52591 results about "Agricultural science" patented technology

The invention relates to the technical field of agricultural irrigation, and discloses a regional crop rolling correction intelligent irrigation decision-making method, which comprises the following steps of S1, collecting basic data and dividing an irrigation decision-making region; s2, crop growth model parameter sensitivity analysis; s3, localizing parameters of the crop growth model; s4, a regional space long-term irrigation decision scheme is formulated; s5, establishing a farmland information database based on remote sensing and Internet of Things technologies; and S6, constructing a regional irrigation system rolling correction system. The invention provides a regional crop rolling correction intelligent irrigation decision-making method. The method comprises the following steps: analyzing a mutual relation and an evolution trend of each element of an irrigation decision-making model; a regional grain and cotton crop irrigation system based on crop water shortage information perception-process recognition-efficient decision is constructed, and irrigation decisions are dynamically adjusted in real time according to future climate changes and field management changes, so that the balance between the water resource utilization efficiency and the crop yield is achieved.

The invention provides a green agricultural product distribution path optimization and inventory control method, and the method comprises the steps: selecting a distribution path with the minimum agricultural product quality influence degree as an optimal path according to a prediction result of the agricultural product quality influence degree, and transmitting the optimized distribution path to a distribution vehicle terminal in real time; after the distribution vehicle terminal receives the optimal distribution route issued by the cloud, the distribution route is navigated in real time through a vehicle-mounted navigation system, a driver is guided to drive along the optimal route, and the distribution route is dynamically adjusted according to the real-time road condition information; meanwhile, according to the real-time position and the driving speed of the distribution vehicle, the arrival time of the agricultural products is dynamically estimated, and the estimated arrival time information is pushed to a destination customer to remind the customer to make preparation for receiving goods in advance.

The invention relates to the technical field of agricultural equipment state monitoring, and discloses an agricultural equipment state monitoring system based on the Internet of Things, which comprises an acquisition unit, an analysis unit, a stripping unit, a fusion unit and a decision unit, and is characterized in that vibration, rotating speed, torque, soil hardness and environmental data are acquired in multiple dimensions through an Internet of Things sensor network; a physical compensation factor is generated through working condition synchronous analysis, interference of soil hardness on vibration data is effectively stripped, a coupling wear index is generated through fusion of a residual feature vector and environment temperature and humidity, a state threshold value is dynamically adjusted through combination of historical fault data, and then interference of soil hardness changes on a vibration spectrum is accurately eliminated. The misjudgment of the abrasion degree caused by frequency spectrum distortion is avoided, the dynamic and accurate monitoring of the abrasion state of the harvester blade is realized, and the accuracy of monitoring the state of agricultural equipment in dry farmland operation is improved.

The invention provides a self-adaptive intelligent control method and system for a poultry breeding environment. The method comprises the following steps: collecting biological signals of poultry individuals; constructing a health state model based on the health indexes; acquiring position coordinates and velocity vectors of poultry, and constructing a time sequence model based on behavior patterns to monitor changes of group behavior patterns in real time; dynamically adjusting the environment control strategy according to the comprehensive environment control index, and performing real-time feedback monitoring after adjustment; constructing a composite growth index for identifying growth stages according to the biological signal, designing a comprehensive optimization mechanism for multi-stage environment control, and integrating data of different growth stages; a self-adaptive multi-objective optimization model is designed, a control strategy is dynamically adjusted according to real-time data, control parameters are dynamically adjusted in combination with historical data, and a multi-level feedback cycle and emergency response mechanism is designed to ensure that the system responds in time in an emergency. The method obviously improves the overall benefits of poultry breeding, and has wide application prospects.

The broken lines in the reproductions depict portions of the plant stand that form no part of the claimed design.

The invention discloses a drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring, and relates to the technical field of irrigation regulation and control, the method comprises the following steps: monitoring a multi-point sensor arranged in a target area to obtain target monitoring data; performing actual demand analysis on the target crop to obtain actual demand data; analyzing the target soil permeability of the target area to obtain a target irrigation period; and performing irrigation processing according to the actual demand data and the target irrigation period to obtain irrigation data. The technical problems that in the prior art, real-time requirements of crops cannot be accurately obtained, irrigation is difficult to implement according to soil characteristics, and the agricultural production efficiency and sustainability are limited are solved. The effects that multiple types of monitoring data are obtained by arranging the multi-point sensors in the target area, precise irrigation is achieved, the crop growth environment is optimized, good physicochemical properties of soil are maintained, and the technical effects of high efficiency and sustainability of agricultural production are improved are achieved.

