CN116310848A - Intelligent valve adjusting control method and system - Google Patents
Intelligent valve adjusting control method and system Download PDFInfo
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Abstract
The invention discloses an intelligent valve adjusting control method and system, which relate to the technical field of data processing, and the method comprises the following steps: region information of the interactive irrigation region; collecting an irrigation area image; carrying out image recognition on the crop image set; reading distribution information of soil moisture content sensors, and generating soil moisture distribution of an irrigation area; the meteorological data are read, data compensation is carried out on soil moisture distribution, and irrigation control data are generated; intelligent regulation and control are carried out on valves in an irrigation area through irrigation control data, and a verification image set is obtained; setting a compensation acquisition period, acquiring crop images, and generating a crop verification image set; and performing irrigation abnormality evaluation, generating irrigation compensation information, and performing intelligent valve regulation and control in an irrigation area. The intelligent valve control method solves the technical problems that in the prior art, the intelligent valve control accuracy is low and the irrigation requirement cannot be met, and achieves the technical effects of improving the valve control quality and improving the irrigation effect.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent valve adjusting and controlling method and system.
Background
With the continuous updating of new equipment and new technology, the automatic irrigation technology is applied to large-area irrigation, however, in the irrigation process, the intelligent valve is controlled by only manual supervision and analysis, so that the feedback period is long, the control precision is low, the irrigation uniformity in the irrigation process is low, and the water demand of crops cannot be guaranteed. In the prior art, the intelligent valve control accuracy is low when irrigation exists, and the technical problem of irrigation requirements cannot be met.
Disclosure of Invention
The application provides an intelligent valve adjusting control method and system, which are used for solving the technical problems that in the prior art, the intelligent valve control accuracy is low and the irrigation requirement cannot be met when irrigation exists.
In view of the above problems, the present application provides a method and a system for controlling adjustment of an intelligent valve.
In a first aspect of the present application, there is provided a method for controlling adjustment of an intelligent valve, the method comprising:
regional information of the interactive irrigation region, wherein the regional information comprises regional distribution information and regional crop information; acquiring image data of the irrigation area, and constructing a crop image set of the area crops; executing the image recognition of the crop image set to generate a crop state distribution result; reading distribution information of soil moisture content sensors of the irrigation area, and generating soil moisture distribution of the irrigation area according to collected data of the soil moisture content sensors and the distribution information; the interactive weather station reads weather data, performs data compensation on the soil moisture distribution according to the crop state distribution result, and generates irrigation control data based on the data compensation result and the weather data; intelligent regulation and control are carried out on valves in the irrigation area through the irrigation control data, verification image acquisition is carried out on the irrigation process through an image acquisition unit, and a verification image set is generated; setting a compensation acquisition period, and controlling the image acquisition unit to acquire crop images through the compensation acquisition period to generate a crop verification image set; and performing irrigation abnormality evaluation according to the verification image set and the crop verification image set, generating irrigation compensation information, and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information.
Further, the method further comprises:
the air pressure monitoring of the irrigation area is carried out through the weather station, and rainfall prediction data are generated, wherein the rainfall prediction data comprise rainfall time nodes and rainfall data; monitoring the temperature and the humidity of the irrigation area through the weather station to generate temperature and humidity data; performing irrigation water demand compensation on the data compensation result according to the rainfall prediction data to generate regional calibration water supplementing quantity; carrying out water loss analysis on the irrigation area according to the temperature and humidity data to generate a water supplementing association coefficient; and carrying out water supplementing control adjustment on the regional calibration water supplementing quantity through the water supplementing associated coefficient, and generating the irrigation control data based on a water supplementing control adjustment result.
Further, the method further comprises:
when the irrigation time node is determined, wind power data acquisition of the irrigation area is carried out through the meteorological station, and a wind power data set is generated, wherein the wind power data set comprises wind direction data and wind power data; valve attribute information and valve distribution information in the irrigation area are obtained, and wind power classification is carried out according to the valve attribute information; performing grade matching on the wind power grade classification result based on the wind power data set to generate compensation data of valve control; generating initial control data of a valve based on the water replenishing control adjustment result, the valve attribute information and the valve distribution information; and controlling and compensating the initial control data according to the compensation data to generate the irrigation control data.
Further, the method further comprises:
generating a calibration irrigation image according to the irrigation control data, wherein the calibration irrigation image is an irrigation expected spraying image of the valve; performing center calibration on the calibrated irrigation image, extracting a coverage area, and taking the coverage area as a first comparison characteristic; extracting the coverage density characteristics of the calibrated irrigation image to generate second comparison characteristics; and carrying out feature comparison of the verification image set through the first comparison feature and the second comparison feature, and obtaining the irrigation compensation information according to a feature comparison result.
Further, the method further comprises:
taking a crop image set corresponding to the crop state distribution result as a basic comparison image, and performing crop mapping on the crop verification image set; constructing a time sequence morphological change characteristic set of crops based on the crop mapping result; performing irrigation effect evaluation in the irrigation area through the time sequence morphological change characteristic set to generate feedback compensation data; and generating the irrigation compensation information according to the feedback compensation data and the characteristic comparison result.
