Agricultural planting method timely adjustment system and method based on big data analysis

Abstract

The invention discloses an agricultural planting method timely adjustment system and method based on big data analysis, and belongs to the technical field of agricultural planting, and the system comprises a data acquisition module, a data processing module, a planting analysis module and an adjustment management and control module. The method solves the problem of poor agricultural planting effect caused by incapability of timely adjusting the agricultural planting method according to the actual growth state of crops in the existing agricultural planting, and determines the agricultural planting characterization data by collecting the agricultural planting real-time data and comprehensively processing the agricultural planting real-time data. The agricultural planting characterization data is analyzed, an agricultural planting analysis result and an agricultural planting method timely adjustment scheme are determined, the agricultural planting method is timely adjusted and controlled based on the agricultural planting method timely adjustment scheme, and the agricultural planting method can be timely adjusted according to the actual growth state of crops. Crops can grow quickly, and the agricultural planting effect can be improved.

Description

Technical Field

[0001] The present invention relates to the field of agricultural planting technology, and specifically to a system and method for timely adjusting agricultural planting methods based on big data analysis. Background Art

[0002] Planting is one of the main components of agriculture. It is a social production sector that uses the life functions of plants to obtain food, non-staple food, feed and industrial raw materials through artificial cultivation. It includes the cultivation of various crops, trees, fruit trees, medicinal and ornamental plants. Among them, agricultural planting greenhouses are often used for planting. Agricultural planting greenhouses can provide an excellent growth environment. The speed of crop germination and growth is faster than in the natural state. Especially in the face of changes in temperature and humidity in the four seasons, agricultural planting greenhouses are necessary facilities for cultivating cross-season plants and agricultural products.

[0003] In the existing agricultural planting, during the growth process of crops, the agricultural planting method cannot be adjusted in time according to the actual growth status of the crops, resulting in slow growth of crops and poor agricultural planting effects. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for timely adjustment of agricultural planting methods based on big data analysis, which can timely adjust the agricultural planting methods according to the actual growth status of crops, so that crops can grow rapidly, improve agricultural planting effects, and solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A system for timely adjusting agricultural planting methods based on big data analysis, comprising:

[0007] A data acquisition module is used to collect environmental parameters and soil parameters during agricultural planting, and determine real-time agricultural planting data based on the collected environmental parameters and soil parameters;

[0008] The data processing module is used to perform comprehensive processing on the real-time data of agricultural planting based on data retrieval, data conversion, feature extraction and data calculation to determine the representation data of agricultural planting;

[0009] A planting analysis module is used to analyze agricultural planting characterization data based on pre-set agricultural planting standard data to determine agricultural planting analysis results;

[0010] The adjustment and control module is used to mine and analyze the agricultural planting analysis results, determine the timely adjustment plan of the agricultural planting method, and timely adjust and control the agricultural planting method based on the timely adjustment plan of the agricultural planting method.

[0011] Preferably, the data acquisition module includes:

[0012] An environmental collection unit is used to collect the environmental temperature, humidity, light and rainfall during the agricultural planting process, and determine the environmental parameters of the agricultural planting process based on the collected environmental temperature, humidity, light and rainfall;

[0013] A soil collection unit is used to collect soil pH, soil nutrients and soil moisture during the agricultural planting process, and to determine soil parameters during the agricultural planting process based on the collected soil pH, soil nutrients and soil moisture;

[0014] Based on the collected environmental parameters and soil parameters, real-time agricultural planting data is determined.

[0015] Preferably, the data acquisition module further includes:

[0016] A real-time monitoring module is used to monitor the data collection time of each sensor involved in environmental collection and soil collection in real time;

[0017] An actual time interval acquisition module is used to acquire the actual time interval of data collection corresponding to each sensor through the data collection moment;

[0018] A time interval difference acquisition module, used for comparing the actual time interval of data collection corresponding to each sensor with the theoretical collection time interval of the sensor to obtain the time interval difference;

[0019] A sensor usage time acquisition module, used to retrieve the sensor usage time when the time interval difference exceeds a preset difference threshold;

[0020] The sensor aging coefficient acquisition module is used to obtain the sensor aging coefficient by using the sensor usage time; wherein the sensor aging coefficient is obtained by the following formula:

[0021]

[0022]

[0023] Where f represents the sensor aging factor; m represents the number of time intervals included in the sensor data acquisition process; t ci represents the time length corresponding to the i-th time interval; T i Indicates the time length corresponding to the theoretical acquisition time interval; λ i represents the humidity ratio of the environment where the sensor is located corresponding to the i-th time interval; n represents the number of complete unit times contained in the usage time experienced by the sensor, and the value range of the unit time is 30 days-60 days; P dIndicates the rate of increase of the theoretical data acquisition response delay per unit time of sensor operation; Δt p Indicates the change in time interval corresponding to each percentage increase in the theoretical data acquisition response delay; t c The length of the time interval corresponding to when the time interval difference exceeds the preset difference threshold; ξ represents the aging coefficient compensation factor;

[0024] An operation stability index parameter acquisition module, used for comparing the sensor aging coefficient with a preset aging coefficient threshold, and acquiring the operation stability index parameter of the current sensor according to the quantitative relationship between the sensor aging coefficient and the preset aging coefficient threshold;

[0025] The abnormal operation alarm module is used to issue a sensor abnormal operation alarm when the operation stability index parameter of the sensor is lower than a preset stability parameter threshold.

