Soil pollution treatment method and system based on microbial remediation

Abstract

The invention discloses a soil pollution abatement method and system based on microbial remediation, and particularly relates to the technical field of soil pollution abatement, by obtaining rotation speed fluctuation information of an air pump rotor, a rotation speed fluctuation coefficient is calculated, and the operation stability of an air pump is evaluated from the perspective of a power source; by monitoring the loosening condition of the pipeline joint, the loosening coefficient of the pipeline joint is calculated so as to quantify the potential hazard of the pipeline joint; the method comprises the steps of analyzing pore blocking information of a filter, calculating a pore blocking coefficient, accurately judging the blocking degree of the filter, constructing a ventilation system deterioration hidden danger evaluation model, generating a ventilation system deterioration hidden danger evaluation index, comprehensively and accurately quantifying the operation state of the ventilation system, and determining whether the ventilation system has potential deterioration hidden danger or not. When the hidden danger exists, the current maintenance time is dynamically adjusted according to the deterioration hidden danger evaluation index and the previous maintenance time, precise maintenance is achieved, the maintenance cost is reduced, and meanwhile the operation efficiency and stability of the ventilation system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution control, and more specifically, to a soil pollution control method and system based on microbial remediation. Background Art

[0002] With the rapid development of industrialization and urbanization, soil pollution is becoming increasingly serious, especially in some highly polluted areas, where harmful substances in the soil, such as heavy metals, pesticides, and petroleum pollutants, have reached levels that endanger the ecological environment and human health. Microbial remediation uses the degradation ability of natural microorganisms to effectively decompose harmful substances in the soil, and is a cutting-edge technology for soil pollution control. In particular, aerobic microorganisms have broad application prospects in degrading organic pollutants. However, deep soil pollution control faces a series of technical challenges. Due to the lack of sufficient oxygen supply in the deep soil, the growth and metabolic activity of microorganisms will be limited, which seriously affects the efficiency of pollution remediation. In order to overcome this problem, the ventilation system is introduced into the microbial remediation process to enhance the oxygen supply and promote the growth and metabolism of aerobic microorganisms, thereby improving the remediation effect.

[0003] The stability and efficient operation of the ventilation system are the key to achieving deep soil pollution remediation. However, due to the long-term unstable operating environment of the ventilation system in the deep soil, its air pumps, pipes and filters may have potential deterioration risks before obvious failures occur, which will lead to insufficient or uneven oxygen supply, thus affecting the repair effect of microorganisms, and may even cause the repair process to stagnate. In addition, the failure to provide timely warnings before failures occur due to the presence of pipelines in the deep soil will further increase the maintenance cost of the ventilation system. Therefore, how to ensure the stability and efficiency of the ventilation system has become a major challenge for microbial remediation technology in the treatment of deep soil pollution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a soil pollution control method and system based on microbial remediation to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A soil pollution control method based on microbial remediation comprises the following steps:

[0007] Step S1, obtaining speed fluctuation information of an air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient according to the speed fluctuation information of the air pump rotor;

[0008] Step S2, obtaining pipeline joint loosening information during the operation of the ventilation system, and calculating the pipeline joint loosening coefficient according to the pipeline joint loosening information;

[0009] Step S3, obtaining pore blockage information of the filter during operation of the ventilation system, and calculating a pore blockage coefficient according to the pore blockage information of the filter;

[0010] Step S4, constructing a ventilation system deterioration risk assessment model according to the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and judging whether the ventilation system has potential deterioration risks;

[0011] Step S5: When there is a potential deterioration risk in the ventilation system, the current maintenance time is adjusted according to the ventilation system deterioration risk assessment index and the previous maintenance time.

[0012] In a preferred embodiment, the speed fluctuation of the air pump rotor is measured by acquiring the speed fluctuation information of the air pump rotor, analyzing the speed fluctuation of the air pump rotor, and calculating the speed fluctuation coefficient;

[0013] The logic for obtaining the speed fluctuation coefficient is as follows:

[0014] A high-precision speed sensor is used to obtain the speed time series zs(t) of the air pump rotor, and an adaptive filtering algorithm is used to denoise the speed time series to obtain a smooth speed time series. Calculate the instantaneous speed deviation Δzs(t), the expression is as follows Calculate the speed root mean square rms, the expression is as follows Where t represents the time unit, and its value range is [0, T]. The speed peak value fzp is calculated as follows: fzp = max(Δzs(t)) - min(Δzs(t)), where max() and min() are the maximum and minimum value acquisition functions used to obtain the maximum and minimum values ​​of the instantaneous speed deviation. The speed skewness ske is calculated as follows: Where μ is the mean speed, σ is the standard deviation of the speed; calculate the speed sharpness kur, the expression is as follows Calculate the speed fluctuation coefficient Zsbd, the expression is as follows Zsbd = a1*rms + a2*fzp + a3*ske + a4*kur, where a1, a2, a3, and a4 represent the preset proportional coefficients of the root mean square speed, the peak speed, the speed skewness, and the speed sharpness, respectively, and a1, a2, a3, and a4 are all greater than 0.

