Intelligent control method and system of induction bundle bag garbage can

Abstract

The invention provides an intelligent control method and system for an induction bundle bag garbage can. According to the method, firstly, the spatial gradient of infrared data of a bag opening is improved through an optical enhancement assembly to generate an annular temperature distribution diagram; dividing urgent and slow levels based on the standing distance of the user, and dynamically intercepting a time domain analysis window; establishing a correlation mapping library of the plastic bag material thickness, the historical failure record and the palm acceleration; after an environment temperature compensation annular graph is combined, a rule decision module outputs a deformation risk index; according to the index and the acceleration peak value, reconstructing a barrel cover damping response function to execute anti-wrinkle opening and closing; and the tension distribution closed-loop correction damping function of the bundle ring is reversely deduced through the vibration characteristics of the motor, so that the anti-wrinkle closed-loop control of the bag opening is realized. Through closed-loop cooperation of infrared space gradient strengthening, user behavior grading response and tension real-time feedback, self-adaptive anti-wrinkle control over the garbage bag opening in the opening and closing process is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent environmental sanitation equipment control, and in particular to an intelligent control method and system for an induction bag bundle garbage can. BACKGROUND

[0002] The intelligent induction bag bundle garbage can needs to automatically open the can cover when the user approaches, and intelligently close the bag bundle opening after garbage is thrown to block odors. The core technical challenge lies in: real-time sensing of the bag bundle opening wrinkle state (such as uneven plastic bag adhesion, local relaxation of the bundle ring, etc.), dynamically coordinating the can cover opening and closing action and the bundle opening tightening mechanism, to prevent permanent deformation or sealing failure of the bag opening due to action mismatch. Especially when the user quickly throws garbage or the bag wall is too thin, the mechanism response strategy needs to be adjusted in time to avoid wrinkles.

[0003] The current mainstream solution uses gesture trajectory analysis based on millimeter wave radar combined with a bag opening pressure sensor array: the radar captures the user's hand movement trajectory to predict the degree of rapidness of the garbage throwing action; at the same time, distributed pressure sensors are embedded along the can to detect the pressure distribution pattern of the contact surface between the garbage bag bundle ring and the can wall in real time, and when a local pressure drop is detected (indicating that the bundle ring tension is unbalanced), the can cover motor torque distribution is immediately adjusted.

[0004] The existing solution relies on the cooperation mechanism of gesture trajectory analysis and contact pressure sensing, and its core defect lies in: the pressure detection has a sensing blind area and cannot capture early deformation signals in the suspended area of the garbage bag bundle ring; 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, there is inherent response lag, which must occur after physical deformation to trigger adjustment, resulting in a significantly increased risk of damage to thin plastic bags in the fast throwing scenario. SUMMARY

[0005] The present application provides an intelligent control method and system for an induction bag bundle garbage can to solve the problem of inherent response lag in the prior art.

[0006] In a first aspect, the present application provides an intelligent control method for an induction bag bundle garbage can, comprising: Collecting infrared data of the induction bag bundle garbage can containing garbage bag bundle opening wrinkle pattern features and user palm movement information, improving the spatial gradient of the infrared data in the circumferential direction of the bag opening through an optical enhancement component, and generating a ring-shaped temperature distribution map; Based on the real-time distance between the user's standing position and the induction bag bundle garbage can, the rapidness level of the user's garbage throwing behavior is divided, and the time domain analysis window corresponding to the ring-shaped temperature distribution map is dynamically intercepted according to the rapidness level; Establishing a mapping relationship library between plastic bag material thickness and historical bag failure records, and correlating the acceleration peak of the palm motion information to obtain a correlated mapping relationship library; Based on the mapping relationship library, the baseline drift of the annular temperature distribution diagram is compensated by integrating the ambient temperature fluctuation, and the compensated annular temperature distribution diagram and the time domain analysis window are processed by a rule decision module to output the deformation risk index of the beam port; Reconstructing the damping response function of the lid mechanism according to the deformation risk index and the acceleration peak value, and executing the anti-wrinkle bag opening and closing action of the induction bundle bag trash can; During the opening and closing process of the lid, the motor vibration characteristics are extracted in real time, the tension distribution of the garbage bag draw ring is obtained by reverse deduction, the damping response function is corrected in a closed loop, and the intelligent control method for preventing wrinkles of the garbage bag draw ring of the induction bag drawstring trash can is realized through closed-loop control of all steps.

[0007] Optionally, reconstructing a damping response function of the lid mechanism according to the deformation risk index and the acceleration peak value, and executing the wrinkle-proof bag opening and closing action of the induction bundle bag trash can, includes: Obtaining the magnitude of the deformation risk index, determining a risk level, reducing the opening and closing speed of the barrel lid when the risk level is high, and increasing the opening and closing speed of the barrel lid when the risk level is low; Obtaining an acceleration peak value, and adjusting a response delay of the barrel cover according to a preset adjustment rule based on the magnitude of the acceleration peak value; Inputting the deformation risk index and the acceleration peak into a predefined function to generate a new damping coefficient; Using the new damping coefficient to update the motion resistance of the barrel cover mechanism, and reconstruct the damping response function of the barrel cover mechanism; Based on the reconstructed damping response function, the motor of the lid of the induction bag trash can is driven to perform the opening or closing action of the anti-wrinkle bag mouth.

[0008] Optionally, during the lid opening and closing process, the motor vibration characteristics are extracted in real time, the tension distribution of the garbage bag band is obtained by reverse deduction, and the damping response function is corrected in a closed loop, including: During the lid opening and closing process, the sensor collects the motor vibration characteristics in real time, including the vibration amplitude and frequency, and inputs the vibration characteristics into the pre-stored correspondence model to output the real-time tension distribution value of the garbage bag tie ring; Based on the tension distribution value, identifying areas with uneven or excessive tension, and modifying the damping coefficient of the damping response function according to a preset adjustment rule; The modified damping response function is updated and applied in real time to close the loop and control the movement of the barrel cover.

[0009] Optionally, the spatial gradient of the infrared data in the circumferential direction of the bag opening is enhanced by the optical enhancement assembly to generate a ring-shaped temperature distribution map, comprising: The collected infrared data containing the crease pattern of the bag opening and the palm movement information of the user are processed using special lenses and filters of the optical enhancement assembly; The spatial gradient of the processed infrared data is enhanced by adjusting the optical path to increase the temperature difference between adjacent points in the circumferential direction of the bag opening; The infrared data with enhanced spatial gradient is converted into a ring-shaped representation, and the temperature value at each position along the circumferential direction of the bag opening is displayed to generate a continuous temperature value map along the circumferential direction of the bag opening.

[0010] Optionally, a time domain analysis window corresponding to the ring-shaped temperature distribution map is dynamically intercepted according to the urgency level, comprising: Based on the divided urgency level, the action is divided into a fast action level or a slow action level; When the urgency level is the fast action level, a short time length time domain analysis window is intercepted, and when the urgency level is the slow action level, a long time length time domain analysis window is intercepted; The time domain analysis window covers the change data of the ring-shaped temperature distribution map in the time domain, and the window length determines the time range of analyzing the ring-shaped temperature distribution map.

