490results about How to "Improve distribution efficiency" patented technology

The invention discloses an RIS-assisted unmanned aerial vehicle network-based communication and sensing integrated system and method. The system comprises a UAV, an MBS module, a user communication association decoupling module and an RIS module. The RIS intelligent reflecting surface and the unmanned aerial vehicle network are applied to the communication and sensing integrated system. In consideration of different service requirements in the system, the process is divided into a communication part and a sensing part. And a deep reinforcement learning algorithm is designed according to the requirements of the system to control the UAV flight path, base station beam forming, user association allocation and RIS phase shift in the system, so that the communication and sensing performance in the communication and sensing integrated system is optimal. According to the invention, while all functions of an original communication and inductance integrated system are reserved, RIS and unmanned aerial vehicle networks are introduced to assist the system, and an alternate iterative optimization method and a deep reinforcement learning algorithm are used to realize scheduling and decision-making of each resource. Compared with the prior art, additional channel gain is provided, and the distribution efficiency of communication sensing resources is improved.

The invention relates to the technical field of supply chains, and discloses an intelligent supply chain and logistics optimization management method and system, and the method comprises the steps: obtaining a real-time order of each supply chain station in a region, and planning a logistics distribution path; data analysis is carried out, the sales volume of orders on the current day is predicted, and the inventory of a supply chain station is predicted in combination with supply volume consumption analysis; and according to calculation of the inventory emergency degree of the supply chain station, a target scheduling station is selected, and an inventory logistics scheduling scheme is generated. According to the invention, full-chain optimization management from order obtaining to logistics distribution path planning, accurate stock prediction and reasonable stock scheduling is realized, the logistics distribution efficiency can be significantly improved, the transportation cost is reduced, refined and scientific management of the stock is realized, the stock cost and the stockout risk are accurately balanced, and the logistics distribution efficiency is improved. The capacity of the supply chain for dealing with market changes and uncertainty is enhanced, the customer satisfaction is improved, the market competitiveness is improved, and efficient, stable and sustainable operation of the supply chain system is guaranteed.

The invention discloses a game theory improved model-based cloud computing node storage calculation training resource competition scheduling method. The method comprises the following steps of S1, constructing a global resource scheduling system; s2, carrying out initial division on the cloud computing nodes in each logic partition; s3, executing a local resource competition game model; s4, the global resource scheduling system collects a local resource competition result of each logic partition and residual available resources in each logic partition; s5, executing the global collaborative game model; s6, dynamically updating the resource state information of the cloud computing node; s7, the global resource scheduling system performs adaptive adjustment on resource demand strategies in the local game model and the global cooperative game model; and S8, the global resource scheduling system stores and analyzes past resource competition results and task execution conditions, and each cloud computing node updates a resource demand strategy according to the data. According to the invention, the problems of resource bottleneck and overload in the prior art are solved, and the resource utilization efficiency and the scheduling flexibility are greatly improved.

The invention discloses a cloud computing task storage and computing resource intelligent scheduling method based on an adaptive graph neural network. The method comprises the following steps: S1, constructing a cloud computing basic information set; s2, constructing a graph neural network model of tasks and resources; s3, using the cloud computing basic information set to initialize the graph neural network model; s4, gradually optimizing a resource allocation strategy of the task; s5, updating the embedded vector of the task node based on the optimized graph neural network model; s6, scheduling computing resources and storage resources in real time according to the task priority and the generated resource allocation decision; s7, dynamically updating the embedding vector and the resource allocation decision of the task node; and S8, optimizing the distribution efficiency of storage and computing resources in the cloud computing environment. The resource utilization rate of the system is improved by about 18%, and meanwhile the task execution success rate is improved by about 9%.

The invention relates to the technical field of distributed file processing, in particular to a distributed file processing method and system, and the method comprises the following steps: based on a task distribution node and a data exchange interface, extracting path characteristics of a task input / output file, analyzing a file exchange sequence of a task path, and calculating a topological structure and length of the path. According to the method, the flexibility and the adaptability of task allocation are realized by combining task state monitoring and real-time adjustment of dependent path weights and task priority marks, resource allocation and a short-time task sequence are optimized by utilizing request frequency and time period records, high-frequency request resource conflicts are effectively relieved, the resource allocation efficiency is improved, and the resource allocation efficiency is improved. Through node historical resource distribution data and time distribution trend analysis, task distribution is adjusted and resource loads are balanced in combination with differences, the stability and balance of the system are enhanced, a file transmission path is optimized based on a load state and a task execution path, and a concurrent task distribution strategy is improved.

