CN113010529A - Crop management method and device based on knowledge graph - Google Patents
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Abstract
The invention discloses a crop management method and device based on a knowledge graph. Wherein, the method comprises the following steps: collecting growth data of crops; 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 by using a knowledge graph and outputting the management measures; and managing the crops based on the management measures, wherein the management measures are measures for managing the growth state of the crops in the growth process of the crops. The invention solves the technical problem of low farm management efficiency caused by managing the farm based on manual experience in the prior art.
Description
Technical Field
The invention relates to the field of farm management, in particular to a crop management method and device based on a knowledge graph.
Background
Agricultural production management refers to management of a series of agricultural production activities, such as basic farmland construction, crop cultivation, pest control, and the like. Wherein the agricultural production management comprises farm management.
At present, in the aspect of farm management, management decisions of crops on a farm are determined based on the knowledge system owned by a manager, and then are traced on a management system. Growers with rich management experience can make accurate decisions by using the current farm management method, but due to the fact that the types, varieties, regions, climates and the like of crops planted in farms are different, most of the growers with less management experience are difficult to make accurate decisions suitable for the crops planted in the farms, so that the efficiency of farm management is reduced, even the yield of the crops planted in the farms can be reduced, and great economic loss is caused to the farms.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a crop management method and device based on a knowledge graph, which at least solve the technical problem of low farm management efficiency caused by the fact that farms are managed based on manual experience in the prior art.
According to an aspect of an embodiment of the present invention, there is provided a crop management method based on a knowledge-graph, including: collecting growth data of crops; 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 by using a knowledge graph and outputting the management measures; and managing the crops based on the management measures, wherein the management measures are measures for managing the growth state of the crops in the growth process of the crops.
Optionally, the growth data comprises at least one of: climate data, regional data, crop type data, soil condition data, time phase data, variety data, and plant phenotype data.
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 comprises the following steps: acquiring at least one preset entity before inputting growth data into a knowledge graph model, wherein different entities correspond to different crops; acquiring current growth data acquired by a sensor for a current crop, wherein the current growth data at least comprises data attributes and data attribute values, and different sensors acquire data corresponding to different data attributes; and constructing the knowledge graph according to the entity, the data attribute and the data attribute value.
Optionally, the elements of the knowledge-graph further comprise: a plurality of entities and relationships between the plurality of entities, the method of crop management based on a knowledge-graph further comprising: acquiring management measures corresponding to different crops; determining the relationship between entities corresponding to different crops according to the management measures corresponding to the different crops; and constructing the knowledge graph according to the entity, the data attribute value and the relationship among the plurality of entities.
Optionally, the crop management method based on the knowledge-map further comprises: detecting attribute values corresponding to crops in the knowledge graph; and acquiring management time and corresponding management measures for managing the crops, which are output by the knowledge graph model according to the attribute values.
Optionally, the crop management method based on the knowledge-map further comprises: after the crops are managed based on the management measures, acquiring a management result of the growth state of the crops based on the management measures; displaying a management result, wherein the management result at least comprises: the first management result represents that the management measures have a positive effect on the growth of the crops, and the second management result represents that the management measures have a negative effect on the growth of the crops.
Optionally, in a case that the management result is the second management result, a processing result obtained after the management measure is optimized is obtained, and the knowledge graph is updated based on the processing result.
Optionally, the crop management method based on the knowledge-map further comprises: displaying the knowledge graph and receiving an adjusting instruction for adjusting the knowledge graph; updating the knowledge graph based on the adjustment instruction; and displaying the updated knowledge graph.
Optionally, the crop management method based on the knowledge-map further comprises: detecting whether entities corresponding to crops exist in the knowledge graph or not under the condition that an adjusting instruction for adjusting the knowledge graph is not received, and obtaining a detection result; and determining an updating measure corresponding to the detection result to update the knowledge graph.
Optionally, the crop management method based on the knowledge-map further comprises: acquiring a plurality of management measures aiming at the crops under the condition that the detection result indicates that the entity corresponding to the crops exists in the knowledge graph; performing big data analysis according to the growth state corresponding to each management measure, and determining the optimal management measure; and updating the attributes and/or attribute values corresponding to the crops in the knowledge graph according to the optimal management measures.
