Personalized recommendation system based on big data

Abstract

A personalized recommendation system based on big data is characterized in that the personalized recommendation system comprises a data acquisition module, an advancing route simulation module, a home arrangement analysis module and an output module, and the advancing route simulation module is used for simulating and monitoring advancing routes of residents in a living room; the home arrangement analysis module evaluates the comfort of the living room of the decorated house of the resident through the advancing route simulation module, and the space comfort is quantitatively expressed through three detection standards of the average width, the average length and the longest walking route through the home arrangement analysis module; according to the method, residents are helped to obtain various specific numerical values of unoccupied space generated by home placement in a design scheme of a decoration designer, then the design scheme is modified according to individual requirements of the residents on the home placement space, and the residents are helped to select a living room space comfort design scheme most suitable for the residents during decoration. The method has the characteristics of high parameter measurement and calculation accuracy and high individuation degree.

Description

Technical Field

[0001] The present invention relates to the technical field of home decoration management, and particularly to a personalized recommendation system based on big data. Background Technique

[0003] In the decoration of the living room, after the household has ordered the furniture according to their personalized needs, the placement of the furniture becomes crucial. The placement of the furniture will not only affect the walkable length of the entire living room, but also affect the overall spatial comfort of the living room. In the prior art, many households choose to let the house design engineer first design the placement of the furniture, and then make adaptive modifications according to the personalized placement needs of the household. However, due to the complexity of the furniture placement, the household cannot understand the specific information such as the walkable gap between the furniture in the design plan and the effective activity space in the living room through the design drawings, which makes it extremely difficult for the household to communicate with the design engineer about the modification opinions. At the same time, although there seems to be a lot of spare area in many positions of the design drawings, due to the too little connection between the positions of the spare areas and other spare areas, the household cannot walk in these spare positions during the actual living process, and these spare areas are actually invalid, which further deepens the household's wrong understanding of the design plan. Therefore, it is very necessary to design a personalized recommendation system and method based on big data with high accuracy of design parameter measurement and high degree of personalization. Summary of the Invention

[0004] The purpose of the present invention is to provide a personalized recommendation system and method based on big data to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A personalized recommendation system and method based on big data, including a data collection module, a travel route simulation module, a furniture layout analysis module, and an output module. The data collection module is used to obtain relevant information of two houses of the household; the travel route simulation module is used to simulate and monitor the travel route of the household in the living room; the furniture layout analysis module evaluates the comfort of the living room decorated by the household through the travel route simulation module; the output module is used to make personalized recommendations to the household based on the evaluation results of the furniture layout analysis module.

[0006] According to the above technical solutions, the data collection module includes a living room decoration information collection module, an original address information collection module, and a reference spare data collection module. The living room decoration information collection module is used to obtain relevant information about the structure of the living room of the decorated house; the original address information collection module is used for relevant information of the household's original address house; the reference spare data collection module is used to provide the household with data on the spare area, length, and width of the living room of the public to help the household modify the design plan based on personalized needs.

[0007] According to the above technical solution, the travel route simulation module includes a trajectory monitoring module, a distance monitoring module, a camera module, and a timing unit. The trajectory monitoring module is used to monitor the travel trajectory of the simulated travel route of the household; the distance monitoring module is used to obtain the relevant distance magnitude of the travel route in the trajectory monitoring module; the camera module is used to take pictures of the overall picture of the household decorating the living room; the timing unit is used to time during the process of simulating the travel route of the household.

[0008] According to the above technical solution, the home layout analysis module includes a reflection times detection module, a reflection interval detection module, and a traveling ray monitoring module. The reflection times detection module obtains the average walking width of the living room by monitoring the number of ray reflections in the simulated travel route of the household; the reflection interval detection module obtains the average straight-line walking length of the living room by monitoring the interval time of ray reflections in the simulated travel route of the household; the traveling ray monitoring module analyzes the maximum walking distance of the living room by combining different ray traveling routes.

