An ai vision-based fly ash treatment device operation control system and method

Abstract

The application discloses an AI vision-based fly ash treatment equipment operation control system and method, and relates to the technical field of fly ash treatment.The system comprises an AI vision detection module, an abnormality detection and processing module, a fly ash treatment module and a parameter regression adjustment module.The AI vision detection module is used for detecting the physical and chemical characteristic information of fly ash.The abnormality detection and processing module is used for analyzing whether humidity abnormality and fly ash abnormal agglomeration occur, and processing abnormal conditions.The fly ash treatment module is used for controlling the fly ash treatment equipment to treat fly ash, and adjusting equipment parameters according to residual amount detection results.The parameter regression adjustment module is used for regression adjustment of equipment parameters.The application improves the accuracy of fly ash treatment and the operation efficiency of fly ash treatment equipment through abnormality detection and processing and real-time adjustment of equipment parameters, and solves the problem of energy waste caused by increasingly high equipment parameters through regression adjustment of equipment parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fly ash treatment, and in particular to a fly ash treatment equipment operation control system and method based on AI vision. BACKGROUND

[0002] Fly ash is a hazardous waste generated by waste incineration, coal-fired power plants, metallurgy and other industries, and its main components are inorganic substances, and also contains heavy metals (such as Pb, Cd, Hg) and dioxins and other toxic substances; if not properly treated, it can cause serious environmental pollution and threaten ecological balance and human health; at the same time, with the increasing strictness of environmental protection regulations, enterprises are facing huge environmental protection pressure and need efficient and compliant fly ash treatment technology to meet the requirements; traditional fly ash treatment methods have some limitations, such as low treatment efficiency and low resource recycling rate; in recent years, with the rapid development of artificial intelligence technology, artificial intelligence technology has been introduced in the control of fly ash treatment equipment, but the current technology still has the problem of being difficult to monitor and accurately control the treatment process in real time, and the parameters of the fly ash treatment equipment cannot be flexibly adjusted, which can cause problems in the fly ash treatment process; if the equipment parameters of the fly ash treatment equipment are too low, the treatment or separation of different components in the fly ash will not be complete, and if the equipment parameters of the fly ash treatment equipment are too high, energy will be wasted and the treatment efficiency will be low. SUMMARY

[0003] The purpose of the present application is to provide a fly ash treatment equipment operation control system and method based on AI vision to solve the problems in the prior art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a fly ash treatment equipment operation control method based on AI vision, comprising the following steps:

[0005] S1, collecting fly ash information by using a high-resolution industrial camera, an infrared sensor and a laser scattering sensor, and extracting fly ash feature data;

[0006] S2, analyzing whether there is humidity abnormality and fly ash abnormal agglomeration according to the real-time detected humidity data and fly ash particle diameter data, and processing the abnormal conditions;

[0007] S3, treating the fly ash by the fly ash treatment equipment, detecting the residual amount of components after each treatment step, and adjusting the equipment parameters in real time according to the residual amount detection result;

[0008] S4, recording and storing the changes of the equipment parameters in the cloud data processing center, and performing regression adjustment of the equipment parameters of the fly ash treatment equipment at fixed periods.

[0009] Further, in step S1: the morphology, color and fly ash particle diameter of fly ash particles are collected by a high-resolution industrial camera and a laser sensor, and the spectral information of different fly ash particles is detected by an infrared sensor; the type of fly ash is analyzed according to the collected particle diameter and spectral information data of fly ash particles by using a convolutional neural network, and the fly ash particles are classified according to toxic substances, heavy metals, salt components and building material substrate components; the proportion of the number of each type of fly ash in the total number of fly ash is detected and calculated, and the proportion of toxic substances is recorded as q d ; the proportion of heavy metals is recorded as q j ; the proportion of salt components is recorded as q y ; and the proportion of building material substrate components is recorded as q c .

[0010] Further, in step S2: the fly ash particles are subjected to vibration anti-agglomeration treatment by a vibrating screen, and the operating power of the vibrating screen is set to P z1 when the device is started; the humidity of the fly ash particles is controlled by a dryer to prevent the fly ash particles from being damp and sticking together, and the operating power of the dryer is set to P h1 when the device is started.

[0011] According to the detected fly ash characteristic data and the humidity data in the fly ash treatment device, abnormal conditions are detected and processed; during the abnormal condition detection process, first, a humidity threshold W max is set, the humidity W in the fly ash treatment device is collected in real time by a humidity sensor, W is compared and analyzed with W max , and it is judged in real time whether the humidity in the fly ash treatment device exceeds the set humidity threshold; if W < W max , it is judged that the humidity in the fly ash treatment device is normal; if W ≥ W max , it is judged that the humidity in the fly ash treatment device is abnormally high, and an abnormal humidity warning is issued, and an instruction is issued to increase the operating power of the dryer by ΔP h .

[0012] While issuing the instruction to increase the power of the dryer for humidity control, abnormal detection is performed on the particle diameter of the fly ash; whether an abnormal agglomeration condition occurs is judged by the collected fly ash particle diameter data; the real-time detected fly ash particle diameter is {D1, D2,..., D n}, the fly ash particle diameter threshold is set as D max , the detected fly ash particle diameter D i is compared and analyzed with D max , if D i < D max , it is judged that there is no abnormal agglomeration condition of fly ash particles; if D i ≥ Dmax , judge the abnormal agglomeration of fly ash particles; wherein i = 1, 2, …, n;

[0013] Different processing schemes are triggered according to the detected abnormal conditions:

[0014] If W ≥ W max and D i < D max , it is judged that the humidity of the fly ash treatment equipment is abnormally high, but the fly ash particles are not abnormally agglomerated, only the instruction to increase the power of the dryer for humidity control is issued;

[0015] If W ≥ W max and D i ≥ D max , it is judged that the humidity of the fly ash equipment is abnormal, and the fly ash particles are abnormally agglomerated; then, in addition to issuing the instruction to increase the power of the dryer for humidity control, the instruction to increase the operating power of the vibrating screen by ΔP z is further issued to separate the abnormally agglomerated fly ash particles; a warning of abnormal composition detection of fly ash particles is issued; and the proportion of each fly ash component detected in step S1 is marked as abnormal data, after the vibrating screen separates the abnormally agglomerated fly ash particles, an instruction is issued to control the detection equipment to re-detect the composition of the fly ash particles, and the proportion of each fly ash component in the total fly ash quantity calculated by re-detection is recorded as q d ', q j ', q y ' and q c ', wherein the proportion of toxic substances is recorded as q d ', the proportion of heavy metals is recorded as q j ', the proportion of salt components is recorded as q y ', and the proportion of building material substrate components is recorded as q c '; through real-time monitoring of the humidity data of the fly ash treatment equipment and the abnormal agglomeration of fly ash particles, the sticking of fly ash particles caused by the increase in humidity can be reduced, and the error in fly ash composition monitoring caused by fly ash particle agglomeration can be prevented.