[0026] Preferably, the operation stability index parameter acquisition module includes:

[0027] A comparison execution module, used for comparing the sensor aging coefficient with a preset aging coefficient threshold value to obtain a comparison result;

[0028] A first operation stability index parameter acquisition module, used for acquiring the operation stability index parameter through a first stability parameter model when the comparison result shows that the sensor aging coefficient is lower than a preset aging coefficient threshold;

[0029] Wherein, the structure of the first stability parameter model is as follows:

[0030]

[0031]

[0032]

[0033] Among them, Q 01 represents the first stability parameter model to obtain the operation stability index parameter; f0 represents the preset coefficient threshold; E 01 and E 02 Represent the first parameter variable and the second parameter variable respectively; P tj represents the acquisition response time delay rate of the sensor corresponding to the jth unit time; j Indicates the average humidity ratio of the sensor environment corresponding to the jth unit time;

[0034] A second operation stability index parameter acquisition module, used for acquiring the operation stability index parameter through a second stability parameter model when the comparison result shows that the sensor aging coefficient is not lower than a preset aging coefficient threshold;

[0035] Wherein, the structure of the second stability parameter model is as follows:

[0036]

[0037]

[0038]

[0039] Among them, Q 02 It indicates that the second stability parameter model obtains the operation stability index parameter; E 03 and E 04 represent the third parameter variable and the fourth parameter variable respectively; k represents the number of unit times when the humidity ratio exceeds the preset humidity threshold; P kj Indicates the sensor acquisition response time delay rate corresponding to the jth unit time when the humidity ratio exceeds the preset humidity threshold; λ kj It indicates the average humidity ratio of the sensor environment corresponding to the jth unit time when the humidity ratio exceeds the preset humidity threshold; λ0 indicates the humidity threshold.

[0040] Preferably, the data processing module includes:

[0041] A data retrieval unit, used to retrieve real-time agricultural planting data;

[0042] Acquire agricultural planting real-time data, search the agricultural planting real-time data one by one based on a sequential search method, filter out the agricultural planting real-time data that is not valuable for timely adjustment of agricultural planting methods, and determine the agricultural planting real-time data that is valuable for timely adjustment of agricultural planting methods;

[0043] A data conversion unit, used for converting the format of the retrieved real-time agricultural planting data;

[0044] Acquire the real-time agricultural planting data that is valuable for timely adjustment of agricultural planting methods, and convert the format of the real-time agricultural planting data to standardize the real-time agricultural planting data to form standardized real-time agricultural planting data;

[0045] A feature extraction unit, used for extracting features from the real-time agricultural planting data after format conversion;

[0046] Acquire standardized real-time agricultural planting data, extract features from the real-time agricultural planting data, and determine agricultural planting feature data;

[0047] A data calculation unit, used for calculating agricultural planting characteristic data;

[0048] Agricultural planting characteristic data are obtained, and the agricultural planting characteristic data are calculated to determine the agricultural planting representation data.

[0049] Preferably, the planting analysis module includes:

[0050] A data storage unit, used to store pre-set agricultural planting standard data;

[0051] A data indexing unit, used to index pre-set agricultural planting standard data;

[0052] Acquire agricultural planting characterization data, and index pre-set agricultural planting standard data based on the agricultural planting characterization data;

[0053] A data retrieval unit, used to retrieve pre-set agricultural planting standard data;

[0054] Obtaining the indexed pre-set agricultural planting standard data, and retrieving the pre-set agricultural planting standard data;

[0055] A comparative analysis unit for analyzing agricultural planting representation data;

[0056] Obtain agricultural planting characterization data and agricultural planting standard data, analyze the agricultural planting characterization data based on the agricultural planting standard data, and determine the agricultural planting analysis results.

[0057] Preferably, the agricultural planting characterization data is analyzed based on the agricultural planting standard data, and the following operations are performed:

[0058] Among them, if the agricultural planting characterization data is within the range of agricultural planting standard data, the agricultural planting analysis result is that the agricultural planting method is normal and there is no need to adjust the agricultural planting method in a timely manner;

[0059] Among them, if the agricultural planting characterization data is not within the range of agricultural planting standard data, the agricultural planting analysis result is that the agricultural planting method is abnormal, and the agricultural planting method needs to be adjusted in time.

[0060] Preferably, the adjustment control module includes:

[0061] A mining and analysis unit, used for mining and analyzing agricultural planting analysis results;

[0062] Obtaining agricultural planting analysis results, and conducting mining and analysis on the agricultural planting analysis results, determining the essential causes of agricultural planting in the agricultural planting analysis results, and determining a timely adjustment plan for agricultural planting methods based on the essential causes of agricultural planting;

[0063] Adjustment and control unit, used to adjust and control agricultural planting methods in a timely manner;

[0064] Obtain timely adjustment plans for agricultural planting methods, and make timely adjustments and controls on agricultural planting methods based on the timely adjustment plans.

