Dynamic monitoring method and system for solid waste treatment

Abstract

This invention discloses a dynamic monitoring method and system for solid waste treatment, relating to the field of intelligent operation control technology for industrial processes. The method includes: setting up buffer units and constructing material queues between adjacent treatment units; assigning unique identifiers to each material unit and recording its source batches; collecting multi-source characterization information and inputting it into a load mapping model to obtain load contribution characterization values. Next, an exchangeability criterion is constructed, candidate exchange pairs are divided, and the exchange matching degree is calculated. Based on this, the material queue is scanned for exchange and rearrangement positions, and the rearranged materials are sequentially released to downstream treatment units, achieving full-process operation control and management of industrial solid waste. This invention solves the technical problems of poor operational stability, unit connection imbalance, and insufficient load control precision in the entire industrial solid waste treatment process, making it difficult to achieve efficient and stable operation. It achieves precise matching of process load and optimized queue arrangement, improving the stability of the entire process operation and the overall treatment efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation control technology for industrial processes, and in particular to a dynamic monitoring method and system for solid waste treatment. Background Technology

[0002] The entire industrial solid waste treatment process is widely used in industrial environmental management and control. Its operational stability and load balance directly affect treatment efficiency, energy consumption levels, and environmental compliance. Precise control based on mechanistic models is the core of ensuring efficient system operation. Currently, the operation control of the entire industrial solid waste treatment process often adopts experience-based scheduling and independent control of single units, relying on manual experience or simple threshold adjustments. These methods have played a certain role in small-scale operations with stable material properties, but as the treatment scale increases and material fluctuations intensify, traditional control technologies reveal many limitations when applied to complex processes involving multiple interconnected units. Current technologies have not constructed a mechanistic model that fits the entire industrial solid waste treatment process, making it impossible to accurately quantify material load contributions and optimize material queues. This results in insufficient precision in the overall process operation control, making it difficult to meet the requirements of efficient, stable, and low-consumption treatment. Summary of the Invention

[0003] This application provides a dynamic monitoring method and system for solid waste treatment, which solves the technical problems of poor operational stability, unbalanced unit connection, and insufficient load control accuracy in the entire process of industrial solid waste treatment, making it difficult to achieve efficient and stable operation.

[0004] The first aspect of this application provides a dynamic monitoring method for solid waste treatment, the method comprising: setting up a buffer unit between adjacent treatment units, and constructing a material queue arranged in the order of entry within the buffer unit; assigning a unique identifier to each entering material unit and recording its source batch; collecting multi-source characterization information of the material units before entering the treatment process, and inputting the multi-source characterization information into a pre-constructed load mapping model to obtain a load contribution characterization value for each corresponding material unit, the load mapping model being used to characterize the degree of influence of the material unit on the treatment load after entering the downstream treatment unit; constructing an exchangeability criterion based on the load contribution characterization value in the material queue, dividing adjacent material units into multiple candidate exchange pairs, and calculating the exchange matching degree corresponding to the candidate exchange pairs; performing segment-by-segment scanning and exchange operations on the material queue according to the exchangeability criterion and the exchange matching degree, and performing a rearrangement of the material units in the queue; and releasing the rearranged materials sequentially to the downstream treatment unit to perform full-process operation control management.

[0005] A second aspect of this application provides a dynamic monitoring system for solid waste treatment, the system comprising: a material queue construction module, used to set up buffer units between adjacent treatment units and construct material queues arranged in the order of entry within the buffer units, assigning a unique identifier to each entering material unit and recording the source batch; a load contribution characterization value acquisition module, used to collect multi-source characterization information of the material units before entering the treatment process, and input the multi-source characterization information into a pre-constructed load mapping model to obtain the load contribution characterization value of each corresponding material unit, the load mapping model being used to characterize the degree of influence of the material units on the treatment load after entering the downstream treatment unit; an exchange matching degree calculation module, used to construct an exchangeability criterion based on the load contribution characterization value in the material queue, divide adjacent material units into multiple candidate exchange pairs, and calculate the exchange matching degree corresponding to the candidate exchange pairs; a position rearrangement execution module, used to perform segment-by-segment scanning and exchange operations on the material queue according to the exchangeability criterion and the exchange matching degree, and execute the position rearrangement of the material units in the queue; and a full-process management execution module, used to release the rearranged materials sequentially to the downstream treatment unit to execute full-process operation control management.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application constructs an orderly material queue by setting up buffer units between various units in industrial solid waste treatment, collects multi-source attribute information of materials, obtains material load contribution data through load mapping model calculation and queue rearrangement optimization, calculates the load distribution and fluctuation trend of the entire process, and makes dynamic adjustments in combination with queue disturbance constraints and load stability requirements. This allows for precise control of material scheduling and operation rhythm throughout the entire industrial solid waste treatment process, making the entire process more stable, efficient, and low-consumption. It achieves the technical effect of precise matching of process load and optimized queue arrangement, improving the stability of the entire process and the overall treatment efficiency. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating the dynamic monitoring method for solid waste treatment provided in the embodiments of this application.

[0010] Figure 2 This is a schematic diagram of the structure of a dynamic monitoring system for solid waste treatment provided in an embodiment of this application.

[0011] Figure labeling: Material queue construction module 1, load contribution characterization value acquisition module 2, exchange matching degree calculation module 3, position rearrangement execution module 4, full process management execution module 5. Detailed Implementation

[0012] This application provides a dynamic monitoring method and system for solid waste treatment, which solves the technical problems of poor operational stability, unbalanced unit connection, and insufficient load control accuracy in the entire process of industrial solid waste treatment, making it difficult to achieve efficient and stable operation.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, a dynamic monitoring method for solid waste treatment is provided, wherein the method includes:

[0016] A buffer unit is set up between adjacent processing units, and a material queue arranged in the order of entry is constructed within the buffer unit. Each material unit entering is assigned a unique identifier and its source batch is recorded.

[0017] In this embodiment, the processing unit is a functional unit in the industrial solid waste treatment process that undertakes the corresponding processing steps, such as crushing, screening, incineration, and stabilization units. It is used to perform predetermined processing operations on industrial solid waste materials, forming a continuous solid waste treatment process chain. The buffer unit is a transitional functional unit located between adjacent processing units, such as a transfer silo, buffer conveyor, or temporary storage hopper. It is used to realize temporary storage and sequential management of materials, ensuring a smooth connection of material supply between upstream and downstream processing units. The material unit is the smallest independently controlled material unit in the industrial solid waste treatment process, such as a single batch of solid waste pile, a quantitatively packaged solid waste group, or a fixed-length material section in continuous conveying. It is the basic object for load calculation and scheduling control in the process. Load refers to the operating load and processing workload borne by the target processing unit when it performs operations such as conveying, reacting, heat conversion, and treating exhaust gas and products of industrial solid waste materials. It is a quantitative indicator used to characterize the operating intensity, energy consumption, and equipment load of the processing unit, such as material conveying load, thermal load, chemical reaction load, equipment wear load, and flue gas and product treatment load.

[0018] Specifically, firstly, buffer units are deployed between adjacent treatment units in the entire industrial solid waste treatment process. These buffer units are positioned between the material output end of the upstream treatment unit and the material input end of the downstream treatment unit, forming a material flow path where the upstream treatment unit, buffer unit, and downstream treatment unit are sequentially connected. After the upstream treatment unit completes its processing, the material units are transported to the buffer unit in the discharge sequence. The buffer unit uses a built-in sequential arrangement structure to arrange the received material units in an orderly manner, forming a material queue arranged according to the order in which the material units entered, maintaining the orderliness and continuity of the queue.

[0019] For each material unit entering the buffer unit, an independent and non-repeating identification information is generated using the coding method in the field of industrial control. This identification information is assigned to the corresponding material unit to achieve unique identification. At the same time, the source batch information corresponding to each material unit is collected and recorded. The identification information and source batch information are associated and stored using conventional data storage methods to provide complete basic data support for subsequent load calculation and queue scheduling.

[0020] Further, an RFID reader-writer, a barcode scanning device, and a batch information collection sensor are arranged at the material inlet position of the buffer unit. At the same time, an encoding generation module is configured in the industrial controller supporting the buffer unit. When the material unit enters the buffer unit, the basic information such as the upstream source and production batch corresponding to the material unit is collected in real time by the batch information collection sensor. The encoding generation module automatically generates unique identification information that is independent and non-repetitive according to the general industrial control encoding rules. The RFID reader-writer or the barcode scanning device writes the unique identification into the identification carrier supporting the material unit and completes the identification binding. The industrial controller associates and integrates the generated unique identification information with the collected source batch information and performs data storage through the built-in storage unit.

[0021] Collect multi-source characterization information of the material unit before entering the processing flow, and input the multi-source characterization information into a pre-constructed load mapping model to obtain a load contribution characterization value corresponding to each material unit. The load mapping model is used to characterize the influence degree of the material unit on the processing load after entering the downstream processing unit.

[0022] Optionally, first, a particle size detection sensor, a moisture detector, a calorific value analyzer, and an in-line component detection device are arranged at the detection station inside the buffer unit close to the release of the material to the downstream processing unit. Each detection device establishes a stable data communication connection with the industrial controller. After the material unit enters the buffer unit and before being released to the downstream processing process, it passes through a preset detection station in sequence. Each detection device synchronously performs on-line collection of the corresponding attributes of the material unit. The multi-dimensional data collected is transmitted to the industrial controller in real time, and the industrial controller completes the integration and temporary storage of the data to form the multi-source characterization information of the material unit. The specific content of the multi-source characterization information will be described in detail in the subsequent steps.

[0023] Next, input the multi-source characterization information into the pre-constructed load mapping model. First, perform data cleaning and hierarchical decoupling in the preprocessing layer to extract primary feature components, then construct a process response mapping in the intermediate processing layer to obtain an intermediate process response quantity, perform coupled correction in the cross-attribute interaction analysis layer to generate a composite response quantity, and then complete scale compression and weight allocation through convergence mapping processing. Finally, establish and output the load contribution characterization value. This step will also be described in detail in the subsequent content.

[0024] Construct an exchangeability criterion based on the load contribution characterization value in the material queue, divide adjacent material units into multiple candidate exchange pairs, and calculate the exchange matching degree corresponding to the candidate exchange pairs.

[0025] In one embodiment of this application, after obtaining the load contribution characterization values ​​of all material units, an exchangeability criterion based on the load contribution characterization values ​​is constructed. First, the actual operating load data of the target processing unit under the same operating conditions over the past 30 days is statistically analyzed. The average load and load standard deviation of the processing unit during this period are calculated. 1.2 times the load standard deviation is taken as the maximum allowable load fluctuation value of the processing unit, which serves as the core threshold of the exchangeability criterion. Specifically, the exchangeability criterion is as follows: when the absolute difference between the load contribution characterization values ​​of two adjacent material units is less than or equal to the maximum allowable load fluctuation value, the two material units are deemed exchangeable; otherwise, they are deemed non-exchangeable.

[0026] Next, continuous material units are grouped in the material queue using a sliding window method. Adjacent material units within each group are combined into candidate exchange pairs. Preliminary screening is then performed based on the differences and gradients of the load contribution characteristics of each material unit to generate multiple candidate exchange pairs. This step will be explained in detail later.

[0027] Finally, the exchange matching degree for each candidate exchange pair is calculated. Specifically: First, the standard deviation of the load contribution characteristics of all material units within the sliding window before the exchange is calculated and recorded as the pre-exchange standard deviation. Then, the positions of the two material units in the candidate exchange pair are simulated for exchange, and the standard deviation of the load contribution characteristics of all material units within the sliding window after the exchange is calculated and recorded as the post-exchange standard deviation. The load leveling gain is calculated, which is equal to the pre-exchange standard deviation minus the post-exchange standard deviation, and then divided by the pre-exchange standard deviation. A material handling priority correction coefficient is introduced, which is set according to the urgency of the material handling, with a value ranging from 0.8 to 1.2. For example, the correction coefficient for urgent materials is 1.2, the correction coefficient for ordinary materials is 1.0, and the correction coefficient for low-priority materials is 0.8. The load leveling gain is multiplied by the material handling priority correction coefficient to obtain the exchange matching degree for the candidate exchange pair.

[0028] By constructing quantitative exchangeability criteria, sliding window grouping and filtering, and exchange matching degree calculation methods, the accurate generation and quantitative evaluation of candidate exchange pairs were achieved, effectively narrowing the search range for subsequent queue optimization and providing an efficient decision-making basis for full-process load leveling control.

[0029] The material queue is scanned and exchanged segment by segment according to the exchangeability criterion and exchange matching degree, and the positions of the material units in the queue are rearranged.

[0030] Specifically, when performing material unit exchange, a queue perturbation constraint is introduced for each exchange operation to configure the limit on the local change range of the queue, and the exchange order is dynamically adjusted according to the limit, thereby realizing the rearrangement of the material units in the queue. This step will be explained in detail later.

[0031] The rearranged materials are released sequentially to downstream processing units to implement full-process operation control and management.

[0032] Specifically, a load change trend curve is constructed based on the load contribution characterization value sequence of continuous material units and divided into segments to identify load increasing, stable and fluctuating transition segments. Then, a differentiated release rhythm is configured according to the identification results to execute full-process operation control management. This step will also be explained in detail in the following content.

[0033] Furthermore, the method provided in this application embodiment includes:

[0034] Multi-source characterization information includes a multi-dimensional dataset that characterizes the physical structure, energy, and reaction properties of material units. This multi-dimensional dataset includes material particle size distribution, moisture content, calorific value, and composition characteristics.

[0035] Specifically, particle size distribution reflects the particle size, particle gradation, and uniformity characteristics of the material unit, clarifying its physical morphology and dispersion state. Moisture content characterizes the moisture content, moisture form, and moisture distribution of the material unit, reflecting its physical humidity characteristics. Calorific value reflects the energy release capacity per unit mass of the material unit, indicating its potential energy output level during processing. Composition characteristics identify the main chemical components, the proportion of inorganic and organic components, and impurity content of the material unit, characterizing its reactivity and processing suitability. These parameters comprehensively and completely characterize the fundamental properties of the material unit from the dimensions of physical structure, energy characteristics, and reactivity, providing accurate data support for subsequent load mapping calculations.

[0036] Furthermore, the method provided in this application embodiment includes:

[0037] After the multi-source characterization information is cleaned and processed by the preprocessing layer, the layered decoupling channel built into the preprocessing layer is used to perform layered decoupling processing according to physical attributes, energy attributes, and reaction attributes, extracting primary feature components that characterize the material structure, energy release potential, and reaction sensitivity, respectively. The intermediate processing layer is activated, and the process response mapping relationship of the material unit in the target processing unit is constructed based on the primary feature components. The action path of each processing feature component in the processing process is expanded to obtain the intermediate process response quantities corresponding to the processing load formation mechanism. The intermediate process response quantities are synchronized to the cross-attribute interactive analysis layer. By establishing the coupling relationship between different intermediate process response quantities, the intermediate process response quantities are interactively corrected to generate a composite response quantity that eliminates the bias influence of a single attribute on the load characterization. The composite response quantity is subjected to convergence mapping processing, uniform scale compression and dynamic weight allocation are performed, and a load contribution characterization value is established and output.

[0038] Optionally, the multi-source characterization information is first input into a pre-built load mapping model. This model is pre-built using an industrial controller, comprising a pre-processing layer, an intermediate processing layer, a cross-attribute interaction analysis layer, and a convergence mapping output unit. The pre-processing layer is pre-configured with an independent data cleaning sub-unit and three parallel hierarchical decoupling channels. The multi-source characterization information is a multi-dimensional dataset containing material particle size distribution, moisture content, calorific value, and compositional characteristics. The pre-processing layer performs independent data cleaning processing on the multi-source characterization information, with data of different attributes undergoing cleaning operations separately within their corresponding decoupling channels. The specific steps are as follows:

[0039] Outlier removal is performed using the 3σ principle. The mean and standard deviation of historical data from the same batch and attribute are calculated, and a reasonable threshold range is set as the mean plus or minus three times the standard deviation. Invalid data exceeding this threshold range are marked and removed. Missing data is handled using a mean-based imputation method. The arithmetic mean of valid data collected from the same batch and attribute is extracted and used to fill in the missing data. Data scaling is performed using a min-max linear normalization method. The maximum and minimum values ​​of data with the same attribute are calculated, and each data value is substituted into the formula (data value - minimum value) / (maximum value - minimum value) to map all data to a uniform numerical range of 0 to 1.

[0040] After data cleaning, the preprocessing layer performs data allocation according to the preset attribute mapping relationship. The cleaned material particle size distribution data and moisture content data are transmitted to the buffer unit of the physical attribute channel, the cleaned calorific value data is transmitted to the buffer unit of the energy attribute channel, and the cleaned component characteristic data is transmitted to the buffer unit of the reaction attribute channel, realizing physical isolation and parallel processing of different attribute data.

[0041] The physical attribute channel extracts physical attribute components through the feature extraction calculation unit, calculating the average particle size, standard deviation of particle size, and moisture content of the material. A weighted summation method is used to calculate the physical attribute components, with a weighting coefficient of 0.6 for average particle size, 0.2 for standard deviation of particle size, and 0.2 for moisture content. The energy attribute channel directly outputs the calorific value data as the energy attribute component. The reaction attribute channel extracts reaction attribute components through the feature extraction calculation unit, calculating the proportion of organic components, inorganic components, and harmful impurities in the component characteristics. A weighted summation method is used to calculate the reaction attribute components, with a weighting coefficient of 0.5 for organic components, 0.3 for inorganic components, and 0.2 for harmful impurities. All weighting coefficients range from 0 to 1 and can be adjusted according to the actual operation of the target processing unit.

[0042] The physical attribute components output from the physical attribute channel, the energy attribute components output from the energy attribute channel, and the reaction attribute components output from the reaction attribute channel are then combined to form a set of primary feature components containing three dimensions, which are then transmitted to the intermediate processing layer for further processing.

[0043] Next, the intermediate processing layer is activated. Based on the differences in the way each primary feature component acts in the processing unit, a multi-path action channel is constructed. Dominant and auxiliary action paths are assigned to each component to form a set of paths covering different load formation mechanisms. The stage response quantities are extracted along the path to form a time-series path response sequence. After nonlinear response characterization, intra-path aggregation, inter-path normalization, and scale unification, intermediate process response quantities corresponding to different load formation mechanisms are generated. This step and the construction of the intermediate processing layer will be described in detail in the following content.

[0044] Then, the intermediate process response quantity is divided into precursor response components, process amplification response components, and constraint inhibition response components according to the action stage, and a directed interaction relationship is constructed. Sequential propagation analysis is performed according to this relationship. The stage response sequence is obtained by excitation of the precursor component and truncation modulation of the constraint component. After local conflict identification and reconstruction, a modified response fragment is formed. Then, the composite response quantity is generated by global consistency convergence, cross-stage splicing, and boundary smoothing constraints. This step and the construction of the cross-attribute interaction analysis layer will also be described in detail in the following content.

[0045] Finally, a convergence mapping output unit for the load mapping model is built in the industrial controller. Convergence mapping processing is performed on the composite response quantities. The convergence mapping output unit uses a linear scaling method to calculate the maximum and minimum values ​​of the composite response quantity set. Each composite response quantity is then substituted into the formula (composite response quantity - minimum value) / (maximum value - minimum value) for calculation, compressing all composite response quantities to a uniform numerical scale of 0 to 1. Next, the convergence mapping output unit reverse-allocates dynamic weighting coefficients according to the current load rate of the target processing unit. The dynamic weighting coefficients range from 0 to 1, and these coefficients are obtained through statistical analysis of historical load data of the target processing unit under the same operating conditions, reflecting the current load-bearing capacity of the processing unit in real time. The convergence mapping output unit performs weighted calculations on the scale-compressed composite response quantities, establishing and outputting the load contribution characterization values ​​for each corresponding material unit.

[0046] By employing multi-dimensional data hierarchical processing, process response mapping, and multi-attribute coupling correction methods, we have achieved accurate quantification of the load contribution of material units to downstream processing units, providing an accurate data foundation for subsequent material queue optimization and full-process load leveling control.

[0047] Furthermore, the method provided in this application embodiment includes:

[0048] Based on the differences in the action modes of each primary feature component in the processing unit, a corresponding multi-path action channel is constructed, and at least one dominant action path and one auxiliary action path are assigned to each primary feature component to form a path set covering different load formation mechanisms. Along the multi-path action channel, path expansion processing is performed on each primary feature component, discretizing the influence process of the primary feature component in the processing into multiple stage nodes, and extracting the corresponding stage response quantity at each stage node to form a path response sequence with temporal evolution characteristics. Nonlinear response characterization processing is performed on the path response sequence, and intra-path aggregation and inter-path normalization processing are performed. The scale of the response contribution between different paths is unified to generate intermediate process response quantities corresponding to different load formation mechanisms.

[0049] Specifically, firstly, an intermediate processing layer for the load mapping model is built in the industrial controller. The initialization parameters of the multi-path action channels, the stage division rules for path expansion, and the calculation logic for nonlinear response characterization are pre-configured in the intermediate processing layer to complete the construction of the intermediate processing layer.

[0050] Based on the differences in the action modes of each primary characteristic component in the processing unit, corresponding multi-path action channels are constructed in the intermediate processing layer. For physical attribute components, channels are constructed with material conveying load as the dominant action path and equipment wear load as the auxiliary action path. For energy attribute components, channels are constructed with heat load as the dominant action path and flue gas treatment load as the auxiliary action path. For reaction attribute components, channels are constructed with chemical reaction load as the dominant action path and product treatment load as the auxiliary action path, forming a set of paths covering different load formation mechanisms.

[0051] Next, along the constructed multi-path action channel, path expansion processing is performed on each primary feature component in the intermediate processing layer. The complete influence process of the primary feature components in the target processing unit is discretized into three consecutive stage nodes according to the processing time sequence: the feeding stage, the reaction stage, and the discharging stage. At each stage node, real-time operating data of the target processing unit under the corresponding material type is collected, and the stage response quantity corresponding to each stage node is extracted. The stage response quantities within the same action channel are arranged in chronological order to form a path response sequence with temporal evolution characteristics.

[0052] Then, in the intermediate processing layer, nonlinear response characterization is performed on all path response sequences. The arithmetic mean method is used to aggregate the response quantities of all stages within the same path, i.e., calculating the arithmetic mean of the response quantities of all stages within the same path to obtain the total response value for a single path. Next, the min-max normalization method is used to perform inter-path normalization on all single-path total response values, mapping all response values ​​to a unified numerical range of 0 to 1. Based on the contribution ratio of different load formation mechanisms to the total processing load—this contribution ratio is obtained through statistical analysis of historical operating data of the target processing unit—corresponding weight coefficients are assigned to each normalized single-path response value, with weight coefficients ranging from 0 to 1. The weighted response values ​​are then summed to generate intermediate process response quantities corresponding to different load formation mechanisms.

[0053] By constructing multi-path action channels, time-series path extension, and nonlinear response characterization methods, we have achieved refined differentiation and quantification of different load formation mechanisms, providing accurate intermediate data support for subsequent cross-attribute interactive correction.

[0054] Furthermore, the method provided in this application embodiment includes:

[0055] Based on the role of each intermediate process response quantity in the load formation process, the intermediate process response quantities are divided into precursor response components, process amplification response components, and constraint suppression response components, and a directed interaction relationship between different types of response components is constructed. According to the directed interaction relationship, sequential propagation analysis is performed on each intermediate process response quantity, so that the precursor response component has an excitation effect on the process amplification response component, and the constraint suppression response component is introduced to truncate and modulate the excessive response in the propagation path, resulting in a propagated and modulated stage response sequence. Local conflict identification and reconstruction processing is performed on the stage response sequence. By identifying the superimposed conflict segments of different response components in the same processing stage, the response components in the superimposed conflict segments are reconstructed in segments to form a corrected response segment that satisfies the continuity of load formation. Global consistency convergence is performed on the corrected response segment, and cross-stage splicing and boundary smoothing constraints are performed to generate a composite response quantity.

[0056] Specifically, a cross-attribute interaction analysis layer of the load mapping model is built in the industrial controller. The stage division rules of the response components, the range of coefficient values ​​for response calculation, the conflict judgment threshold and the smoothing processing parameters are pre-configured in the cross-attribute interaction analysis layer to complete the construction of the cross-attribute interaction analysis layer.

[0057] First, the intermediate process response quantities are synchronized to the cross-attribute interaction analysis layer. These intermediate process response quantities refer to three values ​​corresponding to the three load formation mechanisms: physical transport, energy release, and chemical reaction. Based on the role of each intermediate process response quantity in the load formation process, response component division is performed. The response quantity generated when the corresponding material enters the processing unit is classified as the precursor response component; the response quantity generated when the corresponding material reacts within the processing unit is classified as the process amplification response component; and the response quantity generated when the corresponding material leaves the processing unit is classified as the constraint inhibition response component. A directed interaction relationship is constructed, meaning that the precursor response component first acts on the process amplification response component, and then the process amplification response component acts on the constraint inhibition response component, clarifying the order of action of the three components.

[0058] Next, following the aforementioned directed interaction relationship, sequential propagation analysis is performed. First, the excitation effect of the precursor response component on the process amplification response component is calculated. The precursor response component is multiplied by a preset excitation coefficient to obtain an excitation correction value. This excitation correction value is then added to the original process amplification response component to obtain the excitation-modulated process amplification response component. Next, the truncation modulation effect of the constraint suppression response component on the excessive response is calculated. When the excitation-modulated process amplification response component exceeds a preset safety response threshold, the excess value is calculated. This excess value is multiplied by the corresponding suppression coefficient to obtain a suppression correction value. The suppression correction value is then subtracted from the excitation-modulated process amplification response component to obtain the modulated process amplification response component. The safety response threshold, excitation coefficient, and suppression coefficient are all obtained through statistical analysis of historical operating data from the target processing unit, and their values ​​range from 0 to 1. The original precursor response component, the modulated process amplification response component, and the original constraint suppression response component are arranged in chronological order to obtain the propagated and modulated stage response sequence.

[0059] Next, local conflict identification and reconstruction processing is performed on the stage response sequence. A point-by-point comparison method is used to identify superimposed conflict segments, calculating the sum of the three response components at each time point. When the sum of the values ​​at any time point exceeds a preset conflict threshold, this time point and the three consecutive time points before and after it are marked as a superimposed conflict segment. The conflict threshold is obtained through statistical analysis of historical operating data of the target processing unit, with a value ranging from 0 to 1. Linear interpolation is used to perform segmented reconstruction of the response components within the superimposed conflict segment. Using the response component values ​​at the first and last time points of the conflict segment as endpoints, the response component values ​​at intermediate time points are calculated. This ensures that the sum of the three response component values ​​at each reconstructed time point does not exceed the conflict threshold, while simultaneously guaranteeing continuous change in response values ​​over time, forming a corrected response segment that satisfies the continuity of load formation.

[0060] Finally, a global consistency convergence process is performed on all corrected response fragments. The overall variation range of the maximum value minus the minimum value within the actual load segment under the same operating condition of the target processing unit is statistically analyzed. The ratio of the overall variation range of the maximum value minus the minimum value within the corrected response fragment segment to the overall variation range of the actual load is calculated. All values ​​of the corrected response fragments are then multiplied by this ratio to ensure that the overall variation trend of the corrected response fragments is consistent with the variation trend of the actual load. The converged corrected response fragments are then concatenated sequentially according to the processing time. The arithmetic mean of the two values ​​at the boundary of two adjacent fragments is taken as the final value at the boundary, eliminating abrupt changes in the value at the boundary, and finally generating the composite response quantity.

[0061] By clearly defining the stages and using simple arithmetic operations to achieve response propagation and modulation, by resolving local conflicts through threshold comparison and linear interpolation, and by achieving global convergence and smoothing through proportional adjustment and mean calculation, the bias influence of a single attribute on the load characterization is effectively eliminated, and a composite response quantity that can comprehensively reflect the coupling effect of multiple attributes is generated.

[0062] Furthermore, the method provided in this application embodiment includes:

[0063] In the material queue, continuous material units are grouped in a sliding window manner. Candidate exchange pairs are constructed by combining adjacent material units in each group. Based on the differences and gradients of the load contribution characterization values ​​of each material unit, preliminary screening of candidate exchange pairs is performed, and multiple candidate exchange pairs are generated according to the preliminary screening results.

[0064] Specifically, firstly, consecutive material units are grouped in the material queue using a sliding window method. The size of the sliding window is set to 3 consecutive material units, and the sliding step size is set to 1 material unit. The entire material queue is traversed sequentially, and the material units within each sliding window are grouped. Every two adjacent material units within the window are combined into an initial exchange pair.

[0065] Next, based on the differences and gradients in the load contribution characteristics of each material unit, preliminary screening of candidate exchange pairs is performed. The absolute difference between the load contribution characteristics of the two material units in each initial exchange pair is calculated, and this difference is compared with the core threshold of the exchangeability criterion. If the difference is less than or equal to the core threshold, the initial exchange pair is retained; if the difference is greater than the core threshold, the initial exchange pair is discarded. Simultaneously, the gradient of the load contribution characteristics of three adjacent material units is calculated, and initial exchange pairs with a gradient greater than 0.5 are discarded. The gradient is calculated by dividing the difference in load contribution characteristics of adjacent material units by the average processing time of the material units. Based on the preliminary screening results, several candidate exchange pairs that meet the exchange feasibility requirements are generated.

[0066] Furthermore, the method provided in this application embodiment includes:

[0067] During the material unit exchange process, a queue perturbation constraint is introduced for each exchange operation, and the queue perturbation constraint is used to configure the local change range limit of the queue; the exchange order is dynamically adjusted according to the queue local change range limit.

[0068] In one embodiment, all candidate exchange pairs are first scanned segment by segment from the beginning of the material queue according to the exchangeability criterion and exchange matching degree. The candidate exchange pairs are then sorted from high to low according to the exchange matching degree to determine the initial order of the exchange operations to be performed. During the material unit exchange process, a queue perturbation constraint is introduced. This constraint limits the degree of variation within the local area of ​​the material queue. By collecting historical operating data of the target processing unit under the same working conditions, the average value of the load contribution characterization value is calculated. 10% of this average value is set as the limit for the local variation range of the queue, serving as the specific quantitative standard for the queue perturbation constraint.

[0069] Next, the exchange order is dynamically adjusted according to the local change range limit of the queue. The exchange operation of each candidate exchange pair is simulated one by one. The fluctuation range of the load contribution characterization value of all material units within the sliding window of the three consecutive material units of the corresponding candidate exchange pair after the exchange is calculated. The fluctuation range is obtained by summing the absolute values ​​of the difference between the load contribution characterization values ​​before and after the exchange within the local range. If the calculated fluctuation range is less than or equal to the local change range limit of the queue, the exchange order of the candidate exchange pair remains unchanged. If the fluctuation range is greater than the local change range limit of the queue, the exchange order of the candidate exchange pair is postponed until all candidate exchange pairs have completed the fluctuation range verification, and the dynamic adjustment of the exchange order is completed.

[0070] Finally, according to the dynamically adjusted exchange order, the material units in the material queue are rearranged in sequence, and the exchange operation that meets the queue perturbation constraint is completed one by one. Finally, the optimized material queue is obtained, thereby realizing the rearrangement of the material units in the queue.

[0071] By using segment-by-segment scanning and screening, disturbance amplitude constraints, dynamic adjustment of exchange order, and position rearrangement, queue optimization is achieved while controlling the fluctuation range of the material queue, so as to keep the processing load of the target processing unit stable and ensure the stable operation of the entire industrial solid waste treatment process.

[0072] Furthermore, the method provided in this application embodiment includes:

[0073] A load change trend curve is constructed based on the load contribution characterization value sequence of continuous material units, and the load change trend curve is segmented to identify the load increasing segment, the load stable segment, and the load fluctuation transition segment; a differentiated release rhythm is configured according to the identification results, and differentiated release management is performed.

[0074] Optionally, firstly, the rearranged materials are prepared for release to the downstream processing unit in sequence. The load contribution values ​​of all material units in the material queue are arranged in release order. Using the material unit number as the x-axis and the corresponding load contribution value as the y-axis, a load change trend curve is constructed by connecting adjacent coordinate points with a straight line. The load change trend curve is then segmented. All three consecutive coordinate points on the curve are traversed sequentially, and the load change rate between adjacent coordinate points is calculated. The load change rate equals the load contribution value of the next coordinate point minus the load contribution value of the previous coordinate point, divided by the standard release interval T. When the absolute value of the load change rate of all three consecutive coordinate points is less than or equal to 0.05, the curve segment is marked as a stable load segment. When the load change rate of all three consecutive coordinate points is greater than 0.05, the curve segment is marked as an increasing load segment. The remaining curve portions that belong neither to the stable segment nor the increasing segment are marked as load fluctuation transition segments.

[0075] Subsequently, based on the aforementioned segment identification results, a differentiated release rhythm for releasing materials to downstream processing units is configured. The standard material release interval for the target downstream processing unit is set as T, with the value of T determined based on the rated processing capacity of the processing unit. For stable load segments, material is released to downstream processing units using the standard release interval T. For increasing load segments, the release interval is extended to 1.2T to reduce the material input rate to downstream processing units and alleviate the pressure of increasing load on the processing units. For load fluctuation transition segments, a gradual release rhythm between the standard release interval and the extended release interval is adopted.

[0076] Finally, differentiated release management is implemented, releasing materials to downstream processing units according to the configured rhythm. At the same time, continuity constraints for adjacent sections are executed during the release process. This step will be explained in detail later.

[0077] By constructing load trend curves and dividing sections, configuring differentiated release rhythms, and ensuring smooth transitions between adjacent sections, the precise matching of material release rhythms with the load status of treatment units was achieved, further enhancing the stability and continuity of the entire industrial solid waste treatment process.

[0078] Furthermore, the method provided in this application embodiment includes:

[0079] Implementing differentiated release management also includes implementing continuity constraints on adjacent segment configurations during the release process, wherein the continuity constraints are used to implement smoothing limits on changes in the release rhythm.

[0080] In one embodiment, during the implementation of differentiated release management, a continuity constraint for adjacent segments is introduced to limit the abrupt change in release rhythm between adjacent segments. At the boundary between two adjacent segments, the arithmetic mean of the release intervals of the two segments is taken as the release interval at the boundary. Then, the total quantity of material contained in the transition segment is counted. The total interval difference is obtained by subtracting the release interval of the previous segment from the release interval of the next segment. The total interval difference is divided by the quantity of material in the transition segment to obtain the single-step adjustment value. Starting from the first material in the transition segment, the single-step adjustment value is successively accumulated based on the release interval of the previous segment to complete the linear interpolation calculation. The release interval of the previous segment is gradually and smoothly adjusted to the release interval of the next segment, making the change in release rhythm smooth and continuous, and avoiding sudden jumps in release interval.

[0081] In summary, the dynamic monitoring method for solid waste treatment provided in this application has the following technical effects:

[0082] This application constructs an exchangeability criterion and exchange matching degree by performing load characterization calculation and cross-attribute response analysis on the material queue for industrial solid waste treatment. Combined with queue disturbance constraints, it completes the rearrangement of material positions, identifies load change segments and configures differentiated release rhythms, and introduces continuous constraints to smooth the release rhythm. This achieves stable operation control of the entire industrial solid waste treatment process, making the treatment load management more precise and efficient. It achieves the technical effect of precise matching of process load and optimized queue arrangement, improving the stability of the entire process operation and the overall treatment efficiency.

[0083] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a dynamic monitoring system for solid waste treatment, the system comprising:

[0084] Material queue construction module 1 is used to set up buffer units between adjacent processing units, and to build a material queue arranged in the order of entry within the buffer units, assigning a unique identifier to each material unit entering and recording the source batch.

[0085] The load contribution characterization value acquisition module 2 is used to collect multi-source characterization information of the material unit before it enters the processing flow, and input the multi-source characterization information into a pre-constructed load mapping model to obtain the load contribution characterization value of each material unit. The load mapping model is used to characterize the degree of influence of the material unit on the processing load after it enters the downstream processing unit.

[0086] The exchange matching degree calculation module 3 is used to construct an exchangeability criterion based on the load contribution characterization value in the material queue, divide adjacent material units into multiple candidate exchange pairs, and calculate the exchange matching degree corresponding to the candidate exchange pairs.

[0087] The position rearrangement execution module 4 is used to perform segment-by-segment scanning and exchange operations on the material queue according to the exchangeability criterion and exchange matching degree, and to perform position rearrangement of material units in the queue.

[0088] The full-process management execution module 5 is used to release the rearranged materials sequentially to the downstream processing unit in order to perform full-process operation control and management.

[0089] Furthermore, the load contribution characterization value acquisition module 2 is used to perform the following steps:

[0090] After the multi-source characterization information is cleaned and processed by the preprocessing layer, the layered decoupling channel built into the preprocessing layer is used to perform layered decoupling processing according to physical attributes, energy attributes, and reaction attributes, extracting primary feature components that characterize the material structure, energy release potential, and reaction sensitivity, respectively. The intermediate processing layer is activated, and the process response mapping relationship of the material unit in the target processing unit is constructed based on the primary feature components. The action path of each processing feature component in the processing process is expanded to obtain the intermediate process response quantities corresponding to the processing load formation mechanism. The intermediate process response quantities are synchronized to the cross-attribute interactive analysis layer. By establishing the coupling relationship between different intermediate process response quantities, the intermediate process response quantities are interactively corrected to generate a composite response quantity that eliminates the bias influence of a single attribute on the load characterization. The composite response quantity is subjected to convergence mapping processing, uniform scale compression and dynamic weight allocation are performed, and a load contribution characterization value is established and output.

[0091] Furthermore, the load contribution characterization value acquisition module 2 is used to perform the following steps:

[0092] Based on the role of each intermediate process response quantity in the load formation process, the intermediate process response quantities are divided into precursor response components, process amplification response components, and constraint suppression response components, and a directed interaction relationship between different types of response components is constructed. According to the directed interaction relationship, sequential propagation analysis is performed on each intermediate process response quantity, so that the precursor response component has an excitation effect on the process amplification response component, and the constraint suppression response component is introduced to truncate and modulate the excessive response in the propagation path, resulting in a propagated and modulated stage response sequence. Local conflict identification and reconstruction processing is performed on the stage response sequence. By identifying the superimposed conflict segments of different response components in the same processing stage, the response components in the superimposed conflict segments are reconstructed in segments to form a corrected response segment that satisfies the continuity of load formation. Global consistency convergence is performed on the corrected response segment, and cross-stage splicing and boundary smoothing constraints are performed to generate a composite response quantity.

[0093] Furthermore, the load contribution characterization value acquisition module 2 is used to perform the following steps:

[0094] Based on the differences in the action modes of each primary feature component in the processing unit, a corresponding multi-path action channel is constructed, and at least one dominant action path and one auxiliary action path are assigned to each primary feature component to form a path set covering different load formation mechanisms. Along the multi-path action channel, path expansion processing is performed on each primary feature component, discretizing the influence process of the primary feature component in the processing into multiple stage nodes, and extracting the corresponding stage response quantity at each stage node to form a path response sequence with temporal evolution characteristics. Nonlinear response characterization processing is performed on the path response sequence, and intra-path aggregation and inter-path normalization processing are performed. The scale of the response contribution between different paths is unified to generate intermediate process response quantities corresponding to different load formation mechanisms.

[0095] Furthermore, the exchange matching degree calculation module 3 is used to perform the following steps:

[0096] In the material queue, continuous material units are grouped in a sliding window manner. Candidate exchange pairs are constructed by combining adjacent material units in each group. Based on the differences and gradients of the load contribution characterization values ​​of each material unit, preliminary screening of candidate exchange pairs is performed, and multiple candidate exchange pairs are generated according to the preliminary screening results.

[0097] Furthermore, the position rearrangement execution module 4 is used to perform the following steps:

[0098] During the material unit exchange process, a queue perturbation constraint is introduced for each exchange operation, and the queue perturbation constraint is used to configure the local change range limit of the queue; the exchange order is dynamically adjusted according to the queue local change range limit.

[0099] Furthermore, the full-process management execution module 5 is used to perform the following steps:

[0100] A load change trend curve is constructed based on the load contribution characterization value sequence of continuous material units, and the load change trend curve is segmented to identify the load increasing segment, the load stable segment, and the load fluctuation transition segment; a differentiated release rhythm is configured according to the identification results, and differentiated release management is performed.

[0101] Furthermore, the full-process management execution module 5 is used to perform the following steps:

[0102] Implementing differentiated release management also includes implementing continuity constraints on adjacent segment configurations during the release process, wherein the continuity constraints are used to implement smoothing limits on changes in the release rhythm.

[0103] Furthermore, the load contribution characterization value acquisition module 2 is used to perform the following steps:

[0104] Multi-source characterization information includes a multi-dimensional dataset that characterizes the physical structure, energy, and reaction properties of material units. This multi-dimensional dataset includes material particle size distribution, moisture content, calorific value, and composition characteristics.

[0105] The dynamic monitoring system for solid waste treatment provided in this embodiment of the invention can execute the dynamic monitoring method for solid waste treatment provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0106] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A dynamic monitoring method for solid waste treatment, characterized in that, The method includes: A buffer unit is set up between adjacent processing units, and a material queue arranged in the order of entry is built in the buffer unit. Each material unit entering is assigned a unique identifier and the source batch is recorded. Multi-source characterization information of the material unit before it enters the processing flow is collected, and the multi-source characterization information is input into a pre-constructed load mapping model to obtain the load contribution characterization value of each material unit. The load mapping model is used to characterize the degree of influence of the material unit on the processing load after it enters the downstream processing unit. In the material queue, an exchangeability criterion based on load contribution characterization value is constructed, and adjacent material units are divided into multiple candidate exchange pairs, and the exchange matching degree corresponding to the candidate exchange pairs is calculated. The material queue is scanned and exchanged segment by segment according to the exchangeability criterion and exchange matching degree, and the material units are rearranged in the queue. The rearranged materials are released sequentially to downstream processing units to implement full-process operation control and management.

2. The dynamic monitoring method for solid waste treatment as described in claim 1, characterized in that, Inputting the multi-source representation information into a pre-constructed load mapping model includes: After the multi-source characterization information is cleaned and processed by the preprocessing layer, the layered decoupling channel built into the preprocessing layer is used to perform layered decoupling processing according to physical properties, energy properties, and reaction properties, and primary feature components that characterize the material structure features, energy release potential, and reaction sensitivity are extracted respectively. Activate the intermediate processing layer, construct the process response mapping relationship of the material unit in the target processing unit based on the primary feature components, expand the action path of each processing feature component in the processing process, and obtain the intermediate process response quantities corresponding to the processing load formation mechanism respectively. The intermediate process response quantities are synchronized to the cross-attribute interactive analysis layer. By establishing the coupling relationship between different intermediate process response quantities, the intermediate process response quantities are interactively corrected to generate a composite response quantity that eliminates the bias effect of a single attribute on the load characterization. The composite response is subjected to convergence mapping, uniform scale compression and dynamic weight allocation, and a load contribution characterization value is established and output.

3. The dynamic monitoring method for solid waste treatment as described in claim 2, characterized in that, Synchronizing the intermediate process response to the cross-attribute interaction analysis layer includes: Based on the role of each intermediate process response quantity in the load formation process, the intermediate process response quantities are divided into precursor response components, process amplification response components and constraint suppression response components, and a directed interaction relationship between different types of response components is constructed. According to the directional interaction relationship, sequential propagation analysis is performed on the response quantities of each intermediate process, so that the precursor response component can excite the process amplification response component. At the same time, the constraint suppression response component is introduced to truncate and modulate the excessive response in the propagation path, so as to obtain the stage response sequence after propagation modulation. Local conflict identification and reconstruction processing is performed on the stage response sequence. By identifying superimposed conflict segments of different response components in the same processing stage, the response components in the superimposed conflict segments are reconstructed in segments to form a corrected response segment that satisfies the continuity of load formation. Global consistency convergence is performed on the modified response fragment, and cross-stage splicing and boundary smoothing constraints are applied to generate a composite response.

4. The dynamic monitoring method for solid waste treatment as described in claim 2, characterized in that, Activating the intermediate processing layer also includes: Based on the differences in the function of each primary feature component in the processing unit, a corresponding multi-path action channel is constructed, and at least one dominant action path and one auxiliary action path are assigned to each primary feature component to form a path set covering different load formation mechanisms. Along the multi-path action channel, path expansion processing is performed on each primary feature component, the influence process of the primary feature component in the processing is discretized into multiple stage nodes, and the corresponding stage response quantity is extracted at each stage node to form a path response sequence with temporal evolution characteristics. The path response sequence is subjected to nonlinear response characterization, intra-path aggregation and inter-path normalization are performed, and the response contributions between different paths are scaled to generate intermediate process response quantities corresponding to different load formation mechanisms.

5. The dynamic monitoring method for solid waste treatment as described in claim 1, characterized in that, Adjacent material units are divided into multiple candidate exchange pairs, including: In the material queue, continuous material units are grouped in a sliding window manner. Candidate exchange pairs are constructed by combining adjacent material units in each group. Based on the differences and gradients of the load contribution characterization values ​​of each material unit, preliminary screening of candidate exchange pairs is performed, and multiple candidate exchange pairs are generated according to the preliminary screening results.

6. The dynamic monitoring method for solid waste treatment as described in claim 1, characterized in that, The material queue is scanned and exchanged segment by segment according to the exchangeability criterion and exchange matching degree, including: During the material unit exchange process, a queue perturbation constraint is introduced for each exchange operation, and the queue perturbation constraint is used to configure the local change range limit of the queue. The swapping order is dynamically adjusted based on the local variation limit of the queue.

7. The dynamic monitoring method for solid waste treatment as described in claim 1, characterized in that, The rearranged materials are released sequentially to downstream processing units to perform full-process operation control and management, including: A load change trend curve is constructed based on the load contribution characterization value sequence of continuous material units, and the load change trend curve is segmented to identify the load increasing segment, the load stable segment, and the load fluctuation transition segment. Configure differentiated release rhythms based on the identification results and execute differentiated release management.

8. The dynamic monitoring method for solid waste treatment as described in claim 7, characterized in that, Implementing differentiated release management also includes implementing continuity constraints on adjacent segment configurations during the release process, wherein the continuity constraints are used to implement smoothing limits on changes in the release rhythm.

9. The dynamic monitoring method for solid waste treatment as described in claim 1, characterized in that, Multi-source characterization information includes a multi-dimensional dataset that characterizes the physical structure, energy, and reaction properties of material units. This multi-dimensional dataset includes material particle size distribution, moisture content, calorific value, and composition characteristics.

10. A dynamic monitoring system for solid waste treatment, characterized in that, The system is used to implement the dynamic monitoring method for solid waste treatment according to any one of claims 1-9, the system comprising: The material queue construction module is used to set up buffer units between adjacent processing units, and to build a material queue arranged in the order of entry within the buffer unit, assigning a unique identifier to each material unit entering and recording the source batch; The load contribution characterization value acquisition module is used to collect multi-source characterization information of the material unit before it enters the processing flow, and input the multi-source characterization information into a pre-constructed load mapping model to obtain the load contribution characterization value of each material unit. The load mapping model is used to characterize the degree of influence of the material unit on the processing load after it enters the downstream processing unit. The exchange matching degree calculation module is used to construct an exchangeability criterion based on the load contribution characterization value in the material queue, divide adjacent material units into multiple candidate exchange pairs, and calculate the exchange matching degree corresponding to the candidate exchange pairs. The position rearrangement execution module is used to perform segment-by-segment scanning and exchange operations on the material queue according to the exchangeability criterion and exchange matching degree, and to perform position rearrangement of material units in the queue. The end-to-end management execution module is used to release the rearranged materials sequentially to the downstream processing units in order to perform end-to-end operation control and management.

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