CN116361562A - Knowledge recommendation method and system for agricultural industry Internet - Google Patents
Knowledge recommendation method and system for agricultural industry Internet Download PDFInfo
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Abstract
The invention provides a knowledge recommendation method and system for the Internet of agricultural industry, and relates to the technical field of intelligent recommendation. The method comprises the following steps: extracting knowledge ontology and historical agriculture data in an agriculture scene, and constructing an agriculture industry knowledge base according to the historical agriculture data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under the historical agricultural scene; collecting monitoring data, and comparing the monitoring data with historical agricultural data corresponding to an agricultural industry knowledge base to determine the deviation degree of the monitoring data; and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data. The whole industry of modern agriculture is guided by the agriculture internet, the full-automatic process of the modern agriculture is promoted, the whole process of agricultural production and management is controlled by the internet management, and an industry chain mode of deep integration of the internet and the modern agriculture is formed.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to a knowledge recommendation method and system for the Internet of agricultural industry.
Background
With the application of the Internet in different fields, the Internet has great influence on the production and development of various industries, wherein the combination of agriculture and the Internet is an important trend of modern agriculture development. At present, the main operation mode of the Chinese agricultural industry Internet is that the Internet technology and intelligent equipment deeply apply intelligent agriculture, and the rural electric commerce comprehensively applies Internet marketing, wherein the two modes apply information technology means to exert forces in two directions of production and marketing respectively, and obvious effects are achieved in the aspects of promoting the development of modern agriculture and improving the economic benefits of agriculture.
However, in practical application, in the aspect of promoting the upstream and downstream fusion development of the agricultural industry chain, the two operation modes lack scientific and reasonable technical schemes and products, and an effective conduction mechanism for supplying lateral inversion and upgrading to the demand side is not formed, so that the agricultural Internet is not perfect for guiding the whole industry of modern agriculture.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present invention is to provide a knowledge recommendation method for the internet of agricultural industry, so that the internet can conduct overall guidance on modern agriculture, and advance the full automation process of modern agriculture.
The second aim of the invention is to provide a knowledge recommendation system oriented to the agricultural industry Internet.
The third purpose of the invention is to provide another knowledge recommendation system oriented to the agricultural industry Internet.
A fourth object of the present invention is to propose a non-transitory computer readable storage medium.
A fifth object of the invention is to propose a computer programme product.
To achieve the above objective, an embodiment of a first aspect of the present invention provides a knowledge recommendation method for an agricultural industry internet, including:
extracting a knowledge ontology and historical agricultural data in an agricultural scene, and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under a historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with corresponding historical agricultural data in the agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
In some possible implementations, the building an agricultural industry knowledge base according to the historical agricultural data and the ontology includes:
carrying out ontology construction on the knowledge ontology based on a two-system five-element task paradigm, and determining two-system five elements of the knowledge ontology;
and taking the historical agricultural data as an entity, and constructing a knowledge graph of the entity based on the ontology construction result, wherein the obtained knowledge graph is the agricultural industry knowledge base.
In some possible implementations, the knowledge graph construction of the entity based on the ontology construction result includes:
classifying and grading the knowledge body based on the two-system five-element task paradigm; carrying out knowledge extraction of the corresponding entity according to the distribution type of the knowledge body; and storing the data obtained by knowledge extraction in a knowledge graph storage database, and carrying out knowledge calculation and knowledge operation to realize the construction of the knowledge graph.
In some possible implementations, the making of a management decision recommendation for an agricultural scenario based on the degree of deviation and the monitoring data includes:
and performing scene matching on the monitoring data based on the agricultural industry knowledge base, and generating a management decision based on the deviation degree of the monitoring data under the scene obtained by matching.
In some possible implementations, the monitoring data includes at least one or more of soil data, environmental data, pest data:
the soil data includes at least one or more of soil moisture content, soil nutrients, and soil temperature; the environmental data at least comprises one or more of environmental temperature, environmental humidity, wind speed and rainfall; the pest data includes at least a characteristic number of pests.
