Dust collector automatic adjustment control method and system based on multi-source data

Abstract

The invention relates to the technical field of intelligent household appliances, in particular to a dust collector automatic adjustment control method and system based on multi-source data. The method comprises the following steps: acquiring design data of the dust collector; extracting dust collector structure design data according to the dust collector design data; constructing a dust collector three-dimensional model based on the dust collector structure design data; identifying a ground material type according to the three-dimensional model of the dust collector to obtain carpet material data and tile material data; detecting a particle gas flow based on the carpet material data; identifying the dust coverage based on the tile material data; determining the dust concentration according to the particle gas flow and the dust coverage; extracting air duct structure data according to the dust collector design data; and performing filter screen blockage evolution simulation according to the dust concentration and the air duct structure data to obtain filter screen blockage data. According to the invention, the automatic cleaning path adjustment and obstacle avoidance control accuracy of the dust collector in a multi-material ground environment is improved based on an intelligent household electrical appliance technology.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent household appliances, and particularly to a vacuum cleaner automatic adjustment control method and system based on multi-source data. Background Art

[0002] Vacuum cleaner adjustment control integrates sensor data such as dust concentration, air duct pressure, and floor material type, and uses an adaptive control algorithm to adjust the suction force, cleaning path, and working mode according to real-time environmental changes to ensure the best cleaning effect in different working environments, but there are still many technical defects. Its multi-source data fusion processing ability is limited. Usually, only simple image or pressure sensing data is collected, lacking in-depth fusion modeling of the vacuum cleaner structure design data and three-dimensional geometric model, resulting in the system being unable to perform simulation adjustment and parameter optimization according to the structural characteristics of the vacuum cleaner itself. The accuracy of floor material identification is insufficient, and multi-level physical detection processes such as fiber resilience detection, infrared reflectance calculation, and dust particle path tracking cannot be achieved, leading to deviation in the judgment of dust type and distribution, and ultimately affecting the accuracy of dust concentration calculation. Traditional filter load assessment usually relies on single-sensing feedback, such as air flow rate change or suction reduction signal, without combining multiple factors such as air duct geometric structure, particle concentration, and temperature and humidity air flow for dynamic simulation, so the evolution trend of filter clogging is judged inaccurately. Traditional path planning and obstacle avoidance control mechanisms often adopt fixed cleaning routes or simple obstacle avoidance algorithms, lacking the coupling optimization logic between filter clogging level and cleaning efficiency, and unable to flexibly adjust the cleaning path and obstacle avoidance strategy according to the clogging degree, resulting in reduced cleaning efficiency in high-load or complex environments, and even problems such as path conflicts and obstacle avoidance failure. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a vacuum cleaner automatic adjustment control method and system based on multi-source data to solve at least one of the above technical problems.

[0004] To achieve the above object, a vacuum cleaner automatic adjustment control method based on multi-source data includes the following steps: Step S1: Obtain vacuum cleaner design data; extract vacuum cleaner structure design data according to the vacuum cleaner design data; construct a three-dimensional model of the vacuum cleaner based on the vacuum cleaner structure design data; Step S2: Identify the floor material type according to the three-dimensional model of the vacuum cleaner to obtain carpet material data and ceramic tile material data; detect the particle gas flow based on the carpet material data; identify the dust coverage based on the ceramic tile material data; determine the dust concentration according to the particle gas flow and the dust coverage; Step S3: Extract air duct structure data according to the vacuum cleaner design data; perform a simulation of filter clogging evolution according to the dust concentration and the air duct structure data to obtain filter clogging data; judge the filter load status based on the filter clogging data; Step S4: Divide the clogging level according to the filter load status; adjust the dust cleaning path according to the clogging level; optimize the automatic obstacle avoidance strategy according to the dust cleaning path; transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the automatic obstacle avoidance control task of the vacuum cleaner.

[0005] The present invention realizes the precise modeling of the internal configuration and air duct structure of the vacuum cleaner by integrating the vacuum cleaner structure design data and 3D modeling technology, providing an accurate basis for subsequent simulation analysis. Based on the 3D model, the type of ground material is identified, and combined with physical properties such as fiber resilience and infrared reflectivity, the recognition accuracy of surfaces such as carpets and tiles is improved, thereby enhancing the accuracy of particle path detection and dust concentration evaluation. A filter clogging evolution model is constructed using the dust concentration and air duct structure parameters to dynamically simulate the clogging change process and enhance the ability to judge the filter load status. The cleaning path is dynamically adjusted based on the clogging level, and a regional priority division and spiral path construction strategy are introduced to improve the pertinence and efficiency of path planning. At the same time, the obstacle avoidance strategy is optimized in combination with the clogging state, and the centralized management and remote control of path and obstacle avoidance control data are realized through the management cloud platform to ensure real-time response and fine execution of the cleaning task in a complex environment, significantly improving the overall cleaning efficiency and control accuracy of the system.

[0006] Preferably, this specification also provides a vacuum cleaner automatic adjustment control system based on multi-source data for executing the vacuum cleaner automatic adjustment control method based on multi-source data as described above. This vacuum cleaner automatic adjustment control system based on multi-source data: Vacuum cleaner 3D model construction module: used to obtain the vacuum cleaner design data; extract the vacuum cleaner structure design data according to the vacuum cleaner design data; construct a vacuum cleaner 3D model based on the vacuum cleaner structure design data; Dust concentration detection module: used to identify the type of ground material according to the vacuum cleaner 3D model to obtain carpet material data and tile material data; detect the particle gas flow based on the carpet material data; identify the dust coverage based on the tile material data; determine the dust concentration according to the particle gas flow and the dust coverage; Filter load status identification module: used to extract the air duct structure data according to the vacuum cleaner design data; perform a filter clogging evolution simulation according to the dust concentration and the air duct structure data to obtain filter clogging data; judge the filter load status based on the filter clogging data; Vacuum cleaner automatic obstacle avoidance module: used to divide the clogging level according to the filter load status; adjust the dust cleaning path according to the clogging level; optimize the automatic obstacle avoidance strategy according to the dust cleaning path; transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the automatic obstacle avoidance control task of the vacuum cleaner.

[0007] The automatic adjustment control system of the vacuum cleaner based on multi-source data of the present invention can implement any one of the automatic adjustment control methods of the vacuum cleaner based on multi-source data of the present invention. It is a medium for coordinating the operations and signal transmissions between various modules to complete the automatic adjustment control method of the vacuum cleaner based on multi-source data. The internal modules of the system cooperate with each other to improve the accuracy of the automatic cleaning path adjustment and obstacle avoidance control of the vacuum cleaner in a multi-material floor environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings. Figure 1 It is a schematic flowchart of the steps of an automatic adjustment control method of a vacuum cleaner based on multi-source data of the present invention; Figure 2 It is a detailed schematic flowchart of step S1 in the present invention; Figure 3 It is a detailed schematic flowchart of step S3 in the present invention; The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The technical method of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of the present invention.

[0010] In addition, the drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0012] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a vacuum cleaner automatic adjustment control method based on multi-source data, and the method includes the following steps: Step S1: Obtain vacuum cleaner design data; extract vacuum cleaner structure design data according to the vacuum cleaner design data; construct a three-dimensional model of the vacuum cleaner based on the vacuum cleaner structure design data; In this embodiment, by calling the engineering drawings and structure parameter data in the vacuum cleaner whole machine structure design database, the structure information of each module of the vacuum cleaner is extracted, including the structure parameters of the fan assembly (such as the impeller diameter of 80 mm and the rotation speed of 12,000 rpm), the geometric dimensions of the air duct (such as the main air duct diameter of 25 mm, the secondary air duct diameter of 15 mm, and the lengths of 300 mm and 150 mm respectively), the type and size of the suction port (the suction port opening length of 70 mm, width of 20 mm, and the leading edge inclination angle of 20°), the filter size (such as the filter element diameter of 60 mm and thickness of 30 mm), as well as the position of the battery pack and the distribution of heat sinks. After the above data is exported by the structure design system in JSON format, all structure nodes and boundary constraint relationships are parsed through a parametric modeling tool (such as the SolidWorks API programming interface) to construct a structure parameter database. Subsequently, using the modeling module based on structure parameters in a three-dimensional geometric modeling tool (such as AutoCAD Mechanical or SolidEdge), the spatial form of the air duct, the position of internal components, and the air flow guiding structure are defined, and the three-dimensional geometric figure splicing modeling process is realized through solid modeling and Boolean operations. Finally, a complete three-dimensional geometric model file of the vacuum cleaner (such as.STEP or.IGES format) is output, providing a geometric reference basis for subsequent ground recognition and path simulation.

[0013] Step S2: Identify the type of ground material according to the three-dimensional model of the vacuum cleaner to obtain carpet material data and ceramic tile material data; detect the particulate gas flow based on the carpet material data; identify the dust coverage based on the ceramic tile material data; determine the dust concentration according to the particulate gas flow and the dust coverage; In this embodiment, a near-infrared image acquisition device installed at the bottom of the vacuum cleaner (such as an infrared CMOS sensor with a wavelength of 850 nm, a frame rate of 60 fps, and a resolution of 1280×720) is used to obtain a continuous sequence of ground reflection images during the cleaning process. Carpet materials and ceramic tile materials are classified according to the difference in near-infrared reflectivity. Among them, carpet with a reflectivity lower than 35% is defined as a high-absorption material, and ceramic tile with a reflectivity higher than 60% is defined as a high-reflection material. The image classification process uses a grayscale statistical distribution histogram combined with the K-means clustering algorithm for material segmentation. During the detection of the particulate gas flow, concentration data of suspended particles are obtained through an integrated PM2.5 particle sensor with particle size recognition (detection range: 0.3μm - 10μm, sensitivity ±5%). The sampling value at each point is recorded every 0.1 s, and the particle flow rate (unit: mg / s) is obtained by superimposing the air flow velocity (calculated from the fan speed and the suction port area and calibrated by actual measurement using an anemometer). For the ceramic tile material area, the dust coverage ratio is extracted by calculating the difference between the chromaticity deviation and the mean value of the image pixels in the same area, and a pixel color difference change rate with a threshold of 12% is used as the dust coverage determination criterion. By comprehensively considering the particulate gas flow rate and the dust coverage value, the dust concentration of the current ground area (unit: mg / cm²) is obtained using a linear combination function with weighting factors of 0.7 and 0.3, which is used as the input for the filter simulation.

