A remote monitoring control system and method for tooth cleaner based on mobile phone Internet of Things

Abstract

The present invention belongs to the technical field of tooth cleaners, and discloses a remote monitoring control system and method for tooth cleaners based on a mobile phone Internet of Things; the method comprises: obtaining real-time working status data of the tooth cleaner; constructing a working status data graph of the tooth cleaner; optimizing and analyzing the working status data graph of the tooth cleaner on a cloud server to obtain preliminary optimal working parameters of the tooth cleaner; identifying and marking overlapping cleaning areas in the working status data graph of the tooth cleaner; calculating an optimal working path based on the working status data graph with overlapping cleaning areas marked, combining the optimal working path with preliminary optimal working parameters to obtain a final optimal working strategy; transmitting the final optimal working strategy on the cloud server to a mobile phone terminal via wireless communication; the mobile phone terminal receives the final optimal working strategy, and sends it to the tooth cleaner via wireless communication to adjust the working status of the tooth cleaner, thereby realizing remote intelligent control and optimization of the working status.

Description

Technical Field

[0001] The present invention relates to the technical field of tooth cleaners, and more specifically, to a remote monitoring control system and method for tooth cleaners based on mobile phone Internet of Things. Background Art

[0002] As people's living standards continue to improve, they pay more and more attention to oral hygiene; as a common oral cleaning tool, tooth cleaners have been widely used in daily life; however, existing tooth cleaners still have some shortcomings during use, which affect the cleaning effect and user experience.

[0003] First, during the cleaning process, traditional tooth cleaners are unable to monitor and adjust their own working status in real time, which may lead to unsatisfactory cleaning results; it will affect the cleaning intensity and make the teeth cleaning incomplete; if the cleaning mode is not selected properly, some areas may be missed or cleaned repeatedly; this brings additional mental stress to users; secondly, when cleaning with existing tooth cleaners, repeated cleaning not only wastes time and battery power, but may also cause unnecessary stimulation and damage to the oral mucosa, causing discomfort to users; furthermore, during the cleaning process, existing tooth cleaners are unable to dynamically adjust working parameters according to the actual oral environment, which may affect the cleaning quality; finally, traditional tooth cleaners lack intelligent control and cannot autonomously optimize working paths and working strategies.

[0004] In view of this, the present invention proposes a remote monitoring control system and method for a toothbrush cleaner based on mobile phone Internet of Things to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: A remote monitoring and control system for a tooth cleaner based on the mobile phone Internet of Things, comprising: a connection acquisition module, used to establish a wireless communication connection between the tooth cleaner and a mobile phone terminal, and obtain real-time working status data of the tooth cleaner through the mobile phone terminal;

[0006] A comprehensive construction module, used for transmitting the acquired real-time working status data to a cloud server, and constructing a working status data graph of the toothbrush cleaner on the cloud server;

[0007] An optimization module is used to optimize and analyze the working status data graph of the toothbrush on the cloud server to obtain preliminary optimal working parameters of the toothbrush;

[0008] A strategy fitting module is used to identify and mark overlapping cleaning areas in the working state data graph of the toothbrush; based on the working state data graph with overlapping cleaning areas marked, calculate the optimal working path, combine the optimal working path with the preliminary optimal working parameters, and obtain the final optimal working strategy;

[0009] The sending module is used to transmit the final optimal working strategy on the cloud server to the mobile phone through wireless communication; the mobile phone receives the final optimal working strategy and sends it to the toothbrush through wireless communication to adjust the working state of the toothbrush; each module is connected by wired and / or wireless means.

[0010] Furthermore, the real-time working status data of the toothbrush includes working parameters, position movement parameters and oral environment parameters; the working parameters include rotation speed, vibration frequency, cleaning mode, cleaning force and battery power; the oral environment parameters include oral temperature, oral humidity and saliva pH value;

[0011] The position motion parameters include the three-dimensional coordinate position of the toothbrush in the oral cavity, the motion speed and real-time motion direction, and the coverage rate of the cleaned area.

[0012] Furthermore, the method for obtaining the cleaned area coverage includes:

[0013] A structured light sensor is integrated into the cleaning head of the toothbrush. During the use of the toothbrush, the structured light sensor continuously scans and reconstructs the three-dimensional point cloud data of the area around the cleaning head.

[0014] De-noising the three-dimensional point cloud data to obtain de-noised point cloud data, and filling the de-noised point cloud data to obtain a local three-dimensional point cloud;

[0015] The local 3D point cloud is rigidly registered with the pre-built overall oral 3D model to obtain a real-time oral dynamic model; the real-time oral dynamic model is triangulated to obtain a mesh model composed of triangular facets;

[0016] Based on the three-dimensional coordinate position, movement speed and real-time movement direction of the tooth cleaner in the oral cavity, the movement trajectory of the cleaning head in the real-time oral dynamic model is determined; the movement trajectory is represented by a series of position points;

[0017] For each pose point a on the motion trajectory, calculate the effective cleaning range R of the cleaning head at the pose point a; traverse all triangular facets in the mesh model that are within the effective cleaning range R; for each triangular facet F, if F has been cleaned before, mark it as cleaned, and finally count the proportion of the area of ​​all triangular facets marked as cleaned to the total area of ​​the mesh model to obtain the coverage rate of the cleaned area.

[0018] Furthermore, the method of filling the denoised point cloud data includes:

[0019] Define a three-dimensional coordinate system in the space where the denoised point cloud data is located, calculate the geometric center C of the denoised point cloud data; obtain each point cloud point in the denoised point cloud data Coordinate offset relative to the geometric center C ; Calculate the sum of the coordinate offsets of all point cloud points ; and calculate the comprehensive covariance matrix ;

[0020] ;in, Point cloud point The comprehensive weight of for The transpose of for The transpose of

[0021] Comprehensive weight ;in, Point cloud point The maximum curvature at Point cloud point The minimum curvature at is a tuning parameter, and its value is in the interval (0, 1];

[0022] Covariance matrix Perform eigenvalue decomposition to obtain three eigenvectors and their corresponding eigenvalues; take the direction of the eigenvector corresponding to the largest eigenvalue as the main direction; take the geometric center C as the origin, take the eigenvector corresponding to the main direction as the normal vector, and establish a main plane P;

[0023] Orthogonally project the denoised point cloud data onto the principal plane P along the main direction, and record the two-dimensional point set of the denoised point cloud data under the principal plane P; calculate the new padded point cloud based on the two-dimensional point set; align the new padded point cloud with the original denoised point cloud data, and use statistical removal or moving least squares method to remove overlapping point cloud points to obtain a local three-dimensional point cloud.