[0009] According to the above technical solution, the output module includes a design plan evaluation module and a personalized recommendation module. The design plan evaluation module is used to evaluate the spatial comfort of the current design plan; the personalized recommendation module is used to recommend a personalized decoration modification plan to the owner.

[0010] According to the above technical solution, the operation method of the personalized recommendation system mainly includes the following steps;

[0011] Step S1: The system obtains that the total area of the living room in the owner's original residence is S 11 , and the total area of the living room in the newly decorated house is S 12 , then the total area change rate of the living room after the owner moves to a new home

[0012] Step S2: The system analyzes the spatial comfort of the living room through the travel route simulation module;

[0013] Step S3: The reflection times detection module obtains the maximum value L of the home distance width by analyzing the process of the traveling ray during travel;

[0014] Step S4: The reflection interval detection module obtains the maximum value T of the two adjacent reflection intervals during the travel process of each traveling ray through the timing unit Max and the minimum value T of the reflection interval Min , then the farthest straight-line distance Z 1 that can be traveled in the living room = T Max ·V, and the nearest straight-line distance that can be traveled in the living room, that is, the minimum value Z of the home distance width 2 = TMin ·V;

[0015] Step S5: The traveling ray monitoring module obtains the longest walking length in the living room through trajectory coincidence analysis during the traveling process of the traveling ray;

[0016] Step S6: The living room comfort analysis module obtains the average effective free area after the change in the living room and compares the values of the average effective free area H, the farthest straight-line distance Z that can be traveled in the living room 1 and the longest walking length R in the living room Max with the database to determine whether the current living room situation meets the personalized needs of the household, and recommends modifications to the decoration plan through the output module.

[0017] According to the above technical solution, the step S2 further includes:

[0018] Step S21: Analyze the top view of the living room to obtain a black-and-white reference top view. The black part represents the position of the furniture in the living room, and the white part represents the position without furniture in the living room. Count that there are C adjacent segments between the border of the black-and-white reference view and the white part. Randomly select a point (except the vertex) on each adjacent segment and shoot a virtual traveling ray in the direction of the center point of the reference view. At the same time, shoot two virtual traveling rays in the direction of the two vertices near the symmetric point in sequence. The timing unit starts timing. Assume the speed of the ray The trajectory monitoring module records the movement trajectories of all traveling rays. After the traveling ray encounters the black part, it is reflected. The timing unit records the reflection time and continues timing. After the timing unit reaches 8 seconds, the total number of reflections of the traveling ray is S, and the timing unit stops timing;

[0019] Step S22: Count the number of reflections generated by the three traveling rays shot from each of the C points. If at least one coincidence occurs in the trajectories of the three traveling rays shot from the shooting point within 8 seconds and no coincidence occurs with the trajectories of the traveling rays shot from any other shooting point, it is determined that the corner area where the vertex is located is a separate area and does not affect the spatial comfort of the living room, and the traveling ray trajectory generated by the shooting point is deleted.

[0020] According to the above technical solution, in the step S3, the number of deleted shooting points is counted as I, and the reflection times detection module calculates the minimum number of reflections S of the traveling rays shot from the remaining (C - I) vertices, S = Min{S 1 、S 2 ......S 3(C-I)}, analyze this ray, then the maximum value of the width of the furniture distance during the traveling process of the traveling ray

[0021] According to the above technical solution, step S5 further includes:

[0022] Step S51: When the trajectories of the traveling rays emitted from two different emission points coincide for the first time, the traveling ray monitoring module obtains, through the distance detection module, the trajectory distances traveled by the two emission points as R 1 、R 2 , then the length of a traveling route between the two emission points where λ 1 is the traveling route repetition parameter, which varies with the change in the straight-line distance between the two emission points;

[0023] Step S52: If R 01 ≥L 1 +L 2 or there is no coincidence between the two emission points after that, then this route length is recorded; if R 01 <L 1 +L 2 , the traveling ray monitoring module continues to monitor the two rays. When the traveling rays emitted from two different emission points coincide for the second time, the traveling ray monitoring module obtains, through the distance detection module, the trajectory distances traveled by the two emission points as R 3 、R 4 , then the length of a traveling route between the two emission points where γ is the linear fitting parameter between the first coincidence and the second coincidence;

