CN116976524A

CN116976524A - Garden vegetation hidden danger prediction method and device, electronic equipment and storage medium

Info

Publication number: CN116976524A
Application number: CN202311030757.0A
Authority: CN
Inventors: 周杰
Original assignee: Beijing Lvjiajie Landscape Engineering Co ltd
Current assignee: Beijing Lvjiajie Landscape Engineering Co ltd
Priority date: 2023-08-16
Filing date: 2023-08-16
Publication date: 2023-10-31

Abstract

The application relates to a vegetation hidden danger prediction method and device for gardens, electronic equipment and a storage medium. Relates to the field of garden pest prediction, and the method comprises the following steps: obtaining garden monitoring data; the garden monitoring data comprise data corresponding to at least one vegetation growth factor; determining whether an abnormality exists in the garden range according to the garden monitoring data; if the garden range is abnormal, extracting abnormal data for analysis; determining an abnormal position and an abnormal reason according to the analysis result; and determining hidden vegetation trouble according to the abnormal reasons and abnormal positions. The method provided by the application can realize the purpose of predicting hidden danger. In addition, through obtaining the data corresponding to the factors affecting vegetation, and analyzing the data, whether vegetation hidden danger exists in the garden or not is determined, and judging errors caused by single factors can be reduced, so that judging results are more accurate.

Description

Garden vegetation hidden danger prediction method and device, electronic equipment and storage medium

Technical Field

The application relates to the technical field of garden pest prediction, in particular to a method and device for predicting hidden danger of vegetation for gardens, electronic equipment and a storage medium.

Background

After the garden is built, the influence of plant diseases and insect pests caused by various reasons on the garden is avoided. In this case, timely disposal is required, which may otherwise cause immeasurable losses.

At present, a plurality of methods are to detect and determine whether the insect pest has occurred at the current moment, and if the insect pest is detected to occur, measures are taken to treat the insect pest. However, the plant diseases and insect pests are discovered and then treated, so that not only can certain difficulty be brought to treatment, but also discomfort in appearance can be brought to people, and diseases can be possibly transmitted. But also takes some time during the remediation phase, during which certain damage may have been caused to certain types of vegetation. Therefore, it is necessary to predict possible plant diseases and insect pests in gardens.

Disclosure of Invention

The application provides a method and a device for predicting vegetation hidden danger for gardens, electronic equipment and a storage medium, and aims to solve the technical problems.

In a first aspect, the present application provides a method for predicting a vegetation hidden danger for gardens, including:

obtaining garden monitoring data; the garden monitoring data comprise at least one data corresponding to a vegetation growth factor;

Determining whether an abnormality exists in the garden range according to the garden monitoring data;

if the garden range is abnormal, extracting abnormal data for analysis;

determining an abnormal position and an abnormal reason according to the analysis result;

and determining hidden vegetation trouble according to the abnormal reasons and the abnormal positions.

By the method provided by the application, whether the garden is abnormal or not can be determined through the garden monitoring data, and when the garden is abnormal, whether the vegetation hidden danger possibly exists or not is determined according to the abnormal position and the abnormal reason, so that the purpose of predicting the hidden danger can be realized. In addition, through obtaining the data corresponding to the factors affecting vegetation, and analyzing the data, whether vegetation hidden danger exists in the garden or not is determined, and judging errors caused by single factors can be reduced, so that judging results are more accurate.

Optionally, the determining whether the abnormality exists in the garden range according to the garden monitoring data includes:

analyzing the garden monitoring data, and determining environment monitoring data, vegetation types and vegetation distribution conditions according to analysis results;

determining whether the vegetation introduction of the gardens is abnormal according to the vegetation types;

If the vegetation introduction of the garden is abnormal, determining a vegetation hidden danger according to the abnormal reason and the abnormal position, including:

determining whether the gardens have invasion hidden danger or not according to the introduced abnormal data;

if the vegetation of the gardens is introduced without abnormality, determining whether the vegetation distribution of the gardens is abnormal or not according to the vegetation distribution condition;

if the vegetation distribution of the gardens is abnormal, determining the hidden danger of the vegetation according to the abnormal reasons and the abnormal positions comprises the following steps:

determining whether the gardens have configuration hidden danger according to the distribution abnormal data;

if the vegetation distribution of the gardens is not abnormal, determining whether the environment of the gardens is abnormal or not according to the environment monitoring data;

if the environment of the garden is abnormal, determining a vegetation hidden danger according to the abnormal reason and the abnormal position comprises the following steps:

and determining whether the gardens have environmental hidden danger or not according to the environmental anomaly data.

