Farmland area monitoring method and device and monitoring equipment

Abstract

The invention discloses a farmland area monitoring method and device and monitoring equipment. The method comprises the steps that monitoring data, collected by multiple monitoring devices, of a farmland area are received, and the multiple monitoring devices are deployed at multiple different positions in the farmland area; the monitoring data, collected by the multiple monitoring devices, of the farmland area are fused according to preset weights corresponding to the multiple monitoring devices to obtain fused data, and the preset weights are determined according to the positions of the monitoring devices in the farmland area; early warning information is generated based on the fusion data, the early warning information is used for carrying out early warning on the state of the farmland area, and the state of the farmland area comprises at least one of the following states: the illumination intensity of the farmland area, the temperature of the farmland area, the soil humidity in the farmland area, the soil fertility of the farmland area and the crop growth state of the farmland area. The technical problem of low farmland state monitoring accuracy in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of farmland monitoring technology, and more specifically, to a farmland area monitoring method, device and monitoring equipment. Background Art

[0002] With the development of agriculture, the use of large-scale production machines and the monitoring of environmental conditions by various sensors in agricultural production activities can greatly improve the production efficiency and quality of agricultural production. Many farmers and agricultural production groups have begun to mechanize and intelligentize agricultural production. Different varieties of crops often have different growth stages and different growth states, but they basically need water and fertilizer irrigation and environmental status monitoring. However, the existing processing methods for the monitored data are relatively simple, and generally the data is processed through simple mapping to determine the crop growth status. However, when the farmland area is large, the collected data cannot represent the growth status of all crops in the entire farmland, resulting in low accuracy of the farmland status monitoring results. Summary of the invention

[0003] The embodiments of the present application provide a method, device and monitoring equipment for monitoring a farmland area, so as to at least solve the technical problem of low accuracy of farmland status monitoring in the related art.

[0004] According to one aspect of an embodiment of the present application, a method for monitoring a farmland area is provided, comprising: receiving monitoring data of a farmland area collected by multiple monitoring devices, wherein the multiple monitoring devices are deployed at multiple different positions in the farmland area; fusing the monitoring data of the farmland area collected by the multiple monitoring devices according to preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; generating early warning information based on the fused data, wherein the early warning information is used to warn of the status of the farmland area, and the status of the farmland area includes at least one of the following: light intensity in the farmland area, temperature in the farmland area, soil moisture in the farmland area, soil fertility in the farmland area, and crop growth status in the farmland area.

[0005] Optionally, generating early warning information based on the fused data includes: determining the growth status of crops in the farmland area from the fused data; determining the growth stage of crops in the farmland area based at least on the growth status of crops in the farmland area; determining an evaluation index threshold corresponding to the status of the farmland area based on the growth stage of crops in the farmland area; comparing the evaluation index of the status of the farmland area with the evaluation index threshold, and generating early warning information when the comparison result indicates that the evaluation index of the status of the farmland area is higher than the evaluation index threshold.

[0006] Optionally, the method also includes: determining time series data based on the fused data, the time series data being used to characterize the state of the farmland area at different times; and using a seasonal model to analyze the time series data to predict the state of the farmland area within the period to be predicted.

[0007] Optionally, a seasonal model is used to analyze the time series data to predict the state of the farmland area in the period to be predicted, including: obtaining non-seasonal parameters and seasonal parameters in the seasonal model, the seasonal parameters including seasonal cycle, seasonal difference order, seasonal moving average term and seasonal frequency, the non-seasonal parameters including: autoregressive term, difference order and moving average term; constructing the seasonal model based on the non-seasonal parameters and the seasonal parameters; using the time series data as input of the seasonal model, and outputting the state of the farmland area in the period to be predicted.

[0008] Optionally, obtaining non-seasonal parameters and seasonal parameters in the seasonal model includes: obtaining an autocorrelation function graph and a partial autocorrelation function graph of the time series data, the autocorrelation function graph being used to characterize the degree of correlation of the time series data with itself at different lags, and the partial autocorrelation function graph being used to characterize the correlation between a variable in the time series data and its own past values; determining the non-seasonal parameters and the seasonal parameters based on the autocorrelation function graph and the partial autocorrelation function graph.

[0009] Optionally, the non-seasonal parameters and the seasonal parameters are determined based on the autocorrelation function graph and the partial autocorrelation function graph, including: performing differentiation on the time series data until the time series data is stable, determining the number of differentiations, and determining the number of differentiations as the differentiation order; determining the number of lags before the truncation point in the partial autocorrelation function graph as the autoregressive term; determining the number of lags before the truncation point in the autocorrelation function graph as the moving average term; determining the length of the seasonal cycle as the seasonal frequency; performing seasonal differentiation on the time series data until the time series data is stable, determining the number of seasonal differentiations, and determining the number of seasonal differentiations as the seasonal differentiation order; determining the number of lags before the truncation point in the seasonal partial autocorrelation function graph as the seasonal autoregressive term; determining the number of lags before the truncation point in the seasonal autocorrelation function graph as the seasonal moving average term.

[0010] Optionally, after determining the state of the farmland area within the time period to be predicted, the method also includes: determining the growth stage of crops in the farmland area from the state of the farmland area within the time period to be predicted; determining the maturity date of the crops in the farmland area based on the growth stage of the crops in the farmland area; determining the cultivation strategy for the crops in the farmland area based on the maturity date of the crops in the farmland area and the weather conditions within the target time period, the target time period being the period between the current date and the maturity date; and cultivating the crops in the farmland area according to the cultivation strategy.

[0011] According to another aspect of an embodiment of the present application, a farmland area monitoring device is also provided, including: an acquisition module, used to receive monitoring data of a farmland area collected by multiple monitoring devices, and the multiple monitoring devices are deployed at multiple different positions in the farmland area; a fusion module, used to fuse the monitoring data of the farmland area collected by the multiple monitoring devices according to preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; an early warning module, used to generate early warning information based on the fused data, and the early warning information is used to warn of the status of the farmland area, and the status of the farmland area includes at least one of the following: the light intensity of the farmland area, the temperature of the farmland area, the soil moisture in the farmland area, the soil fertility of the farmland area and the crop growth status of the farmland area.

