Automatic processing system and method for scrapped ammeters

Abstract

The invention relates to the technical field of resource recovery and waste treatment, and discloses an automatic treatment system for a scrapped ammeter, and the system comprises an ammeter recognition and modeling module which is used for carrying out the structure scanning of the scrapped ammeter, recognizing the spatial layout and connection relation of the internal components of the ammeter, and building a topology model representing the connection between the components; the multi-target optimization module is used for carrying out optimization calculation on the disassembly path according to target requirements of efficiency, energy consumption and resource recovery rate and generating an optimal path, the invention further provides an automatic processing method of the scrapped ammeter, and the method comprises the following steps: identifying and modeling the ammeter, scanning the scrapped ammeter to identify the spatial layout and the connection relation of the components, and carrying out automatic processing on the scrapped ammeter; and establishing a topological model. Through the combination of multi-objective optimization, dynamic topology updating and a real-time feedback mechanism, the efficiency and flexibility of scrapped ammeter processing and the comprehensive improvement of the resource recovery rate are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of resource recovery and waste treatment, and particularly to an automatic processing system and method for scrapped electric meters. Background Art

[0002] In modern society, as an important device for energy metering, the scrapping quantity of electric meters increases year by year with equipment updates and technological progress. Scrapped electric meters contain recyclable resources such as metals, plastics, and precious metals, and may also contain potential harmful substances, such as toxic components in batteries or circuit boards. Therefore, scientifically and reasonably treating scrapped electric meters can not only improve resource utilization rates but also effectively reduce environmental pollution.

[0003] In the prior art, the treatment methods for scrapped electric meters mainly rely on manual disassembly or fixed mechanical equipment. Manual disassembly has a certain degree of flexibility and can adjust operation steps according to the complex structure of electric meters, being applicable to various types of electric meters. However, the manual treatment method has low efficiency, and resource losses may occur due to human errors. The use of fixed mechanical equipment improves the disassembly efficiency and can quickly separate and classify batch electric meters, meeting the requirements of large-scale treatment. In addition, due to the standardized process, fixed mechanical equipment reduces manual intervention and lowers safety risks during the treatment process.

[0004] Although the prior art has certain effects, there are still obvious deficiencies. Firstly, the mechanical disassembly equipment with a fixed path has poor flexibility and cannot effectively adapt to the complex and diverse structures of electric meters, resulting in limited treatment effects. Secondly, the disassembly method optimized for a single target fails to comprehensively balance disassembly time, energy consumption, and resource recovery rates, easily damaging other indicators while improving a certain indicator and being difficult to achieve overall optimization. Finally, the prior art lacks a dynamic feedback mechanism, and data such as actual energy consumption and recovery rates during the disassembly process cannot be transmitted back in a timely manner for optimization and adjustment. The treatment system lacks self-adaptive capabilities and is difficult to cope with real-time changing operation requirements. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides an automatic processing system and method for scrapped electric meters, solving the problems of insufficient flexibility, single optimization, and lack of real-time feedback in the prior art for scrapped electric meter processing systems.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An automatic processing system for scrapped electric meters, comprising:

[0007] An electric meter identification and modeling module, configured to perform structural scanning on the scrapped electric meter, identify the spatial layout and connection relationships of the internal components of the electric meter, and establish a topological model representing the connections between the components;

[0008] The multi-objective optimization module is used to optimize and calculate the disassembly path according to the objective requirements of efficiency, energy consumption, and resource recovery rate, and generate the optimal path;

[0009] The dynamic topology update module is used to update the topology model in real time according to the structural changes after the component is removed during the component disassembly process;

[0010] The path planning and execution module is used to control the manipulator and auxiliary equipment to perform disassembly operations based on the optimized path instructions;

[0011] The data acquisition and feedback module is used to collect the time, energy consumption, and recovery rate data during the disassembly process, and feedback the data to the multi-objective optimization module to adjust the optimization parameters.

[0012] Preferably, the meter identification and modeling module includes:

[0013] The AI vision scanning device is used to perform three-dimensional structure scanning on the meter to identify the spatial positions of the shell, circuit board, and battery;

[0014] The model construction unit is used to construct the topology model of the meter components based on the scanning results, with nodes representing components and edges representing the connection relationships between components;

[0015] The parameter measurement module is used to measure the physical parameters of the meter components, including material types and connection methods, and assign initial weights to the connection relationships in the topology model.

[0016] Preferably, the multi-objective optimization module includes:

[0017] The objective function design unit is used to generate optimization objectives according to the disassembly time, energy consumption, and resource recovery rate objectives;

[0018] The Pareto optimization unit is used to calculate multiple optimization solutions based on the non-dominated sorting genetic algorithm and output the Pareto front solution set;

[0019] The weight adjustment unit is used to adjust the weight ratio of the disassembly time, energy consumption, and resource recovery objectives according to actual needs.