Optionally, the crop management method based on the knowledge-map further comprises: acquiring a plurality of management measures for the crops under the condition that the detection result indicates that the entity corresponding to the crops does not exist in the knowledge graph; performing big data analysis according to the growth state corresponding to each management measure, and determining the optimal management measure; and creating entities corresponding to the crops in the knowledge graph, and updating the structure of the knowledge graph based on the optimal management measures.
According to another aspect of the embodiments of the present invention, there is also provided a crop management method based on a knowledge-graph, including: collecting growth data of crops; analyzing the growth data based on the knowledge map to obtain management measures for managing the growth state of the crops; acquiring a management result after the growth state of the crops is managed based on the management measures; and updating the knowledge graph based on the management result.
According to another aspect of the embodiments of the present invention, there is also provided a crop management apparatus based on a knowledge-map, including: the acquisition module is used for acquiring growth data of crops; the acquisition module is used for inputting the growth data into the knowledge map model and acquiring the management measures output by the knowledge map model, wherein the knowledge map model is a model for analyzing the growth data by using a knowledge map and outputting the management measures; and the management module is used for managing the crops based on the management measures, wherein the management measures are measures for managing the growth state of the crops in the growth process of the crops.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program is executed, the apparatus on which the storage medium is located is controlled to perform the above-mentioned crop management method based on the knowledge-map.
According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a program, wherein the program executes the method for crop management based on knowledge-maps.
In the embodiment of the invention, a mode of establishing a farm management knowledge system based on a knowledge graph is adopted, after growth data of crops are obtained, the growth data are input into a knowledge graph model, management measures output by the knowledge graph model are obtained, and finally the crops are managed based on the management measures, wherein the knowledge graph model is a model which analyzes the growth data by using the knowledge graph and outputs the management measures.
As can be seen from the above, the present application uses a knowledge graph to construct a management system (i.e., management measures) for crop management, and manages crops based on the management system. The knowledge map has good mobility to knowledge, so that management knowledge can be copied conveniently, reference decisions can be provided for managers with poor farm management experience, and better management measures can be provided for managers with farm management experience, so that the management efficiency of the farm is improved.
Therefore, the purpose of automatically managing the farm is achieved by the scheme, the technical effect of improving the management efficiency of the farm is achieved, and the technical problem that the farm management efficiency is low due to the fact that the farm is managed based on manual experience in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a method of knowledge-graph based crop management according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an alternative knowledge-graph architecture in accordance with embodiments of the present invention;
FIG. 3 is a block diagram of an alternative method of knowledge-map based crop management according to embodiments of the present invention;
FIG. 4 is a flow chart of a method of knowledge-graph based crop management according to an embodiment of the present invention; and
fig. 5 is a schematic diagram of a crop management apparatus based on a knowledge-map according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a method for knowledge-graph based crop management, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for crop management based on a knowledge-graph according to an embodiment of the present invention, as shown in fig. 1, the method comprising the steps of:
and S102, collecting growth data of crops.
It should be noted that, in this embodiment, the farm management system may be an executive subject of this embodiment, where the farm management system at least includes: the system comprises data acquisition equipment and data processing equipment, wherein the data acquisition equipment can be a sensor, and different data acquisition equipment is used for acquiring different growth data of crops. Optionally, the growth data comprises at least one of: climate data, regional data, crop type data, soil condition data, time phase data, variety data, and plant phenotype data. For example, soil state data is collected using a soil temperature sensor, a soil humidity sensor, a soil nutrient detector, a soil PH tester, and the like, and climate data is collected using an air temperature sensor, an air humidity sensor, a light sensor, and the like.
It should be noted that, by setting the data acquisition device to automatically acquire the growth data of the crops, not only the acquisition time of manually acquiring the data can be saved, but also the problem of inaccurate data acquisition during manually acquiring the data can be avoided, and the efficiency and accuracy of data acquisition are improved.
And step S104, inputting the growth data into a knowledge graph model, and acquiring a management measure output by the knowledge graph model, wherein the knowledge graph model is a model for analyzing the growth data by using a knowledge graph and outputting the management measure.
In step S104, the Knowledge Graph (Knowledge Graph) is a theory that combines theories and methods applying subjects such as mathematics, graphics, information visualization technology, information science, and the like with methods such as metrology citation analysis, co-occurrence analysis, and the like, and uses the visualized Graph to vividly show the core structure, development history, frontier field, and overall Knowledge framework of the subjects to achieve the objective of multidisciplinary fusion. In the application, the knowledge graph can realize the fusion of multiple disciplines such as agriculture, mathematics and the like.
