A purification treatment method for sludge brick making

Abstract

This invention relates to the fields of industrial automation and environmental protection technology, and in particular to a purification treatment method for sludge brick making. The method involves collecting real-time composition data of the sludge to be treated using an online component detection device installed in the sludge purification system, generating a sludge composition dataset; collecting real-time status feedback data from key process nodes of the system, forming a process status dataset; based on this dataset, using the compressive strength of the finished brick, energy consumption, and heavy metal leaching rate as constraints, generating an optimized process parameter set through multi-variable collaborative optimization; dynamically adjusting the execution unit's operating instructions accordingly, generating a process control instruction set; and finally combining the two datasets to drive relevant devices to perform adaptive purification treatment, recording data to generate a control log dataset. This invention achieves deep collaboration between component detection, parameter optimization, and process execution, effectively adapting to fluctuations in sludge composition, and improving the purification efficiency, finished product quality, and resource utilization efficiency of sludge brick making.

Description

Technical Field

[0001] This invention relates to the fields of industrial automation and environmental protection technology, and in particular to a purification treatment method for sludge brick making. Background Technology

[0002] Promoting solid waste reduction at its source and its resource utilization is a crucial step in building waste-free cities and practicing the concept of green development. The utilization of sewage sludge in building materials, especially brick making, is an important way to achieve large-scale disposal. However, this technology still faces regional challenges and common technical bottlenecks in its practical application. For example, in some areas, the sludge produced by wastewater treatment plants has a water content as high as 80%, and in the absence of supporting treatment facilities, simple landfill disposal poses environmental risks. At the same time, existing sludge brick-making technologies generally suffer from high energy consumption, high operating costs, and the risk of secondary pollution, which restricts their large-scale application.

[0003] Currently, various purification processes, including dewatering, drying, pyrolysis, and carbonization, have been developed for sludge brick making. However, existing methods still face significant challenges in terms of treatment efficiency and economics in actual operation. Most processes rely on preset fixed parameters, such as fixed temperature and time settings in the drying stage and uniform heating curves in the carbonization process. This control mode is difficult to flexibly adapt to sludge raw materials from different sources with significant compositional fluctuations. When treating complex industrial sludge, fixed process parameters may not ensure the stable solidification of pollutants; while when treating relatively simple municipal sludge, using the most efficient mode set for the worst-case scenario leads to excessive consumption of energy and materials. Summary of the Invention

[0004] This invention provides a purification treatment method for sludge brick making, which solves the technical problem that existing sludge brick making purification treatment methods cannot optimize process parameters in real time according to the actual composition characteristics of sludge raw materials, cannot adapt to the composition fluctuations of sludge from different sources and batches, and thus result in low resource utilization efficiency.

[0005] The first aspect of this invention provides a purification treatment method for sludge brick making, comprising:

[0006] By using an online component detection device installed in the sludge purification and treatment system, real-time component data of the sludge stream to be treated is collected, and a sludge component dataset is generated.

[0007] Real-time acquisition of status feedback data of key process nodes in the sludge purification and treatment system to generate process status dataset;

[0008] Based on the sludge composition dataset, with the compressive strength of finished bricks, the comprehensive energy consumption of the system, and the heavy metal leaching rate as constraints, a multivariate collaborative optimization solution is performed to generate an optimized process parameter set.

[0009] Based on the optimized process parameter set, the operating instructions of the corresponding execution units in the sludge purification and treatment system are dynamically adjusted to generate a process control instruction set;

[0010] Based on the process control instruction set and the process status dataset, the drying reactor and mixing and batching device in the sludge purification system are driven to perform adaptive purification treatment, and relevant operation and status data are recorded to generate a control log dataset.

[0011] Optionally, the online component detection device installed in the sludge purification and treatment system includes a multispectral imaging sensor, a laser-induced breakdown spectroscopy probe, a data processing unit, an online pH sensor, and an online conductivity sensor; the step of collecting real-time component data of the sludge stream to be treated and generating a sludge component dataset through the online component detection device installed in the sludge purification and treatment system includes:

[0012] The multispectral imaging sensor continuously images the sludge flow on the sludge conveyor belt of the sludge purification system to generate an original spectral image sequence.

[0013] The data processing unit performs noise filtering, image registration, and feature region segmentation on the original spectral image sequence to extract the texture feature parameters and color distribution parameters of the sludge.

[0014] Based on the texture feature parameters and the color distribution parameters, combined with the synchronously collected sludge temperature and flow velocity data, the data processing unit calculates the estimated moisture content and estimated organic matter content.

[0015] The sludge flow is excited and plasma emission spectra are collected by the laser-induced breakdown spectral probe, and the characteristic spectral intensity data of heavy metal elements are obtained by the data processing unit.

[0016] The pH value and conductivity of the sludge flow are simultaneously measured by the online pH sensor and the online conductivity sensor to generate corresponding real-time measurement data;

[0017] The data processing unit fuses the characteristic spectral intensity data, the real-time measurement data, the moisture content prediction data, and the organic matter content prediction data from multiple sources to generate a sludge composition dataset.

[0018] Optionally, the step of calculating the estimated moisture content and estimated organic matter content based on the texture feature parameters and the color distribution parameters, combined with synchronously acquired sludge temperature and flow velocity data, through the data processing unit includes:

[0019] Using the texture feature parameters and color distribution parameters as the core, the synchronously collected sludge temperature and flow rate data are integrated, and weighted combination and nonlinear transformation are performed to generate a high-order feature vector.

[0020] Perform multi-layer perceptron computation on the high-order feature vector, extract features layer by layer and reduce dimensionality, and output a set of abstract features characterizing the core physical properties of sludge;

[0021] Based on the abstract feature set, the moisture content regression value and the organic matter content regression value are respectively parsed through independent regression calculation paths;

[0022] The regression values ​​of moisture content and organic matter content are subjected to confidence interval verification and moving average filtering based on historical data distribution to generate estimated moisture content data and estimated organic matter content data.

[0023] Optionally, the step of performing multivariate collaborative optimization based on the sludge composition dataset, with the finished brick compressive strength, system comprehensive energy consumption, and heavy metal leaching rate as constraint objectives, to generate an optimized process parameter set includes:

[0024] Using the sludge composition dataset as input, a multi-objective optimization problem is constructed with the goal of maximizing the predicted compressive strength of finished bricks and minimizing the predicted comprehensive energy consumption of the system, and with the constraint that the heavy metal leaching rate does not exceed a threshold.

[0025] Based on the dynamic mechanism model of sludge drying and sintering process, a model predictive control framework is used to solve the multi-objective optimization problem in the rolling time domain to generate a set of candidate process parameter solutions.

[0026] From the solution set of the candidate process parameters, key decision variables characterizing the optimal process path are extracted to generate a core decision variable set, which includes the drying temperature curve sequence, sintering holding time, and the optimized blending ratio of sludge and substrate.

