Method and system for analyzing user consumption behaviors in different scenes in commercial space

Abstract

The invention relates to the technical field of space behavior analysis, and discloses a user consumption behavior analysis method and system for different scenes in a commercial space, and the method comprises the steps: obtaining user behavior data through a wireless positioning device and a distributed environment sensor; performing space-time alignment on the multi-source heterogeneous data, and fusing commodity granularity data of a transaction system to construct a three-dimensional trajectory model; based on the three-dimensional track model, identifying residence characteristics and a moving mode of the user, and associating commodity distribution to generate a dynamic thermodynamic diagram; adjusting a guidance strategy and environmental parameters according to the density gradient of the thermodynamic diagram and the user attribute hierarchy; the payment feature data and the user behavior data are integrated to update the user tag, and a cross-regional association strategy is generated, the comprehensive, flexible and efficient commercial space user consumption behavior analysis method is provided, the problem that in the prior art, dynamic user behaviors are understood insufficiently is solved, and therefore the commercial intelligence process is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of spatial behavior analysis, and in particular to a method and system for analyzing user consumption behavior in different scenarios in a commercial space. Background Art

[0002] With the continuous development of commercial space and the increasing complexity of user behavior, enterprises need more accurate and efficient methods to analyze user consumption behavior in order to formulate more scientific market strategies and improve customer experience. In recent years, with the rapid progress of Internet of Things (IoT), big data and cloud computing technologies, relevant technologies have made remarkable achievements in the field of user behavior analysis. User behavior data capture systems in commercial spaces generally use technical means such as wireless sensor networks, mobile device positioning, and visual monitoring to collect user consumption behavior data in real time, and analyze user behavior patterns and preferences based on data analysis models.

[0003] In particular, cloud computing-based analysis systems make it more efficient to extract, process and store information from user behavior data. At the same time, by using advanced algorithms such as machine learning and deep learning to build user models, business analysts can analyze these data to identify user residence characteristics, movement patterns, and generate visual dynamic heat maps. However, existing technologies still have some shortcomings, which does not provide a clear basis for business decisions.

[0004] Existing technologies are limited in the diversity and heterogeneity of data sources. Data sources in commercial spaces are often scattered, involving multiple devices such as positioning devices, environmental sensors, and transaction systems. The lack of unified standards and effective spatiotemporal alignment mechanisms makes data integration difficult and affects the accuracy of analysis results. In addition, existing systems often rely on static models, which make it difficult to adapt to rapid changes in user behavior in real time, and face challenges in maintaining timeliness and accuracy.

[0005] Most existing technologies still use traditional pattern recognition methods, fail to make full use of adaptive learning mechanisms, and are not sensitive enough to the continuous changes in user behavior, resulting in delayed model updates. This makes user portraits and behavior analysis often fail to reflect the real needs of users. In addition, the process of heat map generation lacks the integration of user feedback, making it difficult to mobilize user participation and affecting the effectiveness of decision-making. Summary of the invention

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method for analyzing user consumption behavior in different scenarios within a commercial space, which can solve the problems of data silos, insufficient dynamic adaptability, and lack of user feedback mechanism in the prior art. By comprehensively integrating multi-source data, the real-time response capability is enhanced to realize intelligent management within the commercial space.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: a method for analyzing user consumption behaviors in different scenarios in a commercial space, comprising: obtaining user behavior data through a wireless positioning device and a distributed environmental sensor; aligning multi-source heterogeneous data in time and space, and integrating the commodity granularity data of the transaction system to construct a three-dimensional trajectory model; identifying the user's residence characteristics and movement patterns based on the three-dimensional trajectory model, and generating a dynamic heat map by associating commodity distribution; adjusting the guidance strategy and environmental parameters according to the density gradient of the heat map and the user attribute level; integrating payment feature data and user behavior data to update user tags, and generating a cross-region association strategy.

[0009] As a preferred solution of the method for analyzing user consumption behaviors in different scenarios in a commercial space described in the present invention, the wireless positioning device includes locating the movement trajectory of the user in the commercial space by receiving the signal of the mobile device carried by the user; The distributed environmental sensor includes an RFID reader and a pressure sensor device deployed in a functional area, which are used to record the attribute information and interaction frequency of the operated object.