In an alternative embodiment, the data processing device of the farm management system is capable of acquiring growth data collected by the data collection device and analyzing the growth data using a knowledge map to determine measures to manage the crop. Optionally, the data processing device determines the type and variety of the crop according to the crop type data and the variety data, and further determines the identifier corresponding to the crop in the knowledge graph model, for example, the type is rice, and the identifier corresponding to the crop with the variety wuyou rice No. 2 is 0001, then after the data processing device inputs the crop type data and the variety data into the knowledge graph model, the knowledge graph model may determine the identifier corresponding to the crop to be 0001, and then obtain the attribute and the attribute value of the entity corresponding to 0001 in the knowledge graph, and further determine the management measure of the crop with the identifier 0001, and output the management measure.
And S106, managing the crops based on management measures, wherein the management measures are measures for managing the growth state of the crops in the growth process of the crops.
In step S106, the management measures for managing the crops may include, but are not limited to, pesticide application time, pesticide application dosage, fertilizer application time, fertilizer application dosage, watering time, watering period, watering amount, and the like.
It should be noted that the management measures obtained in step S104 are usually measures that can promote the growth of crops and increase the yield of crops, so that the management of crops based on the management measures in step S106 can better manage the farm and improve the economic efficiency of the farm.
Based on the schemes defined in the above steps S102 to S106, it can be known that, in the present application, a mode of establishing a farm management knowledge system based on a knowledge graph is adopted, after growth data of a crop is obtained, the growth data is input into a knowledge graph model, a management measure output by the knowledge graph model is obtained, and finally the crop is managed based on the management measure, where the knowledge graph model is a model that analyzes the growth data using the knowledge graph and outputs the management measure.
It is easy to note that the application adopts the knowledge map to construct a management system (i.e. management measures) for crop management, and manages crops based on the management system. The knowledge map has good mobility to knowledge, so that management knowledge can be copied conveniently, reference decisions can be provided for managers with poor farm management experience, and better management measures can be provided for managers with farm management experience, so that the management efficiency of the farm is improved.
Therefore, the purpose of automatically managing the farm is achieved by the scheme, the technical effect of improving the management efficiency of the farm is achieved, and the technical problem that the farm management efficiency is low due to the fact that the farm is managed based on manual experience in the prior art is solved.
In an alternative embodiment, prior to 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 firstly acquires at least one preset entity, then acquires current growth data acquired by the sensor for the current crop, and constructs a knowledge graph according to the entity, the data attribute and the data attribute value. Different entities correspond to different crops, current growth data at least comprise data attributes and data attribute values, and different sensors acquire data corresponding to different data attributes.
It should be noted that, in the present application, the entity is a crop, the attribute is an attribute corresponding to growth data of the crop, and the attribute value is growth data of the crop, optionally, fig. 2 shows a structural diagram of an optional knowledge graph, in fig. 2, the entity is rice, and the attribute includes: plant height, illumination duration, leaf age, accumulated temperature, soil temperature, stem breeding number, leaf nitrogen content and the like, wherein attribute values are numerical values corresponding to attributes, and in addition, the knowledge graph shown in FIG. 2 also shows corresponding management measures under different attributes and/or attribute values, for example, when the leaf nitrogen content is less than 0.1, fertilization is carried out; when the plant height is more than 0.5m, insecticide spraying is carried out; watering is carried out when the temperature of the soil is less than 0.5 ℃.
In an alternative embodiment, the elements of the knowledge-graph further comprise: relationships between multiple entities and multiple entities, i.e., the knowledge-graph, may also show relationships between different crops. The data processing equipment firstly acquires management measures corresponding to different crops, then determines the relation between entities corresponding to the different crops according to the management measures corresponding to the different crops, and finally constructs a knowledge graph according to the relation among the entities, the data attributes, the data attribute values and the multiple entities. For example, when the plant height of rice and wheat is greater than 0.5m, and the variety and the dosage of pesticide application are the same, the sub-knowledge map corresponding to the rice and the sub-knowledge map corresponding to the wheat can be associated through the relationship between the plant height and the pesticide application, so that the knowledge maps corresponding to farm management can be obtained.
