A method and system for intelligent control of an inductive beam bag garbage can

Abstract

The application provides a kind of intelligent control method and system of inductive bundle bag garbage can. Among them, the application first generates annular temperature distribution map by improving the spatial gradient of bag opening infrared data through optical enhancement assembly;Based on the user standing distance, the acute and slow grade is divided, and the time domain analysis window is dynamically intercepted;Establish the correlation mapping library of plastic bag material thickness, historical failure record and palm acceleration;After combining with the ambient temperature compensation annular diagram, the deformation risk index is output by the rule decision module;According to the index and acceleration peak value, the damper response function of the bucket cover is reconstructed to execute the anti-wrinkle opening and closing;Then, through the motor vibration characteristics, the tension distribution of the bundle ring is corrected to the damper function in closed loop, and the anti-wrinkle closed loop control of the bag opening is realized. Through the closed loop cooperation of infrared spatial gradient strengthening, user behavior grading response and tension real-time feedback, the application realizes the adaptive anti-wrinkle control of the garbage bag bundle opening in the opening and closing process.

Description

Technical Field

[0001] This application relates to the field of intelligent sanitation equipment control technology, and in particular to an intelligent control method and system for an induction-operated drawstring trash can. Background Technology

[0002] Intelligent sensor-operated drawstring trash cans need to automatically open the lid when a user approaches and intelligently close the drawstring after trash is disposed of to prevent odors. The core technological challenge lies in the real-time sensing of the drawstring's wrinkling state (such as uneven plastic bag adhesion or localized looseness of the drawstring) and dynamically coordinating the lid opening and closing action with the drawstring tightening mechanism to prevent permanent deformation or seal failure due to mismatched actions. Especially when users quickly dispose of trash or the bag walls are too thin, the mechanism's response strategy must be adjusted promptly to avoid wrinkling.

[0003] The current mainstream solution uses a combination of millimeter-wave radar-based gesture trajectory analysis and bag pressure sensor array: the radar captures the user's hand movement trajectory to predict the speed of garbage disposal; at the same time, distributed pressure sensors are embedded in the rim of the bin to detect the pressure distribution pattern of the contact surface between the garbage bag tie and the bin wall in real time. When a sudden drop in local pressure is detected (indicating an imbalance in the tie tension), the torque distribution of the bin lid motor is immediately fine-tuned.

[0004] Existing solutions rely on a collaborative mechanism of gesture trajectory analysis and contact pressure sensing. Their core shortcomings are: pressure detection has a blind spot and cannot capture early deformation signals in the suspended area of ​​the garbage bag loop; it is highly sensitive to changes in environmental temperature and humidity and is prone to sensor misjudgment due to the thermal expansion and contraction effect of plastic bags; more importantly, it has an inherent response lag, which can only trigger adjustment after physical deformation occurs, resulting in a significant increase in the risk of breakage of thin plastic bags in rapid disposal scenarios. Summary of the Invention

[0005] This application provides an intelligent control method and system for a sensor-operated drawstring trash can to solve the problem of inherent response lag in the prior art.

[0006] In a first aspect, this application provides an intelligent control method for a sensor-operated drawstring trash can, including:

[0007] Infrared data from the sensor-controlled drawstring trash can, including the shape characteristics of the drawstring folds of the trash bag and the user's hand movement information, is collected. The spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by an optical enhancement component to generate an annular temperature distribution map.

[0008] Based on the real-time distance between the user's standing position and the sensor-operated trash can, the urgency level of the user's trash disposal behavior is divided, and a time-domain analysis window corresponding to the annular temperature distribution map is dynamically extracted according to the urgency level.

[0009] Establish a mapping relationship library between the thickness of plastic bag material and historical drawstring bag failure records, and associate it with the acceleration peak value of the palm movement information to obtain the associated mapping relationship library;

[0010] Based on the mapping relationship library, the baseline drift of the annular temperature distribution map is compensated by incorporating environmental temperature fluctuations. The compensated annular temperature distribution map and the time domain analysis window are processed by a rule decision module, and the deformation risk index of the bundle opening is output.

[0011] Based on the deformation risk index and the peak acceleration, the damping response function of the lid mechanism is reconstructed, and the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can is executed.

[0012] The vibration characteristics of the motor are extracted in real time during the opening and closing of the lid, and the tension distribution of the garbage bag loop is obtained by reverse calculation. The damping response function is corrected in a closed loop. The intelligent control method for preventing wrinkles at the opening of the garbage bag in the sensor-operated bag garbage can is realized through closed-loop control of all steps.

[0013] Optionally, based on the deformation risk index and the peak acceleration, the damping response function of the lid mechanism is reconstructed, and the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can is executed, including:

[0014] Obtain the magnitude of the deformation risk index, determine the risk level, reduce the opening and closing speed of the lid when the risk level is high, and increase the opening and closing speed of the lid when the risk level is low.

[0015] Obtain the peak acceleration value, and adjust the response delay of the bucket lid according to a preset adjustment rule based on the amplitude of the peak acceleration value;

[0016] The deformation risk index and the peak acceleration are input into a predefined function to generate a new damping coefficient;

[0017] The new damping coefficient is used to update the motion resistance of the lid mechanism, and the damping response function of the lid mechanism is reconstructed.

[0018] Based on the reconstructed damping response function, the motor driving the induction bag bin lid performs the opening or closing action of the anti-wrinkle bag opening.

[0019] Optionally, the vibration characteristics of the motor are extracted in real time during the opening and closing of the lid, and the tension distribution of the garbage bag loop is derived by reverse calculation. The damping response function is then corrected in a closed loop, including:

[0020] During the opening and closing of the lid, the vibration characteristics of the motor are collected in real time by the sensor, including the amplitude and frequency of the vibration. The vibration characteristics are input into the pre-stored correspondence model and the real-time tension distribution value of the garbage bag tie ring is output.

[0021] Based on the tension distribution value, regions with uneven or excessive tension are identified, and the damping coefficient of the damping response function is corrected according to a preset adjustment rule.

[0022] The modified damping response function is updated and applied in real time to control the movement of the bucket lid in a closed loop.

[0023] Optionally, the spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by an optical enhancement component to generate an annular temperature distribution map, including:

[0024] Special lenses and filters with optical enhancement components are used to process the collected infrared data, which includes the morphological features of the garbage bag drawstring folds and information on the user's hand movements.

[0025] By adjusting the optical path, the temperature difference between adjacent points in the circumferential direction of the bag opening is increased, thereby improving the spatial gradient of the processed infrared data.

[0026] The infrared data after spatial gradient enhancement is converted into a ring representation, and the temperature value at each position is displayed along the circumference of the bag opening, generating a continuous temperature value map along the circumference of the bag opening.

[0027] Optionally, dynamically selecting a time-domain analysis window corresponding to the annular temperature distribution map based on the rapidity / slowness level includes:

[0028] Based on the classification of urgency levels, the movements are divided into fast movement levels or slow movement levels.

[0029] When the urgency level is the fast action level, a short time-domain analysis window is extracted; when the urgency level is the slow action level, a long time-domain analysis window is extracted.

[0030] The time-domain analysis window covers the time-domain variation data of the annular temperature distribution map, where the window length determines the time range for analyzing the annular temperature distribution map.

