145 results about "Performance improvement" patented technology

The invention provides a large model cue word design method, system and device in an industrial scene and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a private cue word template library, and forming a domain special knowledge unit set through multi-source industrial data collection, intelligent semantic analysis and classified storage; customizing a four-layer structured template, sequentially establishing a task overview layer, a step disassembly layer, an instruction refining layer and an output specification layer, and constructing a layered mapping model from task definition to output execution; template library management and iterative optimization are carried out, and dynamic updating and performance improvement of a cue word system are realized through centralized system management, multi-source feedback acquisition and data-driven optimization. According to the method, a cue word design and optimization system based on a hierarchical structure is constructed for complex task requirements in an industrial scene, and the task processing performance of a large model in scenes of industrial production, quality detection, equipment operation and maintenance and the like is improved.

The invention provides an intelligent maintenance decision-making method and system based on bridge big data, and belongs to the technical field of bridge safety. The intelligent maintenance decision-making method comprises the steps of 1, collecting basic information, a detection report, a maintenance record and real-time state data of a bridge; 2, key features influencing the bridge performance are mined from the data collected in the step 1, the current state of the bridge is analyzed and evaluated, and the change trend of the bridge condition in a period of time in the future is predicted; 3, generating a plurality of possible maintenance strategy schemes based on the result of the step 2; step 4, carrying out cost benefit analysis on the maintenance strategy scheme generated in the step 3; 5, a multi-objective optimization algorithm is utilized, bridge performance improvement, maintenance cost, construction time and traffic influence factors are comprehensively considered, and an optimal scheme is selected from the maintenance strategy schemes evaluated in the step 4; and 6, formulating a detailed maintenance plan according to the optimal strategy and implementing the detailed maintenance plan.

The invention discloses an RAG performance improvement method based on process optimization. The method comprises the following steps: S1, preprocessing text data; s2, performing hierarchical text segmentation, obtaining a document, storing the text by utilizing a database after the document is segmented, converting the text into vectors, and storing the vectors into a vector knowledge base; s3, querying and rewriting to obtain a rewritten question; s4, combining parent document search with mixed retrieval, and retrieving related contexts for the rewritten questions; s5, performing rearrangement and denoising to obtain a final context; and S6, generating an answer, and sending the final context into a large language model for understanding questions and answers. According to the method, the links of text preprocessing, text partitioning, text embedding, query optimization, retrieval, retrieval post-processing, generation and the like are sequentially optimized according to the data flowing direction, the semantic retrieval precision is improved, and the performance of RAG in the aspects of text retrieval precision, answer generation quality, user interaction and the like can be remarkably improved; and the flexibility and the practicability of the system are enhanced, so that the system is more excellent in practical application.

The embodiment of the invention provides a thread pool operation state monitoring method and device, and the method comprises the steps: dividing the fluctuation range of the ratio of the number of active threads to the number of core threads into a high-load interval, a normal interval and a low-load interval, and calculating the optimization coefficient of the core threads based on the interval distribution characteristics; calculating the variance of the ratio of the total thread count to the maximum thread count, and extracting the adjustment coefficient of the maximum thread through the fluctuation characteristics of a variance curve; taking a difference value between the task growth rate and the processing rate as a calibration factor of the queue capacity; respectively comparing the evaluation results of the three parameters with a preset reasonable interval boundary to generate a parameter rationality score; counting the performance improvement effect of each preset adjustment strategy from a historical adjustment record; calculating a current adjustment step length based on the reference strategy; recording the adjustment suggestions and the calculation basis thereof into a version library, and setting corresponding effective conditions; according to the application, the efficiency and accuracy of monitoring the running state of the thread pool can be improved.

The invention relates to the technical field of industrial equipment control, and discloses an industrial equipment intelligent control system and method based on deep reinforcement learning. The system comprises an equipment state monitoring module, a reinforcement learning decision-making module, a control parameter adjustment module, an abnormity intervention module and an equipment performance optimization module. The equipment state monitoring module collects real-time operation data of equipment, analyzes state change by means of a deep reinforcement learning algorithm, and generates a state feature data set; the reinforcement learning decision-making module calculates a control strategy by using a deep reinforcement learning model and generates a control strategy parameter set; the control parameter adjusting module optimizes the operation efficiency according to the adjusted parameters and generates an optimized control parameter set; the abnormity intervention module monitors abnormity, recognizes intervention through deep reinforcement learning, and generates an abnormity intervention adjustment data set; and the equipment performance optimization module optimizes the performance indexes according to the parameters and generates a performance optimization parameter table. The system realizes accurate monitoring of equipment, intelligent optimization of strategies, timely intervention of abnormities and performance improvement, and meets intelligent management and control requirements of the equipment.

