step S106, managing crops based on the management measures can better manage the farm and improve the economic benefits of the farm.

[0044] Based on the scheme defined by the above steps S102 to S106, it can be known that the present application adopts a method of constructing a farm management knowledge system based on a knowledge graph. After obtaining the growth data of the crops, the growth data is input into the knowledge graph model, and the management measures output by the knowledge graph model are obtained. Finally, the crops are managed based on the management measures, wherein the knowledge graph model is a model that uses a knowledge graph to analyze the growth data and outputs management measures.

[0045] It is easy to notice that this application uses knowledge graphs to construct a management system (i.e., management measures) for crop management, and manages crops based on this management system. Since knowledge graphs have good transferability of knowledge, it is easy to copy management knowledge, provide reference decisions for managers with insufficient farm management experience, and provide more management measures for managers with farm management experience, thereby improving farm management efficiency.

[0046] It can be seen that the solution provided in this application achieves the purpose of automatically managing the farm, thereby realizing the technical effect of improving the management efficiency of the farm, and further solving the technical problem of low farm management efficiency caused by the existing technology of managing the farm based on manual experience.

[0047] In an optional embodiment, before inputting the growth data into the knowledge graph model, it is necessary to construct a knowledge graph, wherein the elements of the knowledge graph include at least entities, attributes, and attribute values. Specifically, the data processing device first obtains at least one preset entity, then obtains the current growth data collected by the sensor for the current crop, and constructs a knowledge graph based on the entity, data attributes, and data attribute values. Different entities correspond to different crops, the current growth data includes at least data attributes and data attribute values, and different sensors collect data corresponding to different data attributes.

[0048] It should be noted that, in this application, the entity is a crop, the attribute is an attribute corresponding to the growth data of the crop, and the attribute value is the growth data of the crop, which is optional. Figure 2 A schematic diagram of the structure of an optional knowledge graph is shown. Figure 2 In the example, the entity is rice, and the attributes include: plant height, light duration, leaf age, accumulated temperature, soil temperature, stem number, leaf nitrogen content, etc. The attribute value is the value corresponding to the attribute. In addition, Figure 2 The knowledge graph shown also shows the corresponding management measures under different attributes and / or attribute values, for example, when the nitrogen content of the leaves is less than 0.1, fertilize; when the plant height is greater than 0.5m, spray pesticides; when the soil temperature is less than 0.5℃, water.

[0049] In an optional embodiment, the elements of the knowledge graph also include: multiple entities and the relationships between multiple entities, that is, the knowledge graph can also show the relationships between different crops. Among them, the data processing device first obtains the management measures corresponding to different crops, and then determines the relationships between entities corresponding to different crops according to the management measures corresponding to different crops, and finally constructs a knowledge graph based on entities, data attributes, data attribute values ​​and the relationships between multiple entities. For example, rice and wheat are both sprayed when the plant height is greater than 0.5m, and the variety and dosage of the sprayed pesticides are the same. The sub-knowledge graph corresponding to rice and the sub-knowledge graph corresponding to wheat can be associated through the relationship between plant height and spraying, thereby obtaining the knowledge graph corresponding to farm management.

[0050] It should be noted that the main forms of knowledge graphs are entity-attribute-attribute value and entity-relationship-entity. In this application, entities are mainly obtained through system settings and customer customization, while attributes and attribute values ​​are mainly obtained through sensor-related data and customer-customized uploaded data, and relationships are associated through customer decision-making plans and related scientific knowledge.

[0051] Optionally, after obtaining the knowledge graph, the data processing device inputs the crop growth data into the knowledge graph model and obtains the management measures output by the knowledge graph model. Specifically, the data processing device detects the attribute values ​​corresponding to the crops in the knowledge graph and obtains the management time and corresponding management measures for managing the crops output by the knowledge graph model according to the attribute values. Figure 2 In the example, the attribute is plant height, and the attribute value is 0.9, which means that the plant height is greater than 0.5m. At this time, the knowledge graph shows that the management measure corresponding to this attribute value is spraying. In this scenario, the knowledge graph model outputs spraying and spraying time ( Figure 2 Not shown), spray dosage ( Figure 2 Not shown in the figure) etc.