[0027] The core decision variable set is subjected to process feasibility and operational boundary verification to generate an optimized process parameter set.

[0028] Optionally, the step of using a model predictive control framework to perform rolling time-domain solution of the multi-objective optimization problem based on the dynamic mechanism model of sludge drying and sintering process to generate a candidate process parameter solution set includes:

[0029] Set the current time as the starting point of the rolling time domain, and obtain the sludge composition dataset and process state dataset at the current time;

[0030] Based on the current sludge composition dataset and process state dataset, the dynamic mechanism model of sludge drying and sintering process is invoked to simulate the process under different control input sequences in the prediction time domain, generating a multi-step forward state prediction dataset.

[0031] Based on the multi-step forward state prediction dataset, and in accordance with the multi-objective optimization problem, optimization calculations are performed in the control time domain to obtain the optimal control sequence in the current rolling time domain.

[0032] The first control variable of the optimal control sequence is extracted as an immediate control command and output to the execution unit corresponding to the sludge purification system. At the same time, the current optimal control sequence is stored as a candidate solution fragment.

[0033] The rolling time domain is advanced by one control cycle, the current time is updated, and the updated process state dataset is obtained.

[0034] Determine if the current time has exceeded the total processing time for the batch;

[0035] If not, then proceed to execute the sludge composition dataset and process state dataset based on the current time, call the dynamic mechanism model of the sludge drying and sintering process, simulate the process under different control input sequences in the prediction time domain, and generate a multi-step forward state prediction dataset.

[0036] If so, all stored candidate solution fragments will be integrated in chronological order to construct a candidate process parameter solution set.

[0037] Optionally, the step of dynamically adjusting the operating instructions of the corresponding execution units in the sludge purification system based on the optimized process parameter set to generate a process control instruction set includes:

[0038] The drying temperature curve sequence in the optimized process parameter set is decomposed into segmented temperature set values ​​for each heating zone of the drying reactor, and the optimized blending ratio of sludge and base material is converted into the batching flow rate set value for each raw material silo in the mixing and batching device.

[0039] Based on the actual operating status of each execution unit in the process status dataset, physical constraint verification and dynamic limiting are performed on the segmented temperature setpoint and the batching flow rate setpoint to generate segmented temperature adjustment value and batching flow rate adjustment value.

[0040] Multi-objective coordination optimization is performed on conflicting setpoints in the segmented temperature adjustment value and the batching flow rate adjustment value to generate conflict-free coordinated control instructions. The coordinated control instructions include temperature control instruction sequences for each heating zone of the drying reactor and flow rate control instruction sequences for each raw material silo in the mixing and batching device.

[0041] Based on the process timing logic of the sludge purification and treatment system, the control instructions in the coordination control instructions are arranged according to the preset execution priority and timing relationship to generate an atomic operation instruction set.

[0042] The atomic operation instruction set is encapsulated according to a preset industrial control protocol to generate a process control instruction set.

[0043] Optionally, the step of driving the drying reactor and mixing and batching device in the sludge purification system to perform adaptive purification treatment based on the process control instruction set and the process status dataset, and recording relevant operation and status data to generate a control log dataset includes:

[0044] The process control instruction set is parsed into the segmented temperature setting curve of the drying reactor and the dynamic proportioning setting sequence of the mixing and batching device in the sludge purification and treatment system.

[0045] Based on the moisture content of the material in the drying section and the oxygen concentration in the kiln as reflected in the process status data, the segmented temperature setting curve is dynamically corrected to generate a safe temperature control curve.

[0046] Based on the material level height and material viscosity in the mixing bin as centrally reflected in the process status data, the execution timing of the dynamic proportioning setting sequence is adjusted to generate an anti-clogging material proportioning control sequence;

[0047] The safe temperature control curve and the anti-clogging material ratio control sequence are executed synchronously, and the drying flue gas temperature, batching motor current and equipment vibration data are collected in real time as execution process status data.

[0048] The safe temperature control curve, the anti-clogging material ratio control sequence, and the corresponding execution process status data are spatiotemporally aligned and correlated with the sludge composition dataset and process status dataset at the current moment to generate a control log dataset.

[0049] A second aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the purification treatment method for sludge brick making as described above.

[0050] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the purification treatment method for sludge brick making as described above.

[0051] A fourth aspect of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer performs the purification treatment method for sludge brick making as described above.

[0052] As can be seen from the above technical solutions, the present invention has the following advantages:

[0053] This invention utilizes an online component detection device within a sludge purification system to collect real-time component data of the sludge stream, generating a sludge component dataset. Simultaneously, it collects status feedback data from key process nodes to form a process status dataset. Based on this sludge component dataset, a multivariate collaborative optimization solution is performed with constraints including the compressive strength of finished bricks, overall system energy consumption, and heavy metal leaching rate, to obtain an optimized set of process parameters. Based on these parameters, the operating instructions of the corresponding execution units are dynamically adjusted to generate a process control instruction set. Finally, combining the process control instruction set and the process status dataset, the drying reactor and mixing and batching device are driven to perform adaptive purification treatment, and operation and status data are recorded to generate a control log dataset. This achieves precise matching between process parameters and sludge component characteristics.

[0054] By capturing sludge composition data in real time through an online component detection device, this method overcomes the limitations of traditional treatment methods that rely on fixed process parameters and cannot promptly detect changes in sludge composition. It accurately grasps the compositional differences and fluctuations of sludge from different sources and batches, providing real-time and reliable data support for process parameter optimization. Multi-variable collaborative optimization is performed with constraints on the finished brick compressive strength, overall system energy consumption, and heavy metal leaching rate, avoiding the imbalance between treatment effect and energy consumption / environmental indicators caused by single-parameter optimization. Optimal process parameter sets can be generated specifically based on the actual compositional characteristics of the sludge, changing the rigidity of traditional process parameters that cannot adapt to sludge composition fluctuations. Based on the optimized process parameter set, the execution unit's operating instructions are dynamically adjusted, driving the device to adaptively execute. This forms a closed-loop control from data acquisition and parameter optimization to instruction execution and status feedback. It can respond to changes in sludge composition in real time, flexibly adjust the treatment process, and ensure stable treatment results even with sludge raw materials exhibiting significant compositional fluctuations, effectively improving the finished product qualification rate of sludge-made bricks. Meanwhile, the closed-loop control mode can precisely regulate the operation of key processes such as drying and batching. Under the premise of ensuring that the heavy metal leaching rate meets the standards and the compressive strength of the finished bricks meets the requirements, it can minimize the overall energy consumption of the system and reduce resource waste. Compared with traditional processes, which suffer from problems such as insufficient sludge resource utilization, excessive energy consumption, and unstable finished product quality due to poor adaptability, it significantly improves the efficiency and economy of sludge resource utilization. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating the steps of a purification treatment method for sludge brick making according to an embodiment of the present invention;

[0057] Figure 2 This is a structural block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0058] This invention provides a purification treatment method for sludge brick making, which solves the technical problem that existing sludge brick making purification treatment methods cannot optimize process parameters in real time according to the actual composition characteristics of sludge raw materials, and cannot adapt to the composition fluctuations of sludge from different sources and batches, resulting in low resource utilization efficiency.

