1240 results about "Real time data acquisition" patented technology

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

The invention relates to the technical field of data transmission, and discloses an intelligent flight data transmission method and system based on satellite communication and a storage medium. The method comprises the following steps: carrying out real-time data acquisition and calculation on signal strength, bandwidth, delay and packet loss probability of an available satellite communication link to obtain a real-time link parameter table and a transmission channel allocation sequence; based on the real-time link parameter table, according to the priority information of the encrypted data packet queue and the transmission channel distribution sequence, performing multi-channel distribution and parallel transmission processing on the data packet to be transmitted to obtain a data packet transmission record; and carrying out statistical analysis and parameter calculation on the transmission success rate, delay time and retransmission times in the data packet transmission record to obtain transmission quality data and a system optimization parameter set. Under the condition of limited satellite communication resources, intelligent hierarchical transmission of flight data is realized, timely and reliable transmission of important data is ensured, and meanwhile, a real-time monitoring and optimization mechanism for data transmission quality is provided.

The invention relates to the technical field of data processing, in particular to a distributed multi-source heterogeneous sensor data processing method and system. The method comprises the following steps: collecting environmental parameters of a leakage area in real time through a distributed sensor array; performing coordinate system unification and timestamp alignment on the environmental parameters of the leakage area to generate a standardized leakage situation data set; extracting gas diffusion characteristics and thermal imaging characteristics of the standardized leakage situation data set to calculate a gas cloud cluster diffusion path probability; performing risk decision instruction generation on the gas cloud cluster diffusion path probability based on a preset leakage level classification neural network to obtain a risk decision instruction; calling a matched emergency plan based on the risk decision instruction; and analyzing the implementation steps of the emergency plan and performing instruction conversion to generate an emergency plan instruction. According to the invention, through real-time data acquisition, intelligent risk assessment and automatic emergency response, the timeliness responsiveness of data processing is improved.

The invention discloses a building ground real-time settlement monitoring device and a use method thereof, and belongs to the field of building structure safety monitoring. The monitoring device comprises a hierarchical sensor network which is used for carrying out multi-time-scale real-time data acquisition and comprehensively obtaining deformation data and related environmental parameters of a building structure; the data processing and analyzing module is used for performing real-time processing and intelligent analysis on the acquired data, and identifying and classifying abnormal deformation characteristics of the building structure in time; the deep learning prediction module is used for quantitatively predicting the probability state and the evolution trend of building settlement by constructing a multi-scale time sequence prediction model; the multi-factor analysis module is used for carrying out coupling modeling and comprehensive analysis on the environmental factors, the structural characteristics and the abnormal evolution process so as to identify key influence factors and action mechanisms thereof; and the risk assessment and early warning module is used for performing grading assessment on the building settlement risk based on the prediction and analysis result and generating corresponding early warning information and decision support schemes.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

The invention discloses a water conservancy reservoir group joint scheduling optimization system based on digital twinning, and the system comprises the following steps: a real-time data collection and preprocessing module which is used for collecting and processing meteorological data; the reservoir group digital twinning modeling module is used for constructing a dynamic digital twinning reservoir group simulation model; the future state prediction module is used for predicting the future state change trend of the reservoir group; the joint scheduling optimization module is used for optimizing a multi-target joint scheduling scheme; the self-adaptive memory mechanism management module is used for maintaining an excellent scheduling strategy memory bank and carrying out intelligent disturbance adjustment; the execution and dynamic adjustment module is used for executing the scheduling scheme and dynamically correcting the scheme; the emergency response module is used for generating an emergency scheduling plan; and the feedback and adaptive optimization module is used for feeding back the real-time execution result to the twin model for dynamic correction. According to the invention, real-time optimization and intelligent emergency response of the reservoir group scheduling system are realized, and the scheduling efficiency and the system stability are obviously improved.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of structure health monitoring, in particular to a breakwater structure health monitoring method based on a sensing network, which comprises the following steps: S1, collecting stress, acceleration, displacement and environmental parameter data of a breakwater; s2, preprocessing the sensing data acquired in the step S1; s3, integrating the data preprocessed in the S2 into a comprehensive data set reflecting the overall structure state of the breakwater; s4, analyzing and calculating the comprehensive data set in combination with a structural mechanical model of the breakwater so as to extract characteristic parameters; s5, constructing a breakwater health assessment model; s6, evaluating the real-time data based on the health evaluation model constructed in the S5, and calculating the health score of the breakwater; according to the invention, through real-time data acquisition based on the sensor network and the sparse Bayesian learning algorithm, comprehensive monitoring and intelligent early warning of the health state of the breakwater structure are realized, and the monitoring precision, the real-time performance and the risk prediction capability are effectively improved.