[0010] As a preferred solution of the method for analyzing user consumption behavior in different scenarios in a commercial space described in the present invention, wherein: the spatiotemporal alignment of multi-source heterogeneous data includes building a central API gateway to receive and route data requests from wireless positioning devices and distributed environmental sensors, and using a mapping table to convert different data structures of various devices into a unified format and transmit it to a data processing platform; Introducing an adaptive heterogeneous data parser to automatically identify and process data from different sources based on data features and remove noisy data and redundant information; Assign a unique timestamp and geolocation identifier to each piece of data, establish a connection with the dynamic environment data stored in the database through the user's activity trajectory, calculate the similarity of timestamps and location identifiers in space and time, and dynamically associate data.

[0011] As a preferred solution of the method for analyzing user consumption behaviors in different scenes in a commercial space described in the present invention, wherein: the construction of the three-dimensional trajectory model includes: the construction of the three-dimensional trajectory model adopts collaborative processing based on cloud computing technology to build a multi-level cloud computing architecture; Edge computing nodes are set up at the user access layer. By setting up edge devices to collect and preliminarily process user data, a lightweight data processing module is developed in each edge node to perform real-time analysis of user behavior data. The cleaned data is transmitted to the central cloud node through a secure encrypted channel for further analysis. Create a RESTful API to enable communication between edge nodes and the cloud, and to perform two-way data transmission and command issuance.

[0012] As a preferred solution of the method for analyzing user consumption behaviors in different scenarios in a commercial space described in the present invention, the identification of the user's residence characteristics and movement patterns includes extracting the behavior characteristics of residence time, frequency and movement speed from the user behavior data to construct a user activity model; Apply the behavior cloning method in reinforcement learning to simulate the typical movement pattern of users in commercial spaces. Through the real-time data feedback mechanism, continuously monitor the changes in user behavior data. Use streaming data analysis technology to capture user behavior trends in real time and adjust the parameters of the user activity model. Through the incremental learning method, the user activity model does not need to be retrained, and only parameters are adjusted based on the newly added data, so that the user activity model always reflects the latest user behavior characteristics.

[0013] As a preferred solution of the method for analyzing user consumption behaviors in different scenes in a commercial space described in the present invention, wherein: generating a dynamic heat map includes converting user behavior data into a heat map format to distinguish the heat of different time periods; Capture nonlinear relationships in user behavior data, identify behavioral characteristics of user activities, and map behavioral characteristics to heat map space through quadratic or cubic polynomial functions, dividing user hotspots into high, medium, and low heat areas. Each heat area corresponds to a different color, and prioritizes planning detours to low-density waiting areas for high-consumption-level users; Integrate a simple user feedback survey function, combine user feedback with heat map data, use a simple weighted model to calculate the impact of user satisfaction on heat data, and dynamically update the heat map based on the results.

[0014] As a preferred solution of the method for analyzing user consumption behaviors in different scenarios in a commercial space described in the present invention, the generation of a cross-region association strategy includes generating a behavior sequence including a timestamp, coordinates, and stay duration by collecting user movement trajectories; aligning the payment time with the stay event in the user trajectory to bind the same user entity; Establish a multi-dimensional fusion index to associate user behavior with consumption characteristics as a unified data object. When a user stays in any functional area for a long time but does not consume, and generates a higher-than-average average customer spending in the remaining areas, it is marked as a potential associated behavior. Build a multi-level labeling system, including basic attribute layer, behavior derivative layer and deep interactive type, and use incremental learning model to dynamically correct label weights: when the behavior of label users deviates from historical patterns, reduce the confidence level of the association between the current label and the category; When the user leaves the current area, the optimal associated area is matched according to the tag: for "price-sensitive" users, navigation to adjacent areas with promotional activities is pushed first; for "experience-oriented" users, the display area path with interactive devices is recommended; When any functional area gathers tag users, the collaborative strategy is automatically triggered to adjust the commodity display density in adjacent areas and highlight related categories; Deploy a module for behavior feedback analysis, and use changes in user behavior after policy execution as optimization signals. When any associated policy fails to achieve the expected goal, the current policy priority is automatically downgraded.