[0031] Optionally, a mapping relationship library is established between the thickness of the plastic bag material and historical drawstring bag failure records, and the acceleration peak value of the hand movement information is associated to obtain the associated mapping relationship library, including:

[0032] Collect plastic bag material thickness data and historical drawstring bag failure records, and associate the material thickness values ​​with the historical failure records to establish an initial mapping relationship library;

[0033] Add the peak acceleration of the hand motion information as an additional dimension to the initial mapping relation library;

[0034] For different plastic bag material thicknesses and acceleration peak values, the corresponding failure frequencies are associated and stored in the initial mapping relationship library, and the associated mapping relationship library containing the material thickness, the acceleration peak value, and the historical failure records is output.

[0035] Optionally, the compensated annular temperature distribution map and the time-domain analysis window are processed using a rule-based decision module to output the deformation risk index of the beam opening, including:

[0036] The rule-based decision-making module extracts the temperature change value in the circumferential direction of the bag opening from the compensated annular temperature distribution map.

[0037] Extract the temperature change rate characteristics within the corresponding time range from the time domain analysis window;

[0038] The temperature change value and the rate of change are analyzed using preset rules. Based on the analysis results, the deformation risk index of the knot is adjusted by increasing or decreasing. Finally, the adjusted deformation risk index value representing the possibility of wrinkles is output.

[0039] Secondly, this application provides an intelligent control system for a sensor-operated drawstring trash can, comprising:

[0040] The data acquisition module is used to acquire infrared data of the sensor-controlled drawstring trash can, including the shape characteristics of the drawstring folds of the trash bag and the user's hand movement information. The spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by an optical enhancement component to generate an annular temperature distribution map.

[0041] The analysis module is used to classify the urgency level of the user's garbage disposal behavior based on the real-time distance between the user's standing position and the sensor-operated bag garbage bin, and dynamically extract the time-domain analysis window corresponding to the annular temperature distribution map according to the urgency level.

[0042] The association module is used to establish a mapping relationship library between the thickness of the plastic bag material and the historical drawstring bag failure records, and to associate the acceleration peak value of the palm movement information to obtain the associated mapping relationship library;

[0043] The processing module is used to compensate for the baseline drift of the annular temperature distribution map by integrating the ambient temperature fluctuation based on the mapping relationship library, and to process the compensated annular temperature distribution map and the time domain analysis window using the rule decision module, and output the deformation risk index of the bundle opening.

[0044] The execution module is used to reconstruct the damping response function of the lid mechanism based on the deformation risk index and the peak acceleration, and to execute the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can.

[0045] The correction module is used to extract the motor vibration characteristics in real time during the opening and closing of the lid, back-calculate the tension distribution of the garbage bag drawstring, and correct the damping response function in a closed loop. Through closed-loop control of all steps, the intelligent control method for preventing wrinkles at the drawstring opening of the induction drawstring garbage can is realized.

[0046] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the intelligent control method for a sensor-operated drawstring trash can as described in the first aspect above.

[0047] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an intelligent control method for an induction-operated drawstring trash can as described in the first aspect.

[0048] This application addresses the blind spot problem in suspended areas of traditional contact sensors by generating an annular temperature distribution map through infrared data acquisition and optical enhancement. Furthermore, it accelerates real-time data processing by classifying the urgency level based on user distance and dynamically capturing time-domain windows, enabling adaptive delivery behavior. Combining a historical mapping library of plastic bag materials with correlation modeling of acceleration peaks quantifies the coupling strength of mechanical risks in human-computer interaction. Then, through environmental temperature compensation and rule-based decision-making modules, it overcomes temperature drift interference and accurately outputs a deformation risk index. Based on this index, it reconstructs the damping response function, predictively adjusting the lid's movement before critical deformation. Finally, it uses motor vibration characteristics to infer tension distribution and corrects the damping function in a closed loop, eliminating execution lag and forming a real-time suppression closed loop for rim deformation. The entire process collaboratively overcomes three major technical bottlenecks: perception blind spots, environmental misjudgment, and response delay.

[0049] Furthermore, by dynamically adjusting the opening and closing speed of the lid in real time based on the deformation risk level (deceleration for high risk, acceleration for low risk), and adaptively adjusting the response delay by combining the peak acceleration amplitude, and inputting the two parameters into a predefined function to generate a new damping coefficient, the motion resistance is finally updated to drive the lid motor to achieve millisecond-level dynamic torque compensation based on the real-time stress state of the plastic bag, so that the tension force of the drawstring and the movement trajectory of the lid always maintain adaptive coordination.

[0050] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart of an intelligent control method for a sensor-operated drawstring trash can provided in this application is shown;

[0053] Figure 2 A scene diagram illustrating an intelligent control method for an induction-operated drawstring trash can provided in this application is shown.

[0054] Figure 3 A schematic diagram of the structure of an intelligent control system for a sensor-operated drawstring trash can provided in this application is shown.

[0055] Figure 4 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0056] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0057] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0058] In the field of intelligent sensor-controlled bag trash cans, existing solutions rely on a collaborative mechanism of millimeter-wave radar gesture tracking and contact pressure sensing along the bin edge. This presents three major technical bottlenecks: limited spatial perception (pressure sensors can only detect the physical contact area between the bag's drawstring and the bin wall, completely failing to detect early wrinkles and deformations in suspended, unattached areas, leading to missed detection of critical deformation signals); environmental interference deficiencies (thermal expansion and contraction of the plastic bag due to temperature and humidity changes causes baseline drift in the pressure sensor, systematically misinterpreting environmental interference as drawstring tension imbalance); and persistent response lag (adjustment mechanisms are only triggered after physical deformation actually occurs, resulting in a mismatch between the lid's movement and the bag's deformation rate, especially for thin plastic bags and in scenarios where users dispose of trash quickly, leading to a high rate of wrinkle breakage). The root cause of these problems lies in the failure of existing technologies to coordinate non-contact deformation sensing, environmental interference decoupling, and predictive control.

[0059] To address the aforementioned shortcomings, this application proposes an intelligent anti-wrinkle method based on infrared thermodynamic sensing and closed-loop damping control. Its core breakthrough lies in: generating an annular temperature distribution map from optically enhanced infrared data to directly capture the dynamic thermal characteristics of wrinkles in the suspended area (overcoming spatial perception limitations); combining user distance dynamics with the time-domain window and the acceleration peak associated with the material-failure mapping library to construct an adaptive risk prediction model for deployment behavior; further integrating environmental temperature compensation to output a deformation risk index and reconstructing the lid's damping response function to achieve pre-deformation intervention (eliminating environmental misjudgment lag); and finally, using motor vibration to reverse-engineer the tension distribution and correct the damping in a closed loop, ensuring that the lid's opening and closing action matches the stress changes at the opening in real time. This solution establishes a system-level solution of "infrared sensing - risk prediction - damping reconstruction - closed-loop calibration" for the first time, simultaneously overcoming three major technical barriers—blind spots in suspended area detection, temperature drift misjudgment, and response lag—without increasing hardware costs.

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] Figure 1 A flowchart illustrating an intelligent control method for a sensor-operated drawstring trash can, as provided in this application embodiment, is shown below. Figure 1 As shown, the method includes:

[0062] 101. Collect infrared data of the sensor-controlled drawstring trash can, including the shape characteristics of the drawstring folds of the trash bag and the user's hand movement information, and enhance the spatial gradient of the infrared data in the circumferential direction of the bag opening through an optical enhancement component to generate an annular temperature distribution map.