[0015] In a preferred embodiment, the looseness of the pipe joint is measured by acquiring the looseness information of the pipe joint, analyzing the looseness of the pipe joint, and calculating the looseness coefficient of the pipe joint;

[0016] The logic for obtaining the looseness coefficient of pipe joints is as follows:

[0017] Collecting pipeline joint loosening data, the pipeline joint loosening data includes but is not limited to joint vibration, joint pressure, joint temperature, and joint displacement characteristic data; performing data preprocessing on the collected pipeline joint loosening data, including data cleaning, missing data filling, and standardization;

[0018] The distance between each pair of pipe joint loosening data points is calculated and recorded in the distance matrix D. The distance between each pair of pipe joint loosening data points is calculated as follows: Where d(x i ,x j ) represents the data point x of the loose pipe joint i and pipe joint loose data point x j distance, n represents the characteristic dimension of the pipeline joint loosening data, k∈{1,2,...,n}; x ik and x jk They are the loose data points x of the pipe joints. i and pipe joint loose data point x j The value of the k-th dimension feature data; each element D in the distance matrix D ij Indicates the data point x of the loose pipe joint i and pipe joint loose data point x j distance;

[0019] Initialize each pipe joint loose data point as an independent cluster, that is, each pipe joint loose data point x i is considered as a cluster C i ={x i};

[0020] Step A1, obtain the minimum distance D in the distance matrix D min and the corresponding cluster C i and cluster C j ;

[0021] Step A2: Merge cluster C i and cluster C j :C ij =C i ∪C j ;

[0022] Step A3, update the distance matrix D, delete D from the distance matrix D ij The rows and columns of ij , calculate the merged cluster C i and cluster C j The distance D ij :Dij =max(D(C i ,C j ));

[0023] Step A4, repeating steps A1, A2, and A3 until all the pipe joint loosening data points are merged;

[0024] Calculate the mean mC of all pipe joint loosening data points in each cluster: where x m is the loose pipe joint data point in the cluster, N is the number of loose pipe joint data points in the cluster, m∈{1,2,...,N};

[0025] Calculate the sum of squared differences pC between each pipe joint loosening data point and its cluster mean within each cluster:

[0026] The cluster weight w is calculated based on the number of loose pipe joint data points in each cluster. r : Where N r is the number of loose pipe joint data points in the rth cluster, r∈{1,2,...,R};

[0027] Calculate the looseness coefficient of pipe joints Gdjs, where w r represents the cluster weight of the rth cluster, pC r represents the sum of squared differences of the rth cluster.

[0028] In a preferred embodiment, the pore blocking information of the filter is obtained, the pore blocking condition of the filter is analyzed, and the pore blocking coefficient is calculated to measure the pore blocking degree of the filter;

[0029] The logic for obtaining the pore blocking coefficient is as follows:

[0030] Install pressure sensors at the filter inlet and outlet to monitor the inlet pressure P in real time. in (t) and outlet pressure P out (t), calculate the real-time pressure difference ΔP(t), the expression is as follows ΔP(t) = P in (t)-P out (t);

[0031] Airflow sensors are installed at the filter inlet and outlet to monitor the inlet airflow Q in real time. in (t) and outlet airflow Q out (t), calculate the real-time pressure difference ΔQ(t), the expression is as follows ΔQ(t) = Q in (t)-Q out (t);

[0032] Calculate the filter permeability K(t), the expression is as follows Calculate the relative change rate of filter permeability RK(t), the expression is as follows Where K0 is the initial filter permeability, that is, the permeability when the filter is not clogged;

[0033] Install an inlet particle concentration sensor to monitor the inlet particle concentration C in real time in (t), calculate the particle accumulation C acc (t), the expression is as follows Where EX is the particle capture efficiency of the filter, ranging from 0 to 1;

[0034] Obtain the filter design life T from the filter design specifications lifespan , calculate the time-weighted blocking coefficient W(t), the expression is as follows Where t represents the time unit, α represents the time weighting factor, and its value range is 0 to 1;

[0035] Calculate the pore blocking coefficient Kzsx, the expression is as follows Where Pnominal represents the rated pressure difference of the filter, that is, the reference pressure difference of the filter under normal operating conditions, Cthreshold represents the maximum adsorption capacity of particulate matter of the filter, β and γ represent the preset proportional factors of the relative change rate of filter permeability and the accumulation of particulate matter, respectively, and β and γ are both greater than 0.

[0036] In a preferred embodiment, a ventilation system deterioration risk assessment model is constructed based on the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient to generate a ventilation system deterioration risk assessment index Tvent. The model is based on the following formula: Wherein b1, b2, and b3 represent preset proportional coefficients of the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blocking coefficient, respectively, and b1, b2, and b3 are all greater than 0.

[0037] In a preferred embodiment, the ventilation system degradation risk assessment index is compared with a preset ventilation system degradation risk assessment index threshold to determine whether the ventilation system has potential degradation risks, as follows:

[0038] If the ventilation system deterioration hidden danger assessment index is greater than the ventilation system deterioration hidden danger assessment index threshold, a deterioration hidden danger signal is generated; if the ventilation system deterioration hidden danger assessment index is less than or equal to the ventilation system deterioration hidden danger assessment index threshold, an operation steady-state signal is generated.