[0016] Further, in step S3, the fly ash treatment equipment processes the fly ash particles according to the following steps:

[0017] S3-1, detoxification treatment of toxic substances contained in fly ash by low-temperature pyrolysis;

[0018] S3-2, removing salt components in fly ash by precipitation and rinsing;

[0019] S3-3, removing heavy metals in fly ash by circulating rinsing;

[0020] S3-4, collect and store the building material substrate components contained in the treated fly ash for subsequent production of building materials;

[0021] After each treatment step, a component residual amount detection is performed, and a component residual amount threshold is set for controlling the component residual amount; a toxic substance residual amount threshold q d0 is set, a salt component residual amount threshold q y0 is set, and a heavy metal residual amount threshold q j0 is set;

[0022] In step S3-1, a low-temperature pyrolysis process with a time length of Δt0 is set to detoxify the toxic substances in the fly ash; after the low-temperature pyrolysis process with a time length of Δt0 ends, a toxic substance residual amount detection is performed on the treated fly ash, and the toxic substance residual amount in the fly ash is recorded as q d ′′, and q d ′′ is compared with the set toxic substance residual amount threshold; if q d ′′≤q d0 , it is judged that the toxic substance residual amount in the fly ash meets the standard, and then the process in step S3-2 is entered; if q d ′′>q d0 , it is judged that the toxic substance residual amount in the fly ash is high, and then an instruction is generated to control the treatment equipment to increase the pyrolysis treatment time length, and a pyrolysis process with a time length of Δt is added, and then a toxic substance residual amount detection is performed again until q d ′′≤q d0 is detected, and then the process in step S3-2 is entered, and the total pyrolysis treatment time length is recorded, where m1 represents the number of times of increasing the pyrolysis treatment time length, and the first treatment time length of the next fly ash pyrolysis treatment is set as Δt′;

[0023] In step S3-2, the addition of chemical additives is used to promote the precipitation of salt components; first, a chemical additive with a mass of s0 is added for the precipitation treatment of salt components, after the first precipitation reaction and filtration, a salt component residual amount detection is performed, and the salt component residual amount is recorded as q y ′′; if q y ′′≤q y0 , it is judged that the salt component residual amount meets the standard, and then the process in step S4-3 is entered; if q y ′′>q y0 , it is judged that the salt component residual amount is high, and then an instruction is generated to control the treatment equipment to add a chemical additive with a mass of Δs to precipitate the salt components again, and then a salt component residual amount detection is performed again until q y ′′≤q y0 is detected, and then the process in step S3-3 is entered, and the total mass of the added chemical additives is recorded. wherein m2 represents the number of times of adding the chemical additive with the mass of Δs; and the mass of the chemical additive added for the first time in the sediment rinsing process of the next fly ash treatment is set as s';

[0024] In step S3-3, the heavy metals contained in the fly ash are first treated by m0 times of circulating rinsing process, and after m0 times of circulating rinsing, the residual amount of heavy metals in the fly ash is detected, and the residual amount of heavy metals in the fly ash is recorded as q j The q j is compared with the set threshold value of the residual amount of heavy metals, if q j ≤ q j0 , it is judged that the residual amount of heavy metals meets the standard, and then the process in step S3-4 is entered; if q j > q j0 , it is judged that the residual amount of heavy metals is high, and then an instruction is generated to control the treatment equipment to add one time of circulating rinsing to treat the residual heavy metals, and then the residual amount of heavy metals is detected again until q j ≤ q j0 , and then the process in step S3-4 is entered, and the final circulating rinsing times m3 is recorded, and the first circulating rinsing times of the next fly ash treatment process is set as m3;

[0025] The residual amount detection after each fly ash treatment step can ensure that the residual amount meets the standard in the fly ash treatment process, and can achieve the effect of improving the recovery rate of different components in the fly ash. And when the residual amount is detected to be abnormal each time, the treatment data of the fly ash treatment equipment is updated, and the equipment parameters of the fly ash treatment equipment are adjusted according to the treatment condition, so that the same residual amount detection and numerical adjustment process as the current treatment process is not repeated in the next treatment, and the efficiency of the fly ash treatment process is improved while the fly ash treatment process is monitored and controlled in real time.

[0026] Further, in step S4: the number of times of fly ash treatment η of the fly ash treatment equipment is recorded in the cloud data processing center, and the parameter change of the fly ash treatment equipment in each fly ash treatment process is recorded, and the equipment parameters of each fly ash treatment are recorded as A η ; and the period of equipment parameter regression adjustment is set as k, and after k times of fly ash treatment, the cloud data processing center performs equipment parameter regression adjustment according to the recorded equipment parameter change; in the regression adjustment process of the fly ash equipment treatment parameters, the number of times of equipment parameter change in k times of fly ash treatment is recorded as k1, and the regression adjustment result of the equipment parameters is calculated by the following formula:

[0027] ;

[0028] Wherein, A represents the device parameters after regression adjustment, A0 represents the initial device parameters set in the fly ash treatment device when fly ash treatment is not performed; the device parameters represent pyrolysis duration, rinsing times and chemical additive mass; and in the parameter regression adjustment process, if the adjusted parameter is rinsing times, the calculation result of the device parameter regression adjustment is calculated by rounding up;

[0029] The cloud data processing center records and stores the regression adjustment times of the device parameters and the device parameter values after each regression adjustment after the device parameters are regression adjusted; and every k2 times of device parameter regression adjustment, the adjusted k2 device parameter adjustment results are compared and analyzed to determine whether the k2 times of regression adjustment results are the same, if it is detected that the k2 times of device parameter regression adjustment results are the same, an instruction is issued to control the fly ash treatment device to adjust the device parameters to the initial device parameters A0; if it is detected that the k2 times of device parameter adjustment results are the initial device parameters A0, an instruction is issued to down-regulate the initial device parameters of the fly ash treatment device, and the initial device parameters are adjusted to A0'=p A0, wherein p is a proportionality coefficient set by the system, and 0<p<1; by setting fixed period device parameter regression adjustment in the cloud data processing center, the device parameters can be regression adjusted after a period of fly ash treatment process, solving the problem that the parameter value becomes higher and higher in the process of real-time adjustment of the device parameters according to the fly ash treatment situation at the device end, preventing the problems of energy waste and long processing time caused by the device parameters always increasing, and the regular parameter regression improves the working efficiency of the fly ash treatment device.

