CN111899818A - Intelligent sewage biological treatment activated sludge monitoring technology and method - Google Patents
Intelligent sewage biological treatment activated sludge monitoring technology and method Download PDFInfo
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Abstract
The invention belongs to the field of sewage treatment, and particularly relates to an intelligent activated sludge monitoring technology and method for ensuring stable operation of a sewage biological treatment facility. The invention realizes the simple, convenient and low-cost monitoring of the performance state of the activated sludge, further grasps the operation condition of the sewage treatment facility and provides technical support for the intelligent operation of the sewage treatment facility.
Description
Technical Field
The invention belongs to the field of sewage treatment, and particularly relates to an intelligent activated sludge monitoring technology and method for ensuring stable operation of a sewage biological treatment facility.
Background
In the field of sewage treatment, particularly decentralized sewage treatment, the number of sewage treatment facilities is large, the distribution is wide, and the number and technical quality of operators do not meet the requirement of guaranteeing stable operation of the facilities. Therefore, the actual operation efficiency of a sewage treatment facility is low, and even the half-paralysis operation state is treated for a long time, so that the resource waste is caused.
Sewage treatment facilities generally include primary, secondary and tertiary treatment. The first-stage treatment is pretreatment mainly comprising mechanical treatment to remove insoluble suspended matters and large-particle matters in the sewage; the secondary treatment is biological treatment, and various pollutants in the sewage are removed through biodegradation; the third-stage treatment is measures such as advanced treatment and disinfection according to the requirements of discharge standards, and the third-stage treatment is not needed when the effluent requirement of a sewage treatment facility is low. In the whole sewage treatment system, secondary treatment is a core unit of the whole treatment process and is also a key point and a difficult point of operation regulation and control. Because the biological treatment system is destroyed to cause the whole sewage treatment system to be crashed, the culture period of domestication and recovery is quite long.
At present, the discussion of the stable operation problem of the sewage treatment facility mainly focuses on the research of a new sewage treatment process and the technical aspect of a matched intelligent operation management platform. The new sewage treatment process is a long-term exploration process, and the problems are difficult to solve fundamentally in a short term and the operation problems of the established sewage treatment facilities cannot be solved. The intelligent operation management platform is suitable for management of decentralized sewage facilities, but the practical application effect is poor, and the intelligent operation management platform is not practical. The reason for this is that a large amount of monitoring data is required to be supported, a large amount of detecting instruments or sensors are required to be added for data acquisition, the monitoring cost is high, and the method is not economical for dispersed sewage treatment facilities with small monomer scale and large number.
Disclosure of Invention
In view of the above problems, the present invention provides an intelligent activated sludge monitoring technology for sewage biological treatment, which combines an artificial intelligence technology with a sewage treatment monitoring technology, so as to grasp comprehensive activated sludge performance data of a sewage treatment biochemical tank under the condition of low cost monitoring hardware investment, and meet the requirement of an intelligent operation management platform for operation guidance.
The intelligent activated sludge monitoring technology for sewage biological treatment comprises a perception monitoring device and an intelligent expert system, wherein the perception monitoring device collects standard images of activated sludge and changes data of dissolved oxygen and transmits the standard images and the dissolved oxygen to the intelligent expert system to comprehensively evaluate the performance of the activated sludge.
The sensing monitoring device comprises a shell, a dissolved oxygen sensor, a magnetic stirrer, an image imaging module, a detection tube, a data acquisition module, a power management and controller and a monitoring program.
The dissolved oxygen sensor is arranged on the detection tube taking and placing port cover of the perception monitoring hardware, the cover is larger than the diameter of the detection tube, a silica gel pad is arranged on the inner side of the detection tube and can compress the detection tube, and one end of the cover is fixed with the shell, and the other end of the cover is provided with a buckle structure and is fixed with the side wall of the shell in a buckle mode.
The magnetic stirrer is an electrically driven liquid mixing and stirring device and consists of a magnetic base and a stirring rotor.
