CN117951530A

CN117951530A - Disease and pest data analysis method and system for walnut with clip

Info

Publication number: CN117951530A
Application number: CN202410348595.3A
Authority: CN
Inventors: 唐小强; 朱欣伟; 王盼; 龚良春; 杨帆; 徐舟; 刘建霞; 张利; 陈玥; 邵晶晶; 任安江; 刘千里; 何建社; 马朝娟; 黄劲松; 杨昌旭; 杨华
Original assignee: Lixian Forestry And Grassland Bureau; Aba Forestry And Grassland Science And Technology Research Institute
Current assignee: Lixian Forestry And Grassland Bureau; Aba Forestry And Grassland Science And Technology Research Institute
Priority date: 2024-03-26
Filing date: 2024-03-26
Publication date: 2024-04-30

Abstract

The application relates to the technical field of big data processing, in particular to a disease and pest data analysis method and system for a walnut, which are used for acquiring a disease metadata set analyzed by a disease and pest association analysis table on candidate diseases and a pest metadata set on candidate pests; deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms; and outputting a disease and pest association analysis table based on the associated symptoms and the associated symptoms which are not associated. The application fully builds the association relation between the diseases and the insect pests more accurately based on the big data content so as to better prevent and treat the diseases and the insect pests of the walnut.

Description

Disease and pest data analysis method and system for walnut with clip

Technical Field

The application relates to the technical field of big data processing, in particular to a disease and pest data analysis method and system for walnut clips.

Background

The method is characterized in that the walnut with the clip is extremely easy to suffer from attack of diseases and insect pests in the growth process, common diseases and insect pests are distinguished from each other but are closely related, and in different areas and different times, symptoms and treatments of the diseases and the insect pests have opposite unified relations, and how to more accurately build the association relation of the diseases and the insect pests based on big data content so as to better prevent and treat the diseases and the insect pests of the walnut with the clip is a problem to be solved urgently.

Disclosure of Invention

In order to achieve the above purpose, the present application provides the following technical solutions:

according to a first aspect of the invention, the invention provides a method for analyzing pest and disease data of a walnut, comprising the following steps:

acquiring a disease metadata set of a disease and pest association analysis table in candidate disease analysis and a pest metadata set on candidate pests;

Deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of the at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms;

And outputting the plant disease and insect pest association analysis table based on the associated symptoms and the associated symptoms which are not associated.

Further, the obtaining the disease metadata set of the disease and pest association analysis table in the analysis of the candidate disease and the pest metadata set in the candidate pest includes:

Acquiring a historical pest metadata set, a plant pest metadata set belonging to the same genus and a regional pest metadata set for the pest association analysis table;

The related symptoms comprise historical data, and if at least two pest metadata sets are obtained and the at least two pest metadata sets have related symptoms at the same attack time stamp of the pest metadata set, the related symptoms of the at least two pest metadata sets at the same attack time stamp are associated to obtain associated related symptoms, including:

When the historical pest metadata sets, the same plant pest metadata sets and at least two pest metadata sets in the regional pest metadata sets are all provided with historical data at the same attack time stamp of the disease metadata sets, the historical data at the same attack time stamp are associated;

After the obtaining the historical pest metadata set, the sibling pest metadata set, and the regional pest metadata set for the pest association analysis table, the method further comprises:

sequentially obtaining the historical pest metadata set, the same plant pest metadata set and related symptoms of the regional pest metadata set on different attack time stamps of the pest metadata set according to the early warning instructions corresponding to the pest association analysis table;

and according to the related symptoms on the different attack time stamps, carrying out data supplementation on the historical insect pest metadata set, the same plant insect pest metadata set and the regional insect pest metadata set in sequence.

Further, if the decision is that at least two pest metadata sets have related symptoms at the same attack time stamp of the pest metadata set, the decision correlates the related symptoms of the at least two pest metadata sets at the same attack time stamp, and after obtaining correlated related symptoms, the method further includes:

if the associated related symptoms meet the preset data semantic similarity comparison conditions, carrying out semantic similarity comparison processing on the associated related symptoms to obtain associated related symptoms subjected to semantic similarity comparison;

If the associated related symptoms meet a preset data semantic similarity comparison condition, performing semantic similarity comparison processing on the associated related symptoms to obtain associated related symptoms subjected to semantic similarity comparison, wherein the method comprises the following steps of:

If the attack distance between the associated related symptoms and the related symptoms caused by the associated related symptoms is smaller than a preset attack distance, acquiring the attack period between the associated related symptoms and the related symptoms caused by the associated related symptoms according to the attack distance;

Carrying out semantic similarity comparison processing on the associated related symptoms according to the period of the attack, and obtaining associated related symptoms subjected to semantic similarity comparison;

The processing of semantic similarity comparison is carried out on the associated related symptoms according to the period of the attack, and the associated related symptoms after the semantic similarity comparison are obtained, which comprises the following steps:

Extracting at least one key data from the associated related symptoms according to the period of the attack, and obtaining the extracted key data as the associated related symptoms after semantic similarity comparison; or according to the period of the attack, obtaining the display data after the de-duplication treatment in the associated related symptoms, and obtaining the display data after the de-duplication treatment as the associated related symptoms after the semantic similarity comparison.

