Building solid waste intelligent disposal and recycling system and method based on internet of things

Abstract

The application relates to the technical field of waste management, in particular to a building solid waste intelligent disposal and recycling system and method based on the Internet of Things, which comprises a solid waste classification optimization module, a solid waste treatment adaptation module, a resourceization path planning module, an environmental impact evaluation module and a scheduling optimization module.In the application, by identifying the particle size, density and energy consumption change of solid waste, the sorting priority is dynamically adjusted, the mixed material identification accuracy and separation efficiency are improved, the path is adjusted by comparing with the equipment capacity, the solid waste attribute is matched, the resource loss and misclassification are avoided, the extraction trend and concentration in the recycling link are extracted, the path matching degree is improved, the proportion of unusable components is reduced, the environmental load and energy consumption change in the treatment process are combined, the high-emission section is adjusted, the environmental impact is relieved, the task switching analysis jump frequency is combined, the adaptability and scheduling continuity of the system to the change interference are enhanced, and the resource utilization continuity, response sensitivity and environmental adaptation level are overall improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of waste management, in particular to a building solid waste intelligent disposal and recycling system and method based on Internet of Things. BACKGROUND

[0002] The technical field of waste management involves the classification, collection, transportation, disposal and resource utilization of various solid wastes such as industrial waste, household garbage and construction waste. The core tasks include the reduction, harmlessness and resource utilization of solid waste, aiming to improve waste disposal efficiency and environmental sustainability through reasonable means. It covers multiple technical paths such as front-end classification and recycling, middle-end transportation and deployment, end processing and disposal, and re-resource utilization. Current methods widely use Internet of Things sensing, automatic identification, mechanical sorting and environmental monitoring for whole-process management and control. Among them, the traditional building solid waste intelligent disposal and recycling system refers to the separation and preliminary processing of reusable components such as concrete blocks, steel bars, bricks, stones and wood in building demolition or reconstruction process mixed solid waste through manual sorting, fixed crushing and screening equipment and physical separation methods. Mobile crushing equipment is often used in combination with manual operation or semi-automatic control for building solid waste decomposition and screening, and the solid waste materials are reclassified and collected according to manual experience or preset program to realize the basic preparation for subsequent resource recovery processing.

[0003] The prior art relies on manual experience and preset program for rough classification of building solid waste, which is difficult to identify the particle size and density variation characteristics in real time, resulting in low matching degree between sorting path and material properties. When processing mixed solid waste, resource mixing or repeated work may occur, affecting the subsequent recycling effect. In actual application, manual identification often produces misjudgment due to material stacking density or equipment load fluctuation, resulting in unstable resource recovery efficiency. In addition, the solid waste treatment process cannot fully evaluate its short-time impact on the environment, lacks identification mechanism for emission peak period, and causes processing tasks to be executed in the energy consumption and pollution concentration period, increasing the external disturbance sensitivity of the system. For example, the equipment emission during the construction peak period cannot effectively avoid the scheduling rhythm, affecting the overall processing stability and environmental compatibility. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art, and to provide a building solid waste intelligent disposal and recycling system and method based on Internet of Things.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme: the building solid waste intelligent disposal and recycling system based on Internet of Things comprises:

[0006] The solid waste classification optimization module extracts a key period of classification efficiency fluctuation and generates a classification priority label value according to the composition characteristics of the building solid waste by analyzing the material particle size distribution, the density difference and the change range of the crushing energy consumption;

[0007] The solid waste treatment adaptation module matches the solid waste treatment equipment operation parameters and the sorting strategy in the corresponding period based on the classification priority label value, compares the equipment load state in the marked section with the current processing capacity, screens the sorting instruction corresponding to the deviation rate, and obtains a sorting path adjustment instruction set;

[0008] The resourceization path planning module extracts the change of resource recovery rate of the node before and after adjustment according to the sorting path adjustment instruction set, identifies the resourceization path concentration under the sorting trend, extracts the change section to form a resource adaptation interval, and obtains a resourceization path optimization trend;

[0009] The environmental impact assessment module calls the resourceization path optimization trend, collects environmental data in the solid waste treatment process, analyzes the coincidence length of the environmental load change and the energy consumption jump period, and outputs the environmental impact action length.

[0010] As a further scheme of the present application, the classification priority label value includes the particle size distribution fluctuation range, the density difference change range and the crushing energy consumption rising feature, the sorting path adjustment instruction set includes the sorting deviation amount, the load state abnormal value and the processing capacity deviation rate, the resourceization path optimization trend includes the resource recovery rate change interval, the resourceization path type and the path response amplitude, and the environmental impact action length includes the energy consumption fluctuation duration, the environmental load jump coincidence period and the disturbance duration.

[0011] As a further scheme of the present application, the solid waste classification optimization module includes:

[0012] The particle size distribution extraction submodule extracts the time sequence of the material particle size distribution based on the composition characteristics of the building solid waste, calculates the particle size difference value of adjacent sampling points, judges the fluctuation period and extracts the peak-to-trough distance, screens the section with a period difference exceeding a threshold value, and obtains a particle size distribution period fluctuation interval value;

[0013] The density difference calculation submodule calls the particle size distribution period fluctuation interval value, identifies the corresponding density difference data, analyzes the absolute density change range of adjacent time periods, compares with a set density difference threshold value, locates the mutation node, and obtains a density difference mutation amplitude interval value;

[0014] The classification state marking submodule identifies corresponding broken energy consumption and material characteristic data according to the density difference mutation amplitude interval value, extracts the energy consumption and characteristic data, calculates a classification complexity offset value in combination with the density difference and the particle size distribution fluctuation, sets an offset threshold value, marks a time section exceeding the threshold value, and obtains a classification priority marking value.

[0015] As a further scheme of the present application, the solid waste treatment adaptation module comprises:

[0016] The sorting data matching submodule extracts solid waste treatment equipment operation parameters and sorting data of the corresponding time period based on the classification priority marking value, calculates a load fluctuation amplitude at a sampling interval, aligns the particle size distribution and the load amplitude at the same time, and obtains a load linkage interval group.

[0017] The sorting deviation judgment submodule extracts equipment state sequences by calling the load linkage interval group, compares sorting changes and state differences, calculates a state difference normalization index, compares the normalized state difference with a preset offset boundary, analyzes the deviation intensity of the sorting point, extracts a sorting position index with a deviation intensity greater than a reference judgment value, and establishes an offset intensity index group.

[0018] The sorting instruction extraction submodule filters the position of the corresponding time point in the task instruction set based on the offset intensity index group, extracts instruction values, and sorts them in time sequence, removes duplicate instructions, and obtains a sorting path adjustment instruction group.

[0019] As a further scheme of the present application, the resource path planning module comprises:

[0020] The resource recovery rate extraction submodule extracts resource recovery rate data of nodes in the cycle before and after adjustment according to the sorting path adjustment instruction group, identifies resource boundary conditions, and obtains a resource recovery rate difference value.

[0021] The resource path identification submodule identifies resource path change trajectories in unit material paths by calling the resource recovery rate difference value, judges the resource quantity trend corresponding to path fluctuation, compares path amplitude reduction and resource quantity fluctuation in adjacent time periods, calculates a path fluctuation coupling degree index, filters synchronous fluctuation segments, and obtains a resource path characteristic section.