[0030] An AI vision-based fly ash treatment device operation control system, the system comprising an AI vision detection module, an abnormality detection processing module, a fly ash treatment module and a parameter regression adjustment module;

[0031] The AI vision detection module detects the physical and chemical characteristic information of fly ash by using a high-resolution industrial camera, an infrared sensor and a laser scattering sensor;

[0032] The abnormality detection processing module analyzes whether humidity abnormality and fly ash abnormal agglomeration occur according to the real-time monitored humidity data and fly ash particle diameter data, and processes the abnormality;

[0033] The fly ash treatment module is used for controlling the fly ash treatment device to treat fly ash, detecting the residual amount of components after each treatment step, and adjusting the device parameters in real time according to the residual amount detection result;

[0034] The parameter regression adjustment module is used for controlling the cloud data processing center to record and store the parameter changes of the device end, and to perform regression adjustment on the device parameters of the fly ash treatment device according to a fixed period.

[0035] Further, the AI visual detection module comprises a physical feature detection unit, a chemical feature detection unit and a fly ash component identification unit; the physical feature detection unit collects the morphology, color and particle diameter of fly ash particles through a high-resolution industrial camera and a laser sensor; the chemical feature detection unit detects the chemical components of fly ash particles through an infrared sensor; and the fly ash component identification unit analyzes the components of fly ash according to the collected fly ash particle data by using a convolutional neural network.

[0036] Further, the abnormality detection processing module comprises a humidity abnormality detection unit, a fly ash particle abnormal agglomeration detection unit and an abnormality condition processing unit; the humidity abnormality detection unit collects the humidity in the fly ash treatment device through a humidity sensor, compares and analyzes the collected humidity data with a set humidity threshold value, and judges whether the humidity in the fly ash treatment device is abnormal; the fly ash particle abnormal agglomeration detection unit compares the fly ash particle diameter with a set fly ash particle diameter threshold value, and judges whether the fly ash particles are in abnormal agglomeration condition; and the abnormality condition processing unit is used for processing abnormality conditions according to the detected abnormality condition types.

[0037] Further, the fly ash treatment module comprises a fly ash treatment unit, a residual amount detection unit and a device parameter updating unit; the fly ash treatment unit detoxifies the toxic substances in fly ash through low-temperature pyrolysis, removes the salt components in fly ash through sedimentation and rinsing, and removes the heavy metals in fly ash through circulating rinsing; the residual amount detection unit is used for setting a residual amount threshold value, comparing the detected residual amount value with the set residual amount threshold value, and judging whether the residual amount of the treated component meets the standard; and the device parameter updating unit is used for updating the device parameters of the fly ash treatment device in real time.

[0038] Further, the parameter regression adjustment module comprises a cloud data storage unit and a device parameter regression adjustment unit; the cloud data storage unit is used for recording and storing the fly ash treatment times and device parameter changes; and the device parameter regression adjustment unit is used for performing periodic regression adjustment on the device parameters of the fly ash treatment device in the cloud data processing center.

[0039] Compared with the prior art, the present application has the following beneficial effects:

[0040] The application provides an AI vision-based fly ash treatment equipment operation control system and method, fly ash particle data is collected through a sensor, and a convolutional neural network is used for fly ash component identification, thereby improving the fly ash component identification efficiency; the humidity data of the fly ash treatment equipment and the abnormal agglomeration of fly ash particles are detected and processed in real time, thereby reducing the fly ash particle bonding caused by humidity increase, and preventing the fly ash component detection inaccuracy caused by fly ash particle agglomeration; the residual amount of the treated component is detected after each fly ash treatment step, the equipment parameters of the fly ash treatment equipment are adjusted in real time according to the treatment condition when the residual amount is monitored to be abnormal each time, thereby improving the resource recovery rate of the fly ash treatment process, and the residual amount detection and numerical adjustment process of the same as the present treatment process are not repeated in the next treatment, thereby improving the fly ash treatment efficiency; and the fixed-period equipment parameter regression adjustment is set in the cloud data processing center, the equipment parameters can be adjusted after a period of fly ash treatment, thereby solving the problem of higher and higher equipment parameter values in the process of real-time adjustment of the equipment parameters according to the fly ash treatment condition, preventing the problems of energy waste and long processing time caused by the continuously increasing equipment parameters, and the regular parameter regression further improves the working efficiency of the fly ash treatment equipment and avoids energy waste. BRIEF DESCRIPTION OF DRAWINGS

[0041] Fig. 1 FIG. 1 is a flowchart of an AI vision-based fly ash treatment equipment operation control method according to the present application;

[0042] Fig. 2 FIG. 2 is a structural diagram of an AI vision-based fly ash treatment equipment operation control system according to the present application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0044] As shown in FIG. 1, the present application provides a technical solution, an AI vision-based fly ash treatment equipment operation control method, as shown in FIG. 2, the method comprises the following steps: Figs. 1-2 Fig. 1 S1, collecting fly ash information by using a high-resolution industrial camera, an infrared sensor and a laser scattering sensor, and extracting fly ash feature data;

[0045] S1, collecting fly ash information by using a high-resolution industrial camera, an infrared sensor and a laser scattering sensor, and extracting fly ash feature data;

[0046] ​S2, analyze whether there are humidity abnormalities and fly ash abnormal agglomeration conditions according to real-time detected humidity data and fly ash particle diameter data, and handle abnormal conditions;

[0047] S3, treat fly ash by fly ash treatment equipment, detect residual amount of ingredients after each treatment step, and adjust equipment parameters in real time according to the residual amount detection results;

[0048] S4, record and store the changes of equipment parameters in the cloud data processing center, and perform regression adjustment of the equipment parameters of the fly ash treatment equipment at fixed periods.

[0049] In step S1: the morphology, color and fly ash particle diameter of fly ash particles are collected by high-resolution industrial cameras and laser sensors, and the spectral information of different fly ash particles is detected by infrared sensors; the type of fly ash is analyzed according to the collected fly ash particle diameter and spectral information data using a convolutional neural network, and the fly ash particles are classified according to toxic substances, heavy metals, salt components and building material substrate components; the proportion of the number of each type of fly ash in the total number of fly ash is calculated, and the proportion of toxic substances is recorded as q d ; the proportion of heavy metals is recorded as q j ; the proportion of salt components is recorded as q y ; and the proportion of building material substrate components is recorded as q c .

