Solid waste recycling method during dismantling of fabricated bridge

Abstract

A solid waste recycling method during dismantling of an assembled bridge comprises the steps that initial stacking data of assembled bridge dismantling waste is obtained, the volume and density distribution of a waste stack are scanned through a sensor, the size range and quality characteristics of waste particles are determined, and a waste attribute set before sorting is obtained; according to the garbage attribute set, the mesh size range of each layer of screening net is calculated, a decreasing algorithm is adopted for arrangement from the top maximum mesh to the bottom minimum mesh, and grading configuration parameters of the multiple layers of screening nets are generated; screening equipment is driven to operate through grading configuration parameters, garbage falls down layer by layer after being thrown from the top, and classification data of the garbage falling to a corresponding collecting tank is obtained according to particles filtered out according to the mesh size of each layer; after running direction parameters of the conveying belt are obtained, directional conveying operation is executed, garbage is guided into the corresponding processing line from the bottom of the collecting groove through the conveying belt, a conveying path is monitored through a sensor, whether path deviation occurs or not is judged, and deviation data are recorded.

Description

Technical Field

[0001] The present invention belongs to the field of bridge solid waste treatment, and particularly relates to a method for recycling solid waste during the demolition of prefabricated bridges. Background Art

[0002] The demolition of prefabricated bridges and the recycling of solid waste are important topics in the cross - field of civil engineering and resource recycling, which are directly related to the goals of sustainable development of the construction industry and reduction of environmental load. With urban renewal and infrastructure aging, the quantity of solid waste generated from the demolition of prefabricated bridges has increased sharply. How to efficiently recycle and maximize its resource value has become a bottleneck that the industry urgently needs to break through. Traditional treatment methods mainly focus on landfilling or simple crushing, ignoring the potential diverse value attributes in the waste, resulting in co - existence of resource waste and the risk of secondary pollution. At present, although some methods attempt to conduct preliminary classification through manual sorting or single - machine screening, they are inefficient, lack precision, and are difficult to adapt to the characteristics of complex components and variable forms of demolition waste, restricting the depth and breadth of recycling.

[0003] The limitations of current solutions lie in the lack of systematic sorting logic and automated connection, and there are generally problems of rough classification and disconnection from subsequent processing. Most technologies only stay at the stage of surface crushing or simple stacking, unable to achieve refined stratification according to the physical properties and potential uses of the waste, resulting in high - value components being inefficiently utilized or even discarded. The core challenges focus on three technical factors: one is how to quickly and accurately identify according to the integrity and repair potential of components, the second is how to achieve efficient separation of multi - gradient levels through physical sorting, and the third is how to seamlessly connect the directional treatment process after sorting. Without solving these factors, the value chain of demolition waste is broken, high - value resources are difficult to be effectively mined, and the processing process still relies on manual intervention, restricting both efficiency and economy.

[0004] Therefore, how to construct an automated sorting and directional transmission system based on component characteristics grading at the prefabricated bridge demolition site to achieve gradient value recovery of solid waste from complete components to filling materials is the technical problem to be solved by the present invention. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for recycling solid waste during the demolition of prefabricated bridges. Through multi - sensor fusion detection and intelligent algorithm analysis, the present invention can accurately identify solid waste of different types and specifications, realizing automated and precise sorting. The system can conduct intelligent grading according to the physical properties of the waste, significantly improving the sorting accuracy and efficiency, and solving the problems of low efficiency and large errors in traditional manual sorting.

[0006] To solve the above - mentioned technical problems, the technical solution adopted by the present invention is: A method for recycling solid waste during the demolition of a prefabricated bridge, the steps are as follows: S1 Obtain the initial accumulation data of the prefabricated bridge demolition waste. By scanning the volume and density distribution of the waste pile with sensors, determine the range of waste particle sizes and mass characteristics, and obtain the waste attribute set before sorting; S1.1 Collect waste pile data through sensors, obtain the volume distribution and density distribution, and determine the initial accumulation state; S1.2 Extract particle size and mass characteristics from the sensor data to obtain the size range and characteristic determination results; S1.3 Use a clustering algorithm to group the particle sizes and density distributions, and judge the classification basis of waste particles; S1.4 Determine the characteristic distribution of the state before sorting according to the classification basis combined with the volume distribution and the attribute set; S1.5 Obtain the distribution area of abnormal particles by comparing the characteristic distribution with the mass characteristics; S1.6 If the abnormal particles exceed the preset threshold, use the support vector machine algorithm to separate the abnormal data and obtain the adjusted attribute set; S1.7 Generate optimized data before sorting according to the adjusted attribute set and the scanning results.

[0007] S2 According to the waste attribute set, calculate the mesh size range of each layer of screening mesh, and arrange them from the largest mesh at the top to the smallest mesh at the bottom using a decreasing algorithm to generate the hierarchical configuration parameters of the multi-layer screening mesh; S2.1 Obtain the initial mesh size range of each layer of screening mesh through the waste attribute set, arrange the hierarchical order using a decreasing algorithm, and obtain the preliminary hierarchical configuration parameters; S2.2 Extract the size ranges of the top mesh and the bottom mesh from the preliminary hierarchical configuration parameters, judge the uniformity of the hierarchical arrangement, and obtain the adjusted mesh size distribution; S2.3 Group the screening mesh layers according to the adjusted mesh size distribution to obtain the configuration parameters of each layer; S2.4 Compare the configuration parameters of each layer with the attribute set to judge whether there are abnormal mesh sizes, and obtain the corrected hierarchical configuration; S2.5 Use the corrected hierarchical configuration, combine with the decreasing algorithm to rearrange the hierarchical order, and obtain the optimized hierarchical arrangement result; S2.6 If the difference between the optimized hierarchical arrangement result and the initial calculation result exceeds the preset threshold, use the support vector machine algorithm to separate the abnormal data and obtain the final hierarchical configuration parameters; S2.7 Generate the complete hierarchical distribution data of the multi-layer screening mesh according to the final hierarchical configuration parameters.