[0022] The path response interval identification submodule analyzes time and path displacement trends, judges direction consistency and amplitude change characteristics, filters fluctuation segments and aggregates them, and obtains a resource path optimization trend according to the resource path characteristic section.

[0023] As a further scheme of the present application, the environmental impact assessment module comprises:

[0024] The path trend extraction submodule calls the resource path optimization trend, extracts node task records and periodic path data, identifies fluctuation amplitude and fluctuation frequency in a period, and obtains fluctuation trend values of the periodic path;

[0025] The energy consumption jump identification submodule selects energy consumption and environmental load records in the same period according to environmental data in the solid waste treatment process based on the fluctuation trend values of the periodic path, compares data change amplitudes by day, screens time nodes with an amplitude greater than an energy consumption jump threshold, and obtains an energy consumption load jump period.

[0026] The environmental impact quantification submodule identifies the intersection length of path amplitude and energy consumption jump according to the energy consumption load jump period, performs weighted processing, and refers to amplitude frequency, combined amplitude value and periodic fluctuation to normalize the use time length in the differentiated period and output the environmental impact action time length.

[0027] As a further scheme of the present application, the system further comprises a scheduling optimization module:

[0028] The scheduling optimization module screens task scheduling instructions affected by environmental interference based on the environmental impact action time length, classifies device switching time and adjustment trigger frequency, identifies load jump overrun periods, and obtains solid waste treatment task load interference impact frequency.

[0029] The solid waste treatment task load interference impact frequency includes device switching frequency, adjustment instruction trigger times, and jump period overrun times.

[0030] As a further scheme of the present application, the scheduling optimization module comprises:

[0031] The instruction screening submodule screens matching task scheduling instructions based on the environmental impact action time length, identifies device time periods and device states, compares disturbance periods and instruction periods, eliminates low matching degree instructions, and obtains an instruction set affected by environmental interference.

[0032] The device classification submodule extracts instruction corresponding device switching time and adjustment trigger frequency from the instruction set affected by environmental interference, arranges them in switching time order, classifies them by frequency interval, counts the corresponding relationship between frequency bands and device time periods, and obtains device adjustment frequency band distribution values.

[0033] The interference identification submodule collects load jump amplitude and duration in the frequency band according to the device adjustment frequency band distribution values, judges whether it exceeds the jump amplitude threshold, identifies the frequency band interference intensity, sorts all frequency band interference intensities, determines the frequency band and jump period of the interference intensity, and obtains the solid waste treatment task load interference impact frequency.

[0034] The method for intelligent disposal and reuse of construction solid waste based on the Internet of Things is performed based on the above-mentioned intelligent disposal and reuse system for construction solid waste based on the Internet of Things, and includes the following steps:

[0035] S1: Based on the composition characteristics of construction solid waste, the particle size distribution and density difference changes are analyzed, the periods of sudden changes in crushing energy consumption peaks and sharp changes in material properties are extracted, the state switching points where density difference and energy consumption changes are synchronized are identified, the task levels are marked, and task state switching identification fragments are generated;

[0036] S2: Based on the task state switching identification segment, extract the sorting change data and the load change amplitude, compare the sorting mutation and the load fluctuation amplitude, analyze the correspondence between the sorting change and the processing capacity response, and obtain the sorting adjustment response trajectory segment;

[0037] S3: Based on the sorting and adjustment response trajectory segments, extract the changes in resource recovery rate values ​​and resource utilization path gradients before and after the response, analyze the node's resource utilization path decrease and resource quantity increase, identify the path attenuation path and compare it with the original cycle data, screen the segments with path decline characteristics, and generate a resource adaptation attenuation path distribution set;

[0038] S4: Based on the resource adaptation attenuation path distribution set, identifying environmental data within a corresponding time period, analyzing the overlapping period of energy consumption jump and path attenuation path, extracting the associated time period, and obtaining the environmental parameter interference section for sorting adjustment;

[0039] S5: Based on the environmental parameter interference section of the sorting adjustment, the scheduling control strategy records within the time period are extracted, the time interval of equipment switching and the adjustment trigger frequency are analyzed, and the high-frequency adjustment strategy fragments are screened to obtain the interference impact frequency of the solid waste treatment task load.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are:

[0041] In the present invention, by identifying the changes in particle size, density differences and energy consumption of construction solid waste during the treatment process, dynamic adjustment of sorting priority is achieved, thereby improving the recognition accuracy and separation efficiency of mixed materials. Combined with the comparison relationship between treatment status and equipment capacity, instructions are dynamically screened and paths are adjusted, so that the sorting process is more in line with the actual properties of solid waste, avoiding resource loss and sorting errors. Furthermore, in the resource recovery link, trend identification and concentration extraction are used to improve the matching degree of material recovery paths, effectively reducing the proportion of unusable components. At the same time, the environmental load and energy consumption change laws are integrated in task execution, and the affected sections are responsively adjusted to suppress the impact of concentrated emission periods on the external environment. Combined with the task switching behavior analysis, the frequency of jump interference in the processing process is improved to improve the system's adaptability to high-frequency changes and scheduling continuity, thereby enhancing the continuity of solid waste resource utilization, the response sensitivity of the treatment process and the environmental adaptability level as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a system flow chart of the present invention;

[0043] Figure 2 This is a flow chart of the solid waste classification optimization module in the present invention;

[0044] Figure 3 This is a flow chart of the solid waste treatment adaptation module in the present invention;

[0045] Figure 4 This is a flow chart of the resource path planning module in the present invention;

[0046] Figure 5 This is a flow chart of the environmental impact assessment module in the present invention;

[0047] Figure 6 This is a flow chart of the scheduling optimization module in the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0050] Please refer to Figure 1 The present application provides a technical solution: a building solid waste intelligent disposal and recycling system based on Internet of Things, comprising:

[0051] The solid waste classification optimization module extracts the key period of classification efficiency fluctuation and generates a classification priority label value by analyzing the material particle size distribution, density difference and change range of crushing energy consumption according to the composition characteristics of building solid waste;

[0052] The solid waste treatment adaptation module matches the solid waste treatment equipment operation parameters and sorting strategy corresponding to the time period based on the classification priority label value, compares the equipment load state in the marked section with the current processing capacity, screens the sorting instructions corresponding to the deviation rate, and obtains a sorting path adjustment instruction set;

[0053] The resource utilization path planning module extracts the change of resource recovery rate of nodes before and after adjustment, identifies the resource utilization path concentration under the sorting trend, extracts the variable section to form a resource adaptation interval, and obtains a resource utilization path optimization trend;

[0054] The environmental impact assessment module calls the resource utilization path optimization trend, collects environmental data in the solid waste treatment process, analyzes the coincidence length of environmental load change and energy consumption jump period, and outputs the environmental impact action time length;

[0055] The scheduling optimization module screens the task scheduling instructions affected by environmental interference based on the environmental impact action time length, classifies the device switching time and adjustment trigger frequency, identifies the load jump out-of-limit period, and obtains the frequency of solid waste treatment task load interference;

[0056] The classification priority mark values ​​include the particle size distribution fluctuation range, the density difference change amplitude, and the crushing energy consumption increase characteristics; the sorting path adjustment instruction group includes the sorting deviation amount, the load status abnormal value, and the processing capacity offset rate; the resource path optimization trend includes the resource recovery rate change range, the resource path type, and the path response amplitude; the environmental impact duration includes the duration of energy consumption fluctuations, the overlapping period of environmental load jumps, and the duration of disturbances; the solid waste treatment task load interference impact frequency includes the equipment switching frequency, the number of adjustment instruction triggers, and the number of jump period limit violations.

