Wearable breast pump detection method, system, device and storage medium

Abstract

The present invention discloses a detection method, system, device and storage medium of a wearable breast pump, the method comprising acquiring model data, lactation data and posture image; calculating the target position and target angle of the breast pump based on the model data and posture image; calculating the position of the breast pump through the characteristic points of the breast model and the marks on the breast pump; calculating the deviation between the position of the breast pump and the target position and then judging to generate a position adjustment signal; analyzing the regularity of lactation status data to obtain the predicted lactation amount; obtaining the target sucking frequency and the target suction value according to the predicted lactation amount mapping; inputting the position adjustment signal, the sucking frequency and the suction value into a control algorithm to generate a first instruction; judging whether the posture image exceeds the preset posture range; if so, calculating the deviation between the current angle and the target angle to generate a second instruction; adjusting the breast pump according to the first instruction and the second instruction. The method can realize the intelligence and high efficiency of the breast pump.

Description

Technical Field

[0001] The present invention relates to the field of medical device control technology, and in particular to a detection method, system, device and storage medium for a wearable breast pump. Background Art

[0002] When using a wearable breast pump, how to accurately sense the position and angle of the breast pump is a key technical issue. Due to the different shapes and sizes of human breasts, the degree of fit between the breast pump and the breast directly affects the efficiency and comfort of milk extraction. If the position of the breast pump deviates or the angle is inappropriate, it will lead to uneven suction, causing breast pain or damage. At the same time, the breast pump will shift during use, especially when the mother is active or changes posture. How to monitor the position change of the breast pump in real time and adjust it in time is also a major challenge. In addition, the suction and frequency control of the breast pump are also crucial. Different mothers have different milk secretion and flow rate at different stages of lactation. If the suction is too strong or the frequency is too fast, it will cause milk duct obstruction or breast pain; conversely, if the suction is too weak or the frequency is too slow, it will affect the efficiency of milk extraction and prolong the milk extraction time. Therefore, how to achieve intelligent adjustment of the suction and frequency of the breast pump according to the individual situation and lactation status of the mother is another technical problem that needs to be solved urgently. During the milk extraction process, the working noise of the breast pump should not be ignored. Excessive noise not only affects the mother's psychological comfort, but also disturbs people around her, especially when used at night or in public places. However, reducing noise will sacrifice the efficiency of the breast pump. How to minimize the noise level while ensuring the milk pumping performance and achieve a quiet and comfortable milk pumping environment is also a technical topic worth exploring.

[0003] In one prior art, a specific implementation includes: using fixed suction force and frequency settings, the user needs to manually adjust the position and angle of the breast pump when using it to ensure the fit between the breast pump and the breast. The breast pump generates negative pressure through a motor to simulate the sucking action of a baby to help the mother discharge milk.

[0004] However, in the prior art, since the breast pump cannot be monitored in real time and cannot be adjusted automatically, the user needs to frequently make manual adjustments, which causes inconvenience in use and low efficiency. Summary of the invention

[0005] The present invention provides a detection method, system, device and storage medium for a wearable breast pump to solve the problem in the prior art that the breast pump cannot be monitored in real time and cannot be adjusted automatically, and the user needs to make frequent manual adjustments, which causes inconvenience and low efficiency.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a detection method for a wearable breast pump, comprising: acquiring breast model data, lactation status data and a real-time posture image; calculating a target position and a target angle of the breast pump based on the breast model data and the real-time posture image; accurately calculating the position of the breast pump through the characteristic points of the breast model and the marks on the breast pump; calculating the deviation between the position of the breast pump and the target position, and generating a position adjustment signal if the deviation exceeds a preset range; performing regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; performing parameter mapping according to the predicted lactation amount to obtain a target milk suction frequency and a target suction value; inputting the position adjustment signal, the target milk suction frequency and the target suction value into a control algorithm to generate a position and milk suction control instruction; making a judgment according to the real-time posture image; if the posture change exceeds the preset posture range, calculating the deviation between the current angle of the breast pump and the target angle to generate an angle adjustment parameter; inputting the angle adjustment parameter into the control algorithm to generate an angle control instruction; controlling the breast pump according to the position and milk suction control instruction and the angle control instruction so that the breast pump is adjusted according to the instruction.

[0007] In an achievable manner of the first aspect, the breast pump is a breast pump that is designed with sound insulation materials and a sound-absorbing structure and uses noise reduction technology to reduce operating noise.

[0008] In an achievable manner of the first aspect, accurately calculating the position of the breast pump through the feature points of the breast model and the marks on the breast pump includes: utilizing a position detection algorithm based on visual SLAM technology to accurately calculate the position of the breast pump in three-dimensional space by identifying the feature points of the individualized breast model and the marks on the breast pump.

