Self-expanding particulate pollutant sediment in-situ disposal wrapping method based on sound wave visualization

Abstract

The invention discloses a self-expanding particulate pollutant sediment in-situ disposal wrapping method based on sound wave visualization, by adding an adsorbent into self-expanding particles, not only is the ability of the particles to form an impermeable layer enhanced, but also the particles can actively adsorb or degrade harmful substances in sediment when laid in a water body. Common adsorbents such as activated carbon, zeolite, titanium dioxide nanoparticles and the like can effectively capture heavy metal ions, organic pollutants and the like, so that the pollutants are treated more thoroughly, and the risk of re-pollution of the water body is further reduced. In addition, in combination with a multi-time intelligent comparison and analysis technology of sound wave detection data, errors possibly occurring in the laying process can be monitored in real time. Through multiple times of comparison of sound wave reflection wave data, the system can automatically identify abnormal data caused by water body disturbance, particle density change or environmental noise, accurate correction is performed through an adaptive correction algorithm, and the accuracy and stability of the data are ensured.

Description

Technical Field

[0001] The invention belongs to the field of water treatment, and in particular relates to an in-situ disposal and packaging method for self-swelling particle pollutant sediment based on acoustic wave visualization. Background Art

[0002] With the continuous increase of human social activities, the intensification of industrial, agricultural and urbanization processes, the problem of water pollution has become more and more serious. A large number of pollutants discharged into rivers, lakes and other water bodies, such as nutrients, heavy metals, organic pollutants, etc., gradually accumulate in the sediment of the water body over time. These pollutants are deposited in the sediment for a long time, forming sediment pollution that cannot be ignored, which seriously affects the ecological environment of the water body. Pollutants in the sediment will not only lead to eutrophication of the water body, promote the proliferation of algae, and then cause ecological disasters such as algal blooms and red tides, but may also be released into the overlying water body through the disturbance or resuspension of the sediment, further aggravating the deterioration of water quality, increasing the concentration of toxic and harmful substances in the water body, and threatening the survival of aquatic organisms and the balance of the water ecosystem. In addition, once the heavy metal pollutants enriched in the sediment are released, they will not only cause irreversible damage to the aquatic ecosystem, but may also be enriched through the food chain, and ultimately threaten human health. Therefore, how to effectively control sediment pollution and protect the water environment has become a difficult problem to be solved in the field of global environmental protection.

[0003] At present, there are two main methods for sediment treatment: ex situ treatment and in situ treatment. Ex situ treatment usually relies on mechanical dredging, which removes contaminated sediment from the water body through equipment such as suction boats and dredgers. However, this method is not only costly, but also often faces the problem of incomplete dredging, and the residual sediment may still become a source of pollution. In addition, the disposal of sediment after dredging is also very difficult. If it is not handled properly, it may also cause secondary pollution. In situ treatment is to treat contaminated sediment directly in the water body by chemical, biological or physical means without transferring the sediment, inhibiting the release of pollutants or promoting their degradation. Although in situ treatment avoids the complexity and high cost of large-scale sediment transfer, existing in situ treatment technologies still have many challenges. Traditional adsorption materials usually have limited capacity and are easily saturated. Once the adsorption capacity is exhausted, the pollutants in the sediment will be released again. In addition, it is difficult for existing technologies to achieve comprehensive treatment of all types of pollutants, especially some persistent organic pollutants and difficult-to-degrade heavy metals, which are often difficult to completely remove through simple in situ treatment methods.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a method for in-situ disposal and packaging of self-expanding particulate pollutant sludge based on acoustic wave visualization, thereby solving the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0007] A method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization comprises the following steps:

[0008] Step S1: obtaining various parameters such as water flow velocity, sediment thickness, and particle distribution density, and dynamically adjusting the placement speed, placement position, and laying thickness of the self-expanding particles according to the obtained parameters;

