CN117322727A - Intelligent lifting table control method and system - Google Patents
Intelligent lifting table control method and system Download PDFInfo
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Abstract
The invention provides a control method and a system of an intelligent lifting table, and relates to the technical field of intelligent lifting table control, wherein the method comprises the following steps: acquiring sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height; acquiring the current height of the desktop, comparing the current height with a target height, and calculating the height deviation; generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop; and adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop. The intelligent control of the lifting table is realized, the height adjustment can be completed without manual operation, and the use convenience is greatly improved.
Description
Technical Field
The invention relates to the technical field of intelligent lifting table control, in particular to an intelligent lifting table control method and system.
Background
Conventional lift tables typically have only manual control to adjust the height of the table top, lacking intelligent control. Thus, the main drawbacks at present are:
Whether the desktop is inclined or not cannot be perceived, the desktop cannot be automatically adjusted to a horizontal state, sitting postures and activities of users cannot be detected, the height cannot be intelligently adjusted according to the user state, and the user cannot automatically recognize the intention of the user only by manually controlling lifting.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent lifting table control method and system, which realize intelligent control of a lifting table, can finish height adjustment without manual operation and greatly improve the use convenience.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a method for controlling an intelligent lifting table, the method comprising:
acquiring height data of a plurality of positions of a desktop, judging whether the desktop is inclined, and calculating the flatness parameters of the desktop according to the inclination state;
acquiring sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height;
acquiring the current height of the desktop, comparing the current height with a target height, and calculating the height deviation;
generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop;
And adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop.
Further, acquiring height data of a plurality of positions of the desktop, judging whether the desktop is inclined, and calculating the flatness parameter of the desktop according to the inclination state, including:
dividing a plurality of measuring areas on the surface of the desktop in advance;
a height sensor is arranged on the surface of each area and is used for detecting the surface height information of the area in real time;
collecting the height sensor data of each area in real time through a serial port or a network interface;
processing the collected height data to obtain first processed data;
calculating a height difference value between each two areas according to the first processing data, judging whether the desktop is inclined or not according to the height difference value, and calculating an inclination angle and an inclination direction when the inclination is detected;
and calculating the flatness parameter according to the inclination state.
Further, obtaining sitting posture and activity data of the user, and analyzing the user state includes:
collecting user state original data according to a preset frequency;
preprocessing the collected original data to obtain second processed data;
encoding the second processed data according to the data format requirement to obtain encoded data;
Adding meta information to the encoded data, including a data source identifier, an acquisition time stamp and data verification information, so as to obtain complete formatted data;
encrypting the formatted data by using an encryption algorithm to obtain encrypted data;
decrypting the encrypted data to obtain decrypted data;
sequencing, grouping and fusing the decrypted data according to the data source identification and the time stamp to form integrated data with a unified structure;
and processing the integrated data according to the integrated data to finally generate a user state analysis result.
Further, according to the user state, predicting the user lifting intention to obtain the target height, including:
collecting a user image, analyzing the gesture of the user, and judging whether the user sits or stands;
detecting the pressure distribution of the seat, analyzing the sitting posture of the user, and judging whether the user leans forward or leans sideways;
collecting hand and head motion data of a user, and analyzing the motion data of the user on a desktop;
establishing a user state model according to the user image, the seat pressure distribution, the motion data and the historical data;
predicting the lifting intention of the user according to the user state model, the user state change trend and the history height adjustment record;
And estimating the target height required by the user according to the lifting intention of the user and preset lifting conditions.
Further, obtaining the current height of the desktop, comparing the current height with the target height, and calculating the height deviation, including:
installing encoders at four corners of the lifting table, and detecting the height data of each corner in real time;
collecting real-time height data of each angle through a data line or a wireless communication module of the encoder;
filtering the collected height data of the four corners to obtain filtered data;
calculating the height average value of the four corners to be used as the current height of the desktop;
and comparing the current height with the target height to obtain a height difference, judging the positive and negative of the height difference, if the current height is positive and is lower than the target height, if the current height is negative, the current height is higher than the target height, and calculating the absolute value of the height difference as a height deviation parameter.
Further, generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor to adjust the current height of the desktop, and the method comprises the following steps:
according to f=w 1 (t)×h+w 2 (t)×v+w 4 (t) x s generating a control strategy and sending a control instruction to the motor according to the generated control strategy to adjust the current height of the desktop, wherein w 1 (t)＝k 1 ×|h(t)|，w 2 (t)＝k 2 ×|w 3 (t)|＝k 3 ×|v(t)|×w 4 (t)＝k 4 ×s(t)；w 1 (t)、w 2 (t)、w 3 (t) and w 4 (t) is a dynamic weight adjusted in real time according to the state at time t, where k 1 、k 2 、k 3 And k 4 Is a normalization coefficient; i h (t) I, I (t) I and s (t) are respectively height deviation and flatnessThe real-time absolute values of the degree and the user state are that h is the height deviation, v is the flatness parameter, s is the user state, and f is the comprehensive evaluation function.