In some possible implementations, the obtaining the characteristic number of pest includes:
and collecting a surface image of the agricultural product, extracting features of the surface image, identifying the features of the plant diseases and insect pests, and counting the features of the plant diseases and insect pests.
In some possible implementations, the determining the degree of deviation of the monitored data includes:
and acquiring a threshold value of the monitoring data in the corresponding scene according to the historical agricultural data, and acquiring the difference of the monitoring data relative to the threshold value as the deviation degree.
In order to achieve the above object, a second aspect of the present invention provides a knowledge recommendation system for the internet of agricultural industry, including:
the knowledge base construction module is used for extracting knowledge ontology and historical agricultural data in an agricultural scene and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under a historical agricultural scene;
the deviation determining module is used for collecting monitoring data and comparing the monitoring data with the historical agricultural data corresponding to the agricultural industry knowledge base so as to determine the deviation degree of the monitoring data;
and the recommendation module is used for carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
To achieve the above object, an embodiment of a third aspect of the present invention provides another knowledge recommendation system for the internet of agricultural industry, including: comprising a memory, a transceiver, and a processor:
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
extracting a knowledge ontology and historical agricultural data in an agricultural scene, and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under a historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with corresponding historical agricultural data in the agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
To achieve the above object, a fourth aspect of the present invention provides a non-transitory computer readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform a knowledge recommendation method for the agricultural industry internet according to the first aspect of the present invention.
In order to achieve the above object, a fifth aspect of the present invention provides a computer program product, which when executed by an instruction processor in the computer program product, performs a knowledge recommendation method for the agricultural industry internet according to the first aspect of the present invention.
Firstly, extracting a knowledge ontology and historical agricultural data in an agricultural scene, and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the standard data represents the optimal environmental data of agricultural products in each weather period in the agricultural scene, and an agricultural industry knowledge base is constructed through the standard data, so that the relation among the standard data can be reflected and used as a database of the optimal environmental data. Collecting monitoring data, and comparing the monitoring data with historical agricultural data corresponding to an agricultural industry knowledge base to determine the deviation degree of the monitoring data; and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data. The agricultural Internet is used for carrying out management guidance on the whole industry of the modern agriculture through management decision recommendation, so that the full-automatic process of the modern agriculture is promoted, the whole process of agricultural production and management is controlled by utilizing the Internet management, and an industrial chain mode of deep integration of the Internet and the modern agriculture is formed. The Internet technology is utilized to break through the traditional isolation of four links of scientific research, production, circulation and consumption of the agricultural industry, supply side technology innovation of producing the Internet as an essence is focused, and the production links are modified to improve the production level.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a knowledge recommendation method for the agricultural industry internet provided by the embodiment of the invention;
FIG. 2 is a schematic flow chart of another knowledge recommendation method for the Internet of agricultural industry according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of another knowledge recommendation method for the Internet of agricultural industry according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a knowledge recommendation system for the agricultural industry internet according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The following describes a knowledge recommendation method and a knowledge recommendation system for the agricultural industry Internet according to the embodiment of the invention with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a knowledge recommendation method for the agricultural industry internet provided by the embodiment of the invention.
The main operation mode of the agricultural industry Internet promotes the development of modern agriculture and promotes the economic benefits of agriculture, but for the fusion development of the upstream and downstream of an agricultural industry chain, a scientific and reasonable technical scheme and a product are lacked, and an effective conduction mechanism for demand side-fall forced supply side transformation upgrading is not formed, so that the agricultural Internet has insufficient guidance on the whole industry of the modern agriculture.
Aiming at the problem, the embodiment of the invention provides a knowledge recommendation method for the Internet of the agricultural industry so as to control the whole production and operation process of the modern agriculture and form an industry chain mode of deep integration of the Internet and the modern agriculture.
In the embodiment of the invention, the soil improvement of the pear garden in Dangshan county is taken as an exemplary scene, and as shown in fig. 1, the method comprises the following steps:
The knowledge source is diversified, and can be derived from national, local and industry standards, agricultural scientific institutions and institutions, the experience of farm technicians and farmers, and historical data; likewise, the source of historical agricultural data as standard data may also be diverse.
