Modern agriculture digital management and control platform based on Internet of Things and big data

Abstract

The invention discloses a modern agricultural digital management and control platform based on the Internet of Things and big data, relates to the technical field of digital management, and solves the technical problem that potential influences existing in agricultural management are further difficult to find due to the fact that only data are monitored and deep analysis cannot be carried out according to anomalies displayed by the data. According to the invention, the regional information of different regions is analyzed, the regional state is identified according to the regional information, and for the abnormal region obtained through identification, the data is analyzed, specific parameters are determined based on the obtained data, and comprehensive analysis is carried out in combination with history. In the analysis process, a corresponding risk prediction model is established through big data, periodic monitoring is performed on the data, then corresponding abnormality adjustment is performed in combination with a periodic monitoring result, and risk prediction is performed by using the risk prediction model, so that the utilization rate of the data is improved, and meanwhile, the agricultural management situation can be integrally analyzed through the data. And proper adjustment is carried out.

Description

Technical Field

[0001] The present invention relates to the field of digital management technology, and in particular to a modern agricultural digital management and control platform based on the Internet of Things and big data. Background Art

[0002] The Modern Agricultural Digital Management and Control Platform is a comprehensive platform that integrates advanced technologies and agricultural management. By leveraging modern information technologies such as the Internet of Things, artificial intelligence, and big data analytics, it enables precise management and efficient operation of the entire agricultural production process, aiming to promote intelligent, efficient, and green agricultural production.

[0003] According to Chinese patent application number CN115953116A, a digital fund security management and control method and platform are disclosed, which includes the following steps: creating and publishing a project; dividing work and recording, and generating a draft file; confirming the draft file; conducting a security check based on the confirmed draft file and generating an inspection report; making security adjustments based on the inspection report; verifying the adjustment results and ending after approval.

[0004] The above invention first creates a project, then divides the work and records it based on the created project. Based on the division of labor, different entities separately confirm the draft documents, ensuring the compliance of the confirmation process. Security checks are then conducted based on the confirmed draft documents, improving inspection efficiency and security. A systematic fund security inspection model is established, consolidating scattered inspection management into a single platform. This significantly shortens the average security inspection time, saves labor costs, and significantly improves work efficiency.

[0005] When some existing digital management and control platforms are applied in modern agriculture, they only provide a monitoring function. When there are abnormal situations, they cannot fundamentally analyze the abnormal problems, which further leads to inconvenience in subsequent management. Secondly, they cannot analyze potential risks from the obtained data and cannot timely discover the potential impact of agricultural management. Summary of the Invention

[0006] In response to the shortcomings of existing technologies, the present invention provides a modern agricultural digital management and control platform based on the Internet of Things and big data, which solves the problem of only monitoring data but not being able to conduct in-depth analysis based on anomalies displayed by the data, and further makes it difficult to discover the potential impact of agricultural management.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a modern agricultural digital management and control platform based on the Internet of Things and big data, comprising:

[0008] The regional information acquisition module is used to acquire regional information of different regions and transmit the acquired regional information to the regional comprehensive analysis module;

[0009] The regional comprehensive analysis module is used to monitor and analyze the regional status of different regions based on the regional information transmitted by the regional information acquisition module, and use standard parameters to compare with the regional information to generate normal monitoring signals and regional analysis signals, and transmit the regional analysis signals to the abnormal regional analysis module;

[0010] The abnormal area analysis module is used to process the acquired regional analysis signals, screen the abnormal parameters of the abnormal area, compare them with the normal area parameters to generate a judgment result, perform a secondary analysis on the normal results in the judgment result, and adjust the abnormal area in combination with historical data to generate adjustment information, and then transmit the adjustment information to the periodic monitoring and analysis module;

[0011] The periodic monitoring and analysis module is used to analyze the acquired adjustment information and perform periodic monitoring of abnormal areas based on the adjustment information. At the same time, it determines the regional status corresponding to the abnormal area after adjustment, performs secondary analysis based on the regional status to determine the cause of the abnormality, and performs risk prediction based on the cause of the abnormality to generate prediction results, and then transmits the prediction results to the management information output module.

[0012] Management information output module, which is used to display the acquired normal monitoring signals and prediction results to the corresponding operators.

