CN116434138A - Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas - Google Patents
Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas Download PDFInfo
- Publication number
- CN116434138A CN116434138A CN202310319658.8A CN202310319658A CN116434138A CN 116434138 A CN116434138 A CN 116434138A CN 202310319658 A CN202310319658 A CN 202310319658A CN 116434138 A CN116434138 A CN 116434138A
- Authority
- CN
- China
- Prior art keywords
- area
- decreasing
- cultivated land
- increasing
- change
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 55
- 230000003247 decreasing effect Effects 0.000 claims abstract description 169
- 230000008859 change Effects 0.000 claims abstract description 111
- 230000008569 process Effects 0.000 claims abstract description 22
- 238000005070 sampling Methods 0.000 claims description 32
- 238000004458 analytical method Methods 0.000 claims description 28
- 230000006870 function Effects 0.000 claims description 26
- 238000011156 evaluation Methods 0.000 claims description 24
- 238000012937 correction Methods 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 description 10
- 230000009467 reduction Effects 0.000 description 6
- 238000005259 measurement Methods 0.000 description 5
- 230000001105 regulatory effect Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Geometry (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention belongs to the technical field of intelligent farmland conservation, and particularly relates to an intelligent farmland conservation real-time dynamic monitoring method applied to a cloudy and foggy region. According to the invention, the optimal cultivated area can be estimated according to the yield of historical crops, and compared with the cultivated area in a real-time state, so that whether the cultivated area is damaged or not can be judged, meanwhile, in the process of changing the cultivated area, the increased area and the decreased area can be monitored in real time, the change trend of the increased area and the decreased area can be calculated respectively, finally, the cultivated area changing result is predicted by combining the changing period, and the decreased area is optimized according to the condition that extra cultivated area is occupied, so that no extra cultivated area is occupied in the process of changing the cultivated area, and the cultivated area in the monitored area is effectively protected.
Description
Technical Field
The invention belongs to the technical field of intelligent farmland conservation, and particularly relates to an intelligent farmland conservation real-time dynamic monitoring method applied to a cloudy and foggy region.
Background
The cultivated land refers to the land for planting crops, including cultivated land, newly developed reclamation land, leisure land, wheel rest land and grassland rotation land, along with the rapid development of economy, more and more civil buildings or industrial buildings are covered into the original cultivated land, so that the cultivated land area of China is continuously reduced, and the cultivation area is greatly reduced to directly threaten agriculture development, thus the real-time forced cultivated land protection is necessary, the real-time forced cultivated land protection is a global strategic problem related to the sustainable development of economy and society of China, along with the development of informatization technology, the intelligent protection system for the cultivated land is applied, so that the cultivated land protection mode of people is changed from field operation to remote operation, particularly for cloudy and foggy areas, artificial investigation is extremely laborious, the occurrence of the time of the infringement of the cultivated land is difficult to be stopped, but the remote monitoring mode can solve the problem, such as low-altitude cruising of an unmanned aerial vehicle, not only can acquire ground information, but also can realize rapid investigation without land, not only save manpower, but also can realize rapid and comprehensive monitoring.
The conventional intelligent farmland protecting system has a cruising function and carries out corresponding treatment when a farmland infringement event is found, however, the upper limit of change of the farmland which cannot be restored possibly occurs in the farmland changing process is reduced, the period of change is correspondingly reduced, the occupation of the farmland is continued at the moment, and the surplus farmland is obviously occupied, so that the intelligent farmland protecting real-time dynamic monitoring method applied to the cloudy and foggy area is provided.
Disclosure of Invention
The invention aims to provide an intelligent real-time dynamic monitoring method for farmland in a cloudy and foggy area, which can monitor an increased area and a decreased area in real time in the process of farmland change and avoid extra occupied farmland in the process of change.
The technical scheme adopted by the invention is as follows:
a real-time dynamic monitoring method for intelligent protection of cultivated land in a cloudy and foggy region comprises the following steps:
acquiring a monitoring area, wherein the monitoring area comprises a cultivated area and a non-cultivated area;
acquiring the areas of the cultivated land area and the non-cultivated land area, inputting the areas into a measuring and calculating model, obtaining the occupation ratio of the cultivated land area, and calibrating the occupation ratio as a parameter to be evaluated;
Acquiring rated parameters of an cultivated land area in the area to be detected, and comparing the rated parameters with the parameters to be evaluated;
if the rated parameters are smaller than or equal to the parameters to be evaluated, judging that the cultivated area of the monitoring area is normal, acquiring a change area of the cultivated area in real time, and summarizing the change area into a data set to be evaluated;
if the rated parameter is larger than the parameter to be evaluated, judging that the cultivated area of the monitoring area is reduced, and generating an alarm signal;
classifying the change areas of the farmland areas in the data set to be evaluated to obtain an increased area and a decreased area;
the increasing area and the decreasing area are respectively input into a trend analysis model, the change trend of the increasing area and the change trend of the decreasing area are respectively obtained, and the increasing trend value and the decreasing trend value are respectively calibrated;
if the increasing trend value is smaller than the decreasing trend value, generating an early warning signal;
if the increasing trend value is larger than the decreasing trend value, judging that the cultivated land area is normal;
the changing periods of the increasing area and the decreasing area are respectively obtained, and are combined with the increasing trend value and the decreasing trend value to be operated, so that a terminal increasing area and a terminal decreasing area are obtained, and are compared;
If the terminal increasing area is smaller than the terminal decreasing area, sending out an early warning signal and generating a correction plan aiming at the decreasing area;
and if the terminal increasing area is larger than or equal to the terminal decreasing area, judging that the cultivated area is normal.
