31 results about "Market competition" patented technology

The invention discloses a multi-modal resource collaborative optimization scheduling method, system and device for a virtual power plant, and relates to the related technical field of virtual power plant optimization scheduling, and the method comprises the steps: carrying out the unified modeling and dynamic aggregation of diversified distributed resources in the virtual power plant, and constructing a standardized resource pool; based on the external market and environment information, generating a prediction sequence of various future scenes; forming a state observation space, making a decision by using a deep reinforcement learning agent, and synchronously generating a real-time scheduling instruction and a joint bidding strategy; and executing a scheduling instruction and submitting market bidding, and performing continuous iteration and optimization according to actual market response and environment feedback. The technical problems that in the prior art, multi-modal resource collaboration is insufficient, response evaluation and scheduling are disjointed, and a multi-market collaboration mechanism is lacked are solved, and the technical effects that the collaborative scheduling instruction and the multi-market joint bidding strategy are generated through the deep learning agent, and the economical efficiency, the reliability and the market competitiveness of virtual power plant operation are improved are achieved.

The invention discloses a coal type sales planning and sales time collaborative management and control method and system based on a coal enterprise, and relates to the technical field of coal sales management. Multi-source data are integrated through technologies such as a data fusion algorithm, the problems of data dispersion and different formats are solved, high-quality data support is provided for follow-up analysis, the sales trend and price fluctuation are accurately predicted by applying a long-short-term memory neural network and a regression analysis model, enterprises are assisted in optimizing sales strategies, the inventory overstock risk is reduced, and the sales quality is improved. By means of linear programming and reinforcement learning algorithms, sales tasks are dynamically adjusted in combination with real-time data, market response speed and sales efficiency are improved, scientificity and flexibility of sales management of coal enterprises are effectively improved, and market competitiveness is enhanced.

The invention relates to the field of multi-currency quotation, in particular to a multi-currency quotation calculation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an original data set related to quotation; acquiring instant and historical exchange rate data of the transaction currency to form a market data set; calculating a cost currency estimated value of each cost composition item and summarizing to obtain a direct cost total amount; analyzing the research data to obtain a risk level, and providing a corresponding insurance cover scheme; calculating a cash flow estimated value under the influence of the exchange rate by combining the cash flow, the historical exchange rate and the insurance cover scheme, generating and sorting a contract quotation amount sample set according to a preset gross profit rate, determining the minimum contract quotation by combining the confidence coefficient, and obtaining the final contract quotation amount according to the premium parameter. According to the method for calculating the multi-currency quotation, the problems that the traditional quotation exchange rate has no standardized calculation logic and the decision is lack of quantitative support are effectively solved, accurate cost accounting and risk management and control are realized, and enterprise profit and market competitiveness are balanced.

The invention belongs to the technical field of power system automation and energy storage regulation and control, particularly relates to a thermal power energy storage frequency modulation auxiliary service benefit maximization method, and aims to solve the problems of disjunction between technical response and economic benefit, large equipment loss and poor market adaptability in a traditional frequency modulation strategy. According to the method, a multi-time-scale instruction analysis model is constructed, thermal power coal consumption and energy storage loss characteristics are fused, a dynamic cost-income coupling function is established, and thermal storage power is distributed in real time based on a rolling time domain optimization algorithm; and dynamically correcting model parameters by executing feedback and an online learning mechanism, and synchronously generating a market bidding strategy and benefit pre-judgment report. The system supports state-of-charge boundary adaptive adjustment and equipment collaborative aging evaluation, and is deployed at an edge computing node to realize millisecond-level response. According to the invention, the economic benefit of frequency modulation is obviously improved by 15-25%, the score of frequency modulation performance is more than 95, the energy storage life is prolonged by more than 30%, the fluctuation of unit income is reduced by 40%, and full-link closed-loop intelligent regulation and control and market competitiveness enhancement are realized.

The application discloses a bidding opportunity prediction and collaborative decision-making method based on multi-source announcement data, relates to the technical field of intelligent bidding, and comprises the following steps: collecting bidding announcement data from no less than two information sources, extracting core information after format conversion, and generating a structured field in JSON format; carrying out integrity and consistency verification on the extracted structured field in sequence, and completing unit and format standardization modification of digital fields; based on the standardized field data, calculating project scale scores and market competition scores respectively, comprehensively obtaining opportunity scores, and dividing potential levels; collecting enterprise multidimensional internal resource data to calculate bearing capacity scores, combining the opportunity scores to calculate bidding scores, and making collaborative decisions.