[0065] Preferably, the agricultural planting method is timely adjusted and controlled based on the agricultural planting method timely adjustment plan, and the following operations are performed:

[0066] Among them, in the case where the agricultural planting method is normal, the agricultural planting method is adjusted in time by real-time monitoring of the planting status of crops planted in the agricultural planting greenhouse, and transmitting the real-time monitored planting status of crops to the remote terminal;

[0067] Among them, in response to abnormal situations in agricultural planting methods, the timely adjustment plan for agricultural planting methods is to provide timely warnings, and timely adjust the fertilization and irrigation system and lighting and ventilation system of the agricultural planting greenhouse through the remote terminal, so that the crops planted in the agricultural planting greenhouse are always in the best planting state.

[0068] According to another aspect of the present invention, a method for timely adjusting agricultural planting methods based on big data analysis is provided, which is implemented based on the above-mentioned system for timely adjusting agricultural planting methods based on big data analysis, and includes the following steps:

[0069] S1: Collect environmental parameters and soil parameters during agricultural planting through the data acquisition module, and determine real-time agricultural planting data based on the collected environmental parameters and soil parameters;

[0070] S2: The data processing module performs comprehensive processing on the real-time agricultural planting data based on data retrieval, data conversion, feature extraction and data calculation to determine the agricultural planting representation data;

[0071] S3: Analyze the agricultural planting characterization data through the agricultural planting standard data pre-set in the planting analysis module to determine the agricultural planting analysis results;

[0072] S4: Through the adjustment and control module, the agricultural planting analysis results are mined and analyzed to determine the timely adjustment plan of the agricultural planting method, and the agricultural planting method is timely adjusted and controlled based on the timely adjustment plan of the agricultural planting method.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention determines real-time agricultural planting data by collecting environmental parameters and soil parameters in the agricultural planting process, comprehensively processes the real-time agricultural planting data, determines agricultural planting characterization data, analyzes the agricultural planting characterization data based on agricultural planting standard data, determines agricultural planting analysis results, mines and analyzes the agricultural planting analysis results, determines a timely adjustment plan for the agricultural planting method, timely adjusts and controls the agricultural planting method based on the timely adjustment plan for the agricultural planting method, can timely adjust the agricultural planting method according to the actual growth status of the crops, so that the crops grow rapidly, and the agricultural planting effect can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A module diagram of a system for timely adjusting an agricultural planting method based on big data analysis of the present invention;

[0076] Figure 2 It is a structural block diagram of the data acquisition module of the present invention;

[0077] Figure 3 The present invention is a flow chart of the method for timely adjusting the agricultural planting method based on big data analysis. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0079] In order to solve the problem that the existing agricultural planting methods cannot be adjusted in time according to the actual growth status of crops during the growth process, resulting in slow crop growth and poor agricultural planting results, please refer to Figure 1-Figure 3 , this embodiment provides the following technical solutions:

[0080] A system for timely adjusting agricultural planting methods based on big data analysis, comprising:

[0081] A data acquisition module is used to collect environmental parameters and soil parameters during agricultural planting, and determine real-time agricultural planting data based on the collected environmental parameters and soil parameters;

[0082] In this embodiment, the data acquisition module includes:

[0083] An environmental collection unit is used to collect the environmental temperature, humidity, light and rainfall during the agricultural planting process, and determine the environmental parameters of the agricultural planting process based on the collected environmental temperature, humidity, light and rainfall;

[0084] It should be noted that the temperature sensor is used to collect the ambient temperature in the agricultural planting process in real time;

[0085] Among them, the temperature sensor is a sensor that can sense the ambient temperature during the agricultural planting process and convert it into a usable output signal. Therefore, the ambient temperature during the agricultural planting process can be collected in real time through the temperature sensor.

[0086] It should be noted that humidity sensors are used to collect the environmental humidity in real time during agricultural planting;

[0087] Among them, the humidity sensor is a sensor that can sense the ambient humidity during agricultural planting and convert it into a usable output signal. Therefore, the ambient humidity during agricultural planting can be collected in real time through the humidity sensor;

[0088] It should be noted that light sensors are used to collect light intensity in real time during agricultural planting;

[0089] Among them, the light sensor uses an advanced photoelectric conversion module to convert the light intensity value into a voltage value, and then the voltage value is converted by the conditioning circuit to obtain the light intensity in the agricultural planting process;

[0090] It should be noted that rainfall sensors are used to collect rainfall in real time during agricultural planting;

[0091] A soil collection unit is used to collect soil pH, soil nutrients and soil moisture during the agricultural planting process, and to determine soil parameters during the agricultural planting process based on the collected soil pH, soil nutrients and soil moisture;

[0092] It should be noted that pH sensors are used to collect soil pH in real time during agricultural planting;

