A sludge solidification treatment method and device, electronic equipment and medium

Abstract

The present application provides a kind of sludge solidification treatment method, device, electronic equipment and medium, method includes obtaining the first monitoring data of sludge solidification treatment equipment;The first monitoring data is input into the monitoring data prediction model trained, to predict the second monitoring data of a period of time in the future, the second monitoring data is input into multi-objective optimization model, and the control parameter of sludge solidification treatment equipment is output;Based on the control parameter, the operation of sludge solidification treatment equipment is controlled.The present application can solve the problem of low sludge solidification efficiency caused by the dependence of existing scheme on historical data to match solidification scheme and the small amount of target optimization.

Description

Technical Field

[0001] This invention relates to the field of sludge solidification treatment technology, and in particular to a sludge solidification treatment method, apparatus, electronic equipment and medium. Background Technology

[0002] Sludge solidification technology, as a core method for achieving sludge reduction and stabilization, has been widely applied in various fields such as river and lake dredging, port dredging, and industrial sludge treatment. For example, in the municipal wastewater treatment industry, sludge solidification equipment effectively reduces the harmfulness of sludge generated from domestic sewage and industrial wastewater, facilitating subsequent treatment and disposal. In the industrial wastewater treatment industry, suitable solidifying agents are selected based on the characteristics of chemical sludge to fix harmful substances in the sludge within the solidified body, preventing their re-release into the environment during subsequent disposal. By adding sludge solidification agents and adjusting the treatment process, it reduces the moisture content of sludge, improves its mechanical strength, and fixes harmful substances such as heavy metals, making it a key link in the resource utilization of sludge (such as roadbed filling and land improvement). Traditional sludge solidification technology mainly relies on experience to set the solidifying agent ratio (such as the dosage of cementitious materials such as cement and fly ash) and treatment parameters (such as mixing time and curing cycle), forming a treatment process of "component detection - experience-based ratio - solidification construction". Some technologies are beginning to explore using machine learning to optimize curing parameters or using intelligent sensing systems to control the dosage of chemicals, thereby improving curing efficiency to some extent.

[0003] However, existing sludge solidification technologies still face the following common problems: Insufficient dynamic adaptability: The composition of silt from different sources (such as river and lake silt, industrial sludge, and marine silt) varies significantly. Existing methods mostly rely on historical data to match solidification schemes, lacking the ability to intelligently respond to real-time fluctuations in composition, which can easily lead to substandard mechanical properties or waste of reagents after solidification.

[0004] The lack of multi-objective optimization: In the solidification process, in addition to improving strength, it is also necessary to ensure that the leaching concentration of heavy metals such as lead is below 5 mg / L to meet environmental standards, while controlling costs. Traditional technologies often only focus on improving a single performance, such as strength, while modern solidification technologies, such as HT sludge solidification technology, through the use of modifiers, can not only improve the chemical and physical properties of sludge in a short period of time, but also effectively reduce the leaching toxicity of heavy metals and improve load-bearing capacity and permeability.

[0005] The processing flow is fragmented: each step, such as the preparation of curing agent, mixing, curing and molding, is mostly controlled independently, lacking a data linkage mechanism for the entire process (for example, changes in moisture content are not fed back to the agent dosing system in real time), and manual intervention still dominates (for example, adjusting curing time based on experience), resulting in a relatively low level of automation and intelligence. Summary of the Invention

[0006] This invention provides a method, apparatus, electronic device, and medium for solidifying sludge, which solves the problems of existing solutions relying on historical data to match solidification schemes and low sludge solidification efficiency caused by insufficient target optimization.

[0007] According to one aspect of the present invention, a method for solidifying sludge is provided, comprising: Obtain the first monitoring data from the sludge solidification treatment equipment; The first monitoring data is input into the trained monitoring data prediction model to predict the second monitoring data for a future period of time. The second monitoring data is then input into the multi-objective optimization model to output the control parameters of the sludge solidification treatment equipment. The operation of the sludge solidification treatment equipment is controlled based on the aforementioned control parameters.

[0008] Optionally, after acquiring the first monitoring data from the sludge solidification treatment device, the method further includes preprocessing the first monitoring data. The preprocessing includes at least data cleaning, filtering, and normalization of the first monitoring data.