[0057] See also Figure 2 , solid waste classification optimization module includes:

[0058] The particle size distribution extraction submodule extracts the time series of the material particle size distribution based on the composition characteristics of construction solid waste, calculates the particle size difference between adjacent sampling points, determines the fluctuation period and extracts the peak-valley distance, and screens the sections where the period difference exceeds the threshold to obtain the particle size distribution period fluctuation interval value;

[0059] Based on the compositional characteristics of construction solid waste, an online particle size analyzer samples the waste every 5 seconds, acquiring particle size distribution data at different time points to form a particle size time series. Over a 10-minute observation period, 120 data points were collected, including particle size data for solid waste such as concrete blocks, brick and tile debris, and wood scraps. The particle size difference between adjacent sampling points was calculated. For example, if the particle size at time t is 50 mm and at time t+5 seconds is 55 mm, the difference is 5 mm. The variation patterns of the particle size difference were analyzed to identify periodic fluctuations. For example, if the particle size difference for five consecutive sampling points is greater than 2 mm, followed by a difference of less than -2 mm for five subsequent sampling points, this is considered a fluctuation cycle. The peak-to-trough distance was extracted. For example, if the particle size increases from 30 mm to 80 mm and then decreases to 35 mm, the peak-to-trough distance is 50 mm. A periodic difference threshold of 10 mm was set to filter out segments with a peak-to-trough distance greater than 10 mm, ultimately yielding the periodic fluctuation interval of the particle size distribution.

[0060] The density difference calculation submodule calls the particle size distribution period fluctuation interval value, identifies the corresponding density difference data, analyzes the absolute density change amplitude of adjacent time periods, compares it with the set density difference threshold, locates the mutation node, and obtains the density difference mutation amplitude interval value;

[0061] Based on the periodic fluctuation intervals in the particle size distribution, construction solid waste density data for the corresponding time period is extracted. For example, when significant particle size fluctuations occur within a certain time period (e.g., from 9:00 to 9:30), density data is collected every minute during that period, for a total of 30 density values. The density fluctuations between adjacent time periods are analyzed. For example, if the density at minute t is 1.8 tons / m³ and at minute t+1 is 2.1 tons / m³, the fluctuation is 0.3 tons / m³. This density fluctuation is compared with a preset density difference threshold (0.2 tons / m³). When the fluctuation exceeds the threshold, it indicates a sudden change in the material composition. This threshold is validated using historical data to ensure a false alarm rate of less than 5% and a false negative rate of less than 3%. For example, if the density fluctuation is 0.35 tons / m³, which is greater than 0.2 tons / m³, this point is considered a sudden change node. All sudden change nodes exceeding the threshold are identified and recorded to obtain the density difference sudden change amplitude interval.

[0062] The classification status marking submodule identifies the corresponding crushing energy consumption and material characteristic data based on the density difference mutation amplitude interval value, extracts the energy consumption and characteristic data, combines the density difference and particle size distribution fluctuation, calculates the classification complexity offset value, sets the offset threshold, marks the time segment that exceeds the threshold, and obtains the classification priority marking value;

[0063] Based on the density difference mutation amplitude interval, the energy consumption and material property data for construction solid waste crushing during the corresponding time period (e.g., 1:00 PM to 1:15 PM) are identified and extracted. Crushing energy consumption data comes from real-time power sensors, and material property data includes hardness, toughness, moisture content, etc. Taking the 1:00 PM to 1:15 PM period as an example, the energy consumption (e.g., 120 kWh) and material properties (e.g., high hardness, 15% moisture content) for this period are extracted. Combining the aforementioned density difference mutation amplitude (0.4 tons / cubic meter) and particle size fluctuation (peak-to-trough spacing of 70 mm), the classification complexity offset value is calculated. The calculation method is: energy consumption × 0.4, material hardness × 0.8 × 0.3, moisture content × 0.6 × 0.3, density difference × 0.2, and particle size fluctuation × 0.1 to obtain the classification complexity offset value. For example, the offset value = 120 × 0.4 + (0.8 × 0.6) × 0.3 + 0.4 × 0.2 + 70 × 0.1 = 55.224. The weighting factors were set based on expert experience and historical data. Experimental verification shows that an energy consumption weight of 0.4 has the greatest impact on classification difficulty. The classification complexity offset threshold is set at 50. An offset value exceeding 50 indicates a significant increase in material classification difficulty and requires priority processing. A calculated offset value of 55.224 indicates that the classification difficulty for that period (e.g., 1:00 to 1:15) is high and should be prioritized. All periods with offset values ​​exceeding the threshold are marked to obtain a classification priority value.

[0064] See also Figure 3 , solid waste treatment adaptation modules include:

[0065] The sorting data matching submodule extracts the solid waste treatment equipment operating parameters and sorting data for the corresponding period based on the classification priority tag value, calculates the load fluctuation amplitude according to the sampling interval, aligns the particle size distribution and load amplitude at the same time, and obtains the load linkage interval group;

[0066] Based on the classification priority tag value, the operating parameters and sorting data of the solid waste treatment equipment in the time period corresponding to the tag value are extracted. For example, when the classification priority tag value indicates that the solid waste in a certain time period (such as 10:00 to 10:15 in the morning) requires high-priority treatment, the equipment operating parameters such as the crusher rotation speed, the conveyor belt speed, the grabbing frequency of the sorting robot arm, etc. in the time period are extracted, as well as the sorting data such as the type, quantity, and location of the sorted materials obtained by the visual recognition system. The load fluctuation amplitude of the solid waste treatment equipment is calculated according to the preset sampling interval (such as once per second). For example, the load fluctuation is reflected by monitoring the change in the crusher motor current. If the current fluctuates from 100 amperes to 100 amperes, the load fluctuation amplitude is 100 amperes. Then, align the obtained particle size distribution data (e.g., average particle size data once every 5 seconds) and the calculated load fluctuation amplitude (e.g., current fluctuation amplitude once every 1 second) at the same time point. For example, interpolate or extrapolate the particle size data points to a time resolution consistent with the load amplitude data points to ensure that each set of particle size data corresponds to a load amplitude data. For example, if the particle size data is a point every 5 seconds and the load amplitude data is a point every 1 second, copy the particle size data 5 times within 5 seconds, or perform linear interpolation on the particle size data, and collect all aligned particle size distribution and load amplitude data to obtain a load linkage interval group.

