Furniture intelligent adaptation recommendation method based on family behavior data

Abstract

This invention belongs to the field of furniture recommendation technology. It discloses a method for intelligent furniture adaptation and recommendation based on family behavior data. The method involves collecting behavioral sensor data, member interaction data, and furniture usage data from the family environment. Based on the behavioral sensor data and member interaction data, a family periodic activity pattern model is constructed, and a behavioral conflict index is calculated. Based on the family periodic activity pattern model and the furniture usage data, a dynamic interaction model between behavior and furniture is constructed, and adaptation adjustment parameters are generated. The dynamic interaction model integrates periodic activity patterns and furniture features. Based on the behavioral conflict index and the adaptation adjustment parameters, an intelligent furniture recommendation strategy is generated, including priority ranking and layout adjustment. Based on the intelligent furniture recommendation strategy, a list of suitable furniture is output, and furniture layout parameters are adjusted, thus realizing intelligent furniture adaptation and recommendation based on family behavior data.

Description

Technical Field

[0001] This invention relates to the field of furniture recommendation technology, and more specifically, to a method for intelligent furniture adaptation and recommendation based on family behavior data. Background Technology

[0002] In the field of furniture recommendation, existing systems mostly rely on static user preference data for recommendations. This approach ignores the dynamic changes in family behavior, leading to a mismatch between recommended results and actual family life. Specifically, static data such as furniture size, color, or style cannot capture the daily activity patterns of family members, such as space occupancy during multi-person interactions, safety needs when children are playing, or comfort requirements when the elderly are resting. This oversight results in conflicting furniture arrangements in actual layouts, such as sofas blocking children's activity paths or beds being too high for the elderly. Furthermore, existing recommendation algorithms lack temporal analysis of cyclical family activity patterns and cannot handle periodic fluctuations in behavioral data, such as differences in activities between weekdays and weekends. This leads to recommendations ignoring time sensitivity, such as the need for multifunctional furniture during weekend gatherings. In addition, existing systems struggle to integrate multi-source behavioral data, such as sensor-captured movement trajectories and voice interaction records, resulting in incomplete input to the recommendation model. Particularly in multi-member families, behavioral conflicts are not identified, such as overlaps between parents' work areas and children's play areas. These problems prevent existing furniture recommendations from adapting to dynamic family environments, limiting user satisfaction and practical application effectiveness.

[0003] In view of this, the present invention proposes a furniture intelligent adaptation and recommendation method based on family behavior data to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a furniture intelligent adaptation and recommendation method based on household behavior data, comprising: Step S1: Collect behavioral sensor data, member interaction data, and furniture usage data in the home environment. The behavioral sensor data includes movement trajectory and activity frequency, the member interaction data includes voice and location interaction, and the furniture usage data includes occupancy duration and interaction frequency. Step S2: Based on the behavioral sensor data and the member interaction data, construct a family periodic activity pattern model and calculate the behavioral conflict index; Step S3: Based on the family periodic activity pattern model and the furniture usage data, construct a dynamic interaction model between behavior and furniture, and generate adaptation adjustment parameters, wherein the dynamic interaction model integrates periodic activity patterns and furniture features; Step S4: Generate a furniture intelligent recommendation strategy based on the behavioral conflict index and the adaptation adjustment parameters, wherein the furniture intelligent recommendation strategy includes priority ranking and layout adjustment; Step S5: Based on the intelligent furniture recommendation strategy, output a list of suitable furniture and adjust the furniture layout parameters, wherein the adjustment takes into account spatial coordinates and behavior matching.

[0005] Preferably, the collection of behavioral sensor data, member interaction data, and furniture usage data in the home environment includes: Data on movement trajectories and activity frequencies are collected by sensors installed in the home space, serving as behavioral sensor data. Recording voice and location interactions between family members as member interaction data, wherein the voice is captured by a microphone and the location interactions are acquired by a positioning module; The occupancy time and interaction frequency of existing furniture are monitored as furniture usage data, wherein the monitoring is achieved through embedded sensors.

[0006] Preferably, the step of constructing a family periodic activity pattern model based on the behavioral sensor data and the member interaction data, and calculating a behavioral conflict index, includes: The behavior sensor data is segmented over time to extract periodic activity patterns, wherein the time segmentation refers to dividing the data into weekday data and weekend data based on a preset time window; By integrating the member interaction data, a family periodic activity pattern model is constructed, wherein the family periodic activity pattern model uses activity patterns as nodes, interaction frequency as edge weights, and embeds member role classifications. The space occupancy conflict value and the time overlap value are calculated based on the family periodic activity pattern model, wherein the space occupancy conflict value is obtained by vector intersection and the time overlap value is obtained by matrix operation; The space occupancy conflict value and the time overlap value are weighted and summed to generate a behavioral conflict index. The weighted summation is calculated using a custom conflict aggregation function that embeds the weights of family member roles and adjusts the function's parameters based on activity frequency.

[0007] Preferably, the step of constructing a dynamic interaction model between behavior and furniture based on the family's periodic activity pattern model and the furniture usage data, and generating adaptation adjustment parameters, includes: Furniture attributes are extracted from furniture usage data to obtain furniture size and functional features, and then feature vectors are used to represent size, material and multifunctional attributes. Based on the family periodic activity pattern model and the functional characteristics of the furniture, a dynamic interaction model is constructed, wherein the dynamic interaction model takes the periodic activity pattern as input, the feature vector as adjustment variable, and a coupling matrix is ​​defined; The dynamic interaction model is iteratively optimized to calculate the fit score, wherein the iterative optimization includes updating the parameters of the dynamic interaction model to minimize the behavior-furniture mismatch. Adaptation adjustment parameters are generated based on the adaptation score.

