Modular collaborative purification system and method for multi-source pollution of deep foundation pit

Abstract

The invention provides a modularized collaborative purification system and method for multi-source pollution of a deep foundation pit. The system comprises a multi-parameter intelligent sensor network module, an intelligent regulation and control center module and a plurality of modularized multi-source pollution treatment modules. The multi-parameter intelligent sensor network module is used for collecting multi-source pollution data of the deep foundation pit; the intelligent regulation and control center module is used for fusing the multi-source pollution data in real time to generate a collaborative purification instruction; and the plurality of modularized multi-source pollution treatment modules are used for executing cross-pollution-source linkage purification operation according to the collaborative purification instruction. Multi-source pollution data are collected in real time through the multi-parameter intelligent sensor network module, data fusion and intelligent analysis are performed through a collaborative purification decision algorithm of the intelligent regulation and control center module to generate an optimization instruction, and the multiple modular multi-source pollution treatment modules are driven to realize cross-pollution-source linkage purification. The collaboration and the response speed of pollution treatment are improved, meanwhile, the flexibility and the expandability of the system are enhanced, and the system can adapt to complex and changeable deep foundation pit pollution scenes.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of environmental engineering and intelligent control, specifically to a modular collaborative purification system and method for multi-source pollution in deep foundation pits. Background Technology

[0002] Deep foundation pit engineering involves multiple pollution sources such as dust, noise, harmful gases, and wastewater in the working environment, and these sources fluctuate dramatically in type, intensity, and distribution as the construction work progresses.

[0003] Existing pollution control technologies have fundamental shortcomings when dealing with such complex scenarios: 1. Single treatment method and lack of synergistic effect: It is designed for a single type of pollution and lacks a mechanism for comprehensive consideration and synergistic treatment of multiple pollutants.

[0004] 2. Low level of intelligence and crude control: Most control equipment relies on manual operation or simple timing, and cannot be accurately controlled according to the real-time dynamics of the pollution status of the work surface.

[0005] 3. Monitoring and control are difficult to strictly correspond: Monitoring systems often focus on data collection and display, and do not form an effective closed-loop collaborative control with various types of pollution treatment equipment across pollutant types.

[0006] 4. Insufficient modularity and integration: Existing equipment is often bulky and inflexible in deployment, making it difficult to adapt to the complex and ever-changing working environment of deep foundation pits with limited space.

[0007] In summary, there is a lack of an integrated system capable of intelligently and collaboratively controlling various types of purification modules based on real-time multidimensional data and the complex interactions between pollutants. This application addresses this technological gap by proposing a novel solution. Summary of the Invention

[0008] This application provides a modular collaborative purification system and method for multi-source pollution in deep foundation pits, which can solve the problem in the existing technology that it is difficult to treat multi-source pollutants in a unified, efficient, low-consumption manner with a truly synergistic effect.

[0009] Firstly, this application provides a modular collaborative purification system for multi-source pollution in deep foundation pits, comprising: A multi-parameter intelligent sensor network module is used to collect multi-source pollution data from deep foundation pits; The intelligent control center module is communicatively connected to the multi-parameter intelligent sensor network module and includes a collaborative purification decision algorithm unit, which is used to fuse the multi-source pollution data in real time to generate collaborative purification instructions. Multiple modular multi-source pollution treatment modules are communicatively connected to the intelligent control center module, and are used to dynamically execute cross-pollution source linkage purification operations according to the collaborative purification command.

[0010] Furthermore, the multi-parameter intelligent sensor network module includes: Dust sensors are deployed in earthwork excavation areas to monitor dust concentration; Noise sensors are deployed at machine operation points to collect noise levels in decibels. Gas sensors, covering the welding area and exhaust gas emission points, are used to detect gas composition; A water quality sensor, integrated into the drain outlet, is used to monitor water quality indicators; Meteorological sensors are used to collect meteorological parameters; The module status sensor communicates with the intelligent control center module to monitor the operating status parameters of each modular pollution treatment module in real time.

