282 results about "Function model" patented technology

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of air conditioning energy consumption optimization, and discloses an HVAC multi-unit air conditioning system energy consumption optimization matching system and method, a polynomial function model is constructed based on historical data and real-time operation parameters, and a nonlinear relation between energy consumption and power distribution of each unit is accurately described; secondly, the adaptability of the model is enhanced by means of principal component analysis, segmented modeling and the like, global optimization of model parameters is realized in combination with a genetic algorithm, and the prediction precision and robustness of the model are improved; in the optimization control level, a multi-unit collaborative weight factor is introduced, a weighted particle swarm optimization algorithm is adopted, a power distribution strategy is dynamically adjusted, and collaborative optimization control among multiple units is achieved. Through a complete acquisition-modeling-optimization-execution closed-loop control structure, intelligent sensing of the running state of the multi-unit air conditioning system and optimal energy consumption scheduling are achieved.

The invention discloses a beneficiation process intelligent optimization control method and system based on a knowledge graph, and relates to the technical field of beneficiation process control, and the method comprises the steps: collecting static attribute data and dynamic operation data, constructing a causal knowledge graph, and obtaining a causal relationship between nodes in the causal knowledge graph and an optimization target constraint; based on a causal relationship between nodes in the causal knowledge graph and optimization target constraints, screening data from a historical database to form a high-value historical data set; according to the high-value historical data set, a reward function model is constructed, a strategy function is trained, and a reinforcement learning control strategy is obtained; running the reinforcement learning control strategy, generating a control instruction, and issuing the control instruction to the target equipment to obtain dynamic optimization control; and performing real-time monitoring on the dynamic optimization control, and performing anti-fact reasoning based on a causal knowledge graph when abnormal deviation of the running state is detected to obtain an anti-fact reasoning result.

The invention discloses a dial plate processing optimization system based on big data, and relates to the technical field of dial plate processing, data is collected, anti-fact sample data is generated, a causal atlas structure including processing process nodes and an edge weight function model is constructed, processing path performance is evaluated based on an abstract objective function, and the processing process is optimized. According to the method, the causal atlas is constructed based on multi-source data such as tool wear degree data and spindle vibration amplitude data, and path deduction and parameter optimization of a target state are realized in combination with an anti-fact sample; self-adaptive control over the machining state is achieved through a structured deviation matrix, a causal relationship and a weight function model are dynamically corrected through a feedback learning mechanism, and data-driven optimization and defect prevention in the machining process are achieved.

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention relates to the field of materials and chemical calculation, in particular to a machine learning potential function model construction method of a solid-solid interface system, which comprises the following steps: constructing the solid-solid interface system; the method comprises the following steps: calculating a solid-solid interface system based on a first principle, recording output data of each configuration in the solid-solid interface system in each step of a simulation process, and forming a high-precision data set; performing fine tuning on a pre-trained MACE model by adopting a high-precision data set, and performing optimization by adopting a physical constraint loss function to obtain a preliminary machine learning potential function model; performing hyper-parameter tuning on the preliminary machine learning potential function model to obtain a fine tuning machine learning potential function model; according to the method, the machine learning potential function model based on the high-order equivariant graph neural network architecture is adopted, and the characteristics of the solid-solid interface system are effectively learned.

The invention provides a chip verification method and device, equipment, a medium and a program product, and the method comprises the steps: receiving a first operation instruction of a to-be-verified module and a second operation instruction of a reference model in parallel, the reference model being a function model used for simulating the expected behavior of the to-be-verified module; searching a first instruction record matched with the first operation instruction from a request queue of the reference model, and searching a second instruction record matched with the second operation instruction from a request queue of the to-be-verified module; and determining a verification result of the to-be-verified module according to a comparison result of the first operation instruction and the first instruction record and / or a comparison result of the second operation instruction and the second instruction record. According to the invention, a structured and reusable universal chip verification method is realized, so that a tedious customized scoreboard design is replaced, and the efficiency and accuracy of verification work are greatly improved.

The invention discloses a method and system for solving a partial differential equation based on a KAN and MLP parallel structure, and belongs to the technical field of deep learning. The method comprises the following steps: constructing a parallel neural network comprising a KAN branch and an MLP branch, and introducing a fusion factor to carry out weighted fusion on the output of the KAN branch and the output of the MLP branch; based on a physical information neural network principle, constructing a loss function comprising an equation residual term, an initial condition term and a boundary condition term; training the parallel neural network by using an adaptive optimization algorithm, and minimizing the loss function; and inputting variables in the partial differential equation into the trained parallel neural network, and outputting a solution of the partial differential equation. By effectively combining the nonlinear expression ability of MLP and the function modeling advantage of KAN, the expression and solution ability of a multi-scale, non-stationary and complex physical field is improved; the modeling precision and convergence stability of a complex physical system, especially a partial differential equation driving problem, can be effectively improved.

