125results about How to "Real-time analysis" patented technology

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention relates to the technical field of artificial intelligence and Internet of Things, in particular to an inspection service management system based on artificial intelligence and Internet of Things, which comprises a data acquisition module, an edge calculation module, a task allocation module, a path planning module, an early warning module and a user interaction module. Wherein the data acquisition module is used for acquiring multi-dimensional data of environment and equipment in real time; the edge calculation module is used for carrying out preliminary analysis and processing on the multi-dimensional data; the task distribution module is used for generating inspection tasks according to inspection rules; the path planning module is used for generating an optimal path of the inspection equipment; the early warning module is used for generating an early warning signal when the potential risk is detected; and the user interaction module is used for displaying the state of the system through a visual interface. According to the invention, by realizing real-time data processing, dynamic task scheduling and path optimization, the response speed and efficiency of the system are improved, and meanwhile, the equipment fault prevention and decision-making capabilities of operators are enhanced.

The invention discloses an automatic trajectory planning and navigation system of a traditional Chinese medicinal material excavating machine, relates to the technical field of traditional Chinese medicinal material excavating, and aims to solve the problems that the efficiency is low during current angelica sinensis excavating, and angelica sinensis plants are different in size due to different growth environments and characteristics, so that the excavating efficiency is low. During digging, the root hair and stem leaves of the angelica sinensis are easily damaged. Comprising a planting area calibration module used for dividing an angelica sinensis planting area and planting point positions in combination with terrain, an image acquisition module used for acquiring growth images of stems and leaves of angelica sinensis in real time, a planting environment data acquisition module used for acquiring planting environment parameters in real time, and a root hair three-dimensional distribution analysis module used for analyzing the root hair distribution condition of angelica sinensis. According to the method, the digging strategy aiming at the maturity is formulated, so that the digging efficiency and quality of the angelica sinensis are remarkably improved, damage to root hairs, stems and leaves of the angelica sinensis due to improper manual operation is avoided, and the technical problems that the efficiency is low and the angelica sinensis is easily damaged are solved.

The invention discloses a multi-source water environment data processing method and system based on intelligent Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: carrying out the standardization processing of obtained multi-source heterogeneous water environment monitoring data, obtaining the standardized data, carrying out the multi-scale decomposition, and obtaining low-dimensional data; a distributed storage architecture is adopted for low-dimensional data, the data are stored on a plurality of nodes in a scattered mode, a data index and metadata management mechanism is established, and data retrieval and access are conducted; cleaning, aggregating and analyzing the low-dimensional data by adopting a stream data processing method, extracting key indexes and events, setting a water quality parameter threshold value and a change rate threshold value, constructing a water quality abnormity early warning rule in combination with a decision tree method, and judging whether water quality abnormity exists or not; and constructing a water quality prediction model through a support vector machine and a random forest machine learning method, and predicting a water quality trend. According to the invention, powerful technical support is provided for water environment monitoring and management.

The invention relates to a streaming speaker log method and system, and the method comprises the steps: obtaining a voice stream, and detecting the voice of a speaker in the voice stream through employing a VAD algorithm; slicing the voice of the speaker, and performing speaker jump detection based on the sliced voice to obtain a speaker jump point; the method comprises the following steps: splicing sliced voice streams before a jump point of a speaker, extracting voiceprint features of spliced voice segments, comparing the extracted voiceprint features in a pre-established temporary voiceprint library, and determining a speaker category corresponding to the voiceprint features after successful comparison. According to the invention, a streaming processing mode is adopted, continuous voice streams can be processed and analyzed in real time, waiting for the completion of processing of the whole voice file is not needed, and the processing efficiency and real-time performance are improved. According to the method, the number of speakers does not need to be preset in advance, the voiceprints are registered step by step in the execution process, the new speaker is found through the temporary voiceprint library, and unlimited processing of the number of speakers is achieved.