In a preferred embodiment, the step of obtaining the area of the cultivated land area and the area of the non-cultivated land area and inputting the area into a measurement model to obtain the occupancy rate of the cultivated land area includes:
acquiring the areas of the cultivated land area and the non-cultivated land area;
obtaining a standard function from the evaluation model;
and inputting the areas of the cultivated land area and the non-cultivated land area into a standard function, and calibrating the calculation result as the occupancy rate of the cultivated land area.
In a preferred embodiment, the step of obtaining the rated parameter of the cultivated land area in the area to be detected includes:
constructing a sampling period, wherein the sampling period comprises a plurality of statistical nodes;
acquiring the historical crop yield under the statistical nodes, and sequencing according to the occurrence time;
obtaining standard yield, comparing the standard yield with all the historical crop yields, screening out all the historical crops with the standard yield or more, and calibrating the historical crops as qualified yields;
Acquiring all cultivated land areas corresponding to the qualified yield, arranging according to the sequence from large to small, and calibrating the minimum cultivated land area as a temporary parameter;
acquiring statistical nodes under the temporary parameters and all the historical crop yields, and inputting the statistical nodes and all the historical crop yields into a judging model to judge whether the temporary parameters can be used as rated parameters or not;
if yes, directly calibrating the temporary parameter as a rated parameter;
if not, continuously acquiring the cultivated land area which is the next time and is larger than the qualified yield, and inputting the cultivated land area into the judging model as a temporary parameter.
In a preferred embodiment, the step of obtaining the statistical node under the temporary parameter includes the following steps:
acquiring the number of statistical nodes under temporary parameters;
obtaining a standard evaluation number and comparing the standard evaluation number with the statistical node number, wherein the value of the standard evaluation number is n, and n is more than or equal to 5;
if the standard evaluation number is greater than the statistical node number, judging that the historical crop yield under the temporary parameters cannot be input into a judging model, and continuously acquiring the cultivated land area of the next level and greater than the qualified yield;
if the standard evaluation number is smaller than or equal to the statistical node number, the historical crop yield under the temporary parameters can be judged to be input into a judging model.
In a preferred embodiment, the step of inputting all the historical crop yields under the temporary parameters into a decision model to decide whether the temporary parameters can be rated parameters, comprises:
acquiring all the historical crop yields under the temporary parameters, and judging whether the historical crop yields smaller than the standard yields exist in the historical crop yields;
if so, calculating the occupancy ratio of the historical crop yield less than the standard yield;
if the ratio of the historical crop yield less than the standard yield is less than or equal to 80%, judging that the temporary parameter cannot be used as the rated parameter;
if the ratio of the historical crop yield less than the standard yield is higher than 80%, judging that the temporary parameter can be used as a rated parameter;
if the temporary parameter does not exist, the temporary parameter is directly judged to be the rated parameter.
In a preferred embodiment, the step of inputting the increasing area and the decreasing area into a trend analysis model to obtain the trend of the increasing area and the decreasing area, and calibrating the increasing area and the decreasing area as the increasing trend value and the decreasing trend value, respectively, includes:
acquiring an initial node and a current monitoring node of the farmland change in the monitoring area to obtain a measuring and calculating period;
Constructing a plurality of sampling nodes in a measuring and calculating period, and respectively acquiring an increasing area and a decreasing area under each sampling node, wherein m sampling nodes are arranged, and m is more than or equal to 10;
and obtaining a trend analysis function from the trend analysis model, and respectively inputting an increasing area and a decreasing area under each sampling node into the trend analysis function to obtain an increasing trend value and a decreasing trend value.
In a preferred scheme, when the increasing area and the decreasing area under each sampling node are respectively obtained, the increasing area and the decreasing area under the adjacent sampling node are compared, and the instantaneous variation of the increasing area and the decreasing area is screened out, wherein the specific process is as follows;
acquiring the difference value between the increased area and the decreased area under adjacent sampling, and calibrating the difference value as the variation to be evaluated;
acquiring an allowable change interval, and comparing the allowable change interval with the change quantity to be evaluated one by one;
if the to-be-evaluated variable quantity is in the allowable variable interval, judging that the to-be-evaluated variable quantity is a normal variable quantity, and adding the normal variable quantity into a trend analysis function;
if the change amount to be evaluated is not in the allowable change interval, judging that the change amount to be evaluated is an instantaneous change amount, and screening out the corresponding increased area or the corresponding decreased area.
In a preferred embodiment, the step of obtaining the change periods of the increasing area and the decreasing area, and combining with the increasing trend value and the decreasing trend value to obtain the terminal increasing area and the terminal decreasing area includes:
respectively acquiring the change periods of the increased area and the decreased area, and comparing the change periods;
if the change period of the added area is greater than or equal to the change period of the reduced area, directly judging that the terminal added area is greater than the terminal reduced area;
if the change period of the increased area is smaller than the change period of the decreased area, acquiring a measuring and calculating function;
and inputting the change period, the increasing trend value and the decreasing trend value into an algorithm function to obtain a terminal increasing area and a terminal decreasing area.
