Garbage flow control method, device and equipment based on elevator and medium

Abstract

The invention relates to a garbage flow control method and device based on an elevator, equipment and a medium. The method comprises the steps that the coverage area of garbage on a conveying belt of the elevator, the weight per unit length, the component type mark of the garbage and the linear speed of the conveying belt are obtained; the coverage area, the weight per unit length, the component type marks and the linear speed of the conveying belt are subjected to synchronous alignment processing, and a synchronous data set with completely synchronous time is obtained; flow calculation is conducted based on the synchronous data set, and the initial instantaneous flow of the garbage on the conveying belt is obtained; processing the initial instantaneous flow rate by adopting adaptive Kalman filtering to obtain a precise instantaneous flow rate; obtaining a flow error and a flow error change rate based on the accurate instantaneous flow; a control instruction is obtained based on the flow error and the flow error change rate; and controlling the operation of the elevator based on the control instruction. Dynamic changes of garbage components and flow can be sensed in real time, operation parameters of the elevator can be rapidly adjusted, and the overall treatment efficiency of the elevator is improved.

Description

Technical Field

[0001] This application relates to the field of waste flow control technology, and in particular to a waste flow control method, device, equipment and medium based on a hoist. Background Technology

[0002] In waste treatment systems, elevators, as key equipment, play a crucial role in transporting waste to processing equipment. The core technical challenge currently facing these systems lies in the precise control of waste flow rate. Due to the highly complex composition of waste (including materials of varying densities such as food waste, plastics, and metals) and its uneven distribution, traditional control methods based on manual experience or simple mechanical adjustments are ill-suited to this dynamic change. This control deficiency leads to frequent fluctuations in the elevator's output flow rate exceeding ±30%, forcing subsequent incinerators or sorting equipment to frequently adjust their operating parameters to adapt to input changes. This unstable material transport not only reduces processing efficiency by 15-20% but also causes equipment to operate under off-design conditions, significantly increasing motor energy consumption (approximately 25%) and mechanical component wear (by 40%). Particularly in continuously operating waste-to-energy plants, this flow rate fluctuation also affects combustion stability, increasing the risk of dioxin and other pollutant formation. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, device, equipment, and medium for controlling garbage flow based on a hoist that can accurately identify cracks, addressing the aforementioned technical problems.

[0004] In a first aspect, this application provides a garbage flow control method based on a hoist, the garbage flow control method based on a hoist includes:

[0005] Obtain the coverage area of ​​waste on the conveyor belt of the elevator, the weight per unit length, the waste composition type markings, and the conveyor belt linear speed;

[0006] The coverage area, the weight per unit length, the component type label, and the conveyor belt speed are synchronized to obtain a synchronized dataset that is completely synchronized in time.

[0007] Flow rate calculation is performed based on the synchronous dataset to obtain the preliminary instantaneous flow rate of waste on the conveyor belt;

[0008] An adaptive Kalman filter is used to process the initial instantaneous flow rate to obtain the accurate instantaneous flow rate;

[0009] Based on the precise instantaneous flow rate, the flow rate error and the rate of change of the flow rate error are obtained;

[0010] Control commands are derived based on the flow error and the rate of change of the flow error;

[0011] The operation of the hoist is controlled based on the control commands.

[0012] In one embodiment, obtaining the coverage area of ​​the waste on the conveyor belt of the elevator includes:

[0013] Acquire images of the waste on the conveyor belt;

[0014] Gamma correction is performed on the garbage image;

[0015] The gamma-corrected garbage image can be converted to the HSV color space;

[0016] Edge detection is performed on the garbage image after HSV color space conversion to obtain the coverage area of ​​the garbage on the conveyor belt of the elevator.

[0017] In one embodiment, the process of synchronizing the coverage area, the weight per unit length, the component type marker, and the conveyor belt linear speed to obtain a time-synchronized dataset includes:

[0018] Add a PLC system clock marker to the coverage area, the weight per unit length, the component type marker, and the conveyor belt linear speed;

[0019] Based on the sampling rate differences of the coverage area, the weight per unit length, the component type label, and the conveyor belt linear speed, a synchronous data sequence is generated using an interpolation algorithm to obtain a synchronous dataset.