The invention provides a photovoltaic power generation power prediction method, system and device and a storage medium, and relates to the technical field of photovoltaic power generation power prediction, and the method comprises the steps: collecting historical data and real-time data of a photovoltaic array region; performing synchronous preprocessing to generate a historical data set and a real-time data set; establishing an initial generation power model, and drawing a curve graph of generation power changing along with time; analyzing climate data and cloud cluster image data in the real-time data, judging the illumination condition of the photovoltaic array area, and calculating the moving direction and the moving speed of the photovoltaic array area under the illumination condition; calculating the actual illuminated area of the photovoltaic array area under each time node, introducing the information into the initial power generation power model for optimization, and generating new prediction data; and carrying out comparison verification on the optimized prediction data and subsequent actual generation power. According to the method, the prediction accuracy is remarkably improved, and the influence of environmental factors on the prediction result is effectively reduced.

The invention discloses a water and soil conservation monitoring system and method based on the Internet of Things technology, and relates to the technical field of water and soil conservation monitoring. A data acquisition module of the Internet of Things technology is utilized to judge small, medium and large drainage basins according to the drainage basin length of a monitoring area; then real-time data of soil humidity, drainage basin correlation and water and soil conservation measures are collected in real time from a monitoring area, a calculation and evaluation module is used for respectively inputting, sequentially calculating and outputting a soil erosion rate TQ, a drainage basin monitoring area evaluation value LY and a water and soil conservation measure adjustment value SBF, and the water and soil conservation measure adjustment value SBF is calculated and output on the basis of the water and soil conservation measure adjustment value SBF; according to the method, the accuracy of monitoring and the pertinence to small, medium and large drainage basins are ensured, a closed-loop feedback mechanism is formed, an innovative monitoring method is realized, and the beneficial effects provide more scientific, comprehensive and effective support for water and soil conservation work.

The invention is suitable for the technical field of data transmission, and provides an illumination lamp data transmission control method, which comprises the following steps of: acquiring data, state data and physiological health data by an illumination lamp; different priorities are set for each type of data, and different data are processed by adopting corresponding compression strategies; a multi-hop transmission ad hoc network is established between the lighting lamp and other nearby lighting lamps, and a network topology structure is dynamically adjusted; the lighting lamp evaluates an optimal sequence of various transmission paths transmitted to the monitoring center based on real-time information in the ad hoc network, and sets corresponding transmission paths and transmission strategies for different data according to priorities; the lighting lamp transmits the data with different priorities to the monitoring center hop by hop through a selected transmission path according to a set transmission strategy; and the monitoring center receives the data of the lighting lamps and carries out corresponding processing according to the priority of the data. According to the invention, the problems of stability, real-time performance and priority management of data transmission in an existing complex mine environment are solved.

The invention discloses an engine multi-mode operation identification method and device based on a multi-dimensional internet of things, and a medium, and aims to solve the problem that the driving state cannot be obtained in time due to inaccurate engine monitoring data. According to the method, multiple key parameters in operation of the engine can be comprehensively covered through different types of sensors, rich information basis is provided for multi-modal operation identification of the engine through multi-dimensional data collection, rich feature data is extracted from the multiple sensors, time domain, frequency domain and time frequency features are covered, and the method is suitable for multi-modal operation identification of the engine. Through construction of a multi-modal operation identification model, data of a plurality of sensors can be comprehensively considered, a neural network model is used for training, and a cross validation method is used for model verification, so that the stability and generalization ability of the model are ensured; and the state of the engine is quickly judged according to the multi-mode operation identification model, so that real-time monitoring and early warning of the state of the engine are realized.

The invention relates to the technical field of power system fault detection, and discloses a 10kV cable single-phase earth fault detection method based on an adaptive algorithm, and the method comprises the following steps: S1, employing a voltage collector and a current collector to collect the power frequency and transient signals of a switching station integrated intelligent terminal strip; s2, filtering and de-noising the collected signals through a self-adaptive digital signal processing algorithm; s3, adaptively determining a single-phase earth fault signal component, and identifying the most significant frequency band; s4, extracting features of the fault signal by using a multi-band multi-criterion adaptive algorithm; and S5, based on the extracted features, selecting the most significant criterion for single-phase earth fault detection. Through an adaptive algorithm and a deep learning model, the detection and recognition precision of a complex single-phase earth fault is improved, real-time intelligent processing is realized through an integrated monitoring module, misjudgment is reduced, the operation and maintenance efficiency is improved, the system adapts to multi-scene application, and the intelligentization and power supply reliability of the system are enhanced.