The invention discloses an adaptive multi-angle elbow airtightness detection method, which comprises the following steps: arranging a flexible pressure sensor array and a micro inertial measurement unit on the inner wall of an elbow, collecting multi-point sealing pressure and attitude signals in real time, fusing geometric parameters and a spatial mapping relation, and completing denoising, normalization and multi-curvature region mapping of pressure data; a distributed pressure-attitude database is combined, a function model associated with sealing pressure and pipeline geometric parameters is established, inflation, exhaust and deformation of each node are dynamically adjusted through an intelligent closed-loop regulation and control algorithm, collaborative optimization of multi-node pressure and a fitting state is achieved, and the sealing performance of the pipeline is improved. According to the method, the accuracy and the self-adaptive adjusting capacity of sealing pressure distribution under a complex bent pipe structure are effectively improved, the tiny leakage real-time positioning and self-learning optimization capacity is achieved, and the method is suitable for sealing quality guarantee of various types of curved surface pipelines.

The invention provides a power equipment three-dimensional dynamic updating method and system for virtual reality training, and the method comprises the steps: collecting the working state data of power equipment in real time through a sensor; processing the working state data based on a preset function model, and calculating the physical state change of the power equipment; according to the physical state change, adjusting physical attributes of the three-dimensional model of the power equipment in real time so as to simulate physical effects of thermal expansion, stress concentration or structural deformation; loading the adjusted three-dimensional model into a virtual reality environment, and performing real-time display through a virtual reality rendering platform; and after the user executes the interactive operation through the virtual reality equipment, the working state of the power equipment is updated, and the influence of the operation on the three-dimensional model of the equipment is dynamically fed back, so that a virtual-real linkage closed-loop training process is formed. The virtual model can change in real time along with the collected data, and the technical verisimilitude and operability of virtual training are remarkably improved.

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention relates to a new energy micro-grid multi-objective optimization control method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time output data of renewable energy sources of the micro-grid, operation parameters of energy storage equipment and load demand records, and performing time sequence analysis to obtain an energy output variation rule and a load demand curve; constructing a multi-objective function model containing economy-stability double objectives and a coupling correction term based on the law and the curve, generating an initial balance scheme through linear programming, and iteratively determining resource collaborative allocation parameters through a particle swarm optimization algorithm; the distribution parameters and the energy storage available capacity are input into the agent model, a real-time optimization decision instruction is generated, and an economic evaluation value is calculated in combination with running log data; and if the evaluation value does not reach the power grid stability threshold value, recalculating a balance scheme through a multi-objective function model, and generating a standardized equipment instruction set. According to the method, collaborative optimization of economy and stability of the micro-grid is realized, and stable operation of the micro-grid in different scenes is guaranteed.

The invention provides a method for additive forming of a high-performance Ti2AlNb three-period minimal curved surface lattice structure in a nitrogen atmosphere, and belongs to the technical field of additive manufacturing. Comprising the following steps: step 1) modeling an implicit function of a TPMS configuration: designing a CAD model of the TPMS configuration and exporting the CAD model as an STL file; 2, pretreatment of the Ti2AlNb lattice structure formed through L-PBF, wherein an STL file is subjected to slicing treatment, and technological parameters are set; 3, L-PBF forming of the Ti2AlNb lattice structure is conducted, specifically, in the nitrogen-argon mixed atmosphere, the Ti2AlNb lattice structure is formed through the L-PBF technology; and (4) the TPMS configuration Ti2AlNb lattice structure is subjected to surface treatment. According to the method, the Ti2AlNb lattice structure with the TPMS configuration is designed through implicit function modeling, the Ti2AlNb lattice structure with the TPMS configuration is formed through L-PBF in the nitrogen-argon reaction atmosphere, the problems that in traditional particle strengthening, reinforcements are distributed unevenly, and the solid solution strengthening effect is limited are solved, and good matching of the strength, plasticity and toughness of the lattice structure is achieved.