[0020] In one embodiment, the step of calculating the flow rate based on the synchronized dataset to obtain the preliminary instantaneous flow rate of waste on the conveyor belt includes:

[0021] The waste coverage density is obtained based on the coverage area and the weight per unit length.

[0022] The initial instantaneous flow rate is obtained based on the garbage coverage density, the coverage area, the conveyor belt length, and the conveyor belt linear speed.

[0023] In one embodiment, the coverage area of ​​waste on the conveyor belt of the elevator, weight per unit length, waste composition type markings, and conveyor belt linear speed are obtained based on sensors; the preliminary instantaneous flow rate is processed using adaptive Kalman filtering to obtain a precise instantaneous flow rate, including:

[0024] Construct a spatial state model;

[0025] Dynamically adjust the noise covariance based on the health status of the sensors;

[0026] Filtering is iteratively optimized to obtain accurate instantaneous flow rate.

[0027] In one embodiment, the step of obtaining control commands based on the flow error and the rate of change of the flow error includes:

[0028] Configure baseline PID parameters;

[0029] The current control mode is obtained based on the flow error;

[0030] The final PID parameters are obtained based on the baseline PID parameters, the current control mode, the flow error, and the flow error change rate.

[0031] The final PID parameters are subjected to anti-saturation processing to obtain control commands.

[0032] Secondly, this application also provides a waste flow control device based on a hoist, comprising:

[0033] Elevator;

[0034] The multimodal sensor data acquisition module is used to acquire the coverage area of ​​waste on the conveyor belt of the elevator, the weight per unit length, the composition type marking of the waste, and the linear speed of the conveyor belt;

[0035] The synchronous dataset acquisition module is used to perform synchronous alignment processing on the coverage area, the weight per unit length, the component type label and the conveyor belt linear speed to obtain a synchronous dataset that is completely synchronized in time.

[0036] The preliminary instantaneous flow rate acquisition module is used to perform flow rate calculation based on the synchronous dataset to obtain the preliminary instantaneous flow rate of the waste on the conveyor belt;

[0037] The precise instantaneous flow rate acquisition module is used to process the preliminary instantaneous flow rate using an adaptive Kalman filter to obtain the precise instantaneous flow rate;

[0038] A flow error and flow error change rate acquisition module is used to obtain the flow error and flow error change rate based on the precise instantaneous flow rate;

[0039] A control command acquisition module is used to obtain control commands based on the flow error and the flow error change rate;

[0040] The control module is used to control the operation of the hoist based on the control commands.

[0041] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0043] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0044] The aforementioned waste flow control method, device, equipment, and medium based on elevators innovatively combine machine vision, weighing sensors, and advanced control algorithms. This enables real-time perception of dynamic changes in waste composition and flow rate, and rapid adjustment of elevator operating parameters, ensuring that the elevator always operates at its optimal condition. This improves the overall processing efficiency of the elevator, especially when handling complex mixed waste. It can automatically identify changes in material characteristics and adjust control strategies to ensure the stable and efficient operation of the entire waste treatment chain. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a garbage flow control method based on a hoist provided in one embodiment;

[0047] Figure 2 This is a structural block diagram of a garbage flow control device based on a hoist provided in another embodiment;

[0048] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] In one embodiment, such as Figure 1 As shown, this application provides a garbage flow control method based on a hoist, which may include the following steps:

[0051] Step S10: Obtain the coverage area of ​​the waste on the conveyor belt of the elevator, the weight per unit length, the waste composition type marking, and the conveyor belt linear speed.

[0052] As an example, step S10, obtaining the coverage area of ​​the waste on the conveyor belt of the elevator, may include the following:

[0053] Acquire images of the waste on the conveyor belt;

[0054] Gamma correction is performed on the garbage image;

[0055] The gamma-corrected garbage image can be converted to the HSV color space;

[0056] Edge detection is performed on the garbage image after HSV color space conversion to obtain the coverage area of ​​the garbage on the conveyor belt of the elevator.

