3622 results about "Multiple device" patented technology

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention belongs to the technical field of power distribution network voltage regulation and control, and discloses a distributed photovoltaic complex power prediction-cluster-cooperation-based power distribution network voltage regulation and control method, which integrates photovoltaic historical data, inputs an improved back propagation neural network model and outputs predicted photovoltaic active power output. Estimating the reactive capacity boundary of each node in real time based on the running state of the network-following inverter; dividing a distributed photovoltaic cluster by establishing a two-dimensional modularity function of a net load index and an equivalent electrical distance; a multi-device differential cooperative control strategy is provided for the voltage out-of-limit risk in the cluster; and constructing an optimization function with minimum network loss and voltage offset as a target, and optimizing and solving the function by using an improved multi-organization particle swarm optimization algorithm to obtain a multi-device adjustment sequence and a device action amount. According to the method, the renewable energy consumption capacity is improved and the network loss is reduced while the voltage stability of the power distribution network is ensured, and the comprehensive adjustment cost is optimized.

The invention relates to a multi-modal large model dynamic compression and reasoning optimization method based on a MoE architecture. The method comprises the following steps: establishing an edge computing system conforming to medical equipment specifications, constructing a medical image analysis network based on an improved hybrid expert MoE architecture, and adopting a three-layer cascade structure of a feature coding layer, a dynamic routing layer and an expert execution layer; executing expert module dynamic loading and video memory optimization; executing knowledge graph compensation and domain knowledge injection; executing hardware instruction level optimization and calculation acceleration; executing multi-expert feature fusion and decision weighting; performing diagnosis result generation and confidence evaluation; performing real-time data return and model iterative optimization; executing multi-device cooperation and load balancing; executing system security monitoring and exception handling; and generating a structured diagnostic report. The problem that the precision loss of a multi-modal large model is difficult to meet actual requirements is solved, and medical feature adaptive dynamic compression, medical hardware collaborative energy efficiency optimization and cross-modal compensation of medical knowledge enhancement are realized.

The invention provides an information transmission device and implementation method based on multi-protocol dynamic conversion and data fusion, and belongs to the technical field of information transmission, and the device comprises a multi-protocol communication interface module, a data analysis unit, a dynamic protocol conversion engine, a data fusion unit, a data assembly reporting unit and a log storage module. According to the method, information transmission is realized by initializing an interface, analyzing multi-protocol original data, dynamically converting the multi-protocol original data into a uniform format, fusing de-duplicated data, packaging, reporting and recording logs. The device and the method solve information interaction obstacles of different protocol devices, support bidirectional conversion of multiple industrial protocols, realize cross-protocol device collaboration and centralized management, improve the system intelligence level, and are suitable for scenes requiring multi-device collaboration, such as electric power, intelligent transportation, intelligent buildings and the like.

The invention relates to the technical field of smart power grids and artificial intelligence, in particular to a substation autonomous intelligent inspection method and system based on multi-modal data, and the method comprises the steps: obtaining inspection data of multiple modals, and generating fusion features; identifying system alarm information; performing intention recognition and task classification to generate an executable task sequence; generating a multi-device cooperative scheduling scheme; executing the multi-device cooperative scheduling scheme; iterative optimization is carried out; according to the intelligent inspection method provided by the invention, more accurate and more robust multi-mode perception and diagnosis are realized, and deep understanding of complex instructions and safe and efficient cooperation of multiple devices are also realized; and meanwhile, through dynamic re-planning and a verification type feedback learning mechanism, high real-time performance and robustness are ensured, and meanwhile, the system is endowed with the capability of iterative optimization.

