620 results about "Network delay" patented technology

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

The invention relates to the technical field of power supply, and particularly discloses a peak and frequency regulation heat supply method for a multi-element energy storage coupling coal power unit, which is used for solving the problems that only active frequency is optimized, electric heating coordination and charge state constraint are lacked and time delay compensation is insufficient in the prior art. The method provided by the invention comprises the steps of system modeling, controller design, coupling constraint, adaptive parameter adjustment, delay compensation and distributed consensus. According to the method, a dual-domain electric power-thermal interconnection model is constructed, a cooperative controller based on delay compensation distributed model prediction control is designed, electric-thermal cooperative penalty and charge state constraint are introduced into a target function, a prediction window and weight parameters are adaptively adjusted, and network delay is identified and compensated online. And a consensus algorithm is adopted to realize multi-region power-heat collaborative optimization.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a cross-platform information interaction method, device and equipment and a medium, and the method comprises the steps: obtaining original information sent by a source platform, and analyzing the original information into a standardized data structure according to a preset data format; extracting multimedia content elements from the structure and performing format conversion to generate standardized multimedia content; packaging the structure and the content into a to-be-transmitted data packet; acquiring network delay and bandwidth parameters of the target platform, and selecting a transmission path based on the parameters; compressing the to-be-transmitted data packet and sending the compressed to-be-transmitted data packet to the target platform through the selected path; and the target platform receives and analyzes the compressed data packet and presents the original information content. Information format compatibility is achieved through a standardized structure and content packaging, transmission efficiency is improved through network parameter perception and path selection, complete presentation of information is guaranteed in combination with compression processing and terminal adaptation, and stability and consistency of cross-platform interaction are enhanced.

The invention is suitable for the technical field of data processing, and provides a distributed data consistency control method and system, and the method comprises the steps: constructing an enhanced vector clock containing a fuzzy membership parameter and a time window deviation value; a dynamic fuzzy inference engine is built in combination with factors such as network delay jitter; calculating conflict event logic precedence relation probability distribution by utilizing an engine; generating an execution sequence hypothesis by integrating the multi-dimensional information; selecting an optimal scheme based on a dynamic confidence threshold; and resources and consistency are balanced through a three-level conflict processing mechanism. According to the method, the problem that a traditional vector clock cannot process conflict event sorting is solved, the accuracy and adaptability of distributed data consistency control are improved, and efficient collaboration of the system is guaranteed.

The invention provides a multi-operator card AIOT management method and system based on the Internet of Things, and the method comprises the following steps: carrying out API butt joint, calling a mobile CMNet API, a Unicom WorAPI and a Telecom CTWing API, and obtaining the state, flow and signal intensity data of an Internet of Things card; a heterogeneous protocol is converted into a unified JSON format through a protocol conversion gateway; network delay and base station loads are monitored in real time, and network scores of all operators are calculated based on the base station loads, the network delay, the signal strength, the tariff cost and the historical stability; and judging whether a switching trigger condition is met or not, and when the switching trigger condition is met, determining to switch the network based on the network scores of the operators, and executing a network switching process. According to the method, the network condition is monitored in real time, and the score of each operator network is calculated based on multiple factors, so that the available network can be quickly switched to when a single operator network has a fault or a poor signal, and the stability of the Internet of Things system is improved.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.

The invention discloses a cross-regional computing power resource collaborative allocation method based on computing power centers, and relates to the technical field of computing power resource scheduling and optimizing.The cross-regional computing power resource collaborative allocation method comprises the steps that real-time load data, historical task execution data and network delay data of all computing power centers are collected, and a regional load feature database is constructed; predicting the load demand of each computing power center in a future time window by using a long short-term memory network model to generate a load prediction value; calculating a resource gap coefficient and a resource margin coefficient of each region according to the load prediction value and the current resource capacity, identifying a resource insufficient region and a resource surplus region, and generating a resource collaborative matching matrix between the regions; and optimizing and solving the cross-regional task allocation scheme by adopting an improved genetic algorithm to generate an optimal resource allocation strategy, and allocating the to-be-processed task to a corresponding computing power center according to the optimal resource allocation strategy. According to the method, the cooperative utilization rate and the distribution efficiency of the computing power resources in the cross-regional scene are effectively improved.

