457 results about "Priority queue" patented technology

The invention relates to the technical field of information retrieval, and discloses a distributed storage and indexing method, which comprises the following steps of: dynamically fragmenting an original file through an improved consistent Hash algorithm to generate a plurality of data blocks with timestamps; constructing a three-dimensional Bloom filter index matrix containing timestamps, data types and content features for the data blocks with the timestamps, associating a B + tree local index with an inverted global index through a hierarchical index structure, and establishing an index update priority queue by adopting a two-channel synchronization mechanism, performing real-time increment synchronization on the hotspot index through a heartbeat mechanism, performing batch synchronization on the cold data layer index according to a cold data synchronization period, predicting a data distribution probability through a distributed query statistics probability table during query, and initiating multi-path query in parallel based on probability weight, and the query path is dynamically optimized according to the node group storage medium type and the inter-node network transmission delay, so that rapid distribution adjustment and access of distributed storage are realized.

The invention provides an AI-based laboratory equipment scheduling optimization method and system, and the method comprises the steps: firstly obtaining a state monitoring data set containing the characteristics of equipment operation power consumption, idle time length, environment interference factors and the like in real time, and then carrying out the multi-dimensional analysis of the state monitoring data set; generating an availability evaluation index set containing characteristics of equipment load fluctuation, maintenance period prediction, compatibility matching and the like, and an experiment task priority queue, performing cross decision analysis on the availability evaluation index set and the experiment task priority queue based on a preset dynamic resource allocation model, and obtaining a scheduling strategy set containing a task allocation path, a cooperative operation rule and a conflict resolution mechanism; scheduling strategy parameters are calibrated according to experimental task operation log data, an optimized execution instruction set is generated, instructions are fed back to an equipment control system to adjust the equipment state, a dynamic resource allocation model is iteratively updated according to execution feedback data, and efficient scheduling optimization of laboratory equipment is achieved.

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

The invention provides a mutual inductor test data cloud edge cooperative processing method and device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the time domain and frequency domain combined feature extraction of the original measurement data of a mutual inductor test through a multi-mode decoupling preprocessing model, and forming a data feature vector set, further generating a confidence label flow through state recognition and Bayesian inference, evaluating a prediction error, and realizing data classification screening and priority queue construction; the data are uploaded to a cloud end in a semantic compression and multi-resolution representation vector mode, so that the transmission efficiency is improved; carrying out deep modeling and model performance monitoring at the cloud, and if degradation is detected, returning edge original data to update the model; and finally, generating a scheduling weight factor according to the prediction error distribution graph, and dynamically optimizing an edge cloud task allocation proportion. According to the invention, the problem of low resource allocation efficiency caused by lack of a dynamic scheduling mechanism based on prediction errors and confidence driving in existing mutual inductor test data edge cloud cooperative processing can be solved.

The invention discloses a production scheduling method and device based on digital twinning, equipment and a medium, and relates to the field of intelligent manufacturing, and the method comprises the steps: collecting production data, generating an initial production state, defining an initial production scheduling constraint condition, and constructing a digital twinning model; predicting the available capacity of the production resources through a digital twinborn model, identifying a risk period, and updating the twinborn state of the production resources; coupling the predicted production resource available capacity with the order set, generating a task priority queue and updating production scheduling constraint conditions; executing scheduling optimization and simulation detection conflicts in the digital twin model, if conflicts exist, executing local rearrangement based on conflict information until requirements are met, and generating an executable scheduling scheme; and issuing the executable scheduling scheme to a production and logistics link, monitoring execution deviation, rescheduling an affected task, completing a production task and updating the digital twin database. According to the invention, the real-time performance and accuracy of scheduling response are improved.

