62 results about "Multi field" patented technology

The invention discloses a fuel cell system modeling and control-oriented joint simulation design method, which comprises the following steps of: based on an electrochemical and heat transfer mechanism, dividing an electric pile into a plurality of nodes along a reaction gas flowing direction by adopting a distributed parameter method, and establishing a one-dimensional solid oxide fuel cell dynamic model by applying a multi-field unified modeling language Modelica; establishing a dynamic model of each auxiliary machine part of the solid oxide fuel cell system; all the components are connected according to a unified interface to form a system overall dynamic model, and dynamic simulation is carried out; carrying out multi-output multi-input state space model identification in SIMULINK by applying dynamic simulation data of the solid oxide fuel cell system; a model prediction controller KF-MPC based on Kalman filtering is developed; and establishing a joint simulation platform. According to the invention, rapid and accurate load tracking and temperature control are realized, and the operation safety of the system is maintained at the same time.

The invention relates to the technical field of large language models, in particular to a complex multi-field guided teaching question and answer generation method, device and equipment and a computer storage medium. According to the complex multi-field guided teaching question and answer generation method, aiming at the problem that the model is single in application field, the corresponding multi-round question and answer pair data set is processed by using a multi-field data source, and the model is finely adjusted based on the multi-round question and answer pair data set, so that the problem that the related field is single is solved, and the applicability and practicability of the teaching question and answer model are enhanced; aiming at the problem that the model lacks guiding ability, the input of the teaching question and answer model is further processed through thinking chain-based question extraction and disassembly and prompt engineering and context learning mechanism-based processing, so that the model question guiding ability is improved; in order to solve the problem that teaching effect evaluation indexes are single, the invention provides a new evaluation system, and a plurality of evaluation indexes can more comprehensively and accurately evaluate and optimize model teaching effects.

The invention relates to an abnormity alarm method and system for a feed production control system, and the method comprises the following steps: collecting multi-field data related in a feed production process in real time, the multi-field data at least comprising production equipment operation parameters, material characteristic parameters, environmental parameters and process control parameters; preprocessing the collected multi-field data, and fusing to generate a multi-field description feature vector representing the current production state; inputting the multi-field description feature vector into a pre-trained fault prediction model, wherein the fault prediction model respectively calculates the confidence of the fault type prediction result and the confidence of the fault point prediction result; wherein the confidence coefficient is in positive correlation with the distance from the feature vector to a known fault mode clustering center or the distance from the feature vector to a decision boundary; and when the fault prediction result indicates that an abnormal risk exists and the corresponding confidence exceeds a preset threshold, triggering a corresponding alarm signal.

The invention discloses a multi-field adaptive RAG optimization method and system, belongs to the technical field of deep learning, and aims to solve the technical problem of how to improve the adaptability and answer quality of an RAG system in a specific field and the generalization and robustness of a model. Comprising the steps of generating initial questions and answers and related contexts from a corpus of a target domain through a universal large language model and a retriever; based on a chained thinking prompting mechanism, guiding the large language model to evaluate and classify the retrieved context, generating a detailed reasoning process, evaluating the generated answer through the large language model, and screening out a training sample; dividing the screened training samples into preferred responses and non-preferred responses, and constructing comparison training samples; and performing joint fine tuning on the large language model and the retriever based on the screened preference data.

The invention discloses a large-model multi-field self-adaption method and system based on an adapter, and the method comprises the steps: collecting and preprocessing data, and obtaining training data; according to the training data, selecting a base universal large model and a parameter fine tuning method, and performing multiple adapter-based parameter fine tuning on the large model to obtain a plurality of fine-tuned adapters; using a deep neural network as an adaptive module, and combining the deep neural network with the base general large model and a plurality of fine-tuned adapters to form a large model multi-field adaptive network based on the adapters; training an adaptive module in the large-model multi-field adaptive network by using the training data to obtain a trained adaptive module; and according to the input of a user, reasoning the input by using the large-model multi-domain adaptive network to obtain the output of the corresponding domain. According to the method, under the condition that only a small number of parameters are added to the large model, the large model can be self-adapted in multiple fields, the large model does not need to be switched, the cross-field adaptive capacity of the large model is improved, and the performance of the large model is enhanced.