The standard data represents the optimal environmental data of agricultural products in each weather period in the agricultural scene, the obtained standard data of each environmental index in each weather period in the historical agricultural scene can be used as reference data in the agricultural scene, an agricultural industry knowledge base is constructed through the standard data, and the relation among the standard data can be reflected to be used as a database of the optimal environmental data.
The monitoring data represent real-time data of the growth environment of crops, and the monitoring data are closer to the standard data, so that the growth environment of agricultural products is more suitable, and management intervention is not needed; conversely, the more management decisions need to be made to optimize the growth environment.
And 103, performing management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
And comparing the acquired monitoring data with corresponding historical agricultural data in an agricultural industry knowledge base to acquire differences between the monitoring data representing the real-time data and standard environmental data under the condition that the physical periods are the same or similar and the environmental relations are the same or similar, and further carrying out management decision recommendation by combining the real-time data according to the differences.
Firstly, extracting a knowledge ontology and historical agricultural data in an agricultural scene, and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; collecting monitoring data, and comparing the monitoring data with historical agricultural data corresponding to an agricultural industry knowledge base to determine the deviation degree of the monitoring data; and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data. The invention uses the constructed agricultural industry knowledge base as guidance, manages and guides the whole industry of the modern agriculture by comparing the monitoring data, promotes the full-automatic process of the modern agriculture, controls the whole process of agricultural production and management by using an internet management, and forms an industrial chain mode of the deep integration of the internet and the modern agriculture.
In order to clearly illustrate the above embodiment, the present embodiment provides another knowledge recommendation method for the agricultural industry internet, and fig. 2 is a flow chart of another knowledge recommendation method for the agricultural industry internet.
As shown in fig. 2, the method may include the steps of:
The historical agricultural data is standard data of each environmental index in each weathered period in the historical agricultural scene, and represents the optimal environmental data of agricultural products in each weathered period in the agricultural scene.
Firstly, ontology construction is carried out on the knowledge ontology based on a two-system five-element task paradigm.
The task paradigm is a structured agricultural industry Internet domain knowledge system, comprises a government system and an object system, and is described by five elements of a subject, a behavior, an object, time and space.
The general knowledge ontology is constructed, the knowledge ontologies in all fields have crossed public parts, classification and classification, relation connection, entity and attribute definition are carried out under a basic framework, and the general knowledge ontology lays a knowledge foundation for the rapid construction of the knowledge ontologies in the fields.
Extracting the five elements of the two systems under the basic framework to obtain the two systems of the knowledge body: a government system and an object system; five elements: subject, object, behavior, time, and space; realizing the construction of the ontology.
Then, a knowledge graph is constructed based on the ontology construction result by taking the historical agricultural data as an entity.
The knowledge graph is a knowledge base based on a semantic network. The embodiment of the invention surrounds a specific task in each link of agricultural production management such as scientific research, planting, processing, storage, sales and transportation in the Dangshan industry, monitors various subjects and behaviors thereof such as purchasing, selling, packaging, logistics, loading and unloading and the like related to the task in the whole chain of the industry in full quantity, real time and on line, and realizes the fusion and association of multi-source heterogeneous static data and dynamic data in the whole chain of the industry by constructing a knowledge graph. Semantic association of various heterogeneous knowledge is realized by constructing an agricultural industry knowledge graph based on an agricultural industry Internet platform.
The domain knowledge graph construction technology comprises ontology construction, knowledge representation, knowledge extraction, knowledge storage, knowledge calculation, knowledge operation and other technologies.
Classifying and grading the knowledge body based on a five-element task paradigm of the two systems; carrying out knowledge extraction of the corresponding entity according to the distribution type of the knowledge body; and storing the data obtained by knowledge extraction in a knowledge graph storage database, and carrying out knowledge calculation and knowledge operation to construct an agricultural knowledge graph.