Through the scheme that this embodiment provided, can analyze the gardens monitoring data that obtains to carry out one-to-one analysis to each factor that probably produces the plant diseases and insect pests, thereby confirm whether current gardens can have the hidden danger. In this way, compared with the prior art, the method has the advantages that the consideration is more comprehensive, the judgment result is more accurate compared with a single factor, the prediction frequency can be reduced, and the prediction efficiency is improved.

Optionally, the environment monitoring data includes humidity data, and determining, according to the environment monitoring data, whether the environment of the garden is abnormal includes:

determining the soil change of the garden according to the humidity data;

determining growth data of different types of vegetation under the influence of the soil change according to the soil change and the vegetation type;

and determining whether the environment of the garden is abnormal according to the growth data.

According to the scheme provided by the embodiment, the change of the soil in the area is determined by detecting the obtained humidity data, and then whether the vegetation growth data is affected is determined, so that whether the environment is abnormal is determined. The method can directly determine whether the vegetation is affected through the change of the vegetation growth data, and when the vegetation is affected, the environment abnormality is indicated. When not affected, the environment is described as changing but is not bad for vegetation. Therefore, the judgment result is more accurate, and the environment is not blindly considered to be abnormal because of the change of the environment monitoring data.

Optionally, the determining the abnormal position and the abnormal reason according to the analysis result includes:

Determining the type of the abnormal data and uploading equipment of the abnormal data according to the analysis result;

the basic information of the uploading equipment is called;

according to the basic information, determining a monitoring range of the uploading equipment, and taking the monitoring range as the abnormal position;

and determining the reason of the abnormality according to the type of the abnormality data.

According to the scheme provided by the embodiment, the uploading equipment for uploading the abnormal data can be determined through the analysis result, and the abnormal position of the abnormal data is determined according to the basic information of the uploading equipment. Since the detection range of the uploading device is known, when the abnormal data is uploaded by a certain uploading device, the abnormal data must belong to the monitoring range of the uploading device, and positioning errors caused by a positioning system can be reduced. In addition, the abnormal reason is determined according to the type of the abnormal data, so that the processing of the data can be reduced, and the efficiency is improved.

Optionally, the determining the reason for the abnormality according to the type of the abnormal data includes:

acquiring operation data of the uploading equipment;

analyzing whether the operation data is abnormal, and if so, matching the abnormal operation data with the abnormal operation data;

Determining whether the abnormality cause is equipment abnormality according to a matching result;

if the operation data is not abnormal, matching the type of the abnormal data with a preset case library, and determining an abnormal reason corresponding to the type.

According to the scheme provided by the embodiment, when abnormal data occurs, the abnormal data is preferentially matched with the operation data of the uploading equipment, whether the abnormal data is caused by the equipment in the operation process of the uploading equipment is determined, misjudgment of results caused by equipment problems is reduced, and the efficiency of determining the abnormal reasons is improved.

Optionally, the determining whether the garden has an intrusion hidden danger according to the abnormal data includes:

determining whether an intrusion relationship exists between at least two vegetation types according to the vegetation types of the gardens;

if an intrusion relationship exists between at least two vegetation types, determining the type of the plant diseases and insect pests generated after intrusion;

and determining whether the gardens have invasion hidden danger according to the judging result.

By the scheme provided by the embodiment, the situation that insects are completely blown out in a net without considering the types of the insects and the influence of the insects on gardens when the invasion relation exists can be avoided.

Optionally, the method further comprises:

determining whether the environment where each type of vegetation is located is safe or not according to the vegetation type and the vegetation distribution condition;

if the environment of each type of vegetation is safe, the growth data of each type of vegetation under the environment monitoring data are called;

comparing the growth data with preset normal growth data, and determining whether plants with abnormal growth exist in each type of vegetation;

if the abnormal plant exists, determining the environment corresponding to the environment monitoring data to cause hidden danger to the abnormal plant.

Through the mode that this embodiment provided, can carry out more careful detection to each type vegetation, whether there is the vegetation of abnormal growth according to environmental monitoring data simultaneously. When the hidden danger exists, the potential danger can be confirmed, attention needs to be paid, omission of hidden danger detection is reduced, and the accuracy of hidden danger detection is improved.

In a second aspect, the present application provides a vegetation hidden danger prediction apparatus for gardens, comprising:

the data acquisition module is used for acquiring garden monitoring data; the garden monitoring data comprise at least one data corresponding to a vegetation growth factor;

The abnormality determining module is used for determining whether abnormality exists in the garden range according to the garden monitoring data;

the analysis module is used for extracting abnormal data for analysis if the garden range is abnormal;

the result determining module is used for determining an abnormal position and an abnormal reason according to the analysis result;

and the hidden danger determining module is used for determining hidden danger of vegetation according to the abnormal reasons and the abnormal positions.

