Market trend prediction and precision marketing system based on deep learning

Abstract

The application relates to the technical field of data analysis and intelligent marketing, and particularly discloses a market trend prediction and precision marketing system based on deep learning. The system comprises a data interface module, an interlocking collaborative analysis engine and a strategy generation module. The interlocking collaborative analysis engine sequentially performs market trend prediction and user behavior analysis, and through a two-way coupling feedback step, converts the market prediction result into an adjustment signal for a user interest model, and feeds the emerging pattern identified in the user behavior to the market prediction model as a correction input; and then updates the two models based on the coupled new data, to realize collaborative evolution. The strategy generation module generates marketing instructions under resource constraints according to the collaborative analysis result. The application realizes the deep integration and co-evolution of market trends and user preferences by forcibly establishing and continuously optimizing a two-way interaction channel between macro prediction and micro analysis, so that the foresight, precision and self-adaptive ability of the marketing strategy are improved.

Description

Technical Field

[0001] This invention relates to the fields of data analysis and intelligent marketing technology, and more specifically, to a market trend prediction and precision marketing system based on deep learning. Background Technology

[0002] Precision marketing systems are playing an increasingly important role in modern business decision-making, aiming to achieve efficient allocation of marketing resources through market trend prediction and user behavior understanding. With the growth of data volume and the development of deep learning technology, using algorithmic models for automated market analysis and personalized recommendations has become a trend.

[0003] However, existing market trend forecasting and precision marketing solutions still have several limitations. On the one hand, many systems design market forecasting and user recommendation modules as independent pipelines. Market forecasting models only extrapolate trends based on macro-level time-series data such as historical sales and economic indicators, and their outputs are often merely used as rough filtering conditions or reports, failing to be deeply embedded in the computational logic of user interest modeling. On the other hand, user recommendation models mostly focus on mining historical static associations between users and products, or on similarity-based recommendations based on collaborative filtering, lacking the ability to proactively respond to future market trends. This fragmented design leads to two key problems: First, recommendation systems may continue to promote soon-to-be-outdated products or categories, failing to capitalize on growth opportunities; second, market forecasting models struggle to capture emerging trends that emerge from micro-level user behavior and are not yet explicit in macro-level data, resulting in insufficient forward-looking predictions. Furthermore, the system models are typically updated through periodic full retraining, unable to quickly adapt and adjust at a fine time granularity based on market feedback and user interaction, making them sluggish when facing sudden trends or rapid shifts in consumer preferences.

[0004] Therefore, existing technical solutions have obvious shortcomings in terms of the deep integration of macro trends and micro insights, and the dynamic real-time adaptation of system models, which leads to the need to improve the timeliness, accuracy and foresight of the marketing strategies they generate. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a market trend prediction and precision marketing system based on deep learning to address the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a market trend prediction and precision marketing system based on deep learning, comprising: The data interface module is used to acquire and standardize market time-series data and user interaction data. This module transforms heterogeneous raw data into a standardized format that can be processed by the internal model, providing a unified input for subsequent analysis.

[0007] An interlocked collaborative analysis engine, connected to the data interface module, is used to execute a collaborative analysis loop, the loop including: S1: Market trend prediction step, based on the market time series data, calculates and generates a market prediction tensor through a time series prediction model; this tensor quantifies the expected change trend of each commodity category in a specific future period.

[0008] S2: User behavior analysis step, based on the user interaction data, calculates and generates a user preference tensor through a hypergraph network model; the tensor represents the user's dynamic and contextualized interest preferences in the form of a dense vector.

[0009] S3: Two-way coupling feedback step, which constitutes the information exchange channel between macroscopic prediction and microscopic analysis, specifically including: S31: Trend Injection Sub-step, which maps the predicted trend value in the market prediction tensor to the adjustment coefficient of the corresponding product node in the hypergraph network model, thereby incorporating macro market signals into the subsequent user interest calculation and realizing trend guidance.

[0010] S32: Pattern Feedback Sub-step, analyzes the user preference tensor, identifies emerging user interest patterns that are not fully reflected by the market prediction tensor, and encodes them as pattern correction vectors; this step aims to extract forward-looking signals from the behavior of micro-user groups.