The invention relates to the field of chip verification, and discloses a construction method of a verification environment, a verification method and a system.The construction method of the verification environment comprises the steps that the connection relation between a to-be-verified component and bus function models is established, and communication connection is established between the matched bus function models and proxy models; initializing each agent model in the software end environment, and completing the initialization process of each agent model after receiving a reset release signal transmitted by the to-be-verified component in the hardware end environment; calling a preset configuration function in the hardware end environment to construct a configuration transaction in the software end environment through the preset configuration function; and driving the configuration transaction through the bus function model in the hardware end environment, and finishing a transaction configuration process of the to-be-verified component in the hardware end environment based on a driving result. According to the technical scheme provided by the invention, the verification process of the to-be-verified component can be accelerated, and the verification efficiency of the to-be-verified component is improved.

The invention discloses a formal verification method based on a hierarchical verification framework, which comprises the following steps of: firstly, dividing a target program into a C code and an assembly code, modeling and verifying the assembly code, then modeling and verifying the C code, and in the modeling process of the C code, carrying out verification on the C code; and defining a specific state and an abstract state of each data structure, establishing mapping through a refinement relationship, defining a function model based on the abstract state, constructing a function API specification, and finally defining system properties for a target program according to the function model and verifying the system properties. According to the method, a hierarchical verification framework and a formalization tool are combined, formalization verification is carried out on the target program from specific implementation to abstract division into three layers, comprehensive verification from bottom-layer codes to high-layer system properties is realized, the risk of security vulnerabilities is reduced, and the reliability of the target program is improved.

The invention discloses an incentive mechanism and power distribution method based on federated learning, and the method comprises the steps: building an FL system model, a time delay model and an edge device and server utility function model according to a system scene, putting forward a two-stage Stackelberg game optimization problem on this basis, and solving a resource optimization configuration scheme through a two-stage convex optimization iterative algorithm (TSPA). According to the method, the earnings of the server and the edge equipment are modeled by utilizing the two-stage Stackelberg game model, so that the power distribution is optimized, the model precision is improved, the model safety is guaranteed, and the training time delay is shortened. In the model, the server is used as a leader of the game and formulates a reward strategy, the edge device is used as a follower, and the uploading power and the interference power of the edge device are optimized based on the reward strategy of the server. And finally, the purpose of maximizing the benefits of both the server and the edge equipment is achieved by analyzing the equilibrium point of the game, iteratively optimizing model uploading and cooperative interference power distribution.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a generative stochastic model and an action-value function model to build a biochemical structure. Indeed, in one or more implementations, the disclosed systems generate a flow measure for a constructive object option in building a biochemical structure and further generate an action-value for the constructive object option. For instance, the disclosed systems combine the flow measure and the action-value to select the constructive object option from a plurality of constructive object options. Moreover, in some instances, the disclosed systems generate the biochemical structure using the selected constructive object option.

The invention discloses a lightweight time sequence information fusion perception control method and system, and relates to the technical field of Internet of Things weak terminal data acquisition, and the method comprises the steps: obtaining key perception data, and tracking the local task load and communication load of a node in real time; based on a feasible space window of various non-linear fitting functions, carrying out adaptive segmentation on input sensing data and channel states; on the basis of curve features of the time series data, preprocessing the time series data, then performing feature extraction on a curve, and selecting important features; analyzing and predicting the compressed curve characteristic data, classifying the time sequence data into curves with different characteristics, selecting a corresponding empirical function model for fitting according to a classification result, and finally dynamically changing the running state of the node by a self-adaptive adjustment algorithm; and performing dynamic control based on the future trend of the data. According to the method, the future trend of the task and the communication load is coupled with the running state of the task and the communication load, and the change rate of node tracking data is combined with the historical condition of the node tracking data, so that the self-adaptive adjustment of the running state of the node is realized.

The invention discloses a new energy system frequency domain modeling and equivalent circuit order reduction analysis method, and relates to the technical field of new energy power system modeling and stability analysis. The method is used for solving the problems that in an existing frequency domain modeling means, the automation degree of data acquisition is low, fitting precision and stability are poor, equivalent circuit universality is poor, and order reduction analysis is not systematic. The method comprises the following steps: acquiring system frequency domain response data based on disturbance injection; constructing a system rational function model by adopting an improved vector fitting algorithm; and then model order reduction is realized through state space modeling and an SVD-based balanced truncation method, and response consistency and stability analysis is carried out in a frequency domain and a time domain. The method can be widely applied to modeling analysis of new energy equipment such as wind power equipment, voltage source converters and high-voltage direct-current power transmission equipment, and has the advantages of being high in automation degree, high in modeling precision, good in visualization, clear in structure, suitable for simulation and control design and the like.