[0063] Optionally, step 101 may specifically include the following steps:

[0064] 1011. Using special lenses and filters with optical enhancement components, infrared data containing the morphological features of the drawstring folds of garbage bags and information on the user's hand movements are processed;

[0065] 1012. By adjusting the optical path, the temperature difference between adjacent points in the circumferential direction of the bag opening is increased, thereby improving the spatial gradient of the processed infrared data.

[0066] 1013. Convert the infrared data after spatial gradient enhancement into a ring representation, display the temperature value at each position along the circumference of the bag opening, and generate a continuous temperature value map along the circumference of the bag opening.

[0067] In the above scheme, the optical enhancement component refers to the hardware system consisting of an aspherical special lens and a band-selective filter; the morphological characteristics of the garbage bag drawstring folds refer to the local temperature field distribution pattern corresponding to the three-dimensional folds formed by the physical tightening operation; the user's hand movement information refers to the operation trajectory and direction vector identified by the spatial displacement of dynamic thermal signals; spatial gradient enhancement refers to the behavior of increasing the difference in radiant flux between adjacent points on the circumference of the bag opening by adjusting the optical path to enhance the resolution of temperature difference in the angular dimension; the ring representation refers to the geometric transformation process of converting Cartesian coordinate system data into a continuous angular function relationship with the center of the bag opening as the origin; and the continuous temperature value map refers to the visual expression of the full-circumference temperature distribution function generated by the interpolation algorithm.

[0068] In this embodiment, firstly, through step 1011, the raw infrared data initially acquired, containing information about the shape of the garbage bag's drawstring folds and the user's hand movements, is processed using a special lens and filter in the optical enhancement assembly. The special lens focuses and magnifies the image onto the drawstring area of ​​the garbage bag, ensuring the target is clear. The filter specifically removes irrelevant thermal signal interference from the environment, leaving only the specific thermal signals related to the bag's drawstring folds and the user's hand movements. For example, before processing, a sensing point might contain mixed information about the bag's temperature and the ambient background temperature. Suppose a point on the bag's drawstring should actually display 31 degrees, but background radiation introduces an interference value of 25 degrees. After processing by the filter, only the valid signal is retained, and the data at that point is corrected to a pure 31 degrees.

[0069] Next, in step 1012, using the clean infrared data obtained in the previous step 1011, the temperature difference between two very close points along the circumference of the garbage bag opening is amplified by adjusting the angle of the reflectors within the optical path. This operation is equivalent to artificially making the temperature change along the circumference more obvious, thus amplifying the originally subtle temperature difference. For example, in the data processed in 1011, suppose there are two points very close together along the circumference of the bag opening: point A has a temperature of 31 degrees Celsius, and the adjacent point B has a temperature of 32 degrees Celsius, with only a slight difference of 1 degree Celsius between them. After adjusting the optical path for enhancement, the enhanced data for point A may become 30 degrees Celsius, and for point B, it may become 34 degrees Celsius. In this way, the temperature difference between the two adjacent points increases from 1 degree Celsius to 4 degrees Celsius, making the temperature change along the entire circumference more significant and easier to distinguish.

[0070] Finally, in step 1013, the data, which has been amplified and enhanced in step 1012 to improve the circumferential temperature variation differences, is reorganized and transformed. Using a specific algorithm, the data points, which might have been arranged in a rectangular grid, are reordered and mapped according to the actual shape of the circumference of the garbage bag opening, generating a continuous temperature value view that unfolds along the circumference of the bag opening. Each circumferential position is clearly marked with its corresponding temperature value. For example, the enhanced data point sequence might show a temperature of 30 degrees Celsius at 0 degrees Celsius, 33 degrees Celsius at 45 degrees Celsius, 36 degrees Celsius at 90 degrees Celsius, and so on. The algorithm connects these points in angular order to form a circular temperature distribution curve, which is then visually displayed on the screen.

[0071] In practical applications, in typical smart trash can systems, built-in infrared sensors collect real-time data on the three-dimensional fold morphology of the trash bag's drawstring area and the user's hand movement trajectory when the user approaches the device. The optical enhancement component, equipped with a special wide-angle lens module and a high-precision infrared filter, first filters out ambient thermal noise and focuses on a specific infrared band. By dynamically adjusting the light path refraction angle of the lens group, it significantly amplifies the temperature difference between adjacent fold points along the circumference of the bag opening, enhancing the spatial resolution of minute scale changes. The processing unit converts the optimized thermal field data into a 360-degree circular coordinate system, generating a continuous temperature distribution map and automatically identifying key temperature change areas, such as the user's finger contact point or folded recesses, ultimately driving the electric drawstring mechanism to perform an adaptive tightening operation.

[0072] This solution significantly enhances the ability to identify key features through optical enhancement, making the microscopic changes in the fold morphology clearly structural features in the infrared spectrum, while improving the accuracy of hand movement trajectory capture; the annular temperature map fully displays the real-time dynamic state of the circumference of the bag opening, including thermal anomalies in potential air leakage areas; the system can accurately respond to complex fold morphology and user operation intentions, reducing the probability of misjudgment in various environments, enhancing the sealing efficiency of garbage bags and reducing the need for manual intervention.

[0073] 102. Based on the real-time distance between the user's standing position and the sensor-operated trash can, the urgency level of the user's trash disposal behavior is divided, and a time-domain analysis window corresponding to the annular temperature distribution map is dynamically extracted according to the urgency level.

[0074] Optionally, step 102 may specifically include the following steps:

[0075] 1021. Based on the classification of speed levels, the movements are divided into fast movement levels or slow movement levels;

[0076] 1022. When the urgency level is the fast action level, extract a short time-domain analysis window; when the urgency level is the slow action level, extract a long time-domain analysis window.

[0077] 1023. The time-domain analysis window covers the time-domain variation data of the annular temperature distribution map, wherein the window length determines the time range for analyzing the annular temperature distribution map.

[0078] In the above scheme, the user's standing position refers to the vertical projection coordinates of the user's feet and the contact surface of the trash can; the real-time distance refers to the dynamically changing Euclidean spatial scale between this coordinate and the geometric center of the trash can; the urgency level refers to the classification of the urgency of the disposal behavior based on the real-time distance threshold; the fast action level refers to the high-speed operation state that the system determines requires an urgent response, and the slow action level refers to the low-speed operation state that the system determines can delay the response; the time domain analysis window refers to the dynamic intercept period covering the time-series changes of the annular temperature distribution map, the short time length window refers to the high-frequency sampling analysis interval adapted to the fast action level, and the long time length window refers to the low-frequency sampling analysis interval adapted to the slow action level; the window length refers to the time range span value determined according to the urgency level.