Optionally, the anomaly determination module is specifically configured to:

determining whether the gardens have invasion hidden dangers according to the introduced abnormal data and the vegetation distribution conditions;

Optionally, the anomaly determination module is specifically further configured to:

determining the soil change of the garden according to the humidity data;

Optionally, the result determining module is specifically configured to:

the basic information of the uploading equipment is called;

Optionally, the result determining module is specifically further configured to:

acquiring operation data of the uploading equipment;

Optionally, the vegetation hidden danger prediction device for gardens further includes a vegetation data analysis module for:

In a third aspect, the present application provides an electronic device comprising: a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and performing the method of the first aspect.

In a fourth aspect, the present application provides a computer readable storage medium storing a computer program capable of being loaded by a processor and performing the method of the first aspect.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;

FIG. 2 is a flowchart of a method for predicting potential vegetation in gardens according to an embodiment of the present application;

FIG. 3 is a flowchart of another method for predicting potential vegetation in gardens according to one embodiment of the present application;

fig. 4 is a schematic structural diagram of a vegetation hidden danger prediction device for gardens according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are 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.

In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.

Embodiments of the application are described in further detail below with reference to the drawings.

The plant diseases and insect pests are discovered and then treated, so that not only can certain difficulty be brought to treatment, but also discomfort on the appearance of tourists can be brought. But also takes some time during the remediation phase, during which certain damage may have been caused to certain types of vegetation. Therefore, it is necessary to predict possible plant diseases and insect pests in gardens. However, most of the current plant diseases and insect pests are detected by single factors such as meteorological data or historical medication information. Because the factors influencing the occurrence of plant diseases and insect pests in gardens are more than one, the detection result obtained by detecting the plant diseases and insect pests by using a single factor is often not representative and has a certain error. This can lead to gardener unable through testing result, knows accurately whether the state of current gardens can cause the plant diseases and insect pests to can't in time handle the plant diseases and insect pests that probably appear in advance.

Based on the above, the application provides a method and a device for predicting vegetation hidden danger for gardens, electronic equipment and a storage medium.

And determining whether an abnormality exists in the garden range according to the obtained garden monitoring data. When an abnormality exists, extracting abnormal data for analysis, determining an abnormal position and an abnormal reason according to an analysis result, and determining a vegetation hidden danger according to the abnormal position and the abnormal reason. Because the obtained garden monitoring data are of various types, such as vegetation types, vegetation distribution conditions, environment monitoring data and the like, the influence caused by various factors is considered in the analysis process. The analysis result is more in line with the situation of the garden, and meanwhile, whether vegetation hidden danger possibly exists in the current garden can be accurately determined.

Fig. 1 is a schematic view of an application scenario provided by the present application. When a certain garden needs to be predicted and whether vegetation hidden danger exists in the current garden is determined, the method provided by the application can be used. In the garden range, a plurality of environment monitoring devices are arranged to continuously monitor the environment, and the data can be uploaded to a server carrying the method of the application in real time. In addition, the vegetation distribution condition and vegetation type of the garden are combined with the received environment monitoring data to obtain garden monitoring data of the garden. After obtaining garden monitoring data, the server performs a series of analyses to determine whether the garden is abnormal or not, and further determines whether vegetation has hidden danger or not. Through comprehensively considering multiple factors, whether vegetation hidden danger exists is determined, and compared with the determination result through single factor, the method is more accurate.

Reference may be made to the following examples for specific implementation.

Fig. 2 is a flowchart of a method for predicting vegetation hidden danger for gardens according to an embodiment of the present application, where the method of the present embodiment may be applied to a server in the above scenario. As shown in fig. 2, the method includes:

s201, obtaining garden monitoring data; the garden monitoring data comprises data corresponding to at least one vegetation influencing growth factor.

In particular, factors affecting vegetation growth may include vegetation type, vegetation distribution, and environmental monitoring data. The vegetation types and vegetation distribution conditions can be stored in a server after the gardens are built, and can be called and used at any time. The environmental monitoring data can be known by a plurality of environmental monitoring devices arranged in the garden range. The environmental monitoring devices can be a temperature and humidity detector, a PH meter, a water quality monitor, a portable soil nutrient detector and the like. The temperature and humidity detector can be used for detecting the temperature and humidity of gardens; the PH meter can be used for detecting the PH value of the garden watering water; the water quality monitor can be used for detecting the components of irrigation water; the portable soil nutrient detector can be used for detecting trace components in soil.