[0008] S3 drives the screening device to operate by grading configuration parameters. After the garbage is input from the top, it falls layer by layer. For the particles filtered by the mesh size of each layer, the garbage classification data falling into the corresponding collection tank is obtained. S3.1 Initializes the operating state of the screening device through grading configuration parameters, and obtains the initial distribution data when the garbage is input from the top. S3.2 Based on the initial distribution data and combined with the mesh size, judges the particle distribution characteristics of the layer-by-layer falling, and obtains the particle quantity data filtered by each layer. S3.3 Adopts the correspondence between the particle quantity data and the collection tank to determine the garbage categories received by each collection tank, and obtains the preliminary classification result. S3.4 Compares the preliminary classification result with the preset threshold. If the difference exceeds the threshold, the support vector machine is used to separate the abnormal particle data to obtain the adjusted classification data. S3.5 According to the adjusted classification data, optimizes the parameter control for the layer-by-layer falling process to obtain the optimized device operating parameters. S3.6 Drives the screening device again through the optimized device operating parameters to obtain the final garbage classification data. S3.7 Based on the final garbage classification data and combined with the data acquisition process, generates the complete classification distribution of multi-layer screening.

[0009] S4 Extracts the garbage weight information from the collection tank, and adopts the preset weight threshold judgment algorithm. If the weight exceeds the complete component threshold, it is marked as the first gradient. If it is between the repair component thresholds, it is marked as the second gradient to obtain the grading mark result. S4.1 Obtains the garbage weight data from the collection tank and gets the weight data through the sensor acquisition technology. S4.2 Adopts the preset threshold to judge the weight data, and determines the complete component or the repair component through the comparison method. S4.3 If the weight data exceeds the complete component threshold, it is marked as the first gradient through the marking algorithm. S4.4 If the weight data is between the repair component thresholds, it is marked as the second gradient through the marking algorithm. S4.5 For the first gradient and the second gradient, adopts the classification algorithm to obtain the grading mark result. S4.6 Saves the grading mark result through the data storage technology to obtain the basis for subsequent processing. S4.7 According to the grading mark result, adopts the scheduling algorithm to allocate the garbage treatment tasks and determine the treatment priorities.

[0010] S5 According to the grading marking result, trigger the conveyor belt operation logic. If it is marked as the first gradient, switch the direction to the complete component processing line; if it is marked as the second gradient, switch to the repaired component processing line, and determine the conveyor belt operation direction parameter; S5.1 Through the grading marking result, obtain the trigger logic data. If it is marked as the first gradient, switch to the complete component processing line to obtain the operation direction parameter; S5.2 From the trigger logic data, judge whether the second gradient mark exists. If it exists, switch to the repaired component processing line to determine the conveyor belt operation status; S5.3 According to the direction switching result, obtain the processing line type, and use the preset rules to judge the path of the complete component or the repaired component to obtain the path allocation result; S5.4 Through the path allocation result, determine the value of the operation direction parameter, and use the conveyor belt control module to adjust the operation logic to obtain the adjusted status; S5.5 Obtain the adjusted status, judge whether the operation direction is consistent with the grading mark. If not, update the direction parameter through the feedback mechanism; S5.6 From the updated direction parameter, obtain the conveyor belt operation data, and use the support vector machine algorithm to optimize the operation logic to determine the final operation direction; S5.7 Through the final operation direction, obtain the processing line operation status, judge whether the component processing is completed, and obtain the component processing result.

[0011] S6 After obtaining the conveyor belt operation direction parameter, perform the directional transmission operation. The garbage is imported from the bottom of the collection tank to the corresponding processing line through the conveyor belt, and the transmission path is monitored by the sensor to judge whether there is a path deviation and record the deviation data; S6.1 Obtain the conveyor belt operation direction parameter, and judge whether the direction meets the set value through the preset threshold to obtain the control signal for directional transmission; S6.2 Perform the directional transmission operation through the control signal, and import the garbage from the bottom of the collection tank to the processing line to determine the completion status of the transmission action; S6.3 The sensor monitors the transmission path in real time, obtains the actual trajectory data of the path, and judges whether there is a path deviation; S6.4 If the path deviation exceeds the preset range, record the deviation data through the deviation calculation formula to obtain the magnitude and direction of the deviation amount; S6.5 Deviation calculation formula: D = |T actual - T set|, where D is the deviation amount, T actual is the actual trajectory, and T set is the set trajectory; S6.6 Use the obtained deviation data to cluster the deviation distribution through the K-means algorithm to determine the concentrated area where the deviation occurs; S6.7 According to the distribution characteristics of the concentrated area, if the deviation is concentrated in a certain transmission path, adjust the conveyor belt running direction parameter to obtain an optimized control signal; S6.8 Re - execute the directional transmission operation with the optimized control signal, and the sensor monitors the transmission path again to determine whether the deviation is reduced below the preset threshold.

[0012] S7 For the path deviation data, use a deviation correction algorithm to adjust the running angle of the conveyor belt. If the deviation is greater than the preset threshold, dynamically correct the direction parameter, and drive the conveyor belt again with the corrected parameter to obtain a stable garbage transmission flow; S7.1 Obtain the deviation data corresponding to the path deviation through the sensor, and use a deviation correction algorithm to process the deviation data to obtain the running angle adjustment value; S7.2 If the running angle adjustment value exceeds the preset threshold, generate a direction parameter through dynamic correction to obtain an adjusted direction parameter; S7.3 Drive the conveyor belt with the adjusted direction parameter to obtain the real - time state data of garbage transmission; S7.4 Judge the fluctuation situation of the transmission flow through the real - time state data to obtain the fluctuation characteristic value; S7.5 Process the transmission flow with a smoothing algorithm according to the fluctuation characteristic value to obtain the judgment basis for the stable state; S7.6 If the fluctuation characteristic value does not reach the stable state, readjust the running angle through the deviation correction algorithm to obtain a new adjustment parameter; S7.7 Drive the conveyor belt with the new adjustment parameter to obtain a stable garbage transmission flow.

[0013] S8 Extract the input data of each processing line from the stable garbage transmission flow, analyze the real - time flow rates of complete components, repairable components, recycled raw materials, and filling materials through an information processing system, and determine the load - balancing state of each processing line; S8.1 Obtain the input data of each processing line from the garbage transmission flow through the sensor and store it in the database to obtain the original data stream of each processing line; S8.2 Use an information processing system to classify the original data stream, identify complete components, repairable components, recycled raw materials, and filling materials, and determine the real - time flow rates of each category; S8.3 Compare the real - time flow rate with a preset threshold. If the real - time flow rate of a certain processing line exceeds the threshold, mark it as a high - load state to obtain the load distribution of each processing line; S8.4 Obtain the load distribution data, calculate the deviation between each processing line and the average load, and judge the load - balancing state; S8.5 For the processing line in a high-load state, predict the changing trend of real-time traffic through a linear regression algorithm to obtain the traffic adjustment requirements. S8.6 According to the traffic adjustment requirements, dynamically allocate the garbage transmission flow to the low-load processing line to determine the adjusted load balancing state. S8.7 Through systematic analysis and comparison of the load balancing states before and after adjustment, obtain the optimized real-time traffic data and judge the operation stability of the processing line.