[0079] In this embodiment, firstly, in step 1021, the real-time distance between the user's current standing position and the trash can is obtained using a distance measuring device on the trash can. The system compares this distance value with an internally set standard threshold. If the actual distance is less than or equal to this threshold, the system classifies the user's action of preparing to throw away trash as a fast action. If the actual distance is greater than the threshold, the system classifies the action as a slow action. For example, the set threshold is 0.5 meters. When the distance measuring device measures the user's position to be 0.4 meters from the trash can, the system immediately determines that this belongs to the fast action level. When the distance measuring device measures the user's position to be 1.2 meters from the trash can, the system immediately determines that this belongs to the slow action level. Secondly, in step 1022, based on the action level classification result obtained in the previous step 1021, the length of the time segment to be intercepted is automatically determined. The specific rule is very clear: if the action level is a fast action level, then a very short time segment is intercepted from the current moment backward as the analysis window. If the action level is slow, then a relatively long time segment is extracted from the current moment backward as the analysis window. The time length itself is a pre-set fixed value. For example, a fast action level corresponds to a fixed time window length of 0.5 seconds, and a slow action level corresponds to a fixed time window length of 2.0 seconds. Assuming that step 1021 has determined it to be a fast action level, the system will immediately determine the time domain analysis window length to be 0.5 seconds and prepare to extract data within this time segment.

[0080] Finally, in step 1023, the specific time window length determined in step 1022 is applied to the continuously updated annular temperature distribution map data. The system knows when each annular temperature distribution map was generated. It extracts data based on the current time point and the window length. This window time length directly indicates how long ago to analyze the changes within that time range. The system extracts all annular temperature distribution maps generated within this time period as the dataset to be analyzed. For example, if step 1022 indicates a window length of 0.5 seconds, and the annular temperature distribution maps are generated continuously at a rate of 10 images per second, then the system will select all temperature distribution maps generated within the 0.5-second time period before the current time point, assuming the 5 most recent images, as the image sequence to be analyzed in depth. This process continues, starting with measuring distance, determining the speed of action based on distance, selecting an appropriate time period length based on the speed, and finally preparing environmental data covering that time period for subsequent analysis.

[0081] In practical applications, within the smart trash can system, infrared distance sensors continuously monitor the distance between the user's standing position and the device. The system categorizes actions into rapid and slow levels based on preset thresholds: a distance less than a set value A indicates a rapid action level, while a distance greater than a set value B indicates a slow action level. If the user is in a rapid action level, the processing unit captures a 0.2-second timeframe of the annular temperature graph change sequence; if in a slow action level, a 2-second timeframe is captured. For example, when a user approaches hastily, the system captures the temperature fluctuations of the bag opening's transient movement; when the user operates slowly, it fully records the gradual thermal field evolution of the bag's folds.

[0082] This solution dynamically classifies actions into fast and slow levels based on distance perception, enabling the time-domain analysis window to accurately adapt to the operation rhythm. Short windows focus on the transient temperature characteristics of fast actions to avoid missing key details, while long windows cover the gradual change of thermal field in slow actions, improving data integrity. Spatiotemporal coupling analysis significantly enhances the ability to interpret user intent and optimizes the rationality of system response and scenario adaptability.

[0083] 103. Establish a mapping relationship library between the material thickness of plastic bags and historical drawstring bag failure records, and associate it with the acceleration peak value of the palm movement information to obtain the associated mapping relationship library;

[0084] Optionally, step 103 may specifically include the following steps:

[0085] 1031. Collect plastic bag material thickness data and historical drawstring bag failure records, and associate the material thickness values ​​with the historical failure records to establish an initial mapping relationship library;

[0086] 1032. Add the peak acceleration of the hand motion information as an additional dimension to the initial mapping relation library;

[0087] 1033. For different plastic bag material thicknesses and acceleration peak values, associate and store the corresponding failure occurrence frequencies into the initial mapping relationship library, and output the associated mapping relationship library containing the material thickness, the acceleration peak value, and the historical failure records.

[0088] In the above scheme, the thickness of the plastic bag material refers to the vertical cross-sectional dimension of the undeformed polymer material; the historical drawstring bag failure record refers to the statistical data of historical events of loosening or damage to the drawstring seal; the initial mapping relationship library refers to the database that stores the correlation data between the material thickness value and the failure record; the peak acceleration of the hand movement information refers to the maximum instantaneous velocity change rate generated by the hand when the user operates the drawstring; the associated mapping relationship library refers to the extended database that adds the peak acceleration dimension and stores the corresponding failure frequency, where the failure frequency refers to the statistical frequency of drawstring bag failure events under a specific combination of material thickness and peak acceleration.

[0089] In this embodiment, firstly, through step 1031, the system collects two key pieces of information from past records: one is the actual thickness of the plastic bag, such as the thickness indicated on the outer packaging of the garbage bag or a specific value measured by a sensor, for example, 0.01 mm; the other is historical records of whether the bag broke or loosened when tightened, marked as "failure" or "success". The system simply pairs each collected thickness value directly with the success or failure result of its corresponding bag and stores them together, for example, associating a thickness of 0.01 mm with the fact that there were 3 failures out of 5 records. All such combinations of thickness values ​​and success or failure records accumulate to form the most basic database, which we call the initial mapping relationship library. Secondly, through step 1032, the system utilizes the hand motion data obtained from the user's garbage disposal action, especially the acceleration information. It focuses on the highest point value of the wrist acceleration reached each time the user tightens the garbage bag drawstring, called the acceleration peak. The system adds this peak value as a new information point to the initial mapping relationship library. The specific method involves adding a new column to the original row storing each thickness-failure record, recording the fastest hand movement speed value corresponding to that value. For example, in the previous record of a 0.01 mm thickness that experienced 3 failures, if the highest acceleration of the user's movement during these 3 failure events was 3 m / s², 3.2 m / s², and 3.5 m / s², then the system would add these three acceleration values ​​to the corresponding three failure records, so that each record contains three pieces of information: thickness value, failure result, and maximum movement speed.

[0090] Finally, in step 1033, the system begins a more in-depth analysis and statistical review of the updated database. It specifically examines the number of times the bag fails (e.g., breaks) when the garbage bag has a specific material thickness and the user's hand movement reaches a certain maximum speed while tightening the bag. The system counts these occurrences separately. For example, for a bag with a thickness of 0.01 mm, if the user's peak acceleration is exactly 3 m / s², there is one failure recorded in the historical data; if the peak is 3.2 m / s², one failure is recorded; and if the peak is 3.5 m / s², another failure is recorded. The system adds these statistically recorded frequencies to each record in the database. This results in a detailed mapping database where each complete record includes the garbage bag's specific material thickness, the maximum acceleration reached by the user's hand movement while tightening the bag, whether the bag was successfully tightened or not, and the total number of times the bag failed under this "thickness + maximum movement speed" combination. For example, the final record might be: thickness 0.01 mm, peak acceleration 3.5 m / s², resulting in failure. This combination of thickness and hand speed failed once in history. The entire process starts by compiling historical thickness and success / failure data, then adds details of the speed of the action, and finally calculates the reliability data of the bag under different combinations of materials and different action speeds.

[0091] In practical applications, the smart trash can system continuously collects material thickness data for different plastic bag samples, such as thin D-type materials and thick E-type materials, and simultaneously records corresponding historical bag failure events, such as bag slippage or tearing. The processing unit establishes an initial mapping relationship library between material thickness values ​​and failure records. When a user performs a bag-binding operation, the system captures the peak value of hand acceleration, such as the high peak value F generated by rapid waving, and adds this peak value data as an independent dimension to the mapping library. Finally, it associates and stores specific combinations of data, such as the failure frequency corresponding to D-type materials combined with peak value F, forming a comprehensive mapping relationship library that includes material thickness, peak acceleration, and failure records.