The invention relates to a training method, a data processing method, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The training method comprises the steps that a training sample is input into a to-be-trained model, forward propagation is executed, output of the to-be-trained model is obtained, the to-be-trained model comprises a plurality of expert layers, and a specific activation value is not stored in the forward propagation process; determining the value of a loss function according to the output of the to-be-trained model; according to the value of the loss function and the loss function, back propagation is executed, in the back propagation process of each expert layer, a recalculation task of a specific activation value and a first communication task are executed in an overlapping mode, and for each expert layer, the first communication task comprises a communication task of data interaction among multiple devices corresponding to the expert layer.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The invention discloses a multi-device cooperative control method applied to deployment of an embedded real-time operating system, and relates to the field of embedded real-time operating systems, which comprises the following steps of: S1, constructing a real-time dependency relationship graph of a task link, a multi-device cooperative task is disassembled into sub-task link units including an execution path, a real-time constraint threshold and a cross-device communication dependency relationship. According to the multi-device cooperative control method applied to the deployment of the embedded real-time operating system, the problems of task priority conflict and resource utilization imbalance in a multi-device heterogeneous environment are effectively solved through a dynamic priority transmission mechanism and a distributed resource elastic perception model. The system can sense equipment load, communication link stability and task real-time requirements in real time, dynamically adjust sub-task weights and execution paths, ensure conflict-free transmission of end-to-end delay constraints, optimize the global resource utilization rate and avoid the problem of equipment overload or idle caused by traditional static scheduling.

The invention provides a distributed energy access-oriented intelligent electric meter multi-device collaborative optimization method and device, equipment and a medium, and the method comprises the steps: carrying out the interpolation of a unified electric meter, an inverter and energy storage data granularity, building a joint time error model, obtaining calibration reference data, and completing the multi-device time synchronization calibration; based on the reference data, acquiring operation data of three devices, and using a joint energy consumption model to distinguish electric meter hardware faults and multi-device collaborative anomalies to complete fault diagnosis; calculating local line loss by combining an inverter-to-ammeter line and electric quantity data, verifying residual electricity metering deviation, and tracing and correcting settlement data to complete line loss-bidirectional metering linkage verification; associating the fault result with the corresponding relation between the long-term statistical fault type of the metering data and the deviation rate and the line loss to establish a model to predict deviation trend early warning; and determining a multi-device cooperative abnormal source and transformer area load interference relation, calculating an interference coefficient, feeding back a scheduling system, and marking a line reconstruction priority. By adopting the method, the distributed energy consumption efficiency and the operation reliability of the power distribution network are improved.

The invention relates to the technical field of equipment cooperative control, and discloses a multi-equipment cooperative control method and system under an autonomous cooperation algorithm, and the method comprises the steps: collecting the physical attribute data, real-time operation data, production task data and a smart factory scene graph of operation equipment under a multi-equipment cooperative task in a smart factory application; based on the physical attribute data and the real-time operation data, identifying a space conflict mode and a time conflict mode of the operation equipment to obtain collaborative conflict information; according to the production task data and the collaboration conflict information, carrying out collaboration conflict decoupling on the operation equipment to obtain a feasible collaboration mode; performing obstacle avoidance reinforcement learning on the operation equipment by using the smart factory scene graph to construct an obstacle avoidance global path of the operation equipment in the smart factory; and based on the feasible coordination mode and the obstacle avoidance global path, constructing a target coordination control scheme of the operation equipment so as to perform coordination control on the operation equipment. According to the invention, the reliability of multi-device cooperative control under the autonomous cooperation algorithm can be improved.

The invention relates to a multi-device cooperative control method and device based on a user state and an air conditioner. The method comprises: monitoring a sleep state of a user; and determining a sleep stage of the user based on the sleep state, and performing cooperative control on each device according to a control strategy matched with the sleep stage. The method and the device can adapt to the difference of dynamic sleep stages, and control the devices to operate cooperatively according to the control strategies matched with the different sleep stages, so that the self-adaptive adjustment and parameter adjustment precision of the devices is improved, and the problems of poor sleep mode adjustment effect and low adaptability in the prior art are solved.