The invention discloses a method for locating the position of a software problem source code through a log and electronic equipment, and the method comprises the steps: obtaining log output points of target software, creating an index record for the log output points, detecting the source code position information of each log output point, analyzing the log content of each log output point based on the source code position information, extracting and generating characteristic values corresponding to the log content, constructing a log index database, receiving an actual running log, extracting the characteristic values of the log, performing matching in the log index database to determine the position of a source code corresponding to a log output point, and positioning the specific position of the source code according to a matching result. According to the method, the code performance and readability are improved, the method does not depend on code position information output in the log, the requirement for the log format is lowered, the code readability is enhanced, and the integrity of the log is improved by scanning the code and proposing suggestions for improving the log.

The invention discloses a method for optimizing edge computing equipment based on a multi-modal large model in reservoir intelligent measurement, and the method achieves the comprehensive and real-time monitoring of a reservoir water source protection region through the combination of the multi-modal large model and an edge computing technology. A high-performance edge computing device is adopted as a platform, a multi-mode large model is deployed to perform real-time analysis on a monitoring video, and illegal behaviors such as burning prohibition, cultivation, water surface floating objects, catering business point garbage and personnel invasion are identified; meanwhile, a small model is designed for specific illegal behaviors for special identification, and the identification precision and efficiency are improved; the edge computing platform supports access of Internet of Things equipment, and integrated analysis of multi-dimensional data such as meteorological data and water quality data is achieved. According to the method, a manager is notified in time through an early warning mechanism, remote access and management are supported, and data transmission security is ensured; the whole scheme is efficient and accurate, is suitable for various reservoir intelligent monitoring scenes, and provides powerful support for reservoir management and protection.

The invention provides an anomaly detection method and device, equipment, a storage medium, a product and a vehicle, the method is applied to the vehicle, the vehicle comprises a plurality of state machines, setting information of each state machine is determined based on a preset abnormal behavior pattern library, and the abnormal behavior pattern library comprises at least one abnormal behavior detection rule. The state table of each state machine comprises a normal state and an abnormal state; the method comprises the following steps: acquiring first information, wherein the first information comprises vehicle control information; when it is determined that first information corresponds to a first state transition event of a first state machine, an event processing function corresponding to the first state transition event is called, and the first state machine is transited to a first state from a current state; and when it is determined that the first state is an abnormal state, executing an exception handling process. According to the invention, the accuracy and efficiency of vehicle anomaly detection can be improved, and the method has high adaptability and expandability.

The invention relates to the technical field of financial management, and discloses a business and financial integrated financial centralized management system, which comprises an integrated data processing center for realizing real-time interaction and analysis of business data and financial data, and further comprises a multi-dimensional report generator in signal connection with the integrated data processing center, the generator can generate a multi-dimensional and dynamic report according to real-time interaction data of each independent financial department, and provides accurate data support for enterprise decision making. The financial centralized management system with business and financial integration is constructed, centralized management, real-time analysis and monitoring of financial data are achieved, the financial management level and the risk control capability are improved, and healthy and stable development of enterprises is ensured. On the basis, through deep learning and big data technologies, the system can provide more accurate decision support for enterprises, assist the enterprises to accurately grasp market pulses, optimize resource allocation and improve operation benefits.

The invention relates to the technical field of ship automatic control and information safety, solves the technical problem that a traditional method cannot effectively monitor in real time and quickly give an early warning to prevent various accidents when an inland ship is in a complex and changeable operating environment, and particularly relates to an abnormal state detection method and system for a shore-based driving and controlling inland ship group. The method comprises four parts: identity authentication, node anomaly detection, dynamic trust evaluation and node adaptive control. According to the method, only reliable entities can participate in communication through a strict verification process and continuous trust evaluation, so that communication safety is enhanced, comprehensive and real-time monitoring of inland ship groups is realized, abnormal conditions such as physical faults or network attacks can be found in time, potential risks can be warned in advance, and the inland ship groups can be monitored in real time. Therefore, the safety and the reliability of the whole navigation are improved.