Further, the method further comprises:
Arranging a pressure sensor and a flow sensor on the valve; real-time data measurement is carried out when valve irrigation is carried out through the pressure sensor and the flow sensor, and a real-time data measurement result is output; generating auxiliary optimization data according to the real-time data measurement result; and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information and the auxiliary optimization data.
Further, the method further comprises:
setting early warning deviation features, wherein the early warning deviation features comprise coverage area deviation features and coverage density deviation features; feature comparison of the verification image set is carried out through the first comparison feature and the second comparison feature, and when a feature deviation result meets the early warning deviation feature, early warning information is generated; and carrying out real-time irrigation early warning according to the early warning information.
In a second aspect of the present application, there is provided a regulating control system for an intelligent valve, the system comprising:
the regional information acquisition module is used for interactively irrigating regional information of the region, wherein the regional information comprises regional distribution information and regional crop information; the crop image set construction module is used for acquiring image data of the irrigation area and constructing a crop image set of the area crop; the distribution result generation module is used for executing image recognition of the crop image set and generating a crop state distribution result; the water distribution generation module is used for reading the distribution information of the soil moisture content sensor of the irrigation area and generating the soil moisture distribution of the irrigation area according to the acquired data of the soil moisture content sensor and the distribution information; the control data generation module is used for interacting with a weather station, reading weather data, carrying out data compensation on the soil moisture distribution through the crop state distribution result, and generating irrigation control data based on the data compensation result and the weather data; the verification image set generation module is used for intelligently regulating and controlling valves in the irrigation area according to the irrigation control data, and collecting verification images in the irrigation process according to the image collection unit to generate a verification image set; the crop verification image generation module is used for setting a compensation acquisition period, controlling the image acquisition unit to acquire crop images through the compensation acquisition period and generating a crop verification image set; the intelligent regulation and control module is used for carrying out irrigation abnormal evaluation according to the verification image set and the crop verification image set, generating irrigation compensation information and carrying out intelligent regulation and control on valves in the irrigation area through the irrigation compensation information.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of carrying out image data acquisition on an irrigation area, constructing a crop image set of the area crop, executing image recognition of the crop image set, generating a crop state distribution result, reading distribution information of a soil moisture sensor of the irrigation area, generating soil moisture distribution of the irrigation area according to acquisition data of the soil moisture sensor and the distribution information, carrying out data compensation on the soil moisture distribution through an interactive weather station, reading weather data, generating irrigation control data based on a data compensation result and the weather data, carrying out intelligent regulation and control on a valve in the irrigation area through the irrigation control data, carrying out verification image acquisition of an irrigation process through an image acquisition unit, generating a verification image set, setting a compensation acquisition period, controlling the image acquisition of the crop image through the compensation acquisition period, generating the crop verification image set, carrying out abnormal evaluation on the verification image set and the verification image set, carrying out data compensation on the soil moisture distribution, carrying out intelligent regulation and control on the valve in the irrigation area through the compensation, and carrying out intelligent regulation and control on the irrigation information. The technical effect of improving the accuracy of valve regulation in the irrigation area and improving the regulation quality through feedback verification adjustment is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an adjustment control method of an intelligent valve according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of generating irrigation control data based on the result of water replenishment control adjustment in the method for controlling adjustment of an intelligent valve according to the embodiment of the present application;
fig. 3 is a schematic flow chart of obtaining irrigation compensation information according to a feature comparison result in the adjustment control method of an intelligent valve provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of an adjustment control system of an intelligent valve according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a region information obtaining module 11, a crop image set constructing module 12, a distribution result generating module 13, a water distribution generating module 14, a control data generating module 15, a verification image set generating module 16, a crop verification image generating module 17 and an intelligent regulation and control module 18.
Detailed Description
The application provides an intelligent valve adjusting control method and system, which are used for solving the technical problems that in the prior art, the intelligent valve control accuracy is low and the irrigation requirement cannot be met when irrigation exists.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Embodiment one: as shown in fig. 1, the present application provides a method for controlling adjustment of an intelligent valve, where the method includes:
Step S100: regional information of the interactive irrigation region, wherein the regional information comprises regional distribution information and regional crop information;
in the embodiment of the application, in order to meet the growth requirement of crops, reasonable irrigation is timely and properly carried out according to the requirement characteristics, the growing stage, the climate and the soil conditions before the crops are sown and in the whole growing period. In the process of irrigation, intelligent control is carried out on the irrigation valve, so that water demands in different periods are met.
In one possible embodiment, the regional information of the irrigation region is obtained by performing data interaction on an irrigation region database storing planning information and crop planting information in the irrigation region by using a data acquisition module, wherein the regional information comprises regional distribution information, regional crop information and the like. Wherein the regional distribution information is information describing the distribution geographical location of the irrigation region. The regional crop information is information describing the condition of crops planted in the irrigation region, and comprises the type of the planted crops, the planting time of the crops and the like. The regional information of the irrigation region is acquired, so that a basis is provided for determining the adjustment control condition of the intelligent valve according to the regional information.