[0012] According to another aspect of an embodiment of the present application, a monitoring device is also provided for collecting monitoring data, including: a main body and a fixed component; a control component is arranged on the top of the main body, wherein the control component includes a processor, the processor is fixedly installed on the top of the main body, a baffle is fixedly installed on the front of the processor, a power connection hole is arranged on the front of the processor, a control key is fixedly installed on the top of the processor, a ventilation grille is arranged on one side of the processor, a fixed frame is fixedly installed on the top of the processor, a display screen is embedded in the front of the fixed frame, a receiver is fixedly installed on the top of the fixed frame, a mounting plate is arranged on the processor, a fixing pin is threadedly connected to the top of the mounting plate, a storage module is fixedly installed on the top of the mounting plate, a circuit board is fixedly installed on the top of the mounting plate, a computing module is embedded in the top of the circuit board, and an execution processing module is fixedly connected to the top of the circuit board.

[0013] Optionally, the fixing assembly includes: a fixing disc, a fixing ground nail is fixedly connected to the bottom of the fixing disc, a fixing rod is fixedly installed on the top of the fixing disc, an adjusting bolt is threadedly connected to one side of the fixing rod, an adjusting rod is slidably connected to the top of the fixing rod, a limiting ring is fixedly connected to the bottom end of the adjusting rod, a mounting disc is fixedly connected to the top of the adjusting rod, a mounting hole is opened on the top of the mounting disc, a rubber tube is penetrated and connected to the inside of the adjusting rod, a driving assembly is arranged on the top of the mounting disc, and the driving assembly includes a shell, the shell is fixedly installed on the top of the mounting disc, a wiring tube is fixedly installed on the bottom of the shell, a data cable is fixedly connected to the bottom of the wiring tube, a driving box is fixedly installed on the inside of the shell, a stator coil is fixedly installed on the inside of the driving box, and the inside of the driving box A limiting groove is opened in the part, and a rotating component is arranged on the top of the shell, the rotating component comprises a rotating seat, the rotating seat is rotatably connected to the top of the shell, a connecting rod is fixedly installed on the bottom of the rotating seat, a rotating magnetic disk is fixedly installed on the bottom end of the connecting rod, a rotating ring is fixedly connected to the bottom of the rotating magnetic disk, the rotating ring is rotatably connected to the inside of the limiting groove, and a monitoring component is arranged on the top of the rotating seat, the monitoring component comprises a mounting frame, the mounting frame is fixedly installed on the top of the rotating seat, a rotating shaft is rotatably connected inside the mounting frame, a protective shell is rotatably connected inside the mounting frame through the rotating shaft, a monitor is fixedly installed inside the protective shell, an adjusting motor is fixedly installed on one side of the mounting frame, the output end of the adjusting motor is fixedly connected to one end of the rotating shaft, and a connecting line is fixedly connected to one side of the adjusting motor.

[0014] According to another aspect of the embodiment of the present application, a computer device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; and the processor is connected to the memory and is used to execute the above-mentioned farmland area monitoring method.

[0015] According to another aspect of the embodiments of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned farmland area monitoring method by running the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is also provided, including computer instructions, which implement the above-mentioned farmland area monitoring method when executed by a processor.

[0017] In an embodiment of the present application, monitoring data of a farmland area collected by multiple monitoring devices are received, and the multiple monitoring devices are deployed at multiple different positions in the farmland area; the monitoring data of the farmland area collected by the multiple monitoring devices are fused according to preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; and early warning information is generated based on the fused data, and the early warning information is used to warn of the status of the farmland area, and the status of the farmland area includes at least one of the following: the light intensity of the farmland area, the temperature of the farmland area, the soil moisture in the farmland area, the soil fertility of the farmland area and the crop growth status of the farmland area, thereby achieving the purpose of fusing and analyzing the monitoring data of multiple positions in the farmland area, thereby realizing the technical effect of improving the monitoring accuracy of the farmland area, and further solving the technical problem of low accuracy of farmland status monitoring in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal for implementing a method for monitoring a farmland area according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for monitoring a farmland area according to an embodiment of the present application;

[0021] Figure 3 is a structural diagram of a farmland area monitoring device according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of the structure of a monitoring device according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of a control component structure according to an embodiment of the present application;

[0024] Figure 6 is a schematic diagram of a processing component structure according to an embodiment of the present application;

[0025] Figure 7 is a schematic diagram of a fixing component structure according to an embodiment of the present application;

[0026] Figure 8 is a schematic diagram of a driving component structure according to an embodiment of the present application;

[0027] Fig. 9is a schematic diagram of a rotating assembly structure according to an embodiment of the present application;

[0028] Fig.10 It is a schematic diagram of the structure of a monitoring component according to an embodiment of the present application.

[0029] Reference numerals: 1. main body; 2. control assembly; 21. processor; 22. baffle; 23. power connection hole; 24. air-permeable grille; 25. control key; 26. fixing frame; 27. display screen; 28. receiver; 3. processing assembly; 31. mounting plate; 32. fixing pin; 33. storage module; 34. circuit board; 35. computing module; 36. execution processing module; 4. fixing assembly; 41. fixing disc; 42. fixing ground nail; 43. fixing rod; 44. adjusting bolt; 45. Adjusting rod; 46, mounting disc; 47, mounting hole; 48, limiting ring; 49, rubber tube; 5, driving assembly; 51, housing; 52, driving box; 53, stator coil; 54, limiting groove; 55, wiring tube; 56, data cable; 6, rotating assembly; 61, rotating seat; 62, connecting rod; 63, rotating disk; 64, rotating ring; 7, monitoring assembly; 71, mounting bracket; 72, rotating shaft; 73, protective shell; 74, monitor; 75, adjusting motor; 76, connecting line. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] The information collected in the embodiments of the present application is information and data authorized by the account or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for the account to choose to authorize or reject the automated decision-making results; if the account chooses to reject, it enters the expert decision-making process.

[0033] In order to solve the problems existing in the related art, the present application embodiment provides a farmland area monitoring method, which can be run on Figure 1 In the computer terminal shown, the computer terminal is explained below.