[0039] In a preferred embodiment, when a degradation potential risk signal is generated, the corresponding generated ventilation system degradation potential risk assessment index is obtained, and the maintenance time interval adjustment coefficient Cadjust is calculated, which is expressed as follows: Tvent 阈 Indicates the threshold value of ventilation system deterioration potential risk assessment index;

[0040] The current maintenance time is regulated according to the maintenance time interval adjustment coefficient. The regulation function is as follows: Where Tprev represents the previous maintenance time, Represents a constant factor that reflects the sensitivity of regulation to the maintenance interval, ranging from 0 to 1.

[0041] In a preferred embodiment, a soil pollution control system based on microbial remediation is characterized by: comprising a speed fluctuation monitoring module, a joint loosening monitoring module, a pore blockage monitoring module, a comprehensive evaluation module, and a maintenance and control module;

[0042] The speed fluctuation monitoring module is used to obtain the speed fluctuation information of the air pump rotor during the operation of the ventilation system, and calculate the speed fluctuation coefficient based on the speed fluctuation information of the air pump rotor;

[0043] A joint loosening monitoring module is used to obtain the loosening information of the pipe joint during the operation of the ventilation system, and calculate the loosening coefficient of the pipe joint according to the loosening information of the pipe joint;

[0044] A pore blockage monitoring module is used to obtain the pore blockage information of the filter during the operation of the ventilation system, and calculate the pore blockage coefficient based on the pore blockage information of the filter;

[0045] Comprehensive assessment module, which is used to construct a ventilation system deterioration risk assessment model based on the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient, generate a ventilation system deterioration risk assessment index, and determine whether the ventilation system has potential deterioration risks;

[0046] The maintenance control module is used to control the current maintenance time according to the ventilation system deterioration risk assessment index and the previous maintenance time when there is a potential deterioration risk in the ventilation system.

[0047] Technical effects and advantages of the present invention:

[0048] 1. The present invention obtains the speed fluctuation information of the air pump rotor, calculates the speed fluctuation coefficient, and evaluates the stability of the air pump operation from the perspective of the power source; monitors the looseness of the pipe joints, calculates the looseness coefficient of the pipe joints, and quantifies the potential hidden dangers at the pipe connection; analyzes the pore blockage information of the filter, calculates the pore blockage coefficient, and accurately determines the blockage degree of the filter. Through the speed fluctuation coefficient, the looseness coefficient of the pipe joint and the pore blockage coefficient, a ventilation system deterioration hidden danger assessment model is constructed, and a ventilation system deterioration hidden danger assessment index is generated. The operating status of the ventilation system is comprehensively and accurately quantified, and it is clear whether the ventilation system has potential deterioration hidden dangers. When the assessment result shows that there is a hidden danger, the current maintenance time is dynamically adjusted according to the deterioration hidden danger assessment index and the previous maintenance time, so as to achieve precise maintenance, reduce maintenance costs, and improve the operating efficiency and stability of the ventilation system.

[0049] 2. The present invention can accurately detect potential hidden dangers before obvious failures occur in the ventilation system, and take maintenance measures in a timely manner, fundamentally ensuring the stability and uniformity of oxygen supply, promoting the efficient restoration of aerobic microorganisms in deep soil, and significantly improving the effect of soil pollution control. In addition, the present invention effectively reduces the maintenance cost of the ventilation system through intelligent monitoring and regulation of the ventilation system, solves the problems of high time consumption, high cost and low efficiency in traditional maintenance modes, and provides a more reliable, efficient and economical technical means for deep soil pollution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0051] Figure 1 This is a flow chart of the method of Example 1 of the present invention;

[0052] Figure 2 This is a flow chart of the system of Example 2 of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Embodiment 1: Figure 1 The present invention provides a soil pollution control method based on microbial remediation, which comprises the following steps:

[0055] Step S1, obtaining speed fluctuation information of an air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient according to the speed fluctuation information of the air pump rotor;

[0056] Step S2, obtaining pipeline joint loosening information during the operation of the ventilation system, and calculating the pipeline joint loosening coefficient according to the pipeline joint loosening information;

[0057] Step S3, obtaining pore blockage information of the filter during operation of the ventilation system, and calculating a pore blockage coefficient according to the pore blockage information of the filter;

[0058] Step S4, constructing a ventilation system deterioration risk assessment model according to the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and judging whether the ventilation system has potential deterioration risks;

[0059] Step S5, when there is a potential deterioration risk in the ventilation system, adjusting the current maintenance time according to the ventilation system deterioration risk assessment index and the previous maintenance time;

[0060] Step S1, obtaining speed fluctuation information of an air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient according to the speed fluctuation information of the air pump rotor;

[0061] The speed fluctuation coefficient is a key indicator used to measure the degree of abnormal fluctuation of the air pump rotor speed during the operation of the ventilation system. Its significance lies in ensuring the stability and efficiency of aerobic microbial remediation in deep soil pollution control. Due to the insufficient natural oxygen supply in deep soil, the metabolic activities of aerobic microorganisms rely on the stable oxygen provided by the ventilation system. As the core equipment for oxygen delivery, the air pump's speed stability directly affects the continuity and uniformity of oxygen supply. When the speed fluctuation coefficient rises abnormally, it may lead to insufficient oxygen delivery or uneven distribution, thereby inhibiting the growth of aerobic microorganisms and reducing the degradation efficiency of organic pollutants. In addition, changes in the speed fluctuation coefficient are usually early signals of mechanical wear, motor failure or load changes of the air pump. By monitoring this coefficient in real time, potential hidden dangers can be identified before obvious equipment failure occurs, avoiding repair interruptions or increased costs. Optimizing the management of the speed fluctuation coefficient can not only improve the efficiency of oxygen delivery, ensure the uniformity of oxygen distribution in deep soil, and reduce the uncertainty of the remediation process, but also reduce equipment energy consumption and maintenance costs, thereby significantly improving the overall benefits of pollution control;