[0014] S9 Dynamically adjust the operation speeds of each processing line according to the load balancing state. If the traffic of a certain processing line exceeds the upper limit, reduce the speed of the corresponding conveyor belt. If the traffic is lower than the lower limit, increase the speed to generate an optimized sorting chain operation plan.

[0015] S9.1 By monitoring the traffic states of each processing line, obtain the real-time load balancing data and determine the processing lines with traffic exceeding the upper limit or lower than the lower limit. S9.2 Extract the threshold values of the upper and lower traffic limits from the real-time load balancing data and judge the operation speed adjustment requirements of each processing line. S9.3 For the operation speed adjustment requirements, adopt the preset speed control rules to calculate the changing trend of the conveyor belt speed. S9.4 According to the changing trend, dynamically adjust the conveyor belt speeds of each processing line to obtain the optimized operation parameters of the sorting chain. S9.5 Through the optimized operation parameters, update the speed control instructions of the processing line to obtain the adjusted traffic state data. S9.6 Extract the equilibrium state index from the adjusted traffic state data and judge whether the load balancing reaches the expected state. S9.7 If the equilibrium state index does not reach the expectation, repeat monitoring the traffic state and adjusting the conveyor belt speed to obtain the final sorting chain operation plan. The present invention can achieve the following beneficial effects: 1. Through multi-sensor fusion detection and intelligent algorithm analysis, the present invention can accurately identify solid wastes of different types and specifications, realizing automatic and precise sorting. The system can perform intelligent grading according to the physical characteristics of the wastes, significantly improving the sorting accuracy and efficiency and solving the problems of low efficiency and large errors in traditional manual sorting.

[0016] 2. The present invention adopts an intelligent grading processing technology, which can effectively distinguish recyclable components, repairable materials, and recycled raw materials, realizing the rational utilization of various construction wastes. By optimizing the sorting process, the utilization rate of recyclable materials is greatly improved, reducing resource waste.

[0017] 3. Through real-time monitoring and intelligent regulation, the system realizes the full-automatic operation from garbage sorting to classified treatment. By adopting adaptive control technology, it can automatically adjust the operation parameters of equipment according to the processing volume, ensuring the stable and efficient operation of the system and significantly reducing the need for manual intervention. Detailed implementation manner

[0018] A method for recycling solid waste during the demolition of prefabricated bridges, the specific steps are as follows: S1. Obtain the initial accumulation data of the prefabricated bridge demolition waste. By scanning the volume and density distribution of the garbage heap through sensors, determine the range of garbage particle sizes and mass characteristics, and obtain the garbage attribute set before sorting.

[0019] Collect garbage heap data through sensors, obtain volume distribution and density distribution, and determine the initial accumulation state. Extract particle size and mass characteristics from sensor data to obtain the size range and characteristic determination results. Use a clustering algorithm to group the particle size and density distribution to judge the classification basis of garbage particles. According to the classification basis, combine the volume distribution and attribute set to determine the characteristic distribution before sorting. By comparing the characteristic distribution with the mass characteristics, obtain the distribution area of abnormal particles. If the abnormal particles exceed the preset threshold, use the support vector machine algorithm to separate the abnormal data and obtain the adjusted attribute set. Generate optimized data before sorting according to the adjusted attribute set and the scanning results.

[0020] For example, when obtaining the initial accumulation data of the prefabricated bridge demolition waste, first use a laser scanner to perform three-dimensional modeling on the garbage heap to obtain its volume data. For example, the measured volume of the garbage heap is 120 cubic meters. Then, use a density sensor to measure the density distribution of the garbage heap. Through multi-point sampling and data analysis, the average density of the garbage heap is obtained as 0.8 tons per cubic meter, and the density distribution range is 0.6 to 1.0 tons per cubic meter. To determine the range of garbage particle sizes, use image recognition technology to scan the garbage heap, analyze the particle sizes through image processing algorithms, and obtain the particle size range of 0.1 to 50 millimeters, among which 80% of the particle sizes are concentrated between 1 and 10 millimeters. In the mass characteristic analysis, weigh the garbage heap through a weight sensor, measure the total mass as 96 tons, and combine the density data to calculate the mass distribution characteristics of the garbage heap. Finally, use a data fusion algorithm to integrate the volume, density, particle size, and mass characteristic data to form a garbage attribute set before sorting. For example, the volume of the garbage heap is 120 cubic meters, the average density is 0.8 tons per cubic meter, the particle size range is 0.1 to 50 millimeters, and the total mass is 96 tons, providing accurate data support for subsequent sorting and treatment.

[0021] Table 1: Parameters for collecting initial garbage attribute data

[0022] S2. Calculate the mesh size range of each layer of screening mesh according to the garbage attribute set, and arrange them from the largest mesh at the top to the smallest mesh at the bottom using a decreasing algorithm to generate the hierarchical configuration parameters of the multi-layer screening mesh.

