1059results about How to "Realize intelligent management" patented technology

The invention relates to the technical field of defect detection, and discloses a pipeline repairing method and system based on defect detection. The method comprises the following steps: performing acoustic detection on a pipeline based on a multi-frequency sound wave signal to obtain pipeline sound field data; carrying out electromagnetic wave scattering detection on the pipeline to obtain electromagnetic scattering data; constructing a sound field distribution diagram in the pipeline and an electromagnetic property distribution diagram on the surface of the pipeline; performing feature extraction on the sound field distribution diagram to obtain a first detection feature set, and performing feature extraction on the electromagnetic characteristic distribution diagram to obtain a second detection feature set; inputting the first detection feature set and the second detection feature set into a space-time diagram convolutional network model for pipeline defect diagnosis to obtain a defect position and a defect type; and performing causal reasoning and repair scheme matching on the pipeline according to the defect position and the defect type to obtain a target pipeline repair scheme, thereby improving the accuracy of pipeline defect detection, thereby improving the accuracy of pipeline repair.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.

The invention provides a seedling cultivation growth condition monitoring method, which comprises the steps of performing data processing on a time series data set of hydrological conditions, obtaining a dynamic association rule among different hydrological parameters, constructing an association model of the hydrological parameters, and quantitatively describing mutual influence and restriction relationships among water temperature, dissolved oxygen and salinity parameters by the association model; performing data processing on the time sequence data set of seedling growth, establishing a seedling growth process model, determining key parameters of a seedling growth curve by analyzing growth rate change rules of seedlings in different growth stages, and correcting the model according to initial quality and stocking density factors of the seedlings; a seedling growth prediction model is combined with an intelligent optimization algorithm, key control parameters in the breeding process are dynamically adjusted, and according to the seedling growth trend predicted by the model, hydrological conditions and feeding strategies are adaptively optimized, so that intelligent management of the breeding process is realized.

The invention provides an electricity price-based permanent magnet motor energy storage and optimization operation method and system, and relates to the technical field of motor energy storage and optimization operation, and the method comprises the steps: predicting the power of a motor through a deep belief neural network, predicting the electricity price through a space-time diagram convolutional neural network, and quantifying a system error in combination with a Gaussian mixture model, based on a prediction result, performing global optimization by adopting a quantum genetic optimization algorithm to obtain an initial optimization strategy set, applying the initial optimization strategy set to a meta-heuristic hybrid optimization model, generating a time sequence association control strategy, performing system modeling through a hybrid Gaussian-hidden Markov model, and constructing a double-layer action evaluation control network to generate an operation control instruction; a distributed reinforcement learning framework is used for cooperative control, and an optimal control strategy is obtained based on adaptive dynamic programming and a meta-learning strategy migration mechanism.

The invention provides an RFID-based power supply management method for a tilt angle sensor of a power tower, and the method comprises the steps: employing a support vector machine and a decision tree, analyzing the historical operation data of the tower, building a tower fault prediction model, predicting the state of the tower through the tower fault prediction model, and obtaining the fault probability and time of the tower; according to a tower fault prediction result, a preventive maintenance plan is made, priority maintenance is arranged for the tower with the fault probability larger than a preset threshold value, and a power supply strategy is adjusted in advance for the tower predicted to have a fault, and load pressure is reduced; a tower group intelligent power supply management system is adopted, state information and a power supply strategy of a single tower are integrated into a whole power grid, the power supply load of each tower is dynamically adjusted through a load balancing algorithm, and for a tower in a dangerous state, the power supply load of the tower is reduced, and the load is transferred to a tower in a good state.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention discloses a factory operator-oriented posture recognition management system and method, and relates to the technical field of factory operation safety management, and the system comprises a video image collection module, a target detection module, a behavior recognition module, an illegal behavior detection module, an alarm module, a recording module and a QT5 integration module which run in sequence. The application scene of the whole management system identifies and manages operators and automation equipment in a factory. The technical key points are as follows: aiming at the problem that in the prior art, false alarm is easy to generate in behavior recognition in a factory scene, the invention provides a system for detecting factory human-object interaction violation behaviors, the system integrates target detection and behavior recognition algorithms, the interaction condition between factory operators and equipment can be monitored in real time, and the system has high practicability. Possible illegal behaviors can be accurately identified, and an alarm signal can be timely sent out, so that the safety management level of a factory can be improved.