The invention discloses a high-frequency power data acquisition adaptive frequency and priority processing method and device based on resource load feedback, and a storage medium, and belongs to the technical field of high-frequency power data processing. The method comprises the following steps: acquiring resource load information of a distribution network side intelligent terminal node in real time; according to the real-time resource load information, judging whether a preset sampling period of the power monitoring data needs to be adjusted, if so, dynamically correcting through a double-layer fast and slow ring adjusting mechanism to obtain a final sampling period, and otherwise, maintaining an original period; acquiring power monitoring data acquired by the node in the corresponding sampling period; and on the basis of a preset priority queue grading rule, a tube queue algorithm is utilized to adopt a differential transmission strategy for the data, so that priority processing of different levels of data is realized. The method does not need to depend on a prediction model, and realizes acquisition link elastic control and key data delay guarantee through real-time quantification of node loads, dual-time-domain closed-loop adjustment of a sampling period and hierarchical queue management messages.

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

The invention discloses a multi-scene multiplexing-oriented VUE dynamic component interface configuration and rendering method, which comprises the following steps of: receiving a multi-scene configuration file, and extracting and coding to generate a scene feature vector; component layout parameters, resource priorities and rendering strategies are automatically generated through the two-stage generative adversarial network, and application parameters are output after the generated parameters are optimized; on the basis of application parameters, asynchronous component loading and priority queue scheduling are adopted, and on-demand loading of components is achieved in combination with dependency topology sorting; dynamically dividing a rendering area by using an adaptive Gaussian algorithm, and rejecting invalid rendering through a boundary box mechanism; the DOM operation frequency is reduced by adopting an optimization Diff algorithm; and constructing a component cache pool through an elimination strategy. According to the method, intelligent configuration and efficient rendering of the interface component under multiple scenes are realized, the development efficiency is improved, and the performance is optimized.

The invention discloses an intelligent-based multi-task self-adaptive scheduling system for a pension accompanying robot, and belongs to the technical field of multi-task self-adaptive scheduling. Comprising a multi-modal task perception fusion module, a dynamic task evaluation module, a dynamic task priority generation module, a quantum causal reasoning module, a quantum optimization task scheduling module, a multi-task collaborative optimization module and a man-machine collaborative interaction module. Task features are mapped to a multi-dimensional weight space, a time attenuation coefficient is introduced to dynamically adjust the influence of historical causal association, a dynamic adjustment mechanism ensures timely execution of high-priority tasks, a quantum superposition state is utilized to parallelly search a global optimal solution through a quantum optimization task scheduling module, and the task scheduling efficiency is improved. Through a dynamic priority queue and a conflict solution feedback mechanism, the scheduling scheme is iteratively optimized, and the feasibility and the resource utilization rate of the scheduling scheme are remarkably improved.

The technology discloses a method for generating a prioritized queue containing reported safety hazards in a site. The system receives a message with at least one issue within the site, and the system generates a command set containing the message and other instructive parameters. The system inputs the command set in an AI model, which identifies issue(s) within the message and integrates the issue(s) in a prioritized queue. Determining where each issue is integrated into the prioritized queue is directed by the instructive parameters in the command set. The system receives the generated prioritized queue from the AI model, in which the system presents to a safety user device.

The invention relates to a lightweight asynchronous message processing method and system for a high-concurrency scene, and the method comprises the steps: receiving a message request of a client, carrying out the preprocessing of the message request, and generating a to-be-processed message; distributing the to-be-processed message to a multi-level priority queue based on a priority scheduling mechanism; dynamically adjusting the number of core threads and the maximum number of threads of the thread pool according to the resource state data, and forming a thread pool resource configuration scheme of the current scheduling period; dynamically calculating the message processing quantity of the current scheduling batch based on the message accumulation quantity of each priority queue, the thread pool resource configuration scheme, the CPU utilization rate in the resource state data and the JVM heap memory utilization rate; and based on the out-of-heap memory buffer area reference, assigning the to-be-processed message object to a corresponding processing thread to execute a service processing logic until the processing operation of the messages in all the scheduling units is completed. The method has the effect of improving the resource adaptability of the asynchronous message processing architecture.