[0067] The sorting deviation judgment submodule calls the load linkage interval group, extracts the equipment status sequence, compares the sorting changes with the status differences, and uses the formula:

[0068] ;

[0069] Calculate the state difference normalization index, compare it with the preset offset boundary after normalization, analyze the deviation intensity of the sorting point, extract the sorting position index with a deviation intensity greater than the benchmark judgment value, and establish an offset intensity index group;

[0070] in, represents the state difference normalization index, n represents the number of device state sequence dimensions involved in the comparison, Represents the state value of the i-th sorting point in the j-th dimension, Represents the historical state mean corresponding to the i-th sorting point in the j-th dimension, Represents the historical state standard deviation of the i-th sorting point in the j-th dimension, Represents the state weight factor of the jth dimension of the i-th sorting point, Represents a very small positive number to prevent division by zero errors;

[0071] Call the load linkage interval group and extract the equipment status sequence contained therein. The equipment status sequence specifically includes real-time operating parameters of multiple dimensions such as crusher speed, conveyor belt speed, robot arm grabbing frequency, material flow, vibrating screen frequency, and winnowing machine wind speed. For example, in a certain load linkage interval group, the equipment status values ​​at time t are included, such as crusher speed of 1500 rpm, conveyor belt speed of 2 m / s, robot arm grabbing frequency of 60 times / min, and material flow of 20 tons / hour. Subsequently, the sorting changes are compared with the equipment status differences. The comparison here is to compare the equipment status values ​​at the current moment with the corresponding historical average status values ​​and historical standard deviations to quantify the degree to which the current status deviates from the normal level. The formula is used. Calculate the state difference normalization index ;

[0072] Among them, n represents the number of device state sequence dimensions involved in the comparison. For example, if the six dimensions of crusher speed, conveyor belt speed, robot arm grabbing frequency, material flow, vibrating screen frequency and winnowing machine wind speed are considered at the same time, then ;

[0073] Representative The sorting point is The state value on the dimension, for example, when (indicates the first sorting point), (indicates the crusher speed), The current crusher speed is 1550 rpm; The current conveyor belt speed is 2.1 m / s; The current robot arm grasping frequency is 62 times / minute; The current material flow rate is 22 tons / hour; The current vibration screen frequency is 50 Hz; The current wind speed of the winnowing machine is 15 m / s;

[0074] Representative The sorting point is The dimension corresponds to the historical state mean, for example, The historical average speed of the crusher is 1500 rpm. The historical average speed of the conveyor belt is 2.0 m / s. The historical average frequency of the robot arm grasping is 60 times / minute. The historical average of material flow is 20 tons / hour. The historical average frequency of the vibrating screen is 48 Hz. The historical average wind speed of the winnowing machine is 14 m / s;

[0075] Representative The sorting point is The dimension corresponds to the historical state standard deviation, for example, The historical standard deviation of the crusher speed is 10 rpm. The historical standard deviation of the conveyor belt speed is 0.1 m / s, The historical standard deviation of the robot arm's grasping frequency is 2 times / minute, The historical standard deviation of material flow is 1 ton / hour, The historical standard deviation of the vibration screen frequency is 1.5 Hz, The historical standard deviation of the wind speed of the winnowing machine is 0.8 m / s;

[0076] Representative Sorting point The state weight factor of the dimension is determined according to the degree of influence of different equipment states on the sorting effect. For example, the crusher speed weight factor Set to 0.3, the conveyor belt speed weight factor Set to 0.25, the robot grasping frequency weight factor Set to 0.2, material flow weight factor Set to 0.15, the vibration screen frequency weight factor Set to 0.05, the wind speed weight factor of the winnowing machine The weight factor is set to 0.05, and the sum of the weight factors is 1. The setting value is determined by combining a large amount of historical data training and expert experience to ensure sensitivity to sorting deviations. For example, through regression analysis of 1,000 sets of historical sorting data, it is found that when the weight is set to the above value, the model's prediction accuracy for the decline in sorting efficiency reaches more than 92%. Represents a very small positive number to prevent division by zero errors, set to , this value ensures that the calculation can be performed even if the standard deviation is 0, for example, when When the parameter values ​​of a sorting point are as follows:

[0077] Table 1: Equipment status sequence data table

[0078]

[0079] Substitute the parameters into the formula for calculation:

[0080] ;

[0081] The state difference normalization index The calculation of the deviation degree of each equipment status dimension is standardized and weighted to sum up, which comprehensively reflects the overall deviation degree of the current sorting point equipment operation status from the historical normal state. The root mean square processing is performed to ensure that the index does not increase infinitely with the increase of the number of dimensions, and is more comparable. The absolute value operation ensures that the deviation direction does not affect the quantification of the deviation intensity. The weight factor makes the deviation of the key equipment parameters have a greater impact on the total index. After the index value is normalized, it is compared with the preset offset boundary. The preset offset boundary is set to 0.8. The determination of this offset boundary is based on a large amount of historical data analysis. When the value exceeds 0.8, it indicates that the equipment operation status has deviated significantly, resulting in a decrease in sorting efficiency or an increase in the risk of failure. Through multiple field tests and data backtracking, when the deviation boundary is set to 0.8, more than 90% of sorting anomalies can be identified in advance, and the deviation intensity of the sorting point can be analyzed. For example, the calculated The value is 0.9707, which is greater than the preset offset boundary of 0.8, indicating that the deviation intensity of the sorting point is high. The sorting position index with a deviation intensity greater than the benchmark judgment value is extracted. The benchmark judgment value is set to 0.8. For example, if there are three sorting points in a certain processing flow, the calculated The values ​​are 0.9707, 0.75, and 0.91 respectively, then the first and third sorting points (the The indexes (eg, sorting position numbers 001 and 003) with values ​​of 0.9707 and 0.91, respectively, are extracted, and an offset strength index group of the extracted sorting position indexes is established. For example, {001, 003} is stored as the offset strength index group.

[0082] The sorting instruction extraction submodule filters the position of the corresponding time point in the task instruction set based on the offset strength index group, extracts the instruction value, sorts it by time series, removes duplicate instructions, and obtains the sorting path adjustment instruction group;

[0083] Based on the offset strength index group, the position in the task instruction set corresponding to the specific time point is filtered. For example, if the offset strength index group is {001, 003}, then the first and third sorting points in the processing flow will be checked for instructions related to the sorting point deviation period in the task instruction set. The task instruction set contains instructions such as sorting material type, equipment adjustment, and path switching. The relevant instruction values ​​are extracted. For example, for sorting point 001, the instructions "adjust the robot arm grabbing frequency to 65 times / minute" and "turn on the vibrating screen power enhancement mode" are extracted; for sorting point 003, the instruction "switch to the glass sorting path" is extracted. The instructions are sorted by time, with the instruction at 10:05 placed before the instruction at 10:20. If there are multiple instructions at the same time, they are sorted by priority. For example, equipment parameter adjustment takes precedence over path switching. Duplicate instructions are eliminated. For example, if the instruction "Adjust the robot arm grasping frequency to 65 times / minute" appears at 10:05 and 10:10 with the same content, only the earliest instruction at 10:05 is retained to obtain a non-duplicate and time-sorted sorting path adjustment instruction group.

