85 results about "Market dynamics" patented technology

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention provides a supply chain collaborative management method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and quantifies collaborative, substitution and complementary relationship strength among commodities by constructing a commodity association graph network; utilizing a graph attention mechanism to identify a commodity group and calculate a demand influence coefficient, and performing time sequence analysis on historical order data to obtain basic demand prediction; meanwhile, a scene recognition model is constructed by adopting reinforcement learning, a basic prediction result is adjusted according to a current market scene, the demand conduction quantity in the commodity group is calculated in combination with a demand influence coefficient, and final demand prediction is obtained; and finally, based on a prediction result, determining a collaborative decision-making scheme of each participant by applying a cooperative game method, and generating an inventory allocation instruction. According to the method, the complex incidence relation between the commodities and the market dynamic change are accurately captured, the supply chain prediction accuracy and the cooperation efficiency are remarkably improved, and inventory optimization and cost reduction are realized.

The invention discloses a public opinion risk dynamic early-warning method and system based on a large language model, and the method and system carry out the semantic recognition, event extraction and situational reasoning of unstructured public opinion data through introducing an advanced large language model, and carry out the dynamic early-warning of the public opinion risk in combination with structured emotion indexes and market dynamics. Potential risk factors in social public opinions can be automatically identified, and risk quantitative evaluation is realized in combination with semantic tags, market behavior data and emotion intensity. The system has strong prospective perception ability and event evolution reasoning ability, can provide rapid and accurate early warning information, and further can generate personalized suggestions and decisions in combination with user risk preferences. The method can be widely applied to a plurality of public opinion sensitive fields such as enterprise brand management, government supervision, financial institutions and media transmission control, and the automation and precision capability of dealing with sudden public opinion risks is effectively improved.

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention belongs to the technical field of advertisement putting management, and particularly relates to an intelligent precise advertisement putting optimization method and system based on dynamic strategy adjustment. The system comprises a user behavior deep insight module, a market dynamic perception module, an advertisement effect intelligent evaluation module, a strategy dynamic generation module, an advertisement intelligent putting execution module and an advertisement putting management end. Various behavior data of users in a network environment are collected and analyzed through the user behavior deep insight module, the market dynamic sensing module monitors and analyzes changes of the market environment in real time, and the advertisement effect intelligent evaluation module evaluates the actual effect of advertisement putting. And the strategy dynamic generation module dynamically generates and adjusts the advertisement putting strategy, the advertisement is accurately put to the target user according to the advertisement putting strategy, the advertisement putting is comprehensively upgraded from multiple dimensions of accuracy, timeliness, effect optimization and user experience, and the increasingly complex and variable advertisement putting requirements are met.

The invention provides a real-time commodity price prediction system based on multi-source data fusion, and the system comprises a data integration module which is used for obtaining multi-source heterogeneous data, carrying out the alignment processing of the multi-source heterogeneous data according to the commodity types and timestamps, and generating a standardized fusion data set; the dynamic weight distribution module is used for dynamically calculating the weight coefficient of each data source according to the relevance between each data source in the standardized fusion data set and the commodity category; the model integration module is used for performing weighted fusion on the price trend prediction result output by the time sequence prediction model and the game correction output by the market game model based on the weight coefficient to generate initial price prediction; and the optimization output module is used for performing periodic rule correction and strategy collaborative analysis on the initial price prediction according to the real-time market dynamic data to obtain a predicted commodity price trend. By adopting the system, the commodity price trend can be dynamically predicted based on multi-source big data, an enterprise is helped to optimize a purchasing strategy, and the inventory overstock risk is reduced.

The invention provides an automobile after-service pricing model optimization method under dynamic supply and demand balance, and relates to the technical field of pricing model optimization. The pricing model optimization method comprises the following steps of accessing a government policy issuing platform and an industry association database, capturing related policies and macroeconomic indicators of automobile post-service in real time, performing semantic analysis on financial news and policy interpretation articles, extracting potential factors, and performing classification and analysis on the potential factors. A competitor official website, an e-commerce platform and a social media account are monitored for 7 * 24 hours, a real-time feedback module is embedded in a service platform, customers are encouraged to evaluate services, prices and new product demands in time, a market signal threshold model is established, and the market quality is improved. And early warning thresholds are set for the policy change frequency, the competitor price fluctuation amplitude and the customer demand growth rate index. Through real-time market dynamic monitoring, scientific quantitative analysis, intelligent and accurate decision making and rapid iteration strategies, the enterprise pricing ability and market competitiveness are effectively improved.