[0024] Step S53: If R 02 ≥L 1 +L 2 or there is no coincidence between the two emission points after that, then this route length is recorded; if R 02 <L 1 +L 2 , the traveling ray monitoring module repeats step S42 until the two traveling rays coincide for the last time, and records the route length;

[0025] Step S54: The traveling ray monitoring module analyzes the coincidence situations of the two traveling rays of all different emission points in sequence; obtains a total of 3 (C-I) route length data, and extracts the maximum value R Max as the longest walking length in the living room.

[0026] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention detects the spatial comfort of the living room design scheme. The home layout analysis module quantifies and presents the spatial comfort through three detection criteria: average width, average length, and longest walking route, helping the household obtain the specific values of the vacant space generated by the furniture placement in the design scheme of the decoration designer, and then modifying the design scheme according to their own personalized needs for the furniture placement space, providing strong support for the personalized decoration of the household and helping the household choose the most suitable living room space comfort design scheme during decoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0028] Figure 1 is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] Please refer to Figure 1 , the present invention provides a technical solution: A personalized recommendation system and method based on big data, including:

[0031] A data acquisition module, a travel route simulation module, a home layout analysis module, and an output module. The data acquisition module is used to obtain relevant information of two houses of the household; the travel route simulation module is used to simulate and monitor the travel route of the household in the living room; the home layout analysis module evaluates the comfort of the living room of the household's decorated house through the travel route simulation module; the output module is used to make a personalized recommendation to the household based on the evaluation result of the home layout analysis module.

[0032] The present invention detects the spatial comfort of the living room design scheme. The home layout analysis module quantifies and presents the spatial comfort through three detection criteria: average width, average length, and longest walking route, helping the household obtain the specific values of the vacant space generated by the furniture placement in the design scheme of the decoration designer, and then modifying the design scheme according to their own personalized needs for the furniture placement space, providing strong support for the personalized decoration of the household and helping the household choose the most suitable living room space comfort design scheme during decoration.

[0033] The data acquisition module includes a living room decoration information acquisition module, an original address information acquisition module, and a reference spare data acquisition module. The living room decoration information acquisition module is used to obtain information related to the structure of the living room of the decorated house; the original address information acquisition module is used for information related to the house of the household's original address; the reference spare data acquisition module is used to provide the household with data on the spare area, length, and width of the living room of the public, helping the household to modify the design plan based on personalized needs.

[0034] The travel route simulation module includes a trajectory monitoring module, a distance monitoring module, a camera module, and a timing unit. The trajectory monitoring module is used to monitor the travel trajectory of the household's simulated travel route; the distance monitoring module is used to obtain the relevant distance size of the travel route in the trajectory monitoring module; the camera module is used to take pictures of the overall picture of the household decorating the living room; the timing unit is used to time during the process of simulating the household's travel route.

[0035] The home layout analysis module includes a reflection times detection module, a reflection interval detection module, and a travel ray monitoring module. The reflection times detection module obtains the average walking width of the living room by monitoring the number of ray reflections in the household's simulated travel route; the reflection interval detection module obtains the average straight-line walking length of the living room by monitoring the interval time of ray reflections in the household's simulated travel route; the travel ray monitoring module analyzes the maximum walking distance of the living room by combining different ray travel routes.

[0036] The output module includes a design plan evaluation module and a personalized recommendation module. The design plan evaluation module is used to evaluate the spatial comfort of the current design plan; the personalized recommendation module is used to recommend a personalized decoration modification plan to the owner.