The image imaging module comprises an image sensor, a lens and a light source. The image sensor should be no less than 500 million pixels; the focal length of the lens is less than 200 mm; the light source is positioned opposite and on the side (left side or right side) of the detection tube relative to the lens, the light source opposite to the detection tube adopts natural scattered light, the light source on the side of the detection tube adopts natural direct light, the direct light sources are vertically arranged along the central line of the detection tube, a reflector which is arranged at 45 degrees is arranged opposite to the detection tube of the direct light source, the direct light source vertically irradiates through the detection tube and then is reflected to the side of the lens through the reflector, and the direct light source and the detection tube can be imaged at different positions of the same image.
The detection tube is a colorless transparent glass tube with good light transmission.
The power management and controller consists of a power management module, a control panel, a control actuator and a data acquisition module. And controlling a power switch, data connection and transmission, light source adjustment and executing a set monitoring program of the sensing monitoring equipment.
The intelligent expert system comprises: the system comprises a data processing module, a data center, an expert analysis module, a user interaction center and a safety guarantee system module.
The data processing module obtains quantitative indexes reflecting the performance of the activated sludge by mining and processing the standard image and the dissolved oxygen change data, wherein the quantitative indexes include but are not limited to the change of the dissolved oxygen reduction rate of reaction liquid, the change of the sludge sedimentation rate, the sludge concentration, the sludge color, the turbidity of supernatant liquid, the color of supernatant liquid, the size and the uniformity of sludge flocs.
The expert analysis module is an activated sludge performance evaluation model established according to internal logic and operation experience between quantitative indexes mined and processed by the data processing module, and can evaluate the performance state of the monitored activated sludge according to the quantitative indexes of the data processing module.
The data center is used for rapidly storing and extracting data information monitored by the perception monitoring device, and storing and extracting data mining and processing information and model evaluation results; and the system is also used for storing and extracting basic information of the detected sewage treatment facility by the detection personnel, including but not limited to specific process, wastewater property, discharge standard, site name and number, treatment scale, hydraulic retention time of the biological pond, starting running time and sludge discharge period.
The intelligent monitoring technology for the biological sewage treatment activated sludge comprises the following detection steps:
and step S1, collecting a sludge-water mixed liquid sample of the sewage treatment biological reaction tank, monitoring a set stirring-precipitation program by using a special sensing monitoring device, collecting a dissolved oxygen sample at regular time by using the stirring program, and collecting standard image information at regular time by using the precipitation program.
And S2, remotely transmitting the data acquired in S1 to an intelligent expert system, processing the data and image information by the intelligent expert system, and extracting useful information including but not limited to dissolved oxygen change characteristics, mud layer height proportion, supernatant liquid height proportion, mud layer color, supernatant liquid color, mud layer scattering illuminance, mud layer direct-injection illuminance, supernatant liquid scattering illuminance, supernatant liquid direct-injection illuminance, sludge floc size and uniformity.
And step S3, performing data processing on the information extracted in the step S2 in a mode of parameter correction with a corresponding standard curve and a corresponding standard color chart to obtain quantitative data of the change of the dissolved oxygen reduction rate of the reaction liquid, the change of the sludge sedimentation rate, the sludge concentration, the sludge volume index, the sludge color, the upper layer liquid turbidity, the upper layer liquid color and the sludge floc size and uniformity.
And S4, substituting the quantitative data obtained in the step S3 into an activated sludge performance evaluation model of corresponding sewage biological process characteristics to obtain an activated sludge comprehensive performance judgment result, trend prediction and credibility.
And step S5, the intelligent expert system sends the judgment result of the comprehensive performance of the activated sludge and the credibility obtained in the step S4 to the monitoring personnel or the user in the step S1.
In the step S1, the stirring-settling procedure is performed in the order of stirring first and then standing, the stirring time is preset to 10-40 minutes, 1 time of dissolved oxygen data is collected every 30-180 seconds, the standing time is preset to 30-60 minutes, and 1 time of image data is shot every 60-300 seconds.