[0034] The farmland area monitoring method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal for implementing a method for monitoring a farmland area. Figure 1 As shown, the computer terminal 10 may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0035] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0036] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the farmland area monitoring method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned farmland area monitoring method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0037] The transmission module 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] The display may be, for example, a touch screen liquid crystal display (LCD), which may enable the account to interact with the account interface of the computer terminal 10 .

[0039] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0040] Under the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a farmland area monitoring method. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] Figure 2 is a flow chart of a method for monitoring a farmland area according to an embodiment of the present application. Figure 2As shown, the method comprises the following steps:

[0042] Step S202, receiving monitoring data of a farmland area collected by a plurality of monitoring devices, where the plurality of monitoring devices are deployed at a plurality of different locations in the farmland area;

[0043] In step S202, the monitoring equipment can be equipped with a variety of sensors, such as: soil moisture sensors, temperature sensors, light sensors, etc., and can also be equipped with cameras to collect images of farmland areas, and then monitor the six agricultural conditions, such as: climate disasters (which can be obtained through air temperature and light sensors), soil moisture conditions (which can be obtained through soil moisture and water level sensors), fertilizer conditions (which can be obtained through soil fertility sensors), etc. At the same time, it is also necessary to take pictures and sample the seedling conditions (such as leaf color, plant height), disease conditions (disease types), and insect conditions (pest conditions) of farmland plants.

[0044] Step S204, fusing the monitoring data of the farmland area collected by the multiple monitoring devices according to the preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area;

[0045] In step S204, all the data collected by the multiple monitoring devices may be uploaded, or the data collected by the multiple monitoring devices may be fused to obtain fused data, and then the fused data may be uploaded. The specific fusion method is shown in the following formula:

[0046]

[0047] In the formula, Data_combined represents the fused data, i represents the monitoring device index, which is a positive integer, n represents the total number of monitoring devices, wi represents the preset weight of the i-th monitoring device, and Data i Represents the data collected by the i-th monitoring device.

[0048] Considering the coverage range of the monitoring equipment, the weight of the data collected by the monitoring equipment located in the center of the farmland area is greater than the weight of the data collected by the sensor located at the edge of the farmland area.

[0049] Step S206, generating early warning information based on the fused data, the early warning information is used to warn the status of the farmland area, the status of the farmland area includes at least one of the following: light intensity in the farmland area, temperature in the farmland area, soil moisture in the farmland area, soil fertility in the farmland area and crop growth status in the farmland area.

[0050] At the same time, during the monitoring process, different monitoring cycles can be determined for different crop growth cycles. Different monitoring cycles indicate that the monitoring equipment has different cycles for collecting monitoring data.

[0051] Through the above steps S202 to S206, the monitoring data of the farmland area collected by multiple monitoring devices is received, and the multiple monitoring devices are deployed at multiple different positions in the farmland area; the monitoring data of the farmland area collected by the multiple monitoring devices are fused according to the preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; based on the fused data, early warning information is generated, and the early warning information is used to warn the state of the farmland area, and the state of the farmland area includes at least one of the following: the light intensity of the farmland area, the temperature of the farmland area, the soil moisture in the farmland area, the soil fertility of the farmland area, and the crop growth state of the farmland area, so as to achieve the purpose of fusing and analyzing the monitoring data of multiple positions in the farmland area, thereby realizing the technical effect of improving the monitoring accuracy of the farmland area, and then solving the technical problem of low accuracy of farmland status monitoring in related technologies. The following is a detailed description.

[0052] In step S206 of the above-mentioned farmland area monitoring method, the specific steps of generating early warning information based on the fused data are as follows: determining the growth status of crops in the farmland area from the fused data; determining the growth stage of crops in the farmland area based on at least the growth status of crops in the farmland area; determining the evaluation index threshold corresponding to the state of the farmland area based on the growth stage of crops in the farmland area; comparing the evaluation index of the state of the farmland area with the evaluation index threshold, and generating early warning information when the comparison result indicates that the evaluation index of the state of the farmland area is higher than the evaluation index threshold.

[0053] It should be noted that the monitoring equipment is wirelessly connected to the terminal computer, the computer monitors and stores the data, and the state of the farmland area is analyzed through the computer's internal computing system. The specific analysis process is as follows: First, the type information of the crops currently planted in the farmland is identified based on the farmland image information, so as to obtain the evaluation index thresholds of the current crop states. Here, each evaluation index threshold is not one, but different evaluation index thresholds are respectively provided for different growth stages of crops. And agricultural warnings are also coupled to each other. Furthermore, the terminal computer can obtain the evaluation index thresholds of some or all states that need to be monitored in the current farmland area based on the crop type information.

[0054] When conducting analysis, the terminal computer can first determine the growth stage of the crops. For example, the growth stage can be determined based on the height of the crops. Furthermore, the threshold values ​​of the evaluation indicators for each farmland status at the corresponding stage are determined, and the growth conditions and growth status of the crops are determined based on the gap between the actual collected farmland status evaluation indicators and the evaluation indicator threshold values. Or it can be determined that the current warning information is generated, and the warning information indicates that the current farmland status does not meet the expected standards. The sowing time of the crops can also be set in advance, and the growth stage that the crops should reach at the current time can be determined based on the time difference between the sowing time and the current time, so as to obtain the corresponding evaluation indicator thresholds for comparison.

[0055] Furthermore, after analyzing the growth status of crops based on the data that has been collected, its future growth trend can be analyzed, so that not only the maturity time of crops can be predicted, but also its growth status can be better monitored. Since crop growth is closely related to time, the embodiment of the present application proposes to use a time series analysis method to construct a crop growth model, and use the growth model to predict the future growth trend of crops. Among them, the specific prediction method is as follows: based on the fused data, the time series data is determined, and the time series data is used to characterize the state of the farmland area at different times; the seasonal model is used to analyze the time series data to predict the state of the farmland area in the period to be predicted.

[0056] Specifically, time series data related to crop growth are extracted from the fused data. These data include at least indicators such as plant height, number of leaves, biomass, soil moisture, temperature, etc. Each indicator is assigned a corresponding timestamp. A timestamp refers to the specific time corresponding to each data point in the time series data related to crop growth. For example, if plant height data is collected, each plant height data point will have a timestamp to indicate when the height was measured. These timestamps can be specific dates, hours, minutes, or even seconds, depending on the frequency and accuracy of data collection.

