2503 results about "Level data" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

A digital product network system includes a set of digital products each having a product processor, a product memory, and a product network interface. The digital product network system includes a product network control tower having a control tower processor, a control tower memory, and a control tower network interface. The product processor and the control tower processor collectively include non-transitory instructions that program the digital product network system to generate product level data at the product processor, transmit the product level data from the product network interface, receive the product level data at the control tower network interface, encode the product level data as a product level data structure configured to convey parameters indicated by the product level data across the set of digital products, and write the product level data structure to at least one of the product memory and the control tower memory.

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention discloses a building deformation analysis method and system based on three-dimensional laser scanning and deep learning, and the method comprises the steps: obtaining original point cloud data, carrying out the hierarchical processing through a feature decomposition method, obtaining the hierarchical feature data, carrying out the fusion analysis in combination with environment parameter data, and eliminating the environment influence. Obtaining high-fidelity point cloud data through quality evaluation optimization and spatial-temporal feature fusion; component segmentation and deformation feature extraction are carried out on the high-fidelity point cloud data, and fusion deformation feature data are obtained through multi-scale analysis; inputting the deformation feature data into a hybrid neural network for feature enhancement and deformation evolution analysis to generate deformation prediction data; and performing physical rule verification, multi-source data cross verification and space-time continuity optimization on the prediction result, and outputting final optimization data and a reliability report. According to the invention, through a multi-level data processing and verification mechanism, the accuracy and reliability of building deformation monitoring are improved.

The invention discloses a hydropower station equipment state monitoring method and system, and relates to the technical field of hydropower station equipment monitoring, and the method comprises the steps: collecting the hydropower station equipment state through multi-layer data, and dynamically adjusting the collection frequency according to the change of the equipment state; performing signal quality evaluation on the collected data to generate fusion data; inputting the fused data into a trend analysis module, and generating a trend model of the equipment state in combination with historical data and real-time data; according to an analysis result of the trend model, combining the plurality of abnormal states into an event mode, and comparing the event mode with a known fault mode to determine a risk level; and generating a corresponding early warning signal based on the risk level, and performing corresponding response processing on early warning events of different levels. According to the hydropower station equipment state monitoring method provided by the invention, through multi-level data acquisition and dynamic frequency adjustment, the accuracy of data acquisition and the resource utilization rate are effectively improved, so that the system can adapt to different equipment states.

The invention relates to the technical field of intelligent security operation and maintenance, in particular to an artificial intelligence network security system based on multi-modal large model training, which comprises a server fault diagnosis module, a network attack detection module, an endpoint security monitoring module, a key management optimization module and a threat analysis feedback module. According to the method, the fault prediction accuracy is improved through multi-dimensional data analysis, service interruption caused by sudden hardware faults is reduced, the network access frequency, source and instruction features are evaluated based on the server abnormality, the attack detection accuracy is improved, the misjudgment risk is reduced, the endpoint equipment execution behavior, resource calling and behavior sequence are extracted, and the service performance of the terminal equipment is improved. Fine-grained security monitoring is realized, attack traceability is enhanced, a key strategy is dynamically adjusted, security adaptability is improved, strategy lag risk is reduced, multi-level data is integrated to calculate threat behavior and attack fitting degree, threat assessment fineness and response speed are enhanced, and global security situation awareness is improved.