In step S2, the processing of the data and the image information by the intelligent expert system includes:
the dissolved oxygen change characteristic is a curve of the dissolved oxygen data collected by the sensing and monitoring device in the step S1 with respect to the corresponding stirring time.
The mud layer height ratio, the supernatant liquid height ratio, the mud layer color, the supernatant liquid color, the mud layer scattered illuminance, the mud layer direct irradiation illuminance, the supernatant liquid scattered illuminance, and the supernatant liquid direct irradiation illuminance are information extracted by data mining according to the image collected by the sensing and monitoring device in the step S1.
In step S3, the data processing includes:
the quantitative processing method for the change of the dissolved oxygen decreasing rate of the reaction liquid is to find the standard dissolved oxygen variation curve with the highest feature similarity in the standard dissolved oxygen variation curve database according to the dissolved oxygen variation features in the step S2, calculate the ratio of the dissolved oxygen decreasing rate of the dissolved oxygen variation features in the step S2 to the standard dissolved oxygen variation curve with the highest feature similarity, and quantitatively determine the reliability value according to the similarity.
The quantitative processing method for the sludge sedimentation rate change is to find a sludge sedimentation change curve with the highest characteristic similarity in a standard sludge sedimentation database according to the sludge layer height proportion and the change curve of the supernatant liquid height proportion along with time in the step S2, calculate the ratio of the sludge layer height proportion in the step S2 and the standard dissolved oxygen change curve with the highest characteristic similarity in a free sedimentation stage, a group sedimentation stage and a compression sedimentation stage in a distribution manner, and quantitatively determine a reliability value according to the similarity.
The data processing method of the supernatant turbidity is to calculate a turbidity value according to the turbidity K × approximate scattered light flux/approximate transmitted light flux, compare the calculation result with the standard supernatant scattered light illuminance/supernatant direct light illuminance-turbidity of the standard database, correct the calculation result as the determined supernatant turbidity value, and calculate a correction coefficient as a reliability value, according to the supernatant scattered light illuminance and the supernatant direct light illuminance in step S2, respectively as an approximate scattered light flux and an approximate transmitted light flux.
The sludge concentration data processing method includes the steps of respectively using the mud layer scattering illuminance and the mud layer direct illuminance in the step S2 as an approximate scattering luminous flux and an approximate transmitting luminous flux, calculating a sludge concentration value according to the mud layer scattering luminous flux/approximate transmitting luminous flux, comparing the calculation result with the standard mud layer scattering luminous flux/mud layer direct illuminance-sludge concentration of a standard database, correcting the calculation result to obtain a determined sludge concentration value, and calculating a correction coefficient to obtain a reliability value.
The data processing method of the sludge volume index is according to a formula: and (3) calculating the reliability of the sludge volume index according to the reliability of SV30 and the sludge concentration value.
The upper layer liquid color data processing method is to find the standard upper layer liquid color with the highest feature similarity and the corresponding attribute numerical value in the standard upper layer liquid color database according to the upper layer liquid color in the step S2 as the upper layer liquid color value, and to quantitatively determine the reliability value according to the similarity.
The sludge color data processing method is that the standard sludge color with the highest feature similarity and the corresponding attribute numerical value thereof are found in the standard sludge color database according to the sludge color in the step S2 to be used as the sludge color value, and the credibility value is quantitatively determined according to the similarity degree.
The data processing method of the size and the uniformity of the sludge flocs is to calculate the ratio of the size and the uniformity of the sludge flocs in the step S2 to the size and the uniformity of the corresponding standard sludge flocs according to the comparison between the size and the uniformity of the sludge flocs in the step S2 and the standard sludge flocs corresponding to a database, and to quantify and determine the reliability value according to the similarity.