Further, the extracting at least one key data from the associated related symptoms according to the period of the attack, and obtaining the extracted key data as the associated related symptoms after semantic similarity comparison includes:

Acquiring key data contained in the associated related symptoms and a corresponding presentation priority sequence by using a preset key database;

If the related symptoms contain a plurality of key data, screening target key data from the key data according to the period of the attack and the presentation priority sequence corresponding to the key data, and obtaining the target key data as related symptoms after semantic similarity comparison;

If the similarity corresponding to the related symptoms is not smaller than a preset similarity threshold, extracting at least one key data from the related symptoms according to the preset similarity threshold, and obtaining the extracted key data as related symptoms after semantic similarity comparison; or if the similarity corresponding to the related symptoms is not smaller than a preset similarity threshold, obtaining display data after the de-duplication processing in the related symptoms according to the preset similarity threshold, and obtaining the display data after the de-duplication processing as related symptoms after semantic similarity comparison.

Further, if the correlation related symptom accords with a preset data semantic similarity comparison condition, performing semantic similarity comparison processing on the correlation related symptom to obtain a correlation related symptom after semantic similarity comparison, including:

obtaining the size of an output platform corresponding to the early warning platform for the diseases and insect pests of the walnut;

The relevant symptoms are adjusted according to the size of the output platform, the adjusted relevant symptoms are obtained, and the adjusted relevant symptoms are displayed;

After the decision if at least two pest metadata sets have associated symptoms at the same attack time stamp of the pest metadata set, correlating the associated symptoms of the at least two pest metadata sets at the same attack time stamp to obtain correlated symptoms, the method further comprises:

receiving a presentation instruction of the associated related symptoms on a target pest metadata set, wherein the presentation instruction has corresponding identification data of the target pest metadata set and a corresponding attack time stamp of the associated related symptoms;

Extracting relevant symptoms of the target pest metadata set on the attack time stamp from the relevant symptoms according to the identification data, and presenting the extracted relevant symptoms on the target pest metadata set;

Deciding whether the size of an output platform corresponding to the walnut plant disease and insect pest early warning platform is not smaller than the size of a preset output platform;

if the size of the pest control unit is not smaller than the size of the preset output platform, acquiring a regional index pest metadata set related to the pest association analysis table according to the related symptoms in the regional pest metadata set;

The obtaining the regional indicator pest metadata set related to the pest association analysis table according to the related symptoms in the regional pest metadata set comprises the following steps:

Acquiring an extracted regional index according to the size of the output platform and the related symptoms in the regional insect pest metadata set, and acquiring a regional index insect pest metadata set corresponding to the regional index;

Extracting parameter data of the regional index on different attack time stamps from related symptoms of the regional pest metadata set;

And carrying out data supplementation on the regional index insect pest metadata set according to the parameter data on the different attack time stamps.

According to a second aspect of the present invention, the present invention provides an instruction protection system for analyzing pest and disease data for walnut, comprising:

the acquisition module is used for acquiring a disease metadata set analyzed by the disease and insect pest association analysis table on the candidate disease and an insect pest metadata set on the candidate insect pest;

The association module is used for deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, the relevant symptoms of the at least two pest metadata sets at the same attack time stamp are associated to obtain associated relevant symptoms;

And the output module is used for outputting the plant diseases and insect pests association analysis table based on the association related symptoms and the related symptoms which are not associated.

Further, the obtaining module includes:

The related symptoms in the association module include historical data, and if at least two pest metadata sets are obtained and the at least two pest metadata sets have related symptoms at the same attack time stamp of the pest metadata set, the related symptoms of the at least two pest metadata sets at the same attack time stamp are associated to obtain associated related symptoms, including:

after the historical pest metadata set, the same genus plant pest metadata set and the regional pest metadata set are obtained for the pest association analysis table, the method further comprises:

Further, after the association module, the method further includes:

Extracting at least one key data from the associated related symptoms according to the period of the attack, and obtaining the extracted key data as the associated related symptoms after semantic similarity comparison; or alternatively

And according to the period of the attack, obtaining display data after the de-duplication treatment in the associated related symptoms, and obtaining the display data after the de-duplication treatment as the associated related symptoms after semantic similarity comparison.

If the similarity corresponding to the related symptoms is not smaller than a preset similarity threshold, extracting at least one key data from the related symptoms according to the preset similarity threshold, and obtaining the extracted key data as related symptoms after semantic similarity comparison; or alternatively

If the similarity corresponding to the related symptoms is not smaller than a preset similarity threshold, obtaining display data after the duplication removal processing in the related symptoms according to the preset similarity threshold, and obtaining the display data after the duplication removal processing as related symptoms after semantic similarity comparison.