[0057] As an example, in step S10, the coverage area of ​​the waste on the conveyor belt of the elevator, the weight per unit length, the component type marking of the waste, and the linear speed of the conveyor belt can be obtained based on sensors.

[0058] Step S20: The coverage area, the weight per unit length, the component type label, and the conveyor belt speed are synchronized to obtain a synchronized dataset that is fully synchronized in time.

[0059] As an example, in step S20, the process of synchronizing and aligning the coverage area, the weight per unit length, the component type label, and the conveyor belt speed to obtain a synchronized dataset that is completely synchronized in time may include the following steps: S201~S202.

[0060] S201: Add a PLC system clock marker to the coverage area, the weight per unit length, the component type marker, and the conveyor belt linear speed.

[0061] S202: Based on the sampling rate differences of the coverage area, the weight per unit length, the component type label, and the conveyor belt linear speed, a synchronous data sequence is generated using an interpolation algorithm to obtain a synchronous dataset.

[0062] Step S30: Calculate the flow rate based on the synchronous dataset to obtain the preliminary instantaneous flow rate of the waste on the conveyor belt.

[0063] As an example, in step S30, the flow calculation based on the synchronous dataset to obtain the preliminary instantaneous flow of waste on the conveyor belt may include the following steps: S301~S302.

[0064] S301: The waste coverage density is obtained based on the coverage area and the weight per unit length.

[0065] S302: The preliminary instantaneous flow rate is obtained based on the garbage coverage density, the coverage area, the conveyor belt length, and the conveyor belt linear speed.

[0066] Step S40: The preliminary instantaneous flow rate is processed using an adaptive Kalman filter to obtain the accurate instantaneous flow rate.

[0067] As an example, in step S40, the process of using an adaptive Kalman filter to process the preliminary instantaneous flow rate to obtain the accurate instantaneous flow rate may include the following steps: S401~S403.

[0068] S401: Construct a spatial state model.

[0069] S402: Dynamically adjust the noise covariance based on the health status of the sensor.

[0070] S403: Perform filter iteration optimization to obtain accurate instantaneous flow rate.

[0071] Step S50: Based on the accurate instantaneous flow rate, obtain the flow error and the flow error change rate.

[0072] Step S60: A control command is obtained based on the flow error and the rate of change of the flow error.

[0073] As an example, in step S60, obtaining the control command based on the flow error and the flow error change rate may include the following steps: S601~S604.

[0074] S601: Configure baseline PID parameters.

[0075] S602: Obtain the current control mode based on the flow error.

[0076] S603: The final PID parameters are obtained based on the baseline PID parameters, the current control mode, the flow error, and the flow error change rate.

[0077] S604: Perform anti-saturation processing on the final PID parameters to obtain control commands.

[0078] Step S70: Control the operation of the hoist based on the control command.

[0079] The waste flow control method based on the elevator proposed in this application innovatively combines machine vision, weighing sensing and advanced control algorithms. It can perceive the dynamic changes in waste composition and flow in real time, and can quickly adjust the operating parameters of the elevator, so that the elevator can always work in the best working condition. It can improve the overall processing efficiency of the elevator, especially when dealing with mixed waste with complex composition. It can automatically identify changes in material characteristics and adjust the control strategy to ensure the stable and efficient operation of the entire waste treatment chain.

[0080] As an example, in step S10, a 2-megapixel industrial camera (IMX307 sensor) can be installed inside a dust cover 1.5 meters directly above the feed inlet of the elevator, along with a 6500K color temperature LED ring light (illuminance ≥1000 lux). After the camera acquires images at a frame rate of 30fps, gamma correction (γ=1.8) is first performed to eliminate uneven lighting. Then, the image is converted to the HSV color space, and the RGB pixel values ​​in the garbage image are used to calculate the hue (H), saturation (S), and lightness (V) components using a non-linear conversion formula. The H component is used to distinguish between organic matter (H=30°~90°) and inorganic matter (H=180°~270°). Edge detection can use a modified Canny operator, first using a 5×5 Gaussian filter (σ=1.2) for noise reduction, then using the Sobel operator to calculate the gradient, and finally using a dual threshold (low threshold 30 / high threshold 80) to determine the effective edges. The waste coverage area is calculated by statistically analyzing the percentage of effective pixels and combining it with a pre-calibrated field of view. Waste composition can be labeled based on organic and inorganic matter distinguished by the H component.