[0093] Among them, the pH sensor is a sensor used to detect the concentration of hydrogen ions in the soil and convert it into a usable output signal. Therefore, the pH sensor can be used to collect the soil acidity and alkalinity in the agricultural planting process in real time;

[0094] It should be noted that soil nitrogen, phosphorus and potassium sensors are used to collect soil nitrogen, phosphorus and potassium content in real time during agricultural planting, and soil nutrients are determined based on the collected soil nitrogen, phosphorus and potassium content;

[0095] Among them, the soil nitrogen, phosphorus and potassium sensor, also known as the soil nutrient rapid tester, is suitable for detecting the content of nitrogen, phosphorus and potassium in the soil. By detecting the content of nitrogen, phosphorus and potassium in the soil, the fertility of the soil can be judged, which is convenient for evaluating the soil condition.

[0096] It should be noted that soil moisture sensors are used to collect soil moisture in real time during agricultural planting;

[0097] The soil moisture sensor is composed of a stainless steel probe and a waterproof probe, which can be buried in the soil for a long time to perform fixed-point monitoring and online measurement of the surface and deep soil moisture conditions.

[0098] A data processing module is used to comprehensively process the real-time data of agricultural planting and determine the agricultural planting representation data;

[0099] Specifically, the data acquisition module further includes:

[0100] A real-time monitoring module is used to monitor the data collection time of each sensor involved in environmental collection and soil collection in real time;

[0101] An actual time interval acquisition module is used to acquire the actual time interval of data collection corresponding to each sensor through the data collection moment;

[0102] A time interval difference acquisition module, used for comparing the actual time interval of data collection corresponding to each sensor with the theoretical collection time interval of the sensor to obtain the time interval difference;

[0103] A sensor usage time acquisition module, used to retrieve the sensor usage time when the time interval difference exceeds a preset difference threshold;

[0104] The sensor aging coefficient acquisition module is used to obtain the sensor aging coefficient by using the sensor usage time; wherein the sensor aging coefficient is obtained by the following formula:

[0105]

[0106]

[0107] Where f represents the sensor aging factor; m represents the number of time intervals included in the sensor data acquisition process; t ci represents the time length corresponding to the i-th time interval; T i Indicates the time length corresponding to the theoretical acquisition time interval; λ i represents the humidity ratio of the environment where the sensor is located corresponding to the i-th time interval; n represents the number of complete unit times contained in the usage time experienced by the sensor, and the value range of the unit time is 30 days-60 days; P d Indicates the rate of increase of the theoretical data acquisition response delay per unit time of sensor operation; Δt pIndicates the change in time interval corresponding to each percentage increase in the theoretical data acquisition response delay; t c The length of the time interval corresponding to when the time interval difference exceeds the preset difference threshold; ξ represents the aging coefficient compensation factor;

[0108] An operation stability index parameter acquisition module, used for comparing the sensor aging coefficient with a preset aging coefficient threshold, and acquiring the operation stability index parameter of the current sensor according to the quantitative relationship between the sensor aging coefficient and the preset aging coefficient threshold;

[0109] The abnormal operation alarm module is used to issue a sensor abnormal operation alarm when the operation stability index parameter of the sensor is lower than a preset stability parameter threshold.

[0110] The technical effect of the above technical solution is: through the real-time monitoring module, the data collection time of each sensor in the environmental collection and soil collection can be tracked in real time to ensure the real-time and accuracy of the data. At the same time, the actual time interval acquisition module can accurately obtain the actual time interval of the data collection corresponding to each sensor, providing a reliable basis for subsequent data analysis and processing. The time interval difference acquisition module can timely detect potential faults or abnormalities by comparing the actual time interval with the theoretical collection time interval. When the time interval difference exceeds the preset difference threshold, the sensor usage time acquisition module will call the sensor usage time, and then calculate the sensor aging coefficient through the sensor aging coefficient acquisition module. This process helps to predict the aging of the sensor and provide a decision-making basis for preventive maintenance and replacement.

[0111] The operation stability index parameter acquisition module can evaluate the operation stability of the current sensor based on the comparison result between the sensor aging coefficient and the preset aging coefficient threshold. When the operation stability index parameter is lower than the preset stability parameter threshold, the operation abnormality alarm module will trigger the alarm mechanism and promptly notify relevant personnel to handle it. This helps to ensure the overall performance and stability of the data acquisition system and improve the efficiency and accuracy of data acquisition. The above technical solution realizes the intelligent and automated management of the data acquisition process through the collaborative work of a series of modules. From real-time monitoring to fault prediction, to performance evaluation and abnormal alarm, the entire process does not require human intervention, which greatly improves the efficiency and reliability of data acquisition.

[0112] In summary, this technical solution provides strong technical support for the stable operation and efficient work of the data acquisition module through real-time monitoring, precise control, fault prediction, performance evaluation and intelligent management.