According to the intelligent inventory management and automatic replenishment e-commerce operation method, sales data, inventory data, return data, commodity attributes, supply chain data and market environment data (such as social media, weather data, economic indicators and the like) of an e-commerce platform are integrated, and a data preprocessing method is adopted for cleaning and denoising, so that the quality and integrity of the data are ensured. A multi-dimensional data fusion and deep learning algorithm is adopted, multiple factors such as commodity historical sales data, seasonal fluctuation, market dynamics, user behaviors and the like can be fully considered, and therefore compared with a traditional single prediction model, the demand prediction accuracy can be greatly improved, the inventory overstock or stockout risk caused by prediction errors can be avoided, and the user experience can be improved. By designing a mechanism for monitoring and analyzing external events in real time, emergency situations (such as emergency promotion, market demand fluctuation, weather change and the like) can be quickly responded, a replenishment strategy can be timely adjusted, and inventory crisis caused by the fact that the system fails to quickly respond to external changes is avoided.

The invention relates to a differential privacy-based competitive intelligence analysis system and method. The differential privacy-based competitive intelligence analysis system comprises a differential privacy processing and budget management module, a differential privacy feature engineering module, a privacy protection analysis and prediction module and a differential privacy synthetic data generation module. According to the design of the invention, an end-to-end advanced analysis framework with mathematics provable privacy protection capability is constructed, so that accurate, timely and reliable insight of market dynamics and competitor strategies is realized on the premise of strictly protecting enterprise sensitive commercial confidentials.

The invention discloses a multi-agent collaborative optimization scheduling method and device based on peak regulation market dynamic rule formulation, and the method comprises the steps: carrying out the refined classification of resources in multiple agents according to the physical characteristics and adjustment flexibility of the resources, building a multi-dimensional classification system, and providing a data basis for the dynamic design of rule parameters; an accurate description model is established for each type of resources, and feasible regions and cost characteristics of the resources in a peak regulation market are quantified; establishing dynamic association between resources and peak regulation market rules, embedding rule parameters into the optimization model, and constructing a power market optimal rule model containing multiple agents; and constructing a double-layer collaborative optimization strategy, solving the model and outputting an optimal rule of the current peak regulation market, and realizing market rule dynamic optimization and resource collaborative scheduling. According to the method, the static limitation of a traditional peak regulation market rule is broken through, dynamic matching of rule parameters and resource characteristics is achieved through multi-agent collaborative optimization, and the new energy consumption capacity and the market operation efficiency are effectively improved.

The invention relates to the technical field of product oil management, and discloses a product oil purchase-sale-stock multi-modal data analysis method and system. The method comprises the following steps: acquiring real-time inventory data, transportation state data and sales terminal data in a product oil supply chain to form a multi-modal data set; performing data cleaning on the data, and extracting key inventory characteristics to generate a cleaned data subset; obtaining associated historical purchase data, regional demand prediction data and market price fluctuation data based on the gas station identification information to form a multi-dimensional auxiliary data set; inputting the features into an inventory feature extraction model, generating a multi-feature vector set, and fusing the multi-feature vector set; inputting the fused feature vectors into an inventory state evaluation model set, and outputting inventory early warning level and abnormal type information; and finally, a dynamic allocation scheme is generated by combining real-time inventory data and the like. According to the method, multi-modal data are integrated, so that the comprehensiveness and accuracy of data analysis are improved, and the purchase-sales-stock management of the product oil is assisted to be more suitable for market dynamics.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

