Regulation and control method and regulation and control system for microorganism solidification of waste slurry

Abstract

The invention provides a regulation and control method and a regulation and control system for microorganism solidification of waste slurry. The regulation and control method comprises the following steps: determining a degradation state of a slurry sample based on distribution characteristics and initial water content of pollutants in the slurry sample; determining the slurry fluidity of the slurry sample according to the degradation state and the degradation speed so as to determine the microbial adaptation amounts of different particle types in the slurry sample, and determining the optimal prediction solid state of the slurry sample according to all the microbial adaptation amounts; determining a plurality of gap margins according to the environment temperature and the slurry fluidity so as to determine the solidification trend of the slurry sample; and determining a regulation confidence value of microorganisms in the target waste slurry according to the optimal predicted solid state and solidification trend, and carrying out confidence regulation on the microorganism content of the target waste slurry in microorganism solidification through the regulation confidence value of the microorganisms. According to the scheme, the influence of non-uniform distribution of pollutants on the target waste slurry during microorganism solidification can be reduced, and the solidification effect of microorganism solidification of the target waste slurry is improved.

Description

Technical Field

[0001] The present application relates to the technical field of microbial solidification, and more specifically, to a control method and control system for waste sludge microbial solidification. Background Art

[0002] Microbial solidification is a technology that uses microorganisms (usually bacteria or fungi) to treat pollutants or solidify soil. This technology uses the metabolic activities of microorganisms to improve the physical and chemical properties of soil or waste, thereby achieving the purpose of environmental remediation or resource recovery.

[0003] Microbial solidification of waste mud is a comprehensive technology. Its successful implementation requires multidisciplinary collaboration and meticulous operational management. Through precise control factors, the effect of microbial solidification can be maximized, and effective pollutant control and resource recovery can be achieved. In the existing waste mud microbial solidification, the microbial content of historical waste mud is used to predict the microbial content of target waste mud microbial solidification, and then the microbial content in the target waste mud is determined. Since the pollutant distribution in the waste mud is uneven and the solidification conditions of different types of particles in the target waste mud are also different, predicting the microbial content in the target waste mud by the microbial content of historical waste mud will reduce the confidence of the predicted microbial content of the target waste mud, thereby reducing the solidification effect of the target waste mud. Therefore, how to reduce the impact of uneven distribution of pollutants on the target waste mud during microbial solidification and improve the solidification effect of microbial solidification of the target waste mud has become a problem faced by the industry. Summary of the invention

[0004] The present application provides a control method and control system for waste mud microbial solidification, which can reduce the impact of uneven distribution of pollutants on target waste mud during microbial solidification and improve the solidification effect of target waste mud microbial solidification.

[0005] In a first aspect, the present application provides a method for regulating waste sludge microbial solidification, comprising the following steps: Collect the initial water content of the mud sample corresponding to the target waste mud; Determining the degradation state of the pollutants in the mud sample when they are degraded based on the distribution characteristics of the pollutants in the mud sample and the initial water content; Obtaining the degradation rate of the mud sample by microorganisms, and then determining the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate, determining the microbial adaptability of the microorganisms when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity, predicting the solidification state of the mud sample through all the microbial adaptabilities, and then obtaining the best predicted solid state of the mud sample; Acquiring the ambient temperature of the target waste mud, determining the gap margin between each adjacent particle in the mud sample according to the ambient temperature and the mud fluidity, and determining the solidification trend of the mud sample according to all the gap margins and the microbial content in the mud sample; The control confidence value of the microorganisms in the target waste mud is determined according to the optimal predicted solid state and the solidification trend, and the microbial content of the target waste mud in the microbial solidification is controlled by the control confidence value of the microorganisms.

[0006] In some embodiments, determining the degradation state of the pollutants in the mud sample when degrading based on the distribution characteristics of the pollutants in the mud sample and the initial water content specifically includes: obtaining a set of contaminant properties in the mud sample; Acquire the spatial position of each pollutant in the mud sample according to the pollutant attribute set, and use the spatial positions of all pollutants as distribution characteristics of the pollutants in the mud sample; determining the spatial distribution entropy of the pollutants in the mud sample according to the distribution characteristics; The degradation state of the pollutants in the mud sample during degradation is determined by the spatial distribution entropy and the initial water content.

[0007] In some embodiments, determining the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate specifically includes: obtaining an initial mud flow velocity of the mud sample; determining the mud flow rate of the mud sample during the microbial solidification process according to the degradation state and the initial mud flow rate; The mud fluidity of the mud sample during the microbial solidification process is determined by the mud flow amount and the degradation rate.

[0008] In some embodiments, determining the microbial adaptability of microorganisms when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity specifically includes: Obtaining the particle size of each particle in the mud sample, and taking all the particle sizes as particle characteristics in the mud sample; determining a plurality of particle types in the mud sample using the particle characteristics; selecting a particle type as a selected particle type, and determining a particle degradation amount of the selected particle type; Determining the microbial adaptability of microorganisms in solidifying a selected particle type in the mud sample based on the particle degradation amount and the mud fluidity; The microbial adaptation of the microorganisms in solidifying the remaining particle types in the slurry sample was then determined.