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0060] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a purification treatment method for sludge brick making, provided in an embodiment of the present invention.

[0061] This invention provides a purification treatment method for sludge brick making, comprising:

[0062] Step 101: Collect real-time composition data of the sludge stream to be treated using an online component detection device installed in the sludge purification system, and generate a sludge composition dataset.

[0063] It should be noted that a sludge purification and treatment system refers to a complete industrial production line integrating functions such as sludge receiving, conditioning, deep dewatering, drying, pyrolysis, and material mixing. The online component detection device is installed in the sludge pretreatment unit of the sludge purification and treatment system. Here, the sludge pretreatment unit specifically refers to the initial stage of the system's process flow, including the sludge receiving bin, homogenizing tank, and related conveying equipment (such as sludge conveyor belts). Its core function is to homogenize the raw sludge, which has complex origins and uneven properties. The online component detection device is a highly integrated intelligent sensing module, fixedly installed in the conveyor belt corridor of the sludge pretreatment unit, enabling non-contact or minimally destructive online analysis of flowing sludge.

[0064] Furthermore, the online component detection device installed in the sludge purification and treatment system includes a multispectral imaging sensor, a laser-induced breakdown spectroscopy probe, a data processing unit, an online pH sensor, and an online conductivity sensor. Step 101 may include the following sub-steps:

[0065] S11. Continuously image the sludge flow on the sludge conveyor belt of the sludge purification system using a multispectral imaging sensor to generate an original spectral image sequence.

[0066] It should be noted that the band configuration of the multispectral imaging sensor is not arbitrarily selected, but precisely matched to the spectral absorption characteristics of organic matter, moisture, and specific pollutants in the sludge. Its operating band covers the visible light band (400-700nm) and the near-infrared band (900-1700nm). The visible light band is mainly used to capture changes in the color and surface texture of the sludge, while the near-infrared band is sensitive to the characteristic absorption peaks of CH and NH bonds in organic matter and OH bonds in water molecules, making it a key spectral region for quantitative analysis. The multispectral imaging sensor uses an industrial-grade linear scan camera with a spatial resolution set to 0.5mm / pixel. This precision is sufficient to distinguish sludge flocs from common impurities (such as sand and plastic fragments), ensuring the effectiveness of subsequent feature extraction.

[0067] In this embodiment of the invention, a multispectral imaging sensor scans the surface of a uniformly moving sludge at a frame rate synchronized with the conveyor belt speed, line by line. By rapidly switching filters between different characteristic bands, multiple bands of reflectance intensity images are acquired for the same sludge region. These images, arranged in chronological order and band sequence, are combined to generate an original spectral image sequence, in which each pixel contains information about its spatial location and multiple spectral dimensions.

[0068] S12. The data processing unit performs noise filtering, image registration, and feature region segmentation on the original spectral image sequence to extract the texture feature parameters and color distribution parameters of the sludge.

[0069] In this embodiment of the invention, the data processing unit first employs a time-frequency domain filtering method based on wavelet transform to remove noise, effectively suppressing random noise introduced by uneven ambient lighting, sensor dark current, etc., while preserving image edge details well. Subsequently, an algorithm based on scale-invariant feature transform is used for image registration to address the problem of positional shift of the same physical point in different spectral band images caused by conveyor belt vibration or uneven sludge surface, thereby achieving strict spatial alignment of multispectral information. Based on this, an adaptive method combining superpixel segmentation and region growing is used for feature region segmentation, aiming to robustly separate the effective sludge area in the image from the conveyor belt background, shadows, and large foreign objects. Specifically, after registration and segmentation are completed, the data processing unit calculates four core indicators—contrast, energy, homogeneity, and correlation—for the extracted effective sludge region within the gray-level co-occurrence matrix framework, thereby obtaining texture feature parameters that quantitatively describe the surface roughness and uniformity of the sludge. Simultaneously, the image is converted to the CIELAB color space, and the mean and standard deviation of all pixels within the region are calculated on the three components of L (brightness), a (red-green axis), and b* (yellow-blue axis), thereby obtaining color distribution parameters.

[0070] S13. Based on texture feature parameters and color distribution parameters, combined with synchronously collected sludge temperature and flow velocity data, the data processing unit calculates the estimated moisture content and estimated organic matter content.

[0071] Furthermore, step S13 may include the following sub-steps:

[0072] S131. Using texture feature parameters and color distribution parameters as the core, and integrating synchronously collected sludge temperature and flow velocity data, weighted combination and nonlinear transformation are performed to generate high-order feature vectors.

[0073] In this embodiment of the invention, weighted combination strengthens key influencing factors and suppresses secondary or noisy information by assigning weights to each original feature; nonlinear transformation captures the complex interaction relationships between features by introducing a nonlinear function, enabling the generated combined features to be better mapped to subsequent prediction targets. The data processing unit takes the received texture feature parameters (4), color distribution parameters (mean and standard deviation of L, a, b*, a total of 6), sludge temperature value (1), and sludge flow velocity value (1), a total of 12 original data, and constructs a 12-dimensional original feature vector. First, this vector is multiplied by a preset weight matrix (dimension 12×16) to achieve weighted combination, resulting in a 16-dimensional intermediate vector. Then, the ReLU (Rectified Linear Unit) activation function is applied element-wise to this intermediate vector for nonlinear transformation. The ReLU function is defined as... It preserves positive features and suppresses negative features, thus introducing nonlinearity. After the above calculations, a 16-dimensional high-order feature vector is finally output.

[0074] S132. Perform multilayer perceptron computation on the high-order feature vectors, extract features layer by layer and reduce dimensionality, and output a set of abstract features that characterize the core physical properties of sludge.

[0075] In this embodiment of the invention, the high-order feature vector is input into a neural network containing two fully connected hidden layers. The first hidden layer has 12 neurons, receives 16-dimensional input, and outputs 12-dimensional features through weight calculation and activation function (such as ReLU); the second hidden layer has 8 neurons, receives 12-dimensional input, and outputs 8-dimensional features. The network weight parameters have been obtained through training with historical sludge sample data. After these two layers of calculation, the original 16-dimensional high-order features are refined and compressed layer by layer into an 8-dimensional abstract feature set.

[0076] S133. Based on the abstract feature set, the regression values ​​of moisture content and organic matter content are analyzed through independent regression calculation paths.