The invention discloses a transformer substation global digital twinborn fusion system, which comprises a data fusion module, a model fusion module and an artificial intelligence fusion module, and is characterized in that the data fusion module is used for collecting multi-source data of a transformer substation, preprocessing the data and performing data fusion on the preprocessed data; the model fusion module is used for constructing a bidirectional dynamic mapping model of a transformer substation physical entity and a digital virtual body, constructing a multi-field model and constructing a model increment updating mechanism, and the artificial intelligence fusion module is used for correcting the deviation of an AI model, performing simulation verification on the output of the AI model and generating a visual alarm. According to the method, the technical problems of incomplete data fusion, poor model fusion collaboration and insufficient AI and digital twinning fusion depth in the existing transformer substation digital twinning system are solved, real-time bidirectional accurate mapping of the transformer substation physical entity and the digital virtual body is realized, and the operation and maintenance efficiency and the decision-making precision are improved.

The invention discloses a design method of an energy-saving integrated water chilling unit. Modularized design is adopted, a compression module, a condensation module, an expansion module, an evaporation module and a control module are included, and all the modules can be flexibly combined; an intelligent control system is arranged, a sensor is used for collecting data, the operation mode is automatically adjusted through an intelligent algorithm, and remote monitoring is supported. And high-efficiency refrigeration technologies, such as a variable-frequency compressor, an optimized heat exchanger and a condensation mode, selection of a low-GWP refrigerant and the like, are applied. The waste heat recovery and utilization device is arranged, environment-friendly refrigerants and energy-saving materials are adopted, fine management is achieved through an energy-saving optimization algorithm and data analysis, and meanwhile the structure is compact and the automation degree is high. The unit is efficient, energy-saving, environment-friendly, intelligent and convenient, is suitable for multiple fields, and effectively improves the comprehensive performance and the use experience.

In recent years, reinforcement learning has a significant effect in multiple fields, but a traditional method has limited adaptability in a dynamic environment or a multi-task situation. The invention provides a multi-task reinforcement learning method named as a priority weighted soft modularization (SM-PHT) method, which is used for enhancing the adaptability and the generalization ability of an intelligent agent, and relates to a multi-task reinforcement learning method named as SM-PHT (SM-PHT), which is used for enhancing the adaptability and the generalization ability of the intelligent agent. SM-PHT integrates three key mechanisms: priority weighted knowledge distillation, and student network robustness is improved through weighted fusion of multi-model knowledge; the hierarchical cache is used for respectively storing low-level experience and high-level parameter optimization efficiency; and task embedding: capturing environment characteristics to enrich task representation so as to promote knowledge transfer. In the Meta-World MT10 challenge, the success rate of SM-PHT is twice that of the existing optimal method, and the average reward is improved by 30%; in the MT50 challenge, the success rate and the average reward are both improved by about 10%. Results prove that the stability and generalization of the mechanical arm in complex tasks highlight the application potential in the field of actual multi-task mechanical arm operation.

The invention discloses a multi-scale and multi-field digital twin model construction method for complex equipment, and belongs to the technical field of intelligent manufacturing and engineering. The implementation method comprises the following steps of: aiming at the characteristic that machine tool equipment comprises multi-scale layers and multi-disciplinary fields, displaying the multi-scale layers and the multi-disciplinary fields of physical entity equipment in a unified modeling mode by adopting a Modelica modeling language through non-causal modeling and connector modeling, and breaking through the limitation of complex coupling characteristics of the equipment; taking the minimum error between the output of the machine tool digital twin model and the test of a machine tool test bed as an optimization target, considering the main shaft rigidity, the main shaft size, the processing temperature, the thermal conductivity and the thermal expansion coefficient correction variables, and constructing a machine tool digital twin model correction optimization problem model; an optimal parameter combination is searched by adopting an improved particle swarm algorithm of a model-elite learning strategy, correction of the machine tool digital twinborn model is realized, the consistency of the twinborn model and physical equipment is improved, and the accuracy of the machine tool equipment digital twinborn model is ensured.