[0009] Step S2: multiple intelligent comparisons and analyses of reflected wave data in acoustic wave detection are performed to automatically correct detection errors;

[0010] Step S3: adding an adsorbent to the self-expanding particles so that the particles can form an impermeable layer while promoting the degradation of pollutants in the sediment or absorbing harmful substances;

[0011] Step S4: installing an acoustic wave transmitter and a receiver at the tail end of the particle delivery pipeline, and realizing real-time visual monitoring of the particle laying process by analyzing the attenuation degree of the acoustic wave reflection wave;

[0012] Step S5: During the laying process, the underwater particle laying process and environmental changes are uploaded to the cloud, and subsequent treatment measures are optimized through big data analysis.

[0013] Optionally, the steps of obtaining multiple parameters such as water flow velocity, bottom mud thickness, and particle distribution density, and dynamically adjusting the delivery speed, delivery position, and laying thickness of the self-expanding particles according to the obtained parameters are as follows:

[0014] Step A1: Detect the flow velocity of the water body in real time through a water flow velocity sensor and measure the thickness and stratification of the bottom mud through an acoustic wave detection device. Finally, monitor the distribution density of the self-expanding particles through an optical or electromagnetic sensor;

[0015] Step A2: Analyze the data obtained in step A1 in real time, and calculate the water flow conditions, bottom mud state and particle delivery effect in combination with the preset model algorithm;

[0016] Step A3: According to the real-time data of water flow velocity and sediment thickness, the release speed of the self-expanding particles is dynamically adjusted, and according to the water flow direction and sediment distribution, the moving path and position of the release pipeline are adjusted in real time. Finally, according to the data of particle distribution density, the release quantity and time of the particles are dynamically adjusted.

[0017] Optionally, the steps of performing multiple intelligent comparisons and analyses on the reflected wave data in the acoustic wave detection and automatically correcting the detection error are as follows:

[0018] Step B1: During the self-expanding particle placement process, the acoustic wave detection device is used to emit acoustic waves, and the acoustic wave data reflected from the bottom mud layer and the particle layer are received in real time;

[0019] Step B2: Intelligently compare the reflected wave data collected multiple times in succession, analyze the differences in waveform change, amplitude attenuation and arrival time each time, and identify abnormal data caused by water disturbance, particle density change or environmental noise;

[0020] Step B3: Using the comparison analysis results, identify potential measurement errors and use the adaptive correction algorithm to correct the identified errors. The expression is: in, is the waveform after correction of the nth acquisition data, S n (t) is the original reflected wave waveform collected for the nth time, β n is the adaptive correction coefficient, E n is the indicator of abnormal data collected for the nth time, N is the size of the sliding window, that is, the number of past data considered when performing weighted averaging of historical data, is the weighted sum of the reflection wave data collected in the past N times and the corresponding abnormal indicators, It is the normalization coefficient of weighted average of historical data;

[0021] Step B4: Feedback the corrected data to the release control system and adjust the release strategy to cope with the current changes in the water environment.

[0022] Optionally, the steps of adding an adsorbent to the self-expanding particles so that the adsorbent can promote the degradation of pollutants in the sediment or adsorb harmful substances while forming an impermeable layer are:

[0023] The adsorbent is mixed with the self-expanding particles in a dry state and the particles are coated with a soluble film. The mixed particles should be immediately sealed and packaged or stored in a dry container.

[0024] Optionally, the adsorbent includes one or a combination of two or more of activated carbon, zeolite, and titanium dioxide nanoparticles, and the adsorbent is added in an amount of 5%-20% of the total weight of the self-expanding particles.