The invention relates to the technical field of equipment collaboration, and discloses a multi-equipment collaboration control method and device, computer equipment and a storage medium, and the method comprises the steps: building a session channel with collaboration equipment according to the authentication information of main control equipment; acquiring operation state parameters of the collaborative devices based on the session channels, and generating a permission allocation matrix according to the operation state parameters of the collaborative devices; and processing task requirements of the main control equipment by utilizing the permission allocation matrix, generating a differential instruction set, and executing synchronous control on the collaborative equipment according to the differential instruction set. According to the invention, the problems of authority conflict, inflexible task allocation and incapability of fast and automatic switching after the main control equipment fails when multiple devices cooperatively control the cloud mobile phone instance in the prior art are solved.

The invention discloses a deep and far sea cage culture typhoon disaster prevention and after-disaster rapid recovery method, which comprises the following steps of: carrying a high-definition camera by using an unmanned aerial vehicle to aerially photograph a cage water surface structure before a disaster, acquiring an underwater image by an underwater robot through sonar hierarchical scanning and the camera, and generating structure data and fish school data before the disaster; weak points such as frames, nettings and anchor chains are analyzed through three-dimensional modeling and are repaired and reinforced, the stocking density is optimized in combination with typhoon information, the risk of facility damage and fish loss is reduced, structural data and fish school data are repeated after a disaster, and the pre-disaster standard serves as a reference to compare and evaluate the frame damage degree, the nettings damage degree, the anchor chain breakage degree and the fish school escape or death degree; and generating a graded repair scheme and a disaster relief plan. According to the method, the detection precision and the evaluation efficiency are improved through multi-device cooperation and data fusion, the damaged part is rapidly positioned, the recovery strategy is optimized, and an efficient disaster prevention and reduction and after-disaster rapid recovery solution is provided for deep and far sea cage culture.

The invention provides an automatic inspection method and device and a readable storage medium, and relates to the technical field of operation and maintenance management, and the method comprises the steps: automatically logging in a plurality of devices through a pre-written automatic login script of terminal simulation software, and executing an inspection command; obtaining an original log output by the plurality of devices after executing the inspection command, and storing the original log in a local database; preprocessing the stored original log, and extracting a key index in the preprocessed original log by using a preset index extraction rule; and carrying out anomaly detection on the extracted key indexes to obtain the anomaly indexes and cross-device association anomaly results of the corresponding devices. According to the method, the device and the readable storage medium, the problems that the manual inspection efficiency is low, the log analysis intelligence degree is insufficient, intelligent assistance is lacked and potential safety hazards exist in data circulation due to the fact that an existing inspection technology mainly depends on manual operation or a basic script tool can be solved.

The invention discloses a cold station multi-equipment combination energy efficiency optimization method and system, relates to the field of cold station energy efficiency optimization, and aims to solve the problems of low energy efficiency and response lag caused by manual adjustment. The method comprises the following steps: S1, acquiring key operation data (power, state, temperature, flow and frequency) of a water chilling unit, a cooling tower and a water pump and external environment data (temperature and humidity, wind speed and load prediction) in real time; s2, constructing an equipment energy efficiency model based on historical data, and quantifying the change of the equipment efficiency along with the load rate, the temperature difference and the environment temperature; s3, according to the current load and environment prediction, simulating and traversing the energy consumption of the equipment start-stop combination and the water pump frequency combination by applying a Monte Carlo algorithm, and screening a combination strategy with the lowest total energy consumption of the system; s4, converting the optimization strategy into a control instruction, and issuing the control instruction to equipment for execution; and S5, comparing the actual energy consumption with the predicted value, and if the deviation exceeds 5%, correcting model parameters by adopting a least square regression algorithm to realize closed-loop optimization. And the energy efficiency and the response speed of the cold station are obviously improved.

