CN114895616A - Intelligent remote monitoring diagnostic system for agricultural greenhouse - Google Patents
Intelligent remote monitoring diagnostic system for agricultural greenhouse Download PDFInfo
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- CN114895616A CN114895616A CN202210540891.4A CN202210540891A CN114895616A CN 114895616 A CN114895616 A CN 114895616A CN 202210540891 A CN202210540891 A CN 202210540891A CN 114895616 A CN114895616 A CN 114895616A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/048—Monitoring; Safety
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
- Y02A40/25—Greenhouse technology, e.g. cooling systems therefor
Abstract
The invention discloses an intelligent remote monitoring and diagnosing system for an agricultural greenhouse, which belongs to the technical field of greenhouse safety monitoring and comprises an analysis module, an acquisition module, an early warning module and a server; the analysis module is used for analyzing the greenhouse data and establishing a monitoring model according to the analysis result; the acquisition module is used for acquiring data, acquiring a monitoring model, identifying a target monitoring point in the monitoring model, setting a corresponding data acquisition device according to the identified target monitoring point position, establishing an acquisition data processing base, processing the acquired data by a corresponding acquisition data processing method stored in the acquisition data processing base to obtain acquisition output data, and sending the acquisition output data to the early warning module; the early warning module is used for carrying out greenhouse safety early warning, acquiring the monitoring model, receiving the collected output data sent by the collecting module, inputting the collected output data into the monitoring model and identifying corresponding standard exceeding data.
Description
Technical Field
The invention belongs to the technical field of greenhouse safety monitoring, and particularly relates to an intelligent remote monitoring and diagnosing system for an agricultural greenhouse.
Background
The greenhouse is also called a greenhouse, can transmit light and keep warm (or heat), and is used for a facility for cultivating plants. In seasons unsuitable for plant growth, the greenhouse can provide a growth period and increase yield, is mainly used for cultivating or growing seedlings of plants such as warm vegetables, flowers and trees in low-temperature seasons, has multiple types, and can be divided into a great variety according to different roof truss materials, lighting materials, shapes, heating conditions and the like, such as a glass greenhouse and a plastic greenhouse; single-span greenhouse, multi-span greenhouse; single-roof greenhouses, double-roof greenhouses; a warm greenhouse, a non-warm greenhouse, etc. The greenhouse structure should be sealed and insulated, but should be convenient for ventilation and cooling. The modern greenhouse is also provided with equipment for controlling conditions such as temperature, humidity, illumination and the like, and the computer is used for automatically controlling and creating the optimal environmental conditions required by the plants.
For an outdoor agricultural planting greenhouse, as the planting technology is mature day by day, the main problem faced by the greenhouse is not the problem of the planting method, but the safety problem brought by severe weather, such as strong wind and heavy snow weather, can not be timely and effectively discovered by farmers under the severe weather, and the early warning problem of the greenhouse can not be timely known due to the lack of enough methods, so that the phenomenon of great economic loss to the farmers due to the severe weather environment can occur every year; therefore, the invention provides an intelligent remote monitoring and diagnosing system for an agricultural greenhouse.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an intelligent remote monitoring and diagnosing system for an agricultural greenhouse.
The purpose of the invention can be realized by the following technical scheme:
an intelligent remote monitoring and diagnosing system for an agricultural greenhouse comprises an analysis module, an acquisition module, an early warning module and a server;
the analysis module is used for analyzing the greenhouse data and establishing a monitoring model according to the analysis result; the acquisition module is used for acquiring data, acquiring a monitoring model, identifying a target monitoring point in the monitoring model, setting a corresponding data acquisition device according to the identified target monitoring point position, establishing an acquisition data processing base, processing the acquired data by a corresponding acquisition data processing method stored in the acquisition data processing base to obtain acquisition output data, and sending the acquisition output data to the early warning module;
the early warning module is used for carrying out greenhouse safety early warning, obtaining a monitoring model, receiving the collected output data sent by the collecting module, inputting the collected output data into the monitoring model, identifying corresponding standard exceeding data, carrying out corresponding marking in the monitoring model, generating alarm information and sending the alarm information to a manager.