[0020] Preferably, the dynamic topology update module includes:

[0021] The component removal unit is used to remove the corresponding nodes and related connection relationships in the topology model after the disassembly of a certain component is completed;

[0022] The weight update unit is used to dynamically update the connection relationship weights in the topology model based on the real-time collected disassembly time and energy consumption data.

[0023] Preferably, the path planning and execution module includes:

[0024] A dynamic programming unit for calculating an optimal disassembly path based on the current topological model;

[0025] An execution control unit for controlling a robotic arm and auxiliary equipment to complete the disassembly operation according to the optimal path, including shell separation, battery removal, and component classification;

[0026] A safety monitoring unit for monitoring the force applied and heating energy consumption during the disassembly process by the robotic arm, and stopping the operation when the safety threshold is exceeded.

[0027] Preferably, the data acquisition and feedback module includes:

[0028] A data acquisition unit for real-time acquisition of time, energy consumption, and recovery rate data generated during the disassembly process;

[0029] A feedback regulation unit for adjusting the optimization target weight ratio of the multi-objective optimization module according to the acquired data and recalculating the disassembly path.

[0030] The present invention also provides an automatic processing method for scrapped electric meters, including the following steps:

[0031] Electric meter identification and modeling, scanning the scrapped electric meter to identify the spatial layout and connection relationship of components, and establishing a topological model;

[0032] Multi-objective optimization, performing optimization calculations on the disassembly path to generate an optimal path meeting the requirements of efficiency, energy consumption, and resource recovery rate;

[0033] Dynamic topology update, real-time updating the topological model according to the structural changes after component removal during the component disassembly process;

[0034] Path planning and execution, controlling the robotic arm to complete the disassembly operation based on the optimal path;

[0035] Data acquisition and feedback, real-time acquisition of time, energy consumption, and recovery rate data generated during the disassembly process and feedback for adjusting the optimization target.

[0036] Preferably, the electric meter identification and modeling includes:

[0037] Using an AI vision scanning device to perform three-dimensional scanning on the electric meter to identify the spatial positions of the shell, circuit board, and battery;

[0038] Constructing a topological model of components based on the scanning results, where the nodes of the topological model represent components and the edges represent the connection relationships between components;

[0039] Assigning an initial weight to the connection relationship, and the weight is calculated by combining disassembly time, energy consumption, and recovery rate parameters.

[0040] Preferably, the multi-objective optimization includes:

[0041] Generate an optimization objective based on the disassembly time, energy consumption, and resource recovery rate targets;

[0042] Use the non-dominated sorting genetic algorithm to calculate the optimization objective and generate a Pareto front solution set;

[0043] Output the optimal path that meets the multi-objective requirements for use in subsequent steps.

[0044] Preferably, the path planning and execution include:

[0045] Calculate the current optimal disassembly path using the dynamic programming method based on the current topological model;

[0046] Control the robotic arm to complete the operations of shell separation, battery removal, and component classification according to the optimal path;

[0047] During the disassembly process, monitor the power and heating energy consumption of the robotic arm in real time, and adjust or stop the operation when it exceeds the threshold.

[0048] The present invention provides an automatic processing system and method for scrapped electric meters. It has the following beneficial effects:

[0049] 1. By combining dynamic topology update and multi-objective optimization technologies, the present invention can adjust the disassembly path in real time, enabling the system to flexibly respond under different electric meter structures. Compared with the traditional method with a fixed path, the present invention effectively reduces resource waste and improves the processing efficiency.

[0050] 2. Adopt a data acquisition and feedback module to obtain key data such as time, energy consumption, and recovery rate during the disassembly process in real time. The present invention dynamically adjusts the optimization parameters and path planning to ensure that the disassembly operation always maintains an optimal state. Compared with the existing methods lacking real-time feedback, the present invention greatly improves the adaptive ability and accuracy of the system.

[0051] 3. The present invention adopts the non-dominated sorting genetic algorithm in path planning, comprehensively balances the disassembly time, energy consumption, and resource recovery rate, and provides a more accurate optimization scheme. At the same time, combined with the dynamic programming algorithm to execute path selection, the optimization effect far exceeds the existing technologies with a single objective, solving the problems of insufficient resource utilization and low efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the system structure diagram of the present invention;

[0053] Figure 2 It is the module architecture diagram of the electric meter identification and modeling module of the present invention;

[0054] Figure 3 It is the module architecture diagram of the multi-objective optimization module of the present invention;

[0055] Figure 4 This is the module architecture diagram of the path planning and execution module of the present invention;

[0056] Figure 5 This is the module architecture diagram of the data acquisition and feedback module of the present invention;

[0057] Figure 6 This is the method flow chart of the present invention. Specific embodiments

[0058] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] Please refer to the attached Figure 1 attachment Figure 5 , the embodiments of the present invention provide an automatic processing system for scrapped electric meters, including:

[0060] An electric meter identification and modeling module, which is used to perform a structural scan on the scrapped electric meter, identify the spatial layout and connection relationship of the internal components of the electric meter, and establish a topological model representing the connection between the components;

[0061] Through the electric meter identification and modeling module of the present invention, the topological structure modeling of the scrapped electric meter is completed, providing data support for multi-objective optimization and dynamic path planning. Generally, the structure of the electric meter is complex and diverse, and there are significant differences in the number and layout of components of different models of electric meters. Therefore, the design of this module fully considers the adaptability of different electric meter models.