The method comprises the following steps:
1) For ontology construction, the ontology is constructed mainly by classifying and grading knowledge in the field of agricultural industry chains by applying an ontology construction method and by summarizing, abstracting and normalizing concepts.
2) For knowledge representation of events, a five-element approach will be employed to achieve event representation for the agricultural industry domain. Including subject, object, behavior, time, space of events. For knowledge representation of non-event types, the entities, and their relationships, will be represented in the form of conventional triples in the knowledge graph.
3) The knowledge extraction can be realized through a learning model in the prior art, and the embodiment of the invention is not repeated.
It should be noted that, in the process of extracting knowledge, the new knowledge can be extracted by manual combing and inputting.
4) For knowledge storage, a mainstream knowledge graph storage database, such as neo4j graph database, will be used.
5) The knowledge calculation is realized through the modes of knowledge representation learning, knowledge linking, knowledge completion, semantic retrieval and the like in the prior art, and the detailed description is omitted in the embodiment of the invention.
6) For knowledge operation, knowledge updating of a knowledge graph is needed to be completed regularly, and timeliness of knowledge provided by a knowledge service is guaranteed.
When the knowledge graph is constructed, an ontology and an entity are taken as nodes, wherein the ontology is a conceptual system and comprises five elements of two systems; the entity is obtained under the concept system of the ontology, the historical agricultural data can respond to category levels corresponding to five elements of the two systems, as an example, fertilization is taken as a behavior, and corresponding objects comprise various fertilizers, such as nitrogenous fertilizer, phosphate fertilizer, potash fertilizer, organic fertilizer and the like, so that the objects and the behaviors establish a semantic association, and the same, different types of fertilizers required by different climatic periods can be different, and correspond to the time; the fertilizer needed by crops in different areas can be different, and the space is corresponding; the main body is the fertilizing main body.
By constructing the agricultural knowledge graph through the process, the method is oriented to an agricultural industry chain, can solidify experience knowledge accumulated in the agricultural industry chain for a long time, realizes semantic association of knowledge in the field of the agricultural industry chain, and better supports application of agricultural industry knowledge services such as accurate pushing of knowledge oriented to individual farmers, accurate matching of farmers and farmers, semantic retrieval of agricultural knowledge, intelligent question-answering of agricultural knowledge and the like.
And 203, performing management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
Step 202 and step 203 can be referred to the related descriptions of the corresponding steps in the foregoing embodiments, which are not repeated in this embodiment.
In this embodiment, another knowledge recommendation method for the agricultural industry internet is provided, and fig. 3 is a flow chart of another knowledge recommendation method for the agricultural industry internet.
As shown in fig. 3, the method may include the steps of:
Step 301 can be referred to the related description of the corresponding steps in the foregoing embodiment, which is not repeated in this embodiment.
Optionally, a threshold value corresponding to the monitoring data is obtained according to the historical agricultural data, and the difference of the monitoring data relative to the threshold value is obtained as the deviation degree.
As a possible implementation, the degree of deviation may be a difference, a variance, or the like, which can reflect the difference.
Optionally, the monitoring data includes at least one or more of soil data, environmental data, pest data.
As a possible implementation manner, the collected soil data can be monitored periodically by a professional detection method, various environmental indexes such as organic matters, minerals, heavy metal elements, soil humidity and the like of the pear garden soil are obtained as threshold values, and the deviation degree of various environmental indexes relative to the corresponding threshold values is monitored.
It should be noted that, soil nutrients can be obtained by a nutrient sensor, and the organic matters, minerals and heavy metal elements all belong to data included in the soil nutrients.
Optionally, the soil data includes at least one or more of soil moisture content, soil nutrients, and soil temperature; the environmental data includes at least one or more of ambient temperature, ambient humidity, wind speed, and rainfall; the pest data includes at least a characteristic number of pests.
As one possible implementation manner, the process of obtaining the characteristic quantity of the plant diseases and insect pests includes:
and collecting surface images of agricultural products, extracting features of the surface images, identifying the features of diseases and insect pests, and counting the features of the diseases and insect pests.