[0024] Furthermore, the method of calculating the filled new point cloud based on the two-dimensional point set includes:

[0025] Calculate the convex hull of the two-dimensional point set to obtain the two-dimensional convex hull boundary; start from the lowest point of the two-dimensional convex hull boundary, and construct a ray L0 horizontally with the lowest point as the starting point, discretize the ray L0 into a series of pixel points, and record the minimum distance between each pixel point on the ray and the point cloud point; move the ray upward row by row, and repeat until the entire main plane P is covered; store each pixel point and its minimum distance as a distance conversion map;

[0026] In the distance conversion diagram, identify the point cloud point corresponding to the minimum distance value of 0, and record it as the seed point; for each seed point, according to the distance value of its 8 neighboring pixel points, if the distance value is greater than 0, then the corresponding seed point is judged to be a boundary point; starting from the boundary point, use the boundary tracking algorithm to obtain the closed boundary line of the boundary point, mark each pixel point on the closed boundary line as a boundary inner point, and form a new seed point set with the boundary inner points; back-project the new seed point set back to the space where the noise cloud data is located, use the distance weighted average interpolation or Gaussian process regression algorithm for the boundary inner points in the new seed point set to obtain the interpolation points, and the point set composed of all interpolation points is the new point cloud after filling.

[0027] Furthermore, the method of constructing the tooth cleaner working status data graph includes:

[0028] Initialize an empty graph G, define nodes to represent the working state of the toothbrush at a certain time point, assign attributes to the working state, and the attributes are the real-time working state data at the corresponding time point; the edges between nodes represent the transfer relationship of the toothbrush from one working state to another;

[0029] Define the span dt of time points, take the working status of the first time point when the toothbrush is turned on as the initial node n0 of the empty graph G, add n0 to the graph G, and based on the span dt, construct its working status as a new node n1 at the next time point, and find out whether there is a node with the same working status as node n1 in the graph G. If so, do not add node n1, and only update the timestamp of node n1; if not, add node n1 to the graph G, and connect node n1 with the node corresponding to the previous time point with an edge, which represents the transfer relationship of the toothbrush from the working status corresponding to node n1 to the working status corresponding to the node at the previous time point; continuously update the graph G to obtain the working status data graph of the toothbrush.

[0030] Furthermore, the method for solving the preliminary optimal working parameters includes:

[0031] The working parameters of the toothbrush are used as decision variables , construct an objective function, which is the weighted sum of the cleaning efficiency sub-function, the battery life sub-function, the noise value sub-function and the cleaning intensity fluctuation sub-function;

[0032] Map the original decision variable onto the n3-dimensional hypersphere to obtain the spherical decision variable y; n3 is an integer greater than 1; the mapping formula is: ;in, is the center position of the n3-dimensional hypersphere; is a positive number; is the nonlinear scaling factor, is the nonlinear mapping parameter;

[0033] Non-linear scaling factor ;in, is the maximum value of the preset nonlinear scaling parameter, is the number of iterations, is the preset maximum number of iterations; is the shape parameter for adaptive adjustment;

[0034] Randomly generate N initial solutions on the n3-dimensional hypersphere to form an initial population, and map these initial solutions back to the space where the decision variables are located to obtain the corresponding decision variables, which are recorded as candidate solutions; substitute each candidate solution into the objective function, and record the calculated value of the objective function as the sphere value;

[0035] According to the sphere value, select the initial solution with the largest sphere value in the current population , based on the initial solution Update the position of the center of the ball; the formula for updating the position of the center of the ball is:

[0036] ;in, is the new center position of the ball after update. , and All are random numbers, and the sum of the three is 1; is the mean of the initial solution of the current population; To randomly find an initial solution on the n3-dimensional hypersphere; for the i-th initial solution in the population , generate a new solution ;

[0037] ;in, is a random number; Reverse mapping back to the space where the decision variable is located, and obtain the decision variable x_i_new corresponding to the new solution; calculate the sphere value of x_i_new, if the sphere value is smaller than the sphere value of the original initial solution, replace the original initial solution with x_i_new, otherwise keep the original initial solution;

[0038] A pause threshold is preset and the process is repeated until the sphere value is less than the pause threshold. Then the decision variable with the smallest sphere value in the current population is output and this decision variable is used as the initial optimal working parameter.

[0039] Furthermore, the method of identifying and marking the overlapping cleaning areas in the toothbrush working state data graph includes:

[0040] The real-time oral dynamic model is projected onto a two-dimensional plane to obtain a two-dimensional grid image. For each posture point on the motion trajectory, the projection area of ​​the effective cleaning range of the cleaning head at the posture point on the two-dimensional grid image is calculated. In the two-dimensional grid image, the pixel value in the projection area is set to 1;

[0041] Preset the cleaning threshold, slide a 3x3 window W on the two-dimensional grid image, and calculate the sum su of the pixel values ​​in the window W; if su is greater than the cleaning threshold, mark the central pixel in the window as the preliminary overlapping cleaning area point; repeatedly traverse all pixels in the two-dimensional grid image; all the preliminary overlapping cleaning area points obtained constitute the preliminary overlapping cleaning area;

[0042] Perform morphological opening operation on the preliminary overlapping clean areas; perform connected domain analysis on the preliminary overlapping clean areas, merge adjacent areas into a connected area, and obtain overlapping clean areas;

[0043] For each node in the toothbrush working status data graph, according to its corresponding time point, find out whether there is an overlapping cleaning area in the two-dimensional grid image; if so, add a mark to the corresponding node to mark that there is an overlapping cleaning area.

[0044] Furthermore, the calculation method of the optimal working path includes:

[0045] Based on the working state data graph of the toothbrush, an undirected graph H is constructed. In the undirected graph H, each node also represents a working state of the toothbrush. For each edge in the working state data graph of the toothbrush, an undirected edge is added to the undirected graph H. If any node is marked as having an overlapping cleaning area, a self-loop edge is added to the undirected graph H. For the undirected edge L(I, J) between two nodes I and J in the undirected graph H, a weight w(I, J) is assigned to it, and the weight is the weighted sum of the time and energy consumption required for the transfer between the working states corresponding to the nodes I and J. If the node I is marked as having an overlapping cleaning area, the weight of the corresponding self-loop edge is set to a preset value. All nodes in the undirected graph H are randomly assigned to K non-overlapping partitions, each partition representing a potential working path.

