29251results about How to "Reduce labor costs" patented technology

The invention discloses an equipment fault intelligent question-answering method and system based on knowledge graph enhancement. The method comprises the following steps: constructing a knowledge graph of a professional field; developing a natural language processing module; designing an enhanced generation model; designing an interactive question and answer interface; the system comprises a user interaction layer, a language understanding processing layer, a knowledge graph query reasoning layer, a question and answer generation layer and a data storage management layer. Through the natural language processing technology, the system can quickly understand the query intention of a user, and the manual information input and interpretation time in traditional fault diagnosis is shortened, so that the overall fault response speed is increased; the large language model has deep learning and generalization capabilities, various complex and unknown fault conditions can be processed, accurate diagnosis and operation suggestions are provided, and the potential capability of the system for processing new fault types is enhanced.

The invention provides an on-line quality monitoring method and system based on optical multispectral fusion, and relates to the technical field of multispectral fusion, and the method comprises the steps: collecting visible light and infrared multispectral images of an object to be measured, carrying out the preprocessing and registration, inputting the images into a variational auto-encoder, and carrying out the probability distribution modeling, and obtaining a spectrum basis vector and an initial abundance graph; and constructing a multi-scale morphological feature pyramid, performing graph segmentation in combination with spatial information to obtain a local consistency region, and fusing the local consistency region with the initial abundance graph to obtain an enhanced abundance feature. And fusing the spectrum basis vector and the enhanced abundance feature by adopting a multi-head cross attention mechanism to obtain a multi-spectrum fusion feature. And inputting the fusion features into a dynamic neural network, analyzing a topological connection relationship between nodes, obtaining a structured representation, performing discriminative enhancement, calculating node significance distribution, determining a quality defect area, analyzing a formation mechanism of the quality defect area, and finally generating a quality monitoring report.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention discloses a multi-sensor fusion unmanned equipment agricultural monitoring method and system, and belongs to the technical field of agricultural monitoring, and the method specifically comprises the steps: obtaining a farmland image and multispectral data through an image shooting device and a multispectral sensor carried by an unmanned aerial vehicle, and recognizing the features of diseases and pests through a deep learning model, generating a distribution diagram and statistical data, and performing early warning and risk assessment in combination with the multispectral data; formulating a pesticide management strategy according to an evaluation result, and pushing the pesticide management strategy to an unmanned equipment control system in real time; meanwhile, according to actual and operation requirements of the farmland, generating an optimal operation path by using a path planning algorithm, setting operation parameters, and allocating tasks by using a task allocation algorithm; in the operation process, environment changes are monitored in real time, an edge computing device is used for finding out abnormity in time and adjusting operation instructions, and efficient and accurate monitoring and intelligent operation management of farmland diseases and insect pests are achieved.

The invention discloses a logistics park vehicle path planning method and system, and the method comprises the steps: obtaining the real-time position and task state information of a vehicle in a park, dynamically constructing a vehicle path planning network model, dividing a road into nodes and edges, and forming a directed weighted graph; according to the directed weighted graph, an improved multi-lane algorithm is adopted, the path length, the congestion degree and the vehicle characteristic factors are comprehensively considered, and the optimal path of each vehicle is calculated; for the optimal path, introducing a virtual lane and a time dimension, pre-judging occupation conditions of multiple vehicles on the virtual lane for each time slice, and constructing a conflict constraint matrix; obtaining park high-precision map data, extracting road network topology, lane lines and traffic sign marking elements, performing lane-level refinement on the optimal path, and generating a driving track; and through a multi-sensor fusion positioning technology, obtaining high-precision position and attitude information of the vehicle, matching the high-precision position and attitude information with the driving track, and calculating a track tracking error.

The invention discloses a rolled metal surface defect automatic labeling method based on a multi-task self-adaptive model, which comprises the following steps: S1, collecting a rolled metal surface image, preprocessing and enhancing the rolled metal surface image, performing data labeling, and constructing a rolled metal surface defect data set; s2, constructing a multi-task adaptive model, and training the multi-task adaptive model by using the data set to obtain an automatic defect labeling model; and S3, inputting a to-be-labeled metal surface image into the defect automatic labeling model, and outputting a surface defect labeling result by executing the target detection task. According to the method, a multi-task learning framework is combined, joint training of target detection and instance segmentation tasks of metal surface defects in the rolling technology is achieved, the target detection task is focused in the reasoning stage, the labeling efficiency is improved, the labor cost is remarkably reduced, and the labeling efficiency is improved. The method can be widely applied to industrial defect detection and data automatic labeling scenes of rolling process metal, and is suitable for defect image automatic labeling tasks in a complex industrial environment.

