719 results about "Distributed control system" patented technology

The invention relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.

The invention relates to the technical field of data acquisition and processing, and provides a real-time data acquisition and processing method and system for a distributed control system, and the method comprises the steps: collecting a data flow in real time through a terminal sensor, analyzing the probability distribution of a data change rate through a sliding window, and obtaining a data change rate; and a sensitivity threshold is intelligently generated by combining dynamic factors such as equipment electric quantity and network load. When data mutation exceeds a threshold value, the system automatically extracts a key change section and generates time, period and variable quantity three-dimensional features, and meanwhile, the frequency domain analysis result is fused to enhance the anti-interference performance. And the event emergency degree is calculated based on the time-space correlation of the spectrum energy entropy and the characteristic parameters, and finally a processing queue sorted according to priorities is formed and the bandwidth is dynamically allocated. According to the method, through multi-dimensional data fusion and resource adaptive adjustment, the response speed of the system to emergencies, the data discrimination precision and the stability in a complex environment are improved.

The invention discloses a dynamic intelligent control method and system for improving the efficiency of a rectifying tower, particularly relates to the technical field of chemical process control, and is used for solving the technical defect that an operation variable is mismatched with a physical state due to the fact that an existing distributed control system cannot recognize a hidden degradation state of equipment. The method comprises the following steps: acquiring temperature gradient distribution of each tower plate of a rectifying tower, thermal load of a reboiler and tower top condensation load parameters in real time, calculating a mass transfer efficiency offset by combining the temperature gradient distribution to generate a scaling trend index, and synchronously calculating a local entropy yield based on the thermal load and the condensation load to generate a thermodynamic irreversibility index. When the double indexes exceed a preset threshold value, it is judged that a recessive degradation state is entered, then a backflow ratio correction coefficient is reversely derived through a dynamic correlation model, and the correction coefficient is input into a distributed control system to dynamically adjust backflow ratio operation parameters; the product purity is maintained, the tower plate scaling process is obviously delayed, and long-term efficient operation of the rectifying tower is guaranteed.

The invention provides a feeding bin intelligent management method and system based on self-adaptive control, and relates to the technical field of feeding bins, and the method comprises the steps: collecting the data of a feeding bin group through multiple sensors, inputting the data into a prediction model to obtain a state prediction result, and correcting the deviation through a Kalman filtering algorithm; a reinforcement learning environment is constructed based on the corrected data, and an optimal control strategy is obtained through deep Q learning network training; the distributed control system generates a control instruction sequence based on a consistency protocol and a model prediction control algorithm to realize cooperative control. According to the invention, intelligent management of the stock bin group is realized, the control precision and the system stability are improved, and the manual intervention cost is reduced.

The invention relates to the technical field of industrial distributed control system architecture, and discloses a dual-redundancy design method for a virtual controller VDPU, which comprises the following steps of: deployment of a four-network-port redundant network architecture: dividing four physical network ports into two groups of independent channels, and simultaneously configuring a network link automatic switching mechanism, the working states of the four network ports are monitored in real time, then double isolation of a physical layer and a protocol layer is achieved, and an independent heartbeat transmission channel is established; main / standby heartbeat monitoring and switching control: the main / standby controller periodically interacts state information packets through a special heartbeat channel, and establishes a three-level fault determination mechanism at the same time to perform seamless main / standby switching; a multi-mode data synchronization mechanism is implemented, namely initialization synchronization, increment synchronization during operation and exception recovery synchronization are started, separation of heartbeat signals and data synchronization can be realized through two groups of independent channels, and the fault-tolerant capability and the switching efficiency of the system are improved, so that the operation stability of the system is guaranteed.

The invention provides a steam extraction energy storage and heat energy storage integrated regulation control method and system, and relates to the technical field, and the method comprises the steps: building a digital twinborn model of a steam extraction energy storage and heat energy storage integrated system, and respectively obtaining corresponding data; establishing a multi-objective optimization model based on the collected data; solving the multi-objective optimization model by using an improved multi-objective differential evolution algorithm in combination with an adaptive variation strategy and a non-dominated sorting mechanism to obtain an optimal adjustment strategy; on the basis of the optimal adjusting strategy, the opening degree of a steam extraction valve of each stage of steam extraction point is adjusted in real time through a distributed control system, and the steam extraction flow is controlled; the inflation power and the deflation power of the compressed air energy storage unit are dynamically adjusted, and meanwhile the charging and discharging power of the flywheel energy storage unit is controlled; the heat charging and discharging rate of the phase change material heat storage unit is adjusted in a self-adaptive mode, and meanwhile the heat charging and discharging flow of the fused salt heat storage unit is controlled.