[0062] In this embodiment, the electric meter identification and modeling module consists of the following sub-units, including an AI vision scanning device, a model construction unit, and a parameter measurement module;

[0063] In this module, the AI vision scanning device is used to obtain the three-dimensional structure information of the scrapped electric meter. Generally, the spatial layout of components such as the outer shell, circuit board, and battery of the electric meter needs to be completely captured through multi-angle scanning.

[0064] As an option, the scanning device can adopt the form of a multi-axis robotic arm equipped with a camera, gradually move the camera to take images of multiple angles of the electric meter, and splice and depth-process these images to form a complete three-dimensional model.

[0065] Specifically, the original images captured by the camera are analyzed through deep learning algorithms. Using a trained convolutional neural network (CNN) model, the boundaries of different components in the electricity meter are identified. For example, the demarcation point between the outer shell and the circuit board can be extracted through edge detection technology, and the contour of the battery can be identified through color differences.

[0066] In one possible implementation, the resolution of the camera and the accuracy of the scanning angle jointly affect the accuracy of the scanning result. For example, when the accuracy of the scanning angle is set to 1°, it can ensure the complete presentation of the complex internal structure of the electricity meter.

[0067] After analyzing the 3D image, the model construction unit converts the physical structure of the electricity meter into a topological model. The topological model is represented by a directed graph G=(V, E), where: V represents the set of components of the electricity meter, and each node v i ∈V represents a specific component, such as the outer shell, battery, circuit board, etc.; E represents the connection relationship between components, and each edge e ij ∈E represents the connection from component v i to component v j

[0068] As an option, the weight of node v j can represent the resource recovery value of the component. This value is determined through material classification and quantitative analysis. For example, the recovery value of the circuit board can be determined by estimating the metal content, and the weight of the outer shell is determined by the plastic recovery rate.

[0069] Specifically, the weight w ij of edge e ij reflects the disassembly cost from v i to v j . This cost is calculated by combining multiple parameters, and the formula is as follows:

[0070] w ij =αT ij +βE ij -γR ij

[0071] where: T ij represents the time cost of disassembling v i and v j , in seconds; E ij represents the energy consumption of the equipment during the disassembly process, in joules; R ij represents the resource recovery value obtained after disassembly, in currency value or resource proportion; α, β, and γ are the weight coefficients of time, energy consumption, and recovery rate respectively, satisfying α + β + γ = 1.

[0072] In some embodiments, the time cost T ijIt can be measured through experiments. For example, the time for the robotic arm to disassemble the outer shell and the battery is approximately 2.5 seconds. And the energy consumption E ij can be calculated by the power of the device and the operation duration. For example, if the power of the robotic arm is 100 watts and the disassembly duration is 3 seconds, then E ij = 100 × 3 = 300 joules.

[0073] In this module, the parameter measurement module is used to provide necessary physical data support for the topological model. Generally, the physical parameters of components (such as material type, connection strength) have an important impact on the selection of disassembly paths.

[0074] As an option, the measurement module can complete data acquisition through a variety of sensors. Specifically, an infrared sensor can detect the material characteristics of components, such as the distinction between plastic and metal; a pressure sensor can measure the connection strength between components, such as the separation force of the outer shell buckle. The data collected by the sensors is used to correct the weights of the edges in the topological model.

[0075] In some embodiments, the measurement result of the connection strength can be determined by the following formula:

[0076] F connect = k·Δx

[0077] Where: F connect represents the connection strength, with the unit of Newton; k represents the elastic coefficient at the connection, with the unit of Newton / meter; Δx represents the deformation amount during component separation, with the unit of meter.

[0078] For example, when the elastic coefficient k = 50 N / m, the separation strength of the outer shell buckle is F connect = 50 × 0.01 = 0.5 N.

[0079] The output of this module directly affects the input of the multi-objective optimization module. The nodes and edge weights in the topological model provide basic data for path selection in the optimization module. In some embodiments, if the scanned type of electric meter is an electronic electric meter, then the model may contain more welded connection edges and the weights may be higher; if it is a mechanical electric meter, the connection methods in the model may be more snap connections and the weights are lower.