[0008] Preferably, generating a smart furniture recommendation strategy based on the behavioral conflict index and the adaptation adjustment parameters includes: The recommendation priority score is calculated by fusing the behavioral conflict index and the adaptation adjustment parameters, wherein the fusing is achieved by weighted average calculation and the nonlinear effect of the conflict index is taken into account. Based on the recommendation priority score, candidate furniture is filtered from the furniture database, wherein the filtering applies a threshold filtering and sorting mechanism; Generate a recommended strategy that includes layout adjustments, wherein the layout adjustments map the conflict index to spatial coordinates through a custom transformation function that embeds the temporal constraints of the periodic activity pattern and defines the coordinate transformation matrix. During the generation process, the geometric data of the home space is integrated.

[0009] Preferably, the step of outputting a list of suitable furniture and adjusting furniture layout parameters based on the intelligent furniture recommendation strategy includes: The candidate furniture is sorted according to the intelligent furniture recommendation strategy, and a list of suitable furniture is output, wherein the sorting is based on the priority score in descending order. Calculate the positional parameters of furniture in a home space, wherein the calculation takes into account room boundaries and movement trajectories; The interaction between the movement trajectory and the furniture boundary is simulated by a custom layout simulation algorithm, and the position coordinates of the furniture are iteratively optimized and the position parameters are adjusted to match the periodic activity pattern. Output the adjusted furniture layout parameters, including furniture rotation angle and spacing values.

[0010] Preferably, the behavior sensor data, member interaction data, and furniture usage data are timestamped and synchronized based on a global clock. After alignment, the data is normalized to form a unified behavior dataset.

[0011] Preferably, the step of calculating the space occupancy conflict value and time overlap value based on the family periodic activity pattern model includes: Space occupancy vectors are extracted from the family periodic activity pattern model through node projection; The intersection value of the space occupancy vectors is calculated using set operations, and this value is used as the space occupancy conflict value. The time overlap matrix is ​​extracted from the family periodic activity pattern model, and the matrix trace is obtained as the time overlap value. The matrix trace is calculated by a custom temporal convolution function that embeds the periodic pattern of member interaction and convolves the time window sequence.

[0012] Preferably, the iterative optimization of the dynamic interaction model and the calculation of the adaptation score include: Initialize the parameters of the dynamic interaction model, wherein the initialization uses a random distribution to set the initial weights; The loss increment for each iteration is calculated using a custom gradient calculation and a feedback mechanism based on a periodic activity pattern, and the fitness score is updated accordingly. During iteration, a learning rate decay strategy is applied to adjust the update step size; The iteration terminates when the behavior-furniture mismatch converges to a preset threshold, and the final fit score is output.

[0013] Preferably, the step of filtering candidate furniture from the furniture database based on the recommendation priority score includes: Based on the median of the score distribution, a threshold filter is applied to the recommendation priority score; The system retrieves matching items from the furniture database by querying the index and filters out furniture with mismatched functional features, using them as candidate furniture. Output the filtered candidate furniture and compile it into a list.

[0014] The technical effects and advantages of the furniture intelligent adaptation and recommendation method based on household behavior data in this invention are as follows: By collecting multidimensional data and constructing a family's periodic activity pattern model and calculating a behavioral conflict index, real-time capture and quantitative analysis of family members' daily activities (such as multi-person interaction, children's play, or elderly people's rest) were achieved. Potential conflicts (such as overlap between parents' work areas and children's play areas) were identified, thus avoiding problems such as furniture recommendations obstructing activity paths or having unsuitable heights in actual layouts.

[0015] A dynamic interaction model is built based on periodic activity pattern models and furniture usage data, and adaptation adjustment parameters are generated. This achieves deep integration of behavioral data and furniture attributes, providing high-precision adaptation parameters that make furniture recommendations more time-sensitive and functionally compatible.

[0016] By integrating behavioral conflict index and adaptation adjustment parameters, a recommendation priority score is calculated, achieving end-to-end optimization from recommendation to layout. It not only outputs a priority-ranked furniture list but also simulates behavioral interactions to adjust parameters, ensuring that furniture is occupied without conflict in the actual environment. This improves the practicality and feasibility of recommendations and enables precise handling of complex family scenarios (such as multi-member interactions). Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the furniture intelligent adaptation and recommendation method based on family behavior data according to the present invention. Detailed Implementation

[0018] 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.

[0019] This invention introduces a modulation or demodulation link for an LED infrared active light source to perform multi-channel timing joint determination of the attenuation of residual heat on the cooktop and the attenuation of oil fume scattering, and adaptively adjusts the shutdown timing and airflow switching strategy.

