Robot tiling control method for garbage bag breaking

Abstract

The invention relates to the technical field of garbage classified storage and conveying, and discloses a robot tiling control method for garbage bag breaking, which comprises the following steps: S1, garbage particle motion modeling: describing the distribution dynamic state of garbage particles on a conveying belt by adopting a continuity equation; s2, stress analysis of the garbage particles: modeling the stress of the garbage particles, and calculating the motion state of the garbage particles on the conveyor belt; and S3, constructing and optimizing an optimal control strategy: constructing an objective function based on a linear quadratic regulation algorithm in an optimal control theory, optimizing the distribution of the garbage particles by using a variational method, and solving the optimal control strategy. According to the invention, by adopting multi-stage classified storage, an intelligent scheduling system and an optimized spiral conveying design, high efficiency, stability and environmental protection of garbage storage and conveying are realized, and the operation efficiency and the resource utilization rate of the system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of garbage classification storage and transportation, and specifically to a robot paving control method for garbage bag breaking. Background Art

[0002] With the acceleration of the global urbanization process and the improvement of environmental protection awareness, the demand for garbage treatment technology is increasing day by day. In daily life, the types of garbage are numerous, including recyclable garbage, non-recyclable garbage, and hazardous garbage, etc. How to reasonably classify and store garbage and efficiently transport it to subsequent treatment links has become a major challenge in urban garbage treatment.

[0003] In the prior art, garbage storage and transportation are generally realized through simple storage bins or conveying devices. In terms of storage, some technologies adopt a sealed storage bin design, and through temperature and humidity control and ventilation systems, harmful gases generated during garbage stacking are reduced to prevent garbage from rotting or mildewing. In terms of garbage transportation, the prior art usually uses screw conveyors, belt conveyors or pneumatic conveying systems. These conveying devices can already handle different types of garbage particles and can reduce the loss of garbage particles to a certain extent.

[0004] Although the prior art has played a certain role in the garbage treatment process, there are still some deficiencies. First of all, the existing garbage storage methods often lack a detailed classification design, and different types of garbage are prone to cross-contamination, resulting in low recycling efficiency. Secondly, due to the large differences in the shape and properties of garbage particles, the existing conveying devices cannot be optimized according to the characteristics of different garbage particles, resulting in low efficiency and frequent blockage problems during the conveying process. In addition, the traditional garbage conveying system generally relies on manual intervention and lacks intelligent scheduling control, and cannot respond in real time to the fluctuations of garbage flow and the changes in storage status. This not only increases the complexity of manual management, but also leads to the lag of garbage treatment, reducing the overall efficiency and safety. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a robot paving control method for garbage bag breaking, which solves the problems of inaccurate classification, low conveying efficiency and insufficient environmental pollution control in the process of garbage storage and transportation in the prior art.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A robot paving control method for garbage bag breaking, including the following steps: S1. Modeling the movement of garbage particles: Using the continuity equation to describe the distribution dynamics of garbage particles on the conveyor belt; S2. Force analysis of garbage particles: By modeling the forces on garbage particles, calculate the movement state of garbage particles on the conveyor belt; S3. Construction and Optimization of Optimal Control Strategy: Based on the linear quadratic regulation algorithm in optimal control theory, construct an objective function and use the variational method to optimize the distribution of waste particles, and solve the optimal control strategy; S4. Optimization of Waste Particle Diffusion: Combine the Navier-Stokes equation and the Fokker-Planck equation in statistical mechanics to establish a waste particle diffusion equation and adjust the air flow field through the LQR control algorithm to promote the uniform distribution of waste particles; S5. Real-time Monitoring and Control: Real-time monitor the distribution of waste particles, and dynamically adjust the stretching force of the robotic arm, the air flow force of the fan, and the conveyor belt speed under the PID control algorithm according to the feedback information to achieve the uniform laying of waste particles.

[0007] Preferably, the motion modeling of the waste particles includes: Use the continuity equation to describe the dynamic changes of waste particles, and the model includes the velocity field and source term of waste particles; The source term is used to describe the process of waste particles being released from the bag-breaking point to the conveyor belt, and the source term is based on the physical properties and release process of waste particles; The motion of waste particles is affected by the combined action of gravity, conveyor belt friction, aerodynamic force, and robotic arm stretching force. The motion trajectory of the particles is calculated by establishing a Lagrangian mechanics model.

[0008] Preferably, the force analysis of the waste particles includes: Analyze the main forces acting on waste particles through the Lagrangian mechanics model, Navier-Stokes equation, Hooke's law, and Coulomb friction model. The main forces include gravity, aerodynamic force, robotic arm stretching force, and friction force; The motion of waste particles is controlled by the force equation. Among them, gravity causes waste particles to fall vertically, aerodynamic force promotes the diffusion of light waste particles, the robotic arm stretching force controls the unfolding of waste particles through the robotic arm, and friction force affects the motion of waste particles along the conveyor belt direction; Consider the mass of waste particles, conveyor belt speed, air flow rate, and friction coefficient in the force analysis.

[0009] Preferably, the construction and optimization of the optimal control strategy include: Use the Navier-Stokes equation and the Fokker-Planck equation to describe the diffusion process of waste particles; Based on the probability density of waste particles, deduce the motion and diffusion equations of waste particles, and optimize and adjust the diffusion path of waste in combination with the air flow field; Use the LQR algorithm in optimal control theory to calculate the adjustment scheme of the air flow to promote the uniform diffusion of waste particles on the conveyor belt.

