CN110751092A - Agricultural monitoring method and device based on Internet of things, storage medium and electronic equipment - Google Patents
Agricultural monitoring method and device based on Internet of things, storage medium and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to the technical field of data processing, in particular to an agricultural monitoring method and device based on the Internet of things, a computer readable storage medium and electronic equipment, wherein the method comprises the following steps: acquiring monitoring data of a target plot in real time through an internet of things corresponding to the target plot; identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result; and triggering an abnormal alarm when the monitoring data meets the preset alarm condition. According to the technical scheme of the embodiment of the disclosure, on one hand, monitoring data collected by various devices can be integrated to realize comprehensive management and utilization of the monitoring data; and on the other hand, the abnormal conditions occurring in the production process can be reflected in real time, so that a manager can process the abnormal conditions in time, and irreparable consequences caused by the abnormal conditions are avoided.
Description
Technical Field
The disclosure relates to the technical field of data processing, in particular to an agricultural monitoring method and device based on the Internet of things, a computer-readable storage medium and electronic equipment.
Background
With the development of economy and the continuous improvement of national living standard, the degree of attention of people on whether agricultural products are pollution-free or not is gradually improved, so that large-scale agricultural production organizations such as many farms and the like begin to trace the agricultural production process, the visualization of people on the agricultural production process is realized, and the recognition degree of people on products is further improved.
In the existing tracing system, equipment such as videos and sensors are often used for collecting production data of agricultural production respectively, and then the data are displayed to users respectively, so that the purpose of visualization of the generation process is completed. In addition, some production data is used in the process of adjusting the production conditions of the agricultural product to improve the quality. For example, the current planting temperature of the agricultural product is determined through the production data, and when the planting temperature is inconsistent with the temperature required by the agricultural product, the temperature is adjusted to improve the quality of the agricultural product.
However, in either of the above-described modes, the collected production data is not fully utilized.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The purpose of the present disclosure is to provide an agricultural monitoring method and apparatus, a computer-readable storage medium, and an electronic device based on the internet of things, so as to overcome the problem of low production data utilization rate at least to a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, an agricultural monitoring method based on the internet of things is provided, which includes:
acquiring monitoring data of a target plot in real time through an internet of things corresponding to the target plot;
identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result;
and triggering an abnormal alarm when the monitoring data meets the preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the monitoring data includes environmental data, and the preset model includes a pre-trained disaster recognition model; the preset alarm condition comprises that the environment type belongs to the environment alarm type;
the identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result, including:
inputting the environment data into the disaster identification model to identify an environment category corresponding to the environment data;
when the environment type belongs to the environment alarm type, judging that the monitoring data meets a preset alarm condition; or
And when the environment type does not belong to the environment alarm type, judging that the monitoring data does not meet the preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the monitoring data includes environmental data and first image data, and the preset model includes a pre-trained insect situation recognition model; the preset alarm condition comprises that the insect condition grade belongs to an insect condition alarm grade;
the identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result, including:
inputting the environment data and the first image data into the insect pest situation recognition model to recognize the insect pest situation grade of the first image data;
when the insect condition grade belongs to the insect condition alarm grade, judging that the monitoring data meet a preset alarm condition; or
And when the insect condition grade does not belong to the insect condition alarm grade, judging that the monitoring data does not meet a preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the obtaining, in real time, the monitoring data of the target parcel through the internet of things corresponding to the target parcel includes:
acquiring target farming information corresponding to the target land parcel; the target farming information comprises execution time corresponding to a target farming;
and extracting the monitoring data of the target plot in the execution time in real time through the Internet of things.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the target farming information includes a category of a target farming; the monitoring data comprises environmental data, the preset model comprises a pre-trained environment recognition model, and the preset alarm condition comprises that the type of the environmental data is a non-executable environment;
the identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result, including:
inputting the type of the target farming and the environmental data into the environmental recognition model to recognize the type of the environmental data;
and when the environment data is a non-executable environment, judging that the monitoring data meets a preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the monitoring data includes second image data, the preset model includes a pre-trained portrait detection model, and the preset alarm condition includes that no portrait data exists in the second image data;
when the environment data is an executable environment, the method further comprises:
inputting the second image data into the portrait detection model to identify whether portrait data exists in the second image data;
when the second image data does not have portrait data, judging that the monitoring data meets a preset alarm condition; or
And when the second image data has portrait data, judging that the monitoring data does not meet a preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the triggering an exception alarm includes:
and sending the preset alarm condition met by the monitoring data to a preset user side.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the monitoring data includes video data, and the method further includes:
and when the monitoring data do not meet the preset alarm condition, uploading the video data to a preset block chain and storing the video data.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, when the monitoring data includes image data, before the monitoring data is identified according to a preset model to obtain an identification result, the method further includes:
and filtering the monitoring data according to a preset algorithm so as to filter error data and repeated data in the monitoring data.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, before the obtaining, in real time, the monitoring data of the target parcel through the internet of things corresponding to the target parcel, the method further includes:
and accessing the target agricultural equipment in the target land block to the Internet of things so that the target agricultural equipment collects the monitoring data in real time and uploads the monitoring data through the Internet of things.