[0009] In some embodiments, determining the gap margin between adjacent particles in the mud sample according to the ambient temperature and the mud fluidity specifically includes: taking the ambient temperature as the ambient temperature of the mud sample; A group of adjacent particles in the mud sample is selected as selected adjacent particles, and a position gap amount of the selected position difference amount is determined according to the distance between the selected adjacent particles and the mud fluidity; determining a gap margin between the selected adjacent particles in the mud sample according to the ambient temperature and the position gap amount; The gap margin between the remaining adjacent particles in the mud sample is then determined.

[0010] In some embodiments, determining the control confidence value of microorganisms in the target waste mud according to the best predicted solid state and the solidification trend specifically includes: obtaining an actual solid concentration of the mud sample; determining a microbial adjustment value of microorganisms in the mud sample based on the optimal predicted solid state, the actual solid state concentration, and the solidification trend; Obtaining the volume of target waste sludge; The control confidence value of the microorganisms in the target waste sludge is determined by the microorganism adjustment value and the volume of the target waste sludge.

[0011] In some embodiments, the confidence control of the microbial content of the target waste mud in the microbial solidification by the control confidence value of the microorganism specifically includes: Obtaining the microbial content of target waste sludge in microbial solidification; Initialize a microbial content adjustment model; Using the control confidence value of the microorganism as a constraint parameter of the microorganism content adjustment model; Using the microbial content as an initial parameter of the microbial content adjustment model; The microbial content is adjusted by the microbial content adjustment model.

[0012] In a second aspect, the present application provides a control system for waste mud microbial solidification, comprising: A collection module, used for collecting the initial water content in the mud sample corresponding to the target waste mud; A processing module, used for determining the degradation state of the pollutants in the mud sample when the pollutants are degraded based on the distribution characteristics of the pollutants in the mud sample and the initial water content; The processing module is further used to obtain the degradation rate of the mud sample by the microorganisms, and then determine the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate, determine the microbial adaptation of the microorganisms when solidifying different particle types in the mud sample based on the particle characteristics in the mud sample and the mud fluidity, predict the solidification state of the mud sample through all the microbial adaptations, and then obtain the best predicted solid state of the mud sample; The processing module is further used to obtain the ambient temperature of the target waste mud, determine the gap margin between each adjacent particle in the mud sample according to the ambient temperature and the mud fluidity, and determine the solidification trend of the mud sample through all the gap margins and the microbial content in the mud sample; An execution module is used to determine a control confidence value of microorganisms in the target waste mud according to the optimal predicted solid state and the solidification trend, and to perform confidence control on the microbial content of the target waste mud in microbial solidification through the control confidence value of the microorganisms.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned method for regulating the microbial solidification of waste mud.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for regulating the microbial solidification of waste mud.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the control method and control system for waste mud microbial solidification provided by the present application, the initial water content of the mud sample corresponding to the target waste mud is first collected; the degradation state of the pollutants in the mud sample when being degraded is determined based on the distribution characteristics of the pollutants in the mud sample and the initial water content; then the degradation rate of the mud sample by the microorganisms is obtained, and then the mud fluidity of the mud sample during the microbial solidification process is determined based on the degradation state and the degradation rate, and the microbial adaptability of the microorganism when solidifying different particle types in the mud sample is determined based on the particle characteristics in the mud sample and the mud fluidity, through All microbial adaptations are used to predict the solidification state of the mud sample, and then the best predicted solid state of the mud sample is obtained; thereby, the ambient temperature of the target waste mud is obtained, and the gap margin between each adjacent particle in the mud sample is determined according to the ambient temperature and the mud fluidity, and the solidification trend of the mud sample is determined by all the gap margins and the microbial content in the mud sample; finally, the control confidence value of the microorganisms in the target waste mud is determined according to the best predicted solid state and the solidification trend, and the microbial content of the target waste mud in microbial solidification is confidence-controlled by the control confidence value of the microorganisms.