If the decision is that at least two pest metadata sets have related symptoms at the same attack time stamp of the pest metadata set, correlating the related symptoms of the at least two pest metadata sets at the same attack time stamp, and after obtaining correlated related symptoms, further comprising:

Drawings

Fig. 1 is a workflow diagram of a method for analyzing pest and disease data for a walnut clip according to an embodiment of the present application;

fig. 2 is a second workflow diagram of a method for analyzing pest and disease data for a walnut with a clip according to an embodiment of the present application;

fig. 3 is a structural block diagram of a pest and disease damage data analysis system for a walnut clip according to an embodiment of the present application;

Fig. 4 is a block diagram of a pest and disease damage data analysis device for a walnut with a clip according to an embodiment of the present application

Reference numerals: the system comprises an acquisition module-31, an association module-32, an output module-33, a processor-41, a memory-42 and a bus-43.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The terms "first," "second," "third," and the like in this disclosure are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "first," "second," and "third" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. All directional indications (such as up, down, left, right, front, back … …) in the embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular gesture (as shown in the drawings), and if the particular gesture changes, the directional indication changes accordingly. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The embodiment of the invention provides a method for analyzing pest and disease data of a walnut, which is shown in fig. 1, and comprises the following steps:

and acquiring a disease metadata set of the disease and pest association analysis table in the analysis of the candidate disease and a pest metadata set on the candidate pest.

Wherein in this embodiment, the pest comprises at least: leafhopper, glossy privet roll She Mianya, fall armyworm, elm cotton aphid, yang Xue moth, walnut limb-lifting moth, pinus tabulaeformis, liu Gandu moth, peach leaf miner, and leaf miner;

The disease comprises at least: plaster disease of walnut, brown spot disease of walnut, felt disease of walnut, anthracnose of walnut, leaf curl disease of walnut, red spot disease of plum and leaf rust disease of willow;

Further, after the disease metadata set and the pest metadata set corresponding to the pest association analysis table are obtained, according to the early warning instruction corresponding to the pest association analysis table, sequentially obtaining relevant symptoms of each pest metadata set on the corresponding attack time stamp of the disease metadata set, and supplementing the relevant symptoms on the corresponding attack time stamp to the pest metadata set, wherein the pest metadata set can be a historical pest metadata set, a same-genus plant pest metadata set and a regional pest metadata set, and the relevant symptoms sequentially supplemented on the historical pest metadata set, the same-genus plant pest metadata set and the regional pest metadata set can be specifically historical data, and the method comprises the following steps: historical data of historical user operation, historical data of the same plant, historical data of equipment and the like, wherein the early warning instruction comprises historical data of operation of a historical user, the data of the same plant related to the manufactured crops, the data of equipment related to a region and historical time corresponding to the data, so that the historical data of a historical pest meta-data set, the historical data of the same plant pest meta-data set and the historical data of the regional pest meta-data set on corresponding attack time stamps of the disease meta-data set can be sequentially obtained based on the early warning instruction of the disease and pest association analysis table, and the obtained historical data is sequentially supplemented into the historical pest meta-data set, the same plant pest meta-data set and the regional pest meta-data set.

And if the at least two pest metadata sets have related symptoms at the same attack time stamp of the pest metadata set, correlating the related symptoms of the at least two pest metadata sets at the same attack time stamp to obtain correlated related symptoms.

For the embodiment of the invention, in order to overcome the defect that in the prior art, if different pest metadata sets obtain relevant symptoms at the same attack time stamp in the process of obtaining the association table, the output of a platform for early warning of the diseases and the pests of the walnut is too complex, in the process of obtaining the association table, the embodiment of the invention can decide whether at least two pest metadata sets obtain relevant symptoms at the same attack time stamp of the disease metadata set, and if so, the relevant symptoms of at least two pest metadata sets at the same attack time stamp are associated to obtain associated relevant symptoms; if only one pest metadata set gets the relevant symptoms at a certain attack time stamp, no data correlation is needed. Therefore, in the process of creating the association table, when different pest metadata sets obtain related symptoms at the same attack time stamp, a user can directly check the associated related symptoms of the corresponding attack time stamp, inconvenience to the user caused by checking a plurality of related symptoms in turn is avoided, and meanwhile, the related symptoms on different pest metadata sets are associated, so that the output of the platform for early warning of the walnut diseases and pests can be compared in a semantic similarity mode.

And outputting a disease and pest association analysis table based on the associated symptoms and the associated symptoms which are not associated.