Step S200: acquiring image data of the irrigation area, and constructing a crop image set of the area crops;
step S300: executing the image recognition of the crop image set to generate a crop state distribution result;
in one embodiment, an image acquisition unit (e.g., video camera, still camera, infrared camera, etc.) is utilized to acquire images of crops in an irrigation area to obtain a crop image set capable of reflecting the status of the crops in the area. The crop image set can intuitively reflect the state condition of the planted crops in the area. And identifying the characteristics of the crop image set capable of reflecting whether the crop water is sufficient or not by carrying out image identification on the crop image set in the irrigation area, so as to obtain the crop state distribution result. Preferably, the image feature set is obtained by carrying out water quantity feature matching according to the type of the crop, wherein the image feature set comprises a blade plumpness feature, a blade curl degree feature, a crop root ground chapping feature and the like. And carrying out image recognition on the crop image set according to the image feature set, obtaining a crop state according to an image recognition result, and generating a crop state distribution result according to the distribution position of crops corresponding to the crop state in an irrigation area. The crop state distribution results can reflect the crop water shortage conditions at different positions in the irrigation area. Illustratively, if the leaves of the crop wilt and a curled state occurs, the ground near the root of the crop has been cracked, indicating that the crop in this area is relatively water-deficient.
Step S400: reading distribution information of soil moisture content sensors of the irrigation area, and generating soil moisture distribution of the irrigation area according to collected data of the soil moisture content sensors and the distribution information;
in the embodiment of the application, the distribution condition of the soil moisture content sensor in the irrigation area is obtained according to the distribution data of the soil moisture content sensor in the irrigation area, wherein the distribution condition comprises distribution positions and distribution quantity. And carrying out data interaction on the data acquired in the soil moisture content sensor through a data acquisition module, thereby acquiring the acquired data. The soil moisture content sensor is a device for continuously monitoring soil moisture content (soil humidity) for a long time. The data acquisition is to acquire data after acquiring soil humidity at different positions in an irrigation area, so that the water content condition of soil at different positions can be reflected. And determining the distribution of soil moisture in an irrigation area by combining the collected data of the soil moisture sensor according to the distribution information of the soil moisture sensor. If the humidity of the soil in the collected data is higher, the moisture in the soil is higher. The soil moisture distribution reflects soil moisture in different areas of the irrigation area.
Step S500: the interactive weather station reads weather data, performs data compensation on the soil moisture distribution according to the crop state distribution result, and generates irrigation control data based on the data compensation result and the weather data;
further, as shown in fig. 2, step S500 in the embodiment of the present application further includes:
step S510: the air pressure monitoring of the irrigation area is carried out through the weather station, and rainfall prediction data are generated, wherein the rainfall prediction data comprise rainfall time nodes and rainfall data;
step S520: monitoring the temperature and the humidity of the irrigation area through the weather station to generate temperature and humidity data;
step S530: performing irrigation water demand compensation on the data compensation result according to the rainfall prediction data to generate regional calibration water supplementing quantity;
step S540: carrying out water loss analysis on the irrigation area according to the temperature and humidity data to generate a water supplementing association coefficient;
step S550: and carrying out water supplementing control adjustment on the regional calibration water supplementing quantity through the water supplementing associated coefficient, and generating the irrigation control data based on a water supplementing control adjustment result.
In one possible embodiment, the rainfall prediction of the irrigation area in a future period of time is obtained by data interaction with a weather station at the location of the irrigation area, that is, the predicted amount of water in the obtained irrigation area, which can be supplemented by natural rainfall, is obtained. The data compensation is performed on the soil moisture conditions reflecting different positions in the soil moisture distribution according to the crop states of different positions in the crop state distribution result, that is, after the soil moisture distribution is determined, the data compensation is performed on the actual soil moisture according to the crop state distribution result of the same position, and the soil moisture content in the soil moisture distribution is lower than 10% of the normal range value, at this time, the crop state distribution result of the same position is that the crop leaves curl and are in a water shortage state, at this time, the water amount required for lifting the soil moisture content to the normal range value is superimposed according to the water amount required for the recovery of the crop state from the water shortage state to the normal state, thereby realizing the data compensation on the soil moisture distribution. The data compensation result is the water quantity to be supplemented, which is obtained after the water quantity to be supplemented in the irrigation area is corrected. And obtaining the irrigation control data by combining the meteorological data according to the data compensation result. The irrigation control data are used for adjusting and controlling the intelligent valve.
In one possible embodiment, the barometric pressure in the irrigation area is monitored by a weather station, and rainfall prediction data is obtained from changes in barometric pressure data. The rainfall prediction data are data for predicting natural rainfall of an irrigation area in a future period of time, and comprise rainfall time nodes and rainfall data. The rainfall time node is data describing a time point when rainfall occurs. The rainfall data is data describing the rainfall corresponding to each rainfall occurrence time point.