[0046] Set the initial temperature T0 and the end temperature Tmin; at the current temperature, randomly select two different partitions P_v and P_u, where v and u are both partition indexes and do not index the same partition at the same time; randomly select a node s1 from P_v and a node s2 from P_u, and calculate the total cost change of moving s1 from P_v to P_u and moving s2 from P_u to P_v ;in, represents the cost change caused by moving s1 from P_v to P_u; represents the cost change caused by moving s2 from P_u to P_v;

[0047] like is less than or equal to 0, then accept the move; otherwise, Accept this move; repeat until the temperature reaches the termination temperature Tmin; For the The temperature of the iteration;

[0048] ;in, For the The temperature of the iteration; For the The total cost change of iterations is For the The total cost of iterations, is a constant; when the temperature reaches the termination temperature Tmin, the partition P* with the smallest total cost is selected from the K partitions. In the partition P*, an optimal working path is constructed according to the connection relationship between the nodes.

[0049] A method for remote monitoring and controlling a tooth cleaner based on a mobile phone Internet of Things is implemented based on the remote monitoring and control system for a tooth cleaner based on a mobile phone Internet of Things, comprising: step 1, establishing a wireless communication connection between the tooth cleaner and a mobile phone terminal, and obtaining real-time working status data of the tooth cleaner through the mobile phone terminal;

[0050] Step 2: Transmit the acquired real-time working status data to the cloud server, and construct a working status data graph of the toothbrush on the cloud server;

[0051] Step 3: On the cloud server, optimize and analyze the working status data graph of the toothbrush to obtain the preliminary optimal working parameters of the toothbrush;

[0052] Step 4, identifying and marking the overlapping cleaning areas in the working state data graph of the toothbrush; calculating the optimal working path based on the working state data graph with the overlapping cleaning areas marked, combining the optimal working path with the preliminary optimal working parameters, and obtaining the final optimal working strategy;

[0053] Step 5: The final optimal working strategy on the cloud server is transmitted to the mobile phone via wireless communication; the mobile phone receives the final optimal working strategy and sends it to the tooth cleaner via wireless communication to adjust the working state of the tooth cleaner.

[0054] The technical effects and advantages of the present invention's remote monitoring control system and method for tooth cleaners based on mobile phone Internet of Things are as follows:

[0055] The present invention can monitor the working status of the toothbrush in real time, and comprehensively obtain data such as oral environment, cleaning force, movement trajectory, etc., laying the foundation for subsequent intelligent analysis and optimization; through big data processing and artificial intelligence algorithms in the cloud, it can independently analyze the working efficiency, energy consumption, noise level, etc. of the toothbrush, and calculate the optimal working parameter combination to maximize the cleaning quality while reducing energy consumption and noise interference; secondly, it can greatly improve the cleaning efficiency, save cleaning time, and effectively extend the battery life of the toothbrush, reducing unnecessary waste of resources; thirdly, through the wireless connection between the mobile phone and the toothbrush, the optimal working strategy calculated in the cloud is sent to the toothbrush, realizing remote intelligent control and optimization of the working status. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a remote monitoring and control system for a tooth cleaner based on mobile phone Internet of Things of the present invention;

[0057] Figure 2 It is a schematic diagram of a remote monitoring and control method of a toothbrush cleaner based on mobile phone Internet of Things of the present invention. DETAILED DESCRIPTION

[0058] 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.

[0059] Example 1

[0060] See also Figure 1 As shown, this embodiment is a remote monitoring and control system for a toothbrush based on mobile phone Internet of Things, including:

[0061] A connection acquisition module is used to establish a wireless communication connection between the tooth cleaner and the mobile phone, and obtain the real-time working status data of the tooth cleaner through the mobile phone;

[0062] A comprehensive construction module, used for transmitting the acquired real-time working status data to a cloud server, and constructing a working status data graph of the toothbrush cleaner on the cloud server;

[0063] An optimization module is used to optimize and analyze the working status data graph of the toothbrush on the cloud server to obtain preliminary optimal working parameters of the toothbrush;

[0064] A strategy fitting module is used to identify and mark overlapping cleaning areas in the working state data graph of the toothbrush; based on the working state data graph with overlapping cleaning areas marked, calculate the optimal working path, combine the optimal working path with the preliminary optimal working parameters, and obtain the final optimal working strategy;

[0065] The sending module is used to transmit the final optimal working strategy on the cloud server to the mobile phone through wireless communication; the mobile phone receives the final optimal working strategy and sends it to the toothbrush through wireless communication to adjust the working state of the toothbrush; each module is connected by wired and / or wireless means to realize data transmission between modules.

[0066] The real-time working status data of the toothbrush includes working parameters, position movement parameters and oral environment parameters; working parameters include rotation speed, vibration frequency, cleaning mode (rotation / vibration / combination, etc.), cleaning force and battery power;

[0067] An encoder or Hall sensor is integrated on the motor or vibrator of the toothbrush to detect the rotation speed and vibration frequency; the control circuit of the toothbrush records the current cleaning mode (rotation / vibration / combination, etc.) according to the user settings; oral environmental parameters include oral temperature, oral humidity and saliva pH value; they are obtained by integrating corresponding sensors.

[0068] The cleaning strength can be obtained by:

[0069] A low-resistance precision sampling resistor is connected in parallel to the power supply circuit of the toothbrush motor or vibrator, and an operational amplifier or a dedicated current detection chip (such as INA219, etc.) is used to amplify the tiny voltage difference across the sampling resistor. The amplified tiny voltage difference is converted into a digital value (ADC) by analog-to-digital conversion, which is the voltage value. The corresponding current value is obtained by dividing the voltage value by the sampling resistor.

[0070] The obtained current value is compared with the pre-calibrated force-current mapping table. During calibration, a standard dynamometer is used to measure the actual cleaning force output by the toothbrush under different currents (powers), and then a force-current mapping table is established. During actual use, the corresponding cleaning force is preliminarily obtained by looking up the table (force-current mapping table) based on the detected current value. Since current fluctuations will cause fluctuations in cleaning force, the cleaning force is smoothed using algorithms such as sliding average filtering and Kalman filtering to obtain accurate cleaning force.

[0071] The position motion parameters include the three-dimensional coordinate position of the toothbrush in the oral cavity, the motion speed and real-time motion direction, and the coverage rate of the cleaned area;

[0072] An inertial measurement unit (IMU) is integrated on the toothbrush, which includes a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer. The three-dimensional coordinate position, movement speed and movement direction of the toothbrush in the oral cavity are obtained by fusing and calculating the data obtained by the inertial measurement unit.