[0080] This module lays a foundation for the subsequent optimization of the disassembly path by achieving high-precision modeling of the electric meter structure;

[0081] The multi-objective optimization module is used to perform optimization calculations on the disassembly path according to the objective requirements of efficiency, energy consumption, and resource recovery rate, and generate the optimal path;

[0082] Based on the relationships between nodes and edges in the topological model, the multi-objective optimization module considers three main optimization objectives: time, energy consumption, and resource recovery rate. Generally, there are conflicts among these objectives. For example, increasing the resource recovery rate may increase the disassembly time, while shortening the disassembly time may lead to an increase in energy consumption. Therefore, the multi-objective optimization module generates a Pareto front solution set through the non-dominated sorting genetic algorithm (NSGA-II), providing multiple balanced solutions for the subsequent path planning and execution module. The input of this module is the topological model generated by the electricity meter identification and modeling module, and the output is the set of optimal disassembly paths.

[0083] In this embodiment, the multi-objective optimization module mainly includes an objective function design unit, a Pareto optimization unit, and a weight adjustment unit.

[0084] In this embodiment, the objective function design unit defines three optimization objectives according to system requirements: minimizing disassembly time, minimizing energy consumption, and maximizing resource recovery rate.

[0085] Generally, the disassembly time affects the processing efficiency, so its optimization objective is to minimize it. The time cost T ij represents the time required for disassembly from component v i to v j , with the unit of seconds. In a possible implementation, the time for the robotic arm to disassemble different components is measured through experiments. For example, the separation time between the housing and the battery is T ij = 2.5s.

[0086] Energy consumption is another important cost indicator for disassembly operations, and its optimization objective is also to minimize it. The energy consumption E ij represents the energy consumption of the robotic arm or auxiliary equipment during disassembly, with the unit of joules. Specifically, the energy consumption can be calculated by the device power P and the operation duration t:

[0087] E ij = P·t

[0088] where: E ij is the energy consumption, with the unit of joules; P is the device power, with the unit of watts; t is the disassembly time, with the unit of seconds.

[0089] As an option, if the device power is 100 watts and the operation duration is 3 seconds, then E ij = 100×3 = 300J.

[0090] The resource recovery rate represents the proportion of recyclable resources after disassembly, and its optimization objective is to maximize it. The resource recovery value R ij is estimated through the material classification and weight of components. For example, the content of precious metals in the circuit board is relatively high, and its recovery value R ij may be higher than that of the plastic housing.

[0091] In this embodiment, the Pareto optimization unit calculates the optimal solution set through the non-dominated sorting genetic algorithm (NSGA-II).

[0092] Specifically, the optimization process includes the following steps:

[0093] In the first step, the population is initialized. Each individual represents a disassembly path. The representation of the path is based on the node order in the topological model. For example, the path P = {v 1 , v 2 , v 3} means disassembling the nodes v 1 , v 2 , v 3 in sequence.

[0094] In the second step, the fitness of each individual is calculated. The fitness function consists of three objective functions, which respectively calculate the total time, total energy consumption, and total resource recovery rate of the path:

[0095]

[0096] Among them: T total is the total disassembly time of the path, in seconds; E total is the total energy consumption of the path, in joules; R recovered is the total resource recovery value of the path.

[0097] In the third step, non-dominated sorting is performed. By comparing the individuals in the population, the Pareto front solution set is generated. A solution x is considered non-dominated if and only if there does not exist another solution y such that F k (y) ≤ F k (x) and there exists F k (y) ≤ F k (x), where F(x) = [T total , E total , -R recovered .

[0098] In the fourth step, the population is updated. Cross-over and mutation operations are performed on the population to generate new individuals. For example, in the path cross-over operation, new paths can be generated by swapping two path segments.

[0099] In the fifth step, the Pareto front solution set is output. Each solution in the solution set represents a possible disassembly path, and the user can select a specific solution according to actual needs.

[0100] In this module, the weight adjustment unit dynamically adjusts the priority of the optimization objectives according to actual needs.

[0101] As an option, when dealing with electronic meters with complex structures, the weight α of the time cost can be increased to ensure the overall efficiency; for meters with higher resource value, the proportion of γ can be appropriately increased to prioritize the recovery of high-value components.

[0102] Specifically, the weight adjustment is achieved through the following formula:

[0103]

[0104] Where: W T ,W E ,W R represent the relative importance of time, energy consumption, and resource recovery rate respectively; α, β, γ are normalized weight values.

[0105] In a possible implementation, if the importance of time, energy consumption, and resource recovery rate are 40, 30, and 30 respectively, then α = 0.4, β = 0.3, γ = 0.3.

[0106] This module directly calls the topology model generated by the meter identification and modeling module, and outputs the optimized path solution set to the path planning and execution module. In some embodiments, if the number of nodes in the input model is large, the Pareto solution set may contain more alternative solutions.

