54 results about "Market response" patented technology

The invention relates to the technical field of e-commerce marketing, in particular to a product marketing service platform system and method, and the system comprises a user behavior tracking module, a consumption intention analysis module, a promotion time optimization module, an inventory monitoring and discount adjustment module, and a precision marketing push module. According to the method, the commodity access frequency and cross browsing data are extracted and analyzed in real time, the consumption trend of the user in a short period is rapidly identified, consumption tendency parameters are generated, potential purchase behaviors are accurately predicted by using browsing behaviors and an interaction mode, the recommendation accuracy is improved, the real-time inventory data are combined, a discount strategy and inventory management are dynamically adjusted, and the recommendation efficiency is improved. According to the method, the promotion activity is ensured to be closely matched with the inventory state, the resource utilization efficiency is improved, precise marketing pushing is performed according to the user equipment preference, the user interaction experience is enhanced, the sales growth is promoted, and the market response speed and the marketing effect are remarkably improved.

The invention discloses an intelligent management system and method for cable production, and the system builds an intelligent decision-making system for demand prediction through the dynamic fusion of historical sales data and market trend information. Specifically, a time sequence feature coding and semantic embedding technology based on deep learning is adopted, cross-modal association is carried out on a local rule of a historical sales window and market dynamic semantics, and deep collaborative decision response is further carried out on historical information and market trends to obtain historical sales-market trend collaborative decision response coding features; and based on this, short-term prediction of the cable market demand is realized. In this way, the timeliness and accuracy of demand prediction are remarkably improved, the risk of supply chain imbalance caused by sudden demand change is effectively relieved, and then dual optimization of the resource utilization rate and the market response capacity is achieved.

Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language market response.

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 main material and equipment price selection method and system for a maintenance project, and relates to the technical field of power maintenance project price selection, and the method comprises the steps: extracting field information from a material price library, and recognizing a core word through a semantic analysis model; constructing a core word index file, and performing word segmentation and matching processing on retrieval keywords; and sorting the matched data based on a weight rule, and generating a recommendation result for a user to refer to. According to the method, a dynamic and real-time data basis is provided for subsequent analysis, so that the system can better adapt to continuously changing market requirements, the reading speed of the index file is increased, the search sensitivity and accuracy are improved, the requirement of a user for instant information is met, and the user experience is improved. According to the method, the recommendation transparency and credibility are improved, and the user experience and participation sense are improved, so that the pertinence and rationality of decision making are improved, and efficient market response and material selection strategy optimization are realized.

The invention provides a data element value measurement and pricing control method, system and device, and belongs to the technical field of data asset value evaluation and management. Pricing information similar to data product fields is collected; analyzing the data element supermarket, and recording data quality, data volume, data timeliness and data application scene information at the same time; constructing a preliminary pricing model, and performing a simulated pricing test on the fields of the known prices by using the preliminary pricing model; determining a final price of the data product; formulating and executing a data directory putting mode; a putting monitoring mechanism is established, and the page view, the click rate and the inquiry amount of the data catalog are tracked in real time to know market response; setting an order quantity monitoring index and a market comparison analysis dimension; and collecting order quantity data, sorting and analyzing to generate a monitoring report. Through a value measurement and pricing management and control method, data products are priced by comprehensively considering various factors, and reasonable flow and configuration of data elements are promoted.

The invention discloses a multi-dimensional commodity sorting method and system based on multi-thread processing, and relates to the technical field of computer platform load balancing, and the method comprises the steps: carrying out the collection of commodity sales data, carrying out the data segmentation construction, and carrying out the batch processing through a multi-thread technology; evaluating the market response of the commodity by adopting an optimized comprehensive scoring model and considering the gross profit contribution degree, the sales volume, the annual accumulated sales volume, the discount strength, the commodity label and the promotion policy; and the system sorts the commodities according to the comprehensive score, and periodically updates and dynamically adjusts the score dimension. The multi-dimensional commodity sorting method based on multi-thread processing provided by the invention not only considers the sales volume, but also combines various factors such as gross profit and promotion policies to evaluate more comprehensively, updates the ranking according to the latest sales data, and ensures the timeliness of the ranking. Manual weight adjustment is allowed, and response can be quickly made according to market changes.