[0050] In step S2: the fly ash particles are subjected to vibration anti-agglomeration treatment by a vibrating screen, and the operating power of the vibrating screen is set to P z1 when the equipment is started; the humidity of the fly ash particles is controlled by a dryer to prevent the fly ash particles from being damp and sticking together, and the operating power of the dryer is set to P h1 when the equipment is started.

[0051] According to the detected fly ash characteristic data and the humidity data in the fly ash treatment equipment, the abnormal conditions are detected and handled; during the abnormal condition detection process, first, set the humidity threshold W max , collect the humidity W in the fly ash treatment equipment in real time by the humidity sensor, compare and analyze W with W max , and judge in real time whether the humidity in the fly ash treatment equipment exceeds the set humidity threshold; if W max , it is judged that the humidity in the fly ash treatment equipment is normal; if W≥W max , it is judged that the humidity in the fly ash treatment equipment is abnormally high, and a humidity abnormality warning is issued, and a command is issued to increase the operating power of the dryer by ΔP h ;

[0052] While issuing a command to increase the dryer power for humidity control, the system also performs anomaly detection on the fly ash particle diameter; it uses the collected fly ash particle diameter data to determine if abnormal agglomeration has occurred; the real-time detected fly ash particle diameter is {D1, D2, ..., D...} n Set the fly ash particle diameter threshold to D. max The detected fly ash particle diameter D i With D max Comparative analysis is performed, if D i <D max To determine if there is any abnormal agglomeration of fly ash particles; if D i ≥D max To determine if fly ash particles exhibit abnormal agglomeration; where i = 1, 2, ..., n;

[0053] Different handling schemes are triggered based on the detected anomalies:

[0054] If W≥W max And D i <D max If the situation is determined to be that the humidity of the fly ash treatment equipment is abnormally high but the fly ash particles are abnormally agglomerated, then only an instruction to increase the power of the dryer is issued to control the humidity.

[0055] If W≥W max And D i ≥D max If the system determines that the fly ash equipment has abnormal humidity and that fly ash particles are abnormally agglomerated, then while issuing a command to increase the dryer power for humidity control, a further command will be issued to increase the operating power of the vibrating screen by ΔP. z This system is used to vibrate and separate abnormally agglomerated fly ash particles; issue an early warning for abnormal fly ash particle composition detection; and mark the proportion of fly ash components detected in step S1 as abnormal data. After the vibrating screen separates the abnormally agglomerated fly ash particles, it issues a command to control the detection equipment to re-detect the fly ash particle composition, and records the proportion of each fly ash component in the total fly ash quantity as q. d ′、q j ′、q y ′ and q c The proportion of toxic substances in '' is denoted as q. d The proportion of heavy metals is denoted as q. j The proportion of salt components is denoted as q. y The proportion of the building material base material component is denoted as q. cBy monitoring the humidity data of the fly ash treatment device and the abnormal agglomeration of fly ash particles in real time, the adhesion of fly ash particles caused by the increase of humidity can be reduced, and the monitoring error of fly ash composition caused by the agglomeration of fly ash particles can be prevented.

[0056] In step S3, the fly ash treatment device processes the fly ash particles according to the following steps:

[0057] S3-1, detoxification treatment of toxic substances contained in fly ash by low-temperature pyrolysis;

[0058] S3-2, removing salt components in fly ash by sedimentation and rinsing;

[0059] S3-3, removing heavy metals in fly ash by circulating rinsing;

[0060] S3-4, collecting and storing the building material substrate components contained in the processed fly ash for subsequent production of building materials;

[0061] After each processing step, the residual amount of the component is detected, and the residual amount threshold of the component is set to control the residual amount of the component. The residual amount threshold of toxic substances is q d0 , the residual amount threshold of salt components is q y0 , and the residual amount threshold of heavy metals is q j0 ;

[0062] In step S3-1, the low-temperature pyrolysis process with a time length of Δt0 is set to detoxify the toxic substances in fly ash. After the low-temperature pyrolysis process with a time length of Δt0 is completed, the residual amount of toxic substances in the processed fly ash is detected, and the residual amount of toxic substances in the fly ash is recorded as q d ′′, and q d ′′ is compared with the set residual amount threshold of toxic substances. If q d ′′≤q d0 , it is judged that the residual amount of toxic substances in fly ash meets the standard, and the processing process of step S3-2 is entered. If q d ′′>q d0 , it is judged that the residual amount of toxic substances in fly ash is high, and an instruction is generated to control the processing device to increase the pyrolysis processing time. A pyrolysis process with a time length of Δt is added, and then the residual amount of toxic substances is detected again until q d ′′≤q d0 , and then the processing process of step S3-2 is entered, and the total pyrolysis processing time is recorded as , where m1 represents the number of times of increasing the pyrolysis processing time, and the first processing time of the next fly ash pyrolysis processing is set as Δt′;

[0063] In step S3-2, the salt component is precipitated by adding chemical additives. First, the amount of chemical additives added for the precipitation of the salt component is set as s0. After the first precipitation reaction and filtration, the residual amount of the salt component is detected and recorded as q y If q y ≤ q y0 , it is determined that the residual amount of the salt component meets the standard, and the process proceeds to step S4-3. If q y > q y0 , it is determined that the residual amount of the salt component is high, and an instruction is generated to control the processing device to add chemical additives with an amount of Δs to precipitate the salt component again. Then, the residual amount of the salt component is detected again until q y ≤ q y0 , and the process proceeds to step S3-3. The total amount of chemical additives added is recorded, where m2 represents the number of times the chemical additives with an amount of Δs are added. The amount of chemical additives added for the first time in the precipitation and rinsing process of the next fly ash treatment is set as s′.

[0064] In step S3-3, the heavy metals contained in the fly ash are treated in m0 cycles of the rinsing process. After m0 cycles of rinsing, the residual amount of heavy metals in the fly ash is detected and recorded as q j . q j is compared with the set threshold value of the residual amount of heavy metals. If q j ≤ q j0 , it is determined that the residual amount of heavy metals meets the standard, and the process proceeds to step S3-4. If q j > q j0 , it is determined that the residual amount of heavy metals is high, and an instruction is generated to control the processing device to increase the number of cycles of rinsing to treat the residual heavy metals. Then, the residual amount of heavy metals is detected again until q j ≤ q j0 , and the process proceeds to step S3-4. The number of cycles of rinsing m3 is recorded, and the number of cycles of rinsing for the first time in the next fly ash treatment process is set as m3.

[0065] ​The residual amount detection after each fly ash treatment step can ensure that the residual amount in the fly ash treatment process meets the standard and can improve the recovery rate of different components in the fly ash. When the residual amount is detected to be abnormal each time, the processing data of the fly ash treatment equipment is updated, the equipment parameters of the fly ash treatment equipment are adjusted according to the processing condition, and the same residual amount detection and numerical adjustment process as the present processing process is repeated in the next processing. Real-time monitoring and control of the fly ash treatment process is realized, and the efficiency of the fly ash treatment process is improved.