It should be noted that the main forms of the knowledge graph include two forms, namely an entity-attribute value and an entity-relationship-entity, in the application, the entity is mainly obtained through system setting and customer customization, the attribute and the attribute value are mainly obtained through associated data of a sensor and customer customization upload data, and the relationship is associated through a customer decision scheme and related scientific knowledge.
Optionally, after obtaining the knowledge graph, the data processing device inputs growth data of the crop into the knowledge graph model, and obtains a management measure output by the knowledge graph model. Specifically, the data processing device detects an attribute value corresponding to a crop in the knowledge graph, and acquires management time and corresponding management measures for managing the crop, which are output by the knowledge graph model according to the attribute value. For example, in fig. 2, the attribute is the plant height, the attribute value is 0.9, that is, the plant height is greater than 0.5m, at this time, the management measure corresponding to the attribute value is the insecticide-spraying according to the knowledge graph, and in this scenario, the knowledge graph model outputs the insecticide-spraying time (not shown in fig. 2), the insecticide-spraying dose (not shown in fig. 2), and the like.
In an alternative embodiment, after the crop is managed based on the management measure, the farm management system further obtains a management result of the growth state of the crop based on the management measure, and displays the management result, wherein the management result at least comprises: the first management result represents that the management measures have a positive effect on the growth of the crops, and the second management result represents that the management measures have a negative effect on the growth of the crops. Optionally, when the management result is the second management result, the processing result obtained after the management measure is optimized is obtained, and the knowledge graph is updated based on the processing result.
It should be noted that, in the present application, the farm management system can record and evaluate the growth states of crops at different stages, track and feed back the corresponding effects of the management measures, perform positive feedback on the management measures that produce positive effects (i.e. show the first management result), and perform negative feedback on the management measures that produce negative effects, so that the farm management system can further optimize the management measures. For example, management measures indicate that pesticide spraying needs to be carried out when the plant height of rice is larger than 0.3m, a farm manager sprays pesticide when the plant height is larger than 0.3m according to the management measures, but the rice yield is not high, namely the management measures have negative effects, at the moment, the farm manager can analyze corresponding attributes and attribute values of the rice based on a knowledge graph to determine that the rice is likely to be damaged if pesticide spraying is carried out on the rice when the plant height is small, and the farm manager can further spray pesticide when the plant height is high (for example, higher than 0.5m), so that the rice yield can be improved.
In an alternative 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.
Optionally, the farm management system further comprises a display device (e.g. a display). The display device can display the knowledge graph, the data processing device receives an adjusting instruction for adjusting the knowledge graph, the knowledge graph is updated based on the adjusting instruction, and finally the display device displays the updated knowledge graph. In this scenario, the farm management system updates the knowledge graph after receiving the adjustment instruction input by the farm manager.
In an alternative embodiment, the farm management system may also automatically update the knowledge-graph. Specifically, the farm management system detects whether an entity corresponding to the crop exists in the knowledge graph or not under the condition that an adjustment instruction for adjusting the knowledge graph is not received, obtains a detection result, and determines an updating measure corresponding to the detection result to update the knowledge graph.
It should be noted that the priority of updating the knowledge graph by the farm management system 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 updating measures.
Optionally, when the detection result indicates that the entity corresponding to the crop exists in the knowledge graph, the farm management system obtains a plurality of management measures for the crop, performs big data analysis according to the growth state corresponding to each management measure, determines an optimal management measure, and finally updates the attribute and/or the attribute value corresponding to the crop in the knowledge graph according to the optimal management measure. For example, the management measure of rice corresponding to the knowledge graph is measure 1, the farm manager performs pesticide spraying when the plant height of rice is greater than 0.6m in the process of managing rice based on measure 1, the yield is greater than the yield corresponding to measure 1, and the farm manager can increase the yield of rice after adjusting the attribute value corresponding to the plant height, that is, measure 2 obtained after the farm manager adjusts measure 1 is superior to measure 1, and at this time, the farm management system automatically modifies the plant height attribute in the knowledge graph corresponding to measure 1 to 0.6 m.
Optionally, the knowledge graph can be updated by re-creation of the entity and by effect analysis and feedback on management measures, and relevant relations and values can be updated by big data analysis. Specifically, under the condition that the detection result indicates that the entity corresponding to the crop does not exist in the knowledge graph, the farm management system acquires a plurality of management measures for the crop, performs big data analysis according to the growth state corresponding to each management measure, determines the optimal management measure, finally creates the entity corresponding to the crop in the knowledge graph, and updates the structure of the knowledge graph based on the optimal management measure.