[0052] In an optional embodiment, after managing crops based on management measures, the farm management system also obtains management results of the growth status of crops based on the management measures and displays the management results, wherein the management results at least include: a first management result and a second management result, the first management result represents that the management measures have a positive effect on the growth of crops, and the second management result represents that the management measures have a negative effect on the growth of crops. Optionally, when the management result is the second management result, the processing result after optimizing the management measures is obtained, and the knowledge graph is updated based on the processing result.

[0053] It should be noted that in this application, the farm management system can record and evaluate the growth status of crops at different stages, and track and feedback the effects of management measures, provide positive feedback (i.e., display the first management result) to management measures that have a positive effect, and provide negative feedback to management measures that have a negative effect, so that the farm management system can further optimize the management measures. For example, the management measures indicate that it is necessary to spray pesticides when the plant height of rice is greater than 0.3m. The farm manager sprays pesticides when the plant height is greater than 0.3m according to the management measures, but the rice yield is not high, that is, the management measures have a negative effect at this time. At this time, the farm manager can analyze the attributes and attribute values ​​corresponding to rice based on the knowledge graph, and determine that when the plant height of rice is small, if the rice is sprayed with pesticides, it may harm the rice. The farm manager can spray pesticides when the plant height is higher (for example, higher than 0.5m), which can increase the rice yield.

[0054] In an optional embodiment, the management measures corresponding to the knowledge graph in the farm management system may not be the optimal management measures, and the knowledge graph needs to be updated.

[0055] Optionally, the farm management system further includes a display device (e.g., a display). The display device can display the knowledge graph, the data processing device receives an adjustment instruction for adjusting the knowledge graph, and updates the knowledge graph based on the adjustment instruction, and finally the display device displays the updated knowledge graph. That is, in this scenario, the farm management system updates the knowledge graph only after receiving the adjustment instruction input by the farm manager.

[0056] In another optional embodiment, the farm management system can also automatically update the knowledge graph. Specifically, when no adjustment instruction is received to adjust the knowledge graph, the farm management system detects whether there is an entity corresponding to the crop in the knowledge graph, obtains the detection result, and determines an update measure corresponding to the detection result to update the knowledge graph.

[0057] It should be noted that the priority of the farm management system to update the knowledge graph based on the adjustment instruction is higher than the priority of automatically updating the knowledge graph. In addition, different detection results also correspond to different update measures.

[0058] Optionally, when the detection result indicates that there are entities corresponding to crops in the knowledge graph, the farm management system obtains multiple management measures for the crops, and performs big data analysis based on the growth status corresponding to each management measure, determines the optimal management measure, and finally updates the attributes and / or attribute values ​​corresponding to the crops in the knowledge graph based on the optimal management measure. For example, the management measure for rice corresponding to the knowledge graph is measure 1. In the process of managing rice based on measure 1, the farm manager sprayed the rice when the plant height was greater than 0.6m, and the yield was greater than the yield corresponding to measure 1. The farm manager can increase the rice yield by adjusting the attribute value corresponding to the plant height, that is, measure 2 obtained by the farm manager after adjusting measure 1 is better than measure 1. At this time, the farm management system automatically modifies the plant height attribute in the knowledge graph corresponding to measure 1 to 0.6m.

[0059] Optionally, the knowledge graph can also be updated by recreating entities and analyzing and providing feedback on the effects of management measures, and updating related relationships and values ​​through big data analysis. Specifically, when the test results indicate that there is no entity corresponding to the crop in the knowledge graph, the farm management system obtains multiple management measures for the crop, and performs big data analysis based on the growth status corresponding to each management measure to determine the optimal management measure. Finally, the entity corresponding to the crop is created in the knowledge graph, and the structure of the knowledge graph is updated based on the optimal management measure.

[0060] For example, the knowledge graph model does not store a sub-knowledge graph about rice planting, but multiple users use a farm management system to manage their own farms, and all of them plant rice. The farm management system records the data collected by each sensor and the farming behavior of each user. When it comes to the crop growth stage, the farm management system uses the same evaluation criteria to evaluate the quality of the current crop (for example, leaf age, number of tillers, plant height, etc.), and then selects the best management measures as knowledge and stores them in the knowledge graph. The stored knowledge includes but is not limited to data in various dimensions such as light, temperature, water, and fertilization, so as to provide decision-making assistance for users who plant rice in the future. If the user subsequently uses management measures that are better than those corresponding to the knowledge graph, the same method mentioned above is used to automatically update the current knowledge structure.

