Intelligent furniture control method and system based on big data

Abstract

The invention discloses an intelligent furniture control method and system based on big data, and relates to the technical field of intelligent furniture control. The method comprises the following steps: acquiring portrait data through a camera, extracting a user of the intelligent furniture according to the portrait data, and recording the user as a basic user; obtaining the exclusive degree of the intelligent furniture, confirming a user using the intelligent furniture in real time according to the exclusive degree, and recording the user as a user; obtaining personal information of all users, and confirming a control range of the intelligent furniture according to the personal information; acquiring use habits of all users, and forming a control scheme according to the use habits and the control range; and the intelligent furniture is controlled and adjusted according to the control scheme, and when the user stops using the intelligent furniture, the standby state of the intelligent furniture is confirmed. According to the invention, the accuracy and intelligence of intelligent furniture control based on big data are improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent furniture control, and particularly to an intelligent furniture control method and system based on big data. Background Art

[0002] The intelligent furniture control system realizes the control of furniture through intelligent devices and network technology. Intelligent furniture control can be widely applied in modern work and family life, providing convenience for people's work and life. And intelligent furniture usually takes families, departments, etc. as the using units, so the users are usually not just one person. In the prior art, the control of intelligent furniture is often executed through user instructions, and usually can only satisfy one user. How to make the control of intelligent furniture satisfy more users, play a better use effect, and make the control more accurate is still a difficult problem in the current intelligent furniture control industry. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent furniture control method and system based on big data to solve the problems raised in the above background art.

[0004] In the first aspect, the intelligent furniture control method based on big data provided by this application adopts the following technical solutions:

[0005] Collect portrait data through a camera, and extract the users of the intelligent furniture from the portrait data and record them as basic users;

[0006] Obtain the exclusivity degree of the intelligent furniture, and confirm the users who are using the intelligent furniture in real time according to the exclusivity degree and record them as using users;

[0007] Obtain the personal information of all using users, and confirm the control range of the intelligent furniture according to the personal information;

[0008] Obtain the usage habits of all using users, and form a control plan according to the usage habits and the control range;

[0009] Control and adjust the intelligent furniture according to the control plan. When the using user stops using, confirm the standby state of the intelligent furniture.

[0010] Preferably, the step of obtaining the exclusivity degree of the intelligent furniture, and confirming the users who are using the intelligent furniture in real time according to the exclusivity degree and recording them as using users is specifically:

[0011] Obtain the exclusivity degree of the intelligent furniture and record it as the furniture exclusivity degree, and judge whether the basic user has the right to use the intelligent furniture according to the furniture exclusivity degree;

[0012] If the basic user has the right to use the intelligent furniture, record the basic user as the authorized user, and obtain the enabling conditions of the intelligent furniture;

[0013] Judge whether the enabling condition is accurate. If the enabling condition is accurate, judge the authorized users who can use the smart furniture in real time according to the enabling condition and record them as the using users.

[0014] If the enabling condition is inaccurate, obtain the living habits of the authorized users, and judge the using users according to the living habits of the authorized users.

[0015] If the basic user does not have the right to use the smart furniture, judge that the basic user is not a using user.

[0016] Preferably, the step of obtaining the exclusivity of the smart furniture and recording it as the furniture exclusivity, and judging whether the basic user has the right to use the smart furniture according to the furniture exclusivity is specifically as follows:

[0017] Obtain the historical using users of the smart furniture, and count the number AL of the historical using users.

[0018] Obtain the house floor plan of the user, divide the area according to the floor plan to obtain the house area map.

[0019] According to the house area map, count the number of users entering different house areas and record it as the area user number, and distinguish the public area and the non-public area according to the area user number.

[0020] Set the area exclusivity of the public area and the non-public area respectively, where the area exclusivity of the non-public area is greater than that of the public area.

[0021] Obtain the area exclusivity corresponding to the area where the smart furniture is located and record it as the furniture area exclusivity AQ.

[0022] According to the furniture exclusivity formula Calculate the furniture exclusivity AJ, where 、 is a proportionality factor and is greater than 0;

[0023] Obtain the historical usage record of the smart furniture, and judge whether the basic user has the right to use the smart furniture according to the furniture exclusivity.

[0024] Preferably, the step of obtaining the historical usage record of the smart furniture and judging whether the basic user has the right to use the smart furniture according to the furniture exclusivity is specifically as follows:

[0025] Extract the appearance frequency of the basic user according to the portrait data, and divide the basic user into family users and non-family users according to the appearance frequency.

[0026] Obtain the historical usage record of the smart furniture, extract the family users who have independently used the smart furniture from the historical usage record and record them as the first users, and judge that the first users have the right to use the smart furniture.

[0027] Household users who have not independently used smart furniture in the historical usage records are recorded as the second users, and it is determined that the second users do not have the right to use smart furniture;

[0028] According to the historical usage records, determine whether non-household users have independently used smart furniture. If they have used it, they are recorded as the third users; if they have not used it, they are recorded as the fourth users, and it is determined that the fourth users do not have the right to use smart furniture;

[0029] Obtain the appearance frequency of the third users and record it as the guest appearance frequency, and find the corresponding furniture exclusivity threshold according to the preset guest appearance frequency - furniture exclusivity threshold table;

[0030] If the furniture exclusivity of the smart furniture is greater than the furniture exclusivity threshold of the third users, it is determined that the third users do not have the right to use it; otherwise, the third users have the right to use the smart furniture.