[0011] S33: Prediction correction sub-step, feeding back the pattern correction vector to the time series prediction model as an additional input for its next round of prediction, so that the macro prediction model can absorb and respond to emerging patterns emerging from the user end, and realize the dynamic calibration of the prediction model.

[0012] S4: Model update step, based on the new data processed by the bidirectional coupling feedback step, iteratively update the parameters of the time series prediction model and the hypergraph network model; this step ensures that the two models can continuously learn from the latest coupling information and achieve co-evolution of analytical capabilities.

[0013] The strategy generation module, connected to the interlocked collaborative analysis engine, is used to generate and output a set of marketing strategy instructions based on the market forecast tensor and user preference tensor currently being output in the loop; this module transforms the collaborative analysis results into specific, executable marketing actions.

[0014] Furthermore, step S2 specifically includes: S21: Based on the session information in the user interaction data, construct a hypergraph structure with users and products as nodes and single sessions as hyperedges; this structure can naturally represent the complex interaction relationship between a user and multiple products in a single session, breaking through the limitations of traditional pairwise relationships.

[0015] S22: Assign an initial feature vector containing category and price attributes to each product node; S23: In the hypergraph network model, the features of product nodes within the hyperedge are aggregated to form a scene vector, and the scene vector is fused and propagated with the user node features to finally generate the user preference tensor. This process makes the user interest representation not only based on the products they have interacted with, but also embedding the contextual scene information of the interaction, thereby improving the depth and interpretability of interest modeling.

[0016] Furthermore, in step S31, the mapping relationship of the adjustment coefficient is a nonlinear piecewise function, and its mapping threshold is dynamically adjusted based on the moving average of the historical prediction accuracy of the corresponding product category. This design differentiates and adaptively controls the intensity of the injected signal based on the historical reliability of the model's predictions for different product categories, resulting in stronger injection intensity for product categories with higher prediction confidence.

[0017] Furthermore, step S32 specifically includes: S321: Cluster the user vectors in the user preference tensor to form several user groups; this method divides users based on interest similarity and aggregates individual behaviors into group patterns to enhance statistical significance.

[0018] S322: For each user group, count the top N most frequently occurring product categories in their historical interaction data to form a core category set; this extracts the most significant common interests of the group at present.

[0019] S323: Query the market forecast tensor to determine whether the forecast trend value of each category in the core category set is lower than a dynamic feedback threshold. S324: If so, extract the interaction features of the user group to generate the pattern correction vector. This logic ensures that a group's interests are only used as a correction signal feedback when they are significant and not captured by current market forecasts, effectively filtering out noise and known trends.

[0020] Furthermore, the dynamic feedback threshold is negatively correlated with the volatility of the market time series data within a recent time window. This volatility is obtained by calculating the ratio of the standard deviation of the market data series within the window to its mean. When the overall market fluctuates drastically, the system lowers the feedback threshold to more actively capture potential change signals; when the market is stable, the threshold is raised to maintain predictive stability, thereby enabling the model feedback mechanism to be environmentally adaptive.

[0021] Furthermore, the time-series prediction model employs a multi-granularity time-series hybrid network architecture, which includes a convolutional neural network path that processes daily granular sales sequences in parallel and a long short-term memory network path that processes weekly granular macroeconomic index sequences. The outputs of the two paths are fused to generate the market prediction tensor. This architecture achieves a more comprehensive and robust multi-scale modeling of market trends by simultaneously capturing short-term detailed fluctuations and long-term macroeconomic patterns.

[0022] Furthermore, the iterative parameter update in step S4 is implemented through a meta-learner; the meta-learner receives the support set data generated in this iteration and outputs targeted update amounts for the parameters of the time-series prediction model and the hypergraph network model. This approach enables the model to achieve rapid and targeted optimization using a small amount of new data, exhibiting higher efficiency and lower risk of catastrophic forgetting compared to traditional gradient descent methods in adapting to non-stationary market environments.

[0023] Furthermore, the process by which the strategy generation module generates marketing strategy instructions includes: S51: For the target user, retrieve their preferred product list based on the user preference tensor, and filter out products with high predictive trend based on the market prediction tensor to form a candidate product set; this step integrates personal historical preferences and future market trends, and achieves personalized and trend-based initial screening.

[0024] S52: Perform conflict detection on the products in the candidate product set. The conflict detection includes querying a global resource status table to determine the overlap of user push history, product inventory availability, and marketing channel occupancy. This step constrains the strategy from an operational feasibility perspective to prevent over-marketing and resource conflicts.