[0011] Furthermore, the intelligent control center module includes: The data receiving and preprocessing unit is communicatively connected to the multi-parameter intelligent sensor network module and is used to preprocess the raw sensor data. The pollution status comprehensive assessment and prediction unit is communicatively connected to the data receiving and preprocessing unit 70, and is used to fuse meteorological data and historical pollution data to output a deep foundation pit comprehensive pollution status characterization vector and a future pollution status prediction curve. The collaborative purification decision-making algorithm unit communicates with the pollution situation comprehensive assessment and prediction unit 80 to dynamically coordinate the operating parameters of each modular pollution treatment module and output the optimal control parameter set. The control command generation and issuance unit is communicatively connected to the collaborative purification decision algorithm unit and is used to convert the optimal control parameter set into collaborative purification commands.

[0012] Furthermore, the plurality of the modular multi-source pollution treatment modules include: The dust control unit is communicatively connected to the intelligent control center module and is used to dynamically adjust the fog cannon spray parameters according to the dust concentration. The noise control unit is communicatively connected to the intelligent control center module and is used to control the working status of the active noise reduction system based on the noise decibel value. The harmful gas purification unit is communicatively connected to the intelligent control center module and is used to automatically switch the gas purification process path according to the gas composition. The wastewater treatment unit is communicatively connected to the intelligent control center module and is used to adjust wastewater treatment parameters according to water quality indicators.

[0013] Furthermore, it also includes: A standardized interface system is used to provide physical and data interaction interfaces for the multiple modular multi-source pollution treatment modules; A collaborative control network, which is communicatively connected to the intelligent control center module, is used to control the synchronization and real-time performance of the multiple modular multi-source pollution treatment modules.

[0014] Furthermore, the collaborative purification decision-making algorithm unit is: The multi-objective optimization unit is configured to solve an optimization objective function that includes a wastewater treatment load penalty term; or, The reinforcement learning unit is configured to employ a policy network with a reward function that includes a reward term for cooperative behavior and a penalty term for conflicting behavior.

[0015] Furthermore, the wastewater treatment load penalty term is defined as a function relating to the spray water volume of the dust control unit and the treatment capacity of the wastewater treatment unit, used to quantify its impact on the wastewater treatment load when optimizing the spray water volume.

[0016] Furthermore, the wastewater treatment load penalty item The calculation formula is as follows:

[0017] in, The total inflow rate of the wastewater treatment unit is a function of the spray water volume. This is the penalty coefficient.

[0018] Secondly, this application provides a modular and collaborative purification method for multi-source pollution in deep foundation pits, comprising the following steps: Multi-source pollution data of the deep foundation pit are collected through a multi-parameter intelligent sensor network module; The multi-source pollution data is integrated in real time through an intelligent control center module containing a collaborative purification decision-making algorithm unit to generate collaborative purification instructions. Based on the collaborative purification command, multiple modular multi-source pollution treatment modules are dynamically controlled to perform cross-pollution source linkage purification operations.

[0019] Furthermore, the real-time fusion of multi-source pollution data to generate collaborative purification instructions specifically includes the following steps: Preprocess the raw sensor data; A Long Short-Term Memory (LSTM) network is used to fuse meteorological data and historical pollution data to output a comprehensive pollution status representation vector for deep foundation pits and a prediction curve of future pollution trends. The collaborative purification decision-making algorithm unit makes a decision, which adopts one of the following methods: A multi-objective optimization algorithm is used to solve an optimization objective function that includes a wastewater treatment load penalty term, so as to dynamically coordinate the operating parameters of each modular pollution treatment module. Alternatively, a policy network with a reward function that includes cooperative behavior reward terms and conflict behavior penalty terms can be used through reinforcement learning algorithms to dynamically coordinate the operating parameters of each modular pollution treatment module; The operating parameters are converted into collaborative purification commands.