[0020] Please see Figure 1 In this embodiment of the invention, a method for intelligent furniture adaptation and recommendation based on family behavior data includes: To address the issues raised in the background technologies, such as "static data cannot capture the daily activity patterns of family members, leading to conflicts in the actual layout of recommended furniture," "existing recommendation algorithms lack time-series analysis of the periodic activity patterns of families, and cannot handle the periodic fluctuations in behavioral data, causing recommendations to ignore time sensitivity," and "existing systems have difficulty integrating multi-source behavioral data, resulting in incomplete input to the recommendation model, especially in multi-member families where behavioral conflicts are not identified," this system is designed as follows: Step S1: Collect behavioral sensor data, member interaction data, and furniture usage data in the home environment. The behavioral sensor data includes movement trajectory and activity frequency, the member interaction data includes voice and location interaction, and the furniture usage data includes occupancy duration and interaction frequency. Specifically: Infrared sensors (which work by detecting the movement of heat sources) or pressure sensors (which work by recording changes in ground pressure) installed in the home space collect movement trajectories (which refer to the spatial coordinate sequence of family members' movements) and activity frequency data (the number of events that occur per unit time, where an event refers to a movement or stay event), as behavioral sensor data; Record the voice and location interactions between family members as member interaction data. The voice is captured by a microphone array (that is, the audio waveform generated by the voice communication between family members, which needs to be converted into digital audio signals). The location interaction is obtained by a positioning module (such as GPS or indoor beacon), that is, to obtain the changes in spatial coordinates during the interaction of family members (that is, to calculate the relative coordinate difference between family member A and family member B). Embedded sensors (such as contact sensors built into furniture) monitor occupancy time (continuous use time) and interaction frequency (number of touches or approaches) as furniture usage data (used to represent the utilization rate of furniture).

[0021] These data are collected through a high-precision sensor network. When a user determines that they need intelligent furniture adaptation recommendations, the system extracts historical data from a period of time, such as the past 30 days, or it can collect data from the next 30 days starting from the current time. This data serves as the input for subsequent recommendations, ensuring real-time performance and accuracy. During the data collection process, a sampling rate of 10 times per second is used for continuous monitoring.

[0022] The behavioral sensor data, member interaction data, and furniture usage data are timestamped and synchronized based on a global clock. After alignment, the data is normalized to form a unified behavior dataset, where normalization is calculated using the z-score method.

[0023] Multidimensional data fusion improves the completeness of model input and solves the problem of unidentified behavioral conflicts; temporal alignment and normalization ensure data consistency and facilitate subsequent analysis; users can choose historical or future data, which is suitable for different family scenarios and significantly improves the matching degree between recommendation results and real life.

[0024] Existing technologies often overlook dynamic changes in family behavior, leading to a mismatch between recommended results and actual family life; they lack time-series analysis of cyclical family activity patterns, failing to handle periodic fluctuations in behavioral data, such as differences in activities between weekdays and weekends; and they struggle to integrate multi-source behavioral data, especially in multi-member families where behavioral conflicts are not identified, such as overlaps between parents' work areas and children's play areas. Based on the above, this system is designed as follows: Step S2: Based on the behavior sensor data and the member interaction data, construct a family periodic activity pattern model and calculate the behavior conflict index; the family periodic activity pattern model is a network structure based on time-series data; the behavior conflict index is an indicator used to quantify incompatible activities (referring to activity patterns that interfere with each other in space or time among family members, for example, when parents are working in the living room, children play in the same area, causing space occupation conflict), to ensure that the model reflects the family rhythm.

[0025] Specifically: The behavioral sensor data is segmented over time, and the frequency components in the time-series data are analyzed using Fourier transform or ARIMA models to identify recurring patterns. Similar sequences are then categorized using clustering algorithms (such as K-means) to extract periodic activity patterns (which are sequences of family activities that repeat within a specific time window, such as breakfast preparation on weekday mornings or family gatherings on weekend afternoons). The data is grouped into daily sequences based on global timestamps. Then, windows are divided according to preset rules (such as weekdays from Monday to Friday and weekends from Saturday to Sunday). Statistical indicators (such as the mean and variance of activity frequency) are calculated within each window, and a sliding window (such as a 1-hour step) is applied to smooth the data to extract patterns, thereby achieving the division of data into weekday data and weekend data based on preset time windows.

[0026] Graph embedding algorithms (such as GraphSAGE) are used to overlay member interaction data (voice and location interaction) as additional features onto the temporally segmented activity patterns. For example, the frequency of voice interaction is used as a weight to adjust the intensity of the activity sequence, thereby achieving the fusion of member interaction data and constructing a family periodic activity pattern model. This model uses activity patterns as nodes, connects each node with edges, and uses interaction frequency as edge weights. Member role classification is embedded, which categorizes family members into groups such as adults (parents), children, and the elderly. Each node (activity pattern) is assigned a role label; for example, a "living room party" node is labeled as "adult + child". Edge weights are calculated based on interaction frequency; for example, the higher the number of voice interactions, the greater the edge weight, thus forming a weighted role embedding graph network. Relevant subgraphs are extracted from the family periodic activity pattern model, for example, selecting a subset of nodes involving the same space; then, for space occupancy conflict values, nodes are projected onto spatial vectors and vector intersections are calculated; simultaneously, for time overlap values, a time matrix is ​​constructed from edge weights and node attributes, and matrix operations are performed. Conflicts in the family periodic activity pattern model are quantified using graph algorithms (such as adjacency matrix representation), for example, by calculating the intersection of spatial vectors through node metrics, or by analyzing the trace of the time matrix through edge paths, wherein the spatial occupancy conflict value is obtained by vector intersection, and the time overlap value is obtained by matrix operations; The space occupancy conflict value and the time overlap value are weighted and summed to generate a behavioral conflict index. The weighted summation is calculated using a custom conflict aggregation function (that is, the weighted summation is not a simple linear addition, but is implemented through a custom function (conflict aggregation function). This function can be non-linear, for example, f(x,y)=αx+βy+γxy, where γ represents the embedded interaction item, x represents the space occupancy conflict value, y represents the time overlap value, and α and β are coefficients based on the member role weights, for example, α=0.6 (emphasizing space conflict) and β=0.4 (emphasizing time conflict). The coefficients can be dynamically adjusted by the activity frequency. This function embeds the family member role weights and adjusts the parameters of the function in combination with the activity frequency.

