1811 results about "Distribution system" patented technology

The invention relates to the technical field of digital marketing, and discloses an omni-channel marketing funnel intelligent attribution model and an automatic budget distribution system, and the model comprises a data collection module which is used for collecting data through a contact recording device and a user identification device which are disposed at a marketing terminal, and a behavior tracking device and a channel classification device which are disposed at a user side. Acquiring information such as multi-channel marketing contact interaction data, user behavior data and channel types; the path analysis module realizes data path analysis through time sequence synchronization of a time alignment unit, feature matching of a path association unit and dynamic adjustment of a computing power scheduling unit; the attribution calculation module generates the conversion attribution weight of each channel based on the matching data; and the budget mapping module establishes a dynamic mapping relationship between the weight and the budget and updates the dynamic mapping relationship. The system outputs the attribution result through the model application module, and the budget execution module distributes the budget according to the dynamic mapping, so that the channel contribution can be accurately evaluated, the scientific distribution of the marketing budget is realized, and the marketing efficiency and the rate of return on investment are improved.

The invention relates to the technical field of intelligent power distribution system state monitoring, in particular to a cable connection state online monitoring method for a low-voltage cable branch box, which comprises the following steps of: applying a disturbance excitation signal to a connection node of the cable branch box, and acquiring transient voltage, current and phase response parameters of the node in different excitation periods; establishing a mapping relation between the node electrical characteristics and the connection state; performing dynamic reference calibration on the acquired signals based on a multi-node space-time coupling matrix to form an adaptive judgment reference of electrical coupling between nodes; a connection degradation sensitive index is extracted through feature sequence differential calculation, a connection state quantitative evaluation result is generated, and real-time updating and anomaly recognition are carried out according to the state change trend; according to the method, the problems of misjudgment and delay caused by a monitoring mode depending on indirect parameters such as temperature and current are solved, high environmental adaptability and degradation identification precision are achieved, and the operation safety and monitoring reliability of the low-voltage power distribution system are greatly improved.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention relates to the technical field of distributed energy storage system control optimization, and discloses a cooperative control and energy optimization distribution system of a distributed energy storage system, and the system comprises an energy storage node data sensing module which obtains a basic sensing unit and a state data collection interval through hierarchical division of an energy storage node operation environment; the multi-source information fusion processing module collects multi-type energy storage operation information according to a preset period, and generates time sequence fusion information; a cooperative control strategy modeling module constructs a state abstract model according to the state abstract model and extracts multi-dimensional control features; the energy distribution state evaluation module identifies a local distribution state and determines an overall distribution state; the dynamic optimization strategy generation module dynamically adjusts energy distribution parameters, and optimizes an energy storage operation environment in real time to obtain a real-time distribution state. According to the system, accurate cooperative control and efficient energy distribution of the distributed energy storage system are realized, and the operation performance of the system is improved.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

A gas-liquid contactor includes at least one packing section, a liquid distribution system, one or more liquid collection devices, and a fan. The at least one packing section includes a plurality of packing supports. Each packing support is removably attached to at least one other packing support and includes a structure defining a bottom surface and a packing perimeter and a plurality of structured packings positioned on the bottom surface within the packing perimeter. The structure includes at least one first attachment and at least one second attachment. The liquid distribution system is configured to flow a carbon dioxide (CO2) capture solution through at least one packing section. The one or more liquid collection devices include a bottom basin positioned beneath the at least one packing section. The fan is operable to form a CO2 lean stream and flow the CO2 lean stream from the at least one packing section.