[0014] Step S3: Extract the air duct structure data according to the vacuum cleaner design data; perform a filter clogging evolution simulation based on the dust concentration and the air duct structure data to obtain filter clogging data; judge the filter load status based on the filter clogging data; In this embodiment, by obtaining specific parameters such as the air duct length, cross-sectional area, and bending angle in the structure parameter database in Step S1 (such as the main air duct length of 300 mm, inner diameter of 25 mm, first turning angle of 30°, and second turning angle of 45°), and the dust concentration value (such as 20 mg / cm²) obtained from Step S2 as input conditions, a particle simulation engine based on the finite volume method (such as the Discrete Phase Model module built in Ansys Fluent) is used to perform a filter clogging evolution modeling. During the simulation, the particle size distribution is set in the range of 0.3μm to 10μm, the initial filter porosity is set to 45%, and as the simulation time progresses (the simulation duration is set to 60 s), the change in particle deposition density is recorded every 0.5 s to obtain a function of the porosity changing with time. The clogging increment coefficient is obtained through curve fitting, and the remaining effective ventilation area of the filter at the end of the simulation is used as the filter clogging data (unit: cm²). The criterion for judging the filter load status is as follows: when the remaining ventilation area is lower than 40% of the initial area (i.e., the ventilation area is reduced by more than 60%), it is defined as a heavy load; when it is lower than 70%, it is defined as a medium load; when it is higher than 90%, it is defined as a light load. The load status will be transmitted to the next clogging level classification module.

[0015] Step S4: Divide the clogging level according to the filter load status; adjust the dust cleaning path according to the clogging level; optimize the automatic obstacle avoidance strategy according to the dust cleaning path; transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the automatic obstacle avoidance control task of the vacuum cleaner.

[0016] In this embodiment, the ratio of the remaining ventilation area of ​​the filter to the initial ventilation area is extracted according to the filter load state data, and the filter load rate is calculated according to the calculation formula of filter load rate = (1-remaining ventilation area / initial ventilation area) × 100%. If the filter load rate is less than 10%, it is classified as a light blockage level, if the filter load rate is between 10% and 30%, it is classified as a moderate blockage level, and if the filter load rate exceeds 30%, it is classified as a heavy blockage level. The blockage level is input into the path adjustment module as a control parameter. The path adjustment module performs path search based on the environment map after grid discretization, and adopts the A algorithm with the heuristic function as the Euclidean distance from the current grid node to the target area to realize path planning. In the state of severe blockage, the dust concentration image data obtained in step S2 is read, and the grid area with dust concentration greater than 15mg / cm² is set as a low priority area. The cost weighting coefficient is increased from the default 1.0 to 2.5 to increase the possibility of avoiding the area in the search path, so as to automatically reduce the cleaning frequency of the area with increased filter load when planning the path. The planning cycle of the cleaning path is set to 30 seconds. In each cycle, the ground data is collected again and the path cost is recalculated. The dynamic obstacle avoidance strategy update module integrates obstacle avoidance rules based on the path, and uses 4 sets of ultrasonic sensors (single set transmission frequency 40kHz, detection range 0.02 The system uses a grid map to map the distance of obstacles in the surrounding environment. The system uses a 3D image ... The control path generated by the new path and obstacle strategy is packaged in JSON format, including fields such as "direction", "distance", "turn_angle", etc., and sent to the vacuum cleaner management cloud platform through a WiFi communication module (compliant with the IEEE802.11n standard, operating frequency of 2.4GHz). The cloud platform parses the received control package and pushes it synchronously to the internal processor of the STM32F429ZIT6 chip of the vacuum cleaner main control module. According to the received path and obstacle avoidance instructions, the execution order and value of the wheel drive module, sensor synchronization module, and fan speed control module are controlled, thereby completing the command closed loop of path adjustment and dynamic obstacle avoidance.

[0017] Preferably, step S1 specifically comprises: Step S11: obtaining vacuum cleaner design data; In this embodiment, by calling the design documents in the vacuum cleaner product structure database, the complete design data of the vacuum cleaner is obtained. The design data includes the overall assembly drawing, part drawings, process drawings, and design specifications. The data format uniformly adopts the STEP (ISO 10303-21) standard, and the reading method is based on an industrial CAD parser. By parsing the STEP file line by line, the geometric description statements, feature dimensions, annotation relationships, and technical tolerances of each part are extracted. The design data also needs to include part numbers, design version numbers, material identifiers, and corresponding part weight parameters. To ensure data consistency, hash value comparison verification (using the SHA-256 hash function) and version number verification are performed on all the extracted data to ensure that the design version is the latest version Vx.1.0 or above before proceeding with subsequent processing.

[0018] Step S12: Extract the vacuum cleaner structure design data according to the vacuum cleaner design data; In this embodiment, according to the vacuum cleaner design data, the structural composition relationship of each part is identified, and the corresponding structural design data is extracted based on the assembly order hierarchy in the assembly drawing. Specifically, the way to extract the structural design data is to analyze each component one by one according to the assembly reference plane and assembly constraint conditions (such as coaxial, parallel, perpendicular, contact, etc.) defined in the drawing, and extract structural parameters such as the outer contour boundary dimensions, mounting hole positions, locating pin slot dimensions, snap structure dimensions, and fastener hole pitches for each component. The data extraction tool is the geometric feature extraction module in the CAD secondary development interface, and the output format is a JSON structure. The fields include "part_id", "bounding_box", "hole_position", "fastener_spec", "constraint_type", etc. The unit is uniformly in millimeters (mm), and the accuracy is controlled within ±0.01 mm.

[0019] Step S13: Construct the geometric profiles of the components based on the vacuum cleaner structure design data; In this embodiment, by calling the geometric processing engine, the geometric profile reconstruction of each extracted part structure design data is performed. The NURBS (Non-Uniform Rational B-Spline) surface reconstruction algorithm is used to perform multi-segment continuous interpolation reconstruction on complex surfaces such as the inner wall of the air duct and the roller brush housing surface. Straight lines and circular structures are defined using analytical expressions, and the boundary point coordinates are obtained from the point position descriptions in the STEP data. The boundary segments are interpolated using B-splines to generate smooth curves. During the reconstruction process, the minimum curvature radius constraint is used, and the minimum value is set to 1 mm to prevent the geometric model from having overly sharp corners. The geometric profile is output in the format of 3D coordinate point cloud data, and the starting point, ending point, curvature information, and patch direction information of each boundary surface are retained for subsequent connection relationship identification.

[0020] Step S14: defining connection relationships according to the geometric outlines of the components; In this embodiment, a connection relationship determination algorithm based on rule matching is adopted to establish a connection map between parts by analyzing the connection hole size, relative position relationship, direction vector and boundary contact line segment length in the geometric contour of the parts. Specifically, it includes three types of connection methods: snap connection, screw connection and slide connection. The connection rules are set as follows: when the aperture size difference between the two parts is less than 0.1mm, the center point distance is less than 0.2mm, and the axis direction cosine value is greater than 0.98, it is determined to be a screw connection; when the edge line segment length difference is less than 0.5mm and the face overlap area exceeds 70%, it is determined to be a slide connection; the snap connection is determined by detecting whether there is a locking step structure in the curve feature of the docking surface. The above connection relationship is stored in the form of a graph structure, with nodes representing parts, edges representing connection methods, and connection information exported in XML format for subsequent virtual assembly.

[0021] Step S15: performing virtual assembly according to the geometrical contours and connection relationships of the components to obtain a geometrical model of the vacuum cleaner components; In this embodiment, virtual assembly is performed based on the constructed geometric contours of parts and connection relationship maps. The assembly method adopts a top-down hierarchical order, with the base of the whole machine as the starting part, and all relative positions and constraints are input into the geometric assembly engine. During the assembly process, the collision detection mechanism is enabled to detect whether there is an interference area between parts in the assembly. The collision judgment method is based on AABB (axis-aligned bounding box) overlap judgment. If there is an overlapping volume greater than 1mm³, the assembly conflict is reported and the process is terminated, requiring the part geometry data to be corrected again. After the entire assembly process is completed, the complete geometric model of the vacuum cleaner parts is output. The data format is X_T (Parasolid format), which contains the spatial coordinate position, assembly relationship and identification number of each component.

[0022] Step S16: injecting material attributes according to the geometric model of the vacuum cleaner parts to generate a three-dimensional model of the vacuum cleaner.

[0023] In this embodiment, material properties are injected into the constructed geometric model. The material properties include physical parameters such as density, Young's modulus, Poisson's ratio, thermal conductivity, resistivity, etc., as well as appearance properties such as color, transparency, and texture images. The property values uniformly reference the internal material database of the enterprise, and are indexed and matched to the specified part numbers in the geometric model using material IDs. The material injection is performed through the property mapping interface of the 3D engine. For example, the material of the main housing part is ABS plastic, with a set density of 1.04 g / cm³, a Young's modulus of 2.1 GPa, a color of RGB(230, 230, 230), and the texture uses a granular pattern texture map. The texture file format is PNG, with a resolution of 1024×1024. After all properties are injected, a complete 3D model structure is generated, in the format of FBX or GLTF, for subsequent rendering or analysis processing.

[0024] Preferably, in step S2, identifying the type of floor material specifically includes: Performing a floor contact simulation on the 3D model of the vacuum cleaner to obtain floor contact data; In this embodiment, a multi-physics field coupling calculation method based on rigid body dynamics and contact force field simulation is used. The 3D model of the vacuum cleaner constructed in step S16 is imported into a 3D CAD platform (such as the SolidWorks Simulation module or Ansys Mechanical), and the center of gravity of the vacuum cleaner host is fixed at the origin of the model coordinate system. In the simulation environment, the contact surfaces between the bottom rollers and the lower edge of the suction port of the vacuum cleaner and the floor are defined, and the total gravity load of the vacuum cleaner is applied. The set mass value is 2.4 kg, and the total normal force on the floor is calculated to be 23.53596 N according to the standard gravitational acceleration of 9.80665 m / s². The floor model is set as a flat rigid plate, with the normal direction being the +Z axis. During the simulation, the penalty function method is used to simulate the contact force, with the contact stiffness set to 1×10 7 N / m, and the damping coefficient set to 5×10³ Ns / m. The contact area, contact pressure, and contact depth of each contact element in the simulation are recorded, and the data is exported as a CSV file, with fields including "contact_id", "contact_area_mm2", "pressure_Pa", and "penetration_mm". The simulation time step is set to 1 ms, and the total simulation time is 3 s.