[0011] Optionally, a mapping relationship library of the thickness of the plastic bag material and the historical bag failure record is established, and the acceleration peak value of the palm movement information is associated to obtain a correlated mapping relationship library, comprising: Collecting plastic bag material thickness data and historical bag failure records, and storing the material thickness value and the historical failure record in association to establish an initial mapping relationship library; Adding the acceleration peak value of the palm movement information as an additional dimension to the initial mapping relationship library; For different plastic bag material thickness and acceleration peak value, the corresponding failure occurrence frequency is associated and stored in the initial mapping relationship library, and the correlated mapping relationship library containing the material thickness, the acceleration peak value and the historical failure record is output.

[0012] Optionally, a rule decision module is used to process the compensated ring-shaped temperature distribution map and the time domain analysis window to output a deformation risk index of the bag opening, comprising: According to the rule decision module, the temperature change value in the circumferential direction of the bag opening is extracted from the compensated ring-shaped temperature distribution map; The temperature change speed feature in the corresponding time range is extracted from the time domain analysis window; The preset rule is applied to analyze the temperature change value and the change speed characteristic, and the deformation risk index of the bundle opening is adjusted to increase or decrease based on the analysis result, and finally the adjusted deformation risk index value indicating the wrinkle possibility is output.

[0013] In a second aspect, the application provides an intelligent control system of a sensing bundle bag garbage can, comprising: The acquisition module is configured to acquire infrared data of the sensing bundle bag garbage can, which contains a garbage bag bundle opening wrinkle morphology feature and user palm movement information, and generate an annular temperature distribution graph by improving a spatial gradient of the infrared data in a bundle opening circumferential direction through an optical enhancement assembly. The analysis module is configured to divide a fast-slow level of a corresponding user garbage disposal behavior based on a real-time distance between a user standing position and the sensing bundle bag garbage can, and dynamically intercept a time domain analysis window corresponding to the annular temperature distribution graph according to the fast-slow level. The correlation module is configured to establish a mapping relationship library of a plastic bag material thickness and a historical bundle bag failure record, and correlate an acceleration peak value of the palm movement information to obtain a correlated mapping relationship library. The processing module is configured to compensate for baseline drift of the annular temperature distribution graph caused by environmental temperature fluctuations based on the mapping relationship library, process the compensated annular temperature distribution graph and the time domain analysis window by using a rule decision module, and output a deformation risk index of the bundle opening. The execution module is configured to reconstruct a damping response function of a can cover mechanism according to the deformation risk index and the acceleration peak value, and perform a bag opening and closing action of the sensing bundle bag garbage can to prevent wrinkle. The correction module is configured to extract a motor vibration feature in real time during the can cover opening and closing process, inversely deduce a garbage bag bundle ring tension distribution, and correct the damping response function in a closed loop to realize an intelligent control method of the sensing bundle bag garbage can to prevent wrinkle of a garbage bag bundle opening.

[0014] In a third aspect, the application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to realize an intelligent control method of a sensing bundle bag garbage can as described in the first aspect.

[0015] In a fourth aspect, the application provides a computer storage medium storing a computer program, which is executed by a computer to realize an intelligent control method of a sensing bundle bag garbage can as described in the first aspect.

[0016] The application generates a ring-shaped temperature distribution map through infrared data acquisition and optical enhancement, solves the problem of blind area detection of traditional contact sensing in the suspended area, further realizes adaptive real-time data processing acceleration of the throwing behavior through user distance-based urgency level division and dynamic time domain window interception, further quantifies the mechanical risk coupling strength in human-computer interaction by combining the historical mapping library of plastic bag material and the associated modeling of acceleration peak value, and further overcomes the temperature drift interference to accurately output the deformation risk index through environmental temperature compensation and rule decision module processing. Based on the index, the damping response function is reconstructed, the bucket cover action is adjusted before the critical deformation, and finally the tension distribution is backstepped through the motor vibration characteristics and the damping function is closed-loop corrected to eliminate the execution hysteresis, form a real-time suppression closed loop of the bundle opening deformation, and break through the three technical bottlenecks of sensing blind area, environmental misjudgment and response delay.

[0017] Further, by dynamically adjusting the opening and closing speed of the bucket cover according to the real-time determination of the deformation risk level, high-risk deceleration and low-risk acceleration are realized, the response delay is adaptively adjusted according to the acceleration peak value amplitude, and a new damping coefficient is generated by inputting the two parameters into a predefined function, so that the motion resistance driving bucket cover motor is finally updated to realize millisecond-level dynamic torque compensation based on the real-time stress state of the plastic bag, so that the bundle opening tension and the bucket cover motion trajectory always maintain adaptive cooperation.

[0018] These aspects or other aspects of the application will be more apparent in the following description of the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of an intelligent control method of a sensing bundle bag garbage can provided by the application is shown; Figure 2 A scene diagram of an intelligent control method of a sensing bundle bag garbage can provided by the application is shown; Figure 3 A structural schematic diagram of an intelligent control system of a sensing bundle bag garbage can provided by the application is shown; Figure 4 A structural schematic diagram of a computing device provided by the application is shown. DETAILED DESCRIPTION

[0021] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0022] In some of the processes described in the specification and claims of the present application and the above-described drawings, a plurality of operations are included which occur in a specific order, but it should be clearly understood that these operations can be executed or performed in parallel, or in a different order from that in which they appear herein, and the serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed or performed in sequence or in parallel. It should be noted that the descriptions herein, such as "first", "second", etc., are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do "first" and "second" represent different types.

[0023] In the field of intelligent sensing bundle bag garbage can control, the existing scheme relies on the cooperative mechanism of millimeter wave radar gesture tracking and barrel along contact type pressure sensing, and there are three technical bottlenecks: spatial perception limitation, the pressure sensor can only detect the physical contact area of the garbage bag bundle ring and the barrel wall, and it is completely ineffective for the early wrinkle deformation of the suspended non-fitting part, resulting in missing detection of key deformation signals; environmental interference defect, the thermal expansion and contraction effect of the plastic bag caused by temperature and humidity changes causes the baseline of the pressure sensor to drift, and the system mistakenly identifies environmental interference as bundle opening tension imbalance; response lag disease, the adjustment mechanism must be triggered after the physical deformation actually occurs, causing the mismatch between the can cover action and the bag opening deformation rate, especially for thin plastic bags and user quick-throw scenarios, the wrinkle breakage rate is high. The root cause of these problems lies in the failure of the existing technology to cooperate in non-contact deformation sensing, environmental coupling interference decoupling and predictive control.

[0024] In view of the above defects, the present application proposes an intelligent anti-wrinkle method based on infrared thermodynamic sensing and closed-loop damping control, the core breakthrough of which is: generating an annular temperature distribution map through optical enhancement of infrared data to directly capture the dynamic thermal characteristics of the wrinkle in the suspended area (breaking through the spatial perception limitation); combining user distance dynamic interception time domain window and material-failure mapping library to accelerate peak value, and constructing a risk prediction model adaptive to throwing behavior; then, combining environmental temperature compensation output deformation risk index to reconstruct the damping response function of the can cover to realize intervention before critical deformation (eliminating environmental misjudgment lag); finally, through motor vibration back-propagation tension distribution closed-loop correction damping, the can cover opening and closing action is real-time matched with the stress change of the bundle opening. This scheme first establishes a system-level solution of "infrared sensing-risk prediction-damping reconstruction-closed-loop calibration", which simultaneously overcomes the three technical barriers of suspended area detection blind area, temperature drift misjudgment and response lag without increasing hardware cost.