The invention discloses a dynamic module operation management system based on a programming language, and the system comprises a module analyzer which manages the dependency relationship between modules in the system, applies a compiling optimization technology before the modules are loaded, and optimizes the code execution through code expansion and constant folding; operation environment establishment: optimizing execution speed and resource scheduling by integrating a just-in-time compiling JIT technology and an optimization measure during operation; the module warehouse is used for controlling the retrieval and loading process of the module by adopting an optimized organization and index strategy; data structure optimization: using a skip list to adjust data structure design, and controlling data processing efficiency; dynamic optimization: the system dynamically adjusts an optimization strategy according to performance feedback and an operation environment, and controls improvement of self-adaptive performance; garbage collection: dynamically adjusting a garbage collection strategy by adopting a self-adaptive garbage collection algorithm, and reducing the module retrieval time, accelerating the module loading process and improving the data access efficiency.

The invention relates to the technical field of intelligent control, and discloses an intelligent control method, device and chip based on processing process management, and the method comprises the steps: setting an anomaly detection instruction through an intelligent control chip, writing the anomaly detection instruction into a control state register, and creating an anomaly detection infrastructure; constructing a data acquisition protocol, and executing feature extraction operation on the processing process data to obtain multi-dimensional feature training data; constructing first anomaly detection sub-models of a plurality of processing stages, and writing the pre-training parameters into the first anomaly detection sub-models to obtain a plurality of initialized anomaly detection models; deploying the plurality of initialized anomaly detection models to the intelligent control chip for model parallel training to obtain a plurality of second anomaly detection sub-models; and calculating sub-model weight coefficients of the plurality of second anomaly detection sub-models, and performing optimization integration and incremental learning to obtain a machining anomaly detection model, thereby ensuring continuous optimization and performance improvement of the anomaly detection model for machining process management.

The invention discloses an instruction scheduling method, device and system, a product and a medium, and relates to the technical field of processor design. The invention provides an instruction scheduling method aiming at the problem that the dynamic change in the instruction transmitting time cannot be accurately scheduled by the existing out-of-order scheduling strategy. The historical delay information of each instruction is stored through the delay cache, and when the instruction execution delay corresponding to the instruction is dynamic delay, the dynamic delay is predicted through the historical delay information stored in the delay cache, so that instruction scheduling is performed. According to the method, the dynamic delay of which the delay time cannot be predetermined is included in the consideration range of instruction scheduling based on the dynamic delay prediction mechanism. On the basis of the method, for dynamic change scenes in instruction transmission time such as memory access delay and cache miss, the optimal instruction transmission opportunity can be accurately predicted, so that the effect of instruction scheduling on performance improvement of a processor is further improved.

The invention discloses a signal processing detection system based on an FPGA architecture, and particularly relates to the technical field of hardware resource management, and the signal processing detection system based on the FPGA architecture comprises a signal acquisition module, a signal characteristic analysis module, a fluctuation prediction module, a self-adaptive hardware reconstruction module and a resource scheduling and optimization module. The signal acquisition module acquires signal data in real time, the signal characteristic analysis module extracts characteristic characteristics, and the fluctuation prediction module predicts signal fluctuation based on an AR model and judges whether hardware reconstruction is triggered or not; the adaptive hardware reconstruction module dynamically adjusts FPGA configuration and monitors the performance improvement effect when a reconstruction mechanism is triggered, and the resource scheduling and optimizing module dynamically allocates hardware resources according to the adjustment result and optimizes the system resource utilization rate; according to the method, when the signal fluctuation is relatively large, the hardware resource adjustment and task scheduling capabilities still have enough guarantee, and the problems of low processing efficiency and system response delay are avoided.

The invention provides a database fault processing method and system based on reinforcement learning, and relates to the technical field of database management, and the method comprises the steps: collecting a performance index, an execution plan and error log data at a fault moment, constructing a causal decision tree to recognize a fault root cause, and generating a propagation link; constructing a reinforcement learning action sequence based on the priority list; dividing a master control action set and an adjustment action set according to the influence degree of the action on the node state; and the execution strategy is dynamically adjusted according to the performance improvement ratio. According to the method, the fault root cause can be accurately positioned, the processing strategy is adaptively optimized, and the fault recovery efficiency and the system reliability are improved.

