Intelligent lifting system for laboratory

Abstract

The invention relates to the technical field of laboratory management, in particular to an intelligent lifting system for a laboratory, the system comprises a plurality of lifting table assemblies placed at intervals, and each lifting table assembly comprises a table top lifting mechanism, a rotating mechanism arranged at the output end of the table top lifting mechanism and a table plate arranged at the output end of the rotating mechanism; the plurality of camera modules are used for detecting the sitting posture of the student in front of each lifting desk assembly; and the processing module is used for receiving and processing the data from the camera module and sending a control instruction to the desktop lifting mechanism or the rotating mechanism according to a preset algorithm and parameters. According to the intelligent lifting system for the laboratory, the sitting posture of the student is obtained, the posture scoring algorithm and the learning feedback mechanism are combined, the height of the table top is automatically adjusted, and the comfort level of the student in the laboratory and the health of the student are gradually improved.

Description

Technical Field

[0001] The invention relates to the technical field of laboratory management, and in particular to an intelligent lifting system for a laboratory. Background Art

[0002] With the popularity of smart devices in modern laboratory environments, more and more schools and scientific research institutions have begun to introduce smart lifting tables to improve students' learning efficiency and comfort in the laboratory. Most existing lifting tables rely on manual or simple preset height adjustment methods, which makes it difficult to automatically adjust the height of the table according to the different sitting and standing postures of individual users, which may cause user fatigue, poor posture and even physical health problems during long-term use.

[0003] Although smart height-adjustable desk systems have gradually appeared on the market, most systems have a single adjustment basis and cannot be adaptively adjusted according to the user's real-time posture and usage habits. In addition, existing smart desktop systems are insufficient in personalization and are difficult to dynamically optimize according to the preferences and feedback of different users. In a laboratory environment, students' long-term sitting or standing posture will have a significant impact on their learning effect and physical health.

[0004] Therefore, it is particularly important to develop an intelligent lifting table system that can monitor and automatically adjust the height of the desktop in real time and improve students' learning comfort. Summary of the invention

[0005] In order to overcome the shortcomings and deficiencies in the prior art, the purpose of the present invention is to provide an intelligent lifting system for laboratories, which realizes automatic adjustment of the height of the desktop by obtaining the sitting and standing postures of students and combining a posture scoring algorithm and a learning feedback mechanism, thereby gradually improving the comfort and health of students in the laboratory.

[0006] The present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention discloses an intelligent lifting system for a laboratory, comprising:

[0008] A plurality of lifting table assemblies placed at intervals, wherein the lifting seat assembly comprises a tabletop lifting mechanism, a rotating mechanism disposed at the output end of the tabletop lifting mechanism, and a tabletop disposed at the output end of the rotating mechanism;

[0009] A number of camera modules for detecting the sitting and standing postures of students in front of each lifting table assembly;

[0010] The processing module is used to receive and process the data from the camera module and send control instructions to the desktop lifting mechanism or rotating mechanism according to preset algorithms and parameters.

[0011] In a second aspect, the present invention further discloses a control method for a laboratory intelligent lifting system, which comprises the following steps:

[0012] S100. Setting the standard key point coordinates of the student's sitting and standing posture in front of the lifting table assembly;

[0013] S200. Obtain image data of the student's sitting posture in front of the lifting table assembly, and obtain real-time key point coordinates of the student's sitting posture;

[0014] S300. Compare the real-time key point coordinates with the standard key point coordinates to calculate the student's posture score;

[0015] S400. According to the posture score, control the desktop lifting mechanism or the rotating mechanism to adjust the height or angle of the lifting table assembly;

[0016] S500. Record the experience scores of students after use, and optimize the adjustment parameters of the desktop lifting mechanism or the rotating mechanism according to the experience scores.

[0017] In combination with the second aspect, further, in step S100, the standard key point coordinates of the student's sitting posture in front of the lifting table assembly are set as P i,standard (x i,standard ,y i,standard );

[0018] In step S200, the image data of the student's sitting posture is output as an image matrix I(x, y), and the feature map F(I) is extracted through a convolutional neural network. The posture estimation function P i =f pose (F(I)) maps the feature map to obtain the coordinates of the key points of the human body P i (x i ,y i ).