[0077] In this embodiment of the invention, an 8-dimensional abstract feature set is simultaneously input into two independent fully connected layers (i.e., regressors). The first fully connected layer is dedicated to water content prediction, having 8 input neurons and 1 output neuron. Through its trained weight parameters, it maps the abstract features to a continuous scalar value, i.e., the water content regression value (e.g., the output range is typically 30% to 80%). The second fully connected layer is dedicated to organic matter content prediction, having the same structure but independent weight parameters. It maps the same abstract feature set to another continuous scalar value, i.e., the organic matter content regression value (e.g., the output range is typically 20% to 60%).

[0078] S134. Perform confidence interval verification and moving average filtering based on historical data distribution on the regression values ​​of moisture content and organic matter content to generate estimated moisture content data and estimated organic matter content data.

[0079] In this embodiment of the invention, the sludge purification system pre-stores reasonable ranges for moisture content (e.g., [35%, 75%]) and organic matter content (e.g., [25%, 55%]) based on long-term historical data statistics. If the regression value of moisture content or organic matter content output in the current step S133 exceeds its corresponding range, the value is replaced with the upper or lower limit of the corresponding range. Then, a moving average filtering is performed: the sludge purification system maintains a first-in, first-out queue of length 5 (configurable) for both moisture content and organic matter content. The verified values ​​are added to the corresponding queues, and the arithmetic mean of all values ​​in the queue is calculated. The final output values ​​are the estimated moisture content data and the estimated organic matter content data.

[0080] S14. The sludge flow is excited and the plasma emission spectrum is collected by a laser-induced breakdown spectral probe, and the characteristic spectral intensity data of heavy metal elements are obtained by analysis through the data processing unit.

[0081] In this embodiment of the invention, laser-induced breakdown spectroscopy (LASPS) detects specific heavy metal elements by analyzing their characteristic emission spectra. For elements such as chromium, cadmium, lead, and copper, which require close monitoring in brick-making sludge, this method selects characteristic spectral lines of their atoms or ions at specific wavelengths for analysis. To achieve reliable detection, the laser pulse energy of the probe must be no less than 50 mJ to ensure sufficient plasma intensity is generated on the sludge surface. The spectrometer resolution must reach approximately 0.1 nm to clearly distinguish adjacent spectral lines and avoid spectral overlap interference. The probe installation position must ensure a constant distance between the laser focal point and the sludge surface and be equipped with a self-cleaning lens to prevent dirt adhesion from affecting laser transmission. Specifically, the probe emits high-energy laser pulses at a set frequency, forming high-temperature plasma in a small area on the sludge surface. During the cooling process, the heavy metal atoms / ions within the plasma undergo excited transitions and emit characteristic light of specific wavelengths. After the spectrometer acquires the emission spectrum and transmits it to the data processing unit, the data processing unit uses peak-finding algorithms and Lorentz fitting to accurately extract the net peak intensity values ​​at the preset target heavy metal characteristic spectral line positions, thereby generating characteristic spectral line intensity data of heavy metal elements. This data is positively correlated with the concentration of the corresponding element.

[0082] S15. The pH value and conductivity of the sludge flow are simultaneously measured by an online pH sensor and an online conductivity sensor to generate corresponding real-time measurement data.

[0083] In this embodiment of the invention, an online pH sensor measures the hydrogen ion activity of the sludge through its composite electrode, and an online conductivity sensor measures the conductivity of the sludge through its four electrodes. Both sensors convert the sensed physicochemical signals into 4-20mA standard current signals and transmit them in real time. The data processing unit acquires and converts the received signals from analog to digital, and performs engineering value conversion based on the sensor's factory calibration curve, thereby directly generating real-time pH measurement data reflecting the current acid-base characteristics of the sludge and real-time conductivity measurement data reflecting the soluble salt content.

[0084] S16. The data processing unit integrates the characteristic spectral intensity data, real-time measurement data, moisture content prediction data, and organic matter content prediction data from multiple sources to generate a sludge composition dataset.

[0085] In this embodiment of the invention, the data processing unit first timestamps all input data using a unified high-precision clock source, and then synchronizes and assembles the data packets within a fixed time window (e.g., 1 second). Subsequently, a fusion framework is constructed using a Kalman filter algorithm. The estimated moisture content and organic matter content output from the image-property correlation model, along with their confidence intervals, are used as the system's state prediction vector and prediction noise covariance. Simultaneously, the characteristic spectral intensity data obtained from laser-induced breakdown spectroscopy analysis, along with real-time measurement data generated by online pH and conductivity sensors, and their respective measurement errors, are used as the observation vector and observation noise covariance. By iteratively executing the Kalman filter's prediction and update loop, the optimal fused estimate of each component index under the minimum mean square error is calculated. Finally, all fused index values ​​(including moisture content, organic matter content, characteristic spectral intensity of various heavy metals, pH value, and conductivity) are encapsulated with their unique timestamps to form a sludge component dataset used to drive subsequent process optimization.

[0086] Step 102: Collect real-time status feedback data of key process nodes in the sludge purification and treatment system to generate a process status dataset.

[0087] In this embodiment of the invention, a key process node refers to a system location that has a decisive impact on the sludge purification effect, energy consumption, and equipment safety, and is equipped with online monitoring instruments. Key process nodes include at least: the conveyor belt drive motor of the sludge pretreatment unit, the heating zones and flue gas outlet of the drying reactor, and the mixing chamber and raw material discharge ports of the mixing and batching device. For these nodes, the system is equipped with corresponding industrial sensors and performs synchronous data acquisition: current sensors and vibration sensors are installed at the conveyor belt drive motor to collect motor operating current and radial vibration acceleration data, which are used to monitor the material conveying load and mechanical health status of the pretreatment unit; K-type thermocouples are installed inside each heating zone of the drying reactor, and a high-temperature gas analyzer (measuring O2 and CO concentrations) is installed at the flue gas outlet to collect real-time temperature and flue gas composition data for each zone, which are used to monitor the heating uniformity, reaction completeness, and combustion safety of the drying process; a material level radar and torque sensor are installed in the mixing chamber of the mixing and batching device, and high-precision weighing sensors are installed at the discharge ports of each raw material silo to collect data on the material level height in the mixing chamber, the real-time torque of the stirring shaft, and the instantaneous discharge flow rate of each raw material, which are used to monitor the mixing uniformity and batching accuracy. All sensor signals are synchronously acquired via an industrial bus (such as PROFINET) at a frequency of not less than 10Hz and transmitted to the central controller. The central controller assigns a uniform, high-precision, millisecond-level timestamp to each data point and, with a 1-second cycle, packages all status data (including current, vibration, temperature, O2 concentration, CO concentration, material level, torque, flow rate, etc.) collected from various nodes at the same time into a structured data record. Multiple such records arranged in chronological order constitute the process status dataset.

[0088] Step 103: Based on the sludge composition dataset, with the compressive strength of finished bricks, the comprehensive energy consumption of the system, and the heavy metal leaching rate as the constraints, perform multivariate collaborative optimization to generate an optimized process parameter set.