[0073] The cleaned area coverage is obtained by:

[0074] A structured light sensor is integrated in the cleaning head of the toothbrush. During the use of the toothbrush, the structured light sensor continuously scans and reconstructs three-dimensional point cloud data of the area around the cleaning head. The three-dimensional point cloud data is denoised to obtain denoised point cloud data, and the denoised point cloud data is filled to obtain a local three-dimensional point cloud.

[0075] Specifically, a three-dimensional coordinate system is defined in the space where the denoised point cloud data is located, and the geometric center C (mass point) of the denoised point cloud data is calculated; each point cloud point in the denoised point cloud data is obtained. Coordinate offset relative to the geometric center C ; Calculate the sum of the coordinate offsets of all point cloud points ; and calculate the comprehensive covariance matrix ;

[0076] ;in, Point cloud point The comprehensive weight of for The transpose of for The transpose of

[0077] Comprehensive weight ;in, Point cloud point The maximum curvature at Point cloud point The minimum curvature at It is an adjustment parameter with a value in the interval (0, 1], which is used to control the influence of the weight.

[0078] Covariance matrix Perform eigenvalue decomposition (EVD) to obtain three eigenvectors and corresponding eigenvalues; take the direction of the eigenvector corresponding to the largest eigenvalue as the main direction; take the geometric center C as the origin, take the eigenvector corresponding to the main direction as the normal vector, and establish a main plane P; orthogonally project the denoised point cloud data onto the main plane P along the main direction, and record the two-dimensional point set of the denoised point cloud data under the main plane P; calculate the new point cloud after filling based on the two-dimensional point set; specifically, perform convex hull calculation on the two-dimensional point set (such as Jarvis walking algorithm, Graham scanning algorithm, etc.) to obtain the two-dimensional convex hull boundary, which is an ordered point sequence.

[0079] Starting from the lowest point of the two-dimensional convex hull boundary (the point with the smallest y coordinate), a ray L0 is constructed horizontally with the lowest point as the starting point, and the ray L0 is discretized into a series of pixel points, and the minimum distance between each pixel point on the ray and the point cloud point is recorded; the ray moves upward row by row, and repeats until the entire main plane P is covered; each pixel point and its minimum distance are stored as a distance conversion map, which gives the distance information from each pixel point to the nearest point cloud point.

[0080] In the distance conversion diagram, the point cloud point corresponding to the minimum distance value of 0 (i.e. the location of the point cloud point) is identified and recorded as the seed point; for each seed point, according to the distance values ​​of its 8 neighboring pixel points, if the distance values ​​are all greater than 0, the corresponding seed point is judged to be a boundary point.

[0081] Starting from the boundary point, the boundary tracking algorithm is used to obtain the closed boundary line of the boundary point, and each pixel point on the closed boundary line is marked as an inner point of the boundary, and the inner points of the boundary constitute a new set of seed points; the new seed point set is back-projected to the space where the noise cloud data is located, and the distance-weighted average interpolation or Gaussian process regression algorithm is used for the inner points of the boundary in the new seed point set to obtain the interpolation points. The point set composed of all interpolation points is the new point cloud after filling; the new point cloud after filling is aligned with the original denoised point cloud data, and the statistical removal or moving least squares method is used to remove the overlapping point cloud points to obtain a local three-dimensional point cloud.

[0082] The local 3D point cloud is rigidly registered with the pre-built overall oral 3D model. Specifically, an iterative closest point (ICP) algorithm or a spline curve registration algorithm is used to solve the rigid body transformation matrix for aligning the local 3D point cloud with the pre-built overall oral 3D model; a real-time oral dynamic model is obtained; the real-time oral dynamic model is triangulated (such as Marching Cubes and other algorithms) to obtain a mesh model composed of a large number of triangular facets.

[0083] It should be noted that the original data is obtained through 3D scanning, and after steps such as data processing, three-dimensional reconstruction, and model optimization, and multiple human body data are collected and integrated, a standardized overall oral three-dimensional model library is finally constructed.

[0084] Based on the three-dimensional coordinate position, movement speed and real-time movement direction of the tooth cleaner in the oral cavity, the movement trajectory of the cleaning head in the real-time oral dynamic model is determined; the movement trajectory is represented by a series of position points;

[0085] For each pose point a on the motion trajectory, calculate the effective cleaning range R of the cleaning head at the pose point a; R is determined by the geometry and size of the cleaning head; traverse all triangular patches in the mesh model that are within the effective cleaning range R; for each triangular patch F, if F has been cleaned before, mark it as cleaned, and finally count the proportion of the area of ​​all triangular patches marked as cleaned to the total area of ​​the mesh model to obtain the coverage rate of the cleaned area. In order to avoid the computational overhead of large-scale patch traversal, an incremental update strategy is adopted, and only patches within a certain area near the motion trajectory of the cleaning head are marked for coverage, while the rest of the area remains unchanged. Acceleration data structures such as spatiotemporal indexes are used to further optimize the efficiency of patch traversal.

[0086] The methods for constructing a data graph of the working status of a toothbrush include:

[0087] Initialize an empty graph G, define nodes to represent the working status of the toothbrush at a certain time point, assign attributes to the working status, and the attributes are the real-time working status data at the corresponding time point; the edges between the nodes represent the transfer relationship of the toothbrush from one working state to another.

[0088] Define the span dt of time points, take the working status of the first time point when the toothbrush is turned on as the initial node n0 of the empty graph G, add n0 to the graph G, and based on the span dt, construct its working status as a new node n1 at the next time point, and find out whether there is a node with the same working status as node n1 in the graph G. If so, do not add node n1, and only update the timestamp of node n1; if not, add node n1 to the graph G, and connect node n1 with the node corresponding to the previous time point with an edge, which represents the transfer relationship of the toothbrush from the working status corresponding to node n1 to the working status corresponding to the node at the previous time point; continuously update the graph G to obtain the working status data graph of the toothbrush.

[0089] The methods for solving the preliminary optimal working parameters include:

[0090] The working parameters of the toothbrush (such as speed, vibration frequency, cleaning mode, cleaning force, etc.) are used as decision variables ; Construct an objective function, which is a weighted sum of a cleaning efficiency sub-function, a battery life sub-function, a noise value sub-function, and a cleaning intensity fluctuation sub-function;

[0091] The cleaning efficiency sub-function is a nonlinear function among the rotation speed, vibration frequency, cleaning mode and cleaning force, such as an exponential function; the battery life sub-function is an inversely proportional function of the rotation speed, vibration frequency and cleaning force; the noise value sub-function is a directly proportional function of the rotation speed and the square of the vibration frequency; the cleaning force fluctuation sub-function is a function that minimizes the sum of the force change rates at any moment and the previous moment; the form of the objective function and sub-function are adjusted according to the specific situation, and the value of each coefficient is obtained by fitting the experimental data to more accurately describe the working characteristics of the toothbrush.