For the embodiment of the present invention, the relevant symptoms and the relevant symptoms which are not associated may be presented on a separate pest metadata set, and the direction of the pest metadata set presenting the relevant symptoms may be the same as or different from the candidate pest, and the embodiment of the present invention is not specifically limited, where relevant symptoms or relevant symptoms which are not associated corresponding to different attack time stamps are recorded on the pest metadata set, and the pest association analysis table is output through the relevant symptoms and the relevant symptoms which are not associated. Therefore, in the process of creating the association table, when different pest metadata sets obtain related symptoms at the same attack time stamp, a user can directly check the associated related symptoms of the corresponding attack time stamp, inconvenience to the user caused by checking a plurality of related symptoms in turn is avoided, and meanwhile, the related symptoms on different pest metadata sets are associated, so that the output of the platform for early warning of the walnut diseases and pests can be compared in a semantic similarity mode.

Compared with the mode that a plurality of data history frames corresponding to related symptoms are presented to a user at the same attack time stamp at present, the disease and pest data analysis method for the carya cathayensis can acquire the disease metadata set analyzed by the disease and pest association analysis table on the candidate disease and the pest metadata set on the candidate pest; deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms; meanwhile, based on the related symptoms and the related symptoms which are not related, a disease and pest association analysis table is output, so that in the disease and pest data analysis process for the carya cathayensis, the related symptoms of at least two pest metadata sets on the same attack time stamp are associated, and related symptoms are presented to a user, so that the user can be prevented from sequentially checking related symptom history frames on different pest metadata sets on the same attack time stamp, the semantic similarity is compared with the output of a platform for early warning of the carya cathayensis in the creation process of the electronic association table, the related symptoms related to different attack time stamps are presented to the user simply, the user can check the related symptoms in the creation association table conveniently, and the user experience is enhanced.

Further, in order to better explain the above pest and disease data analysis process for the walnut, as a refinement and expansion of the above embodiment, the following details the specific implementation process in this embodiment, as shown in fig. 2, the method includes:

For the embodiment of the invention, in order to obtain the pest and disease damage association analysis table, a pest meta-data set is required to be obtained first, and because the history data of the history user operation, the data of the same plant related to the crop and the equipment data related to the area are usually obtained in the association table, the pest and disease damage association analysis table is obtained, and the method specifically comprises the following steps: and obtaining a historical pest metadata set, a same plant pest metadata set and a regional pest metadata set for the pest association analysis table. The historical pest metadata set records historical data of operation of a historical user, and the historical data of the same plant relevant to crops is recorded in the same plant pest metadata set.

Further, after obtaining the historical pest metadata set, the sibling pest metadata set, and the regional pest metadata set for the pest association analysis table, the method further comprises: sequentially obtaining a historical pest metadata set, a plant pest metadata set belonging to the same genus and related symptoms of the regional pest metadata set on different attack time stamps of the pest metadata set according to the early warning instructions corresponding to the pest association analysis table; and according to the related symptoms on different attack time stamps, carrying out data supplementation on the historical insect pest metadata set, the insect pest metadata set of the same plant and the insect pest metadata set of the region in sequence. The early warning instruction comprises historical data operated by a historical user, homonymous plant data related to crops, equipment data related to areas and historical time corresponding to the data, so that according to the early warning instruction, historical data of a historical pest metadata set, homonymous plant pest metadata set and area pest metadata set on corresponding attack time stamps can be obtained sequentially, and then the historical data are supplemented at the corresponding attack time stamps of the historical pest metadata set, homonymous plant pest metadata set and area pest metadata set sequentially.

Further, the number of the obtained pest metadata sets can be adjusted according to the size of the output platform of the walnut pest early warning platform, for example, the mobile phone end can only display three pest metadata sets, so that only a historical pest metadata set, a plant pest metadata set of the same genus and a regional pest metadata set are obtained, and when the plant pest metadata set is converted to the WEB end, because the size of the output platform is larger, regional indexes in the regional pest metadata set can be independently extracted, and the regional index pest metadata set is obtained, based on the method further comprising: deciding whether the size of an output platform corresponding to the walnut plant disease and insect pest early warning platform is not smaller than the size of a preset output platform; and if the size of the regional insect pest metadata set is not smaller than the preset output platform size, acquiring the regional index insect pest metadata set related to the disease and insect pest association analysis table according to the related symptoms in the regional insect pest metadata set. Further, according to the related symptoms in the same regional pest metadata set, obtaining a regional indicator pest metadata set related to the pest association analysis table, including: according to the size of the output platform and the related symptoms in the regional insect pest metadata set, acquiring an extracted regional index, and acquiring a regional index insect pest metadata set corresponding to the regional index; extracting parameter data of the regional indexes on different attack time stamps from related symptoms of the regional insect pest metadata set; and supplementing data on the regional index insect pest metadata set according to the parameter data on the different attack time stamps. The size of the preset output platform can be set according to the disease and pest data analysis instruction aiming at the walnut.