The invention provides a voiceprint-based converter valve saturable reactor screw looseness detection system and method, and the method comprises the steps: collecting a sound wave signal during the operation of a saturable reactor based on a sound wave signal collection module, and carrying out the preprocessing of the collected sound wave signal based on a preprocessing module, the method comprises the following steps: preprocessing a sound wave signal, carrying out time-frequency analysis on the preprocessed sound wave signal based on a wavelet transformation module to obtain time-frequency domain analysis data, converting the time-frequency domain analysis data into a gray fusion image based on a gray fusion image generation module, building a ResNet model based on a ResNet model module, and training the ResNet model based on the obtained gray fusion image to obtain a trained sound wave signal; and inputting a gray fusion image of a sound wave signal to be detected into the trained ResNet model to realize looseness detection. The device can accurately capture the symptom of screw loosening at an early stage, improves the detection sensitivity and reliability, can adapt to a complex operation environment, and is convenient to use.

The invention provides an intelligent financial management system based on data feature analysis, and relates to the technical field of financial management. The intelligent financial management system based on data feature analysis comprises a data acquisition module used for collecting various financial data inside and outside an enterprise; the data preprocessing module is used for carrying out standardization processing on the financial data; the feature extraction and analysis module is used for carrying out feature extraction and analysis on the preprocessed financial data; the model building and training module is used for building a prediction model based on the extracted features and training the model by using historical data; the financial management module is used for realizing comprehensive management of enterprise finance based on a feature analysis result; and the decision support module is used for presenting results of feature analysis and financial management to an enterprise manager in a visual form. The intelligent financial management system not only can improve the financial management efficiency of an enterprise and reduce the operation cost, but also can enhance the market competitiveness and adaptability of the enterprise.

The invention relates to the field of optical detection, in particular to an interference confocal measurement system based on two-dimensional dispersion and a measurement method thereof. White light generated by a light source is collimated into parallel light through a first collimating lens, is shaped into rectangular light through a rectangular diaphragm, and is shaped into a linear light beam through a column focusing lens and a rectangular slit; the light is divided into reference light and object light through a beam splitter, the reference light is achromatic through a first achromatic objective lens, then enters a reference plane mirror and is reflected back to the beam splitter by the reference plane mirror, the object light is focused to positions of different depths of an object to be measured after being dispersed through a dispersion lens and is reflected back to the beam splitter by the object to be measured, and the two beams of light are reconverged in the beam splitter. And after being collimated by the second collimating lens, dispersed by the dispersion grating and achromatic by the second achromatic objective lens in sequence, the light is focused on the target surface of the spectrograph for spectrum and interference analysis. According to the invention, line scanning, dispersion and interference measurement are combined, so that the measurement precision can be greatly improved.

The invention is applicable to the technical field of semantic communication, and particularly relates to a semantic communication system based on zero sample learning, the system comprises a sending end, a receiving end and a database, and by combining data of various modes of communication images, audios and texts and by utilizing the advantages of CLIP and CLAP multi-mode models, the semantic communication system is established. Images and audios are mapped to a shared text coding vector space, so that robust semantic understanding is promoted, the method is suitable for being applied to a complex marine environment with poor communication conditions, data of various sensors on the USV can be processed and analyzed in real time, and the method is suitable for popularization and application. The integration of the dynamically updated knowledge database ensures continuous learning and performance improvement and can explain new and invisible data, reduces the dependence on a large tagged data set, and solves the limitation of a traditional semantic perception system, thereby improving the reliability and efficiency under severe communication conditions.

The invention discloses a network vulnerability detection and repair method, system equipment and a readable storage medium. The method comprises the following steps: obtaining known vulnerabilities, related data and analysis results of the known vulnerabilities; according to the known vulnerabilities, the related data and the analysis result, constructing and training a vulnerability detection model; configuring a vulnerability scanning environment, deploying a vulnerability detection model, and executing vulnerability detection; and formulating and executing a repair scheme according to a vulnerability detection result. A deep learning model is introduced into vulnerability detection and repair, so that the method has higher robustness and accuracy compared with a traditional method, and the security protection level is effectively improved; a large amount of data can be analyzed and processed in real time, and timely response to risks is achieved; and automation of safety detection and protection can be realized, the operation and maintenance efficiency is improved, manual intervention is reduced, and the operation and maintenance cost is also reduced.