The invention discloses an electromechanical equipment distributed control system based on the Internet of Things, which belongs to the technical field of distributed control, and specifically comprises the following steps: splitting a control task into task units, registration dependencies, data entries and task levels at an edge gateway, and generating a candidate scheduling range according to registration items; a calibration heartbeat with a reference segment is issued, and a driving node executes and returns a measurement record in an idle window for calibrating hardware fingerprints and link overhead and dynamically updating matching parameters; when computing power saturation is triggered, splitting a low-level task unit into suspensible fragments, setting a de-duplication execution identifier, opening a state interface, and completing migration check; selecting a target control node and a transmission path according to a calibration result, issuing fragment and state interface information, driving a target node to continue fragments according to an execution template, and guiding a source node to release occupied resources in sequence; and checking and comparing the execution result, and when the rule is triggered, recovering to the previous stable state according to the mapping rollback table.

The invention provides an AGC signal intelligent correction and control method, device, equipment and medium, and the method comprises the steps: collecting an AGC instruction signal of a power grid dispatching side, an actual power feedback signal of a power plant side and a unit operation state parameter, and constructing multi-source heterogeneous data; inputting the multi-source heterogeneous data into a pre-trained digital twinborn model to obtain a predicted value of the real power of the unit; calculating a real-time deviation between the predicted value of the real power and the actual power feedback signal, and dynamically compensating the AGC instruction signal based on the real-time deviation to generate a corrected AGC instruction signal; and on the basis of the corrected AGC instruction signal, a feedforward control signal is generated, and is superposed to a basic control instruction of a unit distributed control system (DCS) in an increment form, so that full-load section accurate control is realized, and the problems of accurate correction of the AGC signal, accurate prediction of real power and dynamic compensation are solved.

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

A power door actuation system for a door of a vehicle is provided. The system includes a housing mounted to the door and an actuator mounted within the housing that includes an electric motor configured to output a motor force. The actuator also includes a geartrain with a geartrain input coupled to an output of the electric motor for receiving the motor force and a geartrain output for applying an output force to the door. An extendible member is configured for extension and retraction in response to actuation by the geartrain output for moving the door. The system determines the output force to compensate for external forces affecting the motion of the door, adjusts the output force determined to an adjusted output force to compensate for internal forces affecting the operation of the actuator, and controls the electric motor to move the door at the adjusted output force.

The invention provides an equipment running state optimization control method and device, electronic equipment and a storage medium. The method comprises the steps that historical data and real-time data of equipment operation are acquired, and key features related to the equipment operation state are screened out; preprocessing the historical data, dividing the preprocessed historical data into a plurality of categories, and generating a reference sample set of each category; performing weight calculation on the key features by using an analytic hierarchy process, and constructing a feature weight matrix; performing state estimation by using an improved multivariate state estimation algorithm; predicting the running state of the equipment according to the state estimation result, and judging whether the running state of the equipment is normal or not; and if the operation state of the equipment is normal, writing back an optimal control parameter generated based on the predicted operation state to the distributed control system. The parameter adjustment precision can be improved, the prediction precision can be improved, and the equipment operation efficiency can be improved.

An actuator assembly of an actuation system for a closure member of a vehicle is provided. The actuator assembly includes an actuator housing including a sensor housing. The actuator assembly also includes an electric motor disposed in the actuator housing and configured to rotate a driven shaft operably coupled to an extensible member that is coupled to one of a body or the closure member for opening or closing the closure member. The actuator assembly also includes an actuator controller disposed in the sensor housing of the actuator housing and coupled to electric motor and an accelerometer configured to sense movement of the closure member. The actuator controller is configured to detect the movement of the closure member using the accelerometer. The actuator controller then controls the opening or closing of the closure member based on the movement of the closure member using the electric motor.