The invention relates to the technical field of industrial data processing, in particular to a suspension spring multi-station production line collaborative scheduling system based on digital twinning, which comprises a station control programming module, a multi-station autonomous collaborative module, a quality pre-control module, a manufacturing process tracing module and a multi-machine execution safety module, according to the system, through real-time data acquisition and monitoring of an integrated digital twin platform, self-adaptive generation of control programs of all stations and programmed issuing of production instructions are carried out; when the production disturbance is sensed, dynamic optimization of a task sequence and real-time self-adaptive adjustment of processing parameters are executed; feedforward control and active compensation are realized in combination with a quality prediction model; constructing a digital thread to support tracing and analysis of the whole manufacturing process; and dynamic program verification of security interlocking and resource conflicts is cooperatively executed on multiple machines. Intelligentized, high-efficiency and high-quality integrated optimization control over the multi-station production process of the suspension spring is achieved.

The invention relates to the technical field of service examination and approval, and discloses a service examination and approval processing method, device and equipment and a storage medium, and the method comprises the steps: receiving a user authorization request, verifying the user authorization request, and obtaining a sub-account certificate with a reading only permission; performing real-time data acquisition on the plurality of e-commerce platforms based on the sub-account credentials to obtain multi-dimensional user data including transaction records, operation data and evaluation information; inputting the multi-dimensional user data into a preset space-time sequence attention network and graph convolution hybrid model to carry out dynamic evaluation on a user behavior pattern to obtain risk quantitative indexes; inputting the risk quantitative indexes into a rule engine integrating an adaptive decision tree and a genetic algorithm for automatic decision making to obtain a graded approval decision; and performing multi-signature encryption on the execution instruction according to the graded approval decision to obtain a safety operation instruction and execution state information, thereby realizing full-process automation from data acquisition, risk assessment to decision execution.

The invention provides a gynecological disease diagnosis and treatment method and system fused with an electronic medical record, and relates to the technical field of medical informationization, and the method comprises the steps: collecting the multi-modal diagnosis and treatment data of a patient, and constructing a personalized medical database through a self-encoding network; deploying a plurality of agents based on a dynamic causal network, and generating an initial treatment scheme in combination with deep reinforcement learning; through real-time data acquisition and a multi-objective optimization algorithm, the scheme is dynamically optimized by fusing expert decision features. According to the invention, personalized precise treatment of gynecological disease diagnosis and treatment can be realized, the treatment effect is improved, and the complication risk is reduced.

The invention relates to the technical field of new energy automobile electric control, and discloses a new energy automobile electric control fault prediction system. The system comprises a real-time data acquisition module, a dynamic fault prediction model construction module, a fault difference calculation module, a multi-dimensional anomaly analysis module, a fault probability positioning module and a self-adaptive maintenance strategy module. The real-time data acquisition module acquires sensor data of the electric control system in real time; the dynamic fault prediction model construction module constructs a dynamic fault prediction model based on historical fault data; the fault difference calculation module inputs real-time data into the model and outputs theoretical fault indexes; the multi-dimensional anomaly analysis module compares the theory with the actually measured fault indexes to generate an anomaly difference matrix; the fault probability positioning module inputs the matrix into a space correlation network to generate a fault probability distribution diagram; and the adaptive maintenance strategy module configures maintenance parameters according to the distribution diagram. According to the system, the fault prediction accuracy and real-time performance can be improved, and stable operation of the new energy automobile electric control system is guaranteed.

The invention discloses a power supply fault rapid detection system and method based on an intelligent diagnosis engine, and belongs to the technical field of power electronic equipment fault diagnosis, and the system comprises a function region division module which is mainly used for dividing a plurality of function regions according to a power supply topological structure; the real-time data acquisition module is mainly used for acquiring parameter data of each functional area in a power supply operation stage; the current model building module is mainly used for building a current time sequence prediction model; the trend prediction module is mainly used for generating a future current change trend; the abnormity judgment module is mainly used for identifying an abnormal condition by performing similarity matching with real-time data; the fault positioning module is mainly used for matching with a fault mode based on an abnormal current waveform and constructing a directed graph to position a fault position; the method has the advantages that early warning and accurate positioning of the power supply fault are realized through multi-modal data fusion and a deep learning model, and the operation and maintenance efficiency and reliability of the power electronic equipment are remarkably improved.