According to a second aspect of the present disclosure, there is provided an agricultural monitoring device based on the internet of things, including:
the data acquisition module is used for acquiring monitoring data of a target plot in real time through the Internet of things corresponding to the target plot;
the data analysis module is used for identifying the monitoring data according to a preset model to obtain an identification result and judging whether the monitoring data meets a preset alarm condition or not according to the identification result;
and the abnormity processing module is used for triggering abnormity alarm when the monitoring data is judged to meet the preset alarm condition.
According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for internet of things based agricultural monitoring as described in the first aspect of the embodiments above.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor; and
a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for internet of things based agricultural monitoring as described in the first aspect of the embodiments above.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the agricultural monitoring method based on the internet of things provided by the embodiment of the disclosure, the monitoring data of a target plot is obtained in real time through the internet of things corresponding to the target plot, the monitoring data is identified according to a preset model to obtain an identification result, whether the monitoring data meets a preset alarm condition or not is judged according to the identification result, and an abnormal alarm is triggered when the monitoring data meets the preset alarm condition, so that the purpose of monitoring agricultural production is achieved. According to the technical scheme of the embodiment of the invention, on one hand, the monitoring data of the target plot is obtained in real time through the Internet of things, and the monitoring data collected by various devices can be integrated to realize comprehensive management and utilization of the monitoring data; on the other hand, the monitoring data are identified through the preset model, and the abnormal condition occurring in the production process can be reflected in real time according to the process of carrying out abnormal alarm according to the identification result, so that a manager can process the abnormal condition in time, and irreparable consequences caused by the abnormal condition are avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
fig. 1 schematically illustrates a flow chart of a method for internet of things-based agricultural monitoring in an exemplary embodiment of the present disclosure;
fig. 2 is a flowchart schematically illustrating a method for recognizing monitoring data according to a preset model to obtain a recognition result and determining whether the monitoring data meets a preset alarm condition according to the recognition result when the monitoring data includes environmental data according to an exemplary embodiment of the present disclosure;
fig. 3 is a flowchart schematically illustrating a method for recognizing monitoring data according to a preset model to obtain a recognition result and determining whether the monitoring data meets a preset alarm condition according to the recognition result when the monitoring data includes environmental data and first image data according to an exemplary embodiment of the present disclosure;
fig. 4 schematically illustrates a flowchart of a method for acquiring monitoring data of a target parcel in real time through an internet of things corresponding to the target parcel in an exemplary embodiment of the present disclosure;
fig. 5 is a flowchart schematically illustrating a method for recognizing the monitoring data according to a preset model to obtain a recognition result and determining whether the monitoring data meets a preset alarm condition according to the recognition result when the target farming information includes a category of the target farming in the exemplary embodiment of the present disclosure;
fig. 6 schematically illustrates a flowchart of a method of determining whether portrait data exists in second image data when the monitoring data includes the second image data in an exemplary embodiment of the present disclosure;
fig. 7 shows a flowchart of an internet-of-things-based agricultural monitoring method in an exemplary embodiment of the present disclosure, for example, when monitoring target farming in a target plot;
fig. 8 schematically illustrates a composition diagram of an internet of things-based agricultural monitoring device in an exemplary embodiment of the disclosure;
FIG. 9 schematically illustrates a structural diagram of a computer system suitable for use with an electronic device that implements an exemplary embodiment of the present disclosure;
fig. 10 schematically illustrates a schematic diagram of a computer-readable storage medium, according to some embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In a related traceability system, production data in agricultural production, including environmental data in the production process, executed farm data, and the like, are collected through video, sensors, and the like. In fact, the production data are consistent with the monitoring data required for monitoring the production process, so that the production data can be integrated as the monitoring data for monitoring the production process in the process of recording the production data by the tracing system, so as to improve the utilization rate of the production data.