The invention belongs to the technical field of data mining, and discloses a standard necessary patent prediction method based on a large language model and a related device. The method comprises the following steps: acquiring a feature group of a patent text to be predicted; inputting the feature group of the patent text to be predicted into a pre-trained large language model for prediction, and obtaining a standard necessary patent prediction result based on the large language model; wherein the pre-trained large language model is obtained by training a general large language model based on a plurality of standard necessary patents. According to the model, a domain knowledge enhanced large language model and a dynamic time sequence analysis framework are fused, and patent domain pre-training and multi-source heterogeneous data fusion are introduced, so that the patent text understanding precision of the model is improved; and meanwhile, a dynamic prediction mechanism is constructed in combination with industrial standard evolution data and technology development trend data, so that prospective pre-judgment of standard necessary patents is realized, and powerful support is provided for enterprise patent strategy formulation, technology research and development decision and market competition.

The invention provides an AI-based product originality automatic generation method and terminal, and the method comprises the steps: predefining a product target, and collecting data from multiple channels according to the product target; respectively carrying out text preprocessing and image preprocessing on the collected text data and image data; training an AI creative generation model based on the preprocessed text data and image data; and automatically generating the final originality of the product in real time by adopting the trained AI originality generation model according to requirements and parameters input in real time. According to the method, inspiration can be automatically drawn from a large number of design cases, and personalized and innovative creative design meeting the market trend and user requirements is automatically generated through the AI creative generation model, so that the innovation efficiency and market competitiveness of product design are improved, the bottleneck of traditional manual creative generation is overcome, time and cost are reduced, and the method is suitable for popularization and application. The speed of responding to market changes is increased, and the systematization and data support capability of product design is enhanced.

According to the reliability distribution method and system considering the design margin and the development cost, the margin coefficient is introduced in the reliability design stage while economic indexes are considered, the average life-cost model of parts is constructed, and the reliability model of complex equipment is constructed based on the Bayesian network. On the premise that the task reliability requirement of considering the design margin is met, an optimization model of reliability distribution considering the design margin is established by taking the minimization of the total development cost as an optimization target, the optimal balance point of reliability and cost is searched based on the Monte Carlo random algorithm, and the reliability distribution optimization model of the design margin is established. And generating an optimal reliability allocation scheme of the complex equipment which meets the task reliability requirement and has the minimum development cost. According to the method, the overall reliability of the complex equipment is ensured, meanwhile, the proper design margin is reserved to cope with possible improvement or uncertainty factors in the future, effective control over the cost and reasonable distribution of the design margin are achieved, and the market competitiveness of the complex equipment and the use satisfaction degree of users can be improved.

The invention belongs to the technical field of competitive product mining, and discloses an AI intelligent marketing hot selling competitive product mining method based on category price, which comprises the following steps: acquiring competitive product data from a plurality of data sources by using a network data acquisition technology and data interface calling; cleaning and converting the obtained multi-source competitive product data; loading the cleaned and converted data into a database or a data warehouse; performing clustering analysis on the competing product data by using a clustering algorithm; comprehensively considering factors such as category popularity, price competitiveness, sales volume and public praise, and calculating a comprehensive score for each competitive product; and generating a competing product analysis report according to the data analysis and calculation result. The method for mining the hot competitive products shows remarkable advantages in the aspects of accuracy, efficiency and comprehensiveness, and can assist enterprises to more accurately grasp market trends, optimize products and marketing strategies and effectively improve market competitiveness.

The application relates to the technical field of market strategy generation of competitors, and discloses a market strategy generation method and device of competitors, electronic equipment and a storage medium, wherein the method comprises the following steps: through integration of multi-platform real-time data and construction of a dynamic user portrait, the influence of market changes on user behavior can be accurately captured, the limitations of a traditional prediction model can be effectively overcome, a method combining causal reasoning and dynamic modeling is adopted, market competition situations can be intelligently analyzed, an optimal response strategy can be automatically generated, the response time is greatly shortened, and finally, through an intelligent strategy generation mechanism, a target market strategy for the target competitor is generated, and the efficiency and precision of the decision-making process are realized.The application has the beneficial effects that the strategy generation efficiency is significantly improved, the scientificity and applicability of the strategy scheme are ensured, and reliable technical support is provided for enterprises to maintain a competitive advantage in a dynamic market environment.