[0020] The beneficial effects of the technical solutions provided in this application include at least the following: Through an integrated system architecture and innovative control methods, a closed-loop management system is achieved, encompassing environmental perception, intelligent analysis, and coordinated purification. Its core collaborative purification decision-making algorithm unit, based on quantitative modeling of interactions between pollutants, enables system-level optimization that goes beyond the simple superposition of individual functions ("1+1>2"). This effectively controls various pollutants while significantly reducing total system energy consumption and operating costs, providing a highly efficient, energy-saving, and intelligent solution for environmental pollution control in the unique and complex environment of deep foundation pits. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall architecture of the multi-source pollution collaborative purification system for deep foundation pit working faces provided in the embodiments of this application; Figure 2 A schematic diagram illustrating the deployment of a multi-parameter intelligent sensor network module provided in an embodiment of this application on a deep foundation pit working face; Figure 3 A functional block diagram of the intelligent control center provided in the embodiments of this application; Figure 4 Example diagram of the modular pollution treatment module provided in the embodiments of this application; Figure 5 A detailed logic block diagram of the collaborative purification decision-making algorithm unit provided in the embodiments of this application; Figure 6 A flowchart of a multi-source pollution synergistic purification method for deep foundation pit working faces provided in this application embodiment. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application. For clarity, in the description of the present application, a "module" refers to a collection for realizing a certain macroscopic function (such as pollution treatment), which may be composed of one or more more functionally specific "units" (such as dust control unit, wastewater treatment unit).

[0023] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0024] In the description of the embodiments in this application, terms such as "exemplary," "for example," or "for instance" are used as examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0025] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0026] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0028] Firstly, such as Figure 1 As shown, this application provides a modular collaborative purification system for multi-source pollution in deep foundation pits, comprising: A multi-parameter intelligent sensor network module is used to collect multi-source pollution data from deep foundation pits; The intelligent control center module 10 is communicatively connected to the multi-parameter intelligent sensor network module and includes a collaborative purification decision algorithm unit, which is used to fuse the multi-source pollution data in real time to generate collaborative purification instructions. Multiple modular multi-source pollution treatment modules are communicatively connected to the intelligent control center module 10, and are used to dynamically execute cross-pollution source linkage purification operations according to the collaborative purification command.

[0029] The modular collaborative purification system for multi-source pollution in deep foundation pits provided in this application collects multi-source pollution data in real time through a multi-parameter intelligent sensor network module. The data is then fused and intelligently analyzed by the collaborative purification decision algorithm of the intelligent control center module 10 to dynamically generate optimization instructions. This drives multiple modular multi-source pollution treatment modules to achieve cross-pollution source linkage purification, significantly improving the synergy and response speed of pollution control. At the same time, the modular design enhances the system's flexibility and scalability, making it adaptable to complex and ever-changing deep foundation pit pollution scenarios.

[0030] In one embodiment, such as Figure 1 As shown, the multi-parameter intelligent sensor network module acts as the system's sensory nerve, responsible for real-time, continuous, and distributed collection of various environmental pollution parameters and auxiliary parameters at the deep foundation pit working face. For example... Figure 1 As shown, the network consists of various types of sensor nodes (including first sensor node 60a, second sensor node 60b, and third sensor node 60c), including but not limited to: Dust sensors 61: such as PM2.5 and PM10 sensors, are deployed in major dust-generating areas such as earthwork excavation and material stockpiling.

[0031] Noise sensor 62: Deployed at major mechanical operation points.

[0032] Gas sensor 63: such as CO, NOx, VOCs sensors, covering welding areas and equipment exhaust emission points.

[0033] Water quality sensor 64: such as pH, turbidity, COD sensor, integrated into the foundation pit drainage outlet or treatment module inlet.

[0034] Meteorological sensor 65: Collects data such as wind speed, wind direction, temperature, and humidity to provide a basis for predicting pollution spread.

[0035] Module status sensor 66: Integrated into each processing module, it monitors its operating status (such as energy consumption, water pressure, air volume) and fault information in real time.

[0036] All sensor data, with precise timestamps and location identifiers, are transmitted in real time to the intelligent control center module via wireless (such as LoRaWAN, Wi-Fi Mesh) or wired (such as industrial Ethernet).

[0037] This embodiment achieves three-dimensional and precise monitoring of construction pollution sources by constructing a multi-parameter intelligent sensor network module: dust sensors provide real-time feedback on the particulate matter diffusion trend in the earthwork excavation area; noise sensors dynamically track the acoustic pollution characteristics of mechanical operations; gas sensors simultaneously capture changes in VOCs and exhaust gas composition in the welding area; water quality sensors continuously track key indicators such as turbidity and pH value at drainage outlets; and meteorological sensors provide environmental parameters such as temperature, humidity, and wind speed for pollution diffusion modeling. Combined with the processing unit operation data returned by the module status sensors, a complete digital monitoring chain of pollution generation, transmission, and treatment is formed. This technical solution significantly improves the spatiotemporal resolution of construction pollution monitoring, provides multi-dimensional real-time data support for the intelligent control center, enhances the response speed of pollution control, and simultaneously achieves closed-loop feedback and energy efficiency optimization of the operating status of treatment equipment.