[0025] S53: Based on preset optimization objectives and constraints, the candidate products that pass conflict detection are combined and optimized, and packaged to generate the final strategy instructions. This process aims to allocate marketing resources globally and maximize the expected comprehensive benefits of the overall marketing campaign.

[0026] Furthermore, the global resource status table records in real time the number of times each user pushes a product, the locked inventory quantity of each product, and the occupancy status of each marketing channel in different time slots. This table serves as the single source of fact for internal resource coordination within the system, ensuring consistency in resource allocation in a multi-threaded, distributed strategy generation environment through atomic updates. This is the technical foundation for avoiding strategy conflicts and resource overselling.

[0027] Furthermore, the interlocked collaborative analysis engine drives the collaborative analysis cycle to execute repeatedly at fixed time intervals or based on market data fluctuations, enabling the system output to continuously evolve. This dual-drive triggering mechanism balances the regularity of system operation with the agility of response to sudden market changes, ensuring that the entire system can continuously operate as a dynamic and adaptive intelligent agent, with its prediction, analysis, and decision-making capabilities constantly iterating and improving over time and with data accumulation.

[0028] The technical effects and advantages of this invention are as follows: Compared to the existing architecture where market forecasting and user recommendation modules are separate, this invention designs a system with an interlocked collaborative analysis engine. This engine sequentially executes market trend forecasting and user behavior analysis within a unified cyclical process, forcibly introducing a bidirectional coupling feedback step. This step transforms market forecasting results into adjustment signals for the user interest modeling process, while simultaneously converting emerging patterns identified in user behavior into corrective inputs for the market forecasting model. This design allows macro trends to directly and quantitatively influence micro-interest calculations, while micro-insights can provide real-time feedback to calibrate macro forecasts. The two are no longer independent processes but constitute a joint computational graph with mutual input and correction, thus solving the problem of the disconnect between forecasting and recommendation and achieving integrated collaborative analysis from data to decision-making.

[0029] Compared to the insufficient adaptability caused by the fixed threshold and static model update strategy in existing technologies, this invention introduces a dynamic threshold judgment and meta-learning-driven incremental update mechanism in a bidirectional coupled feedback loop. The feedback threshold is dynamically adjusted according to the real-time market volatility, lowering the threshold to capture more signals during periods of high volatility and raising it to ensure stability during periods of calm. Model updates are driven by a pre-trained meta-learner, which can quickly calculate targeted parameter updates based on small batches of new data generated in a single loop. This combined mechanism makes the system more sensitive and accurate in responding to environmental changes, enabling it to quickly capture emerging patterns during market changes and rapidly fine-tune model parameters, thus improving the system's overall robustness and adaptive evolutionary capability in non-stationary environments.

[0030] Compared to existing technologies that use time-series models to process macroscopic data and two-dimensional matrix or simple graph models to process user behavior, this invention employs deep network architectures more suited to the characteristics of the data for each of the two core analytical tasks. Market trend prediction utilizes a hybrid network that processes time-series data of different granularities in parallel, enabling the simultaneous capture of short-term details and long-term patterns. User behavior analysis employs a convolutional network based on a session hypergraph, which can naturally model the complex relationships between multiple products involved in a single user interaction session and generate user representations that incorporate contextual information. These dedicated model structures more fully exploit the deep information contained in their respective data sources, providing higher-quality and more interpretable foundational signals for subsequent bidirectional coupling, thereby improving the accuracy and depth of collaborative analysis. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall architecture of the market trend prediction and precision marketing system based on deep learning provided in an embodiment of the present invention.

[0032] Figure 2 This is a schematic diagram of the internal workflow of the interlocking collaborative analysis engine in an embodiment of the present invention.

[0033] Figure 3 This is a schematic diagram of the data exchange and operation mechanism of the bidirectional coupling feedback step in an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1 As attached Figures 1 to 3 The implementation details of the deep learning-based market trend prediction and precision marketing system are as follows: S100: System Initialization and Parameter Configuration Phase Upon system startup, an initialization procedure is executed. This initialization process includes loading the weight files of the trained time-series prediction model and hypergraph network model from persistent storage, loading the model parameters into the computing device's memory, and creating or clearing a key-value store table named `global_resource_state` in the in-memory database. This table is used to record the real-time status of marketing resources. Simultaneously, system operating parameters are configured. Set the base time interval for the collaborative analysis cycle. 3600 seconds; set the base threshold for pattern feedback. Set the activation threshold for trend adjustment to 0.05. The suppression threshold is 0.6. Set the minimum value to 0.3; this is the smallest unit for setting inventory conflict warnings. It is 10.