[0092] Map the original decision variable onto the n3-dimensional hypersphere to obtain the spherical decision variable y; n3 is an integer greater than 1; the mapping formula is: ;in, is the center position of the n3-dimensional hypersphere, which is initially set to the middle value of the decision variable; Be a very small positive number to avoid the denominator being 0; is the nonlinear scaling factor, is a nonlinear mapping parameter used to perform adaptive nonlinear scaling on the mapped y, and Greater than 0.

[0093] Non-linear scaling factor ;in, is the maximum value of the preset nonlinear scaling parameter, is the number of iterations, is the preset maximum number of iterations, is the shape parameter for adaptive adjustment; Control The shape of the value changes, The bigger, The steeper the value change, the better it is at narrowing the search range.

[0094] N initial solutions (spherical decision variables y) are randomly generated on the n3-dimensional hypersphere to form an initial population. These initial solutions are mapped back to the space where the decision variables are located to obtain the corresponding decision variables, which are recorded as candidate solutions. Each candidate solution is substituted into the objective function, and the calculated value of the objective function is recorded as the sphere value.

[0095] According to the sphere value, select the initial solution with the largest sphere value in the current population , based on the initial solution Update the position of the center of the ball; the formula for updating the position of the center of the ball is:

[0096] ;in, is the new center position of the ball after update. , and All of them are random numbers, used to control the moving step length of the ball center, and the sum of the three must be 1; is the mean of the initial solution of the current population; To randomly find an initial solution on the n3-dimensional hypersphere; not only the information of the current optimal solution is used, but also the population mean information and random perturbations are incorporated to make the update of the sphere center position more flexible and diversified; for the i-th initial solution in the population , generate a new solution ;

[0097] ;in, is a random number that controls the deviation of the new solution. Reverse map back to the space where the decision variable is located to obtain the decision variable x_i_new corresponding to the new solution; calculate the sphere value of x_i_new, if the sphere value is smaller than the sphere value of the original initial solution, replace the original initial solution with x_i_new, otherwise retain the original initial solution; preset a pause threshold, repeat the above process until the sphere value is smaller than the pause threshold, then output the decision variable with the smallest sphere value in the current population, and use this decision variable as the preliminary optimal working parameter.

[0098] The methods for identifying and marking overlapping cleaning areas in the working status data graph of the toothbrush include:

[0099] The real-time oral dynamic model is projected onto a two-dimensional plane to obtain a two-dimensional grid image. For each posture point on the motion trajectory, the projection area of ​​the effective cleaning range of the cleaning head at the posture point on the two-dimensional grid image is calculated. In the two-dimensional grid image, the pixel value in the projection area is set to 1; a cleaning threshold is preset, a 3x3 window W is slid on the two-dimensional grid image, and the sum su of the pixel values ​​in the window W is calculated; if su is greater than the cleaning threshold, the central pixel in the window is marked as a preliminary overlapping cleaning area point; all pixels in the two-dimensional grid image are repeatedly traversed; all the preliminary overlapping cleaning area points obtained constitute the preliminary overlapping cleaning area.

[0100] Perform morphological opening operation (corrosion followed by dilation) on the preliminary overlapping cleaning areas to remove isolated noise points; perform connected domain analysis on the preliminary overlapping cleaning areas to merge adjacent areas into a connected area to obtain overlapping cleaning areas; in the toothbrush working status data graph, each node represents the working status of the toothbrush at a certain time point; for each node in the toothbrush working status data graph, search in the two-dimensional grid image for whether there is an overlapping cleaning area according to its corresponding time point; if so, add a mark to the corresponding node to mark the existence of an overlapping cleaning area; indicating that there is an overlapping cleaning area under this working state.

[0101] It should be noted that some adjustments and optimizations of the algorithm are needed to improve recognition accuracy and robustness; for example, the window size and threshold can be adjusted according to the specific situation to balance the recognition accuracy and computational overhead in overlapping areas.

[0102] The calculation method of the optimal working path includes:

[0103] Based on the working status data graph of the toothbrush, an undirected graph H is constructed. In the undirected graph H, each node also represents a working status of the toothbrush. For each edge in the working status data graph of the toothbrush, an undirected edge is added to the undirected graph H, indicating that transfers can be made between working states. If any node is marked as having overlapping cleaning areas, a self-loop edge is added to the undirected graph H, and the self-loop edge indicates continuing cleaning in that state.

[0104] For the undirected edge L(I, J) between two nodes I and J in the undirected graph H, a weight w(I, J) is assigned to it, where the weight is the weighted sum of the time and energy consumption required for the transition between the working states corresponding to nodes I and J.

[0105] If node I is marked as having an overlapping cleaning area, the weight of its corresponding self-loop edge is set to a preset value (very small) to encourage continued cleaning in this state; all nodes in the undirected graph H are randomly assigned to K non-overlapping partitions, each partition represents a potential working path; the initial temperature T0 and the termination temperature Tmin are set.

[0106] At the current temperature, randomly select two different partitions P_v and P_u, where v and u are both partition indexes and do not index the same partition at the same time; randomly select a node s1 from P_v and a node s2 from P_u, and calculate the total cost change of moving s1 from P_v to P_u and moving s2 from P_u to P_v ;in, represents the cost change caused by moving s1 from P_v to P_u; represents the cost change caused by moving s2 from P_u to P_v;

[0107] It should be noted that The sum of the weights of the edges between s1 and other nodes in P_u after s1 is added to P_u, minus the sum of the weights of the edges between s2 and other nodes in P_v. The sum of the edge weights between s2 and other nodes in P_v after s2 is added to P_v, minus the sum of the edge weights between s1 and other nodes in P_u.

[0108] like is less than or equal to 0, then accept the move; otherwise, Accept this move; repeat until the temperature reaches the termination temperature Tmin; For the The temperature of the iteration;

[0109] ;in, For the The temperature of the iteration; For the The total cost change of iterations is For the The total cost of the iteration. Specifically, the total cost is the sum of the internal cost and external cost of each partition. The internal cost is the sum of the weights of the edges between all nodes in the partition. The external cost represents the minimum cost from the corresponding partition to other partitions. That is, for each node in the partition, calculate the sum of the weights of the edges to all nodes in other partitions and take the minimum value of these sums. is a constant that controls the rate at which the temperature drops.