[0010] Preferably, the optimization of the spread of waste particles includes: Using the Navier-Stokes equation and the Fokker-Planck equation to describe the diffusion process of waste particles; Based on the probability density of waste particles, deriving the motion and diffusion equations of waste particles, and combining with the optimization of the air flow field to adjust the diffusion path of waste; Using the LQR algorithm in the optimal control theory to calculate the adjustment scheme of the air flow to promote the uniform spread of waste particles on the conveyor belt.

[0011] Preferably, the real-time monitoring and control includes: Using sensors to monitor the distribution of waste particles on the conveyor belt in real time and calculating the actual distribution state of waste particles; Calculating the deviation between the distribution of waste particles and the target uniform distribution based on the monitoring data, and adjusting the tensile force of the robotic arm, the air flow force of the fan and the conveyor belt speed through the control system; Dynamically adjusting the robotic arm, the air flow and the conveyor belt speed to ensure that the waste particles are always in a uniform distribution state.

[0012] Preferably, the threshold of the target uniform distribution of waste includes: Setting the threshold of the distribution uniformity of waste particles, which is the maximum tolerance range of the distribution deviation of waste particles; Analyzing the real-time monitored waste particle distribution data based on the fuzzy control algorithm. When the deviation is small, only fine-tuning the conveyor belt speed is used to achieve balance. When the deviation is large, the PID control algorithm is further started to dynamically adjust the combined control strategy of the tensile force of the robotic arm, the air flow force of the fan and the conveyor belt speed; The setting of the threshold is adjusted according to the waste type and sorting requirements in the actual operating environment.

[0013] Preferably, the robotic arm control includes: The tensile force of the robotic arm is adjusted in real time through the PID control algorithm to ensure that the waste particles can be fully unfolded and reduce accumulation; through the tensile control of the robotic arm, combined with aerodynamics and conveyor belt friction, the preliminary distribution of waste particles on the conveyor belt is further optimized.

[0014] Preferably, the adjustment of the aerodynamic force includes: Adjusting the intensity and direction of the air flow through the fan to optimize the spread of waste particles on the conveyor belt; The adjustment of the air flow is coordinated with the speed of the conveyor belt and the stretching action of the robotic arm, and they act together on the waste particles to promote uniform distribution.

[0015] Preferably, the adjustment of the conveyor belt speed includes: Adjust the speed of the conveyor belt according to the real-time feedback of the distribution information of garbage particles; By controlling the speed of the conveyor belt, further control the accumulation and diffusion of garbage particles to ensure uniform distribution on the conveyor belt; the speed adjustment of the conveyor belt is coordinated with the stretching of the robotic arm and the adjustment of the air flow to achieve the best garbage distribution effect.

[0016] The present invention provides a robotic tiling control method for garbage bag breaking. It has the following beneficial effects: 1. Through the multi-level storage system and precise classification design, the present invention realizes the efficient storage of garbage particles and avoids cross-contamination between different types of garbage. Compared with the prior art that fails to distinguish storage methods, the present invention effectively solves the problems of mixed garbage storage, space waste, and unclear classification.

[0017] 2. The intelligent monitoring and automatic scheduling technology of the present invention makes the garbage storage and transportation process more intelligent and automated. Compared with the manual intervention and static scheduling in the prior art, the present invention can monitor and adjust the system in real time, improving the garbage treatment efficiency and reducing the operation difficulty.

[0018] 3. The unique optimized design of the flow rate of the screw conveyor in the present invention accurately calculates the transportation flow rate of garbage particles, ensuring the stability and efficiency of the garbage transportation process. Compared with the existing solutions, this design avoids instability and material waste during transportation, making the entire system operate more smoothly.

[0019] 4. The enclosed storage and temperature and humidity control system of the present invention effectively prevents the risks of moisture, corruption, and pollution of garbage during storage. Compared with the traditional storage method, this new design solves the environmental pollution problem in the garbage treatment process and improves the environmental protection benefits of the system. Description of the Drawings

[0020] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiments

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Please refer to the attached Figure 1 , the embodiments of the present invention provide a robotic tiling control method for garbage bag breaking, including the following steps: S1. Modeling the movement of garbage particles: Use the continuity equation to describe the dynamic distribution of garbage particles on the conveyor belt; After the garbage bag is broken, the distribution of garbage particles is not uniform, but is jointly affected by gravity, aerodynamics, the mechanical action of the conveyor belt, and the physical properties of the garbage itself. These factors make the diffusion process of garbage on the conveyor belt have a certain degree of randomness and non-linearity, and in some cases, local accumulation will occur, affecting subsequent intelligent sorting. In this embodiment, mathematical models such as the continuity equation, the garbage source term, and the particle motion equation are used to accurately describe the motion behavior of garbage and provide theoretical support for subsequent optimization control.

[0023] In the present invention, the modeling of garbage particles not only needs to consider the motion trend at the macroscopic level, but also needs to combine the distribution characteristics of microscopic particles. Generally, garbage particles can be regarded as a kind of particle fluid, and its behavior can be described by basic physical laws such as mass conservation, momentum conservation, and external force action. Therefore, in a possible implementation manner of the present invention, the continuity equation is used to describe the distribution evolution of garbage particles on the conveyor belt, and at the same time, a source term model is introduced to characterize the release law of garbage particles.