[0023] Obtain the initial mesh size range of each layer of screening mesh through the garbage attribute set, arrange the hierarchical order using a decreasing algorithm, and get the preliminary hierarchical configuration parameters. Extract the size ranges of the top mesh and the bottom mesh from the preliminary hierarchical configuration parameters, judge the uniformity of the hierarchical arrangement, and obtain the adjusted mesh size distribution. According to the adjusted mesh size distribution, group the screening mesh layers to obtain the configuration parameters of each layer. Compare the configuration parameters of each layer with the attribute set to judge whether there are abnormal mesh sizes, and obtain the corrected hierarchical configuration. Use the corrected hierarchical configuration and combine with the decreasing algorithm to rearrange the hierarchical order to obtain the optimized hierarchical arrangement result. If the difference between the optimized hierarchical arrangement result and the initial calculation result exceeds the preset threshold, separate the abnormal data through the support vector machine algorithm to obtain the final hierarchical configuration parameters. Generate the complete hierarchical distribution data of the multi-layer screening mesh according to the final hierarchical configuration parameters. For example, based on the particle size distribution data in the garbage attribute set, optimize the screening mesh layer configuration using the dynamic programming algorithm. Set the initial maximum mesh size to 50 mm to match the largest particle size in the garbage pile, and then calculate the mesh sizes of each layer according to the exponential decreasing law. The mesh size of the second layer is set to 30 mm, the third layer is 15 mm, the fourth layer is 5 mm, and the bottom layer is 1 mm to ensure that the interception rate of each layer matches the particle cumulative distribution function. Establish an objective function by discretizing the particle size interval, use the gradient descent method to iteratively solve the optimal mesh combination. When the proportion of particle sizes in the range of 1 to 10 mm is 80%, the mesh density of the middle layer needs to be doubled. For example, the number of mesh holes in the third layer is increased to 2000 per square meter to improve the sorting efficiency. Combine with the garbage density distribution data, apply a mesh compressive strength compensation coefficient of 1.5 to the layers corresponding to the high-density area (1.0 ton / m³) to ensure the structural stability of the sieve mesh. Finally, output a five-layer screening mesh configuration parameter table, including process parameters such as the mesh size tolerance of each layer ±0.2 mm, the opening rate of 65% to 80%, and the stainless steel material thickness of 2 to 5 mm, and verify through finite element analysis that the deformation threshold of the sieve mesh under a 96-ton load is less than 0.1 mm.

[0024] S3. Drive the screening equipment to run through the hierarchical configuration parameters. After the garbage is put in from the top and falls layer by layer, obtain the garbage classification data of the particles filtered by the mesh size of each layer and falling into the corresponding collection tank.

[0025] Initialize the operating state of the screening device by grading configuration parameters, obtain the initial distribution data when the garbage is input from the top. According to the initial distribution data and the mesh size, judge the particle distribution characteristics of the layer-by-layer falling, and obtain the particle quantity data after filtering for each layer. Use the corresponding relationship between the particle quantity data and the collection tank to determine the garbage categories received by each collection tank, and obtain the preliminary classification result. Compare the preliminary classification result with the preset threshold. If the difference exceeds the threshold, use the support vector machine to separate the abnormal particle data and obtain the adjusted classification data. According to the adjusted classification data, optimize the parameter control for the layer-by-layer falling process to obtain the optimized operating parameters of the device. Drive the screening device again with the optimized operating parameters of the device to obtain the final garbage classification data. According to the final garbage classification data and combined with the data acquisition process, generate the complete classification distribution of multi-layer screening. For example, after the garbage is input into the screening device from the top, it is driven by a vibration motor with a vibration frequency of 15 Hz to ensure uniform distribution of the particles and layer-by-layer falling. The mesh size of the first layer is 40 mm. The Gaussian distribution model is used to calculate the particle passing rate, and the interception rate is set at 95%. The proportion of garbage with a particle size greater than 40 mm in the collection tank is 85%. The mesh size of the second layer is 25 mm. The Monte Carlo simulation is used to optimize the particle falling path to ensure an interception rate of 90%. The proportion of garbage with a particle size between 25 and 40 mm in the collection tank is 78%. The mesh size of the third layer is 12 mm. Combining the particle density distribution data, the mesh density is adjusted to 1800 holes per square meter using the linear regression algorithm, and the interception rate is 88%. The proportion of garbage with a particle size between 12 and 25 mm in the collection tank is 75%. The mesh size of the fourth layer is 3 mm. The Poisson distribution model is used to analyze the particle passing rate, and the interception rate is 85%. The proportion of garbage with a particle size between 3 and 12 mm in the collection tank is 70%. The mesh size of the bottom layer is 0.5 mm. The neural network algorithm is used to optimize the particle falling trajectory, and the interception rate is 80%. The proportion of garbage with a particle size less than 0.5 mm in the collection tank is 65%. The garbage classification data of each collection tank is collected in real time by sensors and uploaded to the central control system. Combining with the garbage composition analysis algorithm, a garbage classification report is generated for optimizing the subsequent processing process.

[0026] Table 2: Graded configuration parameters of the screening mesh

[0027] Table 3: Operating data of graded screening

[0028] S4. Extract the garbage weight information from the collection tank and use the preset weight threshold judgment algorithm. If the weight exceeds the complete component threshold, it is marked as the first gradient. If it is between the repair component thresholds, it is marked as the second gradient to obtain the grading mark result.

[0029] Obtain the garbage weight data from the collection tank. Use sensor acquisition technology to get the weight data. Perform threshold judgment on the weight data using a preset threshold. Determine the complete component or repaired component through a comparison method. If the weight data exceeds the complete component threshold, mark it as the first gradient through a marking algorithm. If the weight data is between the repaired component thresholds, mark it as the second gradient through a marking algorithm. For the first gradient and the second gradient, use a classification algorithm to obtain the classification marking result. Save the classification marking result through data storage technology to obtain the basis for subsequent processing. According to the classification marking result, use a scheduling algorithm to allocate garbage processing tasks and determine the processing priority. For example, during the extraction of garbage weight information, first, use a pressure sensor to collect the garbage weight data in the collection tank in real time, and set the sampling frequency to 10 Hz to ensure data continuity.

[0030] For example, when the sensor detects that the current weight is 12.5 kg, the system calls a preset weight threshold judgment algorithm for processing. This algorithm uses a dual-threshold classification mechanism, where the complete component threshold is set to 15.0 kg, and the repaired component threshold range is from 5.0 kg to 15.0 kg. When the detected weight data is 18.2 kg, since it exceeds the complete component threshold, the system automatically marks it as the first gradient and triggers the start signal of the compression device. If the detected weight is 9.7 kg, it falls within the repaired component threshold range, is marked as the second gradient, and activates the sorting robotic arm. For lightweight garbage of 3.2 kg, because it is lower than the repaired component threshold, the system classifies it as non-recyclable. The algorithm implementation uses a sliding window mean filter, with the window width set to 5 sampling points to eliminate instantaneous fluctuations. The error between the filtered data and the threshold comparison is controlled within ±0.3 kg. The classification result is output in JSON format, including fields such as timestamp, weight value, and gradient marking, for downstream processing systems to call. The entire process is controlled by a state machine, and the classification operation is only triggered when the weight data exceeds the threshold for 3 seconds continuously, to avoid misjudgment caused by instantaneous interference.