[0009] Optionally, both the first monitoring data and the second monitoring data include at least the moisture content of the sludge at the mixer inlet, the heavy metal ion concentration and viscosity of the sludge at the outlet of the mixing reaction tank in the mixer, the temperature and pH value of the curing agent addition point, the torque of the mixer main shaft, and the pressure of the press. The control parameters include at least the curing agent ratio, the mixer speed, and the press pressure.

[0010] Optionally, before inputting the first monitoring data into the trained monitoring data prediction model to predict the second monitoring data for a future period, and inputting the second monitoring data into the multi-objective optimization model to output the control parameters of the sludge solidification treatment equipment, the method further includes: Obtain historical monitoring data for various types of silt; The historical monitoring data is preprocessed, and a dataset is constructed based on the preprocessed historical monitoring data; The monitoring data prediction model and the multi-objective optimization model are trained based on the constructed dataset to obtain the trained monitoring data prediction model and the multi-objective optimization model.

[0011] Optionally, training the monitoring data prediction model and the multi-objective optimization model based on the constructed dataset includes: The input to the monitoring data prediction model is historical monitoring data within a first time period, and the output is predicted monitoring data within a second time period, which is later than the first time period. The multi-objective optimization model calculates control parameters based on the objective function, constraints, a preset parameter selection strategy, and the predicted monitoring data, and outputs the control parameters. The multi-objectives of the multi-objective optimization model include at least minimizing moisture content deviation, minimizing heavy metal exceedance penalties, and minimizing costs. The constraints include at least pressing machine pressure constraints, mixer torque constraints, and reaction environment constraints. A reward function is set for the multi-objective optimization model to continuously optimize the parameters of the multi-objective optimization model.

[0012] Optionally, the moisture content deviation is minimized as follows:

[0013] Among them, H 实测 H represents the actual moisture content measured after the sludge has solidified. 目标 The preset target moisture content; The penalty for exceeding the heavy metal limit is minimized as follows: (Unit: mg / L) Among them, C Pb C represents the lead concentration in the solidified sludge, where a is the lead concentration threshold. Cd denoted as , and b as the cadmium concentration threshold in the solidified sludge. The cost minimization:

[0014] Where x1, x2, and x3 are the dosages of cement, fly ash, and chelating agent, p1, p2, and p3 are the unit prices of cement, fly ash, and chelating agent, the equipment energy consumption cost is calculated based on the pressing pressure p and time t, and c is the equipment energy consumption cost coefficient. The reward function is:

[0015] In the formula, d is the compliance rate weighting coefficient, e is the compliance rate threshold; f is the cost saving rate weighting coefficient, g is the cost saving rate threshold; and h is the equipment load rate coefficient. Among them, the equipment load rate is:

[0016] , , These are the torque load factor, pressure load factor, and energy load factor, respectively.

[0017] Optionally, the preset parameter filtering strategy includes a priority strategy and a cumulative solution filtering strategy; The priority strategy assigns different weight coefficients to different objective functions based on the different priorities of different modes; that is, when the environmental protection mode is prioritized, the function that minimizes the penalty for exceeding the heavy metal limit is assigned the largest weight coefficient; when the cost mode is prioritized, the function that minimizes the cost is assigned the largest weight coefficient.

[0018] According to another aspect of the present invention, a sludge solidification treatment apparatus is provided, comprising: The data acquisition unit is used to acquire the first monitoring data from the sludge solidification treatment equipment; The parameter calculation unit is used to input the first monitoring data into the trained monitoring data prediction model to predict the second monitoring data for a future period of time, input the second monitoring data into the multi-objective optimization model, and output the control parameters of the sludge solidification treatment equipment. The control unit is used to control the operation of the sludge solidification treatment equipment based on the control parameters. According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the sludge solidification treatment method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the sludge solidification treatment method according to any embodiment of the present invention.

[0020] The technical solution of this invention obtains real-time first monitoring data of the sludge solidification treatment equipment as input, and outputs control parameters of the sludge solidification treatment equipment, including the solidifying agent ratio. This enables intelligent response to real-time component fluctuations, avoiding waste of solidifying agent and poor solidification effect. By inputting the first monitoring data into a trained monitoring data prediction model, and using the predicted second monitoring data for a future period, the second monitoring data is input into a multi-objective optimization model, outputting the control parameters of the sludge solidification treatment equipment. Thus, by focusing on multiple sludge solidification performance indicators, the control parameters of the sludge solidification equipment can be generated, achieving global optimization.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a sludge solidification treatment method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a sludge solidification treatment method according to Embodiment 2 of the present invention; Figure 3 This is a structural diagram of a sludge solidification treatment device provided according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the sludge solidification treatment method of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1 Figure 1This is a flowchart illustrating a sludge solidification treatment method according to Embodiment 1 of the present invention. Figure 1 As shown, the method includes: S101. Obtain the first monitoring data from the sludge solidification treatment equipment.