[0066] In step S4: record the number of times η of fly ash treatment of the fly ash treatment equipment in the cloud data processing center, and record the parameter change of the fly ash treatment equipment in each fly ash treatment process. The equipment parameters of each fly ash treatment are recorded as A η ; and the period of equipment parameter regression adjustment is set to k. After k times of fly ash treatment, the cloud data processing center performs equipment parameter regression adjustment according to the recorded equipment parameter change. In the regression adjustment process of the fly ash equipment treatment parameters, the number of times of change of the equipment parameters in k fly ash treatment processes is recorded as k1, and the regression adjustment result of the equipment parameters is calculated by the following formula:

[0067] ;

[0068] Wherein, A represents the equipment parameters after regression adjustment, A0 represents the initial equipment parameters set in the fly ash treatment equipment when no fly ash treatment is performed; the equipment parameters represent pyrolysis time, rinsing times and chemical addition and quality; in the parameter regression adjustment process, if the adjusted parameter is the rinsing times, the calculation result of the equipment parameter regression adjustment is calculated by rounding up;

[0069] After the cloud data processing center performs regression adjustment on the equipment parameters, the number of times of regression adjustment of the equipment parameters and the equipment parameter values after each regression adjustment are recorded and stored. Every k2 times of equipment parameter regression adjustment, the adjusted k2 equipment parameter adjustment results are compared and analyzed to determine whether the k2 times of regression adjustment results are the same. If it is detected that the k2 times of equipment parameter regression adjustment results are the same, an instruction is sent to control the fly ash treatment equipment to adjust the equipment parameters to the initial equipment parameters A0. If it is detected that the k2 times of equipment parameter adjustment results are the initial equipment parameters A0, an instruction is sent to down-regulate the initial equipment parameters of the fly ash treatment equipment, and the initial equipment parameters are adjusted to A0′=p A0, wherein p is a proportionality coefficient set by the system, and 0<p<1;

[0070] By setting a fixed period of device parameter regression adjustment in the cloud data processing center, the device parameters can be adjusted after a period of fly ash treatment process, solving the problem of increasing parameter values in the process of real-time adjustment of device parameters according to the fly ash treatment situation at the device end, preventing energy waste and long processing time caused by continuously increasing device parameters, and regular parameter regression improves the working efficiency of the fly ash treatment equipment.

[0071] An AI vision-based fly ash treatment equipment operation control system, as shown in Fig. 2 The system includes an AI vision detection module, an abnormality detection processing module, a fly ash treatment module, and a parameter regression adjustment module.

[0072] The AI vision detection module uses high-resolution industrial cameras, infrared sensors, and laser scattering sensors to detect the physical and chemical characteristic information of fly ash.

[0073] The abnormality detection processing module analyzes whether there are humidity abnormalities and fly ash abnormal agglomeration based on real-time monitored humidity data and fly ash particle diameter data, and processes abnormal situations.

[0074] The fly ash treatment module is used to control the fly ash treatment equipment to treat fly ash, detect the residual amount of components after each treatment step, and adjust the equipment parameters in real time based on the residual amount detection results.

[0075] The parameter regression adjustment module is used to control the cloud data processing center to record and store the parameter changes of the device end, and to adjust the device parameters of the fly ash treatment equipment periodically.

[0076] The AI vision detection module includes a physical characteristic detection unit, a chemical characteristic detection unit, and a fly ash component identification unit. The physical characteristic detection unit collects the morphology, color, and particle diameter of fly ash particles through high-resolution industrial cameras and laser sensors. The chemical characteristic detection unit detects the chemical composition of fly ash particles through infrared sensors. The fly ash component identification unit analyzes the composition of fly ash based on the collected fly ash particle data using a convolutional neural network.

[0077] The abnormality detection processing module includes a humidity abnormality detection unit, a fly ash particle abnormal agglomeration detection unit, and an abnormal situation processing unit. The humidity abnormality detection unit compares and analyzes the collected humidity data with the set humidity threshold value to determine whether the humidity in the fly ash treatment equipment is abnormal. The fly ash particle abnormal agglomeration detection unit compares the fly ash particle diameter with the set fly ash particle diameter threshold value to determine whether the fly ash particles are abnormally agglomerated. The abnormal situation processing unit is used to process abnormal situations according to the detected abnormal situation type.

[0078] The fly ash treatment module comprises a fly ash treatment unit, a residual amount detection unit and a device parameter updating unit; the fly ash treatment unit detoxifies toxic substances in fly ash through low-temperature pyrolysis, removes salt components in fly ash through sedimentation and rinsing, and removes heavy metals in fly ash through circulating rinsing; the residual amount detection unit is used to set a residual amount threshold and compare the detected residual amount value with the set residual amount threshold, so as to determine whether the residual amount of the treated component meets the standard; the device parameter updating unit is used to update the device parameters of the fly ash treatment device in real time.

[0079] The parameter regression adjustment module comprises a cloud data storage unit and a device parameter regression adjustment unit; the cloud data storage unit is used to record and store the fly ash treatment times and device parameter changes; the device parameter regression adjustment unit is used to periodically adjust the device parameters of the fly ash treatment device in the cloud data processing center.

[0080] In step S1: the morphology, color and fly ash particle diameter of fly ash particles are collected by a high-resolution industrial camera and a laser sensor, and the spectral information of different fly ash particles is detected by an infrared sensor; the type of fly ash is analyzed according to the collected particle diameter and spectral information data of fly ash particles by using a convolutional neural network, and the fly ash particles are classified according to toxic substances, heavy metals, salt components and building material substrate components; and the proportion of the number of each type of fly ash in the total number of fly ash is detected and calculated, the proportion of the collected toxic substances is denoted as q d =1%; the proportion of heavy metals is denoted as q j =1%; the proportion of salt components is denoted as q y =35%; and the proportion of building material substrate components is denoted as q c =63%.