The invention relates to the field of elevator monitoring, and particularly discloses an elevator equipment monitoring method and system based on a block chain, a terminal and a medium. Elevator equipment parameters are collected through sensor equipment, and data processing is conducted on the elevator equipment parameters; the elevator equipment parameters after data processing are uploaded to a block chain network through the Internet of Things gateway, and the block chain network adopts an alliance chain architecture and a consensus mechanism; the intelligent contract is pre-configured and deployed to the block chain, so that the block chain detects elevator equipment parameters based on the intelligent contract, when the elevator equipment parameters are abnormal, a fault alarm is given out, the elevator maintenance state is detected based on the intelligent contract, when maintenance conditions are met, a maintenance task is generated and issued, and a maintenance result is received. Real-time transparent monitoring of elevator operation data, non-tampering storage of maintenance records and intelligent management of fault early warning are achieved, the safety and reliability of elevator operation are improved, and the maintenance efficiency and the transparency of data management are improved.

The invention provides a substation operation risk analysis method, and relates to the technical field of information, and the method comprises the steps: recognizing whether a person correctly wears a protection device or not in real time through a radio frequency identification reader on the protection device, triggering an alarm if the person does not wear the protection device, and synchronizing the alarm information to a block chain node; the block chain is used for carrying out whole-process recording on the operation behavior of personnel, the operation state of equipment and the disposal condition of a security event to form auditing evidence of security operation, and meanwhile, according to the behavior of the personnel and the state of the equipment, a block chain node dynamically optimizes a security management strategy and an emergency plan to form closed-loop management, so that the security accident is effectively prevented, and the security management efficiency is improved. And the overall safety management level of the transformer substation is improved.

The invention, which relates to the technical field of intelligent logistics management, discloses an AI-based intelligent logistics management system comprising a warehouse management unit, a data fusion acquisition unit and a digital twin supervision unit. The warehouse management unit is used for managing warehouse-in and warehouse-out information and cargo information of a warehouse, reminding inventory risks, and carrying out demand prediction and inventory optimization; the data fusion acquisition unit is used for acquiring personnel, goods, equipment and environment information of a warehouse; the digital twin supervision unit comprises an intelligent AI recognition unit and a data analysis processing unit, the intelligent AI recognition unit is used for carrying out recognition analysis and risk reminding on personnel, goods, equipment and environment of a warehouse through an AI intelligent algorithm, and the data analysis processing unit is used for mapping data of an entity warehouse to a digital twin warehouse. And visual interaction management and intelligent scheduling of personnel, goods and equipment are carried out on the digital twin warehouse, so that the warehouse can be comprehensively monitored, and the goods storage and management efficiency is improved.

The invention provides a green power consumption classification accounting and right tracing method and system of a multi-dimensional account, and relates to the technical field of power, and the method comprises the steps: generating green power transaction matching data through collecting power utilization data and power generation plan data in real time; constructing a distributed account book node network, verifying the matched data, and generating a green power transaction block; and constructing a green power right traceability model based on the transaction block, calculating a green power consumption weight coefficient of the power consumption enterprise, and generating a green power consumption level and a right certificate. According to the invention, precise accounting and right traceability of green power consumption are realized, and the energy management efficiency is improved.

The invention is suitable for the technical field of park management, and provides a park management system and method based on a digital twin platform, and the system comprises a data collection module; an edge computing node module; a digital twin model generation module; a real-time data updating module; an intelligent simulation and prediction module; an intelligent decision module; according to the invention, comprehensive intelligentization and fine control of park management are realized, multi-source data in the park can be acquired and processed in real time, delay of data processing is greatly reduced through edge calculation, timeliness and accuracy of the data are ensured, and meanwhile, the digital twin model generation module is based on the processed data, so that the data processing efficiency is improved. A virtual model corresponding to an actual park is created, and the virtual model is ensured to be highly consistent with an actual environment through an adaptive adjustment mechanism and real-time data updating, so that a reliable basis is provided for subsequent simulation and prediction, and finally, an optimal management strategy can be generated and implemented by the system according to a simulation result through an intelligent decision module.