[0037] In a preferred embodiment, the operation method of the personalized recommendation system mainly includes the following steps;

[0038] Step S1: The system obtains that the total area of the living room of the owner's original residence is S 11 , and the total area of the living room of the newly decorated house is S 12 , then the total area change rate of the living room after the owner moves to a new home

[0039] Step S2: The system analyzes the spatial comfort of the living room through the travel route simulation module;

[0040] Step S3: The reflection times detection module obtains the maximum value L of the home distance width by analyzing the travel process of the travel ray;

[0041] Step S4: The reflection interval detection module obtains the maximum value T of the interval between two adjacent reflections during the travel process of each travel ray through the timing unit Max and the minimum value of the reflection interval is TMin , then the longest straight-line distance Z that can be traveled in the living room 1 =T Max V, the shortest straight-line distance that can be traveled in the living room, that is, the minimum value of the width of the home distance Z 2 =T Min ·V;

[0042] When the reflection interval of the traveling ray is the smallest, the trajectory of the traveling ray is approximately equivalent to bouncing off each other along the two edges within the current free area of ​​the living room, so the shortest straight-line distance that can be traveled in the living room can be regarded as the minimum value of the home distance width.

[0043] Step S5: The traveling ray monitoring module obtains the longest walking length of the living room by analyzing the trajectory overlap of the traveling ray during its traveling process;

[0044] Step S6: The living room comfort analysis module obtains the average effective free area of ​​the living room after the change The average effective free area H, the longest straight-line distance Z that can be traveled in the living room 1 and the longest walking length R in the living room Max The value is compared with the database to determine whether the current living room situation meets the personalized needs of the residents, and the decoration plan modification recommendation is made through the output module.

[0045] In this embodiment, step S2 further includes:

[0046] Step S21: The living room is generally arranged in a rectangular shape. Let the length of the living room be L. 1 (m), width is L 2 (meters), analyze the top view of the living room, obtain a black and white reference map of the top view, the black part represents the location of the furniture in the living room, and the white part represents the location without furniture in the living room. Count the C border segments between the black and white reference map border and the white part, randomly select a point (except the vertex) on each border segment to shoot a virtual traveling ray toward the center of the reference map, and at the same time shoot two virtual traveling rays in turn toward the two vertices near the symmetric point. The timing unit starts timing, and the speed of the ray is set The trajectory monitoring module records the movement trajectory of all traveling rays. When the traveling rays encounter the black part, they are reflected. The timing unit records the reflection time and continues to count. When the timing unit reaches 8 seconds, the total number of reflections of the traveling rays is S, and the timing unit stops counting.

[0047] Step S22: Count the number of reflections generated by the three traveling rays emitted by each of the C points. If the three traveling ray trajectories emitted by the emitting point overlap at least once within 8 seconds and do not overlap with the traveling ray trajectories emitted by any other emitting points, then the corner area where the vertex is located is determined to be a separate area, which does not affect the spatial comfort of the living room, and the traveling ray trajectory generated by the emitting point is deleted.

[0048] Deleting the traveling rays that do not affect the comfort of the living room space reduces the amount of system calculations and alleviates the system's calculation load; at the same time, it reduces the errors caused by traveling rays that have no reference value in the analysis.

[0049] In step S3 of this embodiment, the number of deleted emission points is counted as I, and the reflection number detection module calculates the reflection number S=Min{S 1 , S 2 ......S 3(C-I)}, analyze the ray, and the maximum value of the home distance width of the traveling ray during the traveling process is

[0050] In an ideal situation, when the entire living room is empty, the ray travels in 8 seconds to The speed of the black and white reference map is reflected in the living room about 8 times. Specifically, the number of reflections of the traveling ray emitted from the midpoint of any side of the black and white reference map to the midpoint of one of its adjacent sides within 8 seconds is 8. Therefore, 8 times is used as the standard for testing the width of the home distance during the traveling process of the traveling ray. A coordinate system is established in the black and white reference map. From the equation group containing X and Y Find the ideal width of the traveling ray and through Get the maximum value L of the home distance width of the traveling ray during its travel.

[0051] Here, the traveling ray emitted from the midpoint of any side of the black and white reference image to the midpoint of its adjacent side considers a general traveling ray reflection situation under ideal conditions. However, the situation in which the traveling ray is perpendicular to one side in the black and white reference image is too extreme and is not conducive to reflecting the width of the home under normal home conditions.