The invention relates to the technical field of data acquisition and processing, and provides a real-time data acquisition and processing method and system for a distributed control system, and the method comprises the steps: collecting a data flow in real time through a terminal sensor, analyzing the probability distribution of a data change rate through a sliding window, and obtaining a data change rate; and a sensitivity threshold is intelligently generated by combining dynamic factors such as equipment electric quantity and network load. When data mutation exceeds a threshold value, the system automatically extracts a key change section and generates time, period and variable quantity three-dimensional features, and meanwhile, the frequency domain analysis result is fused to enhance the anti-interference performance. And the event emergency degree is calculated based on the time-space correlation of the spectrum energy entropy and the characteristic parameters, and finally a processing queue sorted according to priorities is formed and the bandwidth is dynamically allocated. According to the method, through multi-dimensional data fusion and resource adaptive adjustment, the response speed of the system to emergencies, the data discrimination precision and the stability in a complex environment are improved.

The invention discloses an on-line automatic measurement and compensation method for digital manufacturing, which comprises the following steps of: scanning the surface of a workpiece to be processed in real time through a high-precision laser scanner to obtain a three-dimensional geometric model of the workpiece; environmental parameters in the laser machining process are monitored in real time through multiple sensors; on the basis of the collected real-time geometrical morphology and environmental parameters, the processing deviation of the workpiece is calculated in real time through a data processing and analyzing module, and a compensation model is generated; according to the calculation result of the compensation model, the central control system adjusts a laser processing path and processing parameters in real time; after each time of processing, a feedback signal of each time of error is used as a basis for updating the compensation model so as to ensure that the compensation model is continuously optimized. The method has the following advantages and effects: dynamic correction and long-term optimization of machining errors can be realized by combining real-time data acquisition and an optimization compensation model technology of a deep learning algorithm, so that the machining precision and efficiency are improved.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention relates to the technical field of mine safety production analysis, and discloses a mine safety production data fusion analysis method based on multi-source perception, which comprises the following steps: step 1, carrying out real-time data acquisition on physical parameters related to safety production in a mine environment through various types of sensors arranged in the mine environment, the collected data are stored as independent data streams with timestamps, and the data of different sensors have time inconsistency according to the difference set by the sampling frequency and the clock; and step 2, carrying out time delay compensation processing on the multi-source data with the timestamps acquired in the step 1. An optimization model is established in combination with a variational method to perform time delay compensation and time synchronization processing on sensor data, the problem of time inconsistency caused by different sampling frequencies and clocks of sensors is eliminated, fusion processing of multi-source sensor data on a unified time reference is realized, and an accurate data basis with consistent time sequence is obtained.

The invention relates to the technical field of quality detection in material manufacturing and coating processes, in particular to an intelligent color difference analysis and detection method for paint, which comprises the following steps: S1, acquiring a multispectral dynamic compensation image; s2, texture-color coupling separation is carried out; s3, carrying out adaptive chromatic aberration modeling; s4, process correlation visualization is carried out, color difference data is mapped into a scalable thermodynamic diagram, and grid density is dynamically adjusted according to detection precision; and analyzing geometric feature parameters of the abnormal region, matching a process defect mode library to generate an optimization suggestion, and superposing the optimization suggestion on the thermodynamic diagram by a visual identifier. Through real-time data acquisition, texture interference elimination, model adaptive optimization and process defect analysis, the overall detection precision and efficiency are improved, the intelligent adaptive characteristic is achieved, and different production environments and base material types can be adapted, so that the coating production process is optimized, and the consistency of the coating quality is ensured.

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention provides a high-efficiency ship automatic control system, and the system comprises a comprehensive control platform which carries out the unified integration of subsystems, sets a target function, and carries out the data sharing, collaborative optimization and global control among the subsystems. The intelligent sensing network module is used for collecting historical data and real-time data; the self-learning control module is used for constructing a control strategy generation model; the ship digital model module is used for constructing a digital model for simulating ship navigation so as to test different ship control strategies; the decision execution module is used for generating and executing a control instruction; and the safety monitoring module detects abnormal behaviors of the ship and gives an alarm. According to the invention, a plurality of subsystem modules can be integrated in a unified manner, data sharing, collaborative optimization and global control are realized, an optimal control strategy can be provided, the cost and risk of actual navigation are reduced, and the control precision, response speed and operation efficiency of a ship are improved.