[0025] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0026] Figure 1 A flow chart of a smart control method of an induction bundle bag garbage can is provided for the embodiments of the present application, as shown in Figure 1 The method comprises the following steps. 101, Collect infrared data of the induction bundle bag garbage can containing bag mouth wrinkle feature and user palm movement information, improve the spatial gradient of the infrared data in the bag mouth circumferential direction through the optical enhancement assembly, and generate an annular temperature distribution map. Optionally, step 101 can specifically include the following steps. 1011, Use the special lens and filter of the optical enhancement assembly to process the collected infrared data containing the bag mouth wrinkle feature and the user palm movement information. 1012, Increase the temperature change difference between adjacent points in the bag mouth circumferential direction by adjusting the optical path, and improve the spatial gradient of the processed infrared data. 1013, Convert the infrared data with improved spatial gradient into an annular representation, display the temperature value of each position along the bag mouth circumferential direction, and generate a continuous temperature value map along the bag mouth circumference.

[0027] In the above scheme, the optical enhancement assembly refers to a hardware system composed of an aspherical special lens and a waveband selective filter, the bag mouth wrinkle feature refers to the local temperature field distribution pattern corresponding to the three-dimensional wrinkle formed by physical tightening operation, the user palm movement information refers to the operation trajectory and direction vector identified by dynamic thermal signal spatial displacement, the spatial gradient improvement refers to the behavior of expanding the radiation flux difference between adjacent points in the bag mouth circumference to strengthen the angle dimension temperature difference resolution by adjusting the optical path, the annular representation refers to the geometric transformation process of converting Cartesian coordinate system data into an angle continuous function relationship with the bag mouth center as the origin, and the continuous temperature value map refers to the visualization expression of the full circumference coverage temperature distribution function generated by the interpolation algorithm.

[0028] The embodiment of the present application first processes the original infrared data containing the crease shape of the bag opening and the user's palm movement information by using special lenses and filters in the optical enhancement assembly in step 1011. The special lenses are responsible for focusing and magnifying the image on the bag opening area, ensuring that the target is clear. The filters are specifically designed to remove irrelevant thermal signal interference in the environment, leaving only specific heat signals related to the bag opening crease and user's palm activity. For example, before processing, a sensing point may contain mixed information of bag opening temperature and environmental background temperature, assuming that a certain point on the bag opening should show 31 degrees, but the background radiation makes its reading mixed with an interference value of 25 degrees. After filter processing, only the valid signal is retained, and the point data is corrected to pure 31 degrees.

[0029] Secondly, by using the pure infrared data obtained by step 1011, the temperature difference between two points very close to each other in the circumferential direction of the bag opening is magnified by adjusting the angle of the mirror in the optical path, etc. in step 1012. This operation is equivalent to artificially making the temperature change in the circumferential direction more obvious, so that the original subtle temperature difference is magnified. For example, in the data after step 1011, assuming that two points very close to each other along the bag opening circumferential direction, point A temperature is 31 degrees, and the adjacent point B temperature is 32 degrees, the difference between them is only 1 degree. After adjusting the optical path for enhancement, the enhanced data of point A may be changed to 30 degrees, and point B to 34 degrees, so that the temperature difference between the two adjacent points increases from 1 degree to 4 degrees, so that the temperature change in the circumferential direction becomes more obvious and easy to distinguish.

[0030] Finally, by step 1013, the data after step 1012 magnification processing, which enhances the temperature change difference in the circumferential direction, is reorganized and converted. Using a specific algorithm, the data points that may be arranged in a rectangular grid are reordered and mapped according to the actual shape of the bag opening circumferential direction, generating a continuous temperature value view along the bag opening circumferential direction. Each circumferential position is clearly marked with its corresponding temperature value. For example, the enhanced data point sequence may show that the temperature at zero degree position is 30 degrees, the temperature at forty-five degree position is 33 degrees, and the temperature at ninety degree position is 36 degrees, etc. The algorithm will connect these points in order of angle to form a ring-shaped temperature distribution curve, which is intuitively displayed on the screen.

[0031] In practical applications, in a typical intelligent garbage can system, when a user approaches the device, the built-in infrared sensor collects real-time three-dimensional wrinkle shape feature data and user palm motion trajectory data in the bag opening area. The optical enhancement assembly is configured with a special wide-angle lens module and a high-precision infrared filter device, which first filters out environmental thermal noise and focuses on a specific infrared band; by dynamically adjusting the light path refraction angle of the lens group, the temperature difference between adjacent wrinkle points in the circumferential direction of the bag opening is significantly expanded, and the spatial resolution of the micro-scale change is enhanced; the processing unit converts the optimized thermal field data into a 360-degree ring coordinate system, generates a continuous temperature distribution map and automatically identifies the key temperature change area, such as the user's finger contact position or the wrinkle depression area, and finally drives the electric bag opening mechanism to perform adaptive tightening operation.

[0032] The present scheme significantly improves the key feature recognition capability through optical enhancement, making the micro changes in wrinkle shape present clear structural features in the infrared map, while improving the palm motion trajectory capture accuracy; the ring temperature map fully displays the real-time dynamic state of the bag opening circumference, including thermal field anomalies in potential air leakage areas; the system can accurately respond to complex wrinkle shapes 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.

[0033] 102、Based on the real-time distance between the user's standing position and the sensing bag garbage can, the urgency level of the corresponding user's garbage throwing behavior is divided, and a time domain analysis window corresponding to the ring temperature distribution map is dynamically intercepted according to the urgency level; Optionally, step 102 can specifically include the following steps: 1021, based on the divided urgency level, the action is divided into a fast action level or a slow action level; 1022, when the urgency level is the fast action level, a short time length time domain analysis window is intercepted, and when the urgency level is the slow action level, a long time length time domain analysis window is intercepted; 1023, the time domain analysis window covers the change data of the ring temperature distribution map in the time domain, and the window length determines the time range of analyzing the ring temperature distribution map.

[0034] In the above scheme, the user standing position refers to the vertical projection coordinates of the user's foot and the contact surface of the garbage can, the real-time distance refers to the dynamic change of the Euclidean space scale between the coordinates and the geometric center of the garbage can, the urgency level refers to the classification of the urgency of the throwing behavior according to the real-time distance threshold, the fast action level refers to the high-speed operation state determined by the system to be urgently responded, the slow action level refers to the low-speed operation state determined by the system to be delayed, the time domain analysis window refers to the dynamic interception time period covering the time sequence change of the ring-shaped 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 decided according to the urgency level.

[0035] In the embodiment of the present application, first, the distance measuring device on the garbage can is used to obtain the real-time distance value from the current standing position of the user to the garbage can through step 1021. The system compares this distance value with the standard threshold value set internally. If the actual distance is less than or equal to the threshold value, the system classifies the user's action of preparing to throw garbage as the fast action level. If the actual distance is greater than the threshold value, the system classifies the action as the slow action level. For example, the set threshold value is 0.5 meters. When the distance measuring device measures that the user's position is 0.4 meters away from the garbage can, the system immediately determines that this belongs to the fast action level. When the distance measuring device measures that the user's position is 1.2 meters away from the garbage can, the system immediately determines that this belongs to the slow action level. Secondly, the time period length to be intercepted is automatically determined according to the action level classification result obtained in the previous step 1021 through step 1022. The specific rule is very clear: if the action level is the fast action level, then a very short time segment is intercepted from the current time as the analysis window. If the action level is the slow action level, then a longer time segment is intercepted from the current time as the analysis window. The time length itself is a pre-set fixed value. For example, the fast action level is fixed to correspond to a time window length of 0.5 seconds, and the slow action level is fixed to correspond to a time window length of 2.0 seconds. Assuming that step 1021 has determined the fast action level, the system will immediately determine that the time domain analysis window length is 0.5 seconds, and prepare to intercept the data in this time period.