[0025] Performing material sampling based on the floor contact data to obtain floor material data; In this embodiment, among the obtained ground contact data, data units with a contact area greater than 200 mm² and a contact pressure between 500 Pa and 2000 Pa are used as valid sampling points, and the ground deformation response is classified according to the indentation depth value. Material sampling is achieved by the method of pressing down the sampling head for identification. The sampling head is installed on the bottom of the vacuum cleaner, and its structure is a hemispherical silica gel head with a diameter of 15 mm and a constant loading force of 1 N. The downward pressing stroke is measured by a grating ruler, and the deformation depth value is recorded. The sampling data acquisition period is 10 ms, and no less than 200 groups of data are continuously sampled. After sampling is completed, a regression fit is performed on the ground reaction force curve. The fitting function uses a cubic polynomial, and the change rate of the curve slope is extracted as the material characteristic factor. If there are obvious mutation points in the second derivative of the fitting curve, the ground material is defined as a heterogeneous ground; if the curve is continuous, smooth and the slope is constant, it is defined as a homogeneous ground. The material data is stored in the form of a matrix, and the fields include "position_x", "position_y", "deflection_mm", and "modulus_estimate_MPa".

[0026] According to the ground material data, resilience detection is carried out to obtain fast-resilience material data and non-resilience material data; In this embodiment, resilience detection is carried out by installing a bidirectional displacement sensor. The measurement frequency of the sensor is 100 Hz, and the accuracy is ±0.01 mm. The time from releasing the pressure after loading to the recovery of the deformation amount is recorded. If the time to recover to the original height is less than 0.3 s, it is defined as a fast-resilience material; if the recovery time is greater than 2 s or there is a residual deformation amount exceeding 0.2 mm, it is defined as a non-resilience material. Each sampling point records its corresponding resilience label one by one, and finally two material category sets are formed in the format of a two-dimensional array structure. Among them, the fast-resilience material data set is identified by "QRM", and the non-resilience material data set is identified by "NRM". Each data item records the coordinates, residual displacement value and resilience duration.

[0027] According to the ground material data, infrared detection is carried out, and the infrared reflectivity is calculated to obtain high-reflectivity ground material data and low-reflectivity ground material data; In this embodiment, an infrared ranging module installed in the middle of the vacuum cleaner chassis is used to sample the ground reflectivity. The module model is VL6180X, the working wavelength band is 940 nm, the emission power is 10 mW, and the reflected light intensity is converted into a digital signal value (0–1023) through the internal ADC module. To eliminate ambient light interference, the visible light band is shielded during the detection process, and a 5-point average filter is applied to the infrared return signal. The method for calculating the infrared reflectivity is to divide the reflected signal intensity value by the emission power and multiply by the constant correction factor of 0.7. If the measured reflectivity is greater than 0.6, the material is marked as a high-reflectivity material; if the reflectivity is less than 0.3, it is marked as a low-reflectivity material. The reflectivity data is stored indexed by coordinates, with a dictionary structure. The key is the "(x,y)" coordinate value, and the value is the "reflectance_ratio", while also marking the material label "HR" or "LR".

[0028] Perform an intersection operation on the carpet material data based on the quick-rebound material data and the low-reflectivity ground material data to obtain the carpet material data; In this embodiment, from the quick-rebound material data set "QRM" and the low-reflectivity material set "LR", the coordinate indices of each data point are extracted respectively, and the points with the same coordinates are identified as carpet material identification points through set intersection operations. The intersection determination method uses Boolean logic calculation: "If QRM(x,y)=True and LR(x,y)=True, then carpet(x,y)=True", generating a Boolean matrix carpet identification image. The intersection operation uses a sparse matrix calculation optimization algorithm to reduce memory occupancy. In the output carpet material data structure, each record item includes the position coordinates, indentation depth, rebound time, and infrared reflectivity value, and is assigned the material label "CARPET".

[0029] Perform an intersection operation on the ceramic tile material data based on the non-rebound material data and the high-reflectivity ground material data to obtain the ceramic tile material data.

[0030] In this embodiment, the non-rebound material data set "NRM" and the high reflectivity material data set "HR" need to be used as inputs to perform an intersection matching operation to identify the ceramic tile material area. First, the ground point coordinates of each piece of data in the "NRM" set are extracted, including the two-dimensional coordinates x and y, with a corresponding coordinate accuracy of 0.1 mm. Subsequently, the coordinate points of each record in the "HR" set are extracted, and a hash index table is established to improve the search efficiency. The set intersection judgment logic is used, that is, a Boolean logic calculation is performed on each coordinate point: if there is "NRM(x,y)=True" and "HR(x,y)=True", then this point is defined as a ceramic tile material point and marked as True. The intersection operation is implemented through a sparse Boolean matrix, and the SciPy sparse matrix library is used to construct the intersection index to compress the storage space and improve the calculation efficiency. After the intersection is successful, a ceramic tile material recognition map is generated. Each ceramic tile material record contains fields "coordinate_x", "coordinate_y", "residual_deformation_mm", "reflectance_ratio", "rebound_time_s", where the residual deformation amount is determined by the deformation amount after release measured by the displacement sensor in step three, the infrared reflectance value comes from the sampling result of the infrared module in step four, and the rebound time is the time difference from loading to deformation recovery recorded in step three. All ceramic tile material data is output in the JSON file structure. The JSON key is in the string format of the coordinate point "(x,y)", and the value is a set of key-value pairs, including "residual_deformation_mm", "reflectance_ratio", "rebound_time_s", and an additional field "material_type" is set to "CERAMIC". This data structure is used in the subsequent vacuum cleaner path control module to implement regional cleaning strategies such as suction adjustment and roller brush control according to the ceramic tile area attributes.

[0031] Preferably, in step S2, the detection of the particulate gas flow is specifically: Identifying the fiber arrangement direction based on the carpet material data; In this embodiment, the coordinates, bounce data, and infrared reflectivity data of each point are extracted from the carpet material data as the analysis basis. The ground contact pressure matrix data and bounce direction data recorded in the three-dimensional contact simulation module are called. By calculating the unit normal of the contact point and the directional gradient of the local contact area, the fiber tilt direction is obtained. The Sobel operator is used to enhance the gradient of the microscopic height difference distribution map of the carpet surface. Edge gradient detection is performed in the x-direction and y-direction respectively, and combined with high-resolution displacement data. By calculating the maximum gradient direction angle, this angle is defined as the fiber arrangement direction. The fiber arrangement direction angle range is set between 0° and 180°, and the accuracy is set to 1°. Finally, the fiber direction of each point is stored in the form of an array and bound to the point coordinate data to form a fiber direction field.

[0032] Calculate the porosity based on the carpet material data; In this embodiment, based on the surface height change data recorded at the carpet points and the indentation depth data generated by the contact simulation, a two-dimensional grid division method is used with a 10mm×10mm unit cell to count the apparent density and the number of fibers per unit volume after compression in each cell. Using the defined formula: porosity = 1 - (compacted density / theoretical maximum density), where the compacted density is obtained by dividing the unit indentation volume by the initial thickness volume, and the theoretical maximum density is obtained by the no-void simulation loading. The porosity value is calculated for each grid point, and the unit is percentage. The porosity value and the coordinate data form a porosity map, and a porosity field (porosity_percent) is provided externally.

[0033] According to the fiber arrangement direction and porosity, dust diffusion simulation is carried out to obtain dust diffusion data; In this embodiment, based on the fiber direction angle data and the porosity map, a two-dimensional diffusion field is constructed with these as input parameters. The finite element method (FEM) is used to establish the dust diffusion equation under the fiber microstructure, and the diffusion initial condition is set to inject 1mg of particulate dust with a diameter of 10μm at the point (x0, y0). The diffusion coefficient D is determined according to the porosity, and its value is set as: D = D0×(1 - porosity), where D0 is set to 1.5×10^-5m² / s. The simulation time step is set to 0.05s, and the total duration is set to 5s. In each time evolution step, the anisotropic diffusion speed is adjusted according to the fiber arrangement direction. The output result is a time-series diffusion concentration map, recording the change of dust concentration at different coordinate points at each time step, forming dust diffusion data.

[0034] Track the dust particle diffusion path based on the dust diffusion data; In this embodiment, during the process of tracking the diffusion path of dust particles based on dust diffusion data, a continuous point sequence with a concentration greater than the threshold c_th = 0.1 mg / m³ is taken from the dust diffusion concentration map. The centroid tracking method is used to locate the position of the concentration center, and interpolation is performed on the positions of consecutive frames to generate a diffusion path line. Each path is composed of a set of coordinate points corresponding to a time stamp. The path coordinates are sampled every 1 mm, and the sampling time interval is 0.05 s. The final output path format is: time-position sequence, recording the specific trajectory of the particulate dust diffusion.

[0035] Perform particle deposition detection on the fiber arrangement direction according to the dust particle diffusion path to obtain particle deposition data; In this embodiment, during the process of performing particle deposition detection on the fiber arrangement direction according to the dust particle diffusion path, each coordinate point on the diffusion path is taken, the fiber arrangement direction of the corresponding point is matched, and the change value of the particle concentration on the path is superimposed. If the change value of the concentration of a certain path segment decreases by more than 50% within 3 frames, then this path segment is defined as a deposition distribution area. The coordinate points of this area are bound to the original fiber arrangement angle, the deposition angle field is recorded, and the output is particle deposition data, and the format includes coordinate position, path ID, deposition angle, deposition amount (unit: mg).

[0036] Identify the gas flow path according to the dust particle diffusion path; In this embodiment, during the process of identifying the gas flow path according to the dust particle diffusion path, the inverse fluid dynamics method is used, that is, a velocity field inversion model is constructed based on the particle propagation velocity and diffusion direction. Calculate the motion velocity vector between each pair of consecutive positions on the path, and unify and standardize the velocity direction to form a unit velocity vector field. Through the vector field aggregation operation, the main gas flow channels are identified. Each point on this path records the gas velocity magnitude, direction angle, and flow density, with the units of mm / s, angle °, and mg / s. The final output is gas flow path data.

[0037] Perform motion behavior fitting on the particle deposition data according to the gas flow path to obtain a particle gas flow.

[0038] In this embodiment, during the process of performing motion behavior fitting on the particle deposition data according to the gas flow path to obtain a particle gas flow, combining the deposition angle, deposition position, and gas velocity field data, calling the Lagrangian particle trajectory fitting algorithm, defining the initial state of each deposition point as the starting position of the particle, and simulating the microscopic trajectory of the gas flow guiding the particle in the fiber structure. Use the fitted path to track the motion direction, velocity, and termination point coordinates of each particle, record the particle gas flow trajectory formed after fitting, and each trajectory contains information such as time series, velocity vector, deposition point label, and fiber angle matching degree. Finally, a particle gas flow data set is formed.