[0024] In this embodiment, the continuity equation of garbage particles is used to describe the density change of garbage on the conveyor belt. Assuming that the density distribution function of garbage particles is u(x, y, t), the mass conservation equation of garbage on the conveyor belt can be expressed as: where: u(x, y, t) is the two-dimensional density distribution function of garbage particles, with the unit of kg / m 2 ; t is time, with the unit of s; represents the spatial change rate of the garbage density field, with the unit of m / s; S(x, y, t) is the source term of garbage particles, representing the release situation of garbage on the conveyor belt, with the unit of kg / m 2 ·s.

[0025] In some embodiments, the garbage source term S(x, y, t) can be described by different mathematical models. For example, at the moment when the garbage bag breaks, the release of garbage often shows a concentrated and explosive release, and then gradually tends to be stable. Therefore, a normal distribution, a Gaussian mixture model (GMM), or an exponential decay model can be used to describe the release process of different types of garbage.

[0026] In a possible implementation manner, assuming that the release process of garbage conforms to a two-dimensional Gaussian distribution, the garbage source term can be expressed as: where: S0 is the maximum release rate of garbage, with the unit of kg / m 2· s; (x, y) are spatial coordinates in meters (m), describing the position on a two-dimensional plane; (x0, y0) is the center position of the garbage bag rupture; σ is the standard deviation of the release distribution, determining the garbage diffusion range, with the unit of m; f(t) is the time evolution function, which can be represented by the exponential decay f(t) = e -λt where λ is the decay factor of the garbage release rate, with the unit of s -1 ; t is the time variable in seconds (s) or other applicable time units, representing the time elapsed since a certain starting moment; e is the base of the natural logarithm.

[0027] In other embodiments, the release of garbage may be intermittent or random, for example: If the garbage bag is a plastic bag, due to the elastic effect of the bag body, the garbage release rate is relatively fast, and the value of λ is relatively large; If the garbage bag is a woven bag, the release of garbage particles is relatively uniform, and the value of the model parameter λ is relatively small; If the garbage bag is a closed package (such as a food box), the garbage release will show a discontinuous pattern and needs to be described by a multi-peak Gaussian distribution.

[0028] In some possible implementation manners, the movement of garbage particles on the conveyor belt is affected not only by gravity but also by factors such as aerodynamic force, conveyor belt friction force, and external force of the robotic arm. To more accurately describe the movement state of garbage particles, a motion equation needs to be introduced.

[0029] In this embodiment, it is assumed that the velocity field v(x, y, t) of the garbage particles satisfies the force balance equation: where: m is the mass of the garbage particle in kilograms (kg); is the acceleration of the garbage particle in m / s 2 ; F g = mg is the gravitational force, with the direction vertically downward; is the aerodynamic force, where: C d is the air resistance coefficient (dimensionless); ρ is the air density in kg / m 3 ; A is the frontal area of the garbage particle in m 2 ; v is the relative air flow velocity of the garbage particle in m / s.

[0030] F m = k m (x - x0) is the tensile force applied by the robotic arm, where: k m is the control gain; x0 is the initial position of the garbage.

[0031] F c = -μmg is the conveyor belt friction force, where: μ is the friction coefficient (dimensionless).

[0032] In a possible implementation, the movement trajectory of the garbage particles can be solved by numerical calculation methods, such as: The Euler method is used to calculate the movement trajectory at discrete time steps; The Runge-Kutta method is used to accurately simulate the force and motion state of the garbage particles; The finite element method (FEM) is used for numerical simulation of large-scale garbage particle distribution.

[0033] S1 describes the movement state of the garbage particles completely through multi-level mathematical modeling such as the continuity equation, source term model, and motion equation. Based on this mathematical model, it can provide data support for the subsequent garbage tiling optimization control strategy and guide the implementation of functions such as conveyor belt speed adjustment, air flow field optimization, and robotic arm intelligent adjustment.

[0034] S2. Force analysis of the garbage particles: By modeling the forces on the garbage particles, calculate the motion state of the garbage particles on the conveyor belt; In the technical solution of the present invention, after the garbage bag is broken, the force condition of the garbage particles on the conveyor belt determines their final distribution state. The force condition not only affects the diffusion range of the garbage particles but also determines whether they will form accumulations, drifts, or deviate from the conveyor path due to external disturbances. Therefore, accurately analyzing the force characteristics of the garbage particles and quantifying the force conditions of different types of garbage particles through mechanical modeling are the keys to achieving precise control and optimized distribution.

[0035] Generally, the forces on the garbage particles on the conveyor belt mainly include gravity, aerodynamic force, robotic arm force, and conveyor belt friction force. In addition, in specific application scenarios, they will also be affected by additional external force disturbances, such as: Electrostatic force: For lightweight garbage such as plastic bags and metal films, static electricity causes the garbage particles to adsorb or aggregate; Inter-particle interaction force: For wet garbage or garbage with adhesive properties, there is an additional adhesive force between its particles, which affects the distribution; Vortex force: The fan or air flow generates local vortices around the garbage particles, affecting the final landing points of the particles.

