Table and chair adjustment method, device, electronic equipment, storage medium and product

Abstract

The present invention provides a table and chair adjustment method, device, electronic device, storage medium and product. The table and chair adjustment model obtained through training can learn the optimal table and chair adjustment scheme corresponding to the posture features in the user posture image, and output the most suitable table and chair height and table and chair spacing. Compared with the prior art, the table and chair parameters can be automatically adjusted for different users. Compared with the existing table and chair adjustment scheme, the table and chair adjustment scheme of the present invention has a high degree of personalized configuration and automatic adjustment, and is easy to promote.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a table and chair adjustment method, device, electronic equipment, storage medium and product. Background Art

[0002] When people work or study in a bad posture for a long time, it is very easy to cause pain in the waist, back, neck or legs. Therefore, it is very important to adjust the position of the desk and chair to ensure human comfort.

[0003] The existing adjustment of tables and chairs often relies on manual adjustment or motor traction adjustment with fixed input parameters. These methods are time-consuming and labor-intensive. At the same time, due to the diversity of adjustment parameters of tables and chairs, how to ensure the accuracy of adjustment while reducing the number of adjustments to achieve efficient adjustment has become an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a table and chair adjustment method, device, electronic device, storage medium and product, which are used to solve the defects of time-consuming and labor-intensive table and chair adjustment in the prior art and realize efficient and accurate table and chair adjustment.

[0005] The present invention provides a table and chair adjustment method, comprising:

[0006] Collect posture images;

[0007] Inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0008] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0009] A table and chair adjustment method provided by the present invention also includes a method for training the table and chair adjustment model:

[0010] Acquire a sample posture image, and mark key points of human posture related to table and chair parameters in the sample posture image;

[0011] Extracting posture features of the sample posture image according to the sample posture image and the human body posture key points;

[0012] The table and chair adjustment scheme is matched according to the posture features of the sample posture image.

[0013] According to a table and chair adjustment method provided by the present invention, the human body posture key point labels include left shoulder joint point, right shoulder joint point, left elbow joint point, right elbow joint point, left wrist joint point, right wrist joint point, top of the head joint point and chest joint point.

[0014] According to a table and chair adjustment method provided by the present invention, the posture characteristics include shoulder width characteristics, arm length characteristics and chest position characteristics.

[0015] According to a table and chair adjustment method provided by the present invention, matching the table and chair adjustment scheme according to the posture feature includes:

[0016] The adjustment scheme of the seat height is obtained according to the shoulder width feature, the adjustment scheme of the table height is obtained according to the shoulder width feature, the arm length feature and the chest position feature, and the adjustment scheme of the distance between the table and the chair is obtained according to the arm length feature.

[0017] According to a table and chair adjustment method provided by the present invention, the adjustment scheme also includes an adjustment sequence between seat height, table top height and table and chair distance.

[0018] The present invention also provides a table and chair adjustment device, comprising:

[0019] A collection module, used for collecting posture images;

[0020] An adjustment module, used for inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0021] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0022] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any of the table and chair adjustment methods described above is implemented.

[0023] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the table and chair adjustment method described in any one of the above methods is implemented.

[0024] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the table and chair adjustment method described above is implemented.

[0025] The present invention provides a table and chair adjustment method, device, electronic device, storage medium and product. By designing a table and chair adjustment model, it is possible to learn the best table and chair adjustment scheme corresponding to the posture features in the image, and output the most suitable table and chair height and table and chair spacing. Compared with the prior art, the table and chair parameters can be automatically adjusted for different users. The present invention obtains the parameters of each position and posture information of the human body by identifying human body information. The most suitable table and chair parameters are formulated through the above parameter information, and the effect that best suits the human body is achieved by adjusting the height and position of the table and chair. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a flow chart of a table and chair adjustment method provided by an embodiment of the present invention;

[0028] Figure 2 is a flowchart of a table and chair adjustment model training process provided by an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of a table and chair adjustment solution provided by an embodiment of the present invention;

[0030] Figure 4 is a structural schematic diagram of a table and chair adjustment device provided by an embodiment of the present invention;

[0031] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Combine the following Figure 1 A table and chair adjustment method according to an embodiment of the present invention is described, comprising the following steps:

[0034] Step 101, collecting posture images;

[0035] It should be noted that the posture image can be collected by a camera set near the table and chair, and the posture image can be a full-body or upper-body posture image of the human body from the front. In this embodiment, the posture image collected is a posture image of the upper-body of the user from the front. The camera position can be set on the desktop and face the user. Since the human body is not necessarily in a state of facing the camera during shooting, it is necessary to collect multiple images of the same user within a certain period of time, and select the image with the highest degree of symmetry and containing all the key points to be extracted as the posture image.