[0009] In an achievable manner of the first aspect, the calculating the deviation between the breast pump position and the target position, and generating a position adjustment signal if the deviation exceeds a preset range, includes: using a position detection algorithm to calculate the deviation between the breast pump position and the target position to obtain a position deviation value; if the deviation exceeds the preset range, performing signal conversion according to the position deviation value to obtain a position adjustment signal.

[0010] In an implementable manner of the first aspect, the inputting the position adjustment signal, the target milk pumping frequency and the target suction force value into a control algorithm to generate a position and milk pumping control instruction includes: removing noise and standardizing the position adjustment signal, the target milk pumping frequency and the target suction force value to obtain standardized data; inputting the standardized data into a data fusion algorithm to calculate a comprehensive sensor value; and inputting the comprehensive sensor value into a control algorithm to generate a position and milk pumping control instruction.

[0011] In an achievable manner of the first aspect, before controlling the breast pump according to the position and the milk pumping control instruction and the angle control instruction so that the breast pump is adjusted according to the instruction, it also includes: acquiring a current milk pumping frequency; performing accuracy calculation according to the breast pump position, the target position, the current milk pumping frequency and the target milk pumping frequency to obtain an accuracy value; comparing the accuracy value with a preset accuracy threshold; if the accuracy value is greater than the preset accuracy threshold, proceeding to the next step; if the accuracy value is less than the preset accuracy threshold, re-acquiring the breast pump position and the current milk pumping frequency and then performing accuracy calculation until the accuracy value reaches the preset accuracy threshold.

[0012] In an achievable manner of the first aspect, performing accuracy calculation according to the breast pump position, the pre-stored target position, the current breast pumping frequency, and the target breast pumping frequency to obtain an accuracy value includes: obtaining the accuracy value by calculating using the following formula: ;in, Indicates the precision value, is the weight of rate deviation in control accuracy evaluation, is the weight of position deviation in control accuracy evaluation, is the deviation between the breast pump position and the target position, is the deviation between the current pumping frequency and the target pumping frequency, represents the target pumping frequency, Indicates the target location.

[0013] In a second aspect, the present invention provides a detection system for a wearable breast pump, comprising: a data acquisition module, used to acquire breast model data, lactation status data and real-time posture images; based on the breast model data and the real-time posture images, a target position and a target angle of the breast pump are calculated; a position capture module, used to accurately calculate the position of the breast pump through the feature points of the breast model and the marks on the breast pump; a position judgment module, used to calculate the deviation between the position of the breast pump and the target position, and if the deviation exceeds a preset range, a position adjustment signal is generated; a data prediction module, used to perform regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; a data mapping module, used to perform a regularity analysis on the lactation status data based on the predicted lactation amount Parameter mapping, to obtain the milk pumping frequency and the suction value; an instruction generation module, for performing parameter conversion according to the position adjustment signal, the milk pumping frequency and the suction value, to obtain the position and milk pumping adjustment parameters, and inputting the position and milk pumping adjustment parameters into the control algorithm to generate the position and milk pumping control instructions; a posture judgment module, for making a judgment according to the real-time posture image; if the posture change exceeds the preset range, calculating the deviation between the current angle of the breast pump and the target angle, and generating the angle adjustment parameter; inputting the angle adjustment parameter into the control algorithm to generate the angle control instruction; a dynamic adjustment module, for controlling the breast pump according to the position and milk pumping control instruction and the angle control instruction, so that the breast pump is adjusted according to the instruction.

[0014] In an achievable manner of the second aspect, the breast pump is a breast pump that is designed with sound insulation materials and a sound-absorbing structure and uses noise reduction technology to reduce operating noise.

[0015] In an achievable manner of the second aspect, the accurately calculating the position of the breast pump through the feature points of the breast model and the marks on the breast pump includes: utilizing a position detection algorithm based on visual SLAM technology to accurately calculate the position of the breast pump in three-dimensional space by identifying the feature points of the individualized breast model and the marks on the breast pump.

[0016] In an achievable manner of the second aspect, the calculating the deviation between the breast pump position and the target position, and generating a position adjustment signal if the deviation exceeds a preset range, includes: using a position detection algorithm to calculate the deviation between the breast pump position and the target position to obtain a position deviation value; if the deviation exceeds the preset range, performing signal conversion according to the position deviation value to obtain a position adjustment signal.

[0017] In an implementable manner of the second aspect, the inputting the position adjustment signal, the milk pumping frequency and the suction force value into a control algorithm to generate a position and milk pumping control instruction includes: removing noise and standardizing the position adjustment signal, the milk pumping frequency and the suction force value to obtain standardized data; inputting the standardized data into a data fusion algorithm to calculate a comprehensive sensor value; and inputting the comprehensive sensor value into a control algorithm to generate a position and milk pumping control instruction.