[0081] As an example, in step S10, a weighing sensor unit can be used for weighing data acquisition. The weighing sensor unit can consist of three S-type load cells (model HBM-PW 15A) evenly distributed at 120° angles below the conveyor belt support roller bearing seat. Each sensor has a range of 500 kg and an accuracy of 0.3%FS. The three-point support structure automatically compensates for off-center loading errors through a hardware summing circuit, performs three-point calibration on the data acquired by the three S-type load cells, and converts it into weight per unit length.

[0082] As an example, in step S10, the conveyor belt linear speed detection can use an Omron E6B2-CWZ6C incremental encoder (1024 pulses / revolution) mounted on the drive wheel shaft. The resolution is increased to 4096 PPR by a 4x frequency multiplication circuit. The conveyor belt linear speed is calculated in conjunction with the conveyor belt circumference calibration value (e.g., 2.356m). The formula for calculating the conveyor belt linear speed is as follows: Conveyor belt linear speed = number of pulses × conveyor belt circumference / 4096.

[0083] As an example, in step S10, all sensor data is connected to the RS485 bus network via shielded twisted-pair cables, with the baud rate set to 115200bps. The PLC employs a sliding window filtering algorithm: a circular buffer of 20 sampling points is set up; after the new data replaces the oldest data, the median of the data within the window is taken as the valid value. Simultaneously, a dynamic threshold detection is set: when the difference between adjacent sampling values ​​exceeds 5% of the full scale, an anomaly flag is triggered; after three consecutive anomalies, the sensor self-test program is initiated.

[0084] As an example, in step S201, a PLC system clock flag can be added to each sensor data (i.e., the sensor data corresponding to the coverage area, the weight per unit length, the component type mark, and the conveyor belt linear speed), with an accuracy of 1ms.

[0085] As an example, in step S202, for the sampling rate differences of the coverage area, the weight per unit length, the component type label and the conveyor belt linear speed, a synchronous data sequence is generated by an interpolation algorithm, which can eliminate the resulting timing deviation.

[0086] As an example, step S20 also includes dynamic buffer management, which may specifically include the following: setting up a three-level data buffer (raw data - time-aligned data - valid data) to ensure smooth data flow; the depth of the buffer is determined based on the sampling rate of the slowest sensor, and the corresponding calculation formula can be as follows:

[0087]

[0088] in, The depth of the buffer; This is the highest sampling frequency among all sensors; This is the lowest sampling frequency among all sensors; 'b' represents the rounding up sign; 'b' represents the amount of redundant buffer.

[0089] As an example, in step S301, the waste coverage density can be obtained based on the coverage area and the weight per unit length. If the waste composition type label indicates that metal has been detected (H component < 30°), density compensation needs to be initiated to obtain the compensated waste coverage density. The corresponding formula is as follows:

[0090]

[0091] in, The compensated garbage cover density; Based on basic waste coverage density; This refers to the content of metallic components; This represents the total content of the material.

[0092] As an example, in step S302, the preliminary instantaneous flow rate is obtained based on the garbage coverage density, the coverage area, the conveyor belt length, and the conveyor belt linear speed. The formula can be expressed as follows:

[0093]

[0094] in, For dynamic weighting coefficients, ; The data collected by the i-th weighing sensor; The j-th garbage image is a valid pixel; When the garbage cover density is such that there is no metal in the garbage, for When metal is detected in the garbage, for ; The linear speed of the conveyor belt; L is the area covered by the garbage; L is the length of the conveyor belt; n is the total number of pixels in the garbage image.

