1153results about How to "Realize intelligent control" patented technology

The invention discloses a trackless rubber-tyred vehicle multi-terminal data fusion system, relates to the technical field of data fusion, and is used for solving the problem of poor trackless rubber-tyred vehicle multi-terminal data fusion. Comprising a path quantification module, an equipment health classification module, an alternative optimization module and a consistency verification module. According to the method, multi-terminal data fusion is carried out on a trackless rubber-tyred vehicle, a sensor dependency graph is dynamically constructed, dependency weights are calculated, a critical path is optimized, health indexes are defined according to a signal-to-noise ratio, output fluctuation and response delay, multi-dimensional abnormity is detected in combination with a sliding window and a mahalanobis distance, and a health score is generated to monitor the state of the sensor. A fault sensor is isolated, substitute data is generated through a redundant data source, the substitute data is incorporated into a fusion process after verification, a dependency graph is adjusted, a path is optimized, global consistency verification analysis is performed, different strategy management modules are triggered, a data fusion rule and task allocation are optimized, and the data fusion reliability of the trackless rubber-tyred vehicle in a complex operation environment is improved.

The invention relates to the technical field of life prediction, and discloses a method and device for predicting the service life of a chemical filter, and the method comprises the steps: collecting the historical multi-dimensional use state data of the chemical filter, and carrying out the standardization preprocessing, and obtaining a time sequence feature data set; carrying out load feature clustering analysis to obtain a filter load mode feature vector; constructing a double-layer performance attenuation prediction model, and outputting a target performance attenuation prediction sequence through physical attenuation coefficient calculation and deep learning network training; performing filter state probability transition calculation to obtain a state transition prediction sequence; performing multi-target joint optimization on the state transition prediction sequence, and calculating to obtain an optimal operation control parameter; and real-time multi-dimensional use state data is collected based on the optimal operation control parameters, parameter optimization is performed on the double-layer performance attenuation prediction model, a service life prediction model is obtained, and the method improves the accuracy and reliability of chemical filter service life prediction.

The invention provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention discloses a rock tunnel intelligent supporting system and method adaptive to geological conditions, and relates to the technical field of tunnel construction automation and intellectualization. The system comprises an intelligent advanced geological forecast module, an intelligent surrounding rock quality evaluation system, a self-adaptive support design optimization module, an intelligent construction equipment monitoring system, an environment monitoring module, a safety early warning system, a construction data analysis and decision support system, a material supply chain management module, a personnel positioning and management system and an integrated control system. Through real-time monitoring and analysis of the intelligent advanced geological forecast module and the safety early warning system, potential geological risks and safety threats in the tunnel construction process can be found in time. And through an adaptive support design optimization algorithm, support parameters and a construction scheme can be dynamically adjusted according to real-time geological data and a surrounding rock quality evaluation result. The integrated control system serves as the core of the whole intelligent supporting system, and unified management and coordination of all the sub-modules are achieved.

The invention discloses a precise control system and method for an intelligent heating digestion furnace, relates to the field of intelligent control, and adopts an artificial intelligence technology based on deep learning to carry out real-time monitoring and data analysis on in-furnace temperature and heating power of the digestion furnace. According to the method, the time sequence change trend of the offset of the in-furnace temperature value relative to the set temperature value and the time sequence change trend of the heating power are extracted, and then the dynamic response mode of the in-furnace temperature relative to the heating power is excavated by performing fine-grained time sequence interaction analysis on the in-furnace temperature value and the time sequence change trend, so that intelligent regulation and control on the heating power are realized. Through the mode, the temperature control stability of the heating digestion furnace can be effectively improved, the energy consumption is reduced, and the efficiency and the reliability of the digestion process are improved.