[0018] In an achievable manner of the second aspect, before controlling the breast pump according to the position and the milk pumping control instruction and the angle control instruction so that the breast pump is adjusted according to the instruction, it also includes: performing accuracy calculation according to the breast pump position, the target position, the current milk pumping frequency and the target milk pumping frequency to obtain an accuracy value; comparing the accuracy value with a preset accuracy threshold; if the accuracy value is greater than the preset accuracy threshold, proceeding to the next step; if the accuracy value is less than the preset accuracy threshold, re-acquiring the breast pump position and the current milk pumping frequency and then performing accuracy calculation until the accuracy value reaches the preset accuracy threshold.

[0019] In an achievable manner of the second aspect, performing accuracy calculation according to the breast pump position, the pre-stored target position, the current breast pumping frequency, and the target breast pumping frequency to obtain an accuracy value includes: obtaining the accuracy value by calculating using the following formula: ;in, Indicates the precision value, is the weight of rate deviation in control accuracy evaluation, is the weight of position deviation in control accuracy evaluation, is the deviation between the breast pump position and the target position, is the deviation between the current pumping frequency and the target pumping frequency, represents the target pumping frequency, Indicates the target location.

[0020] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the wearable breast pump detection method described in any one of the above is implemented.

[0021] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned wearable breast pump detection methods.

[0022] Compared with the prior art, the present invention has the following beneficial effects: the present invention discloses a detection method for a wearable breast pump, including obtaining breast model data, lactation status data and real-time posture images; based on the breast model data and the real-time posture images, the target position and target angle of the breast pump are calculated; taking into account the differences in individual breast conditions and the impact of activities or posture changes during breastfeeding, the optimal target position and target angle of the breast pump are accurately determined, and the position and angle changes of the breast pump are monitored in real time for timely adjustment, which greatly ensures the optimal adjustment of the position and angle during the entire milking process; the present invention performs regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; parameter mapping is performed according to the predicted lactation amount to obtain a target The system can adjust the suction force and target suction force of the breast pump according to the individual situation and lactation status of the mother, analyze the lactation pattern of the mother through deep learning, predict the future status and adjust the parameters in advance, so as to realize the intelligent adjustment of the suction force and the frequency of the breast pump, and overcome the inconvenience and low efficiency caused by manual adjustment in the prior art; it provides mothers with a more personalized and comfortable breastfeeding experience; through continuous optimization and learning, the system can adapt to the unique needs of each mother and make important contributions to the health of mothers and babies; the present invention is designed with sound insulation materials and sound-absorbing structures, and adopts noise reduction technology to reduce working noise, thereby improving the psychological comfort of mothers during breastfeeding. It is especially suitable for use at night or in public places. While ensuring the sucking performance, it minimizes the noise level and realizes a quiet and comfortable sucking environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a schematic flow chart of a detection method for a wearable breast pump provided in a first embodiment of the present invention;

[0024] Figure 2 2 is a schematic diagram of the structure of a detection system for a wearable breast pump provided in a second embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0026] Reference Figure 1 The first embodiment of the present invention provides a method for detecting a wearable breast pump, comprising the following steps:

[0027] S1, obtaining breast model data, lactation status data and real-time posture images; based on the breast model data and real-time posture images, calculating the target position and target angle of the breast pump;

[0028] S2, accurately calculate the position of the breast pump through the characteristic points of the breast model and the marks on the breast pump;

[0029] S3, calculating the deviation between the breast pump position and the target position, and generating a position adjustment signal if the deviation exceeds a preset range;

[0030] S4, performing regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount;

[0031] S5, performing parameter mapping according to the predicted milk production to obtain a target milk pumping frequency and a target suction force value;

[0032] S6, inputting the position adjustment signal, the target breast pumping frequency and the target suction force value into a control algorithm to generate position and breast pumping control instructions;

[0033] S7, making a judgment based on the real-time posture image; if the posture change exceeds the preset posture range, calculating the deviation between the current angle of the breast pump and the target angle, generating an angle adjustment parameter; inputting the angle adjustment parameter into the control algorithm, generating an angle control instruction;

[0034] S8, controlling the breast pump according to the position and milk suction control instruction and the angle control instruction, so that the breast pump is adjusted according to the instruction.

[0035] In step S1, breast model data, lactation status data and real-time posture images are obtained; based on the breast model data and real-time posture images, a target position and a target angle of the breast pump are calculated.

[0036] For example, breast model data can obtain the three-dimensional morphological data of the breast through a depth camera, and generate an individualized breast model using a three-dimensional reconstruction algorithm; lactation status data is measured by sensors and collected in combination with the lactation period information input by the mother, and can include historical lactation status data and real-time monitoring lactation status data, including key features such as lactation volume, lactation time, and lactation frequency; real-time posture images obtain mother posture images through a camera, and use posture recognition algorithms to process the images and extract the mother's current posture data. The target position is the optimal suction point pre-calculated based on the mother's breast model and posture; the target angle is the optimal suction angle determined based on the natural orientation of the nipple in the breast model under the current posture.