[0027] It should be noted that sludge solidification equipment generally includes a mixer, a conveyor belt, and a press. In the process of solidifying sludge, the raw sludge is first transported to the mixer via a conveyor belt. Then, the sludge is mixed with a solidifying agent in the mixing chamber, and then transported by the conveyor belt to the press, where the sludge is dehydrated and solidified, ultimately yielding solidified mud blocks. In this embodiment, a moisture content sensor can be installed at the inlet of the conveyor belt to measure the moisture content entering the mixer; a heavy metal ion electrode can be installed at the outlet of the mixing reaction tank in the mixer to measure the concentration of heavy metal ions in the sludge; a pressure sensor can be installed in the hydraulic cylinder of the press to collect pressure signals; temperature and pH sensors can be installed at the addition point of the curing agent to monitor the temperature and pH data at the addition point; and a torque sensor can be installed on the main shaft of the mixer to monitor the mixing torque of the mixer.

[0028] The first monitoring data obtained from the sludge solidification treatment equipment may include sensor data from various parts of the equipment, real-time monitoring data of the sludge input into the equipment, and environmental data from various parts of the equipment. Furthermore, the first monitoring data is time-series data over a period of time, meaning it can be discrete point data, such as sensor data or environmental data that varies over time.

[0029] S102. Input the first monitoring data into the trained monitoring data prediction model, and input the predicted second monitoring data for a future period into the multi-objective optimization model to output the control parameters of the sludge solidification treatment equipment.

[0030] In this embodiment, the monitoring data prediction model can predict time-series data for a future period based on the input time-series data. That is, when the input is the first monitoring data, the predicted second monitoring data is the monitoring data for a period of time after the time period of the first monitoring data. For example, if the first monitoring data is the monitoring data of the sludge solidification treatment equipment from 0 to 6, then the second monitoring data is the monitoring data from 6 to 7 predicted based on the first monitoring data.

[0031] In this embodiment, the multi-objective optimization model may include multiple objective functions to find a set of optimal solutions that balance these objectives when there are multiple conflicting objectives. Unlike single-objective optimization, multi-objective optimization cannot find an "absolutely optimal solution" that simultaneously optimizes all objectives. Instead, it yields a set of non-dominated solutions called "Pareto optimal solutions," where improvement in any objective requires sacrificing other objectives.

[0032] Specifically, the objective function of the multi-objective optimization model is related to the monitoring data of the sludge in the sludge solidification equipment and the ratio of the solidifying agent. The constraints of the multi-objective optimization model are physical limitations related to the sludge solidification equipment itself, or sludge-related limitations, such as the maximum pressure that the press of the sludge solidification equipment can provide, the minimum pressure required for solidification, and the torque of the mixer. The multi-objective optimization model can obtain the corresponding Pareto optimal solution based on the objective function and constraints, that is, it can obtain the ratio of the solidifying agent and the operating parameters of the equipment (press pressure and mixer torque, etc.).

[0033] Specifically, in this embodiment, both the first monitoring data and the second monitoring data include at least the moisture content of the sludge at the mixer inlet, the heavy metal ion concentration and viscosity of the sludge at the outlet of the mixing reaction tank in the mixer, the temperature and pH value of the curing agent addition point, the torque of the mixer main shaft, and the pressure of the press.

[0034] S103. Control the operation of the sludge solidification treatment equipment based on the control parameters.

[0035] In this embodiment, the control parameters may include the curing agent ratio (cement / fly ash / chelating agent dosage) and equipment control parameters (mixer torque, press pressure, curing time (the time it takes for the solidified mud cake formed after the sludge has been mixed, pressed and dehydrated, to be placed in a specific environment (temperature, humidity) and complete the curing reaction)). Once the control parameters are obtained, the system will convert them into corresponding control commands to adjust the curing agent ratio, or to adjust the mixer torque, press pressure, and curing time in the sludge solidification treatment equipment.