In the step S4, the activated sludge performance evaluation model is established based on association rules of a neural network model and database data types, the association rules of the database data types are established according to internal logic relationships of different sewage biological treatment processes and different activated sludge properties, and support degrees, confidence degrees and promotion degrees of the association rules are assigned respectively.
The different activated sludge properties of the activated sludge performance evaluation model include but are not limited to normal activated sludge, sludge in the early aging stage, activated sludge with serious aging, activated sludge with micro expansion, activated sludge with serious expansion, activated sludge with high inert substances, activated sludge in long-term high-load operation, activated sludge in long-term low-load operation, activated sludge subjected to load impact and poisoned activated sludge.
And obtaining the character diagnosis conclusion of the activated sludge and the operation suggestion of the sewage biological treatment facility according to the comprehensive performance judgment result of the activated sludge and the process model information of the monitored sewage treatment facility.
And the support degree, the confidence degree and the promotion degree of the association rule of the activated sludge performance evaluation model are subjected to enhanced learning and induction on the basis of the big application data of the intelligent expert system, and the provided data statistical result and the provided correction suggestion are corrected after being audited by a professional technical manager, so that the upgrading and perfecting of the system are realized.
In step S4, the data transmission is performed in a wireless or data line manner or in a wireless and data line connection manner.
In an embodiment of the present invention, the data transmission is connected to the smart phone in a wireless connection manner, i.e., WIFI/ZigBee/bluetooth, and interactively communicates with the sensing monitoring hardware through corresponding APP software. The mobile phone APP operation can set the photographing time interval of the sensing monitoring hardware and adjust the light source intensity; receiving information such as images shot by perception monitoring hardware, and uploading the information to the remote intelligent expert system in real time through a 4G/5G network; and the analysis and diagnosis result of the intelligent expert system is transmitted back to the mobile phone APP.
In another embodiment of the present invention, the data transmission and terminal device adopts a remote transmission module, is connected to the sensing and monitoring hardware through a data line, controls and receives monitoring data, and transmits the data to the intelligent expert system through an ethernet; and the analysis and diagnosis result of the intelligent expert system is sent to an intelligent operation management platform of the sewage treatment facility.
The intelligent detection technology can realize intelligent operation management of sewage treatment facilities, and particularly can assist in realizing batch management of decentralized sewage treatment facilities. Through the correlation analysis of the historical monitoring data of the activated sludge and the historical data of the effluent quality, the effluent quality can be predicted to be referred by operation managers.
Drawings
For the understanding of the present invention, the present invention will be described in detail below with reference to the following embodiments and drawings.
FIG. 1 is a flow chart of an intelligent monitoring technique for activated sludge in biological sewage treatment according to an embodiment of the present invention.
Fig. 2 is a schematic structural plan view of a perception monitoring device according to an embodiment of the invention.
Fig. 3 is a schematic structural view of a perception monitoring device according to an embodiment of the invention.
Fig. 4 is a network topology of data transmission according to an embodiment of the present invention.
FIG. 5 is a diagram of a neural network architecture, according to an embodiment of the present invention
In the drawings: the device comprises a shell, a detection tube, a direct light source, a reflector, a scattering light source, a data acquisition module, a control actuator, a lens, a camera lens, an image sensor, a power management module, a control panel, a detection opening cover, a cover buckle, a dissolved oxygen sensor, a magnetic stirrer and a rotor, wherein the shell is 1, the detection tube is 2, the direct light source is 3, the reflector is 4, the scattering light source is 5, the data acquisition module is 6, the control actuator is 7.
Detailed Description
Fig. 1 and 2 are schematic views of a sensing and monitoring device according to an embodiment of the invention, which is used for monitoring by artificial injection. Fig. 3 shows a data transmission and interaction method according to an embodiment of the present invention, in which a smart phone and an APP are used as the data transmission and interaction method. The specific implementation steps are as follows:
first, after the housing 1 of the monitoring device is horizontally placed, the power key of the control panel 11 is pressed. Monitoring personnel are connected with a sensing monitoring device through a smart phone WIFI/ZigBee/Bluetooth; and a stirring-standing monitoring program and parameter setting are set through a mobile phone APP matched with the intelligent expert system, wherein the stirring time is generally set to be 15min, and the standing time is generally set to be 30 min. The perception monitoring means receives the preset information and displays on the display of the control panel 11 the ongoing program steps, the preset time and the ongoing time.