The invention also provides a real-time dynamic monitoring system for the intelligent protection of the cultivated land in the cloudy and foggy region, which is applied to the real-time dynamic monitoring method for the intelligent protection of the cultivated land in the cloudy and foggy region, and comprises the following steps:
the data acquisition module is used for acquiring a monitoring area, wherein the monitoring area comprises a cultivated land area and a non-cultivated land area;
the measuring and calculating module is used for acquiring the areas of the cultivated land area and the non-cultivated land area, inputting the areas into the measuring and calculating model, obtaining the occupation ratio of the cultivated land area, and calibrating the occupation ratio as a parameter to be evaluated;
The judging module is used for acquiring rated parameters of the cultivated land area in the area to be detected and comparing the rated parameters with the parameters to be evaluated;
if the rated parameters are smaller than or equal to the parameters to be evaluated, judging that the cultivated area of the monitoring area is normal, acquiring a change area of the cultivated area in real time, and summarizing the change area into a data set to be evaluated;
if the rated parameter is larger than the parameter to be evaluated, judging that the cultivated area of the monitoring area is reduced, and generating an alarm signal;
the classification module is used for classifying the change areas of the cultivated land areas in the data set to be evaluated to obtain an increased area and a decreased area;
the first evaluation module is used for inputting the increasing area and the decreasing area into a trend analysis model respectively, obtaining the change trend of the increasing area and the decreasing area respectively, and calibrating the increasing area and the decreasing area as an increasing trend value and a decreasing trend value respectively;
if the increasing trend value is smaller than the decreasing trend value, generating an early warning signal;
if the increasing trend value is larger than the decreasing trend value, judging that the cultivated land area is normal;
the second evaluation module is used for respectively acquiring the change periods of the increased area and the decreased area, combining and calculating with the increased trend value and the decreased trend value to obtain a terminal increased area and a terminal decreased area, and comparing the terminal increased area and the terminal decreased area;
If the terminal increasing area is smaller than the terminal decreasing area, sending out an early warning signal and generating a correction plan aiming at the decreasing area;
and if the terminal increasing area is larger than or equal to the terminal decreasing area, judging that the cultivated area is normal.
And, a real-time dynamic monitor terminal is protected to farmland intelligence that is applied to many clouds and fog areas includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the intelligent real-time dynamic monitoring method for the farmland in the cloudy and foggy region.
The invention has the technical effects that:
the invention can evaluate the optimal cultivated area according to the yield of the historical crops, and compares the optimal cultivated area with the cultivated area in a real-time state to judge whether the cultivated area is infringed or not, and can monitor the increased area and the decreased area in real time in the process of changing the cultivated area, respectively calculate the change trend of the increased area and the decreased area, finally predict the cultivated area change result by combining the change period, optimize the decreased area according to the condition that the extra cultivated area is occupied, so that no redundant cultivated area is occupied in the process of changing the cultivated area, and effectively protect the cultivated area in the monitored area.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 and 2, the invention provides a method for intelligent real-time dynamic monitoring of cultivated land in a cloudy and foggy region, which comprises the following steps:
S1, acquiring a monitoring area, wherein the monitoring area comprises a cultivated land area and a non-cultivated land area;
s2, acquiring the areas of the cultivated land area and the non-cultivated land area, inputting the areas into a measuring and calculating model, obtaining the occupation ratio of the cultivated land area, and calibrating the occupation ratio as a parameter to be evaluated;
s3, acquiring rated parameters of an cultivated land area in the area to be detected, and comparing the rated parameters with the parameters to be evaluated;
if the rated parameter is smaller than or equal to the parameter to be evaluated, judging that the cultivated area of the monitoring area is normal, acquiring a change area of the cultivated area in real time, and summarizing the change area into a data set to be evaluated;
if the rated parameter is larger than the parameter to be evaluated, judging that the cultivated area of the monitoring area is reduced, and generating an alarm signal;
s4, classifying the change areas of the farmland areas in the data set to be evaluated to obtain an increased area and a decreased area;
s5, respectively inputting the increasing area and the decreasing area into a trend analysis model to respectively obtain the change trend of the increasing area and the decreasing area, and respectively calibrating the increasing area and the decreasing area as an increasing trend value and a decreasing trend value;
if the increasing trend value is smaller than the decreasing trend value, generating an early warning signal;
if the increasing trend value is larger than the decreasing trend value, judging that the cultivated area is normal;
S6, acquiring change periods of the increased area and the decreased area respectively, combining and calculating with the increased trend value and the decreased trend value to obtain a terminal increased area and a terminal decreased area, and comparing the terminal increased area and the terminal decreased area;
if the terminal increasing area is smaller than the terminal decreasing area, sending out an early warning signal and generating a correction plan aiming at the decreasing area;
and if the terminal increasing area is larger than or equal to the terminal decreasing area, judging that the cultivated area is normal.
As described in the above steps S1-S6, the cultivation protection is a global strategic problem related to economic and social sustainable development of China, a great reduction in cultivation area directly threatens the agricultural development, so that forced cultivation protection measures are required, but some people still infringe the cultivation area, such as illegal construction of a house, illegal construction of a factory and the like, and the cultivation area in China is reduced over time, which obviously does not conform to the global strategic of sustainable development, but the soil area in China is wider, which is obviously not preferable only by means of artificial monitoring, but also is obviously convenient to remotely monitor by adopting phase equipment such as a camera along with the arrival of the informatization age, so that the timeliness of data is ensured, cultivation infringement events can be timely found, and corresponding treatment measures can be made, in the practical situation, however, each area has a corresponding economic development plan, so that cultivated land is collected, and meanwhile, some worn building areas are planned for recovering the cultivated land, in the embodiment, firstly, the area to be monitored is determined, then the cultivated land area and the non-cultivated land area in the area are counted, the cultivated land area mainly comprises crops, the non-cultivated land area comprises the building land, industrial land and the like, the occupation ratio of the cultivated land area in the monitored area can be calculated through a measuring and calculating model, then the judgment is carried out by combining rated parameters, so as to determine whether the cultivated land area in the monitored area is normal or not, correspondingly, for abnormal situations, an alarm signal is generated to inform the supervision department, the supervision department re-plans the cultivated land area and the non-cultivated land area in the area, but, for the cultivated land area corresponding to the normal results, there is still a case where the cultivated land is reduced, that is, the changed area of the cultivated land area proposed in the present embodiment, which can be specifically divided into an increased area and a decreased area, and on the premise that the increased area is larger than the decreased area, the trend analysis model is utilized to analyze the change trend of the increased area and the decreased area so as to judge whether the changed area of the cultivated area shows the decreased trend, if the changed area shows the decreased trend, an early warning signal is sent to inform the supervision department, the supervision department intervenes and suppresses the reduction trend, the method only provides one data support for the supervision department and does not participate in the decision, wherein decisions of the supervision department and the like are made and executed by human subjectivity, the decision is not limited and is excessively detailed in the embodiment, then analyzing the condition that the increased area is larger than the decreased area to prevent excessive cultivated land from being additionally occupied according to the regulation of the supervision department in the process of cultivated land change, for the case that the increasing trend value is larger than the decreasing trend value, further evaluation is required according to the change period, so that the reduction of the area of the cultivated land area caused by the completion of the increasing area and the decreasing area is avoided, in this way, a predictive process is implemented, which helps the regulatory authorities to make corresponding correction plans, such as expanding the area of the increased area and decreasing the area of the decreased area, etc., which are not described here, but instead, when the change period is finished, the area of the increased area is larger than that of the decreased area, the farmland is finally increased, and then the normal monitoring work is continued, by the method, early warning information can be provided for the supervision department before the cultivated land area is reduced, the occurrence of cultivated land invasion is prevented in time, and the purpose of protecting the cultivated land is achieved.