The invention provides a system and method for managing the lifecycle of machine learning models, from development to deployment and ongoing operation, across various environments including on-premises, cloud, and hybrid infrastructures. The system features a model build platform for data processing, feature generation, model development, training, and hyperparameter tuning. A model analytics engine extracts metadata, performs complexity analysis, and generates configuration files specifying environment settings and resource needs. A secure model repository enables version-controlled storage, while a deployment platform retrieves, validates, and deploys models in containerized environments like OpenShift or Kubernetes. The platform dynamically allocates resources, supports real-time and batch scoring, and monitors model performance with guardrails. Customizable agents provide real-time feedback and automated optimization, and the system can securely decommission models while maintaining detailed lifecycle records. The invention enhances the efficiency, security, and scalability of machine learning operations with continuous performance improvement and compliance automation.

The invention discloses a numerical control system feed-forward compensation method based on an edge controller, and the method comprises the steps: enabling the edge controller to obtain a machine tool instruction and a machine tool reality of a numerical control system in real time, calculating a tracking error, calculating a feed-forward compensation amount through an algorithm, transmitting the feed-forward compensation amount to the numerical control system, and installing a PLC function module in the numerical control system. And transmitting the received feed-forward compensation quantity to a servo feed-forward channel through a functional block, thereby realizing the feed-forward compensation function of the numerical control system. Through cooperation of edge calculation and a numerical control system, a feed-forward compensation algorithm is decoupled from a main controller, and high-precision real-time compensation is achieved through the parallel calculation capacity of an edge module. The design not only solves the contradiction between computing power and delay of a traditional system, but also supports multi-source data fusion and adaptive algorithm deployment through an open architecture, and provides a brand new path for performance improvement of a numerical control system.

The invention relates to a ship main engine oil consumption modeling method and system based on noon report data, and the modeling method comprises a noon report data collection and screening step, a noon report data classification step, a ship type classification step, a partial load condition fitting modeling step and a unified fitting and draught correction modeling step. Modeling operand and modeling complexity are greatly reduced, meanwhile, multi-level classification of weather (wind level and wave level) and ship types under different dimensions is carried out, a plurality of ship main engine oil models are constructed by adopting a partial load condition fitting method, a unified fitting method and a draught correction method respectively, ship slip rate is adopted as an intermediate variable, and a ship main engine oil model is constructed. According to the method, the relation between the navigational speed and the ship slip rate and the relation between the oil consumption and the ship slip rate are fitted respectively, the fitting flexibility and the formula complexity are improved, the fitting effect is better, the multiple built ship main engine oil models are accurate and efficient, cost reduction, efficiency improvement and safe and reliable performance improvement of enterprises are facilitated, and environmental protection and compliance management can be promoted.

The invention belongs to the technical field of industrial equipment control, and discloses a dynamic control method and system for feeding of a card breaking machine, and the method comprises the steps: obtaining the multi-dimensional state characteristics of the card breaking machine, and generating a recommended feeding control action through an automatic feeding control strategy generation logic; monitoring an actual feeding control action executed by the operator through the tile breaking machine, and comparing the actual feeding control action with the recommended feeding control action to identify a correction behavior of the operator; when the correction behavior of the operator is identified, monitoring equipment operation data of the mahjong machine in an observation time period after the actual feeding control action is executed, so as to evaluate multi-dimensional equipment performance improvement information; according to the multi-dimensional state features, the recommended feeding control action, the actual feeding control action and the multi-dimensional equipment performance improvement information, an automatic feeding control strategy generation logic is adjusted so as to perform feeding control on the tile breaking machine; therefore, the experience of an operator can be learned, and the self-adaptive capability and performance of the tile breaking machine under complex working conditions are improved.

The invention relates to the technical field of switch testing, and provides a switch testing system data interaction method and device, and the method comprises the steps: obtaining the operation state information and data packet flow information of each port, carrying out the extraction of the data packet flow information, obtaining a data packet feature parameter matrix, and carrying out the dynamic flow scheduling of a switch testing system, obtaining a port flow regulation scheme, and generating test result data based on the port flow regulation scheme and the operation state information; and performing comprehensive evaluation on the performance indexes of the switch based on the test result data, generating corresponding optimal configuration parameters based on an evaluation result, and applying the optimal configuration parameters to the switch test system. By acquiring the running state information and the data packet flow information of each port, the optimal configuration parameters are generated, efficient optimization and performance improvement of the switch test system are realized, and the problems of low response speed and insufficient flexibility in facing a dynamically changing network environment are improved.