The invention relates to the technical field of flow chart automatic testing, and provides a flow chart automatic testing method, system and device of a control system and a storage medium so as to solve the problems of low testing efficiency and poor accuracy. The method comprises the following steps: acquiring a film thickness and transverse tension fluctuation curve by using an intelligent textile sensor array, generating a tension compensation factor based on a phase difference to correct the film thickness, and establishing a thickness distribution topology model; performing non-contact scanning by using a terahertz wave emission array, and activating a defect mark cluster when Doppler frequency shift is detected; transmitting the thickness distribution model and the defect characteristics to a test platform, reconstructing a control logic and generating a test script, and realizing a virtual-real synchronous test through a communication protocol; and generating a process parameter correction factor according to a test result, and feeding back the process parameter correction factor to the distributed control system to optimize the tension closed-loop control module, and generating a matched terahertz verification map. According to the technical scheme provided by the invention, the efficiency and accuracy of automatic testing of the flow chart can be improved.

The invention discloses a DCS operation and maintenance method based on fusion dynamic weight distribution and fault chain deduction, and relates to the technical field of operation and maintenance of a distributed control system, and the method comprises the steps: obtaining a plurality of groups of source data of a monitoring region; corresponding monitoring periods are determined according to the multiple groups of source data, and differential monitoring period information is obtained after combination; according to the technical key points, a multi-source data fusion module is utilized to associate data of a DCS system, an associated production system and a cross-plant cooperative system, and fragmented data such as equipment operation data, production load fluctuation and cross-plant interaction information are woven into a complete data network through adjustment of space-time alignment and multi-dimensional verification; the global sensing capability not only solves the one-sidedness of traditional single data judgment, for example, environmental interference is easily misjudged by only depending on equipment temperature data, but also can capture cross-system implicit associated faults; and secondly, when the multi-source data are mutually verified, an evidence chain is formed through logic consistency verification, so that the misjudgment rate of suspected faults is greatly reduced.

Systems and methods for industrial robotic platforms. Squads of industrial robots autonomously communicate and work together. A control center may monitor the autonomous operations. Software at the control center, squad, and robot levels forms a distributed control system that analyzes various data related to the platform for monitoring of the various systems. Artificial intelligence, such as machine learning, is implemented at the control center, squad, and / or robot levels for swarm behavior driven by intelligent decision making. Each robot may include a universal platform attached to a task-specific tooling system. A robot may be elected as a squad leader, cartographer leader, or task planning leader. A squad leader, cartographer leader, or task planning leader may perform specialized planning functions for a squad of robots. Information generated by robots performing a task may be transmitted to the control center for further development of machine learning models.

The invention relates to the technical field of distributed control systems, and discloses a distributed valve body remote control system which comprises a valve body state acquisition module, a remote communication analysis module and the like. The valve body state acquisition module acquires parameters such as opening, fluid pressure and surface temperature of a multi-area valve body according to a monitoring period, and a high-frequency monitoring mode can be started; the remote communication analysis module evaluates communication delay; the distributed coordination module synchronizes parameters when communication is normal, and calculates a dynamic regulation factor through a formula to generate a regulation instruction; the instruction distribution module determines an instruction distribution sequence according to a priority weight formula, and is provided with delay and forced distribution strategies; and the abnormal response module gives an alarm and marks the position code of the valve body when the communication delay exceeds the limit or the weight is continuously too low. And the system also checks and complements the acquired data. The system improves the monitoring control precision and reliability, and is suitable for remote monitoring of multi-area valve bodies.

The invention discloses a DCS (Distributed Control System)-based ignition and temperature rise mode of a copper anode slime roasting rotary kiln, which specifically comprises the following steps of: setting initial control conditions, drawing a temperature rise curve according to a target temperature, a temperature rise period and a temperature rise gradient, and then starting ignition preparation. Before natural gas is introduced into the combustion chamber for combustion, rotary kiln operation and combustion-supporting air purging are carried out, after purging is carried out for a period of time, combustion-supporting air and natural gas are introduced for ignition at the same time, and meanwhile the ignition condition is judged through a monitor. And if ignition fails, under the condition of no abnormity, purging can be repeated, and natural gas is introduced for re-ignition. According to the method, the natural gas flow and the combustion-supporting air volume are automatically controlled by the DCS according to the drawn temperature rise curve. The system has the beneficial effects that multiple ignition conditions can be controlled at the same time, remote automatic ignition is achieved, the ignition condition is fed back in time, the natural gas flow is flexibly controlled, the combustion temperature is controlled, the ignition effect of the rotary kiln is guaranteed, and meanwhile production operation safety of the rotary kiln is guaranteed.