[0027] For example, suppose a family has parents and children, with a space conflict value of 0.8 (the living room is occupied by parents working and children playing, occupying 80% of the area); and a time overlap value of 0.5 (50% of the time overlaps on weekday evenings). Using the conflict aggregation function f = 0.6 × 0.8 + 0.4 × 0.5 + 0.1 × (0.8 × 0.5) (embedded interaction items), the calculated behavioral conflict index is approximately 0.72 (high conflict, indicating that furniture needs to be adjusted to resolve it). Parameters can also be adjusted; if the child's role has a high weight (e.g., 0.7), then α in the function increases to 0.7 to emphasize space conflict.

[0028] The calculation of space occupancy conflict value and time overlap value based on the family periodic activity pattern model includes: Space occupancy vectors are extracted from the family periodic activity pattern model through node projection (node ​​projection maps nodes in the family periodic activity pattern model to a low-dimensional vector space, for example, using GCN technology to convert node attributes into vector representations). The intersection value of the space occupancy vectors is calculated through set operations (using Jaccard similarity). The intersection value is a measure of the degree of overlap between vectors, for example, Jaccard index = |A∩B|÷|A∪B|, the higher the value, the greater the conflict, where A and B represent different spaces. This intersection value is used as the space occupancy conflict value.

[0029] For example: Assuming a family of four (parents and two children), the family's periodic activity pattern model is a graph network: The nodes include: "Working in the living room on weekdays" (parents' role, space occupation: 80% of the central area of ​​the living room, represented as a vector [living room coordinates x1-y1, area 0.8]), "Children playing on weekends" (children's role, space occupation: the entire living room, represented as a vector [living room coordinates x1-y2, area 1.0]), and "Elderly rest" (elderly role, space occupation: 20% of the corner area of ​​the living room, represented as a vector [living room coordinates x2-y2, area 0.2]), where x1 and x2 represent the x-coordinates and y1 and y2 represent the y-coordinates; Edges connect these nodes, with weights based on interaction frequency (e.g., the edge weight between "working in the living room on weekdays" and "kids playing on weekends" is 0.7, indicating high interaction).

[0030] Extracting space occupancy vectors: Using node projection (GCN algorithm traverses the network and aggregates node attributes), a vector is generated for each node. For example, extract vector V1=[0.8, central area] from the "Weekday living room work" node; extract V2=[1.0, whole area] from the "Weekend children play" node.

[0031] Calculate the intersection value: Apply Jaccard similarity (set operation) to V1 and V2: Assuming V1's occupancy set A1 = {central 80% of pixels} and V2's B1 = {all living room pixels}, then Jaccard = |A∩B| / |A∪B| = 0.8 / 1.0 = 0.8 (representing 80% overlap). If we also consider the "elderly resting" vector V3, calculate the average intersection of the multiple vectors, and the conflict value is 0.75.

[0032] Result: Space occupancy conflict value = 0.75, indicating high conflict (e.g., a 75% overlap between the parents' work area and the children's play area, potentially causing the sofa to block the children's path). This value is used to generate a behavioral conflict index to help recommend adjustments to furniture layout (such as moving the sofa).

[0033] A subset of relevant nodes (e.g., nodes involving the same member or space) is selected from a family periodic activity pattern model. Then, a matrix is ​​constructed from the node's temporal attributes (embedded periodic patterns, such as weekday / weekend labels) and edge weights. Specifically, the network is represented using an adjacency matrix, with edge weights as matrix elements, and a temporal dimension (e.g., each element multiplied by a time window overlap factor) is added to generate an N×N matrix. Rows / columns correspond to nodes, and element values ​​represent the time overlap ratio of two activities (based on timestamp data), serving as the time overlap matrix. The extracted temporal overlap matrix is ​​processed using a trace operation, and the final output trace value serves as the temporal overlap value (a higher value indicates a greater temporal conflict). The matrix trace calculation is performed using a custom temporal convolution function, which smooths and extracts patterns from the time series. Specifically, f(t) = ∑w_k × s_{tk}, where w_k is the convolution kernel weight (a custom parameter), and s_{tk} is the time window sequence. Before matrix trace calculation, this function convolves the matrix elements to capture periodic fluctuations (e.g., kernel size = 7 days, capturing periods). The convolution kernel weights w_k are dynamically adjusted to include member interaction data; for example, interaction frequency (extracted from model edge weights) is embedded as a multiplier in the kernel: w_k = base_w × (interaction frequency factor + periodic pattern embedding). For example, if children's interaction periodic patterns show peaks on weekends, the function amplifies the weights of the weekend window to ensure the convolution output reflects these patterns. The cyclical pattern of member interaction refers to the repetitive pattern of interactions (such as voice or location interaction) between family members over time. For example, a recurring pattern every week (such as brief interactions between parents and children on weekday evenings and intensive interactions throughout the weekend).

[0034] Following the example above, the explanation continues as follows: Nodes: "Living room work on weekdays" (time mode: weekdays 18:00-20:00), "Children's play on weekends" (time mode: weekends 10:00-18:00), "Elderly rest" (time mode: daily 14:00-16:00).