The invention discloses a hydroelectric generating set bolt service stress nephogram inversion optimization algorithm, and relates to the technical field of hydroelectric generating set bolt stress measurement, and the method comprises the following steps: S1, finite element simulation model construction: S2, carrying out data cleaning on physical field simulation data and field measurement data obtained in S1, and obtaining a physical field data set; s3, constructing a stress nephogram inversion model; and S4, searching for optimal parameters. According to the method, the defects in the aspects of actual environment modeling and structure optimization in the prior art are overcome, and a new technical approach is provided for maintenance and performance improvement of the bolts of the hydroelectric generating set. And high-precision simulation in an actual operation environment is realized, and the defects in the aspect of digital modeling in the prior art are overcome. Based on theoretical physical constraints, a reinforcement learning algorithm can be constructed to invert stress distribution of a bolt in long-term service, comprehensive information is provided for structural health monitoring, and certain limitation exists in the aspect in the prior art.

A method for providing automated agent performance improvement includes obtaining a ranking for each agent of a plurality of agents; obtaining customer-agent interactions for each agent; determining a task label for each of the customer-agent interactions; selecting a first set of customer-agent interactions from the customer-agent interactions corresponding to a first task label; receiving application event streams corresponding to the first set of customer-agent interactions and comprising agent application usage data; analyzing the application event streams for statistically relevant differences for one or more activities performed by a first group of agents compared to a second group of agents; determining at least one activity of the one or more activities that is performed by the first group of agents differently than the second group of agents; generating guidance corresponding to the least one activity for the second group of agents; and deploying the guidance to the second group of agents.

The invention discloses an LLMs combined compression method based on joint optimization, an electronic device and a storage medium, a quantization method and a rarefaction method are combined, errors caused by quantization and rarefaction are minimized at the same time, the method comprises the following steps that 128 fragments are sampled from a WikiText2 training corpus, and each fragment comprises 2048 tags; 2, initializing a diagonal transformation matrix by using a statistical index, carrying out preliminary quantization on a weight by using an equivalent transformation operation, determining a quantization step length and a zero point, and calibrating the quantization step length and the zero point through the data sampled in the step 1; calculating a sparse mask, rearranging weights by using an importance index # imgabs0 #, and preferentially sparsening unimportant weights; carrying out joint optimization; and evaluating the compressed model by using different data sets, and verifying the performance improvement of the model.

The embodiment of the invention belongs to the technical field of artificial intelligence, and relates to a feature screening method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the feature screening operation of an original feature set according to a multi-level feature screening mechanism; performing hierarchical processing on the candidate feature set; key feature locking operation is carried out on the layered feature subsets; performing feature selection operation on the key feature subsets according to a greedy algorithm; respectively inputting the feature selection subsets into an initial risk control model for risk control model training operation to obtain performance index data; and performing feature optimization operation on the key feature subset according to the reward function and the performance index data to obtain target feature data. According to the method, when data fluctuation and a nonlinear relation between characteristics are coped, continuous optimization can be performed, and performance improvement can be kept. Particularly, the feature combination can be dynamically optimized based on real-time feedback, so that the screening efficiency is improved, and dependence on a preset rule is reduced.

The invention relates to a self-adaptive loop optimization method, a reconfigurable processor and a device, and the method comprises the steps: carrying out the classification processing of a to-be-optimized initial loop code, obtaining a classification result representing the characteristics of a loop structure in the initial loop code, determining a target optimization strategy from a plurality of pre-established candidate optimization strategies according to the classification result, and carrying out the optimization of the target optimization strategy. And performing optimization processing on the initial loop code by adopting a target optimization strategy to obtain a target loop code. The loop optimization code is automatically generated, manual writing is not needed, the optimization efficiency is improved, the performance of the reconfigurable processor can be improved for the loop-intensive application program through loop optimization, the optimized loop structure can more effectively utilize computing resources and memory bandwidth of the processor, and the performance of the reconfigurable processor is improved. And the execution speed of circulation is accelerated, so that the performance of the whole application program is improved.