The invention relates to a multi-mode AIGC cold-chain logistics path optimization decision-making method and a multi-mode AIGC cold-chain logistics path optimization decision-making system. The method comprises the following steps: inputting position data, temperature data, road condition data and weather data in a cold-chain logistics system into an AIGC model of a stack auto-encoder for feature extraction and reconstruction error calculation to obtain a feature index set; performing space-time interval division and similarity matching on the cold chain distribution path to obtain an interval reference path library; inputting a hierarchical control model for stage processing to obtain an initial control parameter; an objective function including temperature constraint, time window constraint and vehicle dynamics constraint is constructed for solving, and a path optimization scheme is obtained; performing road condition random disturbance and temperature fluctuation influence analysis to obtain a path risk scoring index; and performing feedback correction and iterative optimization on the initial control parameter to obtain a target control parameter. According to the invention, the temperature control precision is ensured, and the distribution efficiency and the system reliability are effectively improved.

The invention relates to the technical field of logistics supply chains, and discloses an intelligent logistics supply chain management method and system, and the method comprises the steps: obtaining the order data of a supply chain in real time; order analysis is carried out according to the receiving information, a delivery task is generated based on an order analysis result, and a first delivery path of the current delivery task is determined; planning an optimal path of supply chain order logistics distribution; inventory information of the supply chain is monitored and updated in real time, and the inventory capacity is dynamically adjusted. According to the method, order division can be realized, refined order management can be realized, a plurality of order sets which can be delivered once in a single pass can be determined, delivery order delivery tasks meeting conditions can be determined through the delivery target model, the optimal path can be dynamically adjusted, the delivery efficiency can be improved to the greatest extent, the delivery time can be shortened, and the logistics cost can be reduced. The inventory information is monitored in real time, the visibility of the inventory can be improved, and the inventory can be managed more scientifically.

The invention discloses a path optimization method and system for unmanned aerial vehicle logistics distribution, and relates to the technical field of unmanned aerial vehicle logistics distribution, and the method comprises the following steps: obtaining all executable distribution paths, screening an unmanned aerial vehicle logistics distribution default execution path, obtaining unmanned aerial vehicle distribution state parameters, and analyzing unmanned aerial vehicle logistics distribution state characterization parameters. Path influence parameters of default distribution sub-paths are collected, unmanned aerial vehicle default distribution sub-path optimization label information is judged, all standby sub-paths are counted, logistics adaptation execution priority values of all the standby sub-paths are analyzed, and therefore unmanned aerial vehicle logistics distribution optimal sub-paths are screened and optimized execution is carried out. The unmanned aerial vehicle distribution efficiency and safety can be improved, complex and changeable meteorological conditions and obstacle conditions can be effectively handled, the distribution time and energy consumption are reduced, the adaptability of the unmanned aerial vehicle in a complex environment is enhanced, and smooth completion of a distribution task is ensured.

The invention relates to the technical field of communication and automation control, and discloses an equipment address automatic allocation method, an address allocation circuit and communication equipment, the allocation method is applied to cascaded communication equipment, and the allocation method comprises the following steps: responding to an addressing trigger operation, reading an address input signal, and determining own equipment attribute according to the address input signal; determining an addressing mode and carrying out addressing according to own equipment attributes; after addressing is completed, a first addressing instruction carrying equipment addressing information is issued to the existing next-level communication equipment; the first addressing instruction is used for instructing the next-level communication equipment to perform addressing. According to the invention, the device address can be automatically addressed, and the correctness and reliability of address allocation are improved.

The invention discloses an unmanned aerial vehicle and unmanned vehicle joint distribution method and system, a terminal and a medium. The method comprises the steps of obtaining a task allocation scheme of cargo distribution of an unmanned aerial vehicle and an unmanned vehicle; according to the task allocation scheme, controlling the unmanned aerial vehicle carrying the goods to sail into a target area; the unmanned aerial vehicle is controlled to throw the goods into the unmanned vehicle located in the target area; according to the task allocation scheme, the unmanned vehicle is controlled to run to a goods receiving position, and goods receiving notification information is generated; and sending the goods receiving notification information to a target device corresponding to a recipient, and when it is detected that the goods are taken out, marking that delivery of the goods is completed. According to the invention, the distribution efficiency is improved through a distribution mode of combining the unmanned aerial vehicle and the unmanned vehicle.