It will be appreciated that the insect pest or disease is displayed in the image as a difference from the normal crop surface, and that by image recognition, the pest area and number can be effectively extracted.
And step 303, performing scene matching on the monitoring data based on the agricultural industry knowledge base, and generating a management decision based on the deviation degree of the monitoring data under the matched scene.
Optionally, the potential relation among the nodes is predicted by using the knowledge graph, so that scene matching of the monitoring data is completed, and further, the corresponding deviation degree is obtained, and a corresponding management decision is generated.
As a possible implementation manner, a targeted improvement strategy can be automatically generated according to the monitoring result, and for environmental indexes which do not reach the corresponding threshold value, a preset soil improvement scheme is automatically adjusted according to the deviation degree, a specific soil improvement scheme is generated, and guidance services are provided for agricultural operations such as fertilization, irrigation, grass growing and the like.
As one possible implementation, corresponding soil moisture content sensor data is collected and compared with standard soil moisture content in real time, an irrigation threshold and a drainage threshold are generated based on the standard soil moisture content, when the soil moisture content sensor data is lower than the irrigation threshold, an irrigation early warning is automatically generated, when the soil moisture content sensor data is higher than the drainage threshold, a drainage early warning is automatically generated, and detailed early warning information is pushed to a pear garden technical manager.
The pear tree needs to absorb various nutrient elements in rhythm and proportion in different growth periods such as flower bud differentiation, flowering and fruiting, fruit expansion and maturity, and the application amount and the application proportion of the various nutrient elements need to be dynamically adjusted so that the proportion of the various nutrient elements is coordinated, thereby meeting the requirements of pear tree on quality and production.
As a possible implementation manner, the soil nutrient content data is collected and compared with the standard soil nutrient content in the corresponding weather period and environmental data in the agricultural industry database to obtain the deviation degree, so that the nutrient proportion of nitrogen, phosphorus, potassium and the like is accurately adjusted.
It should be noted that, the low-temperature protection of the Dangshan pear garden is divided into four time nodes for low-temperature protection management according to the whole life cycle of the pear and the season climate: the structure of fruit tree organs to be protected before and after seedling planting (seeding, seedling planting, grafting), before and after the flowering phase of the fruit tree in spring and before and after the growth period of the fruit in autumn and after the low-temperature period is as follows: flowers, branches, tree bodies and rhizomes.
In combination with environmental data, it can be determined whether an anti-frost scheme needs to be started, for example, according to statistical data, the pollination period of Dangshan pears in Anhui province is basically 3-4 months and 5 days in the next 3 months each year, and the period from the beginning of 3 months to the last 4 months is a dense time zone of Dangshan spring cold, and most of the time is from 20 nights to 8 nights in the next morning, and the temperature T is less than 0 ℃. Then the production plan is pushed forward by D >10-20 days in the time zone according to the most intensive theory and the minimum purchasing period of Chinese agricultural market, and the low-temperature protection task program is started. During the time, the maintenance, the teaching and the replacement of the monitoring instrument and equipment, the operation test of the low-temperature protection net and the night inspection and the arrangement of production technicians and the emergency plan preview are required to be carried out simultaneously.
Further, decision recommendation includes not only crop production behavior recommendation but also commodity recommendation corresponding to production behavior, for example, fertilizer commodity and fertilizer amount are simultaneously recommended when fertilizer application behavior is recommended; and recommending corresponding pesticide commodities when recommending pest and disease protection behaviors.
Alternatively, the merchandise recommendations are scored and ranked according to a quantified commodity factor, which may be price, brand, quality, sales, and public praise.
As a possible implementation manner, for the price, the commodity factor corresponding to the price is quantized by the deviation distance between the commodity price and the average price of the similar commodities; for brands, the like products are graded and quantified into commodity factors corresponding to the brands; for quality, quantifying the deviation degree of the returned goods quantity of the goods in unit time and the average returned goods quantity of the similar goods according to the quality problem into a commodity factor corresponding to the quality; for sales volume, the deviation degree of the commodity purchase quantity and the average purchase quantity of the similar commodities in unit time is quantized into commodity factors corresponding to the sales volume; for the purposes of the public praise, the formation of a public praise grade by extracting the commodity label from the user's evaluation is quantified as the commodity factor corresponding to the public praise.