[0089] Furthermore, step 103 may include the following sub-steps:

[0090] S21. Using the sludge composition dataset as input, construct a multi-objective optimization problem with the goal of maximizing the predicted compressive strength of finished bricks and minimizing the predicted comprehensive energy consumption of the system, and with the constraint that the heavy metal leaching rate does not exceed a threshold.

[0091] In this embodiment of the invention, three pre-set core models are first used to process the sludge composition dataset. The finished brick compressive strength prediction model is a model trained based on support vector regression, taking moisture content, organic matter content, etc., as input, and outputting a predicted strength value (unit: MPa). The system comprehensive energy consumption prediction model is a hybrid model integrating thermodynamic mechanisms and equipment efficiency curves, taking the same composition data and process assumptions as input, and outputting a predicted value of standard coal consumption (unit: kgce) covering steam, fuel gas, and electricity consumption converted to standard coal equivalent for drying and sintering processes. The heavy metal leaching rate assessment model is constructed based on the kinetic equations of heavy metal speciation and sintering solidification, taking the heavy metal spectral intensity and process temperature conditions as input, and outputting a predicted value of heavy metal leaching concentration in Cd (unit: mg / L). Subsequently, the optimization problem is formally defined as follows: within a decision space comprised of "drying temperature (200-950℃)," "sintering holding time (2-8 hours)," and "sludge-to-material blending ratio (10%-35%)," the goal is to find one or a series of solutions that maximize the predicted intensity and minimize the predicted energy consumption, while also satisfying the hard constraint that the predicted leaching rate must be ≤0.5 mg / L. This definition constitutes a standard, constrained biobjective optimization problem.

[0092] S22. Based on the dynamic mechanism model of sludge drying and sintering process, a model predictive control framework is used to solve the multi-objective optimization problem in the rolling time domain and generate a set of candidate process parameters.

[0093] In this embodiment of the invention, based on a dynamic mechanism model including moisture migration, pyrolysis reaction, and heat and mass transfer equations, optimization is initiated at each decision point (e.g., every 2 minutes). The prediction time domain is set to 30 minutes to ensure coverage of the main dynamic processes from drying and heating to temperature stabilization. The control time domain is set to 10 minutes, meaning that the optimal process setpoints for each minute within the next 10 minutes are calculated in this optimization. During the solution process, the optimization algorithm (e.g., NSGA-II) searches within the mathematical problem space defined in S21 to obtain an optimal process parameter trajectory (i.e., a candidate solution) covering the next 30 minutes. Subsequently, only the setpoints for the first minute of this trajectory are implemented. At the next decision point (2 minutes later), the system collects the latest actual process state data, refreshes the initial state of the mechanism model, and rolls the entire optimization window (prediction time domain and control time domain) forward by 2 minutes, repeating the above optimization calculation. The candidate solutions obtained through each rolling optimization are arranged and concatenated in chronological order to form a candidate process parameter solution set covering the next few hours of production, which includes an optimized parameter sequence to cope with various possible future operating conditions.

[0094] S23. Extract key decision variables that characterize the optimal process path from the solution set of candidate process parameters, and generate a core decision variable set. The core decision variable set includes the drying temperature curve sequence, sintering holding time, and the optimized blending ratio of sludge and substrate.

[0095] In this embodiment of the invention, the candidate process parameter solution set is a multidimensional set of parameter trajectories that vary over time. The candidate process parameter solution set is analyzed and aggregated. For the drying process, the setpoint drying temperature per minute within a complete future drying cycle (e.g., 2 hours) is extracted and organized into a time-ordered numerical sequence, i.e., a drying temperature curve sequence. For the sintering process, the duration for which the sintering temperature is maintained at the target value (e.g., 980℃) is extracted from the solution set as the sintering holding time. For the batching stage, the constant ratio of sludge dry basis mass to clay dry basis mass is extracted from the solution set as the optimized blending ratio of sludge and base material. These three variables directly and significantly determine the final strength of the brick, production energy consumption, and heavy metal solidification effect. Extracting and encapsulating them from the solution set generates a core decision variable set that can directly guide the production of each core unit.

[0096] S24. Verify the process feasibility and operational boundaries of the core decision variable set, and generate an optimized process parameter set.

[0097] In this embodiment of the invention, the rate of temperature rise or fall in any consecutive 5-minute time interval within the drying temperature curve sequence is checked to see if it exceeds the maximum safe temperature change rate (set to 5°C / minute) that the kiln refractory material and heater can withstand. Sections exceeding this limit are smoothed using linear interpolation to prevent thermal shock. Secondly, the optimized blending ratio of sludge to base material is checked to see if it exceeds the maximum design capacity of the mixing system or is lower than the minimum requirement to ensure brick forming. If it exceeds this limit, a limiting process is applied. Finally, all verified drying temperature curve sequences, sintering holding times, and optimized blending ratios of sludge and base material are standardized and packaged to generate an optimized process parameter set.

[0098] Furthermore, step S22 may include the following sub-steps:

[0099] S221. Set the current time as the starting point of the rolling time domain, and obtain the sludge composition dataset and process status dataset at the current time.

[0100] It should be noted that each rolling optimization cycle requires a specific starting time point and corresponding operating data as a calculation benchmark. Obtaining the synchronized sludge characteristics and sludge purification system operating status data at that moment provides the correct initial conditions for subsequent accurate model-based predictions.

[0101] In this embodiment of the invention, the control unit of the sludge purification system defines the precise time point at which the current optimization calculation is initiated as the current time, for example, 8:00 AM. The sludge purification system then retrieves the latest batch of sludge composition datasets and process status datasets generated at that time from the real-time database or cache. These two sets of data together constitute the initial input conditions for this rolling optimization calculation.

[0102] S222. Based on the current sludge composition dataset and process state dataset, the dynamic mechanism model of sludge drying and sintering process is invoked to simulate the process under different control input sequences in the prediction time domain, generating a multi-step forward state prediction dataset.

[0103] In this embodiment of the invention, the sludge purification system uses the acquired current-moment data as the initial state of the dynamic mechanism model. This model consists of a set of equations describing dewatering, pyrolysis, and sintering reactions. Within a set prediction time domain, such as the next 30 minutes, the sludge purification system performs numerical simulations on various preset control input sequences in minute increments. Each simulation generates a predicted trajectory of the key process state changing over time. The state trajectories obtained from all simulations are then aggregated to obtain a multi-step forward state prediction dataset.

[0104] S223. Based on the multi-step forward state prediction dataset, and in accordance with the multi-objective optimization problem, optimization calculations are performed in the control time domain to obtain the optimal control sequence in the current rolling time domain.

[0105] In this embodiment of the invention, the optimization algorithm uses the generated multi-step forward state prediction dataset as the evaluation basis. Within the control time domain, for example, the next 10 minutes, the algorithm searches for different control input sequences and calculates whether the prediction result corresponding to each sequence satisfies the objectives of maximizing intensity and minimizing energy consumption, while also adhering to the constraint of heavy metal leaching rate. Through iterative calculation, a comprehensively optimal solution is finally selected, and the control setpoint value for each minute within the next 10 minutes represented by this solution is extracted as the optimal control sequence for the current rolling time domain.