[0031] Preferably, if the enabling condition is inaccurate, obtain the living habits of the authorized user, and the steps of determining the using user according to the living habits of the authorized user are as follows:

[0032] Obtain the living habits of the authorized user, where the living habits include the usage time point, usage frequency BP, and usage scenario of using smart furniture;

[0033] Obtain the real-time time point, and calculate the time difference BC between the real-time time point and the usage time point;

[0034] Obtain the similarity BX between the real-time scenario and the usage scenario, and according to the usage probability correlation function Calculate the usage probability BG of the smart furniture, where 、 、 Are scale factors and are greater than 0;

[0035] Set the usage probability threshold. When the usage probability reaches the usage probability threshold and the enabling condition determines that the authorized user is using the smart furniture, record the authorized user as the using user; otherwise, determine that the authorized user does not use the smart furniture and is not the using user.

[0036] Preferably, the steps of obtaining the personal information of all using users and confirming the control range of the smart furniture according to the personal information are as follows:

[0037] Count the number of all using users and record it as the usage quantity, and determine whether the usage quantity is greater than 1;

[0038] If the usage quantity is greater than 1, obtain the personal information of all using users, where the personal information includes age, height, and physical condition;

[0039] Obtain the limit control range of the parameters of the smart furniture used by the user according to the personal information of the user;

[0040] Obtain the intersection of all limit control ranges, and select the intersection with the smallest range as the control range of the smart furniture;

[0041] If the usage quantity is not greater than 1, obtain the control range of the smart furniture according to the personal information of the user.

[0042] Preferably, the step of obtaining the usage habits of all users and forming a control plan according to the usage habits and the control range is specifically as follows:

[0043] Extract the control plan with the highest usage frequency of all users from the historical usage records of the smart furniture and record it as the standard control plan;

[0044] Extract the parameters of the standard control plan and record them as standard parameters, and calculate the average value of all standard parameters respectively and record it as the parameter average value;

[0045] Judge whether the parameter average value is within the control range. If it is within the control range, use the parameter average value as the control parameter value;

[0046] If it is not within the control range, select the value with the smallest difference from the parameter average value within the control range as the control parameter value;

[0047] Form a control plan for the smart furniture according to all control parameter values.

[0048] Preferably, the step of controlling and adjusting the smart furniture according to the control plan and confirming the standby state of the smart furniture when the user stops using is specifically as follows:

[0049] When the smart furniture detects that the user stops using, obtain the stop state of the smart furniture;

[0050] Judge whether the stop state is safe. If the stop state is safe, control the smart furniture to maintain the stop state for standby;

[0051] If the stop state is not safe, control the smart furniture to adjust to a safe state and standby according to the safe state.

[0052] Preferably, the step of judging whether the stop state is safe is specifically as follows:

[0053] Obtain the injury records of the user, and extract the probability of the stop state of the smart furniture causing the user to be injured in the user's injury records and record it as the injury probability CS;

[0054] Obtain the user information reflected by the smart furniture in the stop state, and obtain the number of personal characteristics CY of the user information;

[0055] According to the status safety value association function Calculate the status safety value CZ, where 、 is a scaling factor and greater than 0;

[0056] Set the status safety value threshold. If the status safety value of the stop state reaches the status safety value threshold, it is determined that the stop state is safe; otherwise, it is determined that the stop state is unsafe.

[0057] In a second aspect, the intelligent furniture control system based on big data provided by the present application adopts the following technical solutions:

[0058] The intelligent furniture control system based on big data includes:

[0059] A basic user module that collects portrait data through a camera, extracts the users of the intelligent furniture from the portrait data, and records them as basic users;

[0060] A using user module that obtains the exclusivity of the intelligent furniture, and confirms the users who are currently using the intelligent furniture according to the exclusivity, and records them as using users;

[0061] A control range module that obtains the personal information of all using users, and confirms the control range of the intelligent furniture according to the personal information;

[0062] A control scheme module that obtains the usage habits of all using users, and forms a control scheme based on the usage habits and the control range;

[0063] A standby control module that controls and adjusts the intelligent furniture according to the control scheme, and confirms the standby state of the intelligent furniture when the using user stops using it.

[0064] In summary, the present application includes at least one of the following beneficial technical effects:

[0065] 1. Collect portrait data through a camera, extract the basic users of the intelligent furniture, and confirm the using users who are currently using the intelligent furniture according to the exclusivity of the intelligent furniture. And use the personal information of all using users to confirm the control range of the intelligent furniture, and form a control scheme in combination with the usage habits of the using users. Finally, control the intelligent furniture according to the control scheme and set the standby state. During the use of the intelligent furniture, the situation of multiple users using the intelligent furniture at the same time is fully considered, providing a control scheme more suitable for the actual use scenario for users, and improving the intelligence of the intelligent furniture control based on big data.

[0066] 2. Obtain the exclusivity of the smart furniture based on the number of historical users of the smart furniture and the regional exclusivity of the area where they are located. Then, in combination with the appearance frequency of the basic users, whether they have independently used the smart furniture, and the probability of using the smart furniture, confirm the users who are actually using the smart furniture. Confirming the users who use the smart furniture is beneficial to providing more appropriate control solutions for the users who are currently using it in the follow-up, making the provided solutions more accurate and improving the accuracy of the control of smart furniture based on big data.