The invention discloses a multi-agent bidirectional competitive cooperation system and method based on a large language model, and belongs to the technical field of artificial intelligence and computational economics crossing. According to the system, a bidirectional dynamic competition framework between a service agent and an experience agent is constructed, and dynamic gaming between the agents is realized through a resource-influence-information three-dimensional competition space in combination with a strategy generator driven by a large language model and a reinforcement learning optimization mechanism. A dynamic constraint mechanism is introduced into the system and comprises elastic resource supply and a reverse monopoly circuit breaker, so that market competitive health and system stability are maintained. The method can be used for scenes such as platform economic governance and meta-universe business simulation, and a computable theoretical tool is provided for digital social competition behavior research.

The invention relates to the technical field of electric vehicle charging and discharging scheduling, and discloses a multi-body double-layer game electric vehicle charging and discharging optimization scheduling system and method, and the method comprises the steps: an upper layer bidding module carries out the modeling of a non-cooperative game among a plurality of electric vehicle aggregators and an electric quantity distribution strategy of a power distribution network operator, so as to describe the competitive behavior of a market side; and the lower-layer scheduling module constructs an evolutionary game model of a large-scale electric vehicle user to simulate the dynamic response of a user side to an electricity price signal, and the system drives the upper-layer module and the lower-layer module to carry out closed-loop iterative solution by taking the charging and discharging electricity price and the total aggregation power as interaction information. According to the method, the game relationship among the independent benefit subjects is described, the behavior characteristics of market competition and user bounded rationality are reflected, and on the premise of protecting user privacy and deciding autonomy, a system stability equilibrium point is found, and an optimized scheduling scheme giving consideration to the benefits of all parties is formed, so that the electric vehicle cluster is guided to be charged and discharged orderly, and the scheduling efficiency is improved. And the stability and economy of power grid operation are improved.

The application relates to the field of intelligent price adjustment of commodities, and discloses an intelligent price adjustment method, device, medium and product for commodities with material specifications. The method comprises the following steps: acquiring a pre-set commodity price adjustment target mode; monitoring and collecting related commodity information of competitive products in real time; identifying whether a price adjustment event is triggered; if the price adjustment event is triggered, outputting a price adjustment decision matched with the price adjustment target mode according to the price adjustment target mode corresponding to the price adjustment event, so that the commodity with the material specification executes the price adjustment decision. The technical problems of the prior art in the aspects of response speed, competition strategy, price stability and risk control in the face of complex and changeable market competition environment can be solved.

The application provides a marketing strategy dynamic simulation optimization system data processing method and device, and relates to the field of data processing. The method comprises the following steps: establishing an industry market demand prediction model with a specified period based on industry characteristic data; establishing a market competition situation and market influence model based on historical market data, and determining the competition type of each market participant in the market competition structure in the target industry through the market competition situation and market influence model; establishing a production capacity and energy storage model based on the production process characteristic data, the upper limit of production capacity data and the inventory turnover level data of the target industry; constructing a market dynamic twin engine based on the production capacity and energy storage model, the market competition situation and market influence model and the industry market demand prediction model; and dynamically simulating the marketing strategies corresponding to different competition types through the market dynamic twin engine to obtain optimal marketing strategy data with a specified period.

This application relates to the field of information technology and discloses a method and apparatus for product selection and advertising in cross-border e-commerce based on regional popularity analysis. The method integrates multi-channel data on regions, products, and users to construct a multi-granularity regional market knowledge graph and extract regional popularity vectors for candidate products. It mines user characteristics and calculates geographical distance weights to quantify the compatibility of products with regional culture, mines product association strength, and calculates dynamic recommendation scores. A decision-making system is built to generate regional product selection lists and personalized recommendation lists, and strategies are dynamically adjusted through anomaly detection. This method abandons subjective experience and the limitations of single data sources, accurately captures differences in regional market popularity, achieves deep matching between product selection and regional needs and user preferences, solves the problem of supply and demand imbalance, ensures accurate and flexible product selection, helps enterprises improve capital turnover efficiency and market competitiveness, and promotes the large-scale and refined operation of cross-border e-commerce.