[0036] In a possible implementation of the present invention, the Lagrangian mechanical modeling method is adopted, combined with the hydrodynamic and Coulomb friction models, to analyze the motion behavior of the garbage particles from the perspective of micro-dynamics and quantify the influence of different external forces. Through this analysis, it can guide the control strategies of the robotic arm, air flow, and conveyor belt speed to achieve more refined optimization of the garbage particle distribution.

[0037] In this embodiment, the kinetic equation disclosed in S1 is used to describe the force on the garbage particles on the conveyor belt, and the motion equation of the garbage particles is established based on Newton's second law.

[0038] In a possible implementation, for lighter garbage (such as plastic sheets, shredded paper), the air resistance F a cannot be ignored and may even become the dominant force. Therefore, in some embodiments, the air resistance coefficients of different types of garbage can be measured using a wind tunnel experiment, and the calculation model can be optimized through experimental data to make the estimation of aerodynamic forces more accurate.

[0039] In some embodiments, a force sensor can be used to measure the frictional force of the garbage particles in real time, and the conveyor belt speed control can be optimized in combination with this data. For example: When the frictional force is small (such as when the garbage particles are relatively smooth), the conveyor belt speed can be reduced to reduce the sliding of the garbage particles; When the frictional force is large (such as wet garbage, heavy garbage), the conveyor belt speed can be appropriately increased to prevent the accumulation of garbage particles.

[0040] F s is the additional electrostatic force or the inter-particle interaction force, with the unit of N, which will affect the distribution state of the garbage in some cases.

[0041] In some embodiments, there is electrostatic adsorption or adhesion force between the garbage particles, which is more obvious especially in wet garbage or electrostatic plastic garbage. Such forces will cause the garbage particles to aggregate with each other, which is not conducive to uniform distribution. Therefore, in some possible implementations of the present invention, surface charge neutralization technology or appropriately changing the conveyor belt material can be adopted to reduce the influence of such additional forces.

[0042] During the implementation of the present invention, the coupling relationship between the force on the garbage particles and the velocity field also needs to be considered, which can be described by the Navier-Stokes equation: where: ρ is the density of air or fluid, with the unit of kg / m 3 , representing the mass of the fluid per unit volume; p is the fluid pressure, with the unit of Pa, describing the pressure distribution of the air on the garbage; μ is the fluid viscosity, with the unit of kg / m·s, affecting the force on the garbage particles in the air flow; is the partial derivative of the velocity with respect to time, with the unit of m / s 2 , representing the instantaneous acceleration of the garbage particles in the fluid, which is a key parameter affecting the particle motion; v is the fluid velocity vector, with the unit of m / s, representing the velocity field of the air around the garbage particles, and its direction is consistent with the air flow direction; is the gradient of the velocity field, with the unit of 1 / s, which is used to describe the change of fluid velocity in space and affects the force state of garbage particles under aerodynamic action; is the pressure gradient term, with the unit of N / m 3 , which represents the change of fluid pressure in space. Its magnitude determines the force exerted by air on garbage particles and is usually affected by factors such as wind speed, garbage shape, and air flow disturbance; is the Laplace operator of the velocity field, with the unit of 1 / s 2 , which represents the diffusion effect of the velocity field and is used to describe the viscous diffusion characteristics of air flow; F is the external force, with the unit of N / m 3 , including air resistance, gravity, electrostatic force, and other external force terms, which determine the final movement trajectory of garbage particles in the air.

[0043] In some embodiments, computational fluid dynamics (CFD) simulation can be used to optimize the force on garbage particles and adjust the air flow control strategy based on the simulation results. For example: For lightweight garbage, the fan position and wind speed can be optimized to prevent garbage particles from drifting; For heavier garbage, the fan direction can be adjusted to make it evenly distributed on the conveyor belt.

[0044] S2 completely describes the force on garbage particles on the conveyor belt through methods such as kinetic modeling, aerodynamic analysis, and friction calculation. These force analyses not only provide theoretical support for subsequent optimization control strategies but also guide key links such as robotic arm adjustment, air flow optimization, and conveyor belt speed adjustment, making the distribution of garbage particles more uniform and stable.

[0045] S3. Construction and optimization of the optimal control strategy: Based on the linear quadratic regulation algorithm in the optimal control theory, construct an objective function and use the variational method to optimize the distribution of garbage particles, and solve the optimal control strategy; In step S3, the motion state of garbage particles on the conveyor belt is affected by various factors, including gravity, aerodynamic force, friction force, robotic arm force, and particle-particle interaction force. Due to the significant differences in the shape, material, and mass of garbage particles, their motion trajectories and final distribution states on the conveyor belt are significantly different. Therefore, the core of this step is to combine the previously disclosed force analysis of garbage to further clarify the dynamic characteristics of garbage particles and optimize their distribution scheme.

[0046] Generally, lightweight garbage (such as plastic films, shredded paper) is more susceptible to aerodynamic forces and may float or get stuck on the conveyor belt. To effectively control its trajectory, the air flow distribution can be adjusted to guide the garbage particles to move along a predetermined path. For example, in the air flow control system, devices that can adjust the fan power and direction can be used to achieve differential treatment for garbage particles of different masses and shapes. In addition, local wind speed gain areas can be set so that lightweight garbage can receive a certain thrust at appropriate positions to enhance its stability.