In some implementations, the area of the garden can be divided into grids, each grid after division can be used as a monitoring area, and the environmental monitoring equipment such as the temperature and humidity detector, the PH meter, the water quality monitor and the portable soil nutrient detector are arranged in each monitoring area, so that each monitoring area can be monitored. When grid division is performed, division standard setting can be performed according to the monitoring range of the environmental monitoring equipment, so that all the ranges of each grid can be monitored.

In other implementations, a plurality of flying devices with image pick-up devices can be further arranged to record the shooting of the areas such as the leaves, the branches and the like of the plants. The photographed video or image can also be uploaded to a server together with vegetation type, vegetation distribution and environmental monitoring data as garden monitoring data.

S202, determining whether abnormality exists in the garden range according to the garden monitoring data.

Specifically, whether the current garden monitoring data is abnormal or not can be judged by normal data in the historical garden monitoring data. For example, in the historical garden monitoring data, the PH value detected by the PH is normal in the range of 6.3-6.7, and if the detected data of a certain PH meter in the present garden monitoring data is 7.2, it can be shown that the data of the PH meter is abnormal.

In other implementations, some vegetation may not cause plant diseases and insect pests by itself, causing vegetation hidden danger, but as different types of vegetation grow in one area, plant diseases and insect pests may occur due to mutual influence, and vegetation growth is affected. For example, spruce and larch are mixedly planted, and larch aphid is easy to occur; pine needle rust is easy to occur, the infection is serious, and the windward side is heavier than the leeward side when the pine needle is mixed with the yellow-corolla. Therefore, all normal data in gardens can be set in advance according to vegetation types and vegetation distribution conditions. For example, rust on pine needles may cause the affected pine needles to fade into a greenish patch of varying length, with papular brown spots appearing on the front of the patch segment, lining up, turning dark brown, and creating yellowish-white to orange-colored scar pockets on the opposite side of the brown papule. At this time, the normal data of the region where the pinus koraiensis and the yellow Porro are mixed may be set as the normal leaf, and the abnormal data may be the above-mentioned pine needle rust. When the gardens are mixed planting of the pinus koraiensis and the yellow sallow, detection is needed according to the onset symptoms of pine needle rust. For example, the state of the leaf is determined according to the uploaded image data, or the environmental monitoring data which is easy to induce the pine needle rust disease period is focused.

And S203, if the garden range is abnormal, extracting abnormal data for analysis.

Specifically, when the above-mentioned garden monitoring data has data different from the history normal data, the data collected at this time may be considered to be abnormal, and at this time, the abnormal data, that is, abnormal data, may be extracted and analyzed.

In some implementations, the source of these anomaly data may be determined by analysis first, i.e., which devices are uploaded. Then, it can be determined in which area the abnormal data are obtained by the device number and the set position of the device itself.

In addition, since the abnormal data are also different in expression form, for example, an image may be obtained by the above-mentioned image pickup device, a numerical value may be obtained by the PH meter, and a trace component such as nitrogen, phosphorus, potassium, etc. may be obtained by the portable soil nutrient detector. The type of data to which the abnormal data belongs can also be determined by the expression form of the abnormal data, so that the cause of the abnormality can also be roughly determined. For example, if the anomaly data is 4.23, it is known by various devices of known configuration that the PH meter can be used to generate such a value, and if the anomaly data is 4.23, it may be indicated that the irrigation water detected by the PH meter is acidic, which is detrimental to vegetation growth.

S204, determining the abnormal position and the abnormal reason according to the analysis result.

The anomaly location may be a location where anomaly data is generated, and may include not only location information, but also the type of vegetation that is present at that location.

The abnormality cause may be the cause of generation of these abnormal data.

Specifically, by the above analysis, an analysis result can be obtained, and thus an abnormality position and an abnormality cause at which such abnormal data is generated can be determined. For example, in step S203 described above, the abnormality position of the abnormality data is determined by the device number of the device and the set position. In addition, there may be a plurality of identical devices in the area where this device is located, so that the abnormal position of the abnormal data may be further determined according to the detection range of this device.

Further, the expression form of the abnormal data and the content of the abnormal data can be determined by the above step S203, and the cause of the abnormality of the abnormal data can be determined. For example, the PH meter in step S203 generates abnormality data 4.23, and the abnormality cause of the abnormality data may be that the irrigation water detected by the PH meter is acidic.

S205, determining hidden vegetation trouble according to the abnormal reasons and abnormal positions.

Specifically, a large number of pest cases can be collected to obtain the abnormal positions and the abnormal reasons corresponding to the abnormal data generated when the pest occurs. And then analyzing the acquired abnormal data to determine the abnormal position and the abnormal reason, and comparing the abnormal position and the abnormal reason with the acquired content, thereby determining whether the abnormal position and the abnormal reason generating the abnormal data bring vegetation hidden danger.