[0035] Extract the time overlap matrix: Construct a 3×3 matrix M, where M_{ij} = the time overlap ratio (e.g., calculated based on timestamps). For example, M[1,2]=0.3 (30% overlap between "working in the living room on weekdays" and "children playing on weekends" on weekend evenings); M[1,1]=1.0 (self-stability); The complete matrix M = [1.0, 0.3, 0.2] 0.3, 1.0, 0.4 [0.2, 0.4, 1.0]; To obtain the matrix trace, first, apply a temporal convolution function f to convolve the matrix rows (embedding the member interaction cycle pattern). Assume the cycle pattern is "children's interaction peaks on weekends" (embedding with kernel weights w=[0.2,0.5,0.3], emphasizing weekends).

[0036] The convolutioned matrix M' = [0.9, 0.4, 0.25] 0.4, 0.95, 0.45 [0.25, 0.45, 0.9] Then, calculate the trace: trace(M') = 0.9 + 0.95 + 0.9 = 2.75. After normalization, the time overlap value is 2.75 / 3 ≈ 0.92 (a high value, indicating severe time conflicts, such as 92% overlap between work and playtime, which may require multifunctional furniture).

[0037] By constructing a model of cyclical family activity patterns and calculating a behavioral conflict index, the system achieves precise capture and quantification of dynamic family behavior. Specific effects include capturing temporal fluctuations (such as weekday / weekend differences), identifying spatial or temporal conflicts (such as overlapping activities), and generating quantitative indicators (conflict indices) as input for subsequent steps, ensuring that furniture recommendations adapt to real-life situations. This addresses the problem of missing temporal analysis, improving the time sensitivity of recommendations; identifying multi-member conflicts, enhancing model completeness; computational efficiency and easy scalability; and enhancing user satisfaction by avoiding layout mismatches.

[0038] Static data fails to capture the daily activity patterns of family members, leading to conflicts between recommended furniture and actual layout; the lack of dynamic integration of behavioral data and furniture characteristics makes it unable to handle time-sensitive factors, such as periodic fluctuations; and it ignores the dynamic interaction between behavior and furniture. Based on these issues, this design is as follows: Step S3: Based on the family periodic activity pattern model and the furniture usage data, construct a dynamic interaction model between behavior and furniture, and generate adaptation adjustment parameters, wherein the dynamic interaction model integrates periodic activity patterns and furniture features; Specifically, through statistical analysis and feature engineering methods, such as applying clustering algorithms (e.g., DBSCAN) or regression models to identify patterns in data on occupancy time and interaction frequency, furniture attributes are extracted from furniture usage data to obtain furniture size and functional characteristics. Furniture size refers to physical specifications, such as length, width, and height; functional characteristics refer to practical attributes, such as adjustability, durability, or specific uses (e.g., whether a sofa can be converted into a bed). Then, feature vectors are used to represent size, material, and multi-functional attributes, such as multi-functional attribute = 0.7 (the score represents the degree of multi-purpose use, such as 0-1 normalization). Based on the family's periodic activity pattern model and the functional characteristics of the furniture, a dynamic interaction model is constructed. This dynamic interaction model takes the periodic activity pattern as input, the feature vector as adjustment variables, and defines a coupling matrix. The dynamic interaction model is a mathematical framework, similar to a coupled neural network or matrix model. For example, it can be a function D=f(A,F,C), where A is the periodic activity pattern; F is the furniture feature vector; and C is the coupling matrix (an M×N matrix whose elements define the interaction strength between the activity pattern and the furniture features). The dynamic interaction model is iteratively optimized to calculate the fit score, wherein the iterative optimization includes updating the parameters of the dynamic interaction model to minimize the behavior-furniture mismatch. The adaptation score is used to generate adaptation adjustment parameters. That is, the adaptation score is converted into parameters through a mapping function. For example, a linear transformation P=k×S+b is used, where S is the adaptation score and k and b are preset coefficients; or a threshold rule is applied. If S>0.8, a positive adjustment parameter is generated (such as increasing the furniture size by 10%). The parameters include specific values, such as layout offset or functional weight adjustment.

[0039] The iterative optimization of the dynamic interaction model and the calculation of the adaptation score include: Initialize the parameters of the dynamic interaction model, wherein the initialization uses a random distribution to set the initial weights; The loss increment for each iteration is calculated using a custom gradient calculation and a feedback mechanism based on a periodic activity pattern, and the fitness score is updated accordingly; (What exactly does this process look like?) During the iteration, a learning rate decay strategy is applied to adjust the update step size (the step size refers to the magnitude of the weight update). The iteration terminates when the behavior-furniture mismatch converges to a preset threshold, and the final fit score is output.

[0040] Suppose a family of three (parents and child) with recurring activity patterns including "working in the living room on weekdays" (input vector A = [frequency = 0.8, duration = 2 hours]) and "children playing on weekends" (A = [frequency = 0.6, duration = 4 hours]). Existing furniture: sofa. Use the data to extract the feature vector F = [size = 180x80x90, material = fabric (encoded 2), multi-functional = 0.5 (foldable)].

[0041] process: Construct a dynamic interaction model D=A×C×F, where C is a 2x3 coupling matrix. The initial weights are randomly set, such as C=[[0.1,-0.05,0.2],[0.15,0.1,-0.1]] (weights represent the interaction intensity).