In one possible embodiment, the temperature and humidity in the irrigation area are monitored in real time through the weather station, and the temperature and humidity data capable of reflecting the temperature and humidity change condition of the irrigation area are obtained. And comparing the rainfall in the rainfall prediction data with the water demand in the data compensation result, and acquiring the water quantity which can be compensated by natural rainfall in the data compensation data to determine the water quantity which still needs to be supplemented by irrigation in the irrigation area, namely the area calibration water supplementing quantity. And part of water flowing into the soil in the irrigation area can be lost after evaporation, the higher the temperature is, the faster the water evaporates, and the higher the humidity is, the slower the water evaporates, so that the water loss condition of the irrigation area is determined according to the temperature and humidity data and the temperature and humidity change condition, and the water loss coefficient is generated by matching from a water loss coefficient set according to the temperature and humidity. The water loss coefficient set is a coefficient set determined by the influence degree of temperature and humidity obtained by staff according to local historical water supplementing data on water loss. The water replenishment correlation coefficient is a coefficient describing the degree of water loss. And carrying out water supplementing control adjustment on the regional calibration water supplementing quantity according to the water supplementing associated coefficient, namely carrying out data compensation on the water quantity lost by the regional calibration water supplementing quantity in irrigation according to the size of the water supplementing associated coefficient, and then obtaining the irrigation control data.
Further, step S550 in the embodiment of the present application further includes:
step S551: when the irrigation time node is determined, wind power data acquisition of the irrigation area is carried out through the meteorological station, and a wind power data set is generated, wherein the wind power data set comprises wind direction data and wind power data;
step S552: valve attribute information and valve distribution information in the irrigation area are obtained, and wind power classification is carried out according to the valve attribute information;
step S553: performing grade matching on the wind power grade classification result based on the wind power data set to generate compensation data of valve control;
step S554: generating initial control data of a valve based on the water replenishing control adjustment result, the valve attribute information and the valve distribution information;
step S555: and controlling and compensating the initial control data according to the compensation data to generate the irrigation control data.
In the embodiment of the application, according to the time point corresponding to the irrigation time node, wind force data of the irrigation area at the irrigation time node are acquired by using the wind force sensor of the weather station, and the wind force data set is obtained. Wherein the wind data set is a data set describing wind conditions within the irrigation area, including wind direction data and wind data. The wind direction data is data describing the direction of wind in the irrigation area, including east wind, southwest wind, etc. The wind data is data describing the wind speed of the wind in the irrigation area, which is, for example, 8 meters per second.
In one possible embodiment, valve attribute information is extracted from valve data within the irrigation area by obtaining it. The valve attribute information is information describing the degree that the irrigation water quantity and the irrigation density in a valve irrigation area are influenced by wind force when the valve sprays water for irrigation, and the attribute information comprises the valve nominal pressure, and the higher the valve nominal pressure is, the higher the capability of resisting the influence of wind force is. The valve spraying pressure level is higher, for example, a valve with nominal pressure PN10-80MPa, the sprayed water flow has higher water speed and water outlet acceleration due to higher water outlet pressure, the capability of resisting the influence of wind force is stronger, the wind force with the wind speed of 5 meters per second cannot influence the water outlet density of the valve, but the wind force with the wind speed of 10 meters per second can influence the initial water speed and the initial irrigation density of the valve spraying, so that the irrigation water in the original spraying area of the valve is unevenly sprayed and moves along with the wind direction. If the wind direction is east wind and the wind force is 10 meters per second, the west water spraying amount of the valve spraying area is reduced, and the east water spraying amount is increased. According to the nominal pressure of the valve, if the nominal pressure is PN10-80MPa, the wind power grade lower than 7 m/s is classified into one grade, the wind power grade of 7-10 m/s is classified into two grade, and the wind power grade of 10-15 m/s is classified into three grade.
Specifically, the wind power grade is determined according to the valve attribute information, then the wind power data in the wind power data set is matched with the wind power grade, and the corresponding wind power grade is obtained, so that the compensation data for compensating the valve control is obtained according to the influence of the wind power grade on the valve spraying. In order to resist the influence of wind force on the valve spraying, the opening and closing degree of the valve can be increased, so that the water outlet speed and the water outlet quantity of the valve are improved. The valve control compensation data is data for adjusting the opening and closing degree of the valve. And further, obtaining the initial control data for controlling the valve according to the water supplementing control adjustment result, the valve attribute information and the valve distribution information, namely determining the initial control data according to the water supplementing amount in the water supplementing control adjustment result, the nominal pressure of the valve and the distribution condition of the valve in the irrigation area. The initial control data comprise valve opening and closing time, valve opening and closing degree and the like. Preferably, the denser the valve distribution, the faster the irrigation speed, and the shorter the valve opening and closing time. And then correspondingly adjusting the initial control data according to the compensation data so as to obtain the irrigation control data. The irrigation control data are data for controlling the irrigation valve, and comprise valve opening and closing time, valve opening and closing degree and the like.
