3376 results about "Computer technology" patented technology

The invention provides a computing resource optimization method and system for analysis tasks, and belongs to the technical field of computers.The computing resource optimization method comprises the steps that the priority of each analysis task is determined according to feature information of each analysis task and current available computing resource state information of the system, and a priority queue is generated; obtaining a task load prediction value according to the historical task load data and the real-time system state data; according to the type of the analysis task, the data scale and the data source position, the analysis task with the real-time requirement higher than a preset standard is allocated to an edge node to be executed, and resource allocation of the edge node is dynamically adjusted according to a task load predicted value; and dynamically allocating available resources from the computing resource pool according to the priority queue and the task load prediction value so as to execute the plurality of parallel analysis tasks. According to the resource management method based on task priority dynamic adjustment, task load prediction and edge computing optimization scheduling, real-time prediction and dynamic self-adaptive scheduling of computing resources are achieved.

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

The embodiment of the invention provides a task scheduling method, system and device based on cloud computing and a storage medium, and belongs to the technical field of computers. According to the method, task priorities are initialized according to task resource requirements, task types and task timeliness requirements, task scheduling strategies are determined according to task metadata and computing resource information, the task metadata comprise the task priorities and the task resource requirements, and the task scheduling strategies are used for representing task scheduling sequences and task allocation objects; task scheduling is carried out according to the task scheduling strategy, load fluctuation information and task demand change information of each computing node are obtained, the task scheduling strategy is adjusted according to the load fluctuation information and the task demand change information, and the step of task scheduling according to the task scheduling strategy is repeatedly executed, so that the task scheduling efficiency is improved in the task execution process. Task scheduling and allocation are adaptively adjusted based on load changes and task demand changes, and the task execution efficiency and the system resource utilization rate are improved.

A watershed pollution control measures optimization method employing response surface methodology coupled with a genetic algorithm, the method relating to the cross-disciplinary field of environmental engineering, environmental system simulation and prediction technology, and computer technology. The present invention solves the problem of low optimization efficiency in existing optimization methods. The invention comprises: by using known data, establishing a geographic database for an area; by using the geographic database, calculating a single load of each pollution source, and accordingly calculating the load reduction amount of each pollution source in a single control measure scenario; calculating the total nitrogen pollution load reduction rate and the total phosphorus pollution load reduction rate, and computing the total economic benefit in the single control measure scenario; setting the maximization of the economic benefit, total nitrogen load reduction rate, and total phosphorus load reduction rate as objectives, and establishing a regression function by adopting response surface methodology; and by adopting an NSGA-II genetic algorithm, performing multi-objective optimization on the regression function, and selecting an optimal management measure for a watershed to undergo pollution control. The present invention is suitable for optimizing pollution control measures for a watershed.

The invention discloses a control method and device for heat dissipation equipment, and relates to the technical field of computers, and the method comprises the steps: employing a load dynamic perception and prediction model cooperative control technology, building a dynamic correlation model of a load and heat dissipation, and achieving the mode upgrade of a heat dissipation system from lagging regulation to advanced response. A control instruction is generated by predicting a load change trend in real time, and a dual-mode protection mechanism is combined, so that energy efficiency loss caused by traditional temperature control delay is avoided, system stability and energy efficiency performance are optimized, an efficient and energy-saving active heat dissipation solution is provided for high-load equipment, and the service life of the system is prolonged. The problems of low control efficiency of the heat dissipation equipment and energy efficiency loss caused by delayed adjustment in related technologies are solved, and the technical effects of improving the control efficiency of the heat dissipation equipment and reducing energy consumption are achieved.

The invention relates to the technical field of computers, in particular to a reasoning acceleration method and device, electronic equipment and a storage medium, and is used for improving the reasoning speed when a model executes a task. The method comprises the steps that when a target model is adopted to execute a to-be-reasoned task, a matched reasoning mode is activated according to hardware state information of computing resources occupied by the target model; when the reasoning mode is an instruction acceleration mode, at least one round of reasoning operation is executed, and each round of reasoning operation comprises the steps that storage continuity check is conducted in computing resources for reasoning dependency data related to the input sequence of the round; the reasoning dependency data is generated and stored through the reasoning operation of the previous round; when the storage position of the reasoning dependency data is in a non-continuous state in the computing resources, the storage position is adjusted to be in a continuous state by rearranging the reasoning dependency data; and calling a specified hardware acceleration instruction to load the adjusted reasoning dependency data, and determining the reasoning result of the round based on the loading result.