For example, the knowledge graph model does not store a sub-knowledge graph related to rice planting, but a plurality of users manage their own farms by using a farm management system, and all the rice is planted, the farm management system records data collected by each sensor and the farming behavior of each user, and by the stage of crop growth, the farm management system evaluates the quality of the current crop (for example, leaf age, tillering number, plant height, and the like) by using the same evaluation criteria, and then selects an optimal management measure as knowledge to be stored in the knowledge graph, wherein the stored knowledge includes, but is not limited to, data of various dimensions such as light, temperature, moisture, fertilization, and the like, so as to provide decision assistance for the users planting rice in the following period; and if the user subsequently uses the management measures better than the management measures corresponding to the knowledge graph, automatically updating the current knowledge structure by adopting the same method.
In an alternative embodiment, fig. 3 shows a frame diagram of a crop management method based on a knowledge-graph, and as can be seen from fig. 3, the present application can generate a decision for managing a crop by analyzing the collected basic data (i.e., growth data) of the crop, and manage the crop based on the decision. And then tracking and feeding back the effect corresponding to the decision, and optimizing the decision which generates negative effect.
According to the method, the management knowledge system is automatically constructed by combining the internet of things data, the retrospective data, the artificial intelligence algorithm and the knowledge map, the knowledge system exists in a background system in the form of the knowledge map, and the knowledge map has good mobility on knowledge, so that the management knowledge can be conveniently copied, reference decisions are provided for farm managers with insufficient management experiences, and better decisions are provided for the farm managers with management experiences.
Example 2
According to an embodiment of the present invention, there is also provided an embodiment of a crop management method based on a knowledge-map, wherein fig. 5 is a flowchart of the crop management method based on a knowledge-map according to an embodiment of the present invention, as shown in fig. 4, the method includes the following steps:
step S402, collecting growth data of crops.
It should be noted that, in this embodiment, the farm management system may be an executive subject of this embodiment, where the farm management system at least includes: the system comprises data acquisition equipment and data processing equipment, wherein the data acquisition equipment can be a sensor, and different data acquisition equipment is used for acquiring different growth data of crops. Optionally, the growth data comprises at least one of: climate data, regional data, crop type data, soil condition data, time phase data, variety data, and plant phenotype data. For example, soil state data is collected using a soil temperature sensor, a soil humidity sensor, a soil nutrient detector, a soil PH tester, and the like, and climate data is collected using an air temperature sensor, an air humidity sensor, a light sensor, and the like.
It should be noted that, by setting the data acquisition device to automatically acquire the growth data of the crops, not only the acquisition time of manually acquiring the data can be saved, but also the problem of inaccurate data acquisition during manually acquiring the data can be avoided, and the efficiency and accuracy of data acquisition are improved.
And S404, analyzing the growth data based on the knowledge graph to obtain management measures for managing the growth state of the crops.
In step S404, the Knowledge Graph (Knowledge Graph) is a theory that combines theories and methods applying mathematics, graphics, information visualization technology, information science, and other disciplines with methods of metrology citation analysis, co-occurrence analysis, and the like, and uses the visualized Graph to vividly show the core structure, development history, frontier domain, and overall Knowledge framework of the disciplines to achieve the objective of multidisciplinary fusion. In the application, the knowledge graph can realize the fusion of multiple disciplines such as agriculture, mathematics and the like.
In an alternative embodiment, the data processing device of the farm management system is capable of acquiring growth data collected by the data collection device and analyzing the growth data using a knowledge map to determine measures to manage the crop. Optionally, the data processing device determines the type and variety of the crop according to the crop type data and the variety data, and further determines the identifier corresponding to the crop in the knowledge graph model, for example, the type is rice, and the identifier corresponding to the crop with the variety wuyou rice No. 2 is 0001, then after the data processing device inputs the crop type data and the variety data into the knowledge graph model, the knowledge graph model may determine the identifier corresponding to the crop to be 0001, and then obtain the attribute and the attribute value of the entity corresponding to 0001 in the knowledge graph, and further determine the management measure of the crop with the identifier 0001, and output the management measure.
Step S406, acquiring a management result after the growth state of the crop is managed based on the management measures.
