Intelligent integrated municipal sludge whole-process treatment system

Abstract

The invention discloses an intelligent integrated municipal sludge whole-process treatment system, which relates to the technical field of municipal environmental protection engineering, comprises a concentration unit, a conditioning unit, a dehydration unit, a drying unit, an incineration unit and a tail gas treatment unit, and is characterized by further comprising a whole-process digital twinborn body module, a whole-process digital twinborn body module and a whole-process digital twinborn body module, and the data fusion unit is connected with the sensor network of each processing unit through a data bus and is used for fusing material characteristics, equipment states and environment parameter data. According to the intelligent integrated municipal sludge whole-process treatment system, the problems of energy efficiency loss and effect fluctuation caused by independent operation of each unit of the sludge treatment system are solved. When the sludge property is suddenly changed, the system can automatically trigger a cross-unit emergency linkage instruction chain to realize dynamic matching of upstream and downstream parameters; and meanwhile, by means of dual verification of digital twinborn rehearsal and a physical equation, it is ensured that an optimization instruction meets the equipment safety boundary and environmental protection requirements, and the stability and reliability of whole-process operation are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of municipal environmental protection engineering, in particular to an intelligent integrated municipal sludge full-process treatment system. BACKGROUND

[0002] In the field of municipal sludge treatment, integration and intelligence are the key development trends to improve treatment efficiency, reduce operating costs and ensure environmental compliance. Existing technologies have developed full-process treatment systems that include thickening, conditioning, dewatering, drying, incineration or resource utilization, and supporting pollution control units, and have applied automatic control and monitoring technology to individual units or local links. However, such systems face a fundamental challenge in actual operation: their intelligence level is often limited to the optimization of single-point equipment or simple start-stop linkage, lacking the ability to globally, collaboratively and adaptively control the entire treatment chain. Due to the wide range of sources, complex composition and significant fluctuations in nature over time, season and external conditions of municipal sludge, combined with changes in the state of internal unit equipment and external energy supply disturbances, the current system is difficult to achieve the core goal of stable and efficient operation. The specific performance is as follows: the parameter changes of the upstream unit cannot be accurately perceived and adaptively adjusted by the downstream unit in time, resulting in large fluctuations in system overall energy efficiency and serious energy waste; when facing sudden changes in sludge properties or external disturbances, the treatment effect is difficult to guarantee; the system often deviates from the global optimal state during long-term operation, relying too much on manual experience intervention for parameter adjustment, which is complex and prone to errors. The root cause lies in the fact that existing technologies have not effectively solved the deep fusion and value mining of full-process multi-source heterogeneous data, lack dynamic process models and prediction capabilities based on real-time data driving, and cannot make collaborative decisions and adaptively adjust parameters across units based on preset global optimization goals. Therefore, the current technical problem to be solved is: how to break through the limitations of single-point intelligence and achieve cross-unit collaborative decision-making and dynamic adaptive control based on the deep fusion of full-process multi-source real-time data and the global optimization goal, in order to ensure the efficient, stable, economic and environmentally friendly operation of the system under complex and variable working conditions. SUMMARY

[0003] To achieve the above purpose, the present application is implemented by the following technical scheme: an intelligent integrated municipal sludge full-process treatment system, comprising a thickening unit, a conditioning unit, a dewatering unit, a drying unit, an incineration unit and a tail gas treatment unit, further comprising: a full-process digital twin module connected to the sensor network of each treatment unit through a data bus, for fusing material properties, equipment states and environmental parameter data; a multi-agent collaborative decision-making module connected to each treatment unit controller and digital twin module; A double closed-loop dynamic optimization engine, input connected to the digital twin module, output connected to the multi-agent collaborative decision-making module; An edge-cloud collaborative security module, deployed on each processing unit edge node and in communication with the cloud server.

[0004] Preferably, the multi-agent collaborative decision-making module comprises: A unit-level agent embedded in each processing unit controller, including a reinforcement learning optimization unit and an expert rule base unit; A process-level coordination agent connected to the unit-level agent through industrial Ethernet, with a dynamic shadow price calculation unit built-in; A plant-level optimization agent interacting with the process-level coordination agent data, outputting global optimization targets to each unit.

[0005] Preferably, the unit-level agent is configured: The reinforcement learning optimization unit generates a first control strategy using the SoftActor-Critic algorithm; The expert rule base unit generates a second control strategy based on the knowledge graph; The confidence weighted fusion unit outputs execution instructions after weight distribution of the first control strategy and the second control strategy.

[0006] Preferably, the confidence weighted fusion unit performs: When the sludge moisture content fluctuates beyond a set threshold, increase the decision weight of the expert rule base unit; When the equipment operating state is stable, increase the decision weight of the reinforcement learning optimization unit.

[0007] Preferably, the process-level coordination agent comprises: A cross-unit impact prediction unit that predicts the steam consumption of the drying unit based on the outlet sludge moisture content of the dewatering unit; An emergency linkage protocol generator that sends parameter adjustment instructions to the dewatering unit, drying unit, and incineration unit simultaneously when a sudden change in sludge heat value is detected.

[0008] Preferably, the emergency linkage protocol generator performs: Send instructions to the dewatering unit to increase the flocculant dosage; Send instructions to the drying unit to reduce the hot air temperature; Send instructions to the incineration unit to increase auxiliary fuel.

[0009] Preferably, the double closed-loop dynamic optimization engine comprises: An inner loop reinforcement learning controller that receives sensor data in real time and outputs device adjustment instructions; An outer loop multi-objective optimizer pre-plays the process scheme in the digital twin module and verifies the physical feasibility through the first principle model.

[0010] Preferably, the outer loop multi-objective optimizer comprises: A Pareto solution set generation unit outputs the day-level maintenance plan, the hour-level process route and the minute-level parameter adjustment scheme. A reverse verification unit requires that all schemes simultaneously satisfy the data-driven prediction result and the thermodynamic conservation constraint condition.

[0011] Preferably, the edge-cloud collaborative security module is provided with: A mechanism-data cross-verification unit performs the following verification on the temperature instruction of the incineration unit: A data-driven model predicts the temperature change curve; A mechanism model calculates the minimum safe burning temperature; A blockchain storage unit records the timestamp and execution result of the passed verification instruction.

[0012] Preferably, the edge-cloud collaborative security module is further configured: When the network is interrupted, the edge node switches to the local security mode, maintaining the constant pressure operation of the dewatering unit and the minimum air supply of the incineration unit.

[0013] The present application provides an intelligent integrated municipal sludge full-process treatment system. The intelligent integrated municipal sludge full-process treatment system solves the problems of energy efficiency loss and effect fluctuation caused by independent operation of each unit in the sludge treatment system through multi-agent collaborative decision-making and double closed-loop verification mechanism. When the nature of the sludge changes, the system can automatically trigger the cross-unit emergency linkage instruction chain to realize dynamic matching of upstream and downstream parameters; at the same time, relying on the double verification of digital twin pre-play and physical equation, it ensures that the optimization instruction meets the safety boundary of the equipment and the environmental protection requirements, effectively improving the stability and reliability of the full-process operation.