[0016] It can be seen that in the regulation process of waste mud microbial solidification, the present application first analyzes the adaptability of the microorganisms required by the pollutants in each particle type through the distribution characteristics of the pollutants in the mud sample and the initial water content, so as to predict the optimal solidification degree of the mud sample in the microbial solidification process, and then obtain the optimal predicted solid state. The optimal predicted solid state can predict the solidification condition of the mud sample, reducing the impact of the uneven distribution of pollutants in the mud sample on the microbial solidification process; secondly, the variable degree of the gap between selected adjacent particles in the mud sample is analyzed through the flow condition of the mud in the mud sample and the ambient temperature, and the trend of the mud sample in the microbial solidification process is determined in turn, and then the solidification trend of the mud sample is obtained. The solidification trend can be used to perform confidence adjustment on the microbial content in the target waste mud; finally, the regulation confidence value of the microorganisms in the target waste mud is determined through the optimal predicted solid state and the solidification trend, and the microbial content of the target waste mud in microbial solidification is confidence regulated through the regulation confidence value of the microorganism. The above scheme can reduce the impact of uneven distribution of pollutants on the target waste mud during microbial solidification, thereby improving the solidification effect of the target waste mud microbial solidification. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a method for regulating waste sludge microbial solidification according to some embodiments of the present application; Figure 2is an exemplary flow chart for determining mud fluidity according to some embodiments of the present application; Figure 3 is an exemplary flow chart of determining a clearance margin according to some embodiments of the present application; Figure 4 is a schematic diagram of exemplary hardware and / or software of a control system for waste mud microbial solidification according to some embodiments of the present application; Figure 5 It is a structural schematic diagram of a computer device for implementing a control method for waste mud microbial solidification according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] The core of the present application is to collect the initial water content in the mud sample corresponding to the target waste mud; determine the degradation state of the pollutants in the mud sample when degrading based on the distribution characteristics of the pollutants in the mud sample and the initial water content; obtain the degradation rate of the mud sample by microorganisms, and then determine the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate, determine the microbial adaptability of the microorganism when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity, predict the solidification state of the mud sample through all the microbial adaptabilities, and then obtain the best predicted solid state of the mud sample; obtain the ambient temperature of the target waste mud, determine the gap margin between each adjacent particle in the mud sample according to the ambient temperature and the mud fluidity, and determine the solidification trend of the mud sample through all the gap margins and the microbial content in the mud sample; determine the control confidence value of the microorganism in the target waste mud according to the best predicted solid state and the solidification trend, and perform confidence control on the microbial content of the target waste mud in microbial solidification through the control confidence value of the microorganism. The above scheme can reduce the impact of uneven distribution of pollutants on the microbial solidification of the target waste mud, and improve the solidification effect of the microbial solidification of the target waste mud.

[0019] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a method for regulating waste sludge microbial solidification according to some embodiments of the present application. The method 100 for regulating waste sludge microbial solidification mainly includes the following steps: In step 101, the initial water content of a mud sample corresponding to the target waste mud is collected.

[0020] It should be noted that the initial moisture content described in the present application represents the moisture content of the mud sample corresponding to the target waste mud in the initial state (without adding microorganisms for solidification). The initial moisture content can be used to judge the moisture content of the target waste mud, and then analyze the condition of the target waste mud during microbial solidification. As a preferred embodiment, the initial moisture content in the mud sample corresponding to the target waste mud can be collected by a centrifugal collection device in the prior art. The algorithm in the centrifugal collection device is a centrifugation method. The centrifugation method is to place the waste mud sample in a high-speed centrifuge for centrifugation. During the centrifugation process, the water will be quickly separated and accumulated at the bottom of the centrifuge tube. The dry weight of the sample after centrifugation and the wet weight of the initial sample can be measured to calculate the initial moisture content. In other embodiments, other methods can also be used for collection, which will not be repeated here.

[0021] In step 102, the degradation state of the pollutants in the mud sample during degradation is determined based on the distribution characteristics of the pollutants in the mud sample and the initial water content.

[0022] In some embodiments, determining the degradation state of the pollutants in the mud sample when degrading based on the distribution characteristics of the pollutants in the mud sample and the initial water content can be achieved by using the following steps: obtaining a set of contaminant properties in the mud sample; Acquire the spatial position of each pollutant in the mud sample according to the pollutant attribute set, and use the spatial positions of all pollutants as distribution characteristics of the pollutants in the mud sample; determining the spatial distribution entropy of the pollutants in the mud sample according to the distribution characteristics; The degradation state of the pollutants in the mud sample during degradation is determined by the spatial distribution entropy and the initial water content.

[0023] It should be noted that the pollutant attribute set described in the present application is a collection of all pollutant attributes, and the pollutant attributes in the pollutant attribute set represent the types of pollutants, wherein the pollutant attributes may be heavy metals, organic pollutants, radioactive substances, etc. As a preferred embodiment, the pollutant attribute set in the mud sample can be obtained through chemical analysis equipment in the prior art, wherein the chemical analysis equipment includes an atomic absorption spectrometer, an inductively coupled plasma mass spectrometer, a gas chromatography-mass spectrometry instrument, a high performance liquid chromatograph, etc.; the spatial position of the pollutant represents the spatial position of the corresponding pollutant in the container holding the mud sample. As a preferred embodiment, the pollutant attribute set to be analyzed in the mud sample can be obtained, and the spatial position of the pollutant to be analyzed can be determined by a transmission electron microscope in the prior art to obtain the spatial position of the pollutant; in other embodiments, other methods can also be used to obtain it, which is not limited here.

[0024] In a specific implementation, determining the spatial distribution entropy of pollutants according to the distribution characteristics can be achieved in the following manner, namely: selecting a group of adjacent spatial positions in the distribution characteristics as selected adjacent spatial positions, taking the distance between two spatial positions in the selected adjacent spatial positions as the position difference amount of the selected adjacent spatial positions, and continuing to determine the position difference amount of the remaining adjacent spatial positions in the distribution characteristics, wherein the position difference amount represents a parameter value of the degree of distance difference between adjacent pollutants, which is used to analyze the state of pollutants in the mud sample, and taking the entropy of all position difference amounts as the spatial distribution entropy of pollutants, wherein the spatial distribution entropy represents a parameter value of the degree of disorder of pollutants in the mud sample, which is used to analyze the state of pollutants in the mud sample. The degradation of pollutants in the sample is analyzed; the degradation state of pollutants in the mud sample during degradation is determined by the spatial distribution entropy and the initial water content, which can be achieved in the following manner, namely: the initial concentration of pollutants in the mud sample is measured by a pollutant concentration measuring instrument in the prior art, and the spatial distribution entropy is first multiplied by the initial water content, and then the inverse of the multiplied value is used as a natural index operation, and then the value obtained by the natural index operation is multiplied by the initial concentration of pollutants in the mud sample, and finally the multiplied value is used as a characterization of the degradation state of pollutants in the mud sample during degradation; in other embodiments, other methods can also be used for determination, which are not limited here.