The invention discloses a rotor surface magnetic field characteristic real-time monitoring and damage assessment system and method, and relates to the technical field of damage assessment. According to the method, a rotor surface magnetic field is monitored in real time under different rotating speed conditions, detailed magnetic field change data information is collected, the monitoring means can dynamically capture the magnetic field fluctuation condition, and the damage assessment accuracy is improved. According to the method, the damage prediction model is constructed by using the deep learning technology, the relevance between the damage prediction model and the rotating speed of the rotor is analyzed, early detection and classified evaluation of the unbalance condition are ensured, accurate data support is provided for further damage analysis, and accurate evaluation of the surface damage of the rotor is realized by using the deep learning technology to construct the damage prediction model and combining the unbalance type and the magnetic field change data. Meanwhile, the damage evaluation index is subjected to linear normalization processing, the influence between different data dimensions can be eliminated, and the evaluation result is more stable and reliable. And whether the damage state of the rotor surface is abnormal or not is judged in time by comparing and analyzing the damage evaluation index and a threshold value.

The invention discloses a photovoltaic grid-connected zone area line loss real-time monitoring system based on data driving, which relates to the field of power systems, and comprises a data collection and preprocessing module, a line loss anomaly detection module, a line loss reason identification module, a line loss prediction module and an optimization control module. A cloud big data platform is adopted to construct a comprehensive power grid model and a photovoltaic power generation model, a time sequence analysis method is used to mine the line loss trend of a distributed photovoltaic grid-connected zone area, line loss prediction and early warning are realized, and an optimization control strategy is made according to the prediction trend, the current power grid operation state and historical optimization records. Therefore, the stability and reliability of power grid operation are improved; the method solves the problems that the line loss of the photovoltaic grid-connected transformer area is difficult to identify and position quickly and accurately and a corresponding line loss optimization strategy is lacked, and can realize real-time monitoring of the line loss of the distributed photovoltaic grid-connected transformer area, effectively reduce the line loss rate and guarantee stable operation of a power grid.

The invention relates to the technical field of electric power system safety early warning, and discloses a fault risk sensing early warning method for a high-low voltage switchgear assembly, and the method carries out the data preprocessing and feature extraction through collecting the state monitoring data, environmental parameters and basic attribute information of a device in historical operation. And based on the feature data sets, constructing a fault risk prediction model to predict the fault risk probability of the equipment in a period of time in the future, obtaining the operation state data and environmental parameters of the equipment in real time, and inputting the prediction model to obtain the fault risk probability. Meanwhile, the equipment is monitored in real time, original monitoring data is cleaned, abnormal values are removed, real-time monitoring data is compared with prediction data, whether the difference exceeds a threshold value or not is judged according to a preset risk threshold value, and risk points are marked. And evaluating the equipment fault risk according to the risk point and the evaluation strategy, and sending a result to related personnel to take prevention measures.

The invention discloses an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: carrying out the comprehensive evaluation of the energy consumption condition of the heating and ventilation equipment, and recognizing the current energy consumption condition and energy-saving potential of the equipment; the system is connected with heating and ventilation equipment through the Internet of Things technology, and collects parameters of the heating and ventilation equipment in real time. Based on the preprocessed data, establishing energy consumption models of the water chilling unit, the cooling tower, the freezing pump and the cooling pump; building an energy consumption prediction regression model by adopting a decision tree algorithm, and predicting the total energy consumption of the heating and ventilation system in real time; according to the predicted energy consumption data, an optimization control strategy of the heating and ventilation equipment is formulated in combination with a set energy consumption control objective function; and applying the formulated optimization control strategy to the heating and ventilation equipment, carrying out real-time monitoring and feedback, and carrying out iterative optimization on the control strategy according to an actual effect. According to the method, comprehensive evaluation, real-time monitoring and optimal control of the energy consumption of the heating and ventilation equipment can be realized, and a powerful guarantee is provided for energy conservation and emission reduction of a building.

The invention discloses a long-distance pipe network danger intelligent early warning system based on an internet platform, and relates to the technical field of pipeline early warning, and the system comprises an acquisition module which is used for carrying out the acquisition of pipeline operation data through at least one sensor disposed on a pipeline of a long-distance pipe network; the Internet platform is used for performing risk scoring on the pipeline operation data acquired by the acquisition module; the Internet platform is used for performing risk scoring on the pipeline operation data acquired by the acquisition module, determining a target inspection strategy corresponding to a target scoring result in a corresponding relationship between the scoring result and the inspection strategy according to the target scoring result, and establishing a target scheduling strategy according to the target inspection strategy; and the scheduling module is used for scheduling the inspection robot in any inspection module according to the target scheduling strategy. The safety and reliability of the oil and gas pipeline are remarkably improved, potential risks can be found and processed in time, stable operation of the oil and gas pipeline is guaranteed, and the method has important practical application value.