[0092] This solution establishes a multi-factor mapping relationship library, enabling the system to dynamically predict the failure risk trend of garbage bags made of specific materials under different operating intensities. For example, it can identify the high tear probability of thin materials under high-intensity operation or the slippage tendency of thick materials under low-intensity operation. This capability allows the sealing mechanism to adaptively adjust the force control strategy, effectively reducing the garbage bag breakage rate and improving sealing reliability.

[0093] 104. Based on the mapping relationship library, the baseline drift of the annular temperature distribution map is compensated by incorporating environmental temperature fluctuations. The compensated annular temperature distribution map and the time domain analysis window are processed by the rule decision module, and the deformation risk index of the bundle opening is output.

[0094] Optionally, step 104 may specifically include the following steps:

[0095] 1041. Based on the rule-based decision-making module, extract the temperature change value in the circumferential direction of the bag opening from the compensated annular temperature distribution map;

[0096] 1042. Extract the temperature change rate characteristics within the corresponding time range from the time domain analysis window;

[0097] 1043. Apply preset rules to analyze the temperature change value and the rate of change characteristics, and adjust the deformation risk index of the knot by increasing or decreasing based on the analysis results, and finally output the adjusted deformation risk index value representing the possibility of wrinkles.

[0098] In the above scheme, ambient temperature fluctuation refers to the background temperature shift caused by changes in external environmental thermodynamic conditions; baseline drift refers to the deviation of the overall temperature field baseline of the annular temperature distribution map caused by ambient temperature fluctuation; compensation refers to the calibration operation that corrects this deviation using data from the mapping relation library; the rule decision module refers to the logical processing unit that performs feature extraction and risk analysis; the temperature change value refers to the temperature difference in the azimuth dimension of the annular temperature distribution map at the bag opening after compensation; the temperature change rate characteristic refers to the rate attribute of temperature evolution over time within the time domain analysis window; the preset rule refers to the judgment logic that associates spatial temperature difference with the time domain change rate; adjustment refers to the behavior of dynamically correcting the deformation risk index value according to the preset rule; the deformation risk index refers to the final output quantitative index characterizing the deformation possibility of the drawstring pleated structure.

[0099] In this embodiment, firstly, through step 1041, the system calls upon previously established database information containing plastic bag characteristics and user behavior habits. Combining this with actual temperature changes in the environment, the system calibrates the real-time generated annular temperature distribution map to eliminate the influence of slow environmental temperature changes on the baseline temperature. For example, if current environmental temperature fluctuations cause the baseline temperature near the trash can to rise by 2 degrees Celsius, the system subtracts these 2 degrees from the temperature values ​​measured at each location on the map, ensuring that the temperature changes on the map purely reflect user actions rather than environmental interference. Next, the rule decision module analyzes this calibrated annular map. It sets multiple detection points around the bag's drawstring position and calculates the difference between the highest and lowest temperatures in each direction. For example, if the 0-degree position on the map displays 31 degrees and the 90-degree position displays 35 degrees, then the maximum temperature difference within that range is 4 degrees Celsius.

[0100] Next, in step 1042, the rule decision module retrieves the time-domain analysis window data prepared in step 102. This window specifies the time period to be analyzed. The module examines the rate of change of all temperature points on the annular temperature distribution chart within this time period. Specifically, it checks the difference in temperature values ​​at the same location on the two annular charts at the beginning and end of the time period, and then divides it by the length of the time period. For example, if the temperature at a location of 0 degrees Celsius is 30 degrees Celsius at the beginning of a 0.5-second window and 36 degrees Celsius at the end, then the rate of change at that point is 36 minus 30 equals 6 degrees Celsius, which, divided by 0.5 seconds, equals an increase of 12 degrees Celsius per second.

[0101] Finally, in step 1043, the rule decision module takes the two core feature values ​​obtained in the first two steps as input and uses preset logical rules for comprehensive judgment. The system stipulates that when the maximum temperature difference at a certain point on the circumference of the neck exceeds a certain preset threshold, and the rate of temperature change in that area also exceeds another preset threshold, the risk of wrinkling and deformation at that location is determined to be high. Based on the triggering of these conditions, the system will adjust an initial deformation risk index of 0 by adding or subtracting points. For example, if a temperature difference of 4 degrees is detected, exceeding the set threshold of 3.5 degrees, and the temperature rise rate at that point is 12 degrees per second, exceeding the set threshold of 10 degrees per second, the rule will require the index to increase by 10 points. If the humidity correction condition is also met, an additional 5 points may be added. The system continues to analyze all locations in this way, accumulating and adjusting the index, and finally outputs a quantitative risk value within the range of 0 to 100 that accurately reflects the degree of risk of wrinkling and tearing that the neck may produce at the current location. For example, if the final calculated index is 65, it represents a moderate risk. The entire process starts by eliminating environmental interference, then quantifies the spatial temperature difference and the rate of change over time, and finally transforms it into an intuitive risk assessment value through clear judgment rules.

[0102] In practical applications, within the intelligent trash can system, the system calls upon pre-established mapping relationship database data and combines it with real-time monitored ambient temperature category G to perform baseline drift compensation. For example, in cold environments, the background thermal radiation value of the annular temperature map is automatically corrected. The rule decision module performs three steps: first, it extracts the temperature change difference between regions H1 and H2 in the circumferential direction of the bag opening from the compensated temperature map; second, it extracts the operation speed characteristics, such as the gradual temperature change pattern generated during slow movements, from the time-domain analysis window; and finally, it analyzes the coupling effect between the temperature difference and the rate of change according to preset rules. When a sudden high-temperature change occurs due to the combination of thin plastic and rapid movement, the wrinkle deformation risk index is increased from the initial value I1 to I2, and the quantitative risk index is output to guide the closing mechanism.

[0103] This solution utilizes a multi-source data fusion and compensation mechanism, enabling the system to reliably identify real risk signals even under extreme temperature conditions. The joint analysis of spatiotemporal features accurately distinguishes between operational force and environmental interference, making the deformation risk index the core basis for adaptive force adaptation of the sealing opening, significantly reducing the breakage rate of plastic bags and improving sealing stability.

[0104] 105. Based on the deformation risk index and the peak acceleration, reconstruct the damping response function of the lid mechanism and execute the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can.

[0105] Optionally, step 105 may specifically include the following steps:

[0106] 1051. Obtain the magnitude of the deformation risk index, determine the risk level, reduce the opening and closing speed of the lid when the risk level is high, and increase the opening and closing speed of the lid when the risk level is low.

[0107] 1052. Obtain the peak acceleration value, and adjust the response delay of the bucket lid according to the preset adjustment rules based on the amplitude of the peak acceleration value;

[0108] 1053. Input the deformation risk index and the peak acceleration into a predefined function to generate a new damping coefficient;

[0109] 1054. Update the motion resistance of the lid mechanism using the new damping coefficient, and reconstruct the damping response function of the lid mechanism;

[0110] 1055. Based on the reconstructed damping response function, drive the motor of the induction bag trash can lid to perform the opening or closing action of the anti-wrinkle bag opening.