The invention discloses an intelligent charging priority distribution system and method based on multi-device identification, and particularly relates to the technical field of charging priority distribution. Equipment identity and charging demand information are obtained through an intelligent identification technology, a priority score is calculated in combination with a preset weight parameter, the real-time performance of priority distribution is ensured by adopting a dynamic threshold adjustment strategy, power demand fluctuation in a short time in the future is predicted based on a load prediction algorithm of machine learning, power distribution is optimized in advance, and the power distribution efficiency is improved. The method comprises the steps of reducing instantaneous load impact, monitoring equipment power in real time in the charging process, comparing the equipment power with the maximum distributable power, and dynamically adjusting the charging power or switching the charging sequence through a self-adaptive power adjustment mechanism if abnormality is detected, so as to guarantee the stability of a power grid and the safety of the equipment, and the method can effectively improve the utilization efficiency of charging resources and reduce the energy consumption. Overload of a power grid is prevented, multi-device charging scheduling is optimized, and safety, reliability and intelligence of the charging process are ensured.

The invention discloses a task load balanced distribution system and method based on a node resource state, and relates to the technical field of distributed computing, and the method comprises the steps: collecting and associating multi-dimensional node resource data and dependency relationship data between tasks; establishing a virtual synaptic connection for each pair of tasks with data interaction, and constructing a task-dependent neural network model; the node resource state is monitored in real time, and the synaptic connection strength is adjusted; dynamically generating an optimal task allocation strategy; when the node resource saturation is continuously too high or the task execution efficiency is obviously reduced, starting a task migration process; and in the migration process, a synaptic cache region is created in the intermediate node, interaction data is cached, a dependency task node mapping relation is updated, data is synchronized, and the collaboration efficiency is verified. According to the method, CPU and GPU resource states and task dependency are considered at the same time, accurate matching and cooperative scheduling are achieved, overload or idling of a single resource is avoided, and the resource utilization rate is increased.

The invention discloses a power supply and distribution control system based on a virtual power plant, and relates to the technical field of power distribution systems. The system comprises five core modules: a digital twin simulation control closed loop module constructs a complete closed loop of data acquisition, simulation calculation, control execution and model iteration, and realizes real-time synchronization of a physical system and a digital mirror image and accurate issuing of a control instruction; the multi-agent source-load-storage optimization module improves the source-load-storage collaborative scheduling efficiency through distributed agent collection and reinforcement learning; the multi-scene energy storage adaptive control module generates a scene-based energy storage strategy based on meteorological, load and electricity price multi-dimensional perception and model prediction control; and the multi-type energy storage ratio linkage module realizes multi-type energy storage cooperative operation based on an energy storage characteristic database and scenarized weight optimization. All the modules work cooperatively, the system response speed, the control precision and the power supply stability are effectively improved, and the system is suitable for industrial parks and other complex power supply and distribution scenes.

The embodiment of the invention discloses a logistics distribution method and device based on an unmanned aerial vehicle cluster, equipment and a medium. The method comprises the steps of obtaining to-be-processed order information and state information of the unmanned aerial vehicle cluster; determining a to-be-delivered task based on the order information, and determining a target unmanned aerial vehicle from an unmanned aerial vehicle cluster based on the delivery information and the state information; determining a flight strategy of the target unmanned aerial vehicle based on a preset relay link, and controlling the unmanned aerial vehicle to execute a distribution task according to the flight strategy; and monitoring real-time state information of the target unmanned aerial vehicle, and determining a real-time control strategy of the target unmanned aerial vehicle based on the real-time state information so as to adjust a flight strategy of the target unmanned aerial vehicle based on the real-time control strategy. According to the embodiment of the invention, an efficient, flexible and safe unmanned aerial vehicle distribution system is constructed, and a solid technical support is provided for large-scale commercial application.

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

The invention relates to an energy efficiency optimization method for a dynamic load of a central air conditioner, which comprises the following steps of: establishing a response relationship among a cold and heat source, a transmission and distribution system and end equipment, identifying a nonlinear decoupling characteristic of a cold and heat response behavior by monitoring operation deflection of each level of equipment under a low-load working condition, and defining and updating a cooling capacity response deviation threshold in real time; on the basis of the cooling capacity response deviation threshold, delay compression control is conducted on response actions of the refrigerant side and the air medium side, and by adjusting the air side response time sequence in advance, dynamic thermodynamic equilibrium is formed between refrigerant transfer delay and air medium response in advance; by establishing the response relationship among the cold and heat source, the transmission and distribution system and the end equipment and introducing a cold capacity response deviation threshold dynamic updating mechanism, nonlinear coupling mismatch in a cold and heat transmission chain can be accurately identified, so that the system is more flexible to operate under low-load and variable-load conditions, frequent start and stop or redundant operation of the cold and heat source is avoided, and the system reliability is improved. And therefore, the overall COP value of the system is remarkably increased, and unit cooling capacity consumption is reduced.