[0052] The minimum number of reflections reflects the trajectory of the traveling ray moving parallel to the area generated in the middle of the home position in the living room to the greatest extent. It can further reflect the width of the space for residents to walk in the living room, affecting the comfort of residents in the living room.

[0053] The reflection times detection module obtains the width of the largest unoccupied area in the living room by screening out the traveling rays with the fewest reflection times within a certain period in the living room. Since the largest unoccupied area in the living room is basically the main path, and most of the main paths are located in places with good ventilation and sunlight, it can reflect the spatial comfort of the living room to the greatest extent and increase the accuracy of calculating the living room comfort level.

[0054] In this embodiment, step S5 is further included:

[0055] Step S51: When the trajectories of the traveling rays emitted from two different emission points first coincide, the traveling ray monitoring module obtains the trajectory distances traveled by the two emission points as R 1 、R 2 , then the length of a traveling route between the two emission points where λ 1 is the traveling route repetition parameter, which changes with the change of the straight-line distance between the two emission points;

[0056] Step S52: If R 01 ≥L 1 +L 2 or there is no coincidence between the two emission points after that, then this route length is recorded; if R 01 <L 1 +L 2 , the traveling ray monitoring module continues to monitor the two rays. When the traveling rays emitted from two different emission points coincide for the second time, the traveling ray monitoring module obtains the trajectory distances traveled by the two emission points as R 3 、R 4 , then the length of a traveling route between the two emission points where γ is the linear fitting parameter between the first coincidence and the second coincidence;

[0057] Step S53: If R 02 ≥L 1 +L 2 or there is no coincidence between the two emission points later, then this route length is recorded; if R 02 <L 1 +L 2 , the traveling ray monitoring module repeats step S42 until the two traveling rays coincide for the last time, and records the route length;

[0058] Step S54: The traveling ray monitoring module analyzes the coincidence situations of the two traveling rays of all different emission points in turn; obtains a total of 3 (C-I) route length data, and extracts the maximum value R Max as the longest walking length in the living room.

[0059] The farther the linear distance between the two emission points, the longer it takes for the two emission points to coincide for the first time, and the greater the total distance of the emission trajectories of the two emission points.

[0060] Since the space in the living room is limited, for each coincidence of the two emission points compared with the previous one, the traveling routes are getting closer and closer to being the same. Therefore, by adding the distance of the previous coincidence route, which accounts for the majority, and the distance of the subsequent coincidence route, which accounts for the minority, the accuracy of the length of the traveling route in the living room can be effectively increased.

[0061] The traveling ray monitoring module analyzes and gradually fits the coincidence of the emission trajectories of the traveling rays, improves the accuracy of measuring the longest effective length of the living room, and enables the household to obtain the specific value of the effective activity length of the living room.