[0031] Table 4: Weight classification threshold parameters

[0032] S5. According to the classification marking result, trigger the conveyor belt operation logic. If it is marked as the first gradient, switch the direction to the complete component processing line. If it is marked as the second gradient, switch to the repaired component processing line, and determine the conveyor belt operation direction parameter.

[0033] By grading the marking results, trigger logic data is obtained. If it is marked as the first gradient, switch to the complete component processing line to obtain the running direction parameter. From the trigger logic data, judge whether the second gradient marking exists. If it exists, switch to the repaired component processing line to determine the running state of the conveyor belt. According to the direction switching result, obtain the type of processing line, and use the preset rules to judge the path of the complete component or the repaired component to obtain the path allocation result. Through the path allocation result, determine the value of the running direction parameter, and use the conveyor belt control module to adjust the running logic to obtain the adjusted state. Obtain the adjusted state, and judge whether the running direction is consistent with the grading marking. If not, update the direction parameter through the feedback mechanism. From the updated direction parameter, obtain the running data of the conveyor belt, and use the support vector machine algorithm to optimize the running logic to determine the final running direction. Through the final running direction, obtain the running state of the processing line, and judge whether the component processing is completed to obtain the component processing result.

[0034] For example, in the conveyor belt control system, first, the surface quality parameters of the component are obtained through the vision detection unit, such as the proportion of the defective area and the crack depth. The convolutional neural network algorithm is used to analyze the component image. When the defective area exceeds 5% or the crack depth is greater than 0.8 mm, it is marked as the second gradient, otherwise it is marked as the first gradient. For the components marked as the first gradient, the conveyor belt direction parameters are set to an angle of 90 degrees, a speed of 1.2 m / s, and the target position is the coordinate point X = 1500, Y = 800 of the complete component processing line. At the same time, the No. 3 robotic arm is activated for the grasping operation. For the second gradient components, the system adjusts the direction parameters to an angle of 135 degrees, a speed of 0.8 m / s, and the target position points to the coordinate point X = 2000, Y = 1200 of the repair line, and triggers the No. 5 pneumatic fixture to be ready to receive. During the direction switching process, the PLC controller monitors the number of pulses fed back by the encoder in real time. When the cumulative pulses reach the preset value of 2500, the braking logic is immediately executed to ensure that the component decelerates 2 meters before the fork. The system writes the running parameters into the Direction field of the database table "Conveyor_Status" through the OPC UA protocol, and at the same time updates the Last_Modified timestamp to the current system time. When two gradient components arrive at the sorting area at the same time, the priority arbitration module schedules according to the time difference. If the time interval is less than 500 milliseconds, the first gradient component is given priority, and a delay instruction is sent to the adjacent station through the RS485 bus.

[0035] Table 5: Conveyor Belt Control Parameters

[0036] After obtaining the conveyor belt running direction parameter, perform the directional transmission operation. Garbage is imported from the bottom of the collection tank to the corresponding processing line through the conveyor belt. Monitor the transmission path through sensors, determine whether there is a path deviation, and record the deviation data.

[0037] Obtain the conveyor belt running direction parameter, and judge whether the direction conforms to the set value through a preset threshold to obtain the control signal for directional transmission. Perform the directional transmission operation through the control signal. Garbage is imported from the bottom of the collection tank to the processing line, and the completion status of the transmission action is determined. The sensor monitors the transmission path in real time, obtains the actual trajectory data of the path, and judges whether there is a path deviation. If the path deviation exceeds the preset range, record the deviation data through the deviation calculation formula to obtain the magnitude and direction of the deviation amount. Deviation calculation formula: D = |Tactual - Tset|, where D is the deviation amount, Tactual is the actual trajectory, and Tset is the set trajectory. Using the obtained deviation data, cluster the deviation distribution through the K-means algorithm to determine the concentrated area where the deviation occurs. According to the distribution characteristics of the concentrated area, if the deviation is concentrated on a certain section of the transmission path, adjust the conveyor belt running direction parameter to obtain the optimized control signal. Re-perform the directional transmission operation through the optimized control signal, and the sensor monitors the transmission path again to judge whether the deviation is reduced below the preset threshold.

[0038] For example, after obtaining the conveyor belt running direction parameter, the system calculates the running speed of the conveyor belt to be 0.5 meters per second through a preset algorithm, and adjusts the inclination angle of the conveyor belt to 15 degrees according to the type and weight of the garbage at the bottom of the collection tank to ensure that the garbage can be smoothly imported into the corresponding processing line. Through the infrared sensors installed on both sides of the conveyor belt, the system monitors the transmission path of the garbage in real time. The sensor collects data every 0.1 seconds and transmits the data to the central processing unit for analysis. The central processing unit uses an algorithm based on Kalman filtering to process the sensor data and judge whether there is a path deviation of the garbage. When it is detected that the deviation exceeds the preset threshold of 5 millimeters, the system will automatically record the deviation data and redirect the garbage to the correct path by adjusting the running speed and direction of the conveyor belt. At the same time, the system will store the deviation data in the database for subsequent analysis and optimization of the transmission path. In this way, the system can ensure that the garbage always maintains the correct path during transmission, improve the processing efficiency and reduce equipment wear.

[0039] S7. For the path deviation data, adopt the deviation correction algorithm to adjust the running angle of the conveyor belt. If the deviation is greater than the preset threshold, dynamically correct the direction parameter, and drive the conveyor belt again through the corrected parameter to obtain a stable garbage transmission flow.