[0036] The technical solution of this invention obtains real-time first monitoring data of the sludge solidification treatment equipment as input, and outputs control parameters of the sludge solidification treatment equipment, including the solidifying agent ratio. This enables intelligent response to real-time component fluctuations, avoiding waste of solidifying agent and poor solidification effect. By inputting the first monitoring data into a trained monitoring data prediction model, and using the predicted second monitoring data for a future period, the second monitoring data is input into a multi-objective optimization model, outputting the control parameters of the sludge solidification treatment equipment. Thus, by focusing on multiple sludge solidification performance indicators, the control parameters of the sludge solidification equipment can be generated, achieving global optimization.

[0037] Example 2 Figure 2 This is a flowchart of a sludge solidification treatment method provided in Embodiment 2 of the present invention. Figure 2 As shown, the method also includes: S201. Obtain historical monitoring data for various types of silt.

[0038] This embodiment can acquire historical monitoring data of various types of silt, such as historical detection data of river silt and industrial sludge, thus ensuring the diversity of sample data in the constructed dataset. Historical monitoring data of silt includes parameters such as moisture content (generally 20% to 80%), heavy metal concentration (generally 0-10 mg / L), and viscosity (1-5000 cP).

[0039] S202. Preprocess the historical monitoring data and construct a dataset based on the preprocessed historical monitoring data.

[0040] To make the prediction model based on the trained monitoring data as accurate as possible, historical monitoring data can be preprocessed.

[0041] In this embodiment, preprocessing includes at least data cleaning, filtering, and normalization of the monitoring data. For example, when cleaning historical monitoring data, negative values ​​or values ​​exceeding the historical normal range of ±10% for moisture content are directly discarded. During the filtering process, pressure data is denoised using a Kalman filter algorithm, while heavy metal data is processed using a moving average filter. Finally, all monitoring data are normalized using the Z-Score method, where the mean μ and standard deviation σ are dynamically updated based on actual conditions.

[0042] S203. Based on the constructed dataset, the monitoring data prediction model and the multi-objective optimization model are trained to obtain the trained monitoring data prediction model and the multi-objective optimization model.

[0043] The input to the monitoring data prediction model is historical monitoring data within a first time period, and the output is predicted monitoring data within a second time period, which is later than the first time period. The multi-objective optimization model calculates control parameters based on the objective function, constraints, a preset parameter selection strategy, and the predicted monitoring data, and outputs the control parameters. The multi-objectives of the multi-objective optimization model include at least minimizing moisture content deviation, minimizing heavy metal exceedance penalties, and minimizing costs. The constraints include at least pressing machine pressure constraints, mixer torque constraints, and reaction environment constraints. A reward function is set for the multi-objective optimization model to continuously optimize the parameters of the multi-objective optimization model.

[0044] In this embodiment, the monitoring data prediction model can adopt an LSTM time series prediction model. Its input can be 24-hour monitoring time series data with a sampling interval of 10 minutes. The feature dimensions include three items: moisture content, Pb, etc. 2+ Concentration and viscosity; the output can be the trend of change in the next hour, i.e., prediction step size = 6, the model structure adopts 2-layer LSTM (64 units) + Dropout (0.2).

[0045] The multi-objective optimization model can adopt the NSGA-II multi-objective optimization model. The input data for the NSGA-II multi-objective optimization model includes at least the real-time composition of the sludge (moisture content, heavy metal concentration, viscosity), the real-time status of the treatment equipment (stirring torque, pressing pressure), and the treatment objectives (moisture content threshold, heavy metal emission standards, cost budget). The output is the optimal reagent ratio (cement / fly ash / chelating agent dosage) and equipment parameters (stirring speed, pressing pressure, curing time). The objective function of the NSGA-II multi-objective optimization model includes: Minimize the moisture content deviation as follows:

[0046] Among them, H 实测 H represents the actual moisture content measured after the sludge has solidified. 目标 The target moisture content is the preset value. This objective function measures the deviation between the actual moisture content and the target moisture content; the smaller the deviation, the closer the moisture content is to the target value.

[0047] The penalty for exceeding the heavy metal limit is minimized as follows: (Unit: mg / L) Among them, C Pb The concentration of lead in the solidified sludge is given by 'a', where 'a' is the lead concentration threshold, typically set to 5 mg / L; C CdLet f1 be the concentration of cadmium in the solidified sludge, and b be the cadmium concentration threshold, which can generally be set to 0.3 mg / L. If the lead concentration exceeds 5 mg / L or the cadmium concentration exceeds 0.3 mg / L, the objective function f2 will produce a value greater than 0, and the magnitude of this value directly reflects the severity of heavy metal contamination. If the concentration does not exceed the standard value, this term is 0. This objective function is used to penalize heavy metal contamination and encourage the treatment process to minimize the risk of heavy metal leaching.