Further, according to the current height and the target height of the desktop, adjusting the control parameters to fine tune the desktop, including:
detecting real-time height data of four corners of a desktop;
calculating the average value of the heights of four corners as the current height, comparing the current height with the target height, and entering a fine adjustment mode if the deviation is within a threshold range;
according to the height deviation value, the rotating speed of the motor is adjusted;
and calculating the time of forward rotation or reverse rotation of the motor, sending a control instruction to the motor, and adjusting the height of the desktop until reaching the target height.
In a second aspect, an intelligent lift table control system includes:
the acquisition module is used for acquiring height data of a plurality of positions of the desktop, judging whether the desktop is inclined, and calculating the flatness parameters of the desktop according to the inclination state; acquiring sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height; acquiring the current height of the desktop, comparing the current height with a target height, and calculating the height deviation;
The processing module is used for generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop; and adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop.
In a third aspect, a computing device includes:
one or more processors;
and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the above-described methods.
In a fourth aspect, a computer readable storage medium stores a program that when executed by a processor implements the above method.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, the intelligent control of the lifting table is realized by acquiring the multisource information such as the height of the table top and the user state and combining the control algorithm, the height adjustment can be completed without manual operation, the use convenience is greatly improved, and the flatness of the table top can be ensured by adopting flatness detection and closed-loop fine adjustment control.
Drawings
Fig. 1 is a flow chart of a control method of an intelligent lifting table according to an embodiment of the invention.
Fig. 2 is a schematic diagram of an intelligent lifting table control system according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described more closely below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention proposes a control method for an intelligent lifting table, where the method includes:
step 11, acquiring height data of a plurality of positions of the desktop, judging whether the desktop is inclined, and calculating the flatness parameters of the desktop according to the inclination state;
step 12, obtaining sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height;
step 13, obtaining the current height of the desktop, comparing the current height with the target height, and calculating the height deviation;
Step 14, generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop;
and 15, adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop.
In the embodiment of the invention, whether the desktop is inclined or not can be detected, and the flatness parameter can be calculated, so that the desktop is controlled and adjusted to be in a horizontal state later, inconvenience to a user caused by the inclination of the desktop is avoided, the state and the intention of the user can be analyzed intelligently, and the height of the desktop can be adjusted actively to adapt to different users and different scenes without manual control of the users; calculating the height deviation, intelligently generating a control strategy according to the deviation, realizing closed-loop feedback of height control, and ensuring that the desktop is adjusted to the height intended by the user; the fine adjustment mechanism can improve the accuracy of the height control, so that the desktop can be adjusted to the most comfortable height of the user; the invention realizes intelligent sensing and closed-loop control on desktop state, user state and height control, so that the lifting table is more intelligent, convenient and friendly to users, and better user experience is provided. Compared with the traditional manual control lifting table, the intelligent lifting control is realized by the method, the operation burden of a user is reduced, and the use convenience is improved.
In a preferred embodiment of the present invention, the step 11 may include:
step 111, dividing a plurality of measurement areas on the surface of the desktop in advance;
step 112, installing a height sensor on the surface of each area, and detecting the surface height information of the area in real time;
step 113, collecting the height sensor data of each area in real time through a serial port or a network interface;
step 114, processing the collected height data to obtain first processed data;
step 115, calculating a height difference value between each two areas according to the first processing data, judging whether the desktop is inclined according to the height difference value, and calculating an inclination angle and direction when the inclination is detected;
in step 116, a flatness parameter is calculated based on the tilt status.
In the embodiment of the present invention, in step 111, a plurality of measurement areas are divided on the desktop, so that height detection on different positions of the desktop can be implemented, so as to determine whether the whole desktop is inclined. In step 112, a height sensor is installed in each region, and height data of each region can be acquired in real time. In step 113, height data of each area is collected, and step 114 performs data processing, so that accurate and reliable height information can be obtained. In step 115, by calculating the height difference between the regions, it can be determined whether the table top is inclined, and the inclination angle and direction can be calculated. In step 116, flatness parameters are calculated to provide a basis for subsequent control to adjust the table top to a level state. Through detecting and processing the heights of a plurality of positions of the desktop, whether the whole desktop is inclined or not and the inclined state can be accurately judged, so that the function of automatically adjusting the desktop to the horizontal state is realized. Therefore, the invention can more accurately judge the inclination condition of the desktop, improve the flatness control effect and enhance the user experience.
In another preferred embodiment of the present invention, in step 111, the step of Dividing a plurality of measuring areas in advance on the surface of the desktop, wherein k is 1 ，k 2 ，k 3 ，k 4 Is a coefficient, where k 1 ＝acos(ωt+φ1)k 2 =bsin (ωt+Φ2); m and n are the number of rows and columns of the measurement area respectively; l (L) 1 And w 1 The length and the width of the tabletop respectively; l (L) 2 And w 2 The length and the width of each measuring area are respectively, ω is angular frequency, and t is time; phi 1 and phi 2 are both primary phases; a and b are both amplitude coefficients; />Is an upward rounding operation; the division of the measurement area can be more flexibly adaptive, k 1 ，k 2 ，k 3 ，k 4 As a time-varying coefficientIntroducing randomness and avoiding occurrence of regular dead zones.