The invention discloses an intelligent building construction operation management method based on big data, and belongs to the technical field of building construction management. Comprising the following steps: step 1, real-time data acquisition of construction site personnel, equipment and environment is realized, and a dynamic information database is established; 2, preprocessing and feature extraction are carried out on the collected multi-source heterogeneous data, and fusion and intelligent analysis of real-time data are achieved; step 3, accurately identifying risk factors and implementing refined early warning and intelligent intervention aiming at a single risk; 4, construction resource configuration is dynamically optimized, the resource utilization efficiency is improved, and meanwhile the cost and the safety risk are reduced; step 5, constructing a block chain data sharing mechanism, and realizing data tracing and intelligent management of a construction full life cycle; and step 6, forming a self-adaptive closed-loop feedback mechanism, and promoting intelligent transformation of construction management from passive response to active prediction.

The invention provides a protocol-based Internet of Things central control host data communication method and system, and is applied to the field of communication data processing. By means of data updating timestamp detection, equipment updating frequency division, local data buffering and the like, the real-time data acquisition capability of the central control host on the multi-protocol Internet of Things equipment is improved, a layered acquisition strategy is adopted, the polling period is optimized for equipment with different frequencies, unnecessary requests are reduced, and the efficiency is improved. Meanwhile, data missing caused by long-time no response of low-frequency updating equipment is avoided, data updating abnormity can be intelligently detected, a data refreshing request is triggered, the polling period is shortened, acquisition of latest data is ensured, finally, data compensation is conducted through historical data trend analysis and missing estimation value calculation, and data integrity and continuity are improved.

The invention relates to the technical field of semiconductor device testing, in particular to a SiC MOSFET power cycle testing method which comprises the following steps: S1, building a composite environment testing platform, configuring dynamic testing parameters, automatically calculating a physical boundary and collecting sensor data in real time; s2, establishing a finite element simulation model based on the physical boundary and sensor data, and outputting optimized dynamic test parameters and a simulated stress distribution diagram; s3, synchronously applying composite stress according to the dynamic test parameters and the stress distribution diagram, and collecting multi-dimensional test data in real time; when the system is used, through dynamic boundary calculation and real-time data acquisition, the intelligent degree and reliability of the test are improved, the test period is shortened, the failure prediction accuracy is improved, the system is suitable for reliability evaluation of SiC MOSFET devices in the fields of new energy, aerospace and the like, a large number of physical tests are avoided through virtual simulation, and the reliability of the SiC MOSFET devices is improved. And reduction of device loss and resource waste is facilitated.

The invention provides a low-altitude three-dimensional traffic road network division method and system. The method comprises the following steps: S1, data acquisition and processing: performing real-time data acquisition and processing on low-altitude three-dimensional traffic road network division areas; s2, dynamically dividing the processed data to obtain divided logic data, and integrating low-altitude air traffic data and low-altitude road surface data to form three-dimensional traffic data; s3, performing structured processing on the dynamically divided logic data and the multi-modal resource integrated three-dimensional traffic data; obtaining a structured result of the low-altitude three-dimensional traffic road network; and S4, calculating a corresponding action specification based on a structured result. Efficient utilization and intelligent management of low-altitude traffic resources are realized through the steps of dynamic division of a low-altitude three-dimensional area, multi-mode resource integration, structured management, intelligent operation standardization and the like.

The invention relates to the technical field of titanium anodes, and provides an automatic coating method of a titanium anode and related equipment, and the method comprises the following steps: obtaining a target coating area of the titanium anode, coating, carrying out data acquisition on the target coating area in the coating process to obtain real-time coating data, and calculating coating uniformity and thickness change conditions; evaluating the coating uniformity, and generating a coating quality evaluation result and a coating defect distribution condition; based on the coating quality evaluation result and the coating defect distribution condition, the thickness change condition is analyzed, and thickness optimization parameters are obtained; and generating a feedback atlas according to the thickness optimization parameters, and adjusting coating parameters of the automatic coating equipment based on the feedback atlas. Through application of automatic coating equipment, a sensor array, real-time data acquisition and a coating analysis model, the problems that when uniformity and thickness in the coating process are controlled, the coating quality is unstable, the coating thickness is not uniform, and coating parameters are difficult to monitor and adjust in real time are solved.