[0015] As a preferred solution of the user consumption behavior analysis system in different scenes in a commercial space described in the present invention, it includes: a data acquisition module, a data processing module, a user behavior analysis module, a dynamic heat map generation module, and a strategy generation and execution module; The data acquisition module acquires the user's behavior data in real time through wireless positioning devices and distributed environmental sensors, is responsible for receiving device signals carried by the user, environmental sensor data and RFID reader data, and records the user's movement trajectory, interaction frequency and related attribute information in the commercial space; The data processing module aligns the collected multi-source heterogeneous data in time and space, cleans and converts the data, and constructs a structured data set that can be used for analysis; The user behavior analysis module extracts the user's residence characteristics and movement patterns based on the cleaned user behavior data, and optimizes the user activity model using an adaptive learning mechanism; The dynamic heat map generation module converts the processed user behavior data into a heat map format, generates user activity heat maps for different time periods, and displays the areas of user attention for merchants; The strategy generation and execution module generates targeted business strategies based on the analysis results, including cross-regional association strategies, and adjusts product displays and user guidance in real time.

[0016] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a method for analyzing user consumption behaviors in different scenarios in a commercial space when executing the computer program.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for analyzing user consumption behavior in different scenarios in a commercial space.

[0018] The beneficial effects of the present invention are: through comprehensive analysis of multi-source data in commercial spaces, the real-time understanding and response capabilities of user consumption behaviors are improved. By integrating wireless positioning technology and user behavior data from distributed environmental sensors, the present invention constructs a new user activity model and three-dimensional trajectory model, thereby more accurately identifying user residence characteristics and movement patterns.

[0019] It can fully capture and deeply analyze user behavior. Through the generation of dynamic heat maps, merchants can clearly identify the distribution of products and consumption trends that users like, thereby optimizing product layout and guidance strategies and improving user experience. At the same time, the introduction of user feedback survey functions and dynamic update mechanisms enables the system to adjust behavior analysis strategies based on real-time data and user satisfaction, integrating users' subjective experience with objective data, and providing merchants with more targeted decision-making basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1 A flowchart of a method for analyzing user consumption behavior in different scenarios in a commercial space provided by an embodiment of the present invention.

[0022] Figure 2 A schematic diagram of working modules of a user consumption behavior analysis system for different scenarios in a commercial space provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0026] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0027] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0028] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0029] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for analyzing user consumption behaviors in different scenarios in a commercial space, including: S1: Obtain user behavior data through wireless positioning devices and distributed environmental sensors.

[0030] Furthermore, the wireless positioning device includes locating the movement trajectory of the user in the commercial space by receiving the signal of the mobile device carried by the user; establishing Bluetooth and Wi-Fi network base stations, and the device records the environmental signal strength; After the user enters the commercial space, the positioning device pairs with the user's mobile phone via Bluetooth and obtains the Wi-Fi signal strength at the same time. Through multi-path propagation and signal back-propagation technology, the precise location of the Bluetooth signal is integrated with the Wi-Fi information to calculate the user's real-time location.

[0031] Distributed environmental sensors include RFID readers and pressure sensing devices deployed in functional areas to record attribute information and interaction frequency of operated items.

[0032] S2: Align multi-source heterogeneous data in time and space, and integrate the commodity granularity data of the trading system to build a three-dimensional trajectory model.

[0033] Furthermore, a central API gateway is built to receive and route data requests from wireless positioning devices and distributed environmental sensors, and the different data structures of each device are converted into a unified format using a mapping table and transmitted to the data processing platform; Introducing an adaptive heterogeneous data parser to automatically identify and process data from different sources based on data features and remove noisy data and redundant information; Each piece of data is assigned a unique timestamp and geographic location identifier, and the user's activity trajectory is linked to the dynamic environment data stored in the database. The spatiotemporal alignment uses an improved sliding window protocol: the RFID trigger event is used as the reference timestamp; the visual data matches the skeleton posture within the ±300ms window; the Wi-Fi signal strength is interpolated and positioned after multipath effect compensation; the attribute distribution of untraded items is calculated by comparing the difference between the number of RFID activations and the POS transaction records.

[0034] A spatial index is established based on location (commercial district, floor), and a time index is established based on time period (hour, day). Through a bidirectional association pointer, time and space information are associated, so that when accessing an index, the associated data of another dimension can be quickly obtained, thereby improving data query efficiency.

[0035] It should be noted that the construction of the three-dimensional trajectory model adopts collaborative processing based on cloud computing technology to build a multi-level cloud computing architecture; The edge computing node is set at the user access layer. By setting up edge devices to collect and preliminarily process user data, and process the original data stream provided by mobile devices, the processing throughput is designed to be 100,000 per second. The data stream includes the user's location information, timestamp, and additional environmental data (such as temperature, humidity, etc.).

