A soil pollution control method and system based on microbial remediation

Abstract

The present invention discloses a soil pollution control method and system based on microbial remediation, which specifically relates to the technical field of soil pollution control. By obtaining the speed fluctuation information of the air pump rotor and calculating the speed fluctuation coefficient, the stability of the air pump operation is evaluated from the perspective of the power source; by monitoring the looseness of the pipeline joints, the pipeline joint looseness coefficient is calculated to quantify the potential hidden dangers at the pipeline connection; by analyzing the pore blockage information of the filter, the pore blockage coefficient is calculated, the blockage degree of the filter is accurately judged, and a ventilation system deterioration hidden danger assessment model is constructed to generate a ventilation system deterioration hidden danger assessment index, which comprehensively and accurately quantifies the operating status of the ventilation system and clarifies whether the ventilation system has potential deterioration hidden dangers. When hidden dangers exist, 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.

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. 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 utilizes 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 deep soil, the growth and metabolic activity of microorganisms will be restricted, which seriously affects the efficiency of pollution remediation. In order to overcome this problem, the ventilation system was 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 key to achieving deep soil pollution remediation. However, due to the long-term operation in the unstable environment of deep soil, the ventilation system's air pumps, pipes, and filters may have potential deterioration risks before obvious failure occurs. This will lead to insufficient or uneven oxygen supply, which in turn affects the remediation effect of microorganisms and may even cause the remediation process to stagnate. In addition, the presence of pipes in deep soil will not provide timely warnings before failure occurs, which 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, the embodiments of the present invention provide 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 the air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient based on the speed fluctuation information of the air pump rotor;

[0008] Step S2, obtaining pipe joint loosening information during the operation of the ventilation system, and calculating the pipe joint loosening coefficient based on the pipe 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 based on the pore blockage information of the filter;

[0010] Step S4: constructing a ventilation system deterioration risk assessment model based on the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and determining 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] Use high-precision speed sensor to obtain the speed time series of the air pump rotor , and the adaptive filtering algorithm is used to denoise the speed time series to obtain a smooth speed time series ; Calculate the instantaneous speed deviation , the expression is as follows ; Calculate the RMS speed , the expression is as follows , where t represents the time unit and its value range is [0, T]; calculate the peak speed , the expression is as follows ,in and The maximum and minimum acquisition functions are used to obtain the maximum and minimum values ​​of the instantaneous deviation of the speed; calculate the speed deviation , the expression is as follows ,in is the mean speed, is the speed standard deviation; calculate the speed sharpness , the expression is as follows ; Calculate the speed fluctuation coefficient , the expression is as follows ,in Respectively represent the preset proportional coefficients of speed root mean square, speed peak, speed skewness, and speed sharpness, and Both are greater than 0.

[0015] In a preferred embodiment, the looseness of the pipe joint is measured by obtaining 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, including but 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] Calculate the distance between each pair of pipe joint loosening data points and record them in the distance matrix In , the distance between each pair of pipe joint looseness data points is calculated as follows: ,in Indicates data points of loose pipe joints and pipe joint loose data points distance, The characteristic dimension of the loose pipe joint data, ; and They are the data points of loose pipe joints and pipe joint loose data points The value of the k-th dimension feature data; distance matrix Each element in Indicates data points of loose pipe joints and pipe joint loose data points distance;

[0019] Initialize each pipe joint loose data point into an independent cluster, that is, each pipe joint loose data point Considered as a cluster ;

[0020] Step A1, obtain the distance matrix The minimum distance in and the corresponding clusters and clusters ;

[0021] Step A2: Merge clusters and clusters : ;

[0022] Step A3, update the distance matrix , from the distance matrix Delete rows and columns, and add new clusters , calculate the merged clusters and clusters The distance between : ;

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

[0024] Calculate the mean of all pipe joint looseness data points in each cluster : ,in is the pipe joint loosening data point within the cluster, is the number of loose pipe joint data points within the cluster, ;

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

[0026] Calculate cluster weights based on the number of loose pipe joint data points in each cluster : ,in is the number of loose pipe joint data points in the rth cluster, ;

[0027] Calculate the looseness coefficient of pipe joints , ,in represents the cluster weight of the r-th cluster, represents the sum of squared differences of the rth cluster.