[0039] Preferably, in step S2, the specific method for identifying the dust coverage is as follows: Calibrate the tile area based on the tile material data, and irradiate the tile area with a laser at an angle of 10° - 30° to obtain a tile laser irradiation image; In this embodiment, read the tile material recognition map, and use the coordinate points marked as "CERAMIC" as the basis for the area boundary. Input the set of coordinate points corresponding to all "CERAMIC" material identifiers into a two-dimensional plane clustering algorithm, and use the DBSCAN algorithm based on the Euclidean distance for clustering. Set the minimum number of samples for clustering to 20 and the neighborhood distance threshold to 15 mm to form a continuous tile material area. For each clustering area, construct a circumscribed rectangle boundary, and use coordinate point interpolation to reconstruct its two-dimensional plane contour, and finally form the spatial boundary data of the tile material area. The boundary data is encoded using floating-point coordinate indexes, and the defined field structure includes area number, boundary point sequence, center point coordinates, and area of the area. In the process of irradiating the tile area with a laser at an angle of 10° - 30° and obtaining a tile laser irradiation image, a vertical irradiation device with a variable-angle laser emitter is used for the irradiation experiment. The laser model uses an adjustable-angle laser module with a wavelength of 532 nm and an output power of 5 mW. Install the laser emitter at a fixed height of 50 cm and set the control stepping motor to adjust the laser emission angle. Irradiate the center point of each tile area with a laser at five angles of 10°, 15°, 20°, 25°, and 30° respectively. Each angle is irradiated for 1 second, and during this period, a high-speed camera with 200 frames per second is used to image the tile area. The camera is arranged perpendicular to the laser emission direction to ensure the recording of laser scattering and reflection behavior. An average brightness image is output for each angle, and a total of five frames of laser irradiation images are generated for the five angles, and are saved with the area number and angle number for identification.

[0040] Perform image difference processing on the tile laser irradiation image to obtain a tile laser irradiation difference image; In this embodiment, in the process of performing image difference processing on the tile laser irradiation image, first perform difference calculation on each angle of the laser irradiation image and the initial background image before irradiation. The image difference method uses a per-pixel gray difference algorithm, and the formula is: Diff(x,y)=|I_L(x,y)-I_B(x,y)|, where I_L is the laser irradiation image and I_B is the background image. The difference image is an 8-bit gray image, and the gray value range of each pixel is 0 - 255. Subsequently, perform binary processing on the difference image, and use a fixed threshold T = 30 for processing, that is, if the pixel gray value is greater than 30, it is set to 1, otherwise it is set to 0, and the binary difference image is output. The difference images of all angles are saved separately and named and managed with "angle - area number".

[0041] Calculate the number of scattering points based on the differential image of laser irradiation on the tile; calculate the size of the scattering points based on the differential image of laser irradiation on the tile; calculate the scattering area ratio based on the number of scattering points and the size of the scattering points; In this embodiment, during the process of calculating the number of scattering points based on the differential image of laser irradiation on the tile, the connected region detection method is used to process each frame of binary differential image. The 4-connected region extraction method is adopted to traverse all white pixel points, mark adjacent pixels and construct separate connected regions. The threshold of the minimum number of pixels in the connected region is set to 20 pixels to filter out the noise regions smaller than this threshold. Finally, the number of effective scattering points in each image is recorded, with the unit of "number", and after statistics, it is written into the attribute record file of each angular image respectively to form a scattering point number table. During the process of calculating the size of the scattering points based on the differential image of laser irradiation on the tile, the total number of pixels of each detected connected region is calculated, and it is converted into an area according to the known image resolution. The image resolution is set to 0.5 mm / pixel², then the area A of each connected region = N×0.25 mm², where N is the number of pixel points. The average area, maximum area and minimum area of all scattering points in each image are calculated, and finally a scattering point area distribution table for each frame of image is formed. During the process of calculating the scattering area ratio based on the number of scattering points and the size of the scattering points, the sum of the areas of all scattering points in each image is divided by the effective observation area of the whole image. The observation area is fixed at 100 mm×100 mm, that is, 10,000 mm². The formula for calculating the scattering area ratio is: R = (ΣA_i) / 10,000, where A_i is the area of the i-th scattering point. The scattering area ratio is recorded in floating-point form, and a set of scattering area ratio values is output for each image.

[0042] Evaluate the dust area based on the scattering area ratio; In this embodiment, during the process of evaluating the dust area based on the scattering area ratio, the empirical dust scattering area threshold is set to 5%. For the image region with a scattering area ratio greater than or equal to 5%, this region is defined as the dust-covered region. In all the image differential maps that meet the conditions, the corresponding connected regions are all marked as dust regions. The dust region marking result is output in the form of a binary map, where the pixel value of 1 represents the dust-covered region and the pixel value of 0 represents the non-dust region.

[0043] Generate a dust mask map based on the dust region; count the dust pixel ratio according to the dust mask map; In this embodiment, during the process of generating a dust mask image based on the dust area, binary images marked as dust areas in all angular images are merged, and a unified dust mask image is generated using the per-pixel maximum fusion method. That is, a pixel value of 1 in the mask image indicates that it is recognized as a dust area at any angle. The output dust mask image has the same resolution and pixel size as the original tile area image to ensure the accuracy of subsequent pixel ratio statistics. During the process of calculating the dust pixel ratio according to the dust mask image, the total number of pixel points N_dust with a value of 1 in the mask image is counted, and the ratio is calculated with the total number of pixels N_total in the image. The dust pixel ratio P = N_dust / N_total, with the unit of percentage and rounded to two decimal places. The statistical results are recorded in the image attribute file.

[0044] Evaluate the dust coverage based on the dust pixel ratio.

[0045] In this embodiment, during the process of evaluating the dust coverage according to the dust pixel ratio, the dust coverage level thresholds are set as: light (<5%), medium (5% - 15%), and heavy (>15%). According to the range of the dust pixel ratio P, the coverage level label is directly assigned, and the result is used to adjust the suction force or cleaning strategy in the control system. The coverage label is output in string format and associated with the corresponding tile area number.

[0046] Preferably, determining the dust concentration in step S2 is specifically as follows: Calculate the gas flow velocity based on the particulate gas flow and identify the flow velocity attenuation degree based on the gas flow velocity; In this embodiment, three sets of hot-film anemometers are installed along the air duct direction in the intake channel of the vacuum cleaner. The model is FS7.0-HF, the accuracy class is ±0.03 m / s, and the measurement range is 0 to 30 m / s. The three sets of sensors are respectively arranged in the inlet section, the middle section, and the outlet section. Each set consists of three sensors arranged in an equidistant linear array, and the sensor spacing is set to 1.2 cm. When obtaining the air flow velocity information, the sampling time is set to sample once every 100 ms, and the total sampling time is set to 2 s, forming 20 frames of time series data. Since particles will change the air flow density and kinetic energy, an independent temperature and humidity sensor (model SHT35) is used for real-time compensation, the gas temperature and relative humidity values are recorded in real time, and then the gas density value is corrected based on the look-up table method. When measuring the gas velocity, the multi-point averaging method is adopted, that is, the three sets of data of each set of sensors are weighted and averaged, and the weights are preset to 0.2 (inlet), 0.3 (middle section), and 0.5 (outlet section) according to the position to enhance the sensitivity to the velocity change at the air outlet. The final flow velocity output is in m / s, and the timestamp, temperature and humidity parameters, the original voltage of the sensor, and the corrected velocity value are recorded for subsequent identification of the flow velocity attenuation degree. First, a sliding window operation is performed. The width of each window is set to 2 s, and the step size is 1 s. The average gas flow velocity obtained in each window is compared with the average flow velocity of the previous window, and the change in the amplitude of the air flow velocity decrease is calculated, in percentage, and two decimal places are reserved. During this process, the attenuation detection threshold is set to 10%. If it is detected that the flow velocity decrease amplitude in two consecutive time windows is greater than 10%, and the decrease amplitude in the current window exceeds 15%, it is recorded as "strong attenuation"; if the decrease amplitude in the current window is between 10% and 15%, it is recorded as "medium attenuation"; if the decrease amplitude is less than 10%, it is recorded as "weak attenuation". The data recording fields include: the start time of the window, the end time, the average flow velocity, the average flow velocity of the previous window, the percentage of the decrease amplitude, the attenuation level label, etc. All data are stored in the local cache module in CSV format and synchronously transmitted to the control unit of the main control chip of the vacuum cleaner. To prevent misjudgment caused by sensor jitter or small particle interference, when judging whether it is a real attenuation state, three consecutive windows need to meet the above conditions before it is determined as an effective attenuation behavior, and then enter the next step of dust accumulation degree evaluation.

[0047] Determine the dust accumulation degree based on the flow velocity attenuation degree; In this embodiment, the real-time flow velocity detection data in the cache is called and compared with the historical initial flow velocity reference value, and the flow velocity value of the current area is obtained point by point according to the sensor number. The initial flow velocity value of each detection point is calculated as the average value after continuously sampling 10 times by the air duct flow velocity sensor (model FS300A) in the first 5 seconds after the device starts, and is used as the initial reference flow velocity of the monitoring area of the sensor. The current detection period is sampled and compared with the attenuation degree once every 2 seconds. In the attenuation judgment, if the flow velocity value in a certain monitoring area is continuously below 80% of the initial value for 3 consecutive detection periods (a total of 6 seconds), the system records the "strong attenuation" state of this area and includes it in the accumulation trend evaluation cache. If this state is detected to occur three times in a row, the system determines that this area has reached the "severe accumulation area" standard. The corresponding accumulation thickness refers to the preset physical quantization range, which is obtained by manually sampling and analyzing different accumulation amount areas in the laboratory with a micrometer caliper (accuracy 0.01 mm), and is set to be between 0.8 mm and 2.0 mm. If the flow velocity value recorded in the detection period continuously shows the "medium attenuation" state 23 times (a total of 46 seconds), that is, the flow velocity is always between 85% and 90% of the initial value, the system marks this area as the "medium accumulation area". Its accumulation thickness is set to be between 0.3 mm and 0.8 mm. All monitoring data below this condition are classified into the "light accumulation area", where the flow velocity attenuation is not obvious, and the accumulation thickness range is set to be between 0.1 mm and 0.3 mm. After the accumulation level judgment is completed, to verify the reliability of the identification, the system compares the airflow velocity map (sampling frequency 2 Hz) of the corresponding detection period with the air duct pressure difference data collected by the air pressure difference sensor (model MPXV7002DP) one by one. The comparison process performs a linear correlation analysis on the curve of the change in the rising amplitude of the pressure difference and the curve of the falling trend of the flow velocity. If the trends of the two are the same, that is, the pressure difference continues to rise and the flow velocity continues to fall, the monitoring result is archived as valid accumulation judgment data. The accumulation information after each judgment is recorded in the following format: including the dust accumulation level (light, medium, heavy), the corresponding area number (the number corresponds to the arrangement order of the device sensors), the start time and end time of the accumulation (the time accuracy is set to the second level), the reference pressure difference (unit Pa), and the current corresponding minimum flow velocity value (unit m / s). All these data will be packed to form an accumulation level map and uploaded to the main control board of the vacuum cleaner for subsequent parameter calling of the suction adjustment module. The generation process of this map calls the dust status layer module, uses each area number as the layer index, and uses the accumulation level as the color rendering standard to realize the dynamic visualization management of the suction intensity of the local area of the vacuum cleaner.