[0048] Cost minimization:

[0049] Where x1, x2, and x3 represent the dosages of cement, fly ash, and chelating agent, respectively; p1, p2, and p3 represent the unit prices of cement, fly ash, and chelating agent, respectively; equipment energy consumption cost is calculated based on pressing pressure p and time t; and c is the equipment energy consumption cost coefficient, which can be set to 0.1. This objective function f3 comprehensively considers both reagent costs and equipment energy consumption costs, striving to minimize the total cost of the entire treatment process.

[0050] The constraints of the NSGA-II multi-objective optimization model include at least the following: molding pressure ≥ 2 MPa (pressure sensor provides real-time feedback to ensure the compressive strength of the cured body meets the standard); stirring torque ≤ 150 N·m (to prevent motor overload); press pressure ≤ 5 MPa (equipment rated threshold); reaction environment constraint: pH ∈ [7, 10] (to ensure reagent activity, monitored in real-time by pH sensor).

[0051] Furthermore, in this embodiment, the parameters of the NSGA-II multi-objective optimization model are gradually optimized using a reinforcement learning parameter tuning module. Specifically, the NSGA-II parameters are automatically optimized with the treatment effect (compliance rate ≥ 95% (probability of the compressive strength of the solidified body meeting the standard) and cost saving rate ≥ 10%) as the reward function: mutation probability (0.05-0.15) and optimal solution population size for sludge treatment (50-200).

[0052] Specifically, the reward function is:

[0053] In the formula, d is the compliance rate weighting coefficient; e is the compliance rate threshold, which can be set to 0.95 in this embodiment; f is the cost saving rate weighting coefficient; g is the cost saving rate threshold, which can be set to 10% in this embodiment; and h is the equipment load rate coefficient.

[0054] The equipment load rate can be:

[0055] , , These are the torque load factor, pressure load factor, and energy load factor, respectively.

[0056] Furthermore, the preset parameter filtering strategy in this embodiment may include a priority strategy and a cumulative solution filtering strategy. The priority strategy assigns different weight coefficients to different objective functions based on the different priorities of different modes; that is, when the environmental protection mode is prioritized, the function that minimizes the penalty for exceeding heavy metal limits is assigned the largest weight coefficient; when the cost mode is prioritized, the function that minimizes cost is assigned the largest weight coefficient.

[0057] For example, in the environmental priority mode under the priority strategy, the weights of the three objective functions can be assigned as f2=0.7, f1=0.2, and f3=0.1. When the Pb concentration reaches or exceeds 4 mg / L, this mode will be automatically triggered.

[0058] In the cost-first model, the weights of the three objective functions can be assigned as f3=0.6, f1=0.2, and f2=0.2.

[0059] In the balanced mode, the weights of the three objective functions can be assigned as follows: f1=0.4, f2=0.3, f3=0.3; operators can customize the weight combinations.

[0060] The Leto solution selection strategy includes: Environmental protection priority mode: Calculate the overall score:

[0061] Choose the solution with the lowest score directly.

[0062] Cost-first mode: directly select the solution with the lowest total cost (while also ensuring that heavy metals do not exceed the standard).

[0063] Dynamic weight adjustment mechanism: When the equipment load rate exceeds the preset threshold (e.g., 0.8), the intelligent decision-making module reduces the weight of the pressing pressure and prioritizes low-torque, low-energy-consumption processing strategies to ensure safe operation of the equipment.

[0064] In this embodiment, after obtaining the optimal strategy, control commands (such as: the dosing device controlling the opening degree of the corresponding curing agent valve, the pressure parameters of the press, and the torque of the mixer) can be generated based on the output optimal strategy and sent to the actuator of the processing equipment to realize the automated control of the processing process.

[0065] The control command generation method includes: calculation of curing agent dosage: calculate valve opening based on curing agent ratio (e.g., 15% cement content corresponds to 45% valve opening).

[0066] Pressing pressure control curve: The pressure rises steadily at a rate of 0.2 MPa per minute to prevent filter plate rupture caused by rapid pressure changes.