The invention relates to a multi-scene self-adaptive intelligent bottom argon blowing control method and system. The method comprises the steps that after a steel ladle reaches a working position, the air permeability is determined, a ladle setting signal is received, argon blowing is started, an infrared thermal imager is started to monitor a picture to judge the liquid steel level, and the liquid level area of the whole steel ladle is completely filled; pLC (Programmable Logic Controller) of all equipment in the whole refining process is intelligently linked, the bottom blowing stirring power is determined, the bottom blowing gas pressure and the bottom blowing gas flow are calculated, and relevant parameters such as the exposed area and the exposed edge slag layer height of molten steel in a continuous video stream are tracked and calculated in real time according to the metallurgical purpose of different stages, so that the bottom blowing control is further optimized; and a flow data instruction is issued to a bottom blowing PLC to control the pressure and flow of bottom blowing gas. According to the requirements of field production, video images of molten steel in the argon blowing state are automatically and continuously received in a zero-delay mode, the argon flow and pressure are automatically distributed through intelligent bottom blowing control, it is guaranteed that each furnace achieves the good bottom blowing stirring effect, field production is guided, and the production labor intensity is reduced.

The invention relates to the technical field of image processing, and discloses a graphite composite insulation board production process control method and system and a storage medium. The control method comprises the following steps: carrying out characteristic analysis and proportion optimization on raw materials to obtain pretreated raw materials; and monitoring the reaction state of the pretreated raw material, regulating process parameters, and generating a modified raw material. And performing formula optimization and performance prediction based on the material parameters of the modified raw material to obtain an optimized formula material. And forming process parameters of the optimized formula material are analyzed, a forming process is regulated and controlled, and a forming material is generated. And the curing process is optimized through the state parameters of the forming material, and a cured product is formed. And finally, evaluating the quality according to the detection data of the cured product, and generating a production control scheme. By establishing a multi-dimensional data acquisition and processing system, intelligent control over the whole production process is achieved, and therefore the stability and consistency of product quality are improved.

The invention relates to the field of voltage regulation and control, in particular to a real-time voltage regulation method for a new energy automobile. Initializing a power distribution network voltage measurement module, and collecting voltage data of each node of the power distribution network; setting a voltage deviation threshold value, and judging whether the voltage is in a normal range or not; collected voltage is compared with a set voltage deviation threshold value, a result obtained after preliminary voltage regulation of an on-load voltage regulation transformer is compared with the set voltage deviation threshold value, and a result obtained after cooperative voltage regulation of an electric vehicle charging pile is compared with the set voltage deviation threshold value. According to a comparison result, tap adjustment of an on-load voltage regulation transformer, cooperative voltage regulation of an electric vehicle charging pile and voltage regulation participated by a distributed photovoltaic system are carried out in sequence; and formulating an optimization strategy based on historical data and results of three times of voltage regulation. By means of cooperative voltage regulation, optimal control, intelligent management and the like, the voltage stability is effectively improved, and the adaptability and flexibility of the system are enhanced.

The invention relates to the technical field of metal processing and casting, and particularly discloses a residual stress control system and method based on metal casting molding, and the system collects simulation process parameters of a metal casting molding process and basic parameters of a to-be-cast assembly, and analyzes a residual stress risk assessment value of the metal casting molding process through big data. Potential casting problems can be recognized and solved from the source, powerful data support is provided for follow-up casting optimization work, the actual effect brought by process optimization is quantified by evaluating the process optimization index of the metal casting forming process after the process optimization set of the metal casting forming process is executed, and the process optimization efficiency is improved. A basis is provided for subsequent adjustment and improvement, the technology optimization set of the metal casting forming process is adjusted, the technology adjustment index of the metal casting forming process is judged, and finally adjustment feedback is conducted on intelligent control over the residual stress of the metal casting forming process, so that the metal casting forming process meets the actual casting requirement.

The invention discloses a pickling waste liquid recycling system and recycling method, and relates to the technical field of industrial wastewater treatment, the pickling waste liquid recycling system comprises: a treatment unit for respectively detecting the concentration of each heavy metal ion in pickling waste liquid, respectively adding corresponding chelating resin into the pickling waste liquid according to each heavy metal ion concentration, and collecting to obtain recycled acid liquid; the monitoring unit is used for collecting the pH value and the conductivity data of the recycled acid liquor in real time, inputting the pH value, the conductivity data and the heavy metal ion concentration data as monitoring data into the control unit, and establishing a monitoring database; and the control unit is used for calculating optimal control parameters in real time based on the monitoring data in the monitoring database, automatically adjusting the processing unit according to the optimal control parameters, and conveying the recycled acid liquor to the pickling process for recycling. Through multi-parameter cooperative monitoring and intelligent self-adaptive control, precise detection, dynamic optimization treatment and efficient recycling of the heavy metal ions in the pickling waste liquid are realized.