The invention relates to the technical field of sales assistance, in particular to an AI technology-based sales assistance system and method, and the system comprises a function modeling module, an interaction analysis module, an inventory decision module, a strategy compiling module and a terminal adaptation module. According to the method, function nodes are extracted through a natural language processing technology, a topological relation is constructed, dynamic hierarchical management of a sales function system is realized, an attention intensity model is established through interaction duration data, a priority strategy is generated in combination with an inventory pressure coefficient, and recommendation weight distribution is optimized when resources are insufficient. A decision tree algorithm is used for carrying out multi-dimensional parameter conflict detection and dynamic sorting, a terminal adaptive personalized verbal skill is generated, the resource allocation real-time response capability is improved, an inventory and strategy linkage mechanism is enhanced, accurate matching of verbal skill and inventory is realized, artificial decision delay is eliminated, customer demands and inventory pressure are balanced, and sales conversion resource allocation targeting is enhanced.

The invention provides an agent control instruction generation method and device, equipment, a medium and a product. The intelligent agent control instruction generation method comprises the following steps: splitting an obtained natural language according to a set rule to obtain splitting units; generating a function corresponding to each splitting unit based on the splitting units and a trained function model; constructing a capability logic expression corresponding to the obtained natural language based on the function corresponding to each splitting unit; and generating an agent control instruction based on the capability logic expression. By analyzing the natural language, the corresponding split unit is converted into the corresponding function, the natural language data is standardized through the function, then the agent control instruction is constructed based on the function, and semantic analysis is decoupled from the internal logic of the agent, so that the intelligent agent control instruction is obtained. Therefore, the instruction generation method can be flexibly and repeatedly used and is convenient to migrate.

The invention discloses a power demand prediction method, system and equipment based on market behavior influence deduction and a conditional diffusion model in the technical field of power market operation and demand prediction. The method comprises the following steps: constructing a market subject profit function model according to obtained power demand influence factors, carding a causal relationship between power demands and the power demand influence factors, and performing system dynamics simulation verification on the power demands to obtain a power demand simulation evaluation result; screening the power demand influence factors according to an evaluation result, performing data preprocessing on a data set of the power demand key influence factors, and expanding the preprocessed data set by using a diffusion model to obtain an expanded data set; performing correlation analysis and frequency domain transformation on the expanded data set to obtain a correlation coding matrix and a frequency domain data set; and inputting the expanded data set, the correlation coding matrix and the frequency domain data set into a Transform model to obtain a power demand prediction result.

The invention provides a multi-source heterogeneous data integrated management method based on a digital main line, a medium and a system, and belongs to the technical field of industrial digital main lines. A digital main line platform is used for collecting data of various formats and conducting standardization processing, a deep ontology fusion model is adopted for achieving cross-domain semantic understanding, and the data of various formats is obtained. Semantic understanding efficiency is improved through adaptive pooling feature dimensionality reduction and separable attention calculation, a storage space is optimized by adopting similarity aggregation de-duplication or a distributed independent storage strategy according to a data overlap ratio, and a digital principal line data consanguinity tracing relation is established to realize full-life-cycle data link construction. Based on the semantic mapping deviation value, a fine fine tuning or coarse tuning mode is adopted to optimize semantic understanding parameters, longitudinal integrated mapping from a function model to a performance model and a physical model and a transverse integrated framework of multi-domain comprehensive simulation are constructed, and the technical problem that cross-domain model semantic understanding accuracy is insufficient is solved.

The invention belongs to the field of artificial intelligence and natural language processing, particularly relates to a multi-round dialogue optimization method based on intelligent customer service and aims to improve system response quality and efficiency. The method comprises the following key steps: S101, receiving data input by a user, converting voice data into characters, performing word segmentation, denoising and feature coding, and generating a vector sequence; s201, an entity is extracted through a SpanBERT model, and a dynamic entity map is initialized and updated in real time; s301, intention recognition is conducted through the dynamic entity map, a slot position is generated, and the slot position is filled with the entity data; s401, generating a pseudo-reply according to the intention and the slot position information, calculating auditing reliability, continuing if the reliability is high, and reprocessing the input data if the reliability is low; s501, calculating task complexity, if the task complexity is lower than a threshold value, generating a natural language response, and if the task complexity is higher than the threshold value, generating a response by using a full-function model; therefore, the technical defects that the context dynamic association capability is insufficient and the manual auditing efficiency is bottleneck in the prior art are overcome.