Step S600: intelligent regulation and control are carried out on valves in the irrigation area through the irrigation control data, verification image acquisition is carried out on the irrigation process through an image acquisition unit, and a verification image set is generated;
step S700: setting a compensation acquisition period, and controlling the image acquisition unit to acquire crop images through the compensation acquisition period to generate a crop verification image set;
in one possible embodiment, according to the parameters in the irrigation control data, the valves in the irrigation area are intelligently regulated, the regulated valves are used for irrigating the irrigation area, and the image acquisition unit is used for acquiring images of the irrigation process in the irrigation area, so that the verification image set is obtained. The verification image set intuitively reflects the working condition of each valve in the irrigation area and the water distribution condition of different positions in the area. The compensation acquisition period is an adjacent time period which is determined according to the type of the crops and is used for supplementing water after irrigation, and the appearance state of the crops is obviously changed, and is set by the staff without limitation. And controlling the image acquisition unit to acquire images of crops in an irrigation area according to the compensation acquisition period to acquire the crop verification image set. The crop verification image set is used for describing the change condition of crops in an irrigation area after the compensation acquisition period.
Step S800: and performing irrigation abnormality evaluation according to the verification image set and the crop verification image set, generating irrigation compensation information, and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information.
Further, as shown in fig. 3, step S800 in the embodiment of the present application further includes:
step S810: generating a calibration irrigation image according to the irrigation control data, wherein the calibration irrigation image is an irrigation expected spraying image of the valve;
step S820: performing center calibration on the calibrated irrigation image, extracting a coverage area, and taking the coverage area as a first comparison characteristic;
step S830: extracting the coverage density characteristics of the calibrated irrigation image to generate second comparison characteristics;
step S840: and carrying out feature comparison of the verification image set through the first comparison feature and the second comparison feature, and obtaining the irrigation compensation information according to a feature comparison result.
In one possible embodiment, the image of the water sprayed by the valve during normal operation is determined from the irrigation control data and used as a calibration irrigation image, i.e. an expected irrigation image of the valve. And determining the center of the calibrated irrigation image according to the position of the valve, obtaining the area where the valve can spray water, and setting the size and the position of the coverage area as a first comparison characteristic. And carrying out feature extraction on the water yield sprayed in a unit area in a valve spraying area in the calibration irrigation image, taking the water yield as the coverage density, and taking the extracted coverage density as a second comparison feature.
Specifically, by comparing the first comparison feature, the second comparison feature and the verification image set, whether the valve irrigation coverage area and the coverage density in the verification image set meet the requirements or not is determined, and if not, the irrigation compensation information is determined according to the feature difference value in the feature comparison result. The irrigation compensation information is used for adjusting the opening and closing degree of the valve, so that the valve can meet the first comparison characteristic and the second comparison characteristic. The intelligent regulation and control of the valve in the irrigation area according to the working state of the valve is realized.
Further, step S800 in the embodiment of the present application further includes:
step S850: taking a crop image set corresponding to the crop state distribution result as a basic comparison image, and performing crop mapping on the crop verification image set;
step S860: constructing a time sequence morphological change characteristic set of crops based on the crop mapping result;
step S870: performing irrigation effect evaluation in the irrigation area through the time sequence morphological change characteristic set to generate feedback compensation data;
step S880: and generating the irrigation compensation information according to the feedback compensation data and the characteristic comparison result.
In the embodiment of the application, the crop mapping is completed by matching the object described in the crop image set corresponding to the crop state distribution result with the object described in the crop verification image set, that is, the crop image before irrigation and the crop image after the compensation acquisition period are in one-to-one correspondence, so as to construct the mapping. And respectively carrying out feature recognition according to the crop image before irrigation in the crop mapping result and the image feature set of the crop in the crop image after the compensation acquisition period, and comparing according to the recognition result to obtain the time sequence morphological change feature set. The time sequence morphological change characteristic set reflects the change condition of crops after irrigation, the irrigation effect is evaluated based on the time sequence morphological change characteristic set, if the change characteristic does not obviously indicate that the crops are not fully irrigated, the feedback compensation data are determined according to the degree of the change characteristic.
In one possible embodiment, a historical compensation data set is constructed by obtaining a plurality of historical time sequence morphological change feature sets and a plurality of historical feedback compensation data, and the historical compensation data set is used for performing supervision training on a feedback compensation model until the model output reaches convergence, so as to obtain the feedback compensation model. The feedback compensation model is an intelligent model which is constructed by taking a BP neural network as a basic framework and performs feedback compensation analysis according to the irrigation effect of crops, input data is a time sequence morphological change characteristic set, and output data is feedback compensation data. And obtaining irrigation compensation information by combining the characteristic comparison result according to the feedback compensation data. Preferably, the irrigation compensation information is determined according to the water supplementing amount required by the feedback compensation data and the opening and closing degree of the valve in the characteristic comparison result.
Further, step S880 of the embodiment of the present application further includes:
step S881: arranging a pressure sensor and a flow sensor on the valve;
step S882: real-time data measurement is carried out when valve irrigation is carried out through the pressure sensor and the flow sensor, and a real-time data measurement result is output;
step S883: generating auxiliary optimization data according to the real-time data measurement result;
step S884: and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information and the auxiliary optimization data.
Further, step S880 of the embodiment of the present application further includes:
step S885: setting early warning deviation features, wherein the early warning deviation features comprise coverage area deviation features and coverage density deviation features;
step S886: feature comparison of the verification image set is carried out through the first comparison feature and the second comparison feature, and when a feature deviation result meets the early warning deviation feature, early warning information is generated;
step S887: and carrying out real-time irrigation early warning according to the early warning information.