[0025] Optionally, an acoustic wave transmitter and a receiver are installed at the tail end of the particle delivery pipeline, and the steps of realizing real-time visual monitoring of the particle laying process by analyzing the attenuation degree of the acoustic wave reflection wave are as follows:

[0026] Step C1: installing an acoustic wave transmitter and a receiver at the tail end of the particle delivery pipeline;

[0027] Step C2: The sound wave transmitter transmits sound waves at a predetermined frequency and power. After passing through the water body and the laid granular layer, these sound waves are partially reflected back by different media, and the receiver captures the signal of the reflected wave in real time;

[0028] Step C3: The receiver converts the captured reflected wave signals into electrical signals and transmits these signals to the control center through the data transmission line;

[0029] Step C4: The control center analyzes the received reflected wave data and calculates the attenuation degree of the reflected wave, which is expressed as A r =A i e -αx , where A r is the amplitude of the received reflected wave, A i is the initial transmission wave amplitude, α is the attenuation coefficient, refer to the empirical formula, and x is the propagation distance;

[0030] Step C5: By analyzing the attenuation data of the reflected wave, the control center generates a real-time visual image of the particle laying process. If it is detected that the laying layer thickness is insufficient or uneven, an alarm is issued and the operator is prompted to adjust the placement parameters.

[0031] Optionally, during the laying process, the underwater particle laying process and environmental changes are uploaded to the cloud, and the steps for optimizing subsequent treatment measures through big data analysis are as follows:

[0032] Analyze the particle distribution pattern under different laying conditions, identify the key factors affecting the laying effect, analyze the long-term impact of environmental changes on the laying effect, predict future environmental change trends, and finally combine historical data to conduct real-time evaluation of the current laying effect to determine whether the expected goal has been achieved. The expression is: Among them, Q(t) is the evaluation value of the laying effect at the current time t, P(x i ,y i , t) is the position at time t (x i ,y i ), H(E(t)) is the environmental impact correction function, which corrects the paving effect based on the accumulated environmental impact value E(t), R i (t) is the reference value in historical data, indicating the laying effect under similar environmental conditions;

[0033] Optionally, based on the results of big data analysis, the cloud system generates optimization suggestions and feeds back the optimization measures to the on-site control system through the cloud to guide subsequent governance work.

[0034] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0035] By adding adsorbents to the self-expanding particles, not only is the ability of the particles to form an impermeable layer enhanced, but also these particles can actively adsorb or degrade harmful substances in the sediment when laid in the water body, and can also effectively capture heavy metal ions, organic pollutants, etc., making the treatment of pollutants more thorough, further reducing the risk of water re-pollution. In addition, combined with multiple intelligent comparison and analysis technology of acoustic wave detection data, this application can monitor the errors that may occur during the laying process in real time. By comparing the acoustic wave reflection wave data multiple times, the system can automatically identify abnormal data caused by water disturbance, particle density changes or environmental noise, and accurately correct them through an adaptive correction algorithm, ensuring the accuracy and stability of the data, making the effect of self-expanding particle laying more reliable.

[0036] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0038] In the figure:

[0039] Figure 1 A flow chart of the in-situ disposal packaging method for self-swelling particulate pollutant sludge;

[0040] Figure 2 Schematic diagram of the package disposal of self-expanding particles.

[0041] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0042] The present invention will now be described in further detail with reference to the accompanying drawings.

[0043] See also Figure 1-2 As shown, in this embodiment, a method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization is provided, comprising the following steps:

[0044] Step S1: Acquire multiple parameters such as water flow velocity, sediment thickness, and particle distribution density, and dynamically adjust the release speed, release position, and laying thickness of the self-expanding particles according to the acquired parameters; ensure that the particles can be accurately laid in a complex and changeable water environment, thereby maximizing the anti-seepage effect and pollutant isolation effect.

[0045] Step S2: Perform multiple intelligent comparisons and analyses on the reflected wave data in the acoustic wave detection, automatically correct the detection errors, ensure the accuracy and reliability of the acoustic wave detection data, and provide a solid data foundation for subsequent laying monitoring.

[0046] Step S3: Adding adsorbents to the self-expanding particles so that they can promote the degradation of pollutants in the sediment or adsorb harmful substances while forming an impermeable layer, thereby enhancing the treatment effect of pollutants and achieving the dual goals of pollution control and ecological restoration.