The invention discloses a memory access method, a program product, equipment and a medium, and relates to the technical field of computers, firmware is utilized to determine to-be-reserved physical memory information, the to-be-reserved physical memory information is analyzed and continuously split to obtain physical memory segments, and the physical memory segments are divided and aligned; dividing the virtual address space, and performing alignment processing on the divided virtual address space; performing memory mapping on the processed physical memory segment and a physical address space corresponding to the processed virtual address space; generating a page table by utilizing a physical address space corresponding to the mapped and processed virtual address space; when an access instruction of a user is obtained, the corresponding target physical address space is screened from the page table to determine the mapped target physical memory segment, so that the user accesses the target physical memory segment based on the user mode process, the range and capacity of the memory segment are dynamically configured, the stability of the memory during key load operation is guaranteed, and the memory access performance is improved.

The invention discloses a model training configuration parameter determination method and device and a storage medium, and relates to the technical field of computers, and the method comprises the steps: dividing configuration items into fixed configuration items and undetermined configuration items, and under the constraint that the fixed configuration items adopt specified configuration, determining the undetermined configuration items; predicting the predicted training performance of the to-be-trained model when the to-be-trained configuration item is trained by adopting different configuration parameters, and selecting a target configuration parameter from multiple configuration parameters of a group of to-be-trained configuration items based on the predicted training performance corresponding to the multiple configuration parameters, when the to-be-trained model is subjected to model training, the target configuration parameters are selected by one group of to-be-determined configuration items, so that the cost required by model training can be remarkably reduced, the resource utilization efficiency is maximized, the resource waste is reduced, the training efficiency is improved, and the problems of high hardware resource demand and high cost in a large model training stage in the related technology are solved.

The invention discloses a method, a product and equipment for sharing a file system by multiple hosts and a storage medium, and relates to the technical field of computers, which comprises the following steps: configuring a shared memory device as a character device, directly mapping to a user mode virtual address space, bypassing a kernel page cache, and directly reading and writing a physical address of the shared memory. A shared memory is divided into a super block area, a metadata area, a log area and a data area during formatting, all hosts perform unified operation through the shared metadata area, stand-alone cache interference is avoided, a hidden metadata file is created to map a non-data area during mounting, a log item is scanned to reconstruct a directory file structure, and the mounting efficiency is improved. All hosts generate a directory file structure completely consistent with the shared memory through log replay, the problem of data consistency caused by a cache strategy in a traditional multi-host file system is solved, and the effects that multiple hosts share a unified memory view, operation is real-time and synchronous, and data access delay is reduced are achieved.

The invention provides an operation and maintenance method and device based on a large language model, electronic equipment, a storage medium and a computer program product, relates to the technical field of computers, in particular to the technical field of artificial intelligence large models, natural language understanding and operation and maintenance, and can be applied to operation and maintenance scenes. The specific implementation scheme is as follows: iteratively executing the following operations through an intelligent agent: determining a link operation and maintenance result corresponding to a completed link in an operation and maintenance task; through a large language model, according to the link operation and maintenance result corresponding to the completed link, determining a target tool for continuing to execute the operation and maintenance task from the tool set; and calling the target tool, and generating a link operation and maintenance result corresponding to the next link in the operation and maintenance task. According to the method and the device, the Agent Loop (agent loop) mode is adopted, the target tool of the next link in the operation and maintenance task is determined according to the link operation and maintenance result corresponding to the completed link in the operation and maintenance task, and the applicability of the operation and maintenance scheme in each operation and maintenance scene and the accuracy of the operation and maintenance result are improved.