[0062] Therefore, by acquiring the speed fluctuation information of the air pump rotor, analyzing the speed fluctuation of the air pump rotor, and calculating the speed fluctuation coefficient, the speed fluctuation degree of the air pump rotor is measured;

[0063] The logic for obtaining the speed fluctuation coefficient is as follows:

[0064] A high-precision speed sensor is used to obtain the speed time series zs(t) of the air pump rotor, and an adaptive filtering algorithm is used to denoise the speed time series to obtain a smooth speed time series. Calculate the instantaneous speed deviation Δzs(t), the expression is as follows Calculate the speed root mean square rms, the expression is as follows Where t represents the time unit, and its value range is [0, T]. The speed peak value fzp is calculated as follows: fzp = max(Δzs(t)) - min(Δzs(t)), where max() and min() are the maximum and minimum value acquisition functions used to obtain the maximum and minimum values ​​of the instantaneous speed deviation. The speed skewness ske is calculated as follows: Where μ is the mean speed, σ is the standard deviation of the speed; calculate the speed sharpness kur, the expression is as follows Calculate the speed fluctuation coefficient Zsbd, the expression is as follows Zsbd = a1*rms + a2*fzp + a3*ske + a4*kur, where a1, a2, a3, a4 represent the preset proportional coefficients of the speed root mean square, speed peak, speed skewness, and speed sharpness, respectively, and a1, a2, a3, a4 are all greater than 0;

[0065] It should be noted that the adaptive filtering algorithm is a signal processing method that dynamically adjusts the filtering parameters based on the changes in the input signal and noise characteristics. In the process of obtaining the air pump speed fluctuation coefficient, the adaptive filtering algorithm is used to denoise the speed time series, remove the high-frequency noise introduced by measurement errors, environmental interference and other factors, and retain the true speed change trend of the air pump rotor. Common adaptive filtering algorithms include wavelet denoising algorithm, Kalman filtering, etc.

[0066] It should be noted that a1, a2, a3, and a4 are set according to actual conditions. For example, the expert empowerment method is adopted, that is, experts in relevant fields are invited to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0067] Step S2, obtaining pipeline joint loosening information during the operation of the ventilation system, and calculating the pipeline joint loosening coefficient according to the pipeline joint loosening information;

[0068] The looseness coefficient of pipe joints is a key indicator used to measure the looseness fluctuation of pipe joints during the operation of the ventilation system. It is of great significance to the control of deep soil pollution. Once the pipe joints in the ventilation system are loose, it will directly lead to unstable flow and pressure of oxygen delivery, which will affect the growth and metabolic efficiency of aerobic microorganisms in deep soil. This not only reduces the degradation rate of pollutants, but also may cause uneven distribution of oxygen, resulting in severe limitation or even failure of the repair effect in some areas. By calculating the looseness coefficient of pipe joints, potential hidden dangers in the operation of the ventilation system can be detected in time, providing important information for ensuring the activity of aerobic microorganisms in deep soil and the overall efficiency of pollution control. According to the data, a larger looseness coefficient of pipe joints indicates that the looseness of pipe joints is high during the operation of the ventilation system, which indicates that there may be a large mechanical relaxation or unstable connection of the pipe joints, resulting in significant fluctuations in the flow and pressure of oxygen delivery. This is usually regarded as a warning of potential hidden dangers in the ventilation system, and timely maintenance and repair measures are required to avoid affecting the efficiency and effect of deep soil pollution remediation. A smaller looseness coefficient of pipe joints indicates that the looseness of pipe joints is low during the operation of the ventilation system, and the pipe joints maintain good mechanical stability, which provides a reliable basic support for deep soil pollution remediation and helps aerobic microorganisms to efficiently degrade pollutants and improve the treatment effect.

[0069] Therefore, the looseness of the pipeline joints is measured by obtaining the looseness information of the pipeline joints, analyzing the looseness of the pipeline joints, and calculating the looseness coefficient of the pipeline joints;

[0070] The logic for obtaining the looseness coefficient of pipe joints is as follows:

[0071] Collecting pipeline joint loosening data, the pipeline joint loosening data includes but is not limited to joint vibration, joint pressure, joint temperature, and joint displacement characteristic data; performing data preprocessing on the collected pipeline joint loosening data, including data cleaning, missing data filling, and standardization;

[0072] The distance between each pair of pipe joint loosening data points is calculated and recorded in the distance matrix D. The distance between each pair of pipe joint loosening data points is calculated as follows: Where d(x i ,x j ) represents the data point x of the loose pipe joint i and pipe joint loose data point x j distance, n represents the characteristic dimension of the pipeline joint loosening data, k∈{1,2,...,n}; x ik and x jk They are the loose data points x of the pipe joints. i and pipe joint loose data point x jThe value of the k-th dimension feature data; each element D in the distance matrix D ij Indicates the data point x of the loose pipe joint i and pipe joint loose data point x j distance;