The invention relates to the technical field of enterprise resource management, in particular to an enterprise management optimization method and system based on big data, and the method comprises the following steps: capturing data of employee skills, a project execution process and customer interaction according to enterprise resource planning in an enterprise, analyzing the captured data, and carrying out the multi-dimensional information classification processing; and constructing an enterprise resource comprehensive data set. According to the method, the flow and utilization efficiency of the resources is improved by establishing the graphic database to integrate the enterprise resources and converting the data elements into the network nodes and the connecting edges, and the resource flow and utilization efficiency is improved by identifying and analyzing the association strength of the nodes and the weights of the edges in the resource network. Through detailed flow path analysis, an enterprise can formulate a scientific resource allocation strategy according to market requirements and current resource situations, resource allocation and use are optimized after dynamic resource allocation is implemented, the strategy is adjusted in time by comparing and analyzing resource efficiency and cost effectiveness before and after optimization, and the market response speed and competitiveness of the enterprise are remarkably improved.

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 discloses a park flexible load aggregation regulation capability quantification and collaborative optimization method and system. The method comprises the following steps: constructing a dynamic coupling feature space of a load group and a multi-element market ecological symbiotic relationship; quantizing the deterministic adjustment capacity, stripping the non-deterministic response spectrum, and quantizing the interaction strength of the physical constraint and the random game to form a reference portrait of the current aggregation adjustment capacity; deducing and ascertaining a performance boundary and an instability critical point through prospective simulation, and generating a dynamic margin map; and when the output dimension of the dynamic margin map is smaller than a preset safety threshold and the adjustment capability toughness is indicated to be insufficient, autonomously deciding a cross-market joint clearing strategy by taking the maximization of the long-term co-evolution value of the park ecology as an optimization target, so as to improve the regulation capability. And extracting load combination and market response in a disturbance scene to reversely reconstruct the dynamic coupling feature space. According to the method, the self-evolution of prospective risk management and decision can be realized, and the long-term collaborative value of the ecology of the park is maximized.

The invention belongs to the technical field of intellectual property, and discloses a patent commercialization value evaluation system and method, and the system comprises a patent information obtaining unit which is used for obtaining target patent information from a patent database; the patent technical analysis unit is used for extracting keyword features in the patent text and analyzing technical features of the patent text according to a similarity comparison result; the market potential prediction unit is used for analyzing the technical field in the patent text and predicting a market response demand conforming to the technical field by using a time sequence analysis technology; and the value comprehensive evaluation unit is used for establishing a comprehensive evaluation model by taking the technical characteristics of the patent text and the market response demand as evaluation indexes, and evaluating the value degree of the target patent by utilizing the comprehensive evaluation model. Through combination of technical characteristics and market response demands, the technical innovation and commercialization potential of patents are comprehensively measured.

The invention belongs to the technical field of virtual power plants, and relates to a power spot market oriented virtual power plant resource dynamic aggregation method. The method comprises the following steps: extracting a distributed resource feature vector and mapping the distributed resource feature vector into a response type label; determining a price fluctuation period according to a spot price sequence, calculating a time sequence matching degree between each resource and a market rhythm by combining the feature vector, and constructing a comprehensive distance by fusing a clustering weight and a response type label; dividing the resource cluster into dynamic aggregation units by taking the variance of the minimum comprehensive distance as a target; a resource aggregation strategy is generated based on the aggregation unit, a control instruction is issued, and clustering weights and aggregation parameters are iteratively updated according to the clearing deviation rate fed back by market clearing. According to the method, the technical problems that the market dynamics is ignored, the clustering model is easy to merge heterogeneous resources and the closed-loop optimization is lacked in the existing static division are solved, and the effects of improving the market response capability of the virtual power plant, the control consistency of the aggregation unit and the adaptive optimization of continuous transactions are achieved.