The invention discloses a comprehensive management system for intelligent power distribution, and the system comprises a sensing layer which is provided with a plurality of sensors, the sensors are disposed on power distribution equipment, and are used for collecting the operation data of a power distribution system in real time, and the operation data comprise a current I, a voltage U, a power P, an equipment temperature T, a power factor cos phi, and an insulation resistance R; the transmission layer is used for transmitting the operation data to a data layer; the data layer is used for preprocessing the operation data to generate standardized data, and preprocessing comprises data cleaning and normalization conversion; and the application layer comprises a fault early warning module, a predictive maintenance module, an energy consumption analysis module and a remote control module, and the application layer realizes collaborative management by calling the standardized data. Multi-dimensional data fusion, a dynamic intelligent algorithm and a module interaction mechanism are introduced, and full-process intelligence of power distribution management is achieved.

A solid distribution system of an agricultural spreader includes a product hopper configured to contain solid particulate material, a blower, a manifold coupled to the blower and having a plurality of outlets, at least one boom arm comprising a plurality of parallel rigid tubes configured to carry the solid particulate material and air from the blower, and a plurality of hoses. The manifold is configured to split air flow from the blower into flows through each of the plurality of outlets. Each valve is in fluid communication with one of the outlets and one of the rigid tubes, and each valve individually controls flow through a respective one of the rigid tubes. Vehicles having such solid distribution systems are also disclosed.

The invention provides a path planning and energy management cooperative regulation and control method for a multi-agent unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model based on a six-degree-of-freedom motion equation of a four-rotor unmanned aerial vehicle, and building an energy consumption model through combining the power of a motor and the discharge characteristics of a battery; sensing the state of the unmanned aerial vehicle in real time, defining a state space and an action space of a deep reinforcement learning algorithm, and generating navigation points by using the deep reinforcement learning algorithm; receiving a delivery request, and dynamically allocating tasks according to the state of the unmanned aerial vehicle by adopting a deep reinforcement learning algorithm; performing path planning on the unmanned aerial vehicle for task allocation by adopting an A * algorithm according to the navigation points in combination with the multi-dimensional cost, and obtaining an optimal path; dispersing the optimal path into a speed instruction, and dynamically adjusting the course angle and the flight speed of the unmanned aerial vehicle through a PID controller; through combination of deep reinforcement learning and an A * algorithm, the problems of path planning and energy management of the multi-agent quad-rotor unmanned aerial vehicle logistics distribution system in complex terrains are effectively solved.

The utility model relates to a power supply control device, power supply equipment and a logistics distribution system, and belongs to the technical field of power supply control. The device comprises a main controller; a plurality of slave controllers, wherein the first ends of the slave controllers are electrically connected with the master controller; the power supply devices are electrically connected with the second ends of the corresponding slave controllers; wherein when the master controller outputs a control signal for operating the first power supply device to the first slave controller, the first slave controller responds to the control signal and controls the first power supply device to work; wherein the first slave controller is one of the plurality of slave controllers, the first power supply device is one of the plurality of power supply devices, and the first power supply device corresponds to the first slave controller.