Further, the working method of the analysis module comprises the following steps:
acquiring characteristic information of the greenhouse, matching a standard greenhouse model according to the acquired characteristic information, identifying a data addition item of the standard greenhouse model, performing corresponding data acquisition on a target greenhouse according to the identified data addition item, processing the acquired data, inputting the processed data into the standard greenhouse model to obtain a preliminary greenhouse model, and performing optimization adjustment on the obtained preliminary greenhouse model to obtain a target greenhouse model; identifying target monitoring points in the target greenhouse model, matching corresponding detection data upper limit models according to the target monitoring points, obtaining material information of the target monitoring points, calculating correction coefficients of the target monitoring points, inputting the correction coefficients and the material information into the detection data upper limit models, obtaining detection data upper limits corresponding to the target monitoring points, marking the detection data upper limits in the target greenhouse model, and marking the current target greenhouse model as a monitoring model.
Further, the method for matching the standard greenhouse model according to the obtained characteristic information comprises the following steps:
acquiring the type of the greenhouse, setting a corresponding standard greenhouse model according to the type of the greenhouse, and marking a data addition item needing data supplement in the standard greenhouse model; setting a characteristic representative library corresponding to the standard greenhouse model; and processing the obtained characteristic information of the greenhouse, inputting the processed characteristic information into a characteristic representative library for matching, and obtaining a corresponding standard greenhouse model.
Further, the method for setting the characteristic representation library corresponding to the standard greenhouse model comprises the following steps:
the method comprises the steps of identifying greenhouse types corresponding to standard greenhouse models, matching corresponding unique features according to the identified greenhouse types, setting a feature representative corresponding to each unique feature according to the obtained unique features, establishing a first database, setting a plurality of storage nodes in the first database, marking the storage nodes with corresponding standard greenhouse model labels, storing the feature representatives into the corresponding storage nodes, and marking the current first database as a feature representative library.
Further, the method for calculating the correction coefficient of the target monitoring point comprises the following steps:
the method comprises the steps of obtaining the structure service time length of a current target monitoring point, marking the structure service time length as SL, obtaining historical weather data of an area where the current greenhouse is located, setting an influence value of a corresponding structure, marking the structure service time length as YS, matching a corresponding adjustment coefficient according to the position of the target monitoring point in the greenhouse, marking the structure service time length as TZ, and calculating a correction coefficient according to a correction formula XZ (b1 multiplied by SL + b2 multiplied by YS). times.b 3 multiplied by TZ, wherein b1, b2 and b3 are proportional coefficients, the value range is 0< b1 < 1, 0< b2 < 1, and 0< b3 < 1.
The system further comprises a function limiting module, wherein the function limiting module is used for controlling corresponding function operation according to actual weather conditions, acquiring the type of the acquired acquisition equipment, setting a corresponding prevention target according to the acquired type of the acquisition equipment, and marking a corresponding prevention target label on the monitoring model; setting weather indexes corresponding to all prevention targets; and acquiring the current weather information in real time, and controlling the corresponding function to operate according to the acquired weather information.