[0042] Iterative optimization: Iteration 1: Input A and F, calculate predicted matching degree (e.g., 0.4); actual matching degree (from the used data) = 0.6; loss L = (0.4 - 0.6)^2 = 0.04. Feedback mechanism: Calculate gradient ∂L / ∂w ≈ 0.1 based on activity pattern; update weights (e.g., C[1,1] = 0.1 - 0.01 × 0.1 = 0.099); fitness score S = 1 - L = 0.96. Loss increment ΔL = 0.04 (initial).

[0043] Iterate 2-5 times: Continue updating, applying learning rate decay (initial η=0.01, decaying to 0.009); gradually reduce the loss to 0.02, 0.015, etc. The feedback mechanism uses activity mode adjustment (e.g., weekend mode amplifies the weights related to children).

[0044] Iteration 6: Loss L = 0.009 < preset threshold 0.01, terminate.

[0045] Output: Final fit score S = 0.991 (high score, indicating that the sofa matches the behavior pattern 99.1%, such as the multi-functional attribute being suitable for weekend play). This score is used to generate adjustment parameters (such as "increase sofa height by 10%" to suit the elderly's rest).

[0046] By integrating multi-source data through feature vectors and coupling matrices, real-time interactions (such as periodic patterns affecting furniture functionality) are captured; iterative optimization (gradient calculation and learning rate decay) ensures efficient convergence of model parameters, and adaptation scores are calculated to quantify the matching degree; adaptation adjustment parameters are generated to support subsequent layout optimization. Overall, this improves the responsiveness of furniture recommendations to dynamic home environments, reduces conflicts, and enhances user satisfaction.

[0047] Step S4: Generate a furniture intelligent recommendation strategy based on the behavioral conflict index and the adaptation adjustment parameters, wherein the furniture intelligent recommendation strategy includes priority ranking and layout adjustment; The behavioral conflict index and the adaptation adjustment parameter are fused to calculate the recommendation priority score. The fusion is achieved through a weighted average operation, taking into account the nonlinear effect of the conflict index. For example, the formula P2=(w1×(1-conflict index)+w2×adjustment parameter) / (w1+w2), where w1 / w2 are the weights, and a nonlinear function (such as sigmoid) is introduced to handle the influence of the conflict index (e.g., if the conflict is high, its negative weight is nonlinearly amplified) to calculate the recommendation priority score for each potential furniture (the higher the score, the higher the priority).

[0048] Based on the recommendation priority score, candidate furniture is filtered from the furniture database, wherein the filtering applies a threshold filtering and sorting mechanism; A recommended strategy incorporating layout adjustments is generated. These adjustments map the conflict index to spatial coordinates using a custom transformation function that embeds temporal constraints of periodic activity patterns and defines a coordinate transformation matrix. The custom transformation function, such as f(conflict index = coordinate offset = a × tanh(conflict index) + b), maps the conflict index to spatial coordinates (e.g., adjusting furniture positions to avoid conflict). Temporal constraints include higher weighting for weekday patterns. The coordinate transformation matrix is ​​a 2D rotation / translation matrix, such as [[cosθ, -sinθ], [sinθ, cosθ]]. The entire process integrates geometric data of the home space (e.g., room dimensions, boundary coordinates).

[0049] During the generation process, the geometric data of the home space is integrated.

[0050] Assume a family of four (parents, child, and elderly) living in a 20m² living room (geometric data: rectangular boundary coordinates [0,0] to [10,10] meters, with a central pillar obstructing the view). The preceding steps have already calculated: Behavioral Conflict Index = 0.75 (high conflict, e.g., 75% spatial / temporal overlap between the parents' work area and the child's play area); Adaptation Adjustment Parameters = [Size Adjustment + 10%, Functional Weight 0.8] (calculated from the recommendation priority score in step S3). Input: Behavioral Conflict Index = 0.75 (high value, indicating that conflict needs to be resolved first); Adaptation Adjustment Parameter = 0.8 (high value, indicating good adaptation potential).

[0051] The weighted average P2 = w1 × (1 - conflict index) + w2 × adjustment parameter is used, where w1 = 0.6 (emphasizing conflict resolution) and w2 = 0.4 (emphasizing adaptation). Considering nonlinear effects: the sigmoid function is applied to adjust the conflict component, sigmoid(0.75) = 0.68 (nonlinear amplification of negative impacts), thus the adjusted (1 - conflict index) = 0.25 × (1 - 0.68) = 0.08.

[0052] Calculation: P=(0.6×0.08)+(0.4×0.8)=0.048+0.32=0.368 (The initial score is low, and there is a conflict due to the high score).

[0053] Repeat the calculation for each piece of furniture in the database (e.g., for "Multifunctional Sofa Ar": adjusted P2=0.75; for "Standard Table and Chair Br": P2=0.45; for "Adjustable Bed Cr": P2=0.82). Result: Generate a list of scores.

[0054] Database example: Sofa Ar (score 0.75, size 180x80, multi-functional 0.7); Table and chair Br (score 0.45, size 100x60, multi-functional 0.4); Bed Cr (score 0.82, size 200x100, multi-functional 0.9); Desk Dr (score 0.65, size 120x70, multi-functional 0.6).

[0055] Set the threshold to 0.6, and only keep scores > 0.6 (Sofa Ar = 0.75, Bed Cr = 0.82, Desk Dr = 0.65; filter out Table and Chair B = 0.45).

[0056] Candidate list sorted in descending order: [Bed Cr (0.82), Sofa Ar (0.75), Desk Dr (0.65)].