[0014] The intelligent integrated municipal sludge full-process treatment system is based on an edge-cloud hierarchical guardian architecture, and the system has strong adaptive ability under extreme working conditions. When the network is interrupted, the edge node can independently maintain the safe operation of the key equipment, avoiding system paralysis caused by sudden failure; after the cloud is restored, the control right is smoothly switched through digital twin pre-play, and the operation is fully traceable combined with blockchain storage. This mechanism greatly reduces the need for manual intervention, while ensuring the dual goals of treatment efficiency and environmental compliance, providing a highly robust intelligent solution for municipal sludge treatment. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1A module interaction schematic diagram of an intelligent integrated municipal sludge full-process treatment system according to the present application; Figure 2 A flowchart of an intelligent integrated municipal sludge full-process treatment method according to the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0017] Please refer to Figure 1 and Figure 2 The present application provides a technical solution: an intelligent integrated municipal sludge full-process treatment system, comprising a thickening unit, a conditioning unit, a dewatering unit, a drying unit, a incineration unit and a tail gas treatment unit, and further comprising: a full-process digital twin module connected to a sensor network of each treatment unit through a data bus, for fusing material property data, equipment state data and environmental parameter data; a multi-agent collaborative decision-making module connected to each treatment unit controller and the digital twin module; a double-closed-loop dynamic optimization engine connected to the digital twin module at the input end and connected to the multi-agent collaborative decision-making module at the output end; an edge-cloud collaborative security module deployed at an edge node of each treatment unit and in communication with a cloud server.

[0018] It needs to be further explained that, during the operation of the intelligent integrated municipal sludge full-process treatment system, first of all, the sensor network deployed in the thickening unit, the conditioning unit, the dewatering unit, the drying unit, the incineration unit and the tail gas treatment unit collects three types of key data in real time: material property data: including sludge moisture content, viscosity, organic matter content and heat value, obtained online by a microwave moisture content meter and a near-infrared spectrometer; equipment state data: covering equipment vibration amplitude, bearing temperature, motor current and valve opening, monitored by a vibration accelerometer and a smart meter; environmental parameter data: involving environmental temperature and humidity, water inflow load and sludge source information, collected by a temperature and humidity sensor and a flow meter.

[0019] All data are transmitted to the full-process digital twin module through the data bus for fusion processing. When constructing a dynamic process model library, the following core operations are performed: when the sludge properties are stable, the data-driven model is preferentially called to predict the parameter changes of the downstream unit, such as predicting the dry vapor consumption based on the moisture content after dewatering; When a sudden change in sludge heat value or abnormal vibration of the equipment is detected, the mechanism model is switched to calculate the safe operation boundary, such as limiting the minimum combustion temperature of the incinerator according to the thermodynamic equation.

[0020] The fused data are input into the multi-agent collaborative decision-making module to drive the cooperation of three levels of agents, and the process is as follows: The unit-level agent runs in the local controller: the dewatering unit agent dynamically adjusts the flocculant dosage according to the real-time moisture content through the reinforcement learning strategy; at the same time, the expert rule library is called to check whether the adjustment amplitude exceeds the historical safety threshold, and if it exceeds, it is forced to correct to the preset range; The process-level coordination agent monitors the cross-unit influence: when the dewatering unit outlet moisture content rises above the set threshold, it immediately sends instructions to the drying unit to reduce the hot air temperature and to the incineration unit to increase the auxiliary fuel; The plant-level optimization agent generates a global target every 24 hours: during the night low-load period, the drying equipment maintenance plan is started, and the incineration unit processing capacity is simultaneously reduced to balance the system load.

[0021] The decision-making instructions are executed after being verified by the double-loop dynamic optimization engine, and the execution process is as follows: Inner loop control: the dewatering unit receives instructions and adjusts the filter press pressure within minutes; Outer loop verification: before the instructions are executed, the digital twin pre-plays the impact of the operation on the whole process: If the pre-play shows that the drying unit energy consumption will exceed the limit, the reverse verification mechanism is triggered, the maximum allowed moisture content output value of the dewatering unit is recalculated through the mass conservation equation, and the instruction parameters are corrected accordingly.

[0022] System safety is guaranteed by the edge-cloud collaborative safety module: the edge node monitors the dewatering unit pressure and the incineration unit temperature in real time, and when the pressure fluctuation exceeds the safety range, it immediately switches to the local constant pressure control mode; all key instructions need to pass through the cloud mechanism model to verify whether the combustion pollutant generation meets the standard, and after verification, the operation timestamp and execution result are recorded by the blockchain.

[0023] The multi-agent collaborative decision-making module includes: The unit-level agent is embedded in each process unit controller and includes a reinforcement learning optimization unit and an expert rule library unit; The process-level coordination agent is connected with the unit-level agent through industrial Ethernet and has a built-in dynamic shadow price calculation unit; The plant-level optimization agent interacts with the process-level coordination agent, and outputs a global optimization target to each unit.

[0024] It should be further explained that, in the implementation process, the multi-agent collaborative decision-making module realizes global optimization through the hierarchical cooperation of the three-layer agents, and the optimization process is as follows: The unit-level agent is embedded in the local controller of each processing unit, and performs the following operations: the dewatering unit agent receives the sludge moisture content data in real time, and when the moisture content is within the normal fluctuation range, i.e. within the set threshold, it calls the reinforcement learning optimization unit to generate a flocculant dosage adjustment strategy; if the moisture content suddenly exceeds the threshold, it switches to the expert rule base unit and limits the adjustment range according to historical safe operation cases; The two strategies input a confidence weighted fusion unit: when the sensor signal is stable and the data quality is high, the reinforcement learning strategy weight dominates; when the equipment is abnormally vibrating or data is missing, the expert rule weight automatically increases to more than 80%.

[0025] The process-level coordination agent obtains all unit-level agent output data through industrial Ethernet, and performs cross-unit collaboration, including: the dynamic shadow price calculation unit gives real-time cost weight to the material flow: when the dewatering unit outputs high-moisture sludge, its "punishment cost" will be increased and fed back to the dewatering agent, prompting it to optimize the dewatering efficiency; when the sludge heat value is abnormally decreased, the emergency linkage protocol generator synchronously triggers three instruction chains within 10 seconds, including: sending "increase filter pressure and flocculant concentration" instruction to the dewatering unit; sending "reduce hot air temperature and extend residence time" instruction to the drying unit; sending "start auxiliary fuel injection" instruction to the incineration unit.

[0026] The plant-level optimization agent coordinates long-term goals and short-term control: during daily low-load periods such as 1:00-5:00, maintenance plan optimization is started: the drying unit is suspended and the filter belt cleaning program is triggered, and the incineration unit is instructed to switch to low-load mode; when energy prices fluctuate, the optimal process route is recalculated: if the real-time electricity price exceeds the critical value, the drying unit is instructed to reduce steam consumption and increase dewatered sludge buffer capacity to stagger operation.

[0027] Unit-level agent configuration: The reinforcement learning optimization unit uses the SoftActor-Critic algorithm to generate the first control strategy; The expert rule base unit generates the second control strategy based on the knowledge graph; The confidence weighted fusion unit outputs the execution instruction after weight distribution of the first control strategy and the second control strategy.