[0111] In the above scheme, the deformation risk index refers to a quantitative assessment value characterizing the possibility of deformation of the bag opening; the risk level refers to the probability level of deformation occurrence divided according to the index; the peak acceleration refers to the maximum instantaneous rate of change of velocity generated by the user's hand movement; the damping response function refers to a mathematical model describing the relationship between the motion resistance and velocity of the lid mechanism; the damping coefficient refers to the function parameter controlling the intensity of motion attenuation of the mechanism; reconstruction refers to the function update process of regenerating the damping coefficient based on the deformation risk index and the peak acceleration; opening and closing speed adjustment refers to the control behavior of reducing the motion speed when the risk level is high or increasing the speed when the risk level is low; response delay adjustment refers to modifying the configuration of the mechanism's start-up delay according to preset rules based on the peak acceleration amplitude; anti-wrinkle bag opening and closing action refers to the operation process of avoiding wrinkles in the garbage bag through the aforementioned adjustments; and the lid motor drive refers to the specific physical action performed after updating the motion resistance of the mechanism with the new damping coefficient.

[0112] In this embodiment, firstly, through step 1051, the system obtains the specific value of the deformation risk index calculated in step 104. This index varies between 0 and 100; a higher value indicates a greater risk of wrinkles and tears in the garbage bag's drawstring. The system automatically divides the index value into three risk levels according to preset rules: low risk (0-30), medium risk (31-70), and high risk (71-100). Then, the appropriate moving speed of the lid is determined based on the level. For example, if the current deformation risk index is 65, which is considered medium risk, the system will maintain the lid opening at the standard speed. However, if the index reaches a high risk level of 80, the system will command the lid to slow down to 50% of the standard speed. Secondly, through step 1052, the system retrieves the data on the highest point of the user's hand movement acceleration recorded in step 103. This value represents the fastest speed at which the user's hand is tying the bag. The system adjusts the lid's response time by simply comparing it to preset threshold values. When the movement speed is particularly fast, exceeding the set upper limit, the lid's closing is delayed to avoid disturbing the user. For example, if the current peak acceleration is 3.5 m / s², and the system's maximum safe action speed threshold is 4.0 m / s², since 3.5 m / s² does not exceed 4.0 m / s², the lid will use a standard response delay of 0.1 seconds. However, if a peak speed of 5.0 m / s² is detected, exceeding the threshold, the system will delay for 0.3 seconds before taking action. Next, in step 1053, the system inputs the two key data points obtained in the first two steps—the deformation risk index and the peak acceleration—into a preset mathematical formula for calculation. This formula causes the lid's movement resistance to increase with increasing deformation risk and decrease with accelerating user movement. For example, when the deformation risk index is 65 and the peak acceleration is 3.5 m / s², the calculation formula first assigns a 70% weight to the deformation risk index and a 30% weight to the peak acceleration, ultimately outputting a new resistance coefficient value of 0.7. Then, in step 1054, the system immediately replaces the original control parameters with the newly calculated resistance coefficient. This new coefficient directly determines the magnitude of friction during the lid's opening and closing process. For example, after obtaining a new coefficient of 0.7, the mechanical structure automatically increases the track damping hydraulic pressure, raising the resistance felt by the lid movement to 1.4 times the original. This is equivalent to reconstructing the control rules for the entire lid movement. Finally, through step 1055, the system drives the lid actuator of the trash can to complete the opening and closing action under the reconstructed resistance control rules. For example, when a user reaches out to trigger the sensor, the system controls the motor to operate with a resistance coefficient of 0.7, causing the lid to open slowly at 60% of the standard speed; when closing, it automatically adjusts the recycling speed according to the new resistance, protecting the plastic bag from tearing while maintaining smooth operation. The entire process starts with risk assessment, combines user behavior characteristics, and gradually adjusts mechanical parameters to ultimately achieve precise intelligent opening and closing control that protects the bag opening.

[0113] In practical applications, within the intelligent trash can system, the system acquires the deformation risk index J and the peak acceleration K of the hand movement in real time. When the risk index J reaches a high threshold, the opening and closing speed of the lid is automatically reduced to a set value L1; when the risk is low, it is increased to L2. Simultaneously, the response delay time is dynamically adjusted according to the amplitude of the peak acceleration K; for example, a high acceleration action corresponds to a short delay mode M1. Subsequently, J and K are input into a predefined function to generate a new damping coefficient N, which is then updated to the hydraulic damper of the lid mechanism. Finally, based on the reconstructed damping response function, the motor is driven to execute the bag opening and closing action; for example, in high-risk scenarios, a slow-speed mode is used to complete the drawstring action to avoid tearing the plastic bag.

[0114] This solution achieves deep coordination between lid movement and risk assessment by dynamically reconstructing the damping response function. In high-risk scenarios, it automatically switches to a slow mode to avoid plastic bag deformation, and shortens the delay to improve real-time response during high-acceleration operations. Adaptive mechanical control significantly reduces the probability of wrinkles during the sealing process and the risk of garbage bag breakage, enhancing user experience and equipment reliability.

[0115] 106. During the opening and closing of the bin lid, the vibration characteristics of the motor are extracted in real time, and the tension distribution of the garbage bag tie ring is obtained by reverse deduction. The damping response function is corrected in a closed loop. The intelligent control method for preventing wrinkles at the opening of the garbage bag in the induction tie bag garbage bin is realized through closed-loop control of all steps.

[0116] Optionally, step 106 may specifically include the following steps:

[0117] 1061. During the opening and closing of the lid, the vibration characteristics of the motor are collected in real time by the sensor, including the amplitude and frequency of the vibration. The vibration characteristics are input into the pre-stored correspondence model and the real-time tension distribution value of the garbage bag tie ring is output.

[0118] 1062. Based on the tension distribution value, identify areas with uneven or excessive tension, and correct the damping coefficient of the damping response function according to a preset adjustment rule;

[0119] 1063. Update and apply the corrected damping response function in real time to control the movement of the bucket lid in a closed loop.

[0120] In the above scheme, motor vibration characteristics refer to the periodic mechanical oscillation signal characteristics generated by the motor when the lid mechanism moves; real-time tension distribution value refers to the set of instantaneous tension data of the garbage bag loop generated by inputting the vibration characteristics into the pre-stored corresponding relationship model; garbage bag loop tension distribution refers to the circumferential tension distribution state characterized by the above data; uneven tension area refers to the dangerous section on the loop circumference where the tension difference exceeds the safety threshold; closed-loop correction refers to the dynamic update operation of the damping coefficient in the damping response function based on the tension distribution anomaly identification result; damping coefficient correction specifically refers to the behavior of adjusting the mechanism resistance parameters according to preset rules; closed-loop control refers to the method of feeding back the tension distribution to the control input to form an automatic adjustment loop; lid movement closed-loop control refers to the anti-wrinkle intelligent execution process realized by continuously applying the corrected damping function.

[0121] In this embodiment, firstly, through step 1061, during the entire process of opening or closing the lid, the system continuously monitors the minute vibrations of the motor using sensors installed on the motor. The sensors record two key data points: the intensity (amplitude) of the vibration and the speed (frequency) of the vibration. This raw vibration data is fed into a pre-prepared analysis model within the system. This model learns the correspondence between the motor vibration pattern and the tightness of the garbage bag's drawstring by studying a large amount of historical data. The model directly outputs the magnitude of the tension at different positions of the drawstring. For example, if the sensor detects that the motor's vibration amplitude is 0.5 mm and the frequency is 500 times per second at a certain moment, the analysis model immediately determines that the tension in the southeast area of ​​the drawstring is 30 Newtons, and in the northwest area it is 20 Newtons.