Taking the pine needle rust related to the steps as an example, the pine needle rust is mainly determined whether the pine needle rust is ill or not by observing the color of the leaves and the states of the leaves, so that the pine needle rust needs to be photographed by a photographing device. The pathogen of pine needle rust invades after autumn, and after successful invasion of the pathogen, the pathogen needs to pay attention to whether the pathogen is mature and looses rust spores or not in the year around four months. If rust spores are scattered, it is necessary to determine whether the leaves of this area are changed from the acquired image data, i.e., whether abnormal data exists in this area from the acquired image data, i.e., whether the leaves are observed to begin to yellow or papular brown spots are present in the image, arranged in a row. If so, determining that the hidden vegetation trouble is pine needle rust according to the position and the generation reason of the phenomenon, and timely processing is needed to avoid the problem of large-area leaves or plants.

In some embodiments, the garden monitoring data includes vegetation type, vegetation distribution, and environmental monitoring data. Analyzing the garden monitoring data to analyze various data, determining whether the garden is abnormal or not, and further determining hidden danger according to the abnormality. The specific implementation flow may refer to fig. 3, and specifically may include: analyzing garden monitoring data, and determining environment monitoring data, vegetation types and vegetation distribution conditions according to analysis results; determining whether the vegetation introduction of gardens is abnormal according to the vegetation types; if the vegetation introduction in gardens is abnormal, determining hidden vegetation trouble according to the abnormal reasons and abnormal positions, including: determining whether the gardens have invasion hidden danger according to the introduced abnormal data and vegetation distribution conditions; if the vegetation of the gardens is introduced without abnormality, determining whether the vegetation distribution of the gardens is abnormal according to the vegetation distribution situation; if the vegetation distribution in gardens is abnormal, determining the hidden danger of vegetation according to the abnormal reasons and abnormal positions, including: determining whether a configuration hidden danger exists in gardens according to the distribution abnormal data; if the vegetation distribution of the gardens is not abnormal, determining whether the environments of the gardens are abnormal according to the environment monitoring data; if the environment of the garden is abnormal, determining a vegetation hidden danger according to the abnormal reason and the abnormal position, including: and determining whether the gardens have hidden environmental hazards according to the environment abnormal data.

The potential for invasion may be considered as a potential for a species invasion to affect vegetation growth. The species invasion described herein may be a vegetation that grows very rapidly in a new environment, and this vegetation exceeds the growth rate of the original vegetation in that environment, potentially resulting in a disruption of the ecosystem and ecological balance of that environment. Thus, when this vegetation type exists in this garden, it can be considered that there is an abnormality in the vegetation introduction of this garden, and this vegetation type and the corresponding growth characteristics and the like can be used as the introduction abnormality data.

The configuration hidden trouble can be considered as hidden trouble that different types of vegetation are not planted reasonably, and diseases and insect pests or other problems are caused. It may be caused by the unreasonable distribution of two or more vegetation. At this time, names, distribution conditions, and the like of at least two kinds of vegetation which may have hidden trouble may be used as the distribution abnormality data.

Environmental hazards may be considered as hazards where environmental changes cause disease and pest outbreaks or other problems. Among the plurality of environmental monitoring devices detected by the environment, if there is data having a large gap from the normal state, it can be regarded as environmental abnormality data.

Specifically, the plurality of data in the garden monitoring data can be classified according to respective digital characteristics in a classification mode, for example, the data characteristics of the PH meter are numbers with decimal points; the data characteristic of vegetation type is the name of vegetation, etc. After classifying all the data, determining the environment monitoring data, vegetation types and vegetation distribution conditions.

It is first determined whether a species with rapid growth and breeding capability is present in this garden according to the vegetation type in this garden, and as such species may affect the growth rate of the remaining plants, and more seriously, may disrupt the local ecosystem and ecological balance. When the vegetation is led in to have abnormality, the distribution position of the vegetation can be determined according to the vegetation distribution condition. In the specific implementation process, the growth environment of the vegetation can be determined through a visual model diagram of gardens and vegetation distribution conditions. Determining whether the vegetation of the category affects the normal growth of other vegetation in the growing environment, thereby determining whether the invasion hidden trouble exists.

When the vegetation in gardens is introduced and does not have abnormality, whether hidden danger possibly exists between at least two vegetation can be determined according to vegetation distribution conditions, namely, the situation that the mutual influence exists between at least two vegetation can not exist, so that the original hidden danger area is caused, and plant diseases and insect pests are generated. For example, the spruce and larch referred to in the above embodiments are mixed together and are prone to the occurrence of myzus persicae. By means of the abnormal distribution data, whether the distribution of the vegetation influences the normal growth of the vegetation or not is determined, whether plant diseases and insect pests are generated or not is determined, for example, the blade states, colors, attachment conditions and the like of the vegetation are analyzed, and whether the garden possibly has a configuration hidden trouble is determined.