[0106] S224. Extract the first control variable of the optimal control sequence as an immediate control instruction and output it to the corresponding execution unit of the sludge purification system. At the same time, store the current optimal control sequence as a candidate solution fragment.

[0107] In this embodiment of the invention, the sludge purification system extracts the control command corresponding to the first time step from the obtained optimal control sequence, such as the target temperature value of the drying zone one minute later, and sends it to the PLC or actuator on site in real time via the industrial network. At the same time, the entire optimal control sequence obtained in this calculation is marked with a start timestamp and stored as an independent candidate solution fragment in the cache or database of the sludge purification system.

[0108] S225. Advance the rolling time domain forward by one control cycle, update the current time, and obtain the updated process state dataset.

[0109] In this embodiment of the invention, after waiting for a preset control period, such as two minutes, the logic clock of the sludge purification system updates the current time to the new time point. Subsequently, the sludge purification system immediately collects data from the field sensors to generate a process state dataset reflecting the actual operating state of the system at the latest moment, which is used to refresh the initial conditions for the next round of optimization.

[0110] S226. Determine whether the current time exceeds the total processing time of the batch. If not, proceed to step S227; if so, proceed to step S228.

[0111] In this embodiment of the invention, the sludge purification system compares the updated current time with the total duration of the planned production time for this batch. If the current time is still within the planned production time range, the process proceeds to step S227 to continue optimizing the cycle; if the current time has reached or exceeded the planned total duration, it means that the batch production has ended, and the process proceeds to step S228 for final data integration.

[0112] S227. Jump to execute the steps based on the sludge composition dataset and process state dataset at the current moment, call the dynamic mechanism model of the sludge drying and sintering process, simulate the process under different control input sequences in the prediction time domain, and generate a multi-step forward state prediction dataset.

[0113] In this embodiment of the invention, when it is determined that the batch has not ended, the program logic will automatically jump back to the beginning of step S222. At this time, the sludge purification and treatment system will use the current time and the corresponding latest dataset updated in step S225 to start a new round of model prediction and optimization calculation, thereby forming a cyclical adaptive control closed loop.

[0114] S228. Integrate all stored candidate solution fragments in chronological order to construct a candidate process parameter solution set.

[0115] In this embodiment of the invention, when the batch is determined to be over, the sludge purification system reads all candidate solution fragments generated and saved during the production of this batch from the storage medium. Based on the timestamp carried by each fragment, the sludge purification system strictly arranges them in order from earliest to latest, and seamlessly splices together the control sequence data contained therein, ultimately forming a coherent process parameter trajectory covering the entire batch production cycle, which is the completed candidate process parameter solution set.

[0116] Step 104: Based on the optimized process parameter set, dynamically adjust the running instructions of the corresponding execution units in the sludge purification and treatment system to generate a process control instruction set.

[0117] Furthermore, step 104 may include the following sub-steps:

[0118] S31. Decompose the drying temperature curve sequence of the optimized process parameters into segmented temperature setpoints for each heating zone of the drying reactor, and convert the optimized blending ratio of sludge and substrate into the batching flow rate setpoints for each raw material silo in the mixing and batching device.

[0119] In this embodiment of the invention, the central controller receives an optimized set of process parameters. For the drying temperature curve sequence, the controller, based on the length ratio of the three heating zones of the drying reactor and the heat load distribution coefficient, decomposes the time-temperature curve values ​​at each sampling moment into three independent, time-varying segmented temperature setpoint sequences. For the optimized blending ratio, the controller, combining the real-time bulk density of sludge and clay with the total processing capacity of the mixing and batching device, calculates the instantaneous feed flow rate setpoints for the sludge screw pump and the clay metering belt scale, thereby converting the optimized blending ratio of sludge and substrate into the batching flow rate setpoints for each raw material silo in the mixing and batching device.

[0120] S32. Based on the actual operating status of each execution unit in the process status data set, perform physical constraint verification and dynamic limiting on the segmented temperature setpoint and batching flow setpoint, and generate segmented temperature adjustment value and batching flow adjustment value.

[0121] In this embodiment of the invention, the system reads the process status dataset in real time, including the measured temperature of each heating zone, the opening degree of the gas valve, and the load current of the feed motor. The system compares the segmented temperature setpoint with the maximum allowable safe heating rate, limits the slope of sections with excessively rapid temperature rise, and generates stable segmented temperature adjustment values. Simultaneously, the system compares the batching flow rate setpoint with the equipment's maximum processing capacity and the current material conveying upper limit, and lowers the limit for values ​​exceeding the limit, generating practically executable batching flow rate adjustment values.

[0122] S33. Perform multi-objective coordination optimization on conflicting setpoints in the segmented temperature adjustment value and the batching flow rate adjustment value to generate conflict-free coordinated control instructions. The coordinated control instructions include the temperature control instruction sequence of each heating zone in the drying reactor and the flow control instruction sequence of each raw material silo in the mixing and batching device.

[0123] In this embodiment of the invention, the sludge purification system identifies potential command conflicts. For example, if the system detects that the instantaneous total power demand of the three heating zones is exceeded if the temperature regulation values ​​of the three heating zones are executed simultaneously, the system will activate a fast linear programming solver to minimize the overall deviation between the actual execution values ​​of each execution unit and the regulation values ​​generated by S32. Constrained by the total power limit and the independent limits of each unit, a new set of setpoints is recalculated. The final output includes a coordinated temperature control command sequence for each heating zone of the drying reactor and a coordinated flow control command sequence for each raw material silo in the mixing and batching device, which together constitute conflict-free coordinated control commands.

[0124] S34. Based on the process timing logic of the sludge purification and treatment system, the control instructions in the coordination control instructions are arranged according to the preset execution priority and timing relationship to generate an atomic operation instruction set.

[0125] In this embodiment, the system arranges the coordination control instructions according to the preset process knowledge base, assigns an execution timestamp and priority to each instruction, and decomposes continuous instructions into discrete atomic operations with time stamps, and finally organizes them into an atomic operation instruction set in an orderly manner according to time and priority.

[0126] S35. Encapsulate the atomic operation instruction set according to the preset industrial control protocol to generate the process control instruction set.

[0127] In this embodiment, the host computer selects the corresponding protocol according to the target controller type, encodes the atomic operation instructions according to the protocol frame format, fills in the target address, function code, register address and control parameters, adds batch identifier and serial number, packages it into a complete process control instruction set, and sends it to the field control station for execution through the industrial network.

[0128] Step 105: Based on the process control instruction set and process status dataset, drive the drying reactor and mixing and batching device in the sludge purification system to perform adaptive purification treatment, record relevant operation and status data, and generate a control log dataset.