[0036] Step 102: input the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0037] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0038] The table and chair adjustment method of the embodiment of the present invention can learn the posture features corresponding to specific key points in the image to obtain the best table and chair adjustment plan by designing a table and chair adjustment model, and automatically adjust the height of the table and chair and the distance between the table and chair. Compared with the prior art, the table and chair adjustment plan output by the present invention can meet the needs of users of different heights and body shapes for table and chair comfort in different life scenarios, and has a high degree of automation.

[0039] It should be noted that, in this embodiment, the sample posture images are obtained by collecting sitting posture pictures of people of different heights, weights, and body shapes, and a ready-made 3D posture data set may also be used, such as. The table and chair adjustment model of the embodiment of the present invention is based on a neural network model, which uses various human sitting posture images with the coordinates of the key points of the skeleton marked as training samples, human sitting posture images as input, the coordinates of the key points of the skeleton as the first-level output, posture features as the first-level output, and matching table and chair adjustment solutions as the final output. Specifically, neural network models such as AlexNet, MoveNet, and PoseNet may be used.

[0040] like Figure 2 As shown, in at least one embodiment of the present invention, a training method for the table and chair adjustment model is also included, and the training method includes the following steps:

[0041] Step 201, input a sample posture image and extract key points of human posture related to table and chair parameters;

[0042] It should be noted that this step corresponds to the recognition layer of the table and chair adjustment model. Since the input sample posture image is marked with the human posture key points related to the table and chair parameters in the sample posture image, this layer is used to learn to extract the key point information required for table and chair adjustment. Without corresponding annotations, the network can extract all the key point information of the human body in the image.

[0043] Step 202: extracting posture features of the sample posture image according to the sample posture image and the human body posture key points;

[0044] It should be noted that this step corresponds to the feature extraction layer of the table and chair adjustment model. Since the human posture key points input by this layer can extract a variety of human postures, the posture features most relevant to the table and chair parameters are set for extraction in the embodiment of the present invention.

[0045] Step 203: Match the table and chair adjustment solution according to the posture features of the sample posture image and output it.

[0046] It should be noted that this step corresponds to the fully connected layer of the table and chair adjustment model, which matches the posture features of the sample image with the table and chair adjustment parameters through pre-set matching rules and outputs the table and chair adjustment plan.

[0047] like Figure 3 As shown, it is a schematic diagram of the adjustment parameters of the table and chair that need to be adjusted in an embodiment of the present invention, wherein the seat height H1, the table height H2 and the distance S between the table and chair are schematically shown.

[0048] In at least one embodiment of the present invention, the human body posture key point labels include a left shoulder joint, a right shoulder joint, a left elbow joint, a right elbow joint, a left wrist joint, a right wrist joint, a top of the head joint and a chest joint.

[0049] It should be noted that the above-mentioned human body posture key point labels may also include left and right clavicle joints and joints on the spine. The joints on the spine may include multiple equidistant spine joints located at different vertical positions.

[0050] In at least one embodiment of the present invention, the posture features include shoulder width features, arm length features and chest position features.

[0051] It should be noted that if the key points of the whole-body image are collected, the posture features can also include height features, leg length features, hip features, etc., and the height, calf length, and upper body length from neck to waist of the user are extracted. The optimal height of the chair is determined according to the calf length of the user, and the optimal height of the table is determined according to the height of the chair and the upper body length from shoulder to waist. Since the embodiments of the present application are mainly described for half-body images, the posture features collected are shoulder width features, arm length features, and chest features. Selecting upper body images can also reduce computational complexity and improve the efficiency of model training and model recognition.

[0052] Specifically, in the embodiment of the present invention, the calculation process of each posture feature is as follows: the arm span L is obtained by summing the distances between the left wrist joint point → the left elbow joint point → the left shoulder joint point → the right shoulder joint point → the right elbow joint point → the right wrist joint point, and the forearm length Q is obtained by averaging the distances between the left wrist joint point → the left elbow joint point and the right wrist joint point → the right elbow joint point. The arm length feature includes the arm span L and the forearm length Q. The shoulder width feature, i.e., the shoulder width W, is obtained by the distance between the left shoulder joint point → the right shoulder joint point, and the chest position feature, i.e., the distance H3 from the top of the head to the chest, is obtained by the distance between the top of the head joint point → the chest joint point.

[0053] In at least one embodiment of the present invention, matching the table and chair adjustment scheme according to the posture feature includes:

[0054] The adjustment scheme of the seat height is obtained according to the shoulder width feature, the adjustment scheme of the table height is obtained according to the shoulder width feature, the arm length feature and the chest position feature, and the adjustment scheme of the distance between the table and the chair is obtained according to the arm length feature.