In addition, when the vegetation distribution of the garden still does not have abnormality, whether abnormal data different from data in a normal state exists can be determined according to the obtained environment monitoring data. For example, the pH value is originally 5.5-6.5, but some data reach 7.2 and 4.3. If it is determined that environmental anomaly data exists, it is determined whether pest production is appropriate and normal growth of vegetation is affected under such environmental change conditions based on the environmental anomaly data.

In some embodiments, the soil variation of the garden may be determined by the humidity data in the environmental monitoring data, and further the vegetation growth data, to determine whether the garden environment is abnormal. Specifically, it may include: determining the soil change of gardens according to the humidity data; determining growth data of different types of vegetation under the influence of soil change according to the soil change and the vegetation type; and determining whether the environment of the garden is abnormal according to the growth data.

Growth data may be considered as data of vegetation during growth, such as leaf growth variation data, branch growth variation data, leaf color variation, branch number variation, and the like.

Specifically, humidity data can be the data that a plurality of temperature and humidity detector that set up in gardens within range detected and uploaded, because humidity can influence the soil humidity in gardens, when soil humidity changes, the growth environment of vegetation will take place slight change to can influence the growth of vegetation. The humidity data may be data from the last prediction end to the current time, or may be obtained from a seasonal change, and the humidity data of the season in which the current time is located.

Therefore, the environment monitoring data can be analyzed in a manner of analyzing the garden monitoring data to obtain humidity data, soil nutrient data and the like, and whether nutrients in the soil are changed in the process of changing the humidity can be determined. If the vegetation is changed, determining the growth data of each type of vegetation in the process of changing the nutrients according to the requirements of different vegetation on soil nutrients, and determining whether the growth data has a larger difference from the growth data in a normal state. If the differences are large, it may be stated that the growth of this type of vegetation is affected by the environment, i.e., the environment is abnormal.

In some embodiments, the uploading device may be determined by the analysis result, and then the abnormality location is determined according to the basic information of the uploading device. And determining the reason of the abnormality according to the type of the abnormality data. Specifically, it may include: determining the type of the abnormal data and uploading equipment of the abnormal data according to the analysis result; basic information of uploading equipment is called; according to the basic information, determining a monitoring range of the uploading equipment, and taking the monitoring range as an abnormal position; and determining the reason of the abnormality according to the type of the abnormality data.

The uploading device may be any one of several devices set in the above embodiments. In a specific implementation manner, each device arranged in the garden needs to be recorded and stored in a database of the server, and recorded contents can include contents such as a device type, a device number, a device monitoring range and the like, and the contents can be used as basic information of the uploading device.

Specifically, by the analysis in the above manner, it is determined which of the above types of the abnormal data belongs to the number or the vegetation name or other types, and which device this abnormal data is uploaded. And then, the basic information of the uploading device is called, the monitoring range of the uploading device is determined from the basic information, and the monitoring range is approximately used as an abnormal position for generating the abnormal data. The cause of this abnormal data is then determined by the type of abnormal data.

In some embodiments, it may be determined whether the anomaly is a device cause by analyzing the operational data of the uploading device. If not, the abnormal data can be matched with a preset case base, and the reason of the abnormality is determined. Specifically, it may include: acquiring operation data of uploading equipment; analyzing whether the operation data is abnormal, and if so, matching the abnormal operation data with the abnormal operation data; determining whether the abnormality cause is equipment abnormality according to the matching result; if the operation data is not abnormal, the type of the abnormal data is matched with a preset case base, and the abnormality reason corresponding to the type is determined.

The preset case library can be considered to contain the contents such as the data type, the abnormality reason, the abnormality position and the like of the abnormal data generated when a plurality of diseases and insect pests occur. The content of the abnormal data, the abnormal reasons, the abnormal positions and the like may be different when different types of diseases and insect pests occur.

Specifically, when the cause of the abnormality is determined according to the type, the operation data of the uploading device may be acquired first, the operation data may be analyzed, when the operation data is abnormal, the abnormal operation data may be matched with the determined abnormal data, and whether the abnormal data is caused by the abnormality of the device may be determined. If the matching finds that the abnormal data is highly similar to the data of the operation abnormality, the abnormality reason of the abnormal data can be considered as that the operation of the uploading equipment has a problem. If a match finds that the anomalous data has no resemblance to the data that is operating abnormally, then this anomalous data may be considered not device-induced. At this time, the type of the abnormal data can be matched with a preset case library, the case which generates the same kind of data in the preset case library is determined, and the abnormal reason of the abnormal data is determined according to the case.