Compared with the prior art, the invention has the beneficial effects that: the analysis module, the acquisition module and the early warning module are matched with each other, so that real-time safety monitoring of the greenhouse is realized, when the early warning requirement is met, corresponding managers are informed in time, sufficient reaction processing time is reserved for the managers, dangerous cases are processed in time, and the economic loss is reduced as much as possible; through setting up the function and injecing the module, according to real-time season and weather condition control corresponding function operation, reduce resource consumption effectively.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an intelligent remote monitoring and diagnosing system for an agricultural greenhouse comprises an analysis module, an acquisition module, an early warning module, a function limiting module and a server;
the analysis module is used for analyzing the greenhouse data and establishing a monitoring model according to an analysis result, and the specific method comprises the following steps:
acquiring characteristic information of the greenhouse, matching a standard greenhouse model according to the acquired characteristic information, identifying a data addition item of the standard greenhouse model, performing corresponding data acquisition on a target greenhouse according to the identified data addition item, processing the acquired data, inputting the processed data into the standard greenhouse model to obtain a preliminary greenhouse model, and performing optimization adjustment on the obtained preliminary greenhouse model to obtain a target greenhouse model; identifying target monitoring points in the target greenhouse model, wherein the target monitoring points are synchronously set by an expert group when a standard greenhouse model is established, namely the points which need to be monitored in the structural stability can be set through the existing mechanical knowledge; matching a corresponding detection data upper limit model according to the target monitoring point, and acquiring material information of the target monitoring point, wherein the material information comprises data information such as material type physical property information and the like which have influence on calculation; and calculating a correction coefficient of the target monitoring point, inputting the correction coefficient and the material information into the detection data upper limit model, obtaining a detection data upper limit corresponding to the target monitoring point, marking the detection data upper limit in the target greenhouse model, and marking the current target greenhouse model as the monitoring model.
The detection data upper limit model is a mathematical model established by an expert group according to the existing mechanical knowledge, and the input data comprises a correction coefficient and material information of a corresponding monitoring point; the specific unpublished part is common knowledge in the art and thus will not be described in detail.
The obtained preliminary greenhouse model is optimized and adjusted in a manual mode, and time and experience of manually establishing the greenhouse model can be greatly saved.
The characteristic information can be the information that this warmhouse booth type can be represented to name, panoramic picture etc. and the panoramic picture indicates the image that can roughly know big-arch shelter kind and shape, can be many image picture combinations to not necessarily only have an image, can adjust in a flexible way as required, and the collection mode of more convenience is shot at the eminence, and it is comparatively convenient to use unmanned aerial vehicle to shoot.
The method for matching the standard greenhouse model according to the obtained characteristic information comprises the following steps:
acquiring the types of the greenhouses, which are applicable to the system, setting corresponding standard greenhouses models by an expert group according to the types of the greenhouses, and marking data addition items needing data supplement in the standard greenhouses; setting a characteristic representative library corresponding to the standard greenhouse model; and processing the obtained characteristic information of the greenhouse, inputting the processed characteristic information into a characteristic representative library for matching, and obtaining a corresponding standard greenhouse model.
The method is mainly applied to planting the greenhouse in ordinary farmers, such as a common greenhouse with a framework and a thin film.
The standard greenhouse model is a model of data to be filled, such as data of adding columns, arch bars, the number of pull rods, the distance, the positions and the like, and is used for quickly establishing a corresponding greenhouse model.
The method for setting the characteristic representative library corresponding to the standard greenhouse model comprises the following steps:
identifying greenhouse types corresponding to the standard greenhouse models, matching corresponding unique characteristics according to the identified greenhouse types, and searching through the Internet, such as special academic names, standard images and the like, setting a characteristic representation corresponding to each unique characteristic according to the obtained unique characteristics, establishing a first database, setting a plurality of storage nodes in the first database, marking the storage nodes with corresponding standard greenhouse model labels, storing the characteristic representations into the corresponding storage nodes, and marking the current first database as a characteristic representation database; the specific unpublished part is common knowledge in the field and can be realized by adopting the prior art.
The processing of the obtained characteristic information of the greenhouse refers to processing of characteristic information which cannot be directly matched, if the characteristic information is a panoramic image, matching is performed after image processing, and all corresponding matching methods can be matched by using the existing matching method.
Corresponding data acquisition is carried out on the target greenhouse according to the identified data addition items, and acquisition processing can be directly carried out through the prior art method, such as acquisition processing through manual positioning of the corresponding supporting position, or acquisition processing through various methods of image identification positioning after image acquisition and the like.