[0048] Detect the dust particle diameter according to the dust coverage; In this embodiment, it is achieved by the multi-point red light reflectance image method. To ensure the high quality and accuracy of the image, a red light LED module with a wavelength of 650 nm is used as the light source, and its emission angle is fixed at 20°. The effective irradiation range can cover an area of 10×10 mm in the sampling area. The imaging module uses a CMOS sensor with a resolution of 800×600 pixels (model number OV2640), and the imaging distance is set to 10 mm to ensure that the projection effect of dust particles is clearly visible during image acquisition, avoiding the situation where the particles are blurred due to too far a distance. The image acquisition period is 500 ms to ensure that the image data collected each time can be obtained stably and quickly. During the image transmission process, the data will be transmitted to the edge detection unit, and the Sobel algorithm is used to perform edge detection on the image to extract the contour information of the dust particles, thereby providing a basis for subsequent particle analysis. During this process, to eliminate the interference of background noise on image analysis, the image gray threshold is set to 85. Pixels with a gray level lower than this value will be determined as background pixels and excluded in subsequent analysis to ensure the precise positioning and identification of the particle part. By calculating the ratio of the area of dust pixels higher than the gray threshold in the image to the total pixel area of the image, the dust coverage is obtained. If the dust coverage exceeds 30%, the further analysis of the particle diameter will be skipped to avoid distortion errors caused by adhesion between particles. If the coverage is between 10% and 30%, all particles in the coverage area will be analyzed. If the coverage is less than 10%, the system will extract and calibrate each particle one by one to ensure accurate measurement of the diameter of each particle. The particle diameter detection method uses the ellipse fitting method. By fitting the edge of each particle in the image, the average value of its major axis and minor axis is calculated as the projected diameter of the particle. This method effectively reduces the measurement error caused by the irregular shape of the particles. After measuring the diameters of all particles, the system will perform statistical analysis on the particle size distribution, excluding outliers that deviate significantly from the median, that is, particle data exceeding ±2 times the standard deviation, to ensure the accuracy of the analysis results. Finally, the effective particle data is retained within the particle size range of 0.150 μm to a certain upper limit, and the number, area, average diameter, and particle diameter distribution range of the particles in each image are extracted. These data will provide a basis for the evaluation of dust concentration and accumulation degree, and assist in the automatic adjustment and control of the vacuum cleaner.

[0049] Determine the dust concentration according to the dust accumulation degree and the dust particle diameter.

[0050] In this embodiment, first, the stacking level of the current area is matched with the average particle diameter. The stacking thickness takes the median value according to the level range defined in the previous steps, 0.2 mm for mild, 0.55 mm for moderate, and 1.4 mm for severe. Taking a 10×10 mm detection area as a unit, calculate the volume of this area (the unit is converted to cm³), and then, assuming the particles are spheres in combination with the average particle diameter, estimate the number of particles per unit volume. The estimation of the number of particles is achieved by dividing the area volume by the volume of a single particle. The particle volume is converted according to the sphere formula, and the unit is converted to cm³. Subsequently, in combination with the density of the particle material, with the density value being 2.5 g / cm³, calculate the total mass and convert it to the unit of mg / m³. Taking mild stacking and a diameter of 2 μm as an example: the stacking volume is 0.2 mm×10 mm×10 mm = 20 mm³ = 0.02 cm³, the volume of a single particle is approximately 4.19×10⁻ 9 cm³, and the estimated number of particles is approximately 4.77×10 6 particles, and the total mass is approximately 0.0497 g, which is converted to 49700 mg / m³ after conversion. The final output fields include the area number, stacking thickness, average particle diameter, estimated number of particles, and mass concentration, which are used by the main control unit to adjust the fan speed according to different concentration levels to complete the automatic adjustment of suction.

[0051] Preferably, step S3 is specifically as follows: Step S31: Extract the air duct structure data according to the vacuum cleaner design data; In this embodiment, first, the geometric structure and dimension information of the vacuum cleaner air duct are extracted. This process requires extracting relevant data from the design drawings of the vacuum cleaner or computer-aided design (CAD) files, including the diameter, length, bending angle of the air duct, and the connection method with the filter interface. By parsing the design data, the internal structural characteristics of the air duct are obtained, specifically including information such as the roughness of the inner surface of the air duct, the change in the air flow path, and the air flow distribution deviation. The extraction process of these data is based on the model parsing tool in CAD software or uses data exchange formats (such as STEP, IGES) to parse the design files. To improve the accuracy of the data, detailed dimension markings and geometric features must be provided in the design files. By accurately obtaining this information, it can provide basic data support for subsequent fluid dynamics simulation and filter clogging evolution simulation.

[0052] Step S32: Perform a filter clogging evolution simulation based on the dust concentration and the air duct structure data to obtain filter clogging data; In this embodiment, based on the geometric parameters of the input air duct and the hydrodynamic characteristics such as air flow velocity and pressure, a computational model of the fluid flow inside the air duct is established. Then, by combining the dust concentration data with the simulation results of the air duct fluid flow, the deposition process of dust particles in the air duct is calculated using the particle deposition model and hydrodynamic simulation. For the filter area, considering the interaction between the fluid and the particles, the accumulation and clogging process of dust particles on the filter surface is simulated. At this time, specific dust particle size distribution data needs to be input, and parameters such as the adhesion force and deposition rate of the particles are set. During the simulation process, factors such as the contact between the particles and the filter, the filtration efficiency, and the flow conditions will all affect the clogging evolution. The simulation results output the clogging degree of the filter at each time point, including data such as the dust accumulation amount and the change in flow resistance. The clogging data obtained through the simulation process will provide a necessary basis for the subsequent filter performance evaluation and automatic adjustment.

[0053] Step S33: Detect the filter material based on the filter clogging data to obtain carbon fiber filter data; In this embodiment, by checking the design and manufacturing information of the filter, the materials used in the filter, such as carbon fiber and polyester fiber, can be identified. To accurately identify the carbon fiber filter, based on the simulation results of the filter clogging evolution, the pressure difference data and the clogging degree of the filter can be extracted, and the differences between these characteristics and the typical response patterns of filters made of different materials can be analyzed. For carbon fiber filters, they usually exhibit a higher pressure rise rate and a longer clogging stable period. By setting specific filtration performance thresholds (such as flow rate decrease, pressure difference change, etc.) and combining the aforementioned clogging data, it is possible to determine whether the filter is made of carbon fiber material based on the time-varying curve of the filter clogging. By matching the characteristic values of filters made of different materials, accurate identification of the filter material can be achieved, and the corresponding carbon fiber filter data can be obtained as the input data for subsequent analysis.

[0054] Step S34: Detect fiber breakage based on the carbon fiber filter data to obtain fiber breakage data; In this embodiment, the fiber breakage of the carbon fiber filter is usually caused by excessive pressure due to over-clogging or strong air flow impact. Therefore, first, it is necessary to analyze the characteristics such as the flow velocity change and pressure rise in the filter clogging data, and combine the mechanical strength and stress distribution of the filter to locate the fiber breakage position. By using the finite element analysis method (FEA) to model the material mechanical properties of the filter, the stress concentration areas that occur during actual use are simulated, and the potential areas of fiber breakage are identified. Secondly, the pressure difference change of the filter is monitored in real time through a pressure difference sensor. If it is found that the rate of change of the pressure difference increases abnormally, it indicates that fiber breakage has occurred. The detection standard for breakage is judged by setting the critical stress value for fiber breakage and the threshold for the breakage position. Combining the sensor data and the simulation results, fiber breakage data is generated, recording information such as the position, time, and pressure change at which the breakage occurs.

[0055] Step S35: Identify the degree of peeling of the carbon fiber layer filter screen based on the fiber breakage data; In this embodiment, the degree of peeling of the filter screen is usually closely related to the number of fiber breaks, the range of breaks, and the pressure distribution at the break position. By analyzing the fiber breakage data, the specific location, number, and degree of breakage are extracted. Combining with the pressure change data, the degree of peeling of the carbon fiber layer can be evaluated. The judgment criteria for the degree of peeling include the ratio of the total number of fiber breaks to the filter screen area and the continuity of the break area. If the continuity of the break area is strong, a higher degree of peeling is determined. The specific judgment method includes setting a break area threshold. When the break area exceeds 10% of the total filter screen area, it is considered that there is an obvious peeling phenomenon in the filter screen. Combining this judgment criterion, the high or low degree of peeling is identified, and the peeling degree data is generated, providing a basis for subsequent decisions on filter screen replacement or maintenance.

[0056] Step S36: Judge the load state of the filter screen based on the degree of peeling of the carbon fiber layer filter screen.

[0057] In this embodiment, the evaluation of the load state needs to combine the degree of peeling of the filter screen, the blockage data, and the filtration efficiency of the filter screen. The load state of the filter screen can be divided into three levels: light load, heavy load, and overload. According to the degree of peeling and the blockage data, if the degree of peeling is less than 5% and the blockage degree is low, the filter screen is rated as light load; if the degree of peeling is between 5% and 20% and the blockage is relatively serious, the filter screen is rated as heavy load; if the degree of peeling exceeds 20% and the blockage is relatively serious, the filter screen is rated as overload. Based on these data, combined with the real-time operation data of the vacuum cleaner, it is judged whether the filter screen needs to be replaced or cleaned, and a basis is provided for the automatic adjustment system.

[0058] Preferably, step S32 is specifically: Step S321: Import the air duct structure data and the dust concentration into the simulation software; In this embodiment, the design data of the air duct is obtained, including the geometric structure, dimensions, and dust concentration data of the air duct. The air duct design data is extracted from CAD design drawings or 3D models, ensuring that detailed parameters such as the length, width, height, and inner wall roughness of the air duct are included. The dust concentration data is usually obtained through air flow sensors or real-time monitoring devices, recording the dust particle concentration at different positions. When importing into the simulation software, the air duct data needs to be converted into an input file that conforms to the format of the simulation software. For example, the geometric information is imported using the STEP format or IGES format, and the dust concentration data can be input in the form of a CSV or text file. The data import format supported by the simulation software must be consistent with its required format for accurate fluid simulation. By selecting the corresponding input file and importing it through the import interface of the simulation software, ensure that the air duct structure data and the dust concentration data are correctly loaded into the simulation environment.