In some implementations, if the operation data of the uploading device is the same as the data types of other environment monitoring devices, after the abnormal data is matched with the operation data to determine dissimilarity, the abnormal data may be matched with the data detected by the environment monitoring device which can generate the same type of data for the second time, so as to determine whether the data detected by the environment monitoring device is abnormal.

In some embodiments, it may be determined whether the garden may have an intrusion hazard by determining an intrusion relationship between vegetation. Specifically, it may include: determining whether an intrusion relationship exists between at least two vegetation types according to vegetation types of gardens; if an intrusion relationship exists between at least two vegetation types, determining the type of the plant diseases and insect pests generated after intrusion; and determining whether the gardens have invasion hidden danger according to the judging result.

Specifically, when abnormal data are introduced, whether an invasion relation exists between at least two vegetation types in gardens can be determined through vegetation types of gardens, and if the invasion relation exists between the at least two vegetation types, the type of diseases and insect pests which possibly occur after the invasion is successful can be determined according to the vegetation types, and the type belongs to the insect pests or beneficial insects. When the insects invade are beneficial insects, the insects can be considered to not have bad influence on vegetation, and even be beneficial to the growth of the vegetation, and measures can be not taken for treatment at the moment. If the insect is a pest, it is believed that if such pest enters the garden, normal growth of vegetation may be affected and some vegetation may even die, thus requiring timely treatment. That is, whether the garden has an invasion hidden trouble is determined by analyzing whether insects possibly generated after invasion are beneficial insects or pests when the invasion relationship exists.

In some embodiments, the treatment mode may be determined by determining warning information for the abnormal location based on the vegetation hidden danger.

Specifically, when there is a hidden vegetation trouble, in order for the person in the garden to find and process the hidden vegetation trouble in time, the content of the hidden vegetation trouble needs to be sent to the relevant person in time, so that the relevant person knows and processes the hidden vegetation trouble. The vegetation hidden danger is determined by the abnormal reason and the abnormal position, so that warning information corresponding to the abnormal position can be generated through the vegetation hidden danger, and then the warning information is sent to related personnel to determine the processing mode.

For example, if the abnormality data 4.5 is generated in the range detected by the PH meter of 0002, the abnormality position of the abnormality data may be the position in the range detected by the PH meter of 0002, and the abnormality may be the acidity of the irrigation water. The vegetation hidden trouble in this area can be regarded as what effect the vegetation in this abnormal position can be caused when the irrigation water is acidic. The warning information may include the cause of the anomaly, the location of the anomaly, the type of vegetation at the location of the anomaly, and the like. In this case, the treatment mode may be to treat the irrigation water so that it can be applied to the vegetation in this area.

Through the scheme provided by the embodiment, when the hidden danger of vegetation is determined, alarm information is timely generated, a processing mode is determined, related personnel are informed of processing, and the influence of the hidden danger on vegetation is reduced.

In some embodiments, whether the environment where each type of vegetation is located is safe or not can be determined according to the vegetation type and vegetation distribution situation; if the environment of each type of vegetation is safe, the growth data of each type of vegetation under the environment monitoring data are called; comparing the growth data with preset normal growth data, and determining whether plants with abnormal growth exist in each type of vegetation; if the abnormal plant exists, determining the environment corresponding to the environment monitoring data to cause hidden danger to the abnormal plant.

The preset normal growth data can be considered as growth data of each type of plant which can normally grow under different environmental influences, such as leaf development condition, branch growth condition and the like. In this example, it can be considered as the slowest data for normal vegetation growth.

After the vegetation types and vegetation distribution conditions are obtained through the embodiments described above, it may be determined whether the growth between each type of vegetation and adjacent vegetation is likely to be invaded or dependent according to the vegetation distribution conditions and the growth dependency between vegetation. If there is an invasion or dependency, it may be stated that a certain class of plants may grow slowly or may even face wilting under the influence of such a relationship. If this relationship does not exist, it can be stated that the growth of each type of plant is safe and unaffected. At this time, the growth data of each type of vegetation corresponding to the environmental monitoring data can be retrieved. Comparing the growth data with preset normal growth data to determine whether abnormal growth of vegetation exists. If not, the situation that hidden danger exists in the current gardens is indicated. If so, it can be considered that there may be a hidden danger.