The invention relates to the technical field of intelligent gateways, and discloses a multi-mode industrial Internet of Things intelligent gateway based on edge computing and an implementation method thereof, and the method comprises the steps: obtaining sensor data streams of a door magnetic sensor, a human body sensor, a temperature and humidity sensor and a smoke detector in an intelligent region; constructing a space-time fusion data matrix based on the sensor data stream; inputting the space-time fusion data matrix into an edge layer quantization neural network, a fog layer recurrent neural network and a cloud layer large model for distributed reasoning to obtain a reasoning result set; and performing priority queue scheduling and zero-copy transmission on emergency events, important events and conventional events in combination with the reasoning result set to generate a control instruction sequence, so that parallel operation of data acquisition and processing is realized, and the intelligent level, the response performance and the operation reliability of an intelligent regional Internet of Things system are improved.

The invention provides a social network hierarchical visualization method and system based on a structure entropy and a coding tree, and the method comprises the steps: obtaining graph data, building the coding tree based on the graph data with the minimized structure entropy, and enabling the coding tree to be used for capturing a hierarchical community structure of a graph; constructing a minimum entropy priority queue, adaptively selecting non-leaf nodes for division by adopting the minimum entropy priority queue, and distributing a current structure entropy value as a weight for a cross-community edge; force-oriented layout based on structure entropy and layout entropy is used, attraction is adjusted based on edge weight, standard repulsive force is kept, and a force model is established; solving a layout entropy maximization target for the force model to calculate a layout result; and for the calculated layout result, outputting and rendering a layout result with a clear community separation effect as a final visualization result.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a dialogue message queue management system based on a large model cluster. The system comprises a message preprocessing module, a message router, a plurality of independent priority queues, a resource scheduler, a dialogue state manager and a large model cluster. The message preprocessing module receives an external dialogue request, constructs a metadata object, allocates a priority queue tag, calculates a static score and records a timestamp; the message router distributes the request to a corresponding priority queue according to the label; the resource scheduler executes a scheduling strategy, calculates a dynamic priority score, realizes load balancing, retrieves historical context information from the dialogue state manager, combines a request and a context into a processing task and dispatches the processing task to a large model cluster; the large model cluster returns model response to the resource scheduler, and the dialogue state manager is responsible for context storage and updating. All the components of the system can efficiently process the dialogue message request through close cooperation.

The invention relates to the technical field of intelligent education management, in particular to a self-adaptive learning progress informatization teaching test system, which comprises the following contents: a knowledge node construction module, a learning state identification module, a test task generation module, a path scheduling adjustment module and a feedback data backtracking module. According to the method, refined numbering management of knowledge points is realized by constructing a directed edge numbering set, a dependency direction is set in combination with a label propagation algorithm, the semantic expression ability of a node relationship is enhanced, mastering weights and stability coefficients are matched based on answering behaviors, a state weight sequence is formed, and the stability of classification and recognition is improved; cognitive ability coding and time-consuming median value linkage state weight are introduced, questions are screened, a priority queue is constructed, task pushing precision is improved, numbering and sorting weight are fused to construct a path chain, dependency intensity is calculated to expand path branches, and individual suitability and task rhythm coordination ability of path scheduling are enhanced.

The invention provides a power grid operation analysis method based on a cloud platform, and relates to the technical field of power grid operation analysis, and the method comprises the steps: configuring a semantic event template library, configuring multi-source power grid operation data access and time reference synchronization, configuring an event triggering rule and an event judgment mechanism, and extracting an event fragment data set, and configuring a semantic abstract vector, executing semantic compression, generating a unique analysis task number, registering a task metadata directory, and setting a task priority and priority queue arrangement. By configuring the semantic event template library and realizing the access and time reference synchronization of the multi-source power grid operation data, the unity and timeliness of the event fragment data set can be ensured in a mass data environment, the problem of analysis errors caused by non-uniform data formats and non-uniform time sequences in the prior art is solved, and the analysis accuracy is improved. Therefore, the reliability of power grid operation state identification and analysis results is improved.