Optionally, the commodity factors are subjected to factor linear weighting to obtain the commodity scores, the sum of the weights of all commodity factors is 1, the assignment of the weights is based on actual conditions, the scores of similar commodities are ordered from high to low, and one or more commodities with the highest scores are recommended, so that the commodity knowledge recommendation of the agricultural industry Internet is realized.
As a possible implementation manner, the assignment method of the weight may be: and screening the same-category historical purchased commodity set of the commodities of the current required category according to all the historical commodities purchased by the user. And respectively obtaining average scores corresponding to the five commodity factors according to five dimensions of price, brand, quality, sales and public praise of all commodities in the set, and normalizing the average scores of the five commodity factors to obtain corresponding weight values.
In order to realize the embodiment, the invention further provides a knowledge recommendation system oriented to the agricultural industry Internet.
Fig. 4 is a schematic structural diagram of a knowledge recommendation system for the agricultural industry internet according to an embodiment of the present invention.
As shown in fig. 4, the system includes: a knowledge base construction module 401, a deviation determination module 402 and a recommendation module 403.
The knowledge base construction module 401 is configured to extract a knowledge ontology and historical agricultural data in an agricultural scene, and construct an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under the historical agricultural scene;
the deviation determining module 402 is configured to collect monitoring data, and compare the monitoring data with historical agricultural data corresponding to the agricultural industry knowledge base to determine a deviation degree of the monitoring data;
and the recommendation module 403 is used for performing management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
Further, in one possible implementation manner of the embodiment of the present invention, the knowledge base construction module 401 further includes:
carrying out ontology construction on the knowledge ontology based on a two-system five-element task paradigm, and determining two-system five elements of the knowledge ontology;
and taking the historical agricultural data as an entity, and constructing a knowledge graph of the entity based on the ontology construction result to obtain a knowledge graph, namely an agricultural industry knowledge base.
Classifying and grading the knowledge body based on a five-element task paradigm of the two systems; carrying out knowledge extraction of the corresponding entity according to the distribution type of the knowledge body; and storing the data obtained by knowledge extraction in a knowledge graph storage database, and carrying out knowledge calculation and knowledge operation to realize the construction of the knowledge graph.
Further, in one possible implementation of the embodiment of the present invention, the deviation determining module 402 further includes:
the monitoring data at least comprises one or more of soil data, environment data and plant diseases and insect pests data;
the soil data includes at least one or more of soil moisture content, soil nutrients, and soil temperature; the environmental data includes at least one or more of ambient temperature, ambient humidity, wind speed, and rainfall; the pest data includes at least a characteristic number of pests.
And collecting surface images of agricultural products, extracting features of the surface images, identifying the features of diseases and insect pests, and counting the features of the diseases and insect pests.
And acquiring a threshold value corresponding to the monitoring data according to the historical agricultural data, and acquiring the difference of the monitoring data relative to the threshold value as the deviation degree.
Further, in a possible implementation manner of the embodiment of the present invention, the recommendation module 403 further includes:
and performing scene matching on the monitoring data based on the agricultural industry knowledge base, and generating a management decision based on the deviation degree of the monitoring data under the scene obtained by matching.
It should be noted that, the explanation of the embodiment of the knowledge recommendation method for the agricultural industry internet is also applicable to the knowledge recommendation system for the agricultural industry internet of the embodiment, and is not repeated here.
In order to implement the above embodiment, the present invention further provides another knowledge recommendation system for the agricultural industry internet, including: a processor, and a memory for storing instructions executable by the processor.