The invention discloses a local legislation compliance intelligent detection system and method based on deep semantic analysis and a multi-modal legal knowledge graph, and relates to the field of computer technology and law crossing technology, the method comprises the following steps: constructing a legal knowledge graph; designing a multi-dimensional rule sub-library with a dynamic weight adjustment mechanism; a large language model based on Deepseek is utilized to construct legal provisions to perform a deep semantic analysis model, so that the intelligence of generation of triads of laws and regulations in the national field is realized, and the field adaptability of triad generation is improved; executing multi-dimensional conflict detection based on the knowledge graph, a conflict detection rule base and a semantic analysis result; and a multi-dimensional law conflict report is automatically generated. According to the method, a legal knowledge graph is constructed, multi-source legal data is integrated, a multi-dimensional rule sub-library is combined, a Deepseek-based special model is used for carrying out deep semantic analysis on legal provisions, and explicit and implicit conflict judgment is carried out on contradictory point locations and contexts.

The invention provides a path planning method and device, electronic equipment and a storage medium, relates to the technical field of computers, in particular to the fields of artificial intelligence, intelligent transportation, automatic driving, deep learning technologies and the like, and can be used for application scenes such as path planning and intelligent driving. According to the specific implementation scheme, a transverse and longitudinal combined search result track is generated according to the vehicle state and obstacle information; generating a decision list according to the transverse and longitudinal combined search result trajectory and the obstacle information; determining a channel corridor constraint and a reference path according to the vehicle state, the obstacle information, the decision list and the environment information; and generating a planned path according to the vehicle state, the passage corridor constraint and the reference path. According to the scheme, the precision and reliability of path planning can be improved.

The invention discloses a robust distillation method and system for a medical visual language large model and a medium, and relates to the technical field of computers, and the method comprises the steps: S1, building an image classification training data set, and selecting a teacher model and a random initialization lightweight student model; s2, constructing gradient calibration loss between the teacher model and the student model based on the teacher model gradient; s3, constructing a dynamic gradient alignment loss between the teacher model and the student model based on the sample and category level; s4, constructing hierarchical disturbance feature alignment loss between the teacher model and the student model based on the intermediate disturbance sample; and S5, finely adjusting the image classification model based on double-level weighted gradient alignment loss, level disturbance feature alignment loss, clean sample cross entropy loss, confrontation sample cross entropy loss and standard knowledge distillation loss. And two original conflicting targets of precision keeping and robustness enhancement are decoupled and cooperatively realized, so that the distillation robustness accuracy is improved.

The invention discloses a health evaluation method, electronic equipment, a storage medium and a product, and relates to the technical field of computers, and the method comprises the steps: based on time sequence index data and log data in a to-be-evaluated system, respectively determining a monitoring index deviation degree corresponding to the time sequence index data and a log abnormal probability corresponding to the log data; based on the monitoring index deviation degree and the log abnormal probability, determining an index fusion weight corresponding to the monitoring index deviation degree and a log fusion weight corresponding to the log abnormal probability; and according to the monitoring index deviation degree, the log abnormal probability, the index fusion weight and the log fusion weight, in combination with a health degree scoring function, determining a health degree score of the to-be-evaluated system, so as to execute a recovery operation on the to-be-evaluated system according to the health degree score. The system fault can be detected and evaluated more accurately, the recovery strategy can be dynamically adjusted according to different fault severity degrees and actual conditions of the system, and the recovery efficiency and fault-tolerant capability of the system are remarkably improved.

The invention discloses a multi-agent distributed task collaboration method and system, and relates to the technical field of computers, and the method comprises the steps: broadcasting a to-be-allocated task set to each agent, enabling the agents to estimate the task expected completion duration of each to-be-allocated task, and generating a corresponding to-be-allocated task expected duration matrix; receiving a to-be-allocated task expected duration matrix and a historical allocation task estimated duration from each agent; constructing a constraint optimization model according to the to-be-allocated task expected duration matrix corresponding to each agent and the historical allocation task estimated duration; and solving the constraint optimization model through a global constraint optimization algorithm, and allocating each task to be allocated to a corresponding agent according to an optimal solution. Therefore, the flexibility of task allocation is improved, and resource waste and unreasonable allocation in task allocation are reduced.