The invention belongs to the technical field of electric power data security management and intelligent question answering, and particularly relates to an electric power data security policy large model question answering system and method based on relation pooling. The system comprises a power data security policy knowledge base, a language model, a question processing module, an answer generation module and a user output module. Data of the electric power data security policy knowledge base comes from an electric power data security policy file; the language model converts a natural language problem into a structured query for knowledge base retrieval; the question processing module retrieves policy terms related to the natural language question to serve as retrieval results; the answer generation module generates a final answer according to the natural language question and a retrieval result of the knowledge base; by introducing the intelligent question-answering system combining the large language model and the knowledge base, the problems of low efficiency, inaccurate interpretation, incapability of dynamically updating and the like in the existing power data security policy query and management are solved.

The invention discloses a cloth defect detection method and system based on machine vision, and relates to the technical field of detection and evaluation, and the method comprises the following steps: optimizing a cloth detection environment, deploying an industrial camera and a sensor to capture an image of a cloth surface and environmental factor data, and preprocessing the collected data; according to the preprocessed data, defect features and environment interference features are extracted, and the extracted defect features and environment interference features are marked. According to the cloth flaw detection method and system based on machine vision, continuous and uninterrupted detection of cloth surface flaws is realized through automatic image acquisition and processing, and compared with a traditional manual detection mode, the detection efficiency is greatly improved, the labor cost is reduced, and the detection efficiency is improved. Automatic detection can ensure that products on a production line quickly pass through a detection area, and defect positions are found and marked in time, so that subsequent treatment is quicker and more accurate.

The invention discloses a reservoir bank slope crack identification method based on unmanned aerial vehicle inspection and digital images, and relates to the technical field of reservoir bank slope inspection. 2, image preprocessing: preprocessing the data by adopting processing methods of graying, gray enhancement and bilateral filtering; 3, crack identification: identifying reservoir bank slope cracks in the image by adopting methods of threshold segmentation, edge detection and supervised classification; 4, crack pixel filtering: performing filtering processing on the extracted crack image by adopting morphological closed operation, length filtering and direction filtering; and 5, model fusion: fusing the threshold segmentation model, the edge detection model and the supervised classification model together by adopting a minimum-risk Bayesian probability method. The method shows remarkable applicability and effect in large-range and multi-form crack identification and investigation, and shows high robustness when processing crack images with different scales, shapes and backgrounds, and the accuracy of the method can stably reach more than 90%.

The invention discloses an unmanned aerial vehicle intelligent dynamic inventory algorithm, and relates to the technical field of warehouse management, the unmanned aerial vehicle flight inventory route is planned according to warehouse layout, shelf distribution and material storage area information, and non-inventory areas are marked; the unmanned aerial vehicle flies to obtain radio frequency data and material image visual data on warehouse materials; the collected radio frequency data and the material image visual data are preprocessed, feature extraction is carried out, and radio frequency features and visual features are obtained; according to the unmanned aerial vehicle intelligent dynamic inventory algorithm, through cooperative application of radio frequency and visual technology, the inventory accuracy is enhanced, radio frequency identification can accurately read material label information and obtain key data of material numbers and category codes, and a visual identification system can be used for capturing material appearance characteristics from multiple angles and multiple distances and assisting in confirming material categories and states; large-area areas in a warehouse can be efficiently checked in a short time, and smooth operation of the warehouse is guaranteed.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention discloses a multi-agent cooperative electric power inspection method, system and device and a storage medium, and relates to the technical field of electric power inspection, and the method comprises the steps: generating a prompt according to a user service instruction through employing a pre-established prompt word agent; analyzing a user service instruction from the prompt by using a pre-established language understanding agent; and according to the analyzed user service instruction, intelligent routing and decision making are carried out among a plurality of service agents by using a pre-established routing agent, and a specific task process is completed through the pre-established service agent. According to the method, a more intelligent and automatic task decomposition and execution mode is adopted to process complex tasks, the capabilities of a large model in language understanding and task arrangement aspects are fully utilized, automatic processing of the complex tasks in a power system is achieved, manual participation in the processing process is reduced, the labor cost is greatly reduced, the task processing efficiency is improved, and the task processing efficiency is improved. And the working threshold is reduced, and the overall stability is improved by using a plurality of service agents to work together.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention discloses an MIM injection molding process of a neodymium-iron-boron magnet, which comprises the steps of raw material ratio adjustment, melt-spinning molding of a strip-shaped sheet, crushing treatment, mixing with a mixed solution of a coupling agent and ethanol, mixing and banburying with a binder, orientation injection molding, extraction degreasing, thermal degreasing, sintering, magnetizing and the like. Through the processes of optimization of raw material components, orientation injection molding, extraction degreasing, thermal degreasing and the like, near-net forming of a complex shape is achieved, meanwhile, the MIM neodymium-iron-boron magnet can achieve the excellent magnetic performance that the residual magnetization is larger than or equal to 1.04 T and the maximum magnetic energy product is larger than or equal to 29.7 MGOe, and the magnet yield and the production efficiency are greatly improved.