[0073] Initialize each pipe joint loose data point as an independent cluster, that is, each pipe joint loose data point x i is considered as a cluster C i ={x i};

[0074] Step A1, obtain the minimum distance D in the distance matrix D min and the corresponding cluster C i and cluster C j ;

[0075] Step A2: Merge cluster C i and cluster C j :C ij =C i ∪C j ;

[0076] Step A3, update the distance matrix D, delete D from the distance matrix D ij The rows and columns of ij , calculate the merged cluster C i and cluster C j The distance D ij :D ij =max(D(C i ,C j ));

[0077] Step A4, repeating steps A1, A2, and A3 until all the pipe joint loosening data points are merged;

[0078] Calculate the mean mC of all pipe joint loosening data points in each cluster: where x m is the loose pipe joint data point in the cluster, N is the number of loose pipe joint data points in the cluster, m∈{1,2,...,N};

[0079] Calculate the sum of squared differences pC between each pipe joint loosening data point and its cluster mean within each cluster:

[0080] The cluster weight w is calculated based on the number of loose pipe joint data points in each cluster. r : Where N r is the number of loose pipe joint data points in the rth cluster, r∈{1,2,...,R};

[0081] Calculate the looseness coefficient of pipe joints Gdjs, where w r represents the cluster weight of the rth cluster, pC r represents the sum of squared differences of the rth cluster;

[0082] Step S3, obtaining pore blockage information of the filter during operation of the ventilation system, and calculating a pore blockage coefficient according to the pore blockage information of the filter;

[0083] The pore blocking coefficient is a key indicator used to measure the degree of blockage in the filter pores during the operation of the ventilation system. Its significance lies in that by monitoring and analyzing the pore blocking coefficient, the filter blockage state can be identified in time, thereby effectively preventing the insufficient or uneven oxygen supply of the air pump system, and ensuring sufficient oxygen supply and stability of the remediation effect during the microbial remediation process. This can avoid the decline or even failure of microbial remediation efficiency caused by filter blockage, and ensure the overall effect of deep soil pollution control. A larger pore blocking coefficient indicates that a large amount of pollutants or impurities have accumulated in the filter pores in the ventilation system, causing serious obstruction of the airflow and poor oxygen supply. This situation may lead to insufficient oxygen during the microbial remediation process, affecting the growth and metabolism of microorganisms, thereby reducing the remediation efficiency and may even cause the entire system to fail. A smaller pore blocking coefficient indicates that the pores of the filter are not blocked too much, the airflow can pass smoothly, and the oxygen supply remains stable. This helps the good growth and metabolism of microorganisms in deep soil, thereby maintaining the high efficiency and stability of pollution remediation. When the pore blocking coefficient is small, it means that the ventilation system is in good maintenance and the pollution control process can continue without significant impact;

[0084] Therefore, the pore blocking information of the filter is obtained, the pore blocking condition of the filter is analyzed, and the pore blocking coefficient is calculated to measure the pore blocking degree of the filter;

[0085] The logic for obtaining the pore blocking coefficient is as follows:

[0086] Install pressure sensors at the filter inlet and outlet to monitor the inlet pressure P in real time. in (t) and outlet pressure P out (t), calculate the real-time pressure difference ΔP(t), the expression is as follows ΔP(t) = P in (t)-P out (t);

[0087] Airflow sensors are installed at the filter inlet and outlet to monitor the inlet airflow Q in real time. in (t) and outlet airflow Q out (t), calculate the real-time pressure difference ΔQ(t), the expression is as follows ΔQ(t) = Qin (t)-Q out (t);

[0088] Calculate the filter permeability K(t), the expression is as follows Calculate the relative change rate of filter permeability RK(t), the expression is as follows Where K0 is the initial filter permeability, that is, the permeability when the filter is not clogged;

[0089] Install an inlet particle concentration sensor to monitor the inlet particle concentration C in real time in (t), calculate the particle accumulation C acc (t), the expression is as follows Where EX is the particle capture efficiency of the filter, ranging from 0 to 1, indicating the ability of the filter to remove particulate matter, and can be preset to a fixed value by a technician in this field according to the filter design specifications;

[0090] Obtain the filter design life T from the filter design specifications lifespan , calculate the time-weighted blocking coefficient W(t), the expression is as follows Wherein t represents the time unit, α represents the time weighting factor, and the value range is 0 to 1. The specific value can be set by technicians in this field according to actual conditions;

[0091] Calculate the pore blocking coefficient Kzsx, the expression is as follows Where Pnominal represents the rated pressure difference of the filter, that is, the reference pressure difference of the filter under normal operating conditions, Cthreshold represents the maximum adsorption capacity of particulate matter of the filter, β and γ represent the preset proportional factors of the relative change rate of filter permeability and the accumulation of particulate matter, respectively, and β and γ are both greater than 0;

[0092] It should be noted that before calculating the pore blocking coefficient, it is necessary to ensure that the real-time pressure difference, the rated pressure difference of the filter, the time-weighted blocking coefficient, the relative change rate of the filter permeability, the particle accumulation, and the maximum adsorption of particles of the filter are all normalized. Commonly used normalization methods include Min-Max normalization and Z-Score normalization.