[0084] See also Figure 4 ,The resource-based path planning module includes:

[0085] The resource recovery rate extraction submodule adjusts the instruction group according to the sorting path, extracts the resource recovery rate data of the node in the cycle before and after the adjustment, identifies the resource boundary conditions, and obtains the resource recovery rate difference value;

[0086] Based on the sorting path adjustment instruction group, the resource recovery rate data of each instruction node in the cycle before and after the adjustment is extracted. The data is obtained by weighing and analyzing the materials before and after processing, reflecting the recycling efficiency of different materials under different sorting paths. For example, when the sorting path adjustment instruction group includes the instruction "switch to the glass sorting path", the glass resource recovery rate data before the execution of this instruction (for example, the glass recovery rate under the original mixed solid waste path is 75%) and after the execution of this instruction (for example, the glass recovery rate after switching to the glass-specific path is 92%) is extracted. At the same time, resource boundary conditions are identified. Boundary conditions refer to the upper and lower limits of recycling for different types of resources in the resource recovery process, as well as the restrictions on resource recycling imposed by specific processing paths. For example, the theoretical recycling limit for glass is set at 95%, and the theoretical recycling limit for metal is set at 98%. It is also clarified that when the moisture content of the solid waste exceeds 20%, the recycling rate of paper will drop significantly. By comparing the resource recovery rate data before and after the adjustment with the identified resource boundary conditions, the difference in resource recovery rate is calculated.

[0087] The resource recovery path identification submodule calls the resource recovery rate difference value to identify the change trajectory of the resource recovery path in the unit material path, judge the resource quantity trend corresponding to the path fluctuation, and compare the path reduction and resource quantity fluctuation in adjacent time periods using the formula:

[0088] ;

[0089] Calculate the path fluctuation coupling index, filter the synchronous fluctuation segments, and obtain the resource path characteristic segments;

[0090] in, represents the path fluctuation coupling index, represents the number of adjacent time periods used for comparison, Represents the change in path drop of the kth path in the tth time period, represents the number of resources on the kth path in the tth time period, represents the number of resources on the kth path in the t-1th time period, represents the smoothing factor used to avoid the denominator being zero;

[0091] The resource recovery rate difference value is called to identify the change trajectory of the resource path in the unit material path. This indicates how resources are transformed from one state (such as mixed solid waste) to another state (such as a single material after classification) in a specific material flow, and how the resource quantity changes in this process. For example, when the resource recovery rate difference value shows that the glass recycling rate has increased significantly, the entire process of glass materials from entering the sorting line to being recycled in the corresponding time period is tracked to determine the resource quantity trend corresponding to the path fluctuation. For example, if a certain path reduction (such as the amount of material loss from mixed crushing to fine sorting) is accompanied by a continuous increase in the amount of recycled specific resources (such as plastics), it indicates that the path optimization is effective. The path reduction in adjacent time periods is compared with the resource quantity fluctuation, and the formula is used. Calculate the path fluctuation coupling index ,in, represents the path fluctuation coupling index, which quantifies the correlation strength between the path decline change and the resource quantity change in the resource utilization path. Represents the number of adjacent time periods used for comparison. For example, if the analysis is performed with each hour as a time period and 10 hours of data are examined, then , Representative The path is in The path drop change in the time period refers to the reduction in resource volume due to loss, improper sorting, etc. during the material processing process. For example, The path is in The material loss rate during the time period changed from 5% to 3%. Representative The path is in The number of resources in a time period, e.g. The path is in The amount of glass resources recycled during this period is 1,000 kg. Representative The path is in The number of resources in a time period, e.g. The path is in The amount of glass resources recycled during the period was 950 kg. The smoothing factor is used to avoid the denominator being zero and is set to a very small positive number, such as , ensuring that even Calculations can be performed even when the value is close to zero;

[0092] The benefit of the formula is that by introducing the path drop change and the number of resources in the preceding and following time periods and , and weighted average processing can more accurately reflect the impact of resource path adjustment on actual resource recovery efficiency, especially considering the negative impact of path reduction on resource quantity, the square root term The denominator is used to scale the amount of resources in the previous time period nonlinearly to prevent the small amount of resources from being The excessive impact makes the indicator more robust, so that the effectiveness of different resource path adjustment plans can be more accurately evaluated in the overall system or method, providing a quantitative basis for subsequent path optimization;

[0093] Assume that a certain plastic recycling route is examined. In 10 consecutive time periods (each time period is 1 hour), the change in the path reduction is and the number of resources and The data are shown in Table 2:

[0094] Table 2: Plastic recycling path data table

[0095]

[0096] but , ;

[0097] Calculate the path fluctuation coupling index :

[0098] ;

[0099] The calculation results show that the path fluctuation coupling index of the plastic recycling path is 0.1508. This index value is used to screen synchronous fluctuation segments. For example, the threshold of the synchronous fluctuation segment is set to 0.12, that is, when When the value exceeds 0.12, it is considered that there is a significant synchronous fluctuation between the path reduction and the resource quantity, indicating that the resource efficiency of the path is greatly affected by the path reduction and needs to be paid special attention to. For example, due to the calculated is greater than the threshold of 0.12, so the plastic recycling path is identified as a resource path characteristic segment. Finally, by Filter the values ​​to obtain the resource path feature segment.

[0100] The path response interval identification submodule analyzes the time and path displacement trends based on the resource utilization path characteristic segments, determines the direction consistency and amplitude change characteristics, screens the fluctuation segments and aggregates them to obtain the resource utilization path optimization trend;

[0101] Based on the characteristic segments of the resource recovery path, the time and path displacement trends are analyzed. This includes analyzing the flow direction and residence time of materials in the processing path within a specific time range, as well as path deviations caused by equipment adjustments or changes in material properties. For example, when it is identified that a certain characteristic segment requires a more refined sorting path, the time axis is analyzed to determine how this type of material moves from coarse sorting to fine sorting, as well as its processing displacement in the fine sorting equipment, to determine the directional consistency and amplitude change characteristics. For example, if the path displacement trend is moving towards a higher recovery rate (directional consistency) and the amplitude of this movement is continuous and significant (amplitude change characteristics), the path adjustment is considered effective. Subsequently, all fluctuation segments that meet the specific directional consistency and amplitude change characteristics (for example, the displacement amplitude is continuously greater than 5%) are screened out and aggregated. For example, multiple adjacent short time segments that all point to an increase in recovery rate and have a displacement amplitude greater than 5% are merged into a longer optimization interval to obtain the resource recovery path optimization trend.