The present disclosure provides a self-propelled full-automatic dense-planting vegetable transplanter and a planting control method thereof. The self-propelled full-automatic dense-planting vegetable transplanter includes a seedling taking and throwing mechanism, a box moving device, a crawler-type walking chassis, a seedling guiding device, a soil covering mechanism, a planting mechanism, and a dispersing device. The box moving device is mounted on the crawler-type walking chassis. The dispersing device is located on one side of the box moving device. The seedling taking and throwing mechanism includes a seedling taking and throwing movement mechanism and a linear movement mechanism. The linear movement mechanism is arranged above the box moving device. The seedling taking and throwing movement mechanism is configured to reciprocate between the dispersing device and the box moving device through the linear movement mechanism. The dispersing device is connected to the seedling guiding device.

The invention discloses a plant pesticide application management method and system based on an intelligent decision-making system. The method comprises the following steps: dividing a target farmland into a plurality of sub-regions, constructing a plant disease and insect pest recognition model by using an image processing algorithm, recognizing disease and insect pests of each sub-region, and generating a recognition result. And then analyzing an identification result based on a clustering algorithm, and determining a pest distribution area. A pesticide application decision-making system is established, a pesticide application scheme of the unmanned aerial vehicle is formulated, influences under different moving speeds and environment wind conditions are analyzed in combination with historical pesticide drift data, and finally the pesticide application scheme is optimized according to real-time environment data. The method can effectively reduce the pesticide consumption, reduce the environmental influence and improve the health level of crops.

The invention discloses an intelligent livestock breeding traceability method and system, and the method comprises the steps: building an overall framework of the intelligent livestock breeding traceability system based on a micro-service architecture, and employing Spring Cloud Al ibaaba as a micro-service technology stack; livestock physiological data and environmental parameters are collected through Internet of Things equipment, and a livestock electronic file is established; an AI intelligent analysis engine is constructed, and livestock health state evaluation, oestrus prediction and abnormal behavior recognition are achieved; the whole life cycle data of livestock from breeding to selling is recorded based on the block chain technology, and a full-chain traceability system is constructed. The method further comprises the functions of integrating a financial service interface, designing a multi-terminal collaborative application platform, constructing a matchmaking transaction platform and the like. According to the invention, the problems of data islanding, incomplete traceability system, low intelligent level, insufficient system expandability and the like in the prior art are solved, and the accuracy, traceability and system stability of livestock breeding are improved.

The invention relates to the technical field of agricultural irrigation, in particular to a rice water-saving intelligent irrigation decision-making method and system, and the method comprises the following steps: determining the basic information of a target rice field, and obtaining a rice intelligent water-saving irrigation path by using a random landmark method and an adaptive heuristic optimization algorithm; the water demand of rice in the target rice field is obtained in real time, and then a water demand prediction model is constructed based on CEEMD and LSTM; predicting the water demand of the target rice field by using a water demand prediction model, and obtaining rice water-saving irrigation parameters of each irrigation point on the rice intelligent water-saving irrigation path in combination with rainfall information and a rice growth stage; according to the rice water-saving irrigation parameters, the irrigation system is used for completing irrigation of the target rice field. According to the method, the accuracy and the intelligent degree of rice irrigation decision making can be improved, the complexity of rice irrigation decision making and the dependence on expert experience are reduced, and more optional schemes are provided for rice irrigation control.

The present invention relates to an intelligent material distribution and material taking device and method for solid fermentation of powdery and granular fertilizers, comprising at least one production line, the production line comprising: a longitudinal conveyor belt parallel to the length direction of the fermentation tank; a transverse conveyor belt arranged above the fermentation tank along the width direction of the fermentation tank; a discharge vehicle, including a longitudinal discharge vehicle and a transverse discharge vehicle; a material distribution conveyor belt for receiving the fermentation raw materials scattered by the longitudinal discharge vehicle; a longitudinal receiving hopper; a mobile spiral material taking machine, which is longitudinally slidably arranged in the fermentation tank to collect the intermediate products in the fermentation tank; a controller, which controls the intelligent material distribution and material taking device for solid fermentation of powdery and granular fertilizers to switch between the two working states of distribution and material taking, and controls the transverse conveyor belt and the longitudinal discharge vehicle to keep synchronous longitudinal movement. The controller is used to switch the working state of the material distribution and material taking device, so as to realize intelligent and automatic material distribution and material taking, so as to meet the needs of solid fermentation of powdery and granular fertilizers.