The invention provides a multi-domain task processing method, system and device based on knowledge fusion and Agent cooperation and a medium, and belongs to the technical field of cross-domain knowledge fusion. Tasks input by a user are decomposed, and a subtask set is generated; building a specialized Agent set according to a subtask calling skill mapping table and a role template library; executing cross-domain knowledge retrieval and fusion on the Agent set, monitoring output consistency and classifying collaborative conflicts; triggering a corresponding extension mechanism according to different conflicts, and updating the knowledge graph or generating a new Agent; meanwhile, field knowledge updating is monitored, and knowledge weights are adjusted and synchronized to related modules. Precise disassembly and specialized processing of complex tasks are realized, and the cross-domain knowledge fusion efficiency is improved; cooperation conflicts are solved, and system adaptability is enhanced; the knowledge is updated in real time, so that the processing timeliness is ensured, and the task processing quality and efficiency of the multi-Agent system are improved.

The present disclosure provides a framework for enhancing artificial intelligence models while reducing its memory requirements, for example, via a multi-field embedding approach. In one exemplary aspect, this framework may be applied to knowledge graphs to mitigate the effects of node degree extremity and to reduce embedding memory demand. For example, this framework may provide multiple graph fields in knowledge graphs that embed nodes in different dimensions to correct for skewness in the distribution of node degrees and decrease memory requirements when training KGE models. In some aspects, the framework may implement a user-specified strategy to split a knowledge graph into a number of distinct fields with similar connectivity patterns. KGEs may then be computed in a model-agnostic manner to allow each field to embed into a different dimensionality, and mathematical transformations may be used to convert embedding dimensions between fields when needed during algorithmic processing.

The invention discloses a polar region activeness analysis method based on multi-field public opinion big data, and relates to the technical field of data processing, and the method comprises the following specific steps: S100, screening polar region sparse features about public opinions, S200, mining implicit association among different polar region sparse features, S300, marking abnormal active points to generate a thermodynamic partition map, and S400, extracting the thermodynamic partition map; according to the polar region activeness analysis method based on the multi-field public opinion big data, the mining depth and breadth of polar region public opinion information can be remarkably improved, the polar region activeness can be effectively analyzed by effectively screening and analyzing polar region sparse features, and the polar region activeness can be effectively analyzed. According to the method, the dependence on high-frequency features is not limited any more, low-frequency but key information can be captured, potential relations between the low-frequency but key information and other features are mined through correlation analysis, the monitoring strength on data is enhanced, and it is ensured that the dynamic state of public opinions is mastered at the first time.

The invention discloses a hybrid power generation system energy prediction method based on multi-field unified modeling, and belongs to the technical field of power system energy control, and the method comprises the steps: selecting system working parameters, and constructing constraint conditions; constructing dynamic working models of the power generation system, wherein the dynamic working models comprise a hydrogen fuel cell working model and a lithium battery dynamic model; deploying a WSN module to collect working parameters of key parts of the hybrid power generation system in real time, and establishing a standard data set by adopting a dynamic filtering algorithm; classifying the standard data set into a corresponding cluster by adopting a clustering space input algorithm; and establishing a fuzzy prediction model based on a four-layer neural network, extracting related data from the standard data set according to a membership function, taking the related data as input of the fuzzy prediction model, and obtaining an energy prediction value. According to the method, adaptive energy prediction is realized through updating iteration, the energy distribution efficiency is improved, and accurate prediction of multi-field energy in the hybrid power generation system can be realized.

The invention discloses a multi-field die cutting part intelligent manufacturing management system and method based on the industrial internet, and relates to the technical field of data analysis, and the method specifically comprises the steps: building a product feature set based on the product demands of die-casting die cutting parts, and calculating the product passing rate of each die-casting die; extracting die-casting dies greater than a preset first qualified threshold value, obtaining pouring features, recording the pouring features as a die feature set, obtaining die features influencing die-casting die cutting piece production based on a correlation algorithm, and recording the die features as key influence die features; and for any die feature in the die feature set, based on the number of times of taking the die feature as the key influence die feature, obtaining a product feature of taking the die feature as the key influence die feature, and counting a constraint set of the die feature, thereby obtaining an optimal feature value range, and selecting an optimal die-casting die according to the optimal feature value range. The key influence mold features are identified based on the correlation algorithm, and the limitation that the quality of the mold is judged only depending on experience or a single parameter traditionally is broken through.