For the embodiment of the invention, in order to compare the semantic similarity with the output of a platform for early warning of the walnut plant diseases and insect pests in the process of creating the electronic association table, the method is convenient for a user to check related symptoms, and specifically comprises the following steps: and when the historical data are all provided with the same attack time stamp of the disease metadata set, the historical data at the same attack time stamp are associated with at least two pest metadata sets in the decision historical pest metadata set, the same plant pest metadata set and the regional pest metadata set.

Further, for the associated symptoms, a part of the data in the associated symptoms can be extracted and displayed in the corresponding pest metadata set, and based on the method, the method further comprises: receiving a presentation instruction of the associated related symptoms on the target pest metadata set, wherein the presentation instruction has corresponding identification data of the target pest metadata set and a corresponding attack time stamp of the associated related symptoms; and extracting the relevant symptoms of the target pest metadata set on the attack time stamp from the relevant symptoms according to the identification data, and presenting the extracted relevant symptoms on the target pest metadata set. The identification data corresponding to the target pest metadata set may be a code or a name of the target pest metadata set. Therefore, the user can not only check the related symptoms associated with a certain pest metadata set from the related symptoms, but also check the related symptoms associated with the pest metadata set by clicking the pest metadata set, so that the user can check the related symptoms more conveniently.

If the associated related symptoms meet the preset data semantic similarity comparison conditions, carrying out semantic similarity comparison processing on the associated related symptoms to obtain associated related symptoms after semantic similarity comparison.

For the embodiment of the invention, when the time interval between the associated symptoms and the related symptoms caused by the associated symptoms is too small, or the content of the associated symptoms is too much, if the content of the associated symptoms is displayed completely, the display of the related symptoms caused by the associated symptoms is likely to be influenced, the platform output is too disordered, and the experience of a user is poor; if the preset semantic similarity comparison condition is not met, the semantic similarity comparison processing of the related symptoms is not needed, and all contents of the related symptoms can be displayed.

For the semantic similarity comparison processing process of the associated related symptoms, the embodiment of the invention specifically provides two implementation manners, and for the first implementation manner, the method specifically comprises the following steps: if the attack distance between the associated related symptoms and the related symptoms caused by the associated related symptoms is smaller than the preset attack distance, obtaining the attack period between the associated related symptoms and the related symptoms caused by the associated related symptoms according to the attack distance; and carrying out semantic similarity comparison treatment on the related symptoms according to the period of the attack, and obtaining the related symptoms after the semantic similarity comparison. Further, the semantic similarity comparison processing is performed on the associated related symptoms according to the period of the attack, so as to obtain the associated related symptoms after the semantic similarity comparison, which comprises the following steps: extracting at least one key data from the associated related symptoms according to the period of the attack, and obtaining the extracted key data as the associated related symptoms after semantic similarity comparison; or according to the period of the attack, obtaining the display data after the de-duplication treatment in the associated related symptoms, and obtaining the display data after the de-duplication treatment as the associated related symptoms after the semantic similarity comparison. The related symptoms caused by the related symptoms can be related symptoms or not, and the preset attack distance can be set according to the disease and pest data analysis instruction aiming at the carya cathayensis, so that the embodiment of the invention is not particularly limited.

Further, when a plurality of key data are obtained from the associated symptoms, it is likely that the plurality of key data cannot be completely presented due to limited attack period, and target key data need to be screened from the plurality of key data to be presented, based on the result, at least one key data is extracted from the associated symptoms according to the attack period, and the extracted key data is obtained as the associated symptoms after semantic similarity comparison, including: acquiring key data contained in the associated related symptoms and a corresponding presentation priority sequence by using a preset key database; if the associated relevant symptoms comprise a plurality of key data, selecting target key data from the plurality of key data according to the length of the attack period and the presentation priority sequence corresponding to the plurality of key data, and obtaining the target key data as the associated relevant symptoms after semantic similarity comparison. The preset key database also stores the presentation priority sequences corresponding to the key data of different pest metadata sets, specifically, because the historical data operated by the historical user is generally important, the highest presentation priority sequence corresponding to the key data of the historical pest metadata set can be set, and then the key data of the same plant pest metadata set and the key data of the regional pest metadata set are set.

An embodiment two of the semantic similarity comparison processing procedure for the associated related symptoms specifically includes: if the similarity corresponding to the related symptoms is not smaller than a preset similarity threshold, extracting at least one key data from the related data according to the preset similarity threshold, and obtaining the extracted key data as related symptoms after semantic similarity comparison; or if the similarity corresponding to the related symptoms is not smaller than the preset similarity threshold, obtaining display data after the de-duplication processing in the related symptoms according to the preset similarity threshold, and obtaining the display data after the de-duplication processing as the related symptoms after the semantic similarity comparison. The preset similarity threshold value can be set according to a disease and pest data analysis instruction aiming at the carya cathayensis, and the embodiment of the invention is not particularly limited.