[0059] Step S322: Set the length, width, height, and inner wall roughness of the air duct in the simulation software. In this embodiment, after entering the simulation software, set the geometric parameters of the air duct, specifically including the length, width, height, and inner wall roughness of the air duct. The setting of these parameters is based on the previously obtained air duct design data. The length and width of the air duct need to be set according to the specific values provided in the design drawings. For example, set the air duct length to 2 meters, the width to 0.3 meters, and the height to 0.3 meters. The inner wall roughness is used to simulate the friction effect of the inner surface of the air duct and is usually determined according to the material type and manufacturing process. The typical value of the inner wall roughness, such as the roughness of the inner wall of a stainless steel air duct, can be set to 0.01 mm. This data is obtained by referring to the material property table or experimental measurement, and the unit needs to be ensured to be consistent during import. In the simulation software, the roughness parameter can be set, and the appropriate value can be input by selecting the "wall roughness" option. After setting all the parameters, save the configuration and prepare to enter the next step of setting the simulation parameters.

[0060] Step S323: Set the particle size range of the dust particles in the simulation software to be 0.1 μm - 100 μm, the particle density range to be 0.5 g / cm³ - 3.0 g / cm³, and the particle concentration range to be 0 - 5000 particles per cubic meter. In this embodiment, in the simulation software, enter the particle input interface and set the particle size range, particle density, and particle concentration range of the dust particles. The particle size range of the dust particles is set to 0.1 μm to 100 μm, based on the common particle size distribution of dust particles in the environment. The particle density is set to 0.5 g / cm³ to 3.0 g / cm³, and the range covers the common densities of different types of dust particles. For example, the density of sand dust particles is relatively high, while the density of organic particles such as wood chips is relatively low. The particle concentration setting range is from 0 particles per cubic meter to 5000 particles per cubic meter, and the concentration data is set based on the monitoring data of the sensor to ensure that various dust concentration situations can be covered in the simulation. The input of each particle requires defining the physical properties of the particle, such as the uniform distribution of the particle size, the movement trajectory of the particle, and the interaction model between the particle and the air flow. At this time, it is necessary to define the particle flow model in detail and select the appropriate particle movement method and transport model, such as the Lagrangian model or the Euler model, in order to accurately simulate the behavior of the particles in the air duct.

[0061] Step S324: Set the air flow velocity range to be 1 m / s - 10 m / s, the temperature range to be 20°C - 40°C, and the humidity range to be 20% - 80% in the simulation software. In this embodiment, first, the velocity range of the airflow is set to be from 1 m / s to 10 m / s. This velocity range is determined by collecting the operation data of the fan in the actual device or through simulation analysis of the device specifications. The setting of the airflow velocity affects the suspension and deposition processes of dust particles. Therefore, the airflow velocity needs to be selected according to the actual working environment. For example, setting the airflow velocity to 5 m / s is suitable for the operation of a vacuum cleaner under medium workload. Then, the temperature range is set to be from 20°C to 40°C, which is the temperature change range under common indoor temperature and humidity environments. The humidity range is set to be 20% - 80%, covering the air humidity in different environments. Humidity and temperature have significant effects on the deposition and flow characteristics of particles. When the humidity is relatively high, particles will aggregate into clusters, while in a low-humidity environment, the possibility of particle suspension is greater. When setting these parameters, ensure that the influence of these environmental variables on the airflow and particle behavior is considered in the simulation model.

[0062] Step S325: Set the porosity, filtration accuracy, and adhesion of the filter material in the simulation software; In this embodiment, set the material properties of the filter screen and enter the filter screen input setting interface. First, input the porosity of the filter screen. The porosity represents the proportion of the void part of the filter screen in the overall material and is usually determined through material tests or parameters provided by the manufacturer. For example, the porosity of a carbon fiber filter screen can be set to 60% - 80%. Then, set the filtration accuracy of the filter screen, which indicates the minimum size of particles that the filter screen can effectively intercept. The filtration accuracy is generally set according to the production specifications or standards of the filter screen. For example, setting the filtration accuracy of the filter screen to 5 μm means that particles larger than 5 μm can be filtered out. The adhesion of the material affects the deposition of particulate matter on the surface of the filter screen, and the set value of the adhesion is determined based on the surface characteristics of the filter screen and the physical characteristics of the particles. For example, for carbon fiber materials, the adhesion can be set to 10 - 50 mN. After inputting these parameters into the simulation software, the behavior model of the filter screen will be used to predict the deposition of particles on the surface of the filter screen, the change in resistance, and the clogging situation.

[0063] Step S326: Run the filter screen clogging evolution program in the simulation software and output the filter screen clogging data.

[0064] In this embodiment, the filter clogging evolution program in the simulation software is run. This program, based on all the previously set parameters (duct structure, dust particles, airflow conditions, filter characteristics, etc.), calculates the deposition and clogging processes of dust particles in the duct and the filter through numerical simulation. During the simulation, the software calculates the movement, deposition, and aggregation processes of the particles at the preset time step, and considers the changes in the filter pores and the evolution of the clogging degree. The simulation results will output filter clogging data, including data such as the resistance change, dust accumulation amount, and filtration efficiency of the filter at each time step. Based on these data, the simulation software will also generate a series of graphs and tables for subsequent analysis. The output clogging data will provide a basis for filter maintenance and automatic adjustment control, and the data includes detailed information such as the time of clogging occurrence, the regional distribution of clogging, and its impact on airflow and suction force.

[0065] Preferably, step S4 is specifically as follows: Step S41: Divide the clogging levels according to the filter load status; extract the mild filter clogging data and the severe filter clogging data according to the clogging levels; In this embodiment, the filter pressure data and the filter images collected by the sensors are obtained. The specific operations are as follows: Extract the pressure difference data from the filter pressure sensor, and set the threshold range to be 15 Pa to 25 Pa. When the measured pressure difference is within this range, it is determined that the filter is in a mild clogging state. Then, use a camera or an image acquisition device to capture the image data of the filter, and obtain the particle information covered on the filter surface. This image data enhances the visibility of the particulate matter through image processing techniques (such as edge enhancement, histogram equalization, etc.). Next, convert the image into a binary image through image segmentation techniques (such as the fixed threshold method or the Otsu algorithm), set the gray value threshold to 90, and mark the pixel regions below this value to represent the clogging regions of the particles. Then, use morphological operations (such as opening and closing operations) to remove small-area noises, and identify each clogging region in the image through a contour extraction algorithm (such as the cv2.findContours function in OpenCV). For each identified region, calculate its area A_region. The area is calculated by counting the number of pixels in the region, and the area of a single pixel is set to 0.0625 mm² (assuming the pixel size is 0.25 mm × 0.25 mm). The regions with small areas and not meeting the clogging conditions will be excluded, and the remaining regions are the mild clogging regions. These regions form a two-dimensional region set R_block, and the subsequent cleaning operations will be based on these regions.

[0066] Step S42: Set a spiral cleaning path according to the mild filter clogging data; In this embodiment, the relative area of each blocked area is calculated through the area A_region, and A_total is the total projected area of the filter screen. Assuming the diameter of the filter screen is 80 mm, A_total is calculated as 5026.5 mm². Then, the area of each region is normalized using the ratio of the regional area A_region to A_total. Secondly, the average gray value G_avg of each region is calculated. The gray value is calculated by statistically averaging the gray values of all pixels within the region, and the gray scale ranges from 0 to 255. The gray value of each region represents the severity of the blockage, and the lower the gray value, the more severe the blockage.

[0067] Particularly importantly, step S42 includes the following steps: Step S421: Calibrate the slightly blocked areas according to the slightly blocked filter screen data; In this embodiment, the filter screen pressure data and the filter screen image collected by the sensor are obtained. The specific operations are as follows: Extract the pressure difference data from the filter screen pressure sensor, and set the threshold range to 15 Pa to 25 Pa. When the measured pressure difference is within this range, it is determined that the filter screen is in a slightly blocked state. Then, use a camera or an image acquisition device to capture the image data of the filter screen and obtain the particle information covered on the surface of the filter screen. This image data enhances the visibility of the particles through image processing techniques (such as edge enhancement, histogram equalization, etc.). Next, the image is converted into a binary image through image segmentation techniques (such as the fixed threshold method or the Otsu algorithm), and the gray value threshold is set to 90. The pixel areas below this value are marked to represent the blocked areas of the particles. Then, morphological operations (such as opening operation and closing operation) are used to remove small-area noises, and each blocked area in the image is identified through a contour extraction algorithm (such as the cv2.findContours function in OpenCV). For each identified area, its area A_region is calculated. The area is calculated by counting the number of pixels within the region, and the area of a single pixel is set to 0.0625 mm² (assuming the pixel size is 0.25 mm × 0.25 mm). Areas that are small and do not meet the blockage conditions are excluded, and the remaining areas are the slightly blocked areas. These areas form a two-dimensional area set R_block, and subsequent cleaning operations will be based on these areas.

[0068] Step S422: Divide the cleaning priorities based on the slightly blocked areas; In this embodiment, the relative area of each blocked area is calculated through the area A_region. A_total is the total projected area of the filter screen. Assuming the diameter of the filter screen is 80 mm, A_total is calculated as 5026.5 mm². Then, the area of each region is normalized using the ratio of the regional area A_region to A_total. Secondly, the average gray value G_avg of each region is calculated. The gray value is calculated by statistically averaging the gray values of all pixel points within the region, and the gray scale ranges from 0 to 255. The gray value of each region represents the severity of the blockage. The lower the gray value, the more severe the blockage. According to the area ratio and the gray mean value, the cleaning priority Priority is set. The specific operation is to calculate the Priority value as the weighted sum of the area normalization value and the gray normalization value, where the area normalization accounts for 70% of the weight and the gray normalization accounts for 30% of the weight. Through this formula, the region with a higher priority is assigned a smaller number (for example, number 1 represents the region with the highest priority). Then, all regions are sorted according to the Priority value, and the region with the highest priority value is ranked first. Finally, the top five regions with the highest priority are selected, and their geometric center points are extracted. The geometric center points are obtained by calculating the center points of the minimum circumscribed rectangles of the boundaries of each region, generating a coordinate point set P_c = {p 1 (x 1 ,y 1 ),p 2 (x 2 ,y 2 ),...,p 5 (x 5 ,y 5 )}, which is used as the candidate base points for subsequent rotational path planning.