The invention discloses a large-scale power dispatching optimization method and system based on agent collaboration, and the method comprises the steps: collecting parameters such as power generation side unit output, power transmission side line transmission, power utilization side load, market side electricity price and the like through an energy Internet intelligent decision platform, and carrying out the dynamic processing of the parameters, and obtaining a power utilization main body market response coefficient; combining the coefficient and a multi-side parameter construction model to determine a multi-agent initial bargaining scheme, inputting the scheme into a reinforcement learning model, and setting a state, action and reward related element training optimization game strategy; and adjusting parameters according to the optimization strategy to generate scheduling plans such as unit start-stop, output distribution and line power flow control, feeding back the scheduling plans to a platform for simulation, outputting indexes such as a network loss rate and a load satisfaction rate, and performing iterative adjustment if a threshold value is not met. According to the system construction method, corresponding functional units operate cooperatively, multi-side parameters can be fully integrated, a closed-loop optimization process is formed, market adaptability and scheduling accuracy are improved, and safe and stable operation of a large-scale power system is guaranteed.

The invention relates to the technical field of power dispatching, in particular to a bidding method, device and equipment for collaboratively participating in a power market based on a double-layer game. Specifically, an upper-layer optimization model and a lower-layer optimization model are constructed; the upper-layer optimization model optimizes day-ahead market bidding and energy storage protocol transaction parameters by taking photovoltaic aggregator expected revenue maximization as a target; and the lower-layer optimization model optimizes charging, discharging and energy states based on an upper-layer result by taking energy storage operator interest arbitrage income maximization as a target. And through iterative optimization to Nash equilibrium, a scheduling plan is determined. According to the method, the problems of limited photovoltaic income and imperfect cooperation mechanism are solved, market rules and physical constraints are considered, the method can be expanded to multi-type resource scenes, and the distributed resource market response and income level is improved.

The invention relates to the field of cross-border e-commerce, and discloses a cross-border e-commerce multi-platform multi-account one-key goods-selling intelligent processing method, which comprises the following steps: S1, inputting goods information and selecting cross-border e-commerce multiple platforms to upload goods; s2, calling an API interface of each e-commerce platform according to the commodity information, and obtaining an API data interface and a platform rule of the platform; and S3, on the basis of the platform rules and the commodity information, constructing a multi-dimensional model for commodity uploading and sales optimization, optimizing a pricing strategy and an inventory scheduling scheme of the commodities on multiple platforms by adopting a nonlinear optimization algorithm, and maximizing the sales potential and the inventory turnover efficiency of the commodities. Through intelligent automatic uploading, real-time data feedback and a multi-dimensional optimization algorithm, collaborative synchronization and dynamic optimization of commodity information, inventory and pricing strategies between cross-border e-commerce platforms are realized, and the operation efficiency and the market response capability are improved.

The invention discloses a machine learning-based cross-border e-commerce self-operation product selection dynamic adjustment method and system, relates to the technical field of e-commerce product selection adjustment, and aims to construct a product selection adsorption quantity model, evaluate the sales attraction of different products in different market situations, analyze the market response condition of each product and improve the product selection efficiency. After the commodities are screened in combination with the output result of the commodity selection adsorption quantity model, the commodities are adaptively adjusted, the racking sequence is determined, the market response condition analysis result is sent to the dynamic optimization module, and based on the market response condition analysis result of the commodities, the discount strength of the commodities is dynamically adjusted by simulating the market dynamic state; according to the analysis of commodity sales data and market feedback, the shelf sequence and marketing promotion strategy of commodities are optimized. According to the adjustment method, through introduction of a machine learning technology and combination of a data driving mode, commodity sales data and market environment changes can be analyzed from multiple dimensions, and commodity selection and adjustment efficiency of a cross-border e-commerce platform is significantly improved.