[0019] In combination with the second aspect, further, in step S300, the calculation formula of the student's posture score is:

[0020]

[0021] Among them, d max is the maximum allowable deviation distance, θ t is the adjustment parameter of the desktop lifting mechanism at time t, and n is the number of key points.

[0022] In combination with the second aspect, further, in step S400, the height adjustment formula of the lifting table assembly is:

[0023] Δh=k h (S target -S)

[0024] Among them, Δh is the height difference of the desktop that needs to be adjusted, k h is the height adjustment factor, S target is the posture score of the target, and S is the current posture score.

[0025] In combination with the second aspect, further, in step S500, the step of optimizing the adjustment parameters of the desktop lifting mechanism includes:

[0026] Set the student experience rating after use to S sat and target satisfaction score S sat,target ;

[0027] Setting the comprehensive loss function in, is the posture score S relative to the target score S target The loss function is Score satisfaction sat The loss function relative to the target satisfaction score, α and β are weight coefficients;

[0028] The optimization formula for adjusting parameters is:

[0029]

[0030] Where η is the learning rate, is the partial derivative of the comprehensive loss function with respect to the parameter θ.

[0031] In a third aspect, the present invention further discloses a computer device, which includes a processor and a storage medium;

[0032] The storage medium is used to store instructions;

[0033] The processor is used to operate according to the instructions to execute the steps of the method described above.

[0034] Beneficial effects of the present invention:

[0035] 1. Real-time posture detection and adjustment: The present invention uses image processing and posture recognition technology to monitor students' sitting and standing postures in real time, automatically calculate posture scores, and adjust the height of the desktop according to the posture scores. This method can effectively avoid physical discomfort caused by long-term poor sitting or standing postures, and improve students' health and learning efficiency.

[0036] 2. Personalized adjustment and feedback optimization: By introducing students' subjective satisfaction scores, the present invention can dynamically optimize system parameters according to students' feedback, so that the desktop height adjustment is not only based on standard ergonomic posture, but also combined with individual needs to improve user experience.

[0037] 3. Learning feedback and continuous optimization: Through the learning feedback mechanism, the system of the present invention can gradually improve the accuracy of the scoring algorithm and make personalized adjustments for different students, so as to continuously improve the comfort level in long-term use.

[0038] 4. Double improvement of health and efficiency: This invention can not only help students maintain a healthy sitting and standing posture, reduce physical discomfort caused by bad posture, but also improve concentration and efficiency during learning. In addition, the personalized adjustment and optimization function of the system ensures that each student can get a customized comfort experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0040] Figure 1 Flow chart of the control method of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0042] With the popularity of smart devices in modern laboratory environments, more and more schools and scientific research institutions have begun to introduce smart lifting tables to improve students' learning efficiency and comfort in the laboratory. Most existing lifting tables rely on manual or simple preset height adjustment methods, which makes it difficult to automatically adjust the height of the table according to the different sitting and standing postures of individual users, which may cause user fatigue, poor posture and even physical health problems during long-term use.

[0043] Although smart height-adjustable desk systems have gradually appeared on the market, most systems have a single adjustment basis and cannot be adaptively adjusted according to the user's real-time posture and usage habits. In addition, existing smart desktop systems are insufficient in personalization and are difficult to dynamically optimize according to the preferences and feedback of different users. In a laboratory environment, students' long-term sitting or standing posture will have a significant impact on their learning effect and physical health.

[0044] Therefore, it is particularly important to develop an intelligent lifting table system that can monitor and automatically adjust the height of the desktop in real time and improve students' learning comfort.

[0045] Example 1

[0046] In order to solve the above problems, this embodiment discloses an intelligent lifting system for a laboratory, which includes:

[0047] A plurality of lifting table assemblies placed at intervals, wherein the lifting seat assembly comprises a tabletop lifting mechanism, a rotating mechanism disposed at the output end of the tabletop lifting mechanism, and a tabletop disposed at the output end of the rotating mechanism;

[0048] A number of camera modules for detecting the sitting and standing postures of students in front of each lifting table assembly;

[0049] The processing module is used to receive and process the data from the camera module and send control instructions to the desktop lifting mechanism or rotating mechanism according to preset algorithms and parameters.