In a preferred embodiment, the step of obtaining the area of the cultivated land area and the area of the non-cultivated land area and inputting the area into the measuring model to obtain the occupancy rate of the cultivated land area comprises the following steps:
s201, acquiring the areas of the cultivated land area and the non-cultivated land area;
s202, obtaining a standard function from an evaluation model;
s203, inputting the areas of the cultivated land area and the non-cultivated land area into a standard function, and calibrating the calculation result as the occupation ratio of the cultivated land area.
As described in the above steps S201 to S203, when calculating the occupancy rate of the cultivated land area, the unmanned aerial vehicle may specifically carry out global photographing scanning on the area, or may alternatively set fixed cameras for collecting the cultivated land area at intervals, extract the areas of the cultivated land area and the non-cultivated land area respectively, and then use standard functions: p=g a /(G a +G b ) Wherein P represents the occupancy ratio of the cultivated land area, i.e. the parameter to be evaluated, G, as set forth in the present embodiment a Representing the area of the cultivated land area G b The area of the non-cultivated land area is represented, the occupation ratio of the cultivated land area can be measured and calculated by adopting the method for different monitoring areas, the cultivated land condition in a real-time state is obtained, the calculated result is immediately compared with the rated parameter, and whether an alarm signal is sent or not is judged.
In a preferred embodiment, the step of obtaining the rated parameters of the cultivated land area in the area to be detected comprises:
s301, constructing a sampling period, wherein the sampling period comprises a plurality of statistical nodes;
s302, acquiring historical crop yield under the statistical nodes, and sequencing according to the occurrence time;
s303, obtaining standard yield, comparing the standard yield with all historical crop yields, screening out all historical crops with the standard yield or more, and calibrating the historical crops as qualified yields;
s304, acquiring the cultivated land areas corresponding to all qualified yields, arranging according to the sequence from large to small, and calibrating the minimum cultivated land area as a temporary parameter;
s305, acquiring statistical nodes under temporary parameters and all the historical crop yields, and inputting the statistical nodes and the historical crop yields into a judging model to judge whether the temporary parameters can be used as rated parameters or not;
if yes, directly calibrating the temporary parameter as a rated parameter;
if not, continuously acquiring the cultivated land area which is the next time and is larger than the qualified yield, and inputting the cultivated land area into the judging model as a temporary parameter.
As described in the above steps S301-S305, with the development of agriculture, people gradually improve the variety of crops to achieve high yield in the inherent cultivated land, so that the corresponding cultivated land can be reduced for economic development, of course, the standard yield in each area is fixed, and is determined according to the population number of China when the standard yield is manufactured, after the standard yield is determined, the historical crops with the standard yield or more are screened out, and compared with the crop yield in the historical state, all the historical crops with the standard yield or more are screened out, and then the corresponding minimum cultivated land area is screened out and calibrated as the temporary parameter, in this embodiment, the cultivated land is changed in a long-term and slow process, and after the cultivated land is changed, the change of the cultivated land can be guaranteed not to happen, or the historical crop yield in this stage is determined, and then the temporary parameter is input into a determination model to determine whether the temporary parameter can be used as the rated parameter.
In a preferred embodiment, the method for obtaining the statistical node under the temporary parameter includes the following steps:
stp1, obtaining the number of statistical nodes under temporary parameters;
stp2, obtaining a standard evaluation number and comparing the standard evaluation number with the number of statistical nodes, wherein the value of the standard evaluation number is n, and n is more than or equal to 5;
if the standard evaluation number is greater than the statistical node number, judging that the historical crop yield under the temporary parameters cannot be input into the judging model, and continuously acquiring the cultivated area of the next time which is greater than the qualified yield;
if the number of standard evaluations is less than or equal to the number of statistical nodes, then the historical crop yield under the determined temporary parameter can be input into the determination model.
As described in the above steps Stp1-Stp2, the continuity of the temporary parameters in the present embodiment is mainly analyzed, for example, the cultivated area under the temporary parameters corresponds to only two sets of historical crop yields, and if there is one set of historical crop yields smaller than the qualified yield, it cannot be determined whether the historical crop yields reach the qualified yield due to the influence of external factors (such as weather and rainwater are in a good quality state), and whether the temporary parameters are lower than the qualified yield due to the influence of external factors (such as heavy rain and drought), so it is necessary to determine whether the temporary parameters meet the conditions input to the determination model in advance.