The invention provides a generator set scheduling method and system in an electricity-carbon market, and the method comprises the steps: obtaining a market response model, a current decision, and a current adjustment range based on the data of the electricity-carbon market and a historical optimal reference point; based on the current decision and the current adjustment range, executing an accelerated solution step to obtain an optimal decision; based on the optimal decision, scheduling the target generator set to obtain a clearing result; the acceleration solving step comprises the following steps: based on a market response model, obtaining trust region sub-problems in a current decision and a current adjustment range, and then carrying out dimensionality reduction solving to obtain a decision adjustment step length; obtaining an update decision amount and an update adjustment range based on the decision adjustment step length; if the updating decision gradient vector is greater than the preset convergence threshold value, executing an accelerated solution step based on the updating decision and the updating adjustment range; otherwise, the accelerated solution is terminated, and the optimal decision is obtained. The scheduling efficiency of the generator set can be improved.

The invention relates to the technical field of cross-border trade process optimization, and discloses a cross-border trade process optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining cargo information and customs clearance information of a to-be-cross-border trade; generating a plurality of preset dimensions based on the cargo information and the customs clearance information; collecting a dimension value corresponding to the preset dimension from a preset knowledge graph to form a state space; wherein the state space is a space corresponding to all preset dimensions; and performing strategy optimization on the state space through a preset learning algorithm to obtain an optimized customs clearance process. The method has the advantages that the cross-border trade process is more flexible and higher in adaptability, the overall operation efficiency can be improved, the customs clearance time can be shortened, an enterprise can optimize resource configuration, the operation cost is reduced, and finally the overall market response ability and customer satisfaction are improved.

The invention relates to the technical field of enterprise management, in particular to an AI-based enterprise growth profit management system and method, and the system comprises a profit growth resource adjustment module, a profit prediction model development module, a pre-allocation resource optimization module, a customer value-added service optimization module, a dynamic service adjustment and feedback module, and a profit management efficiency analysis module. According to the invention, the artificial intelligence technology and the new scheme are comprehensively applied to optimize enterprise growth profit management, the task demand is dynamically analyzed and calculated, the resource use weight is adjusted, the real-time utilization rate and efficiency of resources are improved, the idle time is shortened through resource utilization optimization, and the adaptation to demand fluctuation is enhanced; accurate profit prediction and demand change diagrams help enterprises to optimize pricing and product strategies, adapt to market changes, deeply analyze customer data, convert potential demands into specific service proposals, and enhance customer satisfaction and loyalty, thereby improving market response speed and decision quality.

The invention discloses a carbon reduction result evaluation method and system of an enterprise and a storage medium. The method comprises the following steps: S1, collecting carbon emission related data of the enterprise in the current year and the previous years; s2, confirming current-year evaluation parameters of the enterprise according to the related data, wherein the evaluation parameters comprise a carbon reduction effect, carbon emission intensity, product market response loudness and carbon income; s3, normalizing the carbon reduction effect, the carbon emission intensity and the carbon income obtained in the step S2, substituting the normalized carbon reduction effect, the normalized carbon emission intensity and the normalized carbon income into an evaluation model together with the product market response, obtaining a total score of enterprise carbon reduction results, and giving corresponding evaluation results according to set segments; the method can comprehensively and accurately evaluate benefits brought by carbon reduction to enterprises.

The invention discloses an industrial and commercial power consumer energy control method, and the method comprises the steps: building a day-before-day multi-time scale collaborative optimization framework, enabling a declaration electricity price function of each time period of a power spot market to be embedded into a core variable of an optimization target, enabling an energy storage charging and discharging strategy to actively respond to the real-time electricity price fluctuation instead of a fixed peak-valley time period, and enabling the energy storage to be more stable. The mode upgrading from traditional peak-valley arbitrage to spot price arbitrage is realized; an intra-day electric charge model which is periodically executed in a rolling manner in a day is operated, actual settlement data of an executed time period is locked, only an unexecuted time period is optimized, a prediction error cross-cycle cumulative effect is eliminated, and an industrial control system with real-time market response capability is formed. The technical problem that a traditional energy management system cannot participate in spot market dynamic optimization is solved.