[0107] This module combines the multi-objective optimization theory and the Pareto front method to realize the intelligent optimization of the disassembly path of scrapped meters;

[0108] The dynamic topology update module is used to update the topology model in real time according to the structural changes after component removal during the component disassembly process;

[0109] The dynamic topology update module is used to update the topology model of the meter in real time to ensure that the model is consistent with the actual physical state. As the disassembly process progresses, the removal of components will cause changes in the connection relationship. If the model is not updated in time, it may affect the optimization and execution of the disassembly path. Therefore, this module dynamically adjusts the topology structure, including the removal of nodes and edges and the recalculation of weights. The input of this module comes from the initial topology model of the previous multi-objective optimization module, and the output is the dynamically adjusted topology model, which is provided to the path planning and execution module.

[0110] In this embodiment, the dynamic topology update module mainly includes a component removal unit and a weight correction unit.

[0111] In this embodiment, the component removal unit is used to remove the disassembled component nodes and their related connection relationships in real time.

[0112] Generally, the topological model of the electricity meter is represented by a directed graph G = (V, E), where V is the set of nodes, representing each component of the electricity meter; E is the set of edges, indicating the connection relationships between components. Whenever a component is disassembled, the corresponding node needs to be removed from the set V, and all the connecting edges of this node are deleted simultaneously.

[0113] As an option, when the component v i ∈V is disassembled, all its in-edges and out-edges need to be removed, that is:

[0114] G′ = (V - {v i , E - {e ij , e ji})

[0115] Where: G′ is the updated topological model; V - {v i} means removing the component node v from the set of nodes i ; E - {e ij , e ji} means removing the connections related to v i from the set of edges.

[0116] Specifically, if the robotic arm successfully removes the battery component during a certain operation, the node v battery corresponding to the battery and its related edges (such as the connection edge from the housing to the battery, the connection edge from the PCB to the battery) will be deleted from the topological model simultaneously.

[0117] In a possible implementation, the trigger signal for component removal is sent by the robotic arm after completing the disassembly, and the model update is automatically executed through the control logic within the system.

[0118] In this embodiment, the weight correction unit is responsible for dynamically adjusting the weights of the remaining connection relationships in the topological model. Generally, data such as the actual time and energy consumption of the disassembly operation may differ from the initial estimate, so it is necessary to recalculate the edge weights based on real-time feedback.

[0119] As an option, the correction formula for the edge weight w ij is:

[0120] w′ ij = αT′ ij + βE′ ij - γR′ ij

[0121] Where: w′ ij is the updated weight; T′ ij is the actually measured disassembly time, in seconds; E′ ij is the actually measured disassembly energy consumption, in joules; R′ ijIt is the corrected resource recovery value, with the unit being currency value or resource proportion; α, β, and γ are weight coefficients, satisfying α + β + γ = 1.

[0122] Specifically, when the robotic arm disassembles a certain welding joint, the disassembly time T′ ij can be directly measured by recording the operation duration of the device; the disassembly energy consumption E′ ij is calculated by the product of the device power and the operation duration. For example, if the device power is 200 watts and the operation duration is 4 seconds, then E′ ij = 200 × 4 = 800 J.

[0123] In a possible implementation, the resource recovery value R′ ij is corrected based on the material recovery rate measured in real time. For example, if it is found in actual disassembly that the purity of a certain metal component is reduced due to contamination, its recovery value R′ ij will be correspondingly reduced.

[0124] In some embodiments, the weight correction unit can also adjust the weight coefficients according to the priorities set by the system. For example, when the system is in the high-efficiency mode, the time weight α will increase; when the system is in the energy-saving mode, the energy consumption weight β will increase.

[0125] This module is closely associated with the path planning and execution module. The dynamically updated topological model G′ will be directly used for the next path planning. Generally, the reduction of nodes and edges will narrow the search space, thereby improving the efficiency of path planning.

[0126] In some embodiments, if the input topological model of the previous module contains complex welding structures, the update process of the edge weights may require more real-time feedback data support. These data are provided by the execution results of the path planning and execution module.

[0127] The dynamic topology update module ensures the dynamic consistency and optimization accuracy of the topological model through component removal and weight correction.

[0128] The path planning and execution module is used to control the robotic arm and auxiliary equipment to perform disassembly operations based on the optimized path instructions;

[0129] In this embodiment, the dynamic programming unit is used to calculate the current optimal disassembly path from the topological model output by the dynamic topology update module.

[0130] Specifically, the dynamic programming is based on the current topological model G′ = (V′, E′), and generates the optimal path from the starting point to the ending point by recursively calculating the minimum disassembly cost between each node.