[0036] In each edge node, develop lightweight data processing modules and lightweight data preprocessing, including data cleaning and noise filtering, to analyze user behavior data in real time. The cleaned data is transmitted to the central cloud node through a secure encrypted channel for further analysis. Furthermore, when performing user behavior analysis at the central node, the moving speed is smoothed using cubic spline interpolation, and the user speed data is smoothed with high precision to eliminate noise and obtain more realistic speed characteristics; Use Spark Structured Streaming to process stream data, set the sliding window size to 60 seconds, and perform real-time aggregate analysis. This process includes aggregate statistics on user behavior, such as calculating the average dwell time and movement speed of each user in the last 60 seconds.

[0037] Furthermore, we build a multi-dimensional feature vector, analyze the product categories associated with each dwelling event, and construct corresponding features for each product to improve accuracy. We set up a daily new user behavior data trigger mechanism, and fine-tune the three-dimensional trajectory model on this basis.

[0038] Dynamically slice real-time data based on time range, user identity or geographic location. Each slice contains user activity data within a time period; create a RESTful API to enable communication between edge nodes and the cloud, and perform two-way data transmission and command issuance.

[0039] S3: Identify the user's residence characteristics and movement patterns based on the three-dimensional trajectory model, and generate a dynamic heat map based on the distribution of related products; adjust the guidance strategy and environmental parameters according to the density gradient of the heat map and the user attribute level.

[0040] Furthermore, we extract behavioral features such as dwell time, frequency, and movement speed from user behavior data, and use deep neural networks (DNNs) to fit user movement trajectories and build user activity models; It should be noted that the input layer of the user activity model receives a variety of state information, including but not limited to: The user location information, that is, the current coordinates (x, y), can be the real-time coordinate location of the user in the commercial space.

[0041] Timestamps are used to capture the temporal characteristics of user behavior (e.g., hour, weekend / weekday).

[0042] User historical behavior, including the user’s recent behaviors (such as the time and location of the last stay), helps the user activity model capture the user’s habits.

[0043] The hidden layer of the user activity model uses 3-5 fully connected hidden layers, each containing multiple neurons: The ReLU (Rectified Linear Unit) activation function is used to introduce nonlinearity and accelerate convergence; Dropout is used to reduce the risk of overfitting and improve generalization ability.

[0044] The output layer is used to predict the user's next behavior: When making rough behavior predictions, it is treated as a binary classification problem and two nodes are output, representing the probabilities of "moving" and "staying", respectively. The F1-score of the moving / staying classification is ≥ 0.92, ensuring the reliability of coarse-grained behavior judgment.

[0045] When making an accurate next location prediction, create a regression layer to output the user's next location coordinates (x', y'), providing the specific location of the user's expected behavior.

[0046] Furthermore, the mean Euclidean distance error (MAE) between the predicted position and the true coordinates is Convergence is determined when the meter is reached. Calculation method: ; in, is the total number of samples, , are the horizontal and vertical coordinates of the predicted position, , They are the horizontal and vertical coordinate values ​​of the actual position respectively.

[0047] Apply the behavior cloning method in reinforcement learning to simulate the typical movement pattern of users in commercial spaces, divide the user's trajectory into different activity stages (such as browsing, purchasing, and resting), continuously monitor the changes in user behavior data through a real-time data feedback mechanism, and use streaming data analysis technology to capture user behavior trends in real time and adjust the parameters of the user activity model; Through the incremental learning method, the user activity model does not need to be retrained, and only parameters are adjusted based on the newly added data, so that the user activity model always reflects the latest user behavior characteristics.

[0048] It should be noted that the regularized loss function (such as L2 regularization) is used to calculate the error gradient of the new data to the current model. The formula is: ; in, is the error gradient, α is the dynamic learning rate (adaptively adjusted as the data distribution changes), λ is the regularization coefficient (to prevent overfitting), is the gradient of the loss function for new data, For input data; The parameters of the underlying feature extraction layer of the model (the first three layers of fully connected networks) are fixed, and only the parameters of the top-level behavior prediction layer are fine-tuned to ensure that the basic feature expression ability is not damaged. The confidence is measured by the Softmax probability value. When the prediction confidence of the new data is lower than 0.7, the local parameter update is triggered; otherwise, the original parameters are retained. Since the edge node uploads a data shard every 60 seconds, the central node is updated in mini-batches. The single parameter adjustment range is limited by the dynamic learning rate α to avoid overall model oscillation.