[0095] As an example, in step S401, the formula for constructing the spatial state model can be as follows:

[0096]

[0097] in, Let k be the state variable at time k. , Let K be the initial instantaneous flow of garbage at time k; Let K be the garbage cover density at time k; Let k be the linear velocity of the conveyor belt at time k. This is the state transition matrix; Let k be the state variable at time k-1; The input matrix; To control the input vector; This is the process noise vector; For observation vectors; The observation matrix; This is the observed noise vector.

[0098] As an example, in step S402, during the process of dynamically adjusting the noise covariance based on the sensor's health status, the process noise can be updated in real time according to the sensor's health status. The corresponding formula can be as follows:

[0099]

[0100] in, Construct a function for a diagonal matrix; State variables The fundamental variance; State variables The fundamental variance; State variables The basic variance; t is the time variable; is the time constant.

[0101] As an example, in step S403, after filtering and iterative optimization, the accurate instantaneous flow rate is extracted from the filtered and iteratively optimized state variables.

[0102] As an example, step S601 may include the following:

[0103] Initial parameters are set based on the improved Ziegler-Nichols method, and a three-dimensional parameter lookup table is established. The corresponding formula is as follows:

[0104]

[0105] in, This is the proportionality coefficient; The integral coefficient; These are the differential coefficients; This refers to the system delay time. The system open-loop gain; Type coding for waste cover density (classes 1-5).

[0106] Online tuning employs a constrained particle swarm optimization (PSO) algorithm, and the fitness function can be as follows:

[0107]

[0108] in, is the comprehensive performance index of the fitness function; T is the upper limit of the integration time; w1, w2, and w3 are weight coefficients, w1=0.6, w2=0.3, and w3=0.1; For flow rate error; To control the square of the input vector; To adjust the time.

[0109] As an example, in step S602, a working state machine is designed to describe the absolute value of the flow error. The size and duration of the operation, and the switching logic between three working states (Normal, Fuzzy, Emergency).

[0110] Specifically, Normal (normal state): The flow error is very small, and the absolute value of the flow error is... <5%, in a stable and normal operating mode, the control strategy is usually to maintain the current state smoothly; Fuzzy (fuzzy control state): moderate flow error, absolute value of flow error ≤ 5%. <10% requires a more flexible control strategy (such as fuzzy control) to quickly correct deviations and prevent error expansion; Emergency: System error is large, and the absolute value of the flow error is high. ≥10%. Or moderate error but long duration (absolute value of flow error) If the failure rate is less than 8% and lasts for 2 seconds, emergency control logic (such as strong intervention or protective shutdown) needs to be triggered to prevent loss of control.

[0111] As an example, in step S603, the final PID parameters are obtained based on the baseline PID parameters, the current control mode, the flow error, and the flow error change rate, which may include the following:

[0112] The membership degree of the flow error to the negative large (NB) fuzzy set is calculated based on the following membership function formula. :

[0113]

[0114] Among them, membership degree The value range of is [0,1]. When When = 1, the flow error e belongs entirely to the negative. When = 0, it means that the flow error e is not at all negative; when 0 < When <1, it indicates that the flow error e belongs to the negative largest part; This is the general form of the trapezoidal membership function; These are the four inflection points of the trapezoidal membership function for negative large fuzzy sets.

[0115] The rate of change of flow error is calculated based on the following formula of membership function. Membership degree of a positive small (PS) fuzzy set :

[0116]

[0117] Among them, membership degree The value range of is [0,1]. When When =1, the rate of change of flow error Completely subordinate to Zhengxiao, when When = 0, it represents the rate of change of flow error. Completely not belonging to positive small; when 0 < When <1, it indicates the rate of change of flow error. Some belong to Zhengxiao; This is the general form of the membership function for a triangle.

[0118] Based on the flow error e and the flow error change rate The fuzzification result is matched with 25 Mamdani-type rules to generate the changes in the proportional coefficient, integral coefficient, and differential coefficient.

[0119] By using the center of gravity method formula, the fuzzy compensation amount is converted into an accurate value, which is then superimposed on the baseline PID parameters to obtain the final PID parameters.