[0057] In an optional manner, a statistical analysis method such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc. can be used to calculate the correlation coefficient between each candidate variable and the crop growth status.

[0058] It should be noted that the candidate variable can be fused data.

[0059] Set a correlation threshold (such as 0.5) and select variables with correlation coefficients greater than the threshold as the input of the seasonal model. The seasonal model process is as follows:

[0060] First, build a multivariate ARIMA model: select multiple input variables and build a multivariate ARIMA model. First, build a univariate ARIMA model for each input variable, and then use multivariate statistical analysis methods (such as principal component analysis, partial least squares regression, etc.) to integrate multiple univariate models into a multivariate model.

[0061] Taking into account the seasonal characteristics of crop growth, the multivariate ARIMA model is seasonally expanded by adding seasonal terms (such as sine waves or cosine waves) to capture the impact of seasonal changes on crop growth. By adjusting the coefficients and phases of the seasonal terms, the seasonal model can better fit the seasonal changes in the actual data.

[0062] In some embodiments of the present application, a seasonal model is used to analyze the time series data to predict the state of the farmland area in the time period to be predicted, including: obtaining non-seasonal parameters and seasonal parameters in the seasonal model, the seasonal parameters including seasonal cycle, seasonal difference order, seasonal moving average term and seasonal frequency, the non-seasonal parameters including: autoregressive term, difference order and moving average term; constructing the seasonal model based on the non-seasonal parameters and the seasonal parameters; using the time series data as the input of the seasonal model, and outputting the state of the farmland area in the time period to be predicted.

[0063] Specifically, construct a seasonal ARIMA model: In the ARIMA model, a seasonal ARIMA model is constructed by adding seasonal terms, usually expressed as SARIMA(p,d,q)(P,D,Q,s). Among them, lowercase letters represent the non-seasonal part, p, d, q represent the autoregressive term, the difference order and the moving average term respectively, and uppercase letters represent the seasonal part. P is the seasonal period, D is the seasonal difference order, Q is the seasonal moving average term, and s is the seasonal frequency.

[0064] Among them, the specific method of obtaining the non-seasonal parameters and seasonal parameters in the seasonal model is as follows: obtain the autocorrelation function graph and the partial autocorrelation function graph of the time series data, the autocorrelation function graph is used to characterize the degree of correlation between the time series data and itself at different lags, and the partial autocorrelation function graph is used to characterize the correlation between a variable in the time series data and its own past values; determine the non-seasonal parameters and the seasonal parameters based on the autocorrelation function graph and the partial autocorrelation function graph.

[0065] In an embodiment of the present application, a method for determining the non-seasonal parameter and the seasonal parameter based on the autocorrelation function graph and the partial autocorrelation function graph is provided, which is specifically as follows: the time series data is differentiated until the time series data is stable, the number of differentiations is determined, and the number of differentiations is determined as the differentiation order; the number of lags before the truncation point in the partial autocorrelation function graph is determined as the autoregressive term; the number of lags before the truncation point in the autocorrelation function graph is determined as the moving average term; the length of the seasonal cycle is determined as the seasonal frequency; the time series data is seasonally differentiated until the time series data is stable, the number of seasonal differentiations is determined, and the number of seasonal differentiations is determined as the seasonal differentiation order; the number of lags before the truncation point in the seasonal partial autocorrelation function graph is determined as the seasonal autoregressive term; the number of lags before the truncation point in the seasonal autocorrelation function graph is determined as the seasonal moving average term.

[0066] In actual application scenarios, appropriate p, d, q and P, D, Q, s parameters are identified by analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF) diagrams of time series data.

[0067] Among them, the autocorrelation function (ACF) graph is used to show the correlation between a time series and itself at different time lags. When we observe the ACF graph, if a significant peak appears at lag k, this usually means that a point in the series has a strong correlation with the kth point before or after it. This correlation can be used to infer the autoregressive (AR) or moving average (MA) characteristics of the time series. Specifically: Autoregressive (AR) term: If in the ACF graph, the peak drops rapidly or becomes insignificant after k lags, this may indicate the existence of an autoregressive process in which there is a linear relationship between the current value of the series and its past k values. In other words, a point in the series depends largely on the k points before it. The significance of the lag k value indicates that the order of the autoregressive model is k, that is, the AR(k) model. Moving average (MA) term: In the moving average model, the current value of the signal is a linear combination of its past k error terms (that is, the deviation between the current value and the model prediction value). When the ACF has a significant peak at the kth lag, this may mean that there is a moving average process in which the current value of the series depends on the past k error terms. In this case, the order of the MA model is k, that is, the MA(k) model.

[0068] The non-seasonal parameters are identified as follows:

[0069] d: Observe the ACF and PACF graphs. If the data is not stable, you need to perform a difference. After the difference, check the ACF and PACF graphs again until the data is stable. The difference order when the data is stable is the value of d.

[0070] p: In the PACF diagram, the number of lags before the cutoff point (i.e., the point where the significance drops significantly) is the order p of the AR term.

[0071] q: In the ACF plot, the number of lags before the cutoff point is the order q of the MA term.

[0072] The seasonal parameters are identified as follows:

[0073] s: Select the length of the seasonal period, e.g. for monthly data, if there is annual seasonality, then s=12.

[0074] D: Repeat the non-seasonal differencing step on the seasonally differencing data to determine the order D of the seasonal difference.

[0075] P and Q: For the seasonal part, analyze the seasonal ACF and seasonal PACF plots (obtained from seasonally differencing the data). The number of lags before the cutoff point of the seasonal PACF plot is the order P of the seasonal AR term (autoregressive coefficient), and the number of lags before the cutoff point of the seasonal ACF plot is the order Q of the seasonal MA term (moving average coefficient).

[0076] Taking the above time series data as an example, the non-seasonal ACF and PACF plots show truncation at lag 1, indicating that p = 1, q = 1. The data becomes stationary after the first order difference, so d = 1. The seasonal ACF and PACF plots show truncation at lag 12, indicating that P = 12, Q = 1. The seasonal data becomes stationary after the first order seasonal difference, so D = 1.