The invention discloses a multi-field fusion digital prototype development system and a digital prototype construction method thereof, and belongs to the technical field of digital prototypes. According to the method, by introducing the FACE architecture, professional models in other fields are subjected to virtual modeling again, and multi-scale and multi-dimensional models are unified, so that the professional models in different subject fields can be integrated comprehensively, and a complete digital prototype model is obtained. Meanwhile, after virtualization modeling, the model can be decoupled from the operation platform, and multiplexing of the model on different operation platforms is realized, so that the model can seamlessly run through a system design stage and a system implementation stage, repeated development and test of the model are avoided, and research and development efficiency of a spacecraft system can be further improved.

The application discloses a multi-field fake news detection method based on hierarchical potential field modeling, and belongs to the technical field of fake news detection. The method comprises the following steps: obtaining multi-angle multi-modal feature embedding of to-be-detected news by using a pre-training feature extractor; enhancing original field labels based on multi-modal features by using a field label enhanced memory network; constructing a single-modal and multi-modal processing flow by using a layer-stacking Transformer block, thereby modeling the field dependency relationship in news features in a hierarchical manner while adaptively fusing multi-modal features; introducing a variational mutual information decoupling module to supervise each layer of expert mixed network; obtaining a fake judgment feature by using a field perception Transformer, and outputting a fake judgment result through a classification head. By means of hierarchical field modeling and multi-modal feature fusion, the application effectively solves the distribution difference challenge existing in the multi-field adaptation of social media news.

The invention discloses a multi-field fusion execution method based on a heterogeneous data flow diagram, and belongs to the technical field of computer systems. According to the method, an application program is abstracted into an instruction-level data flow diagram, hierarchical abstraction and fusion are carried out on instruction-level, thread-level and program-block-level data flow nodes, and finally an efficient heterogeneous data flow diagram is formed; a heterogeneous data flow diagram abstract machine is defined, and distribution, storage, scheduling and execution processes of the heterogeneous data flow diagram on a hardware level are described in detail, so that efficient execution of the model is realized; by designing a specific mapping rule, the heterogeneous data flow diagram is accurately mapped to a heterogeneous data flow diagram abstract machine, and finally the heterogeneous data flow diagram is mapped to specific hardware. According to the method, the concurrency of natural data of the data flow diagram is utilized, and the problem of effective fusion of multiple fields is successfully solved.

The invention relates to the technical field of airborne system design, and discloses an airborne system digital forward design method, device, equipment and medium, a design process from demand analysis, architecture design, architecture tradeoff to simulation verification is established, integration and optimization of the airborne system design process are effectively realized, machine learning and multidisciplinary design optimization are combined, and the design efficiency is improved. According to the method, knowledge in multiple fields such as machinery, electronics and software is fused, design rules are mined from historical design cases through deep learning, a multi-target tradeoff analysis model is established, automatic generation and optimization of an architecture scheme are achieved, dependence on artificial experience is remarkably reduced, airborne system design efficiency is improved, design change cost is reduced, and the method is suitable for large-scale popularization and application. And the reliability of the airborne system is improved.

The application provides a dynamic modeling method and system for multi-field skill AI automatic semantic labeling, and relates to information retrieval, medical health and cross-field skill fusion scene. Through nonlinear dynamic modeling and biomedical stability control, the semantic label automatic generation and real-time matching of talent skill and health intervention effectiveness are realized. The application innovatively integrates the sound wave frequency of fetal brain promoting music method, maternal action instruction and blood type nutrition scheme into the "skill-time" frequency domain space, combines the Gevrey smoothing operator and fractional derivative evolution equation, accurately separates the temporary fetal movement fluctuation and low-frequency core characteristics, and breaks through the characterization limitation of traditional models on nonlinear biological coupling effect. By introducing the "intervention effect-time" frequency domain component and local dependency constraint, the short-term effectiveness and long-term value of music intervention on fetal neurodevelopment are quantified, and the model distortion problem caused by extreme data or cross-field parameter resonance in traditional methods is solved.

