Building concrete crushing and reinforcing steel bar recycling integrated intelligent processing system

Abstract

The invention discloses a building concrete crushing and reinforcing steel bar recycling integrated intelligent processing system, and relates to the technical field of concrete recycling, the building concrete crushing and reinforcing steel bar recycling integrated intelligent processing system comprises a sensing module, a control module, a material conveying module, an execution module and a feedback module; the multi-mode sensing modules are arranged at the feeding port of the equipment and the inlet of the crushing cavity, accurate recognition and feature collection of the postures of reinforcing steel bars in concrete are achieved, stress pre-judgment and dynamic adjustment of operation parameters of all the modules are completed in combination with a deep learning model of the control module, the problem that the equipment is stuck is effectively avoided, and the working efficiency is improved. The failure rate of equipment is reduced, bending and damage to reinforcing steel bars in the crushing process are reduced by adopting adaptive operation regulation and control strategies for different reinforcing steel bar postures, the straightening recovery rate of the reinforcing steel bars is improved, all operation links are connected by additionally arranging a material conveying module, and the material conveying efficiency is improved. And a closed-loop control system is matched to form exclusive reinforcing steel bar posture adaptive cooperative control logic, and crushing and separation are converted into space cooperative operation from time sequence step-by-step operation.

Description

Technical Field

[0001] This invention relates to the field of concrete recycling technology, specifically to an integrated intelligent processing system for crushing building concrete and recycling reinforcing steel. Background Technology

[0002] Construction concrete is an artificial stone material formed by mixing and hardening cementitious materials, aggregates, and water in a specific ratio. As a core structural material in building engineering, it is widely used in various infrastructure constructions such as housing, municipal engineering, bridges, and tunnels due to its readily available raw materials, flexible molding, high strength, and good durability. It is a fundamental material supporting the development of the modern construction industry. With the rapid upgrading and iteration of the construction industry, the demolition of a large number of old buildings and the advancement of infrastructure renovation projects have generated a massive amount of waste construction concrete. Its resource utilization has become an important issue for the green development of the construction industry. The crushing of construction concrete and the recycling of steel bars are key operational links in the resource utilization of construction waste, which have multiple significances in terms of ecology, economy, and industrial development. This operation can crush waste concrete into recycled aggregates for reuse, significantly reducing the amount of construction waste going to landfills and saving natural mineral resources such as sand and gravel. At the same time, the steel bars in the waste concrete can be recycled and reused in projects after reprocessing, reducing the consumption of mineral resources in steel production and reducing energy loss in the production process. This aligns with the "dual carbon" development goal, helps to build a resource utilization system for construction waste, and promotes the development of a circular economy in the construction industry.

[0003] However, existing technologies still have certain shortcomings in concrete crushing and rebar recycling operations. Traditional concrete crushing equipment adjusts operating parameters based solely on the overall volume of the material, lacking precise perception and targeted control of the rebar's posture within the concrete. This leads to frequent equipment jams and a high failure rate. Furthermore, the rebar is easily bent and damaged by the crushing mechanism, resulting in a low rate of straight rebar recovery. Moreover, crushing and rebar separation are often sequential and step-by-step operations, requiring multiple material transfers, making the process cumbersome and inefficient. In addition, existing equipment lacks adaptive control logic for rebar posture, making it impossible to achieve coordinated operation between crushing and separation stages. This makes it difficult to meet the integrated and intelligent operation requirements of building concrete crushing and rebar recycling. Therefore, developing an integrated intelligent processing system for building concrete crushing and rebar recycling is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an integrated intelligent processing system for building concrete crushing and rebar recycling. This system utilizes multimodal sensing modules deployed at the equipment inlet and crushing chamber inlet to accurately identify and collect features of the rebar's posture within the concrete. Combined with a deep learning model in the control module, it performs stress prediction and dynamic adjustment of the operating parameters of each module, reducing equipment failure rates. By adopting adaptive operation control strategies for different rebar postures, it reduces bending and damage to the rebar during crushing, improving the straightness and recovery rate of the rebar. Furthermore, by adding a material conveying module to connect each operation stage, and cooperating with a closed-loop control system to form a dedicated rebar posture-adaptive collaborative control logic, it transforms crushing and separation from a sequential, step-by-step operation to a spatially collaborative operation, reducing material transfer links and significantly improving overall operational efficiency. Ultimately, it achieves integrated intelligent processing of building concrete crushing and rebar recycling.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an integrated intelligent processing system for crushing building concrete and recycling steel bars, the system comprising: a sensing module, a control module, a material conveying module, an execution module and a feedback module; The sensing module is deployed at the feed inlet and crushing chamber inlet of the equipment to collect feature information of the concrete block to be processed and the internal steel bars. The sensing module includes a binocular vision sensor, a laser contour sensor and a data processing unit. The control module is electrically connected to the sensing module, the material conveying module, and the execution module, respectively, and is used to receive feature information to generate operation parameter adjustment instructions, and also to receive operation status data to generate parameter correction instructions; The material conveying module connects the equipment's feed end, crushing chamber, and sorting end, and is used to adjust the rhythm of material conveying according to adjustment and correction commands. The execution module includes a jaw plate adjustment mechanism, an impact mechanism, a hydraulic clamping mechanism, a sorting roller mechanism, a lateral guiding combing mechanism, and a sorting auxiliary mechanism, which are used to carry out concrete crushing and rebar separation operations according to adjustment and correction instructions; The feedback module is electrically connected to the execution module, the material conveying module, and the control module respectively, and is used to collect the operation status data of the execution module and the material conveying module. The sensing module transmits the collected feature information to the control module, the control module transmits the operation parameter adjustment instructions to the material conveying module and the execution module respectively, the feedback module transmits the collected operation status data to the control module, and the control module transmits the parameter correction instructions to the material conveying module and the execution module respectively. The modules work together to form a closed-loop control system.