The invention discloses an automatic dubbing method and system based on generative AI. The method comprises the following steps: separating a human voice audio, a background voice audio and a silent video part from an original video, automatically identifying and translating text content in the audio, and combining a translated text with a corresponding time period to generate a translated subtitle text; segmenting the voice audio into segments, marking character attributes for each segmented segment according to voiceprint features, and evaluating the voice quality at the same time; converting the segmented subtitle text into timbre audios of a target language by using a TTS technology, and connecting and merging the audios according to the starting and ending time of the subtitles; and finally, synthesizing the generated target audio with the background sound audio and the silent video to obtain a complete target video. The above steps efficiently complete dubbing work of multiple languages, multiple roles and multiple scenes, and improve dubbing quality and dubbing efficiency.

The invention relates to the technical field of intelligent agriculture, in particular to a mulberry field monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring environment and soil data of a mulberry field through a sensor network to obtain original sensor data; performing time sequence data integration on the original sensor data to obtain an environment parameter data set; the method comprises the following steps: performing multispectral image acquisition on mulberries in a mulberry field through a multispectral camera, and performing image preprocessing to obtain preprocessed image data; estimating the chlorophyll content according to the preprocessed image data to obtain chlorophyll content data; and performing growth health parameter analysis according to the preprocessed image data and the chlorophyll content data to obtain growth health parameters. By combining the sensor technology, the multispectral imaging technology and the intelligent algorithm, comprehensive, accurate and real-time monitoring of the mulberry field is achieved, and an effective technical means is provided for fine management of the mulberry field.

The invention provides a knowledge graph construction system and method based on cue words, and the method comprises the steps: determining a mapping relation corresponding to an obtained unstructured document set through a data collection module, and transmitting the mapping relation to a vector data storage module; sending the obtained rule extraction file to an automatic cue word generation module to obtain a target template, and distributing the target template to a problem auditing module and a cue word judgment module; the vector data storage module obtains vectorized data based on the mapping relation and stores the vectorized data to a vector database; the problem auditing module audits a drawing instruction input by a user according to the template, and if the auditing is passed, the drawing instruction is sent to the cue word judgment module, so that the cue word judgment module inputs the corresponding template and the drawing instruction into the intelligent reasoning and executing module, the drawing instruction is embedded into the template and then is transmitted into a preset large language model, the drawing instruction is disassembled, and the drawing instruction is sent to the intelligent reasoning and executing module. And according to a disassembling result, querying a vector database and calling related tools, so that efficient and accurate knowledge graph construction is realized.

The invention relates to the technical field of chemical engineering and machine learning, and provides a method for predicting the solubility of carbon dioxide in a deep-eutectic solvent based on a machine learning model, and the method comprises the steps: collecting CO2 solubility data, carrying out the coding and feature descriptor conversion of a deep-eutectic solvent molecular structure through an open-source chemoinformatics software package RDKit, and constructing a data set. Four algorithms of XGBoost, random forest, a deep neural network and a convolutional neural network are adopted to construct a model, and hyper-parameters are optimized through grid search and k-fold cross validation. After the model is trained, the prediction performance is evaluated, and the optimal model is selected for CO2 solubility prediction. The method improves prediction efficiency and accuracy, has strong adaptability and generalization ability, and provides an effective tool for formula optimization of the eutectic solvent and improvement of CO2 capture efficiency.