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 relates to the technical field of electric power market bidding decision making, and discloses a market dynamic game bidding decision making method based on multi-stage deep reinforcement learning. According to the method, a market participant bidding behavior data set is obtained through an electricity market data center, and a reference bidding strategy is obtained through historical data trend analysis; according to market subject types, quotation elasticity and market power characteristics of each type are extracted; performing multi-stage deep reinforcement learning training on the features by using a reference bidding strategy to generate an initial bidding strategy matrix; and in combination with the matrix and the corresponding subject type, controlling the bidding decision agent to generate a dynamic bidding sequence in the feasible strategy set. And during market clearing, real-time data is collected to determine a feedback value, and an intelligent agent bidding strategy is adjusted according to the feedback value and the initial strategy value. According to the method, multi-stage learning and dynamic gaming are fused, main body features and real-time states are accurately captured, the adaptability and pertinence of a bidding strategy are improved, and participants are assisted in coping with complex market competition.

The invention belongs to the technical field of computer application, and discloses an anticipated price rolling matchmaking transaction aided decision-making method based on a big data algorithm, which integrates microscopic transaction data, mesoscopic market dynamics and macroscopic environment variables through a system, enables price prediction to more comprehensively fit the actual operation logic of the market, and improves the efficiency of price prediction. The deep forest model has a strong cross-modal feature learning ability, and can deeply mine hidden associations between different types of data, such as indirect influences between international situation changes and bulk commodity prices, conduction effects of upstream and downstream price linkage of an industrial chain on transaction varieties, and the like. A more three-dimensional and more reliable expected basis is provided for transaction decision making, so that a prediction result can better reflect the complex full view of the market, and prediction deviation caused by one-sided information is reduced; through a prediction framework combining a dynamic sliding window and incremental learning, subtle changes of the market can be tracked in real time, and it is ensured that the model is always kept synchronous with the market rhythm.

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 belongs to the technical field of self-media operation and business decision, and particularly relates to a self-media account business intelligent quotation method and system, and the method comprises the following specific steps: S1, collecting self-media account basic data, user portrait data, commercial value data and market dynamic data; s2, encrypting and storing the sensitive data through AES-256; s3, adopting a random forest algorithm to construct a basic quotation model, and combining account operation fluctuation and market nodes to realize dynamic adjustment; s4, displaying quotation composition details to the account party and supporting parameter adjustment preview; and S5, generating a multi-format personalized quotation list, and negotiating and recording full-link data based on the quotation and the cooperation effect. According to the method and the system, through integrating the self-media account basic data, the user portrait data, the commercial value data and the market dynamic data, the key information influencing the quotation is covered in multiple dimensions, the quotation association factors are comprehensively considered, the deviation caused by single data limitation is reduced, and the quotation accuracy is improved.

The invention relates to the technical field of data processing, in particular to a project application competitiveness analysis system which comprises an acquisition module, a preliminary screening module, a judgment module, a determination module, an adjustment module and an output module. According to the method, multi-dimensional data is acquired in real time, firstly, project keywords are matched with policy keywords, and then key projects are judged by utilizing technology integration density and task dependency density; then, a priority list is determined by integrating the competitive strength, the technical optimization rate and the policy matching degree, in addition, according to the dynamic optimization threshold value of the approval rate, self-adaption policy change and market dynamics, not only is the scientificity of analysis improved, but also the practicability and reliability of the system are enhanced, and finally, by outputting an analysis report, a decision basis of data support is provided for a user, and the user experience is improved. The problems of low project screening accuracy and response lag caused by excessive dependence on models and incapability of adapting to changeable markets and policy environments are effectively solved.

Computerized systems and methods for automated AI-driven customer and vendor subdivision and personalized insight delivery are described. The method involves collecting real-time data including purchase behavior and market trends, analyzing it using an AI / ML algorithm for subdivision, and delivering personalized insights through a user-friendly single glass pane user interface (SPoG UI). Transaction details are recorded for continuous enhancement. Based on user feedback and evolved market dynamics, the subdivision effectiveness is monitored and refined. A plurality of data sources and analysis tools are integrated for comprehensive analysis. The user may customize the tessellation parameters, and the delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, thereby enhancing correlation and accuracy.