In the exemplary embodiment, firstly, an agricultural monitoring method based on the internet of things is provided, which can be applied to a monitoring process of agricultural production, for example, a process of large-scale agricultural production such as a farm is monitored. Referring to fig. 1, the above-mentioned agricultural monitoring method based on the internet of things may include the following steps:
s110, acquiring monitoring data of a target plot in real time through an Internet of things corresponding to the target plot;
s120, identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meet a preset alarm condition according to the identification result;
and S130, triggering an abnormal alarm when the monitoring data meets the preset alarm condition.
According to the agricultural monitoring method based on the internet of things provided in the exemplary embodiment, on one hand, the monitoring data of the target plot is obtained in real time through the internet of things, and the monitoring data collected by various devices can be integrated to realize comprehensive management and utilization of the monitoring data. On the other hand, the monitoring data are identified through the preset model, and the abnormal condition occurring in the production process can be reflected in real time according to the process of carrying out abnormal alarm according to the identification result, so that a manager can process the abnormal condition in time, and irreparable consequences caused by the abnormal condition are avoided.
In the following, the steps of the agricultural monitoring method based on the internet of things in the exemplary embodiment will be described in more detail with reference to the drawings and the embodiment.
In step S110, monitoring data of a target parcel is obtained in real time through the internet of things corresponding to the target parcel.
In an example embodiment of the present disclosure, the monitoring data may include monitoring data for monitoring all objects in the target parcel, such as monitoring data for monitoring objects such as temperature, humidity, soil quality, crops, etc. in the parcel; the monitoring data can be in the form of numerical data, image data, video data and other data in various forms. The present disclosure does not particularly limit the monitoring object of the monitoring data and the form of the monitoring data.
In an example embodiment of the present disclosure, before the obtaining, in real time, the monitoring data of the target parcel through the internet of things corresponding to the target parcel, the method further includes: and accessing the target agricultural equipment in the target land block to the Internet of things so that the target agricultural equipment collects the monitoring data in real time and uploads the monitoring data through the Internet of things. In order to obtain all monitoring data of a target plot in real time, all target agricultural equipment for monitoring in the target plot needs to be accessed into the internet of things corresponding to the target plot in advance, so that each target agricultural equipment can upload the monitoring data through the internet of things corresponding to the target plot.
Continuing to refer to fig. 1, in step S120, the monitoring data is identified according to a preset model to obtain an identification result, and whether the monitoring data meets a preset alarm condition is determined according to the identification result.
In an example embodiment of the present disclosure, when the monitoring data includes environmental data, the preset model may include a pre-trained disaster recognition model, and the preset alarm condition may include that an environmental category belongs to an environmental alarm category. At this time, the monitoring data is identified according to a preset model to obtain an identification result, and whether the monitoring data meets a preset alarm condition is determined according to the identification result, as shown in fig. 2, the method includes the following steps S210 to S230:
step S210, inputting the environmental data into the disaster identification model to identify an environmental category corresponding to the environmental data.