The invention discloses a memory prefetching control method and device, a storage medium and a program product, and relates to the technical field of computers. A heterogeneous logic model covering multiple access modes is constructed, a prefetch strategy candidate set is generated based on standard logic models corresponding to different access modes, the expression limitation of a single model on complex multi-mode access features is broken through, and access laws in different scenes can be comprehensively covered; through calculation, sorting and screening of the prefetch cost, targeted evaluation of the strategy is realized, bandwidth load aggravation and resource waste caused by redundant prefetch are avoided, and optimization of the target prefetch strategy in the aspects of low delay and high bandwidth utilization rate is ensured. According to the method and the device, the problems that a fixed logic model is difficult to adapt to dynamic diverse memory access behaviors and resource utilization is low in efficiency in the prior art are solved, and the technical effects that the method does not need to depend on fixed logic, can dynamically adapt to the change of the access mode through flexible combination and strategy screening of heterogeneous models, and is particularly suitable for terminal equipment with limited resources are achieved.

The embodiment of the invention relates to the technical field of computers, and particularly discloses a ceramic spraying intelligent regulation and control method and system based on parameter self-adaption, and the method achieves the global optimization control of a ceramic spraying assembly line process through integrating the real-time collection and dynamic regulation and control mechanism of multi-dimensional equipment parameters. According to the embodiment of the invention, the limitation of independent control of traditional single equipment is broken through, an intelligent spraying control system with an environment self-adaptive characteristic is constructed through parameter collaborative optimization of the equipment group and bidirectional data interaction of the MES system, and comprehensive control processing with multi-parameter collaborative optimization and system-level intelligent decision-making capability is realized.

The invention belongs to the application of a computer technology in the field of shot blasting processing, and discloses a shot blasting strength calculation method and system based on control parameters of intelligent shot blasting equipment. According to the method, an intelligent calculation method system is constructed by establishing a prediction model of the control parameters and the shot blasting strength. According to the method, nozzle pressure, moving speed, spraying angle, shot flow and target distance parameters of shot blasting equipment are collected in real time, workpiece material attribute data are combined, a multi-dimensional data fitting algorithm is adopted for processing, and an accurate shot blasting strength value is output. Meanwhile, a self-adaptive parameter adjusting module is developed, and dynamic calculation optimization can be carried out according to residual stress field distribution and coverage rate requirements. Compared with a traditional Almen test piece method, the method achieves real-time calculation of shot blasting strength and closed-loop control of technological parameters, the calculation error rate is reduced to be within 5%, test piece consumption is reduced by 80% or above, machining efficiency and quality stability are remarkably improved, and an innovative solution is provided for intelligent manufacturing in the field of surface strengthening.

The invention provides a CPU scheduling method and related equipment, and relates to the technical field of computers. The method comprises the following steps: in response to execution of a load process, sampling a last-stage cache failure event of the load process to obtain a memory access hotspot address corresponding to the load process; wherein the last-stage cache failure event is related to a memory access address, and the memory access hotspot address is a first memory address of which the memory access frequency is greater than a first preset threshold value; determining a CPU corresponding to the memory access hotspot address as a first CPU candidate set of the load process; in the first CPU candidate set, determining a target CPU of the load process; and adding the load process into the process scheduling queue of the target CPU. According to the method and the device, the hotspot address is identified by sampling the last-stage cache failure event, the candidate set and the target CPU are determined, the load process is enabled to preferentially run on the CPU with high affinity with the memory hotspot, and the load performance can be effectively improved under a non-uniform cache architecture.

The invention discloses a dominant path identification method and system in a multi-source scene based on a perturbation response model, and relates to the technical field of computers. According to the method, the influence of the link state change on the steady state of the system is dynamically analyzed based on a perturbation response mode instead of depending on a static index, the method is closer to a real interaction mechanism of the network, and the mechanism modeling precision is improved. By traversing the link state, the actual contribution of each link to the response of the target node is quantified, and the complex interaction of weak connection and multi-link coordination is prevented from being ignored. Through the process that the system is evolved to the steady state again after disturbance, flow distribution and competition at the position of a shared node are naturally modeled instead of depending on local optimal addressing, the global reasonability of dominant path recognition is improved, and the problem of multi-source target resource competition is solved. The method is suitable for various networks, and has a core support effect on construction of a propagation risk early warning model based on network science, implementation of a key node intervention strategy and optimization of an intelligent governance mechanism of a social information system.