The application relates to the technical field of transportation, in particular to a layout planning method and device for a component type mobile charging facility, wherein the method comprises the following steps: acquiring actual distribution conditions and actual charging standards of charging equipment of multiple operators; initializing the actual distribution conditions and the actual charging standards to obtain initial distribution conditions and initial charging standards, so as to calculate total revenue of the multiple operators based on the initial distribution conditions and the initial charging standards; and based on the total revenue, iteratively optimizing the initial distribution conditions and the initial charging standards by using a particle swarm algorithm and an evolutionary game strategy to obtain target distribution conditions meeting a preset iteration stop condition. Thus, the problems in the related art that due to the lack of effective pricing strategies, the fierce market competition cannot be effectively responded to, the market share is easily lost, the profit is reduced, and due to the unreasonable distribution of charging facilities, the utilization rate of the charging equipment is possibly reduced and the user experience is affected are solved.

This invention provides a method, apparatus, and electronic device for dynamic pricing optimization in a market with rigid demand, relating to the field of data processing. The method includes: establishing a market forecasting model for rigid demand based on industry characteristic data of the industry; establishing a market competition situation evaluation model based on historical competition data within the industry; converting specified production capacity data and relevant parameters of specified industry safety and environmental standards into quantitative production capacity constraints; establishing an evaluation model of the impact of market prices and freight costs in neighboring regions based on market prices and freight costs in the corresponding neighboring regions; fusing the market forecasting model, the market competition situation evaluation model, and the market price and freight cost impact evaluation model to construct a multi-factor, multi-dimensional spatiotemporal model; determining the optimal market price through the multi-factor, multi-dimensional spatiotemporal model according to a specified optimization objective function and quantitative production capacity constraints, and using the optimal market price as the result of dynamic pricing optimization in the market with rigid demand.

This invention discloses an artificial intelligence-based industrial automation production scheduling system and method, belonging to the field of automated production scheduling technology. The invention includes the following steps: obtaining historical production records of each production line from a database and analyzing the maximum capacity of each production line; obtaining real-time production records of each production line and, combined with the maximum capacity of each production line, analyzing the expected completion date and surplus characteristics of each production line; analyzing whether urgent orders can be placed in place based on the products and quantities of urgent orders, combined with the surplus characteristics of each production line in the first production line set; if urgent orders can be placed in place, analyzing the placed orders and their allocation characteristics based on the urgent delivery date of the urgent orders, combined with the surplus characteristics of each production line in the first production line set. This invention achieves accurate assessment of production line capacity, improves the accuracy and real-time performance of scheduling, and flexibly responds to urgent production needs, shortens delivery cycles, and improves customer satisfaction and market competitiveness.

The invention belongs to the technical field of multi-factor models, and particularly relates to a multi-factor model-based reinsurance pricing calculation method, which comprises the following steps of: S1, determining a target: determining the purpose of reinsurance pricing calculation, and evaluating the rationality of a reinsurance contract or comparing the advantages and disadvantages of different reinsurance schemes; s2, factors are selected, wherein key factors influencing reinsurance price comparison are selected, and the factors comprise insurance company underwriting risk characteristics, historical claim settlement data, market environment and reinsurance market supply and demand conditions; according to the method, the accuracy of reinsurance pricing calculation can be improved by comprehensively considering various key factors, the model utilizes historical and actual data and adapts to different market environments, the accuracy is ensured through cross validation and back test, detailed reports and analysis support decisions are provided, and reinsurance arrangement optimization, cost reduction and market competitiveness improvement are facilitated.

The invention relates to the technical field of reliability engineering, in particular to a multi-level decision model construction and analysis method for life extension of mechanical and electrical products, which comprises the following steps: according to product tree construction and life extension information collection, analyzing life restricting factors, designing factors, environmental factors and management factors, and establishing a multi-level decision model for life extension of mechanical and electrical products. Factors and reasons influencing the life are found out, a multi-level life extension decision model is constructed around the core of the life extension method, five factor decisions including a measure feasibility decision, a weak link decision, an opportunity decision, an effect decision and a cost decision are analyzed, and finally a comprehensive decision life extension scheme is formed. The method can objectively provide a product life extension decision-making method with higher practical operability and perspectiveness, and realizes collaborative improvement of resource utilization efficiency, operation cost control and market competitive advantages.