[0113] Specifically, the operation stability index parameter acquisition module includes:

[0114] A comparison execution module, used for comparing the sensor aging coefficient with a preset aging coefficient threshold value to obtain a comparison result;

[0115] A first operation stability index parameter acquisition module, used for acquiring the operation stability index parameter through a first stability parameter model when the comparison result shows that the sensor aging coefficient is lower than a preset aging coefficient threshold;

[0116] Wherein, the structure of the first stability parameter model is as follows:

[0117]

[0118]

[0119]

[0120] Among them, Q 01 represents the first stability parameter model to obtain the operation stability index parameter; f0 represents the preset coefficient threshold; E 01 and E 02 Represent the first parameter variable and the second parameter variable respectively; P tj represents the acquisition response time delay rate of the sensor corresponding to the jth unit time; j Indicates the average humidity ratio of the sensor environment corresponding to the jth unit time;

[0121] A second operation stability index parameter acquisition module, used for acquiring the operation stability index parameter through a second stability parameter model when the comparison result shows that the sensor aging coefficient is not lower than a preset aging coefficient threshold;

[0122] Wherein, the structure of the second stability parameter model is as follows:

[0123]

[0124]

[0125]

[0126] Among them, Q 02 It indicates that the second stability parameter model obtains the operation stability index parameter; E 03 and E 04 represent the third parameter variable and the fourth parameter variable respectively; k represents the number of unit times when the humidity ratio exceeds the preset humidity threshold; P kj Indicates the sensor acquisition response time delay rate corresponding to the jth unit time when the humidity ratio exceeds the preset humidity threshold; λ kjIt indicates the average humidity ratio of the sensor environment corresponding to the jth unit time when the humidity ratio exceeds the preset humidity threshold; λ0 indicates the humidity threshold.

[0127] The technical effect of the above technical solution is: by comparing the sensor aging coefficient with the preset aging coefficient threshold through the comparison execution module, the module can accurately evaluate the aging degree of the sensor. According to the comparison result, the module can select different stability parameter models to obtain the operation stability index parameters, thereby realizing the classification processing of the sensor operation status.

[0128] According to the different situations of sensor aging coefficient, this module provides two stability parameter models. The first stability parameter model is applicable to the situation where the sensor aging coefficient is lower than the preset threshold, taking into account factors such as the acquisition response time delay rate and the ambient humidity ratio corresponding to the unit time; while the second stability parameter model is applicable to the situation where the sensor aging coefficient is not lower than the preset threshold, and pays special attention to the unit time and related parameters when the humidity ratio exceeds the preset threshold. This design makes the parameter model more flexible and can more comprehensively reflect the operating stability of the sensor.

[0129] The stability parameter model takes into account the humidity ratio of the environment in which the sensor is located. Humidity is one of the key factors affecting sensor performance and stability. By incorporating it into the model calculation, the operating status of the sensor under different environmental conditions can be more accurately evaluated.

[0130] By acquiring and analyzing the operation stability index parameters, the module can promptly detect abnormalities and potential failures in sensor operation. When the operation stability index parameters are lower than the preset stability parameter threshold, the operation abnormality alarm module will trigger the alarm mechanism to remind relevant personnel to handle it. This helps prevent sensor failures and ensure the stable operation of the data acquisition system.

[0131] In summary, this technical solution realizes the functions of accurate evaluation, classification processing and abnormal warning of sensor operation stability through the design of operation stability index parameter acquisition module, thereby improving the reliability and stability of the data acquisition system.

[0132] In this embodiment, the data processing module includes:

[0133] A data retrieval unit, used to retrieve real-time agricultural planting data;

[0134] Acquire agricultural planting real-time data, search the agricultural planting real-time data one by one based on a sequential search method, filter out the agricultural planting real-time data that is not valuable for timely adjustment of agricultural planting methods, and determine the agricultural planting real-time data that is valuable for timely adjustment of agricultural planting methods;

[0135] It should be noted that the real-time data on agricultural planting include A, B, C, D, E, F and G, among which F is missing data and G is duplicate data. The missing data F and duplicate data G are worthless for timely adjustment of agricultural planting methods. Therefore, the missing data F and duplicate data G contained in the real-time data on agricultural planting can be filtered out through the data retrieval unit, which can improve the subsequent processing accuracy and efficiency of the real-time data on agricultural planting.

[0136] A data conversion unit, used for converting the format of the retrieved real-time agricultural planting data;

[0137] Acquire the real-time agricultural planting data that is valuable for timely adjustment of agricultural planting methods, and convert the format of the real-time agricultural planting data to standardize the real-time agricultural planting data to form standardized real-time agricultural planting data;

[0138] It should be noted that the real-time agricultural planting data contains different dimensions and magnitudes. In order to ensure the accuracy of subsequent processing of the real-time agricultural planting data, it is necessary to standardize it to form standardized real-time agricultural planting data;

[0139] A feature extraction unit, used for extracting features from the real-time agricultural planting data after format conversion;

[0140] Acquire standardized real-time agricultural planting data, extract features from the real-time agricultural planting data, and determine agricultural planting feature data;

[0141] A data calculation unit, used for calculating agricultural planting characteristic data;