The invention discloses an article sorting mechanical arm path planning method and system based on vision, and relates to the technical field of mechanical arm path planning. After a light source is automatically controlled, image data in a sorting area are collected, after a target image is screened out, a detected article area is divided into an obstacle and an article to be sorted, and the obstacle and the article to be sorted are sorted; after a grabbing path is marked, planned and obtained on a real-time environment map of the sorting area, a mechanical arm of the sorting robot grabs the to-be-sorted products according to the target path, grabbing scene data serves as feedback, and the movement speed and the grabbing path of the mechanical arm of the sorting robot are optimized; and a grabbing feedback value and a feedback threshold value are constructed according to the grabbing feedback data, and a corresponding sorting processing strategy is adopted according to the relation between the grabbing feedback value and the feedback threshold value. The efficiency in the actual grabbing process is improved, invalid grabbing or low-efficiency grabbing is reduced, and the grabbing feedback effect of the mechanical arm is improved.

The invention discloses a dynamic and static combined urban logistics unmanned aerial vehicle task planning method, which comprises the following steps of (1) inputting immediate and non-immediate distributed dynamic and static combined urban logistics unmanned aerial vehicle task planning urban logistics order data, and preliminarily selecting a logistics unmanned aerial vehicle airport position by using a k-means clustering algorithm improved based on space-time constraints; and (2) determining a static task planning result for non-instant delivery orders by using a static task planning method based on an HPSO algorithm, determining the positions of logistics delivery points, optimizing the site selection of the unmanned aerial vehicle airports according to the distribution of the logistics delivery points, and balancing the number of tasks of each airport. And (3) proposing a dynamic task planning method for improving a DDQN algorithm based on a static task planning result, determining a dynamic task planning result of the instant delivery order, and finally determining a planning and scheduling scheme of the logistics unmanned aerial vehicle fleet. According to the invention, through fusion of urban logistics orders of instant delivery and non-instant delivery, unmanned aerial vehicle facility site selection and order distribution are more reasonable.

The invention provides an intelligent drinking water distribution path optimization method based on a mobile terminal, and the method comprises the steps: calculating an order regularity coefficient and a recovery rate, classifying users into four types, and determining the order basic priority according to the four types; and calculating a time urgency degree weight in combination with the expected order delivery time so as to determine a comprehensive priority value of the order. According to the method, real-time road conditions and deliveryman position information of a delivery area are collected, an order density thermodynamic diagram is generated, distribution area grids are adaptively divided based on the thermodynamic diagram, and a high-density area is covered with a smaller grid size. And a multi-objective optimization model is established by combining the vehicle-mounted capacity state of the deliveryman, and optimization objectives comprise weighted delivery time minimization, vehicle-mounted capacity utilization rate maximization, container recovery efficiency maximization and workload balancing. And solving the optimization model by using an ant colony algorithm, adjusting a distribution path in real time, and dynamically updating path planning according to vehicle-mounted capacity change data so as to realize efficient and flexible intelligent distribution management.

The invention discloses a time-dependent vehicle path planning method, device and equipment and a storage medium, and relates to the technical field of intelligent logistics, the time-dependent vehicle path planning method comprises the following steps: obtaining vehicle information and time-varying road network information, and constructing constraint conditions and a target function according to the vehicle information and the time-varying road network information; obtaining a nectar source set according to the constraint condition, the time-varying road network information and the vehicle information, and constructing a hybrid artificial bee colony strategy according to the nectar source set; and obtaining a target value of a target function according to the constraint condition, the nectar source set and the hybrid artificial bee colony strategy, and performing path planning on the vehicle according to the target value. An objective function value is minimized by constructing an optimization model and adopting a hybrid artificial bee colony strategy, and optimal path planning is carried out on the vehicle according to the objective function value, so that the distribution efficiency is improved, and the cost is reduced.