[0077] After determining the state of the farmland area in the time period to be predicted, the growth stage of the crops in the farmland area can also be determined from the state of the farmland area in the time period to be predicted; the maturity date of the crops in the farmland area is determined based on the growth stage of the crops in the farmland area; the cultivation strategy of the crops in the farmland area is determined based on the maturity date of the crops in the farmland area and the weather conditions in the target time period, the target time period being the period between the current date and the maturity date; and the crops in the farmland area are cultivated according to the cultivation strategy. Specifically, after the configured fertilizer and water amount are determined based on the predicted growth state of the crops, the configuration parameters can be further modified in combination with the weather. For example, under normal growth, the crops will mature in a week, but because of the bad weather in 5 days, more fertilizer will be applied to accelerate the ripening in advance.

[0078] The farmland area monitoring method provided by the embodiment of the present application realizes the intelligent monitoring and early warning of unattended farmland in a large range, no longer relies on manpower for monitoring, can reduce the investment of human capital, and improves the efficiency and accuracy of farmland monitoring in large-scale agricultural production. At the same time, after monitoring the data of the farmland, the traditional simple mapping method is no longer used to determine the crop growth status, but the data is fused and uploaded for analysis. The growth status of crops is analyzed according to the currently collected monitoring data. Specifically, the farmland status and comparison threshold required for monitoring the current farmland are obtained according to the crop type information, and the monitoring analysis is generated according to the comparison results. The farmland feature data terminal computer is stored, and the management can be uploaded by the terminal computer through the real-time observation sensing device. A variety of different farmland feature data, thereby realizing multi-dimensional and remote intelligent monitoring of farmland, improves the monitoring efficiency and management efficiency of farmland. Further, the future growth status of crops can be predicted based on the currently collected monitoring data, and the growth model is constructed using the time series analysis method in the prediction process. The crop growth status is predicted by the model, and the future water volume and fertilizer applied to the crop irrigation are jointly determined in combination with the future weather and the predicted growth status.

[0079] Figure 3 is a structural diagram of a farmland area monitoring device according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0080] An acquisition module 90 is used to receive monitoring data of a farmland area collected by a plurality of monitoring devices, wherein the plurality of monitoring devices are deployed at a plurality of different locations in the farmland area;

[0081] A fusion module 92, configured to fuse the monitoring data of the farmland area collected by the plurality of monitoring devices according to preset weights respectively corresponding to the plurality of monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area;

[0082] The early warning module 94 is used to generate early warning information based on the fused data, and the early warning information is used to warn the status of the farmland area, and the status of the farmland area includes at least one of the following: the light intensity of the farmland area, the temperature of the farmland area, the soil moisture in the farmland area, the soil fertility of the farmland area and the crop growth status of the farmland area.

[0083] Through the above-mentioned farmland area monitoring device, the monitoring data of the farmland area collected by multiple monitoring devices is received, and the multiple monitoring devices are deployed at multiple different positions in the farmland area; the monitoring data of the farmland area collected by the multiple monitoring devices are fused according to the preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; based on the fused data, early warning information is generated, and the early warning information is used to warn the status of the farmland area, and the status of the farmland area includes at least one of the following: the light intensity of the farmland area, the temperature of the farmland area, the soil moisture in the farmland area, the soil fertility of the farmland area and the crop growth status of the farmland area, thereby achieving the purpose of fusing and analyzing the monitoring data of multiple positions in the farmland area, thereby realizing the technical effect of improving the monitoring accuracy of the farmland area, and further solving the technical problem of low accuracy of farmland status monitoring in related technologies.

[0084] The early warning module 94 includes: a generation submodule, which is used to generate early warning information based on the fused data in the above-mentioned farmland area monitoring device, including: determining the growth status of crops in the farmland area from the fused data; determining the growth stage of crops in the farmland area at least based on the growth status of crops in the farmland area; determining the evaluation index threshold corresponding to the state of the farmland area based on the growth stage of crops in the farmland area; comparing the evaluation index of the state of the farmland area with the evaluation index threshold, and generating early warning information when the comparison result indicates that the evaluation index of the state of the farmland area is higher than the evaluation index threshold.

[0085] The generation submodule includes: a prediction unit, which is used to determine time series data based on the fusion data, and the time series data is used to characterize the state of the farmland area at different times; and a seasonal model is used to analyze the time series data to predict the state of the farmland area in the period to be predicted.

[0086] The prediction unit includes an analysis subunit, an acquisition subunit and a determination subunit. The analysis subunit is used to use a seasonal model to analyze the time series data and predict the state of the farmland area in the period to be predicted, including: obtaining non-seasonal parameters and seasonal parameters in the seasonal model, the seasonal parameters include seasonal cycle, seasonal difference order, seasonal moving average term and seasonal frequency, the non-seasonal parameters include: autoregressive term, difference order and moving average term; constructing the seasonal model based on the non-seasonal parameters and the seasonal parameters; using the time series data as the input of the seasonal model, and outputting the state of the farmland area in the period to be predicted.

[0087] An acquisition subunit is used to acquire non-seasonal parameters and seasonal parameters in the seasonal model, including: acquiring an autocorrelation function graph and a partial autocorrelation function graph of the time series data, wherein the autocorrelation function graph is used to characterize the degree of correlation between the time series data and itself at different lags, and the partial autocorrelation function graph is used to characterize the correlation between a variable in the time series data and its own past values; and determining the non-seasonal parameters and the seasonal parameters based on the autocorrelation function graph and the partial autocorrelation function graph.

[0088] A determination subunit, for determining the non-seasonal parameter and the seasonal parameter based on the autocorrelation function graph and the partial autocorrelation function graph, comprising: performing differentiation on the time series data until the time series data is stable, determining the number of differentiations, and determining the number of differentiations as the differentiation order; determining the number of lags before the truncation point in the partial autocorrelation function graph as the autoregressive term; determining the number of lags before the truncation point in the autocorrelation function graph as the moving average term; determining the length of the seasonal cycle as the seasonal frequency; performing seasonal differentiation on the time series data until the time series data is stable, determining the number of seasonal differentiations, and determining the number of seasonal differentiations as the seasonal differentiation order; determining the number of lags before the truncation point in the seasonal partial autocorrelation function graph as the seasonal autoregressive term; determining the number of lags before the truncation point in the seasonal autocorrelation function graph as the seasonal moving average term.