[0142] Acquire agricultural planting characteristic data, and calculate the agricultural planting characteristic data to determine agricultural planting representation data;

[0143] It should be noted that by comprehensively processing the real-time agricultural planting data and determining the agricultural planting characterization data, it is convenient to conduct planting analysis on the agricultural planting method in the future and to make timely and appropriate adjustments to the agricultural planting method;

[0144] A planting analysis module is used to analyze agricultural planting characterization data and determine agricultural planting analysis results;

[0145] In this embodiment, the planting analysis module includes:

[0146] A data storage unit, used to store pre-set agricultural planting standard data;

[0147] It should be noted that by presetting the agricultural planting standard data, it is convenient to analyze the agricultural planting characterization data, wherein the agricultural planting standard data includes the agricultural planting environment standard and the agricultural planting soil standard;

[0148] A data indexing unit, used to index pre-set agricultural planting standard data;

[0149] Acquire agricultural planting characterization data, and index pre-set agricultural planting standard data based on the agricultural planting characterization data;

[0150] It should be noted that by indexing the pre-set agricultural planting standard data, it is convenient to analyze the agricultural planting characterization data later;

[0151] A data retrieval unit, used to retrieve pre-set agricultural planting standard data;

[0152] Obtaining the indexed pre-set agricultural planting standard data, and retrieving the pre-set agricultural planting standard data;

[0153] It should be noted that by retrieving the pre-set agricultural planting standard data, it is convenient to analyze the agricultural planting characterization data later;

[0154] A comparative analysis unit for analyzing agricultural planting representation data;

[0155] Acquire agricultural planting characterization data and agricultural planting standard data, analyze the agricultural planting characterization data based on the agricultural planting standard data, and determine the agricultural planting analysis results;

[0156] Among them, if the agricultural planting characterization data is within the range of agricultural planting standard data, the agricultural planting analysis result is that the agricultural planting method is normal and there is no need to adjust the agricultural planting method in a timely manner;

[0157] Among them, if the agricultural planting characterization data is not within the range of agricultural planting standard data, the agricultural planting analysis result is that the agricultural planting method is abnormal, and the agricultural planting method needs to be adjusted in time;

[0158] Adjustment and control module, used to adjust and control agricultural planting methods in a timely manner;

[0159] In this embodiment, the adjustment control module includes:

[0160] A mining and analysis unit, used for mining and analyzing agricultural planting analysis results;

[0161] Obtaining agricultural planting analysis results, and conducting mining and analysis on the agricultural planting analysis results, determining the essential causes of agricultural planting in the agricultural planting analysis results, and determining a timely adjustment plan for agricultural planting methods based on the essential causes of agricultural planting;

[0162] Adjustment and control unit, used to adjust and control agricultural planting methods in a timely manner;

[0163] Obtain timely adjustment plans for agricultural planting methods, and timely adjust and control agricultural planting methods based on the timely adjustment plans;

[0164] Among them, in the case where the agricultural planting method is normal, the agricultural planting method is adjusted in time by real-time monitoring of the planting status of crops planted in the agricultural planting greenhouse, and transmitting the real-time monitored planting status of crops to the remote terminal;

[0165] Among them, in response to abnormal situations in agricultural planting methods, the timely adjustment plan for agricultural planting methods is to provide timely warnings, and timely adjust the fertilization and irrigation system and lighting and ventilation system of the agricultural planting greenhouse through the remote terminal, so that the crops planted in the agricultural planting greenhouse are always in the best planting state.

[0166] Specifically, when the agricultural planting method is adjusted in time by using the agricultural planting method timely adjustment system based on big data analysis, the timely adjustment of the agricultural planting method is shown in Table 1:

[0167] Table 1: Timely adjustment of agricultural planting methods

[0168]

[0169]

[0170] Therefore, the agricultural planting characterization data is analyzed through the agricultural planting standard data pre-set in the planting analysis module to determine the agricultural planting analysis results. The agricultural planting analysis results are mined and analyzed through the adjustment and control module to determine the timely adjustment plan of the agricultural planting method. The agricultural planting method is timely adjusted and controlled based on the timely adjustment plan of the agricultural planting method. The agricultural planting method can be timely adjusted according to the actual growth status of the crops, so that the crops can grow rapidly and the agricultural planting effect can be improved.

[0171] In order to better demonstrate a timely adjustment process of an agricultural planting method based on big data analysis, this embodiment provides a timely adjustment method of an agricultural planting method based on big data analysis, which is implemented based on the above-mentioned timely adjustment system of an agricultural planting method based on big data analysis, and includes the following steps:

[0172] S1: Collect environmental parameters and soil parameters during agricultural planting through the data acquisition module, and determine real-time agricultural planting data based on the collected environmental parameters and soil parameters;

[0173] S2: The data processing module performs comprehensive processing on the real-time agricultural planting data based on data retrieval, data conversion, feature extraction and data calculation to determine the agricultural planting representation data;

[0174] S3: Analyze the agricultural planting characterization data through the agricultural planting standard data pre-set in the planting analysis module to determine the agricultural planting analysis results;

[0175] S4: Through the adjustment and control module, the agricultural planting analysis results are mined and analyzed to determine the timely adjustment plan of the agricultural planting method, and the agricultural planting method is timely adjusted and controlled based on the timely adjustment plan of the agricultural planting method.