[0028] It needs to be further explained that in the specific implementation process, the unit-level intelligent agent runs in the dewatering unit controller, and the decision-making is executed according to the following logic, and the process is as follows: Data acquisition and preprocessing: real-time receiving of sludge moisture content sensor, filter press pressure gauge and flocculant flowmeter data, when the data fluctuation of continuous 5 sampling periods is less than the set tolerance, it is marked as stable working condition; if the vibration sensor is detected to be abnormal or the current value suddenly increases, the equipment state abnormality flag is triggered immediately.

[0029] Dual-strategy parallel generation: the reinforcement learning optimization unit runs in stable working condition: based on historical operation data, the SoftActor-Critic model is trained, and the flocculant dosage adjustment suggestion is output; the model exploration process is limited within the equipment safety threshold, including that the filter pressure does not exceed 85% of the rated value.

[0030] Expert rule base unit activated in abnormal working condition: when the moisture content suddenly changes more than the historical maximum fluctuation range, the case rules in the knowledge graph are called, such as "rainstorm working condition: flocculant concentration is increased to 120% of the baseline value, and the filter pressure is reduced by 5%"; if the equipment vibration is out of limit, the filter pressure is forcibly locked to the safety value.

[0031] Confidence weighted fusion decision: in normal working condition, the weight of reinforcement learning strategy is set to 70%, and the weight of expert rule is 30%; when any of the following situations occurs, the weight of expert rule is increased to more than 80%: the sludge moisture content sampling value exceeds the threshold interval for 3 times; the filter press driving current fluctuation coefficient exceeds the set safety line; the sensor data quality score is lower than the qualified level.

[0032] The final execution instruction of the fusion unit is output: when the instructions conflict after weight allocation, the high-weight strategy is adopted preferentially; if the conflict and the weight difference is less than 10%, the artificial review process is started.

[0033] Cross-unit influence prediction: before the decision execution, based on the predicted value of the dewatered sludge moisture content, the influence on the downstream drying unit is calculated: if the predicted moisture content rise will lead to the increase of drying steam consumption exceeding the tolerance upper limit, the current dewatering unit processing load is automatically reduced; when the predicted result triggers the drying unit safety alarm, the current instruction is interrupted immediately and switched to the safety mode preset by the expert rule.

[0034] Confidence weighted fusion unit execution: When the sludge moisture content fluctuation exceeds the set threshold, the decision weight of the expert rule base unit is increased; When the equipment running state is stable, the decision weight of the reinforcement learning optimization unit is increased.

[0035] It needs to be further explained that in the specific implementation process, the confidence weighted fusion unit dynamically adjusts the decision weight according to the real-time working condition when the dewatering unit is running: Sludge moisture fluctuation determination: When the moisture change rate exceeds 150% of the historical average change rate for three consecutive sampling periods, it is marked as a severe fluctuation state, and the expert rule base unit decision weight is increased to more than 80%; if the fluctuation continues but does not reach the severe fluctuation threshold, such as only a single sampling exceeds the standard, the expert rule weight is set to 60%, and the reinforcement learning weight remains at 40%.

[0036] Equipment operation stability evaluation: When the vibration amplitude, bearing temperature and driving current of the filter press are all within the normal range, it is determined to be in a stable state, and the weight of the reinforcement learning optimization unit is set to 70%; if any parameter is abnormal, such as the vibration amplitude exceeds the safety baseline and lasts for more than 10 seconds, the expert rule weight is immediately increased to a dominant position, not less than 85%.

[0037] Strategy fusion after weight distribution: When reinforcement learning suggests "increase flocculant dosage by 5%", and expert rules suggest "maintain current dosage": if the weight ratio is 70%:30%, execute the weighted average instruction; if the weight ratio is adjusted to 20%:80%, completely adopt the expert rule instruction.

[0038] When there is a fundamental conflict, such as reinforcement learning requiring the filter pressure to be increased to 8 MPa, and expert rules limiting the pressure to no more than 7.5 MPa: when the weight difference exceeds 25%, the high-weight strategy is executed first; when the weight difference is less than 10% and involves safety parameters: pressure or temperature, a three-level arbitration mechanism is started, including the following: First level: call the digital twin to simulate the results of both strategies; Second level: check if the simulation results meet the mechanical limits of the equipment; Third level: if still cannot be solved, freeze the current operation and report for manual intervention.

[0039] During special working conditions, including the following: For heavy rain water inflow conditions: automatically set the expert rule weight to 90% and force the execution of the pre-set scheme "reduce filter pressure by 5% + increase flocculant concentration by 10%"; the exploration range of the reinforcement learning strategy is compressed to ±2% of the pre-set scheme during this period.

[0040] For sensor data anomalies: if the moisture sensor fails, switch to an alternative model based on filter press power consumption and sludge output; at this time, the expert rule weight is forcibly locked at 100%; after data recovery, consistency needs to be verified for three consecutive sampling periods before gradually restoring the reinforcement learning weight.

[0041] Process-level coordination agents include: Cross-unit impact prediction unit, based on the outlet sludge moisture of the dewatering unit to predict the steam consumption of the drying unit; Emergency linkage protocol generator, when detecting the sudden change of sludge heat value, synchronously sends parameter adjustment instructions to the dewatering unit, drying unit and incineration unit.

[0042] It needs to be further explained that in the specific implementation process, the process level coordination agent monitors the sludge moisture content data at the outlet of the dewatering unit in real time, and when it is detected that the moisture content rises more than the set threshold value of the historical benchmark value, the cross-unit influence prediction process is started: Based on the real-time flow and moisture content of dewatered sludge, the theoretical steam consumption of the drying unit is calculated through a material balance model; if the predicted steam consumption exceeds the upper limit value of the rated processing capacity of the equipment, an optimization instruction is immediately sent to the dewatering unit agent: to reduce the sludge moisture content to the safe range, and to generate a drying unit energy-saving plan.

[0043] When the sludge heat value monitor detects an abnormal decrease in heat value, i.e. lower than the lower limit of the design operating range, the emergency linkage protocol generator executes linkage operations within 8 seconds, including: Dewatering unit instruction chain: increase the flocculant dosage concentration to 115% to 130% of the benchmark value, with the specific value being set according to the heat value drop level; increase the filter press pressure to 90% of the equipment safety upper limit, and extend the filter cycle time by 10% to 15%; wherein the equipment safety upper limit is the maximum operating value allowed by the manufacturer.

[0044] Drying unit instruction chain: reduce the hot air temperature to above the preset safe burning temperature interval; when the sludge viscosity is too high, simultaneously increase the speed of the crusher to prevent clogging.

[0045] Incineration unit instruction chain: start the auxiliary fuel injection system, and the fuel increment is dynamically compensated according to the real-time heat value loss; reduce the feed rate to 70% to 85% of the design value to ensure sufficient burning time.

[0046] Instruction execution process timing coordination control: the dewatering unit parameter adjustment instruction is issued first; the drying unit instruction is started after a delay set time, waiting for the moisture content change to be transmitted to the feed inlet; the incineration unit instruction is executed after the drying unit action, ensuring that the dried sludge properties are stable.