[0006] Furthermore, the sensing module performs the following operations when collecting feature information about the concrete block and its internal reinforcing bars: The sensing module enters the data acquisition mode synchronously when the equipment starts operating. The sensor group deployed at the feed inlet and crushing chamber inlet of the equipment captures the concrete block material entering the working area in real time. The binocular vision sensor in the perception module takes pictures of the concrete block from multiple perspectives and collects the overall appearance and size information of the concrete block. The laser contour sensor emits a detection beam to the concrete block and collects the feature information of the steel bars inside the concrete. The perception module performs preliminary processing on the collected visual and contour data, including noise reduction and normalization, to remove invalid interference data within the perception range. After processing, the effective feature information is organized and transmitted to the control module in real time.

[0007] Furthermore, the control module performs the following operations when analyzing the rebar posture and generating work instructions: The control module receives feature information of concrete blocks and reinforcing bars transmitted by the sensing module, parses and processes the received feature information, and extracts the feature value of the reinforcing bar length. Characteristic values ​​of the bending angle of reinforcing bars Characteristic values ​​of rebar embedment depth and the volume characteristic value of concrete blocks The parsed feature information is then imported into the built-in lightweight deep learning model. The lightweight deep learning model processes the feature information of the reinforcing steel bars using formulas. The comprehensive judgment value of the rebar posture classification was calculated. Then through the formula Calculated predicted values ​​of steel reinforcement stress This allows for the classification of rebar posture and the prediction of rebar stress conditions. , , The weighting coefficients for the rebar posture features are determined iteratively through backpropagation algorithm after training a lightweight deep learning model with a massive number of rebar posture samples. , The stress prediction calibration coefficient is determined by the control module based on the material properties of concrete and the mechanical properties of steel reinforcement, and then verified by actual engineering test samples. Based on the rebar posture classification results and rebar stress prediction results, the control module generates corresponding operation parameter adjustment instructions, and transmits the instructions to the material conveying module and the execution module respectively according to the control requirements of each operation link of the equipment.

[0008] Furthermore, the execution module performs the following operations during concrete crushing and rebar separation: The execution module receives the operation parameter adjustment instructions transmitted by the control module, retrieves the initial operating parameters of each actuator, and simultaneously parses and processes the adjustment instructions to extract the predicted steel reinforcement stress value transmitted by the control module. Characteristic value of steel reinforcement quantity ; The execution module, based on the parsed adjustment instructions, uses formulas... The matching values ​​of the operating parameters of each actuator were calculated. And match values ​​according to the operation parameters. The opening degree of the jaw plate adjustment mechanism, the operating frequency of the impact mechanism, the clamping force of the hydraulic clamping mechanism, and the rotation speed of the sorting roller mechanism are adjusted respectively to match the operating parameters corresponding to the posture of the steel bars. , The calibration coefficients for matching the operation parameters are calibrated by the execution module after actual equipment debugging and multi-condition operation matching tests, and dynamically calibrated by the control module based on historical operation data; Based on the rebar posture classification results, the opening and closing of the lateral guiding sorting mechanism is controlled, and the operating parameters of the sorting auxiliary mechanism are adjusted to carry out crushing and separation operations for rebars with different postures.

[0009] Furthermore, the feedback module performs the following operations when collecting and transmitting job status data: The feedback module is activated synchronously with the start of the execution module and the material conveying module, and captures the real-time operation status of the two modules at all times through the built-in status monitoring element; The collected data on mechanism operating parameters, material handling progress, and equipment operating status are classified and organized, and the organized status data is transmitted to the control module in real time according to the preset transmission frequency. After the control module generates parameter correction instructions based on the feedback data, the feedback module continuously captures the corrected operation status data of each module and transmits the corrected operation status data to the control module in real time.