Secondly, the sludge-water mixed liquid in the biological sewage treatment tank is added into the detection pipe 2 to a fixed liquid level height, and sampling is carried out at a fixed position in the biological sewage treatment tank every time in order to improve the comparability of historical monitoring data.
Furthermore, the detecting tube 2 is arranged at the detecting position of the sensing and monitoring device after being added with a rotor 16 of a magnetic stirrer 15, the cover 12 is buckled, the dissolved oxygen sensor 14 fixed on the cover 12 extends below the liquid level in the detecting tube, and the start key of the control panel 11 is pressed.
Further, the control actuator 7 starts to perform detection according to a preset program, and the dissolved oxygen sensor 14 collects 1 time of dissolved oxygen data every 60 seconds from the 1 st second of stirring starting until the stirring program is finished. And the acquired dissolved oxygen data is transmitted to the intelligent expert system through a smart phone by 4G/5G.
Further, after the stirring program is finished, a preset standing program is entered, and the image imaging module starts to enter the standing program at the 1 st s and shoots for 1 time at intervals of 150s until the standing program is finished. And the acquired image data is transmitted to the intelligent expert system through a smart phone through 4G/5G.
Further, the data processing module of the intelligent expert system performs data mining and data processing on the dissolved oxygen data and the image information to obtain quantitative data as follows:
synchronous, through the input of the detection people and the historical data of the data center, the other information of the monitored sewage treatment facility is obtained:
further, the neural network structure diagram corresponding to the activated sludge performance evaluation model introduced by the intelligent expert system according to the quantitative data of each index and other information is shown in fig. 5, 1 or more comprehensive activated sludge performance judgment results are obtained through calculation of the activated sludge performance evaluation model, and a reliability value is given according to a logical relationship. The results output the first 2 conclusions with confidence > 50%.
Claims (6)
1. The utility model provides an intelligent sewage biological treatment activated sludge monitoring technology, includes perception monitoring devices and intelligent expert system, its characterized in that, perception monitoring devices gathers activated sludge's standard image and dissolved oxygen change data transmission and gives intelligent expert system carries out the comprehensive evaluation of activated sludge performance, intelligent expert system includes: the system comprises a data processing module, a data center, an expert analysis module, a user interaction center and a safety guarantee system module, wherein the data processing module obtains quantitative indexes reflecting the performance of the activated sludge by mining and processing standard images and dissolved oxygen change data, and the quantitative indexes include but are not limited to reaction liquid dissolved oxygen reduction rate change, sludge sedimentation rate change, sludge concentration, sludge color, supernatant turbidity, supernatant color, sludge floc size and uniformity, the expert analysis module is an activated sludge performance evaluation model established according to internal logic and operation experience between the quantitative indexes mined and processed by the data processing module, and the performance state of the monitored activated sludge can be evaluated according to the quantitative indexes of the data processing module.