[0036] 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 the time period from the past to the present that needs to be analyzed. The system extracts all annular temperature distribution maps generated during this period as the data set to be analyzed. For example, if the window length is 0.5 seconds, as determined in step 1022, and annular temperature distribution maps are continuously generated at a rate of 10 per second, the system will select all temperature distribution maps generated within the 0.5-second period before the current time point—assuming the five most recent images—as the image sequence for in-depth analysis. Similarly, the entire process begins with measuring distance, determining the speed level of the action based on the distance, and then selecting an appropriate time period length based on the speed level, ultimately preparing the environmental data covering that time period for subsequent analysis.

[0037] In actual applications, in the smart trash can system, infrared distance sensors detect the distance between the user's standing position and the device in real time. The system divides the action into rapid and slow levels based on preset thresholds: when the distance is less than the set value A, it is determined to be the rapid action level, and when the distance is greater than the set value B, it is determined to be the slow action level. If the user is in the rapid action level, the processing unit captures the 0.2-second period of the annular temperature graph change sequence; if the user is in the slow action level, it captures the 2-second period. For example, when the user approaches hurriedly, the system captures the temperature fluctuations of the transient drawstring movement, while when the user operates slowly, the system fully records the gradual thermal field evolution of the bag opening folds.

[0038] This solution dynamically divides behavior into urgent and slow levels through distance perception, allowing the time domain analysis window to accurately adapt to the operation rhythm. The short window focuses on the transient temperature characteristics of fast actions to avoid missing key details, and the long window covers the slow-changing process of the thermal field of slow actions to improve data integrity. The coupled analysis of time and space dimensions significantly enhances the ability to analyze user delivery intentions and optimizes the rationality of system response and scenario adaptability.

[0039] 103. Establish a mapping relationship library between plastic bag material thickness and historical bag failure records, and associate the acceleration peak value of the palm motion information to obtain an associated mapping relationship library; Optionally, step 103 may specifically include the following steps: 1031. Collecting plastic bag material thickness data and historical bag failure records, and associating and storing the material thickness values ​​with the historical failure records to establish an initial mapping relationship library; 1032. Add the acceleration peak value of the palm motion information as an additional dimension to the initial mapping relationship library; 1033、Correlate and store the corresponding failure occurrence frequency into the initial mapping relationship library for different plastic bag material thickness and acceleration peak value, output the associated mapping relationship library containing the material thickness, the acceleration peak value and the historical failure record.

[0040] In the above scheme, the plastic bag material thickness refers to the vertical cross-sectional dimension of the undeformed polymer material, the historical bag failure record refers to the historical event statistical data of the loose or broken seal of the bag mouth; the initial mapping relationship library refers to the database storing the material thickness value and the failure record correlation data; the acceleration peak value of the palm motion information refers to the maximum instantaneous speed change rate generated by the hand when the user operates the bag mouth; the associated mapping relationship library refers to the extended database adding the acceleration peak value dimension and storing the corresponding failure occurrence frequency, wherein the failure occurrence frequency refers to the statistical frequency of the bag failure event under the combination of a specific material thickness and an acceleration peak value.

[0041] In the embodiment of the present application, first, through step 1031, the system collects two key information from the past records: one is the actual material thickness value of the plastic bag, such as the thickness marked on the outer packaging of the garbage bag or the specific value measured by the sensor, for example, 0.01 millimeter; the other is the record of whether the corresponding bag has failed or loosened during tightening in history, marked as "failure" or "success". The system simply pairs and stores each collected thickness value directly with the success or failure result of the corresponding bag, such as associating the 0.01 millimeter thickness with the fact that 3 out of 5 records have failed. All such combinations of thickness values and success or failure records are accumulated to form the most basic database, which is called the initial mapping relationship library. Secondly, through step 1032, the system uses the hand motion data obtained when processing the user's garbage throwing action, especially the acceleration information. It focuses on the highest point value of the wrist action acceleration during the user's hand tightening the bag mouth, called the acceleration peak value. The system adds this peak value as a new information point to the initial mapping relationship library. The specific method is to add a column of record of the fastest point value of the hand motion speed in the original row storing each thickness-failure record. For example, the previous record of 0.01 millimeter thickness and 3 failures, if the highest acceleration of the user's action in the 3 failure events is 3 meters per second squared, 3.2 meters per second squared and 3.5 meters per second squared respectively, then the system adds these three acceleration values to the corresponding three failure records respectively, so that each record contains three information of thickness value, failure result and maximum action speed.

[0042] Finally through step 1033, the system starts to make further arrangement and statistics in the updated database. It specially looks at how many times the bag fails (such as breaking) when the garbage bag has a certain material thickness and the user's hand action reaches a certain maximum speed value. The system will count separately. For example, for a bag with a thickness of 0.01 millimeters, if the user's action acceleration peak value is exactly 3 meters per second squared, there is 1 failure in the historical record under this condition; if the peak value is 3.2 meters per second squared, there is 1 failure; if the peak value is 3.5 meters per second squared, there is also 1 failure. The system will add these statistical frequency numbers to each record in the database again. Finally, a detailed mapping relationship library is obtained, in which each complete record contains the specific material thickness value of the garbage bag, the maximum acceleration value reached by the user's bagging action, the record state of whether the bag is finally successful or failed, and the statistical number of how many times the bag fails under this "thickness + maximum action speed" combination. For example, a final record may be: thickness 0.01 millimeters, acceleration peak value 3.5 meters per second squared, result failure, under the same thickness and hand speed, the combination failure occurs 1 time. The whole process starts from arranging the historical thickness and success or failure, adds the action speed details, and finally calculates the bag reliability data under different material and different action speed combinations.

[0043] In actual application, in the intelligent garbage can system, the material thickness data of different plastic bag samples such as D type thin material and E type thickened material is continuously collected, and the corresponding historical bagging failure events such as bag mouth slipping or tearing are recorded synchronously; the processing unit establishes an initial mapping relationship library of material thickness value and failure record. When the user performs the bagging operation, the palm acceleration peak value such as the high peak value F generated by rapid waving is captured by the motion sensor, and the peak value data is added to the mapping library as an independent dimension; finally, the corresponding failure frequency of a specific combination such as D type material combined with peak value F is associated and stored, forming a comprehensive mapping relationship library containing material thickness, acceleration peak value and failure record.

[0044] The scheme establishes a multi-factor mapping relationship library, and the system can dynamically predict the failure risk trend of a garbage bag with a specific material under different operation intensities, such as identifying the high tearing probability of a thin material combined with high intensity operation or the slipping tendency of a thick material under low intensity operation; this capability enables the bag mouth mechanism to adaptively adjust the force control strategy, effectively reducing the garbage bag breakage rate and improving the sealing reliability.