The invention relates to the technical field of computers, and discloses a time series data prediction method and device based on a diffusion model, equipment and a medium, and the method can ensure the consistency of different modal data in time by performing time dimension alignment on target time series data and multi-modal evaluation data. And feature splicing is performed on the multi-modal data corresponding to each time step, so that the multi-modal information can be integrated, and the data features of each time step are enriched. And on the basis of a self-attention mechanism, weighted summation is carried out on the fused feature vectors to obtain a self-attention feature matrix, so that mutual correlation between a time sequence dependency relationship and multi-modal features in a feature sequence can be captured, and richer feature representation is generated. And diffusion simulation is carried out on the self-attention feature matrix by using the pre-training diffusion model, more accurate target prediction data is output, and the accuracy of a prediction result is improved. And the application efficiency of the diffusion model in time series data analysis scenes in the financial field and the medical field is expanded.

The invention provides a model training and information replying method and device, a storage medium and a program product, and relates to the technical field of computers. The method comprises the steps of performing continuous pre-training on a base model based on a first training sample to obtain a basic model; performing cold start supervision fine tuning training on the basic model based on the second training sample to obtain a first supervision fine tuning model; performing multiple reasoning based on the third training sample, the target information and the to-be-trained model to obtain a reasoning result; in the Mth reasoning process, the target information comprises information obtained after a target tool determined by previous M-1 reasoning is called; optimizing the to-be-trained model based on the reasoning result to obtain a first reinforcement learning model; and based on the general recognition data and reasoning data output by the first reinforcement learning model, carrying out general recognition alignment training to obtain a target model. According to the method, the target tool can be called to obtain the required target information, so that the information output by the large model is more comprehensive.

The invention discloses a multi-cluster heterogeneous computing power scheduling method oriented to an AI training reasoning task, and belongs to the technical field of computers. According to a scheduling mechanism with issuing of the AI task as a core, the state of a resource pool is monitored in real time, resources are dynamically allocated, it is ensured that the AI task is efficiently executed, the use efficiency of the whole resource pool is improved, and the load state of an AI application example is detected in real time; through an elastic telescoping function, AI application examples are automatically expanded and shrunk, stable operation of AI tasks is ensured, resources of a plurality of clusters are integrated into a unified resource pool, centralized management and cooperative scheduling of the resources are achieved, the load condition of each cluster is monitored in real time, load balancing is automatically carried out among the clusters, and the AI tasks are reasonably distributed to different clusters. And the GPU resources of the specified model are accurately allocated to the AI task according to the actual demand of the AI task, so that the situation that the AI task application runs on non-optimal resources is avoided, and the efficient running of the AI task is ensured.

The invention relates to the field of computer technology and medical image processing, in particular to a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism. In order to solve the problems of scarcity of annotation data, weak model generalization ability, inaccurate segmentation boundary and the like in a current medical image segmentation task, the invention provides a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism, which is called an RCDE segmentation model. The model adopts a shared encoder and double decoder structure, combines a structure-level disturbance generation strategy, enhances the perception ability of the model for image structure change by mixing and recombining labeled images and unlabeled images, generates pseudo labels by utilizing a teacher network, and introduces a dynamic confidence coefficient screening mechanism, so that low-quality pseudo labels are effectively eliminated, and the robustness of the model is improved. The training stability and the pseudo-supervision effect are improved, preprocessing such as size normalization and image enhancement is carried out on the image, and the consistency and robustness of model input are improved.