[0069] Step S423: Set the central rotation point according to the cleaning priority and draw a rotational radius distribution diagram based on the central rotation point; In this embodiment, first, the geometric center of all point pairs in the coordinate point set P_c obtained through step S422 is calculated as the central rotation point P_center. By calculating the average distance between all coordinate points, the average distance from the rotation center is determined as the reference value of the rotation radius. Based on P_center, the area of the rotation path is set and divided into 12 equal-angle sector areas (each sector is 30 degrees). The distance from the center of the blocked area in each sector area to P_center is the rotation radius. For each sector area, the maximum distance and the minimum distance from all area center points in this area to P_center are calculated, and the rotation radius distribution map is drawn accordingly. This distribution map shows the variation of the rotation radius in each area, and the specific values are determined by the maximum radius and the minimum radius of each area. Through this distribution map, the priority position of each area in the rotation path planning can be determined. The area with the maximum radius requires a longer cleaning path length, while the area with the minimum radius has a shorter path. This figure provides the basic data support for the subsequent path planning and pitch adjustment.

[0070] Step S424: Adjust the cleaning pitch based on the rotation radius distribution map; adjust the cleaning density based on the rotation radius distribution map; In this embodiment, based on the rotation radius distribution map obtained through step S423, the radius range of each sector area is determined. According to the distribution of the rotation radius, the cleaning pitch and the cleaning density are adjusted. The adjustment rule of the cleaning pitch is as follows: for the area with a large radius change (for example, the area with a change exceeding 10 mm), the cleaning pitch is set to a smaller value (for example, 5 mm) to ensure that the cleaning path is fully covered. For the area with a small radius change (for example, the area with a change less than 5 mm), the pitch can be increased to 10 mm to reduce the path overlap and thus improve the efficiency. The adjustment of the cleaning density is optimized based on the path overlap degree of each area. In the area with serious blockage, the number of path overlaps is increased to improve the cleaning frequency, and the number of overlaps is set to more than 3 times; while in the relatively clean area, the path overlap is reduced, and only 1 overlap is set. The goal of density adjustment is to ensure that in the cleaning process, the areas with more serious blockage are cleaned more, while the relatively clean areas maintain a higher cleaning frequency.

[0071] Step S425: Set the spiral cleaning path according to the cleaning pitch and the cleaning density.

[0072] In this embodiment, the cleaning pitch and cleaning density parameters obtained through step S424 are used to construct a spiral cleaning path in polar coordinates. Taking the central rotation point P_center determined in step S423 as the reference point, the path is generated according to the calculated cleaning pitch and density. In each loop of the path, the radius r changes dynamically according to the cleaning pitch, and the angle θ increases at 10° intervals according to the set rotation speed. The generation of each path needs to consider the density adjustment of the area, that is, in the area with a larger cleaning density, the number of repetitions of the path is increased, so that the paths overlap more times, thereby enhancing the cleaning effect. When generating the path, the trajectory is drawn at each point along the path to ensure that the final path can cover all areas that need to be cleaned. By continuously increasing the number of rotation loops and the pitch, a spiral path is finally formed until all areas are cleaned. The final path data is transmitted to the vacuum cleaner control system in the form of coordinate points for actual cleaning operations.

[0073] Step S43: Set an energy-saving cleaning path according to the severe filter clogging data; In this embodiment, the severe clogging data usually means that the filter has a greater resistance, and the vacuum cleaner requires more energy during operation. Therefore, when designing the energy-saving cleaning path, the energy consumption problem must be comprehensively considered to reduce unnecessary energy consumption. The energy-saving path design first analyzes the dust accumulation distribution, resistance value and other important factors in the severe clogging data to determine the priority areas that need to be cleaned. Usually, the severely clogged areas are concentrated in certain specific parts. When setting the energy-saving path, avoid repeated cleaning of these parts and give priority to areas with greater resistance. In the path planning, a more straight and simple trajectory is adopted to reduce unnecessary turning and pausing and reduce battery consumption. The path design can reduce energy loss by setting the shortest path from the starting point to the ending point. The specific path planning is achieved by setting the lowest energy consumption mode, and dynamic optimization is carried out using simulation software to adjust the cleaning path in real time to keep the energy consumption within the optimal range.

[0074] Step S44: Integrate the spiral cleaning path and the energy-saving cleaning path to obtain the dust cleaning path; In this embodiment, in order to ensure the balance between cleaning efficiency and energy consumption, a spiral cleaning path is used to handle lightly blocked areas, while an energy-saving cleaning path is used for heavily blocked areas. During integration, it is necessary to preferentially use the energy-saving path in heavily blocked areas according to the blockage level of the cleaning area to ensure the reasonable allocation of resources. The integration process optimizes the path switching through an algorithm. First, it identifies the blockage level of each area and determines which areas are suitable for using the energy-saving path and which areas are suitable for using the spiral path by setting thresholds. For example, when the proportion of lightly blocked areas is greater than 70%, the spiral path can be adopted; if the proportion of heavily blocked areas exceeds 40%, the path is switched to the energy-saving path. Finally, a path plan covering all cleaning areas is generated to ensure that the most suitable path is used in different areas and the transition between paths is as smooth as possible to reduce the energy consumption of the device.

[0075] Step S45: Optimize the automatic obstacle avoidance strategy according to the dust cleaning path; In this embodiment, the built-in sensor data, such as lidar or ultrasonic sensors, is used to continuously monitor the obstacles in the surrounding environment. According to the positions of the obstacles encountered in the dust cleaning path, the path planning of the vacuum cleaner is adjusted. The automatic obstacle avoidance strategy needs to consider each corner, obstacle, and space limitation in the cleaning path to prevent the vacuum cleaner from staying in these areas for too long or deviating from the predetermined path. The optimization process dynamically adjusts the obstacle avoidance strategy and combines the input of the environmental sensors to update the obstacle avoidance algorithm in real time. For example, it can be set that when the sensor detects that the distance to an obstacle is less than 0.5 meters, the cleaning path is automatically adjusted to bypass the obstacle, or the best avoidance method is selected. Through real-time feedback and path adjustment, it is ensured that the vacuum cleaner can efficiently avoid obstacles and prevent accidental collisions or reduced cleaning efficiency.

[0076] Particularly importantly, step S45 includes the following steps: Step S451: Identify the spatial distribution of obstacles according to the dust cleaning path to obtain obstacle distribution data; In this embodiment, the generated dust cleaning path data is processed by expanding coordinate points, and the lidar echo data, ultrasonic reflection data, and infrared depth image frame data of the surrounding space are collected point by point. The lidar uses a 360° scanner with an accuracy of ±2 cm, the scanning radius is set to 3 m, and the scanning frequency is 10 Hz. The ultrasonic sensors are deployed at 5 cm intervals to cover the fan-shaped area in front of the device. When the reflection intensity exceeds the set threshold (e.g., a reflection coefficient of 0.6), it is marked as a hard obstacle. The infrared depth image is obtained by a structured light camera with a resolution of 640×480, the depth distance is within 1.5 m, and the point cloud spacing does not exceed 1 cm. The above multi-source spatial point sets are fused, unified to the dust cleaning path coordinate system through a coordinate registration algorithm, and a spatial obstacle distribution grid is constructed using the Voxel Grid method with a block granularity of 0.1 m³. Finally, an obstacle distribution data file containing the spatial obstacle density corresponding to each cleaning path segment is formed.

[0077] Step S452: Draw an obstacle boundary map based on the obstacle distribution data; In this embodiment, the obstacle distribution grid data is input into the two-dimensional grid map generation module. This module performs threshold division based on the obstacle point density (unit: number of points / m³) in the spatial grid. The set density determination threshold is 50 points / m³. The grid cells above this threshold are marked as real obstacle areas, and those below this value are marked as passable areas. The contour extraction method (such as Marching Squares) in the boundary extraction algorithm is used to construct continuous boundary lines for each real obstacle area one by one. The boundary line coordinates are represented by a two-dimensional integer grid and the vertex sequence is recorded in JSON format. At the same time, the convex hull algorithm is used to perform boundary compression processing on the obstacle contour line to ensure that the boundary line is concise and accurately covers the obstacle area. The finally output obstacle boundary map is in the form of a vector map, containing the polygon coordinate point set of each obstacle and its corresponding number.

[0078] Step S453: Plan an obstacle avoidance path according to the obstacle boundary map; In this embodiment, the dust cleaning path and the obstacle boundary map are jointly input into the path planning algorithm module. The A* search algorithm is used for obstacle avoidance path planning. The grid node spacing is set to 0.1 m, the heuristic cost function uses the Manhattan distance plus the weighted obstacle distance, and the obstacle distance weight is set to 5 times to avoid running close to the obstacle. The end point of the dust cleaning path is set as the target point for the map boundary, the starting point is the current device position, and the non-passable areas in the obstacle area are marked through step-by-step judgment. During the path search process, if the distance of any path node from the obstacle boundary is less than 0.2 m, the path is judged to be invalid and backtracked for re-planning. The finally generated path consists of a series of continuous coordinate points and is stored in CSV format for the path tracking module to call.

[0079] Step S454: Generate an obstacle avoidance action instruction based on the obstacle avoidance path; In this embodiment, according to the sequence of obstacle avoidance path coordinate points, the orientation angle θ (with due north as 0° and increasing clockwise) between adjacent coordinate points is calculated in sequence, and an obstacle avoidance action sequence is constructed based on the distance d (in meters) between points. Each action consists of an instruction pair: a direction instruction and a travel instruction. The direction instruction is represented by "ROT θ", and the rotation angle accuracy is 1°; the travel instruction is represented by "MOV d", and the minimum step size is set to 0.05 meters. The instruction sequence is generated one by one according to the path point order, and a distance merging rule for consecutive same directions (the merging threshold is 0.2 meters) is set to reduce redundancy. Finally, the complete set of obstacle avoidance action instructions is generated as structured text data. Each instruction has four fields: number, type, parameter, and timestamp. The data format is uniformly encoded as UTF-8 and saved in the local temporary instruction buffer.

[0080] Step S455: Arrange the obstacle avoidance instruction sequence according to the obstacle avoidance action instruction to obtain the arranged obstacle avoidance action instruction; In this embodiment, an execution sequence analysis is performed on the preliminary action instruction set. The instruction scheduling optimization module is used to adjust the time sorting of "ROT" and "MOV" type instructions to ensure that each "ROT" operation is completed before executing the "MOV" instruction, avoiding issuing two types of conflicting instructions simultaneously. During the arrangement process, the hardware execution delay time is set to 50 ms and added to the timestamp field of each instruction for cumulative update. The "ROT" operations that are not continuous but have similar directions in space are merged, and the rotation operations with an angle difference less than 5° are integrated into one unified instruction. At the same time, an execution number is added to all instructions for the controller to execute accurately in sequence. The finally generated arranged obstacle avoidance action instruction is a time-serialized instruction set, output in JSON format, and cached in the action control buffer module, waiting for the final strategy optimization call.

[0081] Step S456: Optimize the automatic obstacle avoidance strategy based on the arranged obstacle avoidance action instruction.