In an alternative embodiment, after the crop is managed based on the management measure, the farm management system further obtains a management result of the growth state of the crop based on the management measure, and displays the management result, wherein the management result at least comprises: the first management result represents that the management measures have a positive effect on the growth of the crops, and the second management result represents that the management measures have a negative effect on the growth of the crops.
In step S408, the knowledge graph is updated based on the management result.
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 at this time, the knowledge graph needs to be updated.
Optionally, when the management result is the second management result, the processing result obtained after the management measure is optimized is obtained, and the knowledge graph is updated based on the processing result.
In addition, it should be noted that, in the present application, the farm management system can record and evaluate the growth states of crops at different stages, track and feed back the corresponding effects of the management measures, positively feed back the management measures that have positive effects (i.e. show the first management result), and negatively feed back the management measures that have negative effects, so that the farm management system can further optimize the management measures. For example, management measures indicate that pesticide spraying needs to be carried out when the plant height of rice is larger than 0.3m, a farm manager sprays pesticide when the plant height is larger than 0.3m according to the management measures, but the rice yield is not high, namely the management measures have negative effects, at the moment, the farm manager can analyze corresponding attributes and attribute values of the rice based on a knowledge graph to determine that the rice is likely to be damaged if pesticide spraying is carried out on the rice when the plant height is small, and the farm manager can further spray pesticide when the plant height is high (for example, higher than 0.5m), so that the rice yield can be improved.
In an alternative embodiment, the farm management system further comprises a display device (e.g. a display). The display device can display the knowledge graph, the data processing device receives an adjusting instruction for adjusting the knowledge graph, the knowledge graph is updated based on the adjusting instruction, and finally the display device displays the updated knowledge graph. In this scenario, the farm management system updates the knowledge graph after receiving the adjustment instruction input by the farm manager.
In an alternative embodiment, the farm management system may also automatically update the knowledge-graph. Specifically, the farm management system detects whether an entity corresponding to the crop exists in the knowledge graph or not under the condition that an adjustment instruction for adjusting the knowledge graph is not received, obtains a detection result, and determines an updating measure corresponding to the detection result to update the knowledge graph. Under the condition that an adjusting instruction for adjusting the knowledge graph is not received, the farm management system detects whether an entity corresponding to the crop exists in the knowledge graph or not to obtain a detection result, and determines an updating measure corresponding to the detection result to update the knowledge graph.
Based on the schemes defined in the above steps S402 to S408, it can be known that, in the present application, a mode of establishing a farm management knowledge system based on a knowledge graph is adopted, after growth data of a crop is obtained, management measures for managing the growth state of the crop are obtained by analyzing the growth data based on the knowledge graph, a management result after the growth state of the crop is managed based on the management measures is obtained, and finally, the knowledge graph is updated based on the management result.
It is easy to note that the application adopts the knowledge map to construct a management system (i.e. management measures) for crop management, and manages crops based on the management system. The knowledge map has good mobility to knowledge, so that management knowledge can be copied conveniently, reference decisions can be provided for managers with poor farm management experience, and better management measures can be provided for managers with farm management experience, so that the management efficiency of the farm is improved. In addition, the knowledge graph is updated, so that the knowledge graph can provide optimal management measures.
Therefore, the purpose of automatically managing the farm is achieved by the scheme, the technical effect of improving the management efficiency of the farm is achieved, and the technical problem that the farm management efficiency is low due to the fact that the farm is managed based on manual experience in the prior art is solved.
Example 3
According to an embodiment of the present invention, there is also provided an embodiment of a crop management apparatus based on an intellectual map, wherein fig. 5 is a schematic diagram of the crop management apparatus based on the intellectual map according to the embodiment of the present invention, as shown in fig. 5, the apparatus includes: an acquisition module 501, an acquisition module 503, and a management module 505.
The acquisition module 501 is used for acquiring growth data of crops; an obtaining module 503, configured to input the growth data into a knowledge graph model, and obtain a management measure output by the knowledge graph model, where the knowledge graph model is a model that analyzes the growth data using a knowledge graph and outputs the management measure; and the management module 505 is configured to manage the crops based on management measures, where the management measures are measures for managing the growth states of the crops in the growth process of the crops.
It should be noted here that the acquisition module 501, the acquisition module 503, and the management module 505 correspond to steps S102 to S106 of the above embodiment, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in the above embodiment.