[0047] Step S4: Install an acoustic wave transmitter and receiver at the tail end of the particle delivery pipeline, and realize real-time visual monitoring of the particle laying process by analyzing the attenuation degree of the acoustic wave reflection wave. It can dynamically capture and adjust abnormal situations that occur during the laying process, and ensure the uniformity and continuity of the laying layer.

[0048] Step S5: During the laying process, the underwater particle laying process and environmental changes are uploaded to the cloud, and subsequent governance measures are optimized through big data analysis, and further optimized, so that the entire environmental governance process can be continuously improved and continuously adapted to environmental changes, which not only improves the accuracy and efficiency of governance, but also provides a scientific basis and data support for future water environment restoration.

[0049] In this embodiment, the steps of obtaining multiple parameters such as water flow velocity, bottom mud thickness, and particle distribution density, and dynamically adjusting the delivery speed, delivery position, and laying thickness of the self-expanding particles according to the acquired parameters are as follows:

[0050] Step A1: Use a water velocity sensor to detect the flow rate of the water body in real time and use an acoustic wave detection device to measure the thickness and stratification of the bottom mud. Finally, use an optical or electromagnetic sensor to monitor the distribution density of the self-expanding particles to ensure the comprehensiveness and accuracy of the data. This multi-level data collection system lays a solid foundation for subsequent analysis and decision-making.

[0051] Step A2: Real-time analysis of the data obtained in step A1, combined with the preset model algorithm to calculate the water flow conditions, sediment state and particle delivery effect; precise calculation of water flow conditions, sediment state and particle delivery effect ensures agile response and processing of environmental variables. This data-driven analysis method enables the system to dynamically adapt to changes in water bodies and sediments

[0052] Step A3: According to the real-time data of water flow velocity and sediment thickness, the release speed of self-expanding particles is dynamically adjusted, and the moving path and position of the release pipeline are adjusted in real time according to the water flow direction and sediment distribution. Finally, according to the data of particle distribution density, the release quantity and time of particles are dynamically adjusted. Based on the monitoring data of particle distribution density, the release quantity and time of particles can be dynamically adjusted to achieve the best laying effect without wasting materials. This ability of dynamic adjustment and real-time response ensures the high accuracy and efficiency of the self-expanding particle laying process, enables the technology to cope with complex environmental changes, and ensures the effectiveness and sustainability of water environment management.

[0053] In this embodiment, the steps of performing multiple intelligent comparisons and analyses on the reflected wave data in the acoustic wave detection and automatically correcting the detection error are as follows:

[0054] Step B1: During the self-expanding particle placement process, the acoustic wave detection device is used to emit acoustic waves, and the acoustic wave data reflected from the bottom mud layer and the particle layer are received in real time;

[0055] Step B2: Intelligently compare the reflected wave data collected multiple times in a row, analyze the differences in waveform changes, amplitude attenuation and arrival time each time, and identify abnormal data caused by water disturbance, particle density changes or environmental noise. This detailed analysis enables the system to accurately locate potential sources of error;

[0056] Step B3: Using the comparison analysis results, identify potential measurement errors and use the adaptive correction algorithm to correct the identified errors. The expression is: in, is the waveform after correction of the nth acquisition data, S n (t) is the original reflected wave waveform collected for the nth time, β n is the adaptive correction coefficient, E n is the indicator of abnormal data collected for the nth time, N is the size of the sliding window, that is, the number of past data considered when performing weighted averaging of historical data, is the weighted sum of the reflection wave data collected in the past N times and the corresponding abnormal indicators, The normalization coefficient is the weighted average of historical data; by combining historical data for weighted average, the accuracy of the correction results and the continuity of the data are ensured. This process not only improves the accuracy of the measurement data, but also enables the system to maintain stable performance in complex water environments.