[0028] In a preferred embodiment, the pore blockage degree of the filter is measured by obtaining the pore blockage information of the filter, analyzing the pore blockage condition of the filter, and calculating the pore blockage coefficient;

[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 in real time and outlet pressure , calculate the real-time pressure difference , the expression is as follows ;

[0031] Airflow sensors are installed at the filter inlet and outlet to monitor the inlet airflow in real time and outlet airflow , calculate the real-time airflow difference , the expression is as follows ;

[0032] Calculate filter permeability , the expression is as follows , calculate the relative change rate of filter permeability , the expression is as follows ,in is the initial filter permeability, i.e. the permeability when the filter is not clogged;

[0033] Install an inlet particle concentration sensor to monitor the inlet particle concentration in real time , calculate the amount of particulate matter accumulation , the expression is as follows ,in is the particle capture efficiency of the filter, ranging from 0 to 1;

[0034] Obtain the filter's design life from the filter's design specifications , calculate the time-weighted blocking coefficient , the expression is as follows ,in Indicates the unit of time, Indicates the time weighting factor, ranging from 0 to 1;

[0035] Calculate the pore blockage coefficient , the expression is as follows ,in Indicates the rated pressure difference of the filter, that is, the reference pressure difference of the filter under normal operating conditions. Indicates the maximum adsorption capacity of particulate matter by the filter. They represent the relative change rate of filter permeability and the preset proportional factors of particulate matter accumulation, respectively, and Both are greater than 0.

[0036] In a preferred embodiment, a ventilation system deterioration hidden danger assessment model is constructed based on the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient to generate a ventilation system deterioration hidden danger assessment index. The model is based on the following formula , where They represent the preset proportional coefficients of the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, respectively, and Both are greater than 0.

[0037] In a preferred embodiment, the ventilation system deterioration hidden danger assessment index is compared with a preset ventilation system deterioration hidden danger assessment index threshold to determine whether the ventilation system has potential deterioration hidden dangers, 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 deterioration potential risk signal is generated, the corresponding ventilation system deterioration potential risk assessment index is obtained and the maintenance time interval adjustment coefficient is calculated. , the expression is as follows: Maintenance time interval adjustment coefficient = ventilation system deterioration hidden danger assessment index / ventilation system deterioration hidden danger assessment index threshold;

[0040] The current maintenance time is regulated according to the maintenance time interval adjustment coefficient. The regulation function is as follows: ,in Indicates the last maintenance time. A constant factor that reflects the sensitivity of control 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] The loose joint monitoring module is used to obtain the loose pipe joint information during the operation of the ventilation system and calculate the loose pipe joint coefficient based on the loose pipe joint information;

[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] A comprehensive assessment module 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] The technical effects and advantages of the present invention are as follows:

[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 pipe joint looseness coefficient, 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 judges the blockage degree of the filter. Through the speed fluctuation coefficient, the pipe joint looseness coefficient 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 operation 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 are hidden dangers, the current maintenance time is dynamically adjusted according to the deterioration hidden danger assessment index and the previous maintenance time, thereby achieving precise maintenance, reducing maintenance costs while improving the operation 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 remediation of aerobic microorganisms in deep soil, and significantly improving the effectiveness 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, solving the problems of high time consumption, high cost and low efficiency in traditional maintenance modes, and providing 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 will be further described below with reference to 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

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

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

[0056] Step S2, obtaining pipe joint loosening information during the operation of the ventilation system, and calculating the pipe joint loosening coefficient based on the pipe 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 based on the pore blockage information of the filter;

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

[0059] 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;

[0060] Step S1, obtaining speed fluctuation information of the air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient based on 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 in 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. Optimal management of the speed fluctuation coefficient can not only improve oxygen delivery efficiency, 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 obtaining 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] Use high-precision speed sensor to obtain the speed time series of the air pump rotor , and the adaptive filtering algorithm is used to denoise the speed time series to obtain a smooth speed time series ; Calculate the instantaneous speed deviation , the expression is as follows ; Calculate the RMS speed , the expression is as follows , where t represents the time unit and its value range is [0, T]; calculate the peak speed , the expression is as follows ,in and The maximum and minimum acquisition functions are used to obtain the maximum and minimum values ​​of the instantaneous deviation of the speed; calculate the speed deviation , the expression is as follows ,in is the mean speed, is the speed standard deviation; calculate the speed sharpness , the expression is as follows ; Calculate the speed fluctuation coefficient , the expression is as follows ,in Respectively represent the preset proportional coefficients of speed root mean square, speed peak, speed skewness, and speed sharpness, and 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 role of the adaptive filtering algorithm is to denoise the speed time series, remove 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 Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0067] Step S2, obtaining pipe joint loosening information during the operation of the ventilation system, and calculating the pipe joint loosening coefficient based on the pipe joint loosening information;