[0089] The early warning module 94 also includes: a strategy submodule, which is used to determine the growth stage of crops in the farmland area from the state of the farmland area in the time period to be predicted after determining the state of the farmland area in the time period to be predicted; determine the maturity date of crops in the farmland area based on the growth stage of crops in the farmland area; determine the cultivation strategy of crops in the farmland area based on the maturity date of crops in the farmland area and the weather conditions in the target time period, the target time period being the period between the current date and the maturity date; and cultivate crops in the farmland area according to the cultivation strategy.

[0090] It should be noted that Figure 3 The farmland area monitoring device shown is used to perform Figure 2 The farmland area monitoring method shown, therefore the relevant explanations and instructions in the above-mentioned farmland area monitoring method are also applicable to the farmland area monitoring device, and will not be repeated here.

[0091] The present application also provides a monitoring device for collecting monitoring data, such as Figure 4 As shown, including:

[0092] The main body 1 and the fixing component 4, the top of the main body 1 is provided with a control component 2, such as Figure 5 As shown, the control component 2 includes a processor 21, which is fixedly mounted on the top of the main body 1, a baffle 22 is fixedly mounted on the front of the processor 21, a power connection hole 23 is arranged on the front of the processor 21, a control key 25 is fixedly mounted on the top of the processor 21, a ventilation grille 24 is arranged on one side of the processor 21, a fixing frame 26 is fixedly mounted on the top of the processor 21, a display screen 27 is embedded in the front of the fixing frame 26, a receiver 28 is fixedly mounted on the top of the fixing frame 26, and a processing component 3 is arranged inside the processor 21, as shown in FIG. Figure 6 As shown, the processing component 3 includes a mounting plate 31, which is fixedly mounted inside the processor 21, a fixing pin 32 is threadedly connected to the top of the mounting plate 31, a storage module 33 is fixedly mounted on the top of the mounting plate 31, a circuit board 34 is fixedly mounted on the top of the mounting plate 31, a computing module 35 is embedded in the top of the circuit board 34, and an execution processing module 36 is fixedly connected to the top of the circuit board 34.

[0093] In this implementation case, the storage module 33 can save the monitored data, the calculation module 35 can calculate and analyze the growth trend of crops, and the execution processing module 36 can quantitatively calculate the elements required for crop growth based on the processing data of the calculation module 35, thereby realizing quantitative intelligent control, which is more conducive to the growth of crops.

[0094] like Figure 7 As shown, the fixing assembly 4 includes a fixing disc 41, a fixing ground nail 42 is fixedly connected to the bottom of the fixing disc 41, a fixing rod 43 is fixedly installed on the top of the fixing disc 41, an adjusting bolt 44 is threadedly connected to one side of the fixing rod 43, an adjusting rod 45 is slidably connected to the top of the fixing rod 43, a limiting ring 48 is fixedly connected to the bottom end of the adjusting rod 45, a mounting disc 46 is fixedly connected to the top of the adjusting rod 45, a mounting hole 47 is opened on the top of the mounting disc 46, a rubber tube 49 is penetrated and connected to the inside of the adjusting rod 45, and a driving assembly 5 is arranged on the top of the mounting disc 46, as shown in FIG. Figure 8 As shown, the driving assembly 5 includes a housing 51, which is fixedly mounted on the top of the mounting disc 46, a wiring tube 55 is fixedly mounted on the bottom of the housing 51, a data line 56 is fixedly connected to the bottom of the wiring tube 55, a driving box 52 is fixedly mounted inside the housing 51, a stator coil 53 is fixedly mounted inside the driving box 52, a limiting groove 54 is provided inside the driving box 52, and a rotating assembly 6 is arranged on the top of the housing 51, as shown in FIG. Fig. 9As shown, the rotating assembly 6 includes a rotating seat 61, which is rotatably connected to the top of the housing 51, a connecting rod 62 is fixedly installed at the bottom of the rotating seat 61, a rotating disk 63 is fixedly installed at the bottom end of the connecting rod 62, a rotating ring 64 is fixedly connected to the bottom of the rotating disk 63, and the rotating ring 64 is rotatably connected to the inside of the limiting groove 54. The top monitoring assembly 7 of the rotating seat 61, as shown Fig.10 As shown, the monitoring component 7 includes a mounting frame 71, which is fixedly mounted on the top of the rotating seat 61. The interior of the mounting frame 71 is rotatably connected to a rotating shaft 72. The interior of the mounting frame 71 is rotatably connected to a protective shell 73 through the rotating shaft 72. A monitor 74 is fixedly mounted inside the protective shell 73. An adjusting motor 75 is fixedly mounted on one side of the mounting frame 71. The output end of the adjusting motor 75 is fixedly connected to one end of the rotating shaft 72. A connecting line 76 is fixedly connected to one side of the adjusting motor 75.

[0095] In this embodiment, the fixing rod 43 can be fixed by fixing the ground nail 42, which improves the convenience of fixing the fixing rod 43. The sliding of the adjusting rod 45 can adjust the height of the monitor 74, which is convenient for monitoring crops of different growth heights, meets the monitoring needs of different crops, and improves the scope of application. The adjusting rod 45 can be fixed by rotating the adjusting bolt 44 to prevent the adjusting rod 45 from sliding, thereby improving the stability of the adjusting rod 45 when it is fixed. The stator coil 53 can change the magnetic field after being energized, and the rotating disk 63 can rotate under the action of the magnetic field. The rotation of the rotating disk 63 can drive the connecting rod 45 to rotate. The connecting rod 62 rotates, and the rotation of the connecting rod 62 can drive the monitor 74 to adjust the direction through the rotating seat 61, thereby realizing all-round monitoring of crops and improving the efficiency of monitoring. The limiting groove 54 can limit the rotating ring 64 during rotation, so as to improve the stability during direction adjustment and reduce the shaking caused by the adjustment. The adjusting motor 75 can drive the rotating shaft 72 to rotate, and the rotation of the rotating shaft 72 can drive the protective shell 73 to rotate. The rotation of the protective shell 73 can drive the monitor 74 to adjust the angle, so that the monitor 74 can automatically adjust the angle according to the slope of different terrains to meet the needs of different terrains.