In step 112, n is installed in each region s The distance between every two adjacent sensors is d=d 0 ++ epsilon sin (ωt) with an installation angle of a=a 0 +δcos(ωt+φ a ) Offset angle θ=a 1 sin(ωt+φ θ ) D is the distance between adjacent sensors, a is the installation angle, and θ is the offset angle; d, d 0 An initial installation pitch for the sensor; epsilon is the amplitude of the space disturbance quantity; d is the actual distance between two adjacent sensors; a, a 0 An initial installation angle for the sensor; delta is the amplitude of the disturbance quantity of the installation angle; phi (phi) a Initial phase of the installation angle disturbance; a is the actual installation angle of the sensor; a is that 1 Amplitude of the offset angle disturbance quantity; phi (phi) θ Is the initial phase of the offset angle disturbance; θ is the offset angle of the sensor; the sensor arrangement introduces randomness through the disturbance quantity, so that the comprehensiveness of detection can be increased, and the generation of detection blind areas is avoided.
In step 113, two kinds of interface networks including RS485 and WiFi are adopted to synchronously collect data, and the time synchronization precision is controlled to be in millisecond level, wherein the time synchronization precision reaches deltat<10 -5 Second, dual network acquisition improves the reliability of the data. In step 114, byProcessing the acquired height data to obtain first processed data, wherein db8 is a wavelet base function psi which selects Daubechies8 wavelets; 3 is the wavelet decomposition layer number L, and 3 layers are taken; a is a state transition matrix, wherein +.>B is an input control matrix,>h is the observation matrix, H= [1 0 ]]The method comprises the steps of carrying out a first treatment on the surface of the Q is the process noise covariance matrix q= qI; r is the observed noise covariance matrix, r=ri, where I is the identity matrix, q and R are the variances of the process noise and the observed noiseThe method comprises the steps of carrying out a first treatment on the surface of the x (t) is the original height signal; x is x 1 (t) is a wavelet denoised signal; />WD () is a wavelet denoising function; KF () is a Kalman filter function; Δt is the time interval between two adjacent sampling moments, and the accuracy of signal processing is improved by multistage filtering and reconstruction.
In step 115, according to ΔH i，j ＝H i，j -H i+u，j+v Calculating the height difference of adjacent areas, wherein u and v are row-column offset of the adjacent areas, the value range is-N.ltoreq.u, v.ltoreq.N, N is the maximum offset, H i，j For the height of the i-th row and j-th column region, the height difference of a plurality of adjacent regions in different directions can be calculated; according toScoring the inclination, wherein σ ΔH Is the standard deviation of the height difference, ε is a slightly positive value, and according to +.>Calculating a tilt angle, wherein d i,j For adjacent zone distance, sigma θ Is the standard deviation of angle noise, T i,j The tilt score representing the i-th row, j, column region can effectively reflect the extent of region tilt.
In step 116, according toCalculating the height mean +.>According toCalculating the height variance of the region; according to->Computing the entire desktopFlatness parameter P, where m, n represents that the table top is divided into m rows and n columns of measurement areas S ij Is the height variance of the ith row and j column regions, H ijk The height value of the kth data point in the area is represented, and the overall flatness of the tabletop can be comprehensively estimated through statistical analysis of a plurality of areas; in whole, these steps combine together for the detection to desktop state is more accurate, reliable, and control strategy is also more intelligent and accurate, thereby can effectively promote desktop leveling's effect and use experience.
In a preferred embodiment of the present invention, the step 12 may include:
step 121, collecting user state original data according to a preset frequency;
step 122, preprocessing the collected original data to obtain second processed data;
step 123, coding the second processing data according to the data format requirement to obtain coded data;
step 124, adding meta information to the encoded data, including data source identification, acquisition time stamp, and data verification information, to obtain complete formatted data;
step 125, encrypting the formatted data using an encryption algorithm to obtain encrypted data;
step 126, decrypting the encrypted data to obtain decrypted data;
step 127, sorting, grouping and fusing the decrypted data according to the data source identifier and the timestamp to form integrated data with a unified structure;
and step 128, processing the integrated data according to the integrated data, and finally generating a user state analysis result.
In the embodiment of the present invention, in step 121, the user state raw data is collected periodically, and the user information may be continuously acquired. In step 122, the data is preprocessed; in step 123, the data is encoded, and normalization of the data format and structure may be performed; in step 124, meta information is added, and the data can be marked to identify the source of the data and the time information; in step 126, the data is decrypted, and the security of data transmission and storage can be ensured. Step 127 sorts, groups and fuses the data, and may integrate user data of different sources and times to form complete user information. Through collection, transmission, storage, integration and analysis processing of user data, accurate and reliable user state information can be continuously obtained, so that accurate judgment of user intention and requirement is realized. The encryption and decryption mechanism ensures that the user privacy is not revealed, and the invention realizes intelligent perception and analysis of the user state.