[0110] After the temperature reaches the termination temperature Tmin, the partition P* with the smallest total cost is selected from the K partitions. In the partition P*, an optimal working path is constructed according to the connection relationship between the nodes; specifically, the starting state ns and the ending state nf of the toothbrush are pre-specified. If the working states corresponding to the nodes in the partition P* include ns and nf, they are used as the starting point and end point of the path respectively. If the partition P* does not contain ns or nf, the nodes closest to them are found as the starting point and end point; all nodes in the partition P* are traversed, and for each node h, other nodes connected to it are found, and these connected nodes are added to the adjacency list of h. Based on the adjacency list, a breadth-first search (BFS) or a depth-first search (DFS) is performed to obtain a path from ns to nf. Path Pa(ns, nf), since the path obtained by BFS or DFS may not be optimal, for each pair of adjacent nodes in the path Pa(ns, nf), check whether there is a shorter path Path' that can replace the road segment corresponding to the original adjacent nodes. If such a path exists, replace the original road segment with it to shorten the entire path; repeat the above steps until the path can no longer be shortened, and obtain the optimized path; for the optimized path, check whether each node is marked as having overlapping cleaning areas. If it is marked, insert a self-loop in the path, indicating that cleaning needs to be continued in this state to avoid repeated cleaning; after the above steps, an optimal working path that avoids repeated cleaning is obtained; output the path as the optimal working path of the toothbrush.

[0111] The methods of combining the optimal working path with the preliminary optimal working parameters include:

[0112] The preliminary optimal working parameters are taken as the benchmark parameter set, and each parameter in the benchmark parameter set is the benchmark parameter; the optimal working path is discretized, and the optimal working path is sampled at fixed time intervals or spatial intervals to obtain a series of discrete working state points, each working state point corresponds to the working parameters of the toothbrush at a time point or spatial position; for each discrete working state point on the optimal working path, its corresponding working parameters are extracted and recorded as path parameters, which also include rotation speed, vibration frequency, cleaning mode, cleaning force, etc.; these path parameters are arranged in the order of the optimal working path to form a path parameter sequence.

[0113] For each path parameter in the path parameter sequence, it is compared with the benchmark parameter in the benchmark parameter set. If the path parameter is better than the benchmark parameter (such as higher cleaning efficiency, lower energy consumption, etc.), the benchmark parameter is replaced by the path parameter; if the path parameter is worse than the benchmark parameter, the benchmark parameter is kept unchanged.

[0114] After the above fusion, there may be a sudden change in the working parameters between adjacent time or space points. The fused parameter sequence is smoothed using methods including moving average filtering, Gaussian filtering, Savitzky-Golay filtering, etc. After smoothing, a comprehensive optimal working parameter sequence is obtained, and the optimal working parameter sequence is used as the final optimal working strategy of the toothbrush.

[0115] The cloud server encodes the optimal working strategy into a data packet, including working parameter sequence, timestamp and other information; establishes a communication connection with the mobile phone, and can use HTTP, WebSocket or a custom communication protocol; sends the encoded data packet to the mobile phone through the selected communication protocol; the mobile phone maintains a communication connection with the cloud server; when receiving a data packet from the cloud, decodes and verifies the data packet; if the data packet is legal, parses out the various parameters in the optimal working strategy; encodes the optimal working strategy into a data packet according to the communication protocol agreed with the toothbrush; sends the data packet to the toothbrush through the established wireless connection; reliable transmission mechanisms, such as data segmentation and retransmission mechanisms, can be used to ensure the integrity and correctness of data transmission.

[0116] This embodiment can monitor the working status of the toothbrush in real time, and comprehensively obtain data such as oral environment, cleaning force, and movement trajectory, laying the foundation for subsequent intelligent analysis and optimization; through big data processing and artificial intelligence algorithms in the cloud, it can independently analyze the working efficiency, energy consumption, noise level, etc. of the toothbrush, and calculate the optimal working parameter combination to maximize the cleaning quality while reducing energy consumption and noise interference; secondly, it can greatly improve the cleaning efficiency, save cleaning time, and effectively extend the battery life of the toothbrush, reducing unnecessary waste of resources; thirdly, through the wireless connection between the mobile phone and the toothbrush, the optimal working strategy calculated in the cloud is sent to the toothbrush, realizing remote intelligent control and optimization of the working status.

[0117] Example 2

[0118] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, which provides a remote monitoring and control method for a tooth cleaner based on mobile phone Internet of Things, including:

[0119] Step 1: Establish a wireless communication connection between the tooth cleaner and the mobile phone, and obtain real-time working status data of the tooth cleaner through the mobile phone;

[0120] Step 2: Transmit the acquired real-time working status data to the cloud server, and construct a working status data graph of the toothbrush on the cloud server;

[0121] Step 3: On the cloud server, optimize and analyze the working status data graph of the toothbrush to obtain the preliminary optimal working parameters of the toothbrush;

[0122] Step 4, identifying and marking the overlapping cleaning areas in the working state data graph of the toothbrush; calculating the optimal working path based on the working state data graph with the overlapping cleaning areas marked, combining the optimal working path with the preliminary optimal working parameters, and obtaining the final optimal working strategy;

[0123] Step 5: The final optimal working strategy on the cloud server is transmitted to the mobile phone via wireless communication; the mobile phone receives the final optimal working strategy and sends it to the tooth cleaner via wireless communication to adjust the working state of the tooth cleaner.

[0124] Example 3

[0125] The present embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned method for remote monitoring and control of a toothbrush based on mobile phone Internet of Things is implemented.

[0126] Since the electronic device introduced in this embodiment is an electronic device used to implement a method for remote monitoring and controlling a toothbrush based on a mobile phone Internet of Things in the embodiment of this application, based on the method for remote monitoring and controlling a toothbrush based on a mobile phone Internet of Things introduced in the embodiment of this application, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as a person skilled in the art implements the electronic device used in the method for remote monitoring and controlling a toothbrush based on a mobile phone Internet of Things in the embodiment of this application, it belongs to the scope of protection of this application.