[0057] Step B4: Feedback the corrected data to the delivery control system and adjust the delivery strategy to cope with the current changes in the water environment, thereby ensuring the continuous optimization of the laying effect and maximization of the treatment efficiency.

[0058] In this embodiment, the steps of adding an adsorbent to the self-expanding particles so that the adsorbent can promote the degradation of pollutants in the sediment or adsorb harmful substances while forming an anti-seepage layer are as follows:

[0059] The adsorbent is mixed with the self-expanding particles in a dry state and the particles are coated with a layer of soluble film. The mixed particles should be immediately sealed and packaged or stored in a dry container. This step ensures the stability and effectiveness of the adsorbent and self-expanding particles. The adsorbent in a dry state is mixed with the particles to avoid the risk of moisture absorption or premature reaction, and maintain the integrity and functionality of the particles. Secondly, coating with a layer of soluble material effectively prevents the particles from getting damp or affected by the environment during transportation and storage, thereby ensuring that the performance of the particles is not impaired when they are placed. Finally, sealed packaging or storage in a dry environment further ensures the long-term stability of the material and avoids performance degradation caused by external factors.

[0060] In this embodiment, the adsorbent includes one or a combination of two or more of activated carbon, zeolite, and titanium dioxide nanoparticles, and the adsorbent is added in an amount of 20% of the total weight of the self-expanding particles.

[0061] In this embodiment, the steps of installing an acoustic wave transmitter and a receiver at the tail end of the particle delivery pipeline and realizing real-time visual monitoring of the particle laying process by analyzing the attenuation degree of the acoustic wave reflection wave are as follows:

[0062] Step C1: installing an acoustic wave transmitter and a receiver at the tail end of the particle delivery pipeline;

[0063] Step C2: The sound wave transmitter transmits sound waves at a predetermined frequency and power. After passing through the water body and the laid granular layer, these sound waves are partially reflected back by different media, and the receiver captures the reflected wave signals in real time. By transmitting sound waves at a predetermined frequency and power, the different media of the water body and the laid granular layer can be accurately covered to ensure that comprehensive reflected wave data is obtained.

[0064] Step C3: The receiver converts the captured reflected wave signals into electrical signals and transmits these signals to the control center through data transmission lines, realizing real-time data collection and transmission, providing a basis for subsequent analysis.

[0065] Step C4: The control center analyzes the received reflected wave data and calculates the attenuation degree of the reflected wave, which is expressed as A r =A i e -αx , where A r is the amplitude of the received reflected wave, A iis the initial transmitted wave amplitude, α is the attenuation coefficient, refer to the empirical formula, and x is the propagation distance; by calculating the attenuation degree of the reflected wave, a specific expression is used to conduct a comprehensive analysis of the reflected wave amplitude, attenuation coefficient and propagation distance to ensure an accurate assessment of the thickness and uniformity of the paving layer.

[0066] Step C5: By analyzing the attenuation data of the reflected wave, the control center generates a real-time visual image of the particle laying process. If it is detected that the thickness of the laying layer is insufficient or uneven, an alarm will be issued and the operator will be prompted to adjust the delivery parameters. By generating a visual image through real-time analysis, the operator can intuitively monitor any abnormal conditions in the laying process. In particular, when the thickness of the laying layer is insufficient or uneven, the system can automatically issue an alarm and prompt the operator to adjust the delivery parameters. This full-process automated monitoring and adjustment mechanism not only improves the accuracy and uniformity of the laying process and reduces human errors, but also greatly improves the efficiency and safety of the entire treatment process, ensuring the reliability and stability of the final treatment effect.