[0040] The deviation data corresponding to the path deviation is obtained through sensors, and the deviation correction algorithm is used to process the deviation data to obtain the running angle adjustment value. If the running angle adjustment value exceeds the preset threshold, the direction parameter is generated through dynamic correction to obtain the adjusted direction parameter. The conveyor belt is driven by the adjusted direction parameter, and the real-time status data of garbage transmission is obtained. The fluctuation condition of the transmission flow is judged through the real-time status data to obtain the fluctuation characteristic value. The transmission flow is processed by the smoothing algorithm according to the fluctuation characteristic value to obtain the determination basis of the stable state. If the fluctuation characteristic value does not reach the stable state, the running angle is readjusted through the deviation correction algorithm to obtain the new adjustment parameter. The conveyor belt is driven by the new adjustment parameter to obtain a stable garbage transmission flow. For example, during the operation of the conveyor belt, the lateral deviation data between the center line of the garbage flow and the preset path is collected in real time by a laser sensor. The sampling frequency is set to 100 Hz, and the accuracy is ±0.5 mm. When the average deviation of 5 consecutive sampling points is detected to exceed the threshold of 10 mm, the dynamic correction algorithm is triggered. The PID control algorithm is used to calculate the correction amount, where the proportional coefficient Kp is set to 0.8, the integral time Ti is 0.2 s, and the differential time Td is 0.05 s. According to the deviation e(t) = current value - target value, the output control amount is calculated. The calculated angle correction amount is converted into a stepping motor pulse signal to drive the adjusting roller to adjust the deflection angle of the conveyor belt with an accuracy of 0.1° / step. After each adjustment, the system continuously monitors the deviation data within the next 3 seconds. If the standard deviation drops below 2 mm, the correction is determined to be effective; otherwise, iterative calculation is performed with the current deviation as the initial value until the stability requirement is met. To eliminate the hysteresis effect caused by mechanical clearance, a feed-forward compensation link of 0.3 s is added to the control algorithm to predict the deviation change trend through historical data. All operating parameters and correction records are stored in the database for subsequent analysis of the conveyor belt wear law and optimization of the threshold setting.

[0041] S8. Extract the input data of each processing line from the stable garbage transmission flow, and analyze the real-time flow rates of the complete components, repairable components, recycled raw materials, and filling materials through the information processing system to determine the load balance state of each processing line.

[0042] Obtain the input data of each processing line from the waste transfer stream through sensors and store it in the database to obtain the original data stream of each processing line. Use an information processing system to classify the original data stream, identify complete components, repairable components, recycled raw materials, and filling materials, and determine the real-time flow of each category. Compare the real-time flow with a preset threshold. If the real-time flow of a certain processing line exceeds the threshold, mark it as a high-load state to obtain the load distribution of each processing line. Obtain the load distribution data, calculate the deviation of each processing line from the average load, and judge the load balance state. For the processing lines in the high-load state, predict the change trend of the real-time flow through a linear regression algorithm to obtain the flow adjustment requirements. According to the flow adjustment requirements, dynamically allocate the waste transfer stream to the low-load processing lines to determine the adjusted load balance state. Through systematic analysis and comparison of the load balance state before and after adjustment, obtain the optimized real-time flow data and judge the operating stability of the processing lines.

[0043] For example, extract the input data of each processing line from a stable waste transfer stream. First, collect the waste flow data of each processing line in real time through a sensor network. For example, the hourly processing volume is 50 tons, and the sensor accuracy is ±0.5 tons. These data are transmitted to the information processing system through the Internet of Things protocol, and the system uses the Kalman filter algorithm to denoise the data to ensure the accuracy of the data. Next, the system uses a machine learning model to classify the waste and identify complete components, repairable components, recycled raw materials, and filling materials.

[0044] For example, classify the image data through a convolutional neural network (CNN) with an accuracy rate of 95%. The classified data is recorded in real time, and the flow of each type of material is calculated. For example, the hourly flow of complete components is 10 tons, repairable components is 15 tons, recycled raw materials is 20 tons, and filling materials is 5 tons. Based on these flow data, the system uses a load balancing algorithm (such as the least connection number algorithm) to dynamically adjust the load of each processing line to ensure that the utilization rate of each processing line remains between 80% and 90%.

[0045] For example, when the load of a certain processing line reaches 90%, the system will automatically transfer part of the waste flow to the processing line with a lower load to ensure the stable operation of the overall system. Through real-time monitoring and dynamic adjustment, the system can effectively avoid overloading or idling of the processing lines and improve the overall processing efficiency.

[0046] S9. Dynamically adjust the operating speed of each processing line according to the load balance state. If the flow of a certain processing line exceeds the upper limit, reduce the speed of the corresponding conveyor belt. If the flow is lower than the lower limit, increase the speed to generate an optimized sorting chain operation plan.

[0047] By monitoring the flow status of each processing line, real-time load balancing data is obtained to determine the processing lines with flow exceeding the upper limit or falling below the lower limit. Thresholds for the upper and lower limits of the flow are extracted from the real-time load balancing data to judge the requirements for adjusting the operating speed of each processing line. For the requirements for adjusting the operating speed, a preset speed control rule is adopted to calculate the changing trend of the conveyor belt speed. According to the changing trend, the conveyor belt speeds of each processing line are dynamically adjusted to obtain the optimized operating parameters of the sorting chain. By optimizing the operating parameters, the speed control instructions of the processing line are updated to obtain the adjusted flow status data. An equilibrium status index is extracted from the adjusted flow status data to judge whether the load balancing reaches the expected status. If the equilibrium status index does not reach the expectation, the flow status is repeatedly monitored and the conveyor belt speed is adjusted to obtain the final operating plan of the sorting chain.

[0048] For example, in the dynamic load balancing adjustment system, first, the flow data of each processing line is collected in real time through sensors. For example, the current flow of processing line A is 120 pieces per minute, and that of processing line B is 80 pieces per minute. The preset upper limit of the flow is 100 pieces per minute, and the lower limit is 60 pieces per minute. The system uses the PID control algorithm for calculation. When the flow of processing line A exceeds the upper limit, based on the deviation value of 20 pieces per minute, combined with the integral time constant of 0.5 and the differential time constant of 0.2, the algorithm calculates that the conveyor belt speed needs to be reduced by 15%. At the same time, the flow of processing line B is lower than the lower limit. Based on the deviation value of -20 pieces per minute, the algorithm adjusts the conveyor belt speed to increase by 25%. The system performs secondary optimization on the adjustment amplitude through fuzzy logic. For example, when the historical data shows that the flow of processing line A fluctuates greatly, a decay coefficient of 0.7 is added to correct the speed adjustment amplitude from 15% to 10.5%. The adjusted speed instruction is sent to the frequency converter through the PLC. For example, the frequency of the conveyor belt motor of processing line A is reduced from 50 Hz to 44.75 Hz, and that of processing line B is increased from 40 Hz to 50 Hz. The system performs data sampling and calculation every 5 seconds and records the real-time status of each processing line. For example, the load rate of processing line C is 85%, which is within the normal range, so the current speed remains unchanged. All data is stored in the database for subsequent analysis. For example, through regression analysis, it is found that the energy consumption of processing line D is the lowest when the flow is 90 pieces per minute, and accordingly, its optimal flow range is updated to 85 - 95 pieces per minute.