[0068] The looseness coefficient of pipe joints is a key indicator used to measure the looseness fluctuations 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 become loose, it will directly lead to unstable flow and pressure of oxygen delivery, thereby affecting 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 severely limited or even failed repair effects 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 a timely manner, providing important information for ensuring the activity of aerobic microorganisms in deep soil and the overall efficiency of pollution control. Based on the data, a larger pipe joint looseness coefficient indicates that the pipe joint is more loose during the operation of the ventilation system, which indicates that the pipe joint may have a large mechanical relaxation or unstable connection, resulting in significant fluctuations in the flow rate 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 pipe joint looseness coefficient indicates that the pipe joint is less loose during the operation of the ventilation system, and the pipe connection maintains good mechanical stability, providing a reliable basic support for deep soil pollution remediation, helping 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, including but 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] Calculate the distance between each pair of pipe joint loosening data points and record them in the distance matrix In , the distance between each pair of pipe joint looseness data points is calculated as follows: ,in Indicates data points of loose pipe joints and pipe joint loose data points distance, The characteristic dimension of the loose pipe joint data, ; and They are the data points of loose pipe joints and pipe joint loose data points The value of the k-th dimension feature data; distance matrix Each element in Indicates data points of loose pipe joints and pipe joint loose data points distance;

[0073] Initialize each pipe joint loose data point into an independent cluster, that is, each pipe joint loose data point Considered as a cluster ;

[0074] Step A1, obtain the distance matrix The minimum distance in and the corresponding clusters and clusters ;

[0075] Step A2: Merge clusters and clusters : ;

[0076] Step A3, update the distance matrix , from the distance matrix Delete rows and columns, and add new clusters , calculate the merged clusters and clusters The distance between : ;

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

[0078] Calculate the mean of all pipe joint looseness data points in each cluster : ,in is the pipe joint loosening data point within the cluster, is the number of loose pipe joint data points within the cluster, ;

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

[0080] Calculate cluster weights based on the number of loose pipe joint data points in each cluster : ,in is the number of loose pipe joint data points in the rth cluster, ;

[0081] Calculate the looseness coefficient of pipe joints , ,in represents the cluster weight of the r-th cluster, 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 based on the pore blockage information of the filter;

[0083] The pore blockage coefficient (PoP) is a key indicator used to measure the degree of filter pore blockage during aeration system operation. Its significance lies in the fact that by monitoring and analyzing the PoP, filter blockage can be promptly identified, effectively preventing insufficient or uneven oxygen supply to the air pump system, ensuring adequate oxygen supply and stable remediation results during the microbial remediation process. This prevents the decline or even failure of microbial remediation efficiency due to filter blockage, thereby ensuring the overall effectiveness of deep soil pollution control. A high PoP indicates that a large amount of pollutants or impurities have accumulated in the filter pores of the aeration system, severely obstructing airflow and resulting in a poor oxygen supply. This situation may lead to insufficient oxygen during the microbial remediation process, affecting microbial growth and metabolism, reducing remediation efficiency, and even causing the entire system to fail. A low PoP indicates that the filter pores are not excessively blocked, allowing airflow to pass smoothly and maintaining a stable oxygen supply. This promotes the healthy growth and metabolism of microorganisms in the deep soil, thereby maintaining the efficiency and stability of pollution remediation. A low PoP indicates that the aeration system is well maintained 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 in real time and outlet pressure , calculate the real-time pressure difference , the expression is as follows ;

[0087] Airflow sensors are installed at the filter inlet and outlet to monitor the inlet airflow in real time and outlet airflow , calculate the real-time airflow difference , the expression is as follows ;

[0088] Calculate filter permeability , the expression is as follows , calculate the relative change rate of filter permeability , the expression is as follows ,in is the initial filter permeability, i.e. the permeability when the filter is not clogged;

[0089] Install an inlet particle concentration sensor to monitor the inlet particle concentration in real time , calculate the amount of particulate matter accumulation , the expression is as follows ,in The particle capture efficiency of the filter ranges from 0 to 1, indicating the ability of the filter to remove particulate matter. A fixed value can be preset by a person skilled in the art based on the filter design specifications.