The method for calculating the correction coefficient of the target monitoring point comprises the following steps:
the method comprises the steps of obtaining the structure service time length of a current target monitoring point, marking the structure service time length as SL, obtaining historical weather data of an area where the current greenhouse is located, setting an influence value of a corresponding structure, marking the structure service time length as YS, matching a corresponding adjustment coefficient according to the position of the target monitoring point in the greenhouse, marking the structure service time length as TZ, and calculating a correction coefficient according to a correction formula XZ (b1 multiplied by SL + b2 multiplied by YS). times.b 3 multiplied by TZ, wherein b1, b2 and b3 are proportional coefficients, the value range is 0< b1 < 1, 0< b2 < 1, and 0< b3 < 1.
And setting an influence value of the corresponding structure, namely judging the influence of weather on the current structure according to historical weather data, and setting the corresponding influence value by an expert group.
The corresponding adjustment coefficients are matched according to the positions of the target monitoring points in the greenhouse, the adjustment coefficients are set according to the functions of the target monitoring points in the greenhouse, and specifically, a corresponding structure adjustment coefficient matching table can be synchronously set when a standard greenhouse model is established, and the adjustment coefficients are obtained through matching.
The acquisition module is used for acquiring data, acquiring a monitoring model, identifying a target monitoring point in the monitoring model, setting a corresponding data acquisition device according to the identified target monitoring point position, establishing an acquisition data processing library, processing the acquired data by a corresponding acquisition data processing method stored in the acquisition data processing library to obtain acquisition output data, and sending the acquisition output data to the early warning module.
The corresponding data acquisition device is arranged according to the position of the identified target monitoring point, the corresponding acquisition device is arranged according to the type of the data to be acquired, and the acquisition device can directly use the existing acquisition equipment, particularly the common knowledge in the field, so the detailed description is not needed.
The collected data processing method is used for converting the data collected by the corresponding collecting device into corresponding data which can be input into the monitoring model, and the specific collected data processing method directly uses the existing data processing method to achieve the purpose of data processing.
The early warning module is used for carrying out greenhouse safety early warning, and the specific method comprises the following steps:
and acquiring a monitoring model, receiving the acquired output data sent by the acquisition module, inputting the acquired output data into the monitoring model, identifying corresponding standard exceeding data, namely data exceeding a preset data upper limit, carrying out corresponding marking in the monitoring model, generating alarm information and sending the alarm information to a manager, and not carrying out operation when the standard exceeding data is not exceeded.
The function limiting module is used for controlling the corresponding function to operate according to the actual weather condition, and the specific method comprises the following steps:
acquiring the types of the acquired acquisition equipment, and setting corresponding prevention targets according to the acquired types of the acquisition equipment, such as detection for preventing weather such as heavy snow, heavy wind and the like; marking a corresponding prevention target label in the monitoring model; setting weather indexes corresponding to all prevention targets; the current weather information is acquired in real time, corresponding functions are controlled to operate according to the acquired weather information, namely, the acquisition devices do not work, corresponding data do not need to be processed, and the like.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (6)
1. An intelligent remote monitoring and diagnosing system for an agricultural greenhouse is characterized by comprising an analysis module, an acquisition module, an early warning module and a server;
the analysis module is used for analyzing the greenhouse data and establishing a monitoring model according to the analysis result; the acquisition module is used for acquiring data, acquiring a monitoring model, identifying a target monitoring point in the monitoring model, setting a corresponding data acquisition device according to the identified target monitoring point position, establishing an acquisition data processing base, processing the acquired data by a corresponding acquisition data processing method stored in the acquisition data processing base to obtain acquisition output data, and sending the acquisition output data to the early warning module;
the early warning module is used for carrying out greenhouse safety early warning, obtaining a monitoring model, receiving the collected output data sent by the collecting module, inputting the collected output data into the monitoring model, identifying corresponding standard exceeding data, carrying out corresponding marking in the monitoring model, generating alarm information and sending the alarm information to a manager.