The invention provides a vegetation greening layout optimization method, and relates to the technical field of environmental engineering and garden design, and the method comprises the steps: carrying out the homologous retrieval through taking a target region plant growth influence factor as a constraint, and obtaining a plant type and a planting density; calculating a high-frequency planting density threshold value of the plant type; inputting the plant types into a type-attribute comparison table for matching to obtain time zone attributes, determining a plant combination and performing adaptive screening; carrying out vegetation layout design based on the adaptive plant combination and the high-frequency planting density threshold to generate an initial layout scheme; constructing a simulated growth environment to perform growth prediction and simulated viewing on the layout scheme; and performing evaluation and feasibility evaluation on the simulated landscape according to the landscape evaluation function, and determining an optimal layout scheme, thereby solving the technical problems that the vegetation configuration cannot be accurately performed and the vegetation growth effect and the landscape aesthetic property cannot be effectively predicted and evaluated due to lack of systematic analysis on the plant growth. The technical effect of improving the accuracy and predictability of the plant layout is achieved.

The invention relates to the technical field of agricultural machinery automation, in particular to an intelligent seeding machinery automation control system which comprises a soil variability analysis module, a dynamic seeding mode adjustment module, a soil structure adjustment module, a seeding control and optimization module, an intelligent fault diagnosis module and a visualization module. Soil data are collected in real time through a soil variability analysis module, spatial variability and seeding suitability of soil are analyzed by utilizing a machine learning algorithm, and a dynamic seeding mode adjustment module automatically adjusts the seeding depth, the seeding speed, the seed spacing and the feeding amount according to an analysis result. The soil structure adjusting module can intelligently adjust the structure according to the compaction or loosening condition of soil, the intelligent fault diagnosis module monitors the operation state of the seeder in real time and diagnoses and repairs faults, and the visualization module provides a remote monitoring interface. According to the invention, the seeding process can be accurately controlled according to different soil conditions, the seeding uniformity and the crop germination rate are improved, and the efficiency and precision of agricultural production are remarkably improved.

A travel control system includes a storage to store a travel history including a travel state of the agricultural machine in the past and information on a position of an obstacle candidate detected during travel of the agricultural machine, and a processor configured or programmed to cause the agricultural machine to perform an avoidance operation to avoid an obstacle, based on sensor data obtained by a surrounding environment being sensed and based on the travel history.

The invention discloses a livestock and poultry manure fermentation monitoring method based on digital twinning, and relates to the technical field of livestock and poultry manure treatment. By constructing a high-precision digital twinborn model, fermentation field sensing data are synchronized in real time, the fermentation state is simulated, and the development trend is predicted; setting an intelligent control strategy fusing data relevance and control priority, and dynamically identifying and avoiding rule conflicts; on this basis, control actions such as pile turning, spraying and ventilation are automatically executed, feedback data are recorded, and twin model parameters are continuously optimized based on feedback results; according to the method, intelligent regulation and control and model self-adaptive updating of the fermentation process are realized, and the stability, the energy utilization efficiency and the prediction accuracy of the system are remarkably improved.

A system for adjusting gains for position control of a header of an agricultural harvester includes a header of an agricultural harvester, an actuator configured to adjust a position of the header relative to a field, and a computing system communicatively coupled to the actuator. The computing system is configured to receive data indicative of a contour of the field forward of the agricultural harvester relative to a forward direction of travel of the agricultural harvester. The computing system is further configured to determine an aggressiveness of the contour of the field based at least in part on the data indicative of the contour of the field. Additionally, the computing system is configured to adjust a gain for controlling the actuator based at least in part on the aggressiveness of the contour of the field.

The invention relates to an agricultural biomass characteristic intelligent detection method, system and device based on multi-frequency conductance coupling and a medium, and the method comprises the following steps: applying an electric excitation signal to an agricultural biomass sample, and synchronously collecting multi-physics coupling response data; based on the multi-physics field coupling response data, constructing a multi-scale equivalent circuit model comprising a resonance unit, a constant phase unit and a diffusion impedance unit; synchronously optimizing a resonance unit parameter, a constant phase unit parameter and an impedance unit parameter of the multi-scale equivalent circuit model through a Bayesian joint inversion algorithm to obtain an optimal parameter combination; and according to the optimal parameter combination, calculating a preliminary detection result of biomass components containing cellulose crystallinity, lignin crosslinking degree and water content, combining the collected environment temperature and humidity sensing data to implement dynamic compensation, and outputting a final detection result. Therefore, the detection precision, the environment robustness and the operation reliability are synergistically improved in three aspects of hardware design, model construction and algorithm optimization.