[0102] See also Figure 5 , the environmental impact assessment module includes:

[0103] The path trend extraction submodule calls the resource-based path optimization trend, extracts node task records and periodic path data, and identifies the fluctuation amplitude and frequency within a single cycle using the formula:

[0104] ;

[0105] Get the fluctuation trend value of the cycle path;

[0106] in, represents the fluctuation trend value of the cycle path, representing the total number of sampling nodes within a single cycle, representing the change range of the path resource value corresponding to the representing the number of changes of the path resource value corresponding to the representing the fluctuation frequency adjustment factor, representing the number of node tasks corresponding to the representing the path trend adjustment constant;

[0107] The resource-based path optimization trend is called, and the node task records and periodic path data related to the optimization trend are extracted. The node task records include the specific operations (such as crushing, screening, sorting) of each processing node and the corresponding material processing capacity, energy consumption, time consumption, etc. The periodic path data refers to the movement trajectory and resource conversion of the material in the entire processing flow within a fixed time period (such as every day or every week). For example, when the resource-based path optimization trend shows a fine sorting path for fine-grained construction waste, the system extracts the task records of all the nodes involved in the path, such as crushers, vibrating screens, air separation equipment, etc., and extracts the complete cycle data of the path within a working day. The fluctuation range and fluctuation frequency within a single cycle are identified. The fluctuation range refers to the change range of the resource value (such as recovery rate, energy consumption) in the path, and the fluctuation frequency refers to the number of resource value changes per unit time. For example, in a working cycle of one day, the average recovery rate of the fine sorting path fluctuates from 85% to 90%, with a fluctuation range of 5%, and there are 10 fluctuations of more than 1% within a day, with a fluctuation frequency of 10 times. The formula obtaining the fluctuation trend value of the periodic path wherein, representing the fluctuation trend value of the periodic path, which comprehensively quantifies the potential indication of the change range and frequency of the resource value in the path on the environment, representing the total number of sampling nodes within a single cycle, for example, if sampling once every hour within a day cycle, there are 24 sampling nodes, then , representing the change range of the path resource value corresponding to the For example, at the th sampling point, the resource recovery rate changes from 85% to 88%, then , if the energy consumption changes from 100 kWh to 110 kWh, then representing the number of changes of the path resource value corresponding to the ​​​​​The number of times that the resource value fluctuation exceeds the preset small threshold (for example, 0.5%) in the monitoring time period of the sampling node, 5 times, represent the fluctuation frequency adjustment factor, used to adjust the influence of the fluctuation frequency on the value, set to 0.2, the factor value is obtained by regression analysis on the historical influence data of different frequency fluctuations on stability, experimental verification shows that when , the fluctuation trend value can accurately reflect the dynamics of the path and the potential environmental impact risk, represent the number of node tasks corresponding to the th sampling node, for example, the th sampling node covers three tasks of crushing, screening and sorting, , represent the path trend adjustment constant, used to smooth the influence of the number of node tasks on the value, set to 1.0, this constant value ensures that even if the number of node tasks is small, the denominator will not be too small to cause the value to abnormally increase, through experimental verification, when , the output result of the formula has good stability.

[0108] The benefit of the formula is that by weighting and summing the resource value change amplitude and the change frequency, the dynamics and instability of the periodic path can be comprehensively evaluated, wherein the frequency term adopts a square form, emphasizing the greater impact of high-frequency fluctuations on system stability, thereby more sensitively capturing potential environmental impact risks, the in the denominator considers the dilution effect of the number of node tasks on the fluctuation trend, so that the fluctuation trend value can more reasonably reflect the actual complexity, and this formula can more accurately identify the path fluctuation pattern that leads to increased energy consumption or environmental load, providing a key input for subsequent environmental impact assessment.

[0109] Suppose that in a certain processing period (for example, one day), the data of 4 key sampling nodes are selected for calculation, and the sampling nodes correspond to different processing stages:

[0110] Table 3: Path trend sampling node data table

[0111]

[0112] Calculate the fluctuation trend value of the periodic path : ;

[0113] ;

[0114] The calculation result shows that the fluctuation trend value of the cycle path is 37.480, and this fluctuation trend value reflects the overall fluctuation intensity and complexity of the solid waste treatment path in the current operation cycle. The higher the fluctuation trend value, the more intense or frequent the path fluctuation, and the greater the potential environmental impact risk. This value will be used as the basis for subsequent energy consumption jump identification, for example, when When the value exceeds the preset threshold (for example, 35), the system will focus on the energy consumption and environmental load data in the cycle for more in-depth analysis.

[0115] The energy consumption jump identification submodule selects the energy consumption and environmental load records in the same cycle based on the fluctuation trend value of the cycle path, compares the data change amplitude by day, filters time nodes with an amplitude greater than the energy consumption jump threshold, and obtains the energy consumption load jump period.

[0116] Based on the fluctuation trend value of the cycle path, the environmental data in the solid waste treatment process is analyzed. For example, when the fluctuation trend value T reaches 37.480, the energy consumption and environmental load data in the cycle are analyzed. The environmental data includes device energy consumption, wastewater discharge, exhaust gas emission, and noise level, etc. Through real-time data collection by sensors, the hourly energy consumption and exhaust gas emission records in the same cycle are selected, and the change amplitude is compared by day, for example, the energy consumption at 9:00 am is 2000 kWh, and the energy consumption at 9:00 am the previous day is 1800 kWh, with a change amplitude of 200 kWh. Time nodes with a change amplitude greater than the preset energy consumption jump threshold (150 kWh) are selected. This threshold is based on historical energy consumption fluctuation data, and when the change amplitude exceeds 150 kWh, it indicates an abnormal increase in energy consumption. For example, if the energy consumption change amplitude at a certain time point is 200 kWh, which exceeds the threshold, it is marked as an energy consumption jump node. All time nodes exceeding the threshold are continuously identified, and the energy consumption load jump period is finally obtained, for example, 10:00-11:00 and 14:00-15:00 are identified as energy consumption jump periods in a working day.

[0117] The environmental impact quantification submodule identifies the intersection length of path amplitude and energy consumption jump based on the energy consumption load jump period, performs weighted processing, and refers to amplitude frequency, combined amplitude value, and cycle fluctuation to normalize the usage length in the differentiated cycle, and outputs the environmental impact action length.

[0118] Based on the energy load jump period, identify the intersection duration of path amplitude variation and energy consumption jump. Path amplitude variation is the duration of the change in the material handling path, and energy consumption jump is the duration of time when energy consumption is high. For example, if 10:00-11:00 is the energy consumption jump period, and the path amplitude variation lasts for 45 minutes during the same period, the intersection duration is 45 minutes. The intersection duration is weighted, and the weight coefficient is set according to the intersection duration: 1.5 for more than 30 minutes, 1.0 for 10-30 minutes, and 0.8 for less than 10 minutes. The weight coefficient is based on environmental impact assessments and historical data, highlighting the environmental impact of long-term energy consumption jumps and path amplitude variation overlap. Combined with the amplitude variation frequency, combined amplitude variation value and periodic fluctuations, the duration under different cycles is normalized. For example, if there are 20 path amplitude changes in a month, each lasting 1000 minutes, and the periodic fluctuation value is 37.480, the normalization method is: divide the weighted intersection duration by the total processing time, and multiply by the coefficients for the amplitude change frequency, combined amplitude change, and periodic fluctuation factor. Assuming the weighted intersection duration is 60 minutes, the total processing time is 480 minutes, the amplitude change frequency factor is 0.8, the combined amplitude change factor is 0.83, and the periodic fluctuation factor is 0.75, the normalized environmental impact duration is 0.4225, and the quantized environmental impact duration is output.

[0119] See also Figure 6 , the scheduling optimization module includes:

[0120] The instruction screening submodule screens matching task scheduling instructions based on the duration of environmental impact, identifies device time periods and device states, compares disturbance cycles with instruction cycles, eliminates low-matching instructions, and obtains an instruction set affected by environmental interference.