[0081] In step S2: the fly ash particles are subjected to vibration anti-agglomeration treatment by a vibrating screen, and the operating power of the vibrating screen is set to P z1 =300W when the device is started; the humidity of fly ash particles is controlled by a dryer to prevent fly ash particles from being damp and sticking together, and the operating power of the dryer is set to P h1 =400W when the device is started;

[0082] The humidity threshold W max =5% is set, and the humidity W in the fly ash treatment device is collected in real time by a humidity sensor W=4%; W is compared with W max to determine whether the humidity in the fly ash treatment device exceeds the set humidity threshold; according to the collected data, W max , it is determined that the humidity in the fly ash treatment device is normal;

[0083] While increasing the dryer power for humidity control, the system also performs anomaly detection on the fly ash particle diameter. The collected fly ash particle diameter data is used to determine if abnormal agglomeration has occurred. The real-time detected fly ash particle diameter is {D1, D2, ..., D...}. n Set the fly ash particle diameter threshold to D. max =100μm, the detected fly ash particle diameter D i With D max Comparative analysis is performed, if D i <D max If D is an abnormal agglomeration of fly ash particles, then it is determined that the fly ash particles are not abnormally agglomerated; i ≥D max If i = 1, 2, ..., n, then it is determined that the fly ash particles have abnormally agglomerated; where i = 1, 2, ..., n;

[0084] Furthermore, different handling procedures are triggered depending on the detected anomaly:

[0085] If W≥W max D i <D max If the situation is determined to be an abnormally high humidity in the fly ash treatment equipment but abnormal agglomeration of fly ash particles, then only an instruction to increase the power of the dryer is issued for humidity control.

[0086] If W≥W max And D i ≥D max If the humidity of the fly ash equipment is abnormal and abnormal agglomeration of fly ash particles occurs, then while issuing a command to increase the power of the dryer for humidity control, a further command to increase the power of the vibrating screen is issued to vibrate and separate the abnormally agglomerated fly ash particles. An early warning of abnormal fly ash particle composition detection is also issued. The proportion of fly ash components detected in step S1 is marked as abnormal data. After the vibrating screen separates the abnormally agglomerated fly ash particles, a command is issued to control the detection equipment to re-detect the fly ash particle composition. The proportion of each fly ash component in the total fly ash quantity after re-detection and calculation is recorded as q. d ′、q j ′、q y ′ and q c The proportion of toxic substances in '' is denoted as q. d The proportion of heavy metals is denoted as q. j The proportion of salt components is denoted as q. y The proportion of the building material base material component is denoted as q. c By monitoring the humidity data and abnormal agglomeration of fly ash particles in the fly ash treatment equipment in real time, it is possible to reduce the adhesion of fly ash particles caused by increased humidity and prevent errors in fly ash composition monitoring caused by fly ash particle agglomeration.

[0087] In step S3: the fly ash particles are treated according to the following steps:

[0088] S3-1, detoxification treatment of toxic substances contained in fly ash by low-temperature pyrolysis;

[0089] S3-2, removal of salt components in fly ash by sedimentation and rinsing;

[0090] S3-3, removal of heavy metals in fly ash by circulating rinsing;

[0091] S3-4, collecting and storing the building material substrate components contained in the treated fly ash for subsequent production of building materials;

[0092] After each treatment step, the residual amount of the component is detected, and a component residual amount threshold is set for controlling the residual amount of the component; the toxic substance residual amount threshold is set to q d0 = 0.01%, the salt component residual amount threshold is set to q y0 = 0.1%, and the heavy metal residual amount threshold is set to q j0 = 0.01%;

[0093] In step S3-1, the low-temperature pyrolysis process with a time length of Δt0 = 1 h is used to detoxify the toxic substances in fly ash; after the low-temperature pyrolysis process with a time length of Δt0 = 1 h is completed, the residual amount of toxic substances in the treated fly ash is detected, and the detected residual amount of toxic substances in fly ash is denoted as q d ′′= 0.02%, it is determined that q d ′′> q d0 , it is determined that the residual amount of toxic substances in fly ash is high, and an instruction is generated to control the processing equipment to increase the pyrolysis processing time, and a pyrolysis process with a time length of Δt = 0.5 h is added, and then the residual amount of toxic substances is detected again. If the second detection shows that q d ′′= 0.009%, it is determined that q d ′′≤ q d0 , the process of step S3-2 is entered, and the total pyrolysis processing time is recorded as , where m1 = 1, and the first processing time for the next fly ash pyrolysis processing is set to 1.5 h;

[0094] In step S3-2, chemical additives are added to promote the precipitation of salt components. First, a chemical additive with a mass of s0 = 2 kg is added for the precipitation treatment of salt components. After the first precipitation reaction and filtration, the residual amount of salt components is detected, and the residual amount of salt components is denoted as q y ′′= 0.09%, it is determined that q y ′′≤ q y0If the residual amount of salt component is up to the standard, then the process proceeds to step S3-3.

[0095] In step S3-3, m0=6 is set first, and the residual amount of heavy metal in fly ash is detected after 6 cycles of the circulating rinsing process, and the residual amount of heavy metal in fly ash is recorded as q j If q j ≤q j0 , then it is judged that the residual amount of heavy metal in fly ash is up to the standard, and the process proceeds to step S3-4.

[0096] In step S4, the number of fly ash treatment processes η of the fly ash treatment device is recorded in the cloud data processing center, and the parameter changes of the fly ash treatment device in each fly ash treatment process are recorded, and the device parameters of each fly ash treatment process are recorded as A η , and the period of device parameter regression adjustment is set as k=4, and the cloud data processing center performs device parameter regression adjustment once according to the recorded device parameter changes after every 4 fly ash treatment processes; in the regression adjustment process of the fly ash device treatment parameters, the number of times of device parameter changes in k fly ash treatment processes is recorded as k1=3, and the regression adjustment result of the device parameters is calculated by the following formula:

[0097] ;

[0098] , A0 represents the initial device parameters of the fly ash treatment device when no fly ash treatment is performed; the device parameters can represent pyrolysis duration, rinsing times, and chemical addition and quality; according to the monitoring results, the parameter changes of the pyrolysis duration in 4 fly ash treatment processes are analyzed, A1(Δt)=1.5h, A2(Δt)=2h, A3(Δt)=2h, and A4(Δt)=2.5h; then A(Δt)=1.75h can be calculated; and in the parameter regression adjustment process, if the adjusted parameter is the rinsing times, then the calculation result of the device parameter regression adjustment is calculated by rounding up; then the parameters of the rinsing times are calculated, A1(m3)=7 times, A2(m3)=7 times, A3(m3)=8 times, and A4(m3)=9 times; then A=7.3125, and A(m3)=8 times are obtained by rounding up A.