[0067] 3. Based on the probability of the stop state of the smart furniture causing user injuries, which is recorded as the injury probability, and the number of personal characteristics of the user information reflected by the smart furniture in the stop state, obtain the state safety value of the stop state, so as to judge whether the stop state is safe. If the stop state is not safe, adjust and control the smart furniture to a safe state, improving the safety of the standby state of the smart furniture and the safety of the control of smart furniture based on big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic diagram of the specific steps of an embodiment of the smart furniture control method based on big data of the present invention.

[0069] Figure 2 It is a schematic diagram of the module connection of an embodiment of the smart furniture control system based on big data of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0070] The following combines the embodiments and Figure 1 - Figure 2 further elaborates on the present invention in detail, but the implementation manners of the present invention are not limited thereto.

[0071] The present invention discloses a smart furniture control method based on big data, which specifically includes the following steps:

[0072] Step S1: Collect portrait data through a camera, and extract the users of the smart furniture from the portrait data and record them as basic users.

[0073] Collect portrait data inside the user's family house through a camera authorized by the user, and extract the users who may use the smart furniture from the portrait as basic users. For example, the user authorizes the smart door lock to collect the portrait of the person entering the family house, and 5 people are collected. Since the smart furniture is in the family house, these five people all have the opportunity to use the smart furniture.

[0074] Step S2: Obtain the exclusivity degree of the smart furniture, and confirm the users who are currently using the smart furniture according to the exclusivity degree and record them as using users.

[0075] Step S3: Obtain the personal information of all using users, and confirm the control range of the smart furniture according to the personal information.

[0076] Step S4: Obtain the usage habits of all users, and form a control plan based on the usage habits and control range.

[0077] Step S5: Control and adjust the smart furniture according to the control plan. When the user stops using it, confirm the standby state of the smart furniture.

[0078] In actual application, smart furniture is different from smart appliances. Smart home appliances usually serve users by changing the entire house environment, such as smart fans and smart air conditioners. However, smart furniture is often in contact with the user. This leads to higher customization of smart furniture in the smart home. And the users of smart furniture are usually families, which means there is more than one user. At this time, if the control of smart furniture is only adjusted according to the personal habits of one person, it is obvious that it cannot be applied to all users, bringing a bad experience to the users. For example, if two people are sleeping in a smart bed, adjusting the smart bed only according to the habits of one person will obviously bring discomfort to the other person. By controlling and adjusting according to the actual situation of the two people, on the basis of having a relatively small impact on the users, the comfort brought by the smart furniture to the users can be improved as much as possible. For users, the control and adjustment of the smart bed is more suitable for the two users and is more accurate and intelligent.

[0079] The steps to obtain the exclusivity of smart furniture and confirm the users who are using the smart furniture in real time according to the exclusivity and record them as users are as follows:

[0080] Step S21: Obtain the exclusivity of the smart furniture and record it as furniture exclusivity, and judge whether the basic user has the right to use the smart furniture according to the furniture exclusivity.

[0081] Step S22: If the basic user has the right to use the smart furniture, record the basic user as the authorized user and obtain the activation conditions of the smart furniture.

[0082] Different smart furniture has different activation conditions. For example, the smart sofa starts to adjust and control when it detects that someone is sitting on the sofa, and the smart wardrobe starts to adjust and control when it detects that someone is in front of the wardrobe.

[0083] Step S23: Judge whether the activation condition is accurate. If the activation condition is accurate, judge the authorized user who is using the smart furniture in real time according to the activation condition and record them as users.

[0084] The activation conditions of smart furniture are sometimes not very accurate. For example, the activation condition of the smart sofa is to detect that someone is sitting on the sofa. Then, when the user sits on the sofa, it means the user is using the sofa, and the activation condition is accurate. The activation condition of the smart wardrobe is that the user stands in front of the wardrobe. However, just because the user stands in front of the wardrobe does not necessarily mean the user will use the smart wardrobe. Maybe the user is just passing by. Therefore, the activation condition is inaccurate.

[0085] In step S24, if the enabling condition is inaccurate, obtain the living habits of the authorized user, and determine the using user based on the living habits of the authorized user.

[0086] In step S25, if the basic user does not have the right to use the smart furniture, it is determined that the basic user is not the using user.

[0087] In actual application, based on the portrait data, the basic users who have the opportunity to use the smart furniture can be obtained, but it does not mean that all people entering the house have the right to use the smart furniture. For example, in a house with parents and children, the smart wardrobe is for the parents to use, and the children do not have the right to use the smart wardrobe, so the children will not become the users of the smart furniture. When judging who is using the smart wardrobe, the children are excluded. Determining the users who are using the smart furniture is beneficial to controlling the smart furniture according to the users subsequently, bringing a better experience for the users.

[0088] The steps of obtaining the exclusivity of the smart furniture and recording it as the furniture exclusivity, and judging whether the basic user has the right to use the smart furniture according to the furniture exclusivity are specifically as follows:

[0089] In step S211, obtain the historical using users of the smart furniture, and count the number AL of the historical using users.

[0090] Through the usage records of the smart furniture and combining the data collected by the camera, the historical using users can be extracted, and all the people who have used the smart furniture are superimposed. For example, if there are five people A, B, C, and D who have used the sofa, then the number of historical using users is 5.

[0091] In step S212, obtain the house floor plan of the user, and perform area division according to the floor plan to obtain the house area map.

[0092] According to the house floor plan authorized by the user, divide the house into areas. For example, divide the living areas such as the master bedroom, the second bedroom, and the living room.

[0093] In step S213, count the number of users entering different house areas according to the house area map and record it as the area user number, and distinguish the public area and the non - public area according to the area user number.