The invention discloses a compound fertilizer production nutrient optimization method based on industrial data, relates to the technical field of compound fertilizer production, and firstly solves the problem of unbalanced nutrient release; through the steps S1 and S2, a compound fertilizer production process is decomposed, soil and crop growth related data are collected, an image data processing technology is combined, a plant growth rate Gs and a nutrient supply and demand difference Ns are calculated, and through evaluation of a first supply and demand difference threshold Q1 and a second supply and demand difference threshold Q2, a nutrient release rate is adjusted in time, matching of nutrients and a crop growth cycle is ensured, and the quality of the compound fertilizer is improved. Excessive or insufficient nutrients are avoided; secondly, the trace demand Mn is calculated and evaluated through the step S3, the problem of lack of trace elements is solved, trace element supply is adjusted in time, and it is ensured that crops obtain needed support; and finally, calculating a nutrient loss compensation index Li through the step S4, estimating nutrient loss and adjusting fertilization, so that the applicability of the compound fertilizer under complex climate conditions is improved.

The invention provides a picking mechanical arm motion planning method and system, and relates to the technical field of agricultural picking, and the method comprises the steps: carrying out the collection of mature fruit and vegetable information in a to-be-picked region, generating first picking information M1, carrying out the air collection of visual image data in the to-be-picked region through an unmanned aerial vehicle, and generating second picking information M2; performing comparative analysis on the mature fruit and vegetable information, generating information data of the mature fruits and vegetables shielded by branches and leaves, collecting surrounding environment information of the mature fruits and vegetables shielded by the branches and leaves, and generating third picking information M3; fusing collected data in the first picking information M1, the second picking information M2 and the third picking information M3, analyzing and processing to generate a picking evaluation model P, generating an initial planning path H1 based on a value domain set of the picking evaluation P, and generating a branch and leaf shielding evaluation index ZDZs, and judging picking motion planning of the fruit and vegetable picking mechanical arm according to a comparison threshold value U of the branch and leaf shielding evaluation index ZDZs, and sequentially generating a planning path H2 and a planning path H3.

The invention relates to the technical field of temperature control, in particular to a temperature control method and system for a crop greenhouse cultivation environment, and the method comprises the following steps: calculating the growth rates of crops under various temperature conditions based on the variety information of greenhouse crops, recognizing ideal temperature ranges of a plurality of growth stages, and calculating the temperature of the greenhouse crops according to the ideal temperature ranges; and a crop temperature management model is constructed by integrating ideal temperature range data of a plurality of stages. According to the invention, through calculation of growth rates of crops under various temperature conditions, identification of temperature requirements of the crops in multiple growth stages, calibration of control parameters of a temperature adjusting device, improvement of adaptability and efficiency of a temperature control system, and analysis of an illumination intensity change trend, prediction of internal temperature of a greenhouse is realized. The temperature regulation meets the actual illumination condition, energy is saved, the growth environment is optimized, it is ensured that the environment in the greenhouse is kept in the optimal state, and the market competitiveness of crops and the economic benefits of agricultural producers are improved.

To provide a new type of intelligent rice seeding machine with a water-saving membrane and drought resistance that solves the problem of high production costs. [Solution] The system includes a mobile body 1 and an implement system, which includes a main mounting frame 2, a drip irrigation belt laying mechanism 3, an implement frame lifting mechanism 4, a soil cover removing mechanism 5, a furrow forming mechanism 6, a seeding mechanism 7, a synchronous irrigation mechanism 8, and a laser film cutting mechanism 9. The implement frame lifting mechanism adjusts the lifting of various mechanisms. The soil cover removing mechanism can be used for soil cover removing and transportation. The furrow forming mechanism can dig the soil in the seeding zone and open it into a furrow shape, which enables concentrated positioning and side fertilization. The seeding mechanism moves following the integrated machine. The sowing holes in the hole sowing wheel can perform automatic sowing, but the synchronous irrigation mechanism irrigates a uniform, fixed amount of water only in each seeding hole.