[0047] It needs to be further explained that the historical moisture content benchmark value is dynamically updated according to the season, and the benchmark value automatically floats in the rainy season; when the water inflow load exceeds the design value for 3 consecutive days, the prediction model parameters are recalibrated; when the predicted steam consumption exceeds the limit, the upstream is prioritized for optimization rather than forcing the downstream to run overloaded; when sending the moisture content optimization target to the dewatering unit, the feasible solution is also sent synchronously, such as "reduce the feed rate by 10% + increase the flocculant concentration by 8%".

[0048] Multi-stage heat value compensation strategy, i.e. heat value drop level response mechanism, includes the following: Primary drop (<5%): only increase the flocculant concentration of the dewatering unit; Secondary drop (5%~10%): start the dewatering + drying unit cooperative adjustment; Tertiary drop (>10%): full chain linkage, and includes the incineration unit fuel compensation.

[0049] Fuel increment calculation: according to the heat value loss and sludge treatment amount, calculate the minimum fuel compensation amount according to the chemical equivalent; the actual increment adds a safety margin.

[0050] The dynamic calculation of the delay setting time is as follows: Drying instruction delay time = sludge conveying pipeline length ÷ flow rate + safety margin; Incineration instruction delay time = drying cycle remaining time + buffer bin residence time.

[0051] After each instruction is issued, the execution effect is checked within the set time, such as whether the moisture content after dewatering meets the standard; if not, automatically upgrade the measures, such as increasing the pressure filter pressure again.

[0052] The emergency linkage protocol generator executes: Send the instruction to increase the flocculant dosage to the dewatering unit; Send the instruction to reduce the hot air temperature to the drying unit; Send the instruction to increase the auxiliary fuel to the incineration unit.

[0053] It needs to be further explained that in the specific implementation process, the emergency linkage protocol generator executes the following cross-unit cooperative operation when the sludge heat value abnormally decreases: Dewatering unit instruction execution: when the heat value decreases by a certain amount, i.e. does not reach the significant influence threshold, only increase the flocculant dosage concentration to 105%~110% of the baseline value, and maintain the pressure filter pressure at the original setting; if the heat value enters the secondary drop interval, affecting the stability of incineration, then simultaneously increase the flocculant concentration to 115%~125% and increase the pressure filter pressure to 85% of the equipment safety upper limit, while extending the pressure filter cycle time; when the heat value drops to the tertiary interval, endangering the combustion efficiency, on the basis of the secondary measures, additionally start the sludge buffer bin shunting mechanism to reduce the instantaneous treatment load.

[0054] Drying unit instruction matching: hot air temperature is dynamically adjusted according to the moisture content of the dewatered sludge: for every 1% increase in moisture content, the temperature decreases by a set gradient value; the speed of the crusher is linked to the viscosity of the sludge: when the online viscosity detector detects a value exceeding the high viscosity warning line, the speed is increased to more than 90% of the rated maximum value to prevent clogging of the pipeline.

[0055] Incineration unit fuel compensation: auxiliary fuel injection amount is positively correlated with heat value loss amount: when the loss amount is in the first interval, the injection amount covers 100%-110% of the heat value gap; when the loss amount is in the second interval and above, the injection amount increases to 120%-130% of the gap and the feed rate is reduced to ensure that the sludge residence time is extended to the required length for safe combustion.

[0056] The instruction chain execution process embeds a physical process synchronization mechanism, and the process is as follows: Dehydration unit action takes effect immediately: parameter adjustment is completed within 5 seconds after the instruction is issued; Dryer unit delayed start: delay time is calculated according to the length and flow rate of the sludge conveying pipeline to ensure that the sludge with changed moisture content after adjustment has entered the dryer; Incineration unit secondary delay: fuel compensation and feed speed adjustment are performed after the dried sludge enters the buffer bin to the set capacity, avoiding direct feeding during heat value fluctuation period.

[0057] It needs to be further explained that the heat value grading-measure matching model is as follows: Dynamic interval calibration: the upper limit value of the first interval is updated monthly according to historical data of sludge sources, such as food plant sludge heat value fluctuation benchmark higher than chemical plant; the third interval determination adds a duration dimension, which requires 3 consecutive samples below the threshold to trigger; Double-factor control of reagent-pressure: in the secondary response, the increase of flocculant and the increase of filter pressure are inversely balanced, i.e. when the reagent increases by 15%, the pressure increases by 5%, avoiding excessive energy consumption.

[0058] Double coupling of drying parameters, as follows: Temperature-moisture content dynamic equation: every 1% increase in moisture content corresponds to a temperature drop gradient value, which is automatically corrected according to environmental humidity, i.e. the gradient value increases when the humidity is greater than 80%; Real-time feedback of crushing-viscosity: when the viscosity exceeds the limit, the speed of the crusher is increased to the critical value, and if it does not decrease within 10 seconds, the pipeline flushing program is triggered.

[0059] Timing guarantee of incineration compensation, including: Precise control of fuel injection amount: first compensation: calculate the basic injection amount according to real-time processing amount x heat value gap; second compensation: basic amount x 120% + oxygen content correction value of flue gas; Decoupling of feed rate and residence time: the speed reduction range is calculated according to the heat value loss amount to ensure that the minimum combustion time is greater than or equal to 120% of the design value.

[0060] Physical modeling of cross-unit delay, including: Conveying pipeline delay calculation: delay time = (pipeline volume ÷ real-time flow rate) x safety factor; The cache bin capacity triggering mechanism is: the incineration instruction needs to meet: the cache bin filling rate > 70% and the average heat value of the sludge in the bin has stabilized.

[0061] The double closed-loop dynamic optimization engine includes: The inner loop reinforcement learning controller receives sensor data in real time and outputs device adjustment instructions; The outer loop multi-objective optimizer preforms the process scheme in the digital twin module and verifies the physical feasibility through the first principle model.

[0062] It needs to be further explained that during the specific implementation process, the double closed-loop dynamic optimization engine realizes dynamic optimization through the cooperation of the inner and outer loops, and the process is as follows: The inner loop reinforcement learning controller receives the dewatering unit filter press pressure sensor, flocculant flowmeter and sludge moisture content instrument data in real time, and when it is detected that the moisture content fluctuation exceeds the allowed range, adjustment instructions are generated within 20 seconds: if the moisture content is higher than the target value, the filter pressure is increased to 90% of the safe upper limit, and the flocculant dosage is adjusted, and the increase is controlled within the historical maximum safe increase; if the moisture content is lower than the target value, the filter pressure is reduced to 85% of the reference value, and the flocculant dosage is reduced, not less than the minimum effective dosage.

[0063] After the instructions are generated, they are immediately sent to the actuator, and the pre-performance process of the outer loop multi-objective optimizer is triggered, including: inputting the inner loop instructions into the digital twin module to simulate the future 30-minute full-process state; if the simulation shows that the dry unit steam consumption exceeds the rated load, or the incineration unit flue gas pollutant concentration approaches the limit, it is marked as a high-risk instruction; high-risk instructions are transferred to the first principle verification channel: the maximum allowed sludge moisture content of the dewatering unit is calculated through the thermodynamic conservation equation; check if the inner loop instruction causes the moisture content to break through this limit, if it does, automatically generate a correction parameter.