[0094] Set a user number threshold. If the number of users reaches the user number threshold, it is recorded as the public area, otherwise it is recorded as the non - public area. For example, if only 5 users have entered the bedroom and 20 people have entered the living room, and the user number threshold is 10 people, then the bedroom is the non - public area and the living room is the public area.

[0095] Step S214: Set the regional exclusivity of the public area and the non - public area respectively, where the regional exclusivity of the non - public area is greater than that of the public area.

[0096] Set the regional exclusivity respectively. It can be set by the user himself / herself, but during the setting process, the non - public area needs to be greater than the public area to ensure the accuracy of the user's setting. The regional exclusivity can be set according to the number of historical people who have been in the area. For example, if only 5 users enter the bedroom and 20 people enter the living room, then the exclusivity of the bedroom is higher. Because fewer people enter the bedroom, it is more exclusive to a certain person or a few people.

[0097] Step S215: Obtain the regional exclusivity corresponding to the area where the smart furniture is located and record it as the furniture regional exclusivity AQ.

[0098] Find the corresponding regional exclusivity in the already set regional exclusivities. For example, if the smart bed is in the bedroom and the regional exclusivity of the bedroom is 85, then the furniture regional exclusivity corresponding to the smart bed is 85.

[0099] Step S216: According to the furniture exclusivity formula Calculate the furniture exclusivity AJ, where 、 is a proportionality factor and is greater than 0.

[0100] The higher the regional exclusivity of the area where the smart furniture is placed, the fewer users the smart furniture has, and the more exclusive the furniture is to several people. Therefore, the furniture exclusivity is higher because it serves only a small number of users. The proportionality factor is set by the user himself / herself and can be set according to the actual situation to be more in line with his / her own needs.

[0101] Step S217: Obtain the historical usage record of the smart furniture and judge whether the basic user has the right to use the smart furniture according to the furniture exclusivity.

[0102] In practical applications, the use of smart furniture usually does not require authentication. As a result, some users without permission may use smart furniture, causing trouble to users. Therefore, when controlling and adjusting smart furniture, it is necessary to confirm whether the user has the right to use the smart furniture, which can effectively reduce unnecessary troubles. Different furniture has different degrees of exclusivity. For example, smart sofas, smart tables and chairs in the living room are used for receiving guests, so they serve more people and have a lower degree of exclusivity to a certain person or a few people. Therefore, the exclusivity of the furniture is lower. On the other hand, smart beds and smart wardrobes in the bedroom are for personal use by family users and serve a small number of people, so the exclusivity of the furniture is higher. The higher the exclusivity of the furniture, the lower the possibility of being used by non-family users. Confirming the users through the exclusivity of smart furniture is beneficial to the adjustment of smart furniture. For example, when household A is using the smart wardrobe and household friend B follows household A to the wardrobe, since the smart wardrobe is only used by household A, household friend B does not need to be considered during control and adjustment.

[0103] The steps of obtaining the historical usage records of smart furniture and judging whether the basic user has the right to use the smart furniture according to the furniture exclusivity are as follows:

[0104] Step S2171: Extract the appearance frequency of the basic user according to the portrait data, and divide the basic user into family users and non-family users according to the appearance frequency.

[0105] Set an appearance frequency threshold, and regard the basic user whose appearance frequency reaches the appearance frequency threshold as a family user. Because family users live in the house for a longer time, their appearance frequency is higher.

[0106] Step S2172: Obtain the historical usage records of smart furniture, extract the family users who have used the smart furniture independently from the historical usage records and record them as the first users, and judge that the first users have the right to use the smart furniture.

[0107] If a family user has used the smart furniture independently in the historical usage records of the smart furniture, it means that the family user has the right to use it.

[0108] Step S2173: Record the family users who have not used the smart furniture independently in the historical usage records as the second users, and judge that the second users do not have the right to use the smart furniture.

[0109] If a family user has not used the smart furniture independently, it means that the user does not have the right to use it. For example, a child in the family is using the smart wardrobe, but is accompanied by the mother. This shows that the child is not the main user of the smart wardrobe. Then, if it is detected that the mother and the child come to the wardrobe together, the smart wardrobe is still adjusted and controlled mainly based on the mother because the child does not use the smart wardrobe.

[0110] Step S2174: Determine whether non-household users have independently used smart furniture based on historical usage records. If they have used it, record them as the third type of users; if not, record them as the fourth type of users, and determine that the fourth type of users do not have the right to use smart furniture.

[0111] If non-household users have not independently used smart furniture, it is considered that such users do not have the permission to use it.

[0112] Step S2175: Obtain the appearance frequency of the third type of users and record it as the guest appearance frequency, and find the corresponding furniture exclusivity threshold according to the preset guest appearance frequency - furniture exclusivity threshold table.

[0113] The higher the guest appearance frequency, the higher the corresponding furniture exclusivity threshold. Since the relationships between different guests and household users are different, whether it is necessary to adjust and control smart furniture according to the guests' usage situations requires judging whether the guests have the right to use. For example, if B only visits A's home once and there is no need to use the smart wardrobe, then even if B appears in front of the wardrobe, there is no need to adjust and control for B. The guest appearance frequency - furniture exclusivity threshold table can be set independently by the user, and the user's appearance frequency is extracted from portrait data. For example, if user R appears once a month, the appearance frequency is 1 time / month.