Specifically, when the similarity corresponding to the associated related symptom is not smaller than the preset similarity threshold, it is indicated that the excessive content of the associated related symptom may affect the display of the related symptom caused by the excessive content, or may cause the excessive output of the platform, so that the semantic similarity comparison processing is also required to be performed on the associated related symptom, specifically, the extracted key data can be used as the associated related symptom after the semantic similarity comparison processing on the associated related symptom, so that the semantic similarity comparison processing on the associated related symptom is realized, the specific process of extracting the key data is the same as the key data extraction process in the first embodiment, and is not repeated herein, and in addition, the display data after the duplication removal in the associated related symptom can be obtained according to the preset similarity threshold.

Furthermore, the related symptoms can be adjusted according to the size of the output platform of the early warning platform for the walnut diseases and insect pests, for example, the size of the output platform of the mobile phone end is smaller, so that the whole content of the related symptoms cannot be conveniently presented, and therefore, the related symptoms can be subjected to semantic similarity comparison according to the size of the output platform, and based on the method, the method further comprises the following steps: obtaining the size of an output platform corresponding to the early warning platform for the diseases and insect pests of the walnut; and adjusting the associated symptoms according to the size of the output platform to obtain adjusted associated symptoms, and displaying the adjusted associated symptoms. When the relevant symptoms are specifically adjusted, if the size of the output platform is smaller than a preset size, key data is extracted from the relevant symptoms according to the size of the output platform, the extracted key data is obtained as relevant symptoms after semantic similarity comparison, or display data after de-duplication processing in the relevant symptoms is obtained according to the size of the output platform, and the display data after de-duplication processing is obtained as relevant symptoms after semantic similarity comparison, wherein the preset screen size can be set according to an instruction of creating a correlation table. Therefore, semantic similarity comparison processing can be carried out on related symptoms according to the size of the output platform corresponding to the early warning platform for the diseases and insect pests of the cargoes, and the condition that the output of the early warning platform for the diseases and insect pests of the cargoes is too messy due to too many contents of the related symptoms is avoided.

If only one pest metadata set obtains a relevant symptom at a certain attack time stamp on the pest metadata set, the relevant symptom is a relevant symptom which is not associated, for the embodiment of the invention, in the process of creating the association table, the relevant symptom and the relevant symptom which is not associated can be presented in a separate pest metadata set according to the corresponding attack time stamp, and based on the relevant symptom or the relevant symptom which is not associated on the corresponding attack time stamp of the separate pest metadata set, the pest association analysis table can be output, wherein the direction of the pest metadata set with the relevant symptom can be the same as the direction of the historical pest metadata set, the same-genus plant pest metadata set and the area pest metadata set, or can be different from the direction of the historical pest metadata set, the same-genus plant metadata set and the area pest metadata set, and the direction of the relevant symptom with the relevant metadata set with the relevant symptom is not specifically limited, and the direction of the historical pest metadata set, the same-genus plant metadata set with the direction of the area pest metadata set, and the same-genus plant pest metadata set can be arranged in parallel with the direction of the historical pest metadata set with the area pest metadata set.

Compared with the mode that a plurality of data history frames corresponding to related symptoms are presented to a user at the same attack time stamp at present, the disease and pest data analysis method for the carya cathayensis can acquire a disease metadata set analyzed by the disease and pest association analysis table on the candidate disease and a pest metadata set on the candidate pest; deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms; meanwhile, based on the related symptoms and the related symptoms which are not related, a disease and pest association analysis table is output, so that in the disease and pest data analysis process of the walnut with the hook, the related symptoms of at least two pest metadata sets on the same attack time stamp are associated, and related symptoms are presented to a user, so that the user can be prevented from sequentially checking related symptom history frames on different pest metadata sets on the same attack time stamp, the output of a platform for early warning of the walnut with the hook in the creation process of an electronic association table is compared in a semantic similarity manner, the related symptoms related to different attack time stamps are presented to the user in a simple manner, the user can conveniently check the related symptoms in the creation association table, the user experience is enhanced, in addition, the display of other related symptoms can be prevented from being influenced, and the output of the platform is prevented from being too disordered by carrying out the semantic similarity comparison processing on the related symptoms.

Further, as a specific implementation of fig. 1, an embodiment of the present invention provides a pest and disease data analysis system for a walnut, as shown in fig. 3, where the system includes: an acquisition module 31, an association module 32 and an output module 33.

The obtaining module 31 may be configured to obtain a disease metadata set of the disease association analysis table resolved on the candidate disease and a pest metadata set on the candidate pest.

The association module 32 may be configured to determine that if at least two pest metadata sets have associated symptoms at the same attack time stamp of the pest metadata set, associate the associated symptoms of the at least two pest metadata sets at the same attack time stamp to obtain associated symptoms.

The output module 33 may be configured to output a pest and disease association analysis table based on the associated symptoms and the associated symptoms that are not associated.

Further, the obtaining module 31 may be specifically configured to obtain a historical pest metadata set, a peer pest metadata set, and a regional pest metadata set for the pest association analysis table.