[0037] In step S2, the position of the breast pump in the three-dimensional space is accurately calculated by identifying the feature points of the breast model and the marks on the breast pump.

[0038] In a specific embodiment, the three-dimensional morphological data of the breast is obtained by a depth camera, and a three-dimensional reconstruction algorithm is used to generate an individualized breast model; the position detection algorithm is based on visual SLAM (simultaneous localization and mapping) technology. By identifying feature points in the environment (feature points of the individualized breast model) and marks on the breast pump, the algorithm can accurately calculate the position of the breast pump in three-dimensional space. The system calculates the Euclidean distance between the actual position and the target position, and generates position adjustment parameters in the three directions of x, y, and z. The angle detection algorithm uses the fusion data of the gyroscope and the frequency counter. The current pitch angle, yaw angle, and roll angle of the breast pump are obtained through the inertial measurement unit (IMU).

[0039] In a specific embodiment, a pressure sensor is also used to collect the contact pressure data between the breast pump and the breast in real time, and an angle sensor is used to collect the angle change data of the breast pump in real time. The contact pressure data and the angle change data are transmitted to the central processing unit. In the central processing unit, the contact pressure data is compared with the preset threshold value. If the contact pressure is lower than the preset threshold value, the micro motor is triggered to adjust the position of the breast pump. The angle change data is compared with the preset range. If the angle deviates from the preset range, the micro motor is triggered to adjust the angle of the breast pump. According to the adjusted position and angle data, the fit between the breast pump and the breast is calculated. If the fit does not meet the preset standard, the above adjustment process is repeated until the fit meets the preset standard. During the adjustment process, a machine learning algorithm is used to analyze the contact pressure and angle change data in real time to optimize the adjustment strategy of the micro motor.

[0040] Exemplarily, the acquisition of breast three-dimensional morphological data by a depth camera is the starting point of the whole process. This technology can accurately capture the geometric features of the breast surface by using principles such as structured light or time of flight. For example, a depth camera based on structured light can project a specific pattern onto the breast surface and reconstruct a three-dimensional model by analyzing the pattern deformation. This method can capture subtle surface changes of the breast with millimeter-level accuracy. The three-dimensional reconstruction algorithm is the key to converting raw depth data into a usable breast model. Point cloud registration, mesh reconstruction and other technologies can be applied to this process. For example, the iterative closest point algorithm can align point cloud data from multiple perspectives, and then use the Poisson surface reconstruction method to generate a smooth and continuous breast surface model. This individualized model provides an accurate geometric basis for subsequent breast pump positioning. Determining the optimal position and angle of the breast pump based on the breast model is an optimization problem, which can be achieved by using a surface fitting algorithm.

[0041] The breast pump used in this embodiment uses noise reduction technology to reduce the working noise of the breast pump, and reduces noise propagation through the design of sound insulation materials and silencer structures. Under the premise of ensuring the efficiency of milking, the motor speed and air flow channel are optimized to further reduce the noise level; the specific implementation process includes: obtaining the original noise data of the breast pump when it is working, analyzing the noise spectrum characteristics, and determining the main noise source. According to the noise spectrum characteristics, a sound insulation material with a specific frequency attenuation characteristic is selected, and the relationship between the material thickness and the sound insulation effect is calculated. For the main noise source, a multi-stage silencer structure is designed, the acoustic impedance matching parameters of the silencer structure are calculated, and the structural size is optimized. A mathematical model of motor speed and milking efficiency is established, and the gradient descent algorithm is used to solve the optimal speed range. The acoustic characteristics of the air flow channel are analyzed, the relationship between the channel cross-sectional area and the air flow frequency is calculated, and the channel geometric parameters are optimized. The optimized sound insulation material, silencer structure, motor speed and air flow channel parameters are input into the finite element analysis software for sound field simulation. According to the sound field simulation results, the combination of each parameter is adjusted, and the genetic algorithm is used for iterative optimization to obtain the final noise reduction solution.

[0042] In step S3, the deviation between the current position of the breast pump and the target position is calculated, and if the deviation exceeds a preset range, a position adjustment signal is generated.

[0043] Optionally, a position detection algorithm is used to calculate the deviation between the current position of the breast pump and the target position to obtain a position deviation value; if the deviation exceeds a preset range, a signal conversion is performed according to the position deviation value to obtain a position adjustment signal.