[0038] In one embodiment, such as Figure 2 and 3 As shown, the intelligent control center module includes: The data receiving and preprocessing unit 70 is communicatively connected to the multi-parameter intelligent sensor network module and is used to preprocess the raw sensor data. It is responsible for receiving, verifying, denoising, converting, and initially aggregating the data stream from the sensor network. The pollution status comprehensive assessment and prediction unit 80, communicatively connected to the data receiving and preprocessing unit 70, is used to fuse meteorological data and historical pollution data to output a comprehensive pollution status characterization vector for deep foundation pits and a future pollution status prediction curve. This module performs deep fusion on the preprocessed data to construct a comprehensive pollution status characterization vector for deep foundation pits that can fully reflect the overall pollution status of the deep foundation pits. This vector is not a simple list of the original data, but rather, through algorithms such as weighted averaging, principal component analysis (PCA), or small neural networks, it fuses multidimensional heterogeneous data into a structured feature vector to highlight hidden correlations and dynamic trends that are crucial for coordinated control. In addition, this module uses a Long Short-Term Memory (LSTM) network to fuse meteorological data 65 and historical pollution data to predict the pollution status (such as changes in pollutant concentrations) in the near future and outputs a future pollution status prediction curve. This predictive capability transforms the system's control decision-making from passive response to proactive prevention.

[0039] The collaborative purification decision-making algorithm unit communicates with the pollution situation comprehensive assessment and prediction unit 80 to dynamically coordinate the operating parameters of each modular pollution treatment module and output the optimal control parameter set. The control command generation and issuance unit 97 is communicatively connected to the collaborative purification decision algorithm unit and is used to convert the optimal control parameter set into collaborative purification commands.

[0040] In this embodiment, the intelligent control center module achieves efficient organization of multi-source heterogeneous pollution data through a data preprocessing unit, constructs a dynamic representation vector integrating meteorological and historical data based on the pollution situation assessment unit, and achieves accurate prediction of pollution situation by combining deep learning algorithms; the collaborative purification decision algorithm dynamically optimizes the operating parameters of each processing unit based on real-time pollution characteristics and predicted trends, and finally achieves accurate execution of control strategies through the instruction generation unit, enabling the system to shift from passive governance to proactive prevention and control, and significantly improving the collaborative purification efficiency and response speed in complex pollution scenarios of deep foundation pits.

[0041] In one embodiment, the plurality of the modular multi-source pollution treatment modules include: As "effectors" performing specific purification tasks, all units adopt a standardized interface design, enabling "plug and play" and flexible deployment. For example... Figure 4 As shown, a typical modular pollution treatment module's standardized interface system may include hoisting points 16 and fixing points 11 for convenient transportation and deployment; mechanical quick-connect fittings 12 for rapid physical connection; an adaptive leveling mechanism 13 for adapting to uneven ground; a waterproof electrical interface 14 for power supply; and a communication interface 15 for data interaction and supporting self-diagnosis. The main processing unit includes: Dust control unit 20: Its core components may include a high-pressure water pump for providing a high-pressure water source, an adjustable angle fog cannon nozzle or micro water mist nozzle array whose spray parameters are precisely controlled by the intelligent control center module 10, a high-precision electric gimbal for adjusting the spray direction, a water storage tank, and a precision filter.

[0042] Noise control unit 30: may include a sound-absorbing / sound-insulating barrier unit that can be quickly assembled, and an active noise control unit for noise of a specific frequency, the phase and amplitude parameters of which are optimized by an algorithm based on the results of real-time noise spectrum analysis.

[0043] Harmful gas purification unit 40: It can integrate multiple purification technologies such as activated carbon adsorption and photocatalytic oxidation according to the type of target gas, and is equipped with a corresponding controllable fan. Its speed can be precisely adjusted by the intelligent control center module 10 to coordinately control the air volume.

[0044] Wastewater treatment unit 50: It is usually a multi-stage treatment system, including units such as screens, sedimentation, and filtration. Its operating parameters are controlled by the intelligent control center module 10 based on real-time water quality and quantity.

