11657results about "Programme control in sequence/logic controllers" 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 relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention relates to the technical field of automatic industrial control, and discloses an automatic industrial equipment plc data acquisition monitoring control method and system. The method comprises the following steps: receiving a multi-source PLC data stream, and processing the multi-source PLC data stream through a multi-modal data fusion model to generate an equipment health degree evaluation matrix; constructing a dynamic control strategy generation model, and outputting a multi-target optimization instruction set; simulating the interaction influence of the equipment group by utilizing a process chain collaborative constraint model, and optimizing real-time control parameters of an instruction set; parameters are adjusted in parallel through a distributed edge computing framework, and an equipment cooperative control signal is output to an industrial monitoring center. The system comprises a distributed data acquisition module, a multi-modal fusion calculation module, a dynamic strategy generation module, an edge cooperative control module and an industrial communication gateway. According to the invention, multi-source data can be efficiently processed, equipment health can be accurately evaluated, a control strategy is optimized, equipment collaboration is realized, and industrial production efficiency and energy utilization rate are improved.

The invention discloses a green building intelligent lighting and energy collaborative optimization system based on multi-source data fusion. According to the system, firstly, multi-source information such as indoor and outdoor illumination, weather, personnel occupation and electricity price is subjected to weighted fusion through dynamic confidence, and an accurate real-time environment state is constructed; then, model predictive control is combined with a multi-objective optimization strategy, and a sunshade device, intelligent lighting and a heating ventilation air conditioner are cooperatively adjusted, so that the comprehensive energy consumption or operation cost is minimized on the premise that the indoor illumination and temperature comfort requirements are met; besides, the self-adaptive module continuously learns and adjusts the thermal characteristics and the use mode of the building through online model parameter correction and occupancy probability prediction, and it is ensured that the energy-saving effect is stable for a long time; the system drives each execution device through a standard building self-control interface, is easy to integrate in a newly built or reformed project, and can significantly improve the energy utilization efficiency and the indoor environment quality.

The invention provides a building energy-saving control system based on centralized control of the Internet of Things, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting personnel activity data, environment parameter data and equipment state data in a building in real time; the data preprocessing module is used for carrying out cleaning, denoising and time sequence alignment processing; the area use mode recognition module is used for analyzing the use conditions of equipment in different areas in the building according to a multi-layer classification rule; the energy-saving control strategy generation module is used for analyzing equipment in the building and generating a region-level energy-saving control instruction set; the execution control module is used for controlling an air conditioning system, a lighting system and an elevator system in the building in real time; the feedback regulation module is used for monitoring real-time feedback data of the equipment operation parameter set and feeding back to the energy-saving control strategy generation module in combination with environmental parameter data change; according to the invention, autonomy and accuracy of building energy-saving control are improved.

The invention relates to the field of intelligent titration control, and discloses a high-precision intelligent titration control method and system.The method comprises the steps of sample information input and preprocessing planning, sample automatic preprocessing, multi-sensor system calibration and preparation, intelligent titration parameter setting and scheme optimization, and multi-sensor data real-time collection and fusion in the titration process. Titration end point prediction and judgment based on machine learning, result calculation, verification and storage, system self-cleaning, maintenance and state monitoring. Through multi-module collaborative operation and a series of intelligent technical means, the accuracy, the reliability, the automation degree and the adaptability of high-precision titration in each link are comprehensively improved, and the high requirements on the titration precision and the titration efficiency in a complex and diversified chemical analysis scene are met.