The invention discloses an automatic transportation-distribution device and method for rock-fill dam panel concrete, the device adopts a spiral stirring roller to continuously stir concrete, prevent initial setting or bleeding and maintain slump stability, the bottom of a stock bin is designed to be an adjustable concrete discharge port in the rotation direction, the concrete is automatically discharged through gravity, and the concrete is automatically conveyed and distributed. Precise putting is realized; automatic panel distribution is achieved through belt conveyor conveying and rail moving, and a rail system parallel to a panel is adopted as a moving system, so that the distribution system can flexibly move in the length direction of the panel; according to the method, the discharging efficiency of a chute and horizontal material distribution is quantified, the slump of concrete, the inclination angle of the chute and the friction coefficient are considered in the conveying capacity of the chute, geometric parameters and power parameters of a belt conveyor are considered in the discharging efficiency of the horizontal material distribution, and the discharging condition of the concrete is monitored in real time; parameters of the chute and the belt conveyor are automatically adjusted through an algorithm, and smooth connection of transportation and paving is ensured.

The invention discloses a power distribution network collaborative optimization scheduling method and system considering asymmetric Nash bargaining and producer and consumer renting shared energy storage. The method comprises the following steps: constructing a wholesale market, a retail market and a P2P market, and a multi-agent collaborative transaction framework of mutual influence among a power distribution network operator, a shared energy storage operator and producer and consumer, wherein the multi-agent collaborative transaction framework comprises the wholesale market, the retail market, the P2P market and the mutual influence among the power distribution network operator, the shared energy storage operator and the producer and consumer; constructing a multi-agent optimization scheduling model for renting shared energy storage by the consumer and introducing a bargaining factor to measure the contribution degree of each consumer during electric energy transaction; a cooperative game Nash bargaining theory is introduced to re-model an original multi-body optimization scheduling model, and the model is decomposed into two sub-problems of total cost minimization and payment benefit maximization; a self-adaptive step size ADMM algorithm is adopted to perform distributed optimization solution on the sub-problems to obtain the electricity quantity purchased and sold from the power grid by the producer and the consumer and the shared energy storage, the P2P transaction electricity quantity and the transaction electricity price; and an improved IEEE33 node power distribution system is adopted to carry out example analysis, and a scheduling plan arrangement of multi-agent collaborative optimization operation is obtained.

The invention relates to a real-time scheduling method and system for a port gathering, distributing and transporting energy system based on double-consolidation continuous reinforcement learning. The method comprises the following steps: constructing a port micro-grid; acquiring related data in real time based on the port micro-grid, and performing preprocessing; constructing the port gathering, distributing and transporting energy system into an intelligent agent, and defining a state space, a dynamically limited action space and a reward function based on the pre-processed related data; and on the basis of a double-consolidation continuous reinforcement learning framework fusing a deep learning algorithm and a double-consolidation mechanism, performing optimization scheduling on the intelligent agent, generating an energy storage scheduling strategy, outputting optimal charging and discharging power, and completing a real-time scheduling process. Compared with the prior art, the method has the advantages of realizing dynamic collaborative optimization of clean energy supply and traffic load and the like.

The invention relates to the technical field of electric energy metering of a low-voltage power distribution system, and provides an EM algorithm-based district metering device misalignment identification method and system. The method comprises the following steps: establishing a theoretical misalignment value model of each outgoing line in different metering misalignment states, and determining a mapping relation between the different misalignment states and actual electric quantity data; the method comprises the following steps: constructing theoretical deviation values of each outgoing line metering device in different misalignment states on the basis of a transformer area incoming and outgoing line electric energy data and energy conservation relationship, estimating an initial attribution probability of a line state in combination with an index normalization mapping method, and obtaining a posterior attribution probability of each outgoing line line through Gaussian mixture distribution modeling under dynamic prior guidance; and based on the posterior affiliation probability of each outgoing line, constructing an objective function of joint observation value fitting quality, time continuity and misalignment amplitude balance, solving the objective function, and quantifying the electric quantity misalignment proportion of each outgoing line at each moment so as to judge classification and identification of misalignment behaviors.