[0025] It should be noted that the degradation state in the present application reflects the degree of degradation of pollutants in the mud sample during the solidification process, and can be used to analyze the content of microorganisms in the mud sample. For example: assuming that the characterization value corresponding to the degradation state ranges from 0 to 10, when the characterization value corresponding to the degradation state is between 7 and 10, it means that the state of the pollutants in the mud sample when being degraded is the initial state (i.e., the original state before treatment); when the characterization value corresponding to the degradation state is between 4 and 7, it means that the state of the pollutants in the mud sample when being degraded is a degrading state (i.e., the state when the pollutant concentration is decreasing); when the characterization value corresponding to the degradation state is between 0 and 4, it means that the state of the pollutants in the mud sample when being degraded is a completely degraded state (i.e., the pollutant concentration has met the solidification index).

[0026] In step 103, the degradation rate of the mud sample by microorganisms is obtained, and then the mud fluidity of the mud sample during the microbial solidification process is determined based on the degradation state and the degradation rate. The microbial adaptability of the microorganisms when solidifying different particle types in the mud sample is determined based on the particle characteristics in the mud sample and the mud fluidity. The solidification state of the mud sample is predicted through all the microbial adaptabilities, and the best predicted solid state of the mud sample is obtained.

[0027] It should be noted that the degradation rate described in the present application represents the rate at which the mass of pollutants in the mud sample is reduced by microorganisms. This process involves the metabolism and decomposition of pollutants by microorganisms. As a preferred embodiment, the change in the mass of pollutants in the mud sample can be monitored by analytical instruments in the prior art (high performance liquid chromatograph, gas chromatography-mass spectrometry, etc.), and the change in the mass of pollutants is divided by the monitoring time. The value obtained by the division is used as the degradation rate of the mud sample by microorganisms. In other embodiments, other methods can be used to obtain it, which is not limited here.

[0028] In some embodiments, reference Figure 2 As shown, this figure is a schematic diagram of the process of determining mud fluidity in some embodiments of the present application. In this embodiment, the mud fluidity of the mud sample during the microbial solidification process can be determined according to the degradation state and the degradation rate by the following steps: First, in step 1031, the initial mud flow velocity of the mud sample is obtained; Next, in step 1032, the mud flow rate of the mud sample during the microbial solidification process is determined according to the degradation state and the initial mud flow rate; Finally, in step 1033, the mud fluidity of the mud sample during the microbial solidification process is determined by the mud flow amount and the degradation rate.

[0029] In a specific implementation, the initial mud flow velocity of the mud sample is obtained by a rheometer in the prior art, wherein the initial mud flow velocity represents the flow velocity of the mud in the mud sample after the initial stress is applied, and is used to evaluate the fluidity and viscosity characteristics of the mud sample; the mud flow amount of the mud sample during the microbial solidification process is determined according to the degradation state and the initial mud flow velocity, which can be achieved in the following manner, namely: first, the initial concentration of the pollutant in the mud sample is divided by the value corresponding to the degradation state, and then the value obtained by the division is subjected to a logarithmic operation with a base of 2, and then the value obtained by the logarithmic operation is multiplied by the initial mud flow velocity, and finally the multiplication is performed. The value is used as the mud flow rate of the mud sample during the microbial solidification process, wherein the mud flow rate represents a parameter value of the degree of change of the flow of the mud sample during the microbial solidification process, and is used to analyze the microbial solidification of the mud sample; determining the mud fluidity of the mud sample during the microbial solidification process by the mud flow rate and the degradation rate can be achieved in the following manner, namely: performing a natural exponential operation on the opposite number of the degradation rate, dividing the value obtained by the natural exponential operation by the mud flow rate, and using the value obtained by the division as the mud fluidity of the mud sample during the microbial solidification process; in other embodiments, other methods can also be used for determination, which are not limited here.

[0030] It should be noted that the mud fluidity in the present application represents the parameter value of the flow degree of various substances in the mud sample under initial stress, which can be used to judge the flow condition of the mud sample and further analyze the solidification condition of the mud sample.

[0031] In some embodiments, determining the microbial adaptability of microorganisms when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity can be achieved by the following steps: Obtaining the particle size of each particle in the mud sample, and taking all the particle sizes as particle characteristics in the mud sample; determining a plurality of particle types in the mud sample using the particle characteristics; selecting a particle type as a selected particle type, and determining a particle degradation amount of the selected particle type; Determining the microbial adaptability of microorganisms in solidifying a selected particle type in the mud sample based on the particle degradation amount and the mud fluidity; The microbial adaptation of the microorganisms in solidifying the remaining particle types in the slurry sample was then determined.