The invention relates to the technical field of data processing, and discloses a data processing method and system for a smart city and a storage medium. The method comprises the following steps: acquiring city multi-source data through a distributed sensor network, and performing data cleaning and standardization processing to obtain standardized city data; establishing a domain concept system for semantic mapping to obtain semantic association data; analyzing the spatial-temporal characteristic and regional interaction relationship to obtain dynamic characteristic data; constructing a multi-level abnormal feature library for event recognition to obtain abnormal mode data; carrying out distributed optimization decision making to obtain management decision data; and establishing an evaluation system for strategy optimization to obtain a city management scheme. According to the method, efficient processing and intelligent analysis of the city data are realized, specifically, through a distributed computing architecture and a multi-level data processing method, the problems of low data processing efficiency, insufficient data association analysis, low computing resource utilization rate and the like are solved, and thus the overall performance of smart city data processing is improved.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention relates to the technical field of data verification, and discloses an integrity verification and correction method and system for flight data and a storage medium. The method comprises the following steps: performing spatial-temporal feature extraction processing on a multi-modal feature data stream, and constructing an incidence matrix according to a multi-level data mapping relationship to obtain a spatial-temporal feature association network; performing hierarchical inspection processing on the spatial-temporal feature association network to obtain abnormal mark data; performing regional recursion positioning processing on the abnormal mark data to obtain an abnormal propagation link diagram; performing correction scheme design processing on the abnormal propagation link diagram, and obtaining a data correction execution sequence based on a data reconstruction and influence evaluation optimization scheme; and performing progressive processing on the data correction execution sequence to obtain corrected flight data. According to the method, a data integrity checking and correcting mechanism is established in a distributed data acquisition environment, so that data exception can be accurately identified and positioned, and the corrected data can be ensured to meet physical constraint and space-time consistency requirements.

The present invention relates to a data interaction method, device, equipment and storage medium of a multi-layer stacked memory, comprising the following steps: based on the access frequency clustering result, the target transmission data is mapped to a storage location to obtain a multi-layer storage allocation strategy table; based on the multi-layer storage allocation strategy table, the target transmission data is interactively planned to obtain an optimal interactive path plan; based on the optimal interactive path plan, the target transmission data is hierarchically migrated and scheduled to obtain a data migration execution queue; and the data migration execution queue is transmitted in parallel through an asynchronous pipeline transmission mechanism, which solves the technical problem that the existing data transmission strategies often lack in-depth analysis of the historical access characteristics of the storage hierarchy, resulting in unreasonable data distribution, and frequent cross-level data migration increases system delay and energy consumption.

The invention relates to the technical field of intelligent control, in particular to a dynamic encryption-based data link security management and control system and method.The dynamic encryption-based data link security management and control system comprises a dynamic encryption engine, a key management cluster, a link monitoring unit and a security policy execution array, the switching between the SM9 cryptographic algorithm and the quantum resistance NTRU algorithm is supported; the key management cluster realizes distributed key negotiation and storage through a block chain smart contract, and establishes a bidirectional authentication channel with the dynamic encryption engine; the link monitoring unit is integrated with a multi-dimensional flow probe, and continuously collects link delay jitter, data packet entropy characteristics and protocol compliance indexes; the security policy execution array is deployed at a boundary node of a data link, and is configured with a dynamic access control list and a hardware-level data purification module. Therefore, the problems of insufficient encryption algorithm resistance, significant key update delay, multi-dimensional threat sensing blind areas, dynamic strategy response delay and the like in the prior art are solved.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention relates to a road collapse risk comprehensive assessment method based on multi-source data fusion, and the method comprises the following steps: collecting and preprocessing multi-source data related to road collapse, the multi-source data related to road collapse comprising remote sensing image data, underground water level monitoring data, geological exploration data and surface deformation monitoring data; spatio-temporal feature fusion and multi-level data fusion are carried out on the preprocessed multi-source data, the spatio-temporal feature fusion comprises spatial registration and time alignment, and the multi-level data fusion comprises low-level data fusion and high-level data fusion; and performing feature extraction on the fused multi-level data, inputting a feature extraction result into a road collapse risk assessment model constructed based on a machine learning model, outputting a road collapse risk occurrence probability, and generating a road collapse risk level according to the road collapse risk occurrence probability. Compared with the prior art, the method can comprehensively and accurately predict and evaluate the risk of road collapse.