[0045] In one embodiment, it further includes: A standardized interface system is used to provide physical and data interaction interfaces for the multiple modular multi-source pollution treatment modules; A collaborative control network, which is communicatively connected to the intelligent control center module, is used to control the synchronization and real-time performance of the multiple modular multi-source pollution treatment modules.

[0046] In one embodiment, such as Figure 5 As shown, the collaborative purification decision-making algorithm unit is the core of the intelligent control center and is key to achieving the "1+1>2" synergistic effect. It receives pollution situation characterization vectors, prediction curves, real-time status of each processing module, performance / energy consumption models, inter-module interaction models, and preset environmental protection targets as input. Through a specific optimization algorithm, it outputs a set of collaborative control parameters that achieve system-level optimization. Its core decision-making logic can be implemented in two main ways, such as... Figure 6 As shown: Method 1: Cooperative Control Based on Multi-Objective Optimization (MOO): This method uses a multi-objective optimization solver to solve a carefully constructed weighted objective function that reflects the coupling relationships between modules. For example, when simultaneously controlling dust and wastewater, the objective function can be defined as:

[0047] in, Dust concentration, and These are the energy consumption figures for the dust and wastewater control modules, respectively.

[0048] The key innovation lies in the introduction of a wastewater treatment load penalty item. It will control the water consumption of the dust suppression spray module 20. It is directly related to the load of the wastewater treatment unit 50.

[0049] For example, this penalty item It can be specifically defined as:

[0050] in, .

[0051] In this way, the algorithm determines the amount of water to spray. To reduce At the same time, its impact on The impact of overflow risk penalties is considered to find a globally optimal balance between dust suppression effect and wastewater treatment burden, achieving true synergy.

[0052] In this way, the algorithm determines the amount of water to spray. To reduce At the same time, its impact on The impact of overflow risk penalty term is considered to find a globally optimal balance between dust suppression effect and wastewater treatment burden, achieving true synergy. The weight coefficients in the objective function... to and penalty coefficient It is not arbitrarily set; those skilled in the art can calibrate it using conventional technical means: weighting coefficients Initial settings can be made based on mandatory requirements of national or local environmental regulations, economic considerations of various costs, and management priorities of specific projects, and operators are allowed to dynamically fine-tune them within the preset range; penalty coefficient The algorithm can be optimized through small-scale on-site tests or simulation analysis, taking into account specific working conditions and the potential environmental and economic costs of excessive wastewater discharge. The larger the value, the stronger the algorithm's tendency to control wastewater overflow.

[0053] Method 2: Adaptive Cooperative Control Based on Reinforcement Learning (RL) This method models the system as a reinforcement learning agent.

[0054] State space 92, represented as It consists of pollution status representation vectors, the status of each module, etc.

[0055] Action space 93, represented as Defined as a combination of operation instructions for all controllable processing units.

[0056] The reward function is 94, which is represented as The core innovation lies in the design of the reward function, which not only includes rewards for meeting pollutant standards and penalties for energy consumption, but also introduces an explicit cooperative behavior reward term 95, expressed as... And the penalty item for conflict behavior 96, represented as .

[0057] Example of conflict punishment: When the planned water consumption of dust suppression spraying exceeds the wastewater treatment capacity, a large negative reward (punishment) should be given to deter this behavior of prioritizing one aspect over the other.

[0058] Example of collaborative reward: When the wind direction is detected to be blowing from area A to area B, if the agent can simultaneously activate the dust suppression module in area A and the ventilation and purification module in area B, a positive reward is given to incentivize it to utilize natural conditions to achieve more efficient and lower-consumption collaborative purification. Through training that maximizes long-term cumulative rewards, the agent will autonomously learn non-obvious collaborative control strategies capable of handling complex operating conditions.

[0059] By training to maximize long-term cumulative rewards, the agent will autonomously learn non-obvious cooperative control strategies capable of handling complex situations. Similarly, the weight coefficients in the reward function ( , (etc.) and specific parameters in the collaboration / conflict items (such as...) The parameters are tunable. Those skilled in the art can use historical operational data for offline learning to obtain an initial model, and after actual deployment, use online exploration and learning mechanisms combined with a simulation environment to iteratively optimize these parameters so that the agent's strategy converges to the optimal performance that meets the specific project management objectives.