In another preferred embodiment of the present invention, the step 124 specifically includes: collecting multiple private information such as the name, the identity card number, the mobile phone number, the mailbox, the bank card number and the like of the user, inputting the multiple private information into an SHA3-512 hash algorithm, selecting a Kecak-1600 replacement function, and generating a unique user identification code through 512-bit hash operation; the PPS and 10MHz clock signals connected with a local cesium atomic clock are connected with a counter through an RS-232 serial port to obtain a clock count value; and reading 1024-bit data message, inputting the data message into a CRC-128 algorithm, selecting a Kopman polynomial, calculating 128-bit cyclic redundancy check codes, and inserting the check codes into the tail of the message. The step 125 specifically includes: invoking a key generation function of an ECC-521 algorithm, configuring NIST P-521 curve parameters, and generating 521-bit elliptic curve public keys and private keys; inputting the original data and the encryption public key into an XTS mode encryption function of an AES-512 algorithm, and outputting an encryption ciphertext with a length of 512 bits by using 14 rounds of rotation operation; inputting the encrypted data and the signature private key into an SM2 signature function, configuring SM2 recommendation curve parameters, and generating an SM2 digital signature. The step 126 specifically includes: inputting the encrypted ciphertext and the decryption private key into an XTS mode decryption function of an AES-512 algorithm, and outputting a decrypted original data message by using inverse rotation operation; inputting the digital signature, the decrypted data and the verification public key into an SM2 signature verification function, configuring an SM2 recommendation curve, and verifying signature validity.
In a preferred embodiment of the present invention, the step 12 may further include:
step 129, collecting user images, analyzing the user gestures, and judging whether the user is sitting or standing; detecting the pressure distribution of the seat, analyzing the sitting posture of the user, and judging whether the user leans forward or leans sideways; collecting hand and head motion data of a user, and analyzing the motion data of the user on a desktop; establishing a user state model according to the user image, the seat pressure distribution, the motion data and the historical data; predicting the lifting intention of the user according to the user state model, the user state change trend and the history height adjustment record; and estimating the target height required by the user according to the lifting intention of the user and preset lifting conditions.
In the embodiment of the invention, the multi-source data such as the user image, the seat pressure, the movement data and the like are collected, so that the information such as the gesture, the sitting posture and the movement of the user can be comprehensively judged; the user state model is established, and intelligent prediction of the user intention can be realized by combining the user state change trend and the historical data; target height is estimated according to the lifting intention of the user and preset conditions, so that active response to the user demand is realized; the fusion analysis of the multi-source data realizes the accurate judgment of the user state; through learning and prediction of the user state model, the lifting intention of the user can be actively inferred, and manual control of the user is not needed; the target height is estimated, and the desktop height can be quickly adjusted to meet the requirements of different users and scenes; the intelligent, active and quick response to the lifting intention of the user is realized on the whole, and the use experience is greatly improved. The intelligent lifting table and the intelligent response method realize the active satisfaction and intelligent response of the intelligent lifting table to the user demand.
In another preferred embodiment of the present invention, the step 129 may include:
step 1291, acquiring a front upper body image I of a user at a speed of 30 frames per second by using an RGB camera with 1080P resolution;
step 1292, inputting an image I according to KP=PoseNet (I; W) 1 ，b 1 ，W 2 ，b 2 ) Detecting key points of a human body, wherein KP is a detected key point set and comprises (x, y) coordinates of each key point; w (W) 1 ，b 1 Is a convolutional layer parameter; w (W) 2 ，b 2 Is a full connection layer parameter; according to Defining a sitting posture judging function, wherein w is a weight vector, b is a bias, and ++>Is a feature extraction function, such as a coordinate average; according toAnd finally judging the user gesture, wherein T is a classification threshold value, if Pose=1, judging the user gesture as a sitting gesture, and by designing a sitting gesture judging function and combining key point coordinate distribution characteristics, judging the current gesture of the user, acquiring a clear user image and providing high-quality input for a key point detection algorithm.
Step 1293, according toConstructing a pressure sensing matrix, wherein +_>Gaussian noise, which is a pressure reading; according to->Constructing a pressure thermodynamic diagram, wherein W 4 ，b 4 Is a thermodynamic diagram network parameter; according to y=softmax (W 5 ×ReLU(W 6 ×H+b 6 )+b 5 ) Classifying sitting postures, wherein W 5 ，W 6 ，b 5 ，b 6 Is a convolutional network parameter; p (P) i,j And p i,j Representing raw pressure sensor readings; p represents a matrix of all pressure data; y represents the sitting posture classification result finally obtained by utilizing pressure data prediction, human body key points can be automatically and efficiently detected, accurate judgment on the user posture can be realized by designing a sitting posture judgment function, and the user is obtained by using a pressure sensing matrixThe sitting posture pressure data and the convolutional neural network model are designed to classify the sitting postures, so that whether the user is normal sitting postures or forward leaning/leaning can be judged, the fusion of the images and the pressure data can be realized, the postures and the sitting postures of the user can be judged more comprehensively and accurately, the device has strong characteristic learning and modeling capability, can adapt to complex conditions, can realize more intelligent, accurate and efficient user state analysis, and can effectively fuse multi-source heterogeneous data, thereby improving the accuracy and reliability of user state judgment.