The invention relates to an intelligent customer service method, and aims to provide personalized and accurate customer service reply through advanced technical means. The method comprises the following steps: firstly, acquiring and analyzing a query request of a user regardless of the form of text, voice or image; thirdly, intention recognition is conducted through a preset response model, a user portrait is generated in combination with historical interaction data of the user, and customer service requirements are predicted according to the user portrait. And by correcting the prediction intention data and considering the user preference, an accurate customer service reply is generated. In addition, the system also collects subsequent behavior data of the user, constructs a service scoring data set, and continuously optimizes the service model by using a machine self-learning algorithm. According to the method, the user satisfaction can be effectively improved, the click rate and the conversion rate are increased, the response time is shortened, the problem solving rate is improved, and continuous service optimization and performance improvement are realized.

The invention relates to a dynamic power control method and system for a storage and charging all-in-one machine, and the method comprises the following steps: data collection and monitoring: collecting and analyzing key parameters in real time through multi-source data fusion; dynamic safety threshold calculation: dynamically calculating a safety operation threshold of each subsystem based on real-time data, and ensuring that the system operates in an optimal state; multi-objective optimization control is carried out, an intelligent algorithm is adopted, and an optimal balance point is found among battery safety, power grid stability and charging efficiency; according to a fault processing and redundancy mechanism, a multi-layer fault response system is established, and the safety and continuity of the system under the abnormal working condition are ensured; according to the energy efficiency management and strategy optimization, long-term performance improvement and economical efficiency optimization are achieved based on a data closed loop, and the problem that potential safety hazards are caused due to the fact that a traditional storage and charging all-in-one machine system neglects the common influence of multiple factors such as SOC, temperature and power grid loads is solved.

The invention provides an employee performance prediction method based on multi-source heterogeneous data, and relates to the technical field of enterprise management. Structured data and time sequence data of employees are collected, the two types of data are preprocessed and then associated and fused with unique identifiers of the employees as cores, and a panoramic data wide table is constructed; then static and dynamic time sequence features are extracted from the wide table, the features are input into an artificial intelligence evaluation model, the model calculates basic performance indicators through a rule engine, and performance prediction scores are obtained through comprehensive evaluation in combination with a random forest regression model; the method is further provided with a potential prediction model based on an LSTM architecture integrated attention mechanism, and performance improvement potential scores and confidence intervals in the next three months can be output. According to the method, the problems of multi-source heterogeneous data fusion, model self-adaption, single evaluation dimension and the like are solved, and the employee performance can be predicted and evaluated comprehensively, objectively and prospectively.

The application provides a reliability analysis method for positioning accuracy of a shield tool changing robot, realizes sensitivity analysis of error parameters by constructing a kinematic model and an error proxy model, and accurately evaluates the failure probability of positioning accuracy. The assembly and machining errors are comprehensively considered, the error parameters are treated as random variables, the accuracy and efficiency of the analysis are improved. The mean and standard deviation of the random variable are used for sensitivity calculation, and a failure calculation method of positioning accuracy sensitivity is obtained, which provides an important basis for optimization design. The application not only helps to reduce the tool changing time, improve the construction efficiency and reduce the safety risk, but also provides an accurate and reliable tool changing robot manufacturing scheme for designers. The method has universality and flexibility, and can be widely applied to performance improvement and fault prevention of the shield tool changing robot. The implementation of the application will promote the development of shield construction technology and provide strong support for industrial automation and efficient production.

The invention provides a pressure sensing system compensation method and system, and the method comprises the steps: building a mathematical fitting model of an input and output pressure difference value and time under different conditions through testing and processing the data of a system in a starting time period under different input pressure and working temperature conditions, obtaining an initial environment condition when the system is started, and carrying out the calculation of the initial environment condition after the initial environment condition is obtained; and a corresponding mathematical fitting model is substituted to compensate the output pressure, so that the accurate pressure value in the system starting time period can be quickly calculated, and the performance improvement in the whole time period after the system is started is realized. According to the technical scheme, the technical problem that in the prior art, the pressure measurement precision deviation is large due to the fact that a thermal field is unbalanced in the starting period of a pressure sensing system is solved.

The present invention discloses a method, system and storage medium for making mid- and long-term maintenance decisions for a bridge group. The method first obtains the annual technical condition historical scores of each bridge from the database of the road network bridge group, and determines the actual performance influencing factors of the bridge; then establishes a fuzzy neural network model, takes the parameters corresponding to the actual performance influencing factors as input, and takes the annual technical condition historical scores as label output, trains the model accordingly, and obtains the corresponding bridge performance prediction model after passing the test; uses each bridge performance prediction model to obtain the annual technical condition prediction score of each bridge within a set period in the future; obtains the performance improvement benefits of the bridge under unit maintenance expenditure cost based on the maintenance record of the bridge; finally, establishes a decision generation model based on the multi-objective raccoon optimization algorithm, and outputs the mid- and long-term maintenance decision of the bridge group. The present invention can intelligently provide scientific and reliable mid- and long-term maintenance decisions for bridge groups.