[0032] In specific implementation, the particle size of each particle in the mud sample is obtained by laser particle size analysis in the prior art, wherein the particle size is used to analyze the solidification process of the mud sample; determining multiple particle types in the mud sample through the particle characteristics can be implemented in the following manner, namely: selecting a particle size in the particle characteristics as the selected particle size, taking the set of all particle sizes in the particle characteristics that are the same as the selected particle size as a particle type, and continuing to determine the remaining particle types, wherein the particle type represents a type with the same particle size, which is used to analyze the solidification process of the mud sample; determining the particle degradation amount of the selected particle type can be implemented in the following manner, namely: obtaining the pollutant concentration in the particles corresponding to each particle size in the selected particle type through a pollutant concentration measuring instrument in the prior art, dividing the average value of all pollutant concentrations by the standard deviation of all pollutant concentrations, multiplying the value obtained by the division by the degradation rate, and using the multiplied value as the particle degradation amount of the selected particle type, wherein the particle degradation amount The parameter value representing the degree of degradation of all particles corresponding to the selected particle type is used to analyze the degradation of pollutants in the mud sample; determining the microbial adaptation of microorganisms when solidifying the selected particle type in the mud sample through the particle degradation amount and the mud fluidity can be achieved in the following manner, namely: obtaining the microbial content in the mud sample from a mud sample database, wherein the microbial content represents the number of microorganisms solidifying in the mud sample, and is used to analyze the solidification of the mud sample, observing the microbial content of particles corresponding to each particle size in the selected particle type through an optical microscope in the prior art, first multiplying the particle degradation amount by the mud fluidity, and then performing a natural exponential operation on the inverse of the multiplied value, and then multiplying the value obtained by the natural exponential operation by the sum of all microbial contents corresponding to the selected particle type, and finally using the multiplied value as the microbial adaptation of the microorganism when solidifying the selected particle type in the mud sample; in other embodiments, other methods can also be used for determination, which are not limited here.

[0033] It should be noted that the microbial adaptation amount in this application represents the parameter value of the degree of adaptation of the microorganisms required for the pollutants in the particles of the corresponding particle type, which is used to predict the most suitable microbial content in the mud sample, so as to make the mud sample achieve the best degree of solidification.

[0034] In some embodiments, the solidification state of the mud sample is predicted by all microbial adaptations, and the best predicted solid state of the mud sample is obtained by the following steps: Determining the microbial adaptation concentration of the mud sample based on all microbial adaptation amounts; obtaining a solidification state index of the mud sample; The best predicted solid state of the mud sample is predicted by the microbial adaptation concentration and the solidification state index.

[0035] In specific implementation, determining the microbial adaptation concentration of the mud sample based on all microbial adaptations can be achieved in the following manner, namely: multiplying the standard deviation of the particle size of all particles by the sum of all microbial adaptations, dividing the multiplied value by the volume of the mud sample, and using the divided value as the microbial adaptation concentration of the mud sample, wherein the microbial adaptation concentration represents the number of microorganisms that can survive and grow per unit volume in the mud sample, and is used to judge the adaptability of the microorganisms in the mud sample; obtaining the solidification state index of the mud sample can be achieved in the following manner, namely: the solidification state index can be the physical properties (such as viscosity, hardness, water content) and chemical properties (such as pH value, organic matter content) of the mud sample. As a preferred embodiment, the solidification state index of the mud sample can be obtained from a database of mud samples; the microbial adaptation concentration and the solidification state index are used to predict the mud. The optimal predicted solid state of the sample can be achieved in the following manner, namely: initialize an optimal solid state prediction model, use the solidification state index as the constraint parameter of the optimal solid state prediction model, use the microbial adaptation concentration as the initialization parameter of the optimal solid state prediction model, and predict the optimal predicted solid state index of the mud sample through the optimal solid state prediction model, wherein the optimal solid state prediction model uses a machine learning algorithm (such as a regression algorithm, a neural network, etc.) to establish a prediction function for the optimal solid state, and the prediction function is, for example: the optimal predicted solid state index = microbial adaptation concentration * A + viscosity * B1 in the solidification state index + water content * B2 in the solidification state index +... + hardness * Bn in the solidification state index, wherein A, B1, B2,..., Bn are the regression coefficients (weights) of the model, which can be determined based on the optimal predicted solid state of a large number of mud samples. In other embodiments, other methods can also be used to determine it, which is not limited here.

[0036] It should be noted that the best predicted solid state in this application is a predictive indicator that reflects the best degree of solidification of the mud sample during the microbial solidification process. It is used to predict the solidification of the mud sample, so as to facilitate the corresponding adjustment of the microbial content in the target waste mud so that the target waste mud can achieve the expected solidification effect.

[0037] In step 104, the ambient temperature of the target waste mud is obtained, the gap margin between each adjacent particle in the mud sample is determined according to the ambient temperature and the mud fluidity, and the solidification trend of the mud sample is determined by all the gap margins and the microbial content in the mud sample.

[0038] In specific implementation, the ambient temperature of the target waste mud is obtained by a temperature acquisition device in the prior art, such as a wireless temperature sensor, an infrared thermometer, etc. It should be noted that the ambient temperature of the target waste mud can be used to analyze the state of the microorganisms in the waste mud, and then the number of microorganisms suitable for the waste mud can be obtained.