[0067] In one embodiment, it also includes: Simulation tests were conducted on the system: historical monitoring datasets were injected to verify that the LSTM prediction error was ≤5% and the NSGA-II solution time was ≤30 seconds; pressure over-limit test: the pressing pressure was forcibly set to 5.5MPa, and the emergency stop function of the equipment was checked.

[0068] On-site trial operation of the system: Compliance rate verification: 100 batches of sludge were continuously treated, and the moisture content compliance rate (±2%) and heavy metal compliance rate were statistically analyzed; Cost analysis: The consumption of reagents and energy consumption data before and after optimization using a multi-objective optimization model were compared, and the savings rate was calculated.

[0069] Model self-update mechanism: The system automatically triggers model retraining every month and introduces a sliding window mechanism to retain only the most recent 200 batches of data to avoid interference from historical data and ensure long-term running accuracy.

[0070] The technical solution of this invention obtains real-time first monitoring data of the sludge solidification treatment equipment as input, and outputs control parameters of the sludge solidification treatment equipment, including the solidifying agent ratio. This enables intelligent response to real-time component fluctuations, avoiding waste of solidifying agent and poor solidification effect. By inputting the first monitoring data into a trained monitoring data prediction model, and using the predicted second monitoring data for a future period, the second monitoring data is input into a multi-objective optimization model, outputting the control parameters of the sludge solidification treatment equipment. This allows the control parameters of the sludge solidification equipment to be generated by focusing on multiple sludge solidification performance indicators, achieving global optimization. This application dynamically generates agent ratios and equipment parameter instructions through an LSTM time-series prediction model and an NSGA-II multi-objective optimization model, and significantly improves the automation level and overall efficiency of the treatment process through real-time closed-loop optimization.

[0071] Example 3 Figure 3 This is a schematic diagram of a sludge solidification treatment device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: Data acquisition unit 301 is used to acquire the first monitoring data of the sludge solidification treatment equipment; The parameter calculation unit 302 is used to input the first monitoring data into the trained monitoring data prediction model to predict the second monitoring data for a future period of time, input the second monitoring data into the multi-objective optimization model, and output the control parameters of the sludge solidification treatment equipment. The control unit 303 is used to control the operation of the sludge solidification treatment equipment based on the control parameters.

[0072] The message data processing resource scheduling device provided in this embodiment of the invention can execute the sludge solidification treatment method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0073] Example 4 Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0074] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0075] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a sludge solidification treatment method.

[0077] In some embodiments, a sludge solidification treatment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the sludge solidification treatment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a sludge solidification treatment method by any other suitable means (e.g., by means of firmware).

[0078] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0079] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0080] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0081] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0082] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0083] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0084] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0085] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for solidifying sludge, characterized in that, include: The monitoring data prediction model and the multi-objective optimization model are trained based on the constructed dataset to obtain the trained monitoring data prediction model and the multi-objective optimization model. Specifically, the input of the monitoring data prediction model is historical monitoring data within a first time period, and the output is predicted monitoring data within a second time period, which is later than the first time period. The multi-objective optimization model calculates control parameters based on the objective function, constraints, a preset parameter selection strategy, and the predicted monitoring data, and outputs the control parameters. The multi-objectives of the multi-objective optimization model include at least minimizing moisture content deviation, minimizing heavy metal exceedance penalties, and minimizing costs. The constraints include at least pressing machine pressure constraints, mixer torque constraints, and reaction environment constraints. A reward function is set for the multi-objective optimization model to continuously optimize the parameters of the multi-objective optimization model; The moisture content deviation is minimized as follows: Among them, H 实测 H represents the actual moisture content measured after the sludge has solidified. 目标 The preset target moisture content; The penalty for exceeding the heavy metal limit is minimized as follows: Unit: mg / L; Among them, C Pb C represents the lead concentration in the solidified sludge, where a is the lead concentration threshold. Cd denoted as , and b as the cadmium concentration threshold in the solidified sludge. The cost minimization: Where x1, x2, and x3 are the dosages of cement, fly ash, and chelating agent, p1, p2, and p3 are the unit prices of cement, fly ash, and chelating agent, the equipment energy consumption cost is calculated based on the pressing pressure p and time t, and c is the equipment energy consumption cost coefficient. The reward function is: In the formula, d is the compliance rate weighting coefficient, e is the compliance rate threshold; f is the cost saving rate weighting coefficient, g is the cost saving rate threshold; and h is the equipment load rate coefficient. Among them, the equipment load rate is: , , These are the torque load factor, pressure load factor, and energy consumption load factor, respectively. Obtain the first monitoring data from the sludge solidification treatment equipment; The first monitoring data is input into the trained monitoring data prediction model to predict the second monitoring data for a future period of time. The second monitoring data is then input into the multi-objective optimization model to output the control parameters of the sludge solidification treatment equipment. The operation of the sludge solidification treatment equipment is controlled based on the aforementioned control parameters.