[0057] Integrate the geometric data of the family space (living room boundary [0,0]-[10,10], avoid the central column [4,4]-[5,5]).

[0058] Use a custom transformation function f(conflict index) = offset coordinate = 2 × tanh(0.75) + 1 ≈ 2 × 0.96 + 1 = 2.92 (calculate the offset); embed time constraints (e.g., weekday mode weight 0.7, weekend mode weight 0.3, adjustment function is f_t = 0.7 × f_work + 0.3 × f_weekend).

[0059] Define a coordinate transformation matrix: rotation matrix Me=[[cos(30°),-sin(30°)],[sin(30°),cos(30°)]]≈[[0.866,-0.5],[0.5,0.866]], used to adjust the furniture orientation to resolve conflicts.

[0060] For the bed Cr (high score 0.82), the original position is [3,3]. Apply the transformation: new coordinates = Me × [3,3] + [2.92,0] = [approximately 5.6, 4.1] (move to the edge of the living room to avoid conflicts with children's play paths); consider the geometric data to ensure that the new coordinates are within [0,0]-[10,10] and do not touch the pillars.

[0061] Generation strategy: including priority sorting (1. Bed Cr, 2. Sofa Ar, 3. Desk Dr) and layout adjustment (e.g., "Bed Cr: Rotate 30° and move to [5.6, 4.1] to match the weekend party pattern").

[0062] Step S5: Based on the intelligent furniture recommendation strategy, output a list of suitable furniture and adjust the furniture layout parameters, wherein the adjustment takes into account spatial coordinates and behavior matching.

[0063] Specifically: Candidate furniture is sorted according to the intelligent furniture recommendation strategy, and a list of suitable furniture is output, wherein the sorting is based on descending priority scores; Calculate the positional parameters of furniture within the home space, where the calculation considers room boundaries and movement trajectories. For example, the initial position is assigned via a center point (bed Cr initial [3,3]); boundary checks (ensuring position coordinates ∈ [0,10], not exceeding the room boundaries); trajectory integration (calculating the distance between the trajectory and the furniture boundary, ensuring a minimum distance > 1 meter to avoid obstruction, for example, if the trajectory passes through [4,4], the furniture position is offset to maintain clearance). A custom layout simulation algorithm (an iterative algorithm based on particle swarm optimization (PSO)) simulates the interaction between movement trajectories and furniture boundaries, and iteratively optimizes the furniture's position coordinates, adjusting the position parameters to match periodic activity patterns, i.e., embedding periodic activity patterns (weekday weight 0.7, weekend weight 0.3, for example, optimization function = 0.7 × weekday matching + 0.3 × weekend matching); minimizing mismatches (if overlap > 10%, iterate the offset coordinates until < 5%). Output the adjusted furniture layout parameters, including furniture rotation angle and spacing values.

[0064] The step of filtering candidate furniture from the furniture database based on the recommendation priority score includes: Based on the median of the score distribution, a threshold filter is applied to the recommendation priority score; The system retrieves matching items from the furniture database by querying the index and filters out furniture with mismatched functional features, using them as candidate furniture. Output the filtered candidate furniture and compile it into a list.

[0065] By considering dynamic behaviors and periodic patterns (such as differences between weekdays and weekends), recommended furniture better matches the actual needs of families (e.g., multifunctional furniture for gatherings), avoiding conflicts arising from static recommendations and improving user satisfaction. The output of layout adjustment parameters further simplifies the implementation process, allowing users to directly apply the recommendations.

[0066] Suitable for multi-member families, it embeds member role weights and interaction frequencies to identify and resolve behavioral conflicts (such as children's safety needs versus the comfort needs of the elderly), making recommendations more personalized.

[0067] Time-series analysis and dynamic interaction models capture behavioral fluctuations and generate time-sensitive recommendations (such as multifunctional furniture suitable for weekends), enhancing the system's flexibility and adapting to changes in family life at different stages.

[0068] This invention fills the gap in dynamic behavior analysis in the field of furniture recommendation and can be applied to smart home systems, furniture e-commerce platforms and other fields to improve practical application effects and commercial value.