[0120] Specifically, the formula for the center of gravity method can be as follows:

[0121]

[0122] in, The output value is the precise control value after defuzzification; n is the number of rules obtained from fuzzy inference. This represents the fuzzy control quantity output by the i-th fuzzy rule; The fuzzy control quantity output for the i-th rule The degree of membership.

[0123] As an example, step S604 may include proportional term calculation, integral separation processing, and differential prior processing.

[0124] Specifically, the formula for calculating the proportion term can be as follows:

[0125]

[0126] in, For proportional control components; This is the proportionality coefficient; This is for flow rate error.

[0127] Specifically, the formula for integral separation can be as follows:

[0128]

[0129] in, For integral control components; The integral coefficient; For flow rate error; It is the cumulative integral of the flow error from 0 to time t; This is the integral separation threshold.

[0130] Specifically, the formula for differential-first processing can be as follows:

[0131]

[0132] in, For differential control components; These are the differential coefficients; It represents the rate of change of the actual value of the controlled variable.

[0133] Specifically, the proportion of the component can be controlled. Integral control components and differential control components By integrating them, control commands can be obtained. .

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0135] Based on the same inventive concept, this application also provides a hoist-based waste flow control device for implementing the hoist-based waste flow control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more hoist-based waste flow control device embodiments provided below can be found in the limitations of the hoist-based waste flow control method described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 2As shown, this application also provides a waste flow control device based on a hoist, which includes: a hoist 10, a multimodal sensor data acquisition module 20, a synchronous dataset acquisition module 30, a preliminary instantaneous flow acquisition module 40, a precise instantaneous flow acquisition module 50, a flow error and flow error change rate acquisition module 60, a control command acquisition module 70, and a control module 80, wherein: the multimodal sensor data acquisition module 20 is used to acquire the coverage area of ​​waste on the conveyor belt of the hoist, the weight per unit length, the component type marking of waste, and the linear speed of the conveyor belt; the synchronous dataset acquisition module 30 is used to transmit the coverage area, the weight per unit length, and the component type... The marker and the conveyor belt linear speed are synchronized to obtain a synchronized dataset that is completely synchronized in time; the preliminary instantaneous flow rate acquisition module 40 is used to calculate the flow rate based on the synchronized dataset to obtain the preliminary instantaneous flow rate of the waste on the conveyor belt; the precise instantaneous flow rate acquisition module 50 is used to process the preliminary instantaneous flow rate using an adaptive Kalman filter to obtain the precise instantaneous flow rate; the flow error and flow error change rate acquisition module 60 is used to obtain the flow error and flow error change rate based on the precise instantaneous flow rate; the control command acquisition module 70 is used to obtain control commands based on the flow error and the flow error change rate; and the control module 80 is used to control the operation of the elevator based on the control commands.

[0137] The aforementioned waste flow control device based on the elevator innovatively combines machine vision, weighing sensors, and advanced control algorithms. It can perceive the dynamic changes in waste composition and flow in real time, and quickly adjust the elevator's operating parameters, ensuring that the elevator always operates at its optimal condition. This improves the overall processing efficiency of the elevator, especially when dealing with complex mixed waste. It can automatically identify changes in material characteristics and adjust the control strategy to ensure the stable and efficient operation of the entire waste treatment chain.

[0138] As an example, this application also provides a garbage flow control device based on a hoist that can perform actions such as Figure 1 For details regarding the steps of the waste flow control method based on a hoist and its corresponding embodiments, please refer to the relevant descriptions. Figure 1 The corresponding embodiments are not described here.

[0139] The various modules in the aforementioned garbage flow control device based on the hoist can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0140] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores information such as the coverage area of ​​waste on the conveyor belt of the elevator, weight per unit length, waste composition type markings, and conveyor belt linear speed. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a waste flow control method based on an elevator.

[0141] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the garbage flow control method based on the hoist in any of the above embodiments.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the garbage flow control method based on the hoist in any of the above embodiments.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the hoist-based garbage flow control method in any of the above embodiments.