[0044] In a specific embodiment, the real-time motion data of the device is obtained through a frequency sensor and a gyroscope, and the frequency data and the gyroscope data are fused to obtain the displacement information of the device. According to the preset displacement range, it is determined whether the displacement of the device exceeds the threshold, and if it exceeds, a reset instruction is generated. The sensor data is denoised by a filtering algorithm to obtain smooth motion data, which is used to improve the accuracy of displacement detection.

[0045] In step S4, the lactation status data is analyzed based on a deep learning algorithm to obtain a predicted lactation amount.

[0046] In a specific embodiment, a data cleaning and feature extraction method is used to obtain a preprocessed data set based on the lactation status data collected during the operation of the breast pump. A machine learning model based on a regression algorithm is trained using the preprocessed data set to obtain a predicted lactation amount.

[0047] For example, deep learning models show great potential in optimizing breast pump efficiency. By analyzing historical lactation status data and real-time monitoring of lactation status data, key features such as milk production, lactation time and frequency can be extracted. These feature values ​​are input into a pre-trained deep neural network, which can accurately predict the lactation status and changing trends. For example, a mother's historical data shows that the milk production is high at 8 o'clock every morning, and the model predicts that the milk production will increase between 7:30 and 8:30. Based on these predictions, the breast pump can dynamically adjust its parameters. If the milk production is predicted to increase, the breast pump will start 5 minutes in advance and gradually increase the suction. This predictive adjustment not only improves the efficiency of milk extraction, but also provides a more comfortable experience for mothers.

[0048] In step S5, parameter mapping is performed according to the predicted milk production to obtain a target milk pumping frequency and a target suction force value.

[0049] In a specific implementation, a deep learning model is used to train the lactation law, obtain the predicted value of the lactation state, and judge the change trend of the lactation amount. According to the predicted value combined with the pre-established parameter mapping model corresponding to the lactation amount mapping, the target suction value and the target milk suction frequency are obtained; the suction value is the suction force applied by the breast pump to the nipple, and the milk suction frequency represents the frequency of the suction force applied by the breast pump, specifically, the number of times the suction force is applied per minute; combined with the current parameters of the breast pump, the adjustment parameters of the breast pump are determined, and a dynamic adjustment plan is generated. Through the dynamic adjustment plan, the milk suction efficiency parameters of the breast pump are adjusted in real time to optimize the milk suction efficiency. If the predicted value of the milk production is higher than the preset threshold, the suction parameter of the breast pump is increased to improve the milk suction efficiency. If the predicted value of the milk production is lower than the preset threshold, the suction parameter of the breast pump is reduced to reduce the milk suction time. According to the optimized milk suction efficiency, the historical lactation data is updated, and the deep learning model is re-entered to form a closed-loop optimization process. According to the milk production in this process, the parameters are mapped through the mapping model to obtain the corresponding target milk suction frequency and target suction value.

[0050] For example, a key advantage of deep learning models is their adaptive ability. Over time, the model can learn an individual's unique lactation pattern. For example, the model will find that a mother's milk production is lower on stressful weekdays and higher on weekends. This personalized insight allows the breast pump to adjust parameters more accurately to adapt to different situations. The implementation of dynamic adjustment schemes involves multiple parameters. In addition to suction power, the frequency and duration of milking can also be adjusted based on predictions. For example, if the milk production is predicted to be low, the breast pump will reduce suction power and extend the interval between milking to reduce discomfort. Conversely, when high milk production is predicted, suction power and frequency are increased to maximize milking efficiency. This intelligent adjustment not only improves milking efficiency, but also has a positive impact on breast milk production. By optimizing the milking process, more milk production can be stimulated, forming a virtuous circle. For example, a mother who uses a traditional breast pump produces 300ml of milk per day, while using a smart adjustment breast pump, the production increases to 350ml. The closed-loop optimization process is the core of the system. After each milking, new data is used to update the model to make the prediction more and more accurate. This continuous learning mechanism enables the system to adapt to long-term changes in the mother's lactation pattern, such as changes in demand due to the baby's growth or changes in the mother's eating habits. Overall, this deep learning-based intelligent breastfeeding system not only improves breastfeeding efficiency, but also provides mothers with a more personalized and comfortable breastfeeding experience. Through continuous optimization and learning, the system can adapt to the unique needs of each mother and make an important contribution to maternal and child health.

[0051] In step S6, the position adjustment signal, the target milk pumping frequency and the target suction force value are input into a control algorithm to generate position and milk pumping control instructions.

[0052] In the above step S6, the position adjustment signal, the target breast pumping frequency and the target suction force value are input into a control algorithm to generate position and breast pumping control instructions, which specifically includes the following steps:

[0053] S61, removing noise from and normalizing the position adjustment signal, the target breast pumping frequency, and the target suction force value to obtain standardized data;

[0054] S61: input the standardized data into a data fusion algorithm to calculate a comprehensive sensor value;

[0055] S61 inputs the integrated sensor value into the control algorithm to generate position and milk pumping control instructions.