The invention provides a front-end resource dynamic preloading method, system and device and a medium, and belongs to the technical field of front-end engineering. The method comprises the following steps: constructing and training a user behavior prediction model based on a Transform time sequence model architecture, and deploying the user behavior prediction model to a browser side after compression processing; the method comprises the following steps: acquiring behavior data of a user by monitoring a user interaction event, constructing a time sequence operation sequence, and collecting network information and equipment performance information; inputting the time sequence operation sequence into the model, outputting probability distribution of the next operation, and determining related resources; based on the probability distribution, the resource type and the network information, calculating the priority of the resource, and generating a resource priority queue; according to the network information and the equipment performance information, determining a pre-loaded resource type, and adjusting a resource priority queue to generate a pre-loading strategy; a cache rule is defined, a cache configuration file is generated, and a CDN cache is used for preloading resources; and executing the preloading strategy, and carrying out rollback processing after loading fails.

The invention discloses a CAN message priority dynamic arbitration method and system based on event triggering, and the method comprises the steps: obtaining a discrete sampling signal of a CAN node of an engineering vehicle and a state register value of a CAN controller, converting the discrete sampling signal into a working condition data sequence, and obtaining a bus load rate through the state register value; obtaining an event state sequence according to the working condition data sequence; all CAN messages to be sent are constructed into a message data record, and the message data record with event semantics and dynamic priority labels is obtained according to the protocol data unit and the event state sequence; and according to the bus load rate and the event state sequence, sending a message data record with event semantics and dynamic priority annotations by adopting a token bucket mechanism through a three-level priority queue linked with a three-state state machine. According to the invention, the security key message in the system can still be transmitted in time under high load, and the bandwidth occupation and delay jitter caused by the low-priority message are avoided at the same time.

The invention relates to a tensor core resource allocation method, computer equipment, a readable storage medium and a computer program product. The method comprises the steps of determining a target queue corresponding to a first target calculation instruction from an execution queue based on a priority corresponding to the first target calculation instruction under the condition of determining that the first target calculation instruction from a target thread bundle completes data synchronization, and writing the first target calculation instruction into the target queue, the execution queues comprise at least two execution queues corresponding to different priorities; under the condition that the tensor core resource is idle, the calculation instruction stored in the queue to be allocated is written into the tensor core resource to execute corresponding calculation, and the queue to be allocated is the queue with the highest priority in the execution queues storing the calculation instruction. By adopting the method, the resource utilization rate can be improved.

The invention discloses a low-voltage uninterruptible operation site visual monitoring optimization method based on an edge computing architecture, and the method comprises the following steps: constructing an operation scene graph analysis module, obtaining the distribution of operators, the operation type and the equipment voltage class information of each monitoring region, generating a risk class mapping graph, and carrying out the operation scene graph analysis module; positioning and initial identification of a high-risk area are realized; generating a risk perception matrix; constructing a priority queue of computing resources and risk levels; acquiring identification precision, processing delay and behavior capture integrity indexes, and establishing a task feedback mechanism; constructing a scheduling logic credibility evaluation module, analyzing a scheduling instruction, marking and recording an abnormal path, generating a scheduling behavior record library, and predicting and early warning a priority inversion risk; and iteratively optimizing scheduling model parameters, and dynamically updating risk scores and scheduling logic. According to the method, high-risk task resources are guaranteed preferentially, priority reversal is avoided, and the stability, robustness and safety response timeliness of the system in a complex operation environment are remarkably improved.

The invention provides a linkage control method and system for computer auxiliary equipment, and the method comprises the steps: generating an equipment linkage control instruction sequence through prediction probability distribution, managing a multi-equipment concurrency control request through a priority queue, and dynamically adjusting an instruction execution sequence according to the equipment response time and the resource occupation condition, if the response time of the equipment exceeds a preset delay threshold value, reducing the linkage priority of the equipment and activating standby equipment; and adjusting equipment linkage strategy parameters according to a model updating result, adopting an adaptive threshold mechanism to optimize a prediction trigger condition, calculating a user satisfaction index through a moving average algorithm, and if the satisfaction is lower than a reference value, backtracking and analyzing operation sequence features and recalibrating personalized operation habit model parameters.