[0129] Furthermore, step 105 may include the following sub-steps:

[0130] S41. Parse the process control instruction set into the segmented temperature setting curve of the drying reactor and the dynamic proportioning setting sequence of the mixing and batching device in the sludge purification and treatment system.

[0131] In this embodiment of the invention, the field control station (e.g., a PLC) of the sludge purification system receives a set of process control instructions from the host computer. The control station first unpacks the data according to a predefined communication protocol (e.g., Modbus TCP) and extracts the payload data. Then, based on the instruction type identifier in the data header, the data stream is reconstructed into two independent time series: one is a segmented temperature setpoint curve containing timestamps and temperature value pairs, used to control the three heating zones of the drying reactor; the other is a dynamic proportioning setpoint sequence containing timestamps and instantaneous flow rate setpoints for the mixing and batching device.

[0132] S42. Based on the moisture content of the material in the drying section and the oxygen concentration in the kiln, which are centrally reflected in the process status data, the segmented temperature setting curve is dynamically corrected to generate a safe temperature control curve.

[0133] In this embodiment of the invention, the system reads the material moisture content fed back by the microwave moisture meter in the drying section and the oxygen concentration in the kiln fed back by the flue gas analyzer in real time from the process status dataset. The system embeds a safety correction rule library: when the material moisture content is detected to be higher than a certain threshold (e.g., 50%), the system automatically reduces the heating rate setpoint for the corresponding time window to avoid thermal load shock; when the oxygen concentration in the kiln is detected to be lower than the safety limit (e.g., 8%), the system slightly increases the current temperature setpoint to promote complete combustion. Based on these rules, the system performs real-time and smooth corrections to each future setpoint of the segmented temperature setpoint curve, ultimately outputting a safe temperature control curve that balances optimization objectives with actual operating safety.

[0134] S43. Based on the material level height and material viscosity in the mixing bin as centrally reflected in the process status data, adjust the execution timing of the dynamic proportion setting sequence to generate an anti-blocking material proportion control sequence.

[0135] In this embodiment of the invention, the system monitors the material level height fed back by the radar level gauge in the centralized mixing bin and the material viscosity fed back by the online viscometer (or calculated by motor torque) in real time. The system incorporates anti-clogging logic: when the material level is below the minimum safe level, new instructions to increase sludge feeding are temporarily suspended, and base material is added first to raise the material level; when the material viscosity exceeds a set threshold, the batching cycle is automatically extended, and a "pulse-type" feeding strategy is adopted, i.e., feeding is paused after a short period, and then resumed after the material has flowed, thereby avoiding compaction blockage in the pipeline. Based on these logics, the system rearranges the execution time and duration of each instruction in the dynamic proportioning setting sequence to generate an anti-clogging material proportioning control sequence that can adapt to the material state and effectively prevent blockage.

[0136] S44. Simultaneously execute the safety temperature control curve and the anti-clogging material ratio control sequence, and collect the drying flue gas temperature, batching motor current and equipment vibration data in real time as execution process status data.

[0137] In this embodiment of the invention, the controller of the sludge purification system sends the safe temperature control curve to the temperature control systems of each zone of the drying reactor, and sends the anti-clogging material ratio control sequence to the flow controller of the mixing and batching device. Both are started and executed synchronously according to a unified time base. Simultaneously, the system collects data in real time from three aspects at a frequency of not less than 10Hz: 1) the temperature of the drying flue gas collected from the thermocouple at the outlet of the drying reactor; 2) the current of the batching motor collected from the frequency converters of the drive motors of the sludge screw pump and the clay metering belt scale; 3) the vibration data (including vibration velocity and spectrum) of the equipment (such as the stirring shaft and the induced draft fan) collected from the acceleration sensors of key rotating equipment. These real-time collected data collectively constitute the execution process status data reflecting the instantaneous effect of the command execution.

[0138] S45. Align and correlate the safe temperature control curve, the anti-clogging material ratio control sequence and the corresponding execution process status data with the sludge composition dataset and process status dataset at the current moment in time and space to generate a control log dataset.

[0139] In this embodiment of the invention, the system creates a structured log recording framework. At the end of each new control cycle (e.g., 1 minute), the system performs the following operations: First, based on the timestamp provided by the High Precision Network Time Protocol (NTP) server, it extracts all data generated within that cycle. Then, it packages the safe temperature control curve segment, the anti-clogging material ratio control sequence segment, and the synchronously acquired execution process status data of that cycle, along with a snapshot of the sludge composition dataset at the start of the cycle and the process status dataset for the entire cycle. Each log record contains a unique batch ID and a time range label, and all the aforementioned data dimensions are linked through a relational key between data tables. Finally, multiple such records are arranged in chronological order to generate the control log dataset.

[0140] Please see Figure 2 , Figure 2 This is a structural block diagram of an electronic device provided in an embodiment of the present invention.

[0141] An electronic device according to an embodiment of the present invention includes: a memory 201 and a processor 202. The memory 201 stores a computer program. When the computer program is executed by the processor 202, the processor 202 performs a purification treatment method for sludge brick making as described in any of the above embodiments.

[0142] Memory 201 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 201 has storage space 203 for program code 213 for performing any of the method steps described above. For example, storage space 203 for program code may include individual program codes 213 for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When these codes are run by a computing device, the device causes it to perform the various steps in the purification treatment method for sludge brick making described above.

[0143] This invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the purification treatment method for sludge brick making as described in any of the above embodiments.

[0144] This invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the purification treatment method for sludge brick making as described in any of the above embodiments.

[0145] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A purification treatment method for sludge in brick making, characterized in that, include: By using an online component detection device installed in the sludge purification and treatment system, real-time component data of the sludge stream to be treated is collected, and a sludge component dataset is generated. Real-time acquisition of status feedback data of key process nodes in the sludge purification and treatment system to generate process status dataset; Based on the sludge composition dataset, with the compressive strength of finished bricks, the comprehensive energy consumption of the system, and the heavy metal leaching rate as constraints, a multivariate collaborative optimization solution is performed to generate an optimized process parameter set. Based on the optimized process parameter set, the operating instructions of the corresponding execution units in the sludge purification and treatment system are dynamically adjusted to generate a process control instruction set; Based on the process control instruction set and the process status dataset, the drying reactor and mixing and batching device in the sludge purification system are driven to perform adaptive purification treatment, and relevant operation and status data are recorded to generate a control log dataset.