[0045] 104、Based on the mapping relationship library, the baseline drift of the ring-shaped temperature distribution graph caused by environmental temperature fluctuation is compensated, and a rule decision module is used to process the compensated ring-shaped temperature distribution graph and the time domain analysis window to output the deformation risk index of the bag mouth; Optionally, step 104 can specifically include the following steps: 1041、According to the rule decision module, the temperature change value of the bag opening in the circumferential direction is extracted from the compensated annular temperature distribution map; 1042、The temperature change speed feature in the corresponding time range is extracted from the time domain analysis window; 1043、The preset rule is applied to analyze the temperature change value and the change speed feature, and the deformation risk index of the bag opening is adjusted to increase or decrease based on the analysis result, and finally the adjusted deformation risk index value representing the possibility of wrinkles is output.

[0046] In the above scheme, the environmental temperature fluctuation refers to the background temperature offset caused by the change of external environmental thermodynamic conditions, the baseline drift refers to the phenomenon that the overall temperature field baseline of the annular temperature distribution map deviates due to the environmental temperature fluctuation; compensation refers to the calibration operation for correcting the deviation by using the mapping relationship database data; the rule decision module refers to a logical processing unit for performing feature extraction and risk analysis; the temperature change value refers to the temperature difference value of the compensated annular temperature distribution map in the circumferential direction of the bag opening; the temperature change speed feature refers to the rate attribute of the temperature evolution with time in the time domain analysis window; the preset rule refers to the judgment logic associated with the spatial temperature difference and the time domain change rate; the adjustment refers to the behavior of dynamically correcting the deformation risk index value according to the preset rule; and the deformation risk index refers to the quantitative index representing the possibility of deformation of the bag opening.

[0047] In the embodiment of the present application, first, through step 1041, the system will call the database information containing the characteristics of the plastic bag and the user's action habits established before. Combined with the actual temperature change in the environment, the annular temperature distribution map generated in real time is calibrated to eliminate the influence of the slow change of the environment on the basic temperature. For example, if the current environmental temperature fluctuation causes the basic temperature near the garbage can to rise by 2 degrees, the system will subtract 2 degrees from the temperature value measured at each position on the map to ensure that the temperature change on the map purely reflects the user's action rather than environmental interference. Then the rule decision module starts to analyze the calibrated annular graph. It sets multiple detection points around the bag opening position, calculates the difference between the maximum temperature and the minimum temperature in each direction. For example, the 0-degree position on the graph shows 31 degrees, and the 90-degree position shows 35 degrees. Therefore, the maximum temperature difference in this position range is 4 degrees.

[0048] Secondly, through step 1042, the rule decision module will call the time domain analysis window data prepared by step 102, which defines the time period of information to be analyzed. The module checks the speed of temperature change at all points on the ring-shaped temperature distribution map in this time period. The specific method is to check the difference between the temperature values at the same position on the two ring-shaped maps at the beginning and end of the time period, and then divide by the length of the time period. For example, the temperature at the 0 degree point of a certain position is 30 degrees at the beginning of the 0.5 second window and 36 degrees at the end. The change rate of this point is 6 degrees, which is 36 minus 30, divided by 0.5 seconds, which is 12 degrees per second.

[0049] Finally, through step 1043, the rule decision module takes the two core feature values obtained in the previous two steps as input and uses the pre-set logical rules for comprehensive judgment. The system stipulates that when the maximum temperature difference at a certain position on the bundle opening circumference exceeds a certain pre-set threshold value, and the temperature change rate in that area also exceeds another pre-set threshold value, it is determined that the position has a high risk of wrinkle deformation. According to the triggering conditions of these conditions, the system will adjust the deformation risk index, which is initially 0, by adding or subtracting. For example, if a temperature difference of 4 degrees is detected to have exceeded the set threshold of 3.5 degrees, and the temperature rise rate of 12 degrees per second also exceeds 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, it may increase by an additional 5 points. The system continues to analyze all positions in this way, accumulates the adjusted index, and finally outputs a quantitative risk value within the range of 0 to 100 that accurately reflects the degree of risk of possible wrinkle tearing at the bundle opening. For example, if the final calculation results in an index of 65, it represents a moderate risk. The entire process starts with eliminating environmental interference, then quantifies the spatial temperature difference and the rate of change over time, and finally converts it into an intuitive risk assessment value through explicit judgment rules.

[0050] In practical applications, in the intelligent garbage can system, the system calls the pre-established mapping relationship database data and combines the real-time monitored environmental temperature category G to perform baseline drift compensation. For example, in cold environments, the background thermal radiation value of the ring-shaped temperature map is automatically corrected. The rule decision module performs three operations: first, extracts the temperature change difference in the H1 to H2 area of the bag opening circumference from the compensated temperature map; second, extracts the operation speed characteristics such as the gradual temperature change pattern produced during slow motion from the time domain analysis window; and finally, analyzes the coupling effect of the temperature difference and the change rate according to the pre-set rules. When a high temperature mutation occurs in thin plastic with fast motion, the wrinkle deformation risk index is increased from the initial value I1 to I2, and the quantitative risk index is output to guide the bundle opening mechanism.

[0051] The scheme can reliably identify real risk signals in an extreme temperature environment through multi-source data fusion and compensation mechanism; the spatio-temporal feature joint analysis accurately distinguishes the operation intensity and environmental interference, so that the deformation risk index becomes the core basis for the self-adaptive bundle opening intensity, significantly reduces the plastic bag breakage rate and improves the sealing stability.

[0052] 105、According to the deformation risk index and the acceleration peak value, the damping response function of the bucket cover mechanism is reconstructed, and the anti-wrinkle bag opening and closing action of the inductive bundle bag garbage can is performed; Optionally, step 105 can specifically include the following steps: 1051、Obtain the size of the deformation risk index, determine the risk level, and reduce the opening and closing speed of the bucket cover when the risk level is high, and increase the opening and closing speed of the bucket cover when the risk level is low; 1052、Obtain the acceleration peak value, and adjust the response delay of the bucket cover according to the preset adjustment rule based on the amplitude of the acceleration peak value; 1053、Input the deformation risk index and the acceleration peak value into a predefined function to generate a new damping coefficient; 1054、Update the motion resistance of the bucket cover mechanism using the new damping coefficient, and reconstruct the damping response function of the bucket cover mechanism; 1055、Based on the reconstructed damping response function, drive the inductive bundle bag garbage can bucket cover motor to perform the anti-wrinkle bag opening or closing action.

[0053] In the above scheme, the deformation risk index refers to a quantitative evaluation value representing the possibility of bundle opening wrinkling deformation, the risk level refers to the deformation probability level divided according to the index; the acceleration peak value refers to the maximum instantaneous speed change rate amplitude generated by the user's palm movement; the damping response function refers to a mathematical model describing the relationship between the motion resistance and the speed of the bucket cover mechanism, the damping coefficient refers to a function parameter controlling the motion attenuation intensity of the mechanism; the reconstruction refers to the function updating process of generating a new damping coefficient based on the deformation risk index and the acceleration peak value; the 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, the response delay adjustment refers to modifying the mechanism startup delay according to the acceleration peak value amplitude according to the preset rule; the anti-wrinkle bag opening and closing action refers to the operation process of avoiding garbage bag wrinkling through the foregoing adjustment, and the bucket cover motor driving refers to the specific physical action performed after updating the motion resistance of the mechanism with the new damping coefficient.