[0013] As a further solution of the present invention, the specific method in which the regional comprehensive analysis module generates normal monitoring signals and regional analysis signals by comparing standard parameters with regional information is as follows:

[0014] All areas are acquired and labeled as i, and i = 1, 2, ..., j, where j represents the number of areas. At the same time, the area information corresponding to area i is acquired, and the area information is compared with the standard parameters. When the area information is different from the standard parameters, a regional analysis signal is generated and the regional analysis signal is transmitted to the abnormal area analysis module. On the contrary, when the area information is the same as the standard parameters, a normal monitoring signal is generated and the normal monitoring signal is transmitted to the management information output unit.

[0015] As a further solution of the present invention: the specific manner in which the abnormal region analysis module processes the region analysis signal is as follows:

[0016] All abnormal areas are obtained and recorded as a, and a=1, 2, ..., b, where b represents the number of abnormal data. At the same time, the regional parameters corresponding to the abnormal area a are obtained, and the regional parameters are screened to obtain the abnormal parameters. Then all normal areas are obtained and recorded as n, and n=1, 2, ..., m, where m represents the number of normal areas. At the same time, the regional parameters corresponding to the normal area n are obtained and recorded as normal parameters, and the corresponding normal intervals are generated with the normal parameters. Then the abnormal parameters are matched with the normal intervals.

[0017] As a further solution of the present invention, the abnormal region analysis module matches abnormal parameters with normal intervals in the following specific manner:

[0018] When the abnormal parameter does not exist in the normal interval, it means that the abnormal parameter matches the normal area, and the difference between the abnormal parameter and the normal interval is calculated, and the adjustment information is generated based on the difference. Conversely, when the abnormal parameter exists in the normal interval, it means that the abnormal parameter matches the normal interval, and the historical data corresponding to the abnormal area is obtained, and the abnormal parameters are analyzed in combination with the historical data.

[0019] As a further solution of the present invention, the abnormal area analysis module analyzes abnormal parameters in combination with historical data in the following specific manner:

[0020] Obtain historical data of the abnormal area, and obtain the corresponding records of the same abnormal parameters in the historical data, and record them as similar records. Then determine the corresponding area status in the similar records. If the corresponding area status is normal, generate a normal monitoring signal. If the corresponding area status is abnormal, obtain the corresponding similar records and label them as o, where o = 1, 2, ..., p, where p represents the number of similar records;

[0021] Then the adjustment parameters corresponding to the similar record o are obtained, and then the mean of the adjustment parameters of all similar records o is calculated, and the calculated mean is used as the adjustment standard. At the same time, the abnormal area is adjusted and analyzed according to the adjustment standard and adjustment information is generated. Then the adjustment information is transmitted to the periodic monitoring and analysis module.

[0022] As a further solution of the present invention: the specific manner in which the periodic monitoring and analysis module analyzes the adjustment information is as follows:

[0023] Obtain the generated adjustment information, and adjust the abnormal area according to the adjustment information. At the same time, record the adjusted abnormal area as the monitoring area. Then monitor and analyze the monitoring area with time t as a period, and obtain the regional status corresponding to the monitoring area within the time period t. If the regional status of the monitoring area is abnormal, generate a secondary analysis signal. If the regional status of the monitoring area is normal, generate a normal monitoring signal. At the same time, transmit the normal monitoring signal to the management information output module, and process the generated secondary analysis signal.

[0024] As a further solution of the present invention, the specific method in which the periodic monitoring and analysis module processes the secondary analysis signal is as follows:

[0025] The abnormal parameters in the monitoring area are screened and recorded as parameters to be analyzed, and the parameters to be analyzed are compared with the abnormal parameters. If the parameters to be analyzed are the same as the abnormal parameters, historical data are obtained and a risk prediction model is established. The parameters to be analyzed are substituted into the risk prediction model to generate a prediction result, where the prediction result includes risk signals and normal signals, and the prediction result is transmitted to the management information output module at the same time;

[0026] If the parameter to be analyzed is different from the abnormal parameter, similar records matching the parameter to be analyzed in the historical data are obtained, and adjustment parameters are generated based on the similar records, and the parameter to be analyzed is adjusted. The adjusted parameter to be analyzed is then monitored, and the risk prediction model is used to analyze and generate prediction results, which are then transmitted to the management information output module.