In a preferred embodiment, the step of inputting all the historical crop yields under the temporary parameters into the decision model to decide whether the temporary parameters can be rated parameters, comprises:
stp3, obtaining all the historical crop yields under the temporary parameters, and judging whether the historical crop yields smaller than the standard yields exist in the historical crop yields;
stp4, if present, calculating a historical crop yield occupancy rate that is less than the standard yield;
if the ratio of the historical crop yield less than the standard yield is less than or equal to 80%, judging that the temporary parameter cannot be used as the rated parameter;
if the ratio of the historical crop yield less than the standard yield is higher than 80%, judging that the temporary parameter can be used as the rated parameter;
stp5, if it does not exist, directly judges that the temporary parameter can be regarded as the rated parameter.
As described in the above steps Stp3-Stp5, the determination model is executed by step-by-step nesting according to if … … else condition function, however, other algorithms conforming to the determination process are also possible, and the purpose is to generate different determination results according to different determination conditions, which is a technical means familiar to those skilled in the art, where, without excessive limitation and redundancy, when determining whether the temporary parameter can be used as the rated parameter, the historical crop yield of each statistical node under the temporary parameter is compared, and considering the influence of external climate factors, the crop yield in the cultivated area may have a yield reduction phenomenon, so that a 20% fault tolerance rate is given, that is, after the historical crop yield under the temporary parameter exceeds 80%, the historical crop yield can be determined as the rated parameter, otherwise, the cultivated area under the next time is continuously analyzed until the crop yield reaches 80% and stops.
In a preferred embodiment, the step of inputting the increasing area and the decreasing area into the trend analysis model to obtain the trend of the increasing area and the decreasing area, and calibrating the increasing trend value and the decreasing trend value respectively includes:
s501, acquiring an initial node and a current monitoring node of the farmland change in a monitoring area to obtain a measuring and calculating period;
s502, constructing a plurality of sampling nodes in a measuring and calculating period, and respectively acquiring an increasing area and a decreasing area under each sampling node, wherein m sampling nodes are arranged, and m is more than or equal to 10;
s503, obtaining a trend analysis function from the trend analysis model, and respectively inputting an increasing area and a decreasing area under each sampling node into the trend analysis function to obtain an increasing trend value and a decreasing trend value.
As described in the above steps S501-S503, when a cultivated land change situation occurs in the monitored area, a measurement period is correspondingly constructed, and the cultivated land change is a long-time process, so the measurement period has a longer period, before trend analysis is performed, a plurality of sampling nodes are constructed in the measurement period, and after the number of sampling nodes exceeds m, trend analysis can be performed, otherwise, the problem that an accurate analysis result cannot be obtained due to less data easily occurs, and of course, the larger the value of m is, the more accurate the result of trend analysis is, and when the interval of sampling nodes is determined, the trend analysis function can be set according to specific requirements, wherein: 
In which Q j Representing an increasing trend value or a decreasing trend value, t represents a measuring period, r represents the total amount of sampling nodes, M jh The area of the increased area or the area of the decreased area in the intervals 1 to r is represented by h, the numbers of the area of the increased area and the area of the decreased area in the measurement period are represented by j, and the increased area and the decreased area are represented by jFor example j=1, 2, where number 1 is an increasing region, and number 2 is a decreasing region, which is simply a substitute symbol and does not participate in and affect the operation of the formula.
In a preferred embodiment, when the increasing area and the decreasing area under each sampling node are respectively acquired, the increasing area and the decreasing area under the adjacent sampling node are compared, and the instantaneous variation of the increasing area and the decreasing area is screened out, which comprises the following specific processes;
step 1, obtaining the difference value between an increasing area and a decreasing area under adjacent sampling, and calibrating the difference value as a variation to be evaluated;
step 2, acquiring allowable change intervals, and comparing the allowable change intervals with the change quantity to be evaluated one by one;
if the change amount to be evaluated is in the allowable change interval, judging that the change amount to be evaluated is a normal change amount, and adding the normal change amount into a trend analysis function;
If the change amount to be evaluated is not in the allowable change interval, determining that the change amount to be evaluated is the instantaneous change amount, and screening out the corresponding increased area or the corresponding decreased area.
As described in the above steps 1 to 2, when the cultivated land area changes, the influence of the transient change is unavoidable, for example, in heavy rain, the increasing process and the decreasing process of the cultivated land area are both affected correspondingly, otherwise, the working efficiency of the increasing area or the decreasing area is greatly improved transiently, the change of the cultivated land area is also greatly fluctuated, the fluctuation amount will cause great influence on the trend analysis process, and finally the calculation result error of the increasing trend value and the decreasing trend value will be increased.
t 1 ，t 2 Represents a short period, f represents the number of short periods, M ji ，M jk Representing the area of the increased area or the area of the decreased area in the short term period of time, M as described above jh Meaning that i and k represent numbers that increase the area of the region or decrease the area of the region in different short-term periods, x, y represent the number of sampling nodes in each short-term period, which is also simply a surrogate symbol, the method can accurately calculate a specific numerical value during specific operation, and can calculate an accurate increasing trend value and an accurate decreasing trend value based on the formula because the specific numerical value needs to be analyzed according to specific conditions without specific limitation.
In a preferred embodiment, the steps of obtaining the change periods of the increased area and the decreased area, and combining the change periods with the increased trend value and the decreased trend value to obtain the terminal increased area and the terminal decreased area respectively include:
s601, respectively acquiring change periods of an increased area and a decreased area, and comparing the change periods;
s602, if the change period of the added area is greater than or equal to the change period of the reduced area, directly judging that the terminal added area is greater than the terminal reduced area;
s603, if the change period of the added area is smaller than the change period of the reduced area, acquiring a measuring and calculating function;
s604, inputting the change period, the increasing trend value and the decreasing trend value into the measuring and calculating function to obtain a terminal increasing area and a terminal decreasing area.