[0055] Specifically, in the embodiment of the present invention, the corresponding relationship between each table and chair parameter that needs to be adjusted and the posture parameter is: seat height H1=W, table height H2=L / 2-H3+H1, and table and chair distance S=Q.

[0056] In at least one embodiment of the present invention, the correspondence between the above-mentioned table and chair parameters and the posture parameters may further include a learning factor, wherein the learning factor includes a seat height learning factor μ1, a desktop height learning factor μ2, and a table and chair spacing learning factor μ3. After considering the learning factor, the correspondence between each table and chair parameter that needs to be adjusted and the posture parameter is: seat height H1 = μ1W, desktop height H2 = μ2(L / 2-H3+H1), and table and chair spacing S = μ3Q.

[0057] It should be noted that the learning factor of this embodiment can be intelligently adjusted according to the needs of users of different body shapes. For example, according to the posture characteristics, if the user is short and young and is determined to be a primary school student, in order to correct his sitting posture, the learning factor needs to be set to meet the standard sitting posture, where the learning factor corresponding to the standard sitting posture is 1, and the user's identity is determined based on the average body shape and posture of the age group; if the user is tall, the learning factor can be increased on the basis of the standard sitting posture to adapt to the user's comfort; if the user is thin and tall and is determined to be a female, the learning factor can be reduced on the basis of the standard sitting posture to adapt to the user's comfort.

[0058] Preferably, the initial position of the seat height H1 is the lowest point within the adjustment range, the initial position of the table top height H2 is the lowest point within the adjustment range, and the initial position of the table-chair distance S satisfies that the front end point A of the seat and the rear end point B of the table top are on the same horizontal plane.

[0059] In at least one embodiment of the present invention, the adjustment scheme also includes an adjustment sequence between seat height, table top height and distance between table and chair.

[0060] It should be noted that the adjustment order set in the embodiment of the present invention is directly related to the length of the adjustment distance. After obtaining the absolute values ​​of the seat height, table height and table and chair distance adjustment parameters, the lengths of the three are compared, and the table and chair parameters are adjusted in order from long to short or from short to long.

[0061] The table and chair adjustment device provided by the present invention is described below. The table and chair adjustment device described below and the table and chair adjustment method described above can be referred to each other. Figure 4 As shown, a table and chair adjustment device disclosed in an embodiment of the present invention includes:

[0062] A collection module 401 is used to collect posture images;

[0063] An adjustment module 402 is used to input the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0064] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0065] The table and chair adjustment device of the embodiment of the present invention can learn the posture features corresponding to the key points in the image through the designed table and chair adjustment model to obtain the best table and chair adjustment plan, and automatically adjust the height of the table and chair and the distance between the table and chair. Compared with the existing technology, the table and chair adjustment plan output by the present invention can meet the needs of users of different heights and body shapes for table and chair comfort in different life scenarios, and has a high degree of automation.

[0066] In at least one embodiment of the present invention, the table and chair adjustment model in the adjustment module 402 is obtained by the following training method:

[0067] Get sample posture images and extract human posture key points related to table and chair parameters;

[0068] Extracting posture features of the sample posture image according to the sample posture image and the human body posture key points;

[0069] The table and chair adjustment scheme is matched and outputted according to the posture features of the sample posture image.

[0070] The table and chair adjustment device of the embodiment of the present invention establishes sample posture images as a training set, and uses the key points of human posture related to the table and chair parameters as a label set for training. During the training process, the model can extract the key points of human posture, posture features and table and chair adjustment solutions in turn, thus realizing the end-to-end input and output of human posture and table and chair parameters.

[0071] In at least one embodiment of the present invention, the human body posture key point labels include a left shoulder joint, a right shoulder joint, a left elbow joint, a right elbow joint, a left wrist joint, a right wrist joint, a top of the head joint and a chest joint.

[0072] The table and chair adjustment device of the embodiment of the present invention selects 8 target posture joint points, which can specifically obtain the posture features required by the embodiment of the present invention.

[0073] In at least one embodiment of the present invention, the posture features include shoulder width features, arm length features and chest position features.

[0074] The table and chair adjustment device of the embodiment of the present invention can accurately match different table and chair parameters through the selected three posture features to achieve precise adjustment.

[0075] In at least one embodiment of the present invention, matching the table and chair adjustment scheme according to the posture feature includes:

[0076] The adjustment scheme of the seat height is obtained according to the shoulder width feature, the adjustment scheme of the table height is obtained according to the shoulder width feature, the arm length feature and the chest position feature, and the adjustment scheme of the distance between the table and the chair is obtained according to the arm length feature.

[0077] In at least one embodiment of the present invention, the adjustment scheme also includes an adjustment sequence between seat height, table top height and distance between table and chair.