[0027] Beneficial effects

[0028] This invention provides a modern agricultural digital management and control platform based on the Internet of Things and big data. Compared with existing technologies, it has the following advantages:

[0029] The present invention analyzes regional information of different areas and identifies regional status based on the regional information. For the identified abnormal areas, the present invention further analyzes the data, determines specific parameters based on the acquired data, and conducts comprehensive analysis in combination with history. During the analysis process, a risk prediction model is established through big data, and the data is periodically monitored. Then, corresponding abnormal adjustments are made based on the periodic monitoring results, and risk prediction models are used to predict risks, thereby improving data utilization. At the same time, the agricultural management situation can be analyzed as a whole through data and appropriate adjustments can be made. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] See also Figure 1 This application provides a modern agricultural digital management and control platform based on the Internet of Things and big data, including: regional information collection module, regional comprehensive analysis module, abnormal area analysis module, periodic monitoring and analysis module and management information output module.

[0033] The regional information acquisition module is used to obtain regional information of different regions and transmit the obtained regional information to the regional comprehensive analysis module. The obtained regional information is mainly agricultural environmental information of the corresponding region, such as irrigation volume, environmental index, crop growth conditions and fertilization conditions, etc., and when the above regional information is subsequently analyzed, it is quantified and its numerical value is calculated and analyzed.

[0034] For example, irrigation volume may be measured using flow sensors installed in the field, with units such as cubic meters per mu. For example, if the irrigation volume for a particular area is 800 cubic meters per mu, the environmental index can include a comprehensive assessment of multiple indicators such as temperature, humidity, and light intensity. Assuming the environmental index for a particular area is 80 (a range of values ​​can be set based on specific assessment criteria), crop growth can be quantitatively assessed using characteristics such as plant height, leaf color, and fruit size. For example, the quantitative value of crop growth in a particular area could be 70 (a range of values ​​can also be determined based on specific quantitative criteria). Fertilization can be expressed as the amount of fertilizer applied per unit area, such as 50 kg per mu.

[0035] Regional comprehensive analysis module, which is used to monitor and analyze the regional status of different regions based on the acquired regional information, and use standard parameters to compare with regional information to generate normal monitoring signals and regional analysis signals. The specific comparison method is as follows:

[0036] All areas are acquired and labeled as i, and i = 1, 2, ..., j, where j represents the number of areas. At the same time, the area information corresponding to area i is acquired, and the area information is compared with the standard parameters. The standard parameters here are parameter values ​​preset by the operator, and the values ​​are set according to the actual situation. When the area information is different from the standard parameters, it indicates that there is an abnormality in the corresponding area, and a regional analysis signal is generated. The regional analysis signal is transmitted to the abnormal area analysis module. On the contrary, when the area information is the same as the standard parameters, it indicates that the corresponding area is normal, and a normal monitoring signal is generated. The normal monitoring signal is transmitted to the management information output unit.

[0037] For example, assuming the standard parameter range for irrigation volume is 700-900 cubic meters per mu, if the irrigation volume in area 3 is 950 cubic meters per mu, which exceeds the standard parameter range, this indicates an abnormality in that area. A regional analysis signal is generated and transmitted to the abnormal area analysis module for further analysis of the cause of the abnormality and appropriate treatment measures.

[0038] For the environmental index, assuming the standard parameter is 75-85, if the environmental index of area 4 is 60, which is lower than the standard parameter range, a regional analysis signal will also be generated and transmitted to the abnormal area analysis module.

[0039] For crop growth conditions, assuming the standard quantization value range is 60-80, if the crop growth quantization value of region 2 is 45, which is lower than the standard range, a regional analysis signal will also be generated for transmission.

[0040] For fertilization, assuming the standard parameter is 40-60 kg / mu, if the fertilizer application rate in area 5 is 70 kg / mu, which exceeds the standard range, a regional analysis signal will also be generated.

[0041] Conversely, if the regional information matches the standard parameters, the corresponding area is normal. For example, if the irrigation volume in Area 1 is 850 cubic meters per mu, the environmental index is 82, the crop growth quantitative value is 75, and the fertilizer application rate is 55 kilograms per mu—all within the standard parameter range—a normal monitoring signal is generated and transmitted to the management information output unit, allowing managers to promptly understand the normal operating status of each area.