[0010] Furthermore, the material conveying module includes a conveying roller conveyor, a speed-regulating drive assembly, and a material detection element. The conveying roller conveyor is continuously arranged along the operating direction of the equipment's feed end, crushing chamber, and sorting end. The speed-regulating drive assembly is connected to the conveying roller conveyor via a transmission connection. The material detection element is arranged at the connection positions of each section of the conveying roller conveyor and is connected to the control module via an electrical connection. The material detection element collects information on the material position and quantity on the conveying roller conveyor in real time, and transmits the collected information to the control module in real time. The control module adjusts the conveying speed of the conveying roller conveyor through the speed-regulating drive assembly and directly controls the start and stop timing of the conveying roller conveyor.

[0011] Furthermore, the binocular vision sensor adopts a high-definition industrial vision sensor, which captures stereo images of the concrete block and collects its size information. The laser contour sensor adopts a line laser contour sensor, which collects three-dimensional feature information of the length, bending angle, and embedment depth of the reinforcing bar. The data processing unit adopts an embedded processing unit, which quickly processes the raw data collected by the sensor. The data processing unit is equipped with a data transmission interface, which enables high-speed data interaction between the sensing module and the control module.

[0012] Furthermore, the lateral guiding combing mechanism in the execution module includes a guide roller group and combing blades. The guide roller group is arranged along the discharge direction of the crushing chamber, and the combing blades are uniformly fixed on the roller surface of the guide roller group. The sorting auxiliary mechanism includes a magnetic field adjustment component and an airflow adjustment component. The magnetic field adjustment component realizes stepless adjustment of its own magnetic field strength, and the airflow adjustment component realizes precise adjustment of its own airflow speed. Both the lateral guiding combing mechanism and the sorting auxiliary mechanism are independently equipped with a drive component. The drive component receives the instructions from the control module and realizes the independent start and stop of the lateral guiding combing mechanism and the sorting auxiliary mechanism. The drive component receives the instructions from the control module and adjusts the operating parameters of the lateral guiding combing mechanism and the sorting auxiliary mechanism.

[0013] Furthermore, the control module adopts an industrial-grade controller. The lightweight deep learning model built into the control module is a convolutional neural network model trained with rebar posture samples. This model classifies the rebar posture and predicts the stress on the rebar. The control module is equipped with an instruction storage unit and a parameter adjustment unit. The instruction storage unit stores the operation parameter adjustment instructions generated by the control module in real time. The parameter adjustment unit receives the data returned by the feedback module and dynamically corrects the adjustment instructions generated by the control module. The parameter adjustment unit accurately adjusts the corrected adjustment instructions and then transmits them to the corresponding modules.

[0014] Furthermore, the feedback module includes a vibration monitoring element, a speed monitoring element, a pressure monitoring element, and a data transmission module. The vibration monitoring element is installed on the outer walls of the impact mechanism and the hydraulic clamping mechanism of the execution module. The speed monitoring elements are installed on the roller shafts of the sorting roller mechanism and the conveying roller shafts of the material conveying module. The pressure monitoring element is installed at the clamping end of the hydraulic clamping mechanism. Each monitoring element collects the operating status data of the corresponding mechanism in real time. The data transmission module adopts a wireless transmission module to realize data transmission between the feedback module and the control module. The feedback module receives instructions from the control module and adjusts its own data acquisition frequency.

[0015] Compared with existing technologies, this integrated intelligent processing system for building concrete crushing and steel rebar recycling has the following advantages: This invention achieves accurate identification and feature acquisition of the posture of steel bars inside concrete by deploying multimodal sensing modules at the feed inlet and crushing chamber inlet of the equipment. Combined with the deep learning model of the control module, it completes stress prediction and dynamic adjustment of the operating parameters of each module, effectively avoiding equipment jamming problems and reducing equipment failure rate. By adopting adaptive operation control strategies for different steel bar postures, it reduces bending and damage to steel bars during crushing, thereby improving the straightness recovery rate of steel bars. By adding a material conveying module to connect each operation link, and forming a dedicated steel bar posture adaptive collaborative control logic with the closed-loop control system, it transforms crushing and separation from a time-sequential step-by-step operation to a spatial collaborative operation, reducing material transfer links and significantly improving overall operation efficiency. Ultimately, it realizes integrated intelligent processing of building concrete crushing and steel bar recycling, adapting to the green and efficient operation requirements of modern construction engineering.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 A schematic diagram of an integrated intelligent system for crushing and recycling building concrete and steel bars. Figure 2 A flowchart of the workflow for an integrated intelligent system for crushing building concrete and recycling reinforcing steel. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] Example 1 This embodiment applies an integrated intelligent processing system for concrete crushing and rebar recycling to the resource recovery scenario of construction waste from the demolition of old urban residential buildings. In this scenario, the generated waste concrete components are mostly blocky materials from dismantled floor slabs, beams, and columns. The concrete blocks are of varying sizes, and the internal rebar exhibits complex postures, including large-diameter continuous bars and scattered short bars. Furthermore, the concrete material varies in strength due to differences in the building's age. Traditional crushing and recycling equipment often encounters problems such as equipment jamming and rebar bending damage when operating in this scenario because it cannot identify the rebar posture. Moreover, the crushing and separation processes require multiple material transfers, resulting in low operational efficiency. This embodiment integrates this intelligent processing system into a fixed construction waste processing production line to perform integrated crushing and rebar recycling of waste concrete blocks in this scenario. Relying on the system's multi-module linkage and closed-loop control system, it achieves precise control over different rebar postures, completing efficient concrete crushing and high-quality rebar recycling.