The invention belongs to the technical field of satellite positioning, and particularly discloses a high-precision PPP (Point-to-Point Protocol) positioning method based on SSR (Simple Sequence Repeat) correction prediction, which comprises the following steps that: a receiver receives and stores an SSR correction in real time to obtain an SSR sequence; inputting the stored SSR sequence into a pre-trained prediction model, and outputting an SSR correction number prediction value in a future time period; the broadcast ephemeris is corrected through the predicted SSR correction number, and a precise satellite orbit and clock correction are obtained; and constructing an ionosphere-free combination model based on the double-frequency observation value, and resolving the position of the receiver through Kalman filtering in combination with the corrected satellite orbit and clock correction. According to the method, long-time-sequence prediction is carried out on the SSR correction number through the prediction model, the problem of positioning precision jump caused by satellite correction data interruption is effectively solved, it is guaranteed that PPP positioning continuously outputs centimeter-level precision results under the scenes of signal shielding, network delay and the like, and the limitation that traditional PPP strongly depends on real-time SSR data is broken through.

The invention discloses a storage and calculation separation key value storage delay optimization method based on prediction driving, and aims to solve the problems of performance bottleneck and high tail delay caused by passive management and high network delay in a key value storage system in a storage and calculation separation scene. The method comprises the following steps: time load prediction: deploying a time sequence prediction model at a client, and predicting a future read-write request based on a historical access sequence; active cache prefetching: according to the predicted read request, actively preloading hotspot data from a server side to a client side for caching so as to improve the cache hit rate and hide network delay; active write-in optimization: according to the predicted write request, executing maintenance at a server side through predictive pre-insertion and active node splitting, and moving high index structure adjustment overhead out of a key request path to eliminate a write delay peak; and structure sensing batch synchronization: pre-fragmenting a local write buffer by using a server index top layer model of a client cache, and combining multiple independent remote insertion operations into one efficient batch update to reduce data synchronization overhead. Compared with an existing passive management system, the characteristics of model prediction and active cooperation are fully utilized, and the average delay and the tail delay of the system are reduced.

The invention relates to the technical field of computers, and discloses a proxy gateway scheduling method and device, a storage medium and computer equipment, and the method comprises the steps: receiving and analyzing a user request, determining a target processing demand, and determining a plurality of candidate service nodes meeting the target processing demand from schedulable service nodes; obtaining a first geographic position of the user and a second geographic position of each candidate service node, and calculating a target distance between the user and each candidate service node based on the first geographic position and the second geographic position; obtaining a network delay rate, node load data and node service quality data of each candidate service node, and calculating a comprehensive score of the candidate service node based on the target distance, the network delay rate, the node load data and the node service quality data; and determining a target service node from the plurality of candidate service nodes according to the comprehensive score, and scheduling the request to the target service node. The method can be applied to the fields of financial science and technology and medical health.

The invention relates to the technical field of cloud computing, and particularly provides a data cache prefetching method and device, a storage medium and a program product. According to the method, file access characteristics representing file access preferences of a user are obtained based on historical access data of the user, a target file meeting a cache prefetching strategy is determined based on the file access characteristics, and the target file is cached and prefetched. By dynamically analyzing the historical access data of the user, the system can adjust the cache content in real time, actively load the data possibly required by the user and ensure that the high-frequency access data is always available, and the mechanism not only improves the data access speed, but also remarkably reduces the influence caused by network delay.

The invention discloses a large model deployment method based on heterogeneous resource scheduling and multi-model collaborative reasoning, and relates to the field of artificial intelligence and server system deployment. Comprising the following steps of: 1, calculating a deployment score of a target model by utilizing a deployment scoring function according to a GPU video memory residual rate, a CPU load, network delay and a current task quantity of each node through a heterogeneous resource sensing scheduler, and selecting a node with the highest score of the target model to load or reuse the target model; a cache index key is generated, and whether key value KV pairs supporting multiplexing of the target model exist or not is inquired; step 3, if the cache is hit, skipping a forward propagation stage, and directly decoding based on a cache result, otherwise, executing complete forward reasoning and writing the result into the cache; 4, based on a plurality of user requests, performing fusion according to user priorities, model weights, reasoning costs and cue word lengths to generate execution sorting weights, and scheduling execution models in batches according to the weights; and 5, returning the model response to the user, and releasing or updating node state information.

The invention discloses a dual-channel adaptive network stability testing method and system, and belongs to the technical field of computer network testing. The system adopts a dual-channel asynchronous communication architecture in which a control channel and a data channel are physically separated, instructions and state information are transmitted through an independent TCP control channel, and high-speed test data streams are transmitted through a UDP or TCP data channel. The method comprises the following steps: dynamically and adaptively adjusting a timeout parameter based on real-time network delay detection; nanosecond constant interval packet sending control is realized by using a high-precision monotonic clock source and busy waiting compensation mechanism; a packet loss detection, positioning and ACK confirmation mechanism based on the serial number is realized in an application layer; and real-time statistical display and idle detection automatic termination are realized through multi-thread parallel processing. According to the invention, the precision, the stability and the adaptive capability of network testing are remarkably improved, and the method is suitable for long-time stability testing in various scenes such as a high-speed Ethernet, a satellite link, an industrial Internet of Things gateway and the like.