The invention discloses a trusted task unloading method based on autonomous collaboration of vehicle side cloud. The method can be applied to the dynamic heterogeneous Internet of Vehicles to solve the low-efficiency problems of task accumulation, resource preemption, poor cooperation and the like caused by false tasks and malicious nodes. Firstly, an unloading task and cooperative node trust evaluation model is provided, and false tasks and malicious nodes are accurately identified and filtered by analyzing data sensitivity, resource preemption, historical unloading success rate and cooperative feedback evaluation; then, a discrete particle swarm algorithm is utilized to iteratively obtain a vehicle edge cloud collaborative unloading decision, particle codes, a fitness function and a correction mechanism are redesigned, and random quantities, greedy and mapping functions are introduced to enhance the diversity of unloading decisions. Compared with a traditional task unloading method, the method shows better energy consumption and delay performance in a scene with compact and uniformly distributed tasks, and the unloading efficiency and safety are effectively improved.

The invention discloses a routing method of a quantum key distribution network and a related device. The method comprises the steps that the state of a first QKD network is collected, the value of a corresponding reward function is calculated, the state is input into a preset actor network, and a corresponding action is output; the reward function is a linear combination of a plurality of topological state influence parameters; training the actor network by using a training architecture of the actor-commentator network in combination with the state, the action and the reward function value until a training termination condition is reached; taking the trained actor network as a strategy network; inputting the state of the link between the nodes in the second QKD network of which the route is to be determined into the strategy network, and outputting the weight value of the link between the nodes in the second QKD network through the strategy network; and calculating the optimal route of the second QKD network according to the weight value output by the strategy network. The invention provides a strategy network which is more intelligent and stronger in robustness so as to realize a routing strategy which is higher in distribution efficiency and more balanced in load.

The invention relates to the technical field of computer data management, and discloses a data management method and system capable of reducing computing power occupancy, and the method comprises the steps: S1, based on data distribution analysis, employing a sparse representation algorithm and a principal component analysis method, carrying out the operation elimination of the redundancy of high-dimensional data features through setting an L1 regularization constraint condition, and obtaining an L1 regularization constraint condition; decomposing the data correlation matrix in combination with a matrix operation mode, extracting feature vectors with top-ranked contribution values, and converting the feature vectors into a sparse low-dimensional matrix; and generating a sparse low-dimensional data matrix. Through combination of sparse representation and principal component analysis, L1 regularization is creatively applied to perform feature screening on high-dimensional data, redundant data items are removed, and principal component features with high correlation are extracted, so that the data dimension is effectively reduced, consumption of computing resources is reduced, and meanwhile, main information features of the data are reserved; the efficiency and accuracy of subsequent processing are improved, and distributed scheduling of tasks and efficient utilization of equipment computing power are realized.

The invention provides a multi-source information fusion method and system in an intelligent charging network, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data; preprocessing the obtained original data to obtain preprocessed data; classifying the preprocessed data according to an information source, and performing unified timestamp synchronization on similar data to generate a preliminary fusion data set; performing association analysis on similar data in the preliminary fusion data set, removing redundant information and performing screening to obtain a plurality of simplified data sets; performing cross-class data association on the simplified data set to generate a multi-dimensional fusion data set; grading information in the multi-dimensional fusion data set to form different grades of charging requests; distributing charging power and time periods for the charging requests with different priorities to obtain an optimized scheduling data set; performing conflict detection and adjustment on the optimized scheduling data set to generate an optimized control data set; according to the invention, good fusion data support can be provided for power grid control.

The invention relates to the technical field of distribution management, in particular to an optimization method formed through secondary feedback of a rider, and the method comprises the steps: S1, building a networking platform, and collecting various information of the rider in real time; s2, a feedback mechanism is established, and a rider feeds back important information encountered in the distribution process in real time; s3, collecting the work data of the rider, and generating a personalized work capability evaluation model; s4, the networked map software collects road information, and establishes a route prediction mechanism in combination with feedback data and historical data; s5, monitoring distribution information of the rider in real time, and providing a processing scheme in case of an emergency; and S6, obtaining distribution information, and learning and optimizing an evaluation model and route planning of the rider. The method is beneficial for greatly improving the flexibility and response speed of the platform to cope with emergencies, ensuring dynamic optimization of the distribution plan of the rider, improving the consumer experience, enhancing the participation sense of the rider, and improving the working enthusiasm and initiative of the rider.