[0050] It should be noted that the desktop lifting mechanism in this embodiment is preferably a transmission structure composed of at least a motor, a screw and a screw nut, the rotating mechanism is preferably a servo motor, and the camera module is preferably a high-definition camera; in addition, regarding the camera device in this embodiment obtaining the sitting and standing posture image of the student, the working principle of the control module sending corresponding control instructions to the desktop lifting mechanism or the rotating mechanism according to preset algorithms and parameters are all existing technologies and will not be repeated here.

[0051] Furthermore, this embodiment also provides a control method for a laboratory intelligent lifting system, which is applied to the above-mentioned intelligent lifting system and specifically includes the following steps:

[0052] S100. Setting the standard key point coordinates of the student's sitting and standing posture in front of the lifting table assembly;

[0053] S200. Obtain image data of the student's sitting posture in front of the lifting table assembly, and obtain real-time key point coordinates of the student's sitting posture;

[0054] S300. Compare the real-time key point coordinates with the standard key point coordinates to calculate the student's posture score;

[0055] S400. According to the posture score, control the desktop lifting mechanism or the rotating mechanism to adjust the height or angle of the lifting table assembly;

[0056] S500. Record the experience scores of students after use, and optimize the adjustment parameters of the desktop lifting mechanism or the rotating mechanism according to the experience scores.

[0057] Further, in step S100, the standard key point coordinates of the student's sitting and standing posture in front of the lifting table assembly are set as P i,standard (x i,standard ,y i,standard );

[0058] In step S200, the image data of the student's sitting posture is output as an image matrix I(x, y), and the feature map F(I) is extracted through a convolutional neural network. The posture estimation function P i =f pose (F(I)) maps the feature map to obtain the coordinates of the key points of the human body P i (x i ,y i ).

[0059] It should be noted that, in this embodiment, the convolutional neural network (CNN) processes the image and identifies key features related to the posture; the posture estimation function f pose The OpenPose algorithm is generally used to identify the key points of the human body based on the feature map; the coordinates of the key points of the human body P i (x i ,y i ) can indicate the position of important body joints in the student's posture (such as head, shoulders, knees, etc.).

[0060] Furthermore, in step S300, the calculation formula for the student's posture score is:

[0061]

[0062] Among them, d max is the maximum allowable deviation distance, θ t is the adjustment parameter of the desktop lifting mechanism at time t, and n is the number of key points.

[0063] This formula is used to calculate the gap between the student's current posture and the standard posture, and give a score that reflects the quality of the posture, allowing the system to make adjustments based on this score.

[0064] Furthermore, in step S400, the height adjustment formula of the lifting table assembly is:

[0065] Δh=k h (S target -S)

[0066] Among them, Δh is the height difference of the desktop that needs to be adjusted, k h is the height adjustment factor, S target is the posture score of the target, usually 1, and S is the current posture score, usually a constant between 0 and 1.

[0067] This formula means that the height of the desk is adjusted based on the posture score. If the score is low (poor posture), the system will adjust the height of the desk to help the student improve their posture.

[0068] Furthermore, in step S500, the step of optimizing the adjustment parameters of the desktop lifting mechanism includes:

[0069] Set the student experience rating after use to S sat and target satisfaction score S sat,target ;

[0070] Experience rating is S sat Usually a constant between 0 and 1. Generally speaking, when grading students, the experience will be divided into four levels, namely excellent, good, average and poor. Excellent corresponds to the range of 0.8-1, good corresponds to the range of 0.6-0.8, average corresponds to the range of 0.4-0.6, and poor corresponds to the range of 0-0.2. The above levels can select a constant within this range according to the preset situation.

[0071] Setting the comprehensive loss function in, is the posture score S relative to the target score S target The loss function is Score satisfaction sat The loss function relative to the target satisfaction score, α and γ are weight coefficients;

[0072] The optimization formula for adjusting parameters is:

[0073]

[0074] Among them, η is the learning rate, which is a constant between 0 and 1; It is the partial derivative of the comprehensive loss function with respect to the parameter θ, indicating the impact of parameter changes on the loss.

[0075] This formula is based on the gradient descent method. The system can gradually adjust the parameters of the scoring algorithm according to historical data to make the scoring results more in line with expectations.