The invention discloses a power distribution network robust optimization scheduling method considering a user side, and the method comprises the steps: S1, building an optimization scheduling model of a power distribution network layer, comprehensively considering the power generation cost of each distributed power supply and a micro-grid in a power distribution network, and providing a flexible adjustment service for the power distribution network through excitation of the micro-grid, so as to minimize the operation cost; s2, establishing an optimal scheduling model of a micro-grid layer, comprehensively considering the power generation cost of each distributed power supply of the micro-grid, and flexibly adjusting services by responding to the power distribution network amount so as to minimize the operation cost; s3, establishing a multi-microgrid power distribution system robust optimization model considering the source-load uncertainty, and establishing the robust optimization model to cope with the influence of output fluctuation on the model by considering the uncertainty of source-load output and demand; and S4, establishing a distributed algorithm based on Benders decomposition in combination with CCG to solve the model, and determining the output between the distribution network and the multiple microgrids.

The invention discloses an electrical integrated energy system toughness planning method considering an extreme event tail risk, and relates to the technical field of integrated energy system optimization, and the method comprises the steps: firstly, considering the massive load shedding risk caused by a small-probability extreme event, and constructing a pre-disaster defense-post-disaster emergency response electrical integrated energy system toughness planning frame; secondly, comprehensively considering toughness planning cost, emergency scheduling cost and tail risk loss in a small-probability extreme scene, and establishing an objective function of a planning model; then, for a disaster stage, establishing disaster prevention resource deployment related constraints of the toughness planning model; then, considering operation constraints of the post-disaster power distribution system and the natural gas system, and establishing post-disaster emergency response related constraints; and finally, performing linearization processing on the established planning model, and calling a commercial solver for solving.

The invention discloses an integrated cabinet testing device and a detection method thereof, particularly relates to the technical field of integrated cabinet state detection, and is used for solving the problem that in the prior art, gradual drift of a signal monitoring unit under transient electromagnetic interference cannot be quantized, and consequently equipment degradation characteristics and measurement errors are difficult to decouple. A high-strength transient electromagnetic interference environment conforming to switch operation physical characteristics is simulated in a cabinet; the acquisition end of the signal monitoring unit is connected with a high-precision reference sensor in parallel to realize millisecond-level synchronous acquisition of double-track signals; generating a difference sequence representing drift; self-adaptive fault excitation is dynamically generated based on the interference characteristic frequency band of the difference value sequence; performing covariance matrix orthogonal decomposition on the mixed signal, separating a drift feature vector and a fault feature vector, and calculating a projection scalar value; and finally, the historical reference output drift interference coefficient and the three-level fault confidence report are combined, so that the credibility of predictive maintenance of the intelligent power distribution system is remarkably improved.

The invention discloses an AC arc fault weak signal detection method, device and equipment and a medium, and the method comprises the steps: obtaining a bus current signal of a low-voltage power distribution system, carrying out the amplitude standardization preprocessing of the bus current signal, and generating a normalized signal; performing time-frequency domain feature fusion extraction on the normalized signal to generate a comprehensive feature vector; performing fault diagnosis on the comprehensive feature vector by adopting a nested sliding window mechanism to generate a fault diagnosis mark; and based on the comprehensive feature vector and the fault diagnosis mark, utilizing a pre-trained classification model to generate an arc fault classification result. According to the method, through the synergistic effect of signal noise reduction, feature fusion, real-time diagnosis and intelligent classification, the detection sensitivity is improved to reliable identification in a low signal-to-noise ratio-10dB scene, the real-time performance meets the UL1699 standard requirement, and the core problem that weak alternating current arc fault detection sensitivity and real-time performance are insufficient is solved.

The invention discloses a priority-based charging pile power dynamic distribution system and method, and relates to the technical field of electric vehicle charging. Comprising a vehicle information acquisition module, a charging pile state monitoring module, a priority evaluation module, a power distribution module and a user interaction module, wherein the vehicle information acquisition module is used for acquiring a current SOC value, a target SOC value, vehicle demand power, a vehicle type, a user type and a payment mode of a vehicle in real time; the charging pile state monitoring module is used for monitoring the number of modules, the number of used modules and the number of remaining modules of the charging pile in real time. According to the priority-based charging pile power dynamic distribution system and method provided by the invention, the charging priorities of the vehicles are evaluated, the charging pile modules are reasonably distributed, and meanwhile, it is ensured that the vehicles with small demands occupy at least one power module, so that the problems that the vehicles with small demands occupy too many modules and the vehicle modules with large demands are not enough are solved; and the use efficiency and the user experience of the charging pile are improved.

