5606results about How to "Reduce operating costs" patented technology

The invention provides a predictive maintenance management method and system for an industrial robot, and relates to the technical field of equipment maintenance management, and the method comprises the steps: obtaining multi-modal heterogeneous data of the industrial robot, carrying out the intelligent preprocessing, obtaining first preprocessing data, carrying out the cross-modal correlation fusion of the first preprocessing data, generating fusion data, and carrying out the prediction of the industrial robot; extracting complementary redundant information and constructing a semantic feature map; constructing a health state prediction model, adding the semantic feature map into the health state prediction model, mapping the semantic feature map to an embedding space through linear transformation, extracting time sequence information, carrying out feature interaction to obtain interaction features, and carrying out transformation and feature enhancement through a feedforward neural network layer to obtain health state probability distribution; comprehensive health state distribution is generated through Bayesian reasoning fusion, an optimal maintenance strategy is generated in combination with a detection model, an abnormal mode and a key turning point, collaborative maintenance is conducted on the industrial robot, a maintenance result is uploaded to a server, and the current state is stored.

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a park-level adjustable load-oriented resource regulation and control method and system, and the method comprises the steps: formulating an initial energy scheduling scheme according to the predicted energy demand in combination with the output characteristics of distributed energy in a park and the load condition of a power grid; and training a load regulation and control strategy model by adopting a reinforcement learning algorithm based on the initial energy scheduling scheme, and generating a personalized load regulation and control instruction for each energy consumption unit. According to the scheme, the real-time energy consumption data of the plurality of energy consumption units in the park, the output characteristics of the distributed energy and the power grid load condition can be comprehensively considered, a scientific and reasonable energy scheduling and load regulation and control strategy is formulated, and efficient utilization and green low-carbon development of park energy are realized. Meanwhile, the method also needs to have the characteristics of data sharing, accurate prediction, intelligent scheduling, rapid response and the like so as to adapt to complex and changeable energy requirements and environmental protection requirements of the park.

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 game platform management method and system based on artificial intelligence. The method belongs to the technical field of game platform management, and comprises the following steps: acquiring multi-source data through multiple channels, constructing a user portrait based on the acquired multi-source data, training the user data through a deep learning model, and identifying user preferences, habits and potential demands; the server load, the game popularity and the user distribution are monitored in real time, the game peak period is pre-judged through time sequence analysis based on historical data, and resource allocation is dynamically adjusted according to a prediction result; through collection and analysis of multi-source data, an accurate user portrait is constructed, and in combination with deep learning and an emotion analysis model, preferences, emotion changes and potential demands of the user can be identified and understood, so that personalized game recommendation, customer service and content pushing are provided.

The invention relates to the technical field of artificial intelligence and Internet of Things, in particular to an inspection service management system based on artificial intelligence and Internet of Things, which comprises a data acquisition module, an edge calculation module, a task allocation module, a path planning module, an early warning module and a user interaction module. Wherein the data acquisition module is used for acquiring multi-dimensional data of environment and equipment in real time; the edge calculation module is used for carrying out preliminary analysis and processing on the multi-dimensional data; the task distribution module is used for generating inspection tasks according to inspection rules; the path planning module is used for generating an optimal path of the inspection equipment; the early warning module is used for generating an early warning signal when the potential risk is detected; and the user interaction module is used for displaying the state of the system through a visual interface. According to the invention, by realizing real-time data processing, dynamic task scheduling and path optimization, the response speed and efficiency of the system are improved, and meanwhile, the equipment fault prevention and decision-making capabilities of operators are enhanced.

The invention relates to the technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention relates to the technical field of automatic logistics, in particular to an intelligent warehouse cargo scheduling device, which comprises a dynamic priority adjustment module, a self-adaptive classified storage module, a cargo flow optimization module, a resource allocation module, a fault analysis and maintenance module, a behavior monitoring module, an energy management module and a cost control module. According to the method, the cargo scheduling priority is optimized through a support vector machine algorithm and a decision tree method, the warehouse space utilization rate is improved by utilizing a convolutional neural network and a genetic algorithm, the cargo flow is optimized by adopting linear programming and a network flow analysis algorithm, and the cargo flow trend is estimated through an autoregressive integral moving average model and a recurrent neural network. The system utilizes a long short-term memory network to identify equipment faults and maintain, reduces the risk of accidental shutdown, improves the operation safety through a computer vision technology and a behavior identification algorithm, and utilizes an energy consumption mode analysis and cost-benefit analysis method to reduce the operation cost.