The invention belongs to the technical field of fermentation, and discloses a cloud computing-based fermentation tank fermentation process management and control service system and a cloud computing-based fermentation tank fermentation process management and control service method. Comprising the steps that an equipment end collects fermentation parameters in real time in the fermentation process and sends the fermentation parameters to a cloud computing platform; the fermentation parameters comprise fermentation characteristic parameters and fermentation control parameters; the cloud computing platform analyzes the fermentation characteristic parameters to obtain optimal fermentation control parameters; the cloud computing platform identifies deviation parameters in the fermentation control parameters collected in real time; the cloud computing platform judges and generates an early warning instruction or an adjusting instruction according to the deviation parameter; if the adjusting instruction is generated, the cloud computing platform calculates the adjusting quantity corresponding to each deviation parameter and sends the adjusting quantity to the corresponding equipment end; intelligent monitoring and control in the fermentation process are achieved through the cloud computing platform, the fermentation quality and yield can be effectively controlled, the fermentation efficiency is improved, and therefore the automation level and stability of the fermentation process are improved.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The embodiment of the invention discloses a multi-nest remote take-off and landing unmanned aerial vehicle autonomous inspection method and system, and belongs to the technical field of unmanned aerial vehicles. According to the embodiment of the invention, the target inspection space area is identified, and the related vector and information are acquired, so that the inspection task planning can accurately meet the actual demand. A multi-nest distribution relation vector is input into a multi-agent depth deterministic strategy gradient algorithm, unmanned aerial vehicle path planning spatio-temporal data are discriminated and analyzed by means of a cooperative competition discrimination branch, complex relations such as nest layout and unmanned aerial vehicle paths can be fully considered, and resource allocation is optimized. According to the task indication information, a state vector is generated and a scheduling viewpoint is optimized, and then an autonomous cooperative inspection strategy is generated, so that the cooperative inspection efficiency of multiple unmanned aerial vehicles can be greatly improved, task conflicts are avoided, accurate matching of inspection task paths and task requirements is ensured, and a target area is effectively covered. The labor cost is reduced; and the accuracy and timeliness of routing inspection are improved.

The invention provides a limb nerve rehabilitation state evaluation method based on multi-modal sensor data fusion. Motion inertia data, electromyographic signal data and text data of a corresponding limb nerve rehabilitation action manual of a patient during rehabilitation actions are obtained in real time; inputting the data into a designed and trained multi-modal data feature extraction network to obtain motion inertial data features, electromyographic signal data features and text semantic features; inputting the motion inertia data features, the electromyographic signal data features and the text semantic features into a designed and trained multi-modal data feature fusion network to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a trained prediction output network to obtain a limb nerve rehabilitation state prediction value of the patient. Through multi-modal data fusion and time-dependent feature extraction, the limb nerve rehabilitation state of the patient is evaluated more accurately, and a more reliable diagnosis basis is provided for doctors. Meanwhile, the labor cost is reduced and the evaluation efficiency is improved.

The invention belongs to the technical field of intelligent document management and auditing in the electric power industry, and provides an electric power document autonomous review method and system fusing causal reasoning and compliance review. According to the technical scheme, obtained electric power document data is subjected to semantic analysis, and semantic representation of a document is obtained; based on the semantic representation of the document, performing event extraction on each sentence recognized in the document, and encoding each event to obtain the semantic representation of the event; constructing an initial event causal chain based on the semantic representation of the event; verifying the constructed initial event causal chain by adopting a causal reasoning algorithm, comparing each event in the verified causal chain and the incidence relation between the events with a power industry compliance rule, calculating a compliance score, and calculating the incidence relation between the events of which the compliance score is lower than a set score value or the incidence relation between the events of which the compliance score is lower than the set score value; and generating a correction suggestion, and generating a review report. And the efficiency and the accuracy of document review are obviously improved.

The invention relates to a server application architecture migration method and device and a storage medium, and the method comprises the steps that an automatic detection module analyzes and checks a to-be-migrated application under an X86 architecture, and the to-be-migrated application is subjected to static code analysis, dynamic performance analysis and system compatibility check; according to analysis and check results of the automatic detection module, the automatic adjustment module replaces an X86 specific instruction and a library function, performs memory allocation optimization and memory mapping adjustment, and generates an automatic adjustment report; and the automatic optimization module executes a performance benchmark test, and executes memory access optimization, algorithm optimization, task scheduling optimization and multi-core parallelization adjustment according to a test result. According to the server application architecture migration method and device and the storage medium, the architecture adaptation process can be simplified, performance optimization support and automatic dependency relationship management can be provided, the migration efficiency can be improved, and the cost can be reduced.