[0047] For heavier garbage (such as glass bottles, metal sheets), friction and gravity become the dominant factors affecting its movement. In practical applications, the contact conditions between different garbage particles and the conveyor belt can easily lead to significant differences in friction. Generally speaking, garbage with a smoother surface (such as plastic sheets) has a lower friction coefficient and is prone to slipping, while garbage with a rough surface or a certain humidity (such as food residues, wet wipes) has a higher friction coefficient and will cause retention. Therefore, to optimize the transmission process, the friction coefficient can be monitored in real time, and the conveyor belt speed can be adjusted based on the friction measurement data. For example, the conveyor belt speed can be appropriately reduced in areas with low friction to prevent the garbage from slipping too fast, and the speed can be appropriately increased in areas with high friction to reduce the accumulation of garbage particles.

[0048] In addition, in certain specific situations, the electrostatic and adhesive forces between garbage particles will affect their distribution characteristics, especially when there is a dry environment or a large amount of garbage of specific materials (such as plastic films). These particles aggregate with each other due to electrostatic adsorption, which is not conducive to uniform distribution. To reduce this effect, surface charge neutralization technology can be adopted. For example, an ion generator can be installed near the conveyor belt to eliminate the static charges on the surface of the garbage particles. In addition, by optimizing the conveyor belt material and selecting antistatic materials, the electrostatic adsorption effect can be reduced, and the uniformity of garbage distribution can be improved.

[0049] During the actual operation process, the adjustment effect of the robotic arm on the garbage particles also needs to be considered. The role of the robotic arm not only affects the unfolding degree of the garbage but also changes its motion state to a certain extent. For example, for larger-sized garbage (such as cardboard boxes, plastic bottles), the robotic arm can apply appropriate forces to ensure that the garbage is fully unfolded and prevent it from blocking other garbage particles. For lightweight garbage, the acting force needs to be precisely controlled to prevent the garbage from being overly disturbed and deviating from the predetermined trajectory. In some embodiments, through intelligent control algorithms, combined with a vision recognition system, the positions of the garbage particles can be detected in real time, and the operating parameters of the robotic arm can be dynamically adjusted to optimize the overall distribution of the garbage.

[0050] By combining various methods such as aerodynamic analysis, friction characteristic evaluation, electrostatic effect control, and robotic arm action optimization, S3 achieves precise control of waste particles, making their movement states on the conveyor belt more controllable, thereby improving the efficiency of waste sorting and treatment.

[0051] S4. Optimization of waste particle diffusion: By combining the Navier-Stokes equation and the Fokker-Planck equation in statistical mechanics, establish a waste particle diffusion equation and adjust the air flow field through the LQR control algorithm to promote the uniform distribution of waste particles; In step S4, the sorting of waste particles is a key link in the entire waste treatment process, and it is necessary to ensure that different types of waste particles can be classified according to a predetermined path and transported to the corresponding treatment areas. Since there are a wide variety of waste particle types and there are significant differences in their physical properties (such as mass, size, shape, material, surface properties, etc.), in this step, it is necessary to comprehensively consider factors such as aerodynamics, mechanical sorting principles, electrostatic properties, optical recognition, etc., and combine an intelligent control system to optimize the sorting accuracy and improve the waste sorting efficiency.

[0052] Generally, waste sorting can be divided into multiple stages. First, preliminary classification is carried out through mechanical screening to separate waste with different sizes and shapes. Subsequently, air flow sorting is combined to further sort light and heavy waste, and methods such as electrostatic sorting and optical recognition are used to finely classify specific types of waste. In practical applications, the sorting methods for different wastes need to be dynamically adjusted according to their physical properties to meet different treatment requirements.

[0053] In this embodiment, a multi-stage sorting scheme is adopted, combining air flow sorting, mechanical screening, electrostatic sorting, and optical recognition to ensure the sorting accuracy of waste particles.

[0054] In the initial stage of sorting, use mechanical screening equipment to classify waste particles by size and shape. In this embodiment, mechanical screening adopts a combination of a drum screen and a vibrating screen to improve the sorting accuracy.

[0055] Generally, the drum screen separates waste particles according to different sizes through the centrifugal force and gravity of the rotating screen cylinder. The movement trajectory of waste particles inside the drum screen is mainly determined by their initial velocity, drum inclination, drum rotation speed, and friction force. In some embodiments, the movement trajectory of waste particles can be described by the following formula: Where: r is the displacement of the waste particle inside the drum screen, in m; r0 is the initial position of the waste particle when it enters the screen cylinder, in m; v0 is the initial velocity of the waste particle, in m / s; a is the resultant acceleration of the particle inside the drum screen, in m / s 2; t2 is the movement time of the garbage particles in the sieve drum, with the unit of s.

[0056] In a possible implementation, the screening of different types of garbage can be optimized by adjusting the inclination angle, rotation speed, and sieve hole size of the rotary screen. For example: For larger garbage particles (such as plastic bottles and cardboard boxes), the inclination angle of the sieve drum can be increased to make them roll to the outlet faster; For smaller garbage particles (such as metal particles and glass fragments), the rotation speed can be appropriately reduced to ensure that they are screened out when the sieve mesh aperture is suitable.

[0057] In addition, the vibrating screen is mainly used for further screening of garbage particles. Its working principle is based on high-frequency vibration to make the garbage particles move in different forms and be sorted through the sieve holes. The movement trajectory of the garbage particles on the vibrating screen is affected by the amplitude, vibration frequency, particle mass, and friction force. In this embodiment, the optimization of the vibrating screen can be achieved by experimentally determining its optimal vibration parameters.