The invention belongs to the technical field of distributed control, and discloses an AI intelligent control cabinet distributed control system and method. Comprising the steps of obtaining running state data and control task data; constructing a task priority vector of each intelligent control cabinet in each control period; generating a global task conflict graph based on the task priority vectors of all the intelligent control cabinets in the same control period; obtaining a task conflict probability and a task execution sequence of each intelligent control cabinet in the same control period according to the global task conflict graph; based on the task conflict probability and the task execution sequence, a control task execution strategy of each intelligent control cabinet is adjusted, and an optimized control task sequence of each intelligent control cabinet is obtained; and according to the optimization control task sequence, distributed cooperative operation of each intelligent control cabinet in the next control period is controlled, and the overall operation efficiency and the conflict occurrence rate of the distributed system can be effectively balanced.

The invention provides a visualization method and device for the combustion process of a circulating fluidized bed boiler, and the method comprises the steps: collecting the operation data of the circulating fluidized bed boiler in real time through a distributed control system at an upper computer, and storing the collected data in a CSV format; in the lower computer, a three-dimensional modeling tool is used for establishing a three-dimensional model of the solid boiler, and grid division is carried out; controlling the upper computer to generate boundary condition data of numerical simulation according to the acquired CSV data, transmitting the boundary condition data to the lower computer, and reading the latest boundary condition data by using the lower computer to perform real-time simulation calculation; performing post-processing on a simulation calculation result by utilizing the lower computer to obtain a picture and curve data, and transmitting the picture and curve data to the upper computer through a network; and the upper computer is controlled to display the visual results of the temperature field, the velocity field, the pressure field, the gas component concentration field and the ash combustible content in real time through a platform interface in the forms of static diagrams, dynamic diagrams and trend curves. According to the method, the combustion process of the circulating fluidized bed boiler can be rapidly visualized, the operation state of the boiler can be predicted, and guidance is provided for operation optimization and parameter adjustment.

According to the efficient smelting method and device for the silicon-manganese alloy, the manganese ore and the silica are crushed into proper particle sizes, the contact area between the raw materials is increased, the reaction speed is increased, the raw materials are fully reacted in the furnace, the proportion of the raw materials is adjusted, the reaction is more sufficient, and the qualified rate of products is increased; during smelting, a high-precision sensor is used for monitoring parameters such as temperature, pressure, current and voltage in the furnace in real time, a distributed control system is used for automatically adjusting smelting process variables such as electrode lifting, power supply power and raw material feeding speed according to the parameters monitored in real time, and it is ensured that reaction in the furnace is conducted in the optimal state; smelting hot air is filtered through the filter box and then discharged into the mixing box through the centrifugal suction pump, raw materials are scooped up and fall down while mechanical mixing is conducted, the sufficient mixing and preheating effect is guaranteed in cooperation with hot air introduction, and finally the effects of improving the heat source utilization rate, sufficiently mixing and preheating and efficiently smelting are achieved.

The invention discloses a time correction method and system and a readable storage medium, and relates to the technical field of digital control systems of nuclear power stations. Comprising the following steps: a target device analyzes received timing data to obtain reference time; after the target device obtains the reference time, the system time of the embedded operating system in the target device is obtained; and the target device corrects the system time of the embedded operating system in the target device based on the reference time. Therefore, according to the time correction method, the time correction device is introduced to synchronously send the time correction data to each target device in the distributed control system of the nuclear power station, and the system time of the embedded operating system is automatically corrected based on the unified reference time; the consistency of the system time of multiple target devices in the distributed control system of the nuclear power station is ensured, the precision, efficiency and reliability of time synchronization are remarkably improved, and a powerful guarantee is provided for safe and stable operation of the nuclear power station.