In an example embodiment of the present disclosure, the environmental data may include illumination, wind speed, rainfall, temperature, and the like. The environment type corresponding to the current environment data can be identified through the pre-trained disaster identification model, so that whether natural disasters are possible to occur or not is predicted according to the current environment data. For example, when the environmental data is normal, the disaster identification model may identify the current environmental category as a safe environment; when the rainfall is large, the disaster identification model can identify the current environment type as a heavy rain environment.
And step S220, when the environment type belongs to the environment alarm type, judging that the monitoring data meets a preset alarm condition.
And step S230, when the environment category does not belong to the environment alarm category, judging that the monitoring data does not meet the preset alarm condition.
In an example embodiment of the present disclosure, whether a natural disaster crisis exists in a current crop planting environment may be determined according to whether an environment category belongs to an environment alarm category. For example, when the rainfall is excessively large, the disaster recognition model recognizes the current environment as a rainstorm environment, and if the rainstorm environment belongs to the environmental alarm category, it is determined that the monitoring data satisfies the preset alarm condition. By setting the environment alarm category, when special environment data appears, the monitoring data can be judged to meet the preset alarm condition, and further warning processing can be performed.
In an example embodiment of the present disclosure, when the monitoring data includes environmental data and first image data, the preset model may include a pre-trained insect condition recognition model, and the preset alarm condition may include that the insect condition grade belongs to an insect condition alarm grade. At this time, the monitoring data is identified according to a preset model to obtain an identification result, and whether the monitoring data meets a preset alarm condition is determined according to the identification result, as shown in fig. 3, the method includes the following steps S310 to S330:
step S310, inputting the environment data and the first image data into the insect pest situation recognition model to recognize the insect pest situation grade of the first image data.
In an example embodiment of the present disclosure, the first image data may be a photo of a device such as a bug-sticking board that can collect the presence of bugs in the target area, or a photo of a crop that is photographed or captured from a video. By inputting the environmental data and the first image data into the pre-trained insect situation recognition model, whether insect disasters possibly occur in the current target plot can be judged according to the environmental data and the first image data, so that damage to crops caused by the insect disasters is avoided.
And S320, judging that the monitoring data meets a preset alarm condition when the insect condition grade belongs to the insect condition alarm grade.
And S330, judging that the monitoring data does not meet the preset alarm condition when the insect condition grade does not belong to the insect condition alarm grade.
In an example embodiment of the present disclosure, whether a current crop has a crisis of insect pests may be determined according to whether the insect pest level belongs to the insect pest alarm level. For example, the level with the higher pest situation level may be set as the pest situation alarm level, and when the pest situation level obtained by inputting the pest situation identification model according to the environment data and the first image data is higher, it is determined that the monitoring data meets the preset alarm condition, and further, the further warning processing is performed.
Further, when the monitoring data includes image data, before the monitoring data is identified according to a preset model to obtain an identification result, the method further includes: and filtering the monitoring data according to a preset algorithm so as to filter error data and repeated data in the monitoring data.
In an example embodiment of the disclosure, after the monitoring data of the target parcel is acquired, since the monitoring data uploaded by the internet of things may have an error or a repeated condition, before the monitoring data is identified, the monitoring data may be filtered through some preset algorithms to filter the error data and the repeated data in the monitoring data. For example, two identical second image data are uploaded at the same time in the monitoring data uploaded by the internet of things device, and at this time, one of the two identical second image data can be deleted through a preset algorithm, so that repeated calculation of a corresponding preset model is avoided, and the processing load of the preset model is increased.
Continuing to refer to fig. 1, in step S130, when the monitoring data is determined to satisfy the preset alarm condition, an abnormal alarm is triggered.
In an example embodiment of the present disclosure, triggering an abnormal alarm may include sending a preset alarm condition that the monitoring data satisfies to a preset user side. For example, when the insect situation level belongs to the insect situation alarm level, the information that the insect situation level and the insect situation level belong to the insect situation alarm level and the like can be sent to the preset user side to inform the user that corresponding exception handling can be performed according to specific exception alarm.