[0054] The embodiment of the present application first obtains the specific value of the deformation risk index calculated in step 104 through step 1051. This index varies between 0 and 100, and the higher the value, the greater the risk of crease and tear of the bag opening. The system automatically divides the index value into three risk levels according to the preset rules: low risk is 0 to 30, medium risk is 31 to 70, and high risk is 71 to 100. Then the appropriate moving speed of the lid is determined according to the level. For example, if the current deformation risk index is 65, which belongs to the medium risk, the system will keep the lid open at the standard speed. If the index reaches the high risk level of 80, the system will command the lid to slow down to 50% of the standard speed. Secondly, the system calls the data of the highest point of palm motion acceleration recorded in step 103 through step 1052. This value represents the fastest speed of the user's action when tying the bag. The system adjusts the response time of the lid by simply comparing the preset threshold value. When the action speed is particularly fast and exceeds the set upper limit, the lid is delayed to avoid interfering with the user. For example, if the current acceleration peak value is 3.5 meters per second squared, and the system sets the highest safe action speed threshold to 4.0 meters per second squared, since 3.5 does not exceed 4.0, the lid will adopt a standard response delay of 0.1 seconds; but if a peak value of 5.0 is measured, which exceeds the threshold, the system will delay to 0.3 seconds before acting. Then through step 1053, the system inputs the two key data obtained in the previous two steps, the deformation risk index and the acceleration peak value, into a preset mathematical formula for calculation. This formula will make the movement resistance of the lid increase with the increase of the deformation risk, and decrease with the acceleration of the user's action. For example, when the deformation risk index is 65 and the acceleration peak value is 3.5, the calculation formula first allocates 70% weight to the deformation risk index and 30% weight to the acceleration peak value, and finally outputs a new resistance coefficient value of 0.7 after specific operation. Then through step 1054, the system immediately replaces the original control parameter with the newly calculated resistance coefficient. This new coefficient directly determines the friction force during the opening and closing process of the lid. For example, after obtaining a new coefficient of 0.7, the mechanical structure automatically increases the track damping oil pressure, making the lid feel 1.4 times the original resistance. This is equivalent to reconstructing the control rules of the entire lid movement. Finally, through step 1055, the system drives the lid actuator of the garbage can to complete the opening and closing action under the reconstructed resistance control rules. For example, when the user's hand triggers the sensor, the system controls the motor to run at a resistance coefficient of 0.7, making the lid open slowly at 60% of the standard speed; when closing, the speed is automatically adjusted according to the new resistance to protect the plastic bag from being torn and maintain smooth operation. The entire process starts from risk assessment, combines user behavior characteristics, gradually adjusts mechanical parameters, and finally realizes intelligent opening and closing control of the bag opening.

[0055] In practical applications, in the intelligent garbage can system, the system obtains the deformation risk index J and the palm motion acceleration peak value K in real time. When the risk index J reaches a high threshold, the opening and closing speed of the can cover is automatically reduced to a set value L1, and when the risk is low, it is increased to L2; at the same time, the response delay time is dynamically adjusted according to the amplitude of the acceleration peak value K, for example, high acceleration action corresponds to short delay mode M1. Then input J and K into the pre-defined function to generate a new damping coefficient N, and update to the hydraulic damper of the can cover mechanism; finally, based on the reconstructed damping response function, the motor is driven to perform the bag opening and closing action, for example, in the high-risk scenario, the bag opening is completed in the slow mode to avoid plastic bag tearing.

[0056] The scheme dynamically reconstructs the damping response function, and the system realizes deep cooperation between the can cover action and risk assessment—automatically switches to slow mode in high-risk scenarios to avoid plastic bag deformation, and shortens the delay to improve the real-time response when high acceleration is operated; adaptive mechanical control significantly reduces the probability of wrinkle generation and the risk of garbage bag damage during the bag opening process, enhances user experience and equipment reliability.

[0057] 106、In the process of opening and closing the can cover, the motor vibration characteristics are extracted in real time, and the garbage bag bag ring tension distribution is obtained by back calculation, and the damping response function is corrected in a closed loop, and the intelligent control method for preventing the garbage bag bag opening of the inductive bag garbage can from wrinkling is realized through the closed loop control of all steps.

[0058] Optionally, step 106 can specifically include the following steps: 1061、In the process of opening and closing the can cover, the motor vibration characteristics are extracted in real time through a sensor, including the amplitude and frequency of vibration, and the vibration characteristics are input into a pre-stored corresponding relationship model to output the real-time tension distribution value of the garbage bag bag ring; 1062、Based on the tension distribution value, identify the area with uneven or excessively high tension, and correct the damping coefficient of the damping response function according to the preset adjustment rule; 1063、Real-time update and application of the corrected damping response function, closed loop control of the can cover movement.

[0059] In the above scheme, the motor vibration feature refers to the periodic mechanical oscillation signal characteristics generated by the motor during the movement of the bucket cover mechanism; the real-time tension distribution value refers to the instantaneous tension data set of the garbage bag ring generated by inputting the vibration feature into the pre-stored corresponding relationship model; the garbage bag ring tension distribution refers to the circumferential tension distribution state represented by the above data; the uneven tension area refers to the dangerous section where the tension difference on the circumference of the ring exceeds the safety threshold; the closed-loop correction refers to the dynamic updating operation of the damping coefficient in the damping response function based on the tension distribution abnormality identification result; the damping coefficient correction specifically refers to the behavior of adjusting the mechanism resistance parameter according to the preset rule; the closed-loop control refers to the method of feeding back the tension distribution to the control input end to form an automatic adjustment loop; the bucket cover movement closed-loop control refers to the anti-wrinkle intelligent execution process realized by continuously applying the corrected damping function.

[0060] Firstly, through step 1061, the system continuously monitors the slight jitter of the motor during the whole process of opening or closing the bucket cover by using the sensor installed on the motor. The sensor records two key data: the intensity (amplitude) of the jitter and the fast or slow rhythm (frequency) of the jitter. These original jitter data are sent to the analysis model prepared in advance in the system, which has mastered the corresponding rules between the motor jitter pattern and the tightness of the garbage bag ring through learning a large amount of historical data. The model directly outputs the size of the tension at different positions of the ring. For example, when the sensor detects that the vibration amplitude of the motor at a certain moment is 0.5 mm and the frequency is 500 times per second, the analysis model immediately judges that the tension in the southeast area of the ring is 30 Newton and the tension in the northwest area is 20 Newton.

[0061] Secondly, through step 1062, the system analyzes the tension value distribution diagram of the ring points output by the previous step 1061. It automatically finds out which positions have obviously high tension or which adjacent positions have too large tension difference. Once the abnormal area is found, the system adjusts the resistance parameter of the bucket cover movement according to the preset safety rules. For example, if it is detected that the tension in the southeast area of the ring is 30 Newton, which has exceeded the safety threshold of 25 Newton, and the tension difference between this area and the adjacent northwest area reaches 10 Newton, which also exceeds the difference threshold of 5 Newton, the system determines that there is a risk of wrinkles at this position and immediately increases the coefficient of the control bucket cover moving resistance by 0.15 based on the original value.