The invention discloses a training method of an action decision model of an intelligent agent and an action decision method and device, and belongs to the technical field of computers. According to the method, the state topological graph is constructed on the basis of the historical trajectory, experience distribution of actions of the intelligent agent can be fully reflected, the information utilization rate of the historical trajectory is higher, more information is brought, the action feedback model is guided and trained on the basis of the state topological graph, the accuracy of the action feedback model is improved, and the accuracy of the action feedback model is improved. A state topological graph, an action feedback model and a training process of a constraint action value model are combined to obtain an action value model with better accuracy and better performance, and the action value model is utilized to assist in training to obtain an action decision model with better accuracy, so that an accurate decision on which action is executed by an intelligent agent in a given state is facilitated; and the action decision model can be combined with a large model to mutually promote training, so that the performance of the two parties is improved.

The invention discloses a target multi-attribute identification method based on feature decoupling and cross-task collaboration, and belongs to the technical field of computers of specific calculation models, and the method comprises the following steps: firstly, extracting the initial features of each task through a lightweight backbone network, carrying out feature decoupling in a subspace, and according to the cross-task feature similarity, carrying out feature extraction; according to the target multi-attribute identification method based on feature decoupling and cross-task collaboration, an orthogonal constraint weight is dynamically adjusted, then mutual information confrontation minimization is adopted to further suppress statistical dependence between tasks, task residual errors are injected in a cross-task feature aggregation stage, differentiation enhancement is achieved, and finally unified joint feature representation is formed. According to the method, subspace statistical independence is realized, independence and necessary collaborative information are considered, a stable basis is provided for subsequent fusion, statistical dependence between tasks is further suppressed through mutual information confrontation minimization, complementation information is reserved while independence is ensured, and feature discrimination and robustness are improved.

The provided are a digital currency payment method and apparatus, and relates to the field of computer technology. A specific implementation of the method includes: determining, by a payment device, transaction information of a current digital currency transaction based on an instruction sent by a collection device; invoking the transaction information through a local security chip, and sending the transaction information to the collection device for verification, where the transaction information includes a digital currency or identification information, and the identification information is used for identifying a wallet account or digital currency of the payment device; and updating, by the payment device, a digital currency balance after determining that the transaction information passes the verification.

The invention relates to the technical field of computers, can be applied to the field of science and technology finance / digital medical treatment, and discloses a method and device for dynamically adjusting threads of a thread pool, electronic equipment and a storage medium. The method comprises the steps that initialization setting is conducted on thread pool parameters through a management center of a target service system; when a thread pool parameter adjustment request is received, generating a first thread pool parameter according to the thread pool parameter adjustment request; receiving a configuration change event through a configuration change monitor, and analyzing the configuration change event to obtain a first thread pool parameter; positioning a thread pool configuration field of the distributed service instance according to a mapping relation between a program entity factory and a parameter cache; injecting the first thread pool parameter into a thread pool component instance of the distributed service instance; and calling the setting function of the distributed service instance, and resetting the corresponding configuration parameters in the thread pool component of the distributed service instance. The method improves the adaptability of the system to load change, and breaks through the system performance bottleneck.

The embodiment of the invention discloses a video generation method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The video generation method comprises the following steps: acquiring an audio of a user and a content text of the audio; inputting the content text into a large language model, and enabling the large language model to output a plurality of split description texts of an associated video of the audio based on the content text; generating a plurality of split video generation cue words based on the plurality of split description texts, inputting the plurality of split video generation cue words into a video generation large model, and enabling the video generation large model to generate a plurality of split videos of the associated video based on the plurality of split video generation cue words; and synthesizing the plurality of split videos of the associated video with the audio to generate an audio and video work corresponding to the audio.

The invention relates to the technical field of computers and provides a data transmission method based on remote direct memory access, computer equipment and a medium. The method comprises the following steps: when a sending side sends a plurality of request messages associated with a remote direct memory access transmission request, respectively writing a queue index value of a first queue at a receiving side into respective extension heads of the request messages; on the receiving side, based on the queue index value of the first queue in the extension header of the received request message, the message content is written into the position corresponding to the queue index value in the first queue, and the effective identifier of the corresponding position is set to be effective; and on the receiving side, when the effective identifier of the first position in the first queue is effective, reading contents in the positions with the effective identifiers being effective in the first queue one by one according to the sequence of the first queue, so as to generate a plurality of response messages. Therefore, the problem of out-of-order receiving is solved, and resources are saved.