[0039] In some embodiments, reference Figure 3 As shown, this figure is a schematic diagram of the process of determining the gap margin in some embodiments of the present application. In this embodiment, the gap margin between each adjacent particle in the mud sample is determined according to the ambient temperature and the mud fluidity, which can be achieved by the following steps: First, in step 1041, the ambient temperature is used as the ambient temperature of the mud sample; Next, in step 1042, a group of adjacent particles in the mud sample is selected as selected adjacent particles, and a position gap amount of the selected position difference amount is determined according to the distance between the selected adjacent particles and the mud fluidity; Then, in step 1043, a gap margin between the selected adjacent particles in the mud sample is determined according to the ambient temperature and the position gap amount; Finally, in step 1044, the gap margins between the remaining adjacent particles in the mud sample continue to be determined.

[0040] In a specific implementation, the ambient temperature is used as the ambient temperature of the mud sample, so that the analysis result of the mud sample is more consistent with the target waste mud; the distance between each adjacent particle in the mud sample is obtained by a scanning electron microscope in the prior art, and the position gap amount of the selected position difference amount is determined according to the distance between the selected adjacent particles and the mud fluidity. The following method can be used, namely: the distance between the selected adjacent particles is subtracted from the average value of the distance between all adjacent particles, the value obtained by the subtraction is divided by the standard deviation of the distance between all adjacent particles, and the value obtained by the division is used as the position measurement value of the selected adjacent particle, wherein the position measurement value represents a measurement parameter value of the distance between adjacent particle positions, and is used to measure the concentration of the mud sample. The degree is analyzed, the position measurement value is multiplied by the mud fluidity, and the value obtained by the multiplication is used as the position gap amount of the selected adjacent particles in the mud sample, wherein the position gap amount represents the parameter value of the degree of the position gap between the particles in the mud sample, and is used to judge the particle distribution in the mud sample; determining the gap margin between the selected adjacent particles in the mud sample according to the ambient temperature and the position gap amount can be achieved in the following manner, namely: performing a natural exponential operation on the inverse of the ambient temperature, multiplying the value obtained by the natural exponential operation by the position gap amount, and using the multiplied value as the gap margin between the selected adjacent particles in the mud sample; in other embodiments, other methods can also be used for determination, which are not limited here.

[0041] It should be noted that the gap margin in the present application is a parameter value that reflects the degree of variability of the gap between selected adjacent particles in the mud sample, and is used to analyze the particle gap conditions of the mud sample during the microbial solidification process, so as to facilitate corresponding judgment of the microbial content in the mud sample.

[0042] In some embodiments, determining the solidification tendency of the mud sample by all the gap margins and the microbial content in the mud sample can be achieved by the following steps: determining the gap adaptability of the mud sample according to all gap margins; obtaining a degradation rate of the mud sample by microorganisms; The solidification tendency of the mud sample is determined by the degradation rate, the gap fitness and the microbial content in the mud sample.

[0043] In specific implementation, the gap adaptability of the mud sample can be determined according to all the gap margins in the following manner, namely: the maximum gap margin among all the gap margins is divided by the minimum gap margin among all the gap margins, the divided value is subjected to a logarithmic operation with a base of 2, the value obtained by the logarithmic operation is multiplied by the difference between the maximum gap margin among the gap margins and the minimum gap margin among all the gap margins, and the multiplied value is used as the gap adaptability of the mud sample, wherein the gap adaptability indicates the adaptability of the gaps between the particles in the mud sample (the gaps after solidification). The parameter value of the gap condition) is used to analyze the solidification process of the particle gaps in the mud sample; determining the solidification trend of the mud sample through the degradation rate, the gap fitness and the microbial content in the mud sample can be achieved in the following manner, namely: dividing the microbial content in the mud sample by the degradation rate, multiplying the value obtained by the division by the gap fitness, and using the multiplied value as the solidification trend index of the mud sample; the solidification trend is characterized by the solidification trend index; in other embodiments, other methods can also be used to determine it, which is not limited here.

[0044] It should be noted that the solidification trend in the present application reflects the trend of the mud sample during the microbial solidification process, which is used to judge the situation of the mud sample during the microbial solidification process, and then to adjust the microbial solidification of the target waste mud accordingly.

[0045] In step 105, a control confidence value of microorganisms in the target waste mud is determined according to the optimal predicted solid state and the solidification trend, and the microbial content of the target waste mud in microbial solidification is controlled by confidence according to the control confidence value of the microorganisms.

[0046] In some embodiments, determining the control confidence value of microorganisms in the target waste mud according to the best predicted solid state and the solidification trend can be achieved by the following steps: obtaining an actual solid concentration of the mud sample; determining a microbial adjustment value of microorganisms in the mud sample using the best predicted solid state, the actual solid state concentration, and the solidification trend; Obtaining the volume of target waste sludge; The control confidence value of the microorganisms in the target waste sludge is determined by the microorganism adjustment value and the volume of the target waste sludge.