[0061] In an optional embodiment, Figure 3 The framework diagram of the crop management method based on knowledge graph is shown. Figure 3 It can be seen that the application can generate crop management decisions by analyzing the basic data (i.e., growth data) of the collected crops, and manage the crops based on the decisions. Then, the effects corresponding to the decisions are tracked and feedback is provided, and the decisions that produce negative effects are optimized.

[0062] From the above content, it can be seen that this application automatically constructs a management knowledge system by combining IoT data, traceability data, artificial intelligence algorithms and knowledge graphs. This knowledge system exists in the background system in the form of a knowledge graph. Since the knowledge graph has good transferability for knowledge, it is easy to copy management knowledge, provide reference decisions for farm managers with insufficient management experience, and provide better decisions for farm managers with management experience.

[0063] Example 2

[0064] According to an embodiment of the present invention, an embodiment of a crop management method based on a knowledge graph is also provided, wherein: Figure 5 is a flow chart of a crop management method based on a knowledge graph according to an embodiment of the present invention. Figure 4 As shown, the method comprises the following steps:

[0065] Step S402, collecting crop growth data.

[0066] It should be noted that, in this embodiment, the farm management system can serve as the execution subject of this embodiment, wherein the farm management system at least includes: data acquisition equipment, data processing equipment, wherein the data acquisition equipment can be a sensor, and different data acquisition equipment is used to collect different growth data of crops. Optionally, the growth data includes at least one of the following: climate data, regional data, crop type data, soil state data, time stage data, variety data, and plant phenotypic data. For example, soil temperature sensors, soil moisture sensors, soil nutrient detectors, soil PH testers, etc. are used to collect soil state data, and air temperature sensors, air humidity sensors, light sensors, etc. are used to collect climate data.

[0067] It should be noted that by setting up data collection equipment to automatically collect crop growth data, not only can the time of manual data collection be saved, but also the problem of inaccurate data collection during manual data collection can be avoided, thereby improving the efficiency and accuracy of data collection.

[0068] Step S404: Analyze the growth data based on the knowledge graph to obtain management measures for managing the growth status of the crops.

[0069] In step S404, the knowledge graph is a theory that combines the theories and methods of applied mathematics, graphics, information visualization technology, information science and other disciplines with methods such as metrology citation analysis and co-occurrence analysis, and uses a visualized graph to vividly display the core structure, development history, frontier fields and overall knowledge architecture of the discipline to achieve the purpose of multidisciplinary integration. In this application, the knowledge graph can achieve the integration of multiple disciplines such as agriculture and mathematics.

[0070] In an optional embodiment, the data processing device of the farm management system can obtain the growth data collected by the data collection device, and use the knowledge graph to analyze the growth data to determine the measures for managing the crops. Optionally, the data processing device determines the type and variety of the crop based on the crop type data and variety data, and then determines the identifier corresponding to the crop in the knowledge graph model. For example, the identifier corresponding to the crop of type rice and variety Wuyou Rice No. 2 is 0001. After the data processing device inputs the crop type data and variety data into the knowledge graph model, the knowledge graph model can determine that the identifier corresponding to the crop is 0001, and then obtains the attributes and attribute values ​​of the entity corresponding to 0001 in the knowledge graph, and then determines the management measures for the crop identified as 0001, and outputs the management measures.

[0071] Step S406, obtaining the management result after managing the growth status of the crop based on the management measures.

[0072] In an optional embodiment, after managing the crops based on the management measures, the farm management system also obtains management results of the growth status of the crops based on the management measures, and displays the management results, wherein the management results include at least: a first management result and a second management result, the first management result characterizing that the management measures have a positive effect on the growth of the crops, and the second management result characterizing that the management measures have a negative effect on the growth of the crops.

[0073] Step S408, updating the knowledge graph based on the management results.

[0074] It should be noted that the management measures corresponding to the knowledge graph in the farm management system may not be the optimal management measures, and the knowledge graph needs to be updated at this time.

[0075] Optionally, when the management result is the second management result, the processing result after optimizing the management measure is obtained, and the knowledge graph is updated based on the processing result.