[0049] Table 6: Load Balancing Regulation Parameters

[0050] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for recycling solid waste during the demolition of an assembled bridge, characterized in that, The method includes: Obtain the initial accumulation data of the prefabricated bridge demolition waste. By using sensors to scan the volume and density distribution of the waste pile, determine the range of waste particle sizes and mass characteristics, and obtain the set of waste attributes before sorting. According to the set of waste attributes, calculate the range of mesh sizes for each layer of screening mesh, and arrange them from the largest mesh at the top to the smallest mesh at the bottom using a decreasing algorithm to generate the grading configuration parameters for multiple layers of screening mesh. Drive the screening equipment to operate through the grading configuration parameters. After the waste is input from the top and falls layer by layer, for the particles filtered by the mesh size of each layer, obtain the waste classification data falling into the corresponding collection tank. Extract the waste weight information from the collection tank, and use a preset weight threshold judgment algorithm. If the weight exceeds the complete component threshold, mark it as the first gradient; if it is between the repaired component thresholds, mark it as the second gradient to obtain the grading marking result. According to the grading marking result, trigger the conveyor belt operation logic. If it is marked as the first gradient, switch the direction to the complete component processing line; if it is marked as the second gradient, switch to the repaired component processing line to determine the conveyor belt operation direction parameter. After obtaining the conveyor belt operation direction parameter, perform the directional transmission operation. The waste is imported from the bottom of the collection tank to the corresponding processing line through the conveyor belt, and the transmission path is monitored by sensors to judge whether there is a path deviation and record the deviation data. For the path deviation data, use a deviation correction algorithm to adjust the running angle of the conveyor belt. If the deviation is greater than the preset threshold, dynamically correct the direction parameter, and drive the conveyor belt again through the corrected parameter to obtain a stable waste transmission flow. Extract the input data of each processing line from the stable waste transmission flow, and analyze the real-time flow rates of complete components, repairable components, recycled raw materials, and filling materials through an information processing system to determine the load balance status of each processing line. According to the load balance status, dynamically adjust the running speeds of each processing line. If the flow rate of a certain processing line exceeds the upper limit, reduce the speed of the corresponding conveyor belt; if the flow rate is lower than the lower limit, increase the speed to generate an optimized sorting chain operation plan.

2. The method for recycling solid waste during the demolition of an assembled bridge according to claim 1, wherein The obtaining of the initial accumulation data of the prefabricated bridge demolition waste, by using sensors to scan the volume and density distribution of the waste pile, determining the range of waste particle sizes and mass characteristics, and obtaining the set of waste attributes before sorting, includes: Collect waste pile data through sensors, obtain the volume distribution and density distribution, and determine the initial accumulation state. Extract the particle size and mass characteristics from the sensor data to obtain the size range and characteristic determination result. Use a clustering algorithm to group the particle size and density distribution, and judge the classification basis of waste particles. According to the classification basis, combine the volume distribution and the set of attributes to determine the characteristic distribution in the pre-sorting state. Compare the characteristic distribution with the mass characteristics to obtain the distribution area of abnormal particles. If the abnormal particles exceed the preset threshold, use the support vector machine algorithm to separate the abnormal data to obtain the adjusted set of attributes. Generate the optimized data before sorting according to the adjusted set of attributes and the scanning result.

3. A method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, Calculate the mesh size range of each layer of the screening mesh according to the garbage attribute set, and arrange them from the largest mesh at the top to the smallest mesh at the bottom using a decreasing algorithm to generate the hierarchical configuration parameters of the multi-layer screening mesh, including: Obtain the initial mesh size range of each layer of the screening mesh through the garbage attribute set, arrange the hierarchical order using a decreasing algorithm, and obtain the preliminary hierarchical configuration parameters. Extract the size ranges of the top and bottom meshes from the preliminary hierarchical configuration parameters, judge the uniformity of the hierarchical arrangement, and obtain the adjusted mesh size distribution. According to the adjusted mesh size distribution, group the screening mesh layers to obtain the configuration parameters of each layer. Compare the configuration parameters of each layer with the attribute set to judge whether there are abnormal mesh sizes, and obtain the corrected hierarchical configuration. Use the corrected hierarchical configuration and combine the decreasing algorithm to rearrange the hierarchical order to obtain the optimized hierarchical arrangement result. If the difference between the optimized hierarchical arrangement result and the initial calculation result exceeds the preset threshold, separate the abnormal data through the support vector machine algorithm to obtain the final hierarchical configuration parameters. Generate the complete hierarchical distribution data of the multi-layer screening mesh according to the final hierarchical configuration parameters.

4. The method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, Drive the screening equipment to run through the hierarchical configuration parameters. After the garbage is input from the top, it falls layer by layer. For the particles filtered by the mesh size of each layer, obtain the garbage classification data falling into the corresponding collection tank, including: Initialize the operating state of the screening equipment through the hierarchical configuration parameters, and obtain the initial distribution data when the garbage is input from the top. Combine the mesh size with the initial distribution data to judge the particle distribution characteristics of the falling layer by layer, and obtain the particle quantity data filtered by each layer. Use the corresponding relationship between the particle quantity data and the collection tank to determine the garbage categories received by each collection tank, and obtain the preliminary classification result. Compare the preliminary classification result with the preset threshold. If the difference exceeds the threshold, use the support vector machine to separate the abnormal particle data to obtain the adjusted classification data. According to the adjusted classification data, optimize the parameter control for the falling process layer by layer to obtain the optimized equipment operating parameters. Drive the screening equipment again with the optimized equipment operating parameters to obtain the final garbage classification data. Generate the complete classification distribution of the multi-layer screening according to the final garbage classification data and the data acquisition process.

5. A method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, Extract the garbage weight information from the collection tank and use the preset weight threshold judgment algorithm. If the weight exceeds the complete component threshold, mark it as the first gradient. If it is between the repair component thresholds, mark it as the second gradient to obtain the hierarchical marking result, including: Obtain the garbage weight data from the collection tank, and obtain the weight data through the sensor acquisition technology. Use the preset threshold to judge the weight data, and determine the complete component or the repair component through the comparison method. If the weight data exceeds the complete component threshold, mark it as the first gradient through the marking algorithm. If the weight data is between the repair component thresholds, mark it as the second gradient through the marking algorithm. For the first gradient and the second gradient, obtain the hierarchical marking result through the classification algorithm. Save the hierarchical marking result through the data storage technology to obtain the basis for subsequent processing. According to the hierarchical marking result, use the scheduling algorithm to allocate the garbage processing tasks and determine the processing priorities.