If so, the environmental state corresponding to the current environmental monitoring data can be considered to be not suitable for the normal growth of certain vegetation. The environment of the area where the vegetation is located can be adjusted according to the vegetation type corresponding to the growth data. In addition, it is also possible that some vegetation has abnormal growth due to the shielding of other vegetation or vegetation in the same area. At this time, the flying device can be started to shoot the area with abnormal growth in the field, meanwhile, the shooting pictures can be uploaded to the server, after the shooting pictures are received, the shooting pictures are subjected to image recognition and feature extraction, and whether the area corresponding to a certain plant of vegetation is blocked by other types of vegetation or similar vegetation is determined, if so, the area corresponding to the certain plant of vegetation is blocked by the vegetation. The campus personnel may be alerted to trim them. If not, it can be considered as an abnormality in growth caused by the environmental state.

Fig. 4 is a schematic structural diagram of a vegetation hidden danger prediction device for gardens according to an embodiment of the present application, and as shown in fig. 4, a vegetation hidden danger prediction device 400 for gardens according to the present embodiment includes: a data acquisition module 401, an anomaly determination module 402, an analysis module 403, a result determination module 404, and a hidden danger determination module 405.

A data acquisition module 401, configured to acquire garden monitoring data; the garden monitoring data comprise at least one data corresponding to a vegetation growth factor;

an anomaly determination module 402, configured to determine whether an anomaly exists in a garden range according to the garden monitoring data;

the analysis module 403 is configured to extract abnormal data for analysis if an abnormality exists in the garden range;

the result determining module 404 is configured to determine an abnormal position and an abnormal cause according to the analysis result;

the hidden danger determining module 405 is configured to determine a hidden danger of vegetation according to the abnormality cause and the abnormality location.

Optionally, the anomaly determination module 402 is specifically configured to:

Optionally, the anomaly determination module 402 is specifically further configured to:

determining the soil change of the garden according to the humidity data;

Optionally, the result determining module 404 is specifically configured to:

the basic information of the uploading equipment is called;

Optionally, the result determining module 404 is specifically further configured to:

acquiring operation data of the uploading equipment;

Optionally, the vegetation hidden danger prediction device for gardens further includes a vegetation data analysis module 406, configured to:

The apparatus of this embodiment may be used to perform the method of any of the foregoing embodiments, and its implementation principle and technical effects are similar, and will not be described herein again.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, as shown in fig. 5, an electronic device 500 according to the present embodiment may include: a memory 501 and a processor 502.

The memory 501 has stored thereon a computer program that can be loaded by the processor 502 and that performs the methods of the embodiments described above.

Wherein the processor 502 is coupled to the memory 501, such as via a bus.

Optionally, the electronic device 500 may also include a transceiver. It should be noted that, in practical applications, the transceiver is not limited to one, and the structure of the electronic device 500 is not limited to the embodiment of the present application.

The processor 502 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor 502 may also be a combination of computing functions, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

A bus may include a path that communicates information between the components. The bus may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.

The Memory 501 may be, but is not limited to, ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, EEPROM (Electrically Erasable Programmable Read Only Memory ), CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

The memory 501 is used to store application code for performing the implementation of the present application and is controlled by the processor 502. The processor 502 is configured to execute the application code stored in the memory 501 to implement what is shown in the foregoing method embodiments.

Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. But may also be a server or the like. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present application.

The electronic device of the present embodiment may be used to execute the method of any of the foregoing embodiments, and its implementation principle and technical effects are similar, and will not be described herein.

The present application also provides a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the method in the above embodiments.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.

Claims

1. The method for predicting the hidden danger of the vegetation for gardens is characterized by comprising the following steps:

if the garden range is abnormal, extracting abnormal data for analysis;

2. The method of claim 1, wherein the garden monitoring data comprises vegetation type, vegetation distribution, and environmental monitoring data; determining whether an abnormality exists in the garden range according to the garden monitoring data comprises the following steps:

3. The method of claim 2, wherein determining whether the environment of the garden is abnormal based on the environment monitoring data comprises:

determining the soil change of the garden according to the humidity data;

4. The method of claim 1, wherein determining the anomaly location and the anomaly cause based on the analysis result comprises:

the basic information of the uploading equipment is called;

5. The method of claim 4, wherein determining the cause of the anomaly based on the type of anomaly data comprises:

acquiring operation data of the uploading equipment;

6. The method of claim 2, wherein determining whether the garden has an intrusion hazard based on the incoming anomaly data comprises:

7. The method as recited in claim 1, further comprising:

8. The utility model provides a vegetation hidden danger prediction unit for gardens which characterized in that includes:

9. An electronic device, comprising: a memory and a processor;

the memory is used for storing program instructions;

the processor is configured to call and execute the program instructions in the memory, and execute the vegetation hidden danger prediction method for gardens according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein the computer-readable storage medium has a computer program stored therein; the computer program, when executed by a processor, implements the vegetation hidden danger prediction method for gardens according to any one of claims 1 to 7.