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention discloses a digital twinning-based heterogeneous body-equipped intelligent equipment collaborative management system and method, and belongs to the technical field of digital twinning and body-equipped intelligent systems. The system comprises a digital twin platform, a communication module, a task scheduling module, a cooperative control module and an edge computing node. The digital twinborn platform constructs a high-fidelity three-dimensional model of a physical environment and digital twinborn bodies of the intelligent devices with the bodies; the communication module is used for realizing bidirectional data interaction between the platform and the intelligent equipment; the task scheduling module performs task allocation and path planning by adopting a reinforcement learning algorithm based on the global dynamic situation map; the cooperative control module models an equipment space-time relationship through a graph neural network, predicts conflicts in real time and generates a dynamic avoidance strategy; and the edge computing node carries out real-time preprocessing on the equipment sensing data. According to the method, global optimization scheduling and multi-device intelligent cooperation are realized, and the overall efficiency, robustness and adaptability of the system are remarkably improved.

The invention relates to the technical field of environment intelligent regulation and control, and discloses a slaughter workshop environment temperature and humidity intelligent regulation and control system. The system comprises a data acquisition subsystem used for performing heterogeneous Internet of Things sensor data acquisition on a slaughter workshop to obtain a slaughter process sensing data set; the directed graph modeling subsystem is used for modeling to obtain a slaughter process flow directed graph model; the environment interference prediction subsystem is used for performing process time sequence environment interference prediction of the first stage to obtain an environment interference prediction result; the multi-objective optimization subsystem is used for carrying out control parameter multi-objective optimization in the second stage to obtain a multi-device cooperative control parameter combination; and the multi-device cooperative control subsystem is used for creating an Internet of Things remote device control instruction and issuing the Internet of Things remote device control instruction to the fresh air system, the dehumidification system and the refrigeration system. According to the invention, the precision, efficiency and robustness of the environment temperature and humidity control of the slaughter workshop are improved.

The invention discloses a networking intelligent key dynamic key management system supporting multi-device binding, particularly relates to the technical field of Internet of Things security, and is used for solving the problem that a cryptographic attack path is exposed due to key residue of an existing dynamic key management system. According to the system, an instruction analysis module receives an equipment unbinding instruction and extracts an equipment identifier; the residual positioning module marks residual nodes based on the identification deep search key tree; the fragment confusion module divides the key fragments into length non-geometric progression fragments and generates confusion fragments through elliptic curve encryption; the pollution evaluation module calculates a mutual information value of the confusion fragments and a parent key, and determines a hierarchical pollution coefficient in combination with a key entropy attenuation curve; the path decision module segments or recombines a key path according to a pollution coefficient threshold; and the key updating module constructs a new key tree, distributes updated sub-keys to the binding equipment, clears key residual relevance and blocks generation of a cryptographic attack path.

The invention relates to the technical field of power system monitoring and intelligent scheduling, and discloses a power grid equipment topology dynamic scheduling method and system.The system firstly obtains operation data and spatial data of power grid equipment, and performs data fusion on differences among multi-source heterogeneous data based on the operation data and the spatial data; and then, dynamically adjusting / optimizing the initial power grid topological structure of the power grid equipment through the fusion data after the data fusion processing, and generating a target topological structure. And on the basis, fault prediction or monitoring is performed on nodes or edges of the target topological structure by adopting fusion data. According to the invention, through the multi-source data fusion and dynamic topology reconstruction technology, real-time monitoring, data transmission and scheduling optimization of the power grid state are realized. The technical problem that in the prior art, adaptability of an emergency plan to a complex external environment is not considered, and how to optimize scheduling efficiency in a multi-device cooperation scene is not considered, so that power grid equipment is difficult to schedule in the multi-device cooperation scene is solved.

The invention provides an energy efficiency optimization method and system based on a multi-device coupling relationship, and is applied to the field of data processing. According to the method, the change rate of the collection frequency is dynamically judged by combining various working parameters related to the energy consumption equipment and the coupling relation, accurate regulation and control are performed based on the frequency peak value, the minimum holding time period and the change coefficient, quick response can be performed when the frequency fluctuates abnormally, data over-collection or data missing is avoided, and the accuracy of data collection is improved. And furthermore, by combining emergency process matching and state flag bit identification, the acquisition tasks are layered according to safety influence degrees, and high-frequency monitoring in a high-risk state and low-frequency acquisition in a low-risk state are realized, so that data integrity and resource saving are both considered.

The invention discloses a storage cluster, a system, a data processing method, equipment, a medium and a product, and relates to the technical field of data storage. A plurality of devices are arranged in a controller unit, a switch unit and an extended memory unit; a plurality of switches of the switch unit are correspondingly connected with a plurality of extended memories of the extended memory unit, and copy data of a plurality of controllers are respectively stored in the extended memories. The technical problem that the high availability of a storage system is poor due to the fact that the expansibility of the number of controllers and the number of data copies is poor is solved, and the purposes that memory cache data of the controllers are decoupled from DRAM memories in the controllers to external extended memories, deployment operation is simplified, N-1 controllers are allowed to fail at the same time, and the reliability of the controllers is improved are achieved. The data access processing can be completed under the condition that the M-1 switches simultaneously fail and / or the extended memories corresponding to the P-1 data copies fail, so that the technical effect of high availability of the storage system is improved.

The invention belongs to the technical field of intelligent monitoring and control, and relates to an intelligent monitoring and control system for an agricultural greenhouse. According to the method, multi-environment factor data in a greenhouse is collected, regulation and control actions are encoded into vectors and sampled to construct a sample set, after regulation and control are executed, an environment fluctuation convergence state is detected to construct an action-environment mapping relation, and then candidate actions are generated based on an optimal interval of a crop stage; and selecting an optimal regulation and control combination through a prediction model and real-time state scoring. The system solves the problems of dependence on manpower, slow response, low precision, difficulty in multi-device cooperation, difficulty in dynamic adaptation to crop growth stage changes, low stability and energy efficiency and the like in traditional greenhouse environment regulation and control, and can realize rapid and accurate regulation of multiple factors such as temperature, light, water, gas and the like; and the environmental control automation level, the crop growth quality and the resource utilization efficiency are remarkably improved.

The invention discloses a multi-device high-resolution audio stream intelligent pushing method and device, equipment and a storage medium, which are applied to a multi-device audio system, and the method comprises the following steps: acquiring a current audio stream format and adjusting system working parameters; dynamically adjusting a multi-stage buffer area based on a tone quality priority strategy; operation parameters of multiple devices are synchronously monitored in real time, and data are sent in advance for the device with large delay based on the delay characteristic and the synchronization capability of each device in the system; and dynamically switching system service levels based on the quality of the current audio stream. A protection strategy can be flexibly adjusted according to different audio formats through a format perception pre-detection thread mechanism; the sound quality is kept to the greatest extent while the stability is ensured by the multi-stage buffer region protection with the sound quality priority; a multi-device coordination and timing monitoring mechanism ensures perfect cooperation with a third-party device, and the stability challenge in high-resolution audio transmission is comprehensively solved.

The invention relates to a world model construction method, in particular to a high-fidelity lightweight world model construction method for an end-to-end automatic driving test, which is used for constructing a high-fidelity world model and performing knowledge distillation on the world model to solve the problems of huge parameters, low reasoning efficiency and the like of the world model. Model parameters are reduced on the basis of reserving the world model generation capability, and the reasoning efficiency is improved; a CUDA operator is developed in a user-defined mode for the bottleneck part of world model calculation, video memory allocation is optimized, and the reasoning efficiency of a high-fidelity world model is improved based on a single-device multi-thread scheduling and multi-device cooperative calculation method. According to the method, the high-fidelity lightweight world model for the end-to-end automatic driving test can be constructed, the problems that an existing world model is low in multi-modal information alignment precision, poor in cross-view and cross-frame consistency, low in reasoning efficiency and the like are effectively solved, the confidence coefficient of the end-to-end automatic driving system test process is improved, and the test efficiency of the end-to-end automatic driving system is improved. And the testing efficiency of the end-to-end automatic driving system is greatly improved.