The relevant symptoms include historical data, and the association module 32 may be specifically configured to associate historical data at the same attack time stamp of the disease metadata set when at least two of the historical pest metadata set, the same genus plant pest metadata set, and the regional pest metadata set all have the historical data.

Further, to supplement data on the historical pest metadata set, the sibling pest metadata set, and the regional pest metadata set in sequence, the system further comprises: the obtaining module and the supplementing module.

The acquisition module can be used for sequentially acquiring the historical pest metadata set, the plant pest metadata set of the same genus and the related symptoms of the regional pest metadata set on different attack time stamps of the pest metadata set according to the early warning instructions corresponding to the pest association analysis table.

And the supplementing module can be used for supplementing data on the historical pest metadata set, the plant pest metadata set of the same genus and the regional pest metadata set in sequence according to the related symptoms on different attack time stamps.

Further, in order to perform semantic similarity comparison processing on the associated related symptoms, the system further comprises: semantic similarity comparison module.

The semantic similarity comparison module can be used for carrying out semantic similarity comparison processing on the associated related symptoms if the associated related symptoms meet preset data semantic similarity comparison conditions to obtain the associated related symptoms after the semantic similarity comparison.

Further, the semantic similarity comparison module includes: obtaining a module and a semantic similarity comparison module.

The obtaining module may be configured to obtain, according to the attack distance, a length of an attack period between the associated symptom and the related symptom caused by the associated symptom if the attack distance between the associated symptom and the related symptom caused by the associated symptom is smaller than a preset attack distance.

The semantic similarity comparison module can be used for carrying out semantic similarity comparison processing on the associated related symptoms according to the period of the attack, and obtaining the associated related symptoms after the semantic similarity comparison.

Further, the semantic similarity comparison module can be specifically used for extracting at least one key data from the associated related symptoms according to the period of the attack, and obtaining the extracted key data as the associated related symptoms after semantic similarity comparison; or according to the period of the attack, obtaining the display data after the de-duplication treatment in the associated related symptoms, and obtaining the display data after the de-duplication treatment as the associated related symptoms after the semantic similarity comparison.

Further, the semantic similarity comparison module includes: obtaining a sub-module and a screening sub-module.

The obtaining sub-module can be used for obtaining the key data contained in the associated related symptoms and the corresponding presentation priority sequence by utilizing a preset key database.

And the screening sub-module can be used for screening target key data from the key data according to the length of the attack period and the presentation priority sequence corresponding to the key data if the associated relevant symptoms contain the key data, and obtaining the target key data as the associated relevant symptoms after semantic similarity comparison.

Further, the semantic similarity comparison module may be specifically configured to extract at least one key data from the associated related symptoms according to a preset similarity threshold if the similarity corresponding to the associated related symptoms is not less than the preset similarity threshold, and obtain the extracted key data as the associated related symptoms after semantic similarity comparison; or if the similarity corresponding to the related symptoms is not smaller than the preset similarity threshold, obtaining display data after the de-duplication processing in the related symptoms according to the preset similarity threshold, and obtaining the display data after the de-duplication processing as the related symptoms after the semantic similarity comparison.

Further, the semantic similarity comparison module further includes: and an adjustment module.

The obtaining module can be used for obtaining the size of an output platform corresponding to the walnut disease and pest early warning platform.

The adjustment module can be used for adjusting the associated symptoms according to the size of the output platform to obtain adjusted associated symptoms and displaying the adjusted associated symptoms.

Further, the system further comprises: a receiving module and an extracting module.

The receiving module can be used for receiving a presentation instruction of the associated symptoms on the target insect pest metadata set, wherein the presentation instruction has corresponding identification data of the target insect pest metadata set and a corresponding attack time stamp of the associated symptoms.

And the extraction module is used for extracting the relevant symptoms of the target insect pest metadata set on the attack time stamp from the relevant symptoms according to the identification data, and presenting the extracted relevant symptoms on the target insect pest metadata set.

Further, the system further comprises: the decision module can be used for deciding whether the size of the output platform corresponding to the walnut disease and insect pest early warning platform is not smaller than the size of the preset output platform.

The obtaining module 31 may be further configured to obtain the regional indicator pest metadata set related to the pest association analysis table according to the relevant symptoms in the regional pest metadata set if the regional pest metadata set is not smaller than the preset output platform size.

Further, the acquisition module 31 includes: the device comprises an acquisition module, an extraction module and a supplement module.

The obtaining module can be used for obtaining the extracted regional index according to the size of the output platform and the related symptoms in the regional insect pest metadata set and obtaining the regional index insect pest metadata set corresponding to the regional index.

And the extraction module can be used for extracting the parameter data of the regional indexes on different attack time stamps from the related symptoms of the regional insect pest metadata set.

And the supplementing module can be used for supplementing data on the regional index insect pest metadata set according to the parameter data on different attack time stamps.