[0093] It should be noted that β and γ are set by technicians in this field according to the filter characteristics, for example, by adopting the expert weighting method, that is, inviting experts in related fields to determine the preset proportional coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0094] Step S4, constructing a ventilation system deterioration risk assessment model according to the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and judging whether the ventilation system has potential deterioration risks;

[0095] According to the speed fluctuation coefficient, pipe joint looseness coefficient and pore blockage coefficient, a ventilation system deterioration risk assessment model is constructed to generate the ventilation system deterioration risk assessment index Tvent. The model is based on the following formula: Wherein b1, b2, and b3 represent the preset proportional coefficients of the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blocking coefficient, respectively, and b1, b2, and b3 are all greater than 0;

[0096] It should be noted that before constructing the ventilation system deterioration risk assessment model, it is necessary to ensure that the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient are all normalized; b1, b2, and b3 are set according to actual conditions. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to determine the preset proportional coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0097] It can be seen from the above calculation expression that the larger the speed fluctuation coefficient, the larger the loose coefficient of the pipe joint, and the larger the pore blocking coefficient, the larger the ventilation system deterioration hidden danger assessment index, indicating that the ventilation system has a greater probability of potential deterioration hidden dangers; conversely, the smaller the speed fluctuation coefficient, the smaller the loose coefficient of the pipe joint, and the smaller the pore blocking coefficient, the smaller the ventilation system deterioration hidden danger assessment index, indicating that the ventilation system has a lower probability of potential deterioration hidden dangers;

[0098] The ventilation system deterioration risk assessment index is compared with the preset ventilation system deterioration risk assessment index threshold to determine whether the ventilation system has potential deterioration risks, as follows:

[0099] If the ventilation system deterioration hazard assessment index is greater than the ventilation system deterioration hazard assessment index threshold, it means that there is a potential deterioration hazard in the ventilation system, and a deterioration hazard signal is generated. Timely maintenance measures need to be taken, such as checking and repairing problems such as air pump speed fluctuations, loose pipe joints, and filter blockage, so as to avoid further aggravation of the deterioration problem, thereby ensuring the normal operation of the ventilation system and the effect of deep soil pollution control; if the ventilation system deterioration hazard assessment index is less than or equal to the ventilation system deterioration hazard assessment index threshold, it means that the ventilation system is in normal operation, and an operation steady-state signal is generated. There is no obvious deterioration hazard, and the system can continue to operate according to the original maintenance plan and operation strategy without taking additional maintenance measures immediately, thereby effectively reducing the operation cost and ensuring the continuity and efficiency of deep soil pollution control;

[0100] Step S5, when there is a potential deterioration risk in the ventilation system, adjusting the current maintenance time according to the ventilation system deterioration risk assessment index and the previous maintenance time;

[0101] When a deterioration potential risk signal is generated, the corresponding ventilation system deterioration potential risk assessment index is obtained, and the maintenance time interval adjustment coefficient Cadjust is calculated. The expression is as follows Tvent 阈 Indicates the threshold value of ventilation system deterioration potential risk assessment index;

[0102] The current maintenance time is regulated according to the maintenance time interval adjustment coefficient. The regulation function is as follows: Where Tprev represents the previous maintenance time, Indicates a constant factor, which is used to reflect the sensitivity of the control to the maintenance interval. The value range is 0 to 1 and is set according to actual needs.

[0103] The present invention obtains the speed fluctuation information of the air pump rotor and calculates the speed fluctuation coefficient to evaluate the stability of the air pump operation from the perspective of the power source; monitors the looseness of the pipe joints and calculates the looseness coefficient of the pipe joints to quantify the potential hidden dangers at the pipe connection; analyzes the pore blockage information of the filter and calculates the pore blockage coefficient to accurately determine the blockage degree of the filter, and constructs a ventilation system deterioration hidden danger assessment model through the speed fluctuation coefficient, the pipe joint looseness coefficient and the pore blockage coefficient, generates a ventilation system deterioration hidden danger assessment index, comprehensively and accurately quantifies the operating status of the ventilation system, and clarifies whether the ventilation system has potential deterioration hidden dangers. When the assessment result shows that there is a hidden danger, the current maintenance time is dynamically adjusted according to the deterioration hidden danger assessment index and the previous maintenance time, thereby realizing precise maintenance, reducing maintenance costs while improving the operating efficiency and stability of the ventilation system.

[0104] The present invention can accurately detect potential hidden dangers before obvious failures occur in the ventilation system, and take maintenance measures in a timely manner, fundamentally ensuring the stability and uniformity of oxygen supply, promoting the efficient restoration of aerobic microorganisms in deep soil, and significantly improving the effect of soil pollution control. In addition, the present invention effectively reduces the maintenance cost of the ventilation system through intelligent monitoring and regulation of the ventilation system, solves the problems of high time consumption, high cost and low efficiency in traditional maintenance modes, and provides a more reliable, efficient and economical technical means for deep soil pollution control.