The invention discloses a new energy uncertainty interval fine modeling method based on ELM-Copula. Comprising the following steps: 1) data reading and preprocessing: obtaining a power prediction value and an actual output value by reading historical operation data of a new energy electric field, performing kernel density estimation on the per-unit power prediction value and the actual output value, and calculating an error absolute value according to the per-unit power prediction value and the actual output value; 2) constructing a dynamic Copula function model; 3) measuring goodness of fit of the model; 4) calculating a confidence interval of a prediction error, analyzing uncertainty of power generation power prediction, and giving a confidence interval of a prediction value; according to the method, a multi-type dynamic Copula model is introduced to construct a dynamic dependency structure between a prediction error and power, the ELM is applied to a post-processing stage of a dynamic Copula prediction interval, a correction coefficient is generated by learning historical deviation characteristics, and an original interval is shrunk, so that the prediction precision and practicability are improved on the premise that the coverage rate is not reduced.

A radar confrontation equipment modeling method based on a reconfigurable component technology adopts a modularization modeling mode to construct a radar confrontation equipment model framework required by an electronic confrontation simulation deduction system, and the main design idea is that modularization modeling is carried out on an equipment simulation model, reusable components are not repeatedly modeled any more, the development efficiency is improved, and the modeling cost is reduced. The model stability is improved. Based on the modularization modeling idea, splitting of common model components and fusion with an existing model are fully considered in the model design process, a complex equipment function model is split into independent function components, the modeling complexity of each component is reduced, the function components can be repeatedly used, the modeling workload is reduced, and meanwhile the modeling efficiency is improved. And the stability of the equipment model can be effectively improved.

The invention relates to the technical field of data processing, and discloses a Bellman infinity error-based robust Q learning resisting method and a Bellman infinity error-based robust Q learning resisting system. The method comprises the following steps of: constructing an intrinsic state neighborhood set by utilizing a Bellman optimal Q function according to a state space, an action space and a dynamic transition probability function of a Markov decision process, depicting an optimal adversarial Q function model by utilizing a consistency adversarial robust operator, forming a training target by adopting a Bellman infinite norm, and constructing an intrinsic state neighborhood set; a projection gradient descent algorithm is combined to directly obtain an adversarial sample, or a Q value boundary is estimated through interval boundary propagation so as to obtain a substitute upper bound target of a training target, and finally network training is completed through methods such as stochastic gradient descent or adaptive momentum estimation. On the premise of ensuring the existence of the optimal robust strategy, the deep Q learning method with adversarial robustness and training stability is designed, so that the trained intelligent agent can show consistent excellent performance in a clean environment and an adversarial environment.

The invention relates to a method for establishing an SIL dynamic evaluation model based on Weibull distribution, and belongs to the technical field of safety protection systems. The method comprises the following steps: (1) establishing a dynamic cumulative failure probability function model based on Weibull distribution according to obtained failure data through a reliability test; (2) calculating Weibull distribution parameters by using a median rank method, a linear regression fitting method and a least square method; (3) drawing a function model image by using MATLAB software; and (4) in combination with a fault tree method, establishing an SIL dynamic evaluation model based on Weibull distribution. According to the SIL dynamic evaluation model for the final executive component of the safety instrument system based on Weibull distribution, the dynamic change trend of the final executive component PFDavg along with time obtained by the model basically accords with the reality, the accuracy of SIL evaluation is improved, and scientific method support is provided for design personnel of the safety instrument system.

The invention provides a system reliability analysis method and device based on a SysML model. The method comprises the following steps: obtaining an architecture model and a function model of a system based on a system modeling language (SysML); obtaining a first fault tree event corresponding to the functional object and a second fault tree event corresponding to the special architecture object; the special architecture object is the architecture object which has a corresponding relationship with the plurality of first fault tree events; according to the architecture model and the function model, a first fault tree model is established, the first fault tree model describes the logic relation of all fault tree events and the corresponding relation between all the fault tree events and the architecture object, and all the fault tree events comprise the first fault tree event and the second fault tree event.

The invention discloses a configurable building system and method for an energy management system. The system comprises an infrastructure layer, a configuration management layer, an atomic function library layer, an atomic function combination layer and an application scene layer. The infrastructure layer provides a containerization facility and a distributed data storage facility of the atomic function model; the configuration management layer is used for visualized interaction, gateway and protocol configuration and atomic function model instantiation; the atomic function library layer is used for creating and storing an atomic function model; the atomic function combination layer is used for polymerizing atomic function model examples to obtain atomic function combinations; and the application scene layer is used for calling the atomic function combination and finally forming an energy management system suitable for a corresponding scene. The invention provides a highly flexible, configurable and extensible energy management system development solution.