The invention provides a bonding wire defect detection method based on global-local importance feature fusion, and solves the problems that in an existing method, structure defects cannot be effectively detected, the detection precision is insufficient, and three-dimensional bonding wire defects with rough edges cannot be segmented. And the sensitive area of the input feature and the weak link in the detection process cannot be revealed. The process comprises the following steps: acquiring a depth image of a PCB, converting a bonding wire depth image point column into point cloud data through matrix operation, performing data preprocessing on bonding wire point cloud by using Cloud Compare software to obtain simplified and optimized bonding wire data, and making a marked point cloud bonding wire data set; after the data set is divided, a three-dimensional point cloud bonding wire defect detection model based on global-local importance feature fusion is trained, finally, normal bonding wire detection, radian abnormity detection, wire bending detection and the like are carried out on a segmented bonding wire plane, and instance segmentation is carried out on the eight bonding wire targets.

The invention discloses a contract evaluation method, system and device based on a large model and a medium, and the method comprises the steps: obtaining a target contract, carrying out the data preprocessing, obtaining the preprocessed contract data, and inputting the preprocessed contract data into a preset contract large language model; the preset contract large language model stores a vector knowledge base including coastal bulk cargo transportation knowledge, legal clauses and industry regulations; in a preset contract big language model, performing contract classification and contract summary extraction on the current preprocessed contract data by using a first model agent to obtain contract summary information; performing contract risk assessment on the current preprocessed contract data to obtain risk assessment information and / or risk avoidance suggestion information; performing benefit measurement and calculation on the current preprocessed contract by using a third model agent to obtain benefit measurement and calculation information; according to the method, the examination and evaluation of the large model in the coastal bulk cargo transportation contract are realized, and the purposes of saving the labor cost and improving the business turnover rate are achieved.

The invention discloses an improved YOLOv11 crack identification and quantification method for a strip mine area and a slope thereof. The method comprises the steps of data preprocessing, model improvement and adaptability adjustment, initial identification model training, crack detection by applying a target identification model, local area identification and post-processing. According to the method, technical means such as a feature extraction network improvement method based on an AReLU function and an ACmix model and a self-adaptive local threshold segmentation algorithm based on a gray scale standard deviation are introduced, and efficient and accurate recognition of cracks is achieved. According to the method, the automation degree of crack detection is improved, the labor cost and the time cost are reduced, and the method is of great significance to safety monitoring of the strip mine area.

The invention discloses an air-ground collaborative dam termite trace detection method and device and electronic equipment. The method comprises the steps that an unmanned aerial vehicle is used for cruising and scanning the surface of a dam to obtain image information; constructing a three-dimensional map of the dam by using the image information; extracting termite activity trace characteristics on the image information; according to the image information, a detection and recognition model is constructed, and the detection and recognition model is that an efficient detector for a small target is introduced on the basis of yov8; deploying the detection and recognition model on a ground tracked robot, and performing initial detection and recognition on the termite activity trace characteristics by the unmanned aerial vehicle by using the detection and recognition model; the termite traces recognized through initial detection are determined as suspected features, and the position points of the suspected features are determined and calibrated on a map; and the ground tracked robot plans a one-way planning route map to a position point according to the position point calibrated on the three-dimensional map, then navigates to the position point with the suspected feature, and carries out detection confirmation, so that air-ground collaborative integrated dam termite earth surface exposure feature detection is comprehensively realized.

The invention relates to the technical field of grain storage monitoring, in particular to a wharf granary grain condition monitoring method and system. The method comprises the following steps: acquiring data of wharf granary environment characteristics, grain pile size characteristics, temperature characteristics, humidity characteristics and gas concentration characteristics; based on the data, spatial-temporal characteristic models are constructed, and the spatial-temporal characteristic models comprise a temperature spatial-temporal characteristic model, a humidity spatial-temporal characteristic model and a gas concentration spatial-temporal characteristic model; according to the spatio-temporal characteristic model, spatio-temporal characteristic data are obtained; processing the spatio-temporal characteristic data to obtain optimized spatio-temporal characteristic data; constructing a grain safety risk assessment model through the optimized spatial-temporal characteristic data; and monitoring the grain condition of the wharf granary according to the grain safety risk assessment model. According to the invention, internal sensorless monitoring is carried out on the temperature, humidity and # imgabs0 # concentration of the infinite space point in the grain pile, so that the monitoring cost is greatly reduced, and high-precision assessment of the grain safety risk is realized.