[0114] Step S2176: If the furniture exclusivity of the smart furniture is greater than the furniture exclusivity threshold of the third type of users, determine that the third type of users do not have the right to use; otherwise, the third type of users have the right to use the smart furniture.

[0115] In actual application, there are also some users who are relatives and friends of the household head. Whether they can use smart furniture needs to be determined according to the actual situation. For example, B is the niece of household head A and often goes to A's home to play, and the relationship is relatively close. The corresponding furniture exclusivity threshold is 80. The exclusivity of the smart bed in the bedroom is 90, and the exclusivity of the smart wardrobe is 70. And B has independently used the smart wardrobe, so B can use the smart wardrobe but cannot use the smart bed.

[0116] If the enabling conditions are inaccurate, obtain the living habits of the authorized users, and judge the steps of the using users according to the living habits of the authorized users. Specifically:

[0117] Step S241: Obtain the living habits of the authorized users. The living habits include the usage time points of using smart furniture, the usage frequency BP, and the usage scenarios.

[0118] The usage scenarios include, but are not limited to, usage environments, usage processes, user behaviors, etc. For example, if a user only uses a smart bookshelf after having a meal, then having a meal is a usage scenario of the smart bookshelf. The usage frequency refers to how often the user uses it. For example, the average usage is once every 3 days. Among them, the usage time point, usage frequency, and usage scenario are the data with the highest frequency during the usage process. For example, the user uses the smart sofa at 6 pm, 7 pm, and 8 pm, but the usage frequency is the highest at 7 pm, so 7 pm is used as the usage time point.

[0119] Step S242: Obtain the real-time time point and calculate the time difference BC between the real-time time point and the usage time point.

[0120] For example, if the user usually uses the smart bed at 10 pm and the real-time time point is 7 pm, then the time difference is 3 hours.

[0121] Step S243: Obtain the similarity BX between the real-time scenario and the usage scenario, and calculate the usage probability BG of the smart furniture according to the usage probability correlation function where 、 、 are scale factors and are greater than 0.

[0122] The usage scenario can be represented as a vector, and the distance or angle between these vectors can be calculated to obtain the similarity BX of the usage scenario. It is also possible to use models such as deep learning models and spatial relationship models to obtain the similarity between the real-time scenario and the usage scenario. Just input the data of the real-time scenario and the usage scenario into the existing model.

[0123] Step S244: Set the usage probability threshold. When the usage probability reaches the usage probability threshold and the permission user is using the smart furniture when enabling the condition judgment, mark the permission user as the using user; otherwise, judge that the permission user is not using the smart furniture and is not the using user.

[0124] In actual application, when the enabling condition is inaccurate, whether it is necessary to adjust the smart furniture depends on the situation to determine whether the user is using the smart furniture. For example, if someone approaches the smart bedside table, it is judged that someone is using it, but it is very likely that the user is just passing by. Therefore, whether to perform adjustment control requires judging the usage probability of the user. The usage probability threshold is set to 80%, and the usage probability of user A is 85%. At this time, the smart bedside table also detects that user A is around it, so adjustment is made according to user A at this time. If the usage probability of user B is also 85% and user B is also around the smart bedside table, then adjustment is required according to both user A and user B. However, if the usage probability of user B is 60%, then only the drawer opening and closing of the bedside table need to be adjusted according to user A.

[0125] Steps to obtain the personal information of all users and confirm the control range of smart furniture based on the personal information are as follows:

[0126] Step S31, count the number of all users and record it as the usage quantity, and determine whether the usage quantity is greater than 1.

[0127] Step S32, if the usage quantity is greater than 1, obtain the personal information of all users. The personal information includes age, height, and physical condition.

[0128] The personal information is obtained through the authorization of the users.

[0129] Step S33, obtain the extreme control range of the parameters of the smart furniture used by the users according to their personal information.

[0130] According to the age, height, and physical condition of different users, the range that ensures the users can use the smart furniture can be obtained. For example, the height of the smart bench required by a child is 0 - 30 cm. If it exceeds 30 cm, the child cannot sit on it and thus cannot use it. Therefore, for the child, the extreme control range is 0 - 30 cm.

[0131] Step S34, obtain the intersection of all extreme control ranges, and select the intersection with the smallest range as the control range of the smart furniture.

[0132] For example, user A is an adult and has a physical condition that makes it impossible for user A to squat down. Therefore, the height of the smart bench used by user A is 40 - 60 cm. The height of the smart bench used by user B is 0 - 80 cm, and the height of the smart bench used by user C is 0 - 50 cm. Then the smallest intersection is 40 - 50 cm.

[0133] Step S35, if the usage quantity is not greater than 1, obtain the control range of the smart furniture according to the personal information of the user.

[0134] If the usage quantity is not greater than 1, it means that only one person is using the smart furniture. Then, the control range can be obtained according to the situation of this user.

[0135] In practical applications, if the smart furniture needs to meet the users who use it simultaneously, in order to ensure that each user can use the smart furniture, a feasible control range of the smart furniture needs to be obtained. For example, for the use of a smart table, user A is an adult and user B is a child. If the height of the smart table is too high, user B cannot use it. Therefore, the height limit of the smart table cannot exceed the height at which user B cannot use it to ensure that both user A and user B can use it.

[0136] Steps to obtain the usage habits of all users and form a control plan based on the usage habits and control range are as follows:

[0137] Step S41: Extract the control scheme with the highest usage frequency of all using users from the historical usage records of smart furniture and record it as the standard control scheme.