[0090] Obtain the filter's design life from the filter's design specifications , calculate the time-weighted blocking coefficient , the expression is as follows ,in Indicates the unit of time, represents the time weighting factor, with a value ranging from 0 to 1. The specific value can be set by those skilled in the art according to actual conditions;

[0091] Calculate the pore blockage coefficient , the expression is as follows ,in Indicates the rated pressure difference of the filter, that is, the reference pressure difference of the filter under normal operating conditions. Indicates the maximum adsorption capacity of particulate matter by the filter. They represent the relative change rate of filter permeability and the preset proportional factors of particulate matter accumulation, respectively, and All greater than 0;

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

[0093] It should be noted that The setting is made by those skilled in the art according to the filter characteristics, for example, by adopting the expert weighting method, that is, inviting experts in relevant 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 based on the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and determining whether the ventilation system has potential deterioration risks;

[0095] A ventilation system deterioration hidden danger assessment model is constructed based on the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient, and a ventilation system deterioration hidden danger assessment index is generated. The model is based on the following formula , where They represent the preset proportional coefficients of the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, respectively, and 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; Set it according to the actual situation. For example, adopt the expert empowerment method, that is, invite experts in relevant fields to determine the preset proportion coefficients of various indicators through professional opinion surveys and comprehensive evaluations;

[0097] From the above calculation expression, it can be seen that the larger the speed fluctuation coefficient, the larger the pipe joint looseness coefficient, and the larger the pore blockage coefficient, the larger the ventilation system deterioration hidden danger assessment index, indicating that the probability of potential deterioration hidden dangers in the ventilation system is greater. Conversely, the smaller the speed fluctuation coefficient, the smaller the pipe joint looseness coefficient, and the smaller the pore blockage coefficient, the smaller the ventilation system deterioration hidden danger assessment index, indicating that the probability of potential deterioration hidden dangers in the ventilation system is smaller.

[0098] Compare the ventilation system deterioration risk assessment index 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 hidden danger assessment index is greater than the ventilation system deterioration hidden danger assessment index threshold, it means that there is a potential deterioration hidden danger in the ventilation system, and a deterioration hidden danger 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, to avoid further aggravation of the deterioration problem, thereby ensuring the normal operation of the ventilation system and the effectiveness of deep soil pollution control; if the ventilation system deterioration hidden danger assessment index is less than or equal to the ventilation system deterioration hidden danger assessment index threshold, it means that the ventilation system is in normal operation, an operation steady-state signal is generated, and no obvious deterioration hidden dangers appear. It can continue to operate according to the original maintenance plan and operation strategy without taking additional maintenance measures immediately, thereby effectively reducing operating costs 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, the current maintenance time is adjusted 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 is calculated. , the expression is as follows: Maintenance time interval adjustment coefficient = ventilation system deterioration hidden danger assessment index / ventilation system deterioration hidden danger assessment index threshold, ( ,in represents the ventilation system deterioration hidden danger assessment index threshold);

[0102] The current maintenance time is regulated according to the maintenance time interval adjustment coefficient. The regulation function is as follows: ,in Indicates the last 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, 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 pipe joint looseness coefficient, 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 judges the blockage degree of the filter; 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 are hidden dangers, the current maintenance time is dynamically adjusted according to the deterioration hidden danger assessment index and the previous maintenance time, thereby achieving 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 repair 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, such as Figure 2As 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] The loose joint monitoring module is used to obtain the loose pipe joint information during the operation of the ventilation system and calculate the loose pipe joint coefficient based on the loose pipe joint information;

[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] A comprehensive assessment module 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 formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas 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 using 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 program 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, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer 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 drive.

[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 will 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 the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways.

[0116] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A soil pollution control method based on microbial remediation, characterized by: The steps include: Step S1, obtaining speed fluctuation information of the air pump rotor during operation of the ventilation system, and calculating a speed fluctuation coefficient based on the speed fluctuation information of the air pump rotor; Step S2, obtaining pipe joint loosening information during the operation of the ventilation system, and calculating the pipe joint loosening coefficient based on the pipe joint loosening information; Step S3, obtaining pore blockage information of the filter during operation of the ventilation system, and calculating a pore blockage coefficient based on the pore blockage information of the filter; Step S4: constructing a ventilation system deterioration risk assessment model based on the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, generating a ventilation system deterioration risk assessment index, and determining 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; By obtaining the pore blocking information of the filter, analyzing the pore blocking condition of the filter and calculating the pore blocking coefficient, the degree of pore blocking of the filter can be 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 in real time and outlet pressure , calculate the real-time pressure difference , the expression is as follows ; Airflow sensors are installed at the filter inlet and outlet to monitor the inlet airflow in real time and outlet airflow , calculate the real-time airflow difference , the expression is as follows ; Calculate filter permeability , the expression is as follows , calculate the relative change rate of filter permeability , the expression is as follows ,in is the initial filter permeability, i.e. the permeability when the filter is not clogged; Install an inlet particle concentration sensor to monitor the inlet particle concentration in real time , calculate the amount of particulate matter accumulation , the expression is as follows ,in is the particle capture efficiency of the filter, ranging from 0 to 1; Obtain the filter's design life from the filter's design specifications , calculate the time-weighted blocking coefficient , the expression is as follows ,in Indicates the unit of time, Indicates the time weighting factor, ranging from 0 to 1; Calculate the pore blockage coefficient , the expression is as follows ,in Indicates the rated pressure difference of the filter, that is, the reference pressure difference of the filter under normal operating conditions. Indicates the maximum adsorption capacity of particulate matter by the filter. They represent the relative change rate of filter permeability and the preset proportional factors of particulate matter accumulation, respectively, and Both are greater than 0.