The invention discloses an extensive Beidou terminal equipment management method and system, and belongs to the technical field of Beidou terminal equipment management. The method comprises the following steps: S1, building a modular framework based on a micro-service architecture, and ensuring that each function is realized through an independent module; s2, designing a loose coupling module environment, developing a module unit, and performing module combination and configuration through user permission to construct a personalized workflow; s3, remote sensing monitoring and Beidou positioning technologies are integrated, and natural resource data including geological disaster monitoring data and positioning terminal position and state data are collected and preprocessed in real time; s4, providing natural resource analysis, geological disaster risk assessment, tracking and trajectory analysis, visualization and report generation, and underlying function implementation of an event triggering and alarm mechanism; and step S5, supporting function extension through an API interface, allowing a user to access a third-party service or develop a new module, and supporting import and export of a workflow at the same time.

The invention relates to the technical field of new energy power generation, in particular to a new energy fan master control system improvement method and system, and the method comprises the steps: collecting fault information of a new energy unit; performing electrical and program design according to the fault information; a fan unit is transformed on site according to electrical and program design; and carrying out performance test and acceptance on the transformed fan set. By improving the master control system of the new energy fan set, including upgrading the protection strategy and the control logic of the master control system, the fault probability of the set is reduced, the equipment maintenance time is shortened, the operation stability of the equipment is improved, the operation data and the state information can be monitored and analyzed in real time, and the management and the control of the operation state of the fan are enhanced. By technically improving the variable pitch system and optimizing the control logic of the variable pitch motor, frequent input of an electromagnetic brake, overcurrent of the motor and failure of a driver are avoided, the overall failure rate of the variable pitch system is reduced, the availability and the generating capacity of a unit are improved, and the electric quantity income is increased.

The invention discloses a monorail crane track defect detection system and method, and relates to the technical field of track detection, the monorail crane track defect detection system comprises a vehicle selection module, a data acquisition module, a data judgment module, a secondary acquisition module and a defect division module, comparing the pilot operation data with a standard threshold interval to judge whether the vehicle meets requirements or not; the technical key points are as follows: a vehicle is detected before detection, and the reference of the detection is taken as the reference for subsequently judging whether a defect exists on a monorail crane track or not, so that the subsequent judgment on the defect is more accurate, smaller defects can be identified, and then the defect position is further acquired and analyzed; defect content and defect levels are analyzed, a corresponding defect repair plan is conveniently formulated according to the defect levels subsequently, the amount of data processed in the whole process is small, defect positions can be analyzed in real time, and the method has a good use prospect.

The invention relates to the technical field of network security, in particular to a network security supervision system based on Internet of Things, which comprises a data acquisition module used for acquiring power consumption data and network data of Internet of Things equipment; the network analysis module is used for analyzing the network security of the Internet of Things equipment; the power consumption monitoring module is used for judging the power consumption state of the Internet of Things equipment; the bandwidth management module is used for managing the bandwidth of the next security management period; the risk early warning module is used for carrying out Internet of Things equipment network security risk early warning on the user according to the analysis result of the network security state of the Internet of Things equipment in the risk management period; and the communication analysis module is used for adjusting the network security risk early warning process of the Internet of Things equipment according to the white list communication proportion, the black list communication proportion and the unknown communication proportion of the Internet of Things equipment acquired in the risk management period. According to the invention, the network security supervision efficiency of the Internet of Things equipment is effectively improved.

The invention discloses a chemical production data anomaly detection and safety early warning system based on deep learning, relates to the field of computer and chemical crossed data anomaly detection, and solves the problem that a deep learning model for analyzing acquired data is lacked. The method comprises the following steps of: transmitting acquired temperature, pressure and material flow data to a data processing module for preprocessing, so as to obtain qualified values of the temperature, the pressure and the material flow; giving different weights to construct a parameter total deviation evaluation value in combination with a deep learning model prediction value and a measurement value; and according to the parameter total deviation evaluation value and the parameter total error score evaluation value, constructing a parameter total anomaly calculation value, and according to the parameter total anomaly evaluation calculation value, carrying out grade division on the key equipment position and sending out a corresponding safety early warning signal.