The invention discloses an intelligent monitoring method and system for heating and ventilation of a thousand-level dust-free workshop based on digital twinning. The method specifically comprises the steps that SolidWorks software is used for conducting three-dimensional modeling on heating and ventilation equipment and environment of the thousand-level dust-free workshop; a physical device, an IoT sensor network, a PLC controller and a data transmission module are configured to realize real-time acquisition, processing and transmission of a device operation state and environmental parameters; dynamically optimizing the operation state of the equipment and the environment parameters of the workshop; a three-dimensional visual interface is developed based on an OpenUSD platform, and heating and ventilation equipment and environmental parameters in a workshop are visually presented in a dynamic and real-time mode; an equipment health state curve is generated through a time sequence prediction algorithm, and the equipment health score and trend change are visually displayed, so that a user can grasp the equipment operation state in real time. Accurate control over the temperature and humidity, the cleanliness and the pressure difference in the workshop is achieved, the cleanliness and reliability of the production process are improved, and good adaptability and expansibility are achieved.

The invention relates to a multi-type industrial equipment PLC control method and device based on a hardware abstraction layer, and the method comprises the steps: constructing the hardware abstraction layer comprising a core layer and an adaptation layer; the core layer is provided with a standardized interface set, the adaptation layer is provided with a special adaptation module, and a control protocol is converted through a protocol analysis engine. And generating a unified instruction set based on the standardized interface set, and establishing a data interaction channel supporting priority scheduling. And identifying the newly-added equipment, matching the equipment configuration library, and generating a newly-added adapter drive if the equipment configuration library fails. And acquiring a device state by using a data interaction channel, and constructing a cooperative control logic to realize linkage. Equipment load and task queues are monitored in real time, and dynamic task scheduling and grading fault processing are carried out. And when an adapter configuration updating instruction is detected, the adaptive layer thermally loads the updated drive, and unified, intelligent, efficient and stable PLC control of multiple types of industrial equipment is realized through cooperation of the core layer and the adaptive layer.

A dog training method for intelligently correcting pet behaviors based on multi-modality comprises the steps that multi-modality perception data of a pet is obtained, the multi-modality perception data comprises acoustic signals, motion signals and track data, an acoustic feature tensor, a motion feature tensor and a position feature tensor are constructed, and a unified coupling tensor is formed; inputting the coupling tensor into a pre-trained behavior recognition model, and outputting a behavior tag; the emotion recognition module outputs an emotion label, and updates a character label in a sliding window mode; inputting the behavior label, the emotion label and the character label into an intervention strategy generation model and outputting an adaptive intervention strategy, wherein the intervention strategy comprises a pacifying type strategy and a stimulation type strategy; according to a historical feedback effect record, adjusting a feedback intensity parameter of the intervention strategy, calculating a feedback convergence index and an emotion recovery index, and dynamically correcting a feedback level and a delay strategy of the intervention strategy.

The invention provides a multi-mode control method and system for intelligent variable-frequency cooking equipment, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining a food material image and weight data through an image collection device, extracting feature information through a deep learning processor based on a pre-trained food material feature recognition model, and generating a feature data packet containing food material type, weight and volume information. The controller matches initial cooking mode parameters according to the data packet, collects real-time operation data such as temperature, pressure, heating power and stirring motor rotating speed in the cavity, and calculates power-temperature and motor rotating speed-torque response curves. And the controller generates a frequency conversion control instruction according to the response curve and a preset multi-mode switching judgment rule, and controls the heating unit and the stirring motor to perform cooperative adjustment. In the cooking process, real-time operation data are continuously collected, multi-mode self-adaptive switching is carried out, and the frequency conversion control instruction is optimized until cooking is completed.

The invention belongs to the technical field of automatic industrial sorting, and discloses an automatic sorting digital twin system and a construction method. The construction method comprises the following steps: constructing physical entities for executing different operation functions in automatic sorting based on a physical entity layer, and sending the physical entities to a digital twin platform layer; the digital twin platform layer constructs a three-dimensional virtual model synchronous with different operation functions executed by the physical entity through three-dimensional design software; the three-dimensional virtual model is dynamically updated in real time; and loading the three-dimensional virtual model to a three-dimensional engine of a visual application layer through a communication protocol or a transmission network, monitoring the real-time states of different operation functions of the physical entity in an industrial automatic sorting simulation scene, and visually displaying the real-time states through a user interface. The digital twinning platform of the automatic sorting system is constructed, and visual interaction, real-time data monitoring and virtual and real synchronous operation of the automatic sorting digital twinning system are achieved.