The application relates to the field of power grid dispatching, and provides a medium and long term price prediction method and device, an electronic device and a storage medium. The method comprises the following steps: taking a to-be-predicted date as a starting time point, and collecting historical multi-dimensional feature data sets in reverse; determining power boundary feature data corresponding to the to-be-predicted date based on the historical multi-dimensional feature data sets; screening a target training data set from the historical multi-dimensional feature data sets based on the power boundary feature data and the historical multi-dimensional feature data sets; training an initial medium and long term price prediction model by using the target training data set, so as to obtain a trained medium and long term price prediction model; and inputting multi-dimensional feature data corresponding to the to-be-predicted date into the trained medium and long term price prediction model, and outputting a medium and long term price prediction result. The application effectively solves the problems that the existing technology has poor ability to describe complex market dynamics, the prediction accuracy is difficult to break through the existing bottleneck, and it is difficult to realize high-precision and high-reliability prediction of medium and long term prices.

The invention discloses an e-commerce platform dynamic pricing and subsidy optimization method based on a multi-agent game, and relates to the technical field of e-commerce sales management. The method comprises the following steps: step 100, constructing a multi-agent game model, determining a core game agent in an e-commerce scene, quantifying a revenue function and a constraint condition of each agent, and solving to obtain a pricing and subsidy game equilibrium feasible region; step 200, based on the game equilibrium feasible region, executing a dynamic optimization algorithm, collecting real-time operation data of the e-commerce platform, setting a multi-objective optimization function, and iteratively outputting an optimal pricing strategy and an optimal subsidy strategy through a reinforcement learning algorithm; and step 300, executing the optimal pricing strategy and the optimal subsidy strategy, and constructing a full-link feedback closed loop. The beneficial effects of improving the adaptability of the pricing and subsidy strategy to the market dynamics, optimizing the platform operation resource allocation efficiency and ensuring the long-term stable operation of the e-commerce ecology are achieved.

The application discloses a market maker pricing optimization method for water right transaction, which comprises the following steps: firstly, recording the uncompleted water right transaction; secondly, constructing an expected income function for predicting the income according to the uncompleted transaction; thirdly, establishing a stock risk constraint condition; fourthly, combining the expected income function and the constraint condition to establish a pricing optimization model; fifthly, solving the model to obtain the optimal purchase offer and the optimal sale offer; sixthly, starting the water right transaction by taking the optimal sale offer and the optimal purchase offer as the current offer; seventhly, monitoring all the water right transaction conditions and adjusting the sale offer and the purchase offer according to the transaction conditions; and eighthly, replacing the previous offer with the adjusted offer. The method has the hard constraint of the water right stock quantity, the expected income function is predicted based on the market order information, and the method has high practical value and reliability. The method can adjust the offer according to the market dynamic conditions, and has a positive effect on keeping the stock balance and promoting the transaction.

The invention relates to the technical field of electricity market transaction, in particular to an electricity spot market price prediction and transaction optimization method. According to the technical scheme, the electric power spot market price prediction and transaction optimization method comprises a work flow of electric power spot market price prediction and transaction optimization; according to the method, the robustness and the prediction precision of a price prediction model in the face of market complexity and sudden events are remarkably improved, and a long-term and short-term memory network component can effectively capture and memorize a nonlinear dependency relationship from a historical price sequence and related multi-dimensional features by virtue of a gating mechanism; the model can understand a more abstract market dynamic mode, the autoregressive integral moving average model component is used for capturing inherent linear trends and short-term laws in a time sequence, and the model is allowed to dynamically allocate different weights for input information of past different time steps by setting an attention mechanism when prediction is performed each time.

The invention provides a monitoring method and system for a cargo pledge loan system, and belongs to the technical field of information intelligence. According to the method, a standardized cargo characteristic matrix is established according to cargo characteristics, a reference sample which is the same as a to-be-evaluated cargo is determined according to the standardized cargo characteristic matrix, a value change curve is generated by combining historical price data and market dynamic performance records, and a first value prediction range is generated according to the value change curve. And fusing an external influence factor to obtain a corrected second value prediction range. And determining a loan limit reference value of the to-be-evaluated cargo according to the second value prediction range. The loan limit evaluation precision is improved through standardized and quantitative analysis, the evaluation deviation is reduced through incorporation of multi-dimensional external influence factors, and the accuracy and comprehensiveness of loan limit evaluation are improved. And market changes are responded through real-time dynamic adjustment, so that accurate matching of the loan amount and the actual value of the goods is finally realized, and the pledge risk of a financial institution is effectively reduced.