The invention discloses a public vehicle operation management method based on big data analysis and related equipment, and the method comprises the steps: receiving a battery alarm signal of an electric public vehicle, and obtaining a battery parameter and vehicle operation data; inputting the current environment data and the real-time road condition data into a big data model to obtain a current operation scene of the vehicle; inquiring to obtain a battery parameter interval corresponding to the current operation scene of the vehicle; under the condition that the battery parameters are located in the battery parameter interval, vehicle energy consumption data in the preset kilometer interval are obtained, and according to the battery parameters, the vehicle energy consumption data and the current operation scene of the vehicle, the energy consumption trend of the electric public vehicle in the future preset time period is predicted; generating a power output mode of the electric public vehicle according to the energy consumption trend, the vehicle operation data and the driving behavior data; generating a brake adjusting instruction according to the power output mode; and the brake adjusting instruction is sent to the vehicle terminal, so that the vehicle terminal executes a brake strategy.

The invention provides an intelligent exhibition hall control method and system based on Internet of all things. The method belongs to the technical field of the Internet of Things, and comprises the following steps: constructing an intelligent exhibition hall network architecture based on the Internet of Things, and wirelessly connecting to a central control platform to form a network environment of Internet of all things; acquiring user identity information through an identity recognition device, and constructing a user identity database; and analyzing user preferences through an intelligent algorithm according to the user identity information in combination with user historical behavior data. According to the method, the identity information and the historical behavior data of the user are analyzed through an intelligent algorithm, personalized exhibition recommendation and exhibition routes can be provided for the user, the user can find interested exhibits more quickly, and therefore the visiting experience is improved.

The invention discloses a building energy consumption control system based on deep learning. The building energy consumption control system comprises a data acquisition module, a data processing module, a deep learning module, an optimization control module and a real-time control module, the invention further discloses a control method of the building energy consumption control system based on deep learning. The energy consumption prediction model is constructed through the long-short-term memory network, and the user behavior model is constructed through the convolutional neural network, so that the energy consumption optimization control strategy is generated through the reinforcement learning algorithm based on the output results of the energy consumption prediction model and the user behavior model, and the related data of the building energy consumption system is effectively utilized. Intelligent control over the building energy consumption system is achieved, changes of environmental parameters and user requirements are dynamically adapted by optimizing an energy consumption control strategy, and the comfort level of a user is improved while energy saving and consumption reduction are achieved.

The invention belongs to the field of intelligent control systems, and particularly relates to a shield attitude real-time intelligent control method based on machine learning, according to the method, a data acquisition module can more comprehensively and accurately reflect the actual condition of shield construction, and in the control process, key parameters such as the working state of a jack and the pressure of a hydraulic system are monitored in real time, so that the control accuracy is improved. Abnormal conditions can be found in time, an emergency plan can be started, and the safety of shield construction is guaranteed. According to the real-time monitoring and feedback optimization mechanism, the safety and reliability of shield construction are effectively improved, and the construction risk is reduced.

The invention provides a machining product control system and method of a compound machine tool, relates to the technical field of product machining, and realizes real-time monitoring, intelligent control and fault prediction of a machining process by introducing a multi-sensor module, a data processing platform, an intelligent control module and a fault prediction module. The multi-sensor module can comprehensively process information from a plurality of sensors; the data processing platform can pre-process and analyze the collected data, extract processing state features and provide decision basis for intelligent control; the intelligent control module automatically sets and adjusts cutting parameters according to workpiece parameters input by a user and a machining state analysis result, and intelligent control over the machining process is achieved. And the fault prediction module predicts the operation state of the machine tool based on the historical fault data and the processing state analysis result, and triggers an early warning mechanism when a fault is predicted to occur, so that cooperative response before the fault is realized.

The invention discloses a multi-dimensional balance scheduling shared energy storage optimization method, a medium and a program product, and the method comprises the steps: generating a plurality of optimization objective functions according to a preset multi-dimensional optimization demand; generating a real-time scheduling strategy based on the plurality of optimization objective functions in combination with a dynamic priority rule and a multi-objective optimization algorithm; and in the scheduling process, the output power of the renewable energy, the load demand of the power grid and the energy storage state are obtained in real time, and the real-time scheduling strategy is dynamically adjusted based on a resource conflict decoupling mechanism and a collaborative optimization strategy, so that the energy storage can respond to the load change of the power grid, and collaborative control of the energy storage and the renewable energy is completed. According to the invention, the resource utilization efficiency of the shared energy storage system is improved, the operation cost of the power system is reduced, and the adaptability and reliability of the system to power load demand fluctuation are enhanced.