[0176] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0177] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A timely adjustment system for agricultural planting methods based on big data analysis, characterized in that: include: A data acquisition module is used to collect environmental parameters and soil parameters during agricultural planting, and determine real-time agricultural planting data based on the collected environmental parameters and soil parameters; The data processing module is used to perform comprehensive processing on the real-time data of agricultural planting based on data retrieval, data conversion, feature extraction and data calculation to determine the representation data of agricultural planting; A planting analysis module is used to analyze agricultural planting characterization data based on pre-set agricultural planting standard data to determine agricultural planting analysis results; The adjustment and control module is used to mine and analyze the agricultural planting analysis results, determine the timely adjustment plan of the agricultural planting method, and timely adjust and control the agricultural planting method based on the timely adjustment plan of the agricultural planting method.

2. According to claim 1, a timely adjustment system for agricultural planting methods based on big data analysis is characterized in that: The data acquisition module comprises: An environmental collection unit is used to collect the environmental temperature, humidity, light and rainfall during the agricultural planting process, and determine the environmental parameters of the agricultural planting process based on the collected environmental temperature, humidity, light and rainfall; A soil collection unit is used to collect soil pH, soil nutrients and soil moisture during the agricultural planting process, and to determine soil parameters during the agricultural planting process based on the collected soil pH, soil nutrients and soil moisture; Based on the collected environmental parameters and soil parameters, real-time agricultural planting data is determined.

3. The agricultural planting method timely adjustment system based on big data analysis according to claim 2 is characterized in that: The data acquisition module further includes: A real-time monitoring module is used to monitor the data collection time of each sensor involved in environmental collection and soil collection in real time; An actual time interval acquisition module is used to acquire the actual time interval of data collection corresponding to each sensor through the data collection moment; A time interval difference acquisition module, used for comparing the actual time interval of data collection corresponding to each sensor with the theoretical collection time interval of the sensor to obtain the time interval difference; A sensor usage time acquisition module, used to retrieve the sensor usage time when the time interval difference exceeds a preset difference threshold; The sensor aging coefficient acquisition module is used to obtain the sensor aging coefficient by using the sensor usage time; wherein the sensor aging coefficient is obtained by the following formula: Where f represents the sensor aging factor; m represents the number of time intervals included in the sensor data acquisition process; t ci represents the time length corresponding to the i-th time interval; T i Indicates the time length corresponding to the theoretical acquisition time interval; λ i represents the humidity ratio of the environment where the sensor is located corresponding to the i-th time interval; n represents the number of complete unit times contained in the usage time experienced by the sensor, and the value range of the unit time is 30 days-60 days; P d Indicates the rate of increase of the theoretical data acquisition response delay per unit time of sensor operation; Δt p Indicates the change in time interval corresponding to each percentage increase in the theoretical data acquisition response delay; t c The length of the time interval corresponding to when the time interval difference exceeds the preset difference threshold; ξ represents the aging coefficient compensation factor; An operation stability index parameter acquisition module, used for comparing the sensor aging coefficient with a preset aging coefficient threshold, and acquiring the operation stability index parameter of the current sensor according to the quantitative relationship between the sensor aging coefficient and the preset aging coefficient threshold; The abnormal operation alarm module is used to issue a sensor abnormal operation alarm when the operation stability index parameter of the sensor is lower than a preset stability parameter threshold.

4. According to the agricultural planting method timely adjustment system based on big data analysis according to claim 3, the operation stability index parameter acquisition module comprises: A comparison execution module, used for comparing the sensor aging coefficient with a preset aging coefficient threshold value to obtain a comparison result; A first operation stability index parameter acquisition module, used for acquiring the operation stability index parameter through a first stability parameter model when the comparison result shows that the sensor aging coefficient is lower than a preset aging coefficient threshold; Wherein, the structure of the first stability parameter model is as follows: Among them, Q 01 represents the first stability parameter model to obtain the operation stability index parameter; f0 represents the preset coefficient threshold; E 01 and E 02 Represent the first parameter variable and the second parameter variable respectively; P tj represents the acquisition response time delay rate of the sensor corresponding to the jth unit time; j Indicates the average humidity ratio of the sensor environment corresponding to the jth unit time; A second operation stability index parameter acquisition module, used for acquiring the operation stability index parameter through a second stability parameter model when the comparison result shows that the sensor aging coefficient is not lower than a preset aging coefficient threshold; Wherein, the structure of the second stability parameter model is as follows: Among them, Q 02 It indicates that the second stability parameter model obtains the operation stability index parameter; E 03 and E 04 represent the third parameter variable and the fourth parameter variable respectively; k represents the number of unit times when the humidity ratio exceeds the preset humidity threshold; P kj Indicates the sensor collection response time delay rate corresponding to the jth unit time when the humidity ratio exceeds the preset humidity threshold; λ kj It indicates the average humidity ratio of the sensor environment corresponding to the jth unit time when the humidity ratio exceeds the preset humidity threshold; λ0 indicates the humidity threshold.