[0036] The data interface module starts multiple data listening threads, which connect to the market data warehouse and the user behavior log server, respectively. The data interface module is configured with a data cache to temporarily store the received raw data.

[0037] S200: Analysis of Loop Triggering and Scheduling Phases The collaborative analytics engine is managed by a central scheduler. The scheduler maintains an internal clock and a priority event queue.

[0038] By default, the scheduler schedules every interval Generate a timed trigger signal and add it to the event queue. This signal will initiate a standard co-analysis loop.

[0039] exist During the interval, the monitoring submodule of the data interface module performs real-time calculations on the incoming raw data. The monitoring submodule calculates the rolling standard deviation of the core market indicators every 60 seconds. If the ratio of the standard deviations of two consecutive calculations exceeds 2.5, it is determined that the market has experienced severe fluctuations, and the monitoring submodule immediately sends an event trigger signal to the central scheduler.

[0040] When the scheduler receives an event trigger signal, if no loop is currently running, it will interrupt the sleep state and immediately start an analysis loop; if the current loop is in progress, it will immediately start a new loop after the current loop ends.

[0041] S300: Heterogeneous Data Reception and Standardization Processing Stage When the collaborative analysis cycle starts, the data interface module first performs data preparation.

[0042] For market time-series data, the processing steps are as follows: S301: Extract all original records after the end timestamp of the previous loop, and categorize them by data source and product category.

[0043] S302: For each product category's sales sequence, check for timestamp breaks or missing values. For missing values, fill them with the value from the previous valid timestamp.

[0044] S303: Align all product series with a standard timeline. For series with insufficient data points, pad with zeros at the beginning of the series; for series with excessive data points, truncate the most recent 30 time points.

[0045] S304: Standardize each sequence. Calculate the mean of each category sequence over the past 30 time points. and standard deviation For each value in the sequence Perform the transformation: .in, This represents the sales revenue at a specific point in time within the original sequence. This represents the average value of the product category sequence within a specified historical window. Its standard deviation. After processing, a three-dimensional floating-point tensor is output. .

[0046] The processing steps for user interaction data are as follows: S311: Receive the raw interactive log stream.

[0047] S312: Perform session segmentation. Sort the logs by timestamp, using each user identifier as the unit. If the time difference between two consecutive logs is greater than 1800 seconds, insert a session boundary between them. Assign the same, unique session identifier to all logs belonging to the same session.

[0048] S313: Construct a product feature table. Collect all occurrences of product identifiers (corresponding to the field item_id) and query their attributes from the product database. Convert the category and brand codes into a 16-dimensional dense vector using an embedding table. and After taking the natural logarithm of the price, perform maximum-minimum normalization to obtain the scalar. .Will ... By concatenating the features, we obtain the initial feature vector for each product. The dimension is 33.

[0049] S314: Output structured data: a list The elements are (user index, product index, session index); a dictionary. The key is the product index, and the value is... .

[0050] S400: Interlocking Coordination Analysis Engine Execution Phase After the data preparation is complete, the interlocked collaborative analysis engine begins to sequentially execute market trend prediction, user behavior analysis, and the coupling feedback between the two. The interlocked collaborative analysis engine executes market trend prediction (S401), user behavior analysis (S402) in sequence, and performs bidirectional coupling feedback in between (S403), finally completing the model iterative update (S404). Among them, S403 defines the information exchange steps between the prediction model and the analysis model.

[0051] S401: Market Trend Forecast Based on Encoder-Decoder Architecture This step uses a time series prediction model. This generates quantitative predictions of future market conditions.

[0052] The encoder part of the model is composed of It is composed of stacked Transformer encoders. Input tensor First, a linear projection layer is used to boost the feature dimension to the model's hidden dimension. And add sine position encoding.