[0138] That is, the control scheme with the most usage times. For example, if the user adjusts the height of the smart sofa to 50 cm and the backrest inclination to 30 degrees, such a smart sofa adjustment scheme has the most times, so it is recorded as the standard scheme.

[0139] Step S42: Extract the parameters of the standard control scheme and record them as standard parameters, and calculate the average value of all standard parameters and record it as the parameter average value.

[0140] Extract the standard parameters in the standard control scheme without the same users. For example, the standard control scheme of user A is to adjust the smart sofa to 50 cm and the backrest inclination to 30 degrees, the standard control scheme of user B is to adjust the smart sofa to 30 cm and the backrest inclination to 50 degrees, and the standard control scheme of user C is to adjust the smart sofa to 70 cm and the backrest inclination to 40 degrees. Then the standard parameters are the sofa height and the sofa backrest inclination, and the corresponding parameter average values are: the average value of the sofa height (50 + 30 + 70) / 3 = 50 cm, and the average value of the sofa backrest inclination is (30 + 50 + 40) / 3 = 40 degrees.

[0141] Step S43: Determine whether the parameter average value is within the control range. If it is within the control range, use the parameter average value as the control parameter value.

[0142] If the control range of the sofa height is 30 cm - 60 cm, then the average value of the sofa height of 50 cm is within the control range and is directly used as the control parameter value.

[0143] Step S44: If it is not within the control range, select the value with the smallest difference from the parameter average value within the control range as the control parameter value.

[0144] If the control range of the sofa backrest inclination is 0 degrees - 20 degrees, then 40 degrees is not within the range, and the value with the smallest difference between the parameter average value and the control range is 20 degrees, and 20 degrees is used as the control parameter value.

[0145] Step S45: Form the control scheme of the smart furniture according to all control parameter values.

[0146] Select the corresponding control parameters in the control scheme to form the control scheme. For example, select the control parameters of the sofa height of 50 cm and the sofa backrest inclination of 40 degrees to form the sofa control scheme. If the sofa can also adjust the temperature, add the corresponding temperature control parameter value to form the control scheme.

[0147] In actual use, different users have their own unique habits regarding the use of smart furniture. However, when multiple people use it simultaneously, all users need to be considered comprehensively. Moreover, the settings of smart furniture often involve multiple parameters, and each parameter needs to be considered separately. For example, the most frequently used control scheme for the smart bed by User A is to raise the angle by 10 degrees and heat it to 40 degrees Celsius. The most frequently used control scheme for the smart bed by User B is to raise the angle by 15 degrees and heat it to 30 degrees Celsius. At this time, if User A and User B use the smart bed simultaneously, the calculated average raised angle is 12.5 degrees, and the average heating temperature is 35 degrees Celsius, and both are within the control range. Then, the control scheme is to raise the angle to 12.5 degrees and heat it to 35 degrees Celsius. If the control range for the raised angle is 15 degrees - 30 degrees, then the raised angle of 12.5 degrees is not within the control range, and the value with the smallest difference is 15 degrees. At this time, the raised angle becomes 15 degrees.

[0148] The steps for confirming the standby state of the smart furniture when the user stops using it according to the control scheme are as follows:

[0149] Step S51: When the smart furniture detects that the user has stopped using it, obtain the stop state of the smart furniture.

[0150] That is, when no one is using it, the state of the smart furniture is the stop state. For example, the height of the sofa is adjusted to 50 cm, and then the user gets up and leaves. At this time, the height of the sofa is 50 cm, which is the stop state of the smart sofa.

[0151] Step S52: Determine whether the stop state is safe. If the stop state is safe, control the smart furniture to maintain the stop state for standby.

[0152] Step S53: If the stop state is not safe, control the smart furniture to adjust to a safe state and perform standby according to the safe state.

[0153] The safe state is evaluated based on the state safety value calculated in Step S52. Set the state safety value threshold, and select any state that reaches the state safety value threshold as the safe state to control and adjust the smart furniture. For example, in past evaluations, the inclination angles of the sofa backrest at 30 degrees, 20 degrees, 10 degrees, and 0 degrees are all safe states. Then, the sofa can be adjusted to any one of these inclination angles at will.

[0154] In actual use, when the user stops using, the smart furniture is not necessarily in a safe state. To reduce the impact on the user after the smart furniture stops being used, it is achieved by adjusting the standby state of the smart furniture. For example, when the user uses a smart sofa and the sofa is in a heating state, after the user gets up and leaves, if the sofa remains in the heating state for a long time, it may cause a fire, resulting in the user being injured. Therefore, for the user, the stop state is not safe at this time, and the heating function needs to be turned off. At this time, the inclination angle of the sofa backrest is 20 degrees, which will not cause the user to be injured, so there is no need to adjust, and it can be maintained in this state for standby.

[0155] The steps to determine whether the stop state is safe are specifically as follows:

[0156] Step S521, obtain the user's injury record, extract the probability that the stop state of the smart furniture causes the user to be injured from the user's injury record and record it as the injury probability CS.

[0157] Extract the user's injury record, count the ratio of the number of times the user is injured due to the stop state to the total number of injuries to obtain the injury probability. For example, if the user is injured a total of 10 times and the number of times the user is injured due to the smart furniture without using it is counted as 2 times, then the injury probability is 2 / 10 = 20%.

[0158] Step S522, obtain the user information reflected by the smart furniture in the stop state, and obtain the number of personal characteristics CY of the user information.