The invention discloses a cleaning control method and system based on an electronic cleaning agent, and the method comprises the steps: obtaining the distribution characteristics of pollutants and a material sensitivity judgment result through obtaining the micron-sized particle distribution data, chemical residue concentration data and surface roughness parameters of the pollutants on the surface of an electronic workpiece; according to the pollutant distribution characteristics and the material sensitivity judgment result, matching a candidate formula from a preset cleaning agent formula database, calculating pollutant removal rate predicted values of different concentration and time combinations through a linear regression algorithm, and determining optimized cleaning agent formula parameters and cleaning strength control parameters; and in the cleaning process, the residual quantity of pollutants and the surface damage degree are collected in real time, if the residual quantity of the pollutants exceeds a preset threshold value T1 or the surface damage degree exceeds a preset threshold value T2, cleaning agent injection pressure and cleaning time parameters are dynamically adjusted, and updated cleaning execution parameters are generated. Accurate and efficient cleaning of the surface of the electronic workpiece is achieved.

The invention relates to the technical field of intelligent control, and discloses a light control method and system, a controller and a storage medium, and the method comprises the steps: collecting ambient light, human traffic and user preference data through multi-sensor fusion; after normalization processing, inputting a reinforcement learning model to dynamically calculate target brightness and color temperature, and generating a control instruction to adjust a lamp; and optimizing model parameters in combination with user feedback. The system integrates a data acquisition module, an intelligent processing module, a regulation and control execution module and a feedback optimization module to form a closed-loop control chain. The controller supports PWM dimming and a DMX512 protocol, an emergency module is arranged in the controller, and illumination is maintained according to a historical mean value when communication is interrupted. Through high-precision environment perception calibration, a multi-target dynamic dimming strategy, an intelligent closed-loop learning mechanism and a distributed fault-tolerant control system, the long-standing core problems of light sensation drift, strategy adaptation stiffness, strong cold start dependence, poor cluster stability and the like in the field of intelligent illumination are systematically solved.

The invention relates to an electronic waste automatic classification and recovery system based on artificial intelligence recognition. The electronic waste automatic classification and recovery system comprises a waste conveying module, an artificial intelligence recognition module, an intelligent sorting module, a dynamic sorting control system, a recovery processing module and a data analysis and feedback module. The waste conveying module conveys the electronic waste to a recognition area in order, and the recognition module conducts multi-dimensional feature recognition on the waste through vision, spectrum and 3D morphological analysis technologies. And the intelligent sorting module sorts the materials to a specified sorting box according to the recognition result. The dynamic sorting control system can adjust a sorting path in real time according to task requirements. And the recovery processing module is used for further processing the sorted materials through a crushing device, a magnetic separation device and a rare and noble metal extraction device. And the data analysis and feedback module analyzes and optimizes data of identification and sorting operation, so that the processing efficiency and the recovery rate are improved.