[0053] Subsequently, the data passes through each encoder layer sequentially. In each layer, multi-head self-attention computation is performed first. The input sequence is divided into 8 heads, and each head independently computes the query, key, and value vectors, and the attention weights are calculated using the scaled dot product attention formula.

[0054] This calculation enables the model to model the correlation between data from different product categories at different time points. The attention output, after passing through residual connections and layer normalization, is fed into a feedforward neural network, and then output after passing through residual connections and layer normalization again.

[0055] go through After layer encoding, the vector of the last time step of the last layer output is taken as the global context vector. Its dimensions are 128.

[0056] The decoder section employs an autoregressive long short-term memory network structure. The initial hidden state of the decoder. and cell state From context vector Obtained through mapping using two different linear layers. The first step input to the decoder. It is a learnable start marker. When the mode correction vector is received from the S4032... At that time, the system maps it to the model's hidden dimension through a linear transformation layer and compares it with the context vector output by the encoder. They are spliced ​​together and used together as part of the decoder's initial state.

[0057] As a preferred embodiment, the time-series prediction model employs a multi-granularity temporal hybrid network. This network is designed with two parallel encoding paths, which process input data at different time granularities respectively.

[0058] The first path receives fine-grained sales sequences on a daily basis and processes them using a one-dimensional convolutional neural network. The kernel size is [size missing]. Step size is The number of output channels is 64, followed by the activation of the linear rectifier unit and the maximum pooling layer.

[0059] The second path receives a coarse-grained macro-exponential sequence on a weekly basis and processes it using a Long Short-Term Memory (LSTM) network. This LSM network has 64 hidden units. After processing, the feature vectors output by the two paths are concatenated into a 128-dimensional hybrid feature vector.

[0060] This hybrid feature vector is fed into a time-distributed dense layer, which is computed independently at each future time step and outputs the predicted sales of all categories at that time step.

[0061] Finally, the system calculates the future number... The rate of change in sales volume over a given day relative to the current time is used as the probability value for the growth of this product category. ,all Constitutes the market forecast tensor .

[0062] S402: User Behavior Analysis Based on Attribute-Enhanced Hypergraph Convolutional Networks This step is based on and Construct a dynamic hypergraph and learn user representations.

[0063] The system first constructs a hypergraph data structure. A node set is defined to contain all users and items. A hyperedge is created for each unique session identifier. If a user... In the conversation In terms of goods If interaction occurs, then the hyperedge Connecting nodes This structure uses an association matrix. express, If and only if node Belongs to superedge .

[0064] Each product node initial features Taken from Each user node initial features Initialize it as a vector of all zeros.

[0065] Furthermore, as a preferred embodiment, the user behavior parsing model is implemented using an attribute-enhanced hypergraph convolutional network. This network consists of two hypergraph convolutional layers.

[0066] During the first convolutional layer, for each hyperedge Calculate the mean of the initial features of all product nodes within the hyperedge to obtain the scene vector of the hyperedge. The formula is: .here, Indicates the superedge The total number of product nodes included. This represents a summation operation. (In updating user nodes...) When collecting features, collect all those containing Calculate the mean of the scene vectors of the hyperedges: .

[0067] Then, the user's own characteristics are concatenated with and transformed from the aggregated scene characteristics: .in, and These are the learnable weight matrix and bias vector of the first layer hypergraph convolution, respectively. This represents a vector concatenation operation. This is the activation function for the linear rectifier unit.

[0068] In the second convolutional layer, information transfer occurs at a higher semantic level, ultimately outputting the final embedding vectors for all users. This constitutes the user preference tensor. .

[0069] S403: Two-way Coupling Feedback Execution Phase This step establishes an information link between the predictive model and the analytical model.

[0070] S4031: Market Trend Signal Injection into User Interest Calculation System read Based on the growth probability value of each product category Calculate its adjustment coefficient for commodity nodes. .

[0071] In a preferred embodiment, the probability influence mapping table in the collaborative control module uses a nonlinear piecewise function as its mapping rule. Let... This represents the predicted growth probability value for the product category. and These are the currently calculated dynamic suppression threshold and activation threshold. The adjustment coefficient to be determined is denoted as .

[0072] In practice, the threshold is first dynamically adjusted. Then, the past performance of this product category is queried. The accuracy records of each prediction are used to calculate their moving average. Set the base activation threshold. Basic inhibition threshold .