[0056] The algorithm used in this embodiment is based on the working characteristics of the breast pump. According to the comprehensive position adjustment signal, current suction value and milk sucking frequency of the breast pump when the breast pump is working, the dynamic adjustment parameters required by the breast pump are calculated. The coefficient determined by the characteristics of the breast pump is mainly used to balance the adjustment of suction and frequency. The goal of this formula is to dynamically adjust the suction and frequency according to the real-time position deviation of the breast pump to ensure that the breast pump works in the best state, thereby improving the efficiency and comfort of milk sucking.

[0057] In this embodiment, the specific implementation process includes: obtaining real-time data of the pressure sensor, flow sensor and frequency sensor, preprocessing the pressure value and flow value, removing noise and standardizing. The preprocessed pressure value and flow value are input into the data fusion algorithm, and the comprehensive sensor value is calculated to characterize the current state of the breast pump. According to the comprehensive sensor value, it is judged whether the position value of the breast pump deviates from the preset range. If it deviates, a position adjustment signal is generated. Through the position adjustment signal, combined with the suction value and the frequency value, the position and milk suction adjustment parameters of the breast pump are calculated to optimize the milk suction frequency and position control. The position and milk suction adjustment parameters are input into the control algorithm, and the control instructions of the milk suction frequency and position are generated to adjust the working state of the breast pump in real time. According to the real-time data and control instructions, the optimization target of the breast pump is updated to ensure that the control accuracy meets the preset threshold.

[0058] Exemplarily, the control algorithm may adopt a PID (proportional-integral-differential) control algorithm. The PID control algorithm is a commonly used feedback control algorithm that can adjust the control amount according to the deviation between the set value and the actual value. In the control of the breast pump, the preset working state of the breast pump (such as ideal suction, frequency and position) is used as the set value, and the actual state of the current breast pump reflected by the comprehensive sensor value is used as the actual value, and the deviation between the two is calculated. The proportional link adjusts the control amount proportionally according to the size of the deviation, the integral link integrates the deviation to eliminate the steady-state error of the system, and the differential link predicts the change trend of the deviation according to the rate of change of the deviation, and adjusts the control amount in advance, so that the system can respond quickly and operate stably.

[0059] In step S7, a judgment is made based on the real-time posture image; if the posture change exceeds the preset posture range, the deviation between the current angle of the breast pump and the target angle is calculated to generate an angle adjustment parameter.

[0060] In a specific embodiment, a posture image of the mother is obtained through a camera, and a posture recognition algorithm is used to process the image to extract the mother's current posture data. Whether the mother's posture has changed is determined based on the posture data. If the posture change exceeds a preset threshold, the breast pump adjustment mechanism is triggered. The deviation between the current angle of the breast pump and the target angle is calculated through an angle detection algorithm to generate an angle adjustment parameter. According to the angle adjustment parameter, the breast pump actuator is controlled to adjust the position and angle. A fit detection algorithm is used to monitor the contact state between the breast pump and the mother's body in real time to determine whether the fit meets the preset standard. The suction distribution data is obtained through a suction sensor, and the suction uniformity algorithm is used to analyze the suction distribution. If the suction is uneven, the breast pump angle is readjusted.

[0061] For example, the smart breast pump uses a camera to obtain images of the mother's posture, a step that utilizes computer vision technology. The camera uses a high-resolution CMOS sensor with a wide-angle lens to ensure that the mother's upper body posture is captured completely. The acquired image is preprocessed, such as denoising and contrast enhancement, to improve the accuracy of subsequent recognition. The posture recognition algorithm uses a deep learning model, such as a convolutional neural network (CNN) or a skeletal key point detection network. These models learn the positions of human joints through a large amount of training data and can accurately identify the positions of the mother's shoulders, arms, and torso. The algorithm output is a set of three-dimensional coordinates that represent the positions of each key point in space. When judging a posture change, the system compares the current posture data with the previously stored reference posture. The preset threshold is a change in the position of a key point exceeding 5 cm or an angle change exceeding 10 degrees. Once a change exceeding the threshold is detected, the adjustment mechanism of the breast pump is triggered.

[0062] In step S8, the breast pump is controlled according to the position and milk extraction control instruction and the angle control instruction, so that the breast pump is adjusted according to the instruction.

[0063] It should be noted that, before controlling the breast pump according to the position and breast pumping control instruction and the angle control instruction so that the breast pump is adjusted according to the instruction, the following steps are specifically included: S81, obtaining the current breast pumping frequency.

[0064] It should be noted that the pumping frequency refers to the number of times the breast pump completes the pumping action in a unit of time. The current pumping frequency is obtained through the built-in sensor. The current pumping frequency is used for accuracy calculation to obtain the accuracy value.