The invention relates to the technical field of interaction systems, in particular to an intelligent control-based nursing board interaction system, which comprises a nursing board, and is characterized in that the nursing board internally comprises a voice recognition and control module integration, a board data interaction and display module, a board function page control module, an intelligent equipment control module and a feedback and interaction module; the voice recognition and control module integration, the billboard data interaction and display module, the billboard function page control module, the intelligent equipment control module and the feedback and interaction module are electrically connected. According to the method, a visual nursing billboard large screen is provided, interaction with the visual nursing billboard large screen is carried out through an API interface, instruction analysis and execution are supported, meanwhile, real-time voice and visual feedback and a multi-mode interaction mode are provided, and the user experience is remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a family service method and system based on artificial intelligence, and the method comprises the following steps: obtaining a statement analysis action sequence and a task direction, collecting the feedback of a device to generate a state structure, judging an instruction trend, recombining a statement to generate a behavior chain, and disassembling an action recognition conflict to extract a main control path. Analyzing a behavior time sequence generation prominent trend, and adjusting a display sequence to generate a function scheme. According to the method, through semantic component extraction and equipment state mapping, the accuracy of instruction target recognition is improved, word order rearrangement and logic connection enhance the continuity of a behavior chain, a control path is optimized and sorted according to verb density and object association, the task scheduling precision is improved, and behavior time sequence analysis recognizes a high-frequency operation forward trend; according to the method, priority dynamic adjustment and rearrangement of recommended content in combination with instruction frequency and time sequence characteristics are realized, the response initiative and the service matching degree are enhanced, and collaborative optimization of semantic understanding, behavior prediction and content recommendation is integrally realized.

The invention relates to the technical field of automatic control, in particular to an intelligent aquaculture automatic control system which comprises a feeding feedback control module, a feeding rhythm allocation module, a water body disturbance partition module, a control unit recognition module and a control path construction module. According to the method, the bait residue density is quantified through image recognition, a feeding state dynamic judgment mechanism is constructed, the accuracy and timeliness of feeding response are improved, the feeding speed is calculated through linkage of time difference and density change, a rhythm allocation strategy based on behavior feedback is formulated, the matching degree of the feeding rhythm to the actual feeding ability is enhanced, and the feeding speed is increased. A main flow path continuous block is constructed by combining flow velocity and flow direction, disturbance levels are marked in a grading manner, a key equipment action area is judged according to cross of fish school active density and dissolved oxygen trend, and an equipment linkage path is constructed by connectivity of a physical channel and an inhabitation area. And an intelligent control scheme of cooperative linkage of ingestion behavior driving, disturbance partition recognition and space layout optimization is realized.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention provides an intelligent sorting control method and system for renewable resources based on a YOLO framework, and relates to the technical field of control, and the method comprises the steps: collecting a renewable resource image through a camera arranged above a conveyor belt, carrying out the size normalization processing, inputting an improved YOLOv5 deep neural network, carrying out the target detection, and obtaining the position and category information of the renewable resources. The YOLOv5 network adopts an improved CSPDarknet53 backbone network, and a double attention module is introduced into the YOLOv5 network. And according to the detection result, the predicted time for the targets to arrive at the sorting mechanism is calculated in combination with the speed parameters of the conveying belt, multi-target parallel sorting tasks are optimized and scheduled based on a deep reinforcement learning algorithm, a sorting execution time sequence is generated, and the pneumatic sorting mechanism is controlled to achieve accurate classified putting. According to the invention, the sorting efficiency and accuracy of renewable resources are improved, and the sorting cost is reduced.

The invention belongs to the technical field of cold chain warehouse management, and particularly relates to a cold storage dynamic scheduling cooperative control method and system based on the Internet of Things, and the method comprises the steps: obtaining operation parameters and environment parameters; according to the operation parameters and the environment parameters, an E-LSTM model is used for analyzing the operation state of the refrigeration equipment, an analysis result is generated, and through a TFT neural network model, the environment parameters and the current time and production plan are combined, and the refrigeration demand change trend is predicted; based on the analysis result and the refrigeration demand change trend, a dynamic scheduling strategy of the refrigeration equipment is generated by combining a scheduling rule and an optimization target and utilizing a PPO algorithm; the scheduling strategy is sent to the PLC control end of the refrigeration equipment through the Internet of Things communication network, the PLC control end performs cooperative control on the refrigeration equipment according to the scheduling strategy, and meanwhile, the execution state of the equipment is fed back to the cloud server. Therefore, the problems of rigid scheduling mode, serious energy waste, insufficient emergency capability and the like in the prior art are solved.