[0064] The corrected instructions are reissued for execution, forming a closed loop, including: when the outer loop verification passes, the inner loop instruction is executed directly; when the outer loop is rejected, the modified parameter is executed and the optimization path difference is recorded; every 24 hours, the instruction correction cases are summarized, and the reinforcement learning model optimization reward function weight is trained.

[0065] Special working condition processing: when the equipment suddenly fails, such as filter press hydraulic leakage: the inner loop controller immediately freezes the optimization instruction, switches to the safety mode preset by the expert rule, and maintains constant low pressure operation; the outer loop suspends the pre-performance process and starts the fault impact diffusion simulation to generate cross-unit emergency plan, such as diversion to the standby dewatering line. Data abnormal fluctuation: when the moisture content sensor continuously samples three times and the value jumps more than the reasonable range, the inner loop suspends the decision and calls the historical same period data to replace; the outer loop verification increases the redundancy check, and the conclusions of the digital twin and the first principle model must be consistent to pass.

[0066] Among them, the real-time response of the inner ring safety constraint includes the following: Water content double threshold control: allow fluctuation range to be dynamically adjusted according to sludge source, where food plant sludge range is wider than chemical plant; take different measures when out of range: high water content preferentially increases pressure, low water content preferentially reduces reagent consumption.

[0067] Historical safety amplitude guard: the pressure increase of filter pressing is limited within the maximum safe increase amplitude of equipment operation for three years; the flocculant decrease amplitude cannot be lower than the minimum critical concentration to maintain flocculation effect.

[0068] The outer ring double model verification mechanism includes the following: Three-level early warning of digital twin pre-performance: First-level warning, i.e. 10% steam overload: prompt optimization but not reject instructions; Second-level warning, i.e. 80% pollutant overrun: forcibly start first principle verification; Third-level warning, i.e. device virtual alarm: immediately reject instructions.

[0069] Rigid check of physical equation: maximum allowed water content = f(dryer thermal efficiency, incinerator minimum ignition point); if the inner ring instruction water content exceeds this value, instruction correction is triggered, such as filter pressing pressure reduction to the correction value.

[0070] Cross-layer cooperation of fault scenarios includes the following: Inner ring-outer ring emergency switching: when the equipment fails, the inner ring locks the expert rules, and the outer ring switches to the emergency plan mode; fault simulation includes material transfer path and energy redistribution scheme.

[0071] Progressive processing of data anomalies: first sampling anomaly starts redundant measurement; continuous two anomalies switch data source, where historical data need to match current seasonal working conditions; continuous three anomalies trigger system self-checking and reporting.

[0072] The outer ring multi-objective optimizer includes: Pareto solution set generation unit, output day-level maintenance plan, hour-level process route and minute-level parameter adjustment scheme; Reverse verification unit requires all schemes to simultaneously satisfy data-driven prediction results and thermodynamic conservation constraints.

[0073] It needs to be further explained that in the specific implementation process, when the outer ring multi-objective optimizer performs three-time scale rolling optimization, it processes decision tasks of different periods in layers, and the process is as follows: Daily maintenance schedule optimization: Analyze the sludge production prediction curve every morning, and automatically schedule a maintenance window when the predicted low point is below 60% of the designed load. Four hours before the maintenance task starts, instruct the dewatering unit to increase processing intensity to fill the buffer bin in advance, ensuring continuous feeding of the incinerator during the shutdown of the drying unit.

[0074] Hourly process route switching: Real-time monitoring of energy price signals, when the electricity price jumps to the peak interval, switch to energy-saving mode before the next billing period starts: reduce the drying unit steam temperature to the minimum safe value to maintain moisture evaporation; divert part of the dewatered sludge to the standby buffer bin and delay processing until the low-price period. If it is detected that the organic matter content of the sludge continues to decrease, automatically switch the drying-incineration path to the anaerobic digestion path.

[0075] Minute-level parameter adjustment: Based on the instantaneous fluctuation of the moisture content at the outlet of the dewatering unit, dynamically calculate the drying unit hot air temperature compensation value: for every 1% increase in moisture content, increase the temperature by a set gradient; when the oxygen content of the incinerator flue gas abnormally increases, reduce the feeding rate and increase the secondary air volume within 90 seconds.

[0076] The Pareto solution verification unit performs double-checking on all optimization schemes, the process is as follows: Data-driven forward prediction: Input the candidate scheme into the LSTM prediction model to simulate the system state for the next 2 hours; if any unit parameter exceeds the limit in the simulation results (such as the drying machine outlet temperature exceeding the safety baseline), mark this scheme as high-risk.

[0077] First-principle reverse constraint: Calculate the physical boundaries of each unit through thermodynamic conservation equations: the maximum allowed sludge moisture content at the outlet of the dewatering unit = f (lower limit of drying machine thermal efficiency, minimum heat value required for stable combustion of the incinerator); the minimum hot air temperature of the drying unit = g (initial moisture content of the sludge, tail gas emission standard temperature). Check if the scheme parameters exceed the physical boundaries, and if so, generate a revised parameter set.

[0078] Conflict handling rules: When the data model predicts success but the physical verification fails, the scheme is forced to use the physical boundary values; when the data model warns and the physical verification passes, the scheme is downgraded to observation mode, i.e., execute with a 50% limit and intensify monitoring; when the double conclusions conflict and involve safety parameters, freeze the scheme and start the expert consultation process.

[0079] The time scale decoupling coupling mechanism is: maintenance window opening needs to meet: sludge low point prediction accuracy greater than 90% + buffer bin filling rate greater than 80%; maintenance period incineration feeding amount = buffer bin release rate x heat value compensation coefficient, to avoid combustion interruption. The electricity price peak determination adds trend prediction, and only if it continues to rise for the next 30 minutes will it trigger the switch; the amount of diverted sludge is calculated based on the duration of the peak to ensure that the buffer bin does not overflow.

[0080] The chain reaction control of parameter adjustment includes: dynamically adjusting the temperature gradient value according to the ambient humidity, and increasing the gradient when the humidity is greater than 70%; the compensation upper limit is not more than 90% of the temperature limit of the device. When the oxygen content is abnormal, the feed rate is reduced first, that is, for every 1% of the oxygen content exceeding the standard, the speed is reduced by 5%; if it does not improve for 3 minutes, the secondary air volume is increased, wherein the damper opening degree is proportional to the oxygen content deviation.

[0081] The conflict resolution system of double verification includes: calculating the maximum allowed water content associated with the real-time state of the downstream equipment, such as the scaling rate of the drier > 30%, the boundary is tightened; wherein the scheme that breaks through the physical boundary is directly discarded without correction.

[0082] Data model early warning classification, including the following: Yellow warning, that is, parameter over-limit < 10%: reduction execution; Red alert, that is, parameter over-limit > 10%: observation mode running; Black alert, that is, virtual device damage: immediately discarded.