[0122] Next, in step 1062, the system analyzes the distribution map of tension values ​​at various points of the constriction point output in the previous step 1061. It automatically identifies which locations have significantly higher tension values ​​or where the tension difference between adjacent locations is too large. Once an abnormal area is detected, the system adjusts the resistance parameters of the lid's movement according to preset safety rules. For example, if the system detects that the tension of 30 Newtons in the southeast area of ​​the constriction point exceeds the safety threshold of 25 Newtons, and the tension difference between this area and the adjacent northwest area reaches 10 Newtons, also exceeding the difference threshold of 5 Newtons, the system determines that there is a risk of wrinkling and immediately increases the coefficient controlling the lid's movement resistance by 0.15.

[0123] Finally, in step 1063, the system applies the new resistance coefficient adjusted in step 1062 to the operating lid mechanism in real time. The adjustment command takes effect immediately, and the speed and force of the lid movement change accordingly. Simultaneously, sensors continuously monitor the motor's vibration under the new resistance, generating a new data stream. The entire process of "detecting vibration, calculating tension, identifying problems, adjusting resistance, and re-detecting" repeats dozens of times per second. For example, if the current resistance coefficient has been adjusted to 0.85, and the lid decelerates, a new detection shows the tension in the southeast area dropping to 24 Newtons; the system maintains this resistance. If a local tension exceeding 28 Newtons is still detected, the resistance is further increased to 0.95. Through this millisecond-level dynamic adjustment, it ultimately ensures that the garbage bag's drawstring is evenly stressed throughout the opening and closing process, preventing tearing and wrinkling. The entire process forms a closed-loop self-adjusting system, continuously optimizing the lid's movement, much like a car automatically adjusting its suspension on a bumpy road.

[0124] In practical applications, during the opening and closing phase of the smart trash can lid, built-in sensors collect the vibration characteristics of the motor in real time, including amplitude P and frequency Q. The vibration data is then input into a pre-trained model to infer the real-time tension distribution value of the trash bag loop in the circumferential direction, from R1 to R2. When an abnormal increase in tension is detected in the R3 region, the damping coefficient S of the corresponding lid structure in that region is reduced according to a preset rule. The control unit updates the damping response function parameters within milliseconds, driving the motor to adaptively adjust the opening and closing angle and speed. For example, it automatically switches to a buffer mode in the tension peak region to avoid the formation of local wrinkles.

[0125] This solution uses real-time mapping of motor vibration characteristics and tension distribution to construct a closed-loop path from mechanical state perception to execution control. It accurately identifies local overload areas of the binding ring and immediately adjusts the lid's action strategy to eliminate the potential for wrinkles. The millisecond-level dynamic correction capability ensures that the garbage bag is always under uniform stress during the binding process, significantly improving the robustness of anti-wrinkle control and operational safety.

[0126] Figure 2 This application provides a scenario diagram illustrating an intelligent control method for a sensor-operated drawstring trash can, as shown in the embodiments below. Figure 2 As shown, a complete embodiment of steps 101-106 includes:

[0127] In the workflow of the K-type smart trash can, when a user approaches the device, an infrared sensor collects data on the shape of the trash bag's drawstring folds and hand movements. An optical enhancement component uses a special M-type lens and an N-level infrared filter to process the raw data, enhancing the spatial gradient around the bag opening and generating a ring-shaped temperature distribution map. The distance sensor dynamically classifies the speed level based on the user's position; when the distance is less than a set value A, a fast action level is triggered, capturing a short time window; when the distance is greater than a set value B, a slow action level is triggered, capturing a long time window. The system retrieves failure records from a pre-stored mapping database that correlate the thickness of material P with the peak value of acceleration Q, integrating... The system compensates for baseline drift in the temperature map based on ambient temperature category G; the rule decision module analyzes the temperature change value and time-domain velocity characteristics of the bag opening area after compensation, and outputs the deformation risk index S; the lid mechanism reconstructs the damping response function based on the S value and the real-time acceleration peak value T, automatically switching to low-speed mode U when the risk is high and high-speed mode V when the risk is low; during the opening and closing action of the motor, the vibration sensor captures the amplitude W and frequency X characteristics in real time, and reverses the tension distribution of the drawstring through the pre-trained model, and immediately corrects the damping coefficient after identifying the abnormal area Y; finally, the closed-loop control system continuously optimizes the movement trajectory of the lid to achieve anti-wrinkle drawstring operation.

[0128] This solution achieves multiple optimizations through end-to-end closed-loop control: infrared enhancement and behavior grading improve the accuracy of garbage bag status perception; a multi-dimensional mapping model of material thickness, operating force, and ambient temperature strengthens risk prediction capabilities; a dynamic damping response mechanism enables the lid to adaptively avoid high-tear scenarios; and vibration feedback-driven real-time tension adjustment ensures uniform force distribution during the sealing process. Ultimately, under diverse plastic materials, user operating habits, and environmental conditions, the system reliably eliminates the risk of garbage bag wrinkles and damage, significantly improving the long-term sealing stability of the smart garbage bin and the user experience.

[0129] Figure 3 This application provides a schematic diagram of the intelligent control system for an induction-operated drawstring trash can, as shown in the embodiment. Figure 3 As shown, the system includes:

[0130] The acquisition module 31 is used to acquire infrared data of the sensor drawstring bag trash can, including the shape features of the drawstring folds of the trash bag and the user's hand movement information. The spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by the optical enhancement component to generate an annular temperature distribution map.

[0131] Analysis module 32 is used to classify the urgency level of the user's garbage disposal behavior based on the real-time distance between the user's standing position and the sensor-operated bag garbage bin, and dynamically extract the time domain analysis window corresponding to the annular temperature distribution map according to the urgency level.

[0132] The association module 33 is used to establish a mapping relationship library between the thickness of the plastic bag material and the historical drawstring bag failure records, and to associate the acceleration peak value of the palm movement information to obtain the associated mapping relationship library;

[0133] Processing module 34 is used to compensate for the baseline drift of the annular temperature distribution map by integrating the ambient temperature fluctuation based on the mapping relationship library, and to process the compensated annular temperature distribution map and the time domain analysis window using the rule decision module, and output the deformation risk index of the bundle opening.

[0134] Execution module 35 is used to reconstruct the damping response function of the lid mechanism based on the deformation risk index and the peak acceleration, and execute the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can.

[0135] The correction module 36 is used to extract the motor vibration characteristics in real time during the opening and closing of the lid, back-calculate the tension distribution of the garbage bag tie ring, and correct the damping response function in a closed loop. Through the closed-loop control of all steps, the intelligent control method for preventing wrinkles at the garbage bag tie opening of the induction tie bag garbage can is realized.

[0136] Figure 3 The intelligent control system of the sensor-operated drawstring trash can can perform... Figure 1 The implementation principle and technical effects of the intelligent control method for a sensor-operated drawstring trash can described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the intelligent control system for the sensor-operated drawstring trash can in the above embodiments are described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0137] In one possible design, Figure 3 The intelligent control system of the sensor-operated drawstring trash can shown in the embodiment can be implemented as a computing device, such as... Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42;

[0138] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 42.