The invention discloses a control method and device of a building cold and heat source system self-adaptive to load requirements. The control method comprises the following steps: acquiring historical operation data, physical characteristics and equipment system data of a building, and establishing a building load prediction model; generating the hourly load curve, and predicting the cooling or heating demand of each time period in the future according to the curve; dynamically adjusting the refrigerating or heating load demand, and generating a corresponding cooling or heating curve; and constructing a machine learning algorithm based on an equipment mechanism model, optimizing a control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting control parameters of the equipment. According to the technical scheme, the load of the building cold and heat source system is intelligently predicted and adjusted, the energy efficiency of the system is improved, invalid energy consumption is reduced, energy utilization is optimized, control parameter optimization is achieved through a machine learning algorithm, efficient operation is ensured, indoor comfort is kept, and the purpose of saving energy is achieved.

The invention discloses a distributed new energy storage optimal configuration method and system for a power distribution network, and the method comprises the following steps: building a load prediction model through employing a probability discretization method according to historical power utilization data and real-time weather data; according to load demand data output by the load prediction model, a multi-target optimization model is constructed by taking maximization of distributed new energy power generation utilization rate, minimization of distributed new energy storage energy loss and minimization of operation cost of energy storage and scheduling as optimization targets; according to the multi-objective optimization model, adjusting weight distribution of the three optimization objectives, gradually optimizing power distribution between new energy power generation and energy storage, and generating an optimal new energy complementation strategy; charging and discharging are adjusted according to the optimal new energy complementation strategy, the health state, the battery temperature, the number of charging and discharging cycles, the environment temperature and the load demand change of energy storage equipment are collected in real time, and new energy power generation and energy storage configuration is optimized by dynamically comparing multiple indexes under different energy storage configurations in combination with power grid frequency fluctuation.

The invention discloses an operation regulation and control method and system for a distributed new energy photovoltaic power station, and relates to the technical field of new energy, and the method comprises the steps: recognizing a power station, building a monitoring system, collecting and monitoring key equipment data through the system, so as to obtain historical and real-time operation data, and predicting photovoltaic output and load based on the data to obtain predicted values, calculating power deviation, inputting the deviation into an optimal scheduling model to obtain a strategy, and adaptively regulating and controlling the operation state of the power station according to the strategy. The technical problems of unbalanced power generation and power utilization, low operation efficiency, poor stability, high cost and poor power grid compatibility caused by the fact that efficient and self-adaptive optimal scheduling cannot be performed according to accurate photovoltaic output and load prediction in the prior art are solved, accurate balance of power generation and power utilization is achieved, and the power generation efficiency and the power utilization efficiency are improved. The operation efficiency and stability of the power station are remarkably improved, the operation cost is reduced, and the compatibility with a power grid is enhanced.

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention relates to the technical field of power systems, and discloses a digital twinning-based power grid support performance evaluation system and method. Comprising a data acquisition and processing module, a digital twin model construction and integration module, a multi-scene simulation and prediction evaluation module, an intelligent scheduling and fault prediction module, a multi-source data fusion and big data analysis module, a performance evaluation and optimization decision module, and a visualization and man-machine interaction module. The data acquisition module is used for acquiring real-time states, load data and external environment data of equipment in a physical power grid. Through an intelligent scheduling algorithm, a digital twinborn model and a fault prediction technology, the system can monitor and optimize a new energy access process in real time, solves the challenge of new energy power generation fluctuation on power grid stability, and the algorithm can dynamically adjust power grid load distribution and predict potential faults in advance under the condition of centralized access or fluctuation of new energy. And the stability of power transmission and the safety of equipment are ensured.