[0073] The dynamic threshold is then: ; Then, calculate according to the following formula. : like ,but ; like ,but; like ,but ; After the calculation is completed, before the next round S402 begins, the system traverses all product nodes and initializes their feature vectors. Multiply by the category value.

[0074] S4032: Feedback on Emerging User Patterns and Market Forecast Revision System Analysis Identify patterns of collective user interests that are not fully reflected in market predictions.

[0075] As a preferred implementation, the specific method for identifying consumption patterns is as follows: clustering users according to the user preference tensor to form at least one user group; For each user group, the top N most frequently occurring product categories in their historical interaction data are counted to form a core category set. Query the market forecast tensor to determine whether the forecast trend value of each category in the core category set is lower than a preset feedback threshold. If so, the user group's interest pattern is determined to be an emerging consumption pattern that requires feedback, and feature extraction is performed on the group's interaction data to generate the pattern correction vector.

[0076] During implementation, the system first... Cluster analysis was performed on all user vectors. The DBSCAN algorithm was used, with the neighborhood radius... Set to 0.4, minimum number of samples Set as The algorithm will output several user clusters. And noise points, for each user cluster Retrieve the interaction records of all users within this cluster over the past 24 hours, and count the top [interactions] that occurred most frequently. These categories constitute the core category set. .

[0077] Furthermore, as a preferred embodiment, the feedback threshold is a dynamic threshold whose value is negatively correlated with the recent volatility of the market time series data; the volatility is obtained by calculating the variance or standard deviation of the market time series data within a preset time window.

[0078] The system calculates the standard deviation of total market sales over the past 24 hours. To obtain the relative volatility, divide it by the sequence's historical volatility. Daily average Standardize to obtain .

[0079] Based on the aforementioned dynamic threshold principle, the dynamic feedback threshold for the current loop... The calculation is as follows: .in, is the function that takes the maximum value to ensure the lower limit of the threshold; is the preset sensitivity coefficient used to adjust the intensity of the impact of market volatility on the threshold, set to 0.3.

[0080] For each cluster Query ,get Each category And calculate the average value. .like Then determine This is an emerging cluster of models.

[0081] Subsequently, feature vectors of all interactive items within the cluster are extracted, and the element-wise mean of these vectors is calculated to obtain a 33-dimensional vector. This is the pattern correction vector.

[0082] S404: Iterative Update of Model Parameters Based on Meta-Learning After completing a bidirectional coupling, the system uses the newly generated data to perform rapid incremental updates on the two core models.

[0083] As a preferred implementation, the specific mechanism by which the collaborative control module triggers incremental learning is as follows: after each collaborative optimization cycle, the system creates a mixed training batch containing the latest market data, user interaction data, and the correction vector generated in the previous cycle. The batch is fed into a meta-learner whose task is not to make direct predictions, but to output a set of parameter updates for specific fully connected layers and convolutional kernels in the market trend prediction model and user behavior analysis model. The system uses this update to achieve a quick and targeted adjustment of model parameters.

[0084] In practice, the meta-learner has been pre-trained offline using a model-independent meta-learning algorithm, and its internal parameters encode a general knowledge transfer strategy across tasks. In this scenario, a task refers to prediction or parsing based on data within a time window.

[0085] The training objective of the meta-learner is to enable the main model to quickly achieve a low query loss when faced with new tasks based on new data, using only a small number of updates generated by the meta-learner. During online runtime, it takes the gradients of the loss functions calculated by the temporal prediction model and the hypergraph network model on the current iteration's support set data as input, and uses its internal network to calculate an appropriate update step size and direction. .

[0086] This process simulates an inner loop optimization on the support set, but the update rules are generated by the meta-learner parameterization. The system then performs parameter updates: This process performs a one-time, small-step update of the parameters of the time series prediction model and the hypergraph network model. This update process enables rapid and targeted adjustment of the model parameters.

[0087] At this point, the interlocked collaborative analysis engine has completed a full analysis coupling update cycle. Market trends are injected into the user model through S4031, and emerging user patterns are fed back to the market model through S4032. The model parameters of both are jointly updated in S404 based on the new data generated by this interaction, realizing the bidirectional signal coupling and collaborative evolution defined by the technical solution.