[0058] After the preliminary mechanical screening is completed, an air separation device is used to classify the garbage particles by weight. Air separation is based on the principle of aerodynamics and separates different garbage particles using parameters such as air resistance, mass, and frontal area.

[0059] Specifically, the movement of the garbage particles in the air separation device is affected by aerodynamic forces. Lighter garbage particles (such as plastic bags and shredded paper) are more easily affected by greater air resistance. Therefore, through the action of a directional air flow, they can be transported along a predetermined trajectory to the light garbage recycling channel. Heavier garbage particles (such as metal and glass), on the other hand, have a greater inertia and are not easily pushed by the air flow, so they will continue to slide along the original path to the heavy garbage recycling channel.

[0060] As an option, the optimization of the air separation device can be based on CFD simulation technology. By analyzing the air flow distribution through a computational fluid dynamics (CFD) model, the fan power, wind speed, and diversion structure can be optimized. For example: For garbage particles such as plastic bags and paper scraps, the wind speed can be increased and a buffer area can be added at the outlet to prevent the garbage particles from scattering; For heavier garbage particles, the wind direction can be optimized to make them enter the recycling device along a shorter path.

[0061] In some embodiments, electrostatic separation technology can be used to separate different material garbage particles through an electrostatic field. This method is mainly applicable to the recycling of easily charged garbage particles such as metal and certain plastic materials.

[0062] In this embodiment, the electrostatic separation device consists of a high-voltage electrostatic field, a recovery electrode, and a diversion structure. The movement trajectory of the garbage particles in the electrostatic field is affected by the Coulomb force and can be described by the following formula: F e = qE; Where: F e is the electrostatic force, with the unit of N, which determines the movement trajectory of the waste particles; q is the electric charge carried by the waste particles, with the unit of C, which depends on the material and charged state of the waste particles; E is the electrostatic field strength, with the unit of V / m, which determines the magnitude of the electrostatic force.

[0063] In a possible implementation, the electrostatic separation effect can be optimized by adjusting parameters such as the electric field strength, the distance between the waste particles and the electrode, and the polarity of the recovery electrode. For example: For metal waste (such as aluminum, copper), a stronger electric field can be set to make it more likely to deflect to the recovery electrode; For plastic waste (such as PVC, PET), the electrode polarity can be adjusted so that it is sorted according to different polarities after being charged.

[0064] In order to further improve the sorting accuracy, in some embodiments, an optical recognition system can be combined to perform intelligent classification of waste particles through hyperspectral imaging and machine learning algorithms. In this embodiment, the optical recognition system can achieve waste classification through the following steps: Obtain the reflection spectral characteristics of the waste particles through hyperspectral imaging; Use a deep learning model (such as CNN) to identify and classify waste of different materials; Combine an actuator (such as an air nozzle, a mechanical paddle) to control the sorting path of the waste particles.

[0065] In a possible implementation, the optical recognition system can be combined with multi-channel sensors to achieve automatic classification of different types of waste such as plastics, glass, and metals. For example: Near-infrared spectroscopy can be used to distinguish different types of plastics, such as PET, PP, HDPE, etc.; Visible spectroscopy can be used to identify materials such as glass and paper to optimize the waste sorting strategy.

[0066] S4 achieves precise classification of waste particles through various methods such as mechanical screening, air separation, electrostatic separation, and optical recognition. Each sorting method can be optimized and adjusted according to the specific characteristics of the waste, and combined with an intelligent control system to achieve efficient sorting and improve the waste recovery rate and resource utilization efficiency.

[0067] S5. Real-time monitoring and control: Real-time monitor the distribution of waste particles, and dynamically adjust the tensile force of the robotic arm, the air flow force of the fan, and the conveyor belt speed under the PID control algorithm according to the feedback information to achieve uniform spreading of the waste particles; In step S5, after the waste particles are sorted, they need to go through the storage and transportation processes to ensure the efficiency and stability of the subsequent treatment process. Due to the differences in the volume, density, moisture content, and chemical composition of the waste particles, their storage methods and transportation schemes need to be optimized according to specific characteristics. The core objective of this step is to improve the safety of waste storage, optimize the energy efficiency of the transportation system, and reduce losses and pollution during the storage and transportation of waste particles. Therefore, in this step, it is necessary to combine the physical characteristics of the waste particles, reasonably plan the storage structure, and optimize the transportation parameters to ensure the efficient storage and lossless transportation of waste.

[0068] Generally, waste storage can be divided into short-term storage and long-term storage. Short-term storage is mainly used for waste to be processed. For example, recyclable waste needs to be temporarily stored after classification waiting for further processing, and hazardous waste needs to be stored in a sealed manner before treatment to prevent pollution from spreading. Long-term storage is mainly used for landfill or composting treatment, and factors such as waste degradation, compression, and leachate treatment need to be considered. Waste transportation methods can include gravity transportation, screw transportation, belt transportation, or pneumatic transportation. Different types of waste particles are suitable for different transportation modes. In specific applications, it is necessary to combine the particle size, shape, weight, and fluidity of the waste particles to determine the optimal transportation scheme.