The invention relates to the technical field of digital economy, and provides a digital economy integration and optimization method and system based on an information chain, and the method comprises the following steps: data collection and integration: building a data integration platform, collecting market data, customer data and supply chain data, and integrating the data into a centralized data warehouse through ETL (extraction, conversion and loading); data analysis and mining: analyzing the integrated data based on a Transform model, and mining the market trend and demand; and real-time monitoring and prediction: predicting future market dynamics through an autoregression model by using an information chain and historical data, and supporting strategic decisions. Through ETL and standardization processing, the problem of multi-source data isomerism is solved, through combination of a Transform model and a self-attention mechanism, the market trend analysis accuracy is improved by 30%, the method is significantly superior to a traditional statistical method, through combination of an autoregression model and an information chain, real-time market monitoring and prediction are achieved, and the prediction error rate is reduced to 5% or below.

The invention relates to the technical field of data processing, and provides an asset value data determination method and device, electronic equipment and a readable storage medium. The method comprises the following steps: standardizing house attribute data to obtain standardized house attribute data; performing rationality verification processing on the standardized house attribute data to obtain effective house attribute data; performing multi-factor fusion processing on the effective house attribute data, preset asset static parameters and preset asset dynamic parameters to obtain target house asset value data; and sending the house target asset value data to the target terminal device for display, thereby improving the consistency and normalization of heterogeneous data, improving the data quality and authenticity, enhancing the response speed of asset value evaluation to market dynamics, improving the accuracy and comparability of a pricing result, and improving the user experience. And the decision transparency and the business execution efficiency are improved.

The present application relates to the technical field of intelligent product selection, and particularly relates to an intelligent product selection and infringement early warning method based on cross-platform mining and a knowledge graph, comprising: obtaining product description texts on multiple platforms, user interaction behavior sequences and market dynamic indicators; using natural language processing to analyze the product texts and extract a technical feature vector, combining time series analysis to generate a product heat index, and calculating market saturation based on market dynamics to form a product market potential evaluation indicator; constructing a multi-dimensional product knowledge graph based on the technical feature vector, and selecting candidate products according to the potential indicator; mapping the candidate product technical vector to the knowledge graph to quantitatively generate a patent infringement risk evaluation value; when the risk exceeds a threshold value, generating a design avoidance scheme based on the technical substitution path and design variant scheme in the graph; and outputting intelligent product selection decision support information. The present application improves platform product selection efficiency and accuracy.

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.

The embodiment of the invention provides an LNG price prediction method and system, and belongs to the technical field of energy. The method comprises the steps of performing multi-source data acquisition in a historical database based on preset LNG price related data items; obtaining preprocessed multi-source data and time sequence data, and constructing a training sample based on the preprocessed multi-source data and time sequence data; pre-constructing a training model based on an end-to-end coding-decoding structure, and training the training sample based on the training model to obtain an LNG price prediction model; and collecting current multi-source data related to LNG price prediction, and performing LNG price prediction based on the current multi-source data related to the LNG price prediction and the LNG price prediction model to obtain an LNG price prediction result. According to the scheme, domestic and overseas LNG price prediction can be supported on the prediction space scale, the time scale covers short-term and medium-term price prediction, real-time response to market dynamic changes can be achieved, and prediction flexibility and timeliness are enhanced.

Systems and methods for predicting demand in real estate sales receive project-specific input data such as launch pricing, unit mix, project configuration, location factors, and / or market dynamics; identify comparable real estate projects within a defined geographic range based on similarity of project size, configuration, and / or launch conditions; apply an Initial Timeframe Model to forecast the number of units likely to sell within a first timeframe after launch, using a comparative analysis of the identified comparable projects and the project-specific input data; generate a project-specific demand curve for a subsequent timeframe using a demand curve model in which the model establishes a relationship between unit pricing and demand; dynamically refining the demand curve by incorporating real-time market data and / or housing market trends to align predictions with current economic conditions; and provide a graphical representation of the demand curve to guide optimal pricing and sales strategies for sustained revenue generation.