The invention discloses an electricity transaction agent market prediction method and system based on graph deep learning, and relates to the technical field of electricity transaction, and the method comprises the steps: constructing an electricity market transaction graph comprising a node set and an edge set; performing node and structural feature coding on the electricity market transaction graph, and extracting global embedded representation of the market; and modeling market subjects corresponding to the nodes into power transaction agents, and executing multi-subject strategy interaction learning among the power transaction agents by using global embedded representation and a historical strategy sequence of the power transaction agents. Balance evolution is carried out in the graph reinforcement learning process to dynamically update the strategy weight and the market response threshold value of the power transaction agent; and performing behavior simulation deduction by using the updated power transaction agent to establish a time sequence prediction result. The technical problem that the market price fluctuation prediction precision is insufficient due to the fact that the electricity market transaction relation is complex and the subject behavior is difficult to accurately model in the prior art is solved, and the technical effect of improving the market prediction precision is achieved.

The present application provides a natural language-based enterprise user template production tool and method, which relate to the field of information processing technology. The tool and method receive enterprise users' template generation demand information; analyze store template design requirements based on an NLP model to determine demand feature information; perform template registration on the demand feature information based on template registration recognition multi-channel; perform rendering optimization based on a store product image set and a predetermined product image background set; then, perform animation recommendation comparison on the enterprise users to generate user store templates; and perform correlation adjustment on the user store templates based on real-time store monitoring data, thereby solving the technical problems of poor market response immediacy of user templates, low design accuracy and slow response speed, and achieving the goal of improving the degree of automation and personalization accuracy of template generation, so that the template can reflect the latest status of store operations and market changes in real time, thereby improving efficiency while reducing market investment costs.

The present invention discloses a method for tracking the real-time effect of advertising based on a multi-objective optimization algorithm, including S1, capturing user behavior data related to advertising, collecting market dynamics and competitor advertising performance data from a third-party data provider; S2, formatting, cleaning and preliminarily analyzing the collected user behavior data, market dynamics and competitor advertising performance data; S3, extracting key features from advertising content using natural language processing and image recognition technology; S4, designing an advertising performance optimization model using a multi-objective genetic algorithm; S5, automatically adjusting the advertising delivery strategy according to the output results of the multi-objective genetic algorithm; S6, using a machine learning model to predict the market reaction after the adjusted advertising delivery strategy is delivered; S7, and performing performance data after the advertising is delivered. The present invention uses a multi-objective genetic algorithm to design an advertising performance optimization model to ensure the scientific nature and optimization effect of the advertising delivery strategy.

The invention discloses an intelligent security information pushing method, which comprises the following steps: acquiring real-time market data, user historical behavior data and investment portfolio data, executing normalization processing to generate standardized data, and processing by adopting a segmented accumulation method and a multi-level entropy weight method to generate context feature data and new information feature data; calculating a market mutual information value to construct market associated data, and generating conduction intensity data by adopting an information diffusion model weighted by market mobility; using an LSTM network and an adaptive spectrum analysis method to generate correlation score data; constructing an information conduction graph by adopting a graph neural network to identify a key path and generate path influence data; and a multi-target dynamic programming method is adopted to generate push sequence data. According to the method, the information value can be accurately identified, the market response can be accurately predicted, and the accuracy and timeliness of information pushing can be improved.