The processor is configured to execute the instructions to implement a knowledge recommendation method for the agricultural industry internet:
extracting knowledge ontology and historical agriculture data in an agriculture scene, and constructing an agriculture industry knowledge base according to the historical agriculture data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under the historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with historical agricultural data corresponding to an agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
To achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform a knowledge recommendation method for the agricultural industry internet, the method comprising:
extracting knowledge ontology and historical agriculture data in an agriculture scene, and constructing an agriculture industry knowledge base according to the historical agriculture data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under the historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with historical agricultural data corresponding to an agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
In order to achieve the above embodiments, the present invention further proposes a computer program product, which when executed by an instruction processor in the computer program product, performs a knowledge recommendation method for the agricultural industry internet, the method comprising:
extracting knowledge ontology and historical agriculture data in an agriculture scene, and constructing an agriculture industry knowledge base according to the historical agriculture data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under the historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with historical agricultural data corresponding to an agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The knowledge recommendation method for the agricultural industry Internet is characterized by comprising the following steps of:
extracting a knowledge ontology and historical agricultural data in an agricultural scene, and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under a historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with corresponding historical agricultural data in the agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
2. The method of claim 1, wherein said building an agricultural industry knowledge base from said historical agricultural data, said ontology, comprises:
carrying out ontology construction on the knowledge ontology based on a two-system five-element task paradigm, and determining two-system five elements of the knowledge ontology;
and taking the historical agricultural data as an entity, and constructing a knowledge graph of the entity based on the ontology construction result, wherein the obtained knowledge graph is the agricultural industry knowledge base.
3. The method according to claim 2, wherein the knowledge-graph construction of the entity based on the ontology construction result comprises:
classifying and grading the knowledge body based on the two-system five-element task paradigm; carrying out knowledge extraction of the corresponding entity according to the distribution type of the knowledge body; and storing the data obtained by knowledge extraction in a knowledge graph storage database, and carrying out knowledge calculation and knowledge operation to realize the construction of the knowledge graph.
4. The method of claim 1, wherein said making a management decision recommendation for an agricultural scenario based on said degree of deviation and said monitoring data comprises:
and performing scene matching on the monitoring data based on the agricultural industry knowledge base, and generating a management decision based on the deviation degree of the monitoring data under the scene obtained by matching.
5. The method of claim 1, wherein the monitoring data includes at least one or more of soil data, environmental data, pest data;
the soil data includes at least one or more of soil moisture content, soil nutrients, and soil temperature; the environmental data at least comprises one or more of environmental temperature, environmental humidity, wind speed and rainfall; the pest data includes at least a characteristic number of pests.
6. The method of claim 5, wherein the obtaining the characteristic quantity of the pest comprises:
and collecting a surface image of the agricultural product, extracting features of the surface image, identifying the features of the plant diseases and insect pests, and counting the features of the plant diseases and insect pests.
7. The method of claim 1, wherein said determining the degree of deviation of the monitored data comprises:
and acquiring a threshold value of corresponding monitoring data according to the historical agricultural data, and acquiring the difference of the monitoring data relative to the threshold value as the deviation degree.
8. An agricultural industry internet-oriented knowledge recommendation system, which is characterized by comprising:
the knowledge base construction module is used for extracting knowledge ontology and historical agricultural data in an agricultural scene and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under a historical agricultural scene;
the deviation determining module is used for collecting monitoring data and comparing the monitoring data with the historical agricultural data corresponding to the agricultural industry knowledge base so as to determine the deviation degree of the monitoring data;
and the recommendation module is used for carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
9. The knowledge recommendation system for the agricultural industry Internet is characterized by comprising a memory, a transceiver and a processor:
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
extracting a knowledge ontology and historical agricultural data in an agricultural scene, and constructing an agricultural industry knowledge base according to the historical agricultural data and the knowledge ontology; the historical agricultural data are standard data of each environmental index in each weather period under a historical agricultural scene;
collecting monitoring data, and comparing the monitoring data with corresponding historical agricultural data in the agricultural industry knowledge base to determine the deviation degree of the monitoring data;
and carrying out management decision recommendation on the agricultural scene according to the deviation degree and the monitoring data.
10. A non-transitory computer readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method of any one of claims 1 to 7.
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