[0076] In addition, it should be noted that in the present application, the farm management system can record and evaluate the growth status of crops at different stages, and track and feedback the effects of management measures, provide positive feedback (i.e., display the first management result) to management measures that have a positive effect, and provide negative feedback to management measures that have a negative effect, so that the farm management system can further optimize the management measures. For example, the management measures indicate that it is necessary to spray pesticides when the plant height of rice is greater than 0.3m. The farm manager sprays pesticides when the plant height is greater than 0.3m according to the management measures, but the rice yield is not high, that is, the management measures have a negative effect at this time. At this time, the farm manager can analyze the attributes and attribute values ​​corresponding to rice based on the knowledge graph, and determine that when the plant height of rice is small, if the rice is sprayed with pesticides, it may harm the rice. The farm manager can spray pesticides when the plant height is higher (for example, higher than 0.5m), which can increase the rice yield.

[0077] In an optional embodiment, the farm management system further includes a display device (e.g., a display). The display device can display the knowledge graph, the data processing device receives an adjustment instruction for adjusting the knowledge graph, and updates the knowledge graph based on the adjustment instruction, and finally the display device displays the updated knowledge graph. That is, in this scenario, the farm management system updates the knowledge graph only after receiving the adjustment instruction input by the farm manager.

[0078] In another optional embodiment, the farm management system can also automatically update the knowledge graph. Specifically, in the case where no adjustment instruction is received to adjust the knowledge graph, the farm management system detects whether there are entities corresponding to crops in the knowledge graph, obtains the detection result, and determines the update measures corresponding to the detection result to update the knowledge graph. In the case where no adjustment instruction is received to adjust the knowledge graph, the farm management system detects whether there are entities corresponding to crops in the knowledge graph, obtains the detection result, and determines the update measures corresponding to the detection result to update the knowledge graph.

[0079] Based on the scheme defined by the above steps S402 to S408, it can be known that the present application adopts a method of constructing a farm management knowledge system based on a knowledge graph. After obtaining the growth data of the crops, the growth data is analyzed based on the knowledge graph to obtain management measures for managing the growth status of the crops, and the management results after managing the growth status of the crops based on the management measures are obtained. Finally, the knowledge graph is updated based on the management results.

[0080] It is easy to notice that this application uses knowledge graphs to construct a management system (i.e., management measures) for crop management, and manages crops based on this management system. Since knowledge graphs have good transferability to knowledge, it is easy to copy management knowledge, provide reference decisions for managers with insufficient farm management experience, and provide more management measures for managers with farm management experience, thereby improving farm management efficiency. In addition, this application also updates the knowledge graph so that the knowledge graph can provide the best management measures.

[0081] It can be seen that the solution provided in this application achieves the purpose of automatically managing the farm, thereby realizing the technical effect of improving the management efficiency of the farm, and further solving the technical problem of low farm management efficiency caused by the existing technology of managing the farm based on manual experience.

[0082] Example 3

[0083] According to an embodiment of the present invention, an embodiment of a crop management device based on a knowledge graph is also provided, wherein: Figure 5 is a schematic diagram of a crop management device based on a knowledge graph according to an embodiment of the present invention, such as Figure 5 As shown, the device includes: a collection module 501, an acquisition module 503 and a management module 505.

[0084] Among them, the collection module 501 is used to collect crop growth data; the acquisition module 503 is used to input the growth data into the knowledge graph model to obtain the management measures output by the knowledge graph model, wherein the knowledge graph model is a model that uses the knowledge graph to analyze the growth data and outputs management measures; the management module 505 is used to manage crops based on management measures, wherein the management measures are measures for managing the growth status of crops during the growth process of crops.

[0085] It should be noted here that the above-mentioned collection module 501, acquisition module 503 and management module 505 correspond to steps S102 to S106 of the above-mentioned embodiment, and the three modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the contents disclosed in the above-mentioned embodiment.

[0086] Optionally, the growth data includes at least one of the following: climate data, regional data, crop type data, soil state data, time stage data, variety data and plant phenotypic data.

[0087] In an optional embodiment, the elements of the knowledge graph include at least: entities, attributes and attribute values, wherein the crop management device based on the knowledge graph further includes: a first acquisition module, a second acquisition module and a first construction module. The first acquisition module is used to acquire at least one preset entity before inputting the growth data into the knowledge graph model, wherein different entities correspond to different crops; the second acquisition module is used to acquire the current growth data collected by the sensor for the current crop, wherein the current growth data includes at least data attributes and data attribute values, and different sensors collect data corresponding to different data attributes; the first construction module is used to construct the knowledge graph according to the entities, data attributes and data attribute values.