[0127] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0128] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A remote monitoring and control system for tooth cleaners based on mobile phone Internet of Things, characterized in that: include: A connection acquisition module is used to establish a wireless communication connection between the tooth cleaner and the mobile phone, and obtain the real-time working status data of the tooth cleaner through the mobile phone; A comprehensive construction module is used to transmit the acquired real-time working status data to a cloud server, and to construct a working status data graph of the toothbrush on the cloud server; the working status data graph of the toothbrush includes the three-dimensional coordinate position, movement speed and real-time movement direction of the toothbrush in the oral cavity, and the coverage rate of the cleaned area; Construct a real-time oral dynamic model, and obtain the coverage rate of the cleaned area according to the real-time oral dynamic model; An optimization module is used to optimize and analyze the working status data graph of the toothbrush on the cloud server to obtain preliminary optimal working parameters of the toothbrush; A strategy fitting module is used to identify and mark overlapping cleaning areas in the working state data graph of the toothbrush; Based on the working status data graph with overlapping cleaning areas marked, the optimal working path is calculated, and the optimal working path is combined with the preliminary optimal working parameters to obtain the final optimal working strategy; Based on the working state data graph of the toothbrush, an undirected graph H is constructed. In the undirected graph H, each node also represents a working state of the toothbrush. According to the connection relationship between the nodes, an optimal working path is constructed, and the preliminary optimal working parameters are used as the benchmark parameter set, and each parameter in the benchmark parameter set is a benchmark parameter; the optimal working path is discretized to obtain a series of discrete working state points, each working state point corresponds to the working parameter of the toothbrush at a time point or a spatial position, which is recorded as a path parameter, and these path parameters are arranged in the order of the optimal working path to form a path parameter sequence. For each path parameter in the path parameter sequence, it is compared with the benchmark parameter in the benchmark parameter set to obtain the final optimal working strategy; The sending module is used to transmit the final optimal working strategy on the cloud server to the mobile phone through wireless communication; the mobile phone receives the final optimal working strategy and sends it to the toothbrush through wireless communication to adjust the working state of the toothbrush; each module is connected by wired and / or wireless means.

2. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 1 is characterized in that: The real-time working status data of the toothbrush includes working parameters, position movement parameters and oral environment parameters; the working parameters include rotation speed, vibration frequency, cleaning mode, cleaning force and battery power; the oral environment parameters include oral temperature, oral humidity and saliva pH value.

3. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 2 is characterized in that: The method for obtaining the cleaned area coverage rate includes: A structured light sensor is integrated into the cleaning head of the toothbrush. During the use of the toothbrush, the structured light sensor continuously scans and reconstructs the three-dimensional point cloud data of the area around the cleaning head. De-noising the three-dimensional point cloud data to obtain de-noised point cloud data, and filling the de-noised point cloud data to obtain a local three-dimensional point cloud; The local 3D point cloud is rigidly registered with the pre-built overall oral 3D model to obtain a real-time oral dynamic model; the real-time oral dynamic model is triangulated to obtain a mesh model composed of triangular facets; Based on the three-dimensional coordinate position, movement speed and real-time movement direction of the tooth cleaner in the oral cavity, the movement trajectory of the cleaning head in the real-time oral dynamic model is determined; the movement trajectory is represented by a series of position points; For each pose point a on the motion trajectory, calculate the effective cleaning range R of the cleaning head at the pose point a; traverse all triangular facets in the mesh model that are within the effective cleaning range R; for each triangular facet F, if F has been cleaned before, mark it as cleaned, and finally count the proportion of the area of ​​all triangular facets marked as cleaned to the total area of ​​the mesh model to obtain the coverage rate of the cleaned area.

4. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 3 is characterized in that: The method of filling the denoised point cloud data includes: Define a three-dimensional coordinate system in the space where the denoised point cloud data is located, calculate the geometric center C of the denoised point cloud data; obtain the coordinate offset q_p of each point cloud point p in the denoised point cloud data relative to the geometric center C; calculate the sum sum_q of the coordinate offsets of all point cloud points; and calculate the comprehensive covariance matrix cov; Among them, S(p) is the comprehensive weight of point p in the point cloud, (q_p) T is the transpose of q_p, (sum_q) T is the transpose of sum_q; Comprehensive weight Where p_k1 is the maximum curvature at point p of the point cloud, p_k2 is the minimum curvature at point p of the point cloud, and α is an adjustment parameter whose value is in the interval (0, 1]; Perform eigenvalue decomposition on the covariance matrix cov to obtain three eigenvectors and corresponding eigenvalues; take the direction of the eigenvector corresponding to the largest eigenvalue as the main direction; take the geometric center C as the origin, take the eigenvector corresponding to the main direction as the normal vector, and establish a main plane P; Orthogonally project the denoised point cloud data onto the principal plane P along the main direction, and record the two-dimensional point set of the denoised point cloud data under the principal plane P; calculate the new padded point cloud based on the two-dimensional point set; align the new padded point cloud with the original denoised point cloud data, and use statistical removal or moving least squares method to remove overlapping point cloud points to obtain a local three-dimensional point cloud.

5. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 4 is characterized in that: The method of calculating the filled new point cloud based on the two-dimensional point set includes: Calculate the convex hull of the two-dimensional point set to obtain the two-dimensional convex hull boundary; start from the lowest point of the two-dimensional convex hull boundary, and construct a ray L0 horizontally with the lowest point as the starting point, discretize the ray L0 into a series of pixel points, and record the minimum distance between each pixel point on the ray and the point cloud point; move the ray upward row by row, and repeat until the entire main plane P is covered; store each pixel point and its minimum distance as a distance conversion map; In the distance conversion diagram, identify the point cloud point corresponding to the minimum distance value of 0, and record it as the seed point; for each seed point, according to the distance value of its 8 neighboring pixel points, if the distance value is greater than 0, then the corresponding seed point is judged to be a boundary point; starting from the boundary point, use the boundary tracking algorithm to obtain the closed boundary line of the boundary point, mark each pixel point on the closed boundary line as a boundary inner point, and form a new seed point set with the boundary inner points; back-project the new seed point set back to the space where the noise cloud data is located, use the distance weighted average interpolation or Gaussian process regression algorithm for the boundary inner points in the new seed point set to obtain the interpolation points, and the point set composed of all interpolation points is the new point cloud after filling.

6. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 5 is characterized in that: The method of constructing the working status data graph of the tooth cleaner includes: Initialize an empty graph G, define nodes to represent the working state of the toothbrush at a certain time point, assign attributes to the working state, and the attributes are the real-time working state data at the corresponding time point; the edges between nodes represent the transfer relationship of the toothbrush from one working state to another; Define the span dt of time points, take the working status of the first time point when the toothbrush is turned on as the initial node n0 of the empty graph G, add n0 to the graph G, and based on the span dt, construct its working status as a new node n1 at the next time point, and find out whether there is a node with the same working status as node n1 in the graph G. If so, do not add node n1, and only update the timestamp of node n1; if not, add node n1 to the graph G, and connect node n1 with the node corresponding to the previous time point with an edge, which represents the transfer relationship of the toothbrush from the working status corresponding to node n1 to the working status corresponding to the node at the previous time point; continuously update the graph G to obtain the working status data graph of the toothbrush.

7. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 6 is characterized in that: The method for solving the preliminary optimal working parameters includes: The working parameters of the toothbrush are used as decision variables x to construct an objective function, which is a weighted sum of the cleaning efficiency sub-function, the battery life sub-function, the noise value sub-function, and the cleaning force fluctuation sub-function; Map the original decision variable to the n3-dimensional hypersphere to obtain the spherical decision variable y; n3 is an integer greater than 1; the mapping formula is: Where a1 is the center position of the n3-dimensional hypersphere; ε is a positive number; β is a nonlinear scaling factor, and γ is a nonlinear mapping parameter; Non-linear scaling factor Wherein, β_max is the maximum value of the preset nonlinear scaling parameter, iter is the number of iterations, and max_iter is the preset maximum number of iterations; is the shape parameter for adaptive adjustment; Randomly generate N initial solutions on the n3-dimensional hypersphere to form an initial population, and map these initial solutions back to the space where the decision variables are located to obtain the corresponding decision variables, which are recorded as candidate solutions; substitute each candidate solution into the objective function, and record the calculated value of the objective function as the sphere value; According to the sphere value, select the initial solution ybest with the largest sphere value in the current population, and update the sphere center position based on the initial solution ybest; the formula for updating the sphere center position is: a1_new=a1+r1×(ybest-a1)+r2×(ymean-a1)+r3× (yrand-a1); where a1_new is the new sphere center position after update, r1, r2 and r3 are all random numbers, and the sum of the three is 1; ymean is the mean of the initial solution of the current population; yrand is to randomly find an initial solution on the n3-dimensional hypersphere; for the i-th initial solution y_i in the population, generate a new solution y_i_new; y_i_new=y_i+r×(ybest-y_i)+r×(a1_new-y_i); where r is a random number; map the new solution y_i_new back to the space where the decision variable is located to obtain the decision variable x_i_new corresponding to the new solution; calculate the sphere value of x_i_new, if the sphere value is smaller than the sphere value of the original initial solution, replace the original initial solution with x_i_new, otherwise keep the original initial solution; A pause threshold is preset and the process is repeated until the sphere value is less than the pause threshold. Then the decision variable with the smallest sphere value in the current population is output and this decision variable is used as the initial optimal working parameter.

8. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 7 is characterized in that: The method of identifying and marking the overlapping cleaning area in the tooth cleaner working state data graph includes: The real-time oral dynamic model is projected onto a two-dimensional plane to obtain a two-dimensional grid image. For each posture point on the motion trajectory, the projection area of ​​the effective cleaning range of the cleaning head at the posture point on the two-dimensional grid image is calculated. In the two-dimensional grid image, the pixel value in the projection area is set to 1; Preset the cleaning threshold, slide a 3x3 window W on the two-dimensional grid image, and calculate the sum su of the pixel values ​​in the window W; if su is greater than the cleaning threshold, mark the central pixel in the window as the preliminary overlapping cleaning area point; repeatedly traverse all pixels in the two-dimensional grid image; all the preliminary overlapping cleaning area points obtained constitute the preliminary overlapping cleaning area; Perform morphological opening operation on the preliminary overlapping clean areas; perform connected domain analysis on the preliminary overlapping clean areas, merge adjacent areas into a connected area, and obtain overlapping clean areas; For each node in the toothbrush working status data graph, according to its corresponding time point, find out whether there is an overlapping cleaning area in the two-dimensional grid image; if so, add a mark to the corresponding node to mark that there is an overlapping cleaning area.

9. The remote monitoring and control system for tooth cleaner based on mobile phone Internet of Things according to claim 8, characterized in that: The calculation method of the optimal working path includes: For each edge in the working state data graph of the toothbrush, an undirected edge is added to the undirected graph H. If any node is marked as having an overlapping cleaning area, a self-loop edge is added to the undirected graph H. For the undirected edge L(I, J) between two nodes I and J in the undirected graph H, a weight w(I, J) is assigned to it, and the weight is the weighted sum of the time and energy consumption required for the transfer between the working states corresponding to the nodes I and J. If the node I is marked as having an overlapping cleaning area, the weight of the corresponding self-loop edge is set to a preset value. All nodes in the undirected graph H are randomly assigned to K non-overlapping partitions, each partition representing a potential working path. Set the initial temperature T0 and the end temperature Tmin; at the current temperature, randomly select two different partitions P_v and P_u, where v and u are both partition indexes and do not index the same partition at the same time; randomly select a node s1 from P_v and a node s2 from P_u, and calculate the total cost change ΔC = ΔC1 + ΔC2 for moving s1 from P_v to P_u and s2 from P_u to P_v; ΔC1 represents the cost change value caused by moving s1 from P_v to P_u; ΔC2 represents the cost change value caused by moving s2 from P_u to P_v; If ΔC is less than or equal to 0, then accept the move; otherwise, Accept this move; repeat until the temperature reaches the termination temperature Tmin; T(f) is the temperature of the fth iteration; Wherein, T(f-1) is the temperature of the f-1th iteration; ΔC(f-1) is the total cost change of the f-1th iteration, C(f-1) is the total cost of the f-1th iteration, and γ1 is a constant; after the temperature reaches the termination temperature Tmin, the partition P* with the smallest total cost is selected from the K partitions, and in the partition P*, an optimal working path is constructed according to the connection relationship between the nodes.

10. A method for remote monitoring and control of a tooth cleaner based on mobile phone Internet of Things, which is implemented based on the remote monitoring and control system of a tooth cleaner based on mobile phone Internet of Things according to any one of claims 1 to 9, characterized in that: include: Step 1: Establish a wireless communication connection between the tooth cleaner and the mobile phone, and obtain real-time working status data of the tooth cleaner through the mobile phone; Step 2: Transmit the acquired real-time working status data to the cloud server, and construct a working status data graph of the toothbrush cleaner on the cloud server; Step 3: On the cloud server, optimize and analyze the working status data graph of the toothbrush to obtain the preliminary optimal working parameters of the toothbrush; Step 4, identifying and marking overlapping cleaning areas in the working status data graph of the toothbrush; Based on the working status data graph with overlapping cleaning areas marked, the optimal working path is calculated, and the optimal working path is combined with the preliminary optimal working parameters to obtain the final optimal working strategy; Step 5: The final optimal working strategy on the cloud server is transmitted to the mobile phone via wireless communication; the mobile phone receives the final optimal working strategy and sends it to the tooth cleaner via wireless communication to adjust the working state of the tooth cleaner.

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