[0099] After the cloud data processing center makes the regression adjustment on the device parameters, the number of times of regression adjustment of the device parameters and the device parameter values after each regression adjustment are recorded and stored; and every k2=10 times of device parameter regression adjustment, the adjusted k2 device parameter adjustment results are compared and analyzed to determine whether the results of k2 times of regression adjustment are the same, if the k2 times of device parameter regression adjustment results are detected to be the same, an instruction is sent to control the fly ash treatment device to adjust the device parameters to the initial device parameters A0; if it is detected that the k2 times of device parameter adjustment results are the initial device parameters, an instruction is sent to down-regulate the initial device parameters of the fly ash treatment device, and the initial device parameters are adjusted to A0'=p A0, wherein p=80%; by setting fixed period device parameter regression adjustment in the cloud data processing center, the device parameters can be adjusted after one period of fly ash treatment process, solving the problem of higher and higher parameter values in the process of real-time adjustment of device parameters according to the fly ash treatment situation at the device end, preventing the problems of energy waste and long processing time caused by the device parameters always increasing, and the regular parameter regression improves the working efficiency of the fly ash treatment device.

[0100] It will be obvious to a person skilled in the art that the application is not limited to the details of the above-described exemplary embodiments, but that the application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be considered in all respects as illustrative and not restrictive, and the scope of the application is defined by the appended claims rather than the above description, and it is intended to include all changes falling within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be considered as limiting the claims involved.

Claims

1. A method for controlling the operation of fly ash treatment equipment based on AI vision, characterized in that: Includes the following steps: S1. Use a high-resolution industrial camera, infrared sensor and laser scattering sensor to collect fly ash information and extract fly ash feature data; S2. Analyze whether there are abnormal humidity and abnormal fly ash agglomeration based on the real-time detected humidity data and fly ash particle diameter data, and handle the abnormalities accordingly. S3. The fly ash is treated by the fly ash treatment equipment. The residual content of the components is detected after each treatment step, and the equipment parameters are adjusted in real time according to the residual content detection results. S4. Record and store changes in equipment parameters in the cloud data processing center, and perform regression adjustments on the equipment parameters of the fly ash treatment equipment at fixed intervals; In step S2: the fly ash particles are vibrated to prevent agglomeration using a vibrating screen, and the operating power of the vibrating screen is set to P when the equipment is started. z1 The humidity of fly ash particles is controlled by a dryer, and the dryer's operating power is set to P when the equipment is started. h1 ; Anomalies are detected and handled based on the detected fly ash characteristic data and humidity data in the fly ash treatment equipment. During anomaly detection, a humidity threshold W is first set. max The humidity W in the fly ash treatment equipment is collected in real time by a humidity sensor, and W is compared with W max Comparative analysis is performed to determine in real time whether the humidity in the fly ash treatment equipment exceeds the set humidity threshold; if W <W max Determine if the humidity in the fly ash treatment equipment is normal; if W ≥ W max If the humidity in the fly ash treatment equipment is determined to be abnormally high, a humidity anomaly warning will be issued, and a command will be issued to increase the operating power of the dryer by ΔP. h ; While issuing commands to increase the dryer's operating power for humidity control, the system also performs anomaly detection on the fly ash particle diameter; it uses the collected fly ash particle diameter data to determine if abnormal agglomeration has occurred; the real-time detected fly ash particle diameter is {D1, D2, ..., D...} n Set the fly ash particle diameter threshold to D. max The detected fly ash particle diameter D i With D max Comparative analysis is performed, if D i <D max To determine if there is any abnormal agglomeration of fly ash particles; if D i ≥D max To determine if fly ash particles exhibit abnormal agglomeration; where i = 1, 2, ..., n; Different handling schemes are triggered based on the detected anomalies: If W≥W max And D i <D max If the fly ash treatment equipment is judged to have abnormally high humidity but no abnormal agglomeration of fly ash particles, then only an instruction to increase the operating power of the dryer is issued to control the humidity. If W≥W max And D i ≥D max If the system determines that the fly ash equipment has abnormal humidity and that fly ash particles are abnormally agglomerated, then while issuing a command to increase the dryer power for humidity control, a further command will be issued to increase the operating power of the vibrating screen by ΔP. z It also issues an alert regarding abnormal fly ash particle composition detection. The fly ash component ratio detected in step S1 is marked as abnormal data. After the vibrating screen separates the abnormally agglomerated fly ash particles, a command is issued to control the detection equipment to re-detect the fly ash particle composition. The proportion of each fly ash component in the total fly ash quantity calculated by the re-detection is recorded as q. d ′、q j ′、q y ′ and q c The proportion of toxic substances in '' is denoted as q. d The proportion of heavy metals is denoted as q. j The proportion of salt components is denoted as q. y The proportion of the building material base material component is denoted as q. c ′.

2. The method for controlling the operation of fly ash treatment equipment based on AI vision according to claim 1, characterized in that: In step S1: The morphology, color, and diameter of fly ash particles are acquired using a high-resolution industrial camera and laser sensor; the spectral information of different fly ash particles is detected using an infrared sensor; a convolutional neural network is used to analyze the types of fly ash based on the acquired particle diameter and spectral information data, and the fly ash particles are classified according to toxic substances, heavy metals, salt components, and building material substrate components; the proportion of each type of fly ash to the total amount of fly ash is detected and calculated, and the proportion of toxic substances is recorded as q. d ; Let q be the proportion of heavy metals. j The proportion of salt components is denoted as q. y The proportion of the building material base material is denoted as q. c .