[0088] S500: Precision Marketing Strategy Generation Stage The collaborative analysis engine transmits the final output of this loop—the market forecast tensor and the user preference tensor, which have been mutually corrected through a two-way coupling process—as a unified collaborative analysis result to the strategy generation module.

[0089] S501: Candidate Strategy Generation Based on Multi-Objective Screening The strategy generation module first generates an initial candidate set for the target users.

[0090] In a preferred embodiment, the process by which the strategy generation module generates marketing strategy instructions is as follows: For the target user, retrieve their preferred product list based on the user preference tensor; Based on the market forecast tensor, select products from the preferred product list that belong to the category with high predictive trend to form a candidate product set; Perform conflict detection on the items in the candidate item set; Based on the preset optimization goals, the candidate products that pass the conflict detection are sorted and combined, and packaged to generate the final personalized marketing strategy instructions.

[0091] For each user to be marketed The system from its Starting with an approximate nearest neighbor search algorithm, the algorithm retrieves the 200 items with the highest cosine similarity from the entire product database, forming a preference list. .

[0092] Subsequently, according to Perform trend filtering: For For each product in the list, if its category is Below the action threshold (This is a preset fixed threshold used to filter categories with actionable value), and those categories are removed from the list. Filtered products enter the candidate product set. .

[0093] S502: Conflict Detection Based on Global Resource Status Table To perform policy conflict detection, the system queries its maintained global resource status table.

[0094] In a preferred implementation, the conflict detection is achieved by maintaining and querying a global resource status table; the global resource status table records each user's push history, the inventory status of each product, and the real-time occupancy rate of each marketing channel; the strategy generation module needs to query this table in real time to evaluate conflicts.

[0095] System maintenance The table contains the following data fields: Records the number of times a user pushes a product within 24 hours.

[0096] Records the amount of inventory that is reserved for marketing activities.

[0097] Records the occupancy status of specific channels, dates, and minute-level time slots.

[0098] for Each item in The system initiates a batch query: 1. Receive push notifications to users within 24 hours. commodity Number of times .like This results in duplicate penalties. .

[0099] 2. Obtain the product Total inventory and pre-occupied inventory .like Then the conflict score Set as a very large penalty constant (For example ).

[0100] 3. Assuming the plan is to push notifications via Push notifications at 15:00 tomorrow, check the occupancy status of the corresponding time slot. If it is... Then the conflict score Set to a constant .

[0101] The overall conflict score is: .

[0102] S503: Strategy Optimization and Instruction Packaging The system for each For calculating the final utility score: In the formula, Indicates user With goods The cosine similarity between the embedded vectors, with a range of . ; 0.5 is the preset trend weight factor, used to balance the relative weight of users' historical preferences and future market trends in strategy evaluation; The overall conflict score is calculated using the steps described above.

[0103] Subsequently, the strategy generation module solves a global optimization problem: the goal of this optimization problem is to maximize the sum of the total utility fractions of all selected instructions, with constraints including: (1) The total marketing budget shall not exceed a preset value. ; (2) The number of instructions issued by each marketing channel in any time slot shall not exceed its capacity limit; (3) Each user The number of commands received in this push notification will not exceed a preset limit. This combinatorial optimization problem can be solved using an integer linear programming solver or a greedy algorithm.

[0104] The final selection The instructions are converted into executable instructions.

[0105] S600: Policy Execution and System State Synchronization Phase The generated policy instruction set is sent to the message queue, where it is consumed and executed by the downstream channel executors.

[0106] The system must be updated atomically at the same time the command is issued. A table is used to reflect resource consumption. These update operations are completed within a distributed transaction, thereby maintaining the consistency of the system state.

[0107] S700: Closed-loop feedback and iterative stage A complete collaborative analysis cycle ends after the state update is completed. After recording the metadata for this cycle, the collaborative analysis engine enters a low-power waiting state.

[0108] The central scheduler resets the timer, and the data interface module continues to asynchronously receive new data and monitor fluctuation indicators. When the next... When a new cycle or a new triggering event arrives, a new cycle starts immediately.

[0109] In the new loop, the data interface module provides and It will include user feedback data generated after the previous round of strategy execution. This design allows the system to run in a cyclical manner with a fixed period or event-driven approach. The input of each cycle incorporates the output feedback of the previous cycle, enabling the market forecasting and user analysis models to learn from the market changes and user behavior shifts caused by their own strategies in the next cycle, thus forming a continuously optimized computational workflow.