In one possible embodiment, the real-time data measurement result is obtained by arranging a pressure sensor and a flow sensor at the valve, and then acquiring the water outlet pressure and the water outlet flow rate of the valve in real time by using the pressure sensor and the flow sensor, wherein the real-time data measurement result is real-time water outlet pressure data and real-time water outlet flow rate data. And comparing the data in the real-time data measurement result with the data in the normal operation of the valve, taking the compared difference value as the auxiliary optimization data, and providing compensation basic data for irrigation compensation information according to the auxiliary optimization data so as to intelligently regulate and control the valve in the irrigation area.
In one possible embodiment, the early warning deviation feature is a feature describing the valve operating condition when the degree of deviation of the valve spray during irrigation is insufficient to meet normal irrigation requirements, including a coverage area deviation feature and a coverage density deviation feature. Wherein the coverage area deviation feature is the distance that the coverage area deviates from the original position when the valve operation is not satisfactory. The coverage density deviation feature is a density value of the coverage density deviation from the original coverage density when the valve operation fails to meet the requirement. And comparing the features of the verification image set through the first comparison feature and the second comparison feature, determining the deviation degree of the coverage area and the deviation degree of the coverage density, comparing the coverage area and the deviation degree of the early warning, and generating early warning information when the early warning deviation feature is met. The early warning information is used for reminding workers that the valve working state is deviated from the normal working state. And furthermore, carrying out real-time irrigation early warning on the irrigation process according to the early warning information.
In summary, the embodiments of the present application have at least the following technical effects:
according to the method, the crop state in the irrigation area is analyzed, so that the crop demand condition is determined, the soil moisture distribution in the irrigation area is determined by utilizing the soil moisture content sensor, the water supplementing condition in the area is determined by utilizing the natural precipitation according to the meteorological data, the intelligent regulation and control of the valve in the irrigation area is determined by combining the data compensation result of the soil moisture distribution with the crop state distribution result, then the image acquisition unit is utilized for verifying image acquisition and crop verification image acquisition, the abnormality in irrigation is determined, the irrigation compensation information is obtained, and the intelligent regulation and control of the valve in the irrigation area are realized. The technical effects of improving irrigation control quality and improving control accuracy through feedback verification are achieved.
Embodiment two: based on the same inventive concept as the adjustment control method of an intelligent valve in the foregoing embodiments, as shown in fig. 4, the present application provides an adjustment control system of an intelligent valve, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the regional information obtaining module 11 is used for interactively irrigating regional information of a region, wherein the regional information comprises regional distribution information and regional crop information;
the crop image set construction module 12, wherein the crop image set construction module 12 is used for collecting image data of the irrigation area and constructing a crop image set of the area crop;
a distribution result generation module 13, where the distribution result generation module 13 is configured to perform image recognition of the crop image set, and generate a crop state distribution result;
the water distribution generation module 14 is used for reading the distribution information of the soil moisture content sensor of the irrigation area, and generating the soil moisture distribution of the irrigation area according to the acquired data of the soil moisture content sensor and the distribution information;
The control data generation module 15 is used for interacting with a weather station, reading weather data, performing data compensation on the soil moisture distribution through the crop state distribution result, and generating irrigation control data based on the data compensation result and the weather data;
the verification image set generation module 16, wherein the verification image set generation module 16 is used for intelligently regulating and controlling valves in the irrigation area according to the irrigation control data, and collecting verification images in the irrigation process according to an image collection unit to generate a verification image set;
the crop verification image generation module 17 is used for setting a compensation acquisition period, controlling the image acquisition unit to acquire crop images through the compensation acquisition period, and generating a crop verification image set;
the intelligent regulation and control module 18, the intelligent regulation and control module 18 is used for carrying out irrigation abnormal evaluation according to the verification image set and the crop verification image set, generating irrigation compensation information, and carrying out intelligent regulation and control on valves in the irrigation area through the irrigation compensation information.
Further, the control data generating module 15 is configured to perform the following method:
The air pressure monitoring of the irrigation area is carried out through the weather station, and rainfall prediction data are generated, wherein the rainfall prediction data comprise rainfall time nodes and rainfall data;
monitoring the temperature and the humidity of the irrigation area through the weather station to generate temperature and humidity data;
performing irrigation water demand compensation on the data compensation result according to the rainfall prediction data to generate regional calibration water supplementing quantity;
carrying out water loss analysis on the irrigation area according to the temperature and humidity data to generate a water supplementing association coefficient;
and carrying out water supplementing control adjustment on the regional calibration water supplementing quantity through the water supplementing associated coefficient, and generating the irrigation control data based on a water supplementing control adjustment result.
Further, the control data generating module 15 is configured to perform the following method:
when the irrigation time node is determined, wind power data acquisition of the irrigation area is carried out through the meteorological station, and a wind power data set is generated, wherein the wind power data set comprises wind direction data and wind power data;
valve attribute information and valve distribution information in the irrigation area are obtained, and wind power classification is carried out according to the valve attribute information;
Performing grade matching on the wind power grade classification result based on the wind power data set to generate compensation data of valve control;
generating initial control data of a valve based on the water replenishing control adjustment result, the valve attribute information and the valve distribution information;
and controlling and compensating the initial control data according to the compensation data to generate the irrigation control data.