Optionally, the growth data comprises at least one of: climate data, regional data, crop type data, soil condition data, time phase data, variety data, and plant phenotype data.
In an alternative embodiment, the elements of the knowledge-graph include at least: entities, attributes and attribute values, wherein the crop management apparatus based on a knowledge-graph further comprises: the device comprises a first acquisition module, a second acquisition module and a first construction module. The first acquisition module is used for acquiring at least one preset entity before the growth data are input into the knowledge graph model, wherein different entities correspond to different crops; the second acquisition module is used for acquiring current growth data acquired by the sensors for the current crop, wherein the current growth data at least comprises data attributes and data attribute values, and different sensors acquire data corresponding to different data attributes; the first construction module is used for constructing the knowledge graph according to the entity, the data attribute and the data attribute value.
In an alternative embodiment, the elements of the knowledge-graph further comprise: a plurality of entities and relationships between the plurality of entities, the apparatus further comprising: the device comprises a third acquisition module, a first determination module and a second construction module. The third acquisition module is used for acquiring management measures corresponding to different crops; the first determining module is used for determining the relationship between the entities corresponding to different crops according to the management measures corresponding to the different crops; and the second construction module is used for constructing the knowledge graph according to the entity, the data attribute value and the relationship among the plurality of entities.
In an alternative embodiment, the obtaining module includes: the device comprises a first detection module and a fourth acquisition module. The first detection module is used for detecting attribute values corresponding to crops in the knowledge graph; and the fourth acquisition module is used for acquiring management time and corresponding management measures for managing the crops, which are output by the knowledge graph model according to the attribute values.
In an alternative embodiment, the crop management apparatus based on a knowledge-map further comprises: a fifth obtaining module and a display module. The fifth obtaining module is used for obtaining the management result of the growth state of the crop based on the management measures after the crop is managed based on the management measures; a display module, configured to display a management result, where the management result at least includes: the first management result represents that the management measures have a positive effect on the growth of the crops, and the second management result represents that the management measures have a negative effect on the growth of the crops.
In an alternative embodiment, the crop management apparatus based on a knowledge-map further comprises: and the sixth acquisition module is used for acquiring a processing result after the management measure is optimized under the condition that the management result is the second management result, and updating the knowledge graph based on the processing result.
In an alternative embodiment, the crop management apparatus based on a knowledge-map further comprises: the display device comprises a first display module, a first updating module and a second display module. The first display module is used for displaying the knowledge graph and receiving an adjusting instruction for adjusting the knowledge graph; the first updating module is used for updating the knowledge graph based on the adjusting instruction; and the second display module is used for displaying the updated knowledge graph.
In an alternative embodiment, the crop management apparatus based on a knowledge-map further comprises: a second detection module and a second update module. The second detection module is used for detecting whether entities corresponding to the crops exist in the knowledge graph or not under the condition that an adjustment instruction for adjusting the knowledge graph is not received, and obtaining a detection result; and the second updating module is used for determining an updating measure corresponding to the detection result to update the knowledge graph.
In an alternative embodiment, the second update module comprises: the device comprises a seventh obtaining module, a second determining module and a third updating module. The seventh obtaining module is configured to obtain a plurality of management measures for the crop when the detection result indicates that the entity corresponding to the crop exists in the knowledge graph; the second determining module is used for analyzing big data according to the growth state corresponding to each management measure and determining the optimal management measure; and the third updating module is used for updating the attributes and/or attribute values corresponding to the crops in the knowledge graph according to the optimal management measures.
In an alternative embodiment, the second update module comprises: the device comprises an eighth obtaining module, a third determining module and a fourth updating module. The eighth obtaining module is configured to obtain a plurality of management measures for the crop when the detection result indicates that the entity corresponding to the crop does not exist in the knowledge graph; the third determining module is used for analyzing big data according to the growth state corresponding to each management measure and determining the optimal management measure; and the fourth updating module is used for 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.
Example 4
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program is executed, an apparatus in which the storage medium is controlled performs the method for crop management based on knowledge-maps according to embodiment 1.