The invention discloses a multi-task parallel data stream processing method and device based on an FPGA (Field Programmable Gate Array), which are applied to the technical field of data processing. Carrying out normalization processing on the decoded data to generate a data stream in a unified format; performing coding and ID identification code embedding processing on the normalized data to generate to-be-stored data with an identifier; carrying out FIFO (First In First Out) buffering and write-in priority judgment on the coded data to generate priority queue data; dDR writing and BANK scheduling processing are carried out on the priority queue data, and stably stored parallel data are generated; carrying out reading and output coordination processing on DDR storage data to generate a continuous output data stream, and triggering a reading application by decoding ID identification code positioning data, reading FIFO according to depth scheduling and monitoring data volume by channel temporary storage FIFO; and performing dynamic bandwidth allocation and energy efficiency optimization on the whole data stream processing process to generate an efficient parallel processing result.

The invention provides a large language model application workload scheduling method, system and device, and relates to the technical field of model load scheduling, and the method comprises the steps: modeling a composite large language model application into a directed acyclic graph comprising a conventional stage, an LLM stage and a dynamic stage; modeling execution correlation among stages in the directed acyclic graph through a Bayesian network, dynamically predicting duration distribution of uncompleted stages, and calibrating a duration estimated value of an LLM stage in combination with a real-time batch processing size of an LLM executor; the uncertainty reduction amount of each ready stage is quantitatively scheduled based on information entropy; an epsilon-greedy strategy is adopted, and a JCT priority queue and an uncertainty reduction priority queue are combined to allocate scheduling resources; and assigning the task to a corresponding executor for execution, and repeating the above process until all operations are completed. The technical problem that the scheduling technology in the prior art is difficult to effectively deal with the execution time uncertainty and the structure uncertainty of the composite LLM application is solved.

The invention discloses a mobile robot path planning method combining kinematics constraint and variable density sampling, which comprises the following steps: setting a starting point and a target point, adding the starting point into a vertex set, adding the target point into a sampling set, initializing a node priority queue and an edge priority queue, and establishing kinematics constraint and environment parameters at the same time; searching a main circulation path; if the vertex cost is better, performing vertex expansion and generating candidate edges, and if the edge cost is better, performing feasibility verification and accessing the path tree; when the search is stagnated or trapped in local optimum, an escape mechanism is triggered, and the exploration capability is enhanced through radius expansion and supplementary sampling; an improved heuristic function and a local cost function are established, search convergence is guided, and track smoothness and performability are improved; and when a termination condition is reached, outputting a path planning result. According to the method, the sampling efficiency and the searching speed are improved while the path feasibility is ensured, so that the navigation requirement of the mobile robot in a complex dynamic environment is met.

The invention discloses a space-time density peak value clustering method and system based on shared neighbor weighting, and the method comprises the steps: generating a weight coefficient through quantifying the proximity of a space-time neighbor in time and space dimensions; fusing a weight coefficient, a sample density degree and a shared neighbor similarity, and innovatively calculating a shared neighbor weighted local density which can better reflect spatio-temporal data characteristics; a decision value is generated in combination with the relative distance to accurately select a class cluster center; class cluster division is carried out by adopting a dynamic priority ranking strategy based on a maximum heap priority queue, so that the boundary point distribution precision and efficiency are effectively optimized; and finally, abnormal points are identified through a self-adaptive dynamic threshold value based on statistical distribution characteristics. According to the method, the problems that traditional density peak value clustering is poor in spatio-temporal data adaptability and high in noise sensitivity are solved, and the clustering accuracy, robustness and efficiency in applications such as intelligent traffic flow analysis and regional hot spot monitoring are remarkably improved.

The invention provides an AGV intelligent control method and system, and belongs to the field of intelligent control, and the method comprises the steps: building an environment grid map based on the pose of an AGV and sensor data, defining the actual cost of the g (s) value of a node s from a starting point to the node s, defining the rhs (s) value of the node s as the advanced estimated cost of the node s to a target point, and judging whether the node s is a non-uniform node or not; according to the relative position of the non-consistent node and the AGV and the environment change type, calculating the updating priority of the non-consistent node; and adding the non-consistent nodes into the priority queue, updating the g (s) value map, and generating and executing a new driving path from the current position of the AGV to the target point. According to the method, the re-planning tasks can be intelligently screened and sorted, computing resources are intensively used for processing the most critical environment change, the decision-making efficiency and response timeliness in a complex environment are improved, and the safety and smoothness of AGV operation and the overall passing efficiency are enhanced.