Further, the intelligent regulation module 18 is configured to perform the following method:
generating a calibration irrigation image according to the irrigation control data, wherein the calibration irrigation image is an irrigation expected spraying image of the valve;
performing center calibration on the calibrated irrigation image, extracting a coverage area, and taking the coverage area as a first comparison characteristic;
extracting the coverage density characteristics of the calibrated irrigation image to generate second comparison characteristics;
and carrying out feature comparison of the verification image set through the first comparison feature and the second comparison feature, and obtaining the irrigation compensation information according to a feature comparison result.
Further, the intelligent regulation module 18 is configured to perform the following method:
taking a crop image set corresponding to the crop state distribution result as a basic comparison image, and performing crop mapping on the crop verification image set;
Constructing a time sequence morphological change characteristic set of crops based on the crop mapping result;
performing irrigation effect evaluation in the irrigation area through the time sequence morphological change characteristic set to generate feedback compensation data;
and generating the irrigation compensation information according to the feedback compensation data and the characteristic comparison result.
Further, the intelligent regulation module 18 is configured to perform the following method:
arranging a pressure sensor and a flow sensor on the valve;
real-time data measurement is carried out when valve irrigation is carried out through the pressure sensor and the flow sensor, and a real-time data measurement result is output;
generating auxiliary optimization data according to the real-time data measurement result;
and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information and the auxiliary optimization data.
Further, the intelligent regulation module 18 is configured to perform the following method:
setting early warning deviation features, wherein the early warning deviation features comprise coverage area deviation features and coverage density deviation features;
feature comparison of the verification image set is carried out through the first comparison feature and the second comparison feature, and when a feature deviation result meets the early warning deviation feature, early warning information is generated;
And carrying out real-time irrigation early warning according to the early warning information.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. An intelligent valve regulation control method is characterized by comprising the following steps:
regional information of the interactive irrigation region, wherein the regional information comprises regional distribution information and regional crop information;
acquiring image data of the irrigation area, and constructing a crop image set of the area crops;
executing the image recognition of the crop image set to generate a crop state distribution result;
reading distribution information of soil moisture content sensors of the irrigation area, and generating soil moisture distribution of the irrigation area according to collected data of the soil moisture content sensors and the distribution information;
the interactive weather station reads weather data, performs data compensation on the soil moisture distribution according to the crop state distribution result, and generates irrigation control data based on the data compensation result and the weather data;
intelligent regulation and control are carried out on valves in the irrigation area through the irrigation control data, verification image acquisition is carried out on the irrigation process through an image acquisition unit, and a verification image set is generated;
setting a compensation acquisition period, and controlling the image acquisition unit to acquire crop images through the compensation acquisition period to generate a crop verification image set;
And performing irrigation abnormality evaluation according to the verification image set and the crop verification image set, generating irrigation compensation information, and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information.
2. The method of claim 1, wherein the interactive weather station reads weather data, further comprising:
the air pressure monitoring of the irrigation area is carried out through the weather station, and rainfall prediction data are generated, wherein the rainfall prediction data comprise rainfall time nodes and rainfall data;
monitoring the temperature and the humidity of the irrigation area through the weather station to generate temperature and humidity data;
performing irrigation water demand compensation on the data compensation result according to the rainfall prediction data to generate regional calibration water supplementing quantity;
carrying out water loss analysis on the irrigation area according to the temperature and humidity data to generate a water supplementing association coefficient;
and carrying out water supplementing control adjustment on the regional calibration water supplementing quantity through the water supplementing associated coefficient, and generating the irrigation control data based on a water supplementing control adjustment result.
3. The method of claim 2, wherein the method further comprises:
when the irrigation time node is determined, wind power data acquisition of the irrigation area is carried out through the meteorological station, and a wind power data set is generated, wherein the wind power data set comprises wind direction data and wind power data;
Valve attribute information and valve distribution information in the irrigation area are obtained, and wind power classification is carried out according to the valve attribute information;
performing grade matching on the wind power grade classification result based on the wind power data set to generate compensation data of valve control;
generating initial control data of a valve based on the water replenishing control adjustment result, the valve attribute information and the valve distribution information;
and controlling and compensating the initial control data according to the compensation data to generate the irrigation control data.
4. The method of claim 1, wherein the method further comprises:
generating a calibration irrigation image according to the irrigation control data, wherein the calibration irrigation image is an irrigation expected spraying image of the valve;
performing center calibration on the calibrated irrigation image, extracting a coverage area, and taking the coverage area as a first comparison characteristic;
extracting the coverage density characteristics of the calibrated irrigation image to generate second comparison characteristics;
and carrying out feature comparison of the verification image set through the first comparison feature and the second comparison feature, and obtaining the irrigation compensation information according to a feature comparison result.
5. The method of claim 4, wherein the method further comprises:
taking a crop image set corresponding to the crop state distribution result as a basic comparison image, and performing crop mapping on the crop verification image set;
constructing a time sequence morphological change characteristic set of crops based on the crop mapping result;
performing irrigation effect evaluation in the irrigation area through the time sequence morphological change characteristic set to generate feedback compensation data;
and generating the irrigation compensation information according to the feedback compensation data and the characteristic comparison result.