2. The intelligent remote monitoring and diagnosis system for the agricultural greenhouse as claimed in claim 1, wherein the working method of the analysis module comprises:
acquiring characteristic information of the greenhouse, matching a standard greenhouse model according to the acquired characteristic information, identifying a data addition item of the standard greenhouse model, performing corresponding data acquisition on a target greenhouse according to the identified data addition item, processing the acquired data, inputting the processed data into the standard greenhouse model to obtain a preliminary greenhouse model, and performing optimization adjustment on the obtained preliminary greenhouse model to obtain a target greenhouse model; identifying target monitoring points in the target greenhouse model, matching corresponding detection data upper limit models according to the target monitoring points, obtaining material information of the target monitoring points, calculating correction coefficients of the target monitoring points, inputting the correction coefficients and the material information into the detection data upper limit models, obtaining detection data upper limits corresponding to the target monitoring points, marking the detection data upper limits in the target greenhouse model, and marking the current target greenhouse model as a monitoring model.
3. The intelligent remote monitoring and diagnosis system for the agricultural greenhouse as claimed in claim 2, wherein the method for matching the standard greenhouse model according to the obtained characteristic information comprises:
acquiring the type of the greenhouse, setting a corresponding standard greenhouse model according to the type of the greenhouse, and marking a data addition item needing data supplement in the standard greenhouse model; setting a characteristic representative library corresponding to the standard greenhouse model; and processing the obtained characteristic information of the greenhouse, inputting the processed characteristic information into a characteristic representative library for matching, and obtaining a corresponding standard greenhouse model.
4. The intelligent remote monitoring and diagnosing system for the agricultural greenhouse as claimed in claim 3, wherein the method for setting the characteristic representative library corresponding to the standard greenhouse model comprises the following steps:
the method comprises the steps of identifying greenhouse types corresponding to standard greenhouse models, matching corresponding unique features according to the identified greenhouse types, setting a feature representative corresponding to each unique feature according to the obtained unique features, establishing a first database, setting a plurality of storage nodes in the first database, marking the storage nodes with corresponding standard greenhouse model labels, storing the feature representatives into the corresponding storage nodes, and marking the current first database as a feature representative library.
5. The intelligent remote monitoring and diagnosis system for the agricultural greenhouse as claimed in claim 2, wherein the method for calculating the correction coefficient of the target monitoring point comprises the following steps:
the method comprises the steps of obtaining the structure service time length of a current target monitoring point, marking the structure service time length as SL, obtaining historical weather data of an area where the current greenhouse is located, setting an influence value of a corresponding structure, marking the structure service time length as YS, matching a corresponding adjustment coefficient according to the position of the target monitoring point in the greenhouse, marking the structure service time length as TZ, and calculating a correction coefficient according to a correction formula XZ (b1 multiplied by SL + b2 multiplied by YS). times.b 3 multiplied by TZ, wherein b1, b2 and b3 are proportional coefficients, the value range is 0< b1 < 1, 0< b2 < 1, and 0< b3 < 1.
6. The intelligent remote monitoring and diagnosis system for the agricultural greenhouse as claimed in claim 1, further comprising a function limiting module, wherein the function limiting module is used for controlling corresponding function operation according to actual weather conditions, acquiring the type of the acquired acquisition equipment, setting a corresponding prevention target according to the acquired type of the acquisition equipment, and marking a corresponding prevention target label on the monitoring model; setting weather indexes corresponding to all prevention targets; and acquiring the current weather information in real time, and controlling the corresponding function to operate according to the acquired weather information.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115509285A (en) * | 2022-10-08 | 2022-12-23 | 南通智大信息技术有限公司 | Agricultural greenhouse data processing method and system based on Internet of things |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115509285A (en) * | 2022-10-08 | 2022-12-23 | 南通智大信息技术有限公司 | Agricultural greenhouse data processing method and system based on Internet of things |
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