The invention discloses an intelligent data acquisition system of a special pump, and relates to the technical field of equipment management. Comprising a data acquisition module, a data preprocessing module, a data storage module, a data analysis and prediction module, an intelligent control module, a remote monitoring module, a maintenance and optimization module, a multi-source data fusion module, a digital twinborn simulation module and a collaboration module. According to the intelligent data acquisition system of the special pump, by arranging multi-dimensional data acquisition and an intelligent optimization model based on the self-adaptive neural network and reinforcement learning, the defects that in an existing system, data integration is not comprehensive, a maintenance decision is lagged and intelligent optimization is lacked are overcome, and real-time monitoring and dynamic adjustment of the equipment state are achieved. The system can accurately predict the equipment fault, optimize the maintenance strategy, significantly reduce the operation cost, and improve the operation efficiency and reliability of the equipment.

The invention discloses an intelligent advertisement putting processing method and system based on user data, and relates to the technical field of data processing, and the method comprises the steps: putting an advertisement in an advertisement pool based on an edge device, collecting the video information during the advertisement playing period through the edge device, analyzing the stored video information, extracting the user feature information, and storing the user feature information in the advertisement pool; the user feature information is integrated into user feature vectors, the user feature vectors stored in the edge devices are uploaded to the cloud platform, the cloud platform analyzes the user feature vectors uploaded by the edge devices to generate user portrait information, and the user portrait information is displayed on the basis of the user portrait information generated by the cloud platform. And putting corresponding advertisements to the user groups with similar user portrait information, collecting feedback data, and dynamically optimizing the advertisements put by the edge devices and the user groups based on the feedback data. The method has the advantages that the data transmission delay and the cloud computing pressure are reduced through the cloud edge collaboration method.

The invention provides an industrial Internet of Things data acquisition method based on edge cloud collaboration, and belongs to the technical field of data analysis, and the method comprises the steps: S1, deploying a plurality of edge nodes in an industrial field, collecting original data of industrial equipment, and carrying out the real-time preprocessing; s2, the edge nodes further process the preprocessed data by adopting a hybrid algorithm combined with a deep learning model; s3, in the data processing process, the edge node monitors the operation state of the system through a real-time feedback mechanism, and dynamically adjusts the data acquisition frequency, the data compression rate and the processing strategy through a preset strategy; s4, the cloud performs deep analysis on the received preprocessed data, constructs a multi-dimensional analysis model, and generates an analysis result and an optimization strategy; and S5, the cloud end feeds back the analysis result and the optimization strategy to the edge node, and guides the edge node to adjust the data acquisition and processing strategy. The data acquisition efficiency can be improved, the data security is ensured, and the cloud pressure is relieved.

The invention relates to the technical field of data storage, in particular to a dynamic data distribution storage method and system for an RAID (redundant array of independent disks), and the method comprises the following steps: based on the access frequency of data blocks in a disk, recording the access frequency and timestamp of each data block, carrying out heat rating on the data blocks, and determining thresholds of hot data and cold data. According to the method, the dynamic distribution of data storage is effectively optimized by analyzing the access frequency of the data blocks, and the performance and the economic efficiency of a storage system are remarkably improved by migrating the frequently accessed data to the cache disk and migrating the less accessed data to the low-cost storage; according to the method, unnecessary occupation of high-performance storage resources is reduced, the operation cost is also reduced, heat rating is performed on the data blocks, and the storage positions of the data blocks are correspondingly adjusted, so that the data processing speed is higher, meanwhile, the data security is improved, when a hard disk fails, data recovery is faster, and the service continuity is ensured.

The invention relates to a gas storage assembly applied to a carbon dioxide energy storage system and the carbon dioxide energy storage system, and relates to the technical field of carbon dioxide energy storage. The gas storage assembly comprises a gas storage, a gas distribution pipe and a connecting pipe, the carbon dioxide energy storage system further comprises an energy storage assembly, an energy storage container and an energy release assembly which are connected in sequence. The gas distribution pipe is provided with a plurality of gas ports communicated with a gas storage space of the gas storage; the first end of the connecting pipe is connected with the gas distribution pipe, and the second end of the connecting pipe is communicated with at least one of the energy storage assembly and the energy release assembly directly or through a pipeline structure; the distance between the connecting position of the air distribution pipe and the connecting pipe and one end of the air distribution pipe is 40-60% of the length of the air distribution pipe. According to the gas storage assembly provided by the invention, the balance of the gas inlet flow or the gas outlet flow in different areas of the gas storage space of the gas storage can be improved, the engineering design and debugging difficulty of the gas storage assembly is reduced, and the operation cost is reduced.