1394, installing a 3-axis acceleration sensor and a gyroscope on a desktop, and collecting 3-axis speed and angular speed data of hands and heads of a user; using convolution LSTM network to input user image, pressure map and motion data sequence, and outputting user current state through training; based on a user state time sequence, adopting an RNN model of an attention mechanism to conduct classification prediction of lifting intention; and outputting a matched target height value according to the intention category by combining a preset height adjustment range, speed and comfort level condition.
In a preferred embodiment of the present invention, the step 13 may include:
step 131, installing encoders at four corners of the lifting table, and detecting the height data of each corner in real time;
step 132, collecting real-time height data of each angle through a data line or a wireless communication module of the encoder;
step 133, filtering the collected height data of the four corners to obtain filtered data;
step 134, calculating the height average value of the four corners as the current height of the desktop;
and 135, comparing the current height with the target height to obtain a height difference, judging the positive and negative of the height difference, if the current height is positive and is lower than the target height, if the current height is negative, the current height is higher than the target height, and calculating the absolute value of the height difference as a height deviation parameter.
In the embodiment of the invention, the upper left corner, the upper right corner, the lower left corner and the lower right corner of the lifting table are respectively provided with a linear encoder with the precision of 0.1mm, the linear encoders are used for detecting the height data of each corner in real time, and the encoders are arranged at the four corners to obtain the height data of a plurality of positions of the table top, so that the current height measurement is more accurate; the encoder data are collected through data lines or wireless communication, so that the real-time performance of the data is ensured; the noise can be removed by filtering the collected data, and more accurate height data can be obtained; the average value of the heights of the four corners is calculated, so that the influence of individual data errors can be eliminated, and the accuracy of the calculation of the current height is improved; comparing the current height with the target height, specific height deviation can be calculated, and a basis is provided for subsequent control; judging the positive and negative of the height deviation, and determining whether the height deviation needs to be increased or decreased; calculating the absolute value of the height deviation, and obtaining the specific height quantity to be adjusted; the multipoint acquisition and processing ensures that the current height is more reliable, and is beneficial to the stable control of the system. The invention can obtain more accurate current height, and provides support for the generation of subsequent control strategies and fine adjustment of the height, thereby realizing high-precision desktop height control.
In a preferred embodiment of the present invention, the step 14 may include:
step 141, according to f=w 1 (t)×h+w 2 (t)×v+w 4 (t) x s generating a control strategy and sending a control instruction to the motor according to the generated control strategy to adjust the current height of the desktop, wherein w 1 (t)＝k 1 ×|h(t)|，w 2 (t)＝k 2 ×|w 3 (t)|＝k 3 ×|v(t)|×w 4 (t)＝k 4 ×s(t)；w 1 (t)、w 2 (t)、w 3 (t) and w 4 (t) is a dynamic weight adjusted in real time according to the state at time t, where k 1 、k 2 、k 3 And k 4 Is a normalization coefficient; the I h (t), the I (t) and the S (t) are real-time absolute values of the height deviation, the flatness and the user state respectively, h is the height deviation, v is the flatness parameter, s is the user state and f is the comprehensive evaluation function.
In the embodiment of the invention, the control strategy is generated through the comprehensive evaluation function f, so that a plurality of factors can be fused, and an intelligent control decision can be realized; the evaluation function comprehensively considers the height deviation, the flatness parameter and the user state, so that the control strategy is more comprehensive and intelligent; by a method of dynamically adjusting weights, different factors can be adaptively optimized according to the real-time state, so that the control is more flexible; the weight adopts absolute value, which can reflect the real-time influence degree of each factor and is not influenced by the positive and negative; the normalization coefficient is used to avoid that the decision is influenced by the excessive weight of a certain factor; the evaluation function is simple in form, high in calculation efficiency and easy to adjust in real time; the evaluation function of the multi-factor fusion generates a control strategy, so that the control strategy is more intelligent and comprehensive than a single control mode; the weight is dynamically adjusted to realize self-adaptive control, so that the lifting process meets the actual requirements; the intelligent lifting control system can intelligently generate a control strategy according to various state information on the whole, and achieves accurate, intelligent and stable lifting control.