[0021] In this embodiment, after the system starts, each module first completes initialization. The sensing module is located at the connection between the feed inlet and the jaw crusher inlet of the production line. Its high-definition industrial binocular vision sensor and line laser contour sensor complete lens calibration and parameter initialization. The embedded data processing unit starts the data receiving and preprocessing program. The industrial-grade controller used in the control module loads the lightweight convolutional neural network model trained with a large number of steel bar posture samples. The instruction storage unit and parameter adjustment unit enter the standby state. The conveying rollers of the material conveying module complete idling debugging. The speed control drive component and the material detection elements at each connection position achieve signal communication. The jaw plate adjustment mechanism, impact mechanism, hydraulic clamping mechanism, etc. of the execution module are all reset to the initial working position. The lateral guide combing mechanism and sorting auxiliary mechanism are in standby state. The vibration monitoring element, speed monitoring element, and pressure monitoring element of the feedback module complete zero-point calibration. The wireless data transmission module establishes a stable data connection with the control module. After the initialization of each module is completed, the system enters the formal operation state.

[0022] Concrete blocks from demolished old residential buildings are fed to the production line inlet via a feeding device. The sensing module immediately begins data acquisition upon entering the work area, with sensor arrays deployed at the inlet and crushing chamber entrance capturing the concrete blocks in real time. A binocular vision sensor captures the concrete blocks from different angles, accurately obtaining their length, width, and height dimensions and converting them into volumetric features. A line laser contour sensor emits a detection beam that penetrates the concrete surface, acquiring three-dimensional features such as the length, bending angle, and embedding depth of the internal reinforcing steel. After acquisition, the embedded data processing unit performs preliminary noise reduction and normalization on the visual and contour data, eliminating invalid interference data caused by material splashes and light refraction. The processed concrete block and reinforcing steel feature information is then transmitted to the control module in real time.

[0023] After receiving the feature information transmitted by the sensing module, the control module immediately analyzes and processes it to accurately extract the feature value of the rebar length. Characteristic values ​​of the bending angle of reinforcing bars Characteristic values ​​of rebar embedment depth and the volume characteristic value of concrete blocks The parsed feature information is then imported into the built-in lightweight deep learning model. In the specific implementation of this embodiment, the lightweight deep learning model processes the feature information of the reinforcing steel bars using formulas. The comprehensive judgment value of the rebar posture classification was calculated. ,in , , The weighting coefficients for the rebar posture features are determined by iterative optimization using a backpropagation algorithm after training a lightweight deep learning model with a large number of rebar posture samples. In the specific implementation of this embodiment, the control module uses a formula based on the comprehensive judgment value of the rebar posture classification. Calculated predicted values ​​of steel reinforcement stress This allows for the classification of rebar posture and the prediction of rebar stress conditions. , The stress prediction calibration coefficient is determined by the control module based on the material properties of concrete and the mechanical properties of steel reinforcement, and then verified by actual engineering test samples. In this embodiment, the control module calculates the coefficient... and The steel bars in the work area are precisely divided into two categories: large-diameter through steel bars and scattered short bars. Corresponding operation parameter adjustment instructions are generated for the posture of the two types of steel bars. According to the control requirements of each operation link of the production line, the instructions are transmitted to the material conveying module and the execution module respectively.

[0024] After receiving the operation parameter adjustment instructions from the control module, the material conveying module collects the position and quantity information of the concrete blocks on the conveying roller in real time and transmits the information back to the control module. The control module adjusts the conveying speed of the conveying roller through the speed adjustment drive component according to the actual material conditions, and directly controls the start and stop timing of the conveying roller, so that the concrete blocks enter the crushing chamber from the feed end at a suitable rhythm, realizing the rhythm coordination between material conveying and subsequent crushing and separation operations, and avoiding equipment jamming caused by excessive feeding or material accumulation.