[0131] Generally, the optimal sub-path between nodes satisfies the following recursive relationship:

[0132] C(i,j) = min k∈V′ {C(i,k) + w kj}

[0133] Where: C(i,j) represents the minimum disassembly cost from node i to node j; k is an intermediate node; w kj is the weight of edge e kj , reflecting the disassembly cost from node k to node j.

[0134] As an option, the initial condition of the recursion is set to C(i,i) = 0, that is, the disassembly cost from a node to itself is zero; if there is no direct connection between nodes, then C(i,j) = ∞ is set.

[0135] In some embodiments, if the starting point is the housing node v shell , and the ending point is the core component v core , the system will preferentially select the solution with the lowest total path weight as the current disassembly path.

[0136] In this embodiment, the execution control unit is responsible for converting the optimal path calculated by the dynamic programming unit into specific disassembly operation instructions.

[0137] Generally, the disassembly operations include various tasks such as housing separation, battery removal, circuit board separation, etc. Different disassembly tasks correspond to different device actions. For example:

[0138] Housing separation: Cut the housing along a predetermined path through a cutting device;

[0139] Battery removal: Securely extract the battery through a robotic arm clamping device;

[0140] Circuit board separation: Separate by melting the solder joints through a heating device.

[0141] As an option, the control unit will gradually execute the disassembly tasks according to the node order in the path. Specifically, if the optimal path is P = {v shell , v battery , v PCB}, the system will sequentially execute the housing separation, battery removal, and circuit board separation operations.

[0142] In a possible implementation, the execution control unit adopts real-time position calibration technology to ensure the operation accuracy of the robotic arm. The motion parameters (such as displacement, angle, speed) of the robotic arm are determined by the node positions and connection methods in the path. For example, if the distance between node v battery and node v PCB is 10 centimeters, the movement path of the robotic arm will be planned according to this distance.

[0143] In this embodiment, the safety monitoring unit is used to monitor the mechanical parameters and thermal parameters during the disassembly process in real time to ensure the safety and accuracy of the operation.

[0144] Generally, the force exerted by the robotic arm during disassembly needs to be less than the maximum bearing capacity of the component. As an option, the safety monitoring unit detects the acting force F of the robotic arm in real time through a force sensor applied , and compares it with the maximum bearing capacity F max of the component. When it is detected that F applied > F max , the system will immediately stop the current operation.

[0145] Specifically, if in a certain operation F applied = 5N, while the maximum bearing capacity F max of the component is 4N, the system will trigger an alarm and suspend the task.

[0146] During the disassembly of the welding point, the monitoring of thermal parameters is equally important. The safety monitoring unit detects the actual temperature T of the welding point through a temperature sensor actual , and ensures that it does not exceed the melting threshold T threshold of the material. In some embodiments, if T actual > T threshold , the system will reduce the power of the heating device to avoid overheating.

[0147] The input of this module is the latest topological model G' output by the dynamic topology update module and the solution set provided by the multi-objective optimization module. The dynamic programming unit preferentially selects the path with the lowest cost in the solution set for planning, and the disassembly order of the execution control unit corresponds one by one to the node order in the path.

[0148] In some embodiments, if the connection relationship between nodes changes due to the special properties of the component (such as an increase in adhesive strength), the real-time data of the safety monitoring unit will be fed back to the dynamic programming unit to recalculate the path.

[0149] The path planning and execution module ensures the safety and accuracy of the disassembly task through the efficient path selection of the dynamic programming algorithm and the precise operation of the execution control unit.

[0150] The data acquisition and feedback module is used to collect the time, energy consumption and recovery rate data during the disassembly process, and feed the data back to the multi-objective optimization module to adjust the optimization parameters;

[0151] The data acquisition and feedback module is responsible for real-time acquisition of key data during the disassembly process, including important parameters such as time, energy consumption, and resource recovery rate. These data are transmitted to the multi-objective optimization module and the dynamic topology update module through a feedback mechanism for correcting the weight allocation of the optimization path and subsequent path selection. Generally, various parameters during the disassembly process are affected by equipment performance, material characteristics, and environmental factors. Therefore, real-time acquisition and feedback of data can significantly improve the adaptability and intelligence level of the system.

[0152] In this embodiment, the data acquisition and feedback module mainly includes a data acquisition unit and a feedback control unit.

[0153] In this embodiment, the data acquisition unit is used to monitor in real time the key parameters generated during the disassembly process. Generally, the collected parameters include disassembly time, equipment energy consumption, and material recovery rate. These parameters are collected collaboratively by a variety of sensors and measuring devices.

[0154] Specifically, the disassembly time is calculated from the start and end time intervals of the operation control signal. For example, when the robotic arm executes a certain disassembly task, its start signal t start and completion signal t end are recorded, then the actual disassembly time of the task is:

[0155] T actual = t end - t start

[0156] Where: T actual is the actual disassembly time, in seconds; t start is the start time of the operation; t end is the end time of the operation.