6. The method of claim 1, wherein the method further comprises:
arranging a pressure sensor and a flow sensor on the valve;
real-time data measurement is carried out when valve irrigation is carried out through the pressure sensor and the flow sensor, and a real-time data measurement result is output;
generating auxiliary optimization data according to the real-time data measurement result;
and performing intelligent valve regulation and control in the irrigation area through the irrigation compensation information and the auxiliary optimization data.
7. The method of claim 4, wherein the method further comprises:
setting early warning deviation features, wherein the early warning deviation features comprise coverage area deviation features and coverage density deviation features;
Feature comparison of the verification image set is carried out through the first comparison feature and the second comparison feature, and when a feature deviation result meets the early warning deviation feature, early warning information is generated;
and carrying out real-time irrigation early warning according to the early warning information.
8. An intelligent valve regulation control system, the system comprising:
the regional information acquisition module is used for interactively irrigating regional information of the region, wherein the regional information comprises regional distribution information and regional crop information;
the crop image set construction module is used for acquiring image data of the irrigation area and constructing a crop image set of the area crop;
the distribution result generation module is used for executing image recognition of the crop image set and generating a crop state distribution result;
the water distribution generation module is used for reading the distribution information of the soil moisture content sensor of the irrigation area and generating the soil moisture distribution of the irrigation area according to the acquired data of the soil moisture content sensor and the distribution information;
The control data generation module is used for interacting with a weather station, reading weather data, carrying out data compensation on the soil moisture distribution through the crop state distribution result, and generating irrigation control data based on the data compensation result and the weather data;
the verification image set generation module is used for intelligently regulating and controlling valves in the irrigation area according to the irrigation control data, and collecting verification images in the irrigation process according to the image collection unit to generate a verification image set;
the crop verification image generation module is used for setting a compensation acquisition period, controlling the image acquisition unit to acquire crop images through the compensation acquisition period and generating a crop verification image set;
the intelligent regulation and control module is used for carrying out irrigation abnormal evaluation according to the verification image set and the crop verification image set, generating irrigation compensation information and carrying out intelligent regulation and control on valves in the irrigation area through the irrigation compensation information.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110367097A (en) * | 2019-07-23 | 2019-10-25 | 山东开创云软件有限公司 | A kind of irrigated area water-flow control method and server |
| CN112602563A (en) * | 2020-12-15 | 2021-04-06 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Water-saving irrigation system and accurate irrigation method |
| CN112931150A (en) * | 2021-02-03 | 2021-06-11 | 华南农业大学 | Irrigation system and method based on spectral response of citrus canopy |
| CN114365614A (en) * | 2021-11-22 | 2022-04-19 | 湖南大学 | Water and fertilizer accurate regulation and control method, intelligent equipment and system based on Internet of things |
| CN114581257A (en) * | 2022-03-01 | 2022-06-03 | 河南科技大学 | Agricultural fine planting overall process management system and method |
| CN115633622A (en) * | 2022-06-06 | 2023-01-24 | 华南农业大学 | Intelligent orchard irrigation system and method |
| CN115644037A (en) * | 2022-10-19 | 2023-01-31 | 郑学铖 | Internet of things intelligent irrigation system based on cloud computing |
-
2023
- 2023-05-19 CN CN202310567568.0A patent/CN116310848B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110367097A (en) * | 2019-07-23 | 2019-10-25 | 山东开创云软件有限公司 | A kind of irrigated area water-flow control method and server |
| CN112602563A (en) * | 2020-12-15 | 2021-04-06 | 珠海市现代农业发展中心(珠海市金湾区台湾农民创业园管理委员会、珠海市农渔业科研与推广中心) | Water-saving irrigation system and accurate irrigation method |
| CN112931150A (en) * | 2021-02-03 | 2021-06-11 | 华南农业大学 | Irrigation system and method based on spectral response of citrus canopy |
| CN114365614A (en) * | 2021-11-22 | 2022-04-19 | 湖南大学 | Water and fertilizer accurate regulation and control method, intelligent equipment and system based on Internet of things |
| CN114581257A (en) * | 2022-03-01 | 2022-06-03 | 河南科技大学 | Agricultural fine planting overall process management system and method |
| CN115633622A (en) * | 2022-06-06 | 2023-01-24 | 华南农业大学 | Intelligent orchard irrigation system and method |
| CN115644037A (en) * | 2022-10-19 | 2023-01-31 | 郑学铖 | Internet of things intelligent irrigation system based on cloud computing |
Non-Patent Citations (3)
| Title |
|---|
| MANUEL FIGUEROA: ""Root System Water Consumption Pattern Identification on Time Series Data"", 《MDPI》 * |
| 张旭东: ""基于物候期识别的果园智能灌溉系统设计与实现"", 《中国优秀硕士学位论文全文数据库农业科技辑》 * |
| 毛博识;康义;李文雅;郭渊杰;刘立业;: "基于作物生长模型的大田玉米智能灌溉系统", 电脑知识与技术, no. 22 * |
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