[0042] The abnormal area analysis module is used to process the acquired regional analysis signal, screen the abnormal parameters of the abnormal area, and compare them with the normal area parameters to generate a judgment result. The specific method of generating the judgment result is:

[0043] All abnormal areas are obtained and recorded as a, and a=1, 2, ..., b, where b represents the number of abnormal data, and the abnormal area refers to the area i that generates the regional analysis signal. At the same time, the regional parameters corresponding to the abnormal area a are obtained, and the regional parameters are screened to obtain the abnormal parameters. For example, in the agricultural production scenario, for the irrigation amount parameter, it is assumed that the standard parameter is set to 700-900 cubic meters per mu. If the irrigation volume of an abnormal area is 1000 cubic meters per mu, which is obviously beyond the standard range, the irrigation volume is filtered as the abnormal parameter of the abnormal area. The screening method here is to compare the regional parameter with the standard parameter. At the same time, the standard parameter is set by the operator and is pre-stored data. Then all normal areas are obtained and recorded as n, and n = 1, 2, ..., m, where m represents the number of normal areas. The normal area here refers to the area that generates a normal monitoring signal. At the same time, the regional parameter corresponding to the normal area n is obtained and recorded as the normal parameter. The corresponding normal interval is generated using the normal parameter, and then the abnormal parameter is matched with the normal interval. The specific normal interval represents the interval value consisting of the minimum normal parameter and the maximum normal parameter, which includes the regional parameters of all normal areas. For example, for the fertilizer amount parameter, assuming that the fertilizer amounts of multiple normal areas are 45 kg, 50 kg, 52 kg, etc. per mu, then the minimum normal parameter is 45 kg per mu, the maximum normal parameter is 52 kg per mu, and the normal interval is [45,52] kg / mu.

[0044] When the abnormal parameter does not exist in the normal interval, it means that the abnormal parameter matches the normal area, and the difference between the abnormal parameter and the normal interval is calculated. The difference calculation is performed based on the minimum value of the normal interval, and the adjustment information is generated based on the difference. For example, the amount of fertilizer applied in a certain abnormal area is 60 kg per mu, which exceeds the above-mentioned normal interval [45,52] kg / mu. At this time, the difference between the abnormal parameter and the normal interval is calculated, and the difference calculation is performed based on the minimum value of the normal interval. Assume that the difference here is 60-45=15 kg / mu, and the adjustment information is generated based on the difference. For example, it can be considered to reduce the amount of fertilizer by about 15 kg / mu. On the contrary, when the abnormal parameter exists in the normal interval, it means that the abnormal parameter matches the normal interval, and the historical data corresponding to the abnormal area is obtained, and the abnormal parameters are analyzed in combination with the historical data.

[0045] A secondary analysis is performed on the normal results in the judgment results, and the abnormal areas are adjusted in combination with historical data to generate adjustment information. The specific method of generating the adjustment information is:

[0046] Obtain historical data of the abnormal area, and obtain the corresponding records of the same abnormal parameters in the historical data. The corresponding records exist in the specific default historical data, and it is indicated here that the abnormal parameters are matched. If there are records in the historical data that match the current abnormal parameters, the specific matching is indicated by the same parameters or similar parameter values ​​of the two. For example, the difference between the parameters of the two is within the preset value range. The specific preset value is set by the operator. For example, for the irrigation amount parameter, the preset value is set to a difference range of 50 cubic meters per mu. If the current irrigation volume of the abnormal area is 850 cubic meters per mu, and the historical data records it as 800 cubic meters per mu, then the difference between the two is 50 cubic meters. Within the preset value range, the record is screened and recorded as a similar record. The corresponding records are screened and recorded as similar records. Then, the corresponding area status in the similar records is judged. If the corresponding area status is normal, it means that the current abnormal parameters are normal, and a normal monitoring signal is generated. If the corresponding area status is abnormal, the corresponding similar record is obtained, and the similar record is labeled as o, and o=1, 2,..., p, where p represents the number of similar records.