In an example embodiment of the present disclosure, monitoring may also be performed only for specific target farming. For example, only the farming of the target plot, such as fertilization, watering, etc., may be monitored. Specifically, as shown in fig. 4, acquiring the monitoring data of the target parcel in real time through the internet of things corresponding to the target parcel may include steps S410 to S420:
step S410, obtaining target farming information corresponding to the target plot; the target farming information comprises execution time corresponding to the target farming.
And step S420, extracting the monitoring data of the target plot in the execution time in real time through the Internet of things.
In an example embodiment of the present disclosure, the target farm information corresponding to the target parcel may include an execution time corresponding to the target farm, and may be used to extract corresponding monitoring data in the corresponding internet of things fact according to the execution time, so as to perform further monitoring according to the monitoring data. In addition, the farming information may further include a kind of the target farming, who performs, supplies required for performing, and the like, which is not particularly limited by the present disclosure. For example, the target plot needs to execute the farming of fertilization from 9 o 'clock to 11 o' clock in 1 month and 1 day of 20x9 year, and at this time, the monitoring data from 9 o 'clock to 11 o' clock in 1 month and 1 day of 20x9 year can be extracted through the internet of things for monitoring the execution situation of the target farming.
In an example embodiment of the present disclosure, when the target farm information includes a category of the target farm, the monitoring data may include environment data, the preset model may include a pre-trained environment recognition model, and the corresponding preset alarm condition may include that the type of the environment data is a non-executable environment. At this time, the monitoring data is identified according to a preset model to obtain an identification result, and whether the monitoring data meets a preset alarm condition is determined according to the identification result, as shown in fig. 5, the method may include steps S510 to S520:
step S510, inputting the type of the target farming and the environmental data into the environmental recognition model to recognize the type of the environmental data.
And step S520, when the environment data is a non-executable environment, judging that the monitoring data meets a preset alarm condition.
In an example embodiment of the present disclosure, the environmental data may have some impact on the performance of the target farm, as the target farm may not need to be performed in a particular environment. Therefore, whether the current environment is suitable for executing the target farming can be identified through the environment identification model. Specifically, the type of target farming and the current environmental data may be input into the environment recognition model to recognize whether the current environmental data is suitable for performing the target farming. For example, the target farm is watering, and the current environment is in a condition of large rainfall and is not suitable for watering again, so that the current environment data can be identified as an unexecutable environment, and the monitoring data is judged to meet the preset alarm condition.
In an example embodiment of the present disclosure, when the environment data is an executable environment, the monitoring data may further include second image data, the preset model includes a pre-trained portrait detection model, and the corresponding preset alarm condition includes that no portrait data exists in the second image data. Specifically, the second image data may be detected by a pre-trained portrait detection model to determine whether the target farm is executed in an executable environment.
Specifically, when the environment data is an executable environment, referring to fig. 6, the method further includes the following steps S610 to S630:
step S610, inputting the second image data into the portrait detection model to identify whether portrait data exists in the second image data.
In an example embodiment of the present disclosure, the second image data may be an image photographed with respect to the target parcel, or an image cut out from a video photographed with respect to the target parcel. The second image data may be input to a pre-trained portrait detection model to detect whether a portrait is present in the second image data.
Step S620, when there is no portrait data in the second image data, determining that the monitoring data meets a preset alarm condition.
Step S630, when the second image data has portrait data, judging that the monitoring data does not meet the preset alarm condition.
In an example embodiment of the present disclosure, whether a target farm is executed normally within an execution time may be determined by detecting whether a portrait exists in monitoring data within the execution time corresponding to the target farm. When the portrait exists in the second image data, it can be judged that the target farming is performed on time; and when the portrait does not exist in the second image data, the execution of the target farm affair can be judged to be abnormal, so that the monitoring data is judged to meet the preset alarm condition. Whether the second image data has portrait data or not can be detected, whether the target farm affairs are executed on time or not can be judged, and whether the work of the farm affair executor is on time or normal or not can be further judged.