Claims

1. A personalized recommendation system based on big data, characterized by: It includes a data acquisition module, a travel route simulation module, a home layout analysis module and an output module. The data acquisition module is used to obtain relevant information of two houses of the resident; the travel route simulation module is used to simulate and monitor the travel route of the resident in the living room; the home layout analysis module evaluates the comfort of the living room of the resident's decorated house through the travel route simulation module; and the output module is used to make personalized recommendations to the resident based on the evaluation results of the home layout analysis module; The travel route simulation module includes a trajectory monitoring module, a distance monitoring module, a camera module and a timing unit. The trajectory monitoring module is used to monitor the travel trajectory of the simulated travel route of the resident; the distance monitoring module is used to obtain the relevant distance size of the travel route in the trajectory monitoring module; the camera module is used to take a picture of the overall picture of the resident's decorated living room; the timing unit is used to time the process of simulating the resident's travel route; The home layout analysis module includes a reflection number detection module, a reflection interval detection module and a travel ray monitoring module. The reflection number detection module obtains the average walking width of the living room by monitoring the number of ray reflections in the simulated travel route of the residents; the reflection interval detection module obtains the average straight-line walking length of the living room by monitoring the interval time of ray reflections in the simulated travel route of the residents; The traveling ray monitoring module analyzes the maximum walking distance of the living room by combining different ray traveling routes; The operation method of the personalized recommendation system comprises the following steps: Step S1: The system obtains the total area of ​​the owner's original living room as S 11 , the total area of ​​the newly renovated house living room is S 12 , then the total area change rate of the living room after the owner moves to a new home is Step S2: The system analyzes the spatial comfort of the living room through a travel route simulation module; Step S3: The reflection number detection module obtains the maximum value L of the home distance width by analyzing the traveling ray during its traveling process; Step S4: The reflection interval detection module obtains the maximum value of the interval between two adjacent reflections during the travel of each traveling ray through the timing unit, which is T Max , the minimum value of the reflection interval is T Min , then the longest straight-line distance that can be traveled in the living room is Z1 = T Max V, the shortest straight-line distance that can be traveled in the living room, that is, the minimum value of the width of the home distance Z2 = T Min ·V; Step S5: The traveling ray monitoring module obtains the longest walking length of the living room by analyzing the trajectory overlap of the traveling ray during its traveling process; Step S6: The living room comfort analysis module obtains the average effective free area of ​​the living room after the change The average effective free area H, the longest straight-line distance Z1 that can be traveled in the living room, and the longest walking length R in the living room are calculated. Max The value is compared with the database to determine whether the current living room situation meets the personalized needs of the residents, and the decoration plan modification recommendation is made through the output module; The step S2 further comprises: Step S21: Analyze the top view of the living room to obtain a black and white reference map of the top view. The black part represents the location of the furniture in the living room, and the white part represents the location without furniture in the living room. Count the total number of C border segments between the border of the black and white reference map and the white part. Randomly select a point other than the vertex on each border segment to shoot a virtual traveling ray toward the center of the reference map. At the same time, shoot two virtual traveling rays in turn toward the two vertices near the symmetric point. The timing unit starts timing, and the speed of the ray is set to The trajectory monitoring module records the movement trajectory of all traveling rays. When the traveling rays encounter the black part, they are reflected. The timing unit records the reflection time and continues to count. When the timing unit reaches 8 seconds, the total number of reflections of the traveling rays is S, and the timing unit stops counting. Step S22: Count the number of reflections generated by the three traveling rays emitted by each of the C points. If the three traveling ray trajectories emitted by the emitting point overlap at least once within 8 seconds and do not overlap with the traveling ray trajectories emitted by any other emitting points, then the corner area where the vertex is located is determined to be a separate area, which does not affect the spatial comfort of the living room, and the traveling ray trajectory generated by the emitting point is deleted.

2. A personalized recommendation system based on big data according to claim 1, characterized in that: The step S5 further comprises: Step S51: When the trajectories of the traveling rays emitted from two different emission points overlap for the first time, the traveling ray monitoring module obtains the trajectory distances of the two emission points through the distance detection module, which are R1 and R2 respectively. Then the length of a traveling route between the two emission points is Where λ1 is the path repetition parameter, which varies with the straight-line distance between the two emission points; Step S52: If R 01 ≥L1+L2 or the two exit points do not overlap after this, then this route length is included; if R 01 <L1+L2, the traveling ray monitoring module continues to monitor the two rays. When the traveling rays emitted from two different emission points overlap for the second time, the traveling ray monitoring module obtains the trajectory distances of the two emission points through the distance detection module, which are R3 and R4 respectively. Then the length of a traveling route between the two emission points is Where γ is the linear fitting parameter of the first coincidence to the second coincidence; Step S53: If R 02 ≥L1+L2 or there is no overlap after the two exit points, then this route length is included; if R 02 <L1+L2, the traveling ray monitoring module repeats step S42 until the two traveling rays overlap for the last time, and records the route length; Step S54: The traveling ray monitoring module analyzes the overlap of two traveling rays from all different exit points in turn; obtains a total of 3 (C-I) route length data, extract the maximum value R Max It is the longest walking length in the living room.

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