[0105] Example 2: This example is an introduction to a soil pollution control system based on microbial remediation. Figure 2 As shown, it includes a speed fluctuation monitoring module, a joint loosening monitoring module, a pore blockage monitoring module, a comprehensive evaluation module, and a maintenance and control module;

[0106] The speed fluctuation monitoring module is used to obtain the speed fluctuation information of the air pump rotor during the operation of the ventilation system, and calculate the speed fluctuation coefficient based on the speed fluctuation information of the air pump rotor;

[0107] A joint loosening monitoring module is used to obtain the loosening information of the pipe joint during the operation of the ventilation system, and calculate the loosening coefficient of the pipe joint according to the loosening information of the pipe joint;

[0108] A pore blockage monitoring module is used to obtain the pore blockage information of the filter during the operation of the ventilation system, and calculate the pore blockage coefficient based on the pore blockage information of the filter;

[0109] Comprehensive assessment module, which is used to construct a ventilation system deterioration risk assessment model based on the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient, generate a ventilation system deterioration risk assessment index, and determine whether the ventilation system has potential deterioration risks;

[0110] The maintenance control module is used to control the current maintenance time according to the ventilation system deterioration risk assessment index and the previous maintenance time when there is a potential deterioration risk in the ventilation system;

[0111] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0112] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0113] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0114] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0115] In several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A soil pollution control method based on microbial remediation, characterized in that: The steps include: Step S1, obtaining speed fluctuation information of an air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient according to the speed fluctuation information of the air pump rotor; Step S2, obtaining pipeline joint loosening information during the operation of the ventilation system, and calculating the pipeline joint loosening coefficient according to the pipeline joint loosening information; Step S3, obtaining pore blockage information of the filter during operation of the ventilation system, and calculating a pore blockage coefficient according to the pore blockage information of the filter; Step S4, constructing a ventilation system deterioration risk assessment model according to the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and judging whether the ventilation system has potential deterioration risks; Step S5: When there is a potential deterioration risk in the ventilation system, the current maintenance time is adjusted according to the ventilation system deterioration risk assessment index and the previous maintenance time.

2. A soil pollution control method based on microbial remediation according to claim 1, characterized in that: By acquiring the speed fluctuation information of the air pump rotor, analyzing the speed fluctuation of the air pump rotor, and calculating the speed fluctuation coefficient, the speed fluctuation degree of the air pump rotor is measured; The logic for obtaining the speed fluctuation coefficient is as follows: A high-precision speed sensor is used to obtain the speed time series zs(t) of the air pump rotor, and an adaptive filtering algorithm is used to denoise the speed time series to obtain a smooth speed time series. Calculate the instantaneous speed deviation Δzs(t), the expression is as follows Calculate the speed root mean square rms, the expression is as follows Where t represents the time unit, and its value range is [0, T]. The speed peak value fzp is calculated as follows: fzp = max(Δzs(t)) - min(Δzs(t)), where max() and min() are the maximum and minimum value acquisition functions used to obtain the maximum and minimum values ​​of the instantaneous speed deviation. The speed skewness ske is calculated as follows: Where μ is the mean speed, σ is the standard deviation of the speed; calculate the speed sharpness kur, the expression is as follows Calculate the speed fluctuation coefficient Zsbd, the expression is as follows Zsbd = a1*rms + a2*fzp + a3*ske + a4*kur, where a1, a2, a3, and a4 represent the preset proportional coefficients of the root mean square speed, the peak speed, the speed skewness, and the speed sharpness, respectively, and a1, a2, a3, and a4 are all greater than 0.

3. The soil pollution control method based on microbial remediation according to claim 1 is characterized in that: By obtaining the looseness information of the pipeline joint, analyzing the looseness of the pipeline joint, and calculating the looseness coefficient of the pipeline joint, the looseness degree of the pipeline joint is measured; The logic for obtaining the looseness coefficient of pipe joints is as follows: Collecting pipeline joint loosening data, the pipeline joint loosening data includes but is not limited to joint vibration, joint pressure, joint temperature, and joint displacement characteristic data; performing data preprocessing on the collected pipeline joint loosening data, including data cleaning, missing data filling, and standardization; The distance between each pair of pipe joint loosening data points is calculated and recorded in the distance matrix D. The distance between each pair of pipe joint loosening data points is calculated as follows: Where d(x i ,x j ) represents the data point x of the loose pipe joint i and pipe joint loose data point x j distance, n represents the characteristic dimension of the pipeline joint loosening data, k∈{1,2,...,n}; x ik and x jk They are the loose data points x of the pipe joints. i and pipe joint loose data point x j The value of the k-th dimension feature data; each element D in the distance matrix D ij Indicates the data point x of the loose pipe joint i and pipe joint loose data point x j distance; Initialize each pipe joint loose data point as an independent cluster, that is, each pipe joint loose data point x i is considered as a cluster C i ={x i }; Step A1, obtain the minimum distance D in the distance matrix D min and the corresponding cluster C i and cluster C j ; Step A2: Merge cluster C i and cluster C j :C ij =C i ∪C j ; Step A3, update the distance matrix D, delete D from the distance matrix D ij The rows and columns of ij , calculate the merged cluster C i and cluster C j The distance D ij :D ij =max(D(C i ,C j )); Step A4, repeating steps A1, A2, and A3 until all the pipe joint loosening data points are merged; Calculate the mean mC of all pipe joint loosening data points in each cluster: where x m is the loose pipe joint data point in the cluster, N is the number of loose pipe joint data points in the cluster, m∈{1,2,...,N}; Calculate the sum of squared differences pC between each pipe joint loosening data point and its cluster mean within each cluster: The cluster weight w is calculated based on the number of loose pipe joint data points in each cluster. r : Where N r is the number of loose pipe joint data points in the rth cluster, r∈{1,2,...,R}; Calculate the pipe joint looseness coefficient Gdjs, where w r represents the cluster weight of the rth cluster, pC r represents the sum of squared differences of the rth cluster.