The invention discloses a multi-level supply chain industrial and commercial collaborative distribution method, which comprises the steps of constructing an industrial and commercial collaborative cigarette delivery recommendation system framework based on consumer demands, carrying out multi-source comment data acquisition and preprocessing, carrying out consumer demand feature deep analysis based on preprocessed data, and carrying out cigarette delivery recommendation. Clustering and product similarity calculation are carried out on the demands obtained through analysis, and the standard putting proportion is adjusted according to the clustered demands and the calculated product similarity. According to the invention, through multi-source data acquisition and deep analysis, accurate capture of consumer demand characteristics, and combination of clustering analysis and product similarity calculation, dynamic intelligent adjustment of a product specification delivery proportion is realized, the precision and flexibility of industrial and commercial collaborative distribution are effectively improved, the problem of supply-demand imbalance is reduced, supply chain resource allocation is optimized, the market response capability is enhanced, and the market competitiveness is improved. And a scientific method support is provided for efficient collaboration of the multi-level supply chain.

The invention belongs to the technical field of tourism e-commerce, and particularly relates to an artificial intelligence-based tourism e-commerce big data mining method, which comprises the following steps of: obtaining user behaviors, cross-platform product supply, historical transaction characteristics and social public opinion emotion, and constructing a tourism interest dynamic evolution graph; quantizing a time sequence drift and community diffusion rule of user interests through a time sequence diagram neural network model, constructing a supply-demand matching degree prediction model based on cross-platform product supply and user behavior data, and combining with the updated evolution graph and through a generative adversarial network to obtain a supply-demand matching degree prediction model. And generating a personalized tourism product recommendation list and a dynamic pricing strategy, carrying out intelligent diversion and resource pre-distribution, synchronously collecting user interaction feedback and market conversion efficiency data, and through an online incremental learning algorithm, optimizing a time sequence diagram neural network model, and generating a tourism demand mining and market response evaluation report. Therefore, the problems of insufficient user interest dynamic feature capture, weak user demand mining ability and the like in the prior art are solved.

The invention relates to the technical field of marketing platform management, and discloses a commodity management system of a network marketing platform, which comprises a multi-source information fusion module, a dynamic attribute tag module, an association recommendation module, an inventory and market linkage module and a feedback optimization module. Through cooperation of multi-source information fusion and a dynamic attribute label system, full-dimension integration and real-time updating of commodity information are realized, limitation of single environmental protection attribute or user behavior analysis is broken through, commodity cross-sales efficiency is improved through scene-based association recommendation, and commodity cross-sales efficiency is improved. Static attribute presentation of commodity management is upgraded to dynamic market response, and the control ability and marketing accuracy of the platform for the whole view of the commodity market are remarkably enhanced.

The invention relates to the technical field of information management, in particular to a scientific research project market value analysis method and system.The scientific research project market value analysis method comprises the following steps that S1, related information of a scientific research project and external information related to the scientific research project are obtained, and a market demand model and a market response model are established; s2, a scientific research project market value result is generated according to the relevant information of the scientific research project in combination with the market demand model; s3, performance parameters of all parts of the scientific research project are comprehensively quantified according to the performance evaluation standard, and leading performance parameters are obtained; and S4, generating a performance gap gain parameter according to the leading performance parameter in combination with the market response model, and performing gain correction on the scientific research project market value result through the performance gap gain parameter. The method can generate the market value of the scientific research project in combination with the external information of the scientific research project, thereby better assisting a manager to make a better project decision, and improving the total income of the scientific research project.

The present invention is configured to instruct the at least one processor to perform at least one operation, where the at least one operation may include: providing advertisement data corresponding to each of a plurality of minimum feasible products (MVPs) corresponding to a business model to a social networking service platform server by connecting with an external social networking service platform server; market response data corresponding to the advertisement data are collected from the social network service platform server; based on the collected market response data, using generative artificial intelligence to generate market test result data of a minimum feasible product corresponding to the advertisement data; selecting at least one of the plurality of minimum feasible products based on the marketability test result data; automatically generating a market verification route map of the minimum feasible product based on the market test result data of the at least one minimum feasible product; and providing the marketability verification route map to a client terminal.