The invention provides an intelligent water conservancy digital twinborn simulation system based on multi-source data, and belongs to the technical field of digital monitoring. Minute-level data acquisition and transmission are realized by constructing a space-air-ground three-dimensional sensing network and combining edge intelligent preprocessing, data acquisition, noise reduction and abnormity identification are realized by utilizing sensors such as millimeter wave radar and laser radar, and the system is used for realizing data acquisition and transmission. Dynamic data fusion and intelligent calibration are carried out, and second-level alignment and credible verification of data are realized by means of a space-time calibration algorithm, Kalman filtering and a block chain evidence storage technology; a hybrid simulation and intelligent decision model is established, a physical model, a machine learning architecture and a dynamic threshold decision tree are adopted, flood routing minute-level prediction and emergency response are realized, and the system improves water conservancy monitoring prediction precision and emergency response efficiency.

The invention discloses an electrical fire intelligent identification system based on multi-dimensional sensor fusion. The system comprises the following steps: constructing a reference environment model through multi-sensor scanning, data dimension reduction and intelligent node deployment; multi-sensor time sequence alignment is carried out through edge calculation denoising and dynamic time warping, and high-priority data is processed in real time through a layering mechanism; establishing a fire feature modeling system through LSTM time sequence analysis, mutual information correlation mining and self-supervised learning; through multi-level data fusion, GAN abnormal data generation and fuzzy logic reasoning; through grading alarm, an intelligent fire extinguishing strategy and remote control, full-process coverage from fire detection to emergency response is realized. A fire scene is visualized by means of a three-dimensional thermodynamic diagram, flame dynamic analysis and an augmented reality technology. The system is suitable for fire detection, alarm and response in a complex industrial scene, and can be widely applied to intelligent management of electrical fire.

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

The invention provides a novel power system multi-scale network construction energy storage planning optimization method based on source network load clustering. The method comprises the following steps: firstly, processing source network load data by adopting a multi-dimensional adaptive feature extraction and fusion method, and realizing accurate processing of the data through interval adaptive probability density estimation, adaptive kernel tensor decomposition and marginal information entropy maximization; then, a multi-level data clustering method is adopted, and accurate clustering is achieved through hierarchical distance measurement and boundary self-adaptive adjustment; then, a multi-scale energy storage modeling method is adopted, and accurate modeling of the energy storage system is achieved through the dynamic characteristic matrix and self-adaptive topology embedding; and finally, a multi-objective optimization solution method is adopted, and efficient solution of an optimization problem is realized through adaptive hierarchical decomposition and hybrid evolutionary optimization. According to the invention, the accuracy and reliability of energy storage planning can be obviously improved, and the system operation efficiency is improved.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention relates to the technical field of data processing, and discloses a vehicle-mounted cold chain temperature and humidity intelligent supervision and remote control method, system and device. The method comprises the steps of collecting environment data through a temperature and humidity sensor and adding time codes, dividing priority data packets and encrypting and compressing the priority data packets, performing cleaning and abnormity correction on the data, dividing an early warning threshold value based on feature data and generating an early warning instruction, converting the early warning instruction into control parameters and issuing a remote regulation and control command, and finally, generating a monitoring report in combination with a terminal execution result to form an intelligent supervision scheme. According to the method, a multi-level data processing mechanism is introduced, cleaning, correction and interpolation compensation are carried out on the data in combination with an intelligent algorithm, and the accuracy and reliability of a monitoring result are remarkably improved.

The invention discloses an adaptive protocol stack reconstruction and transaction-level data control method for 5G communication, and aims to solve the technical problems of fixed protocol stack structure, coarse transmission control granularity and the like of an existing communication module. The method comprises the following steps: firstly, analyzing an application layer communication session initialization instruction, generating a communication task label, acquiring 5G network environment state data in real time, and constructing a network state feature vector; then, the communication task label and the network state feature vector are input into a protocol mapping decision map, a protocol reconstruction instruction is generated, loading and unloading of a protocol module in a protocol stack resource pool are controlled, a dynamically-adaptive protocol path is constructed, and a transaction identifier is allocated to the protocol path; and establishing a transaction-level data buffer area based on the transaction identifier, performing priority marking on the data packet, and executing differentiated dynamic flow control operation according to the network state feature vector and the priority label, thereby realizing high-reliability and low-delay 5G communication data management.