5. The agricultural planting method timely adjustment system based on big data analysis according to claim 2 is characterized in that: The data processing module comprises: A data retrieval unit, used to retrieve real-time agricultural planting data; Acquire agricultural planting real-time data, search the agricultural planting real-time data one by one based on a sequential search method, filter out the agricultural planting real-time data that is not valuable for timely adjustment of agricultural planting methods, and determine the agricultural planting real-time data that is valuable for timely adjustment of agricultural planting methods; A data conversion unit, used for converting the format of the retrieved real-time agricultural planting data; Acquire the real-time agricultural planting data that is valuable for timely adjustment of agricultural planting methods, and convert the format of the real-time agricultural planting data to standardize the real-time agricultural planting data to form standardized real-time agricultural planting data; A feature extraction unit, used for extracting features from the real-time agricultural planting data after format conversion; Acquire standardized real-time agricultural planting data, extract features from the real-time agricultural planting data, and determine agricultural planting feature data; A data calculation unit, used for calculating agricultural planting characteristic data; Agricultural planting characteristic data are obtained, and the agricultural planting characteristic data are calculated to determine the agricultural planting representation data.

6. The agricultural planting method timely adjustment system based on big data analysis according to claim 5 is characterized in that: The planting analysis module comprises: A data storage unit, used to store pre-set agricultural planting standard data; A data indexing unit, used to index pre-set agricultural planting standard data; Acquire agricultural planting characterization data, and index pre-set agricultural planting standard data based on the agricultural planting characterization data; A data retrieval unit, used to retrieve pre-set agricultural planting standard data; Obtaining the indexed pre-set agricultural planting standard data, and retrieving the pre-set agricultural planting standard data; A comparative analysis unit for analyzing agricultural planting representation data; Obtain agricultural planting characterization data and agricultural planting standard data, analyze the agricultural planting characterization data based on the agricultural planting standard data, and determine the agricultural planting analysis results.

7. The agricultural planting method timely adjustment system based on big data analysis according to claim 6 is characterized in that: Based on the agricultural planting standard data, the agricultural planting characterization data is analyzed to perform the following operations: Among them, if the agricultural planting characterization data is within the range of agricultural planting standard data, the agricultural planting analysis result is that the agricultural planting method is normal and there is no need to adjust the agricultural planting method in a timely manner; Among them, if the agricultural planting characterization data is not within the range of agricultural planting standard data, the agricultural planting analysis result is that the agricultural planting method is abnormal, and the agricultural planting method needs to be adjusted in time.

8. The agricultural planting method timely adjustment system based on big data analysis according to claim 7 is characterized in that: The adjustment control module includes: A mining and analysis unit, used for mining and analyzing agricultural planting analysis results; Obtaining agricultural planting analysis results, and conducting mining and analysis on the agricultural planting analysis results, determining the essential causes of agricultural planting in the agricultural planting analysis results, and determining a timely adjustment plan for agricultural planting methods based on the essential causes of agricultural planting; Adjustment and control unit, used to adjust and control agricultural planting methods in a timely manner; Obtain timely adjustment plans for agricultural planting methods, and make timely adjustments and controls on agricultural planting methods based on the timely adjustment plans.

9. The agricultural planting method timely adjustment system based on big data analysis according to claim 8 is characterized in that: Based on the timely adjustment plan of agricultural planting methods, timely adjust and control agricultural planting methods, and perform the following operations: Among them, in the case where the agricultural planting method is normal, the agricultural planting method is adjusted in time by real-time monitoring of the planting status of crops planted in the agricultural planting greenhouse, and transmitting the real-time monitored planting status of crops to the remote terminal; Among them, in response to abnormal situations in agricultural planting methods, the timely adjustment plan for agricultural planting methods is to provide timely warnings, and timely adjust the fertilization and irrigation system and lighting and ventilation system of the agricultural planting greenhouse through the remote terminal, so that the crops planted in the agricultural planting greenhouse are always in the best planting state.

10. A method for timely adjusting agricultural planting methods based on big data analysis, implemented based on the system for timely adjusting agricultural planting methods based on big data analysis as claimed in claim 9, characterized in that: The steps include: The environmental parameters and soil parameters in the agricultural planting process are collected through the data collection module, and the real-time data of agricultural planting is determined based on the collected environmental parameters and soil parameters; The data processing module performs comprehensive processing on the real-time data of agricultural planting based on data retrieval, data conversion, feature extraction and data calculation to determine the agricultural planting representation data; Analyze the agricultural planting characterization data through the agricultural planting standard data pre-set in the planting analysis module to determine the agricultural planting analysis results; By adjusting the management and control module, the agricultural planting analysis results are mined and analyzed to determine the timely adjustment plan for the agricultural planting methods, and the agricultural planting methods are timely adjusted and controlled based on the timely adjustment plan for the agricultural planting methods.

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