[0088] In an optional embodiment, the elements of the knowledge graph also include: multiple entities and the relationship between the multiple entities, and the crop management device based on the knowledge graph also includes: a third acquisition module, a first determination module and a second construction module. Among them, the third acquisition module is used to obtain the management measures corresponding to different crops; the first determination module is used to determine the relationship between the entities corresponding to different crops according to the management measures corresponding to different crops; the second construction module is used to construct the knowledge graph according to the entities, data attributes, data attribute values ​​and the relationship between multiple entities.

[0089] In an optional embodiment, the acquisition module includes: a first detection module and a fourth acquisition module. The first detection module is used to detect the attribute value corresponding to the crop in the knowledge graph; the fourth acquisition module is used to obtain the management time and corresponding management measures for managing the crop output by the knowledge graph model according to the attribute value.

[0090] In an optional embodiment, the crop management device based on the knowledge graph further includes: a fifth acquisition module and a display module. The fifth acquisition module is used to acquire the management result of the growth status of the crop based on the management measure after managing the crop based on the management measure; the display module is used to display the management result, wherein the management result includes at least: a first management result and a second management result, the first management result represents that the management measure has a positive effect on the growth of the crop, and the second management result represents that the management measure has a negative effect on the growth of the crop.

[0091] In an optional embodiment, the crop management device based on the knowledge graph also includes: a sixth acquisition module, which is used to obtain a processing result after optimizing the management measure when the management result is the second management result, and update the knowledge graph based on the processing result.

[0092] In an optional embodiment, the crop management device based on the knowledge graph further includes: a first display module, a first update module and a second display module. The first display module is used to display the knowledge graph and receive an adjustment instruction for adjusting the knowledge graph; the first update module is used to update the knowledge graph based on the adjustment instruction; and the second display module is used to display the updated knowledge graph.

[0093] In an optional embodiment, the crop management device based on the knowledge graph further includes: a second detection module and a second update module. The second detection module is used to detect whether there is an entity corresponding to the crop in the knowledge graph and obtain a detection result when no adjustment instruction for adjusting the knowledge graph is received; the second update module is used to determine an update measure corresponding to the detection result to update the knowledge graph.

[0094] In an optional embodiment, the second update module includes: a seventh acquisition module, a second determination module and a third update module. The seventh acquisition module is used to obtain multiple management measures for crops when the detection result indicates that there are entities corresponding to crops in the knowledge graph; the second determination module is used to perform big data analysis based on the growth state corresponding to each management measure to determine the optimal management measure; the third update module is used to update the attributes and / or attribute values ​​corresponding to the crops in the knowledge graph according to the optimal management measure.

[0095] In an optional embodiment, the second update module includes: an eighth acquisition module, a third determination module and a fourth update module. The eighth acquisition module is used to acquire multiple management measures for crops when the detection result indicates that there is no entity corresponding to the crop in the knowledge graph; the third determination module is used to perform big data analysis based on the growth state corresponding to each management measure to determine the optimal management measure; the fourth update module is used to create an entity corresponding to the crop in the knowledge graph and update the structure of the knowledge graph based on the optimal management measure.

[0096] Example 4

[0097] According to another aspect of an embodiment of the present invention, a storage medium is further provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the crop management method based on knowledge graph in the above-mentioned embodiment 1.

[0098] Example 5

[0099] According to another aspect of an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein the crop management method based on the knowledge graph in the above-mentioned embodiment 1 is executed when the program is run.

[0100] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0101] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0104] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0106] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A crop management method based on knowledge graph, characterized in that: include: Collect crop growth data; Input the growth data into a knowledge graph model, and obtain management measures output by the knowledge graph model, wherein the knowledge graph model is a model that uses a knowledge graph to analyze the growth data and outputs the management measures, wherein the elements of the knowledge graph at least include: entities, attributes, and attribute values, wherein the entity is a crop, the attribute is an attribute corresponding to the growth data of the crop, and the attribute value is the growth data of the crop, and the elements of the knowledge graph also include: a plurality of the entities and the relationship between the plurality of the entities, wherein the relationship between the entities is the relationship between the entities corresponding to the different crops determined according to the management measures corresponding to the different crops; Managing the crop based on the management measure, wherein the management measure is a measure for managing the growth state of the crop during the growth of the crop; After managing the crop based on the management measures, the method further includes: Obtaining a management result on the growth status of the crop based on the management measure; Displaying the management results, wherein the management results at least include: a first management result and a second management result, the first management result indicating that the management measure has a positive effect on the growth of the crop, and the second management result indicating that the management measure has a negative effect on the growth of the crop; In a case where the management result is the second management result, a processing result after optimizing the management measure is obtained, and the knowledge graph is updated based on the processing result.