The invention discloses a wheat single grain appearance anomaly detection method based on a deep learning model and hyperspectral imaging. The method comprises the following steps: step 1, collecting different types of wheat grain samples; step 2, acquiring hyperspectral image data of the wheat grains by using a visible light-near infrared and short wave near infrared hyperspectral imaging system, and extracting spectral information and image information; step 3, preprocessing the spectral data, and verifying a preprocessing effect; 4, screening spectral characteristic wave bands, extracting texture characteristics and morphological characteristics in combination with a gray-level co-occurrence matrix, and constructing a middle-level data fusion model; step 5, constructing an atlas feature fusion deep learning model to realize high-level fusion of spectrum and image features; and step 6, pixel-level classification is carried out on the hyperspectral image, and a spatial distribution visualization result of the appearance abnormity of the wheat grains is generated. The method has high precision, nondestructive testing and strong generalization ability, and is suitable for wheat quality grading, processing and sorting and storage safety management.

The invention relates to the technical field of software testing, in particular to a software testing management framework and measurement system construction method and system. The method comprises the following steps: acquiring code structure data of a software system, wherein the code structure data comprises code level data, calling relation data and business function data; identifying and analyzing a key path of the software system according to the code structure data, performing test coverage rate evaluation, and mapping coverage effects under different test scenes to obtain coverage feature map data; performing simulation analysis on the execution efficiency of the different test paths according to the coverage feature map data so as to obtain execution efficiency data; and establishing a test resource allocation model according to the test parameters corresponding to the different paths and the execution efficiency data. According to the method, the key path of the software system can be identified and specifically tested by acquiring the code structure data and constructing the coverage feature map, so that the accuracy and comprehensiveness of the test coverage rate are improved.

The invention discloses a multi-source heterogeneous data fusion method of a sky tower ground well carbon flux observation system, which comprises the following steps of: 1, preprocessing and automatically correcting data to solve the problems of noise, space-time inconsistency, format difference and systematic error in a multi-source heterogeneous data acquisition process; the multi-source heterogeneous data are respectively from satellite remote sensing, unmanned aerial vehicle remote sensing, an eddy covariance flux tower, ground observation and underground observation; step 2, multi-level data fusion: extracting multi-source heterogeneous data from original signals step by step to form unified high-dimensional feature expression, and finally outputting real-time observation data of carbon emission and carbon sink of the coal mining area through a decision model; and step 3, performing comprehensive observation and application output, performing real-time analysis according to the real-time observation data, and outputting a detection result and early warning information. According to the method, an advanced algorithm with adaptive correction and multi-level fusion capability is adopted, and data fusion of five observation modules of the sky tower surface well is realized, so that the key bottleneck in the prior art is broken.

An illustrative method includes receiving, by a data platform configured to monitor the compute environment, runtime workload data collected by an agent deployed to the compute environment, wherein the runtime workload data comprises user space data collected from a user space of the compute environment and kernel space data collected from a kernel space of the compute environment. The method further includes performing, by the data platform, a monitoring operation based on the user space data and the kernel space data of the runtime workload data.

The invention discloses a database management method and management system, and relates to the technical field of general database management, and the system comprises a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis prediction module, a multi-terminal integration module and a safety guarantee module. In the invention, a role-based access control permission model is constructed in a permission control layer and a module definition, and a field-level data control mechanism is implemented for three roles of a platform administrator, a customer enterprise and an employee, for example, the employee can only check a specific public field in a salary list, and sensitive fields such as a salary calculation formula are dynamically shielded; through a permission inheritance mechanism, the system automatically adds a filtering condition that employee ID = current user ID for a query request of an employee role, data isolation is ensured, a multi-dimensional data relation graph of employee-post-customer-contract is constructed, and a dynamic association path between entities is mapped in real time through a visual view.