[0083] The intelligent trigger of expert consultation starts the consultation when the following conditions are met: double verification conclusion conflict, involving incineration temperature or pollutant emission parameters, and the system is in a high load running state of more than 85%. During the consultation, the last cycle parameters are maintained, and if the timeout is not resolved, the safety mode is automatically switched.

[0084] The edge-cloud collaborative safety module is provided with: Mechanism-data cross-verification unit, which performs the following verification on the temperature instruction of the incineration unit: Data-driven model predicts temperature change curve; Mechanism model calculates the minimum safe combustion temperature; Blockchain storage unit, which records the timestamp and execution result of the verified instruction.

[0085] It needs to be further explained that during the specific implementation process, the edge-cloud collaborative safety module runs, and the temperature control instruction of the incineration unit is strictly double-verified, and the process is as follows: Data-driven forward prediction: input the temperature instruction to be executed into the time series prediction model to simulate the furnace temperature change curve in the next 15 minutes; if the prediction curve shows that the temperature cannot reach the sludge ignition point within 120 seconds, it is marked as "burning delay risk"; if the temperature exceeds the limit of the refractory material of the device for more than 60 seconds, it is marked as "over-temperature damage risk".

[0086] Mechanism model reverse constraint: calculate the minimum safe combustion temperature under the current sludge composition based on the combustion thermodynamic equation; back-calculate the pollutant generation threshold temperature according to the tail gas treatment capacity: if the instruction temperature is lower than the lower limit of the dioxin optimal decomposition temperature interval, it is marked as “emission over-standard risk”; if it is higher than the inflection point temperature of nitrogen oxide generation, it is marked as “nitrogen oxide surge risk”.

[0087] Verification result processing rules: when both double verifications pass, the instruction is immediately issued for execution and generates a blockchain evidence; when the data model warns and the mechanism model passes: if it belongs to “combustion delay risk”, additional fuel injection instruction is added to compensate the temperature; if it belongs to “over-temperature damage risk”, the temperature instruction value is forced to reduce to the upper limit of the mechanism model safety; When the mechanism model warns and the data model passes: if it belongs to “emission over-standard risk”, the temperature is raised to the dioxin decomposition interval and the flue gas residence time is extended; if it belongs to “nitrogen oxide surge risk”, the temperature is reduced below the inflection point and the urea injection amount is increased; When both double verifications warn, freeze the instruction and start the three-level emergency response, including: First level: call the digital twin simulation alternative solution; Second level: check the equipment mechanical bearing capacity of the alternative solution; Third level: manually confirm the current parameters before running.

[0088] The blockchain evidence unit records the following key information, including: instruction generation timestamp and original parameters; double verification conclusion and correction measures; actual temperature trajectory and pollutant emission data after execution; sensor snapshot data when abnormal events are triggered.

[0089] Among them, the risk classification response includes compensation for combustion delay and limiting of over-temperature risk; wherein: Compensation for combustion delay: auxiliary fuel increment = (ignition temperature difference x sludge flow) / fuel heat value; after compensation, the target temperature needs to be reached within 60 seconds, otherwise it is upgraded to the second level response.

[0090] Limiting of over-temperature risk: safety upper limit temperature = equipment fire resistance rating x real-time coking coefficient; the coking coefficient is dynamically updated according to the historical cleaning period, wherein the coefficient is down-regulated when the coking rate is greater than 30%.

[0091] The coupling strategy of pollutant control includes: temperature-time coupling of dioxin decomposition: when the temperature rise is limited, the residence time of flue gas in the interval above 850℃ is extended to the design value of 120%; the residence time extension needs to be calculated simultaneously with the heat bearing capacity of the heat exchanger. Nitrogen oxide double-path inhibition: when the temperature is reduced below the inflection point, the urea injection amount is matched with the flue gas flow and the real-time concentration of nitrogen oxides; after injection, the concentration decrease trend needs to be verified within 45 seconds, otherwise secondary injection is started.

[0092] The progression of the double early warning is as follows: digital twin alternative simulation: the alternative needs to meet: equipment safety factor > 1.5 + emission margin > 15%; the simulation results are compared with the physical model twice, and the difference rate > 5% is discarded. Real-time evaluation of mechanical bearing capacity: based on the material fatigue equation to calculate the life loss value after the implementation of the scheme; the scheme whose single loss exceeds the monthly average value needs to be attached to the equipment physical examination plan.

[0093] The multi-dimensional association of blockchain storage includes: abnormal event snapshot capture: record all related sensor data 10 seconds before the trigger time, including temperature, pressure, flow, vibration; snapshot data and corrective measures establish causal association tree. Cross-platform audit tracking: store evidence information to the supervision platform, support multi-dimensional retrieval according to timestamp, device number, and pollutant type; automatically generate device degradation analysis report for three times of the same early warning trigger.

[0094] The edge-cloud collaborative security module is also configured: When the network is interrupted, the edge node switches to the local security mode, maintaining the constant pressure operation of the dewatering unit and the minimum air supply of the incineration unit. It needs to be further explained that in the specific implementation process, when the system detects that the network communication is interrupted, the edge node switches to the local security mode within 100 milliseconds, and performs the following hierarchical protection operation: Dewatering unit constant pressure control: lock the pressure filter pressure to the ± set tolerance range of the last valid instruction value before the network is interrupted, if the network is interrupted during the pressure adjustment process, maintain the change trend to 90% of the safety upper limit and then constant; The flocculant dosing system is switched to flow closed loop control, taking the instantaneous flow before the network is interrupted as the reference value, and starting the standby metering pump when the fluctuation exceeds the allowed threshold.

[0095] Incineration unit minimum air volume guarantee: according to the sludge calorific value data before the network is interrupted, the air volume lower limit is set by level: when the calorific value is normal, maintain 80% of the design air volume and close the variable damper; when the calorific value is below the warning line, increase to 90% of the design air volume and link to auxiliary fuel injection; when the oxygen content sensor in the flue gas fails, switch to time-air volume curve control: increase the air volume by 5% every 30 seconds until the current monitoring detects the fan overload symptom and back off.

[0096] The safety boundary dynamic calculation mechanism includes: Pressure tolerance interval update: under normal network conditions, learn the historical pressure fluctuation characteristics every 8 hours, calculate the new tolerance reference value, such as normal working condition ± 0.5 MPa, storm working condition ± 0.8 MPa; when the network is interrupted, directly call the last learned tolerance value to execute the guardian; Air volume hierarchical threshold adjustment: the calorific value warning line is dynamically calibrated according to the sludge source: the warning line of food plant sludge is higher than that of chemical plant sludge by a set difference; check the sludge properties every 10 minutes during network interruption, and automatically increase the air volume to the next level when the caking rate rises.

[0097] The synchronization mechanism after network recovery includes: the edge node uploads all operation records and sensor data snapshots during the network outage; the cloud verifies the compliance of local decisions: if the constant pressure control does not break through the physical boundary and the pollutant data does not exceed the standard, it is marked as valid autonomous operation; if the air volume adjustment causes fan overload alarm, generate device protection protocol optimization scheme; before restoring cloud control, digital twin simulates the switching process to avoid system oscillation caused by instruction jump.