A business management system with integrated business intelligence and IT-supported optimization, consisting of: a computer device comprising at least one processor and a memory arrangement for storing executable instructions; a data acquisition and integration unit that is operationally connected to the at least one processor and is configured to receive heterogeneous enterprise data from a variety of source systems, including financial transaction systems, operational control systems, human resource information systems, supply chain systems, customer interaction systems, and IT infrastructure monitoring systems, wherein the data acquisition and integration unit is further configured to normalize syntactic formats, resolve temporal inconsistencies, and map source-specific attributes into a unified enterprise data representation stored in the storage system; a business intelligence processing unit that is operationally coupled with the data acquisition and integration unit and is configured to calculate business performance indicators through multidimensional aggregation, correlation analysis, and trend extraction over the unified business data representation, with the calculated business performance indicators being time-stamped and permanently stored; an optimization unit that is operationally coupled with the business intelligence processing unit and is configured to compare the calculated business performance indicators with stored business objectives and constraints and to determine optimized business control parameters that correspond to resource allocation, operational planning, cost efficiency or performance improvement; a control and orchestration unit that is operationally linked to the data acquisition and integration unit, the business intelligence processing unit, and the optimization unit, wherein the control and orchestration unit is configured to coordinate the execution sequence, forward the results of the business intelligence analysis to the optimization unit, and manage feedback by incorporating the effects of applied optimized business control parameters into subsequent data acquisition cycles; and a communication interface that is connected to at least one processor and configured to exchange data and control signals with external enterprise systems and user terminals.

The invention belongs to the technical field of artificial intelligence, and relates to a dynamic controllability management method and system for long-term evolution of a large model, and the method comprises the steps: S1, obtaining the current state information of the large model; s2, calculating a controllability score of the large model based on the output distribution entropy, the distribution variable quantity and the logits drift of the large model, judging whether the current state of the large model deviates from a controllability critical zone or not based on the controllability score, if the current state deviates from the controllability critical zone, entering S3, and otherwise, directly entering S4; s3, actively correcting the offset behavior of the large model in the training process through a controllable calibration head, so that the current state of the large model does not deviate from the controllable critical zone; and S4, dynamically adjusting a training strategy of the large model based on the controllability score and the task category. According to the method, the large model has the capabilities of long-term plasticity maintenance, continuous performance improvement and rapid adaptation to new tasks, and the fundamental upgrade from a static optimization model to a self-evolution model system is realized.

The invention discloses a performance improvement method of a heat pump hot water unit, and relates to the technical field of heat pump hot water units, comprising an intelligent sensor, an intelligent control system, an actuator, a remote monitoring terminal, a remote monitoring platform and a data transmission device; according to the heat pump hot water unit, the intelligent sensor and the actuator are installed, the heat pump hot water unit can sense and respond to environment changes in real time, an intelligent control system accurately adjusts parameters such as the heating temperature and the water flow speed of the unit according to a preset algorithm, and it is ensured that the unit is in the optimal working state all the time; a user can check and analyze data through the remote monitoring platform and remotely control the unit according to needs; when the operation data of the heat pump hot water unit is abnormal or exceeds a preset threshold value, maintenance personnel can obtain fault information through the remote monitoring platform, and remote troubleshooting and guidance are carried out; meanwhile, the intelligent control system can preliminarily diagnose fault causes, accurate information is provided for maintenance personnel, the maintenance time is shortened, and the maintenance cost is reduced.

The invention provides an industrial control parameter adaptive optimization method based on deep reinforcement learning, and relates to the technical field of industrial control, and the method comprises the steps: collecting and preprocessing system operation data, dividing steady-state and fluctuation working conditions according to disturbance characteristics, and building a corresponding sub-network; adaptively switching the network output weight based on the disturbance degree to generate an initial optimization scheme; experience playback is carried out by adopting a self-adaptive parallel sampling strategy; training a deep reinforcement learning model and updating parameters; and the calculation performance improvement value is converted into a reward value to be compared with a dynamic threshold value. According to the invention, adaptive optimization of industrial control parameters can be realized, and the robustness and control precision of the system to different working conditions are improved.