2. The purification treatment method for sludge brick making according to claim 1, characterized in that, The online component detection device installed in the sludge purification and treatment system includes a multispectral imaging sensor, a laser-induced breakdown spectroscopy probe, a data processing unit, an online pH sensor, and an online conductivity sensor; the step of collecting real-time component data of the sludge stream to be treated and generating a sludge component dataset through the online component detection device installed in the sludge purification and treatment system includes: The multispectral imaging sensor continuously images the sludge flow on the sludge conveyor belt of the sludge purification system to generate an original spectral image sequence. The data processing unit performs noise filtering, image registration, and feature region segmentation on the original spectral image sequence to extract the texture feature parameters and color distribution parameters of the sludge. Based on the texture feature parameters and the color distribution parameters, combined with the synchronously collected sludge temperature and flow velocity data, the data processing unit calculates the estimated moisture content and estimated organic matter content. The sludge flow is excited and plasma emission spectra are collected by the laser-induced breakdown spectral probe, and the characteristic spectral intensity data of heavy metal elements are obtained by the data processing unit. The pH value and conductivity of the sludge flow are simultaneously measured by the online pH sensor and the online conductivity sensor to generate corresponding real-time measurement data; The data processing unit fuses the characteristic spectral intensity data, the real-time measurement data, the moisture content prediction data, and the organic matter content prediction data from multiple sources to generate a sludge composition dataset.

3. The purification treatment method for sludge brick making according to claim 2, characterized in that, The step of calculating the estimated moisture content and estimated organic matter content based on the texture feature parameters and color distribution parameters, combined with synchronously collected sludge temperature and flow velocity data, through the data processing unit includes: Using the texture feature parameters and color distribution parameters as the core, the synchronously collected sludge temperature and flow rate data are integrated, and weighted combination and nonlinear transformation are performed to generate a high-order feature vector. Perform multi-layer perceptron computation on the high-order feature vector, extract features layer by layer and reduce dimensionality, and output a set of abstract features characterizing the core physical properties of sludge; Based on the abstract feature set, the moisture content regression value and the organic matter content regression value are respectively parsed through independent regression calculation paths; The regression values ​​of moisture content and organic matter content are subjected to confidence interval verification and moving average filtering based on historical data distribution to generate estimated moisture content data and estimated organic matter content data.

4. The purification treatment method for sludge brick making according to claim 1, characterized in that, The step of performing multivariate collaborative optimization based on the sludge composition dataset, with the compressive strength of finished bricks, the overall energy consumption of the system, and the heavy metal leaching rate as constraints, to generate an optimized process parameter set includes: Using the sludge composition dataset as input, a multi-objective optimization problem is constructed with the goal of maximizing the predicted compressive strength of finished bricks and minimizing the predicted comprehensive energy consumption of the system, and with the constraint that the heavy metal leaching rate does not exceed a threshold. Based on the dynamic mechanism model of sludge drying and sintering process, a model predictive control framework is used to solve the multi-objective optimization problem in the rolling time domain to generate a set of candidate process parameter solutions. From the solution set of the candidate process parameters, key decision variables characterizing the optimal process path are extracted to generate a core decision variable set, which includes the drying temperature curve sequence, sintering holding time, and the optimized blending ratio of sludge and substrate. The core decision variable set is subjected to process feasibility and operational boundary verification to generate an optimized process parameter set.

5. The purification treatment method for sludge brick making according to claim 4, characterized in that, The dynamic mechanism model based on the sludge drying and sintering process, employing a model predictive control framework to perform rolling time-domain solution of the multi-objective optimization problem and generate a candidate process parameter solution set, includes the following steps: Set the current time as the starting point of the rolling time domain, and obtain the sludge composition dataset and process state dataset at the current time; Based on the current sludge composition dataset and process state dataset, the dynamic mechanism model of sludge drying and sintering process is invoked to simulate the process under different control input sequences in the prediction time domain, generating a multi-step forward state prediction dataset. Based on the multi-step forward state prediction dataset, and in accordance with the multi-objective optimization problem, optimization calculations are performed in the control time domain to obtain the optimal control sequence in the current rolling time domain. The first control variable of the optimal control sequence is extracted as an immediate control command and output to the execution unit corresponding to the sludge purification system. At the same time, the current optimal control sequence is stored as a candidate solution fragment. The rolling time domain is advanced by one control cycle, the current time is updated, and the updated process state dataset is obtained. Determine if the current time has exceeded the total processing time for the batch; If not, then proceed to execute the sludge composition dataset and process state dataset based on the current time, call the dynamic mechanism model of the sludge drying and sintering process, simulate the process under different control input sequences in the prediction time domain, and generate a multi-step forward state prediction dataset. If so, all stored candidate solution fragments will be integrated in chronological order to construct a candidate process parameter solution set.

6. The purification treatment method for sludge brick making according to claim 4, characterized in that, The step of dynamically adjusting the operating instructions of the corresponding execution units in the sludge purification system based on the optimized process parameter set to generate a process control instruction set includes: The drying temperature curve sequence in the optimized process parameter set is decomposed into segmented temperature set values ​​for each heating zone of the drying reactor, and the optimized blending ratio of sludge and base material is converted into the batching flow rate set value for each raw material silo in the mixing and batching device. Based on the actual operating status of each execution unit in the process status dataset, physical constraint verification and dynamic limiting are performed on the segmented temperature setpoint and the batching flow rate setpoint to generate segmented temperature adjustment value and batching flow rate adjustment value. Multi-objective coordination optimization is performed on conflicting setpoints in the segmented temperature adjustment value and the batching flow rate adjustment value to generate conflict-free coordinated control instructions. The coordinated control instructions include temperature control instruction sequences for each heating zone of the drying reactor and flow rate control instruction sequences for each raw material silo in the mixing and batching device. Based on the process timing logic of the sludge purification and treatment system, the control instructions in the coordination control instructions are arranged according to the preset execution priority and timing relationship to generate an atomic operation instruction set. The atomic operation instruction set is encapsulated according to a preset industrial control protocol to generate a process control instruction set.

7. The purification treatment method for sludge brick making according to claim 1, characterized in that, The step of driving the drying reactor and mixing and batching device in the sludge purification system to perform adaptive purification treatment based on the process control instruction set and the process status dataset, and recording relevant operation and status data to generate a control log dataset includes: The process control instruction set is parsed into the segmented temperature setting curve of the drying reactor and the dynamic proportioning setting sequence of the mixing and batching device in the sludge purification and treatment system. Based on the moisture content of the material in the drying section and the oxygen concentration in the kiln as reflected in the process status data, the segmented temperature setting curve is dynamically corrected to generate a safe temperature control curve. Based on the material level height and material viscosity in the mixing bin as centrally reflected in the process status data, the execution timing of the dynamic proportioning setting sequence is adjusted to generate an anti-clogging material proportioning control sequence; The safe temperature control curve and the anti-clogging material ratio control sequence are executed synchronously, and the drying flue gas temperature, batching motor current and equipment vibration data are collected in real time as execution process status data. The safe temperature control curve, the anti-clogging material ratio control sequence, and the corresponding execution process status data are spatiotemporally aligned and correlated with the sludge composition dataset and process status dataset at the current moment to generate a control log dataset.

8. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the purification treatment method for sludge brick making as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the purification treatment method for sludge brick making as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the purification treatment method for sludge brick making as described in any one of claims 1-7.

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