The invention provides a dynamic target tracking method and system based on image return, and relates to the technical field of image data processing, and the dynamic target tracking method based on image return comprises the following steps: collecting image data of a target scene, recognizing a to-be-tracked target and position information of the to-be-tracked target by using a double-layer random forest recognition algorithm, and predicting a motion track of the to-be-tracked target; according to the predicted motion track of the to-be-tracked target and the position information of the unmanned aerial vehicle, and based on a target function of the distance between the unmanned aerial vehicle and the to-be-tracked target, using a cooperative control strategy to adjust flight parameters of the unmanned aerial vehicle; and the unmanned aerial vehicle continuously tracks the to-be-tracked target, and after tracking is finished, the cloud server stores the position information and the movement track of the to-be-tracked target in the tracking process. The system has the advantages of real-time performance, high efficiency, intelligence, data storage and subsequent analysis, expansibility and flexibility, safety improvement, risk reduction and the like.

The embodiment of the invention provides a vehicle control method, which is applied to the field of vehicle control, and comprises the following steps: determining a plurality of control instructions by responding to an input operation of a user, respectively determining urgency indexes and resource demand information of the plurality of control instructions according to the control instructions for different subsystems, and determining the urgency indexes and the resource demand information of the plurality of control instructions according to the urgency indexes and the resource demand information; according to the method, the urgency indexes are determined, resource use information of the subsystems is determined, target execution priorities corresponding to the control instructions are determined in combination with the urgency indexes, the resource demand information and the resource use information, the subsystems are scheduled to perform collaborative operation according to the control instructions according to the target execution priorities, and a unified and efficient instruction processing mechanism is provided. And complex coordination and conflicts in a plurality of subsystems in the vehicle are effectively avoided.

The invention relates to a traffic signal control method based on multi-agent deep reinforcement learning. The method comprises the following steps: setting an intelligent agent for an intersection, deploying a fitting network at a cloud end or a server, and performing prediction according to a traffic road network structure and historical traffic flow data to obtain a traffic flow prediction result Y; training each intelligent agent and a fitting network by using the traffic flow prediction result and randomly acquired real-time traffic information of each intersection to obtain each optimized intelligent agent and an optimized fitting network; each optimized intelligent agent performs independent observation according to a traffic flow prediction result Y and respective current real-time traffic information, transmits the result to the optimized fitting network, calculates a global reward and feeds back the global reward to each intelligent agent, and each intelligent agent executes the next action according to the respective current observation result and reward. And forming a current optimal traffic signal control strategy of the target area. According to the invention, the traffic signal coordination control of multiple intersections is realized, and the travel efficiency is obviously improved.

The invention discloses a fiber cement board production line information big data system and method, and the method employs a data processing and analysis algorithm based on artificial intelligence and deep learning to carry out the time sequence feature analysis and fine-grained time sequence feature response correlation coding of real-time temperature, real-time humidity, real-time viscosity and real-time stirring speed. In this way, time sequence fine-grained response interaction characteristics between the temperature, humidity and viscosity and the stirring speed in the stirring process are learned and captured, and time sequence reasoning of stirring speed control is carried out through Bayesian posterior probability calculation among the time sequence fine-grained response interaction characteristics of the temperature, humidity and viscosity in the stirring process, so that the stirring speed value of the next time point is determined. And generating an adjusting instruction. In this way, intelligent control over the stirring process of fiber cement board production can be achieved, and the stability and uniformity of the stirring process can be guaranteed.