[0078] The table and chair adjustment device of the embodiment of the present invention provides a table and chair parameter adjustment solution that adapts to the body shapes of different people. The adjustment method is not specifically limited, including but not limited to realizing automatic adjustment by setting motors and tracks under and between the tables and chairs; or by setting scales at corresponding adjustment positions of seat height, table height and distance between tables and chairs, and after obtaining the table and chair adjustment parameters, reminding the user to make manual adjustments through interface display or voice broadcast.

[0079] In at least one embodiment of the present invention, the table and chair adjustment device includes: a controller, a posture recognition component and a table and chair adjustment component; the table and chair adjustment component and the face recognition component are both connected to the controller;

[0080] The posture recognition component is used to recognize posture information by scanning the upper body image of the user, match the table and chair adjustment plan according to the posture information, and send the table and chair adjustment plan to the controller;

[0081] The controller is used to receive the table and chair adjustment plan, and control the table and chair adjustment component to adjust the parameters of the table and chair to the optimal table and chair parameters of the table and chair adjustment plan.

[0082] In at least one embodiment of the present invention, the gesture recognition component includes an autofocus camera and an upper body image recognition module, the autofocus camera is used to track and take photos of the seated person, and the upper body image recognition module is used to identify whether the photo of the seated person is an upper body image.

[0083] Figure 5 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the table and chair adjustment method, which includes:

[0084] Collect posture images;

[0085] Inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0086] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0087] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0088] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the table and chair adjustment method provided by the above methods, the method includes:

[0089] Collect posture images;

[0090] Inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0091] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0092] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the table and chair adjustment method provided by the above methods is implemented, and the method includes:

[0093] Collect posture images;

[0094] Inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model;

[0095] Among them, the table and chair adjustment model is trained based on sample posture images and human posture key point labels. The table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment plan based on the posture features.

[0096] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0097] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A table and chair adjustment method, characterized in that: include: Collect posture images; Inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model; The table and chair adjustment model is obtained by training based on sample posture images and human posture key point labels, and the table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment scheme based on the posture features; The human body posture key point labels include left shoulder joint point, right shoulder joint point, left elbow joint point, right elbow joint point, left wrist joint point, right wrist joint point, top of head joint point and chest joint point; The posture features include shoulder width features, arm length features and chest position features; The matching of the table and chair adjustment scheme according to the posture feature includes: Acquire a seat height adjustment scheme based on the shoulder width feature, acquire a table height adjustment scheme based on the shoulder width feature, arm length feature and chest position feature, and acquire a table and chair distance adjustment scheme based on the arm length feature; The seat height is equal to the product of the shoulder width and the seat height learning factor; the table height is equal to half the arm span minus the distance from the top of the head to the chest plus the product of the seat height and the table height learning factor; the distance between the table and chair is equal to the product of the forearm length and the table-chair distance learning factor.

2. The table and chair adjustment method according to claim 1, characterized in that: Also included is a training method for the table and chair adjustment model: Get sample posture images and extract human posture key points related to table and chair parameters; Extracting posture features of the sample posture image according to the sample posture image and the human body posture key points; The table and chair adjustment scheme is matched and outputted according to the posture features of the sample posture image.

3. The table and chair adjustment method according to claim 2, characterized in that: The adjustment scheme also includes an adjustment sequence between seat height, table top height and the distance between the table and chairs.

4. A table and chair adjustment device, characterized in that: include: A collection module, used for collecting posture images; An adjustment module, used for inputting the posture image into a table and chair adjustment model to obtain a table and chair adjustment solution output by the table and chair adjustment model; The table and chair adjustment model is obtained by training based on sample posture images and human posture key point labels, and the table and chair adjustment model is used to extract posture features of the posture image based on the human posture key point labels, and match the table and chair adjustment scheme based on the posture features; The human body posture key point labels include left shoulder joint point, right shoulder joint point, left elbow joint point, right elbow joint point, left wrist joint point, right wrist joint point, top of head joint point and chest joint point; The posture features include shoulder width features, arm length features and chest position features; The matching of the table and chair adjustment scheme according to the posture feature includes: Acquire a seat height adjustment scheme based on the shoulder width feature, acquire a table height adjustment scheme based on the shoulder width feature, arm length feature and chest position feature, and acquire a table and chair distance adjustment scheme based on the arm length feature; The seat height is equal to the product of the shoulder width and the seat height learning factor; the table height is equal to half the arm span minus the distance from the top of the head to the chest plus the product of the seat height and the table height learning factor; the distance between the table and chair is equal to the product of the forearm length and the table-chair distance learning factor.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the table and chair adjustment method according to any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the table and chair adjustment method according to any one of claims 1 to 3 is implemented.

7. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the table and chair adjustment method according to any one of claims 1 to 3 is implemented.

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