The invention relates to a method for scientific and technological innovation element data matching, which comprises the following steps: (1) data collection: collecting original data through an enterprise filling form, a sensor node unit filling form, an enterprise technical requirement document, a scientific and technological management department data document and an enterprise project road presentation record; (2) data arrangement: performing screening, verification, standardized cleaning and structured storage on the original data to form an original data set; (3) data screening and matching: performing candidate filtering on an original data set according to enterprise demand vectors, performing scoring by using multi-model fusion, and finally, giving a matching result by using dynamic optimization and combining a differential cold start strategy; and (4) result determination: determining a final matching result by adopting a mode of combining system auxiliary determination and enterprise autonomous determination. The method can meet the requirements of enterprises for efficient and accurate innovation information services, improves the innovation ability and market competitiveness of the enterprises, and has remarkable social and economic benefits.

This invention discloses a method for parameter identification of a FOPDT model with a variable step response duration, comprising the following steps: S1: Initializing a custom matrix accumulation structure; S2: Real-time acquisition of the step response sampling time t, current output data y_current, and step input signal u_step of the FOPDT model, calculating the integral increment of the output data based on the trapezoidal integral formula, and updating the response integral value in the custom matrix accumulation structure in real time; S3: Constructing a 3D feature vector using the response integral value, the product of the step input signal and the sampling time, and a constant 1, and incrementally updating the 3×3 matrix elements in the 3×3 matrix element storage unit through the self-multiplication operation of the 3D feature vector, thereby achieving real-time iterative update of the 3×3 matrix elements. Temperature controllers equipped with the technical solution disclosed in this invention have significant advantages in control accuracy, start-up speed, and ease of operation, exhibiting outstanding product differentiation and competitiveness, and helping enterprises gain a favorable position in market competition.

The invention belongs to the field of data analysis, and discloses a market situation awareness and decision-making method based on space-time diagram convolution and causal inference, and the method comprises the steps: constructing a unified data collection framework, and fusing multi-modal data; establishing a cross-department network through quality evaluation, cleaning and clustering analysis mining association; mapping to a space-time diagram, and generating an operation portrait by means of a convolutional network; analyzing a portrait prediction trend, and mining a causal relationship; and constructing a twin network virtual scene, and carrying out reinforcement learning iteration to obtain an optimal decision. Multi-modal data fusion, accurate situation awareness and intelligent decision making are realized, enterprises are assisted to capture market dynamics and make scientific decisions, the market competitiveness is improved, and the method is suitable for enterprise operation management in a complex market environment.

The invention discloses an intelligent retail terminal dynamic pricing system and method based on big data analysis. The system comprises a data acquisition module, a cloud computing platform, a pricing strategy generation module, an execution module and a feedback optimization module. According to the invention, a multi-module collaborative intelligent closed-loop system is constructed, so that the dependence of traditional manual pricing on experience is greatly reduced. The data acquisition module adopts a multi-source cross validation mechanism to ensure the comprehensiveness and accuracy of regional consumption data, inventory information and competitive product prices; the cloud computing platform realizes accurate analysis of the consumption trend, the inventory state and the competitive product strategy through deep data processing and feature extraction in combination with an advanced algorithm model; the pricing strategy generation module is based on multi-objective optimization logic, the contradiction between stock overstock and sales opportunity loss in traditional pricing is effectively solved while the profit and the market competitiveness are balanced, and the rationality of commodity turnover is remarkably improved.

The invention belongs to the technical field of power system scheduling, and discloses a load aggregator-oriented hierarchical game optimization scheduling and income distribution method. According to the method, unified aggregation of 10 kV / 35 kV multi-voltage-level users is realized through standardized modeling, and multi-market general bidding parameters are extracted; an upper-layer optimization framework covering multiple markets such as electric energy and demand response is constructed, and a particle swarm algorithm and a commercial solver are adopted for cooperative game solving; taking an upper-layer result as input, and combining a voltage grade difference and performance score recursion mechanism to complete lower-layer user scheduling distribution and income allocation; and through physical feasibility and budget balance verification and credibility, reversely correcting closed-loop cooperation of upper-layer bidding until convergence. According to the method, the market default risk can be reduced, the scheduling robustness and profit potential can be improved, the distribution fairness and the user retention stability are considered, the scheme can be ensured to be on the ground, and the market competitiveness and the operation safety of the load aggregator are comprehensively enhanced.