The utility model discloses a multi-field wear-resistant scraper conveyor which mainly solves the technical problem that transportation is affected due to the fact that the bottom of a groove body of an existing scraper conveyor is prone to being abraded in the transportation process. The middle part is provided with a middle groove, and the machine head and the machine tail are correspondingly connected to the two ends of the middle part. The middle groove comprises a groove body, limiting plates, a plurality of supporting rollers and chains, wherein the limiting plates are symmetrically and internally buckled on the tops of the two sides of the groove body and correspondingly form limiting cavities with the groove walls of the two sides of the groove body, the supporting rollers are perpendicular to the axial direction of the groove body and arrayed in the groove body at equal intervals, and the chains circularly run on the upper sides and the lower sides of the supporting rollers along the axis of the groove body. The chain plates are fixed to the chain at equal intervals and are perpendicular to the axis of the groove body, the belts are connected between the adjacent chain plates, and the bottom faces of the belts are in sliding contact with the supporting rollers when materials are conveyed. Limiting parts correspondingly extending into the limiting cavities are arranged on the two sides of the chain plate. The scraper conveyor can avoid direct contact with the bottom of the material tank body, so that the relative integrity of the tank body is ensured.

1. The name of the design product: intelligent AI edge gateway. 2. The use of the design product: provide data encryption security for inter-device communication, smart home, Internet of Vehicles, industrial Internet of Things and other multi-field applications. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application discloses a kind of adaptive control parameter optimization methods for multi-field industrial control system, belong to industrial control and intelligent control technical field;By collecting the multi-source heterogeneous time series data in multi-field industrial control system, a unified system state representation is constructed;The non-invasive virtual state evolution of multiple candidate control parameters is deduced, and the corresponding system response trajectory is generated;Introduce the preferred parameter verification mechanism based on physical residual and parameter risk potential, and screen out the parameter combination that violates physical constraints or has high risk;Based on spectral entropy and spectral geometric center, the frequency domain topology gating analysis is carried out, the stability and safety of the control parameters are constrained in frequency domain, and the application of the control parameters is triggered only when the frequency domain stability condition is met. Realize the safe, robust adaptive optimization of multi-field industrial control system control parameters without interfering with the existing production process, improve the reliability and engineering practicability of complex industrial control system parameter setting.

The invention relates to the technical field of education, and discloses a multi-field talent ability advanced cultivation method and system based on AIQ. According to the method, the professional ability and the AI enabling effect are quantified by constructing the dynamic ability model, so that accurate quantification and dynamic adjustment of the talent cultivation process are realized; layered advanced paths (L1-L5) are designed, a complete culture closed loop is formed from basic cognition to ecological fusion, a corresponding capability target and an implementation mode are defined for each level, and a systematic growth framework is provided for talents in multiple fields; a dynamic tool chain recombination mechanism is used for automatically optimizing AI technology module combination based on user real-time data and field demand changes; a complete closed-loop verification system is constructed through three-dimensional verification indexes of the technology fusion degree, the scene generalization and the commercial value index, and it is ensured that the cultivation result has technical innovation and practical application value.

The invention provides an aviation multi-field data adaptive extraction method and system based on a large model, and relates to the technical field of data retrieval, and the method comprises the steps: obtaining an aviation field multi-field data set, and carrying out the preprocessing of the data set to obtain a preprocessed data set; constructing a plurality of inverted index tables based on the preprocessed data set; performing semantic feature extraction on each paragraph of each text in the preprocessed data set based on a trained BGE model to obtain a semantic feature vector library; performing semantic feature extraction on a text input by a user based on the trained BGE model to obtain an input feature vector; calculating based on the large model to obtain a cosine similarity set and a morpheme similarity set; carrying out normalization processing on the morpheme similarity set to obtain a normalized value set; and performing fusion sorting on the normalized value set and the cosine similarity set to obtain an extraction result. According to the method, the keyword extraction result and the semantic extraction result are fused and sorted at the same time, so that the extraction richness and the sorting accuracy are ensured.

The invention discloses a multi-field time series data prediction method based on language guidance and near-end strategy optimization fine tuning. A used deep learning model comprises a time series encoder based on a Transform structure, a pre-trained large language model and five linear layers arranged in parallel; the method mainly comprises the following steps: carrying out preprocessing and normalization operation on multi-field time series data, dividing the time series data into single-channel input, and carrying out sliding window division processing; designing a structured template, inputting the structured template into a large language model in combination with language background information and a time sequence, and performing a pre-training stage, wherein two tasks of pre-training comprise reconstruction of an input time sequence and prediction of a future time sequence; and a near-end strategy optimization algorithm is adopted to carry out fine adjustment in a specified field, and finally a prediction model is constructed. According to the method, the large language model and the reinforcement learning technology are creatively fused, and the prediction accuracy of the model in multi-field time series data is remarkably improved.