The invention belongs to the technical field of physical and chemical analysis, and particularly discloses a high-precision full-automatic titration system and method based on fusion of a spectrum sensor and a PLC, a multi-channel spectrum sensor is adopted to capture fine spectrum characteristics of solution color change in real time, and the flow rate of titration liquid is dynamically adjusted through a PLC embedded PID control algorithm, so that the titration accuracy is improved. The method realizes submilliliter-level precision control, constructs a real-time compensation mechanism in combination with temperature, humidity, pH and other environmental parameters, improves the accuracy and robustness of titration end point judgment, automatically updates an end point judgment threshold and control parameters in combination with an AI self-learning module, realizes self-adaptive adjustment and sustainable evolution of the system, and improves the accuracy and robustness of titration end point judgment. The device has the characteristics of modular structure, high detection precision, high control response and high operation stability.

The invention belongs to the technical field of intelligent power grids, and particularly relates to a circuit breaker control method, system and device of the intelligent Internet of Things, and the method comprises the steps: obtaining the operation data of a sensor; data extraction is carried out on the operation parameters of the sensor, the operation parameters are transmitted to a cloud server through the wide area Internet of Things for analysis, and whether the circuit breaker has an abnormal condition or not is judged according to a preset rule and a historical data training model; if an abnormal condition exists, based on a risk assessment matrix and real-time data monitoring, generating a safety control strategy and establishing a strategy library, performing different-level response according to an abnormal type, and generating a control instruction; and after an instruction is received, dynamically adjusting parameters according to the state of the circuit breaker, interactively verifying the instruction through the block chain node, operating the circuit breaker according to a safety control strategy after the instruction passes, and meanwhile, feeding back and adjusting the strategy. Therefore, the problems that in the prior art, the data processing dimension is single, strategy response lags behind, and the abnormal type recognition capacity is insufficient are solved.

The invention discloses a cooking robot control method and system based on a knowledge graph, and relates to the technical field of cooking robot control, and the method comprises the steps: collecting multi-source cooking data, constructing the knowledge graph, receiving user demands, recommending dishes based on the graph, determining cooking parameters according to the recommended dishes, and controlling the cooking robot according to the cooking parameters. Comprise time, temperature, seasoning dosage and the like. The parameters are sent to a cooking robot controller, data are collected by a sensor during cooking, the data are compared with standard data, and the parameters are adjusted by a feedback algorithm to ensure that cooking meets the expectation. According to the method, personalized dish recommendation and cooking parameter adaptive adjustment can be realized, dish quality and taste are ensured, user health is considered, nutrition balance is promoted, the intelligent degree of the cooking robot is improved, the market competitiveness of the cooking robot is enhanced, and the requirement of people for high-quality cooking experience is met.

The invention relates to the technical field of PLC control, in particular to a PLC control method and system for an automatic mixing production line, and the method comprises the steps: collecting the rotating speed data and vibration data of a stirring motor of a mixing machine of the automatic mixing production line in real time; acquiring a time range for analyzing the fault condition of the speed sensor; determining a correlation degree between the rotating speed data and the vibration data in the time range; determining the fluctuation rule degree of the rotating speed data in the time range based on the time interval between the response time periods with similar response intensities and the difference of the fluctuation condition of the vibration data between the response time periods in combination with the correlation degree; the abnormal degree is determined; and detecting the fault condition of the speed sensor in the process of controlling the rotating speed of the stirring motor by the PLC of the automatic mixing production line. The invention aims to improve the accuracy of fault detection of the speed sensor in the process of controlling the rotating speed of the stirring motor by the PLC of the automatic mixing production line.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention relates to the technical field of agricultural production, and in particular to a sensor-based real-time monitoring system for the growth status of apricot and plum. A plurality of sensors are arranged to collect key growth status data in an apricot and plum planting area in real time. A data processing module is used to process the growth status data. A growth model construction module uses a long short-term memory network to construct an apricot and plum growth status prediction model, and predicts the growth trend, health status and potential risks of the apricot and plum in real time. An abnormality detection and early warning module compares the real-time data with the model prediction result, identifies abnormal situations in time and issues an early warning to a user. An irrigation and fertilization decision module generates an optimal irrigation, fertilization and environmental regulation strategy. A control execution module automatically regulates related equipment. A visualization module displays monitoring data, prediction results, early warning information and decision strategies. The invention significantly improves the economic benefits and sustainability of apricot and plum planting.