The invention discloses a cross-border intelligent pricing optimization method based on reinforcement learning, and belongs to the cross technical field of artificial intelligence and cross-border electronic commerce. The method comprises the following steps: acquiring and preprocessing multi-source data in a cognitive direct-connected manufacturing network, and constructing a state vector of a fusion structure conservation mapping constraint; a bidirectional reinforcement learning structure is established on the basis, and benefits and risks are collaboratively optimized; an exchange rate sensitive gating unit and an economic constraint condition are introduced to ensure the robustness and compliance of pricing; and finally, cross-market rapid strategy adaptation is realized through migration reinforcement learning. As a key link of a CDM cognitive direct-connection manufacturing technology system, the dynamic pricing problem in a direct-connection scene is effectively solved, and the overall income and market competitiveness of a platform are improved.

The present disclosure relates to the field of low-carbon technology in the power industry, and provides a method and device for evaluating the feasibility of a low-carbon adjustment scheme, a medium, an electronic device and a program product. The method comprises: determining a standard net present value under a standard situation according to historical production and operation data of an enterprise; determining a plurality of selected scenarios from a library of candidate scenarios; determining a first selected net present value corresponding to the plurality of selected scenarios; obtaining a first risk value according to the standard net present value and the first selected net present value; adjusting according to a low-carbon adjustment scheme and determining a second risk value after adjustment; and evaluating the feasibility of the low-carbon adjustment scheme according to the first risk value and the second risk value. This method can accurately evaluate the feasibility of the low-carbon adjustment scheme of the enterprise, help the enterprise avoid risks in the transition, promote technological innovation, and enhance market competitiveness, thereby contributing to the realization of sustainable development goals.

The application discloses a kind of service systems based on automation technology auxiliary completion tender document production, belong to tender service technical field, specifically include: user behavior acquisition module collects the operation behavior data of user in the process of tender document production;Decision preference analysis module generates user decision preference parameters by processing operation behavior data through analysis model;Strategy generation module generates candidate tender strategy set based on historical cases;Scene simulation module constructs tender scene simulation environment and records market competition data in the process of strategy execution;Strategy evaluation module calculates strategy competitiveness index according to market competition data;Strategy execution module filters optimal tender strategy and is converted into specific tender document production scheme;The application effectively improves the accuracy and competitiveness of tender document production by intelligently analyzing user behavior and dynamically simulating tender environment.

The application provides a multi-agent cooperative game decision method and system, relates to the technical field of power market, and comprises the following steps: acquiring sensing data required for decision; constructing a cooperation alliance by a thermal power-energy storage-VPP multi-agent, participating in frequency modulation market bidding as a whole, under the bidding constraint, taking the maximization of expected total income of the alliance as a target, and searching for an optimal bidding strategy through a deep reinforcement learning intelligent agent to assist the alliance bidding; and after the alliance wins the income in the frequency modulation service, an optimal income distribution scheme is determined by combining a cooperative game algorithm to assist the distribution of the alliance income among the alliance members. The application aggregates the dispersed flexible resources (i.e. the thermal power-energy storage-VPP) into a whole (i.e. the cooperative alliance) to participate in the market competition of the frequency modulation auxiliary service, and realizes the scale effect and complementary effect of "1+1>2".

The invention relates to the technical field of competitive product market strategy generation, and discloses a competitive product market strategy generation method and device, electronic equipment and a storage medium, and the method comprises the steps: integrating multi-platform real-time data and constructing a dynamic user portrait, can accurately capture the influence of market changes on user behaviors, and improves the user experience. The limitation of a traditional prediction model is effectively overcome, a method of combining causal reasoning and dynamic modeling is adopted, the market competition situation can be intelligently analyzed, the optimal coping strategy can be automatically generated, the response time is greatly shortened, finally, the target market strategy for the target competitive product is generated through an intelligent strategy generation mechanism, and the market competitiveness of the target competitive product is improved. And high efficiency and precision of the decision-making process are realized. The method has the beneficial effects that the strategy generation efficiency is remarkably improved, the scientificity and applicability of the strategy scheme are ensured, and reliable technical support is provided for an enterprise to maintain competitive advantages in a dynamic market environment.