The invention provides unmanned thermal battery packaging quality inspection equipment based on machine vision, and relates to the technical field of production line detection systems.The unmanned thermal battery packaging quality inspection equipment comprises a moving module, a quality inspection module and a base station assembly, a mounting bottom plate is arranged in the middle of the moving module, and a connecting mechanical arm and a mounting support are arranged at the end of the quality inspection module in an extending mode; mechanical joints are arranged at the connecting positions of the adjacent connecting mechanical arms, the connecting positions of the connecting mechanical arms and the mounting bracket and the connecting positions of the quality inspection module and the connecting mechanical arms, a high-resolution camera and a multi-layer convolutional neural network are adopted, tiny defects can be accurately detected, and compared with a traditional manual detection method, the detection efficiency is greatly improved. According to the automatic quality inspection device, the risks of missing detection and misjudgment are remarkably reduced, the consistency of product quality is ensured, full-automatic quality inspection is achieved through unmanned design, the labor cost and human errors are reduced, and the production efficiency is improved. In addition, an intelligent navigation system and a laser radar technology enable the equipment to move and position autonomously in a complex environment, so that the working flexibility is improved.

The invention discloses a rice phenological period intelligent prediction method based on environmental data and deep learning, and belongs to the technical field of intelligent agriculture. In order to realize accurate prediction of the phenological period of rice, the method comprises the following steps: acquiring environmental data and phenological period data of the whole year of rice growth, and constructing a phenological model data set; performing data preprocessing according to the phenological model data set for subsequent model training and prediction; building a rice phenological period prediction model based on an Encoder-Decoder architecture of a Seq2Seq model, introducing a Bayesian neural network to carry out uncertainty evaluation, and formulating a phenological model update judgment standard; according to eight phenological periods divided by the rice growth cycle, training a rice phenological period prediction model; and performing real-time prediction and updating based on the daily updated environment data. According to the invention, daily meteorological data can be processed and analyzed in real time, the prediction result of the phenological period is dynamically updated, and the efficiency and benefits of agricultural production are improved.

The present application discloses a computer network security analysis method based on big data, which relates to the field of network security, including: collecting user behavior data and interaction data; preprocessing the collected user data set; using a federated learning architecture, taking the preprocessed user data set as a training sample, training a machine learning model locally on the user side for user status analysis; uploading the parameters of the trained machine learning model to a cloud server, performing global aggregation on the cloud service, and obtaining a global machine learning model; based on the global machine learning model and interaction data, using an unsupervised learning method based on anomaly detection to perform security analysis on the user's computer network behavior. In view of the low network security efficiency existing in the prior art, the present application performs real-time analysis of user status through a local BiLSTM neural network model on the user side, and at the same time, uses a federated learning architecture to perform global aggregation on the cloud, etc., thereby improving the efficiency of analysis.

The invention belongs to the technical field of oil well associated gas component analysis, and discloses an oil field associated gas component wellhead online analysis system and method. The wellhead online analysis system for the oilfield associated gas components comprises a circulating suction unit, an oilfield associated gas component analysis unit, a backflow unit and a remote control unit, the circulating suction unit comprises an electric valve, a gas-liquid separation tank and a circulating pump which are connected in sequence, the oilfield associated gas component analysis unit comprises a pressure reducing valve, an electric valve and a gas component detection part which are connected in sequence, and the backflow unit comprises an accumulated liquid backflow unit and an accumulated waste gas emptying unit. The circulating suction unit, the oilfield associated gas component analysis unit and the backflow unit are arranged on a sealed sampling pipeline formed at a wellhead sampling position. According to the wellhead online analysis system for the oil field associated gas components, online detection of the oil field associated gas components is achieved, and the reliability and accuracy of online analysis are improved.

The invention relates to the field of safety monitoring, and discloses an intelligent drilling construction data safety monitoring system based on a management and control cloud platform. The system comprises a data acquisition module, a data transmission and processing module, a construction condition analysis module, a safety risk prediction coefficient calculation module, a safety risk prediction module, a risk level positioning module and a result feedback module. The method comprises the following steps: acquiring drilling construction data, transmitting the drilling construction data to a cloud platform data processing center for processing, carrying out comprehensive analysis and safety risk prediction on the processed drilling construction data, carrying out risk grade division and positioning according to a risk prediction result, and feeding back and early warning to a management personnel terminal, thereby being beneficial to realizing comprehensive, real-time and efficient monitoring and analysis of the drilling construction data. Potential safety risks can be better identified, potential safety hazards in the construction process can be found and processed in time, and the accuracy and timeliness of early warning information are improved.