The invention discloses a water-cooling energy consumption adaptive regulation and control method suitable for a data center, and the method comprises the following steps: obtaining a multi-dimensional operation parameter set of the data center in real time, and generating a real-time thermal load density distribution diagram reflecting the heat distribution condition in a machine room; calculating the overall thermal deviation degree and the local hot spot intensity, and solving through a dynamic energy efficiency optimization function to obtain a target system supply and return water temperature difference and a target cooling liquid flow; inputting into a hydraulic and thermodynamic simulation model, and generating an optimal multi-device coordinated regulation instruction set; and executing the multi-device coordinated regulation instruction set, and then performing parameter calibration on the hydraulic and thermodynamic simulation model. The method has the following advantages and effects that the thermal environment of the machine room can be sensed in real time, the energy consumption and heat dissipation requirements can be intelligently balanced, and multiple devices in the cooling system can be cooperatively regulated and controlled, so that the overall energy consumption of the cooling system is optimized while the local overheating risk of the machine room is thoroughly eliminated.

The invention relates to the technical field of data compression, in particular to an intelligent data compression method and system based on multi-protocol heterogeneous equipment interconnection. The method comprises the following steps: when it is detected that a new heterogeneous device applies to join a network, carrying out communication protocol real-time analysis and protocol conversion adaptation to obtain a heterogeneous device adaptation protocol; carrying out multi-device data stream receiving according to a heterogeneous device adaptation protocol, and generating data streams of a plurality of time windows; adaptive coding and multi-level compression are carried out on the data streams of the multiple time windows, and a multi-device adaptive compression mechanism is constructed; identifying compression load characteristics of each heterogeneous device, and performing dynamic task allocation to obtain an intelligent task allocation strategy; and driving real-time data intelligent compression operation based on an intelligent task allocation strategy and a multi-device adaptive compression mechanism. According to the invention, efficient and adaptive data compression of different devices is realized, and the data transmission efficiency and quality are improved.

The invention relates to the technical field of smelter equipment control, in particular to a smelter feeding multi-equipment cooperative control method, system and equipment, and the method comprises the steps: constructing a real-time communication network between smelter feeding equipment, synchronizing the clock signals of the smelter feeding equipment, and scheduling the transmission time sequence of control instructions according to the priority; continuously collecting operation state data of each smelting furnace feeding device according to a real-time communication network, and generating a device operation index according to a preset abnormality judgment rule; when the device operation index indicates that the first device is in an abnormal state, dynamically allocating a control task of the first device to at least one second device based on a current topological structure of the real-time communication network, and adjusting a link connection relationship of the real-time communication network; and according to the instruction interface type of the target equipment, converting the cooperative control instruction into an instruction format adaptive to the target equipment. According to the invention, the problems of multi-device communication delay, high abnormal detection misjudgment rate and low protocol conversion efficiency in a complex environment are effectively solved.

Methods and systems are provided for improving remote computing experiences. The methods leverage modified PC or console architectures, which enable both local and remote console connections. The systems operate with multiple devices through a peer-to-peer connection. In the context of gaming, the systems are engineered to address obstacles such as low upstream rates and limited bandwidth or quality of service. An AI trained on play style is used as a substitute for a low bandwidth client. Also, methods are provided for inviting remote players, transferring control to a remote user, managing controller connections, and addressing low bandwidth situations using adaptive bitrate. The enhancement of connection, single encoding, and transmission processes contributes to a superior computing and / or gaming experience for all players, irrespective of their bandwidth or quality of service. Related networks, models, apparatuses, devices, techniques, articles, architectures, packets, manifests, tables, data storages, and graphical user interfaces are provided.