The invention discloses a building energy consumption control system based on deep learning. The building energy consumption control system comprises a data acquisition module, a data processing module, a deep learning module, an optimization control module and a real-time control module, the invention further discloses a control method of the building energy consumption control system based on deep learning. The energy consumption prediction model is constructed through the long-short-term memory network, and the user behavior model is constructed through the convolutional neural network, so that the energy consumption optimization control strategy is generated through the reinforcement learning algorithm based on the output results of the energy consumption prediction model and the user behavior model, and the related data of the building energy consumption system is effectively utilized. Intelligent control over the building energy consumption system is achieved, changes of environmental parameters and user requirements are dynamically adapted by optimizing an energy consumption control strategy, and the comfort level of a user is improved while energy saving and consumption reduction are achieved.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

The invention discloses a digital twin-driven photovoltaic module monitoring and self-purification system, belongs to the technical field of photovoltaic module cleaning, and realizes intelligent management and maintenance of a photovoltaic system through mutual cooperation of a sensing layer, an information capturing layer, an information transfer layer, an intelligent analysis layer, a decision support layer and an interactive experience layer. The sensing layer deploys various sensors to collect key data of the photovoltaic module; the information capturing layer transmits the data to a back-end processing system in real time; the information transfer layer ensures efficient transmission of data in the system; the intelligent analysis layer predicts a maintenance demand by using a machine learning algorithm, realizes predictive maintenance, and maintains the optimal working state of the component through an intelligent cleaning mechanism; the decision support layer provides decision support based on data analysis for an operator of the photovoltaic power station, and optimizes a maintenance plan and a productivity plan; and the interactive experience layer provides a visual management interface, so that the component state can be remotely monitored conveniently.

The invention relates to the technical field of variable-frequency water pump control, in particular to a variable-frequency water pump self-adaptive control method and system.The method comprises the steps that motor parameters of a variable-frequency water pump are collected, and whether the motor parameters meet preset starting threshold conditions at the same time or not is judged; under the condition that the starting condition of the motor is met, sensing information of all sensors on the variable frequency water pump is collected; the collected motor parameters and sensing information are preprocessed, filtered and denoised, and key information is extracted; calculating a motor state analysis result based on the processed motor parameters and sensing information, and performing fault diagnosis and prediction to obtain a motor state analysis result; and according to the motor state analysis result, the output voltage, frequency and phase of the frequency converter are dynamically adjusted to optimize the operation efficiency and stability of the water pump. The operation state of the water pump motor is monitored and analyzed in real time, accurate calculation and evaluation are combined, the output parameters of the frequency converter are dynamically adjusted, and efficient and stable operation of the water pump is achieved.

The invention provides a surveying and mapping result management method and system based on big data, and the method comprises the steps: obtaining geographic space surveying and mapping data through employing an unmanned plane, a vehicle-mounted laser radar and a sensor, and recording the time, position and equipment information of the surveying and mapping data in real time; a regularizer based on online linear optimization classifies the surveying and mapping data according to geographic positions, collection time and data types, and uploads the classified surveying and mapping data to a distributed storage system; performing data cleaning, format unification and quality verification on the stored surveying and mapping data to generate standardized surveying and mapping data; constructing a knowledge base based on a large language model (LLM), performing semantic analysis and feature labeling on the standardized surveying and mapping data by using geographic space operators in the knowledge base, and establishing a spatial index of the standardized surveying and mapping data; and receiving a user retrieval request, and performing privacy protection retrieval on the standardized surveying and mapping data based on the spatial index.