[0067] In this embodiment, during the laying process, the underwater particle laying process and environmental changes are uploaded to the cloud, and the steps of optimizing subsequent governance measures through big data analysis are as follows:

[0068] Step D1: Analyze the particle distribution pattern under different laying conditions, identify the key factors affecting the laying effect and analyze the long-term impact of environmental changes on the laying effect, predict the future trend of environmental changes, and finally combine historical data to conduct real-time evaluation of the current laying effect to determine whether the expected goal has been achieved. The expression is: Among them, Q(t) is the evaluation value of the laying effect at the current time t, P(x i ,y i , t) is the position at time t (x i ,y i ), H(E(t)) is the environmental impact correction function, which corrects the paving effect based on the accumulated environmental impact value E(t), R i (t) is the reference value in the historical data, which indicates the laying effect under similar environmental conditions. By analyzing the particle distribution pattern under different laying conditions, the key factors affecting the laying effect are identified, and the long-term impact of environmental changes on the laying effect is further analyzed, which can effectively predict the future environmental change trend. This prediction capability enables the system to respond to potential environmental changes in advance and ensure the continuous stability of the laying effect. At the same time, by combining historical data, the current laying effect is evaluated in real time to ensure that each laying reaches the expected goal.

[0069] Step D2: Based on the results of big data analysis, the cloud system generates optimization suggestions and feeds back the optimization measures to the on-site control system through the cloud to guide subsequent governance work. Further using the results of big data analysis, optimization suggestions are generated through the cloud system, and these optimization measures are fed back to the on-site control system. This mechanism ensures that on-site operations can respond to analysis results in a timely manner and achieve closed-loop management of governance work. The entire process forms an adaptive, self-optimizing laying and governance system through the collaborative work of the cloud and the site, which not only improves the accuracy and reliability of laying, but also enables governance work to be dynamically adjusted according to actual conditions, thereby improving governance efficiency and effectiveness.

[0070] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A method for in-situ disposal and packaging of self-expanding particulate pollutants in sediment based on acoustic wave visualization, characterized in that: The following steps are involved: Step S1: obtaining various parameters such as water flow velocity, sediment thickness, and particle distribution density, and dynamically adjusting the placement speed, placement position, and laying thickness of the self-expanding particles according to the obtained parameters; Step S2: multiple intelligent comparisons and analyses of reflected wave data in acoustic wave detection are performed to automatically correct detection errors; Step S3: adding an adsorbent to the self-expanding particles so that the particles can form an impermeable layer while promoting the degradation of pollutants in the sediment or absorbing harmful substances; Step S4: installing an acoustic wave transmitter and a receiver at the tail end of the particle delivery pipeline, and realizing real-time visual monitoring of the particle laying process by analyzing the attenuation degree of the acoustic wave reflection wave; Step S5: During the laying process, the underwater particle laying process and environmental changes are uploaded to the cloud, and subsequent treatment measures are optimized through big data analysis.

2. According to the method of claim 1, the method is characterized in that: The steps of obtaining multiple parameters such as water flow velocity, sediment thickness, and particle distribution density, and dynamically adjusting the delivery speed, delivery position, and laying thickness of the self-expanding particles according to the obtained parameters are as follows: Step A1: Detect the flow velocity of the water body in real time through a water flow velocity sensor and measure the thickness and stratification of the bottom mud through an acoustic wave detection device. Finally, monitor the distribution density of the self-expanding particles through an optical or electromagnetic sensor; Step A2: Analyze the data obtained in step A1 in real time, and calculate the water flow conditions, bottom mud state and particle delivery effect in combination with the preset model algorithm; Step A3: According to the real-time data of water flow velocity and sediment thickness, the release speed of the self-expanding particles is dynamically adjusted, and according to the water flow direction and sediment distribution, the moving path and position of the release pipeline are adjusted in real time. Finally, according to the data of particle distribution density, the release quantity and time of the particles are dynamically adjusted.