The invention discloses a distributed roof photovoltaic processing method and device based on Bayesian optimization, electronic equipment and a storage medium. Advanced data acquisition and preprocessing technologies, accurate grid division and feature extraction, comprehensive multi-dimensional evaluation index calculation and efficient Bayesian optimization modeling are combined. Through a Gaussian process model and a Bayesian optimization algorithm, accurate capacity prediction and resource allocation can be realized in a complex multi-target optimization space. The method has the advantages that multiple factors such as power generation potential, economic benefit and power grid load are comprehensively considered, and the method has dynamic adjustment capability to adapt to changes in actual operation. The invention discloses a distributed roof photovoltaic processing method and device based on Bayesian optimization, electronic equipment and a storage medium, and aims at remarkably improving the overall benefit and resource utilization efficiency of a photovoltaic system and promoting wider application of renewable energy sources.

The invention relates to the field of large language models, in particular to a resource allocation method and system based on tensor parallelism, and provides a resource allocation method in order to solve the problems of how to meet calculation requirements under the condition of insufficient calculation resources and how to prolong the service life of equipment. Comprising the steps that calculation groups are divided, and the optimal calculation load and the maximum calculation load of the calculation groups are calculated; dividing the sub-modules into tensor modules or assembly line modules, and dividing priorities; calculating load capacity; calculating communication efficiency; calculating the load margin; allocating the sub-modules to each computing group to obtain a computing resource allocation plan; if the current load is greater than or equal to the maximum calculation load, preferentially calculating a part of sub-modules, and marking the sub-modules which are not preferentially calculated as to-be-distributed modules; and distributing the to-be-distributed module to the calculation group for assisted calculation.

The invention discloses a self-service rental vehicle intelligent scheduling and dynamic battery distribution method, system and device and a storage medium. The self-service rental vehicle intelligent scheduling and dynamic battery distribution method comprises the following steps: S1, predicting a battery demand of a rental network in a future set time period through a linear regression model, and feeding back a prediction result to a cloud scheduling platform; s2, a leasing user requests to lease at a leasing network through a user side application program; s3, the cloud scheduling platform extracts the inventory number information of the lease network in the S2; s4, the cloud scheduling platform continues to extract the historical lease information of the lease user in the S2; s5, the cloud scheduling platform performs judgment through a linear regression model in combination with the detailed information of the batteries and the vehicles in the renting network in the S3 and the historical renting information of the renting users in the S4; and S6, the cloud scheduling platform notifies the battery replacement cabinet system to open a corresponding battery compartment door. The method has the advantages of intelligent scheduling and distribution, improved distribution efficiency, prolonged battery life and the like.

The invention provides an APP distribution method and apparatus, an electronic device, a storage medium and a product. The method comprises the steps of obtaining a target APP; judging whether the version of the target APP is a preset version or not; if the judgment result is yes, obtaining first downloading link information corresponding to the target APP; in response to first downloading link information corresponding to the target APP, verifying through a first downloading condition, and distributing the target APP to a target user through at least one first channel; and if the judgment result is no, distributing the target APP to the target user through at least one second channel.

The invention relates to the technical field of waste textile recycling, in particular to a rotary distributing device for waste textiles, which comprises a machine base, a case shell is fixedly mounted at the top of the machine base, a gear motor is arranged on the side surface of the case shell, a middle shaft rod is arranged in the case shell, and a roller part is fixedly mounted outside the middle shaft rod. According to the sorting machine component set, the gear motor drives the middle shaft rod, the roller piece and the hook piece to rotate, automatic hooking and material distribution from piles of waste clothes are achieved, the material distribution efficiency is greatly improved, the problem that clothes are overstocked due to the fact that manual material distribution is low in speed and low in efficiency is solved, and the waste cloth recycling efficiency is improved.

The invention discloses a multimedia task scheduling method based on a table look-up method. The method is applied to a multimedia board card scheduling system, and comprises the following steps of: querying a chip set suitable matching table corresponding to layout, searching all coding and decoding chip combinations capable of meeting a target conference, querying a chip combination table, and determining a coding and decoding chip combination scheme corresponding to each coding and decoding chip combination; traversing all the coding and decoding chips according to the coding and decoding conditions of each coding and decoding chip of each coding and decoding chip combination given by the chip coding and decoding path table, and determining the coding and decoding chip combination with the highest cost performance; and sending a corresponding instruction to the coding and decoding chip in the coding and decoding chip combination with the highest cost performance according to the output layout of each coding and decoding chip given by the chip decoding output layout table. The method does not need to depend on manpower, table look-up scheduling can be automatically carried out according to the actual conference layout of the target conference, coding and decoding resource allocation is dynamically carried out, the resource allocation efficiency is improved, and resources are fully and reasonably utilized.