[0098] The seamless switching in network transient state includes: pressure trend continuation algorithm: when the pressure is in the rising / falling process during network outage, maintain the original change rate until the safe boundary is reached; monitor the equipment vibration amplitude every second during the change process, and freeze the pressure value if it exceeds the limit. Flow closed-loop dual-level guardian: automatically switch to the standby pump when the main metering pump flow deviation is greater than 5%; if the standby pump still exceeds the limit after starting, trigger the flowmeter self-checking, and use the piston stroke number conversion instead of control.

[0099] The triple protection of air volume self-adaptation includes: heat value-air volume dynamic matching: before network outage, when the heat value is in a downward trend, the air volume is increased by a safety margin based on the trend extrapolation value; when there is no heat value data, the theoretical heat value interval is calculated based on the recent average organic matter content. Time-air volume curve intelligent risk avoidance: stop increasing when the fan current reaches 85% of the rated value; execute step-by-step rollback when the current fluctuation coefficient exceeds the limit, with a 3% decrease per step.

[0100] The autonomous learning mechanism of the safety boundary is as follows: pressure tolerance dynamic model: exclude abnormal device data during the learning phase, and the records during the vibration exceedance period are invalid; new tolerance reference value = historical fluctuation extreme value x real-time device health coefficient, wherein the health coefficient is less than 0.8, the tolerance is tightened. Heat value warning line classification and calibration: the heavy metal content correction term is added to the chemical plant sludge warning line, and the warning line is adjusted downward when the content is greater than the limit; the warning line automatically floats up every 2 times of self-checking during network outage found that the sludge viscosity rises.

[0101] The zero-impact mechanism of recovery synchronization includes: digital twin switching rehearsal: simulate the system response of the first instruction after restoring cloud control; if the predicted dehydration unit sludge moisture content fluctuation is greater than the allowed value, use step-by-step callback to complete the adjustment in five steps. Overload backtracking analysis: overload events are associated with operation records and current curves to identify the root cause, such as fan blade fouling causing abnormal load; generate preventive maintenance recommendations and update the expert rule library.

[0102] It needs to be further explained that in the specific implementation process, the municipal sludge flows through the concentration unit, the conditioning unit, the dewatering unit, the drying unit, the incineration unit and the tail gas treatment unit in turn. The sensor network deployed in each unit collects three types of data in real time: material property data including sludge moisture content and heat value, equipment state data including vibration amplitude and motor current, and environmental parameter data including temperature and humidity and water inflow load. All data are transmitted to the central processing platform through the industrial bus.

[0103] The central platform constructs a dynamic process model library and adopts a double-track parallel modeling strategy. When the sludge properties are stable, the data-driven model is called to predict parameter changes, such as calculating the drying steam consumption according to the moisture content after dewatering; when the sludge heat value suddenly changes or the equipment abnormally vibrates, the mechanism model is automatically switched to calculate the safety boundary, such as limiting the minimum combustion temperature of the incinerator according to the thermodynamic principle. The model library adapts the optimization experience of similar water quality areas to the system through the migration learning technology.

[0104] The unit-level intelligent agent is embedded in each unit controller to perform local optimization. The dewatering unit intelligent agent generates flocculant adjustment instructions according to the real-time moisture content: when the fluctuation is normal, the reinforcement learning strategy is used to fine-tune the dosage, and when the fluctuation is severe, the expert rule base is switched to limit the adjustment amplitude. The double strategy is weighted and output through the confidence fusion unit, and if the equipment vibration is abnormal, the expert rule is preferred. The process-level coordination intelligent agent monitors the cross-unit influence, and when the dewatering outlet moisture content exceeds the standard, it immediately sends a cooling instruction to the drying unit and a fuel increasing instruction to the incineration unit. The plant-level intelligent agent generates global targets every day, such as scheduling equipment maintenance and balancing system load at night.

[0105] The inner loop controller responds to parameter fluctuations at the minute level. When the dewatering unit detects that the moisture content exceeds the standard, a pressure filtration pressure adjustment instruction is generated within twenty seconds, and the increase is controlled within the equipment safety upper limit. The outer loop optimizer starts the digital twin pre-performance before the instruction is executed: simulates the state of the whole process for the next thirty minutes, if the drying steam overload or the incineration pollutant exceeds the standard, the first principle verification channel is triggered. The maximum allowed sludge moisture content is calculated through the thermodynamic equation, and if the limit is broken, the instruction parameters are automatically corrected. The corrected instruction is reissued and executed, and the case optimization decision model is summarized every day.

[0106] The incineration unit temperature instruction needs to be verified twice. The data-driven model simulates the furnace temperature change trajectory to identify the risk of combustion delay or over-temperature; the mechanism model calculates the minimum safe combustion temperature to determine the risk of emission exceeding the standard. When the verification conflicts, a hierarchical response is initiated: the data model warning adds fuel compensation or limits the temperature, and the mechanism model warning adjusts the temperature to the pollutant decomposition interval. All instructions and verification results are stored through blockchain, recording timestamps, correction measures and execution effects. When the network is interrupted, the edge node switches to the local safety mode: the dewatering unit maintains the pressure trend before the network interruption to a safe value and then keeps it constant; the incineration unit sets the minimum air volume according to the calorific value, and uses the time gradient to increase the air volume when the calorific value data is missing.

[0107] When the sludge calorific value abnormally decreases, the system executes a hierarchical response. The first level of decrease only increases the flocculant concentration of the dewatering unit; the second level of decrease increases the filter pressure and prolongs the filter time; the third level of decrease simultaneously starts sludge diversion. The instruction chain is embedded in the physical process synchronization: the dewatering unit is immediately executed, the drying unit is delayed until the moisture content change is transmitted to the position, and the incineration unit responds when the dried sludge reaches the capacity of the buffer bin. The delay time is dynamically calculated according to the pipe length and flow rate.

[0108] The device state is locked at the moment of network interruption. The dewatering unit maintains the pressure change trend to the safety boundary, and switches to the standby pump when the flocculant flow deviation exceeds the limit. The incineration unit sets the air volume reference according to the calorific value before the network interruption, and adds a safety margin when the calorific value trend decreases. The sludge properties are detected every ten minutes, and the air volume level is increased when the caking rate rises. After the network is restored, the operation records are uploaded, the cloud verifies the compliance of the decision, and the control right is switched through the digital twin to avoid parameter jumps.

[0109] An intelligent integrated municipal sludge full-process treatment method, comprising the following steps: Step S1: The sensor network collects material characteristics, equipment state and environmental parameter data of the concentration, dewatering, drying and incineration units in real time. When the sludge moisture content fluctuation rate exceeds the historical average, it is marked as a severe fluctuation condition; if the equipment vibration and current are stable, it is determined as a normal operating condition.

[0110] Step S2: The dewatering unit agent executes double-strategy fusion: in normal condition, the reinforcement learning strategy is used to generate flocculant dosage fine-tuning instructions; in severe fluctuation, the expert rule base is switched to limit the filter pressure increase to within the equipment safety limit; the final instruction is output by confidence weighting, and the expert rule weight is increased when the equipment is abnormal.