In a preferred embodiment of the present invention, the step 15 may include:
step 151, detecting real-time height data of four corners of the desktop;
step 152, calculating the average value of the heights of four corners as the current height, comparing the current height with the target height, and entering a fine adjustment mode if the deviation is within a threshold range;
step 153, adjusting the rotating speed of the motor according to the height deviation value;
and 154, calculating the time of forward rotation or reverse rotation of the motor, sending a control instruction to the motor, and adjusting the height of the desktop until the height reaches the target height.
In the embodiment of the invention, the real-time height data of four corners is detected, the average height is calculated, the accurate current height can be obtained, and the accurate current height is compared with the target height to judge whether fine adjustment is needed; setting a deviation threshold range, and entering a fine tuning mode only when the deviation is within the range, so as to avoid unnecessary frequent fine tuning; the rotating speed of the motor is adjusted according to the specific deviation value, so that the fine height control is realized; calculating the forward rotation and reverse rotation time of the motor, accurately controlling the motor to move, and adjusting the height to a target value; the accuracy of the current height calculation is improved by multi-point data acquisition and processing; setting the fine tuning threshold can effectively avoid unnecessary jitter adjustment; the rotating speed control and the accurate calculation of the motor movement time realize the fine adjustment control with high precision; the invention can realize accurate control of the height, adjust the desktop to the most comfortable height of the user, and greatly improve the use experience. The intelligent lifting table realizes the accurate height self-adaptive adjustment of the intelligent lifting table.
In another preferred embodiment of the present invention, the step 151 may include: the four corners of the lifting table are respectively provided with an AB20-40 absolute type linear encoder, and the precision is 0.01mm; the encoder is connected with the STM32 singlechip through an RS485 serial port interface; four serial controllers are arranged in the STM32 and are respectively connected with four encoders; the serial port controller is configured to work at 115200 baud rate, 8-bit data bits, 1-bit stop bit and no check bit; writing a serial port receiving program, and analyzing byte data sent by an encoder in an interrupt service function; configuring the size of a serial port receiving buffer area to be 256 bytes, and discarding new data when the receiving buffer area overflows; analyzing the data frame format of the encoder, extracting height data, and storing the height data into variables; setting the timing period of the serial port receiving timer to be 10ms, namely the sampling frequency of 100 Hz; reading and analyzing the data of each encoder in the timer timeout interrupt service function, and updating the height variable; the data of the four encoders are synchronously acquired in real time through the configuration.
The step 152 may include: sequentially reading the currently detected height values h1, h2, h3 and h4 from the four encoders; calculating the arithmetic average value of the heights of four corners: curr_height= (h1+h2+h3+h4)/4; reading a target height value target_height from a controller storage area; calculating a difference diff=curr_height-target_height between the current height and the target height; and judging whether the absolute value of the height difference value is smaller than a fine tuning threshold range. The step 153 may include: according to Calculating the current height of the desktop +.>Wherein h is i Encoder readings representing the i-th angle, i=1, 2,3,4; according to the current height of the desktop>By->Calculating a height deviation Δh, wherein h target Is the target height; according to-> Calculating the rotational speed rpm, wherein K p ，K i ，K d Is a PID control parameter; according to->Setting the motor rotation speed and rpm motor The target rotating speed of the motor is d is the height deviation, t is the control time, and tau is the integral time variable; input motor target rpm motor This value is output from the PID controller, and the PWM duty cycle is calculated from the rotation speed value, duty cycle=k×rpm motor +b, wherein k and b are a scaling factor and a bias, respectively, limiting the duty cycle range between 20% and 80%; generating PWM waves according to the duty cycle, if, rpm motor >0，pwm＝generate p wm(duty c ycle,freq)else:pwn＝generate p wm(100-duty c ycle, freq), wherein freq is PWM frequency, such as 10kHz; sending the PWM wave to an ENABLE and INPUT pin of a motor drive chip, such as an L298 chip; the motor driving chip decodes the PWM signal, outputs a voltage with a corresponding magnitude to the motor, the rotating speed of the motor is in direct proportion to the PWM duty ratio, and precisely controls the motor to reach the target rotating speed rpm by changing the PWM wave duty ratio motor 。
In the embodiment of the invention, the accuracy of current height detection can be improved by calculating the average height through multipoint acquisition, unnecessary shaking can be prevented by setting the fine tuning threshold value, the motor rotating speed can be regulated in real time according to the height deviation by utilizing a PID algorithm, the accurate control is realized, the PID control mode can carry out closed loop feedback control, the height is enabled to be quickly stabilized at the target value, the accurate rotating speed regulation can be realized by adopting PWM to control the motor rotating speed, thereby fine tuning the height, the multipoint acquisition improves the detection precision, the PID calculation improves the control precision, and the PWM realizes the accurate driving. Overall, the design realizes high-precision desktop height fine adjustment control, and greatly improves system performance.
As shown in fig. 2, an embodiment of the present invention further provides an intelligent lift table control system 20, including:
an acquisition module 21, configured to acquire height data of a plurality of positions of the desktop, determine whether the desktop is tilted, and calculate a flatness parameter of the desktop according to a tilting state; acquiring sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height; acquiring the current height of the desktop, comparing the current height with a target height, and calculating the height deviation;
a processing module 22, configured to generate a control policy according to the height deviation, the flatness parameter, the dynamic threshold value, and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop; and adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop.