[0159] Obtain the user information collected in the stop state of the smart furniture, and count the number of user information that reflects the user's personal characteristics. For example, when the stop state of the smart furniture is that the sofa height is 50 cm and the inclination of the sofa backrest is 30 degrees, there are no user personal characteristics among them. For example, when the smart wardrobe takes a picture of the user when it stops, there are user personal characteristics in the picture, including features such as appearance and height. The extraction of personal characteristics can be carried out through existing models such as convolutional neural networks to count the quantity.

[0160] Step S523, according to the state safety value correlation function Calculate to obtain the state safety value CZ, where 、 is a scaling factor and is greater than 0.

[0161] Step S524, set the state safety value threshold. If the state safety value of the stop state reaches the state safety value threshold, it is determined that the stop state is safe; otherwise, it is determined that the stop state is unsafe.

[0162] In actual use, it is judged whether the stop state is safe according to the probability of user injury and the number of user personal characteristics included in the data. If the stop state of the smart furniture is likely to cause user injury, the stop state is not safe. And if the stop state of the smart furniture is likely to disclose user information or associated data, the stop state is also not safe. For example, if the stop state of the sofa is the lying flat state and it is easy for users to collide, the stop state is not safe. The stop state of the smart bookshelf shows the books recently read by the user, which is likely to disclose the user's reading records and cause data insecurity.

[0163] The intelligent furniture control system based on big data, by applying the intelligent furniture control method based on big data as described above, includes:

[0164] The basic user module collects portrait data through a camera, extracts the user of the intelligent furniture from the portrait data and records it as the basic user.

[0165] The using user module obtains the exclusivity of the intelligent furniture, and confirms the user who is currently using the intelligent furniture according to the exclusivity and records it as the using user.

[0166] The control range module obtains the personal information of all using users, and confirms the control range of the intelligent furniture according to the personal information.

[0167] The control scheme module obtains the usage habits of all using users, and forms a control scheme according to the usage habits and the control range.

[0168] The standby control module controls and adjusts the intelligent furniture according to the control scheme. When the using user stops using it, it confirms the standby state of the intelligent furniture.

[0169] The implementation principle of this system is as follows: First, the camera in the basic user module collects portrait data, and extracts the existing users from the portrait data as the basic users. The using user module then obtains the exclusivity of the intelligent furniture, and confirms the user who is currently using the intelligent furniture according to the exclusivity and records it as the using user. The exclusivity is comprehensively obtained based on the number of historical using users of the intelligent furniture and the regional exclusivity of the location area. Then, the control range module obtains the personal information of all using users, confirms the limit control range of each using user for using the intelligent furniture according to the personal information, forms an intersection according to the limit control ranges, and selects the intersection with the smallest range as the control range of the intelligent furniture. The control scheme module is used to obtain the usage habits of all using users, and forms a control scheme according to the usage habits and the control range. Finally, the standby control module controls and adjusts the intelligent furniture according to the control scheme. When the using user stops using it, it confirms whether the stop state of the intelligent furniture is safe, and sets the standby state of the intelligent furniture according to the confirmation result.

[0170] The above are all preferred embodiments of the present application, and do not limit the protection scope of the present application. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application shall be covered within the protection scope of the present application.

Claims

1. An intelligent furniture control method based on big data, characterized in that, It includes the following steps: Collect portrait data through a camera, extract the user of the smart furniture from the portrait data and record it as the basic user; Obtain the exclusivity level of the smart furniture, confirm the users who are using the smart furniture in real time according to the exclusivity level and record them as using users; Obtain the personal information of all using users, and confirm the control range of the smart furniture according to the personal information; Obtain the usage habits of all using users, and form a control plan according to the usage habits and control range; Control and adjust the smart furniture according to the control plan. When the using user stops using, confirm the standby state of the smart furniture.

2. The intelligent furniture control method based on big data according to claim 1, wherein, The step of obtaining the exclusivity level of the smart furniture, confirming the users who are using the smart furniture in real time according to the exclusivity level and recording them as using users is specifically as follows: Obtain the exclusivity level of the smart furniture and record it as the furniture exclusivity level, and judge whether the basic user has the right to use the smart furniture according to the furniture exclusivity level; If the basic user has the right to use the smart furniture, record the basic user as the authorized user and obtain the enabling conditions of the smart furniture; Judge whether the enabling conditions are accurate. If the enabling conditions are accurate, judge the authorized users who are using the smart furniture in real time according to the enabling conditions and record them as using users; If the enabling conditions are inaccurate, obtain the living habits of the authorized users, and judge the using users according to the living habits of the authorized users; If the basic user does not have the right to use the smart furniture, judge that the basic user is not a using user.

3. The intelligent furniture control method based on big data according to claim 2, wherein The step of obtaining the exclusivity level of the smart furniture and recording it as the furniture exclusivity level, and judging whether the basic user has the right to use the smart furniture according to the furniture exclusivity level is specifically as follows: Obtain the historical using users of the smart furniture, and count the number AL of the historical using users; Obtain the house floor plan of the user, conduct area division according to the floor plan to obtain the house area map; Count the number of users entering different house areas according to the house area map and record it as the area user number, and distinguish the public area and the non-public area according to the area user number; Set the area exclusivity levels of the public area and the non-public area respectively, where the area exclusivity level of the non-public area is greater than that of the public area; Obtain the area exclusivity level corresponding to the area where the smart furniture is located and record it as the furniture area exclusivity level AQ; According to the furniture exclusivity formula the furniture exclusivity AJ is calculated, where , are proportionality factors and greater than 0; Obtain the historical usage records of the smart furniture, and judge whether the basic user has the right to use the smart furniture according to the furniture exclusivity level.