3. The method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization according to claim 1 is characterized in that: The steps for performing multiple intelligent comparisons and analyses on the reflected wave data in acoustic wave detection and automatically correcting the detection errors are as follows: Step B1: During the self-expanding particle placement process, the acoustic wave detection device is used to emit acoustic waves, and the acoustic wave data reflected from the bottom mud layer and the particle layer are received in real time; Step B2: Intelligently compare the reflected wave data collected multiple times in succession, analyze the differences in waveform change, amplitude attenuation and arrival time each time, and identify abnormal data caused by water disturbance, particle density change or environmental noise; Step B3: Using the comparison analysis results, identify potential measurement errors and use the adaptive correction algorithm to correct the identified errors. The expression is: in, is the waveform after correction of the nth acquisition data, S n (t) is the original reflected wave waveform collected for the nth time, β n is the adaptive correction coefficient, E n is the indicator of abnormal data collected for the nth time, N is the size of the sliding window, that is, the number of past data considered when performing weighted averaging of historical data, is the weighted sum of the reflection wave data collected in the past N times and the corresponding abnormal indicators, It is the normalization coefficient of weighted average of historical data; Step B4: Feedback the corrected data to the release control system and adjust the release strategy to cope with the current changes in the water environment.

4. The method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization according to claim 1 is characterized in that: The steps of adding adsorbent to the self-expanding particles to form an impermeable layer while promoting the degradation of pollutants in the sediment or adsorbing harmful substances are as follows: The adsorbent is mixed with the self-expanding particles in a dry state and the particles are coated with a soluble film. The mixed particles should be immediately sealed and packaged or stored in a dry container.

5. The method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization according to claim 1 is characterized in that: The adsorbent comprises one or a combination of two or more of activated carbon, zeolite and titanium dioxide nanoparticles, and the added proportion of the adsorbent is 5%-20% of the total weight of the self-expanding particles.

6. The method for in-situ disposal and packaging of self-expanding particulate pollutants in sediment based on acoustic wave visualization according to claim 1 is characterized in that: Install an acoustic transmitter and receiver at the tail end of the particle delivery pipeline, and analyze the attenuation of the acoustic reflection wave to achieve real-time visual monitoring of the particle laying process. Step C1: installing an acoustic wave transmitter and a receiver at the tail end of the particle delivery pipeline; Step C2: The sound wave transmitter transmits sound waves at a predetermined frequency and power. After passing through the water body and the laid granular layer, these sound waves are partially reflected back by different media, and the receiver captures the signal of the reflected wave in real time; Step C3: The receiver converts the captured reflected wave signals into electrical signals and transmits these signals to the control center through the data transmission line; Step C4: The control center analyzes the received reflected wave data and calculates the attenuation degree of the reflected wave, which is expressed as A r =A i e -αx , where A r is the amplitude of the received reflected wave, A i is the initial transmitted wave amplitude, α is the attenuation coefficient, and x is the propagation distance; Step C5: By analyzing the attenuation data of the reflected wave, the control center generates a real-time visual image of the particle laying process. If it is detected that the laying layer thickness is insufficient or uneven, an alarm is issued and the operator is prompted to adjust the placement parameters.

7. The method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization according to claim 1 is characterized in that: During the laying process, the underwater particle laying process and environmental changes are uploaded to the cloud. The steps for optimizing subsequent treatment measures through big data analysis are as follows: Analyze the particle distribution pattern under different laying conditions, identify the key factors affecting the laying effect, analyze the long-term impact of environmental changes on the laying effect, predict future environmental change trends, and finally combine historical data to conduct real-time evaluation of the current laying effect to determine whether the expected goal has been achieved. The expression is: Among them, Q(t) is the evaluation value of the paving effect at the current time t, P(x i ,y i , t) is the position at time t (x i ,y i ), H(E(t)) is the environmental impact correction function, which corrects the paving effect based on the accumulated environmental impact value E(t), R i (t) is the reference value in historical data, indicating the laying effect under similar environmental conditions.

8. The method for in-situ disposal and packaging of self-expanding particulate pollutant sediment based on acoustic wave visualization according to claim 7 is characterized in that: Based on the results of big data analysis, the cloud system generates optimization suggestions and feeds back the optimization measures to the on-site control system through the cloud to guide subsequent governance work.

Images