[0025] After receiving the operation parameter adjustment command transmitted by the control module, the execution module first retrieves the initial operating parameters of each execution mechanism, such as the jaw plate adjustment mechanism and the impact mechanism. At the same time, it completes the parsing and processing of the adjustment command and extracts the predicted value of the steel reinforcement stress transmitted by the control module. Characteristic value of steel reinforcement quantity In the specific implementation process of this embodiment, the execution module, based on the parsed adjustment instructions, uses formulas... The matching values ​​of the operating parameters of each actuator were calculated. ,in , The calibration coefficients for matching the operation parameters are calibrated by the execution module after actual equipment debugging and multi-condition operation matching tests, and dynamically calibrated by the control module based on historical operation data. The execution module then uses the calculated operation parameter matching values... The operating parameters of each actuator are adjusted as follows: For large-diameter through-reinforcing bars, the opening degree of the jaw plate adjustment mechanism is adjusted, the operating frequency of the impact mechanism is reduced, the clamping force of the hydraulic clamping mechanism is increased, and the speed of the sorting roller mechanism is reduced. At the same time, the lateral guiding combing mechanism is activated, so that the guide roller group runs along the discharge direction of the crushing chamber, and the combing blades on the roller surface guide and comb the through-reinforcing bars. The operating parameters of the sorting auxiliary mechanism are adjusted simultaneously, the magnetic field strength of the magnetic field adjustment component is increased, and the airflow speed of the airflow adjustment component is finely adjusted. For scattered short bars, the opening degree of the jaw plate adjustment mechanism is reduced, the operating frequency of the impact mechanism is increased, the clamping force of the hydraulic clamping mechanism is reduced, and the speed of the sorting roller mechanism is increased. At the same time, the lateral guiding combing mechanism is closed, the magnetic field strength of the magnetic field adjustment component is appropriately reduced, and the airflow speed of the airflow adjustment component is increased, so as to achieve the adaptive operation of concrete crushing and rebar separation under different rebar postures.

[0026] During the operation of the execution module and the material conveying module, the feedback module maintains a synchronized working state, capturing the real-time operating status of both modules around the clock through various built-in status monitoring elements. Vibration monitoring elements collect vibration data from the outer walls of the impact mechanism and the hydraulic clamping mechanism; speed monitoring elements collect speed data from the roller shafts of the sorting roller mechanism and the conveying rollers; and pressure monitoring elements collect pressure data from the clamping end of the hydraulic clamping mechanism. These monitoring elements categorize and organize the collected mechanism operating parameters, material processing progress, and equipment operating status data, which are then transmitted in real-time to the control module via the wireless data transmission module at a preset transmission frequency. Upon receiving the returned operating status data, the control module immediately analyzes and determines whether the current operating parameters are suitable for the actual working conditions. If a mismatch is found between the mechanism operating parameters and the working conditions, a parameter correction command is promptly generated and sent to the material conveying module and the execution module. The feedback module continuously captures the corrected operating status data of each module and transmits it back in real-time, forming a closed-loop control system that ensures each module always operates with optimal parameters.

[0027] This embodiment applies an integrated intelligent processing system for concrete crushing and rebar recycling to the construction waste treatment scenario of demolishing old urban residential buildings. By deploying multimodal sensing modules at the feed inlet and crushing chamber inlet, it achieves accurate identification and feature acquisition of rebars in different postures inside the concrete. Combined with a lightweight deep learning model built into the control module, it completes the prediction of rebar stress and dynamic adjustment of the operating parameters of each module, avoiding equipment jamming problems throughout the process and significantly reducing the equipment failure rate. For large-diameter through rebars and scattered short rebars commonly found in the scenario, the system adopts a highly adaptable operation control strategy. By precisely adjusting the operating parameters of each actuator, and coordinating the operation of the lateral guiding sorting mechanism and the sorting auxiliary mechanism, it minimizes the bending and damage to the rebars during the crushing process, significantly improving the straight rebar recovery rate.

[0028] Example 2 This embodiment applies an integrated intelligent processing system for concrete crushing and rebar recycling to the on-site processing of construction waste in a municipal bridge demolition project. In this scenario, the waste concrete materials are primarily large components from dismantled bridge box girders and piers. The concrete is of high grade and has high overall strength, with internal rebar consisting mainly of large-diameter, densely reinforced, multi-layered continuous bars. Some rebars exhibit complex welded and lapped connections. The work site is an outdoor construction site requiring on-site processing of construction waste. Traditional fixed crushing and recycling equipment is unsuitable for the outdoor operation requirements of this scenario. Furthermore, it cannot handle the complex conditions of high-grade concrete and densely reinforced concrete, easily leading to equipment overload and jamming. Rebars are also prone to severe damage such as twisting and breakage during crushing, resulting in low recycling rates. This embodiment integrates the intelligent processing system with a mobile construction waste processing device. Building upon the previous embodiment, it adapts and optimizes the modules for the specific conditions of bridge demolition, enabling integrated outdoor concrete crushing and rebar recycling operations, thus meeting the on-site processing requirements of the project.