The invention discloses a medical network collaborative penetration test system and method based on artificial intelligence, and belongs to the technical field of medical information security, and the method comprises the steps: integrating a hospital intranet, medical equipment, cloud data center data and historical network delay data to construct a medical network data center; analyzing association between network vulnerabilities and delay data through a graph neural network, constructing an attack path analysis model containing delay weights, and quantifying risk scores to generate a test target priority list; defining a protocol vulnerability and a delay threshold, designing a test scene and generating an attack vector, matching an attack tool to perform a simulation test, calculating a vulnerability triggering success rate and evaluating a risk; generating a penetration test case according to the test target priority list and the risk level, executing multiple rounds of attack tests and generating a report; and monitoring the change of the vulnerability triggering success rate by dynamically adjusting the delay parameter, setting an early warning threshold value, and monitoring and sending a risk early warning notification in real time.

The invention relates to the field of log burying point downloading, and discloses a front-end log classified downloading method and device and a storage medium. The method comprises the steps that a log collection system collects a DOM event triggered by a user behavior, and behavior JSON data is generated; the data processing coordinator performs data filtering processing on the behavior JSON data according to a preset filtering rule to generate filtered JSON data; based on a preset classification algorithm, performing label classification processing on the filtered JSON data to obtain classified JSON constraint data; the storage system performs hierarchical encryption of metadata and content on the classified JSON constraint data to obtain encrypted data; and the transmission system receives a log downloading request, queries corresponding log data from the persistent queue based on the log downloading request, and obtains requested log data. In the embodiment of the invention, log collection, data processing and storage are integrated at the front end, so that the conditions of network delay and data loss are reduced, and the classification efficiency of the log data is improved by utilizing the classification labels for marking.

The invention relates to the field of virtual reality simulation, and discloses a virtual laboratory simulation system and method based on the VR technology, and the method comprises the steps: obtaining user interaction data through a sensor, carrying out the preprocessing, transmitting the user interaction data in real time through a network communication protocol, carrying out the data fusion and synchronous processing, and updating scene data, transmitting the updated scene data to the user; a scene synchronization value is calculated through network delay and equipment performance difference, scene data is fused with space-time three dimensions to perform synchronization verification, and a scene state synchronization algorithm based on a distributed hash table is adopted to monitor the change of a scene state and perform dynamic adjustment; detecting and compensating the operation delay of the user, establishing an operation delay model, predicting the operation action of the user, and executing the operation action in advance in the virtual scene; according to interaction data of an object in a virtual scene, an interactive friction tactile feedback signal is generated in real time, and tactile feedback is controlled by combining a pressure sensor and vibration motor hardware equipment.

The invention discloses a distributed file backup method for a sonar database system, which belongs to the technical field of sonar data processing and comprises the following steps: acquiring file data containing underwater acoustic signal data from the sonar database system; fragmenting the file data to generate data blocks and dynamically adjusting the sizes of the data blocks; generating a unique identifier for the data block based on the content hash value and the metadata; selecting distributed storage nodes based on availability, storage capacity, network delay and geographical distribution factors; the transmission data block uses an encryption protocol; updating the storage position and state of the backup metadata record; periodically verifying the integrity and availability of the backup data; executing a data recovery process to recover the original file data; the dynamically adjusted backup policy is based on real-time operation requirements and data importance levels. According to the method, through distributed storage and dynamic optimization, the backup efficiency, the data security and the system reliability are improved, and the method is suitable for sonar data management in an underwater detection system.

The application relates to a service scheduling and deployment method for Internet of Things edge computing node resources, which is realized based on an Internet of Things edge computing system. The system comprises a cloud data center, a convergence layer core network and a plurality of access layer switch networks connected in sequence. Each access layer switch network is in communication connection with a plurality of edge computing nodes, and each edge computing node is in communication connection with a plurality of data acquisition terminals. The application comprehensively considers the current running state of the edge computing system, network delay problems and running results after deployment, and performs service scheduling and deployment of Internet of Things edge computing node resources on each real-time data stream computing application. The application is suitable for the decision environment of the real-time computing task load and bandwidth dynamic change of the Internet of Things edge computing node, realizes global optimization from the system level, takes into account the single node load capacity, and realizes the optimal scheme and application planning and deployment decision suggestion of the long-period stable operation and real-time task flexible scheduling strategy.