2. The method according to claim 1, characterized in that The growth data includes at least one of the following: climate data, regional data, crop type data, soil state data, time stage data, variety data and plant phenotype data.

3. The method according to claim 1, characterized in that Before inputting the growth data into the knowledge graph model, the method further includes: Acquire at least one preset entity, wherein different entities correspond to different crops; Acquire current growth data of the current crop collected by the sensor, wherein the current growth data at least includes data attributes and data attribute values, and different sensors collect data corresponding to different data attributes; The knowledge graph is constructed according to the entities, the data attributes and the data attribute values.

4. The method according to claim 3, characterized in that: The method further comprises: Obtain management measures corresponding to different crops; Determining the relationship between the entities corresponding to the different crops according to the management measures corresponding to the different crops; The knowledge graph is constructed according to the entities, the data attributes, the data attribute values ​​and the relationships between the multiple entities.

5. The method according to claim 1, characterized in that The management measures for obtaining the output of the knowledge graph model include: Detecting attribute values ​​corresponding to the crops in the knowledge graph; The management time and corresponding management measures for managing the crop output by the knowledge graph model according to the attribute value are obtained.

6. The method according to claim 1, characterized in that The method further comprises: Displaying the knowledge graph and receiving an adjustment instruction for adjusting the knowledge graph; Updating the knowledge graph based on the adjustment instruction; The updated knowledge graph is displayed.

7. The method according to claim 6, characterized in that The method further comprises: In the case where no adjustment instruction for adjusting the knowledge graph is received, detecting whether there is an entity corresponding to the crop in the knowledge graph, and obtaining a detection result; Determine an update measure corresponding to the detection result to update the knowledge graph.

8. The method according to claim 7, characterized in that Determining an update measure corresponding to the detection result to update the knowledge graph includes: When the detection result indicates that an entity corresponding to the crop exists in the knowledge graph, obtaining a plurality of management measures for the crop; Conduct big data analysis based on the growth status corresponding to each management measure to determine the optimal management measure; The attributes and / or attribute values ​​corresponding to the crops in the knowledge graph are updated according to the optimal management measures.

9. The method according to claim 7, characterized in that: Determining an update measure corresponding to the detection result to update the knowledge graph includes: When the detection result indicates that an entity corresponding to the crop does not exist in the knowledge graph, obtaining a plurality of management measures for the crop; Conduct big data analysis based on the growth status corresponding to each management measure to determine the optimal management measure; An entity corresponding to the crop is created in the knowledge graph, and the structure of the knowledge graph is updated based on the optimal management measures.

10. A crop management device based on knowledge graph, characterized in that: include: A collection module is used to collect crop growth data; an acquisition module, used for inputting the growth data into a knowledge graph model, and acquiring management measures output by the knowledge graph model, wherein the knowledge graph model is a model for analyzing the growth data using a knowledge graph and outputting the management measures, wherein the elements of the knowledge graph at least include: entities, attributes, and attribute values, wherein the entities are crops, the attributes are attributes corresponding to the growth data of the crops, and the attribute values ​​are the growth data of the crops, and the elements of the knowledge graph further include: a plurality of the entities and the relationships between the plurality of the entities, wherein the relationships between the entities are relationships between the entities corresponding to the different crops determined according to the management measures corresponding to the different crops; A management module, used for managing the crop based on the management measure, wherein the management measure is a measure for managing the growth state of the crop during the growth of the crop; The device further includes: a fifth acquisition module, which is used to acquire a management result of the growth state of the crop based on the management measure after managing the crop based on the management measure; a display module, which is used to display the management result, wherein the management result at least includes: a first management result and a second management result, the first management result indicates that the management measure has a positive effect on the growth of the crop, and the second management result indicates that the management measure has a negative effect on the growth of the crop; The device also includes: a sixth acquisition module, which is used to obtain a processing result after optimizing the management measure when the management result is the second management result, and update the knowledge graph based on the processing result.

11. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the crop management method based on knowledge graph according to any one of claims 1 to 9.

12. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the crop management method based on the knowledge graph as described in any one of claims 1 to 9.

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