[0157] As an option, the time recording can be directly completed by the built-in clock module of the system without additional external devices.

[0158] The equipment energy consumption is measured by an energy metering device, and the specific calculation formula is:

[0159] E actual = P·T actual

[0160] Where: E actual is the actual energy consumption, in joules; P is the equipment power, in watts; T actual is the disassembly time, in seconds.

[0161] In a possible implementation, if the rated power of the equipment is 200 watts and the actual operation time of a certain task is 4 seconds, then the actual energy consumption of the equipment is:

[0162] Eactual = 200·4 = 800 J

[0163] The collection of the resource recovery rate is based on the quality or value of the material recovery. Generally, a weight sensor is used to measure the mass M of the separated material recovered , the resource recovery rate R actual can be calculated according to the following formula:

[0164]

[0165] Where: R actual is the resource recovery rate, in percentage; M recovered is the actual mass of the recovered material, in kilograms; M total is the total mass of the component, in kilograms.

[0166] For example, for a certain component, its total mass is 5 kilograms, and the mass of the recovered metal material is 3 kilograms, then the resource recovery rate is:

[0167]

[0168] Where: R actual is the resource recovery rate; 3 is the actual mass M of the recovered material recovered ; 5 is the total mass M of the component total ; 100%: the percentage conversion factor;

[0169] In some embodiments, the system can achieve more complex parameter collection through different types of sensors. For example, monitoring the temperature change during the soldering point heating through a temperature sensor, or using an infrared sensor to judge the contact state during the material separation.

[0170] In this embodiment, the feedback control unit adjusts the target weights of the multi-objective optimization module and the edge weights of the dynamic topology update module according to the actual parameters provided by the data collection unit.

[0171] As an option, when the actual disassembly time T actual significantly deviates from the initial estimated value T estimated , the feedback control unit will correct the weight allocation. For example:

[0172]

[0173] Where: α′ is the corrected time weight; α is the initial time weight; T estimated is the initial estimated time; T actual is the actual disassembly time.

[0174] In a possible implementation, if the initial time weight is 0.4, the estimated time is 2 seconds, and the actual time is 4 seconds, then the corrected time weight is:

[0175]

[0176] The feedback control unit also adjusts the edge weights. For example, for the actual energy consumption E actual significantly higher than the estimated energy consumption E estimated the weight w of the edge ij will increase to reduce the selection priority of subsequent paths. The correction formula is:

[0177]

[0178] where: w′ ij is the corrected edge weight; w ij is the initial edge weight; E estimated is the estimated energy consumption; E actual is the actual energy consumption;

[0179] β is the energy consumption weight.

[0180] For example, if the initial weight of a certain edge is 2, the estimated energy consumption is 100 joules, the actual energy consumption is 150 joules, and the energy consumption weight is 0.3, then the corrected edge weight is:

[0181]

[0182] In some embodiments, the feedback control unit can also transmit the weight adjustment result to the dynamic topology update module in real time to update the weight distribution of the entire model.

[0183] The output of this module directly affects the weight adjustment of the multi-objective optimization module and corrects the basis for subsequent path selection through the dynamic topology update module. For example, when the actual disassembly time of a certain component is much higher than expected, the feedback control unit will notify the dynamic programming unit to recalculate the optimal path to avoid the selection of similar high-cost paths.

[0184] The data acquisition and feedback module enhances the adaptability of the system to environmental and operational changes through the combination of real-time acquisition and dynamic adjustment. The key parameters it acquires (time, energy consumption, recovery rate) provide data support for optimizing the path and model correction, while the feedback mechanism further ensures the stability and efficiency of the system operation.

[0185] An automatic processing method for scrapped electric meters described below can be correspondingly referred to the automatic processing system for scrapped electric meters described above.

[0186] Please refer to the attached Figure 6 , the present invention also provides an automatic processing method for scrapped electric meters, including the following steps:

[0187] S1. Meter identification and modeling: Scan the scrapped meters to identify the spatial layout and connection relationships of components, and establish a topological model.

[0188] S2. Multi-objective optimization: Optimize and calculate the disassembly paths to generate the optimal paths that meet the requirements of efficiency, energy consumption, and resource recovery rate.

[0189] S3. Dynamic topology update: During the component disassembly process, update the topological model in real time according to the structural changes after component removal.

[0190] S4. Path planning and execution: Based on the optimal path, control the robotic arm to complete the disassembly operation.

[0191] S5. Data collection and feedback: Collect the time, energy consumption, and recovery rate data generated during the disassembly process in real time and feedback to adjust the optimization objectives.

[0192] The method of this embodiment can be used to implement the above system embodiment, and its principle and technical effects are similar, so they will not be elaborated here.