2. An intelligent monitoring method for sewage biological treatment activated sludge is characterized by comprising the following steps:
step S1, collecting a sludge-water mixed liquid sample of the sewage treatment biological reaction tank, monitoring a set stirring-sedimentation program by using a special sensing and monitoring device, collecting a dissolved oxygen sample at regular time by using the stirring program, and collecting standard image information at regular time by using the sedimentation program;
step S2, remotely transmitting the data acquired in step S1 to an intelligent expert system, processing the data and image information by the intelligent expert system, and extracting useful information including but not limited to dissolved oxygen change characteristics, mud layer height proportion, supernatant liquid height proportion, mud layer color, supernatant liquid color, mud layer scattering illuminance, mud layer direct-injection illuminance, supernatant liquid scattering illuminance, supernatant liquid direct-injection illuminance, sludge floc size and uniformity;
step S3, data processing is carried out on the information extracted in the step S2 in a mode of parameter correction with a corresponding standard curve and a corresponding standard color chart, and quantitative data of the change of the dissolved oxygen reduction rate of the reaction liquid, the change of the sludge sedimentation rate, the sludge concentration, the sludge volume index, the sludge color, the upper layer liquid turbidity, the upper layer liquid color and the sludge floc size and uniformity are obtained;
step S4, substituting the quantitative data of the step S3 into an activated sludge performance evaluation model of corresponding sewage biological process characteristics to obtain an activated sludge comprehensive performance judgment result, trend prediction and credibility;
and step S5, the intelligent expert system sends the judgment result of the comprehensive performance of the activated sludge and the credibility obtained in the step S4 to the monitoring personnel or the user in the step S1.
3. The intelligent activated sludge monitoring technology for sewage biological treatment according to claim 1, wherein in the step S1, the dedicated sensing and monitoring device comprises the sensing and monitoring equipment, which comprises a housing, a dissolved oxygen sensor, a magnetic stirrer, an image imaging module, a detection tube, a data acquisition module, a power management and controller, and a monitoring program;
the dissolved oxygen sensor is arranged on a detection tube taking and placing port cover of the sensing and monitoring hardware, the cover is larger than the diameter of the detection tube, a silica gel pad is arranged on the inner side of the cover and can press the detection tube tightly, one end of the cover is fixed with the shell, and a buckling structure is arranged at the other end of the cover and is fixed with the side wall of the shell in a buckling mode;
the magnetic stirrer is an electrically driven liquid mixing and stirring device and consists of a magnetic base and a stirring rotor;
the image imaging module comprises an image sensor, a lens and a light source, wherein the image sensor is not less than 500 ten thousand pixels; the focal length of the lens is less than 200 mm; the light source is positioned opposite and on the side (left side or right side) of the detection tube relative to the lens, the light source opposite to the detection tube adopts natural scattered light, the light source on the side of the detection tube adopts natural direct light, the direct light sources are vertically arranged along the central line of the detection tube, a reflector which is arranged at an angle of 45 degrees is arranged opposite to the detection tube of the direct light source, the direct light source vertically irradiates through the detection tube and then is reflected to the side of the lens through the reflector, and the direct light source and the detection tube can be imaged at different positions of the same image;
the detection tube is a colorless transparent glass tube with good light transmittance;
the power supply management and controller consists of a power supply management module, a control panel, a control actuator and a data acquisition module, and is used for controlling the power supply switch, the data connection and transmission, the light source regulation and the execution of the set monitoring program of the sensing and monitoring equipment;
the monitoring program is that according to the sequence of stirring and standing, the stirring time is preset to be 10-40 minutes, 1 time of dissolved oxygen data is collected every 30-180 seconds, the standing time is preset to be 30-60 minutes, and 1 time of image data is shot every 60-300 seconds.
4. The intelligent activated sludge monitoring technology for sewage biological treatment as claimed in claim 1, wherein in the step S2, the intelligent expert system processes data and image information, comprising:
the dissolved oxygen change characteristic is a relation curve between the dissolved oxygen data collected by the sensing and monitoring device in the step S1 and the corresponding stirring time;
the mud layer height ratio, the supernatant liquid height ratio, the mud layer color, the supernatant liquid color, the mud layer scattered illuminance, the mud layer direct irradiation illuminance, the supernatant liquid scattered illuminance, and the supernatant liquid direct irradiation illuminance are information extracted by data mining according to the image collected by the sensing and monitoring device in the step S1.