Further, in an example embodiment of the present disclosure, the monitoring data further includes video data. At this time, when the monitoring data does not meet the preset alarm condition, the video data can be uploaded to a preset block chain and stored. By storing the video data in the preset block chain, the authenticity of the video data can be ensured, the video data is prevented from being tampered, and the monitoring on the production process of crops is facilitated.
The following describes in detail the implementation details of the technical solution of the embodiment of the present disclosure, taking monitoring of target farming in a target plot as an example, with reference to fig. 7:
step S710, inputting target farming and corresponding target farming information; the target farming information comprises the type of the target farming and the execution time of the target farming;
s720, collecting monitoring data of target agricultural equipment in a target plot within execution time through the Internet of things; the monitoring data comprises environment data, second image data and video data;
step S730, inputting the environmental data and the types of the target farming events into a pre-trained environmental recognition model to judge whether the current environmental data can execute the target farming events;
step S740, inputting second image data into a pre-trained portrait detection model to judge whether a portrait exists in the second image data;
step S750, triggering a target farm affair abnormity alarm to enable a user to process abnormity;
in step S760, the video data is uploaded to the preset block chain and stored.
Based on the agricultural monitoring method based on the Internet of things, the agricultural monitoring system can comprise an equipment management layer for managing the access and exit of target agricultural equipment; the equipment data management layer is used for receiving monitoring data collected by the target agricultural equipment; the model management layer is used for setting, managing and updating each preset model aiming at various types of monitoring data; and the service management layer is used for judging the identification result of each preset model, triggering an abnormal alarm when the preset alarm condition is met, and sending abnormal alarm information to a preset user. In addition, other management layers may be further configured to perform other processing on the monitoring data, which is not limited in this disclosure.
It is noted that the above-mentioned figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
In addition, in an exemplary embodiment of the disclosure, an agricultural monitoring device based on the internet of things is also provided. Referring to fig. 8, the internet of things-based agricultural monitoring apparatus 800 includes: a data acquisition module 810, a data analysis module 820 and an exception handling module 830.
The data obtaining module 810 may be configured to obtain monitoring data of a target parcel in real time through an internet of things corresponding to the target parcel;
the data analysis module 820 may be configured to identify the monitoring data according to a preset model to obtain an identification result, and determine whether the monitoring data meets a preset alarm condition according to the identification result;
the exception handling module 830 may be configured to trigger an exception alarm when it is determined that the monitoring data meets a preset alarm condition.
In an exemplary embodiment of the disclosure, based on the foregoing, the data analysis module 820 may be configured to input the environmental data into the disaster identification model to identify an environmental category corresponding to the environmental data; when the environment type belongs to the environment alarm type, judging that the monitoring data meets a preset alarm condition; or when the environment type does not belong to the environment alarm type, judging that the monitoring data does not meet the preset alarm condition.
In an exemplary embodiment of the disclosure, based on the foregoing, the data analysis module 820 may be configured to input the environmental data and the first image data into the insect pest situation recognition model to recognize the insect pest level of the first image data; when the insect condition grade belongs to the insect condition alarm grade, judging that the monitoring data meet a preset alarm condition; or when the insect condition grade does not belong to the insect condition alarm grade, judging that the monitoring data does not meet the preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the data obtaining module 810 may be configured to obtain target farming information corresponding to the target plot; the target farming information comprises execution time corresponding to a target farming; and extracting the monitoring data of the target plot in the execution time in real time through the Internet of things.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the data analysis module 820 may be configured to input the category of the target farming affairs and the environmental data into the environmental recognition model to recognize the type of the environmental data; and when the environment data is a non-executable environment, judging that the monitoring data meets a preset alarm condition.
In an exemplary embodiment of the disclosure, based on the foregoing, the data analysis module 820 may be configured to input the second image data into the portrait detection model to identify whether portrait data exists in the second image data; when the second image data does not have portrait data, judging that the monitoring data meets a preset alarm condition; or when the second image data has portrait data, judging that the monitoring data does not meet the preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the exception handling module 830 may be configured to send a preset alarm condition that is met by the monitoring data to a preset user side.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the exception handling module 830 may be configured to upload and store the video data to a preset block chain when the monitoring data does not meet a preset alarm condition.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the data analysis module 820 may be configured to perform filtering processing on the monitoring data according to a preset algorithm to filter error data and repeated data in the monitoring data.