The invention belongs to the technical field of cache prefetching optimization of a firmware layer of a computer storage system, and particularly provides a memory control method, a storage device, a medium and equipment.The instantaneous flow is effectively filtered and persistent hotspot data is accurately recognized through a three-layer structure of combining a first-stage hotspot filtering module with a window cache, a view queue and a protection queue; the second-stage continuous access filtering module dynamically judges a continuous access mode by means of mean-variance statistics of an annular buffer area, and accidental continuous triggering of prefetching is avoided; and the third-stage random access judgment module prohibits invalid prefetching and effectively distinguishes access modes. The pre-fetching bandwidth is limited through the token bucket algorithm, the global priority queue of the skip list is combined, it is ensured that the priority of the write task is higher than that of the pre-fetching task, pre-fetching and foreground I / O resources are isolated, delay jitter control over foreground key I / O can be achieved during full-load pre-fetching, and the problem that delay soaring is caused when pre-fetching preempts the resources is solved.

The invention belongs to the technical field of data center network and clock synchronization, and discloses a dynamic time synchronization path optimization method and system based on topology awareness. The invention provides a closed-loop framework of state awareness-path optimization-resource assurance-dynamic adjustment and optimization: in the first stage, network topology, link load and PTP message performance data are periodically acquired through an SDN controller, and a global state view is constructed; in the second stage, the optimal path is dynamically calculated for the PTP synchronous flow through a multi-constraint optimization algorithm with the purpose of minimizing transmission delay jitter, and deterministic transmission is achieved in combination with bandwidth reservation and a priority queue; and meanwhile, a seamless switching mechanism of building first and disassembling second is introduced to adapt to the network state change in real time. On the basis of multiplexing existing hardware, the time synchronization error is remarkably reduced, the transmission stability and the dynamic adaptability are improved, microsecond-level time synchronization can be efficiently achieved, and the method is suitable for high-precision service scenes.

The invention discloses a statistical data downloading method for supply chain finance, which relates to the technical field of finance and comprises an acceptance permission judgment module, a query resource control module and a desensitization packaging evidence storage module. The acceptance permission judgment module is used for uniformly accessing the statistical application, completing identity verification, main body range binding, index range binding, time range binding and area range binding, and generating an acceptance bill and a judgment bill; the query resource control module is used for generating a query plan under the index aperture mapping dictionary, the partition information and the resource quota, binding a concurrent quota, a throttling quota and a priority queue, and outputting an executable instruction stream; and the desensitization packaging evidence storage module is used for executing differential desensitization according to the main body type and the sensitivity level, performing aggregation calculation and deduplication execution, completing result set packaging, digital watermarking and downloading delivery, generating a downloading certificate and writing a certificate abstract and an evidence index into an evidence storage chain. The method has the characteristic of high consistency.

The invention relates to an intelligent regulation and control method and system for a food processing production line, and belongs to the technical field of food processing automatic control. Formula description information and monitoring instructions are automatically obtained and processed through a PLC control system, production automation is achieved, the system carries out segmented processing according to the processing time period described by a formula, determines processing parameter set values, calculates the process similarity between different products and carries out grouping, further, the processing time period is divided into flavor sub-stages, and the flavor sub-stages are divided into flavor sub-stages. A three-dimensional parameter group is established to finely regulate and control the flavor forming process, meanwhile, an edge calculation terminal is installed to be connected with all devices, the remaining processing time is analyzed in real time, a production sequence is dynamically adjusted based on a priority queue, the production efficiency is improved, the deviation value and the change rate of processing adjustable parameters are calculated, abnormal devices are determined and fed back for maintenance, and stable production is ensured. Manual intervention is reduced, the production automation and intelligence level is improved, and fine control and efficient production are achieved.