5. The intelligent activated sludge monitoring technology for sewage biological treatment as claimed in claim 1, wherein in the step S3, the data processing comprises:
the quantitative processing method for the change of the dissolved oxygen decreasing rate of the reaction liquid is to find a standard dissolved oxygen variation curve with the highest feature similarity in a standard dissolved oxygen variation curve database according to the dissolved oxygen variation features in the step S2, calculate the ratio of the dissolved oxygen decreasing rate of the dissolved oxygen variation features in the step S2 to the standard dissolved oxygen variation curve with the highest feature similarity, and quantitatively determine a reliability value according to the similarity;
the quantitative processing method of the sludge sedimentation rate change is that a sludge sedimentation change curve with the highest characteristic similarity is found in a standard sludge sedimentation database according to the sludge layer height proportion and the change curve of the supernatant liquid height proportion along with time in the step S2, the ratio of the sludge layer height proportion in the step S2 and the standard dissolved oxygen change curve with the highest characteristic similarity in a free sedimentation stage, a group sedimentation stage and a compression sedimentation stage is calculated in a distributed mode, and a reliability value is determined according to the similarity in a quantitative mode;
the data processing method of the supernatant turbidity is that the supernatant scattered illuminance and the supernatant direct illuminance in the step S2 are respectively used as an approximate scattered luminous flux and an approximate transmitted luminous flux, a turbidity value is calculated according to the turbidity K × approximate scattered luminous flux/approximate transmitted luminous flux, the calculation result is compared with the standard supernatant scattered luminous flux/supernatant direct illuminance-turbidity of the standard database, the calculation result is corrected to be used as the determined supernatant turbidity value, and a correction coefficient is calculated to be used as a reliability value;
the sludge concentration data processing method comprises the steps of respectively taking the mud layer scattering illuminance and the mud layer direct illuminance in the step S2 as approximate scattering luminous flux and approximate transmitting luminous flux, calculating a sludge concentration value according to the mud layer scattering luminous flux/approximate transmitting luminous flux, comparing the calculation result with the standard mud layer scattering luminous flux/mud layer direct illuminance-sludge concentration of a standard database, correcting the calculation result to be a determined sludge concentration value, and calculating a correction coefficient to be a reliability value;
the data processing method of the sludge volume index is according to a formula: the sludge volume index is SV 30/sludge concentration, and the reliability of the sludge volume index is calculated according to the reliability of SV30 and the sludge concentration value;
the data processing method of the upper layer liquid color is to find the standard upper layer liquid color with the highest feature similarity and the corresponding attribute numerical value thereof in a standard upper layer liquid color database according to the upper layer liquid color in the step S2 as the upper layer liquid color value, and quantitatively determine the credibility value according to the similarity degree;
the sludge color data processing method comprises the steps of finding a standard sludge color with the highest feature similarity and a corresponding attribute numerical value in a standard sludge color database according to the sludge color in the step S2 as the sludge color value, and quantitatively determining a reliability value according to the similarity degree;
the data processing method of the size and the uniformity of the sludge flocs is to calculate the ratio of the size and the uniformity of the sludge flocs in the step S2 to the size and the uniformity of the corresponding standard sludge flocs according to the comparison between the size and the uniformity of the sludge flocs in the step S2 and the standard sludge flocs corresponding to a database, and to quantify and determine the reliability value according to the similarity.
6. The intelligent activated sludge monitoring technology for sewage biological treatment according to claim 1, wherein in step S4, the activated sludge performance evaluation model is established based on a neural network model and an association rule based on a database data type, the association rule of the database data type is established according to the internal logic relationship of different sewage types and different sewage biological treatment processes, and the support degree, the confidence degree and the promotion degree of the association rule are respectively assigned; and the support degree, the confidence degree and the promotion degree of the association rule are subjected to enhanced learning and induction on the basis of the big application data of the intelligent expert system, and the provided data statistical result and the provided correction suggestion are corrected after being audited by a professional technical manager, so that the upgrading and perfection of the system are realized.
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| CN113387429A (en) * | 2021-06-29 | 2021-09-14 | 黑龙江大学 | Water environment ecological pile |
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