The invention discloses a multi-dimensional information-based adaptive gateway traffic scheduling method, which comprises the following steps of: acquiring server performance indexes, network delay, bandwidth and geographic position data in real time, and establishing a historical database; performance load weights are generated through weighted calculation of CPU, memory and disk indexes, and routing priorities are dynamically adjusted by adopting exponential function mapping; performing normalization processing on network bandwidth, delay and geographic distance, and calculating a service access weight to optimize a transmission path in combination with a step function; analyzing historical traffic data based on an LSTM model to predict a future trend, and generating a traffic prediction weight through a sigmoid function to realize pre-equalization; and combining the three types of weights, weighting to generate dynamic routing configuration, eliminating abnormal nodes in real time in combination with a health check mechanism, triggering high-load early warning and linking a predictive balancing strategy. According to the method, multi-dimensional index collaborative decision-making is realized, routing distribution is dynamically optimized, network delay is effectively reduced, and system throughput and stability in a high-concurrency scene are improved.

The invention discloses a leader node transfer method, a distributed system, electronic equipment and a storage medium, and relates to the field of distributed systems.The method is applied to a leader node in the distributed system and comprises the steps that parameter values of performance parameters of the leader node are determined according to the hardware performance, the load condition and the network delay of the leader node, obtaining a target parameter value; obtaining N parameter values corresponding to N following nodes in the distributed system; determining candidate leading nodes from the N following nodes and the leading nodes according to the target parameter value and the N parameter values; and under the condition of determining to carry out leader node transfer, executing leader node transfer operation, so that the candidate leader node becomes a new leader node in the distributed system. By adopting the scheme, the performance of the distributed system is improved, and the problem that the performance of the distributed system is easy to be poorer due to the fact that the leader node is fixed in a consensus algorithm is solved.

The invention belongs to the technical field of block chains, and particularly relates to a notarist group cross-chain strategy based on a two-stage protocol, and the strategy comprises the following steps: 1, constructing a cross-chain transaction system model composed of a source chain, a target chain and a notarist group, respectively setting public accounts in the source chain and the target chain, and a transaction pool for managing transaction data is established by the notarist group. 2, a request stage: after a transaction failure is detected, a system enters the request stage, key indexes such as network delay, chain load, a notarist response state, exchange rate fluctuation and a historical failure rate are collected, and a feasibility score value of transaction rollback is calculated through normalization processing and a weighting function; the system generates a dynamic rollback threshold value according to the current environment and compares the dynamic rollback threshold value with the score value, if the score value is lower than the threshold value, it is judged that rollback can be executed, and a rollback stage is entered; otherwise, directly turning to the compensation process. 3, the system constructs an on-chain revocation request according to the original transaction, and monitors whether rollback is successful or not within the set monitoring time; if not, retry is tried, and the compensation process is automatically switched to after the threshold value is exceeded. 4, a compensation stage: the system calculates a compensation amount and pushes the compensation amount to the user in combination with the original transaction amount and the exchange rate change, and in-chain compensation transaction is executed after the user authorizes and confirms the compensation amount; and if no response or rejection exists, the transaction compensation process is terminated.

The invention relates to a vehicle lamp control method, computer equipment and a computer readable storage medium, and relates to the technical field of vehicle light control, and the method is applied to a server, and comprises the steps: generating a single vehicle control file of each vehicle participating in a vehicle lamp combined control scene according to the global vehicle lamp configuration information of each frame of picture of the vehicle lamp combined control scene; wherein the single vehicle control file of each vehicle comprises local vehicle lamp configuration information, corresponding to each frame of picture, of the vehicle, and the local vehicle lamp configuration information of different frames of pictures is arranged according to a picture presentation time sequence of the vehicle lamp combined control scene; and sending the corresponding bicycle control file to each bicycle, so that the bicycle performs bicycle lamp control according to the local bicycle lamp configuration information in the corresponding bicycle control file and the global time parameter of the bicycle lamp joint control scene. By adopting the method, the real-time network quality of the vehicle lamp combined control scene performance is not depended on, the requirements on network delay and network bandwidth are reduced, and the performance effect of the vehicle lamp combined control scene is improved.