[0193] 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. An automatic processing system for scrapped electric meters, characterized in that: include: The meter identification and modeling module is used to perform structural scanning on scrapped meters, identify the spatial layout and connection relationship of the internal components of the meter, and establish a topological model that characterizes the connection between components; The multi-objective optimization module is used to optimize the disassembly path and generate the optimal path according to the target requirements of efficiency, energy consumption and resource recovery rate; Dynamic topology update module, used to update the topology model in real time according to the structural changes after the components are removed during the component disassembly process; The path planning and execution module is used to control the robot arm and auxiliary equipment to perform disassembly operations based on the optimized path instructions; The data collection and feedback module is used to collect the time, energy consumption and recovery rate data during the disassembly process, and feed the data back to the multi-objective optimization module to adjust the optimization parameters.

2. The automatic processing system for scrapped electric meters according to claim 1 is characterized in that: The electric meter identification and modeling module includes: AI visual scanning equipment is used to perform three-dimensional structural scanning of the meter to identify the spatial location of the casing, circuit board and battery; A model building unit, used to build a topological model of the electric meter components based on the scanning results, where nodes represent components and edges represent connection relationships between components; The parameter measurement module is used to measure the physical parameters of the meter components, including material type and connection method, and assign initial weights to the connection relationships in the topology model.

3. The automatic processing system for scrapped electric meters according to claim 1 is characterized in that: The multi-objective optimization module includes: An objective function design unit, used to generate optimization targets based on disassembly time, energy consumption and resource recovery rate targets; A Pareto optimization unit, used to calculate multiple optimization solutions based on a non-dominated sorting genetic algorithm and output a Pareto frontier solution set; The weight adjustment unit is used to adjust the weight ratio of the disassembly time, energy consumption and resource recovery targets according to actual needs.

4. The automatic processing system for scrapped electric meters according to claim 1 is characterized in that: The dynamic topology updating module comprises: A component removal unit is used to remove the corresponding nodes and related connection relationships in the topology model after completing the disassembly of a component; The weight updating unit is used to dynamically update the connection relationship weights in the topology model based on the disassembly time and energy consumption data collected in real time.

5. The automatic processing system for scrapped electric meters according to claim 1 is characterized in that: The path planning and execution module includes: A dynamic programming unit, used to calculate the optimal disassembly path based on the current topology model; An execution control unit for controlling the robot arm and auxiliary equipment according to the optimal path to complete the disassembly operation, including shell separation, battery removal and component sorting; A safety monitoring unit is used to monitor the force and heating energy consumption applied during the disassembly process of the robot arm and stop the operation when the safety threshold is exceeded.

6. The automatic processing system for scrapped electric meters according to claim 1 is characterized in that: The data collection and feedback module includes: A data collection unit, used to collect time, energy consumption and recovery rate data generated during the disassembly process in real time; The feedback control unit is used to adjust the optimization target weight ratio of the multi-objective optimization module according to the collected data and recalculate the disassembly path.

7. A method for automatically processing scrapped electric meters, characterized in that: The automatic processing system for scrapped electric meters according to any one of claims 1 to 6 comprises the following steps: Meter identification and modeling: Scan the scrapped meters to identify the spatial layout and connection relationships of the components and build a topological model; Multi-objective optimization: optimize the disassembly path and generate the optimal path that meets the requirements of efficiency, energy consumption and resource recovery rate; Dynamic topology update: during component disassembly, the topology model is updated in real time according to the structural changes after the components are removed; Path planning and execution, controlling the robotic arm based on the optimal path to complete the disassembly operation; Data collection and feedback: real-time collection of time, energy consumption and recovery rate data generated during the disassembly process and feedback to adjust optimization targets.

8. The automatic processing system and method for scrapped electric meters according to claim 7, characterized in that: The electric meter identification and modeling includes: Use AI visual scanning equipment to perform three-dimensional scanning of the meter to identify the spatial location of the casing, circuit board, and battery; Build a component topology model based on the scanning results. The nodes in the topology model represent the components, and the edges represent the connection relationships between the components. Assign initial weights to the connection relationships, which are calculated by combining the disassembly time, energy consumption, and recovery rate parameters.

9. The automatic processing system and method for scrapped electric meters according to claim 7, characterized in that: The multi-objective optimization includes: Generate optimization targets based on disassembly time, energy consumption and resource recovery rate targets; The optimization objective is calculated using a non-dominated sorting genetic algorithm to generate a Pareto front solution set; Output the optimal path that meets multiple objectives for use in subsequent steps.

10. The automatic processing system and method for scrapped electric meters according to claim 7, characterized in that: The path planning and execution includes: Calculate the current optimal disassembly path using dynamic programming method based on the current topology model; Control the robot arm according to the optimal path to complete the shell separation, battery removal and component classification operations; During the disassembly process, the power and heating energy consumption of the robot arm are monitored in real time, and the operation is adjusted or stopped when the threshold is exceeded.

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