[0082] In this embodiment, the acceleration threshold (the maximum acceleration is set to 0.8 m / s²), the minimum turning radius (set to 0.15 m), and the maximum rotational speed limit (set to 1.5 m / s) of the input vacuum cleaner motion control system are input. The executability of each group of "ROT" and "MOV" combinations is calculated through kinematic constraints, and the instruction pairs that violate the acceleration constraint are replaced. The replacement rule adopts the combined execution method of subdividing the rotation angle and decelerating motion, that is, the original instruction "ROT 90°+MOV 0.5m" is decomposed into "ROT45°+MOV 0.25m+ROT 45° + MOV 0.25m". All optimized instructions are again corrected for delay uniformly according to the execution time, and finally integrated into a complete set of automatic obstacle avoidance strategy datasets that meet the dynamic execution limitations of the vacuum cleaner, and are transmitted to the vacuum cleaner body controller through the CAN bus communication protocol. The data structure includes keyword fields such as path number, instruction sequence, execution time per step, and target position coordinates.

[0083] Step S46: Transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the automatic obstacle avoidance control task of the vacuum cleaner.

[0084] In this embodiment, the optimized automatic obstacle avoidance strategy is transmitted to the vacuum cleaner management cloud platform, and real-time data communication is carried out between the cloud platform and the vacuum cleaner device. First, the obstacle avoidance strategy is converted into control instructions that can be executed in the cloud platform, and these instructions are transmitted to the vacuum cleaner control system through wireless communication. The communication protocol can adopt MQTT or other real-time communication protocols to ensure timely and stable information transmission. In the cloud platform, the received instructions will be parsed and transmitted to the hardware control system of the vacuum cleaner to ensure that the vacuum cleaner can execute tasks according to the latest obstacle avoidance strategy. During the data transmission process, it is necessary to ensure the security of the data, and encryption technology is used to ensure the privacy and integrity of the data during the communication process. Once the instruction transmission is successful, the vacuum cleaner will automatically execute the obstacle avoidance task according to the control strategy sent by the cloud platform to ensure that collisions with obstacles are avoided during the cleaning process, and finally complete the entire cleaning process.

[0085] Preferably, this specification also provides a vacuum cleaner automatic adjustment control system based on multi-source data for executing the vacuum cleaner automatic adjustment control method based on multi-source data as described above. This vacuum cleaner automatic adjustment control system based on multi-source data: Vacuum cleaner three-dimensional model construction module: used to obtain vacuum cleaner design data; extract vacuum cleaner structure design data according to the vacuum cleaner design data; construct a vacuum cleaner three-dimensional model based on the vacuum cleaner structure design data; Dust concentration detection module: used to identify the type of floor material based on the 3D model of the vacuum cleaner, obtain carpet material data and ceramic tile material data; detect the particulate gas flow based on the carpet material data; identify the dust coverage based on the ceramic tile material data; determine the dust concentration according to the particulate gas flow and the dust coverage; Filter load status identification module: used to extract the air duct structure data according to the design data of the vacuum cleaner; perform a simulation of the evolution of filter clogging based on the dust concentration and the air duct structure data to obtain filter clogging data; judge the filter load status based on the filter clogging data; Vacuum cleaner automatic obstacle avoidance module: used to divide the clogging level according to the filter load status; adjust the dust cleaning path according to the clogging level; optimize the automatic obstacle avoidance strategy according to the dust cleaning path; transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the vacuum cleaner automatic obstacle avoidance control task.

[0086] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be included in the present invention.

[0087] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A vacuum cleaner automatic adjustment control method based on multi-source data, characterized in that: The following steps are involved: Step S1: obtaining vacuum cleaner design data; Extracting vacuum cleaner structural design data according to vacuum cleaner design data; Construct a three-dimensional model of the vacuum cleaner based on the vacuum cleaner structure design data; Step S2: Identify the type of floor material based on the three-dimensional model of the vacuum cleaner to obtain carpet material data and tile material data; detect the particle gas flow based on the carpet material data; identify the dust coverage based on the tile material data; determine the dust concentration based on the particle gas flow and the dust coverage; Step S3: extracting air duct structure data according to the vacuum cleaner design data; performing filter clogging evolution simulation according to the dust concentration and the air duct structure data to obtain filter clogging data; and determining the filter load state based on the filter clogging data; Step S4: Divide the blockage level according to the filter load status; adjust the dust cleaning path according to the blockage level; optimize the automatic obstacle avoidance strategy according to the dust cleaning path; transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the vacuum cleaner automatic obstacle avoidance control task.

2. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: Step S1 is specifically as follows: Step S11: obtaining vacuum cleaner design data; Step S12: extracting vacuum cleaner structural design data according to the vacuum cleaner design data; Step S13: constructing component geometric profiles based on the vacuum cleaner structural design data; Step S14: defining connection relationships according to the geometric outlines of the components; Step S15: performing virtual assembly according to the geometrical contours and connection relationships of the components to obtain a geometrical model of the vacuum cleaner components; Step S16: injecting material attributes according to the geometric model of the vacuum cleaner parts to generate a three-dimensional model of the vacuum cleaner.

3. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: The ground material type identified in step S2 is specifically: Perform ground contact simulation according to the three-dimensional model of the vacuum cleaner to obtain ground contact data; Material sampling is performed based on ground contact data to obtain ground material data; Perform resilience testing based on ground material data to obtain fast-rebound material data and non-rebound material data; Perform infrared detection based on the ground material data and calculate the infrared reflectivity to obtain high reflectivity ground material data and low reflectivity ground material data; Carpet material intersection operation is performed according to the fast rebound material data and the low reflectivity ground material data to obtain carpet material data; The tile material data is obtained by performing tile material intersection operation according to the no-rebound material data and the high-reflectivity ground material data.

4. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: The specific steps of detecting the particle gas flow in step S2 are as follows: Identify fiber arrangement direction based on carpet material data; Calculate the void ratio based on the carpet material data; According to the fiber arrangement direction and the dust diffusion simulation of the porosity, the dust diffusion data is obtained; Tracking the diffusion path of dust particles based on dust diffusion data; Perform particle deposition detection on the fiber arrangement direction according to the dust particle diffusion path to obtain particle deposition data; Identify gas flow paths based on dust particle diffusion paths; The particle gas flow is obtained by fitting the motion behavior of the particle deposition data according to the gas flow path.

5. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: The dust coverage degree is identified in step S2 as follows: The tile area is calibrated based on the tile material data, and the tile area is irradiated with a laser at an angle of 10°-30° to obtain a tile laser irradiation image; Performing image difference processing on the laser irradiation image of the ceramic tile to obtain a laser irradiation difference image of the ceramic tile; Calculate the number of scattering points based on the difference image of the laser irradiation of the tile; calculate the size of the scattering points based on the difference image of the laser irradiation of the tile; calculate the scattering area ratio based on the number of scattering points and the size of the scattering points; Evaluate dust area based on scattering area ratio; generating a dust mask map based on the dust region; Count the dust pixel ratio according to the dust mask image; Dust coverage is evaluated based on the dust-pixel ratio.

6. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: The dust concentration is determined in step S2 as follows: calculating a gas flow rate according to the particle gas flow, and identifying a flow rate attenuation based on the gas flow rate; Determine the degree of dust accumulation based on the flow velocity decay; Detect dust particle diameter based on dust coverage; The dust concentration is determined based on the degree of dust accumulation and the diameter of the dust particles.

7. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: Step S3 is specifically as follows: Step S31: extracting air duct structure data according to vacuum cleaner design data; Step S32: simulating the filter clogging evolution according to the dust concentration and the air duct structure data to obtain the filter clogging data; Step S33: Detect the filter material based on the filter blockage data to obtain carbon fiber filter data; Step S34: performing fiber breakage detection based on the carbon fiber filter data to obtain fiber breakage data; Step S35: Identifying the degree of peeling of the carbon fiber layer filter according to the fiber breakage data; Step S36: judging the filter load state based on the degree of peeling of the carbon fiber layer filter.

8. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 7, characterized in that: Step S32 is specifically as follows: Step S321: importing the air duct structure data and dust concentration into the simulation software; Step S322: setting the air duct length, width, height, and inner wall roughness of the air duct in the simulation software; Step S323: setting the dust particle size range to 0.1 μm-100 μm, the particle density range to 0.5 g / cm³-3.0 g / cm³, and the particle concentration range to 0-5000 particles / m³ in the simulation software; Step S324: setting the air flow velocity range to 1m / s-10m / s, the temperature range to 20°C-40°C, and the humidity range to 20%-80% in the simulation software; Step S325: setting the porosity, filtration accuracy, and adhesion of the filter material in the simulation software; Step S326: Run the filter clogging evolution program in the simulation software and output the filter clogging data.

9. The vacuum cleaner automatic adjustment control method based on multi-source data according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: classifying the filter blockage level according to the filter load state; extracting the light filter blockage data and the heavy filter blockage data according to the blockage level; Step S42: setting a spiral cleaning path according to the light filter blockage data; Step S43: setting an energy-saving cleaning path according to the heavy filter clogging data; Step S44: integrating the spiral cleaning path and the energy-saving cleaning path to obtain a dust cleaning path; Step S45: Optimizing the automatic obstacle avoidance strategy according to the dust cleaning path; Step S46: Transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the vacuum cleaner automatic obstacle avoidance control task.

10. A vacuum cleaner automatic adjustment control system based on multi-source data, characterized in that: Used to execute the vacuum cleaner automatic adjustment control method based on multi-source data as claimed in claim 1, the vacuum cleaner automatic adjustment control system based on multi-source data comprises: Vacuum cleaner three-dimensional model building module: used to obtain vacuum cleaner design data; extract vacuum cleaner structure design data according to the vacuum cleaner design data; and build a vacuum cleaner three-dimensional model based on the vacuum cleaner structure design data; Dust concentration detection module: used to identify the type of floor material based on the three-dimensional model of the vacuum cleaner, obtain carpet material data and tile material data; detect particle gas flow based on carpet material data; identify dust coverage based on tile material data; determine dust concentration based on particle gas flow and dust coverage; Filter load state identification module: used to extract duct structure data based on vacuum cleaner design data; simulate filter blockage evolution based on dust concentration and duct structure data to obtain filter blockage data; determine filter load state based on filter blockage data; Vacuum cleaner automatic obstacle avoidance module: used to classify blockage levels according to the filter load status; adjust the dust cleaning path according to the blockage level; optimize the automatic obstacle avoidance strategy according to the dust cleaning path; transmit the automatic obstacle avoidance strategy to the vacuum cleaner management cloud platform to execute the vacuum cleaner automatic obstacle avoidance control task.

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