[0047] In specific implementation, the actual solid concentration of the mud sample is obtained by the turbidity method in the prior art, and the actual solid concentration represents the proportion of the solid matter content in the mud sample in the mud sample, which is used to evaluate the concentration of the mud sample; the microbial adjustment value of the microorganisms in the mud sample is determined by the best predicted solid state, the actual solid concentration and the solidification trend, which can be achieved in the following way, namely: the value corresponding to the representation of the best predicted solid state is divided by the actual solid concentration, and the value obtained by the division is used as the first value, the inverse of the absolute value of the product of the actual solid concentration and the solidification trend is subjected to a natural exponential operation, and the value obtained by the natural exponential operation is used as the second value, the first value is multiplied by the second value, and the multiplied value is used as the microorganism in the mud sample. The microbial adjustment value is a parameter value indicating the degree of adjustment of the microorganisms in the microbial solidification process of the mud sample, and is used to adjust the microbial content in the mud sample, thereby obtaining a microbial content that better meets the optimal solid state; the volume of the target waste mud is obtained by a volume measuring device in the prior art; the adjustment confidence value of the microorganisms in the target waste mud is determined by the microbial adjustment value and the volume of the target waste mud in the following manner, namely: performing a logarithmic operation with a base of 2 on the volume of the target waste mud, multiplying the value obtained by the logarithmic operation by the microbial adjustment value, and using the multiplied value as the adjustment confidence value of the microorganisms in the target waste mud. In other embodiments, other methods may also be used for determination, which are not limited here.

[0048] It should be noted that the control confidence value in the present application is a reliable value that reflects the adjustment of the microbial content in the target waste mud, and is used to reliably adjust the microbial content of the target waste mud during the microbial solidification process, so that the microbial solidification result of the target waste mud reaches the expected set result. The larger the control confidence value, the greater the degree of adjustment of the microbial content in the target waste mud, and vice versa, the smaller the degree of adjustment of the microbial content in the target waste mud.

[0049] In some embodiments, the confidence control of the microbial content of the target waste mud in the microbial solidification by the control confidence value of the microorganism can be achieved by the following steps: Obtaining the microbial content of target waste sludge in microbial solidification; Initialize a microbial content adjustment model; Using the control confidence value of the microorganism as a constraint parameter of the microorganism content adjustment model; Using the microbial content as an initial parameter of the microbial content adjustment model; The microbial content is adjusted by the microbial content adjustment model.

[0050] In specific implementation, the microbial content of the target waste mud in microbial solidification is obtained from the database of the target waste mud, wherein the microbial content represents the number of microorganisms in the target waste mud, microorganisms are added to the target waste mud, and the microbial content added to the target waste mud is stored in the database of the target waste mud, so as to facilitate reference for subsequent solidification of the waste mud; in other embodiments, other methods can also be used to obtain it, which is not limited here.

[0051] It should be noted that the microbial content adjustment model described in the present application uses a machine learning algorithm (such as a regression algorithm, a neural network, etc.) to establish an adjustment function of the adjusted microbial content. The adjustment function is, for example: the adjusted microbial content = the microbial control confidence value * A + the microbial content * B, wherein A and B are the weights of the microbial content adjustment model, which can be determined based on the microbial content of a large amount of target waste mud.

[0052] In addition, in another aspect of the present application, in some embodiments, the present application provides a control system for waste mud microbial solidification, referring to Figure 4 , which is a schematic diagram of exemplary hardware and / or software of a control system for waste mud microbial solidification according to some embodiments of the present application, the control system 400 for waste mud microbial solidification includes: a collection module 401, a processing module 402 and an execution module 403, which are described as follows: Collection module 401, in this application, the collection module 401 is mainly used for the regulation of waste mud microbial solidification; Processing module 402, in the present application, the processing module 402 is used to determine the degradation state of the pollutants in the mud sample when they are degraded based on the distribution characteristics of the pollutants in the mud sample and the initial water content; It should be noted that the processing module 402 in the present application is also used to obtain the degradation rate of the mud sample by the microorganisms, and then determine the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate, determine the microbial adaptation of the microorganisms when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity, predict the solidification state of the mud sample through all the microbial adaptations, and then obtain the best predicted solid state of the mud sample; In addition, it should be noted that the processing module 402 in the present application is also used to obtain the ambient temperature of the target waste mud, determine the gap margin between each adjacent particle in the mud sample according to the ambient temperature and the mud fluidity, and determine the solidification trend of the mud sample through all the gap margins and the microbial content in the mud sample; Execution module 403, in the present application, execution module 403 is mainly used to determine the control confidence value of the microorganisms in the target waste mud according to the optimal predicted solid state and the solidification trend, and to perform confidence control on the microbial content of the target waste mud in the microbial solidification through the control confidence value of the microorganisms.

[0053] In addition, the present application also provides a computer device, which includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned method for regulating the microbial solidification of waste mud.

[0054] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing a control method for waste mud microbial solidification according to some embodiments of the present application. The control method for waste mud microbial solidification in the above embodiment can be achieved by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0055] The processor 501 may be a general-purpose central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0056] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0057] The memory 503 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0058] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0059] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0060] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0061] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0062] In addition, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for regulating the microbial solidification of waste mud is implemented.