[0145] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0146] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0147] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling waste flow based on a hoist, characterized in that, The method includes: Obtain the coverage area of ​​waste on the conveyor belt of the elevator, the weight per unit length, the waste composition type markings, and the conveyor belt linear speed; The coverage area, the weight per unit length, the component type label, and the conveyor belt speed are synchronized to obtain a synchronized dataset that is completely synchronized in time. Flow rate calculation is performed based on the synchronous dataset to obtain the preliminary instantaneous flow rate of waste on the conveyor belt; An adaptive Kalman filter is used to process the initial instantaneous flow rate to obtain the accurate instantaneous flow rate; Based on the precise instantaneous flow rate, the flow rate error and the rate of change of the flow rate error are obtained; Control commands are derived based on the flow error and the rate of change of the flow error; The operation of the hoist is controlled based on the control commands.

2. The method according to claim 1, characterized in that, The process of obtaining the coverage area of ​​the waste on the conveyor belt of the elevator includes: Acquire images of the waste on the conveyor belt; Gamma correction is performed on the garbage image; The gamma-corrected garbage image can be converted to the HSV color space; Edge detection is performed on the garbage image after HSV color space conversion to obtain the coverage area of ​​the garbage on the conveyor belt of the elevator.

3. The method according to claim 1, characterized in that, The process of synchronizing and aligning the coverage area, the weight per unit length, the component type label, and the conveyor belt linear speed to obtain a synchronized dataset that is fully synchronized in time includes: Add a PLC system clock marker to the coverage area, the weight per unit length, the component type marker, and the conveyor belt linear speed; Based on the sampling rate differences of the coverage area, the weight per unit length, the component type label, and the conveyor belt linear speed, a synchronous data sequence is generated using an interpolation algorithm to obtain a synchronous dataset.

4. The method according to claim 1, characterized in that, The step of calculating the flow rate based on the synchronized dataset to obtain the preliminary instantaneous flow rate of waste on the conveyor belt includes: The waste coverage density is obtained based on the coverage area and the weight per unit length. The initial instantaneous flow rate is obtained based on the garbage coverage density, the coverage area, the conveyor belt length, and the conveyor belt linear speed.

5. The method according to claim 1, characterized in that, The data is obtained based on sensors, including the coverage area of ​​waste on the conveyor belt of the elevator, weight per unit length, waste composition type markings, and conveyor belt linear speed. The preliminary instantaneous flow rate is processed using an adaptive Kalman filter to obtain a precise instantaneous flow rate, including: Construct a spatial state model; Dynamically adjust the noise covariance based on the health status of the sensors; Filtering is iteratively optimized to obtain accurate instantaneous flow rate.

6. The method according to claim 1, characterized in that, The control command obtained based on the flow error and the rate of change of the flow error includes: Configure baseline PID parameters; The current control mode is obtained based on the flow error; The final PID parameters are obtained based on the baseline PID parameters, the current control mode, the flow error, and the flow error change rate. The final PID parameters are subjected to anti-saturation processing to obtain control commands.

7. A garbage flow control device based on a hoist, characterized in that, include: Elevator; The multimodal sensor data acquisition module is used to acquire the coverage area of ​​waste on the conveyor belt of the elevator, the weight per unit length, the composition type marking of the waste, and the linear speed of the conveyor belt; The synchronous dataset acquisition module is used to perform synchronous alignment processing on the coverage area, the weight per unit length, the component type label and the conveyor belt linear speed to obtain a synchronous dataset that is completely synchronized in time. The preliminary instantaneous flow rate acquisition module is used to perform flow rate calculation based on the synchronous dataset to obtain the preliminary instantaneous flow rate of the waste on the conveyor belt; The precise instantaneous flow rate acquisition module is used to process the preliminary instantaneous flow rate using an adaptive Kalman filter to obtain the precise instantaneous flow rate; A flow error and flow error change rate acquisition module is used to obtain the flow error and flow error change rate based on the precise instantaneous flow rate; A control command acquisition module is used to obtain control commands based on the flow error and the flow error change rate; The control module is used to control the operation of the hoist based on the control commands.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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