[0111] Step S3: When the sludge heat value abnormally decreases: primary decrease: only increase the flocculant concentration of the dewatering unit; secondary decrease: simultaneously increase the filter pressure and extend the filter time; tertiary decrease: divert the sludge to the buffer warehouse and start the full-chain response. Instruction chain timing control: the dewatering unit is immediately executed, the drying unit is delayed until the moisture content change is transmitted, and the incineration unit acts when the buffer warehouse reaches capacity.

[0112] Step S4: Inner ring real-time control: the dewatering unit adjusts the filter parameters at the second level; outer ring pre-verification, including the following steps: a) simulate the full-process state after the instruction is executed by the digital twin; b) if the predicted drying energy consumption exceeds the limit, start the first principle verification; c) calculate the maximum allowable moisture content through thermodynamic equations, and correct the instruction when the limit is broken.

[0113] Step S5: Temperature control instructions need to pass through a data model to pre-verify the furnace temperature change trajectory, identify the risk of combustion delay, and calculate the pollutant generation threshold through a mechanism model to determine the risk of emission exceeding the standard. In case of conflict, the safety value of the mechanism model is used first, and the flue gas residence time is extended.

[0114] Step S6: When the network is interrupted, the dewatering unit maintains the pressure change trend to a safe value and then remains constant, the flocculant flow switches to the standby pump when it exceeds the difference; the incineration unit sets the air volume according to the heat value before the network is interrupted, and increases the air volume according to the time gradient when there is no heat value data; check the sludge caking rate every ten minutes, and automatically increase the air volume level if it rises.

[0115] Step S7: After the network is restored, upload the local operation record, and the digital twin pre-verification control right switching process: if the predicted parameter jump causes system oscillation, use a step-by-step callback strategy; check the pollutant data during the network interruption, and generate an optimization scheme to update the expert rule library for events exceeding the standard.

[0116] Through multi-agent collaborative decision-making and double closed-loop verification mechanism, the energy efficiency loss and effect fluctuation caused by independent operation of each unit in the sludge treatment system are solved. When the sludge properties change suddenly, the system can automatically trigger the cross-unit emergency linkage instruction chain to realize the dynamic matching of upstream and downstream parameters; at the same time, relying on the double verification of digital twin pre-verification and physical equation, it ensures that the optimization instruction meets the safety boundary of the equipment and the environmental protection requirements, effectively improving the stability and reliability of the whole process operation.

[0117] Based on the edge-cloud hierarchical guardian architecture, the system has strong adaptive ability under extreme working conditions. When the network is interrupted, the edge node can independently maintain the safe operation of the key equipment to avoid system paralysis caused by sudden failure; after the cloud is restored, the control right is smoothly switched through digital twin pre-verification, and the operation is fully traceable through blockchain storage. This mechanism greatly reduces the need for manual intervention, while ensuring the dual goals of processing efficiency and environmental compliance, providing a highly robust intelligent solution for municipal sludge treatment.

[0118] It has to be noted that, in the present document, the terms "first", "second", etc. merely serve to identify a subject or action, without necessarily requiring or implying any such actual relationship or order between such subjects or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0119] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous further modifications and changes can be apparent to one skilled in the art without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

Claims

1. An intelligent integrated municipal sludge whole-process treatment system, comprising a thickening unit, a conditioning unit, a dewatering unit, a drying unit, an incineration unit, and a tail gas treatment unit, characterized in that, Also includes: The end-to-end digital twin module is connected to the sensor network of each processing unit via a data bus to integrate data on material characteristics, equipment status, and environmental parameters. The multi-agent collaborative decision-making module is connected to the controllers of each processing unit and the digital twin module via signals. The dual-closed-loop dynamic optimization engine connects to the digital twin module at the input end and to the multi-agent collaborative decision-making module at the output end. The edge-cloud collaborative security module is deployed at the edge nodes of each processing unit and communicates with the cloud server.

2. The intelligent integrated municipal sludge whole-process treatment system according to claim 1, characterized in that: The multi-agent collaborative decision-making module includes: Unit-level intelligent agents are embedded in the controllers of each processing unit and include reinforcement learning optimization units and expert rule base units. A process-level coordinating agent connects to a unit-level agent via an industrial Ethernet network and has a built-in dynamic shadow price calculation unit. The plant-level optimization agent interacts with the process-level coordination agent to output global optimization goals to each unit.

3. The intelligent integrated municipal sludge whole-process treatment system according to claim 2, characterized in that: The unit-level intelligent agent configuration: The reinforcement learning optimization unit uses the SoftActor-Critic algorithm to generate the first control policy; The expert rule base unit generates a second control strategy based on a knowledge graph; The confidence-weighted fusion unit assigns weights to the first control strategy and the second control strategy and then outputs the execution instruction.

4. The intelligent integrated municipal sludge whole-process treatment system according to claim 3, characterized in that: The confidence-weighted fusion unit performs the following: When the sludge moisture content fluctuates beyond a set threshold, the decision weight of the expert rule base unit is increased. When the equipment is operating stably, increase the decision weight of the reinforcement learning optimization unit.

5. The intelligent integrated municipal sludge whole-process treatment system according to claim 2, characterized in that: The process-level coordinating agent includes: Cross-unit impact prediction unit: predict the steam consumption of the drying unit based on the moisture content of the sludge at the outlet of the dewatering unit. The emergency response protocol generator simultaneously sends parameter adjustment instructions to the dewatering unit, drying unit, and incineration unit when it detects a sudden change in the calorific value of the sludge.

6. The intelligent integrated municipal sludge whole-process treatment system according to claim 5, characterized in that: The emergency response protocol generator executes: Send an instruction to the dewatering unit to increase the flocculant dosage; Send a command to the drying unit to reduce the hot air temperature; Send a command to the incineration unit to add auxiliary fuel.

7. The intelligent integrated municipal sludge whole-process treatment system according to claim 1, characterized in that: The dual-closed-loop dynamic optimization engine includes: The inner-loop reinforcement learning controller receives sensor data in real time and outputs device adjustment commands. The outer-loop multi-objective optimizer pre-simulates the process scheme in the digital twin module and verifies its physical feasibility through a first-principles model.

8. The intelligent integrated municipal sludge whole-process treatment system according to claim 7, characterized in that: The outer-loop multi-objective optimizer includes: The Pareto set generation unit outputs a daily maintenance plan, an hourly process route, and a minute-level parameter adjustment scheme. The reverse verification unit requires all schemes to simultaneously satisfy both the data-driven prediction results and the thermodynamic conservation constraints.

9. The intelligent integrated municipal sludge whole-process treatment system according to claim 1, characterized in that: The edge-cloud collaborative security module is equipped with: The mechanism-data cross-validation unit performs the following verifications on the incineration unit temperature command: Data-driven models predict temperature change curves; Mechanism model calculation of minimum safe combustion temperature; The blockchain-based evidence storage unit records the timestamp of the verification command and the execution result.

10. The intelligent integrated municipal sludge whole-process treatment system according to claim 9, characterized in that: The edge-cloud collaborative security module is also configured with: When the network is interrupted, the edge node switches to local security mode to maintain constant pressure operation of the dehydration unit and minimum air volume supply to the incineration unit.

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