The invention discloses a federated learning image classification method based on time extension gradient cumulative average. Each client uses a local data set to train a local model to obtain gradient information of a current communication round, and uploads the gradient information to the central server after compression; the client continues to perform local training in a time period from the time when the client starts to upload the compression gradient information of the current communication round to the time when the server completes the compression gradient information aggregation of the current communication round; the central server side aggregates the compression gradient information to obtain average compression gradient information of the current communication round, and issues the average compression gradient information to the client side to update a local model of the (t + beta) th communication round; and repeating the steps until the training is completed, and inputting the to-be-detected image into the local model to obtain an image classification result. The method shows excellent performance under various data sets and different compression levels, has higher convergence speed and model precision, and solves the problem of low communication efficiency caused by network delay and bandwidth limitation in image classification of federated learning.

The invention belongs to the field of artificial intelligence and energy power system cross control, and particularly relates to a coal-fired unit fused salt combined frequency modulation control method and device based on deep reinforcement learning. According to the method, a deep reinforcement learning agent model is constructed, and boiler fuel quantity, steam turbine valve position, fused salt heat charging and discharging power and pump starting and stopping signals are dynamically output based on state space vectors; and tracking precision, thermal stress, emission and coal consumption indexes are fused through a multi-target reward function, and strategy autonomous optimization is realized by adopting a double-Q network, experience playback and target network delay updating mechanism. After deployment, the boiler, the steam turbine and the molten salt unit can be coordinated on line, power space-time decoupling and closed-loop cooperation are achieved, on the premise that main steam parameter stability and equipment safety are guaranteed, the frequency modulation response speed and precision are remarkably improved, the thermal fatigue risk is reduced, and the unit economy and the power grid adaptability are enhanced. And a flexible adaptive adjustment capability is provided for a high-proportion new energy power system.

The invention discloses an efficient double-link block chain consensus method and device, equipment and a storage medium, and belongs to the field of information security. The method comprises an initialization stage, an input stage, a coding stage, a broadcasting stage and a consensus stage. According to the invention, the parallel design of a coding broadcast link and a consensus confirmation link is adopted, so that the occurrence frequency of transaction congestion is reduced; the integrity required by the asynchronous Byzantine consensus process is realized through three-step broadcasting, the updating problem of the post-quantum component does not need to be considered, and the security is higher; an asynchronous network and a Byzantine opponent model are adopted, so that malicious node attacks which do not exceed the total number of nodes can be tolerated on the premise of not setting a network time delay upper limit; in the encoding stage, the generation of the redundancy share through the RS erasure code can be realized by adopting preprocessing, the data redundancy expansion multiple in the broadcasting stage is far smaller than that of the same type of protocol, and the original transaction can be recovered by collecting part of effective shares in the consensus stage, so that the system time delay is effectively reduced, and the system throughput is improved.

The invention discloses a low-delay lossless digital audio transmission method, a low-delay lossless digital audio transmission system and a storage medium, which relate to the technical field of digital audio transmission and are used for monitoring network environment data in real time and dynamically adjusting audio transmission parameters according to changes of network bandwidth Bw, network delay Dr, packet loss rate Pd and network jitter Wd. Comprising an audio transmission rate Rsv, a data packet size Spd and a redundancy coding ratio Rbf. The adaptive adjustment can ensure that the data transmission quantity is reduced and the packet loss probability is reduced when the network condition is poor, and meanwhile, the transmission efficiency is improved and the audio quality is optimized when the bandwidth condition is good. Compared with a traditional fixed transmission parameter method, lossless audio transmission can be achieved under different network conditions, and meanwhile unnecessary delay is reduced. According to the invention, intelligent compression of the audio data is realized, the data volume is reduced under the condition that the bandwidth is limited, the network burden is reduced, and the stability and integrity of audio transmission in a low-bandwidth environment are ensured.

The invention provides a message sorting method and device, electronic equipment and a program product, original messages are obtained through a distribution thread pool, the messages of the same business process are distributed to the same to-be-sorted queue, a working thread of the sorting thread pool reads the messages and stores the messages in a thread local cache set, the sendability is judged based on the dependency relationship of the messages in the business process, and the message sorting efficiency is improved. The messages meeting the conditions are sent to the subscription queue, and the messages not meeting the conditions are reserved in the cache. According to the mechanism, the problem of disorder of messages of the same service caused by multiple switches and network delay of message middleware is effectively solved, and data inconsistency and service abnormity are avoided; unified sorting logic replaces repeated development of subscribers, so that the system complexity is reduced; through thread pool division, local cache storage and a cache strategy of messages which do not meet conditions, the interaction overhead is reduced, the message orderliness and low delay requirements are considered in a high-frequency transaction scene, and the processing throughput, the real-time performance and the resource utilization rate under high concurrency are improved.