6. The method for recycling solid waste during the demolition of an assembled bridge according to claim 1, characterized in that, According to the grading marking results, trigger the conveyor belt operation logic. If it is marked as the first gradient, switch the direction to the complete component processing line. If it is marked as the second gradient, switch to the repaired component processing line. Determine the conveyor belt operation direction parameters, including: Obtain the trigger logic data through the grading marking results. If it is marked as the first gradient, switch to the complete component processing line to obtain the operation direction parameters; Judge whether the second gradient mark exists in the trigger logic data. If it exists, switch to the repaired component processing line to determine the conveyor belt operation status; According to the direction switching result, obtain the processing line type, and use the preset rules to judge the path of the complete component or the repaired component to obtain the path allocation result; Determine the operation direction parameter value through the path allocation result, and use the conveyor belt control module to adjust the operation logic to obtain the adjusted status; Obtain the adjusted status, and judge whether the operation direction is consistent with the grading mark. If not, update the direction parameter through the feedback mechanism; Obtain the conveyor belt operation data from the updated direction parameter, and use the support vector machine algorithm to optimize the operation logic to determine the final operation direction; Obtain the processing line operation status through the final operation direction, and judge whether the component processing is completed to obtain the component processing result.

7. A method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, After obtaining the conveyor belt operation direction parameters, perform the directional transmission operation. The garbage is imported from the bottom of the collection tank to the corresponding processing line through the conveyor belt. Monitor the transmission path through the sensor, and judge whether there is a path deviation and record the deviation data, including: Obtain the conveyor belt operation direction parameters, and judge whether the direction meets the set value through the preset threshold to obtain the control signal for directional transmission; Perform the directional transmission operation through the control signal. The garbage is imported from the bottom of the collection tank to the processing line to determine the completion status of the transmission action; The sensor monitors the transmission path in real time, obtains the actual trajectory data of the path, and judges whether there is a path deviation; If the path deviation exceeds the preset range, record the deviation data through the deviation calculation formula to obtain the magnitude and direction of the deviation; Deviation calculation formula: D = |T actual - T set|, where D is the deviation amount, T actual is the actual trajectory, and T set is the set trajectory; Use the obtained deviation data to cluster the deviation distribution through the K-means algorithm to determine the concentrated area where the deviation occurs; According to the distribution characteristics of the concentrated area, if the deviation is concentrated on a certain transmission path, adjust the conveyor belt operation direction parameter to obtain the optimized control signal; Perform the directional transmission operation again through the optimized control signal, and the sensor monitors the transmission path again to judge whether the deviation is reduced below the preset threshold.

8. A method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, For the path deviation data, use the deviation correction algorithm to adjust the conveyor belt operation angle. If the deviation is greater than the preset threshold, dynamically correct the direction parameter, and drive the conveyor belt again through the corrected parameter to obtain a stable garbage transmission flow, including: Obtain deviation data corresponding to the path deviation through a sensor, process the deviation data using a deviation correction algorithm to obtain an operating angle adjustment value. If the operating angle adjustment value exceeds a preset threshold, generate a direction parameter through dynamic correction to obtain an adjusted direction parameter. Drive the conveyor belt using the adjusted direction parameter to obtain real-time status data of garbage transmission. Judge the fluctuation condition of the transmission flow through the real-time status data to obtain a fluctuation characteristic value. Process the transmission flow using a smoothing algorithm according to the fluctuation characteristic value to obtain a determination basis for the stable state. If the fluctuation characteristic value does not reach the stable state, readjust the operating angle through the deviation correction algorithm to obtain a new adjustment parameter. Drive the conveyor belt using the new adjustment parameter to obtain a stable garbage transmission flow.

9. A method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, Extract the input data of each processing line from the stable garbage transmission flow, and analyze the real-time flow rates of complete components, repairable components, recycled raw materials, and filling materials through an information processing system to determine the load balance state of each processing line, including: Obtain the input data of each processing line from the garbage transmission flow through a sensor and store it in a database to obtain the original data stream of each processing line; Use an information processing system to classify the original data stream, identify complete components, repairable components, recycled raw materials, and filling materials, and determine the real-time flow rate of each category; Compare the real-time flow rate with a preset threshold. If the real-time flow rate of a certain processing line exceeds the threshold, mark it as a high-load state to obtain the load distribution of each processing line; Obtain load distribution data, calculate the deviation of each processing line from the average load, and judge the load balance state; For the processing line in the high-load state, predict the change trend of the real-time flow rate through a linear regression algorithm to obtain a flow rate adjustment requirement; According to the flow rate adjustment requirement, dynamically allocate the garbage transmission flow to the low-load processing line to determine the adjusted load balance state; Through system analysis and comparison of the load balance states before and after adjustment, obtain optimized real-time flow rate data and judge the operating stability of the processing line.

10. A method for recycling solid waste during the demolition of a prefabricated bridge according to claim 1, characterized in that, According to the load balance state, dynamically adjust the operating speed of each processing line. If the flow rate of a certain processing line exceeds the upper limit, reduce the speed of the corresponding conveyor belt. If the flow rate is lower than the lower limit, increase the speed to generate an optimized sorting chain operation plan, including: Monitor the flow rate status of each processing line to obtain real-time load balance data and determine the processing line with a flow rate exceeding the upper limit or lower than the lower limit; Extract the threshold values of the upper and lower flow rate limits from the real-time load balance data and judge the operating speed adjustment requirement of each processing line; For the operating speed adjustment requirement, use a preset speed control rule to calculate the change trend of the conveyor belt speed; According to the change trend, dynamically adjust the conveyor belt speed of each processing line to obtain optimized operating parameters of the sorting chain; Update the speed control instruction of the processing line through the optimized operating parameters to obtain the adjusted flow rate status data; Extract the equilibrium state index from the adjusted flow rate status data and judge whether the load balance reaches the expected state; If the equilibrium state index does not reach the expectation, repeat monitoring the flow rate status and adjusting the conveyor belt speed to obtain the final sorting chain operation plan.

Images