As described in the above steps S601-S604, for the change of the increased area and the decreased area in the monitored area, both are performed under the premise of reporting to the regulatory department, and for the case of illegal invasive farmland, when the condition is monitored, an alarm signal is immediately sent out, the regulatory department can perform targeted processing according to the alarm signal, the change periods of the increased area and the decreased area may be inconsistent, and on the premise that the area of the increased area is larger than the area of the decreased area and the increase trend value is larger than the decrease trend value, the change period is acquired, and thenAccording to the measuring and calculating function:
wherein Z is j Indicates a terminal increasing area or a terminal decreasing area, T indicates a change period, < ->
The area of the increased area or the area of the decreased area in the current state is represented, and based on the area of the cultivated land under the stop node of the increased area, the upper limit of change of the decreased area is set, so that extra occupied cultivated land is avoided in the process of changing the cultivated land.
The invention also provides a real-time dynamic monitoring system for the intelligent protection of the cultivated land in the cloudy and foggy region, which is applied to the real-time dynamic monitoring method for the intelligent protection of the cultivated land in the cloudy and foggy region, and comprises the following steps:
The data acquisition module is used for acquiring a monitoring area, wherein the monitoring area comprises a cultivated land area and a non-cultivated land area;
the measuring and calculating module is used for acquiring the areas of the cultivated land area and the non-cultivated land area, inputting the areas into the measuring and calculating model, obtaining the occupation ratio of the cultivated land area, and calibrating the occupation ratio as a parameter to be evaluated;
the judging module is used for acquiring rated parameters of the cultivated land area in the area to be detected and comparing the rated parameters with the parameters to be evaluated;
if the rated parameter is smaller than or equal to the parameter to be evaluated, judging that the cultivated area of the monitoring area is normal, acquiring a change area of the cultivated area in real time, and summarizing the change area into a data set to be evaluated;
if the rated parameter is larger than the parameter to be evaluated, judging that the cultivated area of the monitoring area is reduced, and generating an alarm signal;
the classification module is used for classifying the change areas of the cultivated land areas in the data set to be evaluated to obtain an increased area and a decreased area;
the first evaluation module is used for inputting the increasing area and the decreasing area into the trend analysis model respectively, obtaining the change trend of the increasing area and the decreasing area respectively, and calibrating the increasing area and the decreasing area as an increasing trend value and a decreasing trend value respectively;
If the increasing trend value is smaller than the decreasing trend value, generating an early warning signal;
if the increasing trend value is larger than the decreasing trend value, judging that the cultivated area is normal;
the second evaluation module is used for respectively acquiring the change periods of the increased area and the decreased area, combining and calculating with the increased trend value and the decreased trend value to obtain a terminal increased area and a terminal decreased area, and comparing the terminal increased area and the terminal decreased area;
if the terminal increasing area is smaller than the terminal decreasing area, sending out an early warning signal and generating a correction plan aiming at the decreasing area;
and if the terminal increasing area is larger than or equal to the terminal decreasing area, judging that the cultivated area is normal.
When the cultivated land is monitored in real time, the cultivated land area and the non-cultivated land area in the monitored area are firstly obtained through the data acquisition module, then the calculation module is used for calculating the occupation ratio of the cultivated land area, the judgment module is used for judging whether the situation that the cultivated land area is reduced exists or not, in order to fully utilize the ground, the cultivated land is quite common in changing, such as newly-added buildings, and the worn building land is restored to the cultivated land, based on the situation, the change area in the cultivated land area is marked as an increased area and a reduced area, then the first evaluation module is used for judging the increase trend and the reduction trend of the cultivated land, judging whether the change trend of the cultivated land meets the standard, finally the actual area after the cultivated land change is judged according to the second evaluation module, and whether the terminal increase area is larger than the terminal reduction area is predicted, and the additional cultivated land is prevented from being occupied in the cultivated land change.
And, a real-time dynamic monitor terminal is protected to farmland intelligence that is applied to many clouds and fog areas includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, so that the at least one processor can execute the intelligent real-time dynamic monitoring method for the farmland in the cloudy and foggy region.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (10)
1. The intelligent real-time dynamic monitoring method for the farmland in the cloudy and foggy areas is characterized by comprising the following steps of: comprising the following steps:
acquiring a monitoring area, wherein the monitoring area comprises a cultivated area and a non-cultivated area;
acquiring the areas of the cultivated land area and the non-cultivated land area, inputting the areas into a measuring and calculating model, obtaining the occupation ratio of the cultivated land area, and calibrating the occupation ratio as a parameter to be evaluated;
acquiring rated parameters of an cultivated land area in the area to be detected, and comparing the rated parameters with the parameters to be evaluated;
if the rated parameters are smaller than or equal to the parameters to be evaluated, judging that the cultivated area of the monitoring area is normal, acquiring a change area of the cultivated area in real time, and summarizing the change area into a data set to be evaluated;
if the rated parameter is larger than the parameter to be evaluated, judging that the cultivated area of the monitoring area is reduced, and generating an alarm signal;
classifying the change areas of the farmland areas in the data set to be evaluated to obtain an increased area and a decreased area;
the increasing area and the decreasing area are respectively input into a trend analysis model, the change trend of the increasing area and the change trend of the decreasing area are respectively obtained, and the increasing trend value and the decreasing trend value are respectively calibrated;
If the increasing trend value is smaller than the decreasing trend value, generating an early warning signal;
if the increasing trend value is larger than the decreasing trend value, judging that the cultivated land area is normal;
the changing periods of the increasing area and the decreasing area are respectively obtained, and are combined with the increasing trend value and the decreasing trend value to be operated, so that a terminal increasing area and a terminal decreasing area are obtained, and are compared;
if the terminal increasing area is smaller than the terminal decreasing area, sending out an early warning signal and generating a correction plan aiming at the decreasing area;
and if the terminal increasing area is larger than or equal to the terminal decreasing area, judging that the cultivated area is normal.
2. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 1, which is characterized in that: the step of obtaining the area of the cultivated land area and the non-cultivated land area, inputting the area into a measuring and calculating model to obtain the occupation ratio of the cultivated land area comprises the following steps:
acquiring the areas of the cultivated land area and the non-cultivated land area;
obtaining a standard function from the evaluation model;
and inputting the areas of the cultivated land area and the non-cultivated land area into a standard function, and calibrating the calculation result as the occupancy rate of the cultivated land area.
3. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 1, which is characterized in that: the step of obtaining the rated parameters of the cultivated land area in the area to be detected comprises the following steps:
constructing a sampling period, wherein the sampling period comprises a plurality of statistical nodes;
acquiring the historical crop yield under the statistical nodes, and sequencing according to the occurrence time;
obtaining standard yield, comparing the standard yield with all the historical crop yields, screening out all the historical crops with the standard yield or more, and calibrating the historical crops as qualified yields;
acquiring all cultivated land areas corresponding to the qualified yield, arranging according to the sequence from large to small, and calibrating the minimum cultivated land area as a temporary parameter;
acquiring statistical nodes under the temporary parameters and all the historical crop yields, and inputting the statistical nodes and all the historical crop yields into a judging model to judge whether the temporary parameters can be used as rated parameters or not;
if yes, directly calibrating the temporary parameter as a rated parameter;
if not, continuously acquiring the cultivated land area which is the next time and is larger than the qualified yield, and inputting the cultivated land area into the judging model as a temporary parameter.
4. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 3, which is characterized in that: when the statistical node under the temporary parameters is obtained, the method comprises the following steps:
Acquiring the number of statistical nodes under temporary parameters;
obtaining a standard evaluation number and comparing the standard evaluation number with the statistical node number, wherein the value of the standard evaluation number is n, and n is more than or equal to 5;
if the standard evaluation number is greater than the statistical node number, judging that the historical crop yield under the temporary parameters cannot be input into a judging model, and continuously acquiring the cultivated land area of the next level and greater than the qualified yield;
if the standard evaluation number is smaller than or equal to the statistical node number, the historical crop yield under the temporary parameters can be judged to be input into a judging model.
5. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 3, which is characterized in that: inputting all the historical crop yields under the temporary parameters into a judging model, and judging whether the temporary parameters can be used as rated parameters or not, wherein the step of judging comprises the following steps of:
acquiring all the historical crop yields under the temporary parameters, and judging whether the historical crop yields smaller than the standard yields exist in the historical crop yields;
if so, calculating the occupancy ratio of the historical crop yield less than the standard yield;
if the ratio of the historical crop yield less than the standard yield is less than or equal to 80%, judging that the temporary parameter cannot be used as the rated parameter;
If the ratio of the historical crop yield less than the standard yield is higher than 80%, judging that the temporary parameter can be used as a rated parameter;
if the temporary parameter does not exist, the temporary parameter is directly judged to be the rated parameter.
6. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 1, which is characterized in that: the step of inputting the increasing area and the decreasing area into a trend analysis model respectively to obtain the change trend of the increasing area and the decreasing area, and calibrating the increasing area and the decreasing area as an increasing trend value and a decreasing trend value respectively comprises the following steps:
acquiring an initial node and a current monitoring node of the farmland change in the monitoring area to obtain a measuring and calculating period;
constructing a plurality of sampling nodes in a measuring and calculating period, and respectively acquiring an increasing area and a decreasing area under each sampling node, wherein m sampling nodes are arranged, and m is more than or equal to 10;
and obtaining a trend analysis function from the trend analysis model, and respectively inputting an increasing area and a decreasing area under each sampling node into the trend analysis function to obtain an increasing trend value and a decreasing trend value.
7. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 6, which is characterized in that: when the increasing area and the decreasing area under each sampling node are respectively obtained, comparing the increasing area and the decreasing area under the adjacent sampling nodes, and screening out the instantaneous variation of the increasing area and the decreasing area, wherein the specific process is as follows;
Acquiring the difference value between the increased area and the decreased area under adjacent sampling, and calibrating the difference value as the variation to be evaluated;
acquiring an allowable change interval, and comparing the allowable change interval with the change quantity to be evaluated one by one;
if the to-be-evaluated variable quantity is in the allowable variable interval, judging that the to-be-evaluated variable quantity is a normal variable quantity, and adding the normal variable quantity into a trend analysis function;
if the change amount to be evaluated is not in the allowable change interval, judging that the change amount to be evaluated is an instantaneous change amount, and screening out the corresponding increased area or the corresponding decreased area.
8. The intelligent real-time dynamic monitoring method for farmland in cloudy and foggy areas according to claim 1, which is characterized in that: the step of obtaining the change periods of the increased area and the decreased area respectively, and combining and calculating with the increased trend value and the decreased trend value to obtain a terminal increased area and a terminal decreased area comprises the following steps:
respectively acquiring the change periods of the increased area and the decreased area, and comparing the change periods;
if the change period of the added area is greater than or equal to the change period of the reduced area, directly judging that the terminal added area is greater than the terminal reduced area;
If the change period of the increased area is smaller than the change period of the decreased area, acquiring a measuring and calculating function;
and inputting the change period, the increasing trend value and the decreasing trend value into an algorithm function to obtain a terminal increasing area and a terminal decreasing area.