[0076] Example 2

[0077] This embodiment provides a computer device, including a processor and a storage medium;

[0078] The storage medium is used to store instructions;

[0079] The processor is used to operate according to the instructions to execute the steps of any method described in Example 1.

[0080] Example 3

[0081] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0082] In summary, the intelligent lifting system, control method, electronic device and storage medium for a laboratory provided by the present invention have the following effects:

[0083] 1. Real-time posture detection and adjustment: The present invention uses image processing and posture recognition technology to monitor students' sitting and standing postures in real time, automatically calculate posture scores, and adjust the height of the desktop according to the posture scores. This method can effectively avoid physical discomfort caused by long-term poor sitting or standing postures, and improve students' health and learning efficiency.

[0084] 2. Personalized adjustment and feedback optimization: By introducing students' subjective satisfaction scores, the present invention can dynamically optimize system parameters according to students' feedback, so that the desktop height adjustment is not only based on standard ergonomic posture, but also combined with individual needs to improve user experience.

[0085] 3. Learning feedback and continuous optimization: Through the learning feedback mechanism, the system of the present invention can gradually improve the accuracy of the scoring algorithm and make personalized adjustments for different students, so as to continuously improve the comfort level in long-term use.

[0086] 4. Double improvement of health and efficiency: This invention can not only help students maintain a healthy sitting and standing posture, reduce physical discomfort caused by bad posture, but also improve concentration and efficiency during learning. In addition, the personalized adjustment and optimization function of the system ensures that each student can get a customized comfort experience.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. An intelligent lifting system for a laboratory, characterized in that: include: A plurality of lifting table assemblies placed at intervals, wherein the lifting seat assembly includes a tabletop lifting mechanism and a tabletop board arranged at the output end of the tabletop lifting mechanism; A number of camera modules for detecting the sitting and standing postures of students in front of each lifting table assembly; A processing module, used to receive and process data from the camera module, and send control instructions to the desktop lifting mechanism according to preset algorithms and parameters; The intelligent lifting system is also used to perform the following steps: S100. Setting the standard key point coordinates of the student's sitting and standing posture in front of the lifting table assembly; S200. Obtain image data of the student's sitting posture in front of the lifting table assembly, and obtain real-time key point coordinates of the student's sitting posture; S300. Compare the real-time key point coordinates with the standard key point coordinates to calculate the student's posture score; S400. According to the posture score, control the desktop lifting mechanism or the rotating mechanism to adjust the height or angle of the lifting table assembly; S500. Record the experience scores of students after use, and optimize the adjustment parameters of the desktop lifting mechanism or the rotating mechanism according to the experience scores.

2. The intelligent lifting system for a laboratory according to claim 1, characterized in that: In step S100, the standard key point coordinates of the student's sitting and standing posture in front of the lifting table assembly are set as P i,standard (x i,standard ,y i,standard ); In step S200, the image data of the student's sitting posture is output as an image matrix I(x, y), and the feature map F(I) is extracted through a convolutional neural network. The posture estimation function P i =f pose (F(I)) maps the feature map to obtain the coordinates of the key points of the human body P i (x i ,y i ).

3. The intelligent lifting system for a laboratory according to claim 2, characterized in that: In step S300, the calculation formula for the student's posture score is: Among them, d max is the maximum allowable deviation distance, θ t is the adjustment parameter of the desktop lifting mechanism at time t, and n is the number of key points.

4. The intelligent lifting system for a laboratory according to claim 3, characterized in that: In step S400, the height adjustment formula of the lifting table assembly is: Δh=k h (S target -S) Among them, Δh is the height difference of the desktop that needs to be adjusted, k h is the height adjustment factor, S target is the posture score of the target, and S is the current posture score.

5. The intelligent lifting system for a laboratory according to claim 4, characterized in that: In step S500, the step of optimizing the adjustment parameters of the desktop lifting mechanism includes: Set the student experience rating after use to S sat and target satisfaction score S sat,target ; Setting the comprehensive loss function in, is the posture score S relative to the target score S target The loss function is Score satisfaction sat The loss function relative to the target satisfaction score, α and β are weight coefficients; The optimization formula for adjusting parameters is: Where η is the learning rate, is the partial derivative of the comprehensive loss function with respect to the parameter θ.

6. A computer device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 5.

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