[0062] Finally, through step 1063, the system applies the new resistance coefficient adjusted in step 1062 to the running bucket cover mechanism in real time. The adjustment instruction takes effect immediately, and the speed and force of the bucket cover change immediately. At the same time, the sensor continues to monitor the jitter state of the motor under the new resistance to form a new data stream, and the entire process of "detecting jitter, calculating tension, finding problems, adjusting resistance, and detecting again" repeats dozens of times per second. For example, the current resistance coefficient has been adjusted to 0.85, and after the bucket cover slows down, the new monitoring shows that the southeast area tension has dropped to 24 Newton, and the system continues to maintain this resistance; if the local tension is still found to exceed 28 Newton, the resistance will be further increased to 0.95. Through this millisecond-level dynamic adjustment, the final goal is to ensure that the garbage bag cuff is uniformly stressed throughout the opening and closing process, avoiding tearing and wrinkling. The entire process forms a closed-loop self-adjusting system, just like a car automatically adjusting the suspension on a bumpy road to continuously optimize the bucket cover action.

[0063] In actual application, during the execution stage of the opening and closing of the intelligent garbage can bucket cover, the built-in sensor collects the motor vibration characteristics including amplitude P and frequency Q in real time, inputs the vibration data into the pre-trained model to back-propagate the real-time tension distribution values of the garbage bag cuff in the circumferential direction R1 to R2 area; when the tension in the R3 area is abnormally high, the damping coefficient S value of the corresponding bucket cover structure in this area is reduced according to the preset rule; the control unit updates the damping response function parameters within milliseconds, and drives the motor to adaptively adjust the opening and closing angle and speed, for example, automatically switching the buffer mode in the tension peak area to avoid local wrinkles.

[0064] The present scheme realizes real-time mapping of motor vibration characteristics and tension distribution, and the system constructs a closed-loop channel from mechanical state perception to execution control, accurately identifies the local overload area of the cuff and immediately adjusts the bucket cover action strategy, eliminating the risk of wrinkles; the millisecond-level dynamic correction capability keeps the garbage bag cuff process in a uniformly stressed state at all times, greatly improving the robustness and operation safety of the anti-wrinkle control.

[0065] Figure 2 A scene diagram of an intelligent control method of a sensing bag garbage can is provided for the embodiments of the present application, as shown in Figure 2 For a complete embodiment of steps 101-106, it includes: In the K-type intelligent garbage can workflow, when the user approaches the device, the infrared sensor collects the garbage bag bundle opening wrinkle pattern and palm movement data, the optical enhancement component uses a special M lens and an N-level infrared filter to process the original data, improves the spatial gradient in the circumferential direction of the bag opening, and generates a ring-shaped temperature distribution map; the distance sensor dynamically divides the fast and slow levels according to the user's standing position, triggers the fast action level when the distance is less than the set value A and intercepts the short time domain window, and triggers the slow level when the distance is greater than the set value B and intercepts the long time domain window; the system calls the failure record associated with the P material thickness and the Q level acceleration peak value in the pre-stored mapping relationship library, and fuses the environmental temperature category G to compensate for the baseline drift of the temperature map; the rule decision module analyzes the temperature change value of the bag opening area after compensation and the time domain speed characteristics, 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 switches to the low-speed mode U in high-risk situations, and uses the high-speed mode V in low-risk situations; During the motor execution of the opening and closing action, the vibration sensor captures the amplitude W and frequency X characteristics in real time, reverses the bundle tension distribution through the pre-trained model, identifies the abnormal area Y, and immediately corrects the damping coefficient; The final closed-loop control system continuously optimizes the lid motion trajectory to realize the anti-wrinkle bundle opening operation.

[0066] The present scheme realizes multiple optimization through full-link closed-loop control: infrared enhancement and behavior grading improve garbage bag state perception accuracy; multi-dimensional mapping model of material thickness, operation force and environmental temperature enhances risk prediction ability; dynamic damping response mechanism makes the lid action adaptively avoid high tearing scenarios; vibration feedback driven real-time tension adjustment ensures uniform stress during the bundle opening process. Ultimately, under diversified plastic materials, user operation habits and environmental conditions, the system can reliably eliminate the risk of garbage bag wrinkles and damage, significantly improving the long-term sealing stability and user operation experience of the intelligent garbage can.

[0067] Figure 3 A structure diagram of an intelligent control system of a sensing bundle bag garbage can is provided for the embodiments of the present application, as shown in Figure 3 The system comprises: The acquisition module 31 is configured to acquire infrared data of the sensing bundle bag garbage can, which contains garbage bag bundle opening wrinkle pattern characteristics and user palm movement information, and to generate a ring-shaped temperature distribution map by improving the spatial gradient of the infrared data in the circumferential direction of the bag opening through an optical enhancement component. The analysis module 32 is configured to divide the fast and slow levels of the user's garbage throwing behavior based on the user's standing position and the real-time distance of the sensing bundle bag garbage can, and to dynamically intercept the time domain analysis window corresponding to the ring-shaped temperature distribution map according to the fast and slow levels. The association module 33 is configured to establish a mapping relationship library of plastic bag material thickness and historical bundle bag failure records, and to associate the acceleration peak value of the palm movement information to obtain the associated mapping relationship library. The processing module 34 is configured to compensate for baseline drift of the annular temperature distribution map caused by ambient temperature fluctuation based on the mapping relationship library, and output a deformation risk index of the bag opening of the induction bagged garbage can by processing the compensated annular temperature distribution map and the time domain analysis window using a rule decision module. The execution module 35 is configured to reconstruct a damping response function of the bucket cover mechanism according to the deformation risk index and the acceleration peak value, and perform an anti-wrinkling bag opening and closing action of the induction bagged garbage can. The correction module 36 is configured to extract motor vibration features in real time during the opening and closing of the bucket cover, inversely deduce a garbage bag hoop tension distribution, and correct the damping response function in a closed loop, so as to realize the intelligent control method of the induction bagged garbage can.

[0068] Figure 3 The intelligent control system of the induction bagged garbage can can perform Figure 1 The implementation principle and technical effects of the intelligent control method of the induction bagged garbage can are not described again. The specific operation of each module and unit of the intelligent control system of the induction bagged garbage can in the above embodiment has been described in detail in the embodiment related to the method, and will not be described in detail here.

[0069] In one possible design, Figure 3 The intelligent control system of the induction bagged garbage can can be implemented as a computing device, such as Figure 4 The computing device can include a storage component 41 and a processing component 42. The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42.

[0070] The processing component 42 is configured to perform the above Figure 1 The intelligent control method of the induction bagged garbage can.

[0071] The processing component 42 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be 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 elements, for executing the above method.

[0072] The storage component 41 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or nonvolatile storage devices, 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 or optical disk.

[0073] Of course, the computing device can also necessarily include other components, such as an input / output interface, a display component, a communication component, etc.

[0074] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0075] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0076] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be a basic server resource rented or purchased from the cloud computing platform.

[0077] The embodiment of the application further provides a computer storage medium, which stores a computer program, and the computer program can implement the above-mentioned Figure 1 The embodiment of the application further provides a computer storage medium, which stores a computer program, and the computer program can implement the above-mentioned

[0078] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiment, and will not be described here.

[0079] The device embodiment described above is only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. Those skilled in the art can understand and implement without creative labor.

[0080] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and the necessary general hardware platform from the above description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the technical solutions can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a server, etc.) to execute the methods described in the various embodiments or some parts of the methods.