[0065] S82, performing accuracy calculation according to the breast pump position, the target position, the current breast pumping frequency and the target breast pumping frequency to obtain an accuracy value.

[0066] It should be noted that the accuracy value is calculated by the following formula: ;in, Indicates the precision value, is the weight of rate deviation in control accuracy evaluation, is the weight of position deviation in control accuracy evaluation, is the deviation between the breast pump position and the target position, is the deviation between the current pumping frequency and the target pumping frequency, Indicates target pumping frequency, Indicates the target location.

[0067] S83, comparing the accuracy value with a preset accuracy threshold; if the accuracy value is greater than the preset accuracy threshold, proceeding to the next step; if the accuracy value is less than the preset accuracy threshold, re-acquiring the breast pump position and the current milk pumping frequency and then performing accuracy calculation until the accuracy value reaches the preset accuracy threshold.

[0068] It should be noted that the preset accuracy threshold is an accuracy requirement set according to user needs, so that corresponding adjustments are only made when the deviation exceeds a certain range. If the accuracy value is less than the preset accuracy threshold, the system needs to iterate the data for accuracy calculation until the requirements are met, and then stop and proceed to the parameter setting in the next operation.

[0069] In summary, the present invention discloses a detection method for a wearable breast pump, comprising acquiring breast model data, lactation status data and a real-time posture image; calculating a target position and a target angle of the breast pump based on the breast model data and the real-time posture image; accurately calculating the position of the breast pump through the characteristic points of the breast model and the marks on the breast pump; calculating the deviation between the position of the breast pump and the target position, and generating a position adjustment signal if the deviation exceeds a preset range; performing regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; performing parameter mapping according to the predicted lactation amount to obtain a target milk suction frequency and a target suction value; inputting the position adjustment signal, the target milk suction frequency and the target suction value into a control algorithm to generate a position and milk suction control instruction; making a judgment according to the real-time posture image; if the posture change exceeds the preset posture range, calculating the deviation between the current angle of the breast pump and the target angle to generate an angle adjustment parameter; inputting the angle adjustment parameter into the control algorithm to generate an angle control instruction; controlling the breast pump according to the position and milk suction control instruction and the angle control instruction so that the breast pump is adjusted according to the instruction. The present invention uses a depth camera to capture the breast morphology to establish a personalized model, uses multiple sensors to monitor the contact status of the breast pump and the breast in real time, and dynamically adjusts the suction according to the lactation status; uses a machine learning algorithm to optimize the control strategy to improve the accuracy of personalized adjustment; uses deep learning to analyze the mother's lactation pattern, predicts the future status and adjusts the parameters in advance; the present invention also combines posture recognition technology to automatically adjust the position and angle of the breast pump when the mother changes posture; at the same time, uses noise reduction technology and optimized design to reduce working noise; the present invention integrates a variety of advanced technologies, realizes the intelligence, personalization and high efficiency of the breast pump, and significantly improves the breast pumping experience and effect.

[0070] Reference Figure 2The second embodiment of the present invention provides a wearable breast pump detection system, including: a data acquisition module 101, used to acquire breast model data, lactation status data and real-time posture images; based on the breast model data and the real-time posture images, the target position and target angle of the breast pump are calculated; a position capture module 102, which accurately calculates the position of the breast pump through the feature points of the breast model and the marks on the breast pump; a position judgment module 103, which is used to calculate the deviation between the position of the breast pump and the target position, and if the deviation exceeds a preset range, a position adjustment signal is generated; a data prediction module 104, which is used to perform regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; a data mapping module 105, which is used to perform parameter analysis based on the predicted lactation amount. The target milk pumping frequency and the target suction force value are obtained by mapping the position adjustment signal, the target milk pumping frequency and the target suction force value; the instruction generation module 106 is used to perform parameter conversion according to the position adjustment signal, the target milk pumping frequency and the target suction force value to obtain the position and milk pumping adjustment parameters, and input the position and milk pumping adjustment parameters into the control algorithm to generate the position and milk pumping control instructions; the posture judgment module 107 is used to make a judgment according to the real-time posture image; if the posture change exceeds the preset range, the deviation between the current angle of the breast pump and the target angle is calculated to generate the angle adjustment parameter; the angle adjustment parameter is input into the control algorithm to generate the angle control instruction; the dynamic adjustment module 108 is used to control the breast pump according to the position and milk pumping control instruction and the angle control instruction, so that the breast pump is adjusted according to the instruction.

[0071] It should be noted that the wearable breast pump detection system provided in the embodiment of the present invention is used to execute all the process steps of the wearable breast pump detection method in the above embodiment, and the working principles and beneficial effects of the two correspond one to one, so they will not be repeated.