[0049] Furthermore, the user behavior data is converted into a heat map format to distinguish the popularity in different time periods; Capture the nonlinear relationship in user behavior data, identify the behavioral characteristics of user activities, and map the behavioral characteristics to the heat map space through quadratic or cubic polynomial functions. Divide the user hotspots into high, medium, and low heat zones, each with a different color, and prioritize planning detours to low-density waiting areas for high-consumption-level users.

[0050] Integrate a simple user feedback survey function, combine user feedback with heat map data, use a simple weighted model to calculate the impact of user satisfaction on heat data, and dynamically update the heat map based on the results.

[0051] S4: Integrate payment feature data and user behavior data to update user tags and generate cross-region association strategies.

[0052] Furthermore, the collected user movement trajectory is used to generate a behavior sequence including timestamp, coordinates, and dwell time; the payment moment is aligned with the dwell event in the user trajectory to bind the same user entity; Establish a multi-dimensional fusion index to associate user behavior with consumption characteristics as a unified data object. When a user stays in any functional area for a long time but does not consume, and generates a higher-than-average average customer spending in the remaining areas, it is marked as a potential associated behavior. Build a multi-level labeling system, including basic attribute layer, behavior derivative layer and deep interactive type, and use incremental learning model to dynamically correct label weights: when the behavior of label users deviates from historical patterns, reduce the confidence level of the association between the current label and the category; When the user leaves the current area, the optimal associated area is matched according to the tag: for "price-sensitive" users, navigation to adjacent areas with promotional activities is pushed first; for "experience-oriented" users, the display area path with interactive devices is recommended; When any functional area gathers tag users, the collaborative strategy is automatically triggered to adjust the commodity display density in adjacent areas and highlight related categories; Deploy a module for behavior feedback analysis, and use changes in user behavior after policy execution as optimization signals. When any associated policy fails to achieve the expected goal, the current policy priority is automatically downgraded.

[0053] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that: If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0054] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0055] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0056] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination of the two: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0057] Example 3, reference Figure 2 , is an embodiment of the present invention, which provides a user consumption behavior analysis system for different scenarios in a commercial space, including a data collection module, a data processing module, a user behavior analysis module, a dynamic heat map generation module, and a strategy generation and execution module; The data collection module obtains user behavior data in real time through wireless positioning devices and distributed environmental sensors. It is responsible for receiving device signals carried by users, environmental sensor data, and RFID reader data, and records the user's movement trajectory, interaction frequency, and related attribute information in the commercial space; The data processing module aligns the collected multi-source heterogeneous data in time and space, cleans and converts the data, and constructs a structured data set for analysis; The user behavior analysis module extracts the user's residence characteristics and movement patterns based on the cleaned user behavior data, and uses the adaptive learning mechanism to optimize the user activity model; Dynamic heat map generation module converts the processed user behavior data into heat map format, generates user activity heat maps for different time periods, and shows merchants the areas where users are paying attention; The strategy generation and execution module generates targeted business strategies based on the analysis results, including cross-regional association strategies, and real-time adjustments to product display and user guidance.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing user consumption behavior in different scenarios in a commercial space, characterized by: include, Acquire user behavior data through wireless positioning devices and distributed environmental sensors; Align multi-source heterogeneous data in time and space, establish connections with dynamic environment data stored in the database through user activity trajectories, calculate the similarity of timestamps and location identifiers in space and time, dynamically associate data, and build a three-dimensional trajectory model; Identify the user's residence characteristics and movement patterns based on the 3D trajectory model, and generate dynamic heat maps based on the associated product distribution; Adjust the guidance strategy based on the density gradient of the heat map and the user attribute level, combine user feedback with the heat map data, calculate the impact of user satisfaction on the heat data, and dynamically update the heat map based on the results; Integrate payment feature data and user behavior data to update user tags, generate cross-regional association strategies, and use changes in user behavior after strategy execution as optimization signals. When any associated strategy fails to achieve the expected goal, the current strategy priority is automatically downgraded.

2. A method for analyzing user consumption behavior in different scenarios in a commercial space as claimed in claim 1, characterized in that: The wireless positioning device includes locating the movement trajectory of the user in the commercial space by receiving signals from the mobile device carried by the user; The distributed environmental sensor includes an RFID reader and a pressure sensing device deployed in a specific functional area.