[0096] The specific working principle of the above-mentioned monitoring equipment is as follows: the storage module 33 and the circuit board 34 are both fixedly installed on the top of the mounting plate 31, the calculation module 35 and the execution processing module 36 are both fixedly connected to the top of the circuit board 34, when in use, the storage module 33 can save the monitored data, the calculation module 35 can calculate and analyze the growth trend of crops, and the execution processing module 36 can quantitatively calculate the elements required for crop growth according to the processing data of the calculation module 35, so as to realize quantitative intelligent control, which is more conducive to the growth of crops, and the fixed ground nail 42 is fixedly connected to the bottom of the fixed disc 41, and the fixed ground nail 42 can be inserted into the ground of the farmland. When in use, the staff can fix the fixed rod 43 through the fixed ground nail 42 , improves the convenience of fixing the fixing rod 43, the adjusting rod 45 is slidably connected to the top of the fixing rod 43, and the sliding of the adjusting rod 45 can adjust the height of the monitor 74, which is convenient for monitoring crops of different growth heights, meets the monitoring needs of different crops, and improves the scope of application. The adjusting bolt 44 is threadedly connected to one side of the fixing rod 43, and rotating the adjusting bolt 44 can fix the adjusted adjusting rod 45 to prevent the adjusting rod 45 from sliding, thereby improving the stability of the adjusting rod 45 when it is fixed. The stator coil 53 is fixedly installed inside the driving box 52, and the rotating disk 63 is fixedly installed at the bottom end of the connecting rod 62. When in use, the stator coil 53 is energized, and the stator coil 53 can change the magnetic field after being energized, and the rotating disk 63 It can rotate under the action of the magnetic field, and the rotation of the rotating disk 63 can drive the connecting rod 62 to rotate. The rotation of the connecting rod 62 can drive the monitor 74 to adjust the direction through the rotating seat 61, so as to realize all-round monitoring of crops and improve the monitoring efficiency. The limit groove 54 is arranged inside the driving box 52, and the limit groove 54 can limit the rotating ring 64 during rotation, so as to improve the stability during direction adjustment and reduce the shaking caused by the adjustment. The mounting frame 71 is fixedly mounted on the top of the rotating seat 61, and the rotating shaft 72 is rotatably connected to the inside of the mounting frame 71. The adjusting motor 75 is fixedly mounted on one side of the mounting frame 71. When in use, the adjusting motor 75 can drive the rotating shaft 72 to rotate, and the rotation of the rotating shaft 72 can drive the protective shell 73 rotates, and the rotation of the protective shell 73 can drive the monitor 74 to adjust the angle, so that the monitor 74 can automatically adjust the angle according to the slope of different terrains, meet the needs of different terrains, and improve the scope of application. When in use, the monitoring equipment is fixed inside the farmland, and the monitoring equipment monitors the real-time environmental status of the farmland, and records and saves the growth status of the crops. The computer of the monitoring equipment stores the monitored data, and analyzes and calculates the later growth trend of the crops through the computing system inside the processor 21, thereby realizing intelligent monitoring and early warning of unmanned farmland in a large range, no longer relying on manpower for monitoring, which can reduce the investment of human capital and improve the efficiency of farmland monitoring in large-scale agricultural production.According to the crop type information, the agricultural condition parameter type and comparison threshold required for monitoring the current farmland are obtained, the agricultural condition parameters of the farmland are obtained, the agricultural condition parameters are compared with the comparison threshold, and monitoring analysis is generated according to the comparison results. The farmland characteristic data terminal computer is stored, and management can be achieved through the terminal computer real-time observation of a variety of different farmland characteristic data uploaded by the sensing device, thereby realizing multi-dimensional, remote intelligent monitoring of the farmland and improving the monitoring efficiency and management efficiency of the farmland.

[0097] An embodiment of the present application also provides a computer device, including: a memory and a processor, wherein the memory is used to store program instructions; and the processor is connected to the memory and is used to execute the above-mentioned farmland area monitoring method.

[0098] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned farmland area monitoring method by running the computer program.

[0099] An embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the farmland area monitoring method in the present application are implemented.

[0100] An embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the farmland area monitoring method in the present application.

[0101] An embodiment of the present application also provides a computer program, which, when executed by a processor, implements the steps of the farmland area monitoring method in the present application.

[0102] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0103] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0107] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0108] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring a farmland area, characterized in that: include: Receiving monitoring data of a farmland area collected by a plurality of monitoring devices, wherein the plurality of monitoring devices are deployed at a plurality of different locations in the farmland area; The monitoring data of the farmland area collected by the multiple monitoring devices are fused according to the preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; Early warning information is generated based on the fused data, and the early warning information is used to warn of the status of the farmland area, and the status of the farmland area includes at least one of the following: light intensity in the farmland area, temperature in the farmland area, soil moisture in the farmland area, soil fertility in the farmland area and crop growth status in the farmland area.

2. The method according to claim 1, characterized in that Generating early warning information based on the fused data includes: Determining the growth status of crops in the farmland area from the fused data; Determining the growth stage of the crops in the farmland area based at least on the growth status of the crops in the farmland area; Determining an evaluation index threshold corresponding to the state of the farmland area based on the growth stage of the crops in the farmland area; The evaluation index of the state of the farmland area is compared with the evaluation index threshold, and when the comparison result indicates that the evaluation index of the state of the farmland area is higher than the evaluation index threshold, early warning information is generated.

3. The method according to claim 2, characterized in that The method further comprises: Determine time series data based on the fused data, wherein the time series data is used to characterize the state of the farmland area at different times; A seasonal model is used to analyze the time series data to predict the state of the farmland area within the forecast period.