2. The 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: Use high-precision speed sensor to obtain the speed time series of the air pump rotor , and the adaptive filtering algorithm is used to denoise the speed time series to obtain a smooth speed time series ; Calculate the instantaneous speed deviation , the expression is as follows ; Calculate the RMS speed , the expression is as follows , where t represents the time unit and its value range is [0, T]; calculate the peak speed , the expression is as follows ,in and The maximum and minimum acquisition functions are used to obtain the maximum and minimum values ​​of the instantaneous deviation of the speed; calculate the speed deviation , the expression is as follows ,in is the mean speed, is the speed standard deviation; calculate the speed sharpness , the expression is as follows ; Calculate the speed fluctuation coefficient , the expression is as follows ,in Respectively represent the preset proportional coefficients of speed root mean square, speed peak, speed skewness, and speed sharpness, and Both are greater than 0.

3. The soil pollution control method based on microbial remediation according to claim 1, characterized in that: 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, the looseness degree of the pipeline joints can be measured; The logic for obtaining the looseness coefficient of pipe joints is as follows: Collecting pipeline joint loosening data, including but 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; Calculate the distance between each pair of pipe joint loosening data points and record them in the distance matrix In , the distance between each pair of pipe joint looseness data points is calculated as follows: ,in Indicates data points of loose pipe joints and pipe joint loose data points distance, The characteristic dimension of the loose pipe joint data, ; and They are the data points of loose pipe joints and pipe joint loose data points The value of the k-th dimension feature data; distance matrix Each element in Indicates data points of loose pipe joints and pipe joint loose data points distance; Initialize each pipe joint loose data point into an independent cluster, that is, each pipe joint loose data point Considered as a cluster ; Step A1, obtain the distance matrix The minimum distance in and the corresponding clusters and clusters ; Step A2: Merge clusters and clusters : ; Step A3, update the distance matrix , from the distance matrix Delete rows and columns, and add new clusters , calculate the merged clusters and clusters The distance between : ; Step A4, repeating steps A1, A2, and A3 until all pipe joint loosening data points are merged; Calculate the mean of all pipe joint looseness data points in each cluster : ,in is the pipe joint loosening data point within the cluster, is the number of loose pipe joint data points within the cluster, ; Calculate the sum of the squared differences between each pipe joint loosening data point and its cluster mean within each cluster : ; Calculate cluster weights based on the number of loose pipe joint data points in each cluster : ,in is the number of loose pipe joint data points in the rth cluster, ; Calculate the looseness coefficient of pipe joints , ,in represents the cluster weight of the r-th cluster, represents the sum of squared differences of the rth cluster.

4. The soil pollution control method based on microbial remediation according to claim 1, characterized in that: A ventilation system deterioration hidden danger assessment model is constructed based on the speed fluctuation coefficient, pipe joint looseness coefficient, and pore blockage coefficient, and a ventilation system deterioration hidden danger assessment index is generated. The model is based on the following formula , where They represent the preset proportional coefficients of the speed fluctuation coefficient, the pipe joint looseness coefficient, and the pore blockage coefficient, respectively, and Both are greater than 0.

5. The soil pollution control method based on microbial remediation according to claim 4, characterized in that: Compare the ventilation system deterioration risk assessment index 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.

6. The soil pollution control method based on microbial remediation according to claim 5, 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 is calculated. , the expression is as follows: Maintenance time interval adjustment coefficient = ventilation system deterioration hidden danger assessment index / ventilation system deterioration hidden danger assessment index threshold; The current maintenance time is regulated according to the maintenance time interval adjustment coefficient. The regulation function is as follows: ,in Indicates the last maintenance time. A constant factor that reflects the sensitivity of control to the maintenance interval, ranging from 0 to 1.

7. A soil pollution control system based on microbial remediation, used to implement the soil pollution control method based on microbial remediation according to any one of claims 1 to 6, characterized in that: Including speed fluctuation monitoring module, joint loosening monitoring module, pore blockage monitoring module, comprehensive evaluation module, maintenance and 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; The loose joint monitoring module is used to obtain the loose pipe joint information during the operation of the ventilation system and calculate the loose pipe joint coefficient based on the loose pipe joint information; 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; A comprehensive assessment module 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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