It should be noted that, other corresponding descriptions of each functional module related to the pest data analysis system for the carya cathayensis according to the embodiment of the present invention may refer to corresponding descriptions of the method shown in fig. 1, and are not repeated herein.

Based on the above method as shown in fig. 1, correspondingly, the embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements the following steps: acquiring a disease metadata set of a disease and pest association analysis table in candidate disease analysis and a pest metadata set on candidate pests; deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms; and outputting a disease and pest association analysis table based on the associated symptoms and the associated symptoms which are not associated.

Based on the embodiment of the method shown in fig. 1 and the system shown in fig. 3, the embodiment of the invention further provides a physical structure diagram of a pest data analysis device for a walnut, as shown in fig. 4, the computer device includes: a processor 41, a memory 42, and a computer program stored on the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are each arranged on the bus 43 to perform the following steps when the processor 41 executes the program: acquiring a disease metadata set of a disease and pest association analysis table in candidate disease analysis and a pest metadata set on candidate pests; deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms; and outputting a disease and pest association analysis table based on the associated symptoms and the associated symptoms which are not associated.

According to the technical scheme, the disease metadata set of the disease and pest association analysis table in the analysis of the candidate disease and the pest metadata set on the candidate pest can be obtained; deciding that if at least two pest metadata sets have relevant symptoms at the same attack time stamp of the pest metadata set, correlating the relevant symptoms of at least two pest metadata sets at the same attack time stamp to obtain correlated relevant symptoms; meanwhile, based on the related symptoms and the related symptoms which are not related, a disease and pest association analysis table is output, so that in the disease and pest data analysis process for the carya cathayensis, the related symptoms of at least two pest metadata sets on the same attack time stamp are associated, and related symptoms are presented to a user, so that the user can be prevented from sequentially checking related symptom history frames on different pest metadata sets on the same attack time stamp, the semantic similarity is compared with the output of a platform for early warning of the carya cathayensis in the creation process of the electronic association table, the related symptoms related to different attack time stamps are presented to the user simply, the user can check the related symptoms in the creation association table conveniently, and the user experience is enhanced.

It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing system, they may be centralized in a single computing system, or distributed across a network of computing systems, and they may alternatively be implemented in program code that is executable by the computing system, such that they are stored in a memory system and, in some cases, executed in a different order than that shown or described, or they may be implemented in the form of individual integrated circuit modules. Thus, the present invention is not limited to any specific combination of hardware and software.

The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

In the several embodiments provided in the present application, it should be understood that the disclosed system, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some interface, indirect coupling or communication connection of systems or modules, electrical, mechanical, or other form.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may be physically obtained separately, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The foregoing is only the embodiments of the present application, and the patent scope of the application is not limited thereto, but is also covered by the patent protection scope of the application, as long as the equivalent structure or equivalent flow changes made by the description and the drawings of the application or the direct or indirect application in other related technical fields are adopted.

The embodiments of the application have been described in detail above, but they are merely examples, and the application is not limited to the above-described embodiments. It will be apparent to those skilled in the art that any equivalent modifications or substitutions to this application are within the scope of the application, and therefore, all equivalent changes and modifications, improvements, etc. that do not depart from the spirit and scope of the principles of the application are intended to be covered by this application.

Claims

1. The method for analyzing the pest and disease data of the walnut is characterized by comprising the following steps of:

2. The method for analyzing pest data of a walnut tree according to claim 1, wherein the obtaining the pest association analysis table includes:

3. The method of claim 2, wherein if at least two pest metadata sets have associated symptoms at the same attack time stamp of the pest metadata set, the method further comprises, after associating the associated symptoms, associating the associated symptoms of the at least two pest metadata sets at the same attack time stamp:

4. The method for analyzing pest and disease data of a carya cathayensis according to claim 3, wherein the extracting at least one key data from the associated symptoms according to the attack period length, and obtaining the extracted key data as the associated symptoms after semantic similarity comparison comprises:

5. The method for analyzing pest and disease data of a walnut tree according to claim 4, wherein if the related symptoms meet a preset data semantic similarity comparison condition, performing semantic similarity comparison processing on the related symptoms to obtain related symptoms after semantic similarity comparison, comprising:

6. Insect disease data analysis system to checkpost walnut, characterized in that includes:

7. The pest data analysis system for the carya paliurus of claim 6, wherein the acquisition module comprises:

8. The pest data analysis system for the carya paliurus of claim 7, wherein the association module further comprises, after:

9. The system for analyzing pest and disease data of a walnut tree according to claim 8, wherein the extracting at least one key data from the associated symptoms according to the attack period length, and obtaining the extracted key data as the associated symptoms after semantic similarity comparison, comprises:

10. The system for analyzing pest and disease data of walnut hook according to claim 9, wherein if the related symptoms meet a preset data semantic similarity comparison condition, performing semantic similarity comparison processing on the related symptoms to obtain related symptoms after semantic similarity comparison, comprising: