Full-automatic production line management method and system for pretreatment of phosphorus flame retardant

Abstract

The invention relates to the technical field of production line management, and provides a full-automatic production line management method and system for phosphorus flame retardant pretreatment, and the method comprises the steps: building a resource demand model of a multi-medium pollution sample treatment task, and according to the type of a multi-medium pollution sample, a target treatment flow and preset technological parameters, obtaining a resource demand model of the multi-medium pollution sample treatment task; calculating an expected demand of each multi-medium pollution sample processing task on the shared key resource in a future preset duration; aggregating the expected demands of the multi-medium pollution sample processing tasks, and generating a whole production line future resource demand prediction map; obtaining shared resource availability data in real time, and comparing the shared resource availability data with a whole production line future resource demand prediction map and a preset resource supply period to identify a resource shortage risk; when a resource shortage risk is identified, making a resource allocation decision; and executing a resource allocation decision, and controlling the full-automatic production line. The full-automatic production line has the effect of improving the operation efficiency and stability of the full-automatic production line.

Description

Technical Field

[0001] This invention relates to the technical field of production line management, and specifically to a fully automated production line management method and system for the pretreatment of phosphorus-based flame retardants. Background Technology

[0002] In industrial production and environmental monitoring, particularly in the chemical industry and functional materials manufacturing, the pretreatment of multi-media contaminated samples is a crucial step affecting subsequent analysis and extraction results. For example, in the production, application, and related environmental sample analysis of phosphorus-based flame retardants, target phosphorus-based flame retardant compounds may be present in various media such as water, exhaust gas, aerosols, contaminated soil, or solid particles. Before proceeding to the extraction or analysis of phosphorus-based flame retardant compounds, these multi-media contaminated samples typically require differentiated pretreatment processes to achieve sample uniformity, reduce interfering factors, and initially stabilize treatment conditions.

[0003] In existing technologies, different pretreatment devices or units are often used for contaminated samples of different media types. Operators rely on experience to determine the sample type and manually configure the corresponding treatment path and process parameters. This experience-driven pretreatment method not only suffers from low process switching efficiency and poor operational consistency, but also easily leads to problems such as conflicting processing sequences, contention for critical resources, or uneven equipment load when multiple pretreatment units need to operate collaboratively, thus affecting the continuity and stability of the pretreatment stage. These problems are particularly pronounced when multiple batches and types of contaminated samples enter the production line in parallel.

[0004] In fully automated production lines for phosphorus-based flame retardant pretreatment, the processing capacity of current modules (such as reaction units, separation units, or buffer units) may experience a hidden decline due to long-term operation or minor process fluctuations. When this hidden decline is superimposed on a sudden input load of multi-media contaminated samples, and accompanied by competition for shared critical resources among different pretreatment processes, existing production line management systems often lack the ability to comprehensively perceive and analyze the correlation of these complex situations. The system struggles to identify the coupling relationship between changes in pretreatment capacity and sample load fluctuations in a timely manner, and is also unable to accurately assess the overall operational health and resource availability of the production line.

[0005] In this situation, existing production line management systems can typically only respond passively to single equipment or localized anomalies, failing to dynamically adjust the priority of pretreatment tasks for multi-media contaminated samples or replan pretreatment paths based on concurrent anomaly patterns. For example, when some pretreatment modules approach their actual capacity limits, the system struggles to promptly divert high-load samples or prioritize critical resources, leading to forced interruptions in the pretreatment process or fluctuations in processing conditions. This instability will further propagate to subsequent extraction steps, affecting the integrity of the phosphorus-based flame retardant compound release process and the consistency of extraction results.

[0006] Therefore, existing technologies urgently need a fully automated production line management solution that can coordinate and dynamically manage processing tasks, key resources, and processing module status in a unified manner for the pretreatment stage of multi-media contaminated samples, so as to provide a stable, continuous, and controllable pretreatment operating environment before entering the phosphorus-based flame retardant compound extraction step.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] This application discloses a fully automated production line management method and system for the pretreatment of phosphorus-based flame retardants. It aims to solve the problem that existing production line management systems in fully automated production lines for the pretreatment of phosphorus-based flame retardants lack high-level intelligent diagnostic capabilities when facing complex abnormal situations such as the implicit decline in the effective processing capacity of key processing modules, the impact of sudden high-load multi-media contaminated samples, and competition for shared key resources. They are unable to perform in-depth correlation analysis, assess the health status and resource availability of the production line in real time, or intelligently adjust the priority of multi-media contaminated samples and replan the processing path. As a result, the production line cannot effectively self-adjust and optimize, ultimately causing multiple units to stop and requiring time-consuming manual investigation and intervention.

[0009] The technical solution of this application is as follows: In a first aspect, this application discloses a fully automated production line management method for the pretreatment of phosphorus-based flame retardants, comprising the following steps: Establish a resource requirement model for multi-media contaminated sample processing tasks. Based on the multi-media contaminated sample type, target processing flow, and preset process parameters, calculate the expected demand for shared key resources for each multi-media contaminated sample processing task within a preset time period in the future. By aggregating the anticipated needs of various multi-media contamination sample treatment tasks, a forecast map of future resource needs for the entire production line is generated. Real-time acquisition of shared resource availability data, compared with the full production line's future resource demand forecast map and preset resource replenishment cycle, to identify resource shortage risks; When a resource shortage risk is identified, a resource allocation decision is made based on preset task priority rules and the real-time health status of the modules. Implement resource allocation decisions and control the fully automated production line.

[0010] This technical solution enables the establishment of a resource requirement model for multi-media contaminated sample processing tasks, calculation of the expected demand for shared key resources for each task, and aggregation to generate a future resource requirement forecast map for the entire production line. By acquiring and comparing shared resource availability data in real time, resource shortage risks can be identified. When a risk is identified, resource allocation decisions can be made and executed based on preset task priority rules and the real-time health status of modules, thereby effectively controlling the fully automated production line. This solves the problem of existing production line management systems lacking intelligent diagnosis and adjustment capabilities when facing complex abnormal situations, and realizes the self-adjustment and optimization of the production line.

[0011] Furthermore, based on the above, when a resource shortage risk is identified, the steps for making resource allocation decisions according to preset task priority rules and the real-time health status of modules include: Monitor the operating parameters of the delivery pump; Based on the operating parameters of the transfer pump, infer the fluid resistance characteristics of the transferred chemicals and evaluate the actual pumping efficiency of the transfer pump; By comparing fluid resistance characteristics with those of preset standard chemicals, the changes in the physical properties of chemicals can be quantified. Adjust the efficiency factor of the delivery pump based on changes in the physical properties of the chemicals and the actual pumping efficiency. Calculate the actual time required to transport a unit volume of material based on fluid resistance characteristics and efficiency factors; Monitor task progress in real time and adjust the remaining task completion time according to the actual time. Update the future resource demand map for the entire production line based on the calibrated remaining task completion time and corresponding resource occupancy time. Based on the updated future resource requirements map for the entire production line, task priorities and initiation timing are reassessed.

[0012] This technical solution enables the inference of chemical fluid resistance characteristics and pumping efficiency by monitoring the operating parameters of the delivery pump, quantifying changes in the physical properties of the chemicals, and subsequently adjusting the delivery pump efficiency factor to calculate the actual delivery time. By monitoring task progress in real time and calibrating the remaining task completion time, the future resource demand map of the entire production line is updated, and task priorities and start times are reassessed. This allows for more accurate assessment and adjustment of resource allocation under resource shortage risks, optimizing task scheduling and improving the level of refined management of production line operations.

[0013] In some preferred embodiments, when a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Real-time acquisition of operating parameters of key processing modules; Based on the operating parameters of the key processing modules, assess the actual processing capacity of the key processing modules and quantify the types of processing capacity degradation. Adjust the matching strategy between multi-media contaminated sample processing tasks and modules according to the type of processing capacity degradation. Based on the adjusted matching strategy, task priorities and start times will be reassessed.

[0014] This technical solution enables the real-time acquisition of operating parameters from key processing modules, assessment of their actual processing capabilities, and quantification of degradation types. This allows for adjustments to the matching strategy between multi-media contaminated sample processing tasks and modules, as well as a reassessment of task priorities and initiation timing. This allows the production line to dynamically adjust task allocation when facing module processing capability degradation, avoiding inefficiencies or malfunctions caused by module performance decline, and further enhancing the intelligence and adaptability of production line management.

[0015] As an optional approach, when a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Identify the multi-media contaminated sample processing tasks to be handled, and determine the key processing units and environmental compliance indicators involved in each task based on the type of multi-media contaminated sample and the target processing flow. Simulate the potential impact of various resource allocation adjustment schemes on the operational status and environmental compliance indicators of key processing units; Assess the cascading impact of each resource allocation adjustment plan on shared resources; Based on the preset task priority rules, the real-time health status of the modules, potential impacts and cascading effects, calculate the comprehensive risk score of the resource allocation adjustment plan; The resource allocation adjustment plan with the lowest comprehensive risk score shall be selected as the resource allocation decision.

[0016] This technical solution enables the identification of key processing units and environmental compliance indicators for tasks to be processed, and simulates the potential impact and cascading effects of different resource allocation adjustment schemes on operational status, environmental compliance, and shared resources. Through comprehensive risk scoring, the scheme with the lowest risk can be selected as the resource allocation decision, thereby enabling a more comprehensive consideration of multiple factors and making better decisions under resource shortage risks, thus reducing the risk of production line operation.

[0017] Based on the above, when a resource shortage risk is identified, the steps for making resource allocation decisions according to preset task priority rules and the real-time health status of modules include: Predict the fluctuation range and probability distribution of the amount of multi-media contaminated samples generated within a preset time period in the future; Evaluate the probability distribution of the recovery time required for the processing module and the success probability of different recovery paths; Based on the fluctuation range and probability distribution of the amount of multi-media contaminated samples generated, the probability distribution of the time required for the processing module to recover, and the success probability of different recovery paths, the production line operation status of the resource allocation adjustment scheme under different multi-media contaminated sample generation and module recovery scenarios is simulated. Based on the production line operation status, calculate the resource consumption, task completion time, environmental compliance risks, and production line downtime probability of the resource allocation adjustment scheme under different scenarios, and analyze to obtain the comprehensive risk score of each resource allocation adjustment scheme; The resource allocation adjustment plan with the lowest comprehensive risk score shall be selected as the resource allocation decision.

[0018] This technical solution enables the prediction of fluctuations in the generation of multi-media contaminated samples, the recovery time and success probability of the processing module, and the simulation of production line operation under different scenarios. By calculating resource consumption, task completion time, environmental compliance risks, and the probability of production line downtime, a comprehensive risk score is obtained, and the solution with the lowest risk is selected. This allows the production line to make forward-looking plans and decisions when facing uncertainties, improving its ability and robustness in responding to emergencies.

[0019] Furthermore, the steps for evaluating the probability distribution of the recovery time required for the processing module and the success probability of different recovery paths include: When a processing module malfunctions, identify the type of malfunction. Based on the fault type, a preset fault feature library is matched to obtain the fault feature matching result; When the fault feature matching result indicates that the fault type cannot be directly matched, the fault mode is decomposed based on the fault phenomenon, damaged parts, and the degree of impact of the fault on the production line operation, and the complex fault is decomposed into several sub-fault modes. For each sub-fault mode, based on its corresponding historical maintenance data, spare parts inventory, and maintenance personnel skill level, the recovery time range and corresponding probability weight of the sub-fault mode are generated. Based on the recovery time intervals and probability weights of the sub-fault modes, as well as the logical dependencies between the sub-fault modes, the probability distribution of the recovery time required for the processing module is calculated by combining the results. For each sub-failure mode, identify the corresponding feasible recovery path and evaluate the success probability of each recovery path; Based on the success probability of the recovery path of each sub-fault mode and the logical dependencies between each sub-fault mode, the success probability of different recovery paths of the processing module is calculated by combining these factors.

[0020] This technical solution enables the comprehensive calculation of the probability distribution of recovery time required for processing modules and the success probability of different recovery paths through fault type identification, fault mode decomposition, generation of sub-fault mode recovery time and probability weights, and recovery path evaluation. This allows production lines to perform more refined fault diagnosis and recovery time prediction when facing module failures, providing more accurate data support for resource allocation decisions and improving the efficiency and accuracy of fault handling.

[0021] Based on the above, the steps for calculating the probability distribution of the recovery time required for the processing module, according to the recovery time interval and probability weight of the sub-fault modes and the logical dependencies between the sub-fault modes, include: Construct a dependency graph for sub-fault modes; Identify and label parallel and interleaved paths in the dependency graph; Calculate the parallel combination probability distribution of recovery time for sub-fault modes on parallel paths; By analyzing the path, we ensure that the recovery time of each sub-fault mode in the interleaved path is calculated only once, thus obtaining the recovery time data related to the interleaved path. Based on the parallel combination probability distribution and the recovery time data related to the interleaved paths, the probability distribution of the recovery time required for the processing module is calculated.

[0022] This technical solution enables the construction of a sub-fault mode dependency graph, identification of parallel and interleaved paths, and calculation of their recovery time probability distributions. These paths are then combined to obtain the probability distribution of the recovery time required by the processing module. This allows for a more accurate consideration of the dependencies between sub-fault modes when handling complex faults, avoiding redundant calculations and improving the accuracy of recovery time prediction.

[0023] In one implementation, the step of calculating the probability distribution of the recovery time required for the processing module based on the recovery time intervals and probability weights of the sub-fault modes and the logical dependencies between the sub-fault modes includes: Construct a dependency graph for each sub-fault mode; Traverse the dependency graph, identify and mark parallel paths; identify and mark interleaved paths; Perform parallel path combination: For parallel paths, calculate the probability distribution of parallel combination of sub-fault mode recovery times on the parallel path; Perform deduplication of interleaved paths: For interleaved paths, the calculation status of each sub-fault mode is tracked during the calculation process through path analysis to ensure that the recovery time of each sub-fault mode is calculated only once. Perform circular dependency avoidance: When traversing the dependency graph, check in real time whether there is an edge from the current node to a visited node. Once a circular dependency is detected, execute the interruption measure. The recovery time intervals and probability weights of all sub-fault modes after parallel path combination, staggered path deduplication, and circular dependency avoidance processing, along with their corresponding logical dependencies, are combined to calculate the probability distribution of the recovery time required by the processing module.

[0024] This technical solution enables the construction of a dependency graph to identify parallel and interleaved paths, and to perform parallel combination, deduplication, and circular dependency avoidance processing. Finally, it calculates the probability distribution of the recovery time required for the processing module. This allows for a more comprehensive and accurate consideration of various dependencies and potential problems when dealing with complex faults, improving the robustness and accuracy of recovery time prediction.

[0025] To improve the plan, the interruption measures include: Read the result of circular dependency avoidance processing; Based on the result of the circular dependency avoidance process, the calculation of the current path is interrupted, the fault is handled according to the preset fault handling strategy, and a warning is issued.

[0026] This technical solution enables the timely interruption of current path calculation by reading the results of circular dependency avoidance, and allows for processing and warnings based on preset strategies. This allows for timely mitigation of circular dependencies, preventing the system from entering an infinite loop or producing erroneous results, thus improving system stability and reliability.

[0027] Secondly, this application also discloses a fully automated production line management system for the pretreatment of phosphorus-based flame retardants, used to perform fully automated production line management for the pretreatment of phosphorus-based flame retardants, including: The expected demand calculation module is used to establish a resource demand model for multi-media contaminated sample processing tasks. Based on the multi-media contaminated sample type, target processing flow, and preset process parameters, it calculates the expected demand for shared key resources for each multi-media contaminated sample processing task within a preset time period in the future. The demand forecasting graph generation module is used to aggregate the expected demand for various multi-media contamination sample treatment tasks and generate a forecasting graph of future resource demand for the entire production line. The resource shortage risk identification module is used to acquire shared resource availability data in real time and compare it with the future resource demand forecast map of the entire production line and the preset resource replenishment cycle to identify resource shortage risks. The resource allocation decision module is used to make resource allocation decisions based on preset task priority rules and the real-time health status of the module when a risk of resource shortage is identified. The production line control execution module is used to execute resource allocation decisions and control the fully automated production line.

[0028] This technical solution enables intelligent management of a fully automated production line for the pretreatment of phosphorus-based flame retardants through the collaborative work of a demand forecasting module, a demand prediction graph generation module, a resource shortage risk identification module, a resource allocation decision-making module, and a production line control execution module. The system effectively identifies resource shortage risks and formulates and executes resource allocation decisions based on the actual conditions of the production line. This solves the problem of existing production line management systems lacking intelligent diagnosis and adjustment capabilities when facing complex and abnormal situations, achieving self-adjustment and optimization of the production line and improving its operational efficiency and stability.

[0029] Beneficial Effects: This application provides a fully automated production line management method for the pretreatment of phosphorus-based flame retardants. By establishing a resource demand model for multi-media contaminated sample treatment tasks, it calculates the expected demand for shared key resources for each multi-media contaminated sample treatment task within a preset future timeframe, and aggregates this data to generate a prediction map of the entire production line's future resource demand. Based on this, it acquires shared resource availability data in real time and compares it with the prediction map of the entire production line's future resource demand and the preset resource replenishment cycle, effectively identifying resource shortage risks. When a resource shortage risk is identified, this method can intelligently formulate resource allocation decisions based on preset task priority rules and the real-time health status of modules, and execute these decisions to control the fully automated production line.

[0030] Through the above technical solution, this application effectively addresses the problems of existing fully automated production line management systems for phosphorus-based flame retardant pretreatment, which lack high-level intelligent diagnostic capabilities, cannot perform in-depth correlation analysis, cannot assess production line health and resource availability in real time, and cannot intelligently adjust the priority of multi-media contaminated samples and replan treatment paths when facing complex abnormal situations such as the implicit decline in the effective processing capacity of key processing modules, sudden large-load multi-media contaminated sample impacts, and competition for shared key resources. This method, through forward-looking resource demand forecasting, real-time risk identification, and intelligent decision-making, achieves self-adjustment and optimization of the production line, avoids multiple unit shutdowns, reduces reliance on manual inspection and intervention, and significantly improves the operating efficiency, stability, and environmental compliance of the fully automated production line, thereby overcoming the limitations of fragmented fault response mechanisms in existing technologies. Attached Figure Description

[0031] Figure 1This is a flowchart of a fully automated production line management method for pretreatment of phosphorus-based flame retardants according to one embodiment of the present invention; Figure 2 This is a flowchart of a fully automated production line management method for pretreatment of phosphorus-based flame retardants according to another embodiment of the present invention; Figure 3 This is a system block diagram of a fully automated production line management system for pretreatment of phosphorus-based flame retardants according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Fully automated production line management system for pretreatment of phosphorus-based flame retardants; 11. Expected demand calculation module; 12. Demand forecasting graph generation module; 13. Resource shortage risk identification module; 14. Resource allocation decision module; 15. Production line control execution module. Detailed Implementation

[0032] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0033] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] In the production of phosphorus-based flame retardants, waste pretreatment is a complex and critical step. Traditional methods often rely on manual experience, leading to inefficient process switching and a high rate of operational errors. This is especially true when multiple processing units work collaboratively, easily resulting in step conflicts or resource waste. Furthermore, when critical processing modules experience hidden degradation, and simultaneously face sudden surges of high-load, multi-media contaminated samples and competition for shared resources, existing systems lack advanced intelligent diagnostic and correlation analysis capabilities. They are unable to effectively cope with complex and ever-changing anomalies, ultimately causing production line shutdowns and requiring time-consuming manual intervention.

[0035] In response, this application proposes a fully automated production line management method for the pretreatment of phosphorus-based flame retardants, combined with... Figure 1 As shown, it includes: S1. Establish a resource requirement model for multi-media contaminated sample processing tasks. Based on the multi-media contaminated sample type, target processing flow, and preset process parameters, calculate the expected demand for shared key resources for each multi-media contaminated sample processing task within a preset time period in the future. S2 aggregates the expected requirements for various multi-media contamination sample treatment tasks to generate a forecast map of future resource requirements for the entire production line. S3 acquires shared resource availability data in real time and compares it with the future resource demand forecast map of the entire production line and the preset resource replenishment cycle to identify resource shortage risks. S4, when a resource shortage risk is identified, resource allocation decisions are made based on preset task priority rules and the real-time health status of the modules; S5 executes resource allocation decisions and controls the fully automated production line.

[0036] To facilitate understanding of the fully automated production line management method proposed in this application, the key terms involved are explained below.

[0037] "Pretreatment of phosphorus-based flame retardants" refers to the preliminary treatment of various wastes such as wastewater, waste gas, and solid residues generated during the production of phosphorus-based flame retardants, ensuring they meet the requirements for subsequent advanced treatment units or for emissions. "Fully automated production line" refers to a process where the entire multi-media contaminated sample processing is autonomously executed by automated equipment and systems without human intervention.

[0038] "Multi-media contaminated sample processing task" refers to a processing operation performed according to a preset processing procedure and process parameters for a specific type of multi-media contaminated sample. "Shared critical resources" refers to the resources that multiple multi-media contaminated sample processing tasks may share, including chemicals, energy, and the processing capacity of processing modules.

[0039] "Expected Demand" represents the projected consumption of shared critical resources by each multi-media contamination sample treatment task within a preset future timeframe. The "Full Production Line Future Resource Demand Forecast Chart" is derived from the sum of the expected demands of all multi-media contamination sample treatment tasks and reflects the overall resource demand trend of the production line over a future period.

[0040] "Resource shortage risk" refers to the situation where the availability of shared critical resources is lower than the demand shown in the future resource demand forecast map for the entire production line within a certain period, which may result in some tasks being unable to be executed normally. "Task priority rules" are preset rules used to determine the execution order and urgency of different multi-media contaminated sample processing tasks when resources are limited, and can be formulated based on the hazard of multi-media contaminated samples, processing timeliness, etc.

[0041] "Module Real-time Health Status" is used to describe the current operating status, key performance parameters, and potential failure risks of each processing module (such as reactor, pump, filter, etc.) in the production line.

[0042] The core of the fully automated production line management method proposed in this application lies in using intelligent data analysis and decision-making mechanisms to achieve refined operation and management of the phosphorus-based flame retardant pretreatment production line.

[0043] First, when establishing a resource requirement model for multi-media contamination sample treatment tasks, the expected demand for shared key resources for each task within a preset timeframe can be calculated based on the sample type, corresponding treatment process, and preset process parameters. For example, standard treatment processes can be set for different multi-media contamination sample types, such as high-concentration wastewater, low-concentration wastewater, and solid residues. Taking high-concentration wastewater as an example, its treatment process may include neutralization, sedimentation, and biochemical treatment steps. Each treatment step requires specific chemicals (such as acids and alkalis, flocculants, and microbial agents), energy (such as electricity and steam), and occupies the processing capacity of treatment modules (such as neutralization tanks, sedimentation tanks, and biochemical reactors). The expected consumption of corresponding resources within a preset timeframe can be calculated using process parameters such as the required amount of treatment reagents per cubic meter of wastewater and the treatment rate of the biochemical reactor. These parameters can be set through historical data statistical analysis or expert experience.

[0044] Secondly, when aggregating the anticipated needs of various multi-media contamination sample treatment tasks, the system can generate a forecast map of future resource needs for the entire production line. For example, by summarizing the anticipated needs of all scheduled and planned multi-media contamination sample treatment tasks for resources such as neutralizing agents, biochemical reactor processing capacity, and electricity in chronological order over the next 24 hours, a dynamic forecast map reflecting the changing trends of future resource needs can be generated, and can be presented in the form of time series graphs, bar charts, or heat maps.

[0045] Furthermore, when acquiring shared resource availability data in real time, it can be compared with the future resource demand forecast map and resource replenishment cycle of the entire production line to identify resource shortage risks. The system continuously monitors the liquid level of chemical storage tanks, power supply, and the occupancy of processing modules, and matches them with the forecast map. For example, if the forecast map shows that the demand for neutralizing agent at a certain point in the future will exceed the current inventory, and the replenishment cycle (such as ordering and shipping 24 hours in advance) cannot meet the demand, the system can determine that there is a risk of neutralizing agent shortage.

[0046] Finally, after identifying the risk of resource shortage, the system can make resource allocation decisions based on task priority rules and the real-time health status of modules, and control the fully automated production line to execute these decisions. For example, if the processing capacity of the bioreactor is insufficient to support all high-priority tasks in the next few hours, and the backup buffer tank is idle, the system can make decisions based on task priority (e.g., tasks with higher environmental compliance requirements take priority) and the real-time health status of modules (e.g., the current load of the bioreactor is close to its limit), such as temporarily diverting some low-priority wastewater tasks to the buffer tank for pretreatment, or adjusting the start-up time of high-priority tasks to stagger the use of the bioreactor.

[0047] Once a decision is made, the system sends instructions to relevant actuators (such as valves, pumps, and controllers) to adjust the multi-media contaminated sample transport path and processing module operating parameters, achieving real-time control of the fully automated production line and mitigating resource shortage risks. This multi-media contaminated sample pretreatment and production line management scheme, as a preparatory process before the extraction of phosphorus-based flame retardant compounds, coordinates and optimizes sample processing tasks, processing conditions, and key resources before entering the extraction step. After achieving real-time control of the fully automated production line and mitigating resource shortage risks, the extraction of phosphorus-based flame retardant compounds is immediately performed, yielding the compounds.

[0048] In applications involving the extraction of phosphorus-based flame retardant compounds, the target compounds are typically present in water, aerosols, solid particles, or soil matrices in the form of adsorption, encapsulation, or dispersion. Their release and elution processes are highly dependent on the continuity of the pretreatment process, the stability of the treatment conditions, and the order of sample processing. If the sample pretreatment task is interrupted, delayed, or experiences condition drift due to resource contention, fluctuations in the processing module load, or improper task switching before entering the extraction step, it often leads to inconsistencies in subsequent processes, thus affecting the integrity of the phosphorus-based flame retardant compound release and the reproducibility of the extraction results.

[0049] The multi-media contaminated sample pretreatment and production line management scheme of this invention predicts the resource requirements of pretreatment tasks and dynamically formulates resource allocation decisions based on the real-time health status and processing capacity of key processing modules. This enables relevant modules to operate continuously under relatively stable load conditions within a preset time window. This mechanism avoids abrupt changes in process conditions caused by interruptions in the pretreatment process or task conflicts before the sample enters the extraction stage, providing more consistent preconditions for the subsequent extraction process.

[0050] Specifically, by coordinating the delivery capacity, processing sequence, and module occupancy, the differences in sample dwell time during the pretreatment stage can be reduced, and the operating conditions of different batches of samples can be avoided due to different processing histories when entering the extraction step. At the same time, resource allocation decisions can also reduce the instability of flow rate, processing time, or load caused by fluctuations in the performance of key modules, so that the extraction process can be performed under relatively constant conditions.

[0051] Therefore, without changing the specific extraction process itself, this invention achieves a substantial optimization effect on the extraction process of phosphorus-based flame retardant compounds by optimizing the configuration of the pre-extraction preparation process.

[0052] Optional, combined Figure 2 As shown, when S4 identifies a resource shortage risk, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of the modules include: S41, monitors the operating parameters of the delivery pump; S42, based on the operating parameters of the transfer pump, infer the fluid resistance characteristics of the transferred chemicals and evaluate the actual pumping efficiency of the transfer pump; S43, compare fluid resistance characteristics with preset standard chemical characteristics to quantify changes in the physical properties of chemicals; S44, Adjust the efficiency factor of the delivery pump according to the changes in the physical properties of the chemicals and the actual pumping efficiency; S45, calculate the actual time required to transport a unit volume of material based on fluid resistance characteristics and efficiency factor; S46 monitors task progress in real time and calibrates the remaining task completion time based on the actual time. S47, based on the calibrated remaining task completion time and corresponding resource occupancy time, update the future resource demand map for the entire production line; S48, based on the updated full-line future resource requirements map, reassess task priorities and initiation timing.

[0053] Specifically, when making resource allocation decisions, the system first monitors the operating parameters of the delivery pumps. These operating parameters may include the delivery pump inlet pressure, outlet pressure, flow rate, motor current, vibration frequency, and temperature, to reflect the current operating status of the delivery pumps in real time.

[0054] Based on the monitored operating parameters, the system can infer the fluid resistance characteristics of the transported chemicals and evaluate the actual pumping efficiency of the transfer pump. For example, by analyzing the relationship between pressure difference and flow rate, the actual fluid resistance of the chemicals in the pipeline can be calculated; by comparing the motor input power and the output fluid power, the actual pumping efficiency of the transfer pump under the current operating conditions can be obtained.

[0055] The inferred fluid resistance characteristics are then compared with preset standard chemical characteristics to quantify changes in the chemical's physical properties. For example, if the actual fluid resistance is significantly higher than the standard value, it may reflect a change in the chemical's viscosity or density.

[0056] Based on this, the system adjusts the efficiency factor of the transfer pump according to changes in the physical properties of the chemicals and the actual pumping efficiency. This efficiency factor serves as a correction coefficient to more accurately reflect the true performance of the transfer pump when processing the current chemicals.

[0057] Next, the system calculates the actual time required to transport a unit volume of material based on the adjusted fluid resistance characteristics and efficiency factor. This calculation comprehensively considers the actual physical properties of the chemicals and the true operating efficiency of the pump, thereby obtaining an accurate material transport time.

[0058] The system simultaneously monitors the execution progress of each task and calibrates the remaining completion time of the tasks based on the actual delivery time calculated above. If the delivery time exceeds expectations, the remaining completion time of the tasks related to that material will be extended accordingly.

[0059] Based on the calibrated remaining task completion time and its resource usage duration, the system will update the future resource demand map for the entire production line, reflecting the latest changes in task scheduling and dynamic resource demand.

[0060] Ultimately, based on the updated future resource demand map for the entire production line, the system reassesses task priorities and start times, enabling resource allocation decisions to adapt to the actual operating status of the production line and achieve dynamic optimal resource allocation.

[0061] In some preferred embodiments, the above process can be further illustrated. Suppose the production line needs to transport a high-viscosity chemical from a storage tank to a reaction vessel, and the production plan is based on the chemical's standard viscosity parameters and the rated efficiency of the transfer pump. However, due to fluctuations in ambient temperature causing an increase in chemical viscosity, and the transfer pump's pumping efficiency decreasing after long-term operation, deviations occur in the actual transport process.

[0062] The system continuously monitors parameters such as motor current, outlet pressure, and flow rate of the delivery pump during operation, and infers that the current fluid resistance of the chemical is higher than the standard value, while also assessing that the actual pumping efficiency of the delivery pump is lower than the rated value. The system further quantifies changes in the chemical viscosity and adjusts the efficiency factor of the delivery pump accordingly, then calculates the actual time required to deliver a unit volume of chemical. For example, the calculation results show that the actual delivery time is 15% longer than planned.

[0063] Based on the calibrated delivery time, the system updates the remaining completion time of the delivery task in real time and updates the future resource demand map for the entire production line accordingly. For example, the start-up time of a reactor originally planned to start immediately after the delivery task is postponed by 15%. The system reassesses task priorities and execution timing based on the updated demand map, such as advancing cleaning tasks that do not depend on the delivery task to improve resource utilization during the waiting period and prevent reactors from entering an idle state. Through these dynamic adjustments, the production line can effectively avoid process interruptions caused by delivery delays, thereby maintaining the continuous and efficient operation of the entire production line.

[0064] Optionally, when a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Real-time acquisition of operating parameters of key processing modules; Based on the operating parameters of the key processing modules, assess the actual processing capacity of the key processing modules and quantify the types of processing capacity degradation. Adjust the matching strategy between multi-media contaminated sample processing tasks and modules according to the type of processing capacity degradation. Based on the adjusted matching strategy, task priorities and start times will be reassessed.

[0065] Specifically, real-time acquisition of critical processing module operating parameters refers to the continuous acquisition of real-time data related to the operating status of critical processing modules, such as temperature, pressure, flow rate, liquid level, stirring speed, energy consumption, vibration frequency, chemical concentration, and pH value, through various sensors and monitoring systems integrated into the production line. These parameters can reflect the module's current load, internal operating status, and potential anomalies.

[0066] Assessing the actual processing capacity and quantifying the types of capacity degradation based on the operating parameters of key processing modules involves using real-time collected parameters, combined with preset performance models or historical operating data, to analyze the module's processing efficiency, throughput, reaction rate, or separation effect under current conditions. For example, reduced catalyst activity in a reactor may manifest as prolonged reaction time and decreased conversion rate; filter membrane clogging in a filtration unit may manifest as increased pressure differential and decreased filtration speed. Quantifying the types of capacity degradation means clearly defining the form and magnitude of the degradation, such as "processing capacity decreased by 15%" or "processing time increased by 20%".

[0067] In practical applications, adjusting the matching strategy between multi-media contaminated sample processing tasks and modules based on the type of processing capacity degradation means that after identifying processing capacity degradation, the system no longer allocates tasks according to fixed rules, but dynamically adjusts the correspondence between tasks and modules. For example, tasks with high processing capacity requirements are assigned to modules in good condition, while tasks with lower processing efficiency requirements or tolerable performance degradation are assigned to modules with degraded capacity, or overloaded tasks in a certain module are offloaded to other available modules.

[0068] Therefore, re-evaluating task priorities and start times based on the adjusted matching strategy means that the system combines the updated task-module matching relationship, the future resource demand map of the entire production line, and task priority rules to recalculate the priority of each task and determine its optimal start time. This ensures that the overall optimized operation of the production line can still be achieved under dynamic changes in module performance, avoiding resource idleness or task backlog.

[0069] In some preferred embodiments, the above process can be further illustrated. Assume that the production line contains multiple key processing modules for neutralizing multi-media contaminated samples, such as reactor A and reactor B. The system collects operating parameters of reactor A in real time, including the stirring motor current, pH value of the reaction solution, temperature, and inlet / outlet flow rates. Analysis results show that the motor current of reactor A remains consistently high, and the time required for pH stabilization is significantly prolonged when processing specific types of multi-media contaminated samples, indicating a decrease in its stirring efficiency or internal scaling, leading to a decline in its neutralization capacity for some multi-media contaminated samples. Based on this, the system assesses a decrease in its processing capacity of approximately 10%, quantifying this decline as "reduced neutralization reaction efficiency."

[0070] Based on this degradation information, the system adjusts the matching strategy between multi-media contaminated sample processing tasks and modules. For example, tasks with high neutralization speed requirements are preferentially assigned to reactor B, which is in good health; while tasks with relatively relaxed processing time requirements continue to be assigned to reactor A. Simultaneously, the system re-evaluates task priorities according to the new matching strategy. For instance, a high-priority task originally assigned to reactor A, if it has high neutralization efficiency requirements, may have its priority increased and be reassigned to reactor B, or its start-up time may be postponed until reactor A's performance recovers or alternative resources become available. Through this dynamic adjustment mechanism, even if the performance of some key processing modules degrades, the production line can still maintain efficient operation, avoiding a single point of performance decline affecting overall throughput.

[0071] Optionally, when a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Identify the multi-media contaminated sample processing tasks to be handled, and determine the key processing units and environmental compliance indicators involved in each task based on the type of multi-media contaminated sample and the target processing flow. Simulate the potential impact of various resource allocation adjustment schemes on the operational status and environmental compliance indicators of key processing units; Assess the cascading impact of each resource allocation adjustment plan on shared resources; Based on the preset task priority rules, the real-time health status of the modules, potential impacts and cascading effects, calculate the comprehensive risk score of the resource allocation adjustment plan; The resource allocation adjustment plan with the lowest comprehensive risk score shall be selected as the resource allocation decision.

[0072] Specifically, identifying multi-media contaminated sample processing tasks refers to the system automatically or manually confirming the batch of multi-media contaminated samples or processing requests that need to be processed. Based on the type of multi-media contaminated sample (such as high-concentration waste liquid, low-concentration waste liquid, solid waste residue, etc.) and its target processing flow (such as neutralization, precipitation, filtration, incineration, etc.), the system can accurately determine the key processing units (such as reaction vessels, transfer pumps, separators, incinerators, etc.) required for each multi-media contaminated sample processing task, and identify the environmental compliance indicators that the task must meet (such as emission concentration limits, temperature control ranges, pressure safety thresholds, etc.).

[0073] Simulating the potential impact of various resource allocation adjustments on the operational status and environmental compliance indicators of key processing units refers to using simulation models or predictive algorithms to anticipate operational changes that may result from different resource allocation strategies. For example, when a key processing unit is reassigned to other tasks, the resulting changes in load, energy consumption, wear and tear, and their impact on processing efficiency and product quality can be simulated. Simultaneously, the system can predict whether this adjustment may lead to excessive emissions of waste gas and wastewater, or cause other environmental monitoring indicators to fail to meet standards.

[0074] In practical applications, assessing the cascading impact of resource allocation adjustments on shared resources means identifying whether a reallocation of shared resources will indirectly affect other tasks or modules that depend on those resources. For example, when a high-flow-rate pump is prioritized for urgent tasks, other tasks scheduled to use the pump may be delayed, affecting material flow on the production line and the overall rhythm of subsequent processes. Such cascading effects need to be quantified for comprehensive consideration during decision-making.

[0075] Furthermore, based on the preset task priority rules, the real-time health status of modules, and the aforementioned potential and cascading effects, a comprehensive risk score for the resource allocation adjustment plan is calculated. This score can be calculated using a weighted approach, where the task priority rules reflect factors such as the hazard of multi-media contaminated samples and the urgency of handling; the real-time health status of modules reflects the operational stability and failure risk of key equipment; and the potential and cascading effects quantitatively describe the negative consequences that the adjustment plan may cause. Combining these factors yields a quantitative result reflecting the overall risk level of the plan.

[0076] Therefore, the system selects the resource allocation adjustment scheme with the lowest comprehensive risk score as the final resource allocation decision, ensuring that in the case of resource shortage, it can not only solve the current bottleneck, but also minimize the negative impact on the overall operation of the production line, and ensure that environmental compliance is not affected.

[0077] In some preferred embodiments, the above process can be further illustrated. Suppose a critical waste liquid transfer pump in the phosphorus-based flame retardant pretreatment production line malfunctions, reducing pumping capacity and triggering a resource shortage risk. In this case, the system needs to formulate new resource allocation decisions.

[0078] The system first identifies the multi-media contaminated sample task to be processed. For example, task A is to process high-concentration phosphorus-containing waste liquid, which requires the use of a transfer pump and a reaction vessel, and its compliance requirement is that the phosphorus emission concentration is less than 5 mg / L; task B is to process low-concentration phosphorus-containing waste liquid, which requires the use of a transfer pump and a sedimentation tank, and its compliance requirement is that the phosphorus emission concentration is less than 1 mg / L.

[0079] Subsequently, the system simulated different resource allocation adjustment schemes. For example, Scheme 1: Prioritizing Task A, allocating all limited capacity of the transfer pumps to Task A. Simulation results show that Task A can be completed on time, but Task B will be severely delayed, potentially leading to overflow in the storage area, or even generating new pollutants due to prolonged retention, posing environmental compliance risks. Scheme 2: Proportional allocation of transfer pump capacity to Task A and Task B. Simulation results show that both tasks will be delayed, but the degree of delay is controllable, and emission indicators can remain compliant.

[0080] The system also assesses the cascading effects of the two scenarios on shared resources. For example, if the delivery pump capacity is overly concentrated on task A, it may cause the upstream mixing tank or downstream filtration unit to become inefficient or stall due to uneven material supply.

[0081] Finally, the system calculates the comprehensive risk score for each option based on task priority rules (e.g., high-concentration waste liquid has higher priority than low-concentration waste liquid), the real-time health status of the transfer pumps (e.g., decreased pumping efficiency after a failure), the aforementioned potential impacts and cascading effects. If Option 2 has a lower comprehensive risk score, the system will select Option 2 as the final resource allocation decision, that is, allocate the transfer pump capacity proportionally to balance task progress, ensure emission compliance, and minimize the impact on the overall operation of the production line.

[0082] Optionally, when a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Predict the fluctuation range and probability distribution of the amount of multi-media contaminated samples generated within a preset time period in the future; Evaluate the probability distribution of the recovery time required for the processing module and the success probability of different recovery paths; Based on the fluctuation range and probability distribution of the amount of multi-media contaminated samples generated, the probability distribution of the time required for the processing module to recover, and the success probability of different recovery paths, the production line operation status of the resource allocation adjustment scheme under different multi-media contaminated sample generation and module recovery scenarios is simulated. Based on the production line operation status, calculate the resource consumption, task completion time, environmental compliance risks, and production line downtime probability of the resource allocation adjustment scheme under different scenarios, and analyze to obtain the comprehensive risk score of each resource allocation adjustment scheme; The resource allocation adjustment plan with the lowest comprehensive risk score shall be selected as the resource allocation decision.

[0083] Specifically, "predicting the fluctuation range and probability distribution of multi-media contaminated sample generation within a preset time period" refers to using historical data, machine learning models, or statistical analysis methods to predict the quantity, type, and trend of multi-media contaminated samples entering the production line within a future period (e.g., the next 8 hours, 24 hours, or longer). This prediction not only provides a single expected value but, more importantly, provides a fluctuation range (e.g., from minimum to maximum generation) and the probability of occurrence at different generation levels, thereby quantifying the randomness of future multi-media contaminated sample input. "Assessing the probability distribution of the recovery time required for processing modules and the success probability of different recovery paths" refers to predictively analyzing the time required for critical processing modules (e.g., reactors, separators, dryers, etc.) in the production line to return to normal operation when they malfunction or require maintenance. This includes considering factors such as different fault types, spare parts availability, and maintenance personnel skills to generate a probability distribution of recovery time (e.g., a 20% probability of recovery within 2 hours, a 50% probability of recovery within 4 hours, and a 30% probability of recovery within 6 hours). At the same time, it is also necessary to identify and evaluate different recovery strategies or paths (e.g., immediate repair, replacement of spare parts, or waiting for a professional team), and give the probability of success for each path.

[0084] Furthermore, "simulating production line operation under different multi-media contamination sample generation and module recovery scenarios" refers to the system generating multiple possible resource allocation adjustment schemes after identifying resource shortage risks. For each scheme, combining the predicted fluctuations in multi-media contamination sample generation and module recovery uncertainties, the system simulates the production line's operation under various hypothetical scenarios (e.g., high multi-media contamination sample generation and slow recovery of a key module; low multi-media contamination sample generation but simultaneous failure of multiple modules, etc.) using simulation models or digital twin technology. This simulation aims to predict the potential consequences of different decisions in the future. Therefore, "calculating resource consumption, task completion time, environmental compliance risks, and production line shutdown probability under different scenarios for each resource allocation adjustment scheme, and analyzing the comprehensive risk score of each scheme" refers to quantitatively evaluating the performance of each scheme under various scenarios based on the simulated operation status. This includes, but is not limited to, calculating the required energy and material consumption, the estimated completion time of the multi-media contamination sample processing task, whether it will violate environmental emission standards (environmental compliance risk), and the possibility of the production line being forced to shut down due to insufficient resources or failures (production line shutdown probability). These indicators are comprehensively considered, and a comprehensive risk score representing the overall merits of the proposed solution is calculated using pre-defined weights and a scoring model. For example, one solution might have lower resource consumption but higher environmental risk, while another might have a longer completion time but an extremely low probability of stalling. The comprehensive risk score weighs these factors. Ultimately, "selecting the resource allocation adjustment scheme with the lowest comprehensive risk score as the resource allocation decision" means choosing the scheme with the lowest comprehensive risk score from all simulated and evaluated resource allocation adjustment schemes. This scheme is considered the most optimized and robust decision under the current resource shortage risk scenario, after considering future uncertainties.

[0085] Optionally, the steps for evaluating the probability distribution of the time required for the processing module to recover and the success probability of different recovery paths include: When a processing module malfunctions, identify the type of malfunction. Based on the fault type, a preset fault feature library is matched to obtain the fault feature matching result; When the fault feature matching result indicates that the fault type cannot be directly matched, the fault mode is decomposed based on the fault phenomenon, damaged parts, and the degree of impact of the fault on the production line operation, and the complex fault is decomposed into several sub-fault modes. For each sub-fault mode, based on its corresponding historical maintenance data, spare parts inventory, and maintenance personnel skill level, the recovery time range and corresponding probability weight of the sub-fault mode are generated. Based on the recovery time intervals and probability weights of the sub-fault modes, as well as the logical dependencies between the sub-fault modes, the probability distribution of the recovery time required for the processing module is calculated by combining the results. For each sub-failure mode, identify the corresponding feasible recovery path and evaluate the success probability of each recovery path; Based on the success probability of the recovery path of each sub-fault mode and the logical dependencies between each sub-fault mode, the success probability of different recovery paths of the processing module is calculated by combining these factors.

[0086] Specifically, fault type identification refers to using multiple information sources, such as sensor data, operation logs, and operator feedback, to make a preliminary judgment on abnormal situations occurring in the processing module, thereby determining the initial fault category. For example, changes in parameters such as abnormal temperature, pressure fluctuations, and current overload can be used to preliminarily determine whether it is a mechanical fault, an electrical fault, or a control system fault. Matching to a pre-set fault feature library involves comparing the identified fault type with a pre-stored database in the system containing known fault modes and their characteristics. This fault feature library may include historical fault records, expert experience rules, and fault diagnosis manuals provided by equipment manufacturers. The matching result indicates whether the fault is a known type and the corresponding standard recovery procedure. In practical applications, when the fault feature matching result indicates that the fault type cannot be directly matched, fault mode decomposition is required. This typically occurs when the fault manifestation is complex and involves multiple components or system interactions. Fault mode decomposition refers to breaking down a complex, difficult-to-diagnose fault into multiple interrelated or independent, simpler sub-fault modes. The decomposition process can be carried out based on the fault phenomena (such as abnormal noise and leakage), damaged parts (such as pump wear and valve jamming), and the degree of impact of the fault on the production line operation (such as downtime and reduced efficiency).

[0087] Furthermore, for each sub-fault mode, it is necessary to combine its corresponding historical maintenance data, spare parts inventory, and maintenance personnel skill levels to generate the recovery time interval and corresponding probability weights for the sub-fault mode. Historical maintenance data provides information on the actual time spent handling similar faults in the past; spare parts inventory affects spare parts acquisition time; and maintenance personnel skill levels determine maintenance efficiency. Using this information, a recovery time distribution model for each sub-fault mode can be constructed, such as a normal distribution or an exponential distribution, and assigned corresponding probability weights. Therefore, based on the recovery time interval and probability weights of the sub-fault modes, as well as the logical dependencies between the sub-fault modes, a probability distribution of the recovery time required for the processing module can be calculated. Logical dependencies refer to the possible sequential, parallel, or mutually exclusive relationships between sub-fault modes. For example, the repair of one sub-fault must be performed after the repair of another sub-fault. By considering these dependencies, the recovery process of the entire processing module can be simulated more accurately, resulting in a comprehensive recovery time probability distribution.

[0088] Furthermore, for each sub-failure mode, corresponding feasible recovery paths are identified, and the success probability of each recovery path is evaluated. Recovery paths may include different repair solutions, spare parts selections, or personnel configurations. The success probability of each path can be derived based on historical data, expert evaluation, or simulation analysis. Finally, based on the success probabilities of the recovery paths for each sub-failure mode and the logical dependencies between the various sub-failure modes, the success probabilities of different recovery paths for the processing module are calculated. This helps in resource allocation decisions by considering not only recovery time but also the success rates of different recovery schemes, thereby selecting the most reliable or efficient recovery strategy.

[0089] Optionally, the step of calculating the probability distribution of the recovery time required for the processing module based on the recovery time intervals and probability weights of the sub-fault modes, as well as the logical dependencies between the sub-fault modes, includes: Construct a dependency graph for sub-fault modes; Identify and label parallel and interleaved paths in the dependency graph; Calculate the parallel combination probability distribution of recovery time for sub-fault modes on parallel paths; By analyzing the path, we ensure that the recovery time of each sub-fault mode in the interleaved path is calculated only once, thus obtaining the recovery time data related to the interleaved path. Based on the parallel combination probability distribution and the recovery time data related to the interleaved paths, the probability distribution of the recovery time required for the processing module is calculated.

[0090] The construction of the dependency graph for sub-fault modes refers to graphically representing the various sub-fault modes of the processing module and their logical dependencies on each other. This dependency graph can take the form of a directed acyclic graph (DAG), where each node represents a sub-fault mode, and each directed edge indicates that the completion of one sub-fault mode is a prerequisite for the start of another sub-fault mode. This graph clearly shows the execution order and parallel possibilities of the sub-fault modes.

[0091] Identifying and labeling parallel and interleaved paths in a dependency graph refers to using graph traversal algorithms (such as depth-first search or breadth-first search) to identify sequences of sub-fault modes that can be repaired simultaneously (parallel paths) within the constructed dependency graph; and simultaneously identifying sequences of sub-fault modes that may share resources, have temporal overlap, or require specific coordination during the repair process (interleaved paths). Parallel paths typically represent multiple sub-faults that can be processed independently or simultaneously, while interleaved paths may involve resource contention or complex timing constraints.

[0092] Calculating the parallel combination probability distribution of recovery times for sub-fault modes on a parallel path means that for an identified parallel path, the overall recovery time is not simply an additive sum, but depends on the longest-running sub-fault mode. Therefore, a parallel combination method from probability theory is needed, such as calculating the maximum distribution of the recovery time probability distributions of each parallel sub-fault mode to obtain the overall recovery time probability distribution of the parallel path. This reflects that the actual completion time of a parallel task is determined by the slowest task.

[0093] Path analysis ensures that the recovery time of each sub-fault mode in an interleaved path is calculated only once, yielding interleaved path-related recovery time data. This is crucial because interleaved paths, due to potential resource sharing or complex temporal dependencies, require refined path analysis. The analysis aims to avoid redundant calculations of shared resources or repeated consideration of the same sub-fault mode's impact, ensuring that the recovery time of each sub-fault mode is considered only once in the overall interleaved path calculation. This can be achieved by maintaining a list of calculated sub-fault modes' states or by employing specific graph algorithms for tracking and deduplication. Consequently, accurate recovery time data reflecting the characteristics of interleaved paths can be obtained.

[0094] The probability distribution of the recovery time required for the processing module, calculated by combining the parallel combination probability distribution of parallel paths with the recovery time data of interleaved paths, refers to integrating the parallel combination probability distribution of parallel paths with the recovery time data of interleaved paths. This may involve weighted averaging, sequence combination, or more complex probability fusion of the calculation results of different paths to ultimately obtain the total probability distribution of the time required for the entire processing module to fully recover from the occurrence of a fault.

[0095] Optionally, the step of calculating the probability distribution of the recovery time required for the processing module based on the recovery time intervals and probability weights of the sub-fault modes, as well as the logical dependencies between the sub-fault modes, includes: Construct a dependency graph for each sub-fault mode; Traverse the dependency graph, identify and mark parallel paths; identify and mark interleaved paths; Perform parallel path combination: For parallel paths, calculate the probability distribution of parallel combination of sub-fault mode recovery times on the parallel path; Perform deduplication of interleaved paths: For interleaved paths, the calculation status of each sub-fault mode is tracked during the calculation process through path analysis to ensure that the recovery time of each sub-fault mode is calculated only once. Perform circular dependency avoidance: When traversing the dependency graph, check in real time whether there is an edge from the current node to a visited node. Once a circular dependency is detected, execute the interruption measure. The recovery time intervals and probability weights of all sub-fault modes after parallel path combination, staggered path deduplication, and circular dependency avoidance processing, along with their corresponding logical dependencies, are combined to calculate the probability distribution of the recovery time required by the processing module.

[0096] Specifically, constructing a dependency graph for each sub-fault mode means treating each sub-fault mode as a node in the graph and representing the logical dependencies between them (e.g., the resolution of one sub-fault mode is a prerequisite for the resolution of another sub-fault mode) as directed edges. This dependency graph can be stored using an adjacency matrix, adjacency list, or other graph data structures to facilitate subsequent traversal and analysis.

[0097] Traversing the dependency graph and identifying and marking parallel and interleaved paths can be understood as analyzing the connection patterns of nodes and edges in the graph using graph traversal algorithms such as Depth-First Search (DFS) or Breadth-First Search (BFS). Parallel paths refer to multiple sub-fault modes that can be recovered simultaneously, their completion time depending on the longest recovery time. Interleaved paths refer to sub-fault modes that may be depended upon by multiple upstream sub-fault modes, or whose recovery process may affect multiple downstream sub-fault modes, causing them to be considered repeatedly in different computational paths.

[0098] In practical applications, parallel path combination refers to obtaining the overall recovery time probability distribution of an identified parallel path by performing a parallel combination operation on the recovery time probability distributions of each sub-fault mode along the path. For example, if two sub-fault modes A and B can be recovered in parallel, with recovery times TA and TB respectively, then the recovery time of the parallel path T_parallel = max(TA, TB). Its probability distribution can be calculated using mathematical methods such as convolution to accurately reflect the overall time consumption of the parallel task.

[0099] Furthermore, deduplication of interleaved paths refers to maintaining a set of calculated sub-fault modes or state flags during the calculation process. This ensures that the recovery time and probability weight of each sub-fault mode are considered only once in the final combined calculation, avoiding errors caused by repeated calculations. For example, when a sub-fault mode is dependent on multiple paths, its recovery time should only be included once to guarantee the accuracy of the calculation results.

[0100] Furthermore, handling circular dependency avoidance involves continuously detecting edges pointing from the currently visited node to previously visited nodes while traversing the dependency graph. This indicates the existence of a circular dependency. Once a circular dependency is detected, interruption measures are immediately implemented, such as stopping the computation of the current path, and handling the issue according to a pre-defined fault handling strategy, such as issuing a warning or adopting a default value, to prevent the computation from falling into an infinite loop, thereby improving the robustness of the computation process.

[0101] Optional disruption measures include: Read the result of circular dependency avoidance processing; Based on the result of the circular dependency avoidance process, the calculation of the current path is interrupted, the fault is handled according to the preset fault handling strategy, and a warning is issued.

[0102] Specifically, reading the results of circular dependency avoidance processing means that the system obtains information about circular dependencies detected during the traversal of the dependency graph, such as involved nodes and circular paths. Based on the results of circular dependency avoidance processing, interrupting the calculation of the current path means stopping further calculation of the path where a circular dependency is detected, to avoid getting stuck in an infinite loop or producing erroneous results. Simultaneously, processing is carried out according to preset fault handling strategies, which may include logging faults, attempting to backtrack to a loop-free state, or taking other predefined countermeasures based on specific business needs. Furthermore, issuing a warning means that the system sends a notification to operators or relevant management systems, indicating the existence of a circular dependency problem, so that timely intervention can be carried out.

[0103] This application also discloses a fully automated production line management system for pretreatment of phosphorus-based flame retardants, used to perform fully automated production line management for pretreatment of phosphorus-based flame retardants, combined with... Figure 3 As shown, the fully automated production line management system 1 for pretreatment of phosphorus-based flame retardants includes: The expected demand calculation module 11 is used to establish a resource demand model for multi-media contaminated sample processing tasks. Based on the multi-media contaminated sample type, target processing flow and preset process parameters, it calculates the expected demand for shared key resources for each multi-media contaminated sample processing task in the future preset time period. The demand forecasting graph generation module 12 is used to aggregate the expected demand for various multi-media contamination sample treatment tasks and generate a forecasting graph of future resource demand for the entire production line. The resource shortage risk identification module 13 is used to acquire shared resource availability data in real time and compare it with the future resource demand forecast map of the entire production line and the preset resource replenishment cycle to identify resource shortage risks. The resource allocation decision module 14 is used to make resource allocation decisions based on preset task priority rules and the real-time health status of the module when a resource shortage risk is identified. The production line control execution module 15 is used to execute resource allocation decisions and control the fully automated production line.

[0104] The specific operations and objectives of each step in the fully automated production line management method proposed in this application have been described in the above embodiments, and will not be repeated here. It should be emphasized that the fully automated production line management system disclosed in this application achieves automation and intelligence of the above method through the synergistic effect of its various functional modules.

[0105] Specifically, the expected demand calculation module can be implemented as a software service running on a central server. This service calculates the expected demand for shared critical resources for each multi-media contaminated sample treatment task by calling data on multi-media contaminated sample types, processing procedures, and process parameters stored in a database, and combining this data with historical processing data for machine learning training. Alternatively, this module can be designed as a dedicated computing unit embedded in the production line controller, which quickly calculates expected demands using hard-coded algorithms and pre-defined lookup tables, but its flexibility may be relatively lower.

[0106] The demand forecasting map generation module can be implemented as a data visualization and aggregation service. It receives data from the anticipated demand calculation module and uses a graphics processing library to generate a forecast map of future resource demand for the entire production line. This module can be deployed on a cloud server for large-scale data processing and multi-user access. Alternatively, the module can be a locally deployed application, directly integrated with the production line control system, providing real-time, low-latency forecast map updates, but may be limited in data storage and computing power.

[0107] The resource shortage risk identification module can be implemented as a real-time data analysis engine that continuously acquires shared resource availability data from production line sensors and the inventory management system, and compares it with the forecast map provided by the demand forecast map generation module. This engine can employ a rule-based expert system or a machine learning model based on anomaly detection algorithms to identify resource shortage risks. For example, a threshold can be set to trigger a risk alarm when predicted demand exceeds a certain percentage of available resources.

[0108] The resource allocation decision-making module can be implemented as an optimization decision engine, which is activated upon receiving a resource shortage risk alert. This engine can utilize optimization algorithms such as linear programming, genetic algorithms, or reinforcement learning, combined with preset task priority rules and real-time module health status data obtained from the production line monitoring system, to formulate the optimal resource allocation decision. For example, this module can calculate indicators such as resource consumption, task completion time, and environmental compliance risks under different task adjustment schemes, and select the scheme with the best overall score.

[0109] The production line control execution module can be implemented as a programmable logic controller (PLC) or a distributed control system (DCS). It receives instructions from the resource allocation decision module and converts them into control signals for actuators (such as valves, pumps, and robotic arms) within the production line. This module ensures that decisions are executed accurately and promptly, thereby adjusting the transport path of multi-media contaminated samples, the operating parameters of processing modules, and the dosage of chemicals, to achieve real-time control of the fully automated production line.

[0110] The fully automated production line management system proposed in this application represents a significant improvement over traditional production line management methods that rely on manual judgment and configuration, as well as existing automated systems that lack advanced intelligent diagnostics and correlation analysis capabilities. Traditional systems often exhibit delayed responses, low efficiency, and even production line shutdowns when faced with complex, multi-point concurrent anomalies such as latent degradation of critical processing modules, sudden high-load, multi-media contaminated sample impacts, and competition for shared resources. The system in this application, through its modular design, achieves prediction of production line resource needs, risk identification, and intelligent decision-making and execution. The expected demand calculation module and demand forecasting graph generation module provide forward-looking resource management capabilities, enabling the system to anticipate potential resource shortages. The resource shortage risk identification module can identify problems accurately and in real time. More importantly, the resource allocation decision module can comprehensively consider task priorities and module health status to formulate globally optimal solutions, rather than fragmented, passive responses. Finally, the production line control execution module ensures that these intelligent decisions are quickly and effectively translated into actual production line operations. Therefore, this system can significantly improve the operating efficiency and stability of the phosphorus-based flame retardant pretreatment production line, reduce the rate of operational errors and resource waste, and better meet the increasingly stringent environmental regulations, thereby bringing significant economic and environmental benefits to enterprises.

[0111] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A fully automated production line management method for pretreatment of phosphorus-based flame retardants, characterized in that, include: Establish a resource requirement model for multi-media contaminated sample processing tasks. Based on the multi-media contaminated sample type, target processing flow, and preset process parameters, calculate the expected demand for shared key resources for each multi-media contaminated sample processing task within a preset time period in the future. By aggregating the anticipated needs of various multi-media contamination sample treatment tasks, a forecast map of future resource needs for the entire production line is generated. Real-time acquisition of shared resource availability data, compared with the full production line future resource demand forecast map and preset resource replenishment cycle, to identify resource shortage risks; When a resource shortage risk is identified, a resource allocation decision is made based on preset task priority rules and the real-time health status of the modules. The resource allocation decision is executed, and the fully automated production line is controlled to extract the components of phosphorus-based flame retardant compounds.

2. The fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 1, characterized in that, When a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Monitor the operating parameters of the delivery pump; Based on the operating parameters of the transfer pump, infer the fluid resistance characteristics of the transferred chemicals and evaluate the actual pumping efficiency of the transfer pump; By comparing the fluid resistance characteristics with the characteristics of a preset standard chemical, the changes in the physical properties of the chemical can be quantified. Adjust the efficiency factor of the delivery pump based on changes in the physical properties of the chemicals and the actual pumping efficiency. Based on the fluid resistance characteristics and the efficiency factor, calculate the actual time required to transport a unit volume of material. Monitor task progress in real time and calibrate the remaining task completion time based on the actual time. Update the future resource demand map for the entire production line based on the calibrated remaining task completion time and corresponding resource occupancy time. Based on the updated future resource requirements map for the entire production line, task priorities and initiation timing are reassessed.

3. The fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 2, characterized in that, When a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Real-time acquisition of operating parameters of key processing modules; Based on the operating parameters of the key processing modules, assess the actual processing capacity of the key processing modules and quantify the types of processing capacity degradation. Based on the types of processing capacity degradation, adjust the matching strategy between multi-media contaminated sample processing tasks and modules; Based on the adjusted matching strategy, task priorities and start times will be reassessed.

4. The fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 1, characterized in that, When a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Identify the multi-media contaminated sample processing tasks to be handled, and determine the key processing units and environmental compliance indicators involved in each task based on the type of multi-media contaminated sample and the target processing flow. Simulate the potential impact of various resource allocation adjustment schemes on the operational status and environmental compliance indicators of key processing units; Assess the cascading impact of each resource allocation adjustment plan on shared resources; Based on the preset task priority rules, the real-time health status of the modules, the potential impact, and the chain reaction, calculate the comprehensive risk score of the resource allocation adjustment plan; The resource allocation adjustment plan with the lowest comprehensive risk score shall be selected as the resource allocation decision.

5. The fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 1, characterized in that, When a resource shortage risk is identified, the steps for making resource allocation decisions based on preset task priority rules and the real-time health status of modules include: Predict the fluctuation range and probability distribution of the amount of multi-media contaminated samples generated within a preset time period in the future; Evaluate the probability distribution of the recovery time required for the processing module and the success probability of different recovery paths; Based on the fluctuation range and probability distribution of the amount of multi-media contaminated samples generated, the probability distribution of the time required for the processing module to recover, and the success probability of different recovery paths, the production line operation status of the resource allocation adjustment scheme under different multi-media contaminated sample generation and module recovery scenarios is simulated. Based on the production line operation status, calculate the resource consumption, task completion time, environmental compliance risk, and production line shutdown probability of the resource allocation adjustment scheme under different scenarios, and analyze to obtain the comprehensive risk score of each resource allocation adjustment scheme; The resource allocation adjustment plan with the lowest comprehensive risk score shall be selected as the resource allocation decision.

6. A fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 5, characterized in that, The steps for evaluating the probability distribution of the recovery time required by the processing module and the success probability of different recovery paths include: When a processing module malfunctions, identify the type of malfunction. Based on the fault type, a preset fault feature library is matched to obtain the fault feature matching result; When the fault feature matching result indicates that the fault type cannot be directly matched, the fault mode is decomposed according to the fault phenomenon, damaged components, and the degree of impact of the fault on the production line operation, and the complex fault is decomposed into several sub-fault modes. For each sub-fault mode, based on its corresponding historical maintenance data, spare parts inventory, and maintenance personnel skill level, the recovery time range and corresponding probability weight of the sub-fault mode are generated. Based on the recovery time intervals and probability weights of the sub-fault modes, as well as the logical dependencies between the sub-fault modes, the probability distribution of the recovery time required for the processing module is calculated by combining the results. For each sub-failure mode, identify the corresponding feasible recovery path and evaluate the success probability of each recovery path; Based on the success probability of the recovery path of each sub-fault mode and the logical dependencies between each sub-fault mode, the success probability of different recovery paths of the processing module is calculated by combining these factors.

7. A fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 6, characterized in that, The step of calculating the probability distribution of the recovery time required for the processing module based on the recovery time intervals and probability weights of the sub-fault modes and the logical dependencies between the sub-fault modes includes: Construct a dependency graph for sub-fault modes; Identify and label parallel and interleaved paths in the dependency graph; Calculate the parallel combination probability distribution of the recovery time of sub-fault modes on the parallel path; By performing path analysis, we ensure that the recovery time of each sub-fault mode in the interleaved path is calculated only once, thereby obtaining the recovery time data related to the interleaved path. Based on the parallel combination probability distribution and the staggered path related recovery time data, the probability distribution of the recovery time required by the processing module is calculated.

8. A fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 6, characterized in that, The step of calculating the probability distribution of the recovery time required for the processing module based on the recovery time intervals and probability weights of the sub-fault modes and the logical dependencies between the sub-fault modes includes: Construct a dependency graph for each sub-fault mode; Traverse the dependency graph, identify and mark parallel paths; identify and mark interleaved paths; Perform parallel path combination: For the parallel path, calculate the probability distribution of the parallel combination of sub-fault mode recovery time on the parallel path; Perform deduplication of interleaved paths: For the interleaved paths, the calculation status of each sub-fault mode is tracked during the calculation process through path analysis to ensure that the recovery time of each sub-fault mode is calculated only once. Perform circular dependency avoidance: When traversing the dependency graph, check in real time whether there is an edge from the current node to a visited node. Once a circular dependency is detected, execute the interruption measure. The recovery time intervals and probability weights of all sub-fault modes after parallel path combination, staggered path deduplication, and circular dependency avoidance processing, along with their corresponding logical dependencies, are combined to calculate the probability distribution of the recovery time required by the processing module.

9. A fully automated production line management method for pretreatment of phosphorus-based flame retardants according to claim 8, characterized in that, The disruption measures include: Read the result of circular dependency avoidance processing; Based on the result of the circular dependency avoidance process, the calculation of the current path is interrupted, the fault is handled according to the preset fault handling strategy, and a warning is issued.

10. A fully automated production line management system for pretreatment of phosphorus-based flame retardants, used to perform fully automated production line management for pretreatment of phosphorus-based flame retardants, characterized in that, include: The expected demand calculation module is used to establish a resource demand model for multi-media contaminated sample processing tasks. Based on the multi-media contaminated sample type, target processing flow, and preset process parameters, it calculates the expected demand for shared key resources for each multi-media contaminated sample processing task within a preset time period in the future. The demand forecasting graph generation module is used to aggregate the expected demand for various multi-media contamination sample treatment tasks and generate a forecasting graph of future resource demand for the entire production line. The resource shortage risk identification module is used to acquire shared resource availability data in real time and compare it with the future resource demand forecast map of the entire production line and the preset resource replenishment cycle to identify resource shortage risks. The resource allocation decision module is used to make resource allocation decisions based on preset task priority rules and the real-time health status of the module when a risk of resource shortage is identified. The production line control execution module is used to execute the resource allocation decision and control the fully automated production line.

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