[0060] In one embodiment, the two main methods described above are implemented as follows: The multi-objective optimization unit is configured to solve an optimization objective function that includes a wastewater treatment load penalty term; or, The reinforcement learning unit is configured to employ a policy network with a reward function that includes a reward term for cooperative behavior and a penalty term for conflicting behavior.

[0061] In one embodiment, to ensure the integrity and usability of the system, such as Figure 1 As shown, the intelligent control center module 10 may further include: System status monitoring and alarm unit 98: Connected to each sensor and processing unit, it is used to monitor the operating status, communication links and equipment health of the entire system in real time. When abnormal monitoring data occurs or potential risks are diagnosed, it automatically triggers audible and visual or digital alarm signals.

[0062] Human-Computer Interaction and Data Management Unit 99: Provides operators with a graphical user interface (GUI) to visualize real-time pollution cloud maps, operating parameters of each module, historical data curves, and allows authorized operators to configure system control targets, algorithm weights, etc., and generate statistical analysis reports as needed.

[0063] In one embodiment, the internal implementation of the reinforcement learning unit is described below to further clarify the implementation method of the reinforcement learning unit, as follows: Figure 5 As shown, its internal components may specifically include: State Construction: Responsible for receiving the comprehensive pollution state representation vector of the deep foundation pit and the state information of each module, and integrating them into a state vector that meets the algorithm input requirements, denoted as S.

[0064] Policy network: The brain of an intelligent agent, usually composed of deep neural networks, outputs an optimal action, denoted as A, based on the input state, represented by S.

[0065] Value network: Used to evaluate the value of the current state or state-action pair, assisting policy network subunits in making more effective learning and decision-making.

[0066] Reward function calculation: Based on the current state, represented by S, the action performed, represented by A, and the reward function we defined that includes reward items for cooperative behavior and penalty items for conflict behavior, we calculate the immediate reward, represented by R.

[0067] Learning and updating mechanism: As the core driver of the algorithm, based on the reward, represented as R and the evaluation of the value network, the parameters of the policy network sub-units and value network sub-units are continuously updated using methods such as gradient descent, so that the agent can obtain the maximum long-term cumulative reward.

[0068] Secondly, this application provides a modular and collaborative purification method for multi-source pollution in deep foundation pits, comprising the following steps: Step S1: Collect multi-source pollution data of the deep foundation pit through a multi-parameter intelligent sensor network module; Step S2: Through an intelligent control center module containing a collaborative purification decision-making algorithm unit, the multi-source pollution data is integrated in real time to generate a collaborative purification command; Step S3: Based on the collaborative purification command, dynamically control multiple modular multi-source pollution treatment modules to perform cross-pollution source linkage purification operations.

[0069] This application proposes a modular collaborative purification method for multi-source pollution in deep foundation pits, representing a breakthrough in environmental governance. Through a distributed multi-source pollution data acquisition network, the system constructs a comprehensive environmental perception capability, incorporating pollution parameters such as dust, noise, harmful gases, and water quality into a unified monitoring system. The intelligent control center employs advanced collaborative decision-making algorithms to deeply integrate and intelligently analyze heterogeneous data, generating control strategies that balance treatment effectiveness with resource optimization. Modularly designed purification units enable rapid deployment and flexible combination through standardized interfaces, conducting collaborative operations across pollution sources under command-driven conditions. This method significantly improves the timeliness and accuracy of pollution control, effectively solving the problems of response lag and resource waste inherent in traditional segmented treatment methods. Simultaneously, the system possesses excellent scalability, adapting to the complex and ever-changing environmental characteristics of deep foundation pit projects.

[0070] In one embodiment, step S2: generating collaborative purification instructions by real-time fusion of multi-source pollution data, specifically includes the following steps: Step S21: Preprocess the raw sensor data; Step S22: Use a Long Short-Term Memory (LSTM) network to fuse meteorological data and historical pollution data, and output a comprehensive pollution status representation vector for deep foundation pits and a prediction curve for future pollution trends; Step S23: Make a decision through the collaborative purification decision algorithm unit, and the decision is made in one of the following ways: A multi-objective optimization algorithm is used to solve an optimization objective function that includes a wastewater treatment load penalty term, so as to dynamically coordinate the operating parameters of each modular pollution treatment module. Alternatively, a policy network with a reward function that includes cooperative behavior reward terms and conflict behavior penalty terms can be used through reinforcement learning algorithms to dynamically coordinate the operating parameters of each modular pollution treatment module; Step S24: Convert the operating parameters into collaborative purification instructions.