[0069] In this embodiment, the waste storage bin adopts a closed modular structure to ensure that no pollution spreads during waste storage and keep the particle form stable. The design of the storage bin needs to meet the following requirements: Moisture-proof and sealed to prevent waste particles from absorbing moisture and caking, especially for powdery or easily degradable waste; Leakage-proof design to avoid the overflow of hazardous waste or liquid waste and affect the environment; Ventilation and temperature and humidity control to reduce the corruption and odor generation of organic waste.

[0070] In a possible implementation, the optimal volume of the waste storage bin can be calculated according to the following formula: Where: V is the volume of the storage bin, in m 3 , representing the total capacity of the storage bin; M is the total mass of the waste particles, in kg, depending on the waste treatment volume; ρ bulk is the bulk density of the waste particles, in kg / m 3 , and the bulk densities of different types of waste are different. For example: The bulk density of plastic waste is about 50 - 150 kg / m 3 ; The bulk density of metal waste can reach 500 - 800 kg / m 3 ; The bulk density of wet waste is usually between 400 - 600 kg / m 3 between.

[0071] As an option, to prevent the garbage from being affected by humidity during storage, a moisture absorption device or a low-temperature storage system can be configured in the storage bin to ensure the morphological stability of the garbage particles. For example: For paper and plastic garbage, a dry storage mode can be adopted to prevent the garbage from absorbing moisture and reducing its recycling value; For organic garbage, a low-temperature storage mode can be adopted to inhibit the reproduction of microorganisms and reduce the spoilage rate.

[0072] In some embodiments, a gas monitoring system can also be adopted to detect the oxygen concentration, methane concentration and carbon dioxide concentration inside the storage bin in real time, and control the gas emission through a negative pressure exhaust device to reduce the accumulation of harmful gases.

[0073] In this embodiment, the garbage conveying system adopts a combination of a screw conveyor, a belt conveyor and a pneumatic conveying method to meet the conveying requirements of different garbage particles. The selection of the specific conveying method depends on the physical characteristics of the garbage, such as: Garbage with larger particles and regular shapes (such as plastic bottles and glass bottles) is suitable for belt conveying; Garbage with smaller particles and higher density (such as metal particles and glass fragments) is suitable for screw conveying; Light garbage (such as plastic films and paper scraps) is suitable for pneumatic conveying.

[0074] In one possible implementation, the conveying flow rate of the screw conveyor can be calculated by the following formula: Where: Q is the conveying flow rate, in kg / s, representing the conveying capacity of the garbage particles; D is the diameter of the screw blade, in m, determining the size of the conveying channel; S is the pitch, in m, representing the interval between the screw blades; N is the rotational speed of the screw conveyor, in rpm, determining the conveying rate; ρ bulk is the bulk density of the garbage particles, in kg / m 3 ; π2 is the pi, used to calculate the area related to the circular screw blade.

[0075] As an optimization strategy, the blade shape and rotational speed of the screw conveyor can be adjusted according to the physical characteristics of the garbage particles. For example: For high-density garbage, appropriately increase the blade inclination angle to reduce blockage; For low-density garbage, reduce the rotational speed to prevent the garbage particles from being broken or drifting.

[0076] In some embodiments, a variable frequency drive system can be adopted to dynamically adjust the operating parameters of the screw conveyor according to the conveying requirements of the garbage particles and improve the conveying efficiency.

[0077] For light waste, in this embodiment, a pneumatic conveying system is adopted to push waste particles into the conveying pipeline through high-speed air flow. The optimal pressure for pneumatic conveying can be calculated by Bernoulli's equation to ensure that waste particles can be stably conveyed to the target storage area.

[0078] In this embodiment, the storage and conveying system is combined with an intelligent monitoring and automatic scheduling system to optimize the management of waste storage space and improve the operating efficiency of the conveying system. The intelligent monitoring system can real-time monitor the storage quantity, density, humidity and temperature of waste particles, and adjust the storage strategy and conveying rate based on these data.

[0079] As a possible implementation, an Internet of Things sensor network can be adopted to realize the linkage control of waste storage bins and conveying equipment. For example: When the storage bin is approaching full load, the system can automatically increase the conveying rate to avoid waste accumulation; When the humidity is too high, the system can start the dehumidification device to prevent waste caking; When the flow rate of waste particles fluctuates greatly, the conveying path can be dynamically adjusted to balance the load.

[0080] In addition, the intelligent scheduling system can combine machine learning algorithms to predict the storage demand of waste particles and optimize the storage space allocation. For example: For recyclable waste, the system can predict the recycling quantity and reasonably allocate the storage area; For hazardous waste, the system can monitor environmental indicators to ensure safe storage.

[0081] Through airtight storage, dynamic conveying and intelligent monitoring, S5 ensures that waste particles can be efficiently stored after sorting, optimizes the conveying system according to different treatment requirements, thereby reducing possible pollution during waste storage, improving the energy efficiency of the conveying system, and providing stable and efficient support for subsequent waste treatment.

[0082] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A robot paving control method for garbage bag breaking, characterized in that: The following steps are involved: S1. Modeling of garbage particle movement: The continuity equation is used to describe the distribution dynamics of garbage particles on the conveyor belt; S2. Force analysis of garbage particles: By modeling the forces acting on garbage particles, the motion state of garbage particles on the conveyor belt is calculated; S3. Construction and optimization of optimal control strategy: Based on the linear quadratic regulation algorithm in optimal control theory, the objective function is constructed and the distribution of garbage particles is optimized using the variational method to solve the optimal control strategy; S4. Optimization of garbage particle diffusion: Combine the Navier-Stokes equation and Fokker-Planck equation in statistical mechanics to establish the garbage particle diffusion equation and adjust the wind flow field through the LQR control algorithm to promote the uniform distribution of garbage particles; S5. Real-time monitoring and control: monitor the distribution of garbage particles in real time, and dynamically adjust the robot arm stretching force, fan airflow force and conveyor belt speed under the PID control algorithm according to the feedback information to achieve uniform paving of garbage particles.