[0081] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent control method for an induction bag trash can, characterized in that: include: Collect infrared data from the sensor-assisted bag bin, including the wrinkle morphology of the bag opening and the user's palm movement information. Use an optical enhancement component to enhance the spatial gradient of the infrared data in the circumferential direction of the bag opening to generate a ring-shaped temperature distribution map. Based on the real-time distance between the user's standing position and the sensor bundle bag trash can, the user's garbage placement behavior is divided into an urgent and slow level, and a time domain analysis window corresponding to the annular temperature distribution diagram is dynamically intercepted according to the urgent and slow level; Establishing a mapping relationship library between plastic bag material thickness and historical bag failure records, and correlating the acceleration peak of the palm motion information to obtain a correlated mapping relationship library; Based on the mapping relationship library, the baseline drift of the annular temperature distribution diagram is compensated by integrating the ambient temperature fluctuation, and the compensated annular temperature distribution diagram and the time domain analysis window are processed by a rule decision module to output the deformation risk index of the beam port; Reconstructing the damping response function of the lid mechanism according to the deformation risk index and the acceleration peak value, and executing the anti-wrinkle bag opening and closing action of the induction bundle bag trash can; During the opening and closing process of the lid, the motor vibration characteristics are extracted in real time, the tension distribution of the garbage bag draw ring is obtained by reverse deduction, the damping response function is corrected in a closed loop, and the intelligent control method for preventing wrinkles of the garbage bag draw ring of the induction bag drawstring trash can is realized through closed-loop control of all steps.

2. The method according to claim 1, characterized in that Reconstructing the damping response function of the lid mechanism according to the deformation risk index and the acceleration peak value, and executing the wrinkle-proof bag opening and closing action of the induction bundle bag trash can, including: Obtaining the magnitude of the deformation risk index, determining a risk level, reducing the opening and closing speed of the barrel lid when the risk level is high, and increasing the opening and closing speed of the barrel lid when the risk level is low; Obtaining an acceleration peak value, and adjusting a response delay of the barrel cover according to a preset adjustment rule based on the magnitude of the acceleration peak value; Inputting the deformation risk index and the acceleration peak into a predefined function to generate a new damping coefficient; Using the new damping coefficient to update the motion resistance of the barrel cover mechanism, and reconstruct the damping response function of the barrel cover mechanism; Based on the reconstructed damping response function, the motor of the lid of the induction bag trash can is driven to perform the opening or closing action of the anti-wrinkle bag mouth.

3. The method according to claim 1, characterized in that During the lid opening and closing process, the motor vibration characteristics are extracted in real time, the tension distribution of the garbage bag band is obtained by reverse engineering, and the damping response function is corrected in a closed loop, including: During the lid opening and closing process, the sensor collects the motor vibration characteristics in real time, including the vibration amplitude and frequency, and inputs the vibration characteristics into the pre-stored correspondence model to output the real-time tension distribution value of the garbage bag tie ring; Based on the tension distribution value, identifying areas with uneven or excessive tension, and modifying the damping coefficient of the damping response function according to a preset adjustment rule; The modified damping response function is updated and applied in real time to close the loop and control the movement of the barrel cover.

4. The method according to claim 1, wherein The optical enhancement component is used to enhance the spatial gradient of the infrared data in the circumferential direction of the bag opening to generate an annular temperature distribution map, including: Using special lenses and filters in the optical enhancement component, the collected infrared data, including the wrinkle morphology characteristics of the garbage bag's drawstring and the user's palm movement information, is processed; By adjusting the optical path, the temperature difference between adjacent points along the circumference of the bag opening is increased, thereby improving the spatial gradient of the processed infrared data. The infrared data after improving the spatial gradient is converted into a ring representation, and the temperature value of each position is displayed along the circumference of the bag opening, generating a continuous temperature value map along the circumference of the bag opening.

5. The method according to claim 1, wherein Dynamically intercepting a time domain analysis window corresponding to the annular temperature distribution diagram according to the emergency level includes: Based on the divided urgency and slowness levels, the movements are classified as fast movement levels or slow movement levels; When the urgency level is a fast action level, a short time domain analysis window is intercepted; when the urgency level is a slow action level, a long time domain analysis window is intercepted; The time domain analysis window covers the change data of the annular temperature distribution graph in the time domain, wherein the window length determines the time range for analyzing the annular temperature distribution graph.

6. The method according to claim 1, characterized in that A mapping relationship library is established between the thickness of plastic bag materials and historical drawstring bag failure records, and the acceleration peak value of the palm motion information is associated to obtain the associated mapping relationship library, including: Collecting plastic bag material thickness data and historical bag failure records, and associating and storing the material thickness values ​​with the historical failure records to establish an initial mapping relationship library; Adding the acceleration peak value of the palm motion information as an additional dimension to the initial mapping relationship library; For different plastic bag material thicknesses and acceleration peaks, the corresponding failure occurrence frequencies are associated and stored in the initial mapping relationship library, and the mapping relationship library containing the associations of the material thicknesses, the acceleration peaks and the historical failure records is output.

7. The method according to claim 1, characterized in that The rule decision module is used to process the compensated annular temperature distribution map and the time domain analysis window to output the deformation risk index of the beam mouth, including: The temperature variation value in the circumferential direction of the bag opening is extracted from the compensated annular temperature distribution diagram according to the rule decision module; Extract the temperature change speed characteristics within the corresponding time range from the time domain analysis window; The temperature change value and the change speed characteristics are analyzed using preset rules, and the deformation risk index of the bundle mouth is adjusted to increase or decrease based on the analysis results, and finally the adjusted deformation risk index value representing the possibility of wrinkles is output.

8. An intelligent control system for an induction bag trash can, characterized in that: include: Collect infrared data from the sensor-assisted bag bin, including the wrinkle morphology of the bag opening and the user's palm movement information. Use an optical enhancement component to enhance the spatial gradient of the infrared data in the circumferential direction of the bag opening to generate a ring-shaped temperature distribution map. Based on the real-time distance between the user's standing position and the sensor bundle bag trash can, the user's garbage placement behavior is divided into an urgent and slow level, and a time domain analysis window corresponding to the annular temperature distribution diagram is dynamically intercepted according to the urgent and slow level; Establishing a mapping relationship library between plastic bag material thickness and historical bag failure records, and correlating the acceleration peak of the palm motion information to obtain a correlated mapping relationship library; Based on the mapping relationship library, the baseline drift of the annular temperature distribution diagram is compensated by integrating the ambient temperature fluctuation, and the compensated annular temperature distribution diagram and the time domain analysis window are processed by a rule decision module to output the deformation risk index of the beam port; Reconstructing the damping response function of the lid mechanism according to the deformation risk index and the acceleration peak value, and executing the anti-wrinkle bag opening and closing action of the induction bundle bag trash can; During the opening and closing process of the lid, the motor vibration characteristics are extracted in real time, the tension distribution of the garbage bag draw ring is obtained by reverse deduction, the damping response function is corrected in a closed loop, and the intelligent control method for preventing wrinkles of the garbage bag draw ring of the induction bag drawstring trash can is realized through closed-loop control of all steps.

9. 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 used to be called and executed by the processing component to implement an intelligent control method for an induction bag trash can as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, an intelligent control method for an induction bundle bag trash can according to any one of claims 1 to 7 is implemented.

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