9. The intelligent and real-time dynamic monitoring system for the cultivated land in the cloudy and foggy area is applied to the intelligent and real-time dynamic monitoring method for the cultivated land in the cloudy and foggy area, which is characterized in that: comprising the following steps:
the data acquisition module is used for acquiring a monitoring area, wherein the monitoring area comprises a cultivated land area and a non-cultivated land area;
the measuring and calculating module is used for acquiring the areas of the cultivated land area and the non-cultivated land area, inputting the areas into the measuring and calculating model, obtaining the occupation ratio of the cultivated land area, and calibrating the occupation ratio as a parameter to be evaluated;
the judging module is used for acquiring rated parameters of the cultivated land area in the area to be detected and comparing the rated parameters with the parameters to be evaluated;
if the rated parameters are smaller than or equal to the parameters to be evaluated, judging that the cultivated area of the monitoring area is normal, acquiring a change area of the cultivated area in real time, and summarizing the change area into a data set to be evaluated;
If the rated parameter is larger than the parameter to be evaluated, judging that the cultivated area of the monitoring area is reduced, and generating an alarm signal;
the classification module is used for classifying the change areas of the cultivated land areas in the data set to be evaluated to obtain an increased area and a decreased area;
the first evaluation module is used for inputting the increasing area and the decreasing area into a trend analysis model respectively, obtaining the change trend of the increasing area and the decreasing area respectively, and calibrating the increasing area and the decreasing area as an increasing trend value and a decreasing trend value respectively;
if the increasing trend value is smaller than the decreasing trend value, generating an early warning signal;
if the increasing trend value is larger than the decreasing trend value, judging that the cultivated land area is normal;
the second evaluation module is used for respectively acquiring the change periods of the increased area and the decreased area, combining and calculating with the increased trend value and the decreased trend value to obtain a terminal increased area and a terminal decreased area, and comparing the terminal increased area and the terminal decreased area;
if the terminal increasing area is smaller than the terminal decreasing area, sending out an early warning signal and generating a correction plan aiming at the decreasing area;
and if the terminal increasing area is larger than or equal to the terminal decreasing area, judging that the cultivated area is normal.
10. Be applied to real-time dynamic monitor terminal is protected to many clouds and many fog areas cultivated land intelligence, its characterized in that: comprising the following steps:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of intelligent real-time dynamic monitoring of cultivated land in a cloudy and foggy region as claimed in any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310319658.8A CN116434138A (en) | 2023-03-29 | 2023-03-29 | Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310319658.8A CN116434138A (en) | 2023-03-29 | 2023-03-29 | Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116434138A true CN116434138A (en) | 2023-07-14 |
Family
ID=87093691
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310319658.8A Pending CN116434138A (en) | 2023-03-29 | 2023-03-29 | Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116434138A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117953430A (en) * | 2024-03-15 | 2024-04-30 | 湖南省第二测绘院 | Method and system for monitoring farmland damage in real time through communication iron tower video |
-
2023
- 2023-03-29 CN CN202310319658.8A patent/CN116434138A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117953430A (en) * | 2024-03-15 | 2024-04-30 | 湖南省第二测绘院 | Method and system for monitoring farmland damage in real time through communication iron tower video |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111027175B (en) | Method for evaluating social and economic influences of flood based on coupling model integrated simulation | |
| CN110222911B (en) | Rainfall station network optimization layout method based on satellite remote sensing and ground data cooperation | |
| CN109325633B (en) | Weather forecast set member selection method and system | |
| CA2556985A1 (en) | Method and system for automated location dependent probabilistic tropical cyclone forecast | |
| CN102629294A (en) | Probability evaluation method of failure caused by typhoon to power transmission line | |
| CN111624682B (en) | Quantitative precipitation estimation method based on multi-source data fusion | |
| CN111784044A (en) | Landslide prediction method, device, equipment and storage medium | |
| CN107563554A (en) | A kind of screening technique for counting the NO emissions reduction model prediction factor | |
| CN109543870B (en) | Power transmission line tower lightning stroke early warning method based on neighborhood preserving embedding algorithm | |
| CN116434138A (en) | Intelligent and real-time dynamic monitoring method applied to farmland in cloudy and foggy areas | |
| CN107403004B (en) | Remote-measuring rainfall site suspicious numerical inspection method based on terrain data | |
| CN112668821A (en) | Distribution line risk analysis method based on insulator fault probability of sand blown region | |
| Jang et al. | Probabilistic characterization of wind diurnal variability for wind resource assessment | |
| CN113570153B (en) | Rainfall station network point number optimization method | |
| CN109543911B (en) | Sunlight radiation prediction method and system | |
| KR101336551B1 (en) | Climate property modification prediction system and method in accordance with reservoirs construction | |
| CN114414491B (en) | Grass ecology dynamic monitoring and analysis system | |
| CN116758707A (en) | Geological disaster monitoring system and method based on big data | |
| CN116591768A (en) | Tunnel monitoring method, system and device based on distributed network | |
| CN113191568B (en) | Meteorological-based urban operation management big data analysis and prediction method and system | |
| CN113469268B (en) | Error statistical analysis-based rainfall correction method and device for power transmission line tower | |
| CN117113267B (en) | Prediction model training method based on big data and photovoltaic power generation performance detection method | |
| CN117910708B (en) | Urban atmospheric chamber gas monitoring point location method and system | |
| CN117849908B (en) | Plum-entering and plum-exiting date prediction method and device in plum rainy season based on mode circular flow field | |
| CN118153784B (en) | Agricultural product information automatic updating method and system based on multi-source data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |