4283 results about "Multiple device" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention provides a real-time transmission method and system based on internet-of-things sensing data in a digital twinborn scene, and the method comprises the steps: collecting and processing vibration spectrum data, pressure gradient change data and temperature distribution data of equipment in an industrial production line, generating a preprocessing data package, carrying out the analysis to obtain an equipment state fingerprint feature and an environment working condition label, and carrying out the real-time transmission of the environment working condition label. A target virtual control parameter and a target error compensation amount are generated by using a dynamic parameter correction loop of the digital twin, an actual material flow state is generated in combination with material flow rate data, an optimization strategy sequence of multi-device collaborative operation is generated by using a distributed computing architecture, and the optimization strategy sequence is converted into a track compensation parameter of an end effector in the device; in combination with the target error compensation amount, data closed-loop transmission is carried out; according to the invention, millisecond-level time domain matching of vibration, pressure and temperature data and material flow velocity is realized through a dynamic timestamp alignment mechanism of 5G edge computing nodes, and the problem of synchronization error accumulation of traditional internet-of-things sensing data is solved.

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 relates to an intelligent equipment access system and access method based on an AI large model, and relates to the field of artificial intelligence and Internet of Things. According to the technical scheme, a dynamically adaptive universal object model access framework is constructed, functional attributes, control instructions and data formats of intelligent devices of different brands and protocols are mapped into a standardized logic model, meanwhile, a multi-mode AI large model engine is combined, high-precision voice recognition, voiceprint feature detection and multi-level semantic intention analysis capabilities are integrated, and the intelligent devices of different brands and protocols can be obtained. And an intelligent interaction closed loop of end-cloud collaboration is formed. Semantic analysis adopts a mixed strategy, a natural language instruction is converted through function mapping, a local knowledge base is utilized to match user scene requirements, and external data is queried by means of cloud networking to optimize instruction logic. According to the scheme, the multi-device access and operation and maintenance cost is remarkably reduced, the semantic understanding accuracy and scene adaptability are improved, and the method is widely applied to the fields of smart home, smart medical treatment and industrial Internet of Things.

The invention relates to the technical field of charging pile power dispatching, in particular to a service area new energy charging management method and system based on multi-device data analysis. The method comprises the following steps: acquiring real-time operation monitoring parameters of all charging piles in a service area; multi-dimensional feature perception and charging behavior time popularity distribution analysis are carried out, and a dynamic charging behavior hotspot map is constructed; carrying out multi-time-point power sampling according to the dynamic charging behavior hotspot map, carrying out power cooperation path topology evolution, and constructing a power cooperation path network; performing charging pile power trend prediction and power peak congestion evolution according to the power cooperation path network, and generating a power peak congestion state feature of the charging pile; and obtaining new energy traffic flow data of the service area, carrying out unit time traffic flow average calculation, and carrying out traffic flow prediction to obtain a traffic flow prediction thermodynamic diagram. Charging power scheduling is carried out through demand prediction, and the operation efficiency and stability of charging equipment are improved.

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 relates to the technical field of sequence control, in particular to a sequence control test system based on the Internet of Things, which comprises a task decomposition module for analyzing equipment load, decomposing tasks according to equipment operation capability, acquiring task requirements and equipment parameters, judging operation conditions of equipment stations, refining task dependency relationships and performing task scheduling on the basis of a system task sequence; and associating the tasks with the equipment parameters to generate an equipment task sequence table. According to the method, efficient coordination of multiple devices under different loads is ensured by adopting a distributed coordination algorithm, task scheduling is more flexible and efficient by analyzing task dependency and state changes of the devices, optimizing the task execution sequence and arranging the execution time and stations of the devices, and the task scheduling efficiency is improved by judging synchronous execution conditions and obtaining time nodes. The synchronous execution sequence of the tasks is coordinated, repeated occupation or idling of equipment resources is avoided, and the stability and task processing capacity of a sequence control system are improved.

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 relates to the technical field of power adapter temperature management, and discloses a gallium nitride power adapter temperature management system comprising a dynamic thermal model module used for accurately predicting the temperature change trend of each key component; the multi-level temperature monitoring network module is used for forming a complete temperature distribution diagram; the multi-device collaborative sensing module is used for generating a sensing result of the whole heat dissipation environment; the self-adaptive air duct structure module is used for optimizing an air flow path; the heat dissipation path module based on swarm intelligence is used for calculating and generating an optimal multi-device collaborative heat dissipation path by applying a swarm intelligence algorithm; the self-adaptive heat dissipation control strategy module is used for realizing multi-target balance optimization of temperature, noise and energy consumption; the problem that heat dissipation is difficult when multiple adapters are used in a centralized mode is solved, heat dissipation efficiency is improved, energy consumption is reduced, and noise is reduced.

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 relates to the technical field of lithium ion battery charging, and discloses a multi-device compatible intelligent fast-charging lithium ion battery system and a protocol matching method, the system comprises a charging device, the charging interface of which is used for being connected with a target device to charge a lithium ion battery pack of the target device; the battery management equipment acquires an initial communication signal of the connected target equipment in real time when the charging interface is connected with the target equipment, so as to extract a voltage fluctuation feature, a handshake time sequence feature and a protocol identification feature for generating an equipment feature vector; performing similarity matching on the equipment feature vector and the protocol feature parameter to obtain a candidate protocol set; according to the historical charging efficiency data of each candidate protocol in the candidate protocol set and the real-time load state of the target equipment, obtaining an optimal charging protocol and a corresponding charging curve template; and charging the lithium ion battery pack according to the optimal charging protocol and the charging curve template. And efficient, flexible and safe charging control can be realized.

The invention relates to the technical field of high-speed digital interfaces, and discloses a JESD204B multi-device deterministic delay synchronization system and method based on dynamic clock phase compensation, and the system comprises a main node which generates and dynamically distributes clock signals, and constructs a dynamic clock tree structure; the slave equipment is used for realizing cascade error elimination through a distributed compensation mechanism; the dynamic phase compensation engine adopts a hybrid phase detection framework and is combined with a digital-to-time converter (TDC) and an analog phase-locked loop (PLL); and the environment parameter compensation module is used for collecting temperature, voltage and operation time in real time, and calculating phase compensation quantity through a formula that delta phi = alpha * (T-T0) + beta * (V-V0) + gamma * delta t. On the aspect of synchronization precision, the phase error of a single device can be smaller than or equal to + / -200ps, the strict scene requirement is effectively met, the environment adaptability is high, the defects of traditional fixed compensation can be overcome, cascade error accumulation can be efficiently restrained through distributed compensation of slave devices, and on the aspect of real-time performance, an online calibration module can conduct real-time calibration without interrupting services.

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 relates to the technical field of new energy power generation, in particular to an adaptive distributed new energy power generation equipment cooperative control method and system, and the method comprises the steps: extracting the state feature representation of each new energy power generation equipment from the operation parameters of the new energy power generation equipment; based on the state feature representation, determining an action boundary estimated value of each new energy power generation device by using a deep reinforcement learning algorithm; extracting an equipment state-action data pair according to the action boundary estimation value, and generating an empirical extended data set by simulating different disturbance amplitudes; based on the empirical extended data set, obtaining equipment operation reward parameters of the new energy power generation equipment by using a weighting method; obtaining a sharing experience correction set based on the equipment operation reward parameters; and iteratively determining a multi-device cooperative control optimization strategy by using a gradient optimization algorithm based on the shared experience correction set and the state feature representation. According to the invention, cooperative control of the new energy power generation equipment under abnormal fluctuation of the power grid voltage is realized, and the stability and safety of the power grid are improved.

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 field of webpage design, and discloses a webpage generation method and system, computer equipment and a storage medium, the webpage generation method comprises the following steps: sending a webpage configuration request to a server at a client and receiving dynamic configuration data and business logic data returned by the server; a predefined node generation rule is matched according to the function module identifier to generate and mount an interactive page element set, and then a browser rendering engine is triggered to generate a visual webpage; meanwhile, when the user operates on the webpage and packages and submits operation data to the server, the server can update the business logic data and the configuration state and return the business logic data and the configuration state to the client again, and continuous dynamic adaptation and real-time updating of the webpage are achieved. Compared with the prior art, the method has the advantages that the compiling quantity of front-end adaptive and compatible codes is effectively reduced, redundant codes and complexity are reduced, the flexibility and expandability in a multi-environment and multi-device scene are enhanced, and the webpage rendering performance and the user interaction experience are remarkably 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.