The invention discloses an intelligent deep peak regulation system for a coal-fired power generation unit, and particularly relates to the technical field of power generation control, and the system comprises a multi-source data collection module which carries out the real-time parallel collection of all-dimensional operation parameters through a sensing network and unit decentralized control system interface disposed at a key node, and builds a multi-modal time sequence data flow; according to the method, a foresight control strategy is formed by constructing a fusion prediction model, sensing the running state of a unit in real time and predicting the change trend of key parameters, the defect of response lag of a traditional system is effectively overcome, and the purposes of accurate load tracking and advanced regulation and control under the working condition of deep peak regulation are achieved; a global optimization decision-making mechanism is established by adopting a deep reinforcement learning algorithm, optimal control parameters are calculated in real time through the intelligent peak regulation module, a boiler, a steam turbine and an environmental protection system are dynamically coordinated, the problem of insufficient stability caused by subsystem independent control is solved, and the optimal balance of safety, stability and economic benefits is achieved.

The invention relates to the technical field of distributed control systems, and provides a fault diagnosis and early warning method and system for a distributed control system, and the method comprises the steps: collecting mechanical vibration data, equipment surface temperature field distribution data and power supply current waveform data in real time through deploying a plurality of groups of sensor arrays at key nodes of the distributed control system; and comparing the fault mode with a preset safety threshold and a fault mode in a historical fault case library. And when the data is abnormal, the system tracks a vibration propagation path of adjacent nodes through graph structure modeling, locates a fault source in an amplitude difference direction, and matches an undetermined fault topology network generated in real time with a predefined reference fault topology network. The propagation characteristics of the vibration waveform are utilized to reversely deduce the fault origin, and intelligent diagnosis is realized in combination with a historical fault mode library. The dynamic analysis of the fault trend is realized, and the fault identification accuracy is improved through real-time data comparison and topology matching.

The invention discloses a hydroelectric generating set active power distributed control system under multi-station centralized control. The system comprises a data acquisition system, a centralized control system, an energy management controller and a hydroelectric generating set. The data acquisition system acquires historical active power data and operation state data of all hydroelectric generating sets; based on a preset linear or nonlinear objective function and a constraint condition, the centralized control system performs iterative calculation by using the data acquired by the data acquisition system through a numerical optimization algorithm to obtain an optimal active power distribution scheme among all stations in the hydroelectric generating set; the feasibility of the optimal active power distribution scheme is evaluated through stability analysis; wherein the objective function is to minimize the total energy consumption of a power station or balance the output change, and the constraint condition city is determined according to the limitation condition of safe and stable operation of a power grid, the limitation of a power transmission line and / or the safe operation boundary of each station unit; the centralized control system determines an adjustment instruction of each station unit according to the optimal active power distribution scheme, and sends the adjustment instruction to the energy management controller of the corresponding station for real-time regulation and control so as to ensure the consistency of active power output among multiple stations; the energy management controller compares the difference between the current actual active power and the preset value based on the data monitored by the system in real time, and timely adjusts the control strategy to ensure the stability of the system. According to the technical scheme, the active power distributed control system of the hydroelectric generating set under multi-station centralized control can realize intelligent adjustment and optimization of the hydroelectric generating set through the synergistic effect of the components and the technology, so that the efficiency, the reliability and the sustainability of the whole hydroelectric system are improved.

The invention discloses a coal-fired unit hearth temperature soft measurement method based on data fusion. The method comprises the following steps: acquiring a hearth combustion image and operation data in a coal-fired unit distributed control system (DCS) at the same moment; preprocessing the combustion image; establishing a feature extraction network mixed with deconvolution and interpolation upsampling; adding the structural similarity index loss into a network training loss function; training the feature extraction network to obtain a feature extraction model; extracting an M * N two-dimensional feature map obtained by the last layer of encoder; dividing the two-dimensional feature map into S same areas, and adding feature values in the single areas to serve as representative feature values of the areas; dCS operation parameter characteristics are analyzed and selected, and Q characteristics with the highest correlation degree with the hearth temperature are selected; performing data fusion on the S combustion image features and the Q DCS operation parameters; and establishing a bidirectional long-short-term memory neural network prediction model to predict the hearth temperature. According to the invention, the precision and robustness of the hearth temperature soft measurement model can be improved.