The invention discloses an energy-saving ground source heat pump control method and system, and the method comprises the steps: building a ground source heat pump system dynamic model which comprises an indoor heat balance model, a ground source heat pump unit model and a soil heat exchange model, obtaining a topological network through coupling, designing a distributed control and fault-tolerant operation strategy according to the topological network structure, and carrying out the control of the ground source heat pump system. Each node is locally controlled according to the position and adjacent node information, a state space model is constructed, an optimal control action sequence is solved through quadratic programming to balance energy consumption and an indoor and outdoor temperature deviation objective function, the optimal operation state is obtained, and the mapping relation between the optimal operation state and environment parameters is learned through a neural network; a ground source circulation flow control algorithm is designed, and circulation pump flow distribution and unit working parameter automatic adjustment are achieved. According to the method, the control precision and stability of the ground source heat pump system can be effectively improved, reliable operation of the ground source heat pump system under complex working conditions is ensured, the energy utilization efficiency is remarkably improved, energy conservation and consumption reduction are achieved, and meanwhile good interpretability is achieved.

The invention belongs to the technical field of carbon dioxide capture, utilization and storage (CCUS), and discloses a multi-parameter coupled intelligent operation regulation and control method and system for wet CO2 mineralization of base solid waste. The intelligent operation regulation and control method comprises the following steps: monitoring real-time data in a flue gas CO2 generation mechanism, and constructing a CO2 concentration prediction model; calculating the mass of the basic group solid waste required in the mineralization reaction process; preparing base solid waste slurry according to a calculation result, and mixing the base solid waste slurry with flue gas to be treated to perform CO2 mineralization reaction; the change of physical quantity in the mineralization reaction process is monitored in real time, and the amount of base solid waste slurry reacting with the to-be-treated flue gas is finely adjusted in real time according to the monitored result, so that intelligent regulation and control of the CO2 mineralization process are realized. According to the invention, prediction of key operation parameters of the CO2 mineralization system and intelligent regulation and control of key equipment are realized, and a foundation is laid for large-scale CO2 mineralization and storage by using the base solid waste.

The invention discloses a kiln temperature monitoring method based on coupling simulation and Kalman filtering, which belongs to the technical field of kiln temperature monitoring, and comprises the following steps of: simulating temperature field distribution in a kiln through simulation software, and fusing a simulation result and actual measurement data by using a Kalman filtering technology to obtain a simulation result; therefore, a more accurate temperature monitoring result is obtained. The method can effectively improve the accuracy and real-time performance of monitoring, optimize the heat energy utilization efficiency of the kiln, reduce energy consumption and carbon emission, and provide technical support for achieving the carbon neutralization target in industrial production.

The invention relates to the technical field of big data monitoring control, in particular to a cold-chain logistics big data intelligent monitoring method and system. The method comprises the following steps: acquiring a cold-chain logistics original data set; performing data preprocessing on the cold-chain logistics original data set to obtain a standard cold-chain logistics data set; performing environmental information monitoring on the standard cold-chain logistics data set to obtain cold-chain logistics environmental data; performing parameter real-time sensing on the cold-chain logistics environment data to obtain environment parameter real-time data; and abnormal fluctuation identification is carried out on the environment parameter real-time data to generate real-time cold chain environment abnormal data. Therefore, through the Internet of Things technology, the multi-dimensional data analysis technology, the intelligent control technology and the visualization technology, multi-dimensional correlation analysis is carried out on the abnormal conditions of the cold-chain logistics so as to cope with different risks and abnormal conditions; and the cold-chain logistics process is intelligently monitored, regulated and controlled, so that real-time monitoring and accurate control of the cold-chain logistics are realized.

The invention discloses a thermal power generating unit high-pressure heater fault load reduction control method, which comprises the following steps of: 1, judging whether a thermal power generating unit generates high-pressure heater fault splitting or not, and if so, entering a step 2; 2, judging whether a triggering condition of a unit load reduction program is met or not, if yes, sending a unit rapid load reduction instruction, and entering a step 3; 4, whether the auxiliary machine fault load reduction program is completed or not is judged, if the auxiliary machine fault load reduction program is completed, the unit enters a stable transition state, and if the auxiliary machine fault load reduction program is completed, the third step is executed again. The method is reasonable in design, key actions of thermal power generating unit high-pressure heater auxiliary machine fault load reduction are defined and set in control logic, automatic control of the thermal power generating unit high-pressure heater auxiliary machine fault load reduction process has practical operability, intelligent control of the load reduction process is achieved, and the reliability of the load reduction process is improved. And the stability and the safety of the thermal power generating unit after the high-pressure heater is split are ensured.