The invention discloses a coordination control phase difference transition decision-making and distribution method, which comprises the following steps of: correlation adjustment amount calculation: taking any specified time point as a unified clock reference point, and calculating an extended transition adjustment amount required by a period switching moment and a shortened transition adjustment amount corresponding to the extended transition adjustment amount; according to the transition decision model establishment, through association adjustment amount and period phase boundary constraint, through accurate phase difference transition control, differentiated green time ratio distribution, real-time dynamic feedback and multi-field applicability, the defects in dynamic adaptability, multi-objective optimization and engineering economy in the prior art are systematically overcome, and the method has the advantages of high efficiency, high reliability and the like. And a core technical support is provided for intelligent upgrading of urban traffic.

The application provides a multi-field attention mechanism-based electric energy prediction method and system, wherein the method comprises the following steps: acquiring electric energy data and corresponding general features and field features; using a continuous feature encoder and a discrete feature encoder to encode continuous features and discrete features respectively; performing feature fusion on various encoded feature representations, taking the field feature representation as a query in the attention mechanism, and taking the general features as key values and key values in the attention mechanism; calculating the similarity between the query and the key value as the attention weight of the corresponding key value; performing weighted summation on the general features according to the attention weight, splicing the field features, and obtaining mixed features; and using a feedforward neural network to map the mixed features to the electric energy data to obtain an electric energy prediction value. The application can predict the electric energy of various cities and power consumption industries based on a single model, and can fuse electric energy data in different fields to realize accurate electric energy prediction.

The invention discloses a self-adaptive control parameter optimization method for a multi-field industrial control system, and belongs to the technical field of industrial control and intelligent control. Uniform system state representation is constructed by collecting multi-source heterogeneous time series data in a multi-field industrial control system; performing non-intrusive virtual state evolution deduction on the multiple groups of candidate control parameters to generate a corresponding system response track; an optimal parameter verification mechanism based on physical residual errors and parameter risk potential energy is introduced, and parameter combinations which violate physical constraints or have high risks are screened out; based on frequency domain topology gating analysis of spectrum entropy and spectrum geometric gravity center, frequency domain constraint is carried out on stability and safety of control parameters, and application of the control parameters is triggered only when the frequency domain stability condition is met. On the premise that an existing production process is not interfered, safe and steady self-adaptive optimization is carried out on the control parameters of the multi-field industrial control system, and the parameter setting reliability and engineering practicability of the complex industrial control system are improved.

According to the urban rail system based on the rail transit intelligent operation platform and the scene disposal method thereof, the technologies of the Internet of Things, cloud computing, big data, artificial intelligence and the like are fully utilized, and the dilemma that a traditional urban rail system is closed, isolated, solidified and isolated in information is broken through; a plurality of general technology platforms are integrated into a multi-field and multi-service fusion rail transit intelligent operation platform, the rail transit intelligent operation platform is used for system equipment access, data processing and analysis, algorithm construction and scene flow arrangement, customized construction and implementation can be carried out according to differentiated construction requirements of cities, a scene function design mode is adopted, and multi-field and multi-service fusion is realized. Rail transit application scenes such as intelligent passenger service, intelligent transportation, safety emergency command, intelligent train operation, station service management, section field comprehensive management, intelligent energy management, infrastructure protection and enterprise network management are constructed based on the rail transit intelligent operation platform, and the intelligent level and the application development efficiency of an urban rail system are improved.

The present invention discloses a steering gear control method and system based on PCA9685 module, which is realized by the main control module through I 2 The C interface is cascaded with multiple PCA9685 modules to build a unified control framework for multiple servos. Based on the target servo rotation angle, the duty cycle of the PWM signal that controls the servo is calculated, and a binary PWM signal calculation model is constructed in which the nth L298N module connected to each PCA9865 module modulates the i-th servo; and by optimizing the switching angle of the L298N module to control the forward and reverse rotation and high and low level output of each servo, precise control of the servo opening, closing and angle adjustment is achieved. The present invention provides a multi-servo control solution with synchronous control, efficient duty cycle adjustment and modular expansion capabilities, which significantly improves the accuracy, synchronization and energy efficiency of servo movement, and is suitable for multi-field scenarios such as complex industrial automation and robotics.