[0110] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A market trend prediction and precision marketing system based on deep learning, characterized in that: include: The data interface module is used to acquire and standardize market time-series data and user interaction data; An interlocked collaborative analysis engine, connected to the data interface module, is used to execute a collaborative analysis loop, the loop including: S1: Market trend prediction step, based on the market time series data, calculates and generates a market prediction tensor through a time series prediction model; S2: User behavior analysis step, based on the user interaction data, calculates and generates a user preference tensor through a hypergraph network model; S3: Bidirectional coupling feedback step, the step including: S31: Trend Injection Sub-step, which maps the predicted trend value in the market prediction tensor to the adjustment coefficient of the corresponding product node in the hypergraph network model, so as to affect the calculation of the next round of user behavior analysis step; S32: Pattern feedback sub-step, analyze the user preference tensor, identify emerging user interest patterns that are not fully reflected by the market prediction tensor, and encode them as pattern correction vectors; S33: Prediction correction sub-step, feeding back the mode correction vector to the time series prediction model as an additional input for its next round of prediction; S4: Model update step, based on the new data processed by the bidirectional coupling feedback step, iteratively update the parameters of the time series prediction model and the hypergraph network model; The strategy generation module, connected to the interlocked collaborative analysis engine, is used to generate and output a set of marketing strategy instructions based on the market forecast tensor and user preference tensor currently being output in the loop.

2. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, Step S2 specifically includes: S21: Based on the session information in the user interaction data, construct a hypergraph structure with users and products as nodes and single sessions as hyperedges; S22: Assign an initial feature vector containing category and price attributes to each product node; S23: In the hypergraph network model, the features of the product nodes within the hyperedge are aggregated to form a scene vector, and the scene vector is fused and propagated with the user node features to finally generate the user preference tensor.

3. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, In step S31, the mapping relationship of the adjustment coefficient is a non-linear piecewise function, and its mapping threshold is dynamically adjusted according to the moving average of the historical prediction accuracy of the corresponding product category.

4. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, Step S32 specifically includes: S321: Cluster the user vectors in the user preference tensor to form several user groups; S322: For each user group, count the N most frequently occurring product categories in their historical interaction data to form a core category set; S323: Query the market forecast tensor to determine whether the forecast trend value of each category in the core category set is lower than a dynamic feedback threshold. S324: If so, extract the interaction features of the user group to generate the pattern correction vector.

5. The market trend prediction and precision marketing system based on deep learning according to claim 4, characterized in that, The dynamic feedback threshold is negatively correlated with the volatility of the market time series data within a recent time window, and the volatility is obtained by calculating the ratio of the standard deviation of the market data series within the window to its mean.

6. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, The time-series prediction model adopts a multi-granularity time-series hybrid network architecture, which includes a convolutional neural network path that processes daily granularity sales sequences in parallel and a long short-term memory network path that processes weekly granularity macroeconomic index sequences. The outputs of the two paths are fused to generate the market prediction tensor.

7. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, The iterative parameter update in step S4 is implemented through a meta-learner; the meta-learner receives the support set data generated in this iteration and outputs the targeted update amount for the parameters of the time series prediction model and the hypergraph network model.

8. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, The process by which the strategy generation module generates marketing strategy instructions includes: S51: For the target user, retrieve their preferred product list based on the user preference tensor, and filter out products with high prediction trends based on the market prediction tensor to form a candidate product set; S52: Perform conflict detection on the products in the candidate product set. The conflict detection includes querying a global resource status table to determine the overlap of user push history, product inventory availability, and marketing channel occupancy. S53: Based on the preset optimization objectives and constraints, perform combination optimization on the candidate products that have passed the conflict detection, and package them to generate the final strategy instruction.

9. The market trend prediction and precision marketing system based on deep learning according to claim 8, characterized in that, The global resource status table records in real time the number of times each user pushes products, the locked inventory quantity of each product, and the occupancy status of each marketing channel in different time slots.

10. The market trend prediction and precision marketing system based on deep learning according to claim 1, characterized in that, The interlocked collaborative analysis engine drives the collaborative analysis to run repeatedly at fixed time intervals or based on market data fluctuation events, so that the system output continues to evolve.

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