[0071] In one specific embodiment, the modular collaborative purification method for multi-source pollution in deep foundation pits provided in this application has the following workflow: Figure 6 As shown, this is a continuous closed-loop process: Step 1: System Initialization and Deployment: Based on the site survey, design and deploy the sensor network and various processing modules; Step 2: Real-time monitoring of multi-source pollution: The sensor network continuously collects and uploads data.

[0072] Step 3: Data Fusion and Pollution Situation Assessment: The intelligent control center module fuses and processes the data to generate a pollution situation representation vector and predict future trends.

[0073] Step 4: Intelligent Collaborative Purification Decision: The core collaborative purification decision algorithm unit performs calculations and optimizations based on comprehensive input information, and outputs a collaborative control strategy.

[0074] Step 5: Control command issuance and execution: Commands are issued to each processing unit via the network, and each unit executes them automatically.

[0075] Step Six: Real-time Feedback and Dynamic Closed-Loop Control: The system continuously monitors the effectiveness of the purification operation and feeds back new data to the intelligent control center module, initiating a new round of evaluation and decision-making, forming a dynamic closed loop.

[0076] Step 7: Data logging, alarm and report generation: The system records all key data and automatically alarms when an anomaly occurs.

[0077] Through the above implementation methods, the system and method of this application can flexibly adapt to the complex and ever-changing pollution scenarios in deep foundation pit construction, and achieve a highly efficient, energy-saving, and intelligent pollution synergistic purification effect that is difficult to achieve with traditional independent control methods.

[0078] Thirdly, embodiments of this application provide a modular collaborative purification device for multi-source pollution in deep foundation pits. The modular collaborative purification device for multi-source pollution in deep foundation pits can be a personal computer (PC), a laptop computer, a server, or other devices with data processing capabilities.

[0079] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the modular collaborative purification equipment for multi-source pollution in deep foundation pits, as well as interfaces used for interconnecting the equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0080] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0081] The processor can be a general-purpose processor, which can call the modular collaborative purification program for multi-source pollution in deep foundation pits stored in memory and execute the modular collaborative purification method for multi-source pollution in deep foundation pits provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the modular collaborative purification program for multi-source pollution in deep foundation pits is called can refer to the various embodiments of the modular collaborative purification method for multi-source pollution in deep foundation pits in this application, and will not be repeated here.

[0082] Fourthly, embodiments of this application also provide a readable storage medium.

[0083] The present application stores a modular collaborative purification program for multi-source pollution in deep foundation pits on a readable storage medium. When the modular collaborative purification program for multi-source pollution in deep foundation pits is executed by a processor, it implements the steps of the modular collaborative purification method for multi-source pollution in deep foundation pits as described above.

[0084] The method implemented when the modular collaborative purification procedure for multi-source pollution in deep foundation pits is executed can be referred to in the various embodiments of the modular collaborative purification method for multi-source pollution in deep foundation pits of this application, and will not be repeated here.