In an exemplary embodiment of the disclosure, based on the foregoing scheme, the data obtaining module 810 may access the target agricultural equipment in the target land to the internet of things, so that the target agricultural equipment collects the monitoring data in real time and uploads the monitoring data through the internet of things.
As each functional module of the agricultural monitoring device based on the internet of things of the example embodiment of the present disclosure corresponds to the step of the example embodiment of the agricultural monitoring method based on the internet of things, please refer to the embodiment of the agricultural monitoring method based on the internet of things of the present disclosure for details that are not disclosed in the embodiment of the device of the present disclosure.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In addition, in an exemplary embodiment of the disclosure, an electronic device capable of implementing the agricultural monitoring method based on the internet of things is also provided.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 900 according to such an embodiment of the disclosure is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. Components of electronic device 900 may include, but are not limited to: the at least one processing unit 910, the at least one storage unit 920, a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910), and a display unit 940.
Wherein the storage unit stores program code that is executable by the processing unit 910 to cause the processing unit 910 to perform steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary method" of the present specification. For example, the processing unit 910 may execute step S110 as shown in fig. 1: acquiring monitoring data of a target plot in real time through an internet of things corresponding to the target plot; s120: identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result; s130: and triggering an abnormal alarm when the monitoring data meets the preset alarm condition.
As another example, the electronic device may implement the steps shown in fig. 2 to 7.
The storage unit 920 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)921 and/or a cache memory unit 922, and may further include a read only memory unit (ROM) 923.
Storage unit 920 may also include a program/utility 924 having a set (at least one) of program modules 925, such program modules 925 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 930 can be any of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 900 may also communicate with one or more external devices 970 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 900, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interface 950. Also, the electronic device 900 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 960. As shown, the network adapter 960 communicates with the other modules of the electronic device 900 via the bus 930. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the present disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
Referring to fig. 10, a program product 1000 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (13)
1. An agricultural monitoring method based on the Internet of things is characterized by comprising the following steps:
acquiring monitoring data of a target plot in real time through an internet of things corresponding to the target plot;
identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result;
and triggering an abnormal alarm when the monitoring data meets the preset alarm condition.
2. The method of claim 1, wherein the monitoring data comprises environmental data, the pre-set model comprises a pre-trained disaster recognition model; the preset alarm condition comprises that the environment type belongs to the environment alarm type;
the identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result, including:
inputting the environment data into the disaster identification model to identify an environment category corresponding to the environment data;
when the environment type belongs to the environment alarm type, judging that the monitoring data meets a preset alarm condition; or
And when the environment type does not belong to the environment alarm type, judging that the monitoring data does not meet the preset alarm condition.
3. The method of claim 1, wherein the monitoring data comprises environmental data and first image data, and the pre-set model comprises a pre-trained insect situation recognition model; the preset alarm condition comprises that the insect condition grade belongs to an insect condition alarm grade;
the identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result, including:
inputting the environment data and the first image data into the insect pest situation recognition model to recognize the insect pest situation grade of the first image data;
when the insect condition grade belongs to the insect condition alarm grade, judging that the monitoring data meet a preset alarm condition; or
And when the insect condition grade does not belong to the insect condition alarm grade, judging that the monitoring data does not meet a preset alarm condition.
4. The method according to claim 1, wherein the obtaining of the monitoring data of the target parcel in real time through the internet of things corresponding to the target parcel comprises:
acquiring target farming information corresponding to the target land parcel; the target farming information comprises execution time corresponding to a target farming;
and extracting the monitoring data of the target plot in the execution time in real time through the Internet of things.