[0063] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for regulating waste sludge microbial solidification, characterized in that: The steps include: Collect the initial water content of the mud sample corresponding to the target waste mud; Determining the degradation state of the pollutants in the mud sample when they are degraded based on the distribution characteristics of the pollutants in the mud sample and the initial water content; Obtaining the degradation rate of the mud sample by microorganisms, and then determining the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate, determining the microbial adaptability of microorganisms when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity, predicting the solidification state of the mud sample through all the microbial adaptabilities, and then obtaining the best predicted solid state of the mud sample; Acquiring the ambient temperature of the target waste mud, determining the gap margin between each adjacent particle in the mud sample according to the ambient temperature and the mud fluidity, and determining the solidification trend of the mud sample according to all the gap margins and the microbial content in the mud sample; The control confidence value of the microorganisms in the target waste mud is determined according to the optimal predicted solid state and the solidification trend, and the microbial content of the target waste mud in the microbial solidification is controlled by the control confidence value of the microorganisms.

2. The method according to claim 1, characterized in that Determining the degradation state of the pollutants in the mud sample when being degraded based on the distribution characteristics of the pollutants in the mud sample and the initial water content specifically includes: obtaining a set of contaminant properties in the mud sample; Acquire the spatial position of each pollutant in the mud sample according to the pollutant attribute set, and use the spatial positions of all pollutants as distribution characteristics of the pollutants in the mud sample; determining the spatial distribution entropy of the pollutants in the mud sample according to the distribution characteristics; The degradation state of the pollutants in the mud sample during degradation is determined by the spatial distribution entropy and the initial water content.

3. The method according to claim 1, characterized in that Determining the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation speed specifically includes: obtaining an initial mud flow velocity of the mud sample; determining the mud flow rate of the mud sample during the microbial solidification process according to the degradation state and the initial mud flow rate; The mud fluidity of the mud sample during the microbial solidification process is determined by the mud flow amount and the degradation rate.

4. The method according to claim 1, characterized in that Determining the microbial adaptability of microorganisms when solidifying different types of particles in the mud sample based on the particle characteristics in the mud sample and the mud fluidity specifically includes: Obtaining the particle size of each particle in the mud sample, and taking all the particle sizes as particle characteristics in the mud sample; determining a plurality of particle types in the mud sample using the particle characteristics; selecting a particle type as a selected particle type, and determining a particle degradation amount of the selected particle type; Determining the microbial adaptability of microorganisms in solidifying a selected particle type in the mud sample based on the particle degradation amount and the mud fluidity; The microbial adaptation of the microorganisms in solidifying the remaining particle types in the slurry sample was then determined.

5. The method according to claim 1, characterized in that Determining the gap margin between adjacent particles in the mud sample according to the ambient temperature and the mud fluidity specifically includes: taking the ambient temperature as the ambient temperature of the mud sample; A group of adjacent particles in the mud sample is selected as selected adjacent particles, and a position gap amount of the selected position difference amount is determined according to the distance between the selected adjacent particles and the mud fluidity; determining a gap margin between the selected adjacent particles in the mud sample according to the ambient temperature and the position gap amount; The gap margin between the remaining adjacent particles in the mud sample is then determined.

6. The method according to claim 1, characterized in that Determining the control confidence value of microorganisms in the target waste mud according to the best predicted solid state and the solidification trend specifically includes: obtaining an actual solid concentration of the mud sample; determining a microbial adjustment value of microorganisms in the mud sample based on the optimal predicted solid state, the actual solid state concentration, and the solidification trend; Obtaining the volume of target waste mud; The control confidence value of the microorganisms in the target waste sludge is determined by the microorganism adjustment value and the volume of the target waste sludge.

7. The method according to claim 1, characterized in that The confidence control of the microbial content of the target waste mud in the microbial solidification by the control confidence value of the microorganism specifically includes: Obtaining the microbial content of target waste sludge in microbial solidification; Initialize a microbial content adjustment model; Using the control confidence value of the microorganism as a constraint parameter of the microorganism content adjustment model; Using the microbial content as an initial parameter of the microbial content adjustment model; The microbial content is adjusted by the microbial content adjustment model.

8. A control system for waste mud microbial solidification, characterized in that: include: A collection module, used for collecting the initial water content in the mud sample corresponding to the target waste mud; A processing module, used for determining the degradation state of the pollutants in the mud sample when the pollutants are degraded based on the distribution characteristics of the pollutants in the mud sample and the initial water content; The processing module is further used to obtain the degradation rate of the mud sample by the microorganisms, and then determine the mud fluidity of the mud sample during the microbial solidification process according to the degradation state and the degradation rate, determine the microbial adaptation of the microorganisms when solidifying different particle types in the mud sample based on the particle characteristics in the mud sample and the mud fluidity, predict the solidification state of the mud sample through all the microbial adaptations, and then obtain the best predicted solid state of the mud sample; The processing module is further used to obtain the ambient temperature of the target waste mud, determine the gap margin between each adjacent particle in the mud sample according to the ambient temperature and the mud fluidity, and determine the solidification trend of the mud sample through all the gap margins and the microbial content in the mud sample; An execution module is used to determine a control confidence value of microorganisms in the target waste mud according to the optimal predicted solid state and the solidification trend, and to perform confidence control on the microbial content of the target waste mud in microbial solidification through the control confidence value of the microorganisms.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the method for regulating waste sludge microbial solidification according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for regulating the microbial solidification of waste sludge as claimed in any one of claims 1 to 7 is implemented.

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