[0085] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0087] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A deep foundation pit multi-source pollution modular collaborative purification system, characterized in that, The application relates to a multi-source pollution treatment system for deep foundation pits. The system comprises: a multi-parameter intelligent sensor network module for collecting multi-source pollution data of a deep foundation pit; an intelligent control center module in communication connection with the multi-parameter intelligent sensor network module, comprising a collaborative purification decision algorithm unit for real-time fusion of the multi-source pollution data to generate a collaborative purification instruction; 2. The deep foundation pit multi-source pollution modular collaborative purification system of claim 1, wherein, a plurality of modular multi-source pollution treatment modules in communication connection with the intelligent control center module, for dynamic execution of cross-pollution-source linkage purification operation according to the collaborative purification instruction. The multi-parameter intelligent sensor network module comprises: a dust raising sensor arranged in a soil excavation area for monitoring dust raising concentration; a noise sensor arranged at a mechanical operation point for collecting noise decibel value; a gas sensor covering a welding area and an exhaust emission point for detecting gas composition; a water quality sensor integrated in a drainage outlet for monitoring water quality index; a meteorological sensor for collecting meteorological parameters; 3. The deep foundation pit multi-source pollution modular collaborative purification system of claim 1, wherein, a module state sensor in communication connection with the intelligent control center module for real-time monitoring of running state parameters of each modular pollution treatment module. The intelligent control center module comprises: a data receiving and preprocessing unit in communication connection with the multi-parameter intelligent sensor network module for preprocessing original sensor data; a pollution situation comprehensive evaluation and prediction unit in communication connection with the data receiving and preprocessing unit for fusion of meteorological data and historical pollution data, output of a deep foundation pit comprehensive pollution state representation vector and a future pollution situation prediction curve; a collaborative purification decision algorithm unit in communication with the pollution situation comprehensive evaluation and prediction unit for dynamic coordination of running parameters of each modular pollution treatment module, output of an optimal control parameter set; 4. The deep excavation multi-source pollution modular co-purification system of claim 1, wherein, a control instruction generation and delivery unit in communication connection with the collaborative purification decision algorithm unit for conversion of the optimal control parameter set into a collaborative purification instruction. The plurality of modular multi-source pollution treatment modules comprises: a dust raising control unit in communication connection with the intelligent control center module for dynamic adjustment of fog gun spraying parameters according to dust raising concentration; a noise control unit in communication connection with the intelligent control center module for control of a main active noise reduction system working state based on noise decibel value; a harmful gas purification unit in communication connection with the intelligent control center module for automatic switching of a gas purification process path according to gas composition; 5. The deep excavation multi-source pollution modular co-purification system according to claim 1, wherein, a wastewater treatment unit in communication connection with the intelligent control center module for adjustment of wastewater treatment parameters according to water quality index. The system further comprises: a standardized interface system for providing physical and data interaction interfaces for the plurality of modular multi-source pollution treatment modules; 6. The system of claim 3, wherein, a collaborative control network in communication connection with the intelligent control center module for control of synchronism and real-time performance of the plurality of modular multi-source pollution treatment modules. The collaborative purification decision algorithm unit is: a multi-objective optimization unit configured to solve an optimization objective function comprising a wastewater treatment load penalty term; or a reinforcement learning unit configured to adopt a policy network of a reward function comprising a collaborative behavior reward term and a conflict behavior penalty term.

7. The system of claim 6, wherein, The wastewater treatment load penalty term is defined as a function associated with the spraying water amount of the dust control unit and the treatment capacity of the wastewater treatment unit, for quantifying the influence on the wastewater treatment load when optimizing the spraying water amount.

8. The system of claim 7, wherein, The wastewater treatment load penalty term The calculation formula is as follows: wherein Qtot is the total inflow to the wastewater treatment unit and is a function of the amount of spray water, is a penalty factor.

9. A method for multi-source pollution modular collaborative purification of deep foundation pit, characterized in that, The method comprises the following steps: Collecting multi-source pollution data of the deep foundation pit through a multi-parameter intelligent sensor network module; Fusing the multi-source pollution data in real time through an intelligent control center module comprising a collaborative purification decision algorithm unit to generate a collaborative purification instruction; According to the collaborative purification instruction, dynamically controlling a plurality of modular multi-source pollution treatment modules to perform linkage purification operation across pollution sources.

10. The method of claim 9, wherein, The real-time fusion of multi-source pollution data to generate a collaborative purification instruction comprises the following steps: Pretreating the original sensor data; Using a long short-term memory network (LSTM) to fuse meteorological data and historical pollution data, and outputting a deep foundation pit comprehensive pollution state representation vector and a future pollution trend prediction curve; Through the collaborative purification decision algorithm unit, the decision is made in one of the following ways: Solving an optimization objective function comprising a wastewater treatment load penalty term through a multi-objective optimization algorithm to dynamically coordinate the operating parameters of each modular pollution treatment module; Or, using a reward function comprising a collaborative behavior reward term and a conflict behavior penalty term through a reinforcement learning algorithm to dynamically coordinate the operating parameters of each modular pollution treatment module; Converting the operating parameters into a collaborative purification instruction.

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