2. The robot paving control method for garbage bag breaking according to claim 1 is characterized in that: The garbage particle movement modeling includes: The continuity equation is used to describe the dynamic changes of garbage particles, and the model includes the velocity field and source terms of garbage particles; The source term is used to describe the process of garbage particles being released from the bag breaking point onto the conveyor belt. The source term is based on the physical properties of the garbage particles and the release process. The movement of garbage particles is affected by gravity, conveyor belt friction, aerodynamic force and robotic arm tensile force. The movement trajectory of the particles is calculated by establishing a Lagrangian mechanics model.

3. The robot paving control method for garbage bag breaking according to claim 1 is characterized in that: The force analysis of the garbage particles includes: The main forces acting on garbage particles are analyzed by Lagrangian mechanics model, Navier-Stokes equation, Hooke's law and Coulomb friction model. The main forces include gravity, aerodynamic force, mechanical arm stretching force and friction. The movement of garbage particles is controlled by the force equation, in which gravity causes garbage particles to fall vertically, aerodynamic forces push lightweight garbage particles to spread, the mechanical arm stretching force controls the spread of garbage particles through the mechanical arm, and friction affects the movement of garbage particles along the conveyor belt. The mass of the garbage particles, the conveyor belt speed, the air velocity, and the friction coefficient are considered in the force analysis.

4. The robot paving control method for garbage bag breaking according to claim 1 is characterized in that: The optimal control strategy construction and optimization includes: The Navier-Stokes equation and Fokker-Planck equation are used to describe the diffusion process of garbage particles; Based on the probability density of garbage particles, the movement and diffusion equations of garbage particles are derived, and the diffusion path of garbage is optimized and adjusted in combination with the wind flow field; The LQR algorithm in optimal control theory is used to calculate the air flow adjustment plan to promote the uniform diffusion of garbage particles on the conveyor belt.

5. The robot paving control method for garbage bag breaking according to claim 1 is characterized in that: The garbage particle diffusion optimization includes: The Navier-Stokes equation and Fokker-Planck equation are used to describe the diffusion process of garbage particles; Based on the probability density of garbage particles, the movement and diffusion equations of garbage particles are derived, and the diffusion path of garbage is optimized and adjusted in combination with the wind flow field; The LQR algorithm in optimal control theory is used to calculate the air flow adjustment plan to promote the uniform diffusion of garbage particles on the conveyor belt.

6. The robot paving control method for garbage bag breaking according to claim 1, characterized in that: The real-time monitoring and control includes: Use sensors to monitor the distribution of garbage particles on the conveyor belt in real time and calculate the actual distribution status of garbage particles; Calculate the deviation between the garbage particle distribution and the target uniform distribution based on the monitoring data, and adjust the robot arm stretching force, fan airflow force and conveyor belt speed through the control system; Dynamically adjust the robot arm, airflow and conveyor belt speed to ensure that garbage particles are always evenly distributed.

7. The robot paving control method for garbage bag breaking according to claim 1, characterized in that: The target uniformity distribution thresholds of the garbage include: Set the distribution uniformity threshold of garbage particles, which is the maximum tolerance range of garbage particle distribution deviation; The real-time monitored garbage particle distribution data is analyzed based on the fuzzy control algorithm. When the deviation is small, the balance is achieved by only fine-tuning the conveyor belt speed. When the deviation is large, the PID control algorithm is further activated to dynamically adjust the combined control strategy of the tensile force of the robot arm, the fan airflow force and the conveyor belt speed. The setting of the threshold is adjusted according to the garbage type and sorting requirements in the actual operating environment.

8. The robot paving control method for garbage bag breaking according to claim 3 is characterized in that: The robotic arm control includes: The stretching force of the robotic arm is adjusted in real time through the PID control algorithm to ensure that the garbage particles can be fully spread out and reduce accumulation; Through the stretching control of the robotic arm, combined with aerodynamics and conveyor belt friction, the initial distribution of garbage particles on the conveyor belt is further optimized.

9. The robot paving control method for garbage bag breaking according to claim 8, characterized in that: The regulation of the aerodynamic force comprises: The intensity and direction of the air flow are adjusted by fans to optimize the dispersion of waste particles on the conveyor belt; The regulation of the airflow is coordinated with the speed of the conveyor belt and the stretching action of the robotic arm, which together act on the waste particles to promote even distribution.

10. The robot paving control method for garbage bag breaking according to claim 6, characterized in that: The conveyor belt speed adjustment includes: Adjust the conveyor belt speed based on real-time feedback of garbage particle distribution information; By controlling the conveyor belt speed, the accumulation and diffusion of garbage particles can be further controlled to ensure uniform distribution on the conveyor belt; The speed adjustment of the conveyor belt is coordinated with the extension of the robot arm and the regulation of the airflow to achieve the best garbage distribution effect.

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