The invention discloses a hilly slope irrigation control system based on artificial intelligence, and the system comprises a wind speed and wind direction detection module which is used for obtaining wind speed and wind direction data; the sprinkler head adjusting module is used for adjusting the angle and the spraying distance of a sprinkler head based on the wind speed and wind direction data; the soil humidity detection module is used for being connected with a soil humidity sensor and determining irrigation requirements according to data of the soil humidity sensor; the artificial intelligence control module is used for optimizing the irrigation time and the irrigation amount through an artificial intelligence algorithm; the sprinkling irrigation system control module is used for controlling the sprinkling irrigation system to irrigate according to the optimization result; the hilly slope irrigation control system based on artificial intelligence reduces the phenomenon of non-uniform growth of crops, facilitates healthy and uniform growth of the crops, and ensures that the crops can obtain a proper amount of water supply, so that normal growth and development of the crops are promoted, the yield and quality of the crops are improved, and the agricultural production benefits are further improved.

The invention relates to the technical field of intelligent agriculture, and provides a negative pressure type fruit picking mechanical arm and a control system thereof.The negative pressure type fruit picking mechanical arm comprises a connecting chassis, a plurality of wheels are rotationally connected to the outer side wall of the connecting chassis, a storage groove is fixedly connected to the outer side wall of the top end of the connecting chassis, and a picking mechanism is arranged at the top end of the connecting chassis; the picking mechanism comprises a connecting frame which is mounted at the top end of the connecting chassis; by integrating an advanced mechanical arm and a visual recognition technology, the robot can accurately recognize and quickly pick crops, the picking efficiency is greatly improved, the robot is particularly suitable for large-scale agricultural production, the crop quality is improved, crop maturity judgment is conducted through deep learning models such as YOLOv5, immature crops are prevented from being picked by mistake, and the crop quality is improved. The quality of picked crops is guaranteed, the market value of agricultural products is improved, the labor cost is reduced, full-process automatic operation is achieved, the labor demand is reduced, the labor cost of agricultural production is reduced, and meanwhile the labor intensity of farmers is relieved.

An exemplary system for filling a holding tank with agricultural product generally includes a supply line. The supply line includes an inlet, a plurality of outlets, and a plurality of junctions. The inlet is connected with a conveyance device such that the supply line is operable to receive a flowing agricultural product blown by the conveyance device. The outlets are open to a holding space of the holding tank, and includes a first outlet and a second outlet. The plurality of junctions includes a first junction associated with the first outlet, and a second junction associated with the second outlet. The first junction is configured to direct agricultural product to flow out of the first outlet until the first outlet becomes blocked, and to thereafter direct agricultural product to flow toward the second junction. The second junction is configured to direct agricultural product to flow out of the second outlet.

The invention discloses an unmanned automatic seedling taking transplanter and an intelligent control method, belongs to the technical field of intelligent agricultural machinery, and aims at solving the problems that an existing transplanter seedling taking device is low in intelligent and automatic degree and tedious in operation. Comprising a successive seedling supply mechanism, a seedling skin dumping mechanism and a seedling tray recovery mechanism. Seedling trays are loaded on each seedling tray storage bin, a plurality of seedling skins are arranged on the seedling trays, the successive seedling supply mechanism is responsible for successively placing each layer of seedling trays on the two translation guide rails, the translation mechanism is responsible for pushing the seedling trays on the translation guide rails to the turning plate frame, and the seedling skin dumping mechanism is responsible for dumping the plurality of seedling skins on the seedling trays to a seedling box. According to the unmanned automatic seedling taking machine, the complete process of unmanned automatic seedling taking can be achieved, the weight of the whole machine is light, loads borne by all the mechanisms are small, operation is stable and reliable, and the automation degree and the intelligent degree are high.

The invention discloses a sugarcane germplasm resource evaluation and breeding method for smart agriculture. The sugarcane germplasm resource evaluation and breeding method comprises the following steps: step 1, collecting materials with wide genetic diversity; establishing an intelligent incubator and a greenhouse, and monitoring environmental parameters by using an Internet of Things sensor; 2, performing data acquisition on the related germplasm resources by using an unmanned aerial vehicle, a robot and a near infrared spectrum technology, and constructing a phenotype database for data management; meanwhile, genotype identification is carried out by combining molecular marker-assisted selection and genome selection technologies, molecular markers of key genes are mined, and the genetic value of related germplasm is evaluated; step 3, constructing a hybrid combination prediction model by using an AI algorithm, optimizing parent matching of the related germplasm and the cultivated sugarcane, monitoring and collecting the filial generation at the same time, and analyzing and screening in combination with smart agriculture; 4, demonstration planting is conducted on the bred excellent sugarcane strain, and application of the excellent strain is improved through government-enterprise-scientific research institution collaborative popularization and in combination with farmer technical training.