4. The soil pollution control method based on microbial remediation according to claim 1 is characterized in that: By obtaining the pore blocking information of the filter, analyzing the pore blocking of the filter, and calculating the pore blocking coefficient, the pore blocking degree of the filter is measured; The logic for obtaining the pore blocking coefficient is as follows: Install pressure sensors at the filter inlet and outlet to monitor the inlet pressure P in real time. in (t) and outlet pressure P out (t), calculate the real-time pressure difference ΔP(t), the expression is as follows ΔP(t) = P in (t)-P out (t); Airflow sensors are installed at the filter inlet and outlet to monitor the inlet airflow Q in real time. in (t) and outlet airflow Q out (t), calculate the real-time pressure difference ΔQ(t), the expression is as follows ΔQ(t) = Q in (t)-Q out (t); Calculate the filter permeability K(t), the expression is as follows Calculate the relative change rate of filter permeability RK(t), the expression is as follows Where K0 is the initial filter permeability, that is, the permeability when the filter is not clogged; Install an inlet particle concentration sensor to monitor the inlet particle concentration C in real time in (t), calculate the particle accumulation C acc (t), the expression is as follows Where EX is the particle capture efficiency of the filter, ranging from 0 to 1; Obtain the filter design life T from the filter design specifications lifespan , calculate the time-weighted blocking coefficient W(t), the expression is as follows Where t represents the time unit, α represents the time weighting factor, and its value range is 0 to 1; Calculate the pore blocking coefficient Kzsx, the expression is as follows Where Pnominal represents the rated pressure difference of the filter, that is, the reference pressure difference of the filter under normal operating conditions, Cthreshold represents the maximum adsorption capacity of particulate matter of the filter, β and γ represent the preset proportional factors of the relative change rate of filter permeability and the accumulation of particulate matter, respectively, and β and γ are both greater than 0.

5. The soil pollution control method based on microbial remediation according to claim 1 is characterized in that: According to the speed fluctuation coefficient, pipe joint looseness coefficient and pore blockage coefficient, a ventilation system deterioration risk assessment model is constructed to generate the ventilation system deterioration risk assessment index Tvent. The model is based on the following formula: Wherein b1, b2, and b3 represent preset proportional coefficients of the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blocking coefficient, respectively, and b1, b2, and b3 are all greater than 0.

6. A soil pollution control method based on microbial remediation according to claim 5, characterized in that: The ventilation system deterioration risk assessment index is compared with the preset ventilation system deterioration risk assessment index threshold to determine whether the ventilation system has potential deterioration risks, as follows: If the ventilation system deterioration hidden danger assessment index is greater than the ventilation system deterioration hidden danger assessment index threshold, a deterioration hidden danger signal is generated; if the ventilation system deterioration hidden danger assessment index is less than or equal to the ventilation system deterioration hidden danger assessment index threshold, an operation steady-state signal is generated.

7. A soil pollution control method based on microbial remediation according to claim 6, characterized in that: When a deterioration potential risk signal is generated, the corresponding ventilation system deterioration potential risk assessment index is obtained, and the maintenance time interval adjustment coefficient Cadjust is calculated. The expression is as follows Tvent 阈 Indicates the threshold value of ventilation system deterioration potential risk assessment index; The current maintenance time is regulated according to the maintenance time interval adjustment coefficient. The regulation function is as follows: Where Tprev represents the previous maintenance time, Represents a constant factor that reflects the sensitivity of regulation to the maintenance interval, and its value range is 0 to 1.

8. A soil pollution control system based on microbial remediation, used to implement a soil pollution control method based on microbial remediation as claimed in any one of claims 1 to 7, characterized in that: It includes speed fluctuation monitoring module, joint loosening monitoring module, pore blockage monitoring module, comprehensive evaluation module, and maintenance control module; The speed fluctuation monitoring module is used to obtain the speed fluctuation information of the air pump rotor during the operation of the ventilation system, and calculate the speed fluctuation coefficient based on the speed fluctuation information of the air pump rotor; A joint loosening monitoring module is used to obtain the loosening information of the pipe joint during the operation of the ventilation system, and calculate the loosening coefficient of the pipe joint according to the loosening information of the pipe joint; A pore blockage monitoring module is used to obtain the pore blockage information of the filter during the operation of the ventilation system, and calculate the pore blockage coefficient based on the pore blockage information of the filter; Comprehensive assessment module, which is used to construct a ventilation system deterioration risk assessment model based on the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient, generate a ventilation system deterioration risk assessment index, and determine whether the ventilation system has potential deterioration risks; The maintenance control module is used to control the current maintenance time according to the ventilation system deterioration risk assessment index and the previous maintenance time when there is a potential deterioration risk in the ventilation system.

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