5. The method of claim 4, wherein the target farming information includes a category of target farming; the monitoring data comprises environmental data, the preset model comprises a pre-trained environment recognition model, and the preset alarm condition comprises that the type of the environmental data is a non-executable environment;
the identifying the monitoring data according to a preset model to obtain an identification result, and judging whether the monitoring data meets a preset alarm condition according to the identification result, including:
inputting the type of the target farming and the environmental data into the environmental recognition model to recognize the type of the environmental data;
and when the environment data is a non-executable environment, judging that the monitoring data meets a preset alarm condition.
6. The method of claim 5, wherein the monitoring data comprises second image data, the preset model comprises a pre-trained portrait detection model, and the preset alarm condition comprises absence of portrait data from the second image data;
when the environment data is an executable environment, the method further comprises:
inputting the second image data into the portrait detection model to identify whether portrait data exists in the second image data;
when the second image data does not have portrait data, judging that the monitoring data meets a preset alarm condition; or
And when the second image data has portrait data, judging that the monitoring data does not meet a preset alarm condition.
7. The method of claim 1, wherein the triggering an exception alarm comprises:
and sending the preset alarm condition met by the monitoring data to a preset user side.
8. The method of claim 1, wherein the monitoring data comprises video data, the method further comprising:
and when the monitoring data do not meet the preset alarm condition, uploading the video data to a preset block chain and storing the video data.
9. The method according to claim 1, wherein when the monitoring data includes image data, before the monitoring data is recognized according to a preset model to obtain a recognition result, the method further comprises:
and filtering the monitoring data according to a preset algorithm so as to filter error data and repeated data in the monitoring data.
10. The method according to claim 1, wherein before the obtaining of the monitoring data of the target parcel in real time through the internet of things corresponding to the target parcel, the method further comprises:
and accessing the target agricultural equipment in the target land block to the Internet of things so that the target agricultural equipment collects the monitoring data in real time and uploads the monitoring data through the Internet of things.
11. The utility model provides an agricultural monitoring device based on thing networking which characterized in that includes:
the data acquisition module is used for acquiring monitoring data of a target plot in real time through the Internet of things corresponding to the target plot;
the data analysis module is used for identifying the monitoring data according to a preset model to obtain an identification result and judging whether the monitoring data meets a preset alarm condition or not according to the identification result;
and the abnormity processing module is used for triggering abnormity alarm when the monitoring data is judged to meet the preset alarm condition.
12. A computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements the internet of things-based agricultural monitoring method of any one of claims 1 to 10.
13. An electronic device, comprising:
a processor; and
a memory to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the internet of things based agricultural monitoring method of any one of claims 1-10.
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Application publication date: 20200204 Assignee: Chongqing rose Pharmaceutical Co.,Ltd. Assignor: Huazhi Zhongchuang (Beijing) Investment Management Co.,Ltd. Contract record no.: X2023990000869 Denomination of invention: Agricultural monitoring methods and devices, storage media, and electronic devices based on the Internet of Things Granted publication date: 20210430 License type: Common License Record date: 20231016 Application publication date: 20200204 Assignee: Beijing gentong Technology Co.,Ltd. Assignor: Huazhi Zhongchuang (Beijing) Investment Management Co.,Ltd. Contract record no.: X2023990000868 Denomination of invention: Agricultural monitoring methods and devices, storage media, and electronic devices based on the Internet of Things Granted publication date: 20210430 License type: Common License Record date: 20231016 Application publication date: 20200204 Assignee: Beijing Peipei Network Technology Co.,Ltd. Assignor: Huazhi Zhongchuang (Beijing) Investment Management Co.,Ltd. Contract record no.: X2023990000867 Denomination of invention: Agricultural monitoring methods and devices, storage media, and electronic devices based on the Internet of Things Granted publication date: 20210430 License type: Common License Record date: 20231016 Application publication date: 20200204 Assignee: TIANJIN YIYUE INFORMATION TECHNOLOGY Co.,Ltd. Assignor: Huazhi Zhongchuang (Beijing) Investment Management Co.,Ltd. Contract record no.: X2023990000866 Denomination of invention: Agricultural monitoring methods and devices, storage media, and electronic devices based on the Internet of Things Granted publication date: 20210430 License type: Common License Record date: 20231016 |
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