[0029] In this embodiment, after the system starts up, it first completes the initialization and debugging for adapting to outdoor mobile operations. Based on the module initialization of the previous embodiment, the high-definition industrial binocular vision sensor and line laser contour sensor of the perception module complete the debugging of dustproof and waterproof protection components, and at the same time, the lens is calibrated for anti-interference in strong outdoor light environment. The embedded data processing unit loads the outdoor light interference filtering algorithm. The industrial-grade controller used in the control module adds a vehicle power supply adaptation program to ensure the power supply stability of the mobile equipment. The built-in lightweight deep learning model is loaded with the feature recognition library of dense reinforcement of bridge steel bars. The conveying roller of the material conveying module completes the debugging of anti-slip textured roller surface, and at the same time, a lateral limit component is added and reset. The speed control drive component is adapted to the vehicle power system and completes the speed calibration. The various mechanisms of the execution module complete the shock absorption adaptation and debugging of the vehicle equipment. The jaw plate adjustment mechanism is replaced with a high wear-resistant jaw plate, and the combing paddle of the lateral guide combing mechanism completes the working condition adaptation of the wear-resistant coating. The feedback module adds a displacement monitoring element and a vehicle chassis vibration monitoring element and completes zero-point calibration. The wireless data transmission module is switched to an outdoor anti-interference transmission protocol. After each module completes the adaptation initialization, the system enters the outdoor operation state.

[0030] After the bridge demolition, the high-grade concrete blocks are initially disassembled by hydraulic dismantling equipment and then fed to the inlet by the loading mechanism of the mobile equipment. Based on the aforementioned embodiment, the sensing module initiates data acquisition, with sensor arrays deployed at the inlet and crushing chamber entrance capturing the concrete blocks in real time. A binocular vision sensor activates an anti-interference shooting mode for strong outdoor light environments, capturing stereoscopic images of the concrete blocks from multiple perspectives and collecting volumetric feature information. A line laser contour sensor adjusts the detection beam power according to the concrete grade, penetrating the surface of the high-grade concrete to collect feature information such as the length, bending angle, embedment depth, and spacing of the internal reinforcing bars. After acquisition, the data processing unit uses a newly added filtering algorithm to denoise and normalize the visual and contour data, eliminating invalid interference data caused by dust and light in the outdoor environment, and transmitting the processed valid feature information to the control module in real time.

[0031] After receiving the feature information transmitted by the sensing module, the control module performs parsing processing based on the aforementioned embodiment to extract the rebar length feature value. Characteristic values ​​of the bending angle of reinforcing bars Characteristic values ​​of rebar embedment depth and the volume characteristic value of concrete blocks Simultaneously, the feature value of the rebar spacing is extracted as an auxiliary judgment criterion, and the parsed feature information is imported into the built-in lightweight deep learning model. In the specific implementation process of this embodiment, the lightweight deep learning model performs calculations on the rebar feature information, using formulas... The comprehensive judgment value of the rebar posture classification was calculated. Then through the formula Calculated predicted values ​​of steel reinforcement stress It combines the characteristics of rebar spacing to accurately classify complex postures such as dense reinforcement and multi-layer continuous reinforcement. At the same time, it optimizes the stress prediction for the characteristics of high-grade concrete. Based on the judgment and prediction results, it generates operation parameter adjustment instructions with gradient adjustment requirements, which are transmitted to the material conveying module and the execution module respectively.

[0032] After receiving the operation parameter adjustment command from the control module, the material conveying module, based on the aforementioned embodiment, uses a newly added weight detection function to collect the position, quantity, and weight information of the concrete blocks on the conveying roller in real time. This multi-dimensional information is then transmitted back to the control module. The control module, in conjunction with the volume and weight of the concrete blocks, coordinates the operation parameters of the speed-regulating drive component to adjust the conveying speed of the conveying roller. Simultaneously, it controls the opening and closing range of the lateral limit component to prevent large, heavy concrete blocks from shifting during conveying. This achieves dynamic coordination between material conveying and crushing / separation operations, avoiding material conveying jams in outdoor mobile operations.

[0033] After receiving the operation parameter adjustment command with gradient adjustment requirements transmitted by the control module, the execution module retrieves the initial operating parameters of each execution mechanism based on the aforementioned embodiment, and extracts the predicted value of the steel reinforcement stress after parsing and processing. Characteristic value of steel reinforcement quantity In the specific implementation process of this embodiment, the execution module uses the formula The matching values ​​of the operating parameters of each actuator were calculated. Based on this value, the gradient parameters of each mechanism are adjusted: for the large-diameter dense reinforcement of the bridge, the jaw plate adjustment mechanism adopts a graded opening adjustment mode, gradually adjusting the opening width according to the volume of the concrete block; the impact mechanism switches to a low-frequency high-force operation mode; the hydraulic clamping mechanism adopts a multi-point clamping method and adjusts the clamping force according to the reinforcement density gradient; the sorting roller mechanism adopts a low-speed high-torque rotation mode; at the same time, the lateral guide combing mechanism is activated, the guide roller group is adjusted to a stepless speed regulation mode, and the multi-layer through reinforcement is combed layer by layer through the combing paddle; the sorting auxiliary mechanism adopts a dual-zone magnetic field control mode, adjusting the magnetic field strength of different areas according to the embedment depth of the reinforcement; the airflow adjustment component cooperates with the magnetic field to realize layered airflow sweeping, accurately separating the welded lapped reinforcement and aggregate, and realizing the adaptation operation under complex reinforcement posture.