The invention belongs to the field of artificial intelligence and automatic control. The system specifically comprises an in-furnace SO2 prediction model, an out-furnace SO2 prediction model, an in-furnace SO2 removal agent limestone control model, an out-furnace SO2 removal agent slaked lime control model, an in-furnace and out-furnace cooperation model and an early warning and alarm model for monitoring and analyzing the health state of a desulfurization system. The method comprises the following steps: firstly, acquiring real-time data of a desulfurization system associated control system (DCS); transmitting the collected data to an artificial intelligence algorithm system, and analyzing and calculating the data by using artificial intelligence and big data technologies; the calculated strategy is output to a DCS distributed control system, and field equipment is controlled to operate safely and efficiently; and finally, continuing to import the data of the last time period into the algorithm model unit, optimizing the removal process of the sulfur content in the flue gas by using a PID neural network and an operation optimization technology, and training through the model to realize self-optimization and self-adaptive adjustment.

The invention discloses an automatic generation method and system for an electrical principle design drawing of a nuclear safety level DCS cabinet, and relates to the technical field of nuclear power instrument control, and the method comprises the steps: carrying out the modeling of the electrical principle design process of the nuclear safety level DCS cabinet; associating the model of the nuclear security level DCS cabinet electrical principle design process with the input information; the method comprises the following steps of: according to a nuclear security level and classification of DCS cabinet electrical principle design process modeling, making a page macro for each graph page; and according to the model of the electrical principle design process of the nuclear security level DCS cabinet, specific input of a nuclear security level DCS project to be designed is analyzed, and corresponding required page macros are called for batch drawing, so that automatic generation of the electrical principle design drawing of the nuclear security level DCS cabinet is completed. According to the method, the automatic generation of the electrical principle design drawing of the nuclear safety level DCS cabinet is efficiently and accurately realized, the workload in the DCS engineering hardware design process is reduced, the design efficiency is improved, and meanwhile, human errors are reduced.

The invention relates to the technical field of coal-fired units, and discloses a coal-fired unit fault identification method, device and equipment and a medium, and the method comprises the steps: obtaining parameter data of a coal-fired unit, the parameter data comprising operation parameters of a distributed control system in the coal-fired unit, structured data of a coal quality database and environmental data of an environmental sensor; inputting the parameter data into a pre-trained health index prediction model, outputting an equipment health index, and fusing support vector machine anomaly detection and time sequence prediction by the health index prediction model by adopting integrated learning; based on a clustering analysis result of the historical parameter data of the coal-fired unit, setting a self-adaptive early warning threshold value of the coal-fired unit; and if the equipment health index is lower than the self-adaptive early warning threshold value, inputting the parameter data into a pre-trained fault positioning model to predict the fault position of the coal-fired unit.

The invention relates to a distributed calculation method and system for DCS system data based on a multi-task CPU, and the method employs the cooperation of a kernel operation CPU unit, an I / O communication unit and a diagnosis unit, and can achieve the purposes: improving the stability of the system through avoiding the occurrence of a parking event caused by a non-hardware fault of the DCS system; based on a DCS intelligent diagnosis model, the system is responsible for completing software and hardware intelligent diagnosis of a DCS control module and outputting a control strategy to monitor each hardware circuit and software operation condition of the control module so as to realize real-time and accurate diagnosis of the system state, realize intelligent diagnosis of the DCS system and reduce the management difficulty of the DCS system. The multiple CPUs work cooperatively, tasks are processed in a partitioned mode, the operation load of the single CPU can be reduced, work of other parts cannot be affected after the single CPU breaks down, and the operation capacity and stability of the DCS system control module are improved.

The invention discloses a data output method of a distributed control system and a related device, and relates to the field of industrial control, and the method comprises the steps: setting data areas with different output periods in a shared memory, configuring different spin locks for the different data areas, processing obtained target output data by a first core, and outputting the target output data to a second core; and obtaining the spin lock of the target output data area in the shared memory. And writing the target output data into a target output data area, and releasing the spin lock. And an output data notification is sent to the second core, and the second core responds to the notification to obtain the spin lock of the target output data area, reads the data in the target output data area and sends the data to the corresponding IO module through the CAN bus controller. Therefore, according to the scheme, the output data of different periods in the shared memory are protected through different spin locks, the waiting time for obtaining the spin locks is avoided, and the real-time performance of data output is improved. Meanwhile, the utilization rate of SoC resources is improved.