The invention provides a task management method and a project management system for a scientific research platform, and relates to the technical field of data processing, and the method comprises the steps: obtaining a to-be-allocated task in the scientific research platform; splitting the to-be-allocated task into a plurality of sub-tasks according to an execution time sequence; the planned completion time of each subtask is distributed; extracting a feature vector of each subtask; obtaining the feature vector of each person; according to the feature vector of each sub-task and the feature vector of each person, distributing a plurality of execution candidates for each sub-task in combination with a collaborative filtering algorithm; determining a final executor of each sub-task from the execution candidates through a multi-objective optimization algorithm by taking minimization of task completion time and minimization of task failure factors as targets; in the execution process, the task execution progress is monitored according to the planned completion time. The task allocation efficiency can be improved, the task allocation personnel rationality is improved, and the scientific research task completion efficiency is improved.

The invention belongs to the technical field of engineering project material intelligent distribution management, and discloses an intelligent building material distribution management system, which comprises a monitoring area division module, a construction progress analysis module, a material demand identification module, a distribution route planning module, a distribution sequence planning module, a distribution process monitoring module and a distribution abnormity identification module. Whether material distribution needs to be carried out or not is judged based on the construction progress of each construction unit, smooth construction can be guaranteed through the analysis mode, material supply and construction requirements can be accurately matched, resource allocation is greatly optimized, and inventory overstock is reduced. According to the invention, during material distribution, the abnormal material weight condition and the abnormal equipment operation condition in the material distribution process corresponding to each target construction unit are analyzed, whether the material distribution process corresponding to each target construction unit is abnormal is judged, the weight abnormity can be accurately identified, the material allocation can be optimized, the supply and demand balance is ensured, and the material distribution efficiency is improved. Stock overstock and waste are reduced, and cost is reduced.

The embodiment of the invention discloses a distributed self-increasing sequence synchronization method and device, equipment and a storage medium. The method comprises the steps that in a distributed database cluster, a master node and a plurality of slave nodes are arranged, and the master node is used for maintaining a global self-increasing sequence value; the slave node requests to distribute a local self-increasing sequence to the master node according to a preset cache parameter, and the master node calculates an initial value of the local self-increasing sequence according to the global self-increasing sequence value and distributes a preset number of continuous self-increasing sequence values to the slave node; the slave node caches the local auto-increment sequence to the local; and when the local self-increasing sequence cached by the slave node is used up, sending a new allocation request to the master node, and reallocating a new local self-increasing sequence by the master node according to the current global self-increasing sequence value. According to the method, the master node and the slave nodes are arranged in the distributed database cluster environment, and the global self-increasing sequence value is uniformly managed by the master node, so that reliable generation of the self-increasing sequence in the distributed environment is realized.

The invention discloses an intelligent cabin networked multi-cabin cooperative scheduling management system and a method thereof, and relates to the technical field of camping intelligent cabins, and the method comprises the following steps: collecting various parameter information generated in the operation process of a central scheduling system, including system load management information and supply chain robustness information, and after the collection, carrying out cooperative scheduling on the system load management information and the supply chain robustness information; and processing the system load management information and the supply chain robustness information. According to the invention, the load distribution index and the supply chain breakpoint index of the central scheduling system are collected in real time, and the data evaluation index is generated, so that the operation state of the system is accurately analyzed. A high-risk state is judged by setting a threshold value, and when a data evaluation index exceeds a reference threshold value, the system immediately generates a high-risk early warning signal to prompt a potential fault or abnormality. According to the method, the response speed of the system to the abnormity is increased, supply chain unbalance or logistics interruption caused by delay is avoided, particularly under the condition of large demand fluctuation, cargo overstock or shortage can be effectively prevented, and stable operation of the supply chain is ensured.

The invention discloses a test data distribution method and device, equipment, a storage medium and a program product, and relates to the technical field of data processing.The test data distribution method comprises the steps that test data configuration information of a user is received; determining a target data structure, a data generation rule, a target distribution mode and target storage middleware according to the test data configuration information and a preset metadata warehouse; according to the data generation rule, constructing data conforming to the target data structure to obtain test data; and based on the target distribution mode, distributing the test data to the target storage middleware. According to the scheme, the test data can be automatically distributed to the multiple storage middleware after being generated at a time, repeated data generation of the multi-source storage middleware is effectively avoided, the accuracy of the test data is ensured, and the test efficiency is effectively improved.