4. The intelligent furniture control method based on big data according to claim 3, wherein The step of obtaining the historical usage records of the smart furniture, and judging whether the basic user has the right to use the smart furniture according to the furniture exclusivity level is specifically as follows: Extract the appearance frequency of the basic user according to the portrait data, and divide the basic user into family users and non-family users according to the appearance frequency; Obtain the historical usage records of the smart furniture, extract the family users who have independently used the smart furniture from the historical usage records and record them as the first users, and judge that the first users have the right to use the smart furniture; The family users in the historical usage records who have not independently used the smart furniture are recorded as the second users, and judge that the second users do not have the right to use the smart furniture; Judge whether the non-family users have independently used the smart furniture according to the historical usage records. If they have used it, record it as the third user. If they have not used it, record it as the fourth user, and judge that the fourth user does not have the right to use the smart furniture; Obtain the appearance frequency of the third user and record it as the guest appearance frequency, and find the corresponding furniture exclusivity threshold according to the preset guest appearance frequency - furniture exclusivity threshold table; If the furniture exclusivity of the smart furniture is greater than the furniture exclusivity threshold of the third user, it is determined that the third user has no right to use it, otherwise the third user has the right to use the smart furniture.

5. The intelligent furniture control method based on big data according to claim 4, characterized in that If the enabling condition is inaccurate, obtain the living habits of the authorized user, and the steps of judging the using user according to the living habits of the authorized user are specifically as follows: Obtain the living habits of the authorized user, and the living habits include the usage time point of using the smart furniture, the usage frequency BP, and the usage scenario; Obtain the real-time time point, and calculate the time difference BC between the real-time time point and the usage time point; Obtain the similarity BX between the real-time scene and the usage scenario, and according to the usage probability correlation function Calculate the usage probability BG of the smart furniture, where , , are scale factors and greater than 0; Set the usage probability threshold. When the usage probability reaches the usage probability threshold and the enabling condition determines that the authorized user is using the smart furniture, record the authorized user as the using user, otherwise it is determined that the authorized user is not using the smart furniture and is not the using user.

6. The intelligent furniture control method based on big data according to claim 5, wherein The steps of obtaining the personal information of all using users and confirming the control range of the smart furniture according to the personal information are specifically as follows: Count the number of all using users and record it as the usage quantity, and judge whether the usage quantity is greater than 1; If the usage quantity is greater than 1, obtain the personal information of all using users, and the personal information includes age, height, and physical condition; Obtain the limit control range of the parameters for the using users to use the smart furniture according to the personal information of the using users; Obtain the intersection of all limit control ranges, and select the intersection with the smallest range as the control range of the smart furniture; If the usage quantity is not greater than 1, obtain the control range of the smart furniture according to the personal information of the using user.

7. The intelligent furniture control method based on big data according to claim 6, characterized in that, The steps of obtaining the usage habits of all using users and forming a control scheme according to the usage habits and the control range are specifically as follows: Extract the control scheme with the highest usage frequency of all using users from the historical usage records of the smart furniture and record it as the standard control scheme; Extract the parameters of the standard control scheme and record them as standard parameters, and calculate the average value of all standard parameters respectively and record it as the parameter average value; Judge whether the parameter average value is within the control range. If it is within the control range, use the parameter average value as the control parameter value; If it is not within the control range, select the value with the smallest difference from the parameter average value within the control range as the control parameter value; Form a control scheme for the smart furniture according to all control parameter values.

8. The intelligent furniture control method based on big data according to claim 7, wherein The steps of controlling and adjusting the smart furniture according to the control scheme and confirming the standby state of the smart furniture when the using user stops using are specifically as follows: When the smart furniture detects that the user stops using, obtain the stop state of the smart furniture; Judge whether the stop state is safe. If the stop state is safe, control the smart furniture to maintain the stop state for standby; If the stop state is not safe, control the smart furniture to adjust to the safe state and standby according to the safe state.

9. The intelligent furniture control method based on big data according to claim 8, wherein The steps of judging whether the stop state is safe are specifically as follows: Obtain the injury records of the user, and extract the probability of the stop state of the smart furniture causing the user to be injured from the user injury records and record it as the injury probability CS; Obtain the user information reflected by the smart furniture in the stopped state, and obtain the number of personal characteristics CY of the user information; According to the status safety value association function The status safety value CZ is calculated, where 、 are scale factors and greater than 0; Set the threshold value of the status security value. If the status security value in the stopped state reaches the threshold value of the status security value, it is determined that the stopped state is safe; otherwise, it is determined that the stopped state is unsafe.

10. The intelligent furniture control system based on big data is characterized in that, By applying the smart furniture control method based on big data as described in any one of claims 1-9, including: The basic user module collects portrait data through a camera, extracts the user of the smart furniture from the portrait data and records it as the basic user; The using user module obtains the exclusivity of the smart furniture, and confirms the user who is using the smart furniture in real time according to the exclusivity and records it as the using user; The control range module obtains the personal information of all using users, and confirms the control range of the smart furniture according to the personal information; The control scheme module obtains the usage habits of all using users, and forms a control scheme according to the usage habits and the control range; The standby control module controls and adjusts the smart furniture according to the control scheme. When the using user stops using, the standby state of the smart furniture is confirmed.

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