4. The method according to claim 3, characterized in that: The seasonal model is used to analyze the time series data to predict the state of the farmland area in the predicted period, including: Obtaining non-seasonal parameters and seasonal parameters in the seasonal model, wherein the seasonal parameters include seasonal cycle, seasonal difference order, seasonal moving average term and seasonal frequency, and the non-seasonal parameters include autoregressive term, difference order and moving average term; constructing the seasonal model based on the non-seasonal parameters and the seasonal parameters; The time series data is used as the input of the seasonal model, and the state of the farmland area in the period to be predicted is output.

5. The method according to claim 4, characterized in that Obtaining non-seasonal parameters and seasonal parameters in the seasonal model, including: Obtaining an autocorrelation function graph and a partial autocorrelation function graph of the time series data, wherein the autocorrelation function graph is used to characterize the degree of correlation between the time series data and itself at different lags, and the partial autocorrelation function graph is used to characterize the correlation between a variable in the time series data and its own past values; The non-seasonal parameter and the seasonal parameter are determined based on the autocorrelation function graph and the partial autocorrelation function graph.

6. The method according to claim 5, characterized in that Determining the non-seasonal parameter and the seasonal parameter based on the autocorrelation function graph and the partial autocorrelation function graph includes: Determine the number of times of differentiation by performing differentiation on the time series data until the time series data is stable, and determine the number of times of differentiation as the differentiation order; Determining the number of lags before the cutoff point in the partial autocorrelation function graph as the autoregressive term; Determining the number of lags before the cutoff point in the autocorrelation function graph as the moving average term; determining the length of the seasonal period as the seasonal frequency; Perform seasonal differentiation on the time series data until the time series data is stable, determine the number of seasonal differences, and determine the number of seasonal differences as the seasonal difference order; The number of lags before the cutoff point in the seasonal partial autocorrelation function plot was determined as the seasonal autoregressive term; The seasonal moving average term is determined as the number of lags before the cutoff point in the seasonal autocorrelation function graph.

7. The method according to claim 1, characterized in that After determining the state of the farmland area within the period to be predicted, the method further includes: Determine the growth stage of the crops in the farmland area from the state of the farmland area in the time period to be predicted; Determining a maturity date of crops in the farmland area based on the growth stage of the crops in the farmland area; Determining a cultivation strategy for the crops in the farmland area based on the maturity date of the crops in the farmland area and the weather conditions in a target period, wherein the target period is a period between the current date and the maturity date; Crops in the farmland area are cultivated according to the cultivation strategy.

8. A farmland area monitoring device, characterized in that: include: An acquisition module, used for receiving monitoring data of a farmland area collected by a plurality of monitoring devices, wherein the plurality of monitoring devices are deployed at a plurality of different locations in the farmland area; A fusion module, used for fusing the monitoring data of the farmland area collected by the multiple monitoring devices according to preset weights corresponding to the multiple monitoring devices to obtain fused data, wherein the preset weights are determined according to the positions of the monitoring devices in the farmland area; An early warning module is used to generate early warning information based on the fused data, and the early warning information is used to warn the status of the farmland area, and the status of the farmland area includes at least one of the following: light intensity in the farmland area, temperature in the farmland area, soil moisture in the farmland area, soil fertility in the farmland area and crop growth status in the farmland area.

9. A monitoring device, characterized in that: Used to collect monitoring data, including: Main body and fixed components; A control component is provided on the top of the main body, wherein the control component includes a processor, the processor is fixedly installed on the top of the main body, a baffle is fixedly installed on the front of the processor, a power connection hole is provided on the front of the processor, a control key is fixedly installed on the top of the processor, a ventilation grille is provided on one side of the processor, a fixing frame is fixedly installed on the top of the processor, a display screen is embedded in the front of the fixing frame, a receiver is fixedly installed on the top of the fixing frame, a mounting plate is provided on the processor, a fixing pin is threadedly connected to the top of the mounting plate, a storage module is fixedly installed on the top of the mounting plate, a circuit board is fixedly installed on the top of the mounting plate, a computing module is embedded in the top of the circuit board, and an execution processing module is fixedly connected to the top of the circuit board.

10. The monitoring device according to claim 9, characterized in that The fixing assembly comprises: A fixed disc, the bottom of the fixed disc is fixedly connected with a fixed ground nail, the top of the fixed disc is fixedly installed with a fixed rod, one side of the fixed rod is threadedly connected with an adjusting bolt, the top of the fixed rod is slidably connected with an adjusting rod, the bottom end of the adjusting rod is fixedly connected with a limiting ring, the top of the adjusting rod is fixedly connected with a mounting disc, the top of the mounting disc is provided with a mounting hole, the inside of the adjusting rod is connected with a rubber tube, the top of the mounting disc is provided with a driving assembly, the driving assembly includes a shell, the shell is fixedly installed on the top of the mounting disc, the bottom of the shell is fixedly installed with a wiring tube, the bottom of the wiring tube is fixedly connected with a data cable, the inside of the shell is fixedly installed with a driving box, the inside of the driving box is fixedly installed with a stator coil, and the inside of the driving box is provided with a limiting groove. A rotating assembly is provided on the top of the shell, and the rotating assembly includes a rotating seat, which is rotatably connected to the top of the shell, a connecting rod is fixedly installed on the bottom of the rotating seat, a rotating magnetic disk is fixedly installed on the bottom end of the connecting rod, a rotating ring is fixedly connected to the bottom of the rotating magnetic disk, and the rotating ring is rotatably connected to the inside of the limiting groove, and a monitoring assembly is provided on the top of the rotating seat. The monitoring assembly includes a mounting frame, the mounting frame is fixedly installed on the top of the rotating seat, a rotating shaft is rotatably connected inside the mounting frame, a protective shell is rotatably connected inside the mounting frame through the rotating shaft, a monitor is fixedly installed inside the protective shell, an adjusting motor is fixedly installed on one side of the mounting frame, the output end of the adjusting motor is fixedly connected to one end of the rotating shaft, and a connecting line is fixedly connected to one side of the adjusting motor.

11. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory and is used to execute the farmland area monitoring method described in any one of claims 1 to 7.

12. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the farmland area monitoring method described in any one of claims 1 to 7 is implemented.

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