The invention relates to the technical field of fire extinguishing control, in particular to an unmanned aerial vehicle fire extinguishing control method and system. The method comprises the following steps: carrying out hot-flow wind field real-time data acquisition on a high-rise building fire scene through a sensor carried on an unmanned aerial vehicle, carrying out approaching hot-flow wind field intensity difference analysis, and then carrying out unmanned aerial vehicle flight attitude instability numerical value quantification to obtain flight attitude instability numerical value quantification data; time-varying attitude spraying strategy adjustment is carried out according to the flight attitude instability numerical value quantization data, and a time-varying attitude spraying adjustment strategy is obtained; building a fire extinguishing spraying control model for the time-varying attitude spraying adjustment strategy based on a strategy gradient algorithm to obtain a fire extinguishing spraying control model; and sending the fire extinguishing spraying control model to the terminal. The fire extinguishing control technology is optimized, so that the fire extinguishing control technology is more perfect.

The invention provides an intelligent stretching machine control analysis system for tension stringing construction, and relates to the field of power transmission line stringing construction and automatic control. According to the invention, the data acquisition module is used for acquiring the multi-dimensional data of the operation of the stretching machine in real time, the data transmission module transmits the multi-dimensional data to the data terminal and the data processing module, the data processing module processes the received data, and a Markov chain model is used for carrying out depth feature analysis, so that the accuracy of the deep feature analysis is improved. According to the method, the fault risk prediction is carried out, the corresponding early warning operation is carried out according to the prediction result, an accurate braking scheme is given, the wiring construction progress is prevented from being influenced by blind braking or delayed braking, and by integrating various technologies and combining real-time data acquisition, data processing and visual fault analysis, the wiring construction progress is ensured. Comprehensive monitoring and intelligent management of the construction process of the stretching machine are achieved, the operation state of equipment can be monitored in real time, potential faults can be warned in advance, and construction efficiency and safety are improved.

The invention discloses an AI-assisted carbon nanotube growth control method and system, and relates to the technical field of control systems. Comprising the steps that a three-dimensional AI virtual platform builds a carbon nano tube model by collecting carbon nano tube growth data and environment data in unit time, the growth data comprise the growth rate, the direction and the diameter size, the environment data relates to gas flow and electric field intensity, the carbon nano tube model is mapped into a three-dimensional coordinate system, and the three-dimensional coordinate system is constructed; wherein the X axis, the Y axis and the Z axis respectively represent the length, the width and the height of the carbon nanotube, and recording initial coordinates. By using the three-dimensional AI virtual platform and the real-time data acquisition technology, the environment change in the growth process of the carbon nanotubes can be accurately captured, and the environment change can be quickly coped with during actual control by combining the growth data and dynamically adjusting the environment parameters in the growth process; and unstable growth or deviation caused by incapability of timely adjustment in a traditional method is avoided.

The invention relates to the technical field of power systems, and discloses an industrial park distributed power utilization network simulation analysis power utilization behavior evaluation method comprising the following steps: S1, data acquisition: power load data, equipment operation state data and environment data of each power utilization unit in an industrial park are acquired in real time, and the environment data comprise temperature and humidity; s2, data preprocessing: preprocessing the collected data to obtain complete data; and S3, simulation modeling: establishing a power load flow model based on the park information and the real-time data, and performing simulation modeling to generate a dynamic model under various power consumption situations. According to the invention, through real-time data acquisition, dynamic load scheduling and a deep reinforcement learning algorithm, flexible optimal distribution of electric power resources is realized, the efficiency and stability of park electric power management are improved, stable operation of equipment is ensured, and energy waste is reduced.

The invention discloses a distribution network multi-parameter data fusion fault positioning method and system, and the method and system achieve the omnibearing monitoring of a power distribution network fault through the collection of multiple types of data, such as electrical signals, video images, meteorological information and environmental parameters. Compared with a traditional fault positioning method of a single information source, the system can reflect the fault condition more truly, and the positioning accuracy and reliability are remarkably improved. Machine learning or a deep learning model is utilized to analyze fusion data, fault types and positions are automatically identified, intelligent diagnosis is realized, manual intervention is reduced, the operation and maintenance cost is reduced, and the processing efficiency and accuracy are improved. The real-time data acquisition and transmission technology guarantees the system to monitor in real time and rapidly alarm potential faults. According to the method, historical data and an expert knowledge base are combined, fault causes are deeply analyzed, accurate information and maintenance suggestions are provided for maintenance personnel, operation and maintenance decisions are optimized, and the maintenance efficiency and quality are enhanced. The system provides an efficient and intelligent solution for distribution network fault positioning.