[0069] In summary, this invention achieves precise adaptation to dynamic home environments through a behavior-driven intelligent recommendation method, overcoming the limitations of existing technologies and demonstrating significant technical effectiveness and competitive advantages.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0071] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0072] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A furniture intelligent adaptation recommendation method based on family behavior data, characterized in that, include: Step S1: Collect behavioral sensor data, member interaction data, and furniture usage data in the home environment. The behavioral sensor data includes movement trajectory and activity frequency, the member interaction data includes voice and location interaction, and the furniture usage data includes occupancy duration and interaction frequency. Step S2: Based on the behavioral sensor data and the member interaction data, construct a family periodic activity pattern model and calculate the behavioral conflict index, including: The behavior sensor data is segmented over time to extract periodic activity patterns, wherein the time segmentation refers to dividing the data into weekday data and weekend data based on a preset time window; By integrating the member interaction data, a family periodic activity pattern model is constructed, wherein the family periodic activity pattern model uses activity patterns as nodes, interaction frequency as edge weights, and embeds member role classifications. The space occupancy conflict value and the time overlap value are calculated based on the family periodic activity pattern model, wherein the space occupancy conflict value is obtained by vector intersection and the time overlap value is obtained by matrix operation; The space occupancy conflict value and the time overlap value are weighted and summed to generate a behavior conflict index. The weighted summation is calculated using a custom conflict aggregation function that embeds the weight of family member roles and adjusts the function's parameters based on activity frequency. Step S3: Based on the family periodic activity pattern model and the furniture usage data, construct a dynamic interaction model between behavior and furniture, and generate adaptation adjustment parameters, wherein the dynamic interaction model integrates periodic activity patterns and furniture features; The process involves constructing a dynamic interaction model between behavior and furniture based on the family's periodic activity pattern model and the furniture usage data, and generating adaptation adjustment parameters, including: Furniture attributes are extracted from furniture usage data to obtain furniture size and functional features, and then feature vectors are used to represent size, material and multifunctional attributes. Based on the family periodic activity pattern model and the functional characteristics of the furniture, a dynamic interaction model is constructed, wherein the dynamic interaction model takes the periodic activity pattern as input, the feature vector as adjustment variable, and a coupling matrix is ​​defined; The dynamic interaction model is iteratively optimized to calculate the fit score, wherein the iterative optimization includes updating the parameters of the dynamic interaction model to minimize the behavior-furniture mismatch. Generate adaptation adjustment parameters based on the adaptation score; Step S4: Generate a furniture intelligent recommendation strategy based on the behavioral conflict index and the adaptation adjustment parameters, wherein the furniture intelligent recommendation strategy includes priority ranking and layout adjustment; Step S5: Based on the intelligent furniture recommendation strategy, output a list of suitable furniture and adjust the furniture layout parameters, wherein the adjustment takes into account spatial coordinates and behavior matching.

2. The furniture intelligent adaptation and recommendation method based on family behavior data according to claim 1, characterized in that, The collected behavioral sensor data, member interaction data, and furniture usage data in the home environment include: Data on movement trajectories and activity frequencies are collected by sensors installed in the home space, serving as behavioral sensor data. Recording voice and location interactions between family members as member interaction data, wherein the voice is captured by a microphone and the location interactions are acquired by a positioning module; The occupancy time and interaction frequency of existing furniture are monitored as furniture usage data, wherein the monitoring is achieved through embedded sensors. 3.The furniture smart adaptation recommendation method based on home behavior data according to claim 2, characterized in that, The step of generating a smart furniture recommendation strategy based on the behavioral conflict index and the adaptation adjustment parameters includes: The recommendation priority score is calculated by fusing the behavioral conflict index and the adaptation adjustment parameters, wherein the fusing is achieved by weighted average calculation and the nonlinear effect of the conflict index is taken into account. Based on the recommendation priority score, candidate furniture is filtered from the furniture database, wherein the filtering applies a threshold filtering and sorting mechanism; Generate a recommended strategy that includes layout adjustments, wherein the layout adjustments map the conflict index to spatial coordinates through a custom transformation function that embeds the temporal constraints of the periodic activity pattern and defines the coordinate transformation matrix. During the generation process, the geometric data of the home space is integrated. 4.The furniture smart adaptation recommendation method based on home behavior data according to claim 3, characterized in that, The step of outputting a list of suitable furniture based on the intelligent furniture recommendation strategy and adjusting furniture layout parameters includes: The candidate furniture is sorted according to the intelligent furniture recommendation strategy, and a list of suitable furniture is output, wherein the sorting is based on the priority score in descending order. Calculate the positional parameters of furniture in a home space, wherein the calculation takes into account room boundaries and movement trajectories; The interaction between the movement trajectory and the furniture boundary is simulated by a custom layout simulation algorithm, and the position coordinates of the furniture are iteratively optimized and the position parameters are adjusted to match the periodic activity pattern. Output the adjusted furniture layout parameters, including furniture rotation angle and spacing values. 5.The furniture smart adaptation recommendation method based on home behavior data according to claim 4, characterized in that, The behavioral sensor data, member interaction data, and furniture usage data are timestamped and synchronized based on a global clock. After alignment, the data is normalized to form a unified behavior dataset. 6.The furniture smart adaptation recommendation method based on home behavior data according to claim 5, characterized in that, The calculation of space occupancy conflict value and time overlap value based on the family periodic activity pattern model includes: Space occupancy vectors are extracted from the family periodic activity pattern model through node projection; The intersection value of the space occupancy vectors is calculated using set operations, and this value is used as the space occupancy conflict value. The time overlap matrix is ​​extracted from the family periodic activity pattern model, and the matrix trace is obtained as the time overlap value. The matrix trace is calculated by a custom temporal convolution function that embeds the periodic pattern of member interaction and convolves the time window sequence. 7.The furniture smart adaptation recommendation method based on home behavior data according to claim 6, characterized in that, The iterative optimization of the dynamic interaction model and the calculation of the adaptation score include: Initialize the parameters of the dynamic interaction model, wherein the initialization uses a random distribution to set the initial weights; The loss increment for each iteration is calculated using a custom gradient calculation and a feedback mechanism based on a periodic activity pattern, and the fitness score is updated accordingly. During iteration, a learning rate decay strategy is applied to adjust the update step size; The iteration terminates when the behavior-furniture mismatch converges to a preset threshold, and the final fit score is output.

8. The furniture intelligent adaptation and recommendation method based on family behavior data according to claim 7, characterized in that, The step of filtering candidate furniture from the furniture database based on the recommendation priority score includes: Based on the median of the score distribution, a threshold filter is applied to the recommendation priority score; extract matching items from the furniture database by querying the index and filter out the furniture that does not match the functional features as candidate furniture; output the filtered candidate furniture and form a list.

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