Optionally, acquiring height data of a plurality of positions of the desktop, judging whether the desktop is inclined, and calculating the flatness parameter of the desktop according to the inclination state, including:
dividing a plurality of measuring areas on the surface of the desktop in advance;
a height sensor is arranged on the surface of each area and is used for detecting the surface height information of the area in real time;
Collecting the height sensor data of each area in real time through a serial port or a network interface;
processing the collected height data to obtain first processed data;
calculating a height difference value between each two areas according to the first processing data, judging whether the desktop is inclined or not according to the height difference value, and calculating an inclination angle and an inclination direction when the inclination is detected;
and calculating the flatness parameter according to the inclination state.
Optionally, obtaining sitting posture and activity data of the user, and analyzing the user state includes:
collecting user state original data according to a preset frequency;
preprocessing the collected original data to obtain second processed data;
encoding the second processed data according to the data format requirement to obtain encoded data;
adding meta information to the encoded data, including a data source identifier, an acquisition time stamp and data verification information, so as to obtain complete formatted data;
encrypting the formatted data by using an encryption algorithm to obtain encrypted data;
decrypting the encrypted data to obtain decrypted data;
sequencing, grouping and fusing the decrypted data according to the data source identification and the time stamp to form integrated data with a unified structure;
And processing the integrated data according to the integrated data to finally generate a user state analysis result.
Optionally, predicting the lifting intention of the user according to the user state to obtain the target height, including:
collecting a user image, analyzing the gesture of the user, and judging whether the user sits or stands;
detecting the pressure distribution of the seat, analyzing the sitting posture of the user, and judging whether the user leans forward or leans sideways;
collecting hand and head motion data of a user, and analyzing the motion data of the user on a desktop;
establishing a user state model according to the user image, the seat pressure distribution, the motion data and the historical data;
predicting the lifting intention of the user according to the user state model, the user state change trend and the history height adjustment record;
and estimating the target height required by the user according to the lifting intention of the user and preset lifting conditions.
Optionally, obtaining the current height of the desktop, comparing the current height with the target height, and calculating the height deviation includes:
installing encoders at four corners of the lifting table, and detecting the height data of each corner in real time;
collecting real-time height data of each angle through a data line or a wireless communication module of the encoder;
Filtering the collected height data of the four corners to obtain filtered data;
calculating the height average value of the four corners to be used as the current height of the desktop;
and comparing the current height with the target height to obtain a height difference, judging the positive and negative of the height difference, if the current height is positive and is lower than the target height, if the current height is negative, the current height is higher than the target height, and calculating the absolute value of the height difference as a height deviation parameter.
Optionally, generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor to adjust the current height of the desktop, and the method comprises the following steps:
according to f=w 1 (t)×h+w 2 (t)×v+w 4 (t) x s generating a control strategy and sending a control instruction to the motor according to the generated control strategy to adjust the current height of the desktop, wherein w 1 (t)＝k 1 ×|h(t)|，w 2 (t)＝k 2 ×|w 3 (t)|＝k 3 ×|v(t)|×w 4 (t)＝k 4 ×s(t)；w 1 (t)、w 2 (t)、w 3 (t) and w 4 (t) is a dynamic weight adjusted in real time according to the state at time t, where k 1 、k 2 、k 3 And k 4 Is a normalization coefficient; the I h (t), the I (t) and the S (t) are real-time absolute values of the height deviation, the flatness and the user state respectively, h is the height deviation, v is the flatness parameter, s is the user state and f is the comprehensive evaluation function.
Optionally, adjusting the control parameter according to the current height and the target height of the desktop to fine tune the desktop, including:
detecting real-time height data of four corners of a desktop;
calculating the average value of the heights of four corners as the current height, comparing the current height with the target height, and entering a fine adjustment mode if the deviation is within a threshold range;
according to the height deviation value, the rotating speed of the motor is adjusted;
and calculating the time of forward rotation or reverse rotation of the motor, sending a control instruction to the motor, and adjusting the height of the desktop until reaching the target height.
It should be noted that the apparatus is an apparatus corresponding to the above method, and all implementation manners in the above method embodiment are applicable to this embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (10)
1. The intelligent lifting table control method is characterized by comprising the following steps of:
acquiring height data of a plurality of positions of a desktop, judging whether the desktop is inclined, and calculating the flatness parameters of the desktop according to the inclination state;
acquiring sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height;
acquiring the current height of the desktop, comparing the current height with a target height, and calculating the height deviation;
generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop;
and adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop.