The invention discloses a signal processing and intelligent algorithm fused main helium fan residual life prediction method, and relates to the technical field of mechanical equipment state monitoring and maintenance management, and the method comprises the steps: collecting equipment operation data through multiple sensors, carrying out the feature fusion through principal component analysis, and achieving the fault mode recognition through a deep learning algorithm; and the residual life of the equipment is predicted by utilizing transfer learning and an XGBoost algorithm, and a diagnosis and prediction model is integrated into an online monitoring system, so that real-time perception and dynamic prediction of the health state of the equipment are realized, and decision support for maintenance and spare part management is provided. According to the method, the accuracy of fault diagnosis and life prediction of the primary helium fan is effectively improved, and a new thought is provided for safe operation and intelligent maintenance of equipment.

The invention discloses a domain-adaptive retrieval enhancement generation method and system, and the method comprises the steps: carrying out the document analysis and semantic partitioning of a document through a semantic understanding model and a document rule, and storing document fragments; on the basis of the large language model and a preset question library, generating a question and disassembling the question; performing correlation evaluation and sorting on the initial retrieval results in combination with a retrieval technology; candidate content most matched with the cue words is screened out through a large language model, and question-retrieval fragments are scored and marked; performing fusion on the initial retrieval result and the scoring and labeling result to construct positive and negative samples, and constructing a supervised data set; and based on a preset loss function, utilizing the supervised data set to carry out supervised training on the knowledge semantic search model, replacing the RAG mixed search module, and carrying out continuous iteration. By means of the technical scheme, new domain knowledge and operation feedback data can be flexibly applied, and domain self-adaptive retrieval enhancement generation is achieved.

The invention discloses a primary helium fan health state evaluation method based on multi-sensor data fusion. According to the method, a vibration sensor, a temperature sensor, a current sensor and the like are used for comprehensively monitoring the primary helium fan, a deep learning model is used for carrying out feature extraction and fusion on multi-source heterogeneous data, a fault diagnosis knowledge base based on a knowledge graph is constructed, and real-time and accurate evaluation of the health state of the primary helium fan and intelligent diagnosis of a potential fault mode are achieved. Meanwhile, a reinforcement learning algorithm is introduced in the method, the maintenance strategy of the main helium fan is automatically optimized, the equipment reliability is improved to the maximum extent, and the whole life cycle cost is reduced. The method provided by the invention has good adaptability and expandability, can be popularized and applied to nuclear power and other industrial fields, and provides important technical support for improving the intelligent level and operation safety of key equipment.

The invention discloses a container tracking management system and method. The method comprises the following steps: S1, carrying out dynamic encryption binding and multi-dimensional data acquisition on an RFID chip; s2, realizing real-time position data acquisition of the container under different environmental conditions through a multi-source signal fusion and error correction algorithm; s3, various sensor modules are installed inside and outside the container, and data processing is carried out through distributed edge nodes; s4, performing on-chain storage on total data generated in the transportation process through a Hash encryption algorithm, and realizing automatic data verification, state tracking and abnormity alarm in combination with an intelligent contract; s5, carrying out real-time dynamic modeling analysis on the transportation path and resource allocation, and dynamically adjusting the transportation plan by using a distributed scheduling strategy; and S6, integrating the real-time state data, the path optimization model and the transportation scheduling decision, and providing a report for a user. The method has the advantages of high positioning precision, high data security, high transportation efficiency and good real-time performance.

The invention relates to a data processing method and system based on dynamic routing. The method comprises the following steps: receiving a transaction request initiated by a user; the transaction request comprises merchant information, a payment mode and a transaction amount of the user; obtaining a currently available payment channel; performing multi-dimensional scoring on each payment channel to obtain a scoring result of each payment channel; determining an optimal payment channel according to the scoring result of each payment channel; processing the transaction request by using the selected optimal payment channel; and if the transaction fails, re-scoring other payment channels, and selecting a suboptimal payment channel according to a scoring result until the transaction succeeds or all payment channels are tried. The payment channel selection accuracy, the payment efficiency and the user experience can be improved.