[0139] The processing component 42 is used for the above Figure 1 The embodiment describes an intelligent control method for a sensor-operated drawstring trash can.

[0140] The processing component 42 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0141] Storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0142] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0143] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0144] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0145] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0146] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates an intelligent control method for a sensor-operated drawstring trash can.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A smart control method for a sensor-operated drawstring trash can, characterized in that, include: Infrared data from the sensor-controlled drawstring trash can, including the shape characteristics of the drawstring folds of the trash bag and the user's hand movement information, is collected. The spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by an optical enhancement component to generate an annular temperature distribution map. Based on the real-time distance between the user's standing position and the sensor-operated trash can, the urgency level of the user's trash disposal behavior is divided, and a time-domain analysis window corresponding to the annular temperature distribution map is dynamically extracted according to the urgency level. Establish a mapping relationship library between the thickness of plastic bag material and historical drawstring bag failure records, and associate it with the acceleration peak value of the palm movement information to obtain the associated mapping relationship library; Based on the mapping relationship library, the baseline drift of the annular temperature distribution map is compensated by incorporating environmental temperature fluctuations. The compensated annular temperature distribution map and the time domain analysis window are processed by a rule decision module, and the deformation risk index of the bundle opening is output. Based on the deformation risk index and the peak acceleration, the damping response function of the lid mechanism is reconstructed, and the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can is executed. This includes: obtaining the magnitude of the deformation risk index, determining the risk level, reducing the lid opening and closing speed when the risk level is high, and increasing the lid opening and closing speed when the risk level is low; obtaining the peak acceleration, and adjusting the lid response delay based on the amplitude of the peak acceleration according to a preset adjustment rule; inputting the deformation risk index and the peak acceleration into a predefined function to generate a new damping coefficient; updating the motion resistance of the lid mechanism using the new damping coefficient, and reconstructing the damping response function of the lid mechanism; and driving the lid motor of the sensor-operated drawstring trash can to perform the anti-wrinkle bag opening or closing action based on the reconstructed damping response function. The vibration characteristics of the motor are extracted in real time during the opening and closing of the lid, and the tension distribution of the garbage bag loop is obtained by reverse calculation. The damping response function is corrected in a closed loop. The intelligent control method for preventing wrinkles at the opening of the garbage bag in the sensor-operated bag garbage can is realized through closed-loop control of all steps.

2. The method according to claim 1, characterized in that, The vibration characteristics of the motor are extracted in real time during the opening and closing of the lid, and the tension distribution of the garbage bag loop is derived by reverse calculation. The damping response function is then corrected in a closed loop, including: During the opening and closing of the lid, the vibration characteristics of the motor are collected in real time by the sensor, including the amplitude and frequency of the vibration. The vibration characteristics are input into the pre-stored correspondence model and the real-time tension distribution value of the garbage bag tie ring is output. Based on the tension distribution value, regions with uneven or excessive tension are identified, and the damping coefficient of the damping response function is corrected according to a preset adjustment rule. The modified damping response function is updated and applied in real time to control the movement of the bucket lid in a closed loop.

3. The method according to claim 1, characterized in that, By enhancing the spatial gradient of the infrared data in the circumferential direction of the bag opening using optical enhancement components, an annular temperature distribution map is generated, including: Special lenses and filters with optical enhancement components are used to process the collected infrared data, which includes the morphological features of the garbage bag drawstring folds and information on the user's hand movements. By adjusting the optical path, the temperature difference between adjacent points in the circumferential direction of the bag opening is increased, thereby improving the spatial gradient of the processed infrared data. The infrared data after spatial gradient enhancement is converted into a ring representation, and the temperature value at each position is displayed along the circumference of the bag opening, generating a continuous temperature value map along the circumference of the bag opening.

4. The method according to claim 1, characterized in that, Based on the stated rapidity / slowness level, a time-domain analysis window corresponding to the annular temperature distribution map is dynamically extracted, including: Based on the classification of urgency levels, the movements are divided into fast movement levels or slow movement levels. When the urgency level is the fast action level, a short time-domain analysis window is extracted; when the urgency level is the slow action level, a long time-domain analysis window is extracted. The time-domain analysis window covers the time-domain variation data of the annular temperature distribution map, where the window length determines the time range for analyzing the annular temperature distribution map.

5. The method according to claim 1, characterized in that, A mapping database is established between the thickness of the plastic bag material and historical drawstring bag failure records, and the acceleration peak values ​​of the hand movement information are correlated to obtain the correlated mapping database, including: Collect plastic bag material thickness data and historical drawstring bag failure records, and associate and store the material thickness data and the historical drawstring bag failure records to establish an initial mapping relationship library; Add the peak acceleration of the hand motion information as an additional dimension to the initial mapping relation library; For different plastic bag material thicknesses and acceleration peak values, the corresponding failure frequencies are associated and stored in the initial mapping relationship library, and the associated mapping relationship library containing the material thickness data, the acceleration peak value, and the historical drawstring bag failure records is output.

6. The method according to claim 1, characterized in that, The rule-based decision-making module processes the compensated annular temperature distribution map and the time-domain analysis window, outputting the deformation risk index of the beam opening, including: The rule-based decision-making module extracts the temperature change value in the circumferential direction of the bag opening from the compensated annular temperature distribution map. Extract the temperature change rate characteristics within the corresponding time range from the time domain analysis window; The temperature change value and the rate of change are analyzed using preset rules. Based on the analysis results, the deformation risk index of the knot is adjusted by increasing or decreasing. Finally, the adjusted deformation risk index value representing the possibility of wrinkles is output.

7. An intelligent control system for a sensor-operated drawstring trash can, used to execute the intelligent control method for a sensor-operated drawstring trash can according to any one of claims 1 to 6, characterized in that, include: Infrared data from the sensor-controlled drawstring trash can, including the shape characteristics of the drawstring folds of the trash bag and the user's hand movement information, is collected. The spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by an optical enhancement component to generate an annular temperature distribution map. Based on the real-time distance between the user's standing position and the sensor-operated trash can, the urgency level of the user's trash disposal behavior is divided, and a time-domain analysis window corresponding to the annular temperature distribution map is dynamically extracted according to the urgency level. Establish a mapping relationship library between the thickness of plastic bag material and historical drawstring bag failure records, and associate it with the acceleration peak value of the palm movement information to obtain the associated mapping relationship library; Based on the mapping relationship library, the baseline drift of the annular temperature distribution map is compensated by incorporating environmental temperature fluctuations. The compensated annular temperature distribution map and the time domain analysis window are processed by a rule decision module, and the deformation risk index of the bundle opening is output. Based on the deformation risk index and the peak acceleration, the damping response function of the lid mechanism is reconstructed, and the anti-wrinkle bag opening and closing action of the sensor-operated drawstring trash can is executed. The vibration characteristics of the motor are extracted in real time during the opening and closing of the lid, and the tension distribution of the garbage bag loop is obtained by reverse calculation. The damping response function is corrected in a closed loop. The intelligent control method for preventing wrinkles at the opening of the garbage bag in the sensor-operated bag garbage can is realized through closed-loop control of all steps.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the intelligent control method for a sensor-operated drawstring trash can as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements an intelligent control method for a sensor-operated drawstring trash can as described in any one of claims 1 to 6.

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