The invention relates to the technical field of fertilization and watering control, in particular to an automatic fertilization and watering control method and system for landscape garden seedlings. The method comprises the following steps: acquiring and analyzing morphological data and soil state characteristics of landscape garden seedlings through an electronic monitoring and soil monitoring sensor; then, the nursery stock root system density is predicted through the nursery stock form data, abnormal fluctuation of the soil salt content is monitored, and the root system absorption limitation condition is estimated; then, according to root system absorption limited data and soil salt content abnormal fluctuation, the dosage of a slow release fertilizer is matched, and sectional type drip irrigation proportion fusion is carried out; and finally, constructing a fertilization and watering control model based on a strategy gradient algorithm, and implementing automatic fertilization and watering control through a terminal. The fertilization and watering control technology is optimized, so that the fertilization and watering control technology is more perfect.

The invention relates to the technical field of concrete vibration, in particular to an intelligent concrete vibration system and method. The intelligent concrete vibrating system comprises a planning module used for selecting a current vibrating area from a to-be-vibrated range according to construction parameters of the to-be-vibrated range; the multi-dimensional sensor is used for collecting multi-dimensional parameters of the vibrating equipment during vibrating in the current vibrating area; the multi-dimensional parameters comprise vibration frequency, vibration pressure and concrete temperature; the decision module is used for determining a current vibration strategy required by the current vibration area according to multi-dimensional parameters during vibration in the current vibration area, and determining a next vibration area corresponding to the current vibration area in each to-be-vibrated area in the to-be-vibrated range; and the control module is used for controlling the vibrating equipment to perform vibrating operation in the current vibrating area according to the current vibrating strategy, and controlling the vibrating equipment to perform vibrating on the next vibrating area after the vibrating operation of the current vibrating area is executed.

The invention discloses a multi-agent-based automatic processing system and a PLC automatic programming system, and belongs to the technical field of artificial intelligence. The system comprises a distribution agent and a plurality of working agents, the distribution agent is used for acquiring a question input by a user, analyzing the question to determine a target working agent suitable for solving the question, and routing the question to the target working agent; and the working agent is used for answering the received questions to obtain answers corresponding to the questions and returning the answers to the distribution agent. According to the method, the multi-agent framework for managing the large model is built, the distribution function and the work function of the agents are separated, modularization of the system is achieved, the system can flexibly meet various application requirements, large-scale modification does not need to be conducted on the whole process, the customization and maintenance cost of the requirements is reduced, and the implementation efficiency of the system is improved. And the processing efficiency of the automation process is improved.

The invention provides a sewage treatment system and method based on PLC control, and relates to the field of sewage treatment. The system comprises a PLC (Programmable Logic Controller), a data acquisition module and a data processing module, the data acquisition module is used for acquiring detection data of each sewage treatment tank in the sewage treatment device, and the detection data comprises regulation tank detection data, hydrolysis detection data, anaerobic detection data, oxidation detection data, sedimentation tank detection data, sludge concentration detection data and disinfection detection data; the data processing module is used for analyzing the detection data to obtain regulation and control data, and the regulation and control data comprises regulating tank regulation and control data, hydrolysis regulation and control data, anaerobic regulation and control data, oxidation regulation and control data, sedimentation tank regulation and control data, sludge concentration regulation and control data and disinfection regulation and control data; and the PLC is used for generating a control signal according to the regulation and control data. The water treatment effect is improved through the system.