[0034] During the operation of the execution module and the material conveying module, the feedback module, based on the aforementioned embodiments, achieves real-time status capture. In addition to collecting vibration, speed, and pressure data, the newly added displacement monitoring element collects the operational displacement data of the jaw plate adjustment mechanism and the hydraulic clamping mechanism in real time, and the vehicle chassis vibration monitoring element collects the overall vibration data of the equipment. After being classified and organized, the various monitoring data are transmitted to the control module in real time by the anti-interference wireless data transmission module at a transmission frequency dynamically adjusted according to the working conditions. After receiving the feedback data, the control module analyzes and judges the adaptability of the current operating parameters to the outdoor mobile operating conditions. If it finds that the mechanism operating parameters are unreasonable or the equipment chassis vibration exceeds the threshold, it immediately generates parameter correction commands and sends them to each module. The feedback module continuously captures the corrected operating status data and transmits it back in real time, forming a closed-loop control system adapted to complex outdoor working conditions, ensuring the overall operational stability of the equipment.

[0035] This embodiment applies the intelligent processing system to the outdoor on-site processing scenario of municipal bridge demolition projects. Building upon the previous embodiment, and considering the characteristics of high-grade concrete, large-diameter densely reinforced concrete, and the requirements of outdoor mobile operations, the system optimizes the adaptability and upgrades the functions of each module. Through the anti-interference design of the sensing module and the adjustment of laser power, accurate identification of complex rebar postures is achieved. Combined with the bridge rebar feature recognition library in the control module, more realistic stress prediction and command generation are achieved. The gradient parameter adjustment and dual-zone magnetic field and stratified airflow sorting modes of the execution module effectively solve the problems of high-grade concrete crushing difficulty and the easy damage to densely reinforced steel bars, maximizing the integrity of the rebar and improving the rebar recycling rate.

[0036] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An integrated intelligent processing system for crushing building concrete and recycling reinforcing steel, characterized in that, The system includes: a sensing module, a control module, a material conveying module, an execution module, and a feedback module; The sensing module is deployed at the feed inlet and crushing chamber inlet of the equipment to collect feature information of the concrete block to be processed and the internal steel bars. The sensing module includes a binocular vision sensor, a laser contour sensor and a data processing unit. The control module is electrically connected to the sensing module, the material conveying module, and the execution module, respectively, and is used to receive feature information to generate operation parameter adjustment instructions, and also to receive operation status data to generate parameter correction instructions; The material conveying module connects the equipment's feed end, crushing chamber, and sorting end, and is used to adjust the rhythm of material conveying according to adjustment and correction commands. The execution module includes a jaw plate adjustment mechanism, an impact mechanism, a hydraulic clamping mechanism, a sorting roller mechanism, a lateral guiding combing mechanism, and a sorting auxiliary mechanism, which are used to carry out concrete crushing and rebar separation operations according to adjustment and correction instructions; The feedback module is electrically connected to the execution module, the material conveying module, and the control module respectively, and is used to collect the operation status data of the execution module and the material conveying module. The sensing module transmits the collected feature information to the control module, the control module transmits the operation parameter adjustment instructions to the material conveying module and the execution module respectively, the feedback module transmits the collected operation status data to the control module, and the control module transmits the parameter correction instructions to the material conveying module and the execution module respectively. The modules work together to form a closed-loop control system.

2. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The sensing module performs the following operations when collecting feature information about the concrete block and its internal reinforcing bars: The sensing module enters the data acquisition mode synchronously when the equipment starts operating. The sensor group deployed at the feed inlet and crushing chamber inlet of the equipment captures the concrete block material entering the working area in real time. The binocular vision sensor in the perception module takes pictures of the concrete block from multiple perspectives and collects the overall appearance and size information of the concrete block. The laser contour sensor emits a detection beam to the concrete block and collects the feature information of the steel bars inside the concrete. The perception module performs preliminary processing on the collected visual and contour data, including noise reduction and normalization, to remove invalid interference data within the perception range. After processing, the effective feature information is organized and transmitted to the control module in real time.

3. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The control module performs the following operations when performing rebar posture analysis and generating work instructions: The control module receives feature information of concrete blocks and reinforcing bars transmitted by the sensing module, parses and processes the received feature information, and extracts the feature value of the reinforcing bar length. Characteristic values ​​of the bending angle of reinforcing bars Characteristic values ​​of rebar embedment depth and the volume characteristic value of concrete blocks The parsed feature information is then imported into the built-in lightweight deep learning model. The lightweight deep learning model processes the feature information of the reinforcing steel bars using formulas. The comprehensive judgment value of the rebar posture classification was calculated. Then through the formula Calculated predicted values ​​of steel reinforcement stress This allows for the classification of rebar posture and the prediction of rebar stress conditions. , , The weighting coefficient for the posture characteristics of the reinforcing bars. , For force prediction calibration coefficient; Based on the rebar posture classification results and rebar stress prediction results, the control module generates corresponding operation parameter adjustment instructions, and transmits the instructions to the material conveying module and the execution module respectively according to the control requirements of each operation link of the equipment.

4. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The execution module performs the following operations during concrete crushing and rebar separation: The execution module receives the operation parameter adjustment instructions transmitted by the control module, retrieves the initial operating parameters of each actuator, and simultaneously parses and processes the adjustment instructions to extract the predicted values ​​of the reinforcing steel stress transmitted by the control module. Characteristic value of steel reinforcement quantity ; The execution module, based on the parsed adjustment instructions, uses formulas... The matching values ​​of the operating parameters of each actuator were calculated. And match values ​​according to the operation parameters. The opening degree of the jaw plate adjustment mechanism, the operating frequency of the impact mechanism, the clamping force of the hydraulic clamping mechanism, and the rotation speed of the sorting roller mechanism are adjusted respectively to match the operating parameters corresponding to the posture of the steel bars. , Match calibration coefficients to the operation parameters; Based on the rebar posture classification results, the opening and closing of the lateral guiding sorting mechanism is controlled, and the operating parameters of the sorting auxiliary mechanism are adjusted to carry out crushing and separation operations for rebars with different postures.

5. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The feedback module performs the following operations when collecting and transmitting work status data: The feedback module is activated synchronously with the start of the execution module and the material conveying module, and captures the real-time operation status of the two modules at all times through the built-in status monitoring element; The collected data on mechanism operating parameters, material handling progress, and equipment operating status are classified and organized, and the organized status data is transmitted to the control module in real time according to the preset transmission frequency. After the control module generates parameter correction instructions based on the feedback data, the feedback module continuously captures the corrected operation status data of each module and transmits the corrected operation status data to the control module in real time.

6. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The material conveying module includes a conveying roller conveyor, a speed-regulating drive assembly, and material detection elements. The conveying roller conveyor is continuously arranged along the operating direction of the equipment's feed end, crushing chamber, and sorting end. The speed-regulating drive assembly is connected to the conveying roller conveyor via a transmission connection. The material detection elements are arranged at the joint positions of each section of the conveying roller conveyor and are connected to the control module via an electrical connection. The material detection elements collect information on the material position and quantity on the conveying roller conveyor in real time, and transmit the collected information to the control module in real time. The control module adjusts the conveying speed of the conveying roller conveyor through the speed-regulating drive assembly and directly controls the start and stop timing of the conveying roller conveyor.

7. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The binocular vision sensor is a high-definition industrial vision sensor that captures stereo images of the concrete block and collects its size information. The laser profile sensor is a line laser profile sensor that collects three-dimensional feature information of the length, bending angle, and embedment depth of the reinforcing bars. The data processing unit is an embedded processing unit that quickly processes the raw data collected by the sensors. The data processing unit is equipped with a data transmission interface that enables high-speed data interaction between the sensing module and the control module.

8. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The lateral guiding combing mechanism in the execution module includes a guide roller group and combing blades. The guide roller group is arranged along the discharge direction of the crushing chamber, and the combing blades are evenly fixed on the roller surface of the guide roller group. The sorting auxiliary mechanism includes a magnetic field adjustment component and an airflow adjustment component. The magnetic field adjustment component realizes stepless adjustment of its own magnetic field strength, and the airflow adjustment component realizes precise adjustment of its own airflow speed. Both the lateral guiding combing mechanism and the sorting auxiliary mechanism are independently equipped with a drive component. The drive component receives the instructions from the control module and realizes the independent start and stop of the lateral guiding combing mechanism and the sorting auxiliary mechanism. The drive component receives the instructions from the control module and adjusts the operating parameters of the lateral guiding combing mechanism and the sorting auxiliary mechanism.

9. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The control module adopts an industrial-grade controller. The lightweight deep learning model built into the control module is a convolutional neural network model trained with rebar posture samples. This model classifies the rebar posture and predicts the stress on the rebar. The control module is equipped with an instruction storage unit and a parameter adjustment unit. The instruction storage unit stores the operation parameter adjustment instructions generated by the control module in real time. The parameter adjustment unit receives the data returned by the feedback module and dynamically corrects the adjustment instructions generated by the control module. The parameter adjustment unit accurately adjusts the corrected adjustment instructions and then transmits them to the corresponding modules.

10. The integrated intelligent processing system for crushing building concrete and recycling reinforcing steel according to claim 1, characterized in that, The feedback module includes vibration monitoring elements, speed monitoring elements, pressure monitoring elements, and a data transmission module. The vibration monitoring elements are located on the outer walls of the impact mechanism and the hydraulic clamping mechanism of the execution module. The speed monitoring elements are located at the roller shafts of the sorting roller mechanism and the conveying roller shafts of the material conveying module. The pressure monitoring elements are located at the clamping end of the hydraulic clamping mechanism. Each monitoring element collects the operating status data of the corresponding mechanism in real time. The data transmission module adopts a wireless transmission module to realize data transmission between the feedback module and the control module. The feedback module receives instructions from the control module and adjusts its own data acquisition frequency.

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