2. The sludge solidification treatment method according to claim 1, characterized in that, After acquiring the first monitoring data from the sludge solidification treatment equipment, the method further includes preprocessing the first monitoring data. The preprocessing includes at least data cleaning, filtering, and normalization of the first monitoring data.

3. The sludge solidification treatment method according to claim 1, characterized in that, Both the first monitoring data and the second monitoring data include at least the moisture content of the sludge at the mixer inlet, the heavy metal ion concentration and viscosity of the sludge at the outlet of the mixing reaction tank in the mixer, the temperature and pH value at the solidifier addition point, the torque of the mixer main shaft, and the pressure of the press. The control parameters include at least the curing agent ratio, the mixer speed, and the press pressure.

4. The sludge solidification treatment method according to claim 1, characterized in that, The process of training the monitoring data prediction model and the multi-objective optimization model on the constructed dataset to obtain the trained monitoring data prediction model and the multi-objective optimization model includes, prior to: Obtain historical monitoring data for various types of silt; The historical monitoring data is preprocessed, and a dataset is constructed based on the preprocessed historical monitoring data.

5. The sludge solidification treatment method according to claim 1, characterized in that, The preset parameter filtering strategy includes a priority strategy and a Pareto solution filtering strategy; The priority strategy assigns different weight coefficients to different objective functions based on the different priorities of different modes; that is, when the environmental protection mode is prioritized, the function that minimizes the penalty for exceeding the heavy metal limit is assigned the largest weight coefficient; when the cost mode is prioritized, the function that minimizes the cost is assigned the largest weight coefficient.

6. A sludge solidification treatment device, characterized in that, include: The training unit is used to train the monitoring data prediction model and the multi-objective optimization model based on the constructed dataset, so as to obtain the trained monitoring data prediction model and the multi-objective optimization model. Specifically, the input of the monitoring data prediction model is historical monitoring data within a first time period, and the output is predicted monitoring data within a second time period, which is later than the first time period. The multi-objective optimization model calculates control parameters based on the objective function, constraints, a preset parameter selection strategy, and the predicted monitoring data, and outputs the control parameters. The multi-objectives of the multi-objective optimization model include at least minimizing moisture content deviation, minimizing heavy metal exceedance penalties, and minimizing costs. The constraints include at least pressing machine pressure constraints, mixer torque constraints, and reaction environment constraints. A reward function is set for the multi-objective optimization model to continuously optimize the parameters of the multi-objective optimization model; The moisture content deviation is minimized as follows: Among them, H 实测 H represents the actual moisture content measured after the sludge has solidified. 目标 The preset target moisture content; The penalty for exceeding the heavy metal limit is minimized as follows: Unit: mg / L; Among them, C Pb C represents the lead concentration in the solidified sludge, where a is the lead concentration threshold. Cd denoted as , and b as the cadmium concentration threshold in the solidified sludge. The cost minimization: Where x1, x2, and x3 are the dosages of cement, fly ash, and chelating agent, p1, p2, and p3 are the unit prices of cement, fly ash, and chelating agent, the equipment energy consumption cost is calculated based on the pressing pressure p and time t, and c is the equipment energy consumption cost coefficient. The reward function is: In the formula, d is the compliance rate weighting coefficient, e is the compliance rate threshold; f is the cost saving rate weighting coefficient, g is the cost saving rate threshold; and h is the equipment load rate coefficient. Among them, the equipment load rate is: , , These are the torque load factor, pressure load factor, and energy consumption load factor, respectively. The data acquisition unit is used to acquire the first monitoring data of the sludge solidification treatment equipment; The parameter calculation unit is used to input the first monitoring data into the trained monitoring data prediction model to predict the second monitoring data for a future period of time, input the second monitoring data into the multi-objective optimization model, and output the control parameters of the sludge solidification treatment equipment. The control unit is used to control the operation of the sludge solidification treatment equipment based on the control parameters.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the sludge solidification treatment method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the sludge solidification treatment method according to any one of claims 1-5.

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