3. The method for controlling the operation of fly ash treatment equipment based on AI vision according to claim 1, characterized in that: In step S3: The fly ash treatment equipment processes the fly ash particles according to the following steps: S3-1. Detoxification of toxic substances contained in fly ash by low-temperature pyrolysis; S3-2. Remove salt components from fly ash by sedimentation and rinsing; S3-3. Remove heavy metals from fly ash by circulating rinsing; S3-4. Collect and store the building material base components contained in the treated fly ash for subsequent building material production; After each processing step, a residual component level is measured, and a residual component level threshold is set to control the residual component level; the residual threshold for toxic substances is set as q. d0 Set a threshold q for residual salt content. y0 Set the heavy metal residue threshold to q. j0 ; In step S3-1, a low-temperature pyrolysis process with a duration of Δt0 is set to detoxify the toxic substances in the fly ash; after the low-temperature pyrolysis process with a duration of Δt0 is completed, the residual amount of toxic substances in the treated fly ash is detected, and the residual amount of toxic substances in the fly ash is recorded as q. d '', and q d Compare q' with the set threshold for residual toxic substances. d ''≤q d0 If the residual amount of toxic substances in the fly ash is determined to meet the standard, then proceed to step S3-2 of the processing procedure. If q d ''>q d0 If the fly ash is found to have a high level of residual toxic substances, an instruction is generated to control the processing equipment to increase the pyrolysis treatment time, adding an extra pyrolysis process of duration Δt, and then re-detecting the residual toxic substances until q is detected. d ''≤q d0 Then proceed to step S3-2 and record the total pyrolysis processing time. Where m1 represents the number of times the pyrolysis treatment time Δt is increased, and the first treatment time of the next fly ash pyrolysis treatment is set to Δt′; In step S3-2, a chemical additive is added to promote the precipitation of salt components. First, a chemical additive with a mass of s0 is added to precipitate the salt components. After the initial precipitation reaction and filtration, the residual amount of salt components is detected and recorded as q. y If q y ''≤q y0 If the residual salt content is determined to be within the acceptable range, then proceed to step S4-3; if q y ''>q y0 If the residual salt content is determined to be high, an instruction is generated to control the processing equipment to add a chemical additive with a mass of Δs to precipitate the salt content again. Then, the residual salt content is detected again until q is detected. y ''≤q y0 Then proceed to step S3-3 and record the total mass of the added chemical additives. , where m2 represents the number of times a chemical additive with a mass of Δs is added; and the mass of the chemical additive added for the first time during the sedimentation and rinsing process of the next fly ash treatment is set as s′; In step S3-3, m0 cycles of rinsing are first set up to treat the heavy metals contained in the fly ash, and the residual amount of heavy metals is detected after m0 cycles of rinsing. The residual amount of heavy metals in the fly ash is recorded as q. j '', will q j Compare q' with the set heavy metal residue threshold; if q j ''≤q j0 If the heavy metal residue is determined to meet the standard, then proceed to step S3-4 of the processing procedure; If q j ''>q j0 If the system determines that the heavy metal residue is high, it generates an instruction to control the processing equipment to add an extra cycle of rinsing to treat the remaining heavy metals, and then performs heavy metal residue detection again until q is detected. j ''≤q j0 Then proceed to step S3-4, record the final number of rinsing cycles m3, and set the first rinsing cycle number for the next fly ash treatment process to m3.

4. The method for controlling the operation of fly ash treatment equipment based on AI vision according to claim 1, characterized in that: In step S4: The number of times the fly ash treatment equipment performs fly ash treatment η is recorded in the cloud data processing center, and the parameter changes in the fly ash treatment equipment during each fly ash treatment are recorded. The equipment parameters for each fly ash treatment are denoted as A. η The period for adjusting the equipment parameters is set to k. After every k fly ash treatments, the cloud data processing center performs a regression adjustment of the equipment parameters based on the recorded changes. During the regression adjustment of the fly ash equipment treatment parameters, the number of times the equipment parameters change during the k fly ash treatments is recorded as k1. The regression adjustment result of the equipment parameters is calculated using the following formula: ； Where A represents the equipment parameters after regression adjustment, and A0 represents the initial equipment parameters set in the fly ash treatment equipment before fly ash treatment; during the parameter regression adjustment process, if the adjusted parameter is the number of rinsing cycles, the calculation result of the equipment parameter regression adjustment is rounded up. After performing regression adjustments on the equipment parameters, the cloud data processing center records and stores the number of regression adjustments and the parameter values ​​after each adjustment. For every k² regression adjustments, the results are compared and analyzed to determine if they are identical. If the results are identical, a command is issued to adjust the fly ash processing equipment to its initial parameter A0. If the results are all identical, a command is issued to lower the initial parameter A0, setting it to A0′=p. A0, where p is the proportional coefficient set by the system, and 0 <p<1。 5. An AI vision-based fly ash treatment equipment operation control system, applied to the AI ​​vision-based fly ash treatment equipment operation control method as described in claim 1, characterized in that: The system includes: AI visual inspection module, anomaly detection and processing module, fly ash processing module, and parameter regression adjustment module; The AI ​​vision inspection module uses a high-resolution industrial camera, infrared sensor, and laser scattering sensor to detect the physical and chemical characteristics of fly ash. The anomaly detection and processing module analyzes whether there are abnormal humidity or abnormal fly ash agglomeration based on the real-time monitored humidity data and fly ash particle diameter data, and processes the abnormalities accordingly. The fly ash treatment module is used to control the fly ash treatment equipment to treat fly ash, perform residual component detection after each treatment step, and adjust the equipment parameters in real time based on the residual detection results. The parameter regression adjustment module is used to control the cloud data processing center to record and store parameter changes on the device, and to perform regression adjustment on the equipment parameters of the fly ash treatment equipment at fixed intervals.

6. The fly ash treatment equipment operation control system based on AI vision according to claim 5, characterized in that: The AI ​​visual inspection module includes a physical feature detection unit, a chemical feature detection unit, and a fly ash component identification unit. The physical feature detection unit collects the morphology, color, and particle diameter of fly ash particles using a high-resolution industrial camera and a laser sensor. The chemical feature detection unit detects the chemical composition of fly ash particles using an infrared sensor. The fly ash component identification unit uses a convolutional neural network to analyze the composition of fly ash based on the collected fly ash particle data.

7. The fly ash treatment equipment operation control system based on AI vision according to claim 5, characterized in that: The anomaly detection and processing module includes a humidity anomaly detection unit, a fly ash particle abnormal agglomeration detection unit, and an anomaly handling unit. The humidity anomaly detection unit collects humidity data from the fly ash treatment equipment using a humidity sensor, compares the collected humidity data with a set humidity threshold, and determines whether the humidity in the fly ash treatment equipment is abnormal. The fly ash particle abnormal agglomeration detection unit compares the diameter of the fly ash particles with a set fly ash particle diameter threshold to determine whether the fly ash particles are abnormally agglomerated. The anomaly handling unit is used to handle anomalies according to the type of anomaly detected.

8. The fly ash treatment equipment operation control system based on AI vision according to claim 5, characterized in that: The fly ash treatment module includes a fly ash treatment unit, a residual amount detection unit, and an equipment parameter updating unit. The fly ash treatment unit detoxifies toxic substances in fly ash through low-temperature pyrolysis, removes salt components from fly ash through sedimentation and rinsing, and removes heavy metals from fly ash through circulating rinsing. The residual amount detection unit is used to set a residual amount threshold and compare the detected residual amount value with the set residual amount threshold to determine whether the residual amount of the treated component meets the standard. The equipment parameter updating unit is used to update the equipment parameters of the fly ash treatment equipment in real time.

9. The fly ash treatment equipment operation control system based on AI vision according to claim 5, characterized in that: The parameter regression adjustment module includes a cloud data storage unit and an equipment parameter regression adjustment unit; the cloud data storage unit is used to record and store the number of fly ash treatments and changes in equipment parameters; the equipment parameter regression adjustment unit is used to periodically regress and adjust the equipment parameters of the fly ash treatment equipment in the cloud data processing center.

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