[0072] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a wearable breast pump detection program. When the processor executes the computer program, the steps in the above-mentioned wearable breast pump detection method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0073] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0074] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0075] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0076] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0077] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0078] It should be noted that the device embodiments described above are merely schematic, 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. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0079] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting a wearable breast pump, characterized in that: Executed by a computer, including: Acquire breast model data, lactation status data and mother's posture image; calculate and obtain the target position and target angle of the breast pump based on the breast model data and the mother's posture image; The position of the breast pump can be accurately calculated through the characteristic points of the breast model and the marks on the breast pump. Calculating the deviation between the breast pump position and the target position, and generating a position adjustment signal if the deviation exceeds a preset range; Performing regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; Perform parameter mapping according to the predicted milk production to obtain a target milk pumping frequency and a target suction force value; Inputting the position adjustment signal, the target milk pumping frequency and the target suction force value into a control algorithm to generate position and milk pumping control instructions; A judgment is made based on the mother's posture image; if the posture change exceeds the preset posture range, a deviation between the current angle of the breast pump and the target angle is calculated to generate an angle adjustment parameter; and the angle adjustment parameter is input into a control algorithm to generate an angle control instruction; The breast pump is controlled according to the position and milk-sucking control instruction and the angle control instruction, so that the breast pump is adjusted according to the instruction.

2. The detection method of a wearable breast pump according to claim 1, characterized in that: The breast pump is designed with sound insulation materials and a sound-absorbing structure and uses noise reduction technology to reduce working noise.

3. The detection method of a wearable breast pump according to claim 1, characterized in that: The method of accurately calculating the position of the breast pump by using the characteristic points of the breast model and the marks on the breast pump comprises: Using a position detection algorithm and based on visual SLAM technology, the position of the breast pump in three-dimensional space is accurately calculated by identifying the feature points of the individualized breast model and the marks on the breast pump.

4. The detection method of a wearable breast pump according to claim 1, characterized in that: The calculating the deviation between the breast pump position and the target position and generating a position adjustment signal if the deviation exceeds a preset range includes: Use a position detection algorithm to calculate the deviation between the breast pump position and the target position to obtain a position deviation value; If the deviation exceeds a preset range, a signal conversion is performed according to the position deviation value to obtain a position adjustment signal.

5. The detection method of a wearable breast pump according to claim 1, characterized in that: The step of inputting the position adjustment signal, the target breast pumping frequency and the target suction force value into a control algorithm to generate position and breast pumping control instructions comprises: De-noising and standardizing the position adjustment signal, the target breast pumping frequency, and the target suction force value to obtain standardized data; Inputting the standardized data into a data fusion algorithm to calculate a comprehensive sensor value; The combined sensor values ​​are input into a control algorithm to generate position and expression control instructions.

6. The detection method of a wearable breast pump according to claim 1, characterized in that: Before controlling the breast pump according to the position and milk suction control instruction and the angle control instruction so that the breast pump is adjusted according to the instruction, the method further includes: Get the current breast pumping frequency; Performing accuracy calculation according to the breast pump position, the target position, the current breast pumping frequency and the target breast pumping frequency to obtain an accuracy value; The accuracy value is compared with a preset accuracy threshold; if the accuracy value is greater than the preset accuracy threshold, the next step is continued; if the accuracy value is less than the preset accuracy threshold, the breast pump position and the current milk pumping frequency are re-acquired and the accuracy calculation is performed again until the accuracy value reaches the preset accuracy threshold.

7. A wearable breast pump detection system, characterized in that: include: A data acquisition module, used to acquire breast model data, lactation status data and mother posture images; Based on the breast model data and the mother's posture image, the target position and target angle of the breast pump are calculated; A position capture module is used to accurately calculate the position of the breast pump through the feature points of the breast model and the marks on the breast pump; A position determination module is used to calculate the deviation between the breast pump position and the target position, and to generate a position adjustment signal if the deviation exceeds a preset range; A data prediction module, used to perform regularity analysis on the lactation status data based on a deep learning algorithm to obtain a predicted lactation amount; A data mapping module, used for performing parameter mapping according to the predicted milk production to obtain a target milk pumping frequency and a target suction force value; An instruction generation module inputs the position adjustment signal, the target breast pumping frequency and the target suction force value into a control algorithm to generate position and breast pumping control instructions; A posture judgment module is used to make a judgment based on the mother's posture image; if the posture change exceeds a preset range, the deviation between the current angle of the breast pump and the target angle is calculated to generate an angle adjustment parameter; the angle adjustment parameter is input into a control algorithm to generate an angle control instruction; The dynamic adjustment module is used to control the breast pump according to the position and milk suction control instructions and the angle control instructions, so that the breast pump is adjusted according to the instructions.

8. An electronic device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the detection method of the wearable breast pump according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the wearable breast pump detection method according to any one of claims 1 to 6.

Images