The invention discloses a low-voltage line intelligent voltage regulation voltage stabilizer control system, and relates to the technical field of voltage stabilizer control, an intelligent control unit judges whether the current line voltage needs voltage regulation control based on a control algorithm, if yes, a corresponding voltage regulation control instruction is generated, a silicon controlled rectifier switching module executes non-contact on and off operation, and the voltage regulation control is completed. The voltage regulation compensation module is used for carrying out voltage compensation on the basis of the output of the voltage transformation module, so that the output voltage is controlled within a preset range, and when the power grid voltage is detected to be recovered and stable within a set time, and when the power grid voltage is detected to be abnormal, the voltage regulation compensation module is used for carrying out voltage compensation on the basis of the output of the voltage transformation module. And the intelligent control unit is automatically switched into a bypass for operation. According to the control system, a voltage stabilization control framework with high response speed, three-phase independent adjustment capability, an intelligent dormancy mechanism and a complete fault protection system is constructed, and the intelligent level and the operation performance of the low-voltage power distribution system are remarkably improved.

The invention provides a number card distribution system based on message middleware, relates to the field of data management, and solves the technical problem of high data storage security risk of an existing number card distribution system. The system comprises a message center module which is used for processing asynchronous communication and data transmission in the system by adopting a distributed message middleware cluster; the data security module is used for managing distribution data of each region by adopting a data fragmentation storage mechanism; and the demand prediction module is used for predicting the number card demand quantity of the next period according to the sales data and the demographic data of each region, and guiding the formulation of a sales strategy of the next period. The method and the device are used in the whole-process management process of the number card distribution service.

The invention discloses an Internet of Things real-time task distribution system based on AI scheduling, and the system comprises a data collection module which is used for generating a task input vector, and constructing a node resource vector set; the semantic modeling module is used for inputting the task input vector into an improved StructBERT model to construct a task semantic vector; the intention analysis module is used for constructing a historical task intention vector library and a semantic conflict graph; the comparison judgment module is used for executing task conflict judgment and scheduling priority adjustment operation according to the task aggregation degree and the semantic difference vector; and the scheduling execution module is used for executing a scheduling calculation operation, generating a task allocation result and updating the historical task intention vector library and the semantic conflict graph. The method has the advantages of high task semantic understanding precision, high conflict judgment efficiency and high scheduling decision intelligent degree, and is suitable for a real-time task allocation scene in a multi-node heterogeneous Internet of Things environment.

The invention discloses an intelligent distribution system for welding material management and an operation method thereof, and relates to the technical field of welding material pretreatment equipment, and the intelligent distribution system structurally comprises a drying box, a feeding device and a material taking distribution device. A plurality of layers of sliding frames are arranged in the drying box, and material frames are arranged on the sliding frames and can slide out from one side. The feeding device comprises a transferring mechanism and a feeding table, and the transferring mechanism is used for transferring the material frame between the drying box and the feeding table. The material taking and distributing device comprises a mechanical arm and a grabbing mechanism and is used for grabbing and transferring the welding materials in the drying box. The operation method comprises the steps that 1, empty material frames in the drying box are taken out through the transferring mechanism and placed on the workpiece feeding table; 2, a welding material is supplemented into the material frame; 3, taking down the filled material frame through a transferring mechanism, and feeding the material frame into a drying box for drying; and 4, the welding materials are grabbed from the drying box through a material taking and distributing device and put in. By applying the scheme, the technical problems that in the prior art, the working intensity of workers is large, the environment is poor, and whole-process automation is difficult to achieve can be solved.