The invention relates to a modularized farmland water-saving irrigation device applied to the field of agricultural irrigation, through the arrangement of a self-monitoring filter unit, on one hand, the blockage condition can be judged through automatic detection of water pressure, on the other hand, after blockage, a filter disc for effective filtration can be temporarily and automatically replaced, and compared with the prior art, the modularized farmland water-saving irrigation device has the advantages that the cost is reduced; the influence on the irrigation water amount caused by the fact that the filter element is not replaced in time or is not replaced in time is effectively avoided, it is effectively guaranteed that crops can be stably irrigated, and stable growth of the crops is effectively guaranteed; meanwhile, when blockage occurs, the water pressure of one side, far away from a water source, of the multi-position filter is obviously reduced, at the moment, the sensing bin is obviously changed, and in cooperation with the action of a micro camera and an alarm, an alarm can be given after blockage occurs for multiple times and the filter disc is automatically replaced, so that a worker can conveniently position the abnormal multi-position filter, and the working efficiency is improved. A plurality of filter discs can be replaced at a time, so that the manual replacement frequency is effectively reduced, and the influence on the irrigation process is reduced.

An intelligent fertilization and water supply method and system. The method comprises the following steps: step a, comprehensive soil monitoring, involving: comprehensively monitoring various soil indexes by means of a plurality of sensors, so as to obtain real-time data of the soil indexes; b, consideration of crop-specific requirements, involving: for the varieties and growth stages of different crops, thoroughly studying the unique moisture and nutrient requirements of the crops, so as to ensure the rationality and accuracy of a fertilization amount; c, comprehensive consideration of environmental factors, involving: collecting data of surrounding environmental factors; d, utilization of an intelligent irrigation mode, involving: by using a cyclic irrigation mode, an intermittent irrigation mode, etc., in combination with the water requirement pattern of each crop and a change in soil moisture, realizing efficient water utilization; e, implementation of a dynamic fertilization plan, involving: on the basis of a change in a crop growth condition and in soil fertility, dynamically adjusting fertilization time, frequency and dosage, so as to ensure that the nutrient supply matches the requirements of each crop; and f, implementation of a precise fertilization technique, involving: precisely delivering fertilizer to an absorption part of each crop, so as to improve the efficiency of nutrient utilization.

The invention discloses an unmanned agricultural production system based on an agricultural scene and an inspection robot control method. The method comprises the steps that an environment sensing module obtains environment data in the agricultural scene through a plurality of sensors; the data processing module analyzes the state of the crops in the environment data and formulates a plurality of farming tasks according to the state of the crops; a task planning module formulates an inspection robot control strategy according to the priorities of the farming tasks and the state of the inspection robot; the robot control module controls movement of the inspection robot according to the inspection robot control strategy and completes corresponding farming tasks in the moving process, and unmanned production of a farm is achieved; according to the invention, corresponding farming tasks can be formulated according to crop states, the optimal moving path and task execution sequence of the robot in the farm are planned according to the priorities of the farming tasks, the full-range patrol inspection of the farm is realized, the production efficiency is improved, and the unmanned and intelligent farm is realized.

The invention discloses an unmanned agricultural machine path navigation method based on farmland environment perception judgment, and relates to the technical field of path navigation, and the method comprises the steps: continuously perceiving farmland topographic relief features, gradient changes, and agricultural machine current attitude and wheel speed information before and during the operation of an agricultural machine; identifying a terrain disturbance potential risk area based on the sensing data, and predicting the offset and rollover risk value of the terrain fluctuation to the agricultural machine trajectory; according to the method, a pre-correction model fusing terrain prediction data and agricultural machinery dynamic characteristics is called, a pre-correction path containing the terrain compensation amount is generated, and navigation control parameters are adjusted in advance, so that the pre-correction path containing the terrain compensation amount is actively generated by sensing terrain fluctuation and predicting risk and offset in advance; the problem of causal malposition of risk prior to control is avoided from the source.