The invention provides a data center energy efficiency management method and system based on digital twinning. The method comprises the following steps: constructing a digital twinborn body of a data center; synchronizing the actual operation state of the data center to the digital twin body in real time; based on the digital twin, simulating the influence of different energy efficiency optimization strategies on the energy efficiency of the data center; selecting an energy efficiency optimization strategy with an optimal evaluation result on the digital twin, and applying the optimal energy efficiency strategy to energy efficiency management of the data center so as to improve the energy efficiency of the data center and reduce the operation cost; and monitoring the actual energy efficiency change of the data center after the energy efficiency optimization strategy is applied, and feeding back a monitoring result to the digital twinborn body for correcting the model parameters of the digital twinborn body so as to continuously optimize the energy efficiency optimization strategy. According to the technical scheme provided by the invention, the effect of the energy efficiency optimization strategy can be quantified through a systematic evaluation method, so that the flexibility of energy efficiency management of the data center is improved.

The embodiment of the invention provides a humanoid robot flexible assembly method and system based on a thinking chain and a medium, and belongs to the technical field of natural language processing. According to the scheme, the assembly task instruction provided by the user is obtained, the assembly task instruction is subjected to task analysis through the thinking chain model, the weight of each word in the assembly task instruction is calculated through the attention mechanism in the analysis process, and the analysis target related to the current task is dynamically adjusted according to the weight; the task is decomposed into a plurality of subtasks layer by layer according to an analysis target, a task tree structure is obtained, the task tree structure is traversed to determine an assembly plan, the assembly plan comprises an execution arrangement sequence, task path planning and assembly action parameters, and the humanoid robot is controlled to execute assembly operation according to the assembly plan. The task information is automatically extracted by combining a hierarchical thinking chain and an attention mechanism and is converted into assembly operation, a clear execution plan is generated, and the flexibility of task analysis is improved.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

The invention provides a multi-agent cooperation system, which comprises a multi-agent cooperation engine module, a task decomposition and distribution module and a tool calling normal form module, and is characterized in that the task decomposition and distribution module is used for decomposing tasks and distributing decomposed sub-tasks to agents; the multi-agent cooperation engine module is used for managing cooperation and communication between agents; and the tool calling module enables the intelligent agent to dynamically call different tools. According to the multi-agent collaboration system, the collaboration efficiency among the agents, the flexibility of task decomposition, the adaptivity of tool calling and the intelligent level of user interaction are remarkably improved, and remarkable technical advantages and effects are shown in complex task processing and dynamic environment processing.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention provides an intelligent dosing method and system, and the method comprises the following steps: building a multi-source water quality data collection and preprocessing unit, and achieving high-quality data acquisition through employing a self-adaptive sampling frequency adjustment and data complementation algorithm; constructing a parameter feature extraction and correlation analysis system, and establishing a parameter coupling network; an intelligent dosing control strategy optimization system is constructed, and accurate control over the dosing process is achieved through deep reinforcement learning and a multi-objective optimization algorithm; a time-frequency domain dual evaluation system and a space-time two-dimensional feature extraction framework are established, and control effect evaluation and system characteristic analysis are achieved. According to the invention, the accuracy and economical efficiency of dosing control are obviously improved, the standard reaching rate of the effluent quality is improved to 99.5% or above, the use efficiency of chemicals is improved by 40-50%, and the operation cost is reduced by 35-45%.

The invention relates to the technical field of ship energy management, particularly discloses an energy collaborative management system and method based on an energy storage battery, and is used for solving the problems that when an existing ship energy management system integrates energy storage battery information, it is difficult to accurately evaluate actual energy consumption requirements, and consequently energy waste and equipment overload risks are caused. The system comprises an energy storage system unit, a system monitoring unit, a data processing unit and an energy management system unit, and is characterized in that the system operation state is determined according to real-time data monitored by the system monitoring unit; by introducing an adaptive learning rate adjustment formula, dynamically adjusting a weight factor, optimizing an energy efficiency function, adjusting output power of an energy storage battery and a generator in real time, optimizing energy distribution, ensuring stable power supply, reducing overload of the generator and energy waste, improving stability and reliability of a ship electric power system especially in a complex navigation environment, and reducing energy consumption. The service life of equipment is prolonged and the overall operation cost of ships is reduced.