[0121] Based on the duration of the environmental impact, matching task scheduling instructions are selected. Scheduling instructions guide the operation of solid waste treatment equipment and adjust material sorting routes. For example, when the duration of the environmental impact is 0.4225, instructions aimed at reducing energy consumption or optimizing sorting efficiency are prioritized. Equipment time periods and equipment status are identified. Equipment time periods refer to the operating conditions of equipment during a specific time period, while equipment status refers to the equipment's operating parameters and health status. If a scheduling instruction requires adjusting the crusher speed (e.g., 2:00 PM to 3:00 PM), the actual operating status (e.g., speed, energy consumption) of the crusher during that time period is identified. The disturbance period is compared with the instruction period. The disturbance period is the duration of abnormal fluctuations corresponding to the duration of the environmental impact, while the instruction period is the execution window for the scheduling instruction. The overlap ratio between the disturbance period and the instruction period is calculated: overlap ratio = (overlap duration / instruction duration) × 100%. If the overlap ratio is less than 50% (i.e., a low match), the instruction is discarded. A matching threshold of 50% is set. Based on historical data analysis and simulation experiments, only instructions that effectively respond to the current environmental disturbance are retained, resulting in a set of instructions affected by environmental disturbances.

[0122] The device classification submodule calls the set of instructions affected by environmental interference, extracts the device switching time and adjustment trigger frequency corresponding to the instructions, arranges them in order of switching time, classifies them by frequency interval, calculates the correspondence between frequency bands and device time periods, and obtains the distribution value of device adjustment frequency bands;

[0123] Call the set of instructions affected by environmental interference and extract the device switching time and adjustment trigger frequency for each instruction. The device switching time is the point in time when the device switches from one state to another, and the adjustment trigger frequency is the frequency at which the device adjustment operation occurs. For example, from the instruction set {Adjust crusher speed to 1450 rpm, switch to fine screening mode}, we extract that the crusher speed adjustment switch time is 14:00 and the frequency is once per hour, and the fine screening mode switch time is 14:15 and the frequency is once every two hours. Arrange the instructions by device switching time, and if the times are the same, sort them by device priority. Categorize the adjustment trigger frequency into "low frequency (less than 1 time / hour)", "medium frequency (1-5 times / hour)", and "high frequency (greater than 5 times / hour)" intervals. Compute the correspondence between each frequency band and the device time period. For example, calculate the operating time or trigger count of the crusher under "medium frequency" adjustment between 14:00 and 14:30 to obtain the device adjustment frequency band distribution value.

[0124] The interference identification submodule adjusts the frequency band distribution value of the equipment, collects the load jump amplitude and duration within the frequency band, determines whether it exceeds the jump amplitude threshold, identifies the frequency band interference intensity, sorts the interference intensity of all frequency bands, determines the frequency band and jump period of the interference intensity, and obtains the frequency of load interference impact of solid waste treatment tasks;

[0125] Based on the distribution of equipment frequency bands, the load jump amplitude and duration within each frequency band are collected. The load jump amplitude indicates the degree to which the equipment load deviates from the normal load, and the duration indicates how long the jump state lasts. For example, within the medium frequency band, the crusher load jumps from 70% to 95% with a jump amplitude of 25% for 10 minutes. The system then determines whether the jump amplitude exceeds a preset threshold (20%). This threshold is set at 20%. Based on historical fault and energy consumption data analysis, a load jump exceeding 20% ​​indicates an abnormal equipment load, impacting equipment lifespan and efficiency. If the load jump amplitude exceeds the threshold by 25%, it is marked as effective interference. All interference intensities exceeding the threshold are identified and marked, and sorted from highest to lowest by interference intensity to determine the frequency band and jump period with the highest interference intensity. For example, if the interference intensity in the medium frequency band is 25% and the jump period is 2:00 PM to 2:10 PM, the frequency of load interference for the solid waste treatment task is determined. For example, if there were five interference events that day, the medium frequency band was the primary source of interference.

[0126] The method for intelligent disposal and reuse of construction solid waste based on the Internet of Things is implemented based on the above-mentioned intelligent disposal and reuse system for construction solid waste based on the Internet of Things, and includes the following steps:

[0127] S1: Based on the composition characteristics of construction solid waste, the particle size distribution and density difference changes are analyzed, the periods of sudden changes in crushing energy consumption peaks and sharp changes in material properties are extracted, the state switching points where density difference and energy consumption changes are synchronized are identified, the task levels are marked, and task state switching identification fragments are generated;

[0128] S2: Based on the task state switching identification segment, the sorting change data and load change amplitude are extracted, the sorting mutation is compared with the load fluctuation amplitude, the corresponding relationship between the sorting change and the processing capacity response is analyzed, and the sorting adjustment response trajectory segment is obtained;

[0129] S3: Based on the sorting and adjustment response trajectory segments, the changes in resource recovery rate values ​​and resource path gradients before and after the response are extracted, the resource path reduction and resource quantity increase of the node are analyzed, the path attenuation path is identified and compared with the original cycle data, the segments with path decline characteristics are screened, and the resource adaptation attenuation path distribution set is generated;

[0130] S4: Based on the resource adaptation attenuation path distribution set, identify the environmental data within the corresponding time period, analyze the overlapping period of energy consumption jump and path attenuation path, extract the associated time period, and obtain the environmental parameter interference section for sorting adjustment;

[0131] S5: Based on the environmental parameter interference section of sorting adjustment, extract the scheduling control strategy records within the time period, analyze the time interval of equipment switching and the adjustment trigger frequency, screen the high-frequency adjustment strategy fragments, and obtain the interference impact frequency of solid waste treatment task load.

[0132] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The intelligent disposal and recycling system of construction solid waste based on the Internet of Things is characterized by: The system comprises: The solid waste classification optimization module analyzes the composition characteristics of construction solid waste, particle size distribution, density differences, and the variation range of crushing energy consumption, extracts key periods of classification efficiency fluctuations, and generates classification priority marking values; The solid waste classification optimization module includes: The particle size distribution extraction submodule extracts the time series of the material particle size distribution based on the composition characteristics of construction solid waste, calculates the particle size difference between adjacent sampling points, determines the fluctuation period and extracts the peak-valley distance, and screens the sections where the period difference exceeds the threshold to obtain the particle size distribution period fluctuation interval value; The density difference calculation submodule calls the particle size distribution period fluctuation interval value, identifies the corresponding density difference data, analyzes the absolute density change amplitude of adjacent time periods, compares it with the set density difference threshold, locates the mutation node, and obtains the density difference mutation amplitude interval value; The classification status marking submodule identifies the corresponding crushing energy consumption and material characteristic data according to the density difference mutation amplitude interval value, extracts the energy consumption and characteristic data, combines the density difference and particle size distribution fluctuation, calculates the classification complexity offset value, sets the offset threshold, marks the time segment exceeding the threshold, and obtains the classification priority marking value; The solid waste treatment adaptation module matches the solid waste treatment equipment operating parameters and sorting strategies for the corresponding time period based on the classification priority tag value, compares the equipment load status in the marked section with the current processing capacity, selects the sorting instructions corresponding to the deviation rate, and obtains the sorting path adjustment instruction group; The resource recovery path planning module extracts the changes in the resource recovery rate of the node before and after the adjustment based on the sorting path adjustment instruction group, identifies the concentration of the resource recovery path under the unit material in combination with the sorting trend, extracts the change section to form the resource adaptation interval, and obtains the resource recovery path optimization trend; The resource path planning module includes: The resource recovery rate extraction submodule extracts the resource recovery rate data of the node in the cycle before and after the adjustment according to the sorting path adjustment instruction group, identifies the resource boundary conditions, and obtains the resource recovery rate difference value; The resource recovery path identification submodule calls the resource recovery rate difference value to identify the change trajectory of the resource recovery path in the unit material path, determines the resource quantity trend corresponding to the path fluctuation, compares the path reduction and resource quantity fluctuation in adjacent time periods, calculates the path fluctuation coupling index, screens the synchronous fluctuation segments, and obtains the resource recovery path characteristic segment; The path response interval identification submodule analyzes the time and path displacement trends according to the resource utilization path characteristic segments, determines the direction consistency and amplitude change characteristics, screens the fluctuation segments and aggregates them to obtain the resource utilization path optimization trend; The environmental impact assessment module calls the resource utilization path optimization trend, collects environmental data during the solid waste treatment process, analyzes the overlapping duration of the environmental load change and the energy consumption jump period, and outputs the environmental impact duration.

2. The intelligent disposal and recycling system for construction solid waste based on the Internet of Things according to claim 1 is characterized in that: The classification priority mark value includes the particle size distribution fluctuation range, density difference change amplitude, and crushing energy consumption increase characteristics; the sorting path adjustment instruction group includes the sorting deviation amount, load state abnormality value, and processing capacity offset rate; the resource path optimization trend includes the resource recovery rate change range, resource path type, and path response amplitude; the environmental impact duration includes the duration of energy consumption fluctuation, the overlapping period of environmental load jump, and the duration of disturbance.

3. The intelligent disposal and recycling system for construction solid waste based on the Internet of Things according to claim 1 is characterized in that: The solid waste treatment adaptation module includes: The sorting data matching submodule extracts the solid waste treatment equipment operating parameters and sorting data of the corresponding time period based on the classification priority tag value, calculates the load fluctuation amplitude according to the sampling interval, aligns the particle size distribution and load amplitude at the same time, and obtains the load linkage interval group; The sorting deviation judgment submodule calls the load linkage interval group, extracts the equipment state sequence, compares the sorting change with the state difference, calculates the state difference normalization index, compares the normalized state difference with the preset deviation boundary, analyzes the deviation intensity of the sorting point, extracts the sorting position index with a deviation intensity greater than the benchmark judgment value, and establishes a deviation intensity index group; The sorting instruction extraction submodule filters the position of the corresponding time point in the task instruction set based on the offset strength index group, extracts the instruction values, sorts them in time series, eliminates duplicate instructions, and obtains the sorting path adjustment instruction group.

4. The intelligent disposal and recycling system for construction solid waste based on the Internet of Things according to claim 1 is characterized in that: The environmental impact assessment module includes: The path trend extraction submodule calls the resource path optimization trend, extracts node task records and periodic path data, identifies the fluctuation amplitude and frequency within a single cycle, and obtains the fluctuation trend value of the periodic path; The energy consumption jump identification submodule selects energy consumption and environmental load records within the same period based on the fluctuation trend value of the cycle path and the environmental data during the solid waste treatment process, compares the data change amplitude on a daily basis, and selects time nodes with amplitudes greater than the energy consumption jump threshold to obtain the energy consumption load jump period; The environmental impact quantification submodule identifies the intersection duration of path amplitude variation and energy consumption jump according to the energy load jump period, performs weighted processing, and normalizes the usage duration under differentiated cycles with reference to the amplitude variation frequency, combined amplitude variation value and periodic fluctuation, and outputs the environmental impact duration.

5. The intelligent disposal and recycling system for construction solid waste based on the Internet of Things according to claim 1 is characterized in that: The system also includes a scheduling optimization module: The scheduling optimization module filters the task scheduling instructions affected by environmental interference based on the duration of the environmental impact, classifies the equipment switching time and adjusts the trigger frequency, identifies the load jump exceeding the limit period, and obtains the frequency of load interference impact of the solid waste treatment task; The solid waste treatment task load interference impact frequency includes equipment switching frequency, adjustment instruction triggering times, and jump period exceeding times.

6. The intelligent disposal and recycling system for construction solid waste based on the Internet of Things according to claim 5 is characterized in that: The scheduling optimization module includes: The instruction screening submodule screens matching task scheduling instructions based on the duration of the environmental impact, identifies the device time period and device status, compares the disturbance period with the instruction period, eliminates low-matching instructions, and obtains an instruction set affected by environmental interference; The device classification submodule calls the set of instructions affected by environmental interference, extracts the device switching time and adjustment trigger frequency corresponding to the instructions, arranges them in order of switching time, classifies them by frequency interval, counts the correspondence between frequency bands and device time periods, and obtains the device adjustment frequency band distribution value; The interference identification submodule adjusts the frequency band distribution value according to the equipment, collects the load jump amplitude and duration within the frequency band, determines whether it exceeds the jump amplitude threshold, identifies the frequency band interference intensity, sorts the interference intensity of all frequency bands, determines the frequency band and jump period of the interference intensity, and obtains the frequency of load interference impact of solid waste treatment tasks.

7. The method for intelligent disposal and reuse of construction solid waste based on the Internet of Things is characterized by: The method is used to implement the construction solid waste intelligent disposal and reuse system based on the Internet of Things according to any one of claims 1 to 6, comprising the following steps: S1: Based on the composition characteristics of construction solid waste, the particle size distribution and density difference changes are analyzed, the periods of sudden changes in crushing energy consumption peaks and sharp changes in material properties are extracted, the state switching points where density difference and energy consumption changes are synchronized are identified, the task levels are marked, and task state switching identification fragments are generated; S2: Based on the task state switching identification segment, extract the sorting change data and the load change amplitude, compare the sorting mutation and the load fluctuation amplitude, analyze the correspondence between the sorting change and the processing capacity response, and obtain the sorting adjustment response trajectory segment; S3: Based on the sorting and adjustment response trajectory segments, extract the changes in resource recovery rate values ​​and resource utilization path gradients before and after the response, analyze the node's resource utilization path decrease and resource quantity increase, identify the path attenuation path and compare it with the original cycle data, screen the segments with path decline characteristics, and generate a resource adaptation attenuation path distribution set; S4: Based on the resource adaptation attenuation path distribution set, identifying environmental data within a corresponding time period, analyzing the overlapping period of energy consumption jump and path attenuation path, extracting the associated time period, and obtaining the environmental parameter interference section for sorting adjustment; S5: Based on the environmental parameter interference section of the sorting adjustment, the scheduling control strategy records within the time period are extracted, the time interval of equipment switching and the adjustment trigger frequency are analyzed, and the high-frequency adjustment strategy fragments are screened to obtain the interference impact frequency of the solid waste treatment task load.

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