3. The method for analyzing user consumption behavior in different scenarios in a commercial space as claimed in claim 2, characterized in that: The said spatiotemporal alignment of multi-source heterogeneous data includes building a central API gateway to receive and route data requests from wireless positioning devices and distributed environmental sensors, and using a mapping table to convert different data structures of each device into a unified format and transmit it to a data processing platform; Adopt adaptive heterogeneous data parser to automatically identify and process data from different sources according to data characteristics, and remove noise data and redundant information; Assign a unique timestamp and geolocation identifier to each piece of data, establish a connection with the dynamic environment data stored in the database through the user's activity trajectory, calculate the similarity of timestamps and location identifiers in space and time, and dynamically associate data.

4. The method for analyzing user consumption behavior in different scenarios in a commercial space as claimed in claim 3, characterized in that: The constructing of the three-dimensional trajectory model includes: the construction of the three-dimensional trajectory model adopts collaborative processing based on cloud computing technology to build a multi-level cloud computing architecture; Edge computing nodes are set up at the user access layer. By setting up edge devices to collect and preliminarily process user behavior data, a lightweight data processing module is developed in each edge node to perform real-time analysis of user behavior data. The cleaned data is transmitted to the central cloud node through a secure encrypted channel for further analysis. Create a RESTful API to enable communication between edge nodes and the cloud, and to perform two-way data transmission and command issuance.

5. The method for analyzing user consumption behavior in different scenarios in a commercial space according to claim 4, characterized in that: The identifying of the user's residence characteristics and movement patterns includes extracting the behavior characteristics of residence time, frequency and movement speed from the user's behavior data to construct a user activity model; Apply the behavior cloning method in reinforcement learning to simulate the typical movement pattern of users in commercial spaces. Through the real-time data feedback mechanism, continuously monitor the changes in user behavior data. Use streaming data analysis technology to capture user behavior trends in real time and adjust the parameters of the user activity model. Through the incremental learning method, the user activity model does not need to be retrained, and only parameters are adjusted based on the newly added data, so that the user activity model always reflects the latest user behavior characteristics.

6. A method for analyzing user consumption behavior in different scenarios in a commercial space as claimed in claim 5, characterized in that: Generating a dynamic heat map includes converting user behavior data into a heat map format and distinguishing the heat of different time periods; Capture the nonlinear relationship in user behavior data, identify the behavioral characteristics of user activities, and map the behavioral characteristics to the heat map space through quadratic or cubic polynomial functions. Divide the user hotspots into high, medium, and low heat zones, each with a different color, and prioritize planning detours to low-density waiting areas for high-consumption-level users.

7. A method for analyzing user consumption behaviors in different scenarios in a commercial space as claimed in claim 6, characterized in that: The generation of the cross-region association strategy includes generating a behavior sequence including timestamp, coordinates, and dwell time by collecting the user's movement trajectory; aligning the payment time with the dwell event in the user's trajectory to bind the same user entity; Establish a multi-dimensional fusion index to associate user behavior with consumption characteristics as a unified data object. When a user stays in any functional area for a long time but does not consume, and generates a higher-than-average average customer spending in the remaining areas, it is marked as a potential associated behavior. Construct a multi-level labeling system, which includes a basic attribute layer, a behavior-derived layer, and a deep interactive layer. Use an incremental learning model to dynamically modify label weights: when the behavior of the labeling user deviates from the historical pattern, reduce the confidence level of the association between the current label and the category; When the user leaves the current area, the optimal associated area is matched according to the tag; when any functional area gathers tagged users, the collaborative strategy is automatically triggered to adjust the commodity display density in adjacent areas and highlight related categories.

8. A system using the method for analyzing user consumption behaviors in different scenarios in a commercial space as claimed in any one of claims 1 to 7, characterized in that: It includes data collection module, data processing module, user behavior analysis module, dynamic heat map generation module, strategy generation and execution module; The data acquisition module receives device signals carried by users, environmental sensor data, and RFID reader data, and records the user's movement trajectory, interaction frequency, and related attribute information in the commercial space; The data processing module performs spatiotemporal alignment on the collected multi-source heterogeneous data, and performs data cleaning and conversion; The user behavior analysis module extracts the user's residence characteristics and movement patterns, and optimizes the user activity model; The dynamic heat map generation module converts the processed user behavior data into a heat map; The strategy generation and execution module generates a cross-region association strategy based on the analysis result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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