The invention relates to the technical field of wet cleaning in a semiconductor manufacturing process, and discloses a semiconductor wet cleaning process optimization method based on PLC programming, and the method specifically comprises the following operation steps: constructing a PLC programming dissolution rate matching system, designing and integrating a set of PLC-based dissolution rate matching system, and optimizing the PLC-based dissolution rate matching system. The system needs to have the functions of data acquisition, processing and feedback, a dissolution rate model is established according to cleaning liquid components and wafer material characteristics, an algorithm is preset to be matched with the actual dissolution process, and sensors are deployed in cleaning equipment and used for monitoring key parameters such as the temperature and concentration of cleaning liquid and the dissolution rate of the surface of a wafer in real time. According to the semiconductor wet cleaning process optimization method based on PLC programming, the rotating speed and cleaning liquid distribution in the cleaning process are accurately controlled, the problem of flowing particle defects caused by the poor dissolving speed in traditional wet cleaning is effectively solved, and the cleanliness and the yield of semiconductor wafers are improved.

The invention relates to the technical field of network advertisements, in particular to an advertisement data management system based on market trend analysis, which comprises a data acquisition module, an analysis module, a prediction module, a conversion rate calculation module and an adjustment module. The advertisement effect is reflected by acquiring click and purchase data after advertisement putting, the change trend of user behaviors is accurately recognized through the analysis module, and the user change rate is calculated, so that data support is provided for an advertisement putting strategy, and the advertisement putting strategy is more accurate by analyzing historical data, predicting the future purchase trend and comparing with predicted data in real time. The conversion rate change of the advertisement is accurately evaluated, a basis is provided for optimizing the advertisement effect, whether the advertisement putting duration needs to be adjusted or not is judged according to the comprehensive analysis of the user change rate and the conversion rate change rate, efficient utilization of advertisement resources is ensured, the accuracy and flexibility of advertisement putting are remarkably improved, the conversion rate and the return on investment of the advertisement are facilitated, and the user experience is improved. And continuous optimization and improvement of the advertisement effect are realized.

The invention provides a battery cell echelon utilization battery BMS charging management method comprising the following steps: obtaining historical usage record data of each battery, the historical usage record data comprising charging and discharging times, charging and discharging current values, voltage values and temperature values, and constructing a battery state original data set; according to the standardized battery state data set, calculating and establishing a battery health assessment model, a battery capacity attenuation rate, an internal resistance growth rate and charge and discharge efficiency, and calculating according to a preset weight ratio to obtain a battery health score; based on the health scores of the batteries, grouping the batteries by adopting a clustering algorithm, and generating battery grouping data according to a preset threshold value; and after each charging is completed, updating the original data set of the battery state, recalculating the health score of the battery, and dynamically updating the grouping data of the battery.

The invention discloses an energy storage cabinet temperature control method based on a cloud platform and a temperature control energy storage cabinet, and relates to the technical field of electric energy storage, the energy storage cabinet comprises a cabinet body, an energy storage module, a data acquisition module, a communication module and a temperature control module, and the temperature control method comprises the following steps: S1, acquiring environmental parameters and battery state data; s2, determining a temperature adjustment factor of the energy storage cabinet through the battery state data; s3, generating a composite temperature index based on the environmental parameters and the temperature adjustment factor; s4, performing region division on the energy storage cabinet, determining temperature requirements of different regions according to the composite temperature index, and formulating an energy storage cabinet temperature control strategy based on the temperature requirements in combination with the environment prediction temperature; s5, the cloud platform generates a control instruction based on the temperature control strategy and issues the control instruction to the energy storage cabinet to execute the control instruction; accurate control over the temperature in the energy storage cabinet can be achieved, it is ensured that energy storage equipment operates within the optimal temperature range, the energy storage efficiency is improved, and the service life of the equipment is prolonged.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The invention discloses a bioreactor process optimization system and method, and relates to the technical field of bioreactors. Comprising the following steps: collecting historical reaction process data of a bioreactor, and obtaining real-time reaction process data of the bioreactor; analyzing the historical reaction process data to obtain different influence results of different adjustment modes on the biological reaction process; establishing a cell culture model according to a corresponding relation obtained by different influence results of different adjustment modes on the biological reaction process; the real-time reaction process data is obtained, the biological culture process is influenced by adjusting the bioreactor, the specific adjustment mode is obtained through the real-time reaction process data and the required adjustment result, the required adjustment result can be obtained after the reaction in the adjustment mode obtained through backstepping, hysteresis is avoided, materials are saved, and the efficiency is improved. And the problem of poor subsequent biological reaction caused by excessive material addition, excessive design parameter adjustment and poor subsequent biological reaction due to hysteresis is also avoided.