[0047] Next, the adjustment parameters corresponding to the similar record o are obtained. The specific parameters obtained are the same data as the current abnormal parameter. For example, if the current parameter is irrigation volume, the obtained parameter is also irrigation volume. Then, the adjustment parameters of all similar records o are averaged and the calculated average is used as the adjustment standard. At the same time, the abnormal area is adjusted and analyzed according to the adjustment standard and adjustment information is generated. The adjustment information is then transmitted to the periodic monitoring and analysis module. For example, if the current parameter is irrigation volume, the obtained parameter is also irrigation volume. Suppose there are three similar records with irrigation volume adjustment parameters of 30 cubic meters per mu, 40 cubic meters per mu, and 35 cubic meters per mu respectively.

[0048] The periodic monitoring and analysis module is used to analyze the acquired adjustment information, and periodically monitor the abnormal area based on the adjustment information, and determine the regional status corresponding to the abnormal area after adjustment. The specific determination method is:

[0049] Obtain the generated adjustment information, and adjust the abnormal area according to the adjustment information. At the same time, record the adjusted abnormal area as the monitoring area. Then monitor and analyze the monitoring area with time t as a period, and obtain the regional status corresponding to the monitoring area within the time period t. The specific method is to compare the regional parameters of the monitoring area with the standard parameters. If the regional status of the monitoring area is abnormal, a secondary analysis signal is generated. If the regional status of the monitoring area is normal, a normal monitoring signal is generated, and the normal monitoring signal is transmitted to the management information output module.

[0050] At the same time, secondary analysis is performed based on the regional status to determine the cause of the abnormality, and risk prediction is performed based on the abnormality to generate prediction results. The specific method of generating the prediction results is:

[0051] The abnormal parameters of the monitoring area are screened and recorded as parameters to be analyzed, and the parameters to be analyzed are compared with the abnormal parameters. The abnormal parameters here are the parameters corresponding to the abnormal area before adjustment. If the parameters to be analyzed are the same as the abnormal parameters, it specifically means that the data types corresponding to the parameters to be analyzed and the abnormal parameters are the same. For example, the irrigation volume is also abnormal. Assuming that the irrigation volume of the abnormal area before adjustment is 1,200 cubic meters per mu, after screening, the parameters to be analyzed are also abnormal irrigation volume. Then, historical data is obtained and a risk prediction model is established. The risk prediction model is established through an artificial intelligence algorithm and combined with big data. For example, historical data of similar abnormal irrigation volume situations in the past can be collected, including subsequent developments, measures taken, and final results. The model is trained using these data. The specific establishment method is existing technology and will not be elaborated on here. The parameters to be analyzed are substituted into the risk prediction model, and the risk prediction model generates a prediction result through analysis, wherein the prediction result includes risk signals and normal signals. At the same time, the prediction result is transmitted to the management information output module.

[0052] If the parameter to be analyzed and the abnormal parameter differ, similar records matching the parameter to be analyzed are retrieved from the historical data. For example, if the abnormal parameter is an abnormal irrigation amount, and the parameter to be analyzed is abnormal soil pH, similar records with abnormal soil pH are searched in the historical data. Adjustment parameters are generated based on these similar records and used to adjust the parameter to be analyzed. For example, if similar records reveal that soil pH reaches a certain value, adding a specific soil conditioner can adjust the pH. Based on this information, adjustment parameters are generated and the current parameter to be analyzed (abnormal soil pH) is adjusted. The adjusted parameter to be analyzed is then monitored and analyzed using a risk prediction model to generate a prediction result, which includes both risk signals and normal signals. The prediction result is then transmitted to the management information output module.

[0053] Management information output module, which is used to display the acquired normal monitoring signals and prediction results to the corresponding operators.

[0054] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0055] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A modern agricultural digital management and control platform based on the Internet of Things and big data, characterized by: include: The regional comprehensive analysis module is used to monitor and analyze the regional status of different regions based on the regional information transmitted by the regional information acquisition module, and use standard parameters to compare with the regional information to generate normal monitoring signals and regional analysis signals, and transmit the regional analysis signals to the abnormal regional analysis module; The abnormal area analysis module is used to process the acquired regional analysis signals, screen the abnormal parameters of the abnormal area, compare them with the normal area parameters to generate a judgment result, perform a secondary analysis on the normal results in the judgment result, and adjust the abnormal area in combination with historical data to generate adjustment information, and then transmit the adjustment information to the periodic monitoring and analysis module; The periodic monitoring and analysis module is used to analyze the acquired adjustment information and perform periodic monitoring of abnormal areas based on the adjustment information. At the same time, it determines the regional status corresponding to the abnormal area after adjustment, performs secondary analysis based on the regional status to determine the cause of the abnormality, and performs risk prediction based on the cause of the abnormality to generate prediction results, and then transmits the prediction results to the management information output module.

2. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 1, characterized in that: It also includes a regional information collection module and a management information output module; The regional information acquisition module is used to acquire regional information of different regions and transmit the acquired regional information to the regional comprehensive analysis module; Management information output module, which is used to display the acquired normal monitoring signals and prediction results to the corresponding operators.

3. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 1, characterized in that: The specific method in which the regional comprehensive analysis module generates normal monitoring signals and regional analysis signals by comparing standard parameters with regional information is as follows: Obtain all regions i and their corresponding region information, where i = 1, 2, ..., j, where j represents the number of regions, and compare the region information with the standard parameters. When the region information is different from the standard parameters, a regional analysis signal is generated. Conversely, when the region information is the same as the standard parameters, a normal monitoring signal is generated. Then the regional analysis signal is transmitted to the abnormal region analysis module, and the normal monitoring signal is transmitted to the management information output unit.

4. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 1, characterized in that: The specific method in which the abnormal region analysis module processes the region analysis signal is as follows: Obtain all abnormal regions a and their corresponding regional parameters, where a=1, 2, ..., b, where b represents the number of abnormal regions, and filter the regional parameters to obtain abnormal parameters. At the same time, obtain all normal regions n and their corresponding normal parameters, where n=1, 2, ..., m, where m represents the number of normal regions, and generate corresponding normal intervals with normal parameters. Then match the abnormal parameters with the normal intervals.

5. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 4, characterized in that: The specific way in which the abnormal area analysis module matches abnormal parameters with normal intervals is as follows: When the abnormal parameter does not exist in the normal range, the difference between the abnormal parameter and the normal range is calculated, and the adjustment information is generated based on the difference. Conversely, when the abnormal parameter exists in the normal range, the historical data corresponding to the abnormal area is obtained, and the abnormal parameter is analyzed in combination with the historical data.

6. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 5, characterized in that: The specific method by which the abnormal area analysis module analyzes abnormal parameters in combination with historical data is as follows: Obtain the same abnormal parameter records in the historical data of the abnormal area, record them as similar records, and determine the corresponding area status in the similar records. If the corresponding area status is normal, generate a normal monitoring signal. If the corresponding area status is abnormal, obtain the corresponding similar records and label them as o, where o = 1, 2, ..., p, where p represents the number of similar records; Then the adjustment parameters corresponding to the similar record o are obtained, and then the mean of the adjustment parameters of all similar records o is calculated, and the calculated mean is used as the adjustment standard. At the same time, the abnormal area is adjusted and analyzed according to the adjustment standard and adjustment information is generated. Then the adjustment information is transmitted to the periodic monitoring and analysis module.

7. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 1, characterized in that: The specific method in which the periodic monitoring and analysis module analyzes the adjustment information is as follows: The abnormal interval adjusted according to the adjustment information is recorded as a monitoring area, and the regional status corresponding to the monitoring area within the time period t is obtained. If the regional status of the monitoring area is abnormal, a secondary analysis signal is generated; if the regional status of the monitoring area is normal, a normal monitoring signal is generated; At the same time, the normal monitoring signal is transmitted to the management information output module, and the generated secondary analysis signal is processed.

8. A modern agricultural digital management and control platform based on the Internet of Things and big data according to claim 7, characterized in that: The specific way in which the periodic monitoring and analysis module processes the secondary analysis signal is as follows: The abnormal parameters in the monitoring area are screened and recorded as parameters to be analyzed, and the parameters to be analyzed are compared with the abnormal parameters. If the parameters to be analyzed are the same as the abnormal parameters, the parameters to be analyzed are substituted into the risk prediction model to generate a prediction result, where the prediction result includes risk signals and normal signals; If the parameter to be analyzed is different from the abnormal parameter, similar records matching the parameter to be analyzed in the historical data are obtained, and adjustment parameters are generated based on the similar records, and the parameter to be analyzed is adjusted. The adjusted parameter to be analyzed is then monitored, and the risk prediction model is used to analyze and generate prediction results, which are then transmitted to the management information output module.

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