Example 5
According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a program, wherein the program executes the method for crop management based on knowledge-maps in embodiment 1.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (15)
1. A crop management method based on a knowledge graph is characterized by comprising the following steps:
collecting growth data of crops;
inputting the growth data into a knowledge graph model, and acquiring a management measure output by the knowledge graph model, wherein the knowledge graph model is a model for analyzing the growth data by using a knowledge graph and outputting the management measure;
and managing the crops based on the management measures, wherein the management measures are measures for managing the growth state of the crops in the growth process of the crops.
2. The method of claim 1, wherein the growth data comprises at least one of: climate data, regional data, crop type data, soil condition data, time phase data, variety data, and plant phenotype data.
3. The method of claim 1, wherein the elements of the knowledge-graph comprise at least: entities, attributes, and attribute values, wherein prior to inputting the growth data into a knowledge graph model, the method further comprises:
acquiring at least one preset entity, wherein different entities correspond to different crops;
acquiring current growth data acquired by a sensor for a current crop, wherein the current growth data at least comprises data attributes and data attribute values, and different sensors acquire data corresponding to different data attributes;
and constructing the knowledge graph according to the entity, the data attribute and the data attribute value.
4. The method of claim 3, wherein the elements of the knowledge-graph further comprise: a plurality of said entities and a plurality of relationships between said entities, said method further comprising:
acquiring 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;
and constructing the knowledge graph according to the entity, the data attribute value and the relationship among the entities.
5. The method of claim 1, wherein obtaining management measures for the knowledge graph model output comprises:
detecting attribute values corresponding to the crops in the knowledge graph;
and acquiring management time and corresponding management measures for managing the crops, which are output by the knowledge graph model according to the attribute values.
6. The method of claim 1, wherein after managing the crop based on the management measure, the method further comprises:
acquiring a management result of the growth state of the crop based on the management measure;
displaying the management result, wherein the management result at least comprises: a first management outcome characterizing that the management action has a positive effect on the growth of the crop and a second management outcome characterizing that the management action has a negative effect on the growth of the crop.
7. The method according to claim 6, wherein, when the management result is the second management result, a processing result obtained by optimizing the management measure is obtained, and the knowledge graph is updated based on the processing result.
8. The method of claim 1, further comprising:
displaying the knowledge graph and receiving an adjusting instruction for adjusting the knowledge graph;
updating the knowledge-graph based on the adjustment instruction;
and displaying the updated knowledge graph.
9. The method of claim 8, further comprising:
detecting whether an entity corresponding to the crop exists in the knowledge graph or not under the condition that an adjusting instruction for adjusting the knowledge graph is not received, and obtaining a detection result;
and determining an updating measure corresponding to the detection result to update the knowledge graph.
10. The method of claim 9, wherein determining that the update measure corresponding to the detection result updates the knowledge-graph comprises:
acquiring a plurality of management measures for the crop when the detection result indicates that the entity corresponding to the crop exists in the knowledge graph;
performing big data analysis according to the growth state corresponding to each management measure, and determining the optimal management measure;
and updating the attributes and/or attribute values corresponding to the crops in the knowledge graph according to the optimal management measures.
11. The method of claim 9, wherein determining that the update measure corresponding to the detection result updates the knowledge-graph comprises:
acquiring a plurality of management measures for the crop when the detection result indicates that the entity corresponding to the crop does not exist in the knowledge graph;
performing big data analysis according to the growth state corresponding to each management measure, and determining the optimal management measure;
and 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.
12. A crop management method based on a knowledge graph is characterized by comprising the following steps:
collecting growth data of crops;
analyzing the growth data based on a knowledge graph to obtain a management measure for managing the growth state of the crop;
acquiring a management result after the growth state of the crop is managed based on the management measure;
and updating the knowledge graph based on the management result.
13. A crop management apparatus based on a knowledge-graph, comprising:
the acquisition module is used for acquiring growth data of crops;
the acquisition module is used for inputting the growth data into a knowledge map model and acquiring management measures output by the knowledge map model, wherein the knowledge map model is a model for analyzing the growth data by using a knowledge map and outputting the management measures;
and the management module is used for managing the crops based on the management measures, wherein the management measures are measures for managing the growth state of the crops in the growth process of the crops.
14. A storage medium comprising a stored program, wherein the apparatus on which the storage medium is stored is controlled to perform the method of crop management based on knowledge-graph according to any one of claims 1 to 11 when the program is executed.
15. A processor, wherein the processor is configured to run a program, wherein the program when executed performs the method of crop management based on knowledge-maps of any one of claims 1 to 11.
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