The invention provides a high-efficiency and environment-friendly sandstone material processing control system, and relates to the technical field of sandstone material processing control, and the system comprises a production demand determination module, a blank raw material determination module, a processing workshop control module, a processing workshop fault evaluation module, an environment-friendly monitoring processing module and a sandstone aggregate online detection module. According to the invention, intelligent control of gravel production is realized, physical online detection of finished aggregate detects particle size and shape of the finished aggregate, chemical online detection of harmful substances chloride and methylene blue in the finished aggregate is realized, the detection efficiency is improved, the labor intensity of testers is reduced, and the production cost is reduced. Online collection, real-time detection, remote analysis and dynamic feedback of the finished aggregate are realized, and the quality control level of the finished aggregate is improved.

The invention relates to the technical field of soilless culture, in particular to an intelligent cultivation tank system and a plant cultivation method. The system comprises a sensor configured to collect environmental data of a cultivation tank; the artificial intelligence module is configured to identify and analyze the environment data, obtain different types of sub-environment data, and generate a first nutrient solution adjusting instruction based on the different types of sub-environment data; the digital twinning module is configured to perform matrix conversion on the sub-environment data and map the sub-environment data to a digital twinning scene; the digital twinning module is configured to analyze spatio-temporal data of the plant in real time in a digital twinning scene and trigger a second nutrient solution adjusting instruction when the growth stage of the plant changes; the nutrient solution circulating device is configured to adjust the concentration of the nutrient solution in the cultivation tank based on the first nutrient solution adjusting instruction and / or the second nutrient solution adjusting instruction. According to the system, the intelligence of the soilless culture system is improved, and precise control over the plant growth environment is achieved.

The utility model discloses a moxibustion fumigation traditional Chinese medicine external treatment device which comprises a display control bin, a radio frequency induction bin and an independently-installed chip medicine cake box which are connected with one another. A radio frequency chip, a radio frequency coil and a temperature-resistant protective cover are installed in the radio frequency induction bin, a heat transfer through hole and a heater are arranged at the bin bottom, and an independent chip medicine cake box is installed at the lower end of the outer bottom. According to the device, information of the chip medicine cake box is read through a radio frequency chip and a coil of the radio frequency induction bin, and parameters such as optimal working time, working temperature and working wavelength of different traditional Chinese medicine external treatment medicines or non-medicine materials such as moxibustion or fumigation are matched; convenience, digitization, intelligentization, safety and standardization of moxibustion fumigation and other hundreds of traditional Chinese medicine external treatment methods can be achieved.

The invention discloses an intelligent regulation and control task chain generation method and apparatus, an electronic device and a medium. The method comprises the steps of obtaining content texts of multiple types of regulation and control security regulations and regulation and control operations; inputting the content text into a large language model agent for content extraction so as to extract regulation and control operation sequences and calling contents under various conditions; intelligently generating a knowledge graph in a decision tree form in the knowledge graph through an intelligent agent according to the regulation and control operation sequence and the calling content under various conditions; acquiring a text instruction, performing intention recognition by an intelligent agent, intelligently generating a task chain for regulating and controlling operation logic in the generated knowledge graph after autonomous understanding, and autonomously generating a thinking chain for task execution; and according to the generated thinking chain, the intelligent agent intelligently calls related modules for regulation and control so as to realize intelligent regulation and control. According to the method, the quality and efficiency of obtaining the text information can be improved, the regulation and control thinking chain logic architecture is established by using the decision tree type knowledge graph, the accuracy is improved, and the use threshold of operation is reduced.

The invention relates to the technical field of power transmission line nondestructive flaw detection, in particular to a power transmission line X-ray flaw detection system and method based on an unmanned aerial vehicle. Comprising a framework, a control system arranged on the framework, a pulley mechanism arranged on the framework and used for being erected on the wire and a flaw detection mechanism arranged in the framework, the framework is provided with a space for containing the wire and an opening for the wire to enter the space, and the flaw detection mechanism comprises an X-ray machine driven by a servo motor to rotate around the wire. And the imaging plate is arranged at the central position of the framework and rotatably receives the X-rays penetrating through the to-be-detected wire. The multifunctional and multi-angle detection of four-bundle conductors and single wires is realized, and the flaw detection efficiency, accuracy and comprehensiveness of tension-bearing fragile fittings and connecting fittings of power transmission lines are improved.