2. The intelligent lifting table control method according to claim 1, wherein acquiring the height data of a plurality of positions of the table top, judging whether the table top is inclined, and calculating the flatness parameter of the table top according to the inclination state, comprises:
Dividing a plurality of measuring areas on the surface of the desktop in advance;
a height sensor is arranged on the surface of each area and is used for detecting the surface height information of the area in real time;
collecting the height sensor data of each area in real time through a serial port or a network interface;
processing the collected height data to obtain first processed data;
calculating a height difference value between each two areas according to the first processing data, judging whether the desktop is inclined or not according to the height difference value, and calculating an inclination angle and an inclination direction when the inclination is detected;
and calculating the flatness parameter according to the inclination state.
3. The intelligent lift table control method of claim 2, wherein acquiring user sitting and activity data, analyzing user status, comprises:
collecting user state original data according to a preset frequency;
preprocessing the collected original data to obtain second processed data;
encoding the second processed data according to the data format requirement to obtain encoded data;
adding meta information to the encoded data, including a data source identifier, an acquisition time stamp and data verification information, so as to obtain complete formatted data;
encrypting the formatted data by using an encryption algorithm to obtain encrypted data;
Decrypting the encrypted data to obtain decrypted data;
sequencing, grouping and fusing the decrypted data according to the data source identification and the time stamp to form integrated data with a unified structure;
and processing the integrated data according to the integrated data to finally generate a user state analysis result.
4. The intelligent elevating table control method as set forth in claim 3, wherein predicting the elevating intention of the user based on the user state to obtain the target height comprises:
collecting a user image, analyzing the gesture of the user, and judging whether the user sits or stands;
detecting the pressure distribution of the seat, analyzing the sitting posture of the user, and judging whether the user leans forward or leans sideways;
collecting hand and head motion data of a user, and analyzing the motion data of the user on a desktop;
establishing a user state model according to the user image, the seat pressure distribution, the motion data and the historical data;
predicting the lifting intention of the user according to the user state model, the user state change trend and the history height adjustment record;
and estimating the target height required by the user according to the lifting intention of the user and preset lifting conditions.
5. The intelligent lift table control method of claim 4, wherein obtaining a current height of the table top, comparing the current height with a target height, and calculating a height deviation comprises:
Installing encoders at four corners of the lifting table, and detecting the height data of each corner in real time;
collecting real-time height data of each angle through a data line or a wireless communication module of the encoder;
filtering the collected height data of the four corners to obtain filtered data;
calculating the height average value of the four corners to be used as the current height of the desktop;
and comparing the current height with the target height to obtain a height difference, judging the positive and negative of the height difference, if the current height is positive and is lower than the target height, if the current height is negative, the current height is higher than the target height, and calculating the absolute value of the height difference as a height deviation parameter.
6. The intelligent lift table control method of claim 5, wherein a control strategy is generated based on the height deviation, the flatness parameter, the dynamic threshold, and the user status; according to the control strategy, a control instruction is sent to the motor to adjust the current height of the desktop, and the method comprises the following steps:
according to f=w 1 (t)×h+w 2 (t)×v+w 4 (t) x s generating a control strategy and sending a control instruction to the motor according to the generated control strategy to adjust the current height of the desktop, wherein w 1 (t)＝k 1 ×|h(t)|，w 2 (t)＝k 2 ×|w 3 (t)|＝k 3 ×|v(t)|×w 4 (t)＝k 4 ×s(t)；w 1 (t)、w 2 (t)、w 3 (t) and w 4 (t) is a dynamic weight adjusted in real time according to the state at time t, where k 1 、k 2 、k 3 And k 4 Is a normalization coefficient; the I h (t), the I (t) and the S (t) are real-time absolute values of the height deviation, the flatness and the user state respectively, h is the height deviation, v is the flatness parameter, s is the user state and f is the comprehensive evaluation function.
7. The intelligent lift table control method of claim 6, wherein adjusting control parameters to fine tune the table top based on the current height and the target height of the table top comprises:
detecting real-time height data of four corners of a desktop;
calculating the average value of the heights of four corners as the current height, comparing the current height with the target height, and entering a fine adjustment mode if the deviation is within a threshold range;
according to the height deviation value, the rotating speed of the motor is adjusted;
and calculating the time of forward rotation or reverse rotation of the motor, sending a control instruction to the motor, and adjusting the height of the desktop until reaching the target height.
8. An intelligent lift table control system, characterized by comprising:
the acquisition module is used for acquiring height data of a plurality of positions of the desktop, judging whether the desktop is inclined, and calculating the flatness parameters of the desktop according to the inclination state; acquiring sitting posture and activity data of a user, analyzing the user state, and predicting the lifting intention of the user according to the user state to obtain the target height; acquiring the current height of the desktop, comparing the current height with a target height, and calculating the height deviation;
The processing module is used for generating a control strategy according to the height deviation, the flatness parameter, the dynamic threshold value and the user state; according to the control strategy, a control instruction is sent to the motor so as to adjust the current height of the desktop; and adjusting control parameters according to the current height and the target height of the desktop so as to finely adjust the desktop.
9. A computing device, comprising:
one or more processors;
storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1-7.
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