The invention relates to the technical field of hydrological monitoring, and discloses a river flow monitoring method and system fusing multi-source data, and the method comprises the steps: collecting river flow data and river water level data through employing a high-precision sensor group, and collecting river image data through employing an unmanned plane; filtering the river flow data and the river water level data by adopting a Kalman filtering method to obtain filtered river flow data and filtered river water level data; performing edge detection on the riverway image data by adopting a Canny edge detection method to obtain riverway boundary information; on the basis of the riverway boundary information, a cubic spline interpolation method is adopted for interpolation, a smooth first boundary line and a smooth second boundary line are obtained, and the actual flow of the riverway is calculated on the basis of the filtered riverway water level data and the riverway flow velocity data; judging whether the actual flow of the riverway is greater than a riverway flow preset value, and if yes, sending alarm information; the actual flow of the river channel is calculated by fusing the multi-source data, and the accuracy of monitoring the flow of the river channel is improved.

The invention discloses an energy optimization decision-making system based on cloud computing, and belongs to the technical field of energy optimization, and the method specifically comprises the steps: collecting energy consumption data and environmental parameter data of energy consumption equipment, carrying out the preprocessing, extracting space-time joint feature vectors of the energy consumption data and the environmental parameter data of the energy consumption equipment, and carrying out the calculation of the space-time joint feature vectors; constructing a space-time collaborative prediction model of dynamic heterogeneous graph fusion, predicting energy load in a future preset time period, establishing a multi-target optimization model including economic cost, carbon emission and equipment loss, and solving the multi-target optimization model to generate an optimal scheduling instruction; according to the method, millisecond-level data analysis and scheduling optimization are realized through the dynamic heterogeneous graph fused space-time collaborative prediction model and cloud computing, and the method is suitable for large-scale complex scenes and cross-domain collaborative scheduling.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

The invention discloses an artificial intelligence-driven supply chain risk early warning system, which relates to the technical field of supply chain risk early warning, and performs closed-loop operation according to five steps of cross-level data acquisition, semantic alignment, graph expansion causal prediction and scene synthesis. The method comprises the following steps: firstly, converging heterogeneous data in milliseconds by using an adapter and constructing a named initial graph; calling an industry ontology to complete node and edge standardization so as to generate a semantic unified graph; inferring implicit dependency by using a multi-scale threshold and revising an edge weight to obtain an implicit dependency enhanced graph; then, a causal mask and time sequence attention are applied to the enhanced graph, and a risk vector combining the node influence degree and the propagation probability is output; and finally, according to the service context and the resource constraint optimization matching strategy template, pushing a signature slow-release instruction and returning the signature slow-release instruction. The method has the advantages of data real-time consistency, explainable risk quantification and auditable instruction execution, and improves the toughness and compliance level of the supply chain.

The invention provides a deep learning-based farmland disease and pest monitoring and prevention method, system and device, and the method comprises the steps: carrying out the data collection of a target farmland through a plurality of sensors, obtaining the sensor data corresponding to the target farmland, carrying out the multi-level data preprocessing, obtaining the structured sensor data of the target farmland, and carrying out the multi-level data preprocessing of the structured sensor data; and performing data analysis on the crop growth image to obtain growth parameters of crops, performing pest recognition, positioning and counting on the pest situation information image through a deep learning algorithm to obtain a pest population dynamic state, constructing a pest occurrence prediction model to obtain a pest occurrence probability, and generating early warning information of a pest control opportunity. And finally obtaining a farming management decision scheme of the target farmland. According to the method, environmental factors, crop growth conditions and insect pest information are